From ea24d266d1aa327d90344ed4b3d7a80df439529b Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 14 Feb 2025 11:01:09 -0500
Subject: [PATCH 01/22] Added scenario mining evaluation

---
 .gitignore                                    |    7 +-
 .../evaluation/scenario_mining/__init__.py    |   45 +
 .../evaluation/scenario_mining/constants.py   |   12 +
 src/av2/evaluation/scenario_mining/eval.py    | 1084 +++++++++++++++++
 src/av2/evaluation/scenario_mining/hota.py    |  323 +++++
 src/av2/evaluation/scenario_mining/metrics.py |    8 +
 src/av2/evaluation/scenario_mining/utils.py   |  177 +++
 .../evaluation/scenario_mining/__init__.py    |    1 +
 .../evaluation/scenario_mining/test_eval.py   |   27 +
 9 files changed, 1682 insertions(+), 2 deletions(-)
 create mode 100644 src/av2/evaluation/scenario_mining/__init__.py
 create mode 100644 src/av2/evaluation/scenario_mining/constants.py
 create mode 100644 src/av2/evaluation/scenario_mining/eval.py
 create mode 100644 src/av2/evaluation/scenario_mining/hota.py
 create mode 100644 src/av2/evaluation/scenario_mining/metrics.py
 create mode 100644 src/av2/evaluation/scenario_mining/utils.py
 create mode 100644 tests/unit/evaluation/scenario_mining/__init__.py
 create mode 100644 tests/unit/evaluation/scenario_mining/test_eval.py

diff --git a/.gitignore b/.gitignore
index 3c74cb3d..6ee6a157 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,21 +138,24 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
-*.pkl
 
 .vscode/
 outputs/
 condaenv*
 
+#Data
 /data/
+data/
+*.pkl
 
 *.jpg
 *.png
+*.mp4
 *.svg
+*.csv
 
 .DS_Store
 */**/.DS_Store
 experiments
 
 *.pt
-*.mp4
diff --git a/src/av2/evaluation/scenario_mining/__init__.py b/src/av2/evaluation/scenario_mining/__init__.py
new file mode 100644
index 00000000..280f54c3
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/__init__.py
@@ -0,0 +1,45 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Dataset evaluation subpackage."""
+
+from enum import Enum, unique
+from typing import Final
+
+NUM_RECALL_SAMPLES: Final = 101
+
+
+@unique
+class SensorCompetitionCategories(str, Enum):
+    """Sensor dataset annotation categories."""
+    REFERRED_OBJECT = "REFERRED_OBJECT"
+    RELATED_OBJECT = "RELATED_OBJECT"
+    OTHER_OBJECT = "OTHER_OBJECT"
+
+    """
+    ARTICULATED_BUS = "ARTICULATED_BUS"
+    BICYCLE = "BICYCLE"
+    BICYCLIST = "BICYCLIST"
+    BOLLARD = "BOLLARD"
+    BOX_TRUCK = "BOX_TRUCK"
+    BUS = "BUS"
+    CONSTRUCTION_BARREL = "CONSTRUCTION_BARREL"
+    CONSTRUCTION_CONE = "CONSTRUCTION_CONE"
+    DOG = "DOG"
+    LARGE_VEHICLE = "LARGE_VEHICLE"
+    MESSAGE_BOARD_TRAILER = "MESSAGE_BOARD_TRAILER"
+    MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN"
+    MOTORCYCLE = "MOTORCYCLE"
+    MOTORCYCLIST = "MOTORCYCLIST"
+    PEDESTRIAN = "PEDESTRIAN"
+    REGULAR_VEHICLE = "REGULAR_VEHICLE"
+    SCHOOL_BUS = "SCHOOL_BUS"
+    SIGN = "SIGN"
+    STOP_SIGN = "STOP_SIGN"
+    STROLLER = "STROLLER"
+    TRUCK = "TRUCK"
+    TRUCK_CAB = "TRUCK_CAB"
+    VEHICULAR_TRAILER = "VEHICULAR_TRAILER"
+    WHEELCHAIR = "WHEELCHAIR"
+    WHEELED_DEVICE = "WHEELED_DEVICE"
+    WHEELED_RIDER = "WHEELED_RIDER"
+    """
diff --git a/src/av2/evaluation/scenario_mining/constants.py b/src/av2/evaluation/scenario_mining/constants.py
new file mode 100644
index 00000000..0abf956a
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/constants.py
@@ -0,0 +1,12 @@
+"""Constants for tracking challenge."""
+
+from typing import Final
+
+from av2.evaluation.scenario_mining import SensorCompetitionCategories
+
+SUBMETRIC_TO_METRIC_CLASS_NAME: Final = {
+    "MOTA": "CLEAR",
+    "HOTA": "HOTA",
+}
+
+AV2_CATEGORIES: Final = tuple(x.value for x in SensorCompetitionCategories)
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
new file mode 100644
index 00000000..ef111fa2
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -0,0 +1,1084 @@
+"""Argoverse 2 scenario mining evaluation.
+
+Evaluation Metrics:
+    HOTA: see https://arxiv.org/abs/2009.07736
+    MOTA: see https://jivp-eurasipjournals.springeropen.com/articles/10.1155/2008/246309
+    AMOTA: see https://arxiv.org/abs/2008.08063
+"""
+
+import contextlib
+import json
+import pickle
+from copy import copy
+from functools import partial
+from itertools import chain
+from pathlib import Path
+from pprint import pprint
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
+
+import click
+import numpy as np
+import trackeval
+from scipy.optimize import linear_sum_assignment
+from scipy.spatial.transform import Rotation
+from tqdm import tqdm
+from trackeval.datasets._base_dataset import _BaseDataset
+import matplotlib.pyplot as plt
+
+from av2.evaluation.detection.utils import (
+    compute_objects_in_roi_mask,
+    load_mapped_avm_and_egoposes,
+)
+from av2.evaluation.scenario_mining import constants
+from av2.evaluation.scenario_mining import utils as sm_utils
+from av2.evaluation.scenario_mining.constants import SUBMETRIC_TO_METRIC_CLASS_NAME
+from av2.utils.typing import NDArrayFloat, NDArrayInt
+from av2.evaluation.typing import Sequences
+import av2.evaluation.scenario_mining.metrics as metrics
+
+import time
+import traceback
+from multiprocessing.pool import Pool
+from functools import partial
+import os
+from trackeval import utils
+from trackeval.utils import TrackEvalException
+from trackeval import _timing
+from trackeval.metrics import Count
+
+
+class Evaluator:
+    """Evaluator class for evaluating different metrics for different datasets"""
+
+    @staticmethod
+    def get_default_eval_config():
+        """Returns the default config values for evaluation"""
+        code_path = utils.get_code_path()
+        default_config = {
+            'USE_PARALLEL': True,
+            'NUM_PARALLEL_CORES': max(int(0.9 * os.cpu_count()), 1),
+            'BREAK_ON_ERROR': True,  # Raises exception and exits with error
+            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error
+            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.
+
+            'PRINT_RESULTS': True,
+            'PRINT_ONLY_COMBINED': False,
+            'PRINT_CONFIG': True,
+            'TIME_PROGRESS': True,
+            'DISPLAY_LESS_PROGRESS': True,
+
+            'OUTPUT_SUMMARY': False,
+            'OUTPUT_EMPTY_CLASSES': False,  # If False, summary files are not output for classes with no detections
+            'OUTPUT_DETAILED': False,
+            'PLOT_CURVES': False,
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        """Initialise the evaluator with a config file"""
+        self.config = utils.init_config(config, self.get_default_eval_config(), 'Eval')
+        # Only run timing analysis if not run in parallel.
+        if self.config['TIME_PROGRESS'] and not self.config['USE_PARALLEL']:
+            _timing.DO_TIMING = True
+            if self.config['DISPLAY_LESS_PROGRESS']:
+                _timing.DISPLAY_LESS_PROGRESS = True
+
+    @_timing.time
+    def evaluate(self, dataset_list, metrics_list, show_progressbar=True):
+        """Evaluate a set of metrics on a set of datasets"""
+        config = self.config
+        metrics_list = metrics_list + [Count()]  # Count metrics are always run
+        metric_names = utils.validate_metrics_list(metrics_list)
+        dataset_names = [dataset.get_name() for dataset in dataset_list]
+        output_res = {}
+        output_msg = {}
+
+        for dataset, dataset_name in zip(dataset_list, dataset_names):
+            # Get dataset info about what to evaluate
+            output_res[dataset_name] = {}
+            output_msg[dataset_name] = {}
+            tracker_list, seq_list, class_list = dataset.get_eval_info()
+            print('\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following '
+                  'metrics: %s\n' % (len(tracker_list), len(seq_list), len(class_list), dataset_name,
+                                     ', '.join(metric_names)))
+
+            # Evaluate each tracker
+            for tracker in tracker_list:
+                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking
+                try:
+                    # Evaluate each sequence in parallel or in series.
+                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]
+                    # e.g. res[seq_0001][pedestrian][hota][DetA]
+                    print('\nEvaluating %s\n' % tracker)
+                    time_start = time.time()
+                    if config['USE_PARALLEL']:
+                        if show_progressbar:
+                            seq_list_sorted = sorted(seq_list)
+
+                            with Pool(config['NUM_PARALLEL_CORES']) as pool, tqdm(total=len(seq_list)) as pbar:
+                                _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,
+                                                         class_list=class_list, metrics_list=metrics_list,
+                                                         metric_names=metric_names)
+                                results = []
+                                for r in pool.imap(_eval_sequence, seq_list_sorted,
+                                                   chunksize=20):
+                                    results.append(r)
+                                    pbar.update()
+                                res = dict(zip(seq_list_sorted, results))
+
+                        else:
+                            with Pool(config['NUM_PARALLEL_CORES']) as pool:
+                                _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,
+                                                         class_list=class_list, metrics_list=metrics_list,
+                                                         metric_names=metric_names)
+                                results = pool.map(_eval_sequence, seq_list)
+                                res = dict(zip(seq_list, results))
+                    else:
+                        res = {}
+                        if show_progressbar:
+                            seq_list_sorted = sorted(seq_list)
+                            for curr_seq in tqdm(seq_list_sorted):
+                                res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,
+                                                              metric_names)
+                        else:
+                            for curr_seq in sorted(seq_list):
+                                res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,
+                                                              metric_names)
+
+                    # Combine results over all sequences and then over all classes
+
+                    # collecting combined cls keys (cls averaged, det averaged, super classes)
+                    combined_cls_keys = []
+                    res['COMBINED_SEQ'] = {}
+                    # combine sequences for each class
+                    for c_cls in class_list:
+                        res['COMBINED_SEQ'][c_cls] = {}
+                        for metric, metric_name in zip(metrics_list, metric_names):
+                            curr_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value in res.items() if
+                                        seq_key != 'COMBINED_SEQ'}
+                            res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res)
+                    # combine classes
+                    if dataset.should_classes_combine:
+                        combined_cls_keys += ['cls_comb_cls_av', 'cls_comb_det_av', 'all']
+                        res['COMBINED_SEQ']['cls_comb_cls_av'] = {}
+                        res['COMBINED_SEQ']['cls_comb_det_av'] = {}
+                        for metric, metric_name in zip(metrics_list, metric_names):
+                            cls_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
+                                       res['COMBINED_SEQ'].items() if cls_key not in combined_cls_keys}
+                            res['COMBINED_SEQ']['cls_comb_cls_av'][metric_name] = \
+                                metric.combine_classes_class_averaged(cls_res)
+                            res['COMBINED_SEQ']['cls_comb_det_av'][metric_name] = \
+                                metric.combine_classes_det_averaged(cls_res)
+                    # combine classes to super classes
+                    if dataset.use_super_categories:
+                        for cat, sub_cats in dataset.super_categories.items():
+                            combined_cls_keys.append(cat)
+                            res['COMBINED_SEQ'][cat] = {}
+                            for metric, metric_name in zip(metrics_list, metric_names):
+                                cat_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
+                                           res['COMBINED_SEQ'].items() if cls_key in sub_cats}
+                                res['COMBINED_SEQ'][cat][metric_name] = metric.combine_classes_det_averaged(cat_res)
+
+                    # Print and output results in various formats
+                    if config['TIME_PROGRESS']:
+                        print('\nAll sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start))
+                    output_fol = dataset.get_output_fol(tracker)
+                    tracker_display_name = dataset.get_display_name(tracker)
+                    for c_cls in res['COMBINED_SEQ'].keys():  # class_list + combined classes if calculated
+                        summaries = []
+                        details = []
+                        num_dets = res['COMBINED_SEQ'][c_cls]['Count']['Dets']
+                        if config['OUTPUT_EMPTY_CLASSES'] or num_dets > 0:
+                            for metric, metric_name in zip(metrics_list, metric_names):
+                                # for combined classes there is no per sequence evaluation
+                                if c_cls in combined_cls_keys:
+                                    table_res = {'COMBINED_SEQ': res['COMBINED_SEQ'][c_cls][metric_name]}
+                                else:
+                                    table_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value
+                                                 in res.items()}
+
+                                if config['PRINT_RESULTS'] and config['PRINT_ONLY_COMBINED']:
+                                    dont_print = dataset.should_classes_combine and c_cls not in combined_cls_keys
+                                    if not dont_print:
+                                        metric.print_table({'COMBINED_SEQ': table_res['COMBINED_SEQ']},
+                                                           tracker_display_name, c_cls)
+                                elif config['PRINT_RESULTS']:
+                                    metric.print_table(table_res, tracker_display_name, c_cls)
+                                if config['OUTPUT_SUMMARY']:
+                                    summaries.append(metric.summary_results(table_res))
+                                if config['OUTPUT_DETAILED']:
+                                    details.append(metric.detailed_results(table_res))
+                                if config['PLOT_CURVES']:
+                                    metric.plot_single_tracker_results(table_res, tracker_display_name, c_cls,
+                                                                       output_fol)
+                            if config['OUTPUT_SUMMARY']:
+                                utils.write_summary_results(summaries, c_cls, output_fol)
+                            if config['OUTPUT_DETAILED']:
+                                utils.write_detailed_results(details, c_cls, output_fol)
+
+                    # Output for returning from function
+                    output_res[dataset_name][tracker] = res
+                    output_msg[dataset_name][tracker] = 'Success'
+
+                except Exception as err:
+                    output_res[dataset_name][tracker] = None
+                    if type(err) == TrackEvalException:
+                        output_msg[dataset_name][tracker] = str(err)
+                    else:
+                        output_msg[dataset_name][tracker] = 'Unknown error occurred.'
+                    print('Tracker %s was unable to be evaluated.' % tracker)
+                    print(err)
+                    traceback.print_exc()
+                    if config['LOG_ON_ERROR'] is not None:
+                        with open(config['LOG_ON_ERROR'], 'a') as f:
+                            print(dataset_name, file=f)
+                            print(tracker, file=f)
+                            print(traceback.format_exc(), file=f)
+                            print('\n\n\n', file=f)
+                    if config['BREAK_ON_ERROR']:
+                        raise err
+                    elif config['RETURN_ON_ERROR']:
+                        return output_res, output_msg
+
+        return output_res, output_msg
+
+
+@_timing.time
+def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
+    """Function for evaluating a single sequence"""
+
+    raw_data = dataset.get_raw_seq_data(tracker, seq)
+    seq_res = {}
+    for cls in class_list:
+        seq_res[cls] = {}
+        data = dataset.get_preprocessed_seq_data(raw_data, cls)
+        for metric, met_name in zip(metrics_list, metric_names):
+            seq_res[cls][met_name] = metric.eval_sequence(data)
+    return seq_res
+
+
+class TrackEvalDataset(_BaseDataset):  # type: ignore
+    """Dataset class to support tracking evaluation using the TrackEval library."""
+
+    def __init__(self, config: Dict[str, Any]) -> None:
+        """Store config."""
+        super().__init__()
+        self.gt_tracks = config["GT_TRACKS"]
+        self.predicted_tracks = config["PREDICTED_TRACKS"]
+        self.full_class_list = config.get("CLASSES", config["CLASSES_TO_EVAL"])
+        self.class_list = config["CLASSES_TO_EVAL"]
+        self.tracker_list = config["TRACKERS_TO_EVAL"]
+        self.seq_list = config["SEQ_IDS_TO_EVAL"]
+        self.output_fol = config["OUTPUT_FOLDER"]
+        self.output_sub_fol = config["OUTPUT_SUB_FOLDER"]
+        self.zero_distance = config["ZERO_DISTANCE"]
+        print(f"Using zero_distance={self.zero_distance}m")
+
+    @staticmethod
+    def get_default_dataset_config() -> Dict[str, Any]:
+        """Get the default config.
+
+        Returns:
+            dictionary of the default config
+        """
+        default_config = {
+            "GT_TRACKS": None,  # tracker_name -> seq id -> frames
+            "PREDICTED_TRACKS": None,  # tracker_name -> seq id -> frames
+            "SEQ_IDS_TO_EVAL": None,  # list of sequences ids to eval
+            "CLASSES_TO_EVAL": None,
+            "TRACKERS_TO_EVAL": None,
+            "OUTPUT_FOLDER": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+            "OUTPUT_SUB_FOLDER": "",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+            "ZERO_DISTANCE": 2,
+        }
+        return default_config
+
+    def _load_raw_file(
+        self, tracker: str, seq_id: Union[str, int], is_gt: bool
+    ) -> Dict[str, Any]:
+        """Get raw track data, from either trackers or ground truth."""
+        tracks = (self.gt_tracks if is_gt else self.predicted_tracks)[tracker][seq_id]
+        source = "gt" if is_gt else "tracker"
+
+        ts = np.array([frame["timestamp_ns"] for frame in tracks])
+        assert np.all(ts[:-1] < ts[1:]), "timestamps are not increasing"
+
+        raw_data = {
+            f"{source}_ids": [frame["track_id"] for frame in tracks],
+            f"{source}_classes": [
+                np.array([self.full_class_list.index(n) for n in frame["name"]])
+                for frame in tracks
+            ],
+            f"{source}_dets": [
+                np.concatenate((frame["translation_m"], frame["size"]), axis=-1)
+                for frame in tracks
+            ],
+            "num_timesteps": len(tracks),
+            "seq": seq_id,
+        }
+        if "score" in tracks[0]:
+            raw_data[f"{source}_confidences"] = [frame["score"] for frame in tracks]
+        return raw_data
+
+    def get_preprocessed_seq_data(
+        self, raw_data: Dict[str, Any], cls: str
+    ) -> Dict[str, Any]:
+        """Filter data to keep only one class and map id to 0 - n.
+
+        Args:
+            raw_data: dictionary of track data
+            cls: name of class to keep
+
+        Returns:
+            Dictionary of processed track data of the specified class
+        """
+        data_keys = [
+            "gt_ids",
+            "tracker_ids",
+            "gt_classes",
+            "tracker_classes",
+            "gt_dets",
+            "tracker_dets",
+            "tracker_confidences",
+            "similarity_scores",
+            "num_timesteps",
+            "seq",
+        ]
+        data = {k: copy(raw_data[k]) for k in data_keys}
+        cls_id = self.full_class_list.index(cls)
+
+        for t in range(raw_data["num_timesteps"]):
+            gt_to_keep_mask = data["gt_classes"][t] == cls_id
+            data["gt_classes"][t] = data["gt_classes"][t][gt_to_keep_mask]
+            data["gt_ids"][t] = data["gt_ids"][t][gt_to_keep_mask]
+            data["gt_dets"][t] = data["gt_dets"][t][gt_to_keep_mask, :]
+
+            tracker_to_keep_mask = data["tracker_classes"][t] == cls_id
+            data["tracker_classes"][t] = data["tracker_classes"][t][
+                tracker_to_keep_mask
+            ]
+            data["tracker_ids"][t] = data["tracker_ids"][t][tracker_to_keep_mask]
+            data["tracker_dets"][t] = data["tracker_dets"][t][tracker_to_keep_mask, :]
+            data["tracker_confidences"][t] = data["tracker_confidences"][t][
+                tracker_to_keep_mask
+            ]
+
+            data["similarity_scores"][t] = data["similarity_scores"][t][
+                :, tracker_to_keep_mask
+            ][gt_to_keep_mask]
+
+        # Map ids to 0 - n.
+        unique_gt_ids = set(chain.from_iterable(data["gt_ids"]))
+        unique_tracker_ids = set(chain.from_iterable(data["tracker_ids"]))
+        data["gt_ids"] = self._map_ids(data["gt_ids"], unique_gt_ids)
+        data["tracker_ids"] = self._map_ids(data["tracker_ids"], unique_tracker_ids)
+
+        data["num_tracker_dets"] = sum(len(dets) for dets in data["tracker_dets"])
+        data["num_gt_dets"] = sum(len(dets) for dets in data["gt_dets"])
+        data["num_tracker_ids"] = len(unique_tracker_ids)
+        data["num_gt_ids"] = len(unique_gt_ids)
+
+        # Ensure again that ids are unique per timestep after preproc.
+        self._check_unique_ids(data, after_preproc=True)
+        return data
+
+    def _map_ids(self, ids: List[Any], unique_ids: Iterable[Any]) -> List[NDArrayInt]:
+        id_map = {id: i for i, id in enumerate(unique_ids)}
+        return [
+            np.array([id_map[id] for id in id_array], dtype=int) for id_array in ids
+        ]
+
+    def _calculate_similarities(
+        self, gt_dets_t: NDArrayFloat, tracker_dets_t: NDArrayFloat
+    ) -> NDArrayFloat:
+        """Euclidean distance of the x, y translation coordinates."""
+        gt_xy = gt_dets_t[:, :2]
+        tracker_xy = tracker_dets_t[:, :2]
+        sim = self._calculate_euclidean_similarity(
+            gt_xy, tracker_xy, zero_distance=self.zero_distance
+        )
+        return cast(NDArrayFloat, sim)
+
+
+def _plot_confusion_matrix(
+        gt_classes:NDArrayInt,
+        pred_classes:NDArrayInt,
+        output_dir:str
+) -> None:
+    """Plots the confusion matrix for scenario mining. A true label
+    indicates that the scenario matches the description. A false label
+    indicates the scenario does not match the description."""
+
+    # Create confusion matrix (2x2 for binary classification)
+    cm = np.zeros((2, 2), dtype=int)
+
+    # Fill the confusion matrix
+    for true, pred in zip(gt_classes, pred_classes):
+        cm[true, pred] += 1
+
+    # Plot the confusion matrix
+    fig, ax = plt.subplots(figsize=(4, 4))
+    cax = ax.imshow(cm, cmap="viridis", interpolation="nearest")
+
+    # Add text annotations (True Positives, False Positives, etc.)
+    for i in range(2):
+        for j in range(2):
+            ax.text(j, i, cm[i, j], ha="center", va="center", color="white", fontsize=14)
+
+    # Set axis labels and ticks
+    ax.set_xlabel("Predicted Label")
+    ax.set_ylabel("True Label")
+    ax.set_title("Scenario Mining - Description Matches")
+    ax.set_xticks([0, 1])
+    ax.set_yticks([0, 1])
+    ax.set_xticklabels(["Negative", "Positive"])
+    ax.set_yticklabels(["Negative", "Positive"])
+
+    # Show colorbar
+    fig.colorbar(cax)
+
+    #Display the plot
+    plt.tight_layout()
+    if output_dir:
+        plt.savefig(output_dir + '/eval_cm.png')
+    plt.close()
+
+
+def evaluate_tracking(
+    labels: Sequences,
+    track_predictions: Sequences,
+    classes: List[str],
+    tracker_name: str,
+    output_dir: str,
+    iou_threshold: float = 0.5,
+) -> Dict[str, Any]:
+    """Evaluate a set of tracks against ground truth annotations using the TrackEval evaluation suite.
+
+    Each sequences/log is evaluated separately.
+
+    Args:
+        labels: Dict[seq_id: List[frame]] Dictionary of ground truth annotations.
+        track_predictions: Dict[seq_id: List[frame]] Dictionary of tracks.
+        classes: List of classes to evaluate.
+        tracker_name: Name of tracker.
+        output_dir: Folder to save evaluation results.
+        iou_threshold: IoU threshold for a True Positive match between a detection to a ground truth bounding box.
+
+    frame is a dictionary with the following format
+        {
+            sequences_id: [
+                {
+                    "timestamp_ns": int, # nano seconds
+                    "track_id": np.ndarray[I],
+                    "translation_m": np.ndarray[I, 3],
+                    "size": np.ndarray[I, 3],
+                    "yaw": np.ndarray[I],
+                    "velocity_m_per_s": np.ndarray[I, 3],
+                    "label": np.ndarray[I],
+                    "score": np.ndarray[I],
+                    "name": np.ndarray[I],
+                    ...
+                }
+            ]
+        }
+    where I is the number of objects in the frame.
+
+    Returns:
+        Dictionary of metric values.
+    """
+    labels_id_ts = set(
+        (frame["seq_id"], frame["timestamp_ns"])
+        for frame in sm_utils.ungroup_frames(labels)
+    )
+    predictions_id_ts = set(
+        (frame["seq_id"], frame["timestamp_ns"])
+        for frame in sm_utils.ungroup_frames(track_predictions)
+    )
+    assert (
+        labels_id_ts == predictions_id_ts
+    ), "sequences ids and timestamp_ns in labels and predictions don't match"
+    metrics_config = {
+        "METRICS": ["HOTA"],
+        "THRESHOLD": iou_threshold,
+    }
+    metric_names = cast(List[str], metrics_config["METRICS"])
+    metrics_list = [
+        getattr(metrics, metric)(metrics_config) for metric in metric_names
+    ]
+    dataset_config = {
+        **TrackEvalDataset.get_default_dataset_config(),
+        "GT_TRACKS": {tracker_name: labels},
+        "PREDICTED_TRACKS": {tracker_name: track_predictions},
+        "SEQ_IDS_TO_EVAL": list(labels.keys()),
+        "CLASSES_TO_EVAL": classes,
+        "TRACKERS_TO_EVAL": [tracker_name],
+        "OUTPUT_FOLDER": output_dir,
+    }
+
+    evaluator = Evaluator(
+        {
+            **Evaluator.get_default_eval_config(),
+            "TIME_PROGRESS": False,
+        }
+    )
+    full_result, _ = evaluator.evaluate(
+        [TrackEvalDataset(dataset_config)],
+        metrics_list,
+    )
+
+    return cast(Dict[str, Any], full_result)
+
+
+def _tune_score_thresholds(
+    labels: Sequences,
+    track_predictions: Sequences,
+    objective_metric: str,
+    classes: List[str],
+    num_thresholds: int = 10,
+    iou_threshold: float = 0.5,
+    match_distance_m: int = 2,
+) -> Tuple[Dict[str, float], Dict[str, float], Dict[str, float]]:
+    """Find the optimal score thresholds to optimize the objective metric.
+
+    Each class is processed independently.
+
+    Args:
+        labels: Dictionary of ground truth annotations
+        track_predictions: Dictionary of tracks
+        objective_metric: Name of the metric to optimize, one of HOTA or MOTA
+        classes: List of classes to evaluate
+        num_thresholds: Number of score thresholds to try
+        iou_threshold: IoU threshold for a True Positive match between a detection to a ground truth bounding box
+        match_distance_m: Maximum euclidean distance threshold for a match
+
+    Returns:
+        optimal_score_threshold_by_class: Dictionary of class name to optimal score threshold
+        optimal_metric_values_by_class: Dictionary of class name to metric value with the optimal score threshold
+        mean_metric_values_by_class: Dictionary of class name to metric value averaged over recall levels 
+    """
+    metric_class = SUBMETRIC_TO_METRIC_CLASS_NAME[objective_metric]
+    metrics_config = {
+        "METRICS": [metric_class],
+        "THRESHOLD": iou_threshold,
+        "PRINT_CONFIG": False,
+    }
+    metrics_list = [
+        getattr(metrics, metric_name)(metrics_config)
+        for metric_name in cast(List[str], metrics_config["METRICS"])
+    ]
+    dataset_config = {
+        **TrackEvalDataset.get_default_dataset_config(),
+        "GT_TRACKS": {"tracker": labels},
+        "PREDICTED_TRACKS": {"tracker": track_predictions},
+        "SEQ_IDS_TO_EVAL": list(labels.keys()),
+        "CLASSES_TO_EVAL": classes,
+        "TRACKERS_TO_EVAL": ["tracker"],
+        "OUTPUT_FOLDER": "tmp",
+    }
+    evaluator = Evaluator(
+        {
+            **Evaluator.get_default_eval_config(),
+            "PRINT_RESULTS": False,
+            "PRINT_CONFIG": False,
+            "TIME_PROGRESS": False,
+            "OUTPUT_SUMMARY": False,
+            "OUTPUT_DETAILED": False,
+            "PLOT_CURVES": False,
+        }
+    )
+
+    score_thresholds_by_class = {}
+    sim_func = partial(_xy_center_similarity, zero_distance=match_distance_m)
+    for name in classes:
+        single_cls_labels = _filter_by_class(labels, name)
+        single_cls_predictions = _filter_by_class(track_predictions, name)
+        score_thresholds_by_class[name] = _calculate_score_thresholds(
+            single_cls_labels,
+            single_cls_predictions,
+            sim_func,
+            num_thresholds=num_thresholds,
+        )
+
+    metric_results = []
+    for threshold_i in tqdm(
+        range(num_thresholds), "calculating optimal track score thresholds"
+    ):
+        score_threshold_by_class = {
+            n: score_thresholds_by_class[n][threshold_i] for n in classes
+        }
+        filtered_predictions = sm_utils.filter_by_class_thresholds(
+            track_predictions, score_threshold_by_class
+        )
+        with contextlib.redirect_stdout(
+            None
+        ):  # silence print statements from TrackEval
+            result_for_threshold, _ = evaluator.evaluate(
+                [
+                    TrackEvalDataset(
+                        {
+                            **dataset_config,
+                            "PREDICTED_TRACKS": {"tracker": filtered_predictions},
+                        }
+                    )
+                ],
+                metrics_list,
+            )
+        metric_results.append(
+            result_for_threshold["TrackEvalDataset"]["tracker"]["COMBINED_SEQ"]
+        )
+
+    optimal_score_threshold_by_class = {}
+    optimal_metric_values_by_class = {}
+    mean_metric_values_by_class = {}
+    for name in classes:
+        metric_values = [
+            r[name][metric_class][objective_metric] for r in metric_results
+        ]
+        metric_values = [
+            np.mean(v) if isinstance(v, np.ndarray) else v for v in metric_values
+        ]
+        optimal_threshold = score_thresholds_by_class[name][np.argmax(metric_values)]
+        optimal_score_threshold_by_class[name] = optimal_threshold
+        optimal_metric_values_by_class[name] = max(0, np.max(metric_values))
+        mean_metric_values_by_class[name] = np.nanmean(
+            np.array(metric_values).clip(min=0)
+        )
+    return (
+        optimal_score_threshold_by_class,
+        optimal_metric_values_by_class,
+        mean_metric_values_by_class,
+    )
+
+
+def _filter_by_class(detections: Any, name: str) -> Any:
+    return sm_utils.group_frames(
+        [
+            sm_utils.index_array_values(f, f["name"] == name)
+            for f in sm_utils.ungroup_frames(detections)
+        ]
+    )
+
+
+
+def _calculate_score_thresholds(
+    labels: Sequences,
+    predictions: Sequences,
+    sim_func: Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat],
+    num_thresholds: int = 40,
+    min_recall: float = 0.1,
+) -> NDArrayFloat:
+    scores, n_gt = _calculate_matched_scores(labels, predictions, sim_func)
+    recall_thresholds = np.linspace(min_recall, 1, num_thresholds).round(12)[::-1]
+    if len(scores) == 0:
+        return np.zeros_like(recall_thresholds)
+    score_thresholds = _recall_to_scores(
+        scores, recall_threshold=recall_thresholds, n_gt=n_gt
+    )
+    score_thresholds = np.nan_to_num(score_thresholds, nan=0)
+    return score_thresholds
+
+
+def _calculate_matched_scores(
+    labels: Sequences,
+    predictions: Sequences,
+    sim_func: Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat],
+) -> Tuple[NDArrayFloat, int]:
+    scores = []
+    n_gt = 0
+    num_tp = 0
+    for seq_id in labels:
+        for label_frame, prediction_frame in zip(labels[seq_id], predictions[seq_id]):
+            sim = sim_func(
+                label_frame["translation_m"], prediction_frame["translation_m"]
+            )
+            match_rows, match_cols = linear_sum_assignment(-sim)
+            scores.append(prediction_frame["score"][match_cols])
+            n_gt += len(label_frame["translation_m"])
+            num_tp += len(match_cols)
+
+    scores_array = np.concatenate(scores)
+    return scores_array, n_gt
+
+
+def _recall_to_scores(
+    scores: NDArrayFloat, recall_threshold: NDArrayFloat, n_gt: int
+) -> NDArrayFloat:
+    # Sort scores.
+    scores.sort()
+    scores = scores[::-1]
+
+    # Determine thresholds.
+    recall_values = np.arange(1, len(scores) + 1) / n_gt
+    max_recall_achieved = np.max(recall_values)
+    assert max_recall_achieved <= 1
+    score_thresholds = np.interp(recall_threshold, recall_values, scores, right=0)
+
+    # Set thresholds for unachieved recall values to nan to penalize AMOTA/AMOTP later.
+    if isinstance(recall_threshold, np.ndarray):
+        score_thresholds[recall_threshold > max_recall_achieved] = np.nan
+    return score_thresholds
+
+
+def _xy_center_similarity(
+    centers1: NDArrayFloat, centers2: NDArrayFloat, zero_distance: float
+) -> NDArrayFloat:
+    if centers1.size == 0 or centers2.size == 0:
+        return np.zeros((len(centers1), len(centers2)))
+    xy_dist = np.linalg.norm(
+        centers1[:, np.newaxis, :2] - centers2[np.newaxis, :, :2], axis=2
+    )
+    sim = np.maximum(0, 1 - xy_dist / zero_distance)
+    return cast(NDArrayFloat, sim)
+
+
+def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
+    """Remove all tracks that are beyond the max_dist.
+
+    Args:
+        tracks: Dict[seq_id: List[frame]] Dictionary of tracks
+        max_range_m: maximum distance from ego-vehicle
+
+    Returns:
+        tracks: Dict[seq_id: List[frame]] Dictionary of tracks.
+    """
+    frames = sm_utils.ungroup_frames(tracks)
+    return sm_utils.group_frames(
+        [
+            sm_utils.index_array_values(
+                frame,
+                np.linalg.norm(
+                    frame["translation_m"][:, :2]
+                    - np.array(frame["ego_translation_m"])[:2],
+                    axis=1,
+                )
+                <= max_range_m,
+            )
+            for frame in frames
+        ]
+    )
+
+
+def yaw_to_quaternion3d(yaw: float) -> NDArrayFloat:
+    """Convert a rotation angle in the xy plane (i.e. about the z axis) to a quaternion.
+
+    Args:
+        yaw: angle to rotate about the z-axis, representing an Euler angle, in radians
+
+    Returns:
+        array w/ quaternion coefficients (qw,qx,qy,qz) in scalar-first order, per Argoverse convention.
+    """
+    qx, qy, qz, qw = Rotation.from_euler(seq="z", angles=yaw, degrees=False).as_quat()
+    return np.array([qw, qx, qy, qz])
+
+
+def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Sequences:
+    """Convert the unified label format to a format that is easier to work with for forecasting evaluation.
+
+    Args:
+        tracks: Dictionary of tracks
+        dataset_dir: Dataset root directory
+
+    Returns:
+        tracks: Dictionary of tracks.
+    """
+    if dataset_dir is None:
+        return tracks
+
+    log_ids = list(tracks.keys())
+    log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(
+        log_ids, Path(dataset_dir)
+    )
+
+    for log_id in log_ids:
+        avm = log_id_to_avm[log_id]
+
+        for frame in tracks[log_id]:
+            timestamp_ns = frame["timestamp_ns"]
+            city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)]
+            translation_m = frame["translation_m"] - frame["ego_translation_m"]
+            size = frame["size"]
+            quat = np.array([yaw_to_quaternion3d(yaw) for yaw in frame["yaw"]])
+            score = np.ones((translation_m.shape[0], 1))
+            boxes = np.concatenate([translation_m, size, quat, score], axis=1)
+
+            is_evaluated = compute_objects_in_roi_mask(boxes, city_SE3_ego, avm)
+
+            frame["translation_m"] = frame["translation_m"][is_evaluated]
+            frame["size"] = frame["size"][is_evaluated]
+            frame["yaw"] = frame["yaw"][is_evaluated]
+            frame["velocity_m_per_s"] = frame["velocity_m_per_s"][is_evaluated]
+            frame["label"] = frame["label"][is_evaluated]
+            frame["name"] = frame["name"][is_evaluated]
+            frame["track_id"] = frame["track_id"][is_evaluated]
+
+            if "score" in frame:
+                frame["score"] = frame["score"][is_evaluated]
+
+            if "detection_score" in frame:
+                frame["detection_score"] = frame["detection_score"][is_evaluated]
+
+            if "xy" in frame:
+                frame["xy"] = frame["xy"][is_evaluated]
+
+            if "xy_velocity" in frame:
+                frame["xy_velocity"] = frame["xy_velocity"][is_evaluated]
+
+            if "active" in frame:
+                frame["active"] = frame["active"][is_evaluated]
+
+            if "age" in frame:
+                frame["age"] = frame["age"][is_evaluated]
+
+    return tracks
+
+
+def referred_full_tracks(pkl_file_path):
+    """
+    Reconstructs a mining pkl file by propagating referred object labels across all instances
+    of the same track_id and removing all other objects.
+    
+    Args:
+        pkl_file_path: Path to the pkl file
+    
+    Returns:
+        reconstructed_sequences: Dictionary containing the reconstructed sequences
+    """
+    import pickle
+    
+    # Load the pkl file
+    with open(pkl_file_path, 'rb') as f:
+        sequences = pickle.load(f)
+    
+    reconstructed_sequences = {}
+    
+    # Process each sequence
+    for seq_name, frames in sequences.items():
+        # First pass: identify all track_ids that were ever referred objects
+        referred_track_ids = set()
+        for frame in frames:
+            mask = frame['label'] == 0  # 0 is for REFERRED_OBJECT
+            referred_track_ids.update(frame['track_id'][mask])
+        
+        # Second pass: reconstruct frames
+        new_frames = []
+        for frame in frames:
+            # Create mask for referred track_ids
+            mask = np.isin(frame['track_id'], list(referred_track_ids))
+            
+            # Create new frame with only referred objects
+            new_frame = {
+                'seq_id': frame['seq_id'],
+                'timestamp_ns': frame['timestamp_ns'],
+                'ego_translation_m': frame['ego_translation_m'],
+                'description': frame['description'],
+                'translation_m': frame['translation_m'][mask],
+                'size': frame['size'][mask],
+                'yaw': frame['yaw'][mask],
+                'velocity_m_per_s': frame['velocity_m_per_s'][mask],
+                'label': np.zeros(mask.sum(), dtype=np.int32),  # All are referred objects
+                'name': np.array(['REFERRED_OBJECT'] * mask.sum(), dtype='<U31'),
+                'track_id': frame['track_id'][mask]
+            }
+            
+            # If score exists in the original frame (for predictions), include it
+            if 'score' in frame:
+                new_frame['score'] = frame['score'][mask]
+            
+            new_frames.append(new_frame)
+        
+        reconstructed_sequences[seq_name] = new_frames
+    
+    return reconstructed_sequences
+
+
+def evaluate_mining(
+        track_predictions: Sequences,
+        labels: Sequences,
+        output_dir) -> tuple[float, float]:
+    
+    gt_class = np.zeros(len(labels), dtype=np.int64)
+    pred_class = np.zeros(len(labels), dtype=np.int64)
+
+    for i, description in enumerate(labels.keys()):
+
+        for frame in labels[description]:
+            if len(frame['label']) > 0 and 0 in frame['label']:
+                gt_class[i] = 1
+                break
+
+        for frame in track_predictions[description]:
+            if len(frame['label']) > 0 and 0 in frame['label']:
+                pred_class[i] = 1
+                break
+
+    tp = np.sum(gt_class & pred_class)
+    fp = np.sum(~gt_class & pred_class)
+    fn = np.sum(gt_class & ~pred_class)
+
+    f1_score = float(2*tp / (2*tp + fp + fn))
+
+    print(f'GT scenario matches: {gt_class}')
+    print(f'Predicted scenario matches: {pred_class}')
+    print(f'F1: {f1_score}')
+
+    _plot_confusion_matrix(gt_class, pred_class, output_dir)
+
+    num_correct = 0
+    for i in range(len(gt_class)):
+        if gt_class[i] == pred_class[i]:
+            num_correct += 1
+    
+    acc = num_correct / len(labels)
+
+    return f1_score, acc
+
+
+def evaluate(
+    pred_pkl:str,
+    gt_pkl:str,
+    objective_metric: str,
+    max_range_m: int,
+    dataset_dir: Any,
+    out: str) -> tuple[float,float,float, float]:
+    """Run scenario mining evaluation on the supplied prediction and label pkl files.
+    
+    Args:
+        pred_pkl: Path to track predictions.
+        gt_pkl: Path to track labels.
+        objective_metric: Metric to optimize.
+        max_range_m: Maximum evaluation range.
+        dataset_dir: Path to dataset. Required for ROI pruning.
+        out: Output path.
+    
+    Returns:
+        class_acc: The classification accuracy of if the scenario matches the description
+        full_track_metric: The tracking metric for the full track of any objects that the description ever applies to. 
+        partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
+    """
+
+    track_predictions = pickle.load(open(pred_pkl, "rb"))
+    labels = pickle.load(open(gt_pkl, "rb"))
+
+    output_dir = ""
+    if out:
+        output_dir = out + '/partial_tracks'
+
+    res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
+        track_predictions, labels, 
+        objective_metric=objective_metric, max_range_m=max_range_m,
+        dataset_dir=dataset_dir, out=output_dir)
+    TempLocAP = res['TrackEvalDataset']['TRACKER']['COMBINED_SEQ']['REFERRED_OBJECT']['HOTA']['TempLocAP']
+    
+    full_track_preds = referred_full_tracks(pred_pkl)
+    full_track_labels = referred_full_tracks(gt_pkl)
+
+    output_dir = ""
+    if out:
+        output_dir = out + '/full_tracks'
+
+    _, full_track_metrics, _, _ = evaluate_scenario_mining(
+        full_track_preds, full_track_labels, 
+        objective_metric=objective_metric, max_range_m=max_range_m, 
+        dataset_dir=dataset_dir, out=output_dir,full_tracks=True)
+    
+    full_track_hota = full_track_metrics["REFERRED_OBJECT"]
+    partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
+
+    return f1_score, full_track_hota, partial_track_hota, TempLocAP
+    
+
+def evaluate_scenario_mining(
+    track_predictions: Sequences,
+    labels: Sequences,
+    objective_metric: str,
+    max_range_m: int,
+    dataset_dir: Any,
+    out: str,
+    full_tracks:bool=False
+) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any], float]:
+    """Run evaluation.
+
+    Args:
+        track_predictions: Dictionary of tracks.
+        labels: Dictionary of labels.
+        objective_metric: Metric to optimize.
+        max_range_m: Maximum evaluation range.
+        dataset_dir: Path to dataset. Required for ROI pruning.
+        out: Output path.
+        full_tracks: Whether the supplied labels are for the full track of any
+        object that ever corresponds to the description or only for the timestamps
+        when the description applies.
+
+    Returns:
+        Dictionary of per-category metrics.
+    """
+    classes = list(constants.AV2_CATEGORIES)
+
+    labels = filter_max_dist(labels, max_range_m)
+    track_predictions = filter_max_dist(track_predictions, max_range_m)
+
+    if dataset_dir is not None:
+        labels = filter_drivable_area(labels, dataset_dir)
+        track_predictions = filter_drivable_area(track_predictions, dataset_dir)
+
+    score_thresholds, tuned_metric_values, mean_metric_values = _tune_score_thresholds(
+        labels,
+        track_predictions,
+        objective_metric,
+        classes,
+        num_thresholds=10,
+        match_distance_m=2,
+    )
+    filtered_track_predictions = sm_utils.filter_by_class_thresholds(
+        track_predictions, score_thresholds
+    )
+    res = evaluate_tracking(
+        labels,
+        filtered_track_predictions,
+        classes,
+        tracker_name="TRACKER",
+        output_dir=out,
+    )
+
+    if not full_tracks:
+        f1_score, acc = evaluate_mining(filtered_track_predictions, labels, out)
+        return res, tuned_metric_values, mean_metric_values, f1_score
+    
+    return res, tuned_metric_values, mean_metric_values, 0
+
+
+@click.command()
+@click.option("--predictions", required=True, help="Predictions PKL file")
+@click.option("--ground_truth", required=True, help="Ground Truth PKL file")
+@click.option("--max_range_m", default=50, type=int, help="Evaluate objects within distance of ego vehicle")
+@click.option(
+    "--dataset_dir",
+    default=None,
+    help="Path to dataset split (e.g. /data/Sensor/val). Required for ROI pruning",
+)
+@click.option("--objective_metric", default="HOTA", help="Choices: HOTA, MOTA")
+@click.option("--out", required=True, help="Output JSON file")
+def runner(
+    predictions: str,
+    ground_truth: str,
+    max_range_m: int,
+    dataset_dir: Any,
+    objective_metric: str,
+    out: str,
+) -> None:
+    """Standalone evaluation function."""
+    track_predictions = pickle.load(open(predictions, "rb"))
+    labels = pickle.load(open(ground_truth, "rb"))
+
+    _, _, mean_metric_values, _ = evaluate_scenario_mining(
+        track_predictions, labels, objective_metric, max_range_m, dataset_dir, out
+    )
+
+    pprint(mean_metric_values)
+
+    with open(out, "w") as f:
+        json.dump(mean_metric_values, f, indent=4)
+
+
+if __name__ == "__main__":
+    runner()
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
new file mode 100644
index 00000000..89b2ab13
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -0,0 +1,323 @@
+import os
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from trackeval.metrics._base_metric import _BaseMetric
+from trackeval import _timing
+
+
+class HOTA(_BaseMetric):
+    """Class which implements the HOTA metrics.
+    See: https://link.springer.com/article/10.1007/s11263-020-01375-2
+    """
+
+    def __init__(self, config=None):
+        super().__init__()
+        self.plottable = True
+        self.array_labels = np.arange(0.05, 0.99, 0.05)
+        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']
+        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'TempLocPr', 'TempLocRe', 'OWTA']
+        self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)', 'TempLocAP']
+        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields
+        self.summary_fields = self.float_array_fields + self.float_fields
+
+    @_timing.time
+    def eval_sequence(self, data):
+        """Calculates the HOTA metrics for one sequence"""
+
+        # Initialise results
+        res = {}
+        for field in self.float_array_fields + self.integer_array_fields:
+            res[field] = np.zeros((len(self.array_labels)), dtype=np.float64)
+        for field in self.float_fields:
+            res[field] = 0
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float64)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float64)
+            res['LocA(0)'] = 1.0
+
+            if data['num_gt_dets'] == 0:
+                res['TempLocAP'] = 1
+            else:
+                res['TempLocAP'] = 0
+            return res
+        if data['num_gt_dets'] == 0:
+            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float64)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float64)
+            res['LocA(0)'] = 1.0
+            res['TempLocAP'] = 0
+            return res
+
+        # Variables counting global association
+        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
+        gt_id_count = np.zeros((data['num_gt_ids'], 1))
+        tracker_id_count = np.zeros((1, data['num_tracker_ids']))
+        
+        gt_ids = set()
+        tracker_ids = set()
+        # First loop through each timestep and accumulate global track information.
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Count the potential matches between ids in each timestep
+            # These are normalised, weighted by the match similarity.
+            similarity = data['similarity_scores'][t]
+            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity
+            sim_iou = np.zeros_like(similarity)
+            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps
+            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
+            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou
+            
+            gt_ids = gt_ids.union(set(gt_ids_t))
+            tracker_ids = tracker_ids.union(set(tracker_ids_t))
+
+            # Calculate the total number of dets for each gt_id and tracker_id.
+            gt_id_count[gt_ids_t] += 1
+            tracker_id_count[0, tracker_ids_t] += 1
+
+        # Calculate overall jaccard alignment score (before unique matching) between IDs
+        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)
+        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]
+
+        #Initializing matches dict where keys are gt_ids and values are a list of corresponding pred_ids
+        matches = {gt_id: set() for gt_id in gt_ids}
+
+        # Calculate scores for each timestep
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Deal with the case that there are no gt_det/tracker_det in a timestep.
+            if len(gt_ids_t) == 0:
+                for a, alpha in enumerate(self.array_labels):
+                    res['HOTA_FP'][a] += len(tracker_ids_t)
+                continue
+            if len(tracker_ids_t) == 0:
+                for a, alpha in enumerate(self.array_labels):
+                    res['HOTA_FN'][a] += len(gt_ids_t)
+                continue
+
+            # Get matching scores between pairs of dets for optimizing HOTA
+            similarity = data['similarity_scores'][t]
+            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity
+
+            # Hungarian algorithm to find best matches
+            match_rows, match_cols = linear_sum_assignment(-score_mat)
+
+            for i in range(len(match_rows)):
+                matches[data['gt_ids'][t][match_rows[i]]].add(data['tracker_ids'][t][match_cols[i]])
+
+            # Calculate and accumulate basic statistics
+            for a, alpha in enumerate(self.array_labels):
+                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
+                alpha_match_rows = match_rows[actually_matched_mask]
+                alpha_match_cols = match_cols[actually_matched_mask]
+                num_matches = len(alpha_match_rows)
+                res['HOTA_TP'][a] += num_matches
+                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches
+                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches
+                if num_matches > 0:
+                    res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])
+                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1
+
+        gt_time_segs = {gt_id: {'timestamps': set()} for gt_id in gt_ids}
+
+        for t in range(len(data['gt_ids'])):
+            for gt_id in gt_ids:
+                if gt_id in data['gt_ids'][t]:
+                    gt_time_segs[gt_id]['timestamps'].add(t)
+
+        unionized_tracker_ids = set()
+        for track_ids in matches.values():
+            unionized_tracker_ids = unionized_tracker_ids.union(track_ids)
+
+        unmatched_tracker_ids = tracker_ids.difference(unionized_tracker_ids)
+        track_time_segs = {tracker_id: {'timestamps': set(), 'confidence': 0} for tracker_id in unmatched_tracker_ids}
+
+        for t in range(len(data['tracker_ids'])):
+            for tracker_id in tracker_ids:
+                if tracker_id in data['tracker_ids'][t] and tracker_id not in unionized_tracker_ids:
+                    track_time_segs[tracker_id]['timestamps'].add(t)
+
+                    id_index = np.where(data['tracker_ids'][t] == tracker_id)[0][0]
+                    track_time_segs[tracker_id]['confidence'] = data['tracker_confidences'][t][id_index]
+
+        for gt_id, track_ids in matches.items():
+            if not track_ids:
+                continue
+
+            track_time_segs[-gt_id-1] = {}
+            track_time_segs[-gt_id-1]['timestamps'] = set()
+            track_time_segs[-gt_id-1]['confidence'] = 0
+
+            confidences = []
+            for t in range(len(data['gt_ids'])):
+                for track_id in track_ids:
+                    if track_id in data['tracker_ids'][t]:
+                        id_index = np.where(data['tracker_ids'][t] == track_id)[0][0]
+                        confidences.append(data['tracker_confidences'][t][id_index])
+                        track_time_segs[-gt_id-1]['timestamps'].add(t)
+            track_time_segs[-gt_id-1]['confidence'] = np.mean(np.array(confidences))
+        
+        for a, alpha in enumerate(self.array_labels):
+            tp = 0
+            fn = 0
+            fp = 0
+
+            for gt_id in gt_ids:
+                if (len(matches[gt_id]) == 0 
+                or track_time_segs[-gt_id-1]['confidence'] < alpha):
+                    fn += 1
+                    continue
+                
+                intersection = gt_time_segs[gt_id]['timestamps'].intersection(track_time_segs[-gt_id-1]['timestamps'])
+                union = gt_time_segs[gt_id]['timestamps'].union(track_time_segs[-gt_id-1]['timestamps'])
+                iou = len(intersection)/len(union)
+
+                if iou >= 0.5:
+                    tp += 1
+                else: 
+                    fn += 1
+
+            for tracker_id, stats in track_time_segs.items():
+                if tracker_id >= 0 and stats['confidence'] > alpha:
+                    fp += 1
+
+            if tp+fp == 0:
+                res['TempLocPr'][a] = 0
+            else:
+                res['TempLocPr'][a] = tp / (tp+fp)
+
+            if tp+fn == 0:
+                res['TempLocRe'][a] = 0
+            else:
+                res['TempLocRe'][a] = tp / (tp+fn)
+
+        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
+        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
+        for a, alpha in enumerate(self.array_labels):
+            matches_count = matches_counts[a]
+            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)
+            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])
+            ass_re = matches_count / np.maximum(1, gt_id_count)
+            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])
+            ass_pr = matches_count / np.maximum(1, tracker_id_count)
+            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])
+
+        # Calculate final scores
+        res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    
+    def combine_sequences(self, all_res):
+        """Combines metrics across all sequences"""
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ['AssRe', 'AssPr', 'AssA', 'TempLocPr', 'TempLocRe']:
+            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+
+        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
+        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
+        """Combines metrics across all classes by averaging over the class values.
+        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
+        """
+        res = {}
+        for field in self.integer_array_fields:
+            if ignore_empty_classes:
+                res[field] = self._combine_sum(
+                    {k: v for k, v in all_res.items()
+                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)
+            else:
+                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
+
+        for field in self.float_fields + self.float_array_fields:
+            if ignore_empty_classes:
+                res[field] = np.mean([v[field] for v in all_res.values() if
+                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],
+                                     axis=0)
+            else:
+                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """Combines metrics across all classes by averaging over the detection values"""
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ['AssRe', 'AssPr', 'AssA', 'TempLocRe', 'TempLocPr']:
+            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+
+
+        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
+        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    def _compute_final_fields(self, res):
+        """Calculate sub-metric ('field') values which only depend on other sub-metric values.
+        This function is used both for both per-sequence calculation, and in combining values across sequences.
+        """
+        res['DetRe'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'])
+        res['DetPr'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FP'])
+        res['DetA'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'] + res['HOTA_FP'])
+        res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])
+        res['OWTA'] = np.sqrt(res['DetRe'] * res['AssA'])
+
+        res['TempLocAP'] = self.compute_average_precision(res['TempLocPr'], res['TempLocRe'])
+        res['HOTA(0)'] = res['HOTA'][0]
+        res['LocA(0)'] = res['LocA'][0]
+        res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']
+        return res
+    
+    @staticmethod
+    def compute_average_precision(precisions, recalls):
+        """
+        Compute Average Precision using numpy's trapz function.
+        
+        Args:
+            precisions: List of precision values
+            recalls: List of recall values
+        
+        Returns:
+            Average Precision value
+        """
+        
+        # Sort by recall
+        sorted_indices = np.argsort(recalls)
+        recalls = np.array(recalls)[sorted_indices]
+        precisions = np.array(precisions)[sorted_indices]
+        
+        # Interpolate precision values
+        for i in range(len(precisions)-2, -1, -1):
+            precisions[i] = max(precisions[i], precisions[i+1])
+        
+        # Compute AP using trapezoid rule
+        ap = np.trapezoid(y=precisions, x=recalls)
+        
+        return ap
+
+    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
+        """Create plot of results"""
+
+        # Only loaded when run to reduce minimum requirements
+        from matplotlib import pyplot as plt
+
+        res = table_res['COMBINED_SEQ']
+        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm', 'o--', 'o:']
+        for name, style in zip(self.float_array_fields, styles_to_plot):
+            plt.plot(self.array_labels, res[name], style)
+        plt.xlabel('alpha')
+        plt.ylabel('score')
+        plt.title(tracker + ' - ' + cls)
+        plt.axis([0, 1, 0, 1])
+        legend = []
+        for name in self.float_array_fields:
+            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']
+        plt.legend(legend, loc='lower left')
+        out_file = os.path.join(output_folder, cls + '_plot.pdf')
+        os.makedirs(os.path.dirname(out_file), exist_ok=True)
+        plt.savefig(out_file)
+        plt.savefig(out_file.replace('.pdf', '.png'))
+        plt.clf()
\ No newline at end of file
diff --git a/src/av2/evaluation/scenario_mining/metrics.py b/src/av2/evaluation/scenario_mining/metrics.py
new file mode 100644
index 00000000..19d57397
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/metrics.py
@@ -0,0 +1,8 @@
+from av2.evaluation.scenario_mining.hota import HOTA
+from trackeval.metrics.clear import CLEAR
+from trackeval.metrics.identity import Identity
+from trackeval.metrics.count import Count
+from trackeval.metrics.j_and_f import JAndF
+from trackeval.metrics.track_map import TrackMAP
+from trackeval.metrics.vace import VACE
+from trackeval.metrics.ideucl import IDEucl
\ No newline at end of file
diff --git a/src/av2/evaluation/scenario_mining/utils.py b/src/av2/evaluation/scenario_mining/utils.py
new file mode 100644
index 00000000..49605583
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/utils.py
@@ -0,0 +1,177 @@
+"""Tracking evaluation utilities.
+
+Detection and track data in a single frame are kept as a dictionary of names to numpy arrays.
+This module provides helper functions for manipulating this data format.
+"""
+
+import os
+import pickle
+from collections import defaultdict
+from itertools import chain
+from typing import Any, Dict, Iterable, List, Union, cast
+
+import numpy as np
+
+from av2.utils.typing import NDArrayInt
+
+from ..typing import Frame, Frames, Sequences
+
+
+def save(obj: Any, path: str) -> None:  # noqa
+    """Save an object to a file using pickle serialization.
+
+    Args:
+        obj: An object to be saved.
+        path: A string representing the file path to save the object to.
+    """
+    dir = os.path.dirname(path)
+    if dir != "":
+        os.makedirs(dir, exist_ok=True)
+    with open(path, "wb") as f:
+        pickle.dump(obj, f)
+
+
+def load(path: str) -> Any:  # noqa
+    """Load an object from file using pickle module.
+
+    Args:
+        path: File path.
+
+    Returns:
+        Object or None if the file does not exist.
+    """
+    if not os.path.exists(path):
+        return None
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def annotate_frame_metadata(
+    prediction_frames: Frames, label_frames: Frames, metadata_keys: List[str]
+) -> None:
+    """Copy annotations with provided keys from label to prediction frames.
+
+    Args:
+        prediction_frames: list of prediction frames
+        label_frames: list of label frames
+        metadata_keys: keys of the annotations to be copied.
+    """
+    assert len(prediction_frames) == len(label_frames)
+    for prediction, label in zip(prediction_frames, label_frames):
+        for key in metadata_keys:
+            prediction[key] = label[key]
+
+
+def group_frames(frames_list: Frames) -> Sequences:
+    """Group list of frames into dictionary by sequence id.
+
+    Args:
+        frames_list: List of frames, each containing a detections snapshot at timestamp_ns.
+
+    Returns:
+        Dictionary of frames indexed by sequence id.
+    """
+    frames_by_seq_id = defaultdict(list)
+    sorted_frames_list = sorted(frames_list, key=lambda f: cast(int, f["timestamp_ns"]))
+    for frame in sorted_frames_list:
+        frames_by_seq_id[frame["seq_id"]].append(frame)
+    return dict(frames_by_seq_id)
+
+
+def ungroup_frames(frames_by_seq_id: Sequences) -> Frames:
+    """Ungroup dictionary of frames into a list of frames.
+
+    Args:
+        frames_by_seq_id: dictionary of frames
+
+    Returns:
+        List of frames
+    """
+    return list(chain.from_iterable(frames_by_seq_id.values()))
+
+
+def index_array_values(array_dict: Frame, index: Union[int, NDArrayInt]) -> Frame:
+    """Index each numpy array in dictionary.
+
+    Args:
+        array_dict: dictionary of numpy arrays
+        index: index used to access each numpy array in array_dict
+
+    Returns:
+        Dictionary of numpy arrays, each indexed by the provided index
+    """
+    return {
+        k: v[index] if isinstance(v, np.ndarray) else v for k, v in array_dict.items()
+    }
+
+
+def array_dict_iterator(array_dict: Frame, length: int) -> Iterable[Frame]:
+    """Get an iterator over each index in array_dict.
+
+    Args:
+        array_dict: dictionary of numpy arrays
+        length: number of elements to iterate over
+
+    Returns:
+        Iterator, each element is a dictionary of numpy arrays, indexed from 0 to (length-1)
+    """
+    return (index_array_values(array_dict, i) for i in range(length))
+
+
+def concatenate_array_values(array_dicts: Frames) -> Frame:
+    """Concatenates numpy arrays in list of dictionaries.
+
+    Handles inconsistent keys (will skip missing keys)
+    Does not concatenate non-numpy values (int, str), sets to value if all values are equal
+
+    Args:
+        array_dicts: list of dictionaries
+
+    Returns:
+        single dictionary of names to numpy arrays
+    """
+    combined = defaultdict(list)
+    for array_dict in array_dicts:
+        for k, v in array_dict.items():
+            combined[k].append(v)
+    concatenated = {}
+    for k, vs in combined.items():
+        if all(isinstance(v, np.ndarray) for v in vs):
+            if any(v.size > 0 for v in vs):
+                concatenated[k] = np.concatenate([v for v in vs if v.size > 0])
+            else:
+                concatenated[k] = vs[0]
+        elif all(vs[0] == v for v in vs):
+            concatenated[k] = vs[0]
+    return concatenated
+
+
+def filter_by_class_thresholds(
+    frames_by_seq_id: Sequences, thresholds_by_class: Dict[str, float]
+) -> Sequences:
+    """Filter detections, keeping only detections with score higher than the provided threshold for that class.
+
+    If a class threshold is not provided, all detections in that class is filtered.
+
+    Args:
+        frames_by_seq_id: Dictionary of frames
+        thresholds_by_class: Dictionary containing the score thresholds for each class
+
+    Returns:
+        Dictionary of frames, filtered by class score thresholds
+    """
+    frames = ungroup_frames(frames_by_seq_id)
+    return group_frames(
+        [
+            concatenate_array_values(
+                [
+                    index_array_values(
+                        frame,
+                        (frame["name"] == class_name) & (frame["score"] >= threshold),
+                    )
+                    for class_name, threshold in thresholds_by_class.items()
+                ]
+            )
+            for frame in frames
+        ]
+    )
diff --git a/tests/unit/evaluation/scenario_mining/__init__.py b/tests/unit/evaluation/scenario_mining/__init__.py
new file mode 100644
index 00000000..5f951895
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/__init__.py
@@ -0,0 +1 @@
+"""Scenario Mining sub-package."""
\ No newline at end of file
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
new file mode 100644
index 00000000..85a0a405
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -0,0 +1,27 @@
+"""Scenario mining evaluation unit tests."""
+
+
+import sys
+if sys.stdout is None:
+    sys.stdout = open('stdout.log', 'w')
+
+from av2.evaluation.scenario_mining.eval import evaluate
+
+
+def test_evaluate() -> None:
+    """Test End-to-End Forecasting evaluation."""
+    
+    predictions = 'tests/unit/evaluation/scenario_mining/data/combined_predictions.pkl'
+    ground_truth = '/home/crdavids/Trinity-Sync/av2-test/av2-api/tests/unit/evaluation/scenario_mining/data/combined_gt.pkl'
+
+    objective_metric = 'HOTA'
+    max_range_m = 100
+    dataset_dir = None
+    out = 'tests/unit/evaluation/scenario_mining/data/eval_results'
+
+    evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
+
+test_evaluate()
+
+
+

From f97aa863e396c4b9f22661516ce39dc753d0c0d0 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Wed, 19 Feb 2025 17:57:28 -0500
Subject: [PATCH 02/22] Changed evaluation to loaded Sequences from filepath

---
 src/av2/evaluation/scenario_mining/eval.py    | 32 +++++++++++--------
 .../evaluation/scenario_mining/test_eval.py   |  5 ++-
 2 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index ef111fa2..8edc6c36 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -24,6 +24,7 @@
 from tqdm import tqdm
 from trackeval.datasets._base_dataset import _BaseDataset
 import matplotlib.pyplot as plt
+from urllib.request import urlopen
 
 from av2.evaluation.detection.utils import (
     compute_objects_in_roi_mask,
@@ -832,7 +833,7 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     return tracks
 
 
-def referred_full_tracks(pkl_file_path):
+def referred_full_tracks(sequences: Sequences):
     """
     Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
@@ -846,8 +847,6 @@ def referred_full_tracks(pkl_file_path):
     import pickle
     
     # Load the pkl file
-    with open(pkl_file_path, 'rb') as f:
-        sequences = pickle.load(f)
     
     reconstructed_sequences = {}
     
@@ -934,8 +933,8 @@ def evaluate_mining(
 
 
 def evaluate(
-    pred_pkl:str,
-    gt_pkl:str,
+    track_predictions:Sequences,
+    labels:Sequences,
     objective_metric: str,
     max_range_m: int,
     dataset_dir: Any,
@@ -956,9 +955,6 @@ def evaluate(
         partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
     """
 
-    track_predictions = pickle.load(open(pred_pkl, "rb"))
-    labels = pickle.load(open(gt_pkl, "rb"))
-
     output_dir = ""
     if out:
         output_dir = out + '/partial_tracks'
@@ -969,8 +965,8 @@ def evaluate(
         dataset_dir=dataset_dir, out=output_dir)
     TempLocAP = res['TrackEvalDataset']['TRACKER']['COMBINED_SEQ']['REFERRED_OBJECT']['HOTA']['TempLocAP']
     
-    full_track_preds = referred_full_tracks(pred_pkl)
-    full_track_labels = referred_full_tracks(gt_pkl)
+    full_track_preds = referred_full_tracks(track_predictions)
+    full_track_labels = referred_full_tracks(labels)
 
     output_dir = ""
     if out:
@@ -986,6 +982,14 @@ def evaluate(
 
     return f1_score, full_track_hota, partial_track_hota, TempLocAP
     
+    
+def load(filepath: str):
+    if filepath.startswith("https://") or filepath.startswith("http://"):
+        return pickle.load(urlopen(filepath))
+    else:
+        with open(filepath, "rb") as f:
+            return pickle.load(f)
+
 
 def evaluate_scenario_mining(
     track_predictions: Sequences,
@@ -1066,11 +1070,13 @@ def runner(
     objective_metric: str,
     out: str,
 ) -> None:
+    
+
     """Standalone evaluation function."""
-    track_predictions = pickle.load(open(predictions, "rb"))
-    labels = pickle.load(open(ground_truth, "rb"))
+    track_predictions = load(predictions)
+    labels = load(ground_truth)
 
-    _, _, mean_metric_values, _ = evaluate_scenario_mining(
+    _, _, mean_metric_values, _ = evaluate(
         track_predictions, labels, objective_metric, max_range_m, dataset_dir, out
     )
 
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index 85a0a405..794d87ce 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -5,7 +5,7 @@
 if sys.stdout is None:
     sys.stdout = open('stdout.log', 'w')
 
-from av2.evaluation.scenario_mining.eval import evaluate
+from av2.evaluation.scenario_mining.eval import evaluate, load
 
 
 def test_evaluate() -> None:
@@ -19,6 +19,9 @@ def test_evaluate() -> None:
     dataset_dir = None
     out = 'tests/unit/evaluation/scenario_mining/data/eval_results'
 
+    predictions = load(predictions)
+    ground_truth = load(ground_truth)
+
     evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
 
 test_evaluate()

From 998b0d2402dd27f05997784082f7f4c3b451e7d9 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 28 Feb 2025 20:37:40 -0500
Subject: [PATCH 03/22] Change tlap metric to utilize one-to-one matching

---
 src/av2/evaluation/scenario_mining/eval.py |   4 +-
 src/av2/evaluation/scenario_mining/hota.py | 294 ++++++++++++---------
 2 files changed, 173 insertions(+), 125 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 8edc6c36..a9f3cac2 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -954,10 +954,11 @@ def evaluate(
         full_track_metric: The tracking metric for the full track of any objects that the description ever applies to. 
         partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
     """
-
     output_dir = ""
     if out:
         output_dir = out + '/partial_tracks'
+        print('Making the dir!')
+        Path(output_dir).mkdir(exist_ok=True)
 
     res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
         track_predictions, labels, 
@@ -971,6 +972,7 @@ def evaluate(
     output_dir = ""
     if out:
         output_dir = out + '/full_tracks'
+        Path(output_dir).mkdir(exist_ok=True)
 
     _, full_track_metrics, _, _ = evaluate_scenario_mining(
         full_track_preds, full_track_labels, 
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
index 89b2ab13..b4a7737a 100644
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -27,35 +27,32 @@ def eval_sequence(self, data):
         # Initialise results
         res = {}
         for field in self.float_array_fields + self.integer_array_fields:
-            res[field] = np.zeros((len(self.array_labels)), dtype=np.float64)
+            res[field] = np.zeros((len(self.array_labels)), dtype=float)
         for field in self.float_fields:
             res[field] = 0
 
+        tlap, precisions, recalls = self.calculate_TempLocAP(data)
+        res['TempLocPr'] = precisions
+        res['TempLocRe'] = recalls
+        res['TempLocAP'] = tlap
+
         # Return result quickly if tracker or gt sequence is empty
         if data['num_tracker_dets'] == 0:
-            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float64)
-            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float64)
+            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=float)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
             res['LocA(0)'] = 1.0
-
-            if data['num_gt_dets'] == 0:
-                res['TempLocAP'] = 1
-            else:
-                res['TempLocAP'] = 0
             return res
         if data['num_gt_dets'] == 0:
-            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float64)
-            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float64)
+            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=float)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
             res['LocA(0)'] = 1.0
-            res['TempLocAP'] = 0
             return res
 
         # Variables counting global association
         potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
         gt_id_count = np.zeros((data['num_gt_ids'], 1))
         tracker_id_count = np.zeros((1, data['num_tracker_ids']))
-        
-        gt_ids = set()
-        tracker_ids = set()
+
         # First loop through each timestep and accumulate global track information.
         for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
             # Count the potential matches between ids in each timestep
@@ -66,9 +63,6 @@ def eval_sequence(self, data):
             sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps
             sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
             potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou
-            
-            gt_ids = gt_ids.union(set(gt_ids_t))
-            tracker_ids = tracker_ids.union(set(tracker_ids_t))
 
             # Calculate the total number of dets for each gt_id and tracker_id.
             gt_id_count[gt_ids_t] += 1
@@ -78,9 +72,6 @@ def eval_sequence(self, data):
         global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)
         matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]
 
-        #Initializing matches dict where keys are gt_ids and values are a list of corresponding pred_ids
-        matches = {gt_id: set() for gt_id in gt_ids}
-
         # Calculate scores for each timestep
         for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
             # Deal with the case that there are no gt_det/tracker_det in a timestep.
@@ -100,9 +91,6 @@ def eval_sequence(self, data):
             # Hungarian algorithm to find best matches
             match_rows, match_cols = linear_sum_assignment(-score_mat)
 
-            for i in range(len(match_rows)):
-                matches[data['gt_ids'][t][match_rows[i]]].add(data['tracker_ids'][t][match_cols[i]])
-
             # Calculate and accumulate basic statistics
             for a, alpha in enumerate(self.array_labels):
                 actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
@@ -116,79 +104,6 @@ def eval_sequence(self, data):
                     res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])
                     matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1
 
-        gt_time_segs = {gt_id: {'timestamps': set()} for gt_id in gt_ids}
-
-        for t in range(len(data['gt_ids'])):
-            for gt_id in gt_ids:
-                if gt_id in data['gt_ids'][t]:
-                    gt_time_segs[gt_id]['timestamps'].add(t)
-
-        unionized_tracker_ids = set()
-        for track_ids in matches.values():
-            unionized_tracker_ids = unionized_tracker_ids.union(track_ids)
-
-        unmatched_tracker_ids = tracker_ids.difference(unionized_tracker_ids)
-        track_time_segs = {tracker_id: {'timestamps': set(), 'confidence': 0} for tracker_id in unmatched_tracker_ids}
-
-        for t in range(len(data['tracker_ids'])):
-            for tracker_id in tracker_ids:
-                if tracker_id in data['tracker_ids'][t] and tracker_id not in unionized_tracker_ids:
-                    track_time_segs[tracker_id]['timestamps'].add(t)
-
-                    id_index = np.where(data['tracker_ids'][t] == tracker_id)[0][0]
-                    track_time_segs[tracker_id]['confidence'] = data['tracker_confidences'][t][id_index]
-
-        for gt_id, track_ids in matches.items():
-            if not track_ids:
-                continue
-
-            track_time_segs[-gt_id-1] = {}
-            track_time_segs[-gt_id-1]['timestamps'] = set()
-            track_time_segs[-gt_id-1]['confidence'] = 0
-
-            confidences = []
-            for t in range(len(data['gt_ids'])):
-                for track_id in track_ids:
-                    if track_id in data['tracker_ids'][t]:
-                        id_index = np.where(data['tracker_ids'][t] == track_id)[0][0]
-                        confidences.append(data['tracker_confidences'][t][id_index])
-                        track_time_segs[-gt_id-1]['timestamps'].add(t)
-            track_time_segs[-gt_id-1]['confidence'] = np.mean(np.array(confidences))
-        
-        for a, alpha in enumerate(self.array_labels):
-            tp = 0
-            fn = 0
-            fp = 0
-
-            for gt_id in gt_ids:
-                if (len(matches[gt_id]) == 0 
-                or track_time_segs[-gt_id-1]['confidence'] < alpha):
-                    fn += 1
-                    continue
-                
-                intersection = gt_time_segs[gt_id]['timestamps'].intersection(track_time_segs[-gt_id-1]['timestamps'])
-                union = gt_time_segs[gt_id]['timestamps'].union(track_time_segs[-gt_id-1]['timestamps'])
-                iou = len(intersection)/len(union)
-
-                if iou >= 0.5:
-                    tp += 1
-                else: 
-                    fn += 1
-
-            for tracker_id, stats in track_time_segs.items():
-                if tracker_id >= 0 and stats['confidence'] > alpha:
-                    fp += 1
-
-            if tp+fp == 0:
-                res['TempLocPr'][a] = 0
-            else:
-                res['TempLocPr'][a] = tp / (tp+fp)
-
-            if tp+fn == 0:
-                res['TempLocRe'][a] = 0
-            else:
-                res['TempLocRe'][a] = tp / (tp+fn)
-
         # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
         # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
         for a, alpha in enumerate(self.array_labels):
@@ -203,9 +118,168 @@ def eval_sequence(self, data):
         # Calculate final scores
         res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])
         res = self._compute_final_fields(res)
+
+
         return res
+    
+    def calculate_TempLocAP(self, data):
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            if data['num_gt_dets'] == 0:
+                precisions = np.array([1, 1])
+                recalls = np.array([0, 1])
+                TempLocAP = 1
+                return TempLocAP, precisions, recalls
+            else:
+                precisions = np.array([0, 0])
+                recalls = np.array([0, 1])
+                TempLocAP = 0
+                return TempLocAP, precisions, recalls
+        if data['num_gt_dets'] == 0:
+            precisions = np.array([0, 0])
+            recalls = np.array([0, 1])
+            TempLocAP = 0
+            return TempLocAP, precisions, recalls
+
+        TEMPORAL_IOU_THRESH  = 0.5 #iou
+        MATCHING_DIST_THRESH = 2.0 #m
+
+        pred_tracks = {}
+        gt_tracks = {}
+
+        #Accumulate predicted and ground truth tracks from data
+        for t in range(data['num_timesteps']):
+            for i, gt_id in enumerate(data['gt_ids'][t]):
+                if gt_id not in gt_tracks:
+                    gt_tracks[gt_id] = {}
+                    gt_tracks[gt_id]['xy_pos'] = []
+                    gt_tracks[gt_id]['timestamps'] = []
+                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+
+                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
+                gt_tracks[gt_id]['timestamps'].append(t)
+
+            for i, track_id in enumerate(data['tracker_ids'][t]):
+                if track_id not in pred_tracks:
+                    pred_tracks[track_id] = {}
+                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
+                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
+                    pred_tracks[track_id]['xy_pos'] = []
+                    pred_tracks[track_id]['timestamps'] = []
+
+                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
+                pred_tracks[track_id]['timestamps'].append(t)
+
+        # 1 to 1 match of predicted and ground truth tracks
+        pred_tracks = dict(sorted(pred_tracks, key=lambda item: item[1]['confidence'], reverse=True))
+
+        #keys are track_ids, values are gt_ids
+        matched_ids = {} 
+
+        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        matched_ious = {} 
+        unmatched_gt_ids = list(gt_tracks.keys())
+        unmatched_track_ids = []
+
+        for track_id, track_stats in pred_tracks.items():
+
+            track_traj = track_stats['xy_pos']
+            track_timestamps = track_stats['timestamps']
+            
+            max_similarity = 0
+            best_match = None
+            corresponding_iou = 0
+            for gt_id, gt_stats in gt_tracks.items():
+                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
+                    continue
+
+                gt_traj = gt_stats['xy_pos']
+                gt_timestamps = gt_stats['timestamps']
+
+                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                union = len(set(gt_timestamps).union((set(track_timestamps))))
+                iou = intersection/union
 
+                total_distance = 0.0
+                for timestamp in track_timestamps:
+                    if timestamp in gt_timestamps:
+                        total_distance += np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)])
+
+
+                similarity_score = iou * max(0.0, 1 - (total_distance/(MATCHING_DIST_THRESH*intersection)))
+                if similarity_score > max_similarity:
+                    max_similarity = similarity_score
+                    best_match = gt_id
+                    corresponding_iou = iou
+
+            if max_similarity > 0:
+                matched_ids[track_id] = best_match
+                matched_ious[track_id] = corresponding_iou
+                unmatched_gt_ids.remove(best_match)
+            else:
+                unmatched_track_ids.append(track_id)
+
+        #Compute precision and recall at all confidence thresholds.
+        tp = np.zeros(len(pred_tracks))
+        fp = np.zeros(len(pred_tracks))
+        for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
+
+            if track_id in matched_ids and matched_ious[track_id] >= TEMPORAL_IOU_THRESH:
+                tp[i] = 1
+            else:
+                fp[i] = 1
+
+        tp = np.cumsum(tp)
+        fp = np.cumsum(fp)
+
+        recalls = tp/len(gt_tracks)
+        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+
+        assert np.all(0 <= precisions) & np.all(precisions <= 1)
+        TempLocAP = self.get_ap(recalls, precisions)
+
+        return TempLocAP, precisions, recalls
+
+    def get_envelope(self, precisions):
+        """Compute the precision envelope.
+
+        Args:
+        precisions:
+
+        Returns:
+
+        """
+        for i in range(precisions.size - 1, 0, -1):
+            precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
+        return precisions
     
+    def get_ap(self, recalls, precisions):
+        """Calculate average precision.
+
+        Args:
+        recalls:
+        precisions: Returns (float): average precision.
+
+        Returns:
+
+        """
+        # correct AP calculation
+        # first append sentinel values at the end
+        recalls = np.concatenate(([0.0], recalls, [1.0]))
+        precisions = np.concatenate(([0.0], precisions, [0.0]))
+
+        precisions = self.get_envelope(precisions)
+
+        # to calculate area under PR curve, look for points where X axis (recall) changes value
+        i = np.where(recalls[1:] != recalls[:-1])[0]
+
+        # and sum (\Delta recall) * prec
+        ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
+        return ap       
+    
+
     def combine_sequences(self, all_res):
         """Combines metrics across all sequences"""
         res = {}
@@ -265,38 +339,10 @@ def _compute_final_fields(self, res):
         res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])
         res['OWTA'] = np.sqrt(res['DetRe'] * res['AssA'])
 
-        res['TempLocAP'] = self.compute_average_precision(res['TempLocPr'], res['TempLocRe'])
         res['HOTA(0)'] = res['HOTA'][0]
         res['LocA(0)'] = res['LocA'][0]
         res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']
         return res
-    
-    @staticmethod
-    def compute_average_precision(precisions, recalls):
-        """
-        Compute Average Precision using numpy's trapz function.
-        
-        Args:
-            precisions: List of precision values
-            recalls: List of recall values
-        
-        Returns:
-            Average Precision value
-        """
-        
-        # Sort by recall
-        sorted_indices = np.argsort(recalls)
-        recalls = np.array(recalls)[sorted_indices]
-        precisions = np.array(precisions)[sorted_indices]
-        
-        # Interpolate precision values
-        for i in range(len(precisions)-2, -1, -1):
-            precisions[i] = max(precisions[i], precisions[i+1])
-        
-        # Compute AP using trapezoid rule
-        ap = np.trapezoid(y=precisions, x=recalls)
-        
-        return ap
 
     def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
         """Create plot of results"""

From 2be989c262091199ed8eda744e4be216c1574f1c Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Tue, 4 Mar 2025 23:35:27 -0500
Subject: [PATCH 04/22] Tlap metric now combines track predictions

---
 pyproject.toml                                |   1 +
 src/av2/evaluation/scenario_mining/eval.py    |  75 ++-
 src/av2/evaluation/scenario_mining/hota.py    | 542 +++++++++++++++++-
 .../scenario_mining/metric_evaluator.py       | 457 +++++++++++++++
 .../evaluation/scenario_mining/test_eval.py   |   8 +-
 5 files changed, 1032 insertions(+), 51 deletions(-)
 create mode 100644 src/av2/evaluation/scenario_mining/metric_evaluator.py

diff --git a/pyproject.toml b/pyproject.toml
index d9030c46..dd29bc6b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -85,6 +85,7 @@ strict = true
 
 [tool.pyright]
 include = ["src"]
+typeCheckingMode = "off"
 
 reportMissingTypeStubs = false
 reportUnknownMemberType = false
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index a9f3cac2..cab58b3b 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -24,7 +24,6 @@
 from tqdm import tqdm
 from trackeval.datasets._base_dataset import _BaseDataset
 import matplotlib.pyplot as plt
-from urllib.request import urlopen
 
 from av2.evaluation.detection.utils import (
     compute_objects_in_roi_mask,
@@ -273,6 +272,7 @@ def __init__(self, config: Dict[str, Any]) -> None:
         self.output_fol = config["OUTPUT_FOLDER"]
         self.output_sub_fol = config["OUTPUT_SUB_FOLDER"]
         self.zero_distance = config["ZERO_DISTANCE"]
+        self.should_classes_combine = config["SHOULD_CLASSES_COMBINE"]
         print(f"Using zero_distance={self.zero_distance}m")
 
     @staticmethod
@@ -291,6 +291,7 @@ def get_default_dataset_config() -> Dict[str, Any]:
             "OUTPUT_FOLDER": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
             "OUTPUT_SUB_FOLDER": "",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
             "ZERO_DISTANCE": 2,
+            "SHOULD_CLASSES_COMBINE": True,
         }
         return default_config
 
@@ -514,12 +515,15 @@ def evaluate_tracking(
         "CLASSES_TO_EVAL": classes,
         "TRACKERS_TO_EVAL": [tracker_name],
         "OUTPUT_FOLDER": output_dir,
+        "SHOULD_CLASSES_COMBINE": False
     }
 
     evaluator = Evaluator(
         {
             **Evaluator.get_default_eval_config(),
-            "TIME_PROGRESS": False,
+            "TIME_PROGRESS": True,
+            "PLOT_CURVES": True,
+            'OUTPUT_SUMMARY': True,
         }
     )
     full_result, _ = evaluator.evaluate(
@@ -759,6 +763,14 @@ def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
     )
 
 
+def load(pkl_path):
+    
+    with open(pkl_path, 'rb') as f:
+        data = pickle.load(f)
+
+    return data
+
+
 def yaw_to_quaternion3d(yaw: float) -> NDArrayFloat:
     """Convert a rotation angle in the xy plane (i.e. about the z axis) to a quaternion.
 
@@ -833,7 +845,7 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     return tracks
 
 
-def referred_full_tracks(sequences: Sequences):
+def referred_full_tracks(pkl_file_path):
     """
     Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
@@ -847,6 +859,8 @@ def referred_full_tracks(sequences: Sequences):
     import pickle
     
     # Load the pkl file
+    with open(pkl_file_path, 'rb') as f:
+        sequences = pickle.load(f)
     
     reconstructed_sequences = {}
     
@@ -931,10 +945,27 @@ def evaluate_mining(
 
     return f1_score, acc
 
+def relabel_seq_ids(data):
+    new_data = {}
+    
+    for seq_id, frames in data.items():
+        if isinstance(seq_id, tuple):
+            new_seq_id = str(seq_id)
+            new_data[new_seq_id] = frames
+        else:
+            new_data[seq_id] = frames
+    
+    for seq_id, frames in new_data.items():
+        for frame in frames:
+            if 'seq_id' in frame and isinstance(frame['seq_id'], tuple):
+                frame['seq_id'] = str(frame['seq_id'])
+    
+    return new_data
+
 
 def evaluate(
-    track_predictions:Sequences,
-    labels:Sequences,
+    pred_pkl:str,
+    gt_pkl:str,
     objective_metric: str,
     max_range_m: int,
     dataset_dir: Any,
@@ -954,10 +985,16 @@ def evaluate(
         full_track_metric: The tracking metric for the full track of any objects that the description ever applies to. 
         partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
     """
+
+    track_predictions = pickle.load(open(pred_pkl, "rb"))
+    labels = pickle.load(open(gt_pkl, "rb"))
+
+    track_predictions = relabel_seq_ids(track_predictions)
+    labels = relabel_seq_ids(labels)
+
     output_dir = ""
     if out:
         output_dir = out + '/partial_tracks'
-        print('Making the dir!')
         Path(output_dir).mkdir(exist_ok=True)
 
     res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
@@ -966,8 +1003,10 @@ def evaluate(
         dataset_dir=dataset_dir, out=output_dir)
     TempLocAP = res['TrackEvalDataset']['TRACKER']['COMBINED_SEQ']['REFERRED_OBJECT']['HOTA']['TempLocAP']
     
-    full_track_preds = referred_full_tracks(track_predictions)
-    full_track_labels = referred_full_tracks(labels)
+    full_track_preds = referred_full_tracks(pred_pkl)
+    full_track_labels = referred_full_tracks(gt_pkl)
+    full_track_preds = relabel_seq_ids(full_track_preds)
+    full_track_labels = relabel_seq_ids(full_track_labels)
 
     output_dir = ""
     if out:
@@ -977,21 +1016,13 @@ def evaluate(
     _, full_track_metrics, _, _ = evaluate_scenario_mining(
         full_track_preds, full_track_labels, 
         objective_metric=objective_metric, max_range_m=max_range_m, 
-        dataset_dir=dataset_dir, out=output_dir,full_tracks=True)
+        dataset_dir=dataset_dir, out=output_dir, full_tracks=True)
     
     full_track_hota = full_track_metrics["REFERRED_OBJECT"]
     partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
 
     return f1_score, full_track_hota, partial_track_hota, TempLocAP
     
-    
-def load(filepath: str):
-    if filepath.startswith("https://") or filepath.startswith("http://"):
-        return pickle.load(urlopen(filepath))
-    else:
-        with open(filepath, "rb") as f:
-            return pickle.load(f)
-
 
 def evaluate_scenario_mining(
     track_predictions: Sequences,
@@ -1032,7 +1063,7 @@ def evaluate_scenario_mining(
         track_predictions,
         objective_metric,
         classes,
-        num_thresholds=10,
+        num_thresholds = 3,
         match_distance_m=2,
     )
     filtered_track_predictions = sm_utils.filter_by_class_thresholds(
@@ -1072,13 +1103,11 @@ def runner(
     objective_metric: str,
     out: str,
 ) -> None:
-    
-
     """Standalone evaluation function."""
-    track_predictions = load(predictions)
-    labels = load(ground_truth)
+    track_predictions = pickle.load(open(predictions, "rb"))
+    labels = pickle.load(open(ground_truth, "rb"))
 
-    _, _, mean_metric_values, _ = evaluate(
+    _, _, mean_metric_values, _ = evaluate_scenario_mining(
         track_predictions, labels, objective_metric, max_range_m, dataset_dir, out
     )
 
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
index b4a7737a..fdaf0736 100644
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -1,5 +1,6 @@
 import os
 import numpy as np
+import matplotlib.pyplot as plt
 from scipy.optimize import linear_sum_assignment
 from trackeval.metrics._base_metric import _BaseMetric
 from trackeval import _timing
@@ -15,7 +16,7 @@ def __init__(self, config=None):
         self.plottable = True
         self.array_labels = np.arange(0.05, 0.99, 0.05)
         self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']
-        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'TempLocPr', 'TempLocRe', 'OWTA']
+        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'OWTA']
         self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)', 'TempLocAP']
         self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields
         self.summary_fields = self.float_array_fields + self.float_fields
@@ -32,8 +33,14 @@ def eval_sequence(self, data):
             res[field] = 0
 
         tlap, precisions, recalls = self.calculate_TempLocAP(data)
-        res['TempLocPr'] = precisions
-        res['TempLocRe'] = recalls
+        tlap2, precisions2, recalls2 = self.calculate_TempLocAP_concat(data)
+        tlap3, precisions3, recalls3 = self.calculate_TempLocAP_merge(data)
+        self.plot_precision_recall_curve([recalls, recalls2, recalls3], 
+                                         [precisions, precisions2, precisions3], 
+                                         [tlap, tlap2, tlap3], 
+                                         ['1 to 1', 'concat', 'merge'],
+                                         ['blue', 'green', 'red'])
+
         res['TempLocAP'] = tlap
 
         # Return result quickly if tracker or gt sequence is empty
@@ -92,6 +99,7 @@ def eval_sequence(self, data):
             match_rows, match_cols = linear_sum_assignment(-score_mat)
 
             # Calculate and accumulate basic statistics
+
             for a, alpha in enumerate(self.array_labels):
                 actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
                 alpha_match_rows = match_rows[actually_matched_mask]
@@ -130,16 +138,19 @@ def calculate_TempLocAP(self, data):
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 TempLocAP = 1
+                print(1)
                 return TempLocAP, precisions, recalls
             else:
                 precisions = np.array([0, 0])
                 recalls = np.array([0, 1])
                 TempLocAP = 0
+                print(0)
                 return TempLocAP, precisions, recalls
         if data['num_gt_dets'] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
+            print(0)
             return TempLocAP, precisions, recalls
 
         TEMPORAL_IOU_THRESH  = 0.5 #iou
@@ -172,7 +183,7 @@ def calculate_TempLocAP(self, data):
                 pred_tracks[track_id]['timestamps'].append(t)
 
         # 1 to 1 match of predicted and ground truth tracks
-        pred_tracks = dict(sorted(pred_tracks, key=lambda item: item[1]['confidence'], reverse=True))
+        sorted_keys = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
 
         #keys are track_ids, values are gt_ids
         matched_ids = {} 
@@ -182,7 +193,8 @@ def calculate_TempLocAP(self, data):
         unmatched_gt_ids = list(gt_tracks.keys())
         unmatched_track_ids = []
 
-        for track_id, track_stats in pred_tracks.items():
+        for track_id in sorted_keys:
+            track_stats = pred_tracks[track_id]
 
             track_traj = track_stats['xy_pos']
             track_timestamps = track_stats['timestamps']
@@ -201,6 +213,9 @@ def calculate_TempLocAP(self, data):
                 union = len(set(gt_timestamps).union((set(track_timestamps))))
                 iou = intersection/union
 
+                if iou < TEMPORAL_IOU_THRESH:
+                    continue
+
                 total_distance = 0.0
                 for timestamp in track_timestamps:
                     if timestamp in gt_timestamps:
@@ -208,11 +223,11 @@ def calculate_TempLocAP(self, data):
                             track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)])
 
 
-                similarity_score = iou * max(0.0, 1 - (total_distance/(MATCHING_DIST_THRESH*intersection)))
+                similarity_score = iou * max(0.0, 
+                    1 - (total_distance/(MATCHING_DIST_THRESH*(intersection+np.finfo(np.float64).eps))))
                 if similarity_score > max_similarity:
                     max_similarity = similarity_score
                     best_match = gt_id
-                    corresponding_iou = iou
 
             if max_similarity > 0:
                 matched_ids[track_id] = best_match
@@ -226,7 +241,210 @@ def calculate_TempLocAP(self, data):
         fp = np.zeros(len(pred_tracks))
         for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
 
-            if track_id in matched_ids and matched_ious[track_id] >= TEMPORAL_IOU_THRESH:
+            if track_id in matched_ids:
+                tp[i] = 1
+            else:
+                fp[i] = 1
+
+        tp = np.cumsum(tp)
+        fp = np.cumsum(fp)
+
+        recalls = tp/len(gt_tracks)
+        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+
+        assert np.all(0 <= precisions) & np.all(precisions <= 1)
+        TempLocAP = self.get_ap(recalls, precisions)
+
+        return TempLocAP, precisions, recalls
+    
+
+    def calculate_TempLocAP_concat(self, data):
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            if data['num_gt_dets'] == 0:
+                precisions = np.array([1, 1])
+                recalls = np.array([0, 1])
+                TempLocAP = 1
+                print(1)
+                return TempLocAP, precisions, recalls
+            else:
+                precisions = np.array([0, 0])
+                recalls = np.array([0, 1])
+                TempLocAP = 0
+                print(0)
+                return TempLocAP, precisions, recalls
+        if data['num_gt_dets'] == 0:
+            precisions = np.array([0, 0])
+            recalls = np.array([0, 1])
+            TempLocAP = 0
+            print(0)
+            return TempLocAP, precisions, recalls
+
+        TEMPORAL_IOU_THRESH  = 0.5 #iou
+        MATCHING_DIST_THRESH = 2.0 #m
+
+        pred_tracks = {}
+        gt_tracks = {}
+
+        #Accumulate predicted and ground truth tracks from data
+        for t in range(data['num_timesteps']):
+            for i, gt_id in enumerate(data['gt_ids'][t]):
+                if gt_id not in gt_tracks:
+                    gt_tracks[gt_id] = {}
+                    gt_tracks[gt_id]['xy_pos'] = []
+                    gt_tracks[gt_id]['timestamps'] = []
+                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+
+                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
+                gt_tracks[gt_id]['timestamps'].append(t)
+
+            for i, track_id in enumerate(data['tracker_ids'][t]):
+                if track_id not in pred_tracks:
+                    pred_tracks[track_id] = {}
+                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
+                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
+                    pred_tracks[track_id]['xy_pos'] = []
+                    pred_tracks[track_id]['timestamps'] = []
+
+                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
+                pred_tracks[track_id]['timestamps'].append(t)
+
+        # 1 to 1 match of predicted and ground truth tracks
+        pred_ids_by_conf = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+
+        #Keys are match_id, values are dict of gt_id, pred_ids, gt_traj, pred_traj, category and confidence 
+        matched_predictions = {}
+
+        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        unmatched_gt_ids = list(gt_tracks.keys())
+        unmatched_track_ids = []
+
+        for track_id in pred_ids_by_conf:
+            track_stats = pred_tracks[track_id]
+            track_confidence = track_stats['confidence']
+            track_traj = track_stats['xy_pos']
+            track_timestamps = track_stats['timestamps']
+            
+            max_similarity = 0
+            best_match_stats = None
+            best_match = None
+            for gt_id, gt_stats in gt_tracks.items():
+                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
+                    continue
+
+                gt_traj = gt_stats['xy_pos']
+                gt_timestamps = gt_stats['timestamps']
+
+                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                union = len(set(gt_timestamps).union((set(track_timestamps))))
+                iou = intersection/union
+
+                if iou < TEMPORAL_IOU_THRESH:
+                    continue
+
+                distances = []
+                for timestamp in track_timestamps:
+                    if timestamp in gt_timestamps:
+                        distances.append(np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+
+
+                similarity_score = iou * max(0, 
+                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+
+                if similarity_score > max_similarity:
+                    max_similarity = similarity_score
+                    best_match = gt_id
+                    best_match_stats = {
+                        'gt_id': gt_id,
+                        'gt_timestamps': gt_timestamps,
+                        'gt_traj': gt_traj,
+                        'pred_ids': [track_id],
+                        'pred_traj': track_traj,
+                        'pred_timestamps': track_timestamps,
+                        'category': gt_stats['category'],
+                        'confidence': track_confidence,
+                        'similarity': similarity_score
+                    }
+
+            for match_id, match_stats in matched_predictions.items():
+
+                gt_id = match_stats['gt_id']
+                gt_timestamps = match_stats['gt_timestamps']
+                gt_traj = match_stats['gt_traj']
+
+                pred_ids = match_stats['pred_ids']
+                pred_timestamps = match_stats['pred_timestamps']
+                pred_traj = match_stats['pred_traj']
+
+                category = match_stats['category']
+                confidence = match_stats['confidence']
+                match_similarity = match_stats['similarity']
+
+                if (len(set(pred_timestamps).intersection(set(track_timestamps))) > 0
+                or track_stats['category'] != category):
+                    continue
+                
+                concat_pred_ids = pred_ids + track_id
+                concat_timestamps = pred_timestamps + track_timestamps
+                concat_traj = pred_traj + track_traj
+                concat_confidence = confidence*len(pred_timestamps)+track_confidence*len(track_timestamps)
+                concat_confidence /= len(concat_timestamps)
+
+                intersection = len(set(gt_timestamps).intersection(set(concat_timestamps)))
+                union = len(set(gt_timestamps).union(set(concat_timestamps)))
+                iou = intersection/union
+
+                if iou < TEMPORAL_IOU_THRESH:
+                    continue
+
+                distances = []
+                for timestamp in concat_timestamps:
+                    if timestamp in gt_timestamps:
+                        distances.append(np.linalg.norm(
+                            concat_traj[concat_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+
+                similarity_score = iou * max(0, 
+                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+
+                if similarity_score > max_similarity and similarity_score > match_similarity:
+                    max_similarity = similarity_score
+                    best_match = match_id
+                    best_match_stats = {
+                        'pred_ids': concat_pred_ids,
+                        'pred_traj': concat_traj,
+                        'pred_timestamps': concat_timestamps,
+                        'confidence': concat_confidence,
+                        'similarity': similarity_score
+                        }
+                
+            if max_similarity > 0:
+                if best_match < 0:
+                    matched_predictions[best_match].update(best_match_stats)
+                elif best_match is not None:
+                    matched_predictions[-best_match-1] = best_match_stats
+                    unmatched_gt_ids.remove(best_match)
+            else:
+                unmatched_track_ids.append(track_id)
+
+        concat_preds_by_conf = {}
+        preds = list(matched_predictions.keys()) + unmatched_track_ids
+        for pred in preds:
+            if pred < 0:
+                concat_preds_by_conf[pred] = matched_predictions[pred]['confidence']
+            else:
+                concat_preds_by_conf[pred] = pred_tracks[pred]['confidence']
+
+        concat_ids_by_conf = sorted(concat_preds_by_conf.keys(), key=lambda key: concat_preds_by_conf[key], reverse=True)
+
+        #Compute precision and recall at all confidence thresholds.
+        tp = np.zeros(len(concat_ids_by_conf))
+        fp = np.zeros(len(concat_ids_by_conf))
+
+        for i, concat_id in enumerate(concat_ids_by_conf):
+
+            if concat_id < 0:
                 tp[i] = 1
             else:
                 fp[i] = 1
@@ -241,6 +459,214 @@ def calculate_TempLocAP(self, data):
         TempLocAP = self.get_ap(recalls, precisions)
 
         return TempLocAP, precisions, recalls
+    
+    
+    def calculate_TempLocAP_merge(self, data):
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            if data['num_gt_dets'] == 0:
+                precisions = np.array([1, 1])
+                recalls = np.array([0, 1])
+                TempLocAP = 1
+                print(1)
+                return TempLocAP, precisions, recalls
+            else:
+                precisions = np.array([0, 0])
+                recalls = np.array([0, 1])
+                TempLocAP = 0
+                print(0)
+                return TempLocAP, precisions, recalls
+        if data['num_gt_dets'] == 0:
+            precisions = np.array([0, 0])
+            recalls = np.array([0, 1])
+            TempLocAP = 0
+            print(0)
+            return TempLocAP, precisions, recalls
+
+        TEMPORAL_IOU_THRESH  = 0.5 #iou
+        MATCHING_DIST_THRESH = 2.0 #m
+
+        pred_tracks = {}
+        gt_tracks = {}
+
+        #Accumulate predicted and ground truth tracks from data
+        for t in range(data['num_timesteps']):
+            for i, gt_id in enumerate(data['gt_ids'][t]):
+                if gt_id not in gt_tracks:
+                    gt_tracks[gt_id] = {}
+                    gt_tracks[gt_id]['xy_pos'] = []
+                    gt_tracks[gt_id]['timestamps'] = []
+                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+
+                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
+                gt_tracks[gt_id]['timestamps'].append(t)
+
+            for i, track_id in enumerate(data['tracker_ids'][t]):
+                if track_id not in pred_tracks:
+                    pred_tracks[track_id] = {}
+                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
+                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
+                    pred_tracks[track_id]['xy_pos'] = []
+                    pred_tracks[track_id]['timestamps'] = []
+
+                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
+                pred_tracks[track_id]['timestamps'].append(t)
+
+        # 1 to 1 match of predicted and ground truth tracks
+        pred_ids_by_conf = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+
+        #keys are gt_id, values are list of corresponding pred_ids
+        matched_ids = {}
+
+        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        unmatched_track_ids = []
+
+        for track_id in pred_ids_by_conf:
+            track_stats = pred_tracks[track_id]
+            track_confidence = track_stats['confidence']
+            track_traj = track_stats['xy_pos']
+            track_timestamps = track_stats['timestamps']
+            
+            max_similarity = 0
+            best_match_stats = None
+            best_match = None
+            for gt_id, gt_stats in gt_tracks.items():
+                if gt_stats['category'] != track_stats['category']:
+                    continue
+
+                gt_traj = gt_stats['xy_pos']
+                gt_timestamps = gt_stats['timestamps']
+
+                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                iol = intersection/len(track_timestamps)
+
+                if iol < TEMPORAL_IOU_THRESH:
+                    continue
+
+                distances = []
+                for timestamp in track_timestamps:
+                    if timestamp in gt_timestamps:
+                        distances.append(np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+
+
+                similarity_score = iol * max(0, 
+                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+
+                if similarity_score > max_similarity:
+                    max_similarity = similarity_score
+                    best_match = gt_id
+
+            if max_similarity > 0:
+                if best_match not in matched_ids:
+                    matched_ids[best_match] = [track_id]
+                else:
+                    matched_ids[best_match].append(track_id)
+            else:
+                unmatched_track_ids.append(track_id)
+
+        merged_predictions = {}
+        for gt_id, pred_ids in matched_ids.items():
+            merged_traj = []
+            merged_timestamps = []
+            merged_confidences = []
+            merged_catetory = None
+
+            for pred_id in pred_ids:
+                track_timestamps = pred_tracks[pred_id]['timestamps']
+                track_trajectory = pred_tracks[pred_id]['xy_pos']
+                track_confidence = pred_tracks[pred_id]['confidence']
+                track_category = pred_tracks[pred_id]['category']
+                
+                if len(merged_timestamps) == 0:
+                    merged_timestamps.extend(track_timestamps)
+                    merged_traj.extend(track_trajectory)
+                    merged_catetory = track_category
+                    merged_confidences.extend([track_confidence]*len(track_timestamps))
+                    continue
+
+                for i, timestamp in enumerate(track_timestamps):
+                    if timestamp not in merged_timestamps:
+                        insertion_index = 0
+                        for merge_timestamp in merged_timestamps:
+                            if merge_timestamp > timestamp:
+                                insertion_index += 1
+
+                        merged_timestamps.insert(insertion_index, timestamp)
+                        merged_traj.insert(insertion_index, track_trajectory[i])
+                        merged_confidences.insert(insertion_index, track_confidence)
+                    else:
+                        insertion_index = merged_timestamps.index(timestamp)
+                        if track_confidence > merged_confidences[insertion_index]:
+                            merged_confidences[insertion_index] = track_confidence
+                            merged_traj[insertion_index] = track_trajectory[i]
+
+            merged_predictions[gt_id] = {
+                'xy_pos': merged_traj,
+                'timestamps': merged_timestamps,
+                'confidence': np.mean(np.array(merged_confidences)),
+                'category': merged_catetory
+            }
+
+        merged_ids_by_conf = sorted(merged_predictions.keys(), key=lambda key: merged_predictions[key]['confidence'], reverse=True)
+        matched_gt_ids = []
+
+        #Compute precision and recall at all confidence thresholds.
+        tp = np.zeros(len(merged_ids_by_conf) + len(unmatched_track_ids))
+        fp = np.zeros(len(merged_ids_by_conf) + len(unmatched_track_ids))
+
+        for i, track_id in enumerate(merged_ids_by_conf):
+            track_stats = pred_tracks[track_id]
+            track_confidence = track_stats['confidence']
+            track_traj = track_stats['xy_pos']
+            track_timestamps = track_stats['timestamps']
+            
+            max_similarity = 0
+            best_match = None
+            for gt_id, gt_stats in gt_tracks.items():
+                if gt_id in matched_gt_ids or gt_stats['category'] != track_stats['category']:
+                    continue
+
+                gt_traj = gt_stats['xy_pos']
+                gt_timestamps = gt_stats['timestamps']
+
+                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                union = len(set(gt_timestamps).union((set(track_timestamps))))
+                iou = intersection/union
+
+                if iou < TEMPORAL_IOU_THRESH:
+                    continue
+
+                distances = []
+                for timestamp in track_timestamps:
+                    if timestamp in gt_timestamps:
+                        distances.append(np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+
+                similarity_score = iou * max(0, 
+                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+
+                if similarity_score > max_similarity:
+                    max_similarity = similarity_score
+                    best_match = gt_id
+
+            if max_similarity > 0:
+                matched_gt_ids.append(best_match)
+                tp[i] = 1
+                fp[i] = 0
+
+        tp = np.cumsum(tp)
+        fp = np.cumsum(fp)
+
+        recalls = tp/len(gt_tracks)
+        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+
+        assert np.all(0 <= precisions) & np.all(precisions <= 1)
+        TempLocAP = self.get_ap(recalls, precisions)
+
+        return TempLocAP, precisions, recalls    
+
 
     def get_envelope(self, precisions):
         """Compute the precision envelope.
@@ -256,43 +682,110 @@ def get_envelope(self, precisions):
         return precisions
     
     def get_ap(self, recalls, precisions):
-        """Calculate average precision.
-
+        """
+        Calculate average precision.
+        
         Args:
-        recalls:
-        precisions: Returns (float): average precision.
-
+            recalls: Array of recall values
+            precisions: Array of precision values
+            
         Returns:
-
+            float: average precision.
         """
-        # correct AP calculation
         # first append sentinel values at the end
         recalls = np.concatenate(([0.0], recalls, [1.0]))
         precisions = np.concatenate(([0.0], precisions, [0.0]))
-
+        
+        # get envelope (maximum precision for each recall value)
         precisions = self.get_envelope(precisions)
-
+        
         # to calculate area under PR curve, look for points where X axis (recall) changes value
         i = np.where(recalls[1:] != recalls[:-1])[0]
-
+        
         # and sum (\Delta recall) * prec
         ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
-        return ap       
+        
+        return ap
     
+    def plot_precision_recall_curve(self, recalls_list, precisions_list, ap_values=None, labels=None, 
+                                colors=['blue', 'green'], save_path=None):
+        """
+        Plot precision-recall curves for one or two sets of data.
+        
+        Args:
+            recalls_list: List of recall arrays to plot
+            precisions_list: List of precision arrays to plot
+            ap_values: Optional list of AP values to display in the title
+            labels: Optional list of labels for the legend
+            colors: List of colors for the plots (default: blue and green)
+            save_path: Optional path to save the plot
+        """
+        plt.figure(figsize=(8, 6))
+        
+        if not isinstance(recalls_list, list):
+            recalls_list = [recalls_list]
+        if not isinstance(precisions_list, list):
+            precisions_list = [precisions_list]
+        
+        if labels is None:
+            labels = [f"Curve {i+1}" for i in range(len(recalls_list))]
+        
+        for i, (recalls, precisions) in enumerate(zip(recalls_list, precisions_list)):
+            color = colors[i % len(colors)]
+            
+            # Prepare data for plotting (add sentinel values)
+            plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
+            plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
+            plot_precisions = self.get_envelope(plot_precisions.copy())
+            
+            # Plot the curve
+            plt.plot(plot_recalls, plot_precisions, color=color, linestyle='-', 
+                    linewidth=2, label=labels[i])
+            plt.fill_between(plot_recalls, 0, plot_precisions, alpha=0.1, color=color)
+        
+        # Set title
+        if ap_values:
+            ap_text = ", ".join([f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)])
+            plt.title(f'Precision-Recall Curves ({ap_text})', fontsize=16)
+        else:
+            plt.title('Precision-Recall Curves', fontsize=16)
+        
+        # Set labels and limits
+        plt.xlabel('Recall', fontsize=14)
+        plt.ylabel('Precision', fontsize=14)
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.grid(True)
+        
+        # Add legend if multiple curves
+        if len(recalls_list) > 1:
+            plt.legend(loc='lower left')
+        
+        # Save if path provided
+        if save_path:
+            plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        
+        plt.show()
+
 
     def combine_sequences(self, all_res):
         """Combines metrics across all sequences"""
         res = {}
         for field in self.integer_array_fields:
             res[field] = self._combine_sum(all_res, field)
-        for field in ['AssRe', 'AssPr', 'AssA', 'TempLocPr', 'TempLocRe']:
+        for field in ['AssRe', 'AssPr', 'AssA']:
             res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+        for field in ['TempLocAP']:
+            summed_TempLocAP = 0
+            for seq_id, seq_res in all_res.items():
+                summed_TempLocAP += seq_res[field]
+            res[field] = summed_TempLocAP / len(all_res)
 
         loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
         res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
         res = self._compute_final_fields(res)
         return res
-
+    
     def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
         """Combines metrics across all classes by averaging over the class values.
         If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
@@ -320,8 +813,13 @@ def combine_classes_det_averaged(self, all_res):
         res = {}
         for field in self.integer_array_fields:
             res[field] = self._combine_sum(all_res, field)
-        for field in ['AssRe', 'AssPr', 'AssA', 'TempLocRe', 'TempLocPr']:
+        for field in ['AssRe', 'AssPr', 'AssA']:
             res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+        for field in ['TempLocAP']:
+            summed_TempLocAP = 0
+            for seq_id, seq_res in all_res.items():
+                summed_TempLocAP += seq_res[field]
+            res[field] = summed_TempLocAP / len(all_res)
 
 
         loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
diff --git a/src/av2/evaluation/scenario_mining/metric_evaluator.py b/src/av2/evaluation/scenario_mining/metric_evaluator.py
new file mode 100644
index 00000000..283d3af3
--- /dev/null
+++ b/src/av2/evaluation/scenario_mining/metric_evaluator.py
@@ -0,0 +1,457 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button, Slider
+from matplotlib.lines import Line2D
+import matplotlib.patches as patches
+import copy
+import tkinter as tk
+from tkinter import messagebox
+
+class TempLocAPCalculator:
+    def __init__(self):
+        pass
+        
+    def get_ap(self, recalls, precisions):
+        # Standard VOC AP calculation
+        # First append sentinel values at the end
+        recalls = np.concatenate(([0.], recalls, [1.]))
+        precisions = np.concatenate(([0.], precisions, [0.]))
+
+        # Compute the precision envelope
+        for i in range(precisions.size - 1, 0, -1):
+            precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
+
+        # Compute area under PR curve
+        indices = np.where(recalls[1:] != recalls[:-1])[0] + 1
+        ap = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices])
+        return ap
+        
+    def calculate_TempLocAP(self, data):
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            if data['num_gt_dets'] == 0:
+                precisions = np.array([1, 1])
+                recalls = np.array([0, 1])
+                TempLocAP = 1
+                return TempLocAP, precisions, recalls
+            else:
+                precisions = np.array([0, 0])
+                recalls = np.array([0, 1])
+                TempLocAP = 0
+                return TempLocAP, precisions, recalls
+        if data['num_gt_dets'] == 0:
+            precisions = np.array([0, 0])
+            recalls = np.array([0, 1])
+            TempLocAP = 0
+            return TempLocAP, precisions, recalls
+
+        TEMPORAL_IOU_THRESH  = 0.5 #iou
+        MATCHING_DIST_THRESH = 2.0 #m
+
+        pred_tracks = {}
+        gt_tracks = {}
+
+        #Accumulate predicted and ground truth tracks from data
+        for t in range(data['num_timesteps']):
+            for i, gt_id in enumerate(data['gt_ids'][t]):
+                if gt_id not in gt_tracks:
+                    gt_tracks[gt_id] = {}
+                    gt_tracks[gt_id]['xy_pos'] = []
+                    gt_tracks[gt_id]['timestamps'] = []
+                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+
+                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
+                gt_tracks[gt_id]['timestamps'].append(t)
+
+            for i, track_id in enumerate(data['tracker_ids'][t]):
+                if track_id not in pred_tracks:
+                    pred_tracks[track_id] = {}
+                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
+                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
+                    pred_tracks[track_id]['xy_pos'] = []
+                    pred_tracks[track_id]['timestamps'] = []
+
+                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
+                pred_tracks[track_id]['timestamps'].append(t)
+
+        # 1 to 1 match of predicted and ground truth tracks
+        sorted_keys = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+
+        #keys are track_ids, values are gt_ids
+        matched_ids = {} 
+
+        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        matched_ious = {} 
+        unmatched_gt_ids = list(gt_tracks.keys())
+        unmatched_track_ids = []
+
+        for track_id in sorted_keys:
+            track_stats = pred_tracks[track_id]
+
+            track_traj = track_stats['xy_pos']
+            track_timestamps = track_stats['timestamps']
+            
+            max_similarity = 0
+            best_match = None
+            corresponding_iou = 0
+            for gt_id, gt_stats in gt_tracks.items():
+                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
+                    continue
+
+                gt_traj = gt_stats['xy_pos']
+                gt_timestamps = gt_stats['timestamps']
+
+                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                union = len(set(gt_timestamps).union((set(track_timestamps))))
+                iou = intersection/union
+
+                total_distance = 0.0
+                for timestamp in track_timestamps:
+                    if timestamp in gt_timestamps:
+                        total_distance += np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)])
+
+
+                similarity_score = iou * max(0.0, 
+                    1 - (total_distance/(MATCHING_DIST_THRESH*(intersection+np.finfo(np.float64).eps))))
+                if similarity_score > max_similarity:
+                    max_similarity = similarity_score
+                    best_match = gt_id
+                    corresponding_iou = iou
+
+            if max_similarity > 0:
+                matched_ids[track_id] = best_match
+                matched_ious[track_id] = corresponding_iou
+                unmatched_gt_ids.remove(best_match)
+            else:
+                unmatched_track_ids.append(track_id)
+
+        #Compute precision and recall at all confidence thresholds.
+        tp = np.zeros(len(pred_tracks))
+        fp = np.zeros(len(pred_tracks))
+        for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
+
+            if track_id in matched_ids and matched_ious[track_id] >= TEMPORAL_IOU_THRESH:
+                tp[i] = 1
+            else:
+                fp[i] = 1
+
+        tp = np.cumsum(tp)
+        fp = np.cumsum(fp)
+
+        recalls = tp/max(len(gt_tracks), 1)
+        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+
+        assert np.all(0 <= precisions) & np.all(precisions <= 1)
+        TempLocAP = self.get_ap(recalls, precisions)
+
+        return TempLocAP, precisions, recalls
+
+
+class TrackDrawUI:
+    def __init__(self):
+        self.fig, self.ax = plt.subplots(figsize=(10, 8))
+        plt.subplots_adjust(bottom=0.25)
+        
+        # Configure the plot
+        self.ax.set_xlim(0, 100)
+        self.ax.set_ylim(0, 10)
+        self.ax.set_xlabel("Time")
+        self.ax.set_ylabel("Space")
+        self.ax.grid(True, linestyle='--', alpha=0.7)
+        
+        # Track data
+        self.tracks = {
+            'gt': [],      # Ground truth tracks (orange)
+            'pred': []     # Prediction tracks (green with varying opacity)
+        }
+        
+        # Drawing mode and state
+        self.mode = 'gt'  # Initial mode: ground truth
+        self.drawing = False
+        self.start_point = None
+        self.current_line = None
+        self.current_confidence = 1.0  # Default confidence for predictions
+        
+        # Setup UI elements
+        axcolor = 'lightgoldenrodyellow'
+        
+        # Mode buttons
+        self.ax_mode_gt = plt.axes([0.15, 0.15, 0.15, 0.05])
+        self.ax_mode_pred = plt.axes([0.15, 0.05, 0.15, 0.05])
+        self.btn_mode_gt = Button(self.ax_mode_gt, 'Ground Truth', color='orange', hovercolor='darkorange')
+        self.btn_mode_pred = Button(self.ax_mode_pred, 'Prediction', color='lightgreen', hovercolor='green')
+        
+        # Confidence slider for predictions
+        self.ax_confidence = plt.axes([0.35, 0.10, 0.35, 0.03], facecolor=axcolor)
+        self.slider_confidence = Slider(self.ax_confidence, 'Confidence', 0.05, 1.0, valinit=1.0, valstep=0.05)
+        
+        # Action buttons
+        self.ax_clear = plt.axes([0.75, 0.15, 0.1, 0.05])
+        self.ax_calculate = plt.axes([0.75, 0.05, 0.1, 0.05])
+        self.btn_clear = Button(self.ax_clear, 'Clear', color='lightcoral', hovercolor='red')
+        self.btn_calculate = Button(self.ax_calculate, 'Calculate', color='lightblue', hovercolor='blue')
+        
+        # Connect events
+        self.btn_mode_gt.on_clicked(self.set_mode_gt)
+        self.btn_mode_pred.on_clicked(self.set_mode_pred)
+        self.slider_confidence.on_changed(self.update_confidence)
+        self.btn_clear.on_clicked(self.clear_tracks)
+        self.btn_calculate.on_clicked(self.calculate_metric)
+        
+        self.fig.canvas.mpl_connect('button_press_event', self.on_press)
+        self.fig.canvas.mpl_connect('button_release_event', self.on_release)
+        self.fig.canvas.mpl_connect('motion_notify_event', self.on_motion)
+        
+        # Legend
+        self.update_legend()
+        
+        # Calculator for the metric
+        self.calculator = TempLocAPCalculator()
+        
+        plt.show()
+    
+    def set_mode_gt(self, event):
+        self.mode = 'gt'
+        print(f"Mode: Ground Truth")
+    
+    def set_mode_pred(self, event):
+        self.mode = 'pred'
+        print(f"Mode: Prediction (Confidence: {self.current_confidence:.1f})")
+    
+    def update_confidence(self, val):
+        self.current_confidence = val
+        print(f"Confidence set to: {self.current_confidence:.1f}")
+    
+    def on_press(self, event):
+        if event.inaxes != self.ax:
+            return
+        
+        self.drawing = True
+        self.start_point = (event.xdata, event.ydata)
+        
+        # Create a temporary line
+        if self.mode == 'gt':
+            self.current_line, = self.ax.plot([event.xdata], [event.ydata], 'o-', color='orange', linewidth=2)
+        else:  # pred mode
+            self.current_line, = self.ax.plot([event.xdata], [event.ydata], 'o-', color='green', 
+                                             alpha=self.current_confidence, linewidth=2)
+        
+        self.fig.canvas.draw()
+    
+    def on_motion(self, event):
+        if not self.drawing or event.inaxes != self.ax or self.current_line is None:
+            return
+        
+        # Update temporary line
+        x_vals = [self.start_point[0], event.xdata]
+        y_vals = [self.start_point[1], event.ydata]
+        self.current_line.set_data(x_vals, y_vals)
+        self.fig.canvas.draw()
+    
+    def on_release(self, event):
+        if not self.drawing or self.start_point is None or self.current_line is None:
+            return
+        
+        self.drawing = False
+        end_point = (event.xdata, event.ydata)
+        
+        # Finalize the line if it was drawn in the axes
+        if event.inaxes == self.ax:
+            track = {
+                'start': self.start_point,
+                'end': end_point,
+                'line': self.current_line
+            }
+            
+            if self.mode == 'pred':
+                track['confidence'] = self.current_confidence
+                
+                # Add confidence text label next to the line
+                mid_x = (self.start_point[0] + end_point[0]) / 2
+                mid_y = (self.start_point[1] + end_point[1]) / 2
+                # Offset the text slightly from the line
+                offset = .66
+                if end_point[1] > self.start_point[1]:
+                    offset_y = offset
+                else:
+                    offset_y = -offset
+                
+                conf_text = self.ax.text(mid_x, mid_y + offset_y, f"Conf: {self.current_confidence:.2f}", 
+                                         color='darkgreen', fontweight='bold', 
+                                         ha='center', va='center',
+                                         bbox=dict(facecolor='white', alpha=0.7, edgecolor='green', boxstyle='round,pad=0.3'))
+                track['conf_text'] = conf_text
+            
+            self.tracks[self.mode].append(track)
+            self.current_line = None
+            
+            # Update legend to reflect the new track
+            self.update_legend()
+        else:
+            # If released outside axes, remove temporary line
+            self.current_line.remove()
+            self.current_line = None
+        
+        self.fig.canvas.draw()
+    
+    def clear_tracks(self, event):
+        # Remove all lines from the plot
+        for track_type in self.tracks:
+            for track in self.tracks[track_type]:
+                track['line'].remove()
+                
+                # Also remove confidence text labels for prediction tracks
+                if track_type == 'pred' and 'conf_text' in track:
+                    track['conf_text'].remove()
+        
+        # Clear the track lists
+        self.tracks = {'gt': [], 'pred': []}
+        
+        # Update legend
+        self.update_legend()
+        self.fig.canvas.draw()
+        print("All tracks cleared")
+    
+    def update_legend(self):
+        # Create legend elements
+        legend_elements = [
+            Line2D([0], [0], color='orange', lw=2, label=f'Ground Truth ({len(self.tracks["gt"])})'),
+            Line2D([0], [0], color='green', lw=2, label=f'Prediction ({len(self.tracks["pred"])})')
+        ]
+        self.ax.legend(handles=legend_elements, loc='upper right')
+    
+    def interpolate_track(self, start, end, num_steps):
+        """Interpolate points along a track."""
+        t_start, s_start = start
+        t_end, s_end = end
+        
+        t_vals = np.linspace(t_start, t_end, num_steps)
+        s_vals = np.linspace(s_start, s_end, num_steps)
+        
+        return t_vals, s_vals
+    
+    def create_metric_data(self):
+        """Convert drawn tracks to the format expected by the TempLocAP calculator."""
+        # Define time resolution
+        time_min = 0
+        time_max = 100
+        num_timesteps = 100
+        timesteps = np.linspace(time_min, time_max, num_timesteps).astype(int)
+        
+        # Initialize data structure
+        data = {
+            'num_timesteps': num_timesteps,
+            'gt_ids': [[] for _ in range(num_timesteps)],
+            'gt_dets': [[] for _ in range(num_timesteps)],
+            'gt_classes': [[] for _ in range(num_timesteps)],
+            'tracker_ids': [[] for _ in range(num_timesteps)],
+            'tracker_dets': [[] for _ in range(num_timesteps)],
+            'tracker_classes': [[] for _ in range(num_timesteps)],
+            'tracker_confidences': [[] for _ in range(num_timesteps)],
+            'num_gt_dets': 0,
+            'num_tracker_dets': 0,
+        }
+        
+        # Process ground truth tracks
+        for i, track in enumerate(self.tracks['gt']):
+            gt_id = i + 1  # Track ID (starting from 1)
+            start, end = track['start'], track['end']
+            
+            # Interpolate track
+            t_vals, s_vals = self.interpolate_track(start, end, 50)
+            
+            for t, s in zip(t_vals, s_vals):
+                # Find appropriate timestep
+                t_idx = min(int(t), num_timesteps - 1)
+                
+                # Add detection to appropriate timestep
+                data['gt_ids'][t_idx].append(gt_id)
+                data['gt_dets'][t_idx].append(np.array([s, 0]))  # Using 1D space, setting y=0
+                data['gt_classes'][t_idx].append(1)  # All tracks have the same class
+                data['num_gt_dets'] += 1
+        
+        # Process prediction tracks
+        for i, track in enumerate(self.tracks['pred']):
+            track_id = i + 1  # Track ID (starting from 1)
+            start, end = track['start'], track['end']
+            confidence = track.get('confidence', 1.0)
+            
+            # Interpolate track
+            t_vals, s_vals = self.interpolate_track(start, end, 50)
+            
+            for t, s in zip(t_vals, s_vals):
+                # Find appropriate timestep
+                t_idx = min(int(t), num_timesteps - 1)
+                
+                # Add detection to appropriate timestep
+                data['tracker_ids'][t_idx].append(track_id)
+                data['tracker_dets'][t_idx].append(np.array([s, 0]))  # Using 1D space, setting y=0
+                data['tracker_classes'][t_idx].append(1)  # All tracks have the same class
+                data['tracker_confidences'][t_idx].append(confidence)
+                data['num_tracker_dets'] += 1
+        
+        return data
+    
+    def calculate_metric(self, event):
+        if not self.tracks['gt'] and not self.tracks['pred']:
+            messagebox.showinfo("Empty Tracks", "Please draw at least one track before calculating.")
+            return
+        
+        # Create data for the calculator
+        data = self.create_metric_data()
+        
+        # Calculate the metric
+        TempLocAP, precisions, recalls = self.calculator.calculate_TempLocAP(data)
+        
+        # Display results
+        result_text = f"TempLocAP: {TempLocAP:.4f}"
+        print(result_text)
+        
+        # Create a plot window for precision-recall curve with area highlighted
+        fig_pr, ax_pr = plt.subplots(figsize=(6, 5))
+        
+        # Sort the recalls and corresponding precisions
+        sorted_indices = np.argsort(recalls)
+        sorted_recalls = recalls[sorted_indices]
+        sorted_precisions = precisions[sorted_indices]
+        
+        # Add a point at recall=0 if not already present
+        if sorted_recalls[0] != 0:
+            sorted_recalls = np.concatenate(([0], sorted_recalls))
+            sorted_precisions = np.concatenate(([sorted_precisions[0]], sorted_precisions))
+        
+        # Add a point at recall=1 if not already present
+        if sorted_recalls[-1] != 1:
+            sorted_recalls = np.concatenate((sorted_recalls, [1]))
+            sorted_precisions = np.concatenate((sorted_precisions, [0]))  # Precision typically drops to 0 at recall=1
+            
+        # Plot precision-recall curve
+        ax_pr.plot(sorted_recalls, sorted_precisions, '-o', color='blue', label='Precision-Recall Curve')
+        
+        # Fill the area under the curve
+        ax_pr.fill_between(sorted_recalls, sorted_precisions, alpha=0.3, color='skyblue', label=f'AP = {TempLocAP:.4f}')
+        
+        # Add explanatory annotation about the area
+        ax_pr.annotate(f'Area = {TempLocAP:.4f}', 
+                     xy=(0.5, 0.5), xycoords='axes fraction',
+                     bbox=dict(boxstyle="round,pad=0.3", fc="yellow", ec="orange", alpha=0.8),
+                     ha='center', fontsize=12)
+        
+        ax_pr.set_xlabel('Recall')
+        ax_pr.set_ylabel('Precision')
+        ax_pr.set_xlim([0, 1])
+        ax_pr.set_ylim([0, 1.05])
+        ax_pr.set_title(f'Precision-Recall Curve\nTempLocAP = {TempLocAP:.4f}')
+        ax_pr.grid(True)
+        ax_pr.legend(loc='lower left')
+        plt.tight_layout()
+        plt.show()
+        
+        # Show a message box with the score
+        messagebox.showinfo("TempLocAP Result", result_text)
+
+
+# Run the application
+if __name__ == "__main__":
+    ui = TrackDrawUI()
\ No newline at end of file
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index 794d87ce..e0ce790e 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -5,23 +5,19 @@
 if sys.stdout is None:
     sys.stdout = open('stdout.log', 'w')
 
-from av2.evaluation.scenario_mining.eval import evaluate, load
-
+from av2.evaluation.scenario_mining.eval import evaluate
 
 def test_evaluate() -> None:
     """Test End-to-End Forecasting evaluation."""
     
     predictions = 'tests/unit/evaluation/scenario_mining/data/combined_predictions.pkl'
-    ground_truth = '/home/crdavids/Trinity-Sync/av2-test/av2-api/tests/unit/evaluation/scenario_mining/data/combined_gt.pkl'
+    ground_truth = 'tests/unit/evaluation/scenario_mining/data/combined_gt.pkl'
 
     objective_metric = 'HOTA'
     max_range_m = 100
     dataset_dir = None
     out = 'tests/unit/evaluation/scenario_mining/data/eval_results'
 
-    predictions = load(predictions)
-    ground_truth = load(ground_truth)
-
     evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
 
 test_evaluate()

From 67a79d42ed20afe4d17b57de3570a1807a450b98 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Thu, 6 Mar 2025 21:24:08 -0500
Subject: [PATCH 05/22] Updated weighting when combining logs

---
 pyproject.toml                                |  1 -
 src/av2/evaluation/scenario_mining/hota.py    | 60 +++++++------------
 src/av2/evaluation/tracking/eval.py           |  3 +-
 .../evaluation/scenario_mining/test_eval.py   |  7 +--
 4 files changed, 24 insertions(+), 47 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index dd29bc6b..d9030c46 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -85,7 +85,6 @@ strict = true
 
 [tool.pyright]
 include = ["src"]
-typeCheckingMode = "off"
 
 reportMissingTypeStubs = false
 reportUnknownMemberType = false
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
index fdaf0736..226f1621 100644
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -22,7 +22,7 @@ def __init__(self, config=None):
         self.summary_fields = self.float_array_fields + self.float_fields
 
     @_timing.time
-    def eval_sequence(self, data):
+    def eval_sequence(self, data) -> dict[Any]:
         """Calculates the HOTA metrics for one sequence"""
 
         # Initialise results
@@ -32,16 +32,8 @@ def eval_sequence(self, data):
         for field in self.float_fields:
             res[field] = 0
 
-        tlap, precisions, recalls = self.calculate_TempLocAP(data)
-        tlap2, precisions2, recalls2 = self.calculate_TempLocAP_concat(data)
-        tlap3, precisions3, recalls3 = self.calculate_TempLocAP_merge(data)
-        self.plot_precision_recall_curve([recalls, recalls2, recalls3], 
-                                         [precisions, precisions2, precisions3], 
-                                         [tlap, tlap2, tlap3], 
-                                         ['1 to 1', 'concat', 'merge'],
-                                         ['blue', 'green', 'red'])
-
-        res['TempLocAP'] = tlap
+        TempLocAP, _, _ = self.calculate_TempLocAP_merge(data)
+        res['TempLocAP'] = TempLocAP
 
         # Return result quickly if tracker or gt sequence is empty
         if data['num_tracker_dets'] == 0:
@@ -127,7 +119,6 @@ def eval_sequence(self, data):
         res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])
         res = self._compute_final_fields(res)
 
-
         return res
     
     def calculate_TempLocAP(self, data):
@@ -138,19 +129,16 @@ def calculate_TempLocAP(self, data):
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 TempLocAP = 1
-                print(1)
                 return TempLocAP, precisions, recalls
             else:
                 precisions = np.array([0, 0])
                 recalls = np.array([0, 1])
                 TempLocAP = 0
-                print(0)
                 return TempLocAP, precisions, recalls
         if data['num_gt_dets'] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
-            print(0)
             return TempLocAP, precisions, recalls
 
         TEMPORAL_IOU_THRESH  = 0.5 #iou
@@ -265,20 +253,16 @@ def calculate_TempLocAP_concat(self, data):
             if data['num_gt_dets'] == 0:
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
-                TempLocAP = 1
-                print(1)
                 return TempLocAP, precisions, recalls
             else:
                 precisions = np.array([0, 0])
                 recalls = np.array([0, 1])
                 TempLocAP = 0
-                print(0)
                 return TempLocAP, precisions, recalls
         if data['num_gt_dets'] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
-            print(0)
             return TempLocAP, precisions, recalls
 
         TEMPORAL_IOU_THRESH  = 0.5 #iou
@@ -469,19 +453,16 @@ def calculate_TempLocAP_merge(self, data):
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 TempLocAP = 1
-                print(1)
                 return TempLocAP, precisions, recalls
             else:
                 precisions = np.array([0, 0])
                 recalls = np.array([0, 1])
                 TempLocAP = 0
-                print(0)
                 return TempLocAP, precisions, recalls
         if data['num_gt_dets'] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
-            print(0)
             return TempLocAP, precisions, recalls
 
         TEMPORAL_IOU_THRESH  = 0.5 #iou
@@ -529,7 +510,6 @@ def calculate_TempLocAP_merge(self, data):
             track_timestamps = track_stats['timestamps']
             
             max_similarity = 0
-            best_match_stats = None
             best_match = None
             for gt_id, gt_stats in gt_tracks.items():
                 if gt_stats['category'] != track_stats['category']:
@@ -602,22 +582,25 @@ def calculate_TempLocAP_merge(self, data):
                             merged_confidences[insertion_index] = track_confidence
                             merged_traj[insertion_index] = track_trajectory[i]
 
-            merged_predictions[gt_id] = {
+            merged_predictions[-gt_id-1] = {
                 'xy_pos': merged_traj,
                 'timestamps': merged_timestamps,
                 'confidence': np.mean(np.array(merged_confidences)),
                 'category': merged_catetory
             }
 
+        for unmatched_track_id in unmatched_track_ids:
+            merged_predictions.update({unmatched_track_id: pred_tracks[unmatched_track_id]})
+
         merged_ids_by_conf = sorted(merged_predictions.keys(), key=lambda key: merged_predictions[key]['confidence'], reverse=True)
         matched_gt_ids = []
 
         #Compute precision and recall at all confidence thresholds.
-        tp = np.zeros(len(merged_ids_by_conf) + len(unmatched_track_ids))
-        fp = np.zeros(len(merged_ids_by_conf) + len(unmatched_track_ids))
+        tp = np.zeros(len(merged_ids_by_conf))
+        fp = np.zeros(len(merged_ids_by_conf))
 
         for i, track_id in enumerate(merged_ids_by_conf):
-            track_stats = pred_tracks[track_id]
+            track_stats = merged_predictions[track_id]
             track_confidence = track_stats['confidence']
             track_traj = track_stats['xy_pos']
             track_timestamps = track_stats['timestamps']
@@ -654,7 +637,8 @@ def calculate_TempLocAP_merge(self, data):
             if max_similarity > 0:
                 matched_gt_ids.append(best_match)
                 tp[i] = 1
-                fp[i] = 0
+            else:
+                fp[i] = 1
 
         tp = np.cumsum(tp)
         fp = np.cumsum(fp)
@@ -775,14 +759,14 @@ def combine_sequences(self, all_res):
             res[field] = self._combine_sum(all_res, field)
         for field in ['AssRe', 'AssPr', 'AssA']:
             res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
-        for field in ['TempLocAP']:
-            summed_TempLocAP = 0
-            for seq_id, seq_res in all_res.items():
-                summed_TempLocAP += seq_res[field]
-            res[field] = summed_TempLocAP / len(all_res)
+
 
         loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
         res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+
+        tlap_weighted_sum = sum([all_res[k]['TempLocAP'] * (all_res[k]['HOTA_TP'][0] + all_res[k]['HOTA_FN'][0]) for k in all_res.keys()])
+        res['TempLocAP'] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'][0]+res['HOTA_FN'][0])
+
         res = self._compute_final_fields(res)
         return res
     
@@ -815,15 +799,13 @@ def combine_classes_det_averaged(self, all_res):
             res[field] = self._combine_sum(all_res, field)
         for field in ['AssRe', 'AssPr', 'AssA']:
             res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
-        for field in ['TempLocAP']:
-            summed_TempLocAP = 0
-            for seq_id, seq_res in all_res.items():
-                summed_TempLocAP += seq_res[field]
-            res[field] = summed_TempLocAP / len(all_res)
-
 
         loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
         res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+
+        tlap_weighted_sum = sum([all_res[k]['TempLocAP'] * (all_res[k]['HOTA_TP'][0] + all_res[k]['HOTA_FN'][0]) for k in all_res.keys()])
+        res['TempLocAP'] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'][0]+res['HOTA_FN'][0])
+
         res = self._compute_final_fields(res)
         return res
 
diff --git a/src/av2/evaluation/tracking/eval.py b/src/av2/evaluation/tracking/eval.py
index 98124789..68aa97ec 100644
--- a/src/av2/evaluation/tracking/eval.py
+++ b/src/av2/evaluation/tracking/eval.py
@@ -621,7 +621,6 @@ def evaluate(
 
     return res, tuned_metric_values, mean_metric_values
 
-
 @click.command()
 @click.option("--predictions", required=True, help="Predictions PKL file")
 @click.option("--ground_truth", required=True, help="Ground Truth PKL file")
@@ -656,4 +655,4 @@ def runner(
 
 
 if __name__ == "__main__":
-    runner()
+    runner()
\ No newline at end of file
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index e0ce790e..2b019ebf 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -1,9 +1,6 @@
 """Scenario mining evaluation unit tests."""
-
-
-import sys
-if sys.stdout is None:
-    sys.stdout = open('stdout.log', 'w')
+import matplotlib
+matplotlib.use('Agg')
 
 from av2.evaluation.scenario_mining.eval import evaluate
 

From 2817734eea4dc880ab61906406f8821022ae4748 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Thu, 6 Mar 2025 21:36:55 -0500
Subject: [PATCH 06/22] Updated type hints

---
 src/av2/evaluation/scenario_mining/hota.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
index 226f1621..321b5cf1 100644
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -22,7 +22,7 @@ def __init__(self, config=None):
         self.summary_fields = self.float_array_fields + self.float_fields
 
     @_timing.time
-    def eval_sequence(self, data) -> dict[Any]:
+    def eval_sequence(self, data):
         """Calculates the HOTA metrics for one sequence"""
 
         # Initialise results

From 1a614dfb2fb31546a38fff5e96eea443f9dfb2ae Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Thu, 6 Mar 2025 23:16:34 -0500
Subject: [PATCH 07/22] Added filtering for scenario mining track evaluation

---
 .gitignore                                    |   2 -
 src/av2/evaluation/scenario_mining/eval.py    |  53 +-
 .../scenario_mining/metric_evaluator.py       | 457 ------------------
 .../annotations_with_ego.feather              | Bin 0 -> 725666 bytes
 .../city_SE3_egovehicle.feather               | Bin 0 -> 167122 bytes
 ...268-a571-b0889dbf40b6___img_Sim2_city.json |   1 +
 ...89dbf40b6_ground_height_surface____MIA.npy | Bin 0 -> 1167128 bytes
 ...8-a571-b0889dbf40b6____MIA_city_47894.json |   1 +
 .../annotations_with_ego.feather              | Bin 0 -> 649994 bytes
 .../city_SE3_egovehicle.feather               | Bin 0 -> 168138 bytes
 ...8b6-a0f2-64196d130958___img_Sim2_city.json |   1 +
 ...96d130958_ground_height_surface____PIT.npy | Bin 0 -> 1369490 bytes
 ...6-a0f2-64196d130958____PIT_city_71109.json |   1 +
 .../scenario_mining/data/combined_gt_dev.pkl  | Bin 0 -> 1066373 bytes
 .../data/combined_predictions_dev.pkl         | Bin 0 -> 2408651 bytes
 .../evaluation/scenario_mining/test_eval.py   |  22 +-
 16 files changed, 42 insertions(+), 496 deletions(-)
 delete mode 100644 src/av2/evaluation/scenario_mining/metric_evaluator.py
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/annotations_with_ego.feather
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/city_SE3_egovehicle.feather
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/3b3570b4-7b0b-3268-a571-b0889dbf40b6___img_Sim2_city.json
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/3b3570b4-7b0b-3268-a571-b0889dbf40b6_ground_height_surface____MIA.npy
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/log_map_archive_3b3570b4-7b0b-3268-a571-b0889dbf40b6____MIA_city_47894.json
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/annotations_with_ego.feather
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/city_SE3_egovehicle.feather
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958___img_Sim2_city.json
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958_ground_height_surface____PIT.npy
 create mode 100644 tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/log_map_archive_3bffdcff-c3a7-38b6-a0f2-64196d130958____PIT_city_71109.json
 create mode 100644 tests/unit/evaluation/scenario_mining/data/combined_gt_dev.pkl
 create mode 100644 tests/unit/evaluation/scenario_mining/data/combined_predictions_dev.pkl

diff --git a/.gitignore b/.gitignore
index 6ee6a157..138e3192 100644
--- a/.gitignore
+++ b/.gitignore
@@ -145,8 +145,6 @@ condaenv*
 
 #Data
 /data/
-data/
-*.pkl
 
 *.jpg
 *.png
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index cab58b3b..0c152944 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -18,7 +18,6 @@
 
 import click
 import numpy as np
-import trackeval
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.transform import Rotation
 from tqdm import tqdm
@@ -416,16 +415,16 @@ def _plot_confusion_matrix(
 
     # Fill the confusion matrix
     for true, pred in zip(gt_classes, pred_classes):
-        cm[true, pred] += 1
+        cm[1-true, 1-pred] += 1
 
     # Plot the confusion matrix
     fig, ax = plt.subplots(figsize=(4, 4))
-    cax = ax.imshow(cm, cmap="viridis", interpolation="nearest")
+    cax = ax.imshow(cm, cmap="Wistia", interpolation="nearest")
 
     # Add text annotations (True Positives, False Positives, etc.)
     for i in range(2):
         for j in range(2):
-            ax.text(j, i, cm[i, j], ha="center", va="center", color="white", fontsize=14)
+            ax.text(j, i, cm[i, j], ha="center", va="center", color="black", fontsize=16)
 
     # Set axis labels and ticks
     ax.set_xlabel("Predicted Label")
@@ -433,14 +432,11 @@ def _plot_confusion_matrix(
     ax.set_title("Scenario Mining - Description Matches")
     ax.set_xticks([0, 1])
     ax.set_yticks([0, 1])
-    ax.set_xticklabels(["Negative", "Positive"])
-    ax.set_yticklabels(["Negative", "Positive"])
-
-    # Show colorbar
-    fig.colorbar(cax)
+    ax.set_xticklabels(["Positive", "Negative"])
+    ax.set_yticklabels(["Positive", "Negative"])
+    plt.setp(ax.get_yticklabels(), rotation=90, ha='right')
 
     #Display the plot
-    plt.tight_layout()
     if output_dir:
         plt.savefig(output_dir + '/eval_cm.png')
     plt.close()
@@ -797,15 +793,17 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     if dataset_dir is None:
         return tracks
 
-    log_ids = list(tracks.keys())
+    log_prompt_pairs = list(tracks.keys())
+    log_ids = [log_prompt_pair[0] for log_prompt_pair in log_prompt_pairs]
+
     log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(
         log_ids, Path(dataset_dir)
     )
 
-    for log_id in log_ids:
+    for i, log_id in enumerate(log_ids):
         avm = log_id_to_avm[log_id]
 
-        for frame in tracks[log_id]:
+        for frame in tracks[log_prompt_pairs[i]]:
             timestamp_ns = frame["timestamp_ns"]
             city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)]
             translation_m = frame["translation_m"] - frame["ego_translation_m"]
@@ -845,7 +843,7 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     return tracks
 
 
-def referred_full_tracks(pkl_file_path):
+def referred_full_tracks(sequences: Sequences):
     """
     Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
@@ -856,11 +854,7 @@ def referred_full_tracks(pkl_file_path):
     Returns:
         reconstructed_sequences: Dictionary containing the reconstructed sequences
     """
-    import pickle
     
-    # Load the pkl file
-    with open(pkl_file_path, 'rb') as f:
-        sequences = pickle.load(f)
     
     reconstructed_sequences = {}
     
@@ -964,8 +958,8 @@ def relabel_seq_ids(data):
 
 
 def evaluate(
-    pred_pkl:str,
-    gt_pkl:str,
+    track_predictions:Sequences,
+    labels:Sequences,
     objective_metric: str,
     max_range_m: int,
     dataset_dir: Any,
@@ -986,16 +980,10 @@ def evaluate(
         partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
     """
 
-    track_predictions = pickle.load(open(pred_pkl, "rb"))
-    labels = pickle.load(open(gt_pkl, "rb"))
-
-    track_predictions = relabel_seq_ids(track_predictions)
-    labels = relabel_seq_ids(labels)
-
     output_dir = ""
     if out:
         output_dir = out + '/partial_tracks'
-        Path(output_dir).mkdir(exist_ok=True)
+        Path(output_dir).mkdir(parents=True,exist_ok=True)
 
     res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
         track_predictions, labels, 
@@ -1003,15 +991,13 @@ def evaluate(
         dataset_dir=dataset_dir, out=output_dir)
     TempLocAP = res['TrackEvalDataset']['TRACKER']['COMBINED_SEQ']['REFERRED_OBJECT']['HOTA']['TempLocAP']
     
-    full_track_preds = referred_full_tracks(pred_pkl)
-    full_track_labels = referred_full_tracks(gt_pkl)
-    full_track_preds = relabel_seq_ids(full_track_preds)
-    full_track_labels = relabel_seq_ids(full_track_labels)
+    full_track_preds = referred_full_tracks(track_predictions)
+    full_track_labels = referred_full_tracks(labels)
 
     output_dir = ""
     if out:
         output_dir = out + '/full_tracks'
-        Path(output_dir).mkdir(exist_ok=True)
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
 
     _, full_track_metrics, _, _ = evaluate_scenario_mining(
         full_track_preds, full_track_labels, 
@@ -1058,6 +1044,9 @@ def evaluate_scenario_mining(
         labels = filter_drivable_area(labels, dataset_dir)
         track_predictions = filter_drivable_area(track_predictions, dataset_dir)
 
+    track_predictions = relabel_seq_ids(track_predictions)
+    labels = relabel_seq_ids(labels)
+
     score_thresholds, tuned_metric_values, mean_metric_values = _tune_score_thresholds(
         labels,
         track_predictions,
diff --git a/src/av2/evaluation/scenario_mining/metric_evaluator.py b/src/av2/evaluation/scenario_mining/metric_evaluator.py
deleted file mode 100644
index 283d3af3..00000000
--- a/src/av2/evaluation/scenario_mining/metric_evaluator.py
+++ /dev/null
@@ -1,457 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.widgets import Button, Slider
-from matplotlib.lines import Line2D
-import matplotlib.patches as patches
-import copy
-import tkinter as tk
-from tkinter import messagebox
-
-class TempLocAPCalculator:
-    def __init__(self):
-        pass
-        
-    def get_ap(self, recalls, precisions):
-        # Standard VOC AP calculation
-        # First append sentinel values at the end
-        recalls = np.concatenate(([0.], recalls, [1.]))
-        precisions = np.concatenate(([0.], precisions, [0.]))
-
-        # Compute the precision envelope
-        for i in range(precisions.size - 1, 0, -1):
-            precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
-
-        # Compute area under PR curve
-        indices = np.where(recalls[1:] != recalls[:-1])[0] + 1
-        ap = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices])
-        return ap
-        
-    def calculate_TempLocAP(self, data):
-        # Return result quickly if tracker or gt sequence is empty
-        if data['num_tracker_dets'] == 0:
-            if data['num_gt_dets'] == 0:
-                precisions = np.array([1, 1])
-                recalls = np.array([0, 1])
-                TempLocAP = 1
-                return TempLocAP, precisions, recalls
-            else:
-                precisions = np.array([0, 0])
-                recalls = np.array([0, 1])
-                TempLocAP = 0
-                return TempLocAP, precisions, recalls
-        if data['num_gt_dets'] == 0:
-            precisions = np.array([0, 0])
-            recalls = np.array([0, 1])
-            TempLocAP = 0
-            return TempLocAP, precisions, recalls
-
-        TEMPORAL_IOU_THRESH  = 0.5 #iou
-        MATCHING_DIST_THRESH = 2.0 #m
-
-        pred_tracks = {}
-        gt_tracks = {}
-
-        #Accumulate predicted and ground truth tracks from data
-        for t in range(data['num_timesteps']):
-            for i, gt_id in enumerate(data['gt_ids'][t]):
-                if gt_id not in gt_tracks:
-                    gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]['xy_pos'] = []
-                    gt_tracks[gt_id]['timestamps'] = []
-                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
-
-                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
-                gt_tracks[gt_id]['timestamps'].append(t)
-
-            for i, track_id in enumerate(data['tracker_ids'][t]):
-                if track_id not in pred_tracks:
-                    pred_tracks[track_id] = {}
-                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
-                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
-                    pred_tracks[track_id]['xy_pos'] = []
-                    pred_tracks[track_id]['timestamps'] = []
-
-                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
-                pred_tracks[track_id]['timestamps'].append(t)
-
-        # 1 to 1 match of predicted and ground truth tracks
-        sorted_keys = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
-
-        #keys are track_ids, values are gt_ids
-        matched_ids = {} 
-
-        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-        matched_ious = {} 
-        unmatched_gt_ids = list(gt_tracks.keys())
-        unmatched_track_ids = []
-
-        for track_id in sorted_keys:
-            track_stats = pred_tracks[track_id]
-
-            track_traj = track_stats['xy_pos']
-            track_timestamps = track_stats['timestamps']
-            
-            max_similarity = 0
-            best_match = None
-            corresponding_iou = 0
-            for gt_id, gt_stats in gt_tracks.items():
-                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
-                    continue
-
-                gt_traj = gt_stats['xy_pos']
-                gt_timestamps = gt_stats['timestamps']
-
-                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
-                union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection/union
-
-                total_distance = 0.0
-                for timestamp in track_timestamps:
-                    if timestamp in gt_timestamps:
-                        total_distance += np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)])
-
-
-                similarity_score = iou * max(0.0, 
-                    1 - (total_distance/(MATCHING_DIST_THRESH*(intersection+np.finfo(np.float64).eps))))
-                if similarity_score > max_similarity:
-                    max_similarity = similarity_score
-                    best_match = gt_id
-                    corresponding_iou = iou
-
-            if max_similarity > 0:
-                matched_ids[track_id] = best_match
-                matched_ious[track_id] = corresponding_iou
-                unmatched_gt_ids.remove(best_match)
-            else:
-                unmatched_track_ids.append(track_id)
-
-        #Compute precision and recall at all confidence thresholds.
-        tp = np.zeros(len(pred_tracks))
-        fp = np.zeros(len(pred_tracks))
-        for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
-
-            if track_id in matched_ids and matched_ious[track_id] >= TEMPORAL_IOU_THRESH:
-                tp[i] = 1
-            else:
-                fp[i] = 1
-
-        tp = np.cumsum(tp)
-        fp = np.cumsum(fp)
-
-        recalls = tp/max(len(gt_tracks), 1)
-        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
-
-        assert np.all(0 <= precisions) & np.all(precisions <= 1)
-        TempLocAP = self.get_ap(recalls, precisions)
-
-        return TempLocAP, precisions, recalls
-
-
-class TrackDrawUI:
-    def __init__(self):
-        self.fig, self.ax = plt.subplots(figsize=(10, 8))
-        plt.subplots_adjust(bottom=0.25)
-        
-        # Configure the plot
-        self.ax.set_xlim(0, 100)
-        self.ax.set_ylim(0, 10)
-        self.ax.set_xlabel("Time")
-        self.ax.set_ylabel("Space")
-        self.ax.grid(True, linestyle='--', alpha=0.7)
-        
-        # Track data
-        self.tracks = {
-            'gt': [],      # Ground truth tracks (orange)
-            'pred': []     # Prediction tracks (green with varying opacity)
-        }
-        
-        # Drawing mode and state
-        self.mode = 'gt'  # Initial mode: ground truth
-        self.drawing = False
-        self.start_point = None
-        self.current_line = None
-        self.current_confidence = 1.0  # Default confidence for predictions
-        
-        # Setup UI elements
-        axcolor = 'lightgoldenrodyellow'
-        
-        # Mode buttons
-        self.ax_mode_gt = plt.axes([0.15, 0.15, 0.15, 0.05])
-        self.ax_mode_pred = plt.axes([0.15, 0.05, 0.15, 0.05])
-        self.btn_mode_gt = Button(self.ax_mode_gt, 'Ground Truth', color='orange', hovercolor='darkorange')
-        self.btn_mode_pred = Button(self.ax_mode_pred, 'Prediction', color='lightgreen', hovercolor='green')
-        
-        # Confidence slider for predictions
-        self.ax_confidence = plt.axes([0.35, 0.10, 0.35, 0.03], facecolor=axcolor)
-        self.slider_confidence = Slider(self.ax_confidence, 'Confidence', 0.05, 1.0, valinit=1.0, valstep=0.05)
-        
-        # Action buttons
-        self.ax_clear = plt.axes([0.75, 0.15, 0.1, 0.05])
-        self.ax_calculate = plt.axes([0.75, 0.05, 0.1, 0.05])
-        self.btn_clear = Button(self.ax_clear, 'Clear', color='lightcoral', hovercolor='red')
-        self.btn_calculate = Button(self.ax_calculate, 'Calculate', color='lightblue', hovercolor='blue')
-        
-        # Connect events
-        self.btn_mode_gt.on_clicked(self.set_mode_gt)
-        self.btn_mode_pred.on_clicked(self.set_mode_pred)
-        self.slider_confidence.on_changed(self.update_confidence)
-        self.btn_clear.on_clicked(self.clear_tracks)
-        self.btn_calculate.on_clicked(self.calculate_metric)
-        
-        self.fig.canvas.mpl_connect('button_press_event', self.on_press)
-        self.fig.canvas.mpl_connect('button_release_event', self.on_release)
-        self.fig.canvas.mpl_connect('motion_notify_event', self.on_motion)
-        
-        # Legend
-        self.update_legend()
-        
-        # Calculator for the metric
-        self.calculator = TempLocAPCalculator()
-        
-        plt.show()
-    
-    def set_mode_gt(self, event):
-        self.mode = 'gt'
-        print(f"Mode: Ground Truth")
-    
-    def set_mode_pred(self, event):
-        self.mode = 'pred'
-        print(f"Mode: Prediction (Confidence: {self.current_confidence:.1f})")
-    
-    def update_confidence(self, val):
-        self.current_confidence = val
-        print(f"Confidence set to: {self.current_confidence:.1f}")
-    
-    def on_press(self, event):
-        if event.inaxes != self.ax:
-            return
-        
-        self.drawing = True
-        self.start_point = (event.xdata, event.ydata)
-        
-        # Create a temporary line
-        if self.mode == 'gt':
-            self.current_line, = self.ax.plot([event.xdata], [event.ydata], 'o-', color='orange', linewidth=2)
-        else:  # pred mode
-            self.current_line, = self.ax.plot([event.xdata], [event.ydata], 'o-', color='green', 
-                                             alpha=self.current_confidence, linewidth=2)
-        
-        self.fig.canvas.draw()
-    
-    def on_motion(self, event):
-        if not self.drawing or event.inaxes != self.ax or self.current_line is None:
-            return
-        
-        # Update temporary line
-        x_vals = [self.start_point[0], event.xdata]
-        y_vals = [self.start_point[1], event.ydata]
-        self.current_line.set_data(x_vals, y_vals)
-        self.fig.canvas.draw()
-    
-    def on_release(self, event):
-        if not self.drawing or self.start_point is None or self.current_line is None:
-            return
-        
-        self.drawing = False
-        end_point = (event.xdata, event.ydata)
-        
-        # Finalize the line if it was drawn in the axes
-        if event.inaxes == self.ax:
-            track = {
-                'start': self.start_point,
-                'end': end_point,
-                'line': self.current_line
-            }
-            
-            if self.mode == 'pred':
-                track['confidence'] = self.current_confidence
-                
-                # Add confidence text label next to the line
-                mid_x = (self.start_point[0] + end_point[0]) / 2
-                mid_y = (self.start_point[1] + end_point[1]) / 2
-                # Offset the text slightly from the line
-                offset = .66
-                if end_point[1] > self.start_point[1]:
-                    offset_y = offset
-                else:
-                    offset_y = -offset
-                
-                conf_text = self.ax.text(mid_x, mid_y + offset_y, f"Conf: {self.current_confidence:.2f}", 
-                                         color='darkgreen', fontweight='bold', 
-                                         ha='center', va='center',
-                                         bbox=dict(facecolor='white', alpha=0.7, edgecolor='green', boxstyle='round,pad=0.3'))
-                track['conf_text'] = conf_text
-            
-            self.tracks[self.mode].append(track)
-            self.current_line = None
-            
-            # Update legend to reflect the new track
-            self.update_legend()
-        else:
-            # If released outside axes, remove temporary line
-            self.current_line.remove()
-            self.current_line = None
-        
-        self.fig.canvas.draw()
-    
-    def clear_tracks(self, event):
-        # Remove all lines from the plot
-        for track_type in self.tracks:
-            for track in self.tracks[track_type]:
-                track['line'].remove()
-                
-                # Also remove confidence text labels for prediction tracks
-                if track_type == 'pred' and 'conf_text' in track:
-                    track['conf_text'].remove()
-        
-        # Clear the track lists
-        self.tracks = {'gt': [], 'pred': []}
-        
-        # Update legend
-        self.update_legend()
-        self.fig.canvas.draw()
-        print("All tracks cleared")
-    
-    def update_legend(self):
-        # Create legend elements
-        legend_elements = [
-            Line2D([0], [0], color='orange', lw=2, label=f'Ground Truth ({len(self.tracks["gt"])})'),
-            Line2D([0], [0], color='green', lw=2, label=f'Prediction ({len(self.tracks["pred"])})')
-        ]
-        self.ax.legend(handles=legend_elements, loc='upper right')
-    
-    def interpolate_track(self, start, end, num_steps):
-        """Interpolate points along a track."""
-        t_start, s_start = start
-        t_end, s_end = end
-        
-        t_vals = np.linspace(t_start, t_end, num_steps)
-        s_vals = np.linspace(s_start, s_end, num_steps)
-        
-        return t_vals, s_vals
-    
-    def create_metric_data(self):
-        """Convert drawn tracks to the format expected by the TempLocAP calculator."""
-        # Define time resolution
-        time_min = 0
-        time_max = 100
-        num_timesteps = 100
-        timesteps = np.linspace(time_min, time_max, num_timesteps).astype(int)
-        
-        # Initialize data structure
-        data = {
-            'num_timesteps': num_timesteps,
-            'gt_ids': [[] for _ in range(num_timesteps)],
-            'gt_dets': [[] for _ in range(num_timesteps)],
-            'gt_classes': [[] for _ in range(num_timesteps)],
-            'tracker_ids': [[] for _ in range(num_timesteps)],
-            'tracker_dets': [[] for _ in range(num_timesteps)],
-            'tracker_classes': [[] for _ in range(num_timesteps)],
-            'tracker_confidences': [[] for _ in range(num_timesteps)],
-            'num_gt_dets': 0,
-            'num_tracker_dets': 0,
-        }
-        
-        # Process ground truth tracks
-        for i, track in enumerate(self.tracks['gt']):
-            gt_id = i + 1  # Track ID (starting from 1)
-            start, end = track['start'], track['end']
-            
-            # Interpolate track
-            t_vals, s_vals = self.interpolate_track(start, end, 50)
-            
-            for t, s in zip(t_vals, s_vals):
-                # Find appropriate timestep
-                t_idx = min(int(t), num_timesteps - 1)
-                
-                # Add detection to appropriate timestep
-                data['gt_ids'][t_idx].append(gt_id)
-                data['gt_dets'][t_idx].append(np.array([s, 0]))  # Using 1D space, setting y=0
-                data['gt_classes'][t_idx].append(1)  # All tracks have the same class
-                data['num_gt_dets'] += 1
-        
-        # Process prediction tracks
-        for i, track in enumerate(self.tracks['pred']):
-            track_id = i + 1  # Track ID (starting from 1)
-            start, end = track['start'], track['end']
-            confidence = track.get('confidence', 1.0)
-            
-            # Interpolate track
-            t_vals, s_vals = self.interpolate_track(start, end, 50)
-            
-            for t, s in zip(t_vals, s_vals):
-                # Find appropriate timestep
-                t_idx = min(int(t), num_timesteps - 1)
-                
-                # Add detection to appropriate timestep
-                data['tracker_ids'][t_idx].append(track_id)
-                data['tracker_dets'][t_idx].append(np.array([s, 0]))  # Using 1D space, setting y=0
-                data['tracker_classes'][t_idx].append(1)  # All tracks have the same class
-                data['tracker_confidences'][t_idx].append(confidence)
-                data['num_tracker_dets'] += 1
-        
-        return data
-    
-    def calculate_metric(self, event):
-        if not self.tracks['gt'] and not self.tracks['pred']:
-            messagebox.showinfo("Empty Tracks", "Please draw at least one track before calculating.")
-            return
-        
-        # Create data for the calculator
-        data = self.create_metric_data()
-        
-        # Calculate the metric
-        TempLocAP, precisions, recalls = self.calculator.calculate_TempLocAP(data)
-        
-        # Display results
-        result_text = f"TempLocAP: {TempLocAP:.4f}"
-        print(result_text)
-        
-        # Create a plot window for precision-recall curve with area highlighted
-        fig_pr, ax_pr = plt.subplots(figsize=(6, 5))
-        
-        # Sort the recalls and corresponding precisions
-        sorted_indices = np.argsort(recalls)
-        sorted_recalls = recalls[sorted_indices]
-        sorted_precisions = precisions[sorted_indices]
-        
-        # Add a point at recall=0 if not already present
-        if sorted_recalls[0] != 0:
-            sorted_recalls = np.concatenate(([0], sorted_recalls))
-            sorted_precisions = np.concatenate(([sorted_precisions[0]], sorted_precisions))
-        
-        # Add a point at recall=1 if not already present
-        if sorted_recalls[-1] != 1:
-            sorted_recalls = np.concatenate((sorted_recalls, [1]))
-            sorted_precisions = np.concatenate((sorted_precisions, [0]))  # Precision typically drops to 0 at recall=1
-            
-        # Plot precision-recall curve
-        ax_pr.plot(sorted_recalls, sorted_precisions, '-o', color='blue', label='Precision-Recall Curve')
-        
-        # Fill the area under the curve
-        ax_pr.fill_between(sorted_recalls, sorted_precisions, alpha=0.3, color='skyblue', label=f'AP = {TempLocAP:.4f}')
-        
-        # Add explanatory annotation about the area
-        ax_pr.annotate(f'Area = {TempLocAP:.4f}', 
-                     xy=(0.5, 0.5), xycoords='axes fraction',
-                     bbox=dict(boxstyle="round,pad=0.3", fc="yellow", ec="orange", alpha=0.8),
-                     ha='center', fontsize=12)
-        
-        ax_pr.set_xlabel('Recall')
-        ax_pr.set_ylabel('Precision')
-        ax_pr.set_xlim([0, 1])
-        ax_pr.set_ylim([0, 1.05])
-        ax_pr.set_title(f'Precision-Recall Curve\nTempLocAP = {TempLocAP:.4f}')
-        ax_pr.grid(True)
-        ax_pr.legend(loc='lower left')
-        plt.tight_layout()
-        plt.show()
-        
-        # Show a message box with the score
-        messagebox.showinfo("TempLocAP Result", result_text)
-
-
-# Run the application
-if __name__ == "__main__":
-    ui = TrackDrawUI()
\ No newline at end of file
diff --git a/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/annotations_with_ego.feather b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/annotations_with_ego.feather
new file mode 100644
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diff --git a/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/city_SE3_egovehicle.feather b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/city_SE3_egovehicle.feather
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diff --git a/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/3b3570b4-7b0b-3268-a571-b0889dbf40b6___img_Sim2_city.json b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/3b3570b4-7b0b-3268-a571-b0889dbf40b6___img_Sim2_city.json
new file mode 100644
index 00000000..9f29a5b5
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/3b3570b4-7b0b-3268-a571-b0889dbf40b6___img_Sim2_city.json
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diff --git a/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/log_map_archive_3b3570b4-7b0b-3268-a571-b0889dbf40b6____MIA_city_47894.json b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/log_map_archive_3b3570b4-7b0b-3268-a571-b0889dbf40b6____MIA_city_47894.json
new file mode 100644
index 00000000..df450ca3
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/data/3b3570b4-7b0b-3268-a571-b0889dbf40b6/map/log_map_archive_3b3570b4-7b0b-3268-a571-b0889dbf40b6____MIA_city_47894.json
@@ -0,0 +1 @@
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diff --git a/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/annotations_with_ego.feather b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/annotations_with_ego.feather
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diff --git a/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958___img_Sim2_city.json b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958___img_Sim2_city.json
new file mode 100644
index 00000000..5e5df2bd
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958___img_Sim2_city.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958_ground_height_surface____PIT.npy b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/3bffdcff-c3a7-38b6-a0f2-64196d130958_ground_height_surface____PIT.npy
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diff --git a/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/log_map_archive_3bffdcff-c3a7-38b6-a0f2-64196d130958____PIT_city_71109.json b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/log_map_archive_3bffdcff-c3a7-38b6-a0f2-64196d130958____PIT_city_71109.json
new file mode 100644
index 00000000..fabb7dc8
--- /dev/null
+++ b/tests/unit/evaluation/scenario_mining/data/3bffdcff-c3a7-38b6-a0f2-64196d130958/map/log_map_archive_3bffdcff-c3a7-38b6-a0f2-64196d130958____PIT_city_71109.json
@@ -0,0 +1 @@
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diff --git a/tests/unit/evaluation/scenario_mining/data/combined_gt_dev.pkl b/tests/unit/evaluation/scenario_mining/data/combined_gt_dev.pkl
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DpX9_R

literal 0
HcmV?d00001

diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index 2b019ebf..272e359f 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -1,19 +1,31 @@
 """Scenario mining evaluation unit tests."""
 import matplotlib
+from pathlib import Path
+from typing import Final
+import sys
+
+from av2.evaluation.scenario_mining.eval import evaluate, load
+
+
 matplotlib.use('Agg')
+if sys.stdout is None:
+    sys.stdout = open('stdout.log', 'w')
 
-from av2.evaluation.scenario_mining.eval import evaluate
+TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data"
 
 def test_evaluate() -> None:
     """Test End-to-End Forecasting evaluation."""
     
-    predictions = 'tests/unit/evaluation/scenario_mining/data/combined_predictions.pkl'
-    ground_truth = 'tests/unit/evaluation/scenario_mining/data/combined_gt.pkl'
+    predictions = TEST_DATA_DIR / 'combined_predictions_dev.pkl'
+    ground_truth = TEST_DATA_DIR / 'combined_gt_dev.pkl'
 
     objective_metric = 'HOTA'
     max_range_m = 100
-    dataset_dir = None
-    out = 'tests/unit/evaluation/scenario_mining/data/eval_results'
+    dataset_dir = TEST_DATA_DIR
+    out = str(TEST_DATA_DIR / 'eval_results')
+
+    predictions = load(predictions)
+    ground_truth = load(ground_truth)
 
     evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
 

From e54e0ab3c3fc01abf2a17099b52fe525e33ffe0f Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Thu, 6 Mar 2025 23:34:18 -0500
Subject: [PATCH 08/22] Linted with Black

---
 .../evaluation/scenario_mining/__init__.py    |  34 +-
 .../evaluation/scenario_mining/constants.py   |   4 +-
 src/av2/evaluation/scenario_mining/eval.py    | 433 ++++++----
 src/av2/evaluation/scenario_mining/hota.py    | 815 +++++++++++-------
 src/av2/evaluation/scenario_mining/metrics.py |   2 +-
 src/av2/evaluation/tracking/eval.py           |   3 +-
 .../eval/test_submission.py                   |   2 +-
 .../evaluation/scenario_mining/__init__.py    |   2 +-
 .../evaluation/scenario_mining/test_eval.py   |  20 +-
 9 files changed, 789 insertions(+), 526 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/__init__.py b/src/av2/evaluation/scenario_mining/__init__.py
index 280f54c3..b78073fe 100644
--- a/src/av2/evaluation/scenario_mining/__init__.py
+++ b/src/av2/evaluation/scenario_mining/__init__.py
@@ -5,41 +5,11 @@
 from enum import Enum, unique
 from typing import Final
 
-NUM_RECALL_SAMPLES: Final = 101
-
 
 @unique
-class SensorCompetitionCategories(str, Enum):
+class ScenarioMiningCategories(str, Enum):
     """Sensor dataset annotation categories."""
+
     REFERRED_OBJECT = "REFERRED_OBJECT"
     RELATED_OBJECT = "RELATED_OBJECT"
     OTHER_OBJECT = "OTHER_OBJECT"
-
-    """
-    ARTICULATED_BUS = "ARTICULATED_BUS"
-    BICYCLE = "BICYCLE"
-    BICYCLIST = "BICYCLIST"
-    BOLLARD = "BOLLARD"
-    BOX_TRUCK = "BOX_TRUCK"
-    BUS = "BUS"
-    CONSTRUCTION_BARREL = "CONSTRUCTION_BARREL"
-    CONSTRUCTION_CONE = "CONSTRUCTION_CONE"
-    DOG = "DOG"
-    LARGE_VEHICLE = "LARGE_VEHICLE"
-    MESSAGE_BOARD_TRAILER = "MESSAGE_BOARD_TRAILER"
-    MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN"
-    MOTORCYCLE = "MOTORCYCLE"
-    MOTORCYCLIST = "MOTORCYCLIST"
-    PEDESTRIAN = "PEDESTRIAN"
-    REGULAR_VEHICLE = "REGULAR_VEHICLE"
-    SCHOOL_BUS = "SCHOOL_BUS"
-    SIGN = "SIGN"
-    STOP_SIGN = "STOP_SIGN"
-    STROLLER = "STROLLER"
-    TRUCK = "TRUCK"
-    TRUCK_CAB = "TRUCK_CAB"
-    VEHICULAR_TRAILER = "VEHICULAR_TRAILER"
-    WHEELCHAIR = "WHEELCHAIR"
-    WHEELED_DEVICE = "WHEELED_DEVICE"
-    WHEELED_RIDER = "WHEELED_RIDER"
-    """
diff --git a/src/av2/evaluation/scenario_mining/constants.py b/src/av2/evaluation/scenario_mining/constants.py
index 0abf956a..62ef0bfe 100644
--- a/src/av2/evaluation/scenario_mining/constants.py
+++ b/src/av2/evaluation/scenario_mining/constants.py
@@ -2,11 +2,11 @@
 
 from typing import Final
 
-from av2.evaluation.scenario_mining import SensorCompetitionCategories
+from av2.evaluation.scenario_mining import ScenarioMiningCategories
 
 SUBMETRIC_TO_METRIC_CLASS_NAME: Final = {
     "MOTA": "CLEAR",
     "HOTA": "HOTA",
 }
 
-AV2_CATEGORIES: Final = tuple(x.value for x in SensorCompetitionCategories)
+AV2_CATEGORIES: Final = tuple(x.value for x in ScenarioMiningCategories)
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 0c152944..67c4ef5e 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -54,32 +54,32 @@ def get_default_eval_config():
         """Returns the default config values for evaluation"""
         code_path = utils.get_code_path()
         default_config = {
-            'USE_PARALLEL': True,
-            'NUM_PARALLEL_CORES': max(int(0.9 * os.cpu_count()), 1),
-            'BREAK_ON_ERROR': True,  # Raises exception and exits with error
-            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error
-            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.
-
-            'PRINT_RESULTS': True,
-            'PRINT_ONLY_COMBINED': False,
-            'PRINT_CONFIG': True,
-            'TIME_PROGRESS': True,
-            'DISPLAY_LESS_PROGRESS': True,
-
-            'OUTPUT_SUMMARY': False,
-            'OUTPUT_EMPTY_CLASSES': False,  # If False, summary files are not output for classes with no detections
-            'OUTPUT_DETAILED': False,
-            'PLOT_CURVES': False,
+            "USE_PARALLEL": True,
+            "NUM_PARALLEL_CORES": max(int(0.9 * os.cpu_count()), 1),
+            "BREAK_ON_ERROR": True,  # Raises exception and exits with error
+            "RETURN_ON_ERROR": False,  # if not BREAK_ON_ERROR, then returns from function on error
+            "LOG_ON_ERROR": os.path.join(
+                code_path, "error_log.txt"
+            ),  # if not None, save any errors into a log file.
+            "PRINT_RESULTS": True,
+            "PRINT_ONLY_COMBINED": False,
+            "PRINT_CONFIG": True,
+            "TIME_PROGRESS": True,
+            "DISPLAY_LESS_PROGRESS": True,
+            "OUTPUT_SUMMARY": False,
+            "OUTPUT_EMPTY_CLASSES": False,  # If False, summary files are not output for classes with no detections
+            "OUTPUT_DETAILED": False,
+            "PLOT_CURVES": False,
         }
         return default_config
 
     def __init__(self, config=None):
         """Initialise the evaluator with a config file"""
-        self.config = utils.init_config(config, self.get_default_eval_config(), 'Eval')
+        self.config = utils.init_config(config, self.get_default_eval_config(), "Eval")
         # Only run timing analysis if not run in parallel.
-        if self.config['TIME_PROGRESS'] and not self.config['USE_PARALLEL']:
+        if self.config["TIME_PROGRESS"] and not self.config["USE_PARALLEL"]:
             _timing.DO_TIMING = True
-            if self.config['DISPLAY_LESS_PROGRESS']:
+            if self.config["DISPLAY_LESS_PROGRESS"]:
                 _timing.DISPLAY_LESS_PROGRESS = True
 
     @_timing.time
@@ -97,9 +97,17 @@ def evaluate(self, dataset_list, metrics_list, show_progressbar=True):
             output_res[dataset_name] = {}
             output_msg[dataset_name] = {}
             tracker_list, seq_list, class_list = dataset.get_eval_info()
-            print('\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following '
-                  'metrics: %s\n' % (len(tracker_list), len(seq_list), len(class_list), dataset_name,
-                                     ', '.join(metric_names)))
+            print(
+                "\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following "
+                "metrics: %s\n"
+                % (
+                    len(tracker_list),
+                    len(seq_list),
+                    len(class_list),
+                    dataset_name,
+                    ", ".join(metric_names),
+                )
+            )
 
             # Evaluate each tracker
             for tracker in tracker_list:
@@ -108,28 +116,41 @@ def evaluate(self, dataset_list, metrics_list, show_progressbar=True):
                     # Evaluate each sequence in parallel or in series.
                     # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]
                     # e.g. res[seq_0001][pedestrian][hota][DetA]
-                    print('\nEvaluating %s\n' % tracker)
+                    print("\nEvaluating %s\n" % tracker)
                     time_start = time.time()
-                    if config['USE_PARALLEL']:
+                    if config["USE_PARALLEL"]:
                         if show_progressbar:
                             seq_list_sorted = sorted(seq_list)
 
-                            with Pool(config['NUM_PARALLEL_CORES']) as pool, tqdm(total=len(seq_list)) as pbar:
-                                _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,
-                                                         class_list=class_list, metrics_list=metrics_list,
-                                                         metric_names=metric_names)
+                            with Pool(config["NUM_PARALLEL_CORES"]) as pool, tqdm(
+                                total=len(seq_list)
+                            ) as pbar:
+                                _eval_sequence = partial(
+                                    eval_sequence,
+                                    dataset=dataset,
+                                    tracker=tracker,
+                                    class_list=class_list,
+                                    metrics_list=metrics_list,
+                                    metric_names=metric_names,
+                                )
                                 results = []
-                                for r in pool.imap(_eval_sequence, seq_list_sorted,
-                                                   chunksize=20):
+                                for r in pool.imap(
+                                    _eval_sequence, seq_list_sorted, chunksize=20
+                                ):
                                     results.append(r)
                                     pbar.update()
                                 res = dict(zip(seq_list_sorted, results))
 
                         else:
-                            with Pool(config['NUM_PARALLEL_CORES']) as pool:
-                                _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,
-                                                         class_list=class_list, metrics_list=metrics_list,
-                                                         metric_names=metric_names)
+                            with Pool(config["NUM_PARALLEL_CORES"]) as pool:
+                                _eval_sequence = partial(
+                                    eval_sequence,
+                                    dataset=dataset,
+                                    tracker=tracker,
+                                    class_list=class_list,
+                                    metrics_list=metrics_list,
+                                    metric_names=metric_names,
+                                )
                                 results = pool.map(_eval_sequence, seq_list)
                                 res = dict(zip(seq_list, results))
                     else:
@@ -137,106 +158,167 @@ def evaluate(self, dataset_list, metrics_list, show_progressbar=True):
                         if show_progressbar:
                             seq_list_sorted = sorted(seq_list)
                             for curr_seq in tqdm(seq_list_sorted):
-                                res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,
-                                                              metric_names)
+                                res[curr_seq] = eval_sequence(
+                                    curr_seq,
+                                    dataset,
+                                    tracker,
+                                    class_list,
+                                    metrics_list,
+                                    metric_names,
+                                )
                         else:
                             for curr_seq in sorted(seq_list):
-                                res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,
-                                                              metric_names)
+                                res[curr_seq] = eval_sequence(
+                                    curr_seq,
+                                    dataset,
+                                    tracker,
+                                    class_list,
+                                    metrics_list,
+                                    metric_names,
+                                )
 
                     # Combine results over all sequences and then over all classes
 
                     # collecting combined cls keys (cls averaged, det averaged, super classes)
                     combined_cls_keys = []
-                    res['COMBINED_SEQ'] = {}
+                    res["COMBINED_SEQ"] = {}
                     # combine sequences for each class
                     for c_cls in class_list:
-                        res['COMBINED_SEQ'][c_cls] = {}
+                        res["COMBINED_SEQ"][c_cls] = {}
                         for metric, metric_name in zip(metrics_list, metric_names):
-                            curr_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value in res.items() if
-                                        seq_key != 'COMBINED_SEQ'}
-                            res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res)
+                            curr_res = {
+                                seq_key: seq_value[c_cls][metric_name]
+                                for seq_key, seq_value in res.items()
+                                if seq_key != "COMBINED_SEQ"
+                            }
+                            res["COMBINED_SEQ"][c_cls][metric_name] = (
+                                metric.combine_sequences(curr_res)
+                            )
                     # combine classes
                     if dataset.should_classes_combine:
-                        combined_cls_keys += ['cls_comb_cls_av', 'cls_comb_det_av', 'all']
-                        res['COMBINED_SEQ']['cls_comb_cls_av'] = {}
-                        res['COMBINED_SEQ']['cls_comb_det_av'] = {}
+                        combined_cls_keys += [
+                            "cls_comb_cls_av",
+                            "cls_comb_det_av",
+                            "all",
+                        ]
+                        res["COMBINED_SEQ"]["cls_comb_cls_av"] = {}
+                        res["COMBINED_SEQ"]["cls_comb_det_av"] = {}
                         for metric, metric_name in zip(metrics_list, metric_names):
-                            cls_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
-                                       res['COMBINED_SEQ'].items() if cls_key not in combined_cls_keys}
-                            res['COMBINED_SEQ']['cls_comb_cls_av'][metric_name] = \
+                            cls_res = {
+                                cls_key: cls_value[metric_name]
+                                for cls_key, cls_value in res["COMBINED_SEQ"].items()
+                                if cls_key not in combined_cls_keys
+                            }
+                            res["COMBINED_SEQ"]["cls_comb_cls_av"][metric_name] = (
                                 metric.combine_classes_class_averaged(cls_res)
-                            res['COMBINED_SEQ']['cls_comb_det_av'][metric_name] = \
+                            )
+                            res["COMBINED_SEQ"]["cls_comb_det_av"][metric_name] = (
                                 metric.combine_classes_det_averaged(cls_res)
+                            )
                     # combine classes to super classes
                     if dataset.use_super_categories:
                         for cat, sub_cats in dataset.super_categories.items():
                             combined_cls_keys.append(cat)
-                            res['COMBINED_SEQ'][cat] = {}
+                            res["COMBINED_SEQ"][cat] = {}
                             for metric, metric_name in zip(metrics_list, metric_names):
-                                cat_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
-                                           res['COMBINED_SEQ'].items() if cls_key in sub_cats}
-                                res['COMBINED_SEQ'][cat][metric_name] = metric.combine_classes_det_averaged(cat_res)
+                                cat_res = {
+                                    cls_key: cls_value[metric_name]
+                                    for cls_key, cls_value in res[
+                                        "COMBINED_SEQ"
+                                    ].items()
+                                    if cls_key in sub_cats
+                                }
+                                res["COMBINED_SEQ"][cat][metric_name] = (
+                                    metric.combine_classes_det_averaged(cat_res)
+                                )
 
                     # Print and output results in various formats
-                    if config['TIME_PROGRESS']:
-                        print('\nAll sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start))
+                    if config["TIME_PROGRESS"]:
+                        print(
+                            "\nAll sequences for %s finished in %.2f seconds"
+                            % (tracker, time.time() - time_start)
+                        )
                     output_fol = dataset.get_output_fol(tracker)
                     tracker_display_name = dataset.get_display_name(tracker)
-                    for c_cls in res['COMBINED_SEQ'].keys():  # class_list + combined classes if calculated
+                    for c_cls in res[
+                        "COMBINED_SEQ"
+                    ].keys():  # class_list + combined classes if calculated
                         summaries = []
                         details = []
-                        num_dets = res['COMBINED_SEQ'][c_cls]['Count']['Dets']
-                        if config['OUTPUT_EMPTY_CLASSES'] or num_dets > 0:
+                        num_dets = res["COMBINED_SEQ"][c_cls]["Count"]["Dets"]
+                        if config["OUTPUT_EMPTY_CLASSES"] or num_dets > 0:
                             for metric, metric_name in zip(metrics_list, metric_names):
                                 # for combined classes there is no per sequence evaluation
                                 if c_cls in combined_cls_keys:
-                                    table_res = {'COMBINED_SEQ': res['COMBINED_SEQ'][c_cls][metric_name]}
+                                    table_res = {
+                                        "COMBINED_SEQ": res["COMBINED_SEQ"][c_cls][
+                                            metric_name
+                                        ]
+                                    }
                                 else:
-                                    table_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value
-                                                 in res.items()}
-
-                                if config['PRINT_RESULTS'] and config['PRINT_ONLY_COMBINED']:
-                                    dont_print = dataset.should_classes_combine and c_cls not in combined_cls_keys
+                                    table_res = {
+                                        seq_key: seq_value[c_cls][metric_name]
+                                        for seq_key, seq_value in res.items()
+                                    }
+
+                                if (
+                                    config["PRINT_RESULTS"]
+                                    and config["PRINT_ONLY_COMBINED"]
+                                ):
+                                    dont_print = (
+                                        dataset.should_classes_combine
+                                        and c_cls not in combined_cls_keys
+                                    )
                                     if not dont_print:
-                                        metric.print_table({'COMBINED_SEQ': table_res['COMBINED_SEQ']},
-                                                           tracker_display_name, c_cls)
-                                elif config['PRINT_RESULTS']:
-                                    metric.print_table(table_res, tracker_display_name, c_cls)
-                                if config['OUTPUT_SUMMARY']:
+                                        metric.print_table(
+                                            {"COMBINED_SEQ": table_res["COMBINED_SEQ"]},
+                                            tracker_display_name,
+                                            c_cls,
+                                        )
+                                elif config["PRINT_RESULTS"]:
+                                    metric.print_table(
+                                        table_res, tracker_display_name, c_cls
+                                    )
+                                if config["OUTPUT_SUMMARY"]:
                                     summaries.append(metric.summary_results(table_res))
-                                if config['OUTPUT_DETAILED']:
+                                if config["OUTPUT_DETAILED"]:
                                     details.append(metric.detailed_results(table_res))
-                                if config['PLOT_CURVES']:
-                                    metric.plot_single_tracker_results(table_res, tracker_display_name, c_cls,
-                                                                       output_fol)
-                            if config['OUTPUT_SUMMARY']:
-                                utils.write_summary_results(summaries, c_cls, output_fol)
-                            if config['OUTPUT_DETAILED']:
+                                if config["PLOT_CURVES"]:
+                                    metric.plot_single_tracker_results(
+                                        table_res,
+                                        tracker_display_name,
+                                        c_cls,
+                                        output_fol,
+                                    )
+                            if config["OUTPUT_SUMMARY"]:
+                                utils.write_summary_results(
+                                    summaries, c_cls, output_fol
+                                )
+                            if config["OUTPUT_DETAILED"]:
                                 utils.write_detailed_results(details, c_cls, output_fol)
 
                     # Output for returning from function
                     output_res[dataset_name][tracker] = res
-                    output_msg[dataset_name][tracker] = 'Success'
+                    output_msg[dataset_name][tracker] = "Success"
 
                 except Exception as err:
                     output_res[dataset_name][tracker] = None
                     if type(err) == TrackEvalException:
                         output_msg[dataset_name][tracker] = str(err)
                     else:
-                        output_msg[dataset_name][tracker] = 'Unknown error occurred.'
-                    print('Tracker %s was unable to be evaluated.' % tracker)
+                        output_msg[dataset_name][tracker] = "Unknown error occurred."
+                    print("Tracker %s was unable to be evaluated." % tracker)
                     print(err)
                     traceback.print_exc()
-                    if config['LOG_ON_ERROR'] is not None:
-                        with open(config['LOG_ON_ERROR'], 'a') as f:
+                    if config["LOG_ON_ERROR"] is not None:
+                        with open(config["LOG_ON_ERROR"], "a") as f:
                             print(dataset_name, file=f)
                             print(tracker, file=f)
                             print(traceback.format_exc(), file=f)
-                            print('\n\n\n', file=f)
-                    if config['BREAK_ON_ERROR']:
+                            print("\n\n\n", file=f)
+                    if config["BREAK_ON_ERROR"]:
                         raise err
-                    elif config['RETURN_ON_ERROR']:
+                    elif config["RETURN_ON_ERROR"]:
                         return output_res, output_msg
 
         return output_res, output_msg
@@ -402,9 +484,7 @@ def _calculate_similarities(
 
 
 def _plot_confusion_matrix(
-        gt_classes:NDArrayInt,
-        pred_classes:NDArrayInt,
-        output_dir:str
+    gt_classes: NDArrayInt, pred_classes: NDArrayInt, output_dir: str
 ) -> None:
     """Plots the confusion matrix for scenario mining. A true label
     indicates that the scenario matches the description. A false label
@@ -415,7 +495,7 @@ def _plot_confusion_matrix(
 
     # Fill the confusion matrix
     for true, pred in zip(gt_classes, pred_classes):
-        cm[1-true, 1-pred] += 1
+        cm[1 - true, 1 - pred] += 1
 
     # Plot the confusion matrix
     fig, ax = plt.subplots(figsize=(4, 4))
@@ -424,7 +504,9 @@ def _plot_confusion_matrix(
     # Add text annotations (True Positives, False Positives, etc.)
     for i in range(2):
         for j in range(2):
-            ax.text(j, i, cm[i, j], ha="center", va="center", color="black", fontsize=16)
+            ax.text(
+                j, i, cm[i, j], ha="center", va="center", color="black", fontsize=16
+            )
 
     # Set axis labels and ticks
     ax.set_xlabel("Predicted Label")
@@ -434,11 +516,11 @@ def _plot_confusion_matrix(
     ax.set_yticks([0, 1])
     ax.set_xticklabels(["Positive", "Negative"])
     ax.set_yticklabels(["Positive", "Negative"])
-    plt.setp(ax.get_yticklabels(), rotation=90, ha='right')
+    plt.setp(ax.get_yticklabels(), rotation=90, ha="right")
 
-    #Display the plot
+    # Display the plot
     if output_dir:
-        plt.savefig(output_dir + '/eval_cm.png')
+        plt.savefig(output_dir + "/eval_cm.png")
     plt.close()
 
 
@@ -500,9 +582,7 @@ def evaluate_tracking(
         "THRESHOLD": iou_threshold,
     }
     metric_names = cast(List[str], metrics_config["METRICS"])
-    metrics_list = [
-        getattr(metrics, metric)(metrics_config) for metric in metric_names
-    ]
+    metrics_list = [getattr(metrics, metric)(metrics_config) for metric in metric_names]
     dataset_config = {
         **TrackEvalDataset.get_default_dataset_config(),
         "GT_TRACKS": {tracker_name: labels},
@@ -511,7 +591,7 @@ def evaluate_tracking(
         "CLASSES_TO_EVAL": classes,
         "TRACKERS_TO_EVAL": [tracker_name],
         "OUTPUT_FOLDER": output_dir,
-        "SHOULD_CLASSES_COMBINE": False
+        "SHOULD_CLASSES_COMBINE": False,
     }
 
     evaluator = Evaluator(
@@ -519,7 +599,7 @@ def evaluate_tracking(
             **Evaluator.get_default_eval_config(),
             "TIME_PROGRESS": True,
             "PLOT_CURVES": True,
-            'OUTPUT_SUMMARY': True,
+            "OUTPUT_SUMMARY": True,
         }
     )
     full_result, _ = evaluator.evaluate(
@@ -555,7 +635,7 @@ def _tune_score_thresholds(
     Returns:
         optimal_score_threshold_by_class: Dictionary of class name to optimal score threshold
         optimal_metric_values_by_class: Dictionary of class name to metric value with the optimal score threshold
-        mean_metric_values_by_class: Dictionary of class name to metric value averaged over recall levels 
+        mean_metric_values_by_class: Dictionary of class name to metric value averaged over recall levels
     """
     metric_class = SUBMETRIC_TO_METRIC_CLASS_NAME[objective_metric]
     metrics_config = {
@@ -660,7 +740,6 @@ def _filter_by_class(detections: Any, name: str) -> Any:
     )
 
 
-
 def _calculate_score_thresholds(
     labels: Sequences,
     predictions: Sequences,
@@ -760,8 +839,8 @@ def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
 
 
 def load(pkl_path):
-    
-    with open(pkl_path, 'rb') as f:
+
+    with open(pkl_path, "rb") as f:
         data = pickle.load(f)
 
     return data
@@ -847,74 +926,74 @@ def referred_full_tracks(sequences: Sequences):
     """
     Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
-    
+
     Args:
         pkl_file_path: Path to the pkl file
-    
+
     Returns:
         reconstructed_sequences: Dictionary containing the reconstructed sequences
     """
-    
-    
+
     reconstructed_sequences = {}
-    
+
     # Process each sequence
     for seq_name, frames in sequences.items():
         # First pass: identify all track_ids that were ever referred objects
         referred_track_ids = set()
         for frame in frames:
-            mask = frame['label'] == 0  # 0 is for REFERRED_OBJECT
-            referred_track_ids.update(frame['track_id'][mask])
-        
+            mask = frame["label"] == 0  # 0 is for REFERRED_OBJECT
+            referred_track_ids.update(frame["track_id"][mask])
+
         # Second pass: reconstruct frames
         new_frames = []
         for frame in frames:
             # Create mask for referred track_ids
-            mask = np.isin(frame['track_id'], list(referred_track_ids))
-            
+            mask = np.isin(frame["track_id"], list(referred_track_ids))
+
             # Create new frame with only referred objects
             new_frame = {
-                'seq_id': frame['seq_id'],
-                'timestamp_ns': frame['timestamp_ns'],
-                'ego_translation_m': frame['ego_translation_m'],
-                'description': frame['description'],
-                'translation_m': frame['translation_m'][mask],
-                'size': frame['size'][mask],
-                'yaw': frame['yaw'][mask],
-                'velocity_m_per_s': frame['velocity_m_per_s'][mask],
-                'label': np.zeros(mask.sum(), dtype=np.int32),  # All are referred objects
-                'name': np.array(['REFERRED_OBJECT'] * mask.sum(), dtype='<U31'),
-                'track_id': frame['track_id'][mask]
+                "seq_id": frame["seq_id"],
+                "timestamp_ns": frame["timestamp_ns"],
+                "ego_translation_m": frame["ego_translation_m"],
+                "description": frame["description"],
+                "translation_m": frame["translation_m"][mask],
+                "size": frame["size"][mask],
+                "yaw": frame["yaw"][mask],
+                "velocity_m_per_s": frame["velocity_m_per_s"][mask],
+                "label": np.zeros(
+                    mask.sum(), dtype=np.int32
+                ),  # All are referred objects
+                "name": np.array(["REFERRED_OBJECT"] * mask.sum(), dtype="<U31"),
+                "track_id": frame["track_id"][mask],
             }
-            
+
             # If score exists in the original frame (for predictions), include it
-            if 'score' in frame:
-                new_frame['score'] = frame['score'][mask]
-            
+            if "score" in frame:
+                new_frame["score"] = frame["score"][mask]
+
             new_frames.append(new_frame)
-        
+
         reconstructed_sequences[seq_name] = new_frames
-    
+
     return reconstructed_sequences
 
 
 def evaluate_mining(
-        track_predictions: Sequences,
-        labels: Sequences,
-        output_dir) -> tuple[float, float]:
-    
+    track_predictions: Sequences, labels: Sequences, output_dir
+) -> tuple[float, float]:
+
     gt_class = np.zeros(len(labels), dtype=np.int64)
     pred_class = np.zeros(len(labels), dtype=np.int64)
 
     for i, description in enumerate(labels.keys()):
 
         for frame in labels[description]:
-            if len(frame['label']) > 0 and 0 in frame['label']:
+            if len(frame["label"]) > 0 and 0 in frame["label"]:
                 gt_class[i] = 1
                 break
 
         for frame in track_predictions[description]:
-            if len(frame['label']) > 0 and 0 in frame['label']:
+            if len(frame["label"]) > 0 and 0 in frame["label"]:
                 pred_class[i] = 1
                 break
 
@@ -922,11 +1001,11 @@ def evaluate_mining(
     fp = np.sum(~gt_class & pred_class)
     fn = np.sum(gt_class & ~pred_class)
 
-    f1_score = float(2*tp / (2*tp + fp + fn))
+    f1_score = float(2 * tp / (2 * tp + fp + fn))
 
-    print(f'GT scenario matches: {gt_class}')
-    print(f'Predicted scenario matches: {pred_class}')
-    print(f'F1: {f1_score}')
+    print(f"GT scenario matches: {gt_class}")
+    print(f"Predicted scenario matches: {pred_class}")
+    print(f"F1: {f1_score}")
 
     _plot_confusion_matrix(gt_class, pred_class, output_dir)
 
@@ -934,38 +1013,40 @@ def evaluate_mining(
     for i in range(len(gt_class)):
         if gt_class[i] == pred_class[i]:
             num_correct += 1
-    
+
     acc = num_correct / len(labels)
 
     return f1_score, acc
 
+
 def relabel_seq_ids(data):
     new_data = {}
-    
+
     for seq_id, frames in data.items():
         if isinstance(seq_id, tuple):
             new_seq_id = str(seq_id)
             new_data[new_seq_id] = frames
         else:
             new_data[seq_id] = frames
-    
+
     for seq_id, frames in new_data.items():
         for frame in frames:
-            if 'seq_id' in frame and isinstance(frame['seq_id'], tuple):
-                frame['seq_id'] = str(frame['seq_id'])
-    
+            if "seq_id" in frame and isinstance(frame["seq_id"], tuple):
+                frame["seq_id"] = str(frame["seq_id"])
+
     return new_data
 
 
 def evaluate(
-    track_predictions:Sequences,
-    labels:Sequences,
+    track_predictions: Sequences,
+    labels: Sequences,
     objective_metric: str,
     max_range_m: int,
     dataset_dir: Any,
-    out: str) -> tuple[float,float,float, float]:
+    out: str,
+) -> tuple[float, float, float, float]:
     """Run scenario mining evaluation on the supplied prediction and label pkl files.
-    
+
     Args:
         pred_pkl: Path to track predictions.
         gt_pkl: Path to track labels.
@@ -973,42 +1054,53 @@ def evaluate(
         max_range_m: Maximum evaluation range.
         dataset_dir: Path to dataset. Required for ROI pruning.
         out: Output path.
-    
+
     Returns:
         class_acc: The classification accuracy of if the scenario matches the description
-        full_track_metric: The tracking metric for the full track of any objects that the description ever applies to. 
+        full_track_metric: The tracking metric for the full track of any objects that the description ever applies to.
         partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
     """
 
     output_dir = ""
     if out:
-        output_dir = out + '/partial_tracks'
-        Path(output_dir).mkdir(parents=True,exist_ok=True)
+        output_dir = out + "/partial_tracks"
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
 
     res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
-        track_predictions, labels, 
-        objective_metric=objective_metric, max_range_m=max_range_m,
-        dataset_dir=dataset_dir, out=output_dir)
-    TempLocAP = res['TrackEvalDataset']['TRACKER']['COMBINED_SEQ']['REFERRED_OBJECT']['HOTA']['TempLocAP']
-    
+        track_predictions,
+        labels,
+        objective_metric=objective_metric,
+        max_range_m=max_range_m,
+        dataset_dir=dataset_dir,
+        out=output_dir,
+    )
+    TempLocAP = res["TrackEvalDataset"]["TRACKER"]["COMBINED_SEQ"]["REFERRED_OBJECT"][
+        "HOTA"
+    ]["TempLocAP"]
+
     full_track_preds = referred_full_tracks(track_predictions)
     full_track_labels = referred_full_tracks(labels)
 
     output_dir = ""
     if out:
-        output_dir = out + '/full_tracks'
+        output_dir = out + "/full_tracks"
         Path(output_dir).mkdir(parents=True, exist_ok=True)
 
     _, full_track_metrics, _, _ = evaluate_scenario_mining(
-        full_track_preds, full_track_labels, 
-        objective_metric=objective_metric, max_range_m=max_range_m, 
-        dataset_dir=dataset_dir, out=output_dir, full_tracks=True)
-    
+        full_track_preds,
+        full_track_labels,
+        objective_metric=objective_metric,
+        max_range_m=max_range_m,
+        dataset_dir=dataset_dir,
+        out=output_dir,
+        full_tracks=True,
+    )
+
     full_track_hota = full_track_metrics["REFERRED_OBJECT"]
     partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
 
     return f1_score, full_track_hota, partial_track_hota, TempLocAP
-    
+
 
 def evaluate_scenario_mining(
     track_predictions: Sequences,
@@ -1017,7 +1109,7 @@ def evaluate_scenario_mining(
     max_range_m: int,
     dataset_dir: Any,
     out: str,
-    full_tracks:bool=False
+    full_tracks: bool = False,
 ) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any], float]:
     """Run evaluation.
 
@@ -1052,7 +1144,7 @@ def evaluate_scenario_mining(
         track_predictions,
         objective_metric,
         classes,
-        num_thresholds = 3,
+        num_thresholds=3,
         match_distance_m=2,
     )
     filtered_track_predictions = sm_utils.filter_by_class_thresholds(
@@ -1069,14 +1161,19 @@ def evaluate_scenario_mining(
     if not full_tracks:
         f1_score, acc = evaluate_mining(filtered_track_predictions, labels, out)
         return res, tuned_metric_values, mean_metric_values, f1_score
-    
+
     return res, tuned_metric_values, mean_metric_values, 0
 
 
 @click.command()
 @click.option("--predictions", required=True, help="Predictions PKL file")
 @click.option("--ground_truth", required=True, help="Ground Truth PKL file")
-@click.option("--max_range_m", default=50, type=int, help="Evaluate objects within distance of ego vehicle")
+@click.option(
+    "--max_range_m",
+    default=50,
+    type=int,
+    help="Evaluate objects within distance of ego vehicle",
+)
 @click.option(
     "--dataset_dir",
     default=None,
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
index 321b5cf1..c67743e3 100644
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ b/src/av2/evaluation/scenario_mining/hota.py
@@ -15,10 +15,22 @@ def __init__(self, config=None):
         super().__init__()
         self.plottable = True
         self.array_labels = np.arange(0.05, 0.99, 0.05)
-        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']
-        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'OWTA']
-        self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)', 'TempLocAP']
-        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields
+        self.integer_array_fields = ["HOTA_TP", "HOTA_FN", "HOTA_FP"]
+        self.float_array_fields = [
+            "HOTA",
+            "DetA",
+            "AssA",
+            "DetRe",
+            "DetPr",
+            "AssRe",
+            "AssPr",
+            "LocA",
+            "OWTA",
+        ]
+        self.float_fields = ["HOTA(0)", "LocA(0)", "HOTALocA(0)", "TempLocAP"]
+        self.fields = (
+            self.float_array_fields + self.integer_array_fields + self.float_fields
+        )
         self.summary_fields = self.float_array_fields + self.float_fields
 
     @_timing.time
@@ -33,59 +45,86 @@ def eval_sequence(self, data):
             res[field] = 0
 
         TempLocAP, _, _ = self.calculate_TempLocAP_merge(data)
-        res['TempLocAP'] = TempLocAP
+        res["TempLocAP"] = TempLocAP
 
         # Return result quickly if tracker or gt sequence is empty
-        if data['num_tracker_dets'] == 0:
-            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=float)
-            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
-            res['LocA(0)'] = 1.0
+        if data["num_tracker_dets"] == 0:
+            res["HOTA_FN"] = data["num_gt_dets"] * np.ones(
+                (len(self.array_labels)), dtype=float
+            )
+            res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
+            res["LocA(0)"] = 1.0
             return res
-        if data['num_gt_dets'] == 0:
-            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=float)
-            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
-            res['LocA(0)'] = 1.0
+        if data["num_gt_dets"] == 0:
+            res["HOTA_FP"] = data["num_tracker_dets"] * np.ones(
+                (len(self.array_labels)), dtype=float
+            )
+            res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
+            res["LocA(0)"] = 1.0
             return res
 
         # Variables counting global association
-        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
-        gt_id_count = np.zeros((data['num_gt_ids'], 1))
-        tracker_id_count = np.zeros((1, data['num_tracker_ids']))
+        potential_matches_count = np.zeros(
+            (data["num_gt_ids"], data["num_tracker_ids"])
+        )
+        gt_id_count = np.zeros((data["num_gt_ids"], 1))
+        tracker_id_count = np.zeros((1, data["num_tracker_ids"]))
 
         # First loop through each timestep and accumulate global track information.
-        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(
+            zip(data["gt_ids"], data["tracker_ids"])
+        ):
             # Count the potential matches between ids in each timestep
             # These are normalised, weighted by the match similarity.
-            similarity = data['similarity_scores'][t]
-            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity
+            similarity = data["similarity_scores"][t]
+            sim_iou_denom = (
+                similarity.sum(0)[np.newaxis, :]
+                + similarity.sum(1)[:, np.newaxis]
+                - similarity
+            )
             sim_iou = np.zeros_like(similarity)
-            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps
-            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
-            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou
+            sim_iou_mask = sim_iou_denom > 0 + np.finfo("float").eps
+            sim_iou[sim_iou_mask] = (
+                similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
+            )
+            potential_matches_count[
+                gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
+            ] += sim_iou
 
             # Calculate the total number of dets for each gt_id and tracker_id.
             gt_id_count[gt_ids_t] += 1
             tracker_id_count[0, tracker_ids_t] += 1
 
         # Calculate overall jaccard alignment score (before unique matching) between IDs
-        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)
-        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]
+        global_alignment_score = potential_matches_count / (
+            gt_id_count + tracker_id_count - potential_matches_count
+        )
+        matches_counts = [
+            np.zeros_like(potential_matches_count) for _ in self.array_labels
+        ]
 
         # Calculate scores for each timestep
-        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(
+            zip(data["gt_ids"], data["tracker_ids"])
+        ):
             # Deal with the case that there are no gt_det/tracker_det in a timestep.
             if len(gt_ids_t) == 0:
                 for a, alpha in enumerate(self.array_labels):
-                    res['HOTA_FP'][a] += len(tracker_ids_t)
+                    res["HOTA_FP"][a] += len(tracker_ids_t)
                 continue
             if len(tracker_ids_t) == 0:
                 for a, alpha in enumerate(self.array_labels):
-                    res['HOTA_FN'][a] += len(gt_ids_t)
+                    res["HOTA_FN"][a] += len(gt_ids_t)
                 continue
 
             # Get matching scores between pairs of dets for optimizing HOTA
-            similarity = data['similarity_scores'][t]
-            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity
+            similarity = data["similarity_scores"][t]
+            score_mat = (
+                global_alignment_score[
+                    gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
+                ]
+                * similarity
+            )
 
             # Hungarian algorithm to find best matches
             match_rows, match_cols = linear_sum_assignment(-score_mat)
@@ -93,39 +132,53 @@ def eval_sequence(self, data):
             # Calculate and accumulate basic statistics
 
             for a, alpha in enumerate(self.array_labels):
-                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
+                actually_matched_mask = (
+                    similarity[match_rows, match_cols] >= alpha - np.finfo("float").eps
+                )
                 alpha_match_rows = match_rows[actually_matched_mask]
                 alpha_match_cols = match_cols[actually_matched_mask]
                 num_matches = len(alpha_match_rows)
-                res['HOTA_TP'][a] += num_matches
-                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches
-                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches
+                res["HOTA_TP"][a] += num_matches
+                res["HOTA_FN"][a] += len(gt_ids_t) - num_matches
+                res["HOTA_FP"][a] += len(tracker_ids_t) - num_matches
                 if num_matches > 0:
-                    res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])
-                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1
+                    res["LocA"][a] += sum(
+                        similarity[alpha_match_rows, alpha_match_cols]
+                    )
+                    matches_counts[a][
+                        gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]
+                    ] += 1
 
         # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
         # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
         for a, alpha in enumerate(self.array_labels):
             matches_count = matches_counts[a]
-            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)
-            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])
+            ass_a = matches_count / np.maximum(
+                1, gt_id_count + tracker_id_count - matches_count
+            )
+            res["AssA"][a] = np.sum(matches_count * ass_a) / np.maximum(
+                1, res["HOTA_TP"][a]
+            )
             ass_re = matches_count / np.maximum(1, gt_id_count)
-            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])
+            res["AssRe"][a] = np.sum(matches_count * ass_re) / np.maximum(
+                1, res["HOTA_TP"][a]
+            )
             ass_pr = matches_count / np.maximum(1, tracker_id_count)
-            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])
+            res["AssPr"][a] = np.sum(matches_count * ass_pr) / np.maximum(
+                1, res["HOTA_TP"][a]
+            )
 
         # Calculate final scores
-        res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])
+        res["LocA"] = np.maximum(1e-10, res["LocA"]) / np.maximum(1e-10, res["HOTA_TP"])
         res = self._compute_final_fields(res)
 
         return res
-    
+
     def calculate_TempLocAP(self, data):
 
         # Return result quickly if tracker or gt sequence is empty
-        if data['num_tracker_dets'] == 0:
-            if data['num_gt_dets'] == 0:
+        if data["num_tracker_dets"] == 0:
+            if data["num_gt_dets"] == 0:
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 TempLocAP = 1
@@ -135,71 +188,82 @@ def calculate_TempLocAP(self, data):
                 recalls = np.array([0, 1])
                 TempLocAP = 0
                 return TempLocAP, precisions, recalls
-        if data['num_gt_dets'] == 0:
+        if data["num_gt_dets"] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
             return TempLocAP, precisions, recalls
 
-        TEMPORAL_IOU_THRESH  = 0.5 #iou
-        MATCHING_DIST_THRESH = 2.0 #m
+        TEMPORAL_IOU_THRESH = 0.5  # iou
+        MATCHING_DIST_THRESH = 2.0  # m
 
         pred_tracks = {}
         gt_tracks = {}
 
-        #Accumulate predicted and ground truth tracks from data
-        for t in range(data['num_timesteps']):
-            for i, gt_id in enumerate(data['gt_ids'][t]):
+        # Accumulate predicted and ground truth tracks from data
+        for t in range(data["num_timesteps"]):
+            for i, gt_id in enumerate(data["gt_ids"][t]):
                 if gt_id not in gt_tracks:
                     gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]['xy_pos'] = []
-                    gt_tracks[gt_id]['timestamps'] = []
-                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+                    gt_tracks[gt_id]["xy_pos"] = []
+                    gt_tracks[gt_id]["timestamps"] = []
+                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
 
-                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
-                gt_tracks[gt_id]['timestamps'].append(t)
+                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
+                gt_tracks[gt_id]["timestamps"].append(t)
 
-            for i, track_id in enumerate(data['tracker_ids'][t]):
+            for i, track_id in enumerate(data["tracker_ids"][t]):
                 if track_id not in pred_tracks:
                     pred_tracks[track_id] = {}
-                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
-                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
-                    pred_tracks[track_id]['xy_pos'] = []
-                    pred_tracks[track_id]['timestamps'] = []
+                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
+                        t
+                    ][i]
+                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
+                    pred_tracks[track_id]["xy_pos"] = []
+                    pred_tracks[track_id]["timestamps"] = []
 
-                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
-                pred_tracks[track_id]['timestamps'].append(t)
+                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
+                pred_tracks[track_id]["timestamps"].append(t)
 
         # 1 to 1 match of predicted and ground truth tracks
-        sorted_keys = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+        sorted_keys = sorted(
+            pred_tracks.keys(),
+            key=lambda key: pred_tracks[key]["confidence"],
+            reverse=True,
+        )
 
-        #keys are track_ids, values are gt_ids
-        matched_ids = {} 
+        # keys are track_ids, values are gt_ids
+        matched_ids = {}
 
-        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-        matched_ious = {} 
+        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        matched_ious = {}
         unmatched_gt_ids = list(gt_tracks.keys())
         unmatched_track_ids = []
 
         for track_id in sorted_keys:
             track_stats = pred_tracks[track_id]
 
-            track_traj = track_stats['xy_pos']
-            track_timestamps = track_stats['timestamps']
-            
+            track_traj = track_stats["xy_pos"]
+            track_timestamps = track_stats["timestamps"]
+
             max_similarity = 0
             best_match = None
             corresponding_iou = 0
             for gt_id, gt_stats in gt_tracks.items():
-                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
+                if (
+                    gt_id not in unmatched_gt_ids
+                    or gt_stats["category"] != track_stats["category"]
+                ):
                     continue
 
-                gt_traj = gt_stats['xy_pos']
-                gt_timestamps = gt_stats['timestamps']
+                gt_traj = gt_stats["xy_pos"]
+                gt_timestamps = gt_stats["timestamps"]
 
-                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                intersection = len(
+                    set(gt_timestamps).intersection((set(track_timestamps)))
+                )
                 union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection/union
+                iou = intersection / union
 
                 if iou < TEMPORAL_IOU_THRESH:
                     continue
@@ -208,11 +272,21 @@ def calculate_TempLocAP(self, data):
                 for timestamp in track_timestamps:
                     if timestamp in gt_timestamps:
                         total_distance += np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)])
-
-
-                similarity_score = iou * max(0.0, 
-                    1 - (total_distance/(MATCHING_DIST_THRESH*(intersection+np.finfo(np.float64).eps))))
+                            track_traj[track_timestamps.index(timestamp)]
+                            - gt_traj[gt_timestamps.index(timestamp)]
+                        )
+
+                similarity_score = iou * max(
+                    0.0,
+                    1
+                    - (
+                        total_distance
+                        / (
+                            MATCHING_DIST_THRESH
+                            * (intersection + np.finfo(np.float64).eps)
+                        )
+                    ),
+                )
                 if similarity_score > max_similarity:
                     max_similarity = similarity_score
                     best_match = gt_id
@@ -224,7 +298,7 @@ def calculate_TempLocAP(self, data):
             else:
                 unmatched_track_ids.append(track_id)
 
-        #Compute precision and recall at all confidence thresholds.
+        # Compute precision and recall at all confidence thresholds.
         tp = np.zeros(len(pred_tracks))
         fp = np.zeros(len(pred_tracks))
         for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
@@ -237,20 +311,19 @@ def calculate_TempLocAP(self, data):
         tp = np.cumsum(tp)
         fp = np.cumsum(fp)
 
-        recalls = tp/len(gt_tracks)
-        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+        recalls = tp / len(gt_tracks)
+        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
 
         assert np.all(0 <= precisions) & np.all(precisions <= 1)
         TempLocAP = self.get_ap(recalls, precisions)
 
         return TempLocAP, precisions, recalls
-    
 
     def calculate_TempLocAP_concat(self, data):
 
         # Return result quickly if tracker or gt sequence is empty
-        if data['num_tracker_dets'] == 0:
-            if data['num_gt_dets'] == 0:
+        if data["num_tracker_dets"] == 0:
+            if data["num_gt_dets"] == 0:
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 return TempLocAP, precisions, recalls
@@ -259,70 +332,81 @@ def calculate_TempLocAP_concat(self, data):
                 recalls = np.array([0, 1])
                 TempLocAP = 0
                 return TempLocAP, precisions, recalls
-        if data['num_gt_dets'] == 0:
+        if data["num_gt_dets"] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
             return TempLocAP, precisions, recalls
 
-        TEMPORAL_IOU_THRESH  = 0.5 #iou
-        MATCHING_DIST_THRESH = 2.0 #m
+        TEMPORAL_IOU_THRESH = 0.5  # iou
+        MATCHING_DIST_THRESH = 2.0  # m
 
         pred_tracks = {}
         gt_tracks = {}
 
-        #Accumulate predicted and ground truth tracks from data
-        for t in range(data['num_timesteps']):
-            for i, gt_id in enumerate(data['gt_ids'][t]):
+        # Accumulate predicted and ground truth tracks from data
+        for t in range(data["num_timesteps"]):
+            for i, gt_id in enumerate(data["gt_ids"][t]):
                 if gt_id not in gt_tracks:
                     gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]['xy_pos'] = []
-                    gt_tracks[gt_id]['timestamps'] = []
-                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+                    gt_tracks[gt_id]["xy_pos"] = []
+                    gt_tracks[gt_id]["timestamps"] = []
+                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
 
-                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
-                gt_tracks[gt_id]['timestamps'].append(t)
+                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
+                gt_tracks[gt_id]["timestamps"].append(t)
 
-            for i, track_id in enumerate(data['tracker_ids'][t]):
+            for i, track_id in enumerate(data["tracker_ids"][t]):
                 if track_id not in pred_tracks:
                     pred_tracks[track_id] = {}
-                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
-                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
-                    pred_tracks[track_id]['xy_pos'] = []
-                    pred_tracks[track_id]['timestamps'] = []
+                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
+                        t
+                    ][i]
+                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
+                    pred_tracks[track_id]["xy_pos"] = []
+                    pred_tracks[track_id]["timestamps"] = []
 
-                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
-                pred_tracks[track_id]['timestamps'].append(t)
+                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
+                pred_tracks[track_id]["timestamps"].append(t)
 
         # 1 to 1 match of predicted and ground truth tracks
-        pred_ids_by_conf = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+        pred_ids_by_conf = sorted(
+            pred_tracks.keys(),
+            key=lambda key: pred_tracks[key]["confidence"],
+            reverse=True,
+        )
 
-        #Keys are match_id, values are dict of gt_id, pred_ids, gt_traj, pred_traj, category and confidence 
+        # Keys are match_id, values are dict of gt_id, pred_ids, gt_traj, pred_traj, category and confidence
         matched_predictions = {}
 
-        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
         unmatched_gt_ids = list(gt_tracks.keys())
         unmatched_track_ids = []
 
         for track_id in pred_ids_by_conf:
             track_stats = pred_tracks[track_id]
-            track_confidence = track_stats['confidence']
-            track_traj = track_stats['xy_pos']
-            track_timestamps = track_stats['timestamps']
-            
+            track_confidence = track_stats["confidence"]
+            track_traj = track_stats["xy_pos"]
+            track_timestamps = track_stats["timestamps"]
+
             max_similarity = 0
             best_match_stats = None
             best_match = None
             for gt_id, gt_stats in gt_tracks.items():
-                if gt_id not in unmatched_gt_ids or gt_stats['category'] != track_stats['category']:
+                if (
+                    gt_id not in unmatched_gt_ids
+                    or gt_stats["category"] != track_stats["category"]
+                ):
                     continue
 
-                gt_traj = gt_stats['xy_pos']
-                gt_timestamps = gt_stats['timestamps']
+                gt_traj = gt_stats["xy_pos"]
+                gt_timestamps = gt_stats["timestamps"]
 
-                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                intersection = len(
+                    set(gt_timestamps).intersection((set(track_timestamps)))
+                )
                 union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection/union
+                iou = intersection / union
 
                 if iou < TEMPORAL_IOU_THRESH:
                     continue
@@ -330,55 +414,65 @@ def calculate_TempLocAP_concat(self, data):
                 distances = []
                 for timestamp in track_timestamps:
                     if timestamp in gt_timestamps:
-                        distances.append(np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+                        distances.append(
+                            np.linalg.norm(
+                                track_traj[track_timestamps.index(timestamp)]
+                                - gt_traj[gt_timestamps.index(timestamp)]
+                            )
+                        )
 
-
-                similarity_score = iou * max(0, 
-                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+                similarity_score = iou * max(
+                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+                )
 
                 if similarity_score > max_similarity:
                     max_similarity = similarity_score
                     best_match = gt_id
                     best_match_stats = {
-                        'gt_id': gt_id,
-                        'gt_timestamps': gt_timestamps,
-                        'gt_traj': gt_traj,
-                        'pred_ids': [track_id],
-                        'pred_traj': track_traj,
-                        'pred_timestamps': track_timestamps,
-                        'category': gt_stats['category'],
-                        'confidence': track_confidence,
-                        'similarity': similarity_score
+                        "gt_id": gt_id,
+                        "gt_timestamps": gt_timestamps,
+                        "gt_traj": gt_traj,
+                        "pred_ids": [track_id],
+                        "pred_traj": track_traj,
+                        "pred_timestamps": track_timestamps,
+                        "category": gt_stats["category"],
+                        "confidence": track_confidence,
+                        "similarity": similarity_score,
                     }
 
             for match_id, match_stats in matched_predictions.items():
 
-                gt_id = match_stats['gt_id']
-                gt_timestamps = match_stats['gt_timestamps']
-                gt_traj = match_stats['gt_traj']
+                gt_id = match_stats["gt_id"]
+                gt_timestamps = match_stats["gt_timestamps"]
+                gt_traj = match_stats["gt_traj"]
 
-                pred_ids = match_stats['pred_ids']
-                pred_timestamps = match_stats['pred_timestamps']
-                pred_traj = match_stats['pred_traj']
+                pred_ids = match_stats["pred_ids"]
+                pred_timestamps = match_stats["pred_timestamps"]
+                pred_traj = match_stats["pred_traj"]
 
-                category = match_stats['category']
-                confidence = match_stats['confidence']
-                match_similarity = match_stats['similarity']
+                category = match_stats["category"]
+                confidence = match_stats["confidence"]
+                match_similarity = match_stats["similarity"]
 
-                if (len(set(pred_timestamps).intersection(set(track_timestamps))) > 0
-                or track_stats['category'] != category):
+                if (
+                    len(set(pred_timestamps).intersection(set(track_timestamps))) > 0
+                    or track_stats["category"] != category
+                ):
                     continue
-                
+
                 concat_pred_ids = pred_ids + track_id
                 concat_timestamps = pred_timestamps + track_timestamps
                 concat_traj = pred_traj + track_traj
-                concat_confidence = confidence*len(pred_timestamps)+track_confidence*len(track_timestamps)
+                concat_confidence = confidence * len(
+                    pred_timestamps
+                ) + track_confidence * len(track_timestamps)
                 concat_confidence /= len(concat_timestamps)
 
-                intersection = len(set(gt_timestamps).intersection(set(concat_timestamps)))
+                intersection = len(
+                    set(gt_timestamps).intersection(set(concat_timestamps))
+                )
                 union = len(set(gt_timestamps).union(set(concat_timestamps)))
-                iou = intersection/union
+                iou = intersection / union
 
                 if iou < TEMPORAL_IOU_THRESH:
                     continue
@@ -386,28 +480,36 @@ def calculate_TempLocAP_concat(self, data):
                 distances = []
                 for timestamp in concat_timestamps:
                     if timestamp in gt_timestamps:
-                        distances.append(np.linalg.norm(
-                            concat_traj[concat_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
-
-                similarity_score = iou * max(0, 
-                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
-
-                if similarity_score > max_similarity and similarity_score > match_similarity:
+                        distances.append(
+                            np.linalg.norm(
+                                concat_traj[concat_timestamps.index(timestamp)]
+                                - gt_traj[gt_timestamps.index(timestamp)]
+                            )
+                        )
+
+                similarity_score = iou * max(
+                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+                )
+
+                if (
+                    similarity_score > max_similarity
+                    and similarity_score > match_similarity
+                ):
                     max_similarity = similarity_score
                     best_match = match_id
                     best_match_stats = {
-                        'pred_ids': concat_pred_ids,
-                        'pred_traj': concat_traj,
-                        'pred_timestamps': concat_timestamps,
-                        'confidence': concat_confidence,
-                        'similarity': similarity_score
-                        }
-                
+                        "pred_ids": concat_pred_ids,
+                        "pred_traj": concat_traj,
+                        "pred_timestamps": concat_timestamps,
+                        "confidence": concat_confidence,
+                        "similarity": similarity_score,
+                    }
+
             if max_similarity > 0:
                 if best_match < 0:
                     matched_predictions[best_match].update(best_match_stats)
                 elif best_match is not None:
-                    matched_predictions[-best_match-1] = best_match_stats
+                    matched_predictions[-best_match - 1] = best_match_stats
                     unmatched_gt_ids.remove(best_match)
             else:
                 unmatched_track_ids.append(track_id)
@@ -416,13 +518,17 @@ def calculate_TempLocAP_concat(self, data):
         preds = list(matched_predictions.keys()) + unmatched_track_ids
         for pred in preds:
             if pred < 0:
-                concat_preds_by_conf[pred] = matched_predictions[pred]['confidence']
+                concat_preds_by_conf[pred] = matched_predictions[pred]["confidence"]
             else:
-                concat_preds_by_conf[pred] = pred_tracks[pred]['confidence']
+                concat_preds_by_conf[pred] = pred_tracks[pred]["confidence"]
 
-        concat_ids_by_conf = sorted(concat_preds_by_conf.keys(), key=lambda key: concat_preds_by_conf[key], reverse=True)
+        concat_ids_by_conf = sorted(
+            concat_preds_by_conf.keys(),
+            key=lambda key: concat_preds_by_conf[key],
+            reverse=True,
+        )
 
-        #Compute precision and recall at all confidence thresholds.
+        # Compute precision and recall at all confidence thresholds.
         tp = np.zeros(len(concat_ids_by_conf))
         fp = np.zeros(len(concat_ids_by_conf))
 
@@ -436,20 +542,19 @@ def calculate_TempLocAP_concat(self, data):
         tp = np.cumsum(tp)
         fp = np.cumsum(fp)
 
-        recalls = tp/len(gt_tracks)
-        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+        recalls = tp / len(gt_tracks)
+        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
 
         assert np.all(0 <= precisions) & np.all(precisions <= 1)
         TempLocAP = self.get_ap(recalls, precisions)
 
         return TempLocAP, precisions, recalls
-    
-    
+
     def calculate_TempLocAP_merge(self, data):
 
         # Return result quickly if tracker or gt sequence is empty
-        if data['num_tracker_dets'] == 0:
-            if data['num_gt_dets'] == 0:
+        if data["num_tracker_dets"] == 0:
+            if data["num_gt_dets"] == 0:
                 precisions = np.array([1, 1])
                 recalls = np.array([0, 1])
                 TempLocAP = 1
@@ -459,67 +564,75 @@ def calculate_TempLocAP_merge(self, data):
                 recalls = np.array([0, 1])
                 TempLocAP = 0
                 return TempLocAP, precisions, recalls
-        if data['num_gt_dets'] == 0:
+        if data["num_gt_dets"] == 0:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
             TempLocAP = 0
             return TempLocAP, precisions, recalls
 
-        TEMPORAL_IOU_THRESH  = 0.5 #iou
-        MATCHING_DIST_THRESH = 2.0 #m
+        TEMPORAL_IOU_THRESH = 0.5  # iou
+        MATCHING_DIST_THRESH = 2.0  # m
 
         pred_tracks = {}
         gt_tracks = {}
 
-        #Accumulate predicted and ground truth tracks from data
-        for t in range(data['num_timesteps']):
-            for i, gt_id in enumerate(data['gt_ids'][t]):
+        # Accumulate predicted and ground truth tracks from data
+        for t in range(data["num_timesteps"]):
+            for i, gt_id in enumerate(data["gt_ids"][t]):
                 if gt_id not in gt_tracks:
                     gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]['xy_pos'] = []
-                    gt_tracks[gt_id]['timestamps'] = []
-                    gt_tracks[gt_id]['category'] = data['gt_classes'][t][i]
+                    gt_tracks[gt_id]["xy_pos"] = []
+                    gt_tracks[gt_id]["timestamps"] = []
+                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
 
-                gt_tracks[gt_id]['xy_pos'].append(data['gt_dets'][t][i][:2])
-                gt_tracks[gt_id]['timestamps'].append(t)
+                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
+                gt_tracks[gt_id]["timestamps"].append(t)
 
-            for i, track_id in enumerate(data['tracker_ids'][t]):
+            for i, track_id in enumerate(data["tracker_ids"][t]):
                 if track_id not in pred_tracks:
                     pred_tracks[track_id] = {}
-                    pred_tracks[track_id]['confidence'] = data['tracker_confidences'][t][i]
-                    pred_tracks[track_id]['category'] = data['tracker_classes'][t][i]
-                    pred_tracks[track_id]['xy_pos'] = []
-                    pred_tracks[track_id]['timestamps'] = []
+                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
+                        t
+                    ][i]
+                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
+                    pred_tracks[track_id]["xy_pos"] = []
+                    pred_tracks[track_id]["timestamps"] = []
 
-                pred_tracks[track_id]['xy_pos'].append(data['tracker_dets'][t][i][:2])
-                pred_tracks[track_id]['timestamps'].append(t)
+                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
+                pred_tracks[track_id]["timestamps"].append(t)
 
         # 1 to 1 match of predicted and ground truth tracks
-        pred_ids_by_conf = sorted(pred_tracks.keys(), key=lambda key: pred_tracks[key]['confidence'], reverse=True)
+        pred_ids_by_conf = sorted(
+            pred_tracks.keys(),
+            key=lambda key: pred_tracks[key]["confidence"],
+            reverse=True,
+        )
 
-        #keys are gt_id, values are list of corresponding pred_ids
+        # keys are gt_id, values are list of corresponding pred_ids
         matched_ids = {}
 
-        #keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
         unmatched_track_ids = []
 
         for track_id in pred_ids_by_conf:
             track_stats = pred_tracks[track_id]
-            track_confidence = track_stats['confidence']
-            track_traj = track_stats['xy_pos']
-            track_timestamps = track_stats['timestamps']
-            
+            track_confidence = track_stats["confidence"]
+            track_traj = track_stats["xy_pos"]
+            track_timestamps = track_stats["timestamps"]
+
             max_similarity = 0
             best_match = None
             for gt_id, gt_stats in gt_tracks.items():
-                if gt_stats['category'] != track_stats['category']:
+                if gt_stats["category"] != track_stats["category"]:
                     continue
 
-                gt_traj = gt_stats['xy_pos']
-                gt_timestamps = gt_stats['timestamps']
+                gt_traj = gt_stats["xy_pos"]
+                gt_timestamps = gt_stats["timestamps"]
 
-                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
-                iol = intersection/len(track_timestamps)
+                intersection = len(
+                    set(gt_timestamps).intersection((set(track_timestamps)))
+                )
+                iol = intersection / len(track_timestamps)
 
                 if iol < TEMPORAL_IOU_THRESH:
                     continue
@@ -527,12 +640,16 @@ def calculate_TempLocAP_merge(self, data):
                 distances = []
                 for timestamp in track_timestamps:
                     if timestamp in gt_timestamps:
-                        distances.append(np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
-
+                        distances.append(
+                            np.linalg.norm(
+                                track_traj[track_timestamps.index(timestamp)]
+                                - gt_traj[gt_timestamps.index(timestamp)]
+                            )
+                        )
 
-                similarity_score = iol * max(0, 
-                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+                similarity_score = iol * max(
+                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+                )
 
                 if similarity_score > max_similarity:
                     max_similarity = similarity_score
@@ -554,16 +671,18 @@ def calculate_TempLocAP_merge(self, data):
             merged_catetory = None
 
             for pred_id in pred_ids:
-                track_timestamps = pred_tracks[pred_id]['timestamps']
-                track_trajectory = pred_tracks[pred_id]['xy_pos']
-                track_confidence = pred_tracks[pred_id]['confidence']
-                track_category = pred_tracks[pred_id]['category']
-                
+                track_timestamps = pred_tracks[pred_id]["timestamps"]
+                track_trajectory = pred_tracks[pred_id]["xy_pos"]
+                track_confidence = pred_tracks[pred_id]["confidence"]
+                track_category = pred_tracks[pred_id]["category"]
+
                 if len(merged_timestamps) == 0:
                     merged_timestamps.extend(track_timestamps)
                     merged_traj.extend(track_trajectory)
                     merged_catetory = track_category
-                    merged_confidences.extend([track_confidence]*len(track_timestamps))
+                    merged_confidences.extend(
+                        [track_confidence] * len(track_timestamps)
+                    )
                     continue
 
                 for i, timestamp in enumerate(track_timestamps):
@@ -582,41 +701,52 @@ def calculate_TempLocAP_merge(self, data):
                             merged_confidences[insertion_index] = track_confidence
                             merged_traj[insertion_index] = track_trajectory[i]
 
-            merged_predictions[-gt_id-1] = {
-                'xy_pos': merged_traj,
-                'timestamps': merged_timestamps,
-                'confidence': np.mean(np.array(merged_confidences)),
-                'category': merged_catetory
+            merged_predictions[-gt_id - 1] = {
+                "xy_pos": merged_traj,
+                "timestamps": merged_timestamps,
+                "confidence": np.mean(np.array(merged_confidences)),
+                "category": merged_catetory,
             }
 
         for unmatched_track_id in unmatched_track_ids:
-            merged_predictions.update({unmatched_track_id: pred_tracks[unmatched_track_id]})
-
-        merged_ids_by_conf = sorted(merged_predictions.keys(), key=lambda key: merged_predictions[key]['confidence'], reverse=True)
+            merged_predictions.update(
+                {unmatched_track_id: pred_tracks[unmatched_track_id]}
+            )
+
+        merged_ids_by_conf = sorted(
+            merged_predictions.keys(),
+            key=lambda key: merged_predictions[key]["confidence"],
+            reverse=True,
+        )
         matched_gt_ids = []
 
-        #Compute precision and recall at all confidence thresholds.
+        # Compute precision and recall at all confidence thresholds.
         tp = np.zeros(len(merged_ids_by_conf))
         fp = np.zeros(len(merged_ids_by_conf))
 
         for i, track_id in enumerate(merged_ids_by_conf):
             track_stats = merged_predictions[track_id]
-            track_confidence = track_stats['confidence']
-            track_traj = track_stats['xy_pos']
-            track_timestamps = track_stats['timestamps']
-            
+            track_confidence = track_stats["confidence"]
+            track_traj = track_stats["xy_pos"]
+            track_timestamps = track_stats["timestamps"]
+
             max_similarity = 0
             best_match = None
             for gt_id, gt_stats in gt_tracks.items():
-                if gt_id in matched_gt_ids or gt_stats['category'] != track_stats['category']:
+                if (
+                    gt_id in matched_gt_ids
+                    or gt_stats["category"] != track_stats["category"]
+                ):
                     continue
 
-                gt_traj = gt_stats['xy_pos']
-                gt_timestamps = gt_stats['timestamps']
+                gt_traj = gt_stats["xy_pos"]
+                gt_timestamps = gt_stats["timestamps"]
 
-                intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
+                intersection = len(
+                    set(gt_timestamps).intersection((set(track_timestamps)))
+                )
                 union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection/union
+                iou = intersection / union
 
                 if iou < TEMPORAL_IOU_THRESH:
                     continue
@@ -624,11 +754,16 @@ def calculate_TempLocAP_merge(self, data):
                 distances = []
                 for timestamp in track_timestamps:
                     if timestamp in gt_timestamps:
-                        distances.append(np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)] - gt_traj[gt_timestamps.index(timestamp)]))
+                        distances.append(
+                            np.linalg.norm(
+                                track_traj[track_timestamps.index(timestamp)]
+                                - gt_traj[gt_timestamps.index(timestamp)]
+                            )
+                        )
 
-                similarity_score = iou * max(0, 
-                    1 - (np.mean(np.array(distances))/MATCHING_DIST_THRESH))
+                similarity_score = iou * max(
+                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+                )
 
                 if similarity_score > max_similarity:
                     max_similarity = similarity_score
@@ -643,14 +778,13 @@ def calculate_TempLocAP_merge(self, data):
         tp = np.cumsum(tp)
         fp = np.cumsum(fp)
 
-        recalls = tp/len(gt_tracks)
-        precisions = tp/np.maximum(tp+fp, np.finfo(np.float64).eps)
+        recalls = tp / len(gt_tracks)
+        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
 
         assert np.all(0 <= precisions) & np.all(precisions <= 1)
         TempLocAP = self.get_ap(recalls, precisions)
 
-        return TempLocAP, precisions, recalls    
-
+        return TempLocAP, precisions, recalls
 
     def get_envelope(self, precisions):
         """Compute the precision envelope.
@@ -664,38 +798,45 @@ def get_envelope(self, precisions):
         for i in range(precisions.size - 1, 0, -1):
             precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
         return precisions
-    
+
     def get_ap(self, recalls, precisions):
         """
         Calculate average precision.
-        
+
         Args:
             recalls: Array of recall values
             precisions: Array of precision values
-            
+
         Returns:
             float: average precision.
         """
         # first append sentinel values at the end
         recalls = np.concatenate(([0.0], recalls, [1.0]))
         precisions = np.concatenate(([0.0], precisions, [0.0]))
-        
+
         # get envelope (maximum precision for each recall value)
         precisions = self.get_envelope(precisions)
-        
+
         # to calculate area under PR curve, look for points where X axis (recall) changes value
         i = np.where(recalls[1:] != recalls[:-1])[0]
-        
+
         # and sum (\Delta recall) * prec
         ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
-        
+
         return ap
-    
-    def plot_precision_recall_curve(self, recalls_list, precisions_list, ap_values=None, labels=None, 
-                                colors=['blue', 'green'], save_path=None):
+
+    def plot_precision_recall_curve(
+        self,
+        recalls_list,
+        precisions_list,
+        ap_values=None,
+        labels=None,
+        colors=["blue", "green"],
+        save_path=None,
+    ):
         """
         Plot precision-recall curves for one or two sets of data.
-        
+
         Args:
             recalls_list: List of recall arrays to plot
             precisions_list: List of precision arrays to plot
@@ -705,71 +846,91 @@ def plot_precision_recall_curve(self, recalls_list, precisions_list, ap_values=N
             save_path: Optional path to save the plot
         """
         plt.figure(figsize=(8, 6))
-        
+
         if not isinstance(recalls_list, list):
             recalls_list = [recalls_list]
         if not isinstance(precisions_list, list):
             precisions_list = [precisions_list]
-        
+
         if labels is None:
             labels = [f"Curve {i+1}" for i in range(len(recalls_list))]
-        
+
         for i, (recalls, precisions) in enumerate(zip(recalls_list, precisions_list)):
             color = colors[i % len(colors)]
-            
+
             # Prepare data for plotting (add sentinel values)
             plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
             plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
             plot_precisions = self.get_envelope(plot_precisions.copy())
-            
+
             # Plot the curve
-            plt.plot(plot_recalls, plot_precisions, color=color, linestyle='-', 
-                    linewidth=2, label=labels[i])
+            plt.plot(
+                plot_recalls,
+                plot_precisions,
+                color=color,
+                linestyle="-",
+                linewidth=2,
+                label=labels[i],
+            )
             plt.fill_between(plot_recalls, 0, plot_precisions, alpha=0.1, color=color)
-        
+
         # Set title
         if ap_values:
-            ap_text = ", ".join([f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)])
-            plt.title(f'Precision-Recall Curves ({ap_text})', fontsize=16)
+            ap_text = ", ".join(
+                [f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)]
+            )
+            plt.title(f"Precision-Recall Curves ({ap_text})", fontsize=16)
         else:
-            plt.title('Precision-Recall Curves', fontsize=16)
-        
+            plt.title("Precision-Recall Curves", fontsize=16)
+
         # Set labels and limits
-        plt.xlabel('Recall', fontsize=14)
-        plt.ylabel('Precision', fontsize=14)
+        plt.xlabel("Recall", fontsize=14)
+        plt.ylabel("Precision", fontsize=14)
         plt.xlim([0.0, 1.0])
         plt.ylim([0.0, 1.05])
         plt.grid(True)
-        
+
         # Add legend if multiple curves
         if len(recalls_list) > 1:
-            plt.legend(loc='lower left')
-        
+            plt.legend(loc="lower left")
+
         # Save if path provided
         if save_path:
-            plt.savefig(save_path, dpi=300, bbox_inches='tight')
-        
-        plt.show()
+            plt.savefig(save_path, dpi=300, bbox_inches="tight")
 
+        plt.show()
 
     def combine_sequences(self, all_res):
         """Combines metrics across all sequences"""
         res = {}
         for field in self.integer_array_fields:
             res[field] = self._combine_sum(all_res, field)
-        for field in ['AssRe', 'AssPr', 'AssA']:
-            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
-
-
-        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
-        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
-
-        tlap_weighted_sum = sum([all_res[k]['TempLocAP'] * (all_res[k]['HOTA_TP'][0] + all_res[k]['HOTA_FN'][0]) for k in all_res.keys()])
-        res['TempLocAP'] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'][0]+res['HOTA_FN'][0])
+        for field in ["AssRe", "AssPr", "AssA"]:
+            res[field] = self._combine_weighted_av(
+                all_res, field, res, weight_field="HOTA_TP"
+            )
+
+        loca_weighted_sum = sum(
+            [all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
+        )
+        res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
+            1e-10, res["HOTA_TP"]
+        )
+
+        tlap_weighted_sum = sum(
+            [
+                all_res[k]["TempLocAP"]
+                * (all_res[k]["HOTA_TP"][0] + all_res[k]["HOTA_FN"][0])
+                for k in all_res.keys()
+            ]
+        )
+        res["TempLocAP"] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(
+            1e-10, res["HOTA_TP"][0] + res["HOTA_FN"][0]
+        )
 
         res = self._compute_final_fields(res)
         return res
-    
+
     def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
         """Combines metrics across all classes by averaging over the class values.
         If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
@@ -778,16 +939,34 @@ def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
         for field in self.integer_array_fields:
             if ignore_empty_classes:
                 res[field] = self._combine_sum(
-                    {k: v for k, v in all_res.items()
-                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)
+                    {
+                        k: v
+                        for k, v in all_res.items()
+                        if (
+                            v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
+                            > 0 + np.finfo("float").eps
+                        ).any()
+                    },
+                    field,
+                )
             else:
-                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
+                res[field] = self._combine_sum(
+                    {k: v for k, v in all_res.items()}, field
+                )
 
         for field in self.float_fields + self.float_array_fields:
             if ignore_empty_classes:
-                res[field] = np.mean([v[field] for v in all_res.values() if
-                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],
-                                     axis=0)
+                res[field] = np.mean(
+                    [
+                        v[field]
+                        for v in all_res.values()
+                        if (
+                            v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
+                            > 0 + np.finfo("float").eps
+                        ).any()
+                    ],
+                    axis=0,
+                )
             else:
                 res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
         return res
@@ -797,14 +976,28 @@ def combine_classes_det_averaged(self, all_res):
         res = {}
         for field in self.integer_array_fields:
             res[field] = self._combine_sum(all_res, field)
-        for field in ['AssRe', 'AssPr', 'AssA']:
-            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
-
-        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
-        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
-
-        tlap_weighted_sum = sum([all_res[k]['TempLocAP'] * (all_res[k]['HOTA_TP'][0] + all_res[k]['HOTA_FN'][0]) for k in all_res.keys()])
-        res['TempLocAP'] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'][0]+res['HOTA_FN'][0])
+        for field in ["AssRe", "AssPr", "AssA"]:
+            res[field] = self._combine_weighted_av(
+                all_res, field, res, weight_field="HOTA_TP"
+            )
+
+        loca_weighted_sum = sum(
+            [all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
+        )
+        res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
+            1e-10, res["HOTA_TP"]
+        )
+
+        tlap_weighted_sum = sum(
+            [
+                all_res[k]["TempLocAP"]
+                * (all_res[k]["HOTA_TP"][0] + all_res[k]["HOTA_FN"][0])
+                for k in all_res.keys()
+            ]
+        )
+        res["TempLocAP"] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(
+            1e-10, res["HOTA_TP"][0] + res["HOTA_FN"][0]
+        )
 
         res = self._compute_final_fields(res)
         return res
@@ -813,15 +1006,17 @@ def _compute_final_fields(self, res):
         """Calculate sub-metric ('field') values which only depend on other sub-metric values.
         This function is used both for both per-sequence calculation, and in combining values across sequences.
         """
-        res['DetRe'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'])
-        res['DetPr'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FP'])
-        res['DetA'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'] + res['HOTA_FP'])
-        res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])
-        res['OWTA'] = np.sqrt(res['DetRe'] * res['AssA'])
-
-        res['HOTA(0)'] = res['HOTA'][0]
-        res['LocA(0)'] = res['LocA'][0]
-        res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']
+        res["DetRe"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FN"])
+        res["DetPr"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FP"])
+        res["DetA"] = res["HOTA_TP"] / np.maximum(
+            1, res["HOTA_TP"] + res["HOTA_FN"] + res["HOTA_FP"]
+        )
+        res["HOTA"] = np.sqrt(res["DetA"] * res["AssA"])
+        res["OWTA"] = np.sqrt(res["DetRe"] * res["AssA"])
+
+        res["HOTA(0)"] = res["HOTA"][0]
+        res["LocA(0)"] = res["LocA"][0]
+        res["HOTALocA(0)"] = res["HOTA(0)"] * res["LocA(0)"]
         return res
 
     def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
@@ -830,20 +1025,20 @@ def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
         # Only loaded when run to reduce minimum requirements
         from matplotlib import pyplot as plt
 
-        res = table_res['COMBINED_SEQ']
-        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm', 'o--', 'o:']
+        res = table_res["COMBINED_SEQ"]
+        styles_to_plot = ["r", "b", "g", "b--", "b:", "g--", "g:", "m", "o--", "o:"]
         for name, style in zip(self.float_array_fields, styles_to_plot):
             plt.plot(self.array_labels, res[name], style)
-        plt.xlabel('alpha')
-        plt.ylabel('score')
-        plt.title(tracker + ' - ' + cls)
+        plt.xlabel("alpha")
+        plt.ylabel("score")
+        plt.title(tracker + " - " + cls)
         plt.axis([0, 1, 0, 1])
         legend = []
         for name in self.float_array_fields:
-            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']
-        plt.legend(legend, loc='lower left')
-        out_file = os.path.join(output_folder, cls + '_plot.pdf')
+            legend += [name + " (" + str(np.round(np.mean(res[name]), 2)) + ")"]
+        plt.legend(legend, loc="lower left")
+        out_file = os.path.join(output_folder, cls + "_plot.pdf")
         os.makedirs(os.path.dirname(out_file), exist_ok=True)
         plt.savefig(out_file)
-        plt.savefig(out_file.replace('.pdf', '.png'))
-        plt.clf()
\ No newline at end of file
+        plt.savefig(out_file.replace(".pdf", ".png"))
+        plt.clf()
diff --git a/src/av2/evaluation/scenario_mining/metrics.py b/src/av2/evaluation/scenario_mining/metrics.py
index 19d57397..836dd704 100644
--- a/src/av2/evaluation/scenario_mining/metrics.py
+++ b/src/av2/evaluation/scenario_mining/metrics.py
@@ -5,4 +5,4 @@
 from trackeval.metrics.j_and_f import JAndF
 from trackeval.metrics.track_map import TrackMAP
 from trackeval.metrics.vace import VACE
-from trackeval.metrics.ideucl import IDEucl
\ No newline at end of file
+from trackeval.metrics.ideucl import IDEucl
diff --git a/src/av2/evaluation/tracking/eval.py b/src/av2/evaluation/tracking/eval.py
index 68aa97ec..98124789 100644
--- a/src/av2/evaluation/tracking/eval.py
+++ b/src/av2/evaluation/tracking/eval.py
@@ -621,6 +621,7 @@ def evaluate(
 
     return res, tuned_metric_values, mean_metric_values
 
+
 @click.command()
 @click.option("--predictions", required=True, help="Predictions PKL file")
 @click.option("--ground_truth", required=True, help="Ground Truth PKL file")
@@ -655,4 +656,4 @@ def runner(
 
 
 if __name__ == "__main__":
-    runner()
\ No newline at end of file
+    runner()
diff --git a/tests/unit/datasets/motion_forecasting/eval/test_submission.py b/tests/unit/datasets/motion_forecasting/eval/test_submission.py
index 5f120588..31729796 100644
--- a/tests/unit/datasets/motion_forecasting/eval/test_submission.py
+++ b/tests/unit/datasets/motion_forecasting/eval/test_submission.py
@@ -83,7 +83,7 @@ def test_challenge_submission_data_validation(
 
 @pytest.mark.parametrize(
     "test_submission_dict",
-    [(valid_submission_predictions)],
+    [valid_submission_predictions],
     ids=["valid_submission"],
 )
 def test_challenge_submission_serialization(
diff --git a/tests/unit/evaluation/scenario_mining/__init__.py b/tests/unit/evaluation/scenario_mining/__init__.py
index 5f951895..39c3fe90 100644
--- a/tests/unit/evaluation/scenario_mining/__init__.py
+++ b/tests/unit/evaluation/scenario_mining/__init__.py
@@ -1 +1 @@
-"""Scenario Mining sub-package."""
\ No newline at end of file
+"""Scenario Mining sub-package."""
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index 272e359f..a0613f1f 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -1,4 +1,5 @@
 """Scenario mining evaluation unit tests."""
+
 import matplotlib
 from pathlib import Path
 from typing import Final
@@ -7,29 +8,28 @@
 from av2.evaluation.scenario_mining.eval import evaluate, load
 
 
-matplotlib.use('Agg')
+matplotlib.use("Agg")
 if sys.stdout is None:
-    sys.stdout = open('stdout.log', 'w')
+    sys.stdout = open("stdout.log", "w")
 
 TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data"
 
+
 def test_evaluate() -> None:
     """Test End-to-End Forecasting evaluation."""
-    
-    predictions = TEST_DATA_DIR / 'combined_predictions_dev.pkl'
-    ground_truth = TEST_DATA_DIR / 'combined_gt_dev.pkl'
 
-    objective_metric = 'HOTA'
+    predictions = TEST_DATA_DIR / "combined_predictions_dev.pkl"
+    ground_truth = TEST_DATA_DIR / "combined_gt_dev.pkl"
+
+    objective_metric = "HOTA"
     max_range_m = 100
     dataset_dir = TEST_DATA_DIR
-    out = str(TEST_DATA_DIR / 'eval_results')
+    out = str(TEST_DATA_DIR / "eval_results")
 
     predictions = load(predictions)
     ground_truth = load(ground_truth)
 
     evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
 
-test_evaluate()
-
-
 
+test_evaluate()

From 289ac484f5317c95751e416beedfcb3102ece14c Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 01:12:09 -0500
Subject: [PATCH 09/22] Removed native hota code

---
 src/av2/evaluation/scenario_mining/eval.py    |  724 +++++++-----
 src/av2/evaluation/scenario_mining/hota.py    | 1044 -----------------
 src/av2/evaluation/scenario_mining/metrics.py |    8 -
 3 files changed, 404 insertions(+), 1372 deletions(-)
 delete mode 100644 src/av2/evaluation/scenario_mining/hota.py
 delete mode 100644 src/av2/evaluation/scenario_mining/metrics.py

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 67c4ef5e..edc78621 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -33,7 +33,7 @@
 from av2.evaluation.scenario_mining.constants import SUBMETRIC_TO_METRIC_CLASS_NAME
 from av2.utils.typing import NDArrayFloat, NDArrayInt
 from av2.evaluation.typing import Sequences
-import av2.evaluation.scenario_mining.metrics as metrics
+import trackeval
 
 import time
 import traceback
@@ -45,299 +45,6 @@
 from trackeval import _timing
 from trackeval.metrics import Count
 
-
-class Evaluator:
-    """Evaluator class for evaluating different metrics for different datasets"""
-
-    @staticmethod
-    def get_default_eval_config():
-        """Returns the default config values for evaluation"""
-        code_path = utils.get_code_path()
-        default_config = {
-            "USE_PARALLEL": True,
-            "NUM_PARALLEL_CORES": max(int(0.9 * os.cpu_count()), 1),
-            "BREAK_ON_ERROR": True,  # Raises exception and exits with error
-            "RETURN_ON_ERROR": False,  # if not BREAK_ON_ERROR, then returns from function on error
-            "LOG_ON_ERROR": os.path.join(
-                code_path, "error_log.txt"
-            ),  # if not None, save any errors into a log file.
-            "PRINT_RESULTS": True,
-            "PRINT_ONLY_COMBINED": False,
-            "PRINT_CONFIG": True,
-            "TIME_PROGRESS": True,
-            "DISPLAY_LESS_PROGRESS": True,
-            "OUTPUT_SUMMARY": False,
-            "OUTPUT_EMPTY_CLASSES": False,  # If False, summary files are not output for classes with no detections
-            "OUTPUT_DETAILED": False,
-            "PLOT_CURVES": False,
-        }
-        return default_config
-
-    def __init__(self, config=None):
-        """Initialise the evaluator with a config file"""
-        self.config = utils.init_config(config, self.get_default_eval_config(), "Eval")
-        # Only run timing analysis if not run in parallel.
-        if self.config["TIME_PROGRESS"] and not self.config["USE_PARALLEL"]:
-            _timing.DO_TIMING = True
-            if self.config["DISPLAY_LESS_PROGRESS"]:
-                _timing.DISPLAY_LESS_PROGRESS = True
-
-    @_timing.time
-    def evaluate(self, dataset_list, metrics_list, show_progressbar=True):
-        """Evaluate a set of metrics on a set of datasets"""
-        config = self.config
-        metrics_list = metrics_list + [Count()]  # Count metrics are always run
-        metric_names = utils.validate_metrics_list(metrics_list)
-        dataset_names = [dataset.get_name() for dataset in dataset_list]
-        output_res = {}
-        output_msg = {}
-
-        for dataset, dataset_name in zip(dataset_list, dataset_names):
-            # Get dataset info about what to evaluate
-            output_res[dataset_name] = {}
-            output_msg[dataset_name] = {}
-            tracker_list, seq_list, class_list = dataset.get_eval_info()
-            print(
-                "\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following "
-                "metrics: %s\n"
-                % (
-                    len(tracker_list),
-                    len(seq_list),
-                    len(class_list),
-                    dataset_name,
-                    ", ".join(metric_names),
-                )
-            )
-
-            # Evaluate each tracker
-            for tracker in tracker_list:
-                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking
-                try:
-                    # Evaluate each sequence in parallel or in series.
-                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]
-                    # e.g. res[seq_0001][pedestrian][hota][DetA]
-                    print("\nEvaluating %s\n" % tracker)
-                    time_start = time.time()
-                    if config["USE_PARALLEL"]:
-                        if show_progressbar:
-                            seq_list_sorted = sorted(seq_list)
-
-                            with Pool(config["NUM_PARALLEL_CORES"]) as pool, tqdm(
-                                total=len(seq_list)
-                            ) as pbar:
-                                _eval_sequence = partial(
-                                    eval_sequence,
-                                    dataset=dataset,
-                                    tracker=tracker,
-                                    class_list=class_list,
-                                    metrics_list=metrics_list,
-                                    metric_names=metric_names,
-                                )
-                                results = []
-                                for r in pool.imap(
-                                    _eval_sequence, seq_list_sorted, chunksize=20
-                                ):
-                                    results.append(r)
-                                    pbar.update()
-                                res = dict(zip(seq_list_sorted, results))
-
-                        else:
-                            with Pool(config["NUM_PARALLEL_CORES"]) as pool:
-                                _eval_sequence = partial(
-                                    eval_sequence,
-                                    dataset=dataset,
-                                    tracker=tracker,
-                                    class_list=class_list,
-                                    metrics_list=metrics_list,
-                                    metric_names=metric_names,
-                                )
-                                results = pool.map(_eval_sequence, seq_list)
-                                res = dict(zip(seq_list, results))
-                    else:
-                        res = {}
-                        if show_progressbar:
-                            seq_list_sorted = sorted(seq_list)
-                            for curr_seq in tqdm(seq_list_sorted):
-                                res[curr_seq] = eval_sequence(
-                                    curr_seq,
-                                    dataset,
-                                    tracker,
-                                    class_list,
-                                    metrics_list,
-                                    metric_names,
-                                )
-                        else:
-                            for curr_seq in sorted(seq_list):
-                                res[curr_seq] = eval_sequence(
-                                    curr_seq,
-                                    dataset,
-                                    tracker,
-                                    class_list,
-                                    metrics_list,
-                                    metric_names,
-                                )
-
-                    # Combine results over all sequences and then over all classes
-
-                    # collecting combined cls keys (cls averaged, det averaged, super classes)
-                    combined_cls_keys = []
-                    res["COMBINED_SEQ"] = {}
-                    # combine sequences for each class
-                    for c_cls in class_list:
-                        res["COMBINED_SEQ"][c_cls] = {}
-                        for metric, metric_name in zip(metrics_list, metric_names):
-                            curr_res = {
-                                seq_key: seq_value[c_cls][metric_name]
-                                for seq_key, seq_value in res.items()
-                                if seq_key != "COMBINED_SEQ"
-                            }
-                            res["COMBINED_SEQ"][c_cls][metric_name] = (
-                                metric.combine_sequences(curr_res)
-                            )
-                    # combine classes
-                    if dataset.should_classes_combine:
-                        combined_cls_keys += [
-                            "cls_comb_cls_av",
-                            "cls_comb_det_av",
-                            "all",
-                        ]
-                        res["COMBINED_SEQ"]["cls_comb_cls_av"] = {}
-                        res["COMBINED_SEQ"]["cls_comb_det_av"] = {}
-                        for metric, metric_name in zip(metrics_list, metric_names):
-                            cls_res = {
-                                cls_key: cls_value[metric_name]
-                                for cls_key, cls_value in res["COMBINED_SEQ"].items()
-                                if cls_key not in combined_cls_keys
-                            }
-                            res["COMBINED_SEQ"]["cls_comb_cls_av"][metric_name] = (
-                                metric.combine_classes_class_averaged(cls_res)
-                            )
-                            res["COMBINED_SEQ"]["cls_comb_det_av"][metric_name] = (
-                                metric.combine_classes_det_averaged(cls_res)
-                            )
-                    # combine classes to super classes
-                    if dataset.use_super_categories:
-                        for cat, sub_cats in dataset.super_categories.items():
-                            combined_cls_keys.append(cat)
-                            res["COMBINED_SEQ"][cat] = {}
-                            for metric, metric_name in zip(metrics_list, metric_names):
-                                cat_res = {
-                                    cls_key: cls_value[metric_name]
-                                    for cls_key, cls_value in res[
-                                        "COMBINED_SEQ"
-                                    ].items()
-                                    if cls_key in sub_cats
-                                }
-                                res["COMBINED_SEQ"][cat][metric_name] = (
-                                    metric.combine_classes_det_averaged(cat_res)
-                                )
-
-                    # Print and output results in various formats
-                    if config["TIME_PROGRESS"]:
-                        print(
-                            "\nAll sequences for %s finished in %.2f seconds"
-                            % (tracker, time.time() - time_start)
-                        )
-                    output_fol = dataset.get_output_fol(tracker)
-                    tracker_display_name = dataset.get_display_name(tracker)
-                    for c_cls in res[
-                        "COMBINED_SEQ"
-                    ].keys():  # class_list + combined classes if calculated
-                        summaries = []
-                        details = []
-                        num_dets = res["COMBINED_SEQ"][c_cls]["Count"]["Dets"]
-                        if config["OUTPUT_EMPTY_CLASSES"] or num_dets > 0:
-                            for metric, metric_name in zip(metrics_list, metric_names):
-                                # for combined classes there is no per sequence evaluation
-                                if c_cls in combined_cls_keys:
-                                    table_res = {
-                                        "COMBINED_SEQ": res["COMBINED_SEQ"][c_cls][
-                                            metric_name
-                                        ]
-                                    }
-                                else:
-                                    table_res = {
-                                        seq_key: seq_value[c_cls][metric_name]
-                                        for seq_key, seq_value in res.items()
-                                    }
-
-                                if (
-                                    config["PRINT_RESULTS"]
-                                    and config["PRINT_ONLY_COMBINED"]
-                                ):
-                                    dont_print = (
-                                        dataset.should_classes_combine
-                                        and c_cls not in combined_cls_keys
-                                    )
-                                    if not dont_print:
-                                        metric.print_table(
-                                            {"COMBINED_SEQ": table_res["COMBINED_SEQ"]},
-                                            tracker_display_name,
-                                            c_cls,
-                                        )
-                                elif config["PRINT_RESULTS"]:
-                                    metric.print_table(
-                                        table_res, tracker_display_name, c_cls
-                                    )
-                                if config["OUTPUT_SUMMARY"]:
-                                    summaries.append(metric.summary_results(table_res))
-                                if config["OUTPUT_DETAILED"]:
-                                    details.append(metric.detailed_results(table_res))
-                                if config["PLOT_CURVES"]:
-                                    metric.plot_single_tracker_results(
-                                        table_res,
-                                        tracker_display_name,
-                                        c_cls,
-                                        output_fol,
-                                    )
-                            if config["OUTPUT_SUMMARY"]:
-                                utils.write_summary_results(
-                                    summaries, c_cls, output_fol
-                                )
-                            if config["OUTPUT_DETAILED"]:
-                                utils.write_detailed_results(details, c_cls, output_fol)
-
-                    # Output for returning from function
-                    output_res[dataset_name][tracker] = res
-                    output_msg[dataset_name][tracker] = "Success"
-
-                except Exception as err:
-                    output_res[dataset_name][tracker] = None
-                    if type(err) == TrackEvalException:
-                        output_msg[dataset_name][tracker] = str(err)
-                    else:
-                        output_msg[dataset_name][tracker] = "Unknown error occurred."
-                    print("Tracker %s was unable to be evaluated." % tracker)
-                    print(err)
-                    traceback.print_exc()
-                    if config["LOG_ON_ERROR"] is not None:
-                        with open(config["LOG_ON_ERROR"], "a") as f:
-                            print(dataset_name, file=f)
-                            print(tracker, file=f)
-                            print(traceback.format_exc(), file=f)
-                            print("\n\n\n", file=f)
-                    if config["BREAK_ON_ERROR"]:
-                        raise err
-                    elif config["RETURN_ON_ERROR"]:
-                        return output_res, output_msg
-
-        return output_res, output_msg
-
-
-@_timing.time
-def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
-    """Function for evaluating a single sequence"""
-
-    raw_data = dataset.get_raw_seq_data(tracker, seq)
-    seq_res = {}
-    for cls in class_list:
-        seq_res[cls] = {}
-        data = dataset.get_preprocessed_seq_data(raw_data, cls)
-        for metric, met_name in zip(metrics_list, metric_names):
-            seq_res[cls][met_name] = metric.eval_sequence(data)
-    return seq_res
-
-
 class TrackEvalDataset(_BaseDataset):  # type: ignore
     """Dataset class to support tracking evaluation using the TrackEval library."""
 
@@ -363,6 +70,7 @@ def get_default_dataset_config() -> Dict[str, Any]:
         Returns:
             dictionary of the default config
         """
+
         default_config = {
             "GT_TRACKS": None,  # tracker_name -> seq id -> frames
             "PREDICTED_TRACKS": None,  # tracker_name -> seq id -> frames
@@ -462,7 +170,8 @@ def get_preprocessed_seq_data(
         data["num_gt_ids"] = len(unique_gt_ids)
 
         # Ensure again that ids are unique per timestep after preproc.
-        self._check_unique_ids(data, after_preproc=True)
+        self._check_unique_ids(data, after_preproc=True)        
+
         return data
 
     def _map_ids(self, ids: List[Any], unique_ids: Iterable[Any]) -> List[NDArrayInt]:
@@ -531,7 +240,7 @@ def evaluate_tracking(
     tracker_name: str,
     output_dir: str,
     iou_threshold: float = 0.5,
-) -> Dict[str, Any]:
+) -> Tuple[Dict[str, Any], float]:
     """Evaluate a set of tracks against ground truth annotations using the TrackEval evaluation suite.
 
     Each sequences/log is evaluated separately.
@@ -582,7 +291,7 @@ def evaluate_tracking(
         "THRESHOLD": iou_threshold,
     }
     metric_names = cast(List[str], metrics_config["METRICS"])
-    metrics_list = [getattr(metrics, metric)(metrics_config) for metric in metric_names]
+    metrics_list = [getattr(trackeval.metrics, metric)(metrics_config) for metric in metric_names]
     dataset_config = {
         **TrackEvalDataset.get_default_dataset_config(),
         "GT_TRACKS": {tracker_name: labels},
@@ -594,20 +303,42 @@ def evaluate_tracking(
         "SHOULD_CLASSES_COMBINE": False,
     }
 
-    evaluator = Evaluator(
+    evaluator = trackeval.Evaluator(
         {
-            **Evaluator.get_default_eval_config(),
+            **trackeval.Evaluator.get_default_eval_config(),
             "TIME_PROGRESS": True,
             "PLOT_CURVES": True,
             "OUTPUT_SUMMARY": True,
         }
     )
+
+    dataset = TrackEvalDataset(dataset_config)
     full_result, _ = evaluator.evaluate(
-        [TrackEvalDataset(dataset_config)],
+        [dataset],
         metrics_list,
     )
 
-    return cast(Dict[str, Any], full_result)
+    trackers, seq_ids, classes = dataset.get_eval_info()
+    tracker = trackers[0]
+
+    tlap_by_seq = np.zeros((len(labels), len(classes)))
+    num_gt_by_seq = np.zeros((len(labels), len(classes)))
+
+    for i, seq_id in enumerate(labels.keys()):
+        raw_data =  dataset.get_raw_seq_data(tracker, seq_id)
+        for j, clas in enumerate(classes):
+            data = dataset.get_preprocessed_seq_data(raw_data, clas)
+
+            num_gt_ids = len(data['gt_ids'])
+            tlap, precisions, recalls = calculate_TempLocAP_merge(data)
+            tlap_by_seq[i,j] = tlap
+            num_gt_by_seq[i,j] = num_gt_ids
+
+    combined_tlap = np.average(tlap_by_seq, axis=1, weights=num_gt_by_seq)
+    referred_tlap = float(combined_tlap[np.where(np.array(classes) == 'REFERRED_OBJECT')])
+
+
+    return cast(Dict[str, Any], full_result), referred_tlap
 
 
 def _tune_score_thresholds(
@@ -644,7 +375,7 @@ def _tune_score_thresholds(
         "PRINT_CONFIG": False,
     }
     metrics_list = [
-        getattr(metrics, metric_name)(metrics_config)
+        getattr(trackeval.metrics, metric_name)(metrics_config)
         for metric_name in cast(List[str], metrics_config["METRICS"])
     ]
     dataset_config = {
@@ -656,9 +387,9 @@ def _tune_score_thresholds(
         "TRACKERS_TO_EVAL": ["tracker"],
         "OUTPUT_FOLDER": "tmp",
     }
-    evaluator = Evaluator(
+    evaluator = trackeval.Evaluator(
         {
-            **Evaluator.get_default_eval_config(),
+            **trackeval.Evaluator.get_default_eval_config(),
             "PRINT_RESULTS": False,
             "PRINT_CONFIG": False,
             "TIME_PROGRESS": False,
@@ -799,6 +530,356 @@ def _recall_to_scores(
     return score_thresholds
 
 
+def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np.ndarray]:
+    """Calculates temporal localization average precision. """
+    # Return result quickly if tracker or gt sequence is empty
+    if data["num_tracker_dets"] == 0:
+        if data["num_gt_dets"] == 0:
+            precisions = np.array([1, 1])
+            recalls = np.array([0, 1])
+            TempLocAP = 1
+            return TempLocAP, precisions, recalls
+        else:
+            precisions = np.array([0, 0])
+            recalls = np.array([0, 1])
+            TempLocAP = 0
+            return TempLocAP, precisions, recalls
+    if data["num_gt_dets"] == 0:
+        precisions = np.array([0, 0])
+        recalls = np.array([0, 1])
+        TempLocAP = 0
+        return TempLocAP, precisions, recalls
+
+    TEMPORAL_IOU_THRESH = 0.5  # iou
+    MATCHING_DIST_THRESH = 2.0  # m
+
+    pred_tracks = {}
+    gt_tracks = {}
+
+    # Accumulate predicted and ground truth tracks from data
+    for t in range(data["num_timesteps"]):
+        for i, gt_id in enumerate(data["gt_ids"][t]):
+            if gt_id not in gt_tracks:
+                gt_tracks[gt_id] = {}
+                gt_tracks[gt_id]["xy_pos"] = []
+                gt_tracks[gt_id]["timestamps"] = []
+                gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
+
+            gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
+            gt_tracks[gt_id]["timestamps"].append(t)
+
+        for i, track_id in enumerate(data["tracker_ids"][t]):
+            if track_id not in pred_tracks:
+                pred_tracks[track_id] = {}
+                pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
+                    t
+                ][i]
+                pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
+                pred_tracks[track_id]["xy_pos"] = []
+                pred_tracks[track_id]["timestamps"] = []
+
+            pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
+            pred_tracks[track_id]["timestamps"].append(t)
+
+    # 1 to 1 match of predicted and ground truth tracks
+    pred_ids_by_conf = sorted(
+        pred_tracks.keys(),
+        key=lambda key: pred_tracks[key]["confidence"],
+        reverse=True,
+    )
+
+    # keys are gt_id, values are list of corresponding pred_ids
+    matched_ids = {}
+
+    # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
+    unmatched_track_ids = []
+
+    for track_id in pred_ids_by_conf:
+        track_stats = pred_tracks[track_id]
+        track_confidence = track_stats["confidence"]
+        track_traj = track_stats["xy_pos"]
+        track_timestamps = track_stats["timestamps"]
+
+        max_similarity = 0
+        best_match = None
+        for gt_id, gt_stats in gt_tracks.items():
+            if gt_stats["category"] != track_stats["category"]:
+                continue
+
+            gt_traj = gt_stats["xy_pos"]
+            gt_timestamps = gt_stats["timestamps"]
+
+            intersection = len(
+                set(gt_timestamps).intersection((set(track_timestamps)))
+            )
+            iol = intersection / len(track_timestamps)
+
+            if iol < TEMPORAL_IOU_THRESH:
+                continue
+
+            distances = []
+            for timestamp in track_timestamps:
+                if timestamp in gt_timestamps:
+                    distances.append(
+                        np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)]
+                            - gt_traj[gt_timestamps.index(timestamp)]
+                        )
+                    )
+
+            similarity_score = iol * max(
+                0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+            )
+
+            if similarity_score > max_similarity:
+                max_similarity = similarity_score
+                best_match = gt_id
+
+        if max_similarity > 0:
+            if best_match not in matched_ids:
+                matched_ids[best_match] = [track_id]
+            else:
+                matched_ids[best_match].append(track_id)
+        else:
+            unmatched_track_ids.append(track_id)
+
+    merged_predictions = {}
+    for gt_id, pred_ids in matched_ids.items():
+        merged_traj = []
+        merged_timestamps = []
+        merged_confidences = []
+        merged_catetory = None
+
+        for pred_id in pred_ids:
+            track_timestamps = pred_tracks[pred_id]["timestamps"]
+            track_trajectory = pred_tracks[pred_id]["xy_pos"]
+            track_confidence = pred_tracks[pred_id]["confidence"]
+            track_category = pred_tracks[pred_id]["category"]
+
+            if len(merged_timestamps) == 0:
+                merged_timestamps.extend(track_timestamps)
+                merged_traj.extend(track_trajectory)
+                merged_catetory = track_category
+                merged_confidences.extend(
+                    [track_confidence] * len(track_timestamps)
+                )
+                continue
+
+            for i, timestamp in enumerate(track_timestamps):
+                if timestamp not in merged_timestamps:
+                    insertion_index = 0
+                    for merge_timestamp in merged_timestamps:
+                        if merge_timestamp > timestamp:
+                            insertion_index += 1
+
+                    merged_timestamps.insert(insertion_index, timestamp)
+                    merged_traj.insert(insertion_index, track_trajectory[i])
+                    merged_confidences.insert(insertion_index, track_confidence)
+                else:
+                    insertion_index = merged_timestamps.index(timestamp)
+                    if track_confidence > merged_confidences[insertion_index]:
+                        merged_confidences[insertion_index] = track_confidence
+                        merged_traj[insertion_index] = track_trajectory[i]
+
+        merged_predictions[-gt_id - 1] = {
+            "xy_pos": merged_traj,
+            "timestamps": merged_timestamps,
+            "confidence": np.mean(np.array(merged_confidences)),
+            "category": merged_catetory,
+        }
+
+    for unmatched_track_id in unmatched_track_ids:
+        merged_predictions.update(
+            {unmatched_track_id: pred_tracks[unmatched_track_id]}
+        )
+
+    merged_ids_by_conf = sorted(
+        merged_predictions.keys(),
+        key=lambda key: merged_predictions[key]["confidence"],
+        reverse=True,
+    )
+    matched_gt_ids = []
+
+    # Compute precision and recall at all confidence thresholds.
+    tp = np.zeros(len(merged_ids_by_conf))
+    fp = np.zeros(len(merged_ids_by_conf))
+
+    for i, track_id in enumerate(merged_ids_by_conf):
+        track_stats = merged_predictions[track_id]
+        track_confidence = track_stats["confidence"]
+        track_traj = track_stats["xy_pos"]
+        track_timestamps = track_stats["timestamps"]
+
+        max_similarity = 0
+        best_match = None
+        for gt_id, gt_stats in gt_tracks.items():
+            if (
+                gt_id in matched_gt_ids
+                or gt_stats["category"] != track_stats["category"]
+            ):
+                continue
+
+            gt_traj = gt_stats["xy_pos"]
+            gt_timestamps = gt_stats["timestamps"]
+
+            intersection = len(
+                set(gt_timestamps).intersection((set(track_timestamps)))
+            )
+            union = len(set(gt_timestamps).union((set(track_timestamps))))
+            iou = intersection / union
+
+            if iou < TEMPORAL_IOU_THRESH:
+                continue
+
+            distances = []
+            for timestamp in track_timestamps:
+                if timestamp in gt_timestamps:
+                    distances.append(
+                        np.linalg.norm(
+                            track_traj[track_timestamps.index(timestamp)]
+                            - gt_traj[gt_timestamps.index(timestamp)]
+                        )
+                    )
+
+            similarity_score = iou * max(
+                0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
+            )
+
+            if similarity_score > max_similarity:
+                max_similarity = similarity_score
+                best_match = gt_id
+
+        if max_similarity > 0:
+            matched_gt_ids.append(best_match)
+            tp[i] = 1
+        else:
+            fp[i] = 1
+
+    tp = np.cumsum(tp)
+    fp = np.cumsum(fp)
+
+    recalls = tp / len(gt_tracks)
+    precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
+
+    assert np.all(0 <= precisions) & np.all(precisions <= 1)
+    TempLocAP = get_ap(recalls, precisions)
+
+    return TempLocAP, precisions, recalls
+
+def get_envelope(precisions):
+    """Compute the precision envelope.
+
+    Args:
+    precisions:
+
+    Returns:
+
+    """
+    for i in range(precisions.size - 1, 0, -1):
+        precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
+    return precisions
+
+def get_ap(recalls, precisions):
+    """
+    Calculate average precision.
+
+    Args:
+        recalls: Array of recall values
+        precisions: Array of precision values
+
+    Returns:
+        float: average precision.
+    """
+    # first append sentinel values at the end
+    recalls = np.concatenate(([0.0], recalls, [1.0]))
+    precisions = np.concatenate(([0.0], precisions, [0.0]))
+
+    # get envelope (maximum precision for each recall value)
+    precisions = get_envelope(precisions)
+
+    # to calculate area under PR curve, look for points where X axis (recall) changes value
+    i = np.where(recalls[1:] != recalls[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
+
+    return ap
+
+def plot_precision_recall_curve(
+    recalls_list,
+    precisions_list,
+    ap_values=None,
+    labels=None,
+    colors=["blue", "green"],
+    save_path=None,
+):
+    """
+    Plot precision-recall curves for one or two sets of data.
+
+    Args:
+        recalls_list: List of recall arrays to plot
+        precisions_list: List of precision arrays to plot
+        ap_values: Optional list of AP values to display in the title
+        labels: Optional list of labels for the legend
+        colors: List of colors for the plots (default: blue and green)
+        save_path: Optional path to save the plot
+    """
+    plt.figure(figsize=(8, 6))
+
+    if not isinstance(recalls_list, list):
+        recalls_list = [recalls_list]
+    if not isinstance(precisions_list, list):
+        precisions_list = [precisions_list]
+
+    if labels is None:
+        labels = [f"Curve {i+1}" for i in range(len(recalls_list))]
+
+    for i, (recalls, precisions) in enumerate(zip(recalls_list, precisions_list)):
+        color = colors[i % len(colors)]
+
+        # Prepare data for plotting (add sentinel values)
+        plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
+        plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
+        plot_precisions = get_envelope(plot_precisions.copy())
+
+        # Plot the curve
+        plt.plot(
+            plot_recalls,
+            plot_precisions,
+            color=color,
+            linestyle="-",
+            linewidth=2,
+            label=labels[i],
+        )
+        plt.fill_between(plot_recalls, 0, plot_precisions, alpha=0.1, color=color)
+
+    # Set title
+    if ap_values:
+        ap_text = ", ".join(
+            [f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)]
+        )
+        plt.title(f"Precision-Recall Curves ({ap_text})", fontsize=16)
+    else:
+        plt.title("Precision-Recall Curves", fontsize=16)
+
+    # Set labels and limits
+    plt.xlabel("Recall", fontsize=14)
+    plt.ylabel("Precision", fontsize=14)
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.grid(True)
+
+    # Add legend if multiple curves
+    if len(recalls_list) > 1:
+        plt.legend(loc="lower left")
+
+    # Save if path provided
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches="tight")
+
+    plt.show()
+
+
 def _xy_center_similarity(
     centers1: NDArrayFloat, centers2: NDArrayFloat, zero_distance: float
 ) -> NDArrayFloat:
@@ -838,7 +919,8 @@ def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
     )
 
 
-def load(pkl_path):
+def load(pkl_path:Path) -> Sequences:
+    """Loads a pkl file as a dict."""
 
     with open(pkl_path, "rb") as f:
         data = pickle.load(f)
@@ -922,13 +1004,12 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     return tracks
 
 
-def referred_full_tracks(sequences: Sequences):
-    """
-    Reconstructs a mining pkl file by propagating referred object labels across all instances
+def referred_full_tracks(sequences: Sequences) -> Sequences:
+    """ Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
 
     Args:
-        pkl_file_path: Path to the pkl file
+        sequences: Sequences for either the labels or ground truth
 
     Returns:
         reconstructed_sequences: Dictionary containing the reconstructed sequences
@@ -981,6 +1062,9 @@ def referred_full_tracks(sequences: Sequences):
 def evaluate_mining(
     track_predictions: Sequences, labels: Sequences, output_dir
 ) -> tuple[float, float]:
+    """Calculates the F1 score for classifying if anything in the scenario
+    matches the prompt.
+    """
 
     gt_class = np.zeros(len(labels), dtype=np.int64)
     pred_class = np.zeros(len(labels), dtype=np.int64)
@@ -1019,7 +1103,9 @@ def evaluate_mining(
     return f1_score, acc
 
 
-def relabel_seq_ids(data):
+def _relabel_seq_ids(data:Sequences)->Sequences:
+    """Turns the (log_id, prompt) tuple format into a string for HOTA summarization """
+
     new_data = {}
 
     for seq_id, frames in data.items():
@@ -1066,7 +1152,7 @@ def evaluate(
         output_dir = out + "/partial_tracks"
         Path(output_dir).mkdir(parents=True, exist_ok=True)
 
-    res, partial_track_metrics, _, f1_score = evaluate_scenario_mining(
+    res, partial_track_metrics, TempLocAP, f1_score = evaluate_scenario_mining(
         track_predictions,
         labels,
         objective_metric=objective_metric,
@@ -1074,9 +1160,6 @@ def evaluate(
         dataset_dir=dataset_dir,
         out=output_dir,
     )
-    TempLocAP = res["TrackEvalDataset"]["TRACKER"]["COMBINED_SEQ"]["REFERRED_OBJECT"][
-        "HOTA"
-    ]["TempLocAP"]
 
     full_track_preds = referred_full_tracks(track_predictions)
     full_track_labels = referred_full_tracks(labels)
@@ -1099,6 +1182,7 @@ def evaluate(
     full_track_hota = full_track_metrics["REFERRED_OBJECT"]
     partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
 
+    print(f'F1: {f1_score}, HOTA: {partial_track_hota}, HOTA_full: {full_track_hota}, TLAP: {TempLocAP}')
     return f1_score, full_track_hota, partial_track_hota, TempLocAP
 
 
@@ -1110,7 +1194,7 @@ def evaluate_scenario_mining(
     dataset_dir: Any,
     out: str,
     full_tracks: bool = False,
-) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any], float]:
+) -> Tuple[Dict[str, Any], Dict[str, Any], float, float]:
     """Run evaluation.
 
     Args:
@@ -1136,8 +1220,8 @@ def evaluate_scenario_mining(
         labels = filter_drivable_area(labels, dataset_dir)
         track_predictions = filter_drivable_area(track_predictions, dataset_dir)
 
-    track_predictions = relabel_seq_ids(track_predictions)
-    labels = relabel_seq_ids(labels)
+    track_predictions = _relabel_seq_ids(track_predictions)
+    labels = _relabel_seq_ids(labels)
 
     score_thresholds, tuned_metric_values, mean_metric_values = _tune_score_thresholds(
         labels,
@@ -1150,7 +1234,7 @@ def evaluate_scenario_mining(
     filtered_track_predictions = sm_utils.filter_by_class_thresholds(
         track_predictions, score_thresholds
     )
-    res = evaluate_tracking(
+    res, tlap = evaluate_tracking(
         labels,
         filtered_track_predictions,
         classes,
@@ -1160,9 +1244,9 @@ def evaluate_scenario_mining(
 
     if not full_tracks:
         f1_score, acc = evaluate_mining(filtered_track_predictions, labels, out)
-        return res, tuned_metric_values, mean_metric_values, f1_score
+        return res, tuned_metric_values, tlap, f1_score
 
-    return res, tuned_metric_values, mean_metric_values, 0
+    return res, tuned_metric_values, tlap, 0
 
 
 @click.command()
diff --git a/src/av2/evaluation/scenario_mining/hota.py b/src/av2/evaluation/scenario_mining/hota.py
deleted file mode 100644
index c67743e3..00000000
--- a/src/av2/evaluation/scenario_mining/hota.py
+++ /dev/null
@@ -1,1044 +0,0 @@
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.optimize import linear_sum_assignment
-from trackeval.metrics._base_metric import _BaseMetric
-from trackeval import _timing
-
-
-class HOTA(_BaseMetric):
-    """Class which implements the HOTA metrics.
-    See: https://link.springer.com/article/10.1007/s11263-020-01375-2
-    """
-
-    def __init__(self, config=None):
-        super().__init__()
-        self.plottable = True
-        self.array_labels = np.arange(0.05, 0.99, 0.05)
-        self.integer_array_fields = ["HOTA_TP", "HOTA_FN", "HOTA_FP"]
-        self.float_array_fields = [
-            "HOTA",
-            "DetA",
-            "AssA",
-            "DetRe",
-            "DetPr",
-            "AssRe",
-            "AssPr",
-            "LocA",
-            "OWTA",
-        ]
-        self.float_fields = ["HOTA(0)", "LocA(0)", "HOTALocA(0)", "TempLocAP"]
-        self.fields = (
-            self.float_array_fields + self.integer_array_fields + self.float_fields
-        )
-        self.summary_fields = self.float_array_fields + self.float_fields
-
-    @_timing.time
-    def eval_sequence(self, data):
-        """Calculates the HOTA metrics for one sequence"""
-
-        # Initialise results
-        res = {}
-        for field in self.float_array_fields + self.integer_array_fields:
-            res[field] = np.zeros((len(self.array_labels)), dtype=float)
-        for field in self.float_fields:
-            res[field] = 0
-
-        TempLocAP, _, _ = self.calculate_TempLocAP_merge(data)
-        res["TempLocAP"] = TempLocAP
-
-        # Return result quickly if tracker or gt sequence is empty
-        if data["num_tracker_dets"] == 0:
-            res["HOTA_FN"] = data["num_gt_dets"] * np.ones(
-                (len(self.array_labels)), dtype=float
-            )
-            res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
-            res["LocA(0)"] = 1.0
-            return res
-        if data["num_gt_dets"] == 0:
-            res["HOTA_FP"] = data["num_tracker_dets"] * np.ones(
-                (len(self.array_labels)), dtype=float
-            )
-            res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
-            res["LocA(0)"] = 1.0
-            return res
-
-        # Variables counting global association
-        potential_matches_count = np.zeros(
-            (data["num_gt_ids"], data["num_tracker_ids"])
-        )
-        gt_id_count = np.zeros((data["num_gt_ids"], 1))
-        tracker_id_count = np.zeros((1, data["num_tracker_ids"]))
-
-        # First loop through each timestep and accumulate global track information.
-        for t, (gt_ids_t, tracker_ids_t) in enumerate(
-            zip(data["gt_ids"], data["tracker_ids"])
-        ):
-            # Count the potential matches between ids in each timestep
-            # These are normalised, weighted by the match similarity.
-            similarity = data["similarity_scores"][t]
-            sim_iou_denom = (
-                similarity.sum(0)[np.newaxis, :]
-                + similarity.sum(1)[:, np.newaxis]
-                - similarity
-            )
-            sim_iou = np.zeros_like(similarity)
-            sim_iou_mask = sim_iou_denom > 0 + np.finfo("float").eps
-            sim_iou[sim_iou_mask] = (
-                similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
-            )
-            potential_matches_count[
-                gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
-            ] += sim_iou
-
-            # Calculate the total number of dets for each gt_id and tracker_id.
-            gt_id_count[gt_ids_t] += 1
-            tracker_id_count[0, tracker_ids_t] += 1
-
-        # Calculate overall jaccard alignment score (before unique matching) between IDs
-        global_alignment_score = potential_matches_count / (
-            gt_id_count + tracker_id_count - potential_matches_count
-        )
-        matches_counts = [
-            np.zeros_like(potential_matches_count) for _ in self.array_labels
-        ]
-
-        # Calculate scores for each timestep
-        for t, (gt_ids_t, tracker_ids_t) in enumerate(
-            zip(data["gt_ids"], data["tracker_ids"])
-        ):
-            # Deal with the case that there are no gt_det/tracker_det in a timestep.
-            if len(gt_ids_t) == 0:
-                for a, alpha in enumerate(self.array_labels):
-                    res["HOTA_FP"][a] += len(tracker_ids_t)
-                continue
-            if len(tracker_ids_t) == 0:
-                for a, alpha in enumerate(self.array_labels):
-                    res["HOTA_FN"][a] += len(gt_ids_t)
-                continue
-
-            # Get matching scores between pairs of dets for optimizing HOTA
-            similarity = data["similarity_scores"][t]
-            score_mat = (
-                global_alignment_score[
-                    gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
-                ]
-                * similarity
-            )
-
-            # Hungarian algorithm to find best matches
-            match_rows, match_cols = linear_sum_assignment(-score_mat)
-
-            # Calculate and accumulate basic statistics
-
-            for a, alpha in enumerate(self.array_labels):
-                actually_matched_mask = (
-                    similarity[match_rows, match_cols] >= alpha - np.finfo("float").eps
-                )
-                alpha_match_rows = match_rows[actually_matched_mask]
-                alpha_match_cols = match_cols[actually_matched_mask]
-                num_matches = len(alpha_match_rows)
-                res["HOTA_TP"][a] += num_matches
-                res["HOTA_FN"][a] += len(gt_ids_t) - num_matches
-                res["HOTA_FP"][a] += len(tracker_ids_t) - num_matches
-                if num_matches > 0:
-                    res["LocA"][a] += sum(
-                        similarity[alpha_match_rows, alpha_match_cols]
-                    )
-                    matches_counts[a][
-                        gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]
-                    ] += 1
-
-        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
-        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
-        for a, alpha in enumerate(self.array_labels):
-            matches_count = matches_counts[a]
-            ass_a = matches_count / np.maximum(
-                1, gt_id_count + tracker_id_count - matches_count
-            )
-            res["AssA"][a] = np.sum(matches_count * ass_a) / np.maximum(
-                1, res["HOTA_TP"][a]
-            )
-            ass_re = matches_count / np.maximum(1, gt_id_count)
-            res["AssRe"][a] = np.sum(matches_count * ass_re) / np.maximum(
-                1, res["HOTA_TP"][a]
-            )
-            ass_pr = matches_count / np.maximum(1, tracker_id_count)
-            res["AssPr"][a] = np.sum(matches_count * ass_pr) / np.maximum(
-                1, res["HOTA_TP"][a]
-            )
-
-        # Calculate final scores
-        res["LocA"] = np.maximum(1e-10, res["LocA"]) / np.maximum(1e-10, res["HOTA_TP"])
-        res = self._compute_final_fields(res)
-
-        return res
-
-    def calculate_TempLocAP(self, data):
-
-        # Return result quickly if tracker or gt sequence is empty
-        if data["num_tracker_dets"] == 0:
-            if data["num_gt_dets"] == 0:
-                precisions = np.array([1, 1])
-                recalls = np.array([0, 1])
-                TempLocAP = 1
-                return TempLocAP, precisions, recalls
-            else:
-                precisions = np.array([0, 0])
-                recalls = np.array([0, 1])
-                TempLocAP = 0
-                return TempLocAP, precisions, recalls
-        if data["num_gt_dets"] == 0:
-            precisions = np.array([0, 0])
-            recalls = np.array([0, 1])
-            TempLocAP = 0
-            return TempLocAP, precisions, recalls
-
-        TEMPORAL_IOU_THRESH = 0.5  # iou
-        MATCHING_DIST_THRESH = 2.0  # m
-
-        pred_tracks = {}
-        gt_tracks = {}
-
-        # Accumulate predicted and ground truth tracks from data
-        for t in range(data["num_timesteps"]):
-            for i, gt_id in enumerate(data["gt_ids"][t]):
-                if gt_id not in gt_tracks:
-                    gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]["xy_pos"] = []
-                    gt_tracks[gt_id]["timestamps"] = []
-                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
-
-                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
-                gt_tracks[gt_id]["timestamps"].append(t)
-
-            for i, track_id in enumerate(data["tracker_ids"][t]):
-                if track_id not in pred_tracks:
-                    pred_tracks[track_id] = {}
-                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
-                        t
-                    ][i]
-                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
-                    pred_tracks[track_id]["xy_pos"] = []
-                    pred_tracks[track_id]["timestamps"] = []
-
-                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
-                pred_tracks[track_id]["timestamps"].append(t)
-
-        # 1 to 1 match of predicted and ground truth tracks
-        sorted_keys = sorted(
-            pred_tracks.keys(),
-            key=lambda key: pred_tracks[key]["confidence"],
-            reverse=True,
-        )
-
-        # keys are track_ids, values are gt_ids
-        matched_ids = {}
-
-        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-        matched_ious = {}
-        unmatched_gt_ids = list(gt_tracks.keys())
-        unmatched_track_ids = []
-
-        for track_id in sorted_keys:
-            track_stats = pred_tracks[track_id]
-
-            track_traj = track_stats["xy_pos"]
-            track_timestamps = track_stats["timestamps"]
-
-            max_similarity = 0
-            best_match = None
-            corresponding_iou = 0
-            for gt_id, gt_stats in gt_tracks.items():
-                if (
-                    gt_id not in unmatched_gt_ids
-                    or gt_stats["category"] != track_stats["category"]
-                ):
-                    continue
-
-                gt_traj = gt_stats["xy_pos"]
-                gt_timestamps = gt_stats["timestamps"]
-
-                intersection = len(
-                    set(gt_timestamps).intersection((set(track_timestamps)))
-                )
-                union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection / union
-
-                if iou < TEMPORAL_IOU_THRESH:
-                    continue
-
-                total_distance = 0.0
-                for timestamp in track_timestamps:
-                    if timestamp in gt_timestamps:
-                        total_distance += np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)]
-                            - gt_traj[gt_timestamps.index(timestamp)]
-                        )
-
-                similarity_score = iou * max(
-                    0.0,
-                    1
-                    - (
-                        total_distance
-                        / (
-                            MATCHING_DIST_THRESH
-                            * (intersection + np.finfo(np.float64).eps)
-                        )
-                    ),
-                )
-                if similarity_score > max_similarity:
-                    max_similarity = similarity_score
-                    best_match = gt_id
-
-            if max_similarity > 0:
-                matched_ids[track_id] = best_match
-                matched_ious[track_id] = corresponding_iou
-                unmatched_gt_ids.remove(best_match)
-            else:
-                unmatched_track_ids.append(track_id)
-
-        # Compute precision and recall at all confidence thresholds.
-        tp = np.zeros(len(pred_tracks))
-        fp = np.zeros(len(pred_tracks))
-        for i, (track_id, track_stats) in enumerate(pred_tracks.items()):
-
-            if track_id in matched_ids:
-                tp[i] = 1
-            else:
-                fp[i] = 1
-
-        tp = np.cumsum(tp)
-        fp = np.cumsum(fp)
-
-        recalls = tp / len(gt_tracks)
-        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
-
-        assert np.all(0 <= precisions) & np.all(precisions <= 1)
-        TempLocAP = self.get_ap(recalls, precisions)
-
-        return TempLocAP, precisions, recalls
-
-    def calculate_TempLocAP_concat(self, data):
-
-        # Return result quickly if tracker or gt sequence is empty
-        if data["num_tracker_dets"] == 0:
-            if data["num_gt_dets"] == 0:
-                precisions = np.array([1, 1])
-                recalls = np.array([0, 1])
-                return TempLocAP, precisions, recalls
-            else:
-                precisions = np.array([0, 0])
-                recalls = np.array([0, 1])
-                TempLocAP = 0
-                return TempLocAP, precisions, recalls
-        if data["num_gt_dets"] == 0:
-            precisions = np.array([0, 0])
-            recalls = np.array([0, 1])
-            TempLocAP = 0
-            return TempLocAP, precisions, recalls
-
-        TEMPORAL_IOU_THRESH = 0.5  # iou
-        MATCHING_DIST_THRESH = 2.0  # m
-
-        pred_tracks = {}
-        gt_tracks = {}
-
-        # Accumulate predicted and ground truth tracks from data
-        for t in range(data["num_timesteps"]):
-            for i, gt_id in enumerate(data["gt_ids"][t]):
-                if gt_id not in gt_tracks:
-                    gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]["xy_pos"] = []
-                    gt_tracks[gt_id]["timestamps"] = []
-                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
-
-                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
-                gt_tracks[gt_id]["timestamps"].append(t)
-
-            for i, track_id in enumerate(data["tracker_ids"][t]):
-                if track_id not in pred_tracks:
-                    pred_tracks[track_id] = {}
-                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
-                        t
-                    ][i]
-                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
-                    pred_tracks[track_id]["xy_pos"] = []
-                    pred_tracks[track_id]["timestamps"] = []
-
-                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
-                pred_tracks[track_id]["timestamps"].append(t)
-
-        # 1 to 1 match of predicted and ground truth tracks
-        pred_ids_by_conf = sorted(
-            pred_tracks.keys(),
-            key=lambda key: pred_tracks[key]["confidence"],
-            reverse=True,
-        )
-
-        # Keys are match_id, values are dict of gt_id, pred_ids, gt_traj, pred_traj, category and confidence
-        matched_predictions = {}
-
-        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-        unmatched_gt_ids = list(gt_tracks.keys())
-        unmatched_track_ids = []
-
-        for track_id in pred_ids_by_conf:
-            track_stats = pred_tracks[track_id]
-            track_confidence = track_stats["confidence"]
-            track_traj = track_stats["xy_pos"]
-            track_timestamps = track_stats["timestamps"]
-
-            max_similarity = 0
-            best_match_stats = None
-            best_match = None
-            for gt_id, gt_stats in gt_tracks.items():
-                if (
-                    gt_id not in unmatched_gt_ids
-                    or gt_stats["category"] != track_stats["category"]
-                ):
-                    continue
-
-                gt_traj = gt_stats["xy_pos"]
-                gt_timestamps = gt_stats["timestamps"]
-
-                intersection = len(
-                    set(gt_timestamps).intersection((set(track_timestamps)))
-                )
-                union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection / union
-
-                if iou < TEMPORAL_IOU_THRESH:
-                    continue
-
-                distances = []
-                for timestamp in track_timestamps:
-                    if timestamp in gt_timestamps:
-                        distances.append(
-                            np.linalg.norm(
-                                track_traj[track_timestamps.index(timestamp)]
-                                - gt_traj[gt_timestamps.index(timestamp)]
-                            )
-                        )
-
-                similarity_score = iou * max(
-                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-                )
-
-                if similarity_score > max_similarity:
-                    max_similarity = similarity_score
-                    best_match = gt_id
-                    best_match_stats = {
-                        "gt_id": gt_id,
-                        "gt_timestamps": gt_timestamps,
-                        "gt_traj": gt_traj,
-                        "pred_ids": [track_id],
-                        "pred_traj": track_traj,
-                        "pred_timestamps": track_timestamps,
-                        "category": gt_stats["category"],
-                        "confidence": track_confidence,
-                        "similarity": similarity_score,
-                    }
-
-            for match_id, match_stats in matched_predictions.items():
-
-                gt_id = match_stats["gt_id"]
-                gt_timestamps = match_stats["gt_timestamps"]
-                gt_traj = match_stats["gt_traj"]
-
-                pred_ids = match_stats["pred_ids"]
-                pred_timestamps = match_stats["pred_timestamps"]
-                pred_traj = match_stats["pred_traj"]
-
-                category = match_stats["category"]
-                confidence = match_stats["confidence"]
-                match_similarity = match_stats["similarity"]
-
-                if (
-                    len(set(pred_timestamps).intersection(set(track_timestamps))) > 0
-                    or track_stats["category"] != category
-                ):
-                    continue
-
-                concat_pred_ids = pred_ids + track_id
-                concat_timestamps = pred_timestamps + track_timestamps
-                concat_traj = pred_traj + track_traj
-                concat_confidence = confidence * len(
-                    pred_timestamps
-                ) + track_confidence * len(track_timestamps)
-                concat_confidence /= len(concat_timestamps)
-
-                intersection = len(
-                    set(gt_timestamps).intersection(set(concat_timestamps))
-                )
-                union = len(set(gt_timestamps).union(set(concat_timestamps)))
-                iou = intersection / union
-
-                if iou < TEMPORAL_IOU_THRESH:
-                    continue
-
-                distances = []
-                for timestamp in concat_timestamps:
-                    if timestamp in gt_timestamps:
-                        distances.append(
-                            np.linalg.norm(
-                                concat_traj[concat_timestamps.index(timestamp)]
-                                - gt_traj[gt_timestamps.index(timestamp)]
-                            )
-                        )
-
-                similarity_score = iou * max(
-                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-                )
-
-                if (
-                    similarity_score > max_similarity
-                    and similarity_score > match_similarity
-                ):
-                    max_similarity = similarity_score
-                    best_match = match_id
-                    best_match_stats = {
-                        "pred_ids": concat_pred_ids,
-                        "pred_traj": concat_traj,
-                        "pred_timestamps": concat_timestamps,
-                        "confidence": concat_confidence,
-                        "similarity": similarity_score,
-                    }
-
-            if max_similarity > 0:
-                if best_match < 0:
-                    matched_predictions[best_match].update(best_match_stats)
-                elif best_match is not None:
-                    matched_predictions[-best_match - 1] = best_match_stats
-                    unmatched_gt_ids.remove(best_match)
-            else:
-                unmatched_track_ids.append(track_id)
-
-        concat_preds_by_conf = {}
-        preds = list(matched_predictions.keys()) + unmatched_track_ids
-        for pred in preds:
-            if pred < 0:
-                concat_preds_by_conf[pred] = matched_predictions[pred]["confidence"]
-            else:
-                concat_preds_by_conf[pred] = pred_tracks[pred]["confidence"]
-
-        concat_ids_by_conf = sorted(
-            concat_preds_by_conf.keys(),
-            key=lambda key: concat_preds_by_conf[key],
-            reverse=True,
-        )
-
-        # Compute precision and recall at all confidence thresholds.
-        tp = np.zeros(len(concat_ids_by_conf))
-        fp = np.zeros(len(concat_ids_by_conf))
-
-        for i, concat_id in enumerate(concat_ids_by_conf):
-
-            if concat_id < 0:
-                tp[i] = 1
-            else:
-                fp[i] = 1
-
-        tp = np.cumsum(tp)
-        fp = np.cumsum(fp)
-
-        recalls = tp / len(gt_tracks)
-        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
-
-        assert np.all(0 <= precisions) & np.all(precisions <= 1)
-        TempLocAP = self.get_ap(recalls, precisions)
-
-        return TempLocAP, precisions, recalls
-
-    def calculate_TempLocAP_merge(self, data):
-
-        # Return result quickly if tracker or gt sequence is empty
-        if data["num_tracker_dets"] == 0:
-            if data["num_gt_dets"] == 0:
-                precisions = np.array([1, 1])
-                recalls = np.array([0, 1])
-                TempLocAP = 1
-                return TempLocAP, precisions, recalls
-            else:
-                precisions = np.array([0, 0])
-                recalls = np.array([0, 1])
-                TempLocAP = 0
-                return TempLocAP, precisions, recalls
-        if data["num_gt_dets"] == 0:
-            precisions = np.array([0, 0])
-            recalls = np.array([0, 1])
-            TempLocAP = 0
-            return TempLocAP, precisions, recalls
-
-        TEMPORAL_IOU_THRESH = 0.5  # iou
-        MATCHING_DIST_THRESH = 2.0  # m
-
-        pred_tracks = {}
-        gt_tracks = {}
-
-        # Accumulate predicted and ground truth tracks from data
-        for t in range(data["num_timesteps"]):
-            for i, gt_id in enumerate(data["gt_ids"][t]):
-                if gt_id not in gt_tracks:
-                    gt_tracks[gt_id] = {}
-                    gt_tracks[gt_id]["xy_pos"] = []
-                    gt_tracks[gt_id]["timestamps"] = []
-                    gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
-
-                gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
-                gt_tracks[gt_id]["timestamps"].append(t)
-
-            for i, track_id in enumerate(data["tracker_ids"][t]):
-                if track_id not in pred_tracks:
-                    pred_tracks[track_id] = {}
-                    pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
-                        t
-                    ][i]
-                    pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
-                    pred_tracks[track_id]["xy_pos"] = []
-                    pred_tracks[track_id]["timestamps"] = []
-
-                pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
-                pred_tracks[track_id]["timestamps"].append(t)
-
-        # 1 to 1 match of predicted and ground truth tracks
-        pred_ids_by_conf = sorted(
-            pred_tracks.keys(),
-            key=lambda key: pred_tracks[key]["confidence"],
-            reverse=True,
-        )
-
-        # keys are gt_id, values are list of corresponding pred_ids
-        matched_ids = {}
-
-        # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-        unmatched_track_ids = []
-
-        for track_id in pred_ids_by_conf:
-            track_stats = pred_tracks[track_id]
-            track_confidence = track_stats["confidence"]
-            track_traj = track_stats["xy_pos"]
-            track_timestamps = track_stats["timestamps"]
-
-            max_similarity = 0
-            best_match = None
-            for gt_id, gt_stats in gt_tracks.items():
-                if gt_stats["category"] != track_stats["category"]:
-                    continue
-
-                gt_traj = gt_stats["xy_pos"]
-                gt_timestamps = gt_stats["timestamps"]
-
-                intersection = len(
-                    set(gt_timestamps).intersection((set(track_timestamps)))
-                )
-                iol = intersection / len(track_timestamps)
-
-                if iol < TEMPORAL_IOU_THRESH:
-                    continue
-
-                distances = []
-                for timestamp in track_timestamps:
-                    if timestamp in gt_timestamps:
-                        distances.append(
-                            np.linalg.norm(
-                                track_traj[track_timestamps.index(timestamp)]
-                                - gt_traj[gt_timestamps.index(timestamp)]
-                            )
-                        )
-
-                similarity_score = iol * max(
-                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-                )
-
-                if similarity_score > max_similarity:
-                    max_similarity = similarity_score
-                    best_match = gt_id
-
-            if max_similarity > 0:
-                if best_match not in matched_ids:
-                    matched_ids[best_match] = [track_id]
-                else:
-                    matched_ids[best_match].append(track_id)
-            else:
-                unmatched_track_ids.append(track_id)
-
-        merged_predictions = {}
-        for gt_id, pred_ids in matched_ids.items():
-            merged_traj = []
-            merged_timestamps = []
-            merged_confidences = []
-            merged_catetory = None
-
-            for pred_id in pred_ids:
-                track_timestamps = pred_tracks[pred_id]["timestamps"]
-                track_trajectory = pred_tracks[pred_id]["xy_pos"]
-                track_confidence = pred_tracks[pred_id]["confidence"]
-                track_category = pred_tracks[pred_id]["category"]
-
-                if len(merged_timestamps) == 0:
-                    merged_timestamps.extend(track_timestamps)
-                    merged_traj.extend(track_trajectory)
-                    merged_catetory = track_category
-                    merged_confidences.extend(
-                        [track_confidence] * len(track_timestamps)
-                    )
-                    continue
-
-                for i, timestamp in enumerate(track_timestamps):
-                    if timestamp not in merged_timestamps:
-                        insertion_index = 0
-                        for merge_timestamp in merged_timestamps:
-                            if merge_timestamp > timestamp:
-                                insertion_index += 1
-
-                        merged_timestamps.insert(insertion_index, timestamp)
-                        merged_traj.insert(insertion_index, track_trajectory[i])
-                        merged_confidences.insert(insertion_index, track_confidence)
-                    else:
-                        insertion_index = merged_timestamps.index(timestamp)
-                        if track_confidence > merged_confidences[insertion_index]:
-                            merged_confidences[insertion_index] = track_confidence
-                            merged_traj[insertion_index] = track_trajectory[i]
-
-            merged_predictions[-gt_id - 1] = {
-                "xy_pos": merged_traj,
-                "timestamps": merged_timestamps,
-                "confidence": np.mean(np.array(merged_confidences)),
-                "category": merged_catetory,
-            }
-
-        for unmatched_track_id in unmatched_track_ids:
-            merged_predictions.update(
-                {unmatched_track_id: pred_tracks[unmatched_track_id]}
-            )
-
-        merged_ids_by_conf = sorted(
-            merged_predictions.keys(),
-            key=lambda key: merged_predictions[key]["confidence"],
-            reverse=True,
-        )
-        matched_gt_ids = []
-
-        # Compute precision and recall at all confidence thresholds.
-        tp = np.zeros(len(merged_ids_by_conf))
-        fp = np.zeros(len(merged_ids_by_conf))
-
-        for i, track_id in enumerate(merged_ids_by_conf):
-            track_stats = merged_predictions[track_id]
-            track_confidence = track_stats["confidence"]
-            track_traj = track_stats["xy_pos"]
-            track_timestamps = track_stats["timestamps"]
-
-            max_similarity = 0
-            best_match = None
-            for gt_id, gt_stats in gt_tracks.items():
-                if (
-                    gt_id in matched_gt_ids
-                    or gt_stats["category"] != track_stats["category"]
-                ):
-                    continue
-
-                gt_traj = gt_stats["xy_pos"]
-                gt_timestamps = gt_stats["timestamps"]
-
-                intersection = len(
-                    set(gt_timestamps).intersection((set(track_timestamps)))
-                )
-                union = len(set(gt_timestamps).union((set(track_timestamps))))
-                iou = intersection / union
-
-                if iou < TEMPORAL_IOU_THRESH:
-                    continue
-
-                distances = []
-                for timestamp in track_timestamps:
-                    if timestamp in gt_timestamps:
-                        distances.append(
-                            np.linalg.norm(
-                                track_traj[track_timestamps.index(timestamp)]
-                                - gt_traj[gt_timestamps.index(timestamp)]
-                            )
-                        )
-
-                similarity_score = iou * max(
-                    0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-                )
-
-                if similarity_score > max_similarity:
-                    max_similarity = similarity_score
-                    best_match = gt_id
-
-            if max_similarity > 0:
-                matched_gt_ids.append(best_match)
-                tp[i] = 1
-            else:
-                fp[i] = 1
-
-        tp = np.cumsum(tp)
-        fp = np.cumsum(fp)
-
-        recalls = tp / len(gt_tracks)
-        precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
-
-        assert np.all(0 <= precisions) & np.all(precisions <= 1)
-        TempLocAP = self.get_ap(recalls, precisions)
-
-        return TempLocAP, precisions, recalls
-
-    def get_envelope(self, precisions):
-        """Compute the precision envelope.
-
-        Args:
-        precisions:
-
-        Returns:
-
-        """
-        for i in range(precisions.size - 1, 0, -1):
-            precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
-        return precisions
-
-    def get_ap(self, recalls, precisions):
-        """
-        Calculate average precision.
-
-        Args:
-            recalls: Array of recall values
-            precisions: Array of precision values
-
-        Returns:
-            float: average precision.
-        """
-        # first append sentinel values at the end
-        recalls = np.concatenate(([0.0], recalls, [1.0]))
-        precisions = np.concatenate(([0.0], precisions, [0.0]))
-
-        # get envelope (maximum precision for each recall value)
-        precisions = self.get_envelope(precisions)
-
-        # to calculate area under PR curve, look for points where X axis (recall) changes value
-        i = np.where(recalls[1:] != recalls[:-1])[0]
-
-        # and sum (\Delta recall) * prec
-        ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
-
-        return ap
-
-    def plot_precision_recall_curve(
-        self,
-        recalls_list,
-        precisions_list,
-        ap_values=None,
-        labels=None,
-        colors=["blue", "green"],
-        save_path=None,
-    ):
-        """
-        Plot precision-recall curves for one or two sets of data.
-
-        Args:
-            recalls_list: List of recall arrays to plot
-            precisions_list: List of precision arrays to plot
-            ap_values: Optional list of AP values to display in the title
-            labels: Optional list of labels for the legend
-            colors: List of colors for the plots (default: blue and green)
-            save_path: Optional path to save the plot
-        """
-        plt.figure(figsize=(8, 6))
-
-        if not isinstance(recalls_list, list):
-            recalls_list = [recalls_list]
-        if not isinstance(precisions_list, list):
-            precisions_list = [precisions_list]
-
-        if labels is None:
-            labels = [f"Curve {i+1}" for i in range(len(recalls_list))]
-
-        for i, (recalls, precisions) in enumerate(zip(recalls_list, precisions_list)):
-            color = colors[i % len(colors)]
-
-            # Prepare data for plotting (add sentinel values)
-            plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
-            plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
-            plot_precisions = self.get_envelope(plot_precisions.copy())
-
-            # Plot the curve
-            plt.plot(
-                plot_recalls,
-                plot_precisions,
-                color=color,
-                linestyle="-",
-                linewidth=2,
-                label=labels[i],
-            )
-            plt.fill_between(plot_recalls, 0, plot_precisions, alpha=0.1, color=color)
-
-        # Set title
-        if ap_values:
-            ap_text = ", ".join(
-                [f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)]
-            )
-            plt.title(f"Precision-Recall Curves ({ap_text})", fontsize=16)
-        else:
-            plt.title("Precision-Recall Curves", fontsize=16)
-
-        # Set labels and limits
-        plt.xlabel("Recall", fontsize=14)
-        plt.ylabel("Precision", fontsize=14)
-        plt.xlim([0.0, 1.0])
-        plt.ylim([0.0, 1.05])
-        plt.grid(True)
-
-        # Add legend if multiple curves
-        if len(recalls_list) > 1:
-            plt.legend(loc="lower left")
-
-        # Save if path provided
-        if save_path:
-            plt.savefig(save_path, dpi=300, bbox_inches="tight")
-
-        plt.show()
-
-    def combine_sequences(self, all_res):
-        """Combines metrics across all sequences"""
-        res = {}
-        for field in self.integer_array_fields:
-            res[field] = self._combine_sum(all_res, field)
-        for field in ["AssRe", "AssPr", "AssA"]:
-            res[field] = self._combine_weighted_av(
-                all_res, field, res, weight_field="HOTA_TP"
-            )
-
-        loca_weighted_sum = sum(
-            [all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
-        )
-        res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
-            1e-10, res["HOTA_TP"]
-        )
-
-        tlap_weighted_sum = sum(
-            [
-                all_res[k]["TempLocAP"]
-                * (all_res[k]["HOTA_TP"][0] + all_res[k]["HOTA_FN"][0])
-                for k in all_res.keys()
-            ]
-        )
-        res["TempLocAP"] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(
-            1e-10, res["HOTA_TP"][0] + res["HOTA_FN"][0]
-        )
-
-        res = self._compute_final_fields(res)
-        return res
-
-    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
-        """Combines metrics across all classes by averaging over the class values.
-        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
-        """
-        res = {}
-        for field in self.integer_array_fields:
-            if ignore_empty_classes:
-                res[field] = self._combine_sum(
-                    {
-                        k: v
-                        for k, v in all_res.items()
-                        if (
-                            v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
-                            > 0 + np.finfo("float").eps
-                        ).any()
-                    },
-                    field,
-                )
-            else:
-                res[field] = self._combine_sum(
-                    {k: v for k, v in all_res.items()}, field
-                )
-
-        for field in self.float_fields + self.float_array_fields:
-            if ignore_empty_classes:
-                res[field] = np.mean(
-                    [
-                        v[field]
-                        for v in all_res.values()
-                        if (
-                            v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
-                            > 0 + np.finfo("float").eps
-                        ).any()
-                    ],
-                    axis=0,
-                )
-            else:
-                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
-        return res
-
-    def combine_classes_det_averaged(self, all_res):
-        """Combines metrics across all classes by averaging over the detection values"""
-        res = {}
-        for field in self.integer_array_fields:
-            res[field] = self._combine_sum(all_res, field)
-        for field in ["AssRe", "AssPr", "AssA"]:
-            res[field] = self._combine_weighted_av(
-                all_res, field, res, weight_field="HOTA_TP"
-            )
-
-        loca_weighted_sum = sum(
-            [all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
-        )
-        res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
-            1e-10, res["HOTA_TP"]
-        )
-
-        tlap_weighted_sum = sum(
-            [
-                all_res[k]["TempLocAP"]
-                * (all_res[k]["HOTA_TP"][0] + all_res[k]["HOTA_FN"][0])
-                for k in all_res.keys()
-            ]
-        )
-        res["TempLocAP"] = np.maximum(1e-10, tlap_weighted_sum) / np.maximum(
-            1e-10, res["HOTA_TP"][0] + res["HOTA_FN"][0]
-        )
-
-        res = self._compute_final_fields(res)
-        return res
-
-    def _compute_final_fields(self, res):
-        """Calculate sub-metric ('field') values which only depend on other sub-metric values.
-        This function is used both for both per-sequence calculation, and in combining values across sequences.
-        """
-        res["DetRe"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FN"])
-        res["DetPr"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FP"])
-        res["DetA"] = res["HOTA_TP"] / np.maximum(
-            1, res["HOTA_TP"] + res["HOTA_FN"] + res["HOTA_FP"]
-        )
-        res["HOTA"] = np.sqrt(res["DetA"] * res["AssA"])
-        res["OWTA"] = np.sqrt(res["DetRe"] * res["AssA"])
-
-        res["HOTA(0)"] = res["HOTA"][0]
-        res["LocA(0)"] = res["LocA"][0]
-        res["HOTALocA(0)"] = res["HOTA(0)"] * res["LocA(0)"]
-        return res
-
-    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
-        """Create plot of results"""
-
-        # Only loaded when run to reduce minimum requirements
-        from matplotlib import pyplot as plt
-
-        res = table_res["COMBINED_SEQ"]
-        styles_to_plot = ["r", "b", "g", "b--", "b:", "g--", "g:", "m", "o--", "o:"]
-        for name, style in zip(self.float_array_fields, styles_to_plot):
-            plt.plot(self.array_labels, res[name], style)
-        plt.xlabel("alpha")
-        plt.ylabel("score")
-        plt.title(tracker + " - " + cls)
-        plt.axis([0, 1, 0, 1])
-        legend = []
-        for name in self.float_array_fields:
-            legend += [name + " (" + str(np.round(np.mean(res[name]), 2)) + ")"]
-        plt.legend(legend, loc="lower left")
-        out_file = os.path.join(output_folder, cls + "_plot.pdf")
-        os.makedirs(os.path.dirname(out_file), exist_ok=True)
-        plt.savefig(out_file)
-        plt.savefig(out_file.replace(".pdf", ".png"))
-        plt.clf()
diff --git a/src/av2/evaluation/scenario_mining/metrics.py b/src/av2/evaluation/scenario_mining/metrics.py
deleted file mode 100644
index 836dd704..00000000
--- a/src/av2/evaluation/scenario_mining/metrics.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from av2.evaluation.scenario_mining.hota import HOTA
-from trackeval.metrics.clear import CLEAR
-from trackeval.metrics.identity import Identity
-from trackeval.metrics.count import Count
-from trackeval.metrics.j_and_f import JAndF
-from trackeval.metrics.track_map import TrackMAP
-from trackeval.metrics.vace import VACE
-from trackeval.metrics.ideucl import IDEucl

From 45029a34276030361eda56168506c36cc5401182 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 01:35:32 -0500
Subject: [PATCH 10/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py    | 105 +++++++++---------
 .../evaluation/scenario_mining/test_eval.py   |   9 +-
 2 files changed, 56 insertions(+), 58 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index edc78621..a448e5ee 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -45,6 +45,7 @@
 from trackeval import _timing
 from trackeval.metrics import Count
 
+
 class TrackEvalDataset(_BaseDataset):  # type: ignore
     """Dataset class to support tracking evaluation using the TrackEval library."""
 
@@ -70,7 +71,6 @@ def get_default_dataset_config() -> Dict[str, Any]:
         Returns:
             dictionary of the default config
         """
-
         default_config = {
             "GT_TRACKS": None,  # tracker_name -> seq id -> frames
             "PREDICTED_TRACKS": None,  # tracker_name -> seq id -> frames
@@ -170,7 +170,7 @@ def get_preprocessed_seq_data(
         data["num_gt_ids"] = len(unique_gt_ids)
 
         # Ensure again that ids are unique per timestep after preproc.
-        self._check_unique_ids(data, after_preproc=True)        
+        self._check_unique_ids(data, after_preproc=True)
 
         return data
 
@@ -197,8 +197,8 @@ def _plot_confusion_matrix(
 ) -> None:
     """Plots the confusion matrix for scenario mining. A true label
     indicates that the scenario matches the description. A false label
-    indicates the scenario does not match the description."""
-
+    indicates the scenario does not match the description.
+    """
     # Create confusion matrix (2x2 for binary classification)
     cm = np.zeros((2, 2), dtype=int)
 
@@ -291,7 +291,9 @@ def evaluate_tracking(
         "THRESHOLD": iou_threshold,
     }
     metric_names = cast(List[str], metrics_config["METRICS"])
-    metrics_list = [getattr(trackeval.metrics, metric)(metrics_config) for metric in metric_names]
+    metrics_list = [
+        getattr(trackeval.metrics, metric)(metrics_config) for metric in metric_names
+    ]
     dataset_config = {
         **TrackEvalDataset.get_default_dataset_config(),
         "GT_TRACKS": {tracker_name: labels},
@@ -318,25 +320,26 @@ def evaluate_tracking(
         metrics_list,
     )
 
-    trackers, seq_ids, classes = dataset.get_eval_info()
+    trackers, _, classes = dataset.get_eval_info()
     tracker = trackers[0]
 
     tlap_by_seq = np.zeros((len(labels), len(classes)))
     num_gt_by_seq = np.zeros((len(labels), len(classes)))
 
     for i, seq_id in enumerate(labels.keys()):
-        raw_data =  dataset.get_raw_seq_data(tracker, seq_id)
+        raw_data = dataset.get_raw_seq_data(tracker, seq_id)
         for j, clas in enumerate(classes):
             data = dataset.get_preprocessed_seq_data(raw_data, clas)
 
-            num_gt_ids = len(data['gt_ids'])
+            num_gt_ids = len(data["gt_ids"])
             tlap, precisions, recalls = calculate_TempLocAP_merge(data)
-            tlap_by_seq[i,j] = tlap
-            num_gt_by_seq[i,j] = num_gt_ids
+            tlap_by_seq[i, j] = tlap
+            num_gt_by_seq[i, j] = num_gt_ids
 
     combined_tlap = np.average(tlap_by_seq, axis=1, weights=num_gt_by_seq)
-    referred_tlap = float(combined_tlap[np.where(np.array(classes) == 'REFERRED_OBJECT')])
-
+    referred_tlap = float(
+        combined_tlap[np.where(np.array(classes) == "REFERRED_OBJECT")]
+    )
 
     return cast(Dict[str, Any], full_result), referred_tlap
 
@@ -530,8 +533,10 @@ def _recall_to_scores(
     return score_thresholds
 
 
-def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np.ndarray]:
-    """Calculates temporal localization average precision. """
+def calculate_TempLocAP_merge(
+    data: dict[str, Any],
+) -> tuple[float, np.ndarray, np.ndarray]:
+    """Calculates temporal localization average precision."""
     # Return result quickly if tracker or gt sequence is empty
     if data["num_tracker_dets"] == 0:
         if data["num_gt_dets"] == 0:
@@ -571,9 +576,7 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
         for i, track_id in enumerate(data["tracker_ids"][t]):
             if track_id not in pred_tracks:
                 pred_tracks[track_id] = {}
-                pred_tracks[track_id]["confidence"] = data["tracker_confidences"][
-                    t
-                ][i]
+                pred_tracks[track_id]["confidence"] = data["tracker_confidences"][t][i]
                 pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
                 pred_tracks[track_id]["xy_pos"] = []
                 pred_tracks[track_id]["timestamps"] = []
@@ -609,9 +612,7 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
             gt_traj = gt_stats["xy_pos"]
             gt_timestamps = gt_stats["timestamps"]
 
-            intersection = len(
-                set(gt_timestamps).intersection((set(track_timestamps)))
-            )
+            intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
             iol = intersection / len(track_timestamps)
 
             if iol < TEMPORAL_IOU_THRESH:
@@ -660,9 +661,7 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
                 merged_timestamps.extend(track_timestamps)
                 merged_traj.extend(track_trajectory)
                 merged_catetory = track_category
-                merged_confidences.extend(
-                    [track_confidence] * len(track_timestamps)
-                )
+                merged_confidences.extend([track_confidence] * len(track_timestamps))
                 continue
 
             for i, timestamp in enumerate(track_timestamps):
@@ -689,9 +688,7 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
         }
 
     for unmatched_track_id in unmatched_track_ids:
-        merged_predictions.update(
-            {unmatched_track_id: pred_tracks[unmatched_track_id]}
-        )
+        merged_predictions.update({unmatched_track_id: pred_tracks[unmatched_track_id]})
 
     merged_ids_by_conf = sorted(
         merged_predictions.keys(),
@@ -722,9 +719,7 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
             gt_traj = gt_stats["xy_pos"]
             gt_timestamps = gt_stats["timestamps"]
 
-            intersection = len(
-                set(gt_timestamps).intersection((set(track_timestamps)))
-            )
+            intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
             union = len(set(gt_timestamps).union((set(track_timestamps))))
             iou = intersection / union
 
@@ -766,22 +761,24 @@ def calculate_TempLocAP_merge(data: dict[str, Any])->tuple[float, np.ndarray, np
 
     return TempLocAP, precisions, recalls
 
-def get_envelope(precisions):
+
+def _get_envelope(precisions: np.ndarray) -> np.ndarray:
     """Compute the precision envelope.
 
     Args:
-    precisions:
+        precisions: A set of precision values <= 1
 
     Returns:
+        The monotonically non-increasing precision values
 
     """
     for i in range(precisions.size - 1, 0, -1):
         precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
     return precisions
 
+
 def get_ap(recalls, precisions):
-    """
-    Calculate average precision.
+    """Calculate average precision.
 
     Args:
         recalls: Array of recall values
@@ -805,6 +802,7 @@ def get_ap(recalls, precisions):
 
     return ap
 
+
 def plot_precision_recall_curve(
     recalls_list,
     precisions_list,
@@ -812,9 +810,8 @@ def plot_precision_recall_curve(
     labels=None,
     colors=["blue", "green"],
     save_path=None,
-):
-    """
-    Plot precision-recall curves for one or two sets of data.
+) -> None:
+    """Plot precision-recall curves for one or two sets of data.
 
     Args:
         recalls_list: List of recall arrays to plot
@@ -919,9 +916,8 @@ def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
     )
 
 
-def load(pkl_path:Path) -> Sequences:
+def load(pkl_path: Path) -> Sequences:
     """Loads a pkl file as a dict."""
-
     with open(pkl_path, "rb") as f:
         data = pickle.load(f)
 
@@ -1005,7 +1001,7 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
 
 
 def referred_full_tracks(sequences: Sequences) -> Sequences:
-    """ Reconstructs a mining pkl file by propagating referred object labels across all instances
+    """Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
 
     Args:
@@ -1014,7 +1010,6 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
     Returns:
         reconstructed_sequences: Dictionary containing the reconstructed sequences
     """
-
     reconstructed_sequences = {}
 
     # Process each sequence
@@ -1062,10 +1057,12 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
 def evaluate_mining(
     track_predictions: Sequences, labels: Sequences, output_dir
 ) -> tuple[float, float]:
-    """Calculates the F1 score for classifying if anything in the scenario
-    matches the prompt.
-    """
+    """Calculates the F1 score.
 
+    F1 is a binary classification metric. A true postive when both
+    the ground-truth and predictions sequences contains no tracks
+    or both contain at least one track corresponding to the prediction.
+    """
     gt_class = np.zeros(len(labels), dtype=np.int64)
     pred_class = np.zeros(len(labels), dtype=np.int64)
 
@@ -1103,9 +1100,8 @@ def evaluate_mining(
     return f1_score, acc
 
 
-def _relabel_seq_ids(data:Sequences)->Sequences:
-    """Turns the (log_id, prompt) tuple format into a string for HOTA summarization """
-
+def _relabel_seq_ids(data: Sequences) -> Sequences:
+    """Turns the (log_id, prompt) tuple format into a string for HOTA summarization."""
     new_data = {}
 
     for seq_id, frames in data.items():
@@ -1134,19 +1130,20 @@ def evaluate(
     """Run scenario mining evaluation on the supplied prediction and label pkl files.
 
     Args:
-        pred_pkl: Path to track predictions.
-        gt_pkl: Path to track labels.
+        track_predictions: Prediction sequences.
+        labels: Ground truth sequences.
         objective_metric: Metric to optimize.
         max_range_m: Maximum evaluation range.
         dataset_dir: Path to dataset. Required for ROI pruning.
         out: Output path.
 
     Returns:
-        class_acc: The classification accuracy of if the scenario matches the description
-        full_track_metric: The tracking metric for the full track of any objects that the description ever applies to.
-        partial_track_metric: The tracking metric for the tracks that contain only the timestamps for which the description applies.
+        F1_score: The F1 score for if the scenario matches the description
+        full_track_HOTA: The tracking metric for the full track of any objects that the description ever applies to.
+        partial_track_HOTA: The tracking metric for the tracks that contain only the timestamps for which the description applies.
+        tlap: Temporal localization average precision calculates how well predictions are temporally localized,
+            with some built in give for ambiguous annotations
     """
-
     output_dir = ""
     if out:
         output_dir = out + "/partial_tracks"
@@ -1182,7 +1179,9 @@ def evaluate(
     full_track_hota = full_track_metrics["REFERRED_OBJECT"]
     partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
 
-    print(f'F1: {f1_score}, HOTA: {partial_track_hota}, HOTA_full: {full_track_hota}, TLAP: {TempLocAP}')
+    print(
+        f"F1: {f1_score}, HOTA: {partial_track_hota}, HOTA_full: {full_track_hota}, TLAP: {TempLocAP}"
+    )
     return f1_score, full_track_hota, partial_track_hota, TempLocAP
 
 
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index a0613f1f..b84c2dd5 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -17,17 +17,16 @@
 
 def test_evaluate() -> None:
     """Test End-to-End Forecasting evaluation."""
-
-    predictions = TEST_DATA_DIR / "combined_predictions_dev.pkl"
-    ground_truth = TEST_DATA_DIR / "combined_gt_dev.pkl"
+    pred_pkl = TEST_DATA_DIR / "combined_predictions_dev.pkl"
+    gt_pkl = TEST_DATA_DIR / "combined_gt_dev.pkl"
 
     objective_metric = "HOTA"
     max_range_m = 100
     dataset_dir = TEST_DATA_DIR
     out = str(TEST_DATA_DIR / "eval_results")
 
-    predictions = load(predictions)
-    ground_truth = load(ground_truth)
+    predictions = load(pred_pkl)
+    ground_truth = load(gt_pkl)
 
     evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
 

From 3e135ff752337453ef5b50b95357c637dc861e9c Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 01:50:43 -0500
Subject: [PATCH 11/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 49 ++++++++++++----------
 1 file changed, 26 insertions(+), 23 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index a448e5ee..597ad0c4 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -195,9 +195,10 @@ def _calculate_similarities(
 def _plot_confusion_matrix(
     gt_classes: NDArrayInt, pred_classes: NDArrayInt, output_dir: str
 ) -> None:
-    """Plots the confusion matrix for scenario mining. A true label
-    indicates that the scenario matches the description. A false label
-    indicates the scenario does not match the description.
+    """Plots the confusion matrix for scenario mining. 
+    
+    A true label indicates that the scenario matches the description. 
+    A false label indicates the scenario does not match the description.
     """
     # Create confusion matrix (2x2 for binary classification)
     cm = np.zeros((2, 2), dtype=int)
@@ -535,7 +536,7 @@ def _recall_to_scores(
 
 def calculate_TempLocAP_merge(
     data: dict[str, Any],
-) -> tuple[float, np.ndarray, np.ndarray]:
+) -> Tuple[float, NDArrayFloat, NDArrayFloat]:
     """Calculates temporal localization average precision."""
     # Return result quickly if tracker or gt sequence is empty
     if data["num_tracker_dets"] == 0:
@@ -558,8 +559,8 @@ def calculate_TempLocAP_merge(
     TEMPORAL_IOU_THRESH = 0.5  # iou
     MATCHING_DIST_THRESH = 2.0  # m
 
-    pred_tracks = {}
-    gt_tracks = {}
+    pred_tracks:dict[int, Any] = {}
+    gt_tracks:dict[int, Any] = {}
 
     # Accumulate predicted and ground truth tracks from data
     for t in range(data["num_timesteps"]):
@@ -628,7 +629,7 @@ def calculate_TempLocAP_merge(
                         )
                     )
 
-            similarity_score = iol * max(
+            similarity_score = iol * np.maximum(
                 0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
             )
 
@@ -647,7 +648,7 @@ def calculate_TempLocAP_merge(
     merged_predictions = {}
     for gt_id, pred_ids in matched_ids.items():
         merged_traj = []
-        merged_timestamps = []
+        merged_timestamps:list[int] = []
         merged_confidences = []
         merged_catetory = None
 
@@ -736,7 +737,7 @@ def calculate_TempLocAP_merge(
                         )
                     )
 
-            similarity_score = iou * max(
+            similarity_score = iou * np.maximum(
                 0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
             )
 
@@ -762,7 +763,7 @@ def calculate_TempLocAP_merge(
     return TempLocAP, precisions, recalls
 
 
-def _get_envelope(precisions: np.ndarray) -> np.ndarray:
+def _get_envelope(precisions:NDArrayFloat) -> NDArrayFloat:
     """Compute the precision envelope.
 
     Args:
@@ -777,7 +778,7 @@ def _get_envelope(precisions: np.ndarray) -> np.ndarray:
     return precisions
 
 
-def get_ap(recalls, precisions):
+def get_ap(recalls:NDArrayFloat, precisions:NDArrayFloat)->float:
     """Calculate average precision.
 
     Args:
@@ -792,24 +793,24 @@ def get_ap(recalls, precisions):
     precisions = np.concatenate(([0.0], precisions, [0.0]))
 
     # get envelope (maximum precision for each recall value)
-    precisions = get_envelope(precisions)
+    precisions = _get_envelope(precisions)
 
     # to calculate area under PR curve, look for points where X axis (recall) changes value
     i = np.where(recalls[1:] != recalls[:-1])[0]
 
     # and sum (\Delta recall) * prec
-    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
+    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1]).__float__()
 
     return ap
 
 
 def plot_precision_recall_curve(
-    recalls_list,
-    precisions_list,
-    ap_values=None,
-    labels=None,
-    colors=["blue", "green"],
-    save_path=None,
+    recalls_list:list[NDArrayFloat],
+    precisions_list:list[NDArrayFloat],
+    ap_values:Union[list[float],None]=None,
+    labels:Union[list[str],None]=None,
+    colors:list[str]=["blue", "green"],
+    save_path:Union[str,None]=None,
 ) -> None:
     """Plot precision-recall curves for one or two sets of data.
 
@@ -837,7 +838,7 @@ def plot_precision_recall_curve(
         # Prepare data for plotting (add sentinel values)
         plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
         plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
-        plot_precisions = get_envelope(plot_precisions.copy())
+        plot_precisions = _get_envelope(plot_precisions.copy())
 
         # Plot the curve
         plt.plot(
@@ -916,7 +917,7 @@ def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
     )
 
 
-def load(pkl_path: Path) -> Sequences:
+def load(pkl_path: Path) -> Any:
     """Loads a pkl file as a dict."""
     with open(pkl_path, "rb") as f:
         data = pickle.load(f)
@@ -1001,7 +1002,9 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
 
 
 def referred_full_tracks(sequences: Sequences) -> Sequences:
-    """Reconstructs a mining pkl file by propagating referred object labels across all instances
+    """Expands the predicted partial tracks to the whole length of the track_id.
+    
+    Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
 
     Args:
@@ -1055,7 +1058,7 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
 
 
 def evaluate_mining(
-    track_predictions: Sequences, labels: Sequences, output_dir
+    track_predictions: Sequences, labels: Sequences, output_dir:str
 ) -> tuple[float, float]:
     """Calculates the F1 score.
 

From beb01060795956801ef99a7be9f083a623f88c73 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 01:59:09 -0500
Subject: [PATCH 12/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 34 +++++++++++-----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 597ad0c4..f3e7a99f 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -195,9 +195,9 @@ def _calculate_similarities(
 def _plot_confusion_matrix(
     gt_classes: NDArrayInt, pred_classes: NDArrayInt, output_dir: str
 ) -> None:
-    """Plots the confusion matrix for scenario mining. 
-    
-    A true label indicates that the scenario matches the description. 
+    """Plots the confusion matrix for scenario mining.
+
+    A true label indicates that the scenario matches the description.
     A false label indicates the scenario does not match the description.
     """
     # Create confusion matrix (2x2 for binary classification)
@@ -559,8 +559,8 @@ def calculate_TempLocAP_merge(
     TEMPORAL_IOU_THRESH = 0.5  # iou
     MATCHING_DIST_THRESH = 2.0  # m
 
-    pred_tracks:dict[int, Any] = {}
-    gt_tracks:dict[int, Any] = {}
+    pred_tracks: dict[int, Any] = {}
+    gt_tracks: dict[int, Any] = {}
 
     # Accumulate predicted and ground truth tracks from data
     for t in range(data["num_timesteps"]):
@@ -648,7 +648,7 @@ def calculate_TempLocAP_merge(
     merged_predictions = {}
     for gt_id, pred_ids in matched_ids.items():
         merged_traj = []
-        merged_timestamps:list[int] = []
+        merged_timestamps: list[int] = []
         merged_confidences = []
         merged_catetory = None
 
@@ -763,7 +763,7 @@ def calculate_TempLocAP_merge(
     return TempLocAP, precisions, recalls
 
 
-def _get_envelope(precisions:NDArrayFloat) -> NDArrayFloat:
+def _get_envelope(precisions: NDArrayFloat) -> NDArrayFloat:
     """Compute the precision envelope.
 
     Args:
@@ -778,7 +778,7 @@ def _get_envelope(precisions:NDArrayFloat) -> NDArrayFloat:
     return precisions
 
 
-def get_ap(recalls:NDArrayFloat, precisions:NDArrayFloat)->float:
+def get_ap(recalls: NDArrayFloat, precisions: NDArrayFloat) -> float:
     """Calculate average precision.
 
     Args:
@@ -799,18 +799,18 @@ def get_ap(recalls:NDArrayFloat, precisions:NDArrayFloat)->float:
     i = np.where(recalls[1:] != recalls[:-1])[0]
 
     # and sum (\Delta recall) * prec
-    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1]).__float__()
+    ap = float(np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1]))
 
     return ap
 
 
 def plot_precision_recall_curve(
-    recalls_list:list[NDArrayFloat],
-    precisions_list:list[NDArrayFloat],
-    ap_values:Union[list[float],None]=None,
-    labels:Union[list[str],None]=None,
-    colors:list[str]=["blue", "green"],
-    save_path:Union[str,None]=None,
+    recalls_list: list[NDArrayFloat],
+    precisions_list: list[NDArrayFloat],
+    ap_values: Union[list[float], None] = None,
+    labels: Union[list[str], None] = None,
+    colors: list[str] = ["blue", "green"],
+    save_path: Union[str, None] = None,
 ) -> None:
     """Plot precision-recall curves for one or two sets of data.
 
@@ -1003,7 +1003,7 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
 
 def referred_full_tracks(sequences: Sequences) -> Sequences:
     """Expands the predicted partial tracks to the whole length of the track_id.
-    
+
     Reconstructs a mining pkl file by propagating referred object labels across all instances
     of the same track_id and removing all other objects.
 
@@ -1058,7 +1058,7 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
 
 
 def evaluate_mining(
-    track_predictions: Sequences, labels: Sequences, output_dir:str
+    track_predictions: Sequences, labels: Sequences, output_dir: str
 ) -> tuple[float, float]:
     """Calculates the F1 score.
 

From 1cf900df8bd85f65b1d784b9044c3c3d2a1cf192 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 02:12:46 -0500
Subject: [PATCH 13/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index f3e7a99f..2b9b32fb 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -543,17 +543,17 @@ def calculate_TempLocAP_merge(
         if data["num_gt_dets"] == 0:
             precisions = np.array([1, 1])
             recalls = np.array([0, 1])
-            TempLocAP = 1
+            TempLocAP = 1.0
             return TempLocAP, precisions, recalls
         else:
             precisions = np.array([0, 0])
             recalls = np.array([0, 1])
-            TempLocAP = 0
+            TempLocAP = 0.0
             return TempLocAP, precisions, recalls
     if data["num_gt_dets"] == 0:
         precisions = np.array([0, 0])
         recalls = np.array([0, 1])
-        TempLocAP = 0
+        TempLocAP = 0.0
         return TempLocAP, precisions, recalls
 
     TEMPORAL_IOU_THRESH = 0.5  # iou

From 6a0c08e307ae2a26ec17a3eb279d39b415be757a Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 02:41:45 -0500
Subject: [PATCH 14/22] fixed deprecated trackeval types

---
 src/av2/evaluation/scenario_mining/eval.py | 9 +++++++++
 src/av2/evaluation/tracking/eval.py        | 1 +
 2 files changed, 10 insertions(+)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 2b9b32fb..1f397550 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -18,6 +18,15 @@
 
 import click
 import numpy as np
+from packaging import version
+
+#Replacing deprecated numpy types in TrackEval
+if version.parse(np.__version__) >= version.parse("1.24.0"):
+    print('hi')
+    np.float = np.float32
+    np.int = np.int32
+    np.bool = bool
+
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.transform import Rotation
 from tqdm import tqdm
diff --git a/src/av2/evaluation/tracking/eval.py b/src/av2/evaluation/tracking/eval.py
index 98124789..ec83bbd1 100644
--- a/src/av2/evaluation/tracking/eval.py
+++ b/src/av2/evaluation/tracking/eval.py
@@ -18,6 +18,7 @@
 
 import click
 import numpy as np
+
 import trackeval
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.transform import Rotation

From 1156a6136aa83f82186576a5721fbaf0d54849ee Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 02:51:58 -0500
Subject: [PATCH 15/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 1f397550..3a2e3f00 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -20,12 +20,17 @@
 import numpy as np
 from packaging import version
 
-#Replacing deprecated numpy types in TrackEval
+# Handling deprecated NumPy types for compatibility with TrackEval
 if version.parse(np.__version__) >= version.parse("1.24.0"):
-    print('hi')
-    np.float = np.float32
-    np.int = np.int32
-    np.bool = bool
+    # Use type annotations to avoid linter errors
+    numpy_float = np.float32
+    numpy_int = np.int32
+    numpy_bool = np.bool_  # Use np.bool_ instead of trying to assign to bool
+    
+    # Monkey patch numpy namespace for backward compatibility
+    setattr(np, "float", numpy_float)
+    setattr(np, "int", numpy_int)
+    setattr(np, "bool", numpy_bool)
 
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.transform import Rotation

From 49bb41194f0a392c1bb2387012aac80b861719b5 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 02:59:45 -0500
Subject: [PATCH 16/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 3a2e3f00..a845b077 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -26,7 +26,7 @@
     numpy_float = np.float32
     numpy_int = np.int32
     numpy_bool = np.bool_  # Use np.bool_ instead of trying to assign to bool
-    
+
     # Monkey patch numpy namespace for backward compatibility
     setattr(np, "float", numpy_float)
     setattr(np, "int", numpy_int)

From ae6efd91c5e0179af4a61edf3d6ca994235caf8d Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Fri, 7 Mar 2025 17:53:02 -0500
Subject: [PATCH 17/22] fixed eval edge cases

---
 .gitignore                                 |  3 ++
 src/av2/evaluation/scenario_mining/eval.py | 35 ++++++++--------------
 2 files changed, 15 insertions(+), 23 deletions(-)

diff --git a/.gitignore b/.gitignore
index 138e3192..6d3a5c9b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -124,6 +124,9 @@ venv.bak/
 # mkdocs documentation
 /site
 
+# ruff
+.ruff_cache/
+
 # mypy
 .mypy_cache/
 .dmypy.json
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index a845b077..3e4e8408 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -15,6 +15,7 @@
 from pathlib import Path
 from pprint import pprint
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
+from copy import deepcopy
 
 import click
 import numpy as np
@@ -27,7 +28,7 @@
     numpy_int = np.int32
     numpy_bool = np.bool_  # Use np.bool_ instead of trying to assign to bool
 
-    # Monkey patch numpy namespace for backward compatibility
+    # Hotfix numpy namespace for backward compatibility
     setattr(np, "float", numpy_float)
     setattr(np, "int", numpy_int)
     setattr(np, "bool", numpy_bool)
@@ -347,7 +348,7 @@ def evaluate_tracking(
             data = dataset.get_preprocessed_seq_data(raw_data, clas)
 
             num_gt_ids = len(data["gt_ids"])
-            tlap, precisions, recalls = calculate_TempLocAP_merge(data)
+            tlap, precisions, recalls = calculate_TempLocAP(data)
             tlap_by_seq[i, j] = tlap
             num_gt_by_seq[i, j] = num_gt_ids
 
@@ -548,7 +549,7 @@ def _recall_to_scores(
     return score_thresholds
 
 
-def calculate_TempLocAP_merge(
+def calculate_TempLocAP(
     data: dict[str, Any],
 ) -> Tuple[float, NDArrayFloat, NDArrayFloat]:
     """Calculates temporal localization average precision."""
@@ -1042,28 +1043,16 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
         for frame in frames:
             # Create mask for referred track_ids
             mask = np.isin(frame["track_id"], list(referred_track_ids))
+            new_frame = deepcopy(frame)
 
-            # Create new frame with only referred objects
-            new_frame = {
-                "seq_id": frame["seq_id"],
-                "timestamp_ns": frame["timestamp_ns"],
-                "ego_translation_m": frame["ego_translation_m"],
-                "description": frame["description"],
-                "translation_m": frame["translation_m"][mask],
-                "size": frame["size"][mask],
-                "yaw": frame["yaw"][mask],
-                "velocity_m_per_s": frame["velocity_m_per_s"][mask],
-                "label": np.zeros(
-                    mask.sum(), dtype=np.int32
-                ),  # All are referred objects
-                "name": np.array(["REFERRED_OBJECT"] * mask.sum(), dtype="<U31"),
-                "track_id": frame["track_id"][mask],
-            }
-
-            # If score exists in the original frame (for predictions), include it
-            if "score" in frame:
-                new_frame["score"] = frame["score"][mask]
+            new_names = np.zeros(len(frame["name"]), dtype="<U31")
+            for i in range(len(frame["name"])):
+                if mask[i]:
+                    new_names[i] = "REFERRED_OBJECT"
+                else:
+                    new_names[i] = frame["name"][i]
 
+            new_frame["name"] = new_names
             new_frames.append(new_frame)
 
         reconstructed_sequences[seq_name] = new_frames

From 99314e6d7af1f827943c8110746ce703c29f740b Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Sun, 9 Mar 2025 23:13:42 -0400
Subject: [PATCH 18/22] simplified scenario mining eval

---
 .../evaluation/scenario_mining/__init__.py    |    9 +
 .../evaluation/scenario_mining/constants.py   |   12 -
 src/av2/evaluation/scenario_mining/eval.py    | 1066 ++---------------
 src/av2/evaluation/scenario_mining/utils.py   |  177 ---
 tutorials/map_tutorial.ipynb                  |   83 +-
 5 files changed, 205 insertions(+), 1142 deletions(-)
 delete mode 100644 src/av2/evaluation/scenario_mining/constants.py
 delete mode 100644 src/av2/evaluation/scenario_mining/utils.py

diff --git a/src/av2/evaluation/scenario_mining/__init__.py b/src/av2/evaluation/scenario_mining/__init__.py
index b78073fe..03953641 100644
--- a/src/av2/evaluation/scenario_mining/__init__.py
+++ b/src/av2/evaluation/scenario_mining/__init__.py
@@ -13,3 +13,12 @@ class ScenarioMiningCategories(str, Enum):
     REFERRED_OBJECT = "REFERRED_OBJECT"
     RELATED_OBJECT = "RELATED_OBJECT"
     OTHER_OBJECT = "OTHER_OBJECT"
+
+
+"""Constants for scenario mining challenge."""
+SUBMETRIC_TO_METRIC_CLASS_NAME: Final = {
+    "MOTA": "CLEAR",
+    "HOTA": "HOTA",
+}
+
+AV2_CATEGORIES: Final = tuple(x.value for x in ScenarioMiningCategories)
diff --git a/src/av2/evaluation/scenario_mining/constants.py b/src/av2/evaluation/scenario_mining/constants.py
deleted file mode 100644
index 62ef0bfe..00000000
--- a/src/av2/evaluation/scenario_mining/constants.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""Constants for tracking challenge."""
-
-from typing import Final
-
-from av2.evaluation.scenario_mining import ScenarioMiningCategories
-
-SUBMETRIC_TO_METRIC_CLASS_NAME: Final = {
-    "MOTA": "CLEAR",
-    "HOTA": "HOTA",
-}
-
-AV2_CATEGORIES: Final = tuple(x.value for x in ScenarioMiningCategories)
diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 3e4e8408..8f48e6d3 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -2,24 +2,17 @@
 
 Evaluation Metrics:
     HOTA: see https://arxiv.org/abs/2009.07736
-    MOTA: see https://jivp-eurasipjournals.springeropen.com/articles/10.1155/2008/246309
-    AMOTA: see https://arxiv.org/abs/2008.08063
+    scenario-level F1: see https://jivp-eurasipjournals.springeropen.com/articles/10.1155/2008/246309
+    timestamp-level F1: see https://arxiv.org/abs/2008.08063
 """
 
-import contextlib
-import json
-import pickle
-from copy import copy
-from functools import partial
-from itertools import chain
 from pathlib import Path
-from pprint import pprint
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
+from typing import Any, Dict, Optional, Tuple, Union
 from copy import deepcopy
-
+import pickle
 import click
-import numpy as np
 from packaging import version
+import numpy as np
 
 # Handling deprecated NumPy types for compatibility with TrackEval
 if version.parse(np.__version__) >= version.parse("1.24.0"):
@@ -33,194 +26,37 @@
     setattr(np, "int", numpy_int)
     setattr(np, "bool", numpy_bool)
 
-from scipy.optimize import linear_sum_assignment
-from scipy.spatial.transform import Rotation
-from tqdm import tqdm
-from trackeval.datasets._base_dataset import _BaseDataset
 import matplotlib.pyplot as plt
 
-from av2.evaluation.detection.utils import (
-    compute_objects_in_roi_mask,
-    load_mapped_avm_and_egoposes,
+from av2.map.map_api import ArgoverseStaticMap, RasterLayerType
+from av2.utils.typing import NDArrayBool
+from av2.structures.cuboid import Cuboid, CuboidList
+from av2.evaluation.detection.utils import load_mapped_avm_and_egoposes
+from av2.evaluation.tracking.eval import (
+    yaw_to_quaternion3d,
+    filter_max_dist,
+    _tune_score_thresholds,
+    evaluate_tracking,
 )
-from av2.evaluation.scenario_mining import constants
-from av2.evaluation.scenario_mining import utils as sm_utils
-from av2.evaluation.scenario_mining.constants import SUBMETRIC_TO_METRIC_CLASS_NAME
-from av2.utils.typing import NDArrayFloat, NDArrayInt
+from av2.evaluation.scenario_mining import AV2_CATEGORIES
+from av2.evaluation.tracking import utils as sm_utils
+from av2.utils.typing import NDArrayFloat
 from av2.evaluation.typing import Sequences
-import trackeval
-
-import time
-import traceback
-from multiprocessing.pool import Pool
-from functools import partial
-import os
-from trackeval import utils
-from trackeval.utils import TrackEvalException
-from trackeval import _timing
-from trackeval.metrics import Count
-
-
-class TrackEvalDataset(_BaseDataset):  # type: ignore
-    """Dataset class to support tracking evaluation using the TrackEval library."""
-
-    def __init__(self, config: Dict[str, Any]) -> None:
-        """Store config."""
-        super().__init__()
-        self.gt_tracks = config["GT_TRACKS"]
-        self.predicted_tracks = config["PREDICTED_TRACKS"]
-        self.full_class_list = config.get("CLASSES", config["CLASSES_TO_EVAL"])
-        self.class_list = config["CLASSES_TO_EVAL"]
-        self.tracker_list = config["TRACKERS_TO_EVAL"]
-        self.seq_list = config["SEQ_IDS_TO_EVAL"]
-        self.output_fol = config["OUTPUT_FOLDER"]
-        self.output_sub_fol = config["OUTPUT_SUB_FOLDER"]
-        self.zero_distance = config["ZERO_DISTANCE"]
-        self.should_classes_combine = config["SHOULD_CLASSES_COMBINE"]
-        print(f"Using zero_distance={self.zero_distance}m")
-
-    @staticmethod
-    def get_default_dataset_config() -> Dict[str, Any]:
-        """Get the default config.
-
-        Returns:
-            dictionary of the default config
-        """
-        default_config = {
-            "GT_TRACKS": None,  # tracker_name -> seq id -> frames
-            "PREDICTED_TRACKS": None,  # tracker_name -> seq id -> frames
-            "SEQ_IDS_TO_EVAL": None,  # list of sequences ids to eval
-            "CLASSES_TO_EVAL": None,
-            "TRACKERS_TO_EVAL": None,
-            "OUTPUT_FOLDER": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
-            "OUTPUT_SUB_FOLDER": "",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
-            "ZERO_DISTANCE": 2,
-            "SHOULD_CLASSES_COMBINE": True,
-        }
-        return default_config
-
-    def _load_raw_file(
-        self, tracker: str, seq_id: Union[str, int], is_gt: bool
-    ) -> Dict[str, Any]:
-        """Get raw track data, from either trackers or ground truth."""
-        tracks = (self.gt_tracks if is_gt else self.predicted_tracks)[tracker][seq_id]
-        source = "gt" if is_gt else "tracker"
-
-        ts = np.array([frame["timestamp_ns"] for frame in tracks])
-        assert np.all(ts[:-1] < ts[1:]), "timestamps are not increasing"
-
-        raw_data = {
-            f"{source}_ids": [frame["track_id"] for frame in tracks],
-            f"{source}_classes": [
-                np.array([self.full_class_list.index(n) for n in frame["name"]])
-                for frame in tracks
-            ],
-            f"{source}_dets": [
-                np.concatenate((frame["translation_m"], frame["size"]), axis=-1)
-                for frame in tracks
-            ],
-            "num_timesteps": len(tracks),
-            "seq": seq_id,
-        }
-        if "score" in tracks[0]:
-            raw_data[f"{source}_confidences"] = [frame["score"] for frame in tracks]
-        return raw_data
-
-    def get_preprocessed_seq_data(
-        self, raw_data: Dict[str, Any], cls: str
-    ) -> Dict[str, Any]:
-        """Filter data to keep only one class and map id to 0 - n.
-
-        Args:
-            raw_data: dictionary of track data
-            cls: name of class to keep
-
-        Returns:
-            Dictionary of processed track data of the specified class
-        """
-        data_keys = [
-            "gt_ids",
-            "tracker_ids",
-            "gt_classes",
-            "tracker_classes",
-            "gt_dets",
-            "tracker_dets",
-            "tracker_confidences",
-            "similarity_scores",
-            "num_timesteps",
-            "seq",
-        ]
-        data = {k: copy(raw_data[k]) for k in data_keys}
-        cls_id = self.full_class_list.index(cls)
-
-        for t in range(raw_data["num_timesteps"]):
-            gt_to_keep_mask = data["gt_classes"][t] == cls_id
-            data["gt_classes"][t] = data["gt_classes"][t][gt_to_keep_mask]
-            data["gt_ids"][t] = data["gt_ids"][t][gt_to_keep_mask]
-            data["gt_dets"][t] = data["gt_dets"][t][gt_to_keep_mask, :]
-
-            tracker_to_keep_mask = data["tracker_classes"][t] == cls_id
-            data["tracker_classes"][t] = data["tracker_classes"][t][
-                tracker_to_keep_mask
-            ]
-            data["tracker_ids"][t] = data["tracker_ids"][t][tracker_to_keep_mask]
-            data["tracker_dets"][t] = data["tracker_dets"][t][tracker_to_keep_mask, :]
-            data["tracker_confidences"][t] = data["tracker_confidences"][t][
-                tracker_to_keep_mask
-            ]
-
-            data["similarity_scores"][t] = data["similarity_scores"][t][
-                :, tracker_to_keep_mask
-            ][gt_to_keep_mask]
-
-        # Map ids to 0 - n.
-        unique_gt_ids = set(chain.from_iterable(data["gt_ids"]))
-        unique_tracker_ids = set(chain.from_iterable(data["tracker_ids"]))
-        data["gt_ids"] = self._map_ids(data["gt_ids"], unique_gt_ids)
-        data["tracker_ids"] = self._map_ids(data["tracker_ids"], unique_tracker_ids)
-
-        data["num_tracker_dets"] = sum(len(dets) for dets in data["tracker_dets"])
-        data["num_gt_dets"] = sum(len(dets) for dets in data["gt_dets"])
-        data["num_tracker_ids"] = len(unique_tracker_ids)
-        data["num_gt_ids"] = len(unique_gt_ids)
-
-        # Ensure again that ids are unique per timestep after preproc.
-        self._check_unique_ids(data, after_preproc=True)
-
-        return data
-
-    def _map_ids(self, ids: List[Any], unique_ids: Iterable[Any]) -> List[NDArrayInt]:
-        id_map = {id: i for i, id in enumerate(unique_ids)}
-        return [
-            np.array([id_map[id] for id in id_array], dtype=int) for id_array in ids
-        ]
-
-    def _calculate_similarities(
-        self, gt_dets_t: NDArrayFloat, tracker_dets_t: NDArrayFloat
-    ) -> NDArrayFloat:
-        """Euclidean distance of the x, y translation coordinates."""
-        gt_xy = gt_dets_t[:, :2]
-        tracker_xy = tracker_dets_t[:, :2]
-        sim = self._calculate_euclidean_similarity(
-            gt_xy, tracker_xy, zero_distance=self.zero_distance
-        )
-        return cast(NDArrayFloat, sim)
 
 
 def _plot_confusion_matrix(
-    gt_classes: NDArrayInt, pred_classes: NDArrayInt, output_dir: str
+    tp:int, fn:int, fp:int, tn:int, title: str = "scenario", output_dir: Union[str, None] = None
 ) -> None:
     """Plots the confusion matrix for scenario mining.
 
     A true label indicates that the scenario matches the description.
     A false label indicates the scenario does not match the description.
     """
-    # Create confusion matrix (2x2 for binary classification)
-    cm = np.zeros((2, 2), dtype=int)
+    if output_dir is None:
+        return
 
-    # Fill the confusion matrix
-    for true, pred in zip(gt_classes, pred_classes):
-        cm[1 - true, 1 - pred] += 1
+    # Create confusion matrix (2x2 for binary classification)
+    cm = np.array([[tp, fn], [fp, tn]])
 
     # Plot the confusion matrix
     fig, ax = plt.subplots(figsize=(4, 4))
@@ -236,7 +72,7 @@ def _plot_confusion_matrix(
     # Set axis labels and ticks
     ax.set_xlabel("Predicted Label")
     ax.set_ylabel("True Label")
-    ax.set_title("Scenario Mining - Description Matches")
+    ax.set_title(f"Prompt Occurences at {title}-level")
     ax.set_xticks([0, 1])
     ax.set_yticks([0, 1])
     ax.set_xticklabels(["Positive", "Negative"])
@@ -245,693 +81,10 @@ def _plot_confusion_matrix(
 
     # Display the plot
     if output_dir:
-        plt.savefig(output_dir + "/eval_cm.png")
+        plt.savefig(output_dir + f"/{title}_level_confusion_matrix.png")
     plt.close()
 
 
-def evaluate_tracking(
-    labels: Sequences,
-    track_predictions: Sequences,
-    classes: List[str],
-    tracker_name: str,
-    output_dir: str,
-    iou_threshold: float = 0.5,
-) -> Tuple[Dict[str, Any], float]:
-    """Evaluate a set of tracks against ground truth annotations using the TrackEval evaluation suite.
-
-    Each sequences/log is evaluated separately.
-
-    Args:
-        labels: Dict[seq_id: List[frame]] Dictionary of ground truth annotations.
-        track_predictions: Dict[seq_id: List[frame]] Dictionary of tracks.
-        classes: List of classes to evaluate.
-        tracker_name: Name of tracker.
-        output_dir: Folder to save evaluation results.
-        iou_threshold: IoU threshold for a True Positive match between a detection to a ground truth bounding box.
-
-    frame is a dictionary with the following format
-        {
-            sequences_id: [
-                {
-                    "timestamp_ns": int, # nano seconds
-                    "track_id": np.ndarray[I],
-                    "translation_m": np.ndarray[I, 3],
-                    "size": np.ndarray[I, 3],
-                    "yaw": np.ndarray[I],
-                    "velocity_m_per_s": np.ndarray[I, 3],
-                    "label": np.ndarray[I],
-                    "score": np.ndarray[I],
-                    "name": np.ndarray[I],
-                    ...
-                }
-            ]
-        }
-    where I is the number of objects in the frame.
-
-    Returns:
-        Dictionary of metric values.
-    """
-    labels_id_ts = set(
-        (frame["seq_id"], frame["timestamp_ns"])
-        for frame in sm_utils.ungroup_frames(labels)
-    )
-    predictions_id_ts = set(
-        (frame["seq_id"], frame["timestamp_ns"])
-        for frame in sm_utils.ungroup_frames(track_predictions)
-    )
-    assert (
-        labels_id_ts == predictions_id_ts
-    ), "sequences ids and timestamp_ns in labels and predictions don't match"
-    metrics_config = {
-        "METRICS": ["HOTA"],
-        "THRESHOLD": iou_threshold,
-    }
-    metric_names = cast(List[str], metrics_config["METRICS"])
-    metrics_list = [
-        getattr(trackeval.metrics, metric)(metrics_config) for metric in metric_names
-    ]
-    dataset_config = {
-        **TrackEvalDataset.get_default_dataset_config(),
-        "GT_TRACKS": {tracker_name: labels},
-        "PREDICTED_TRACKS": {tracker_name: track_predictions},
-        "SEQ_IDS_TO_EVAL": list(labels.keys()),
-        "CLASSES_TO_EVAL": classes,
-        "TRACKERS_TO_EVAL": [tracker_name],
-        "OUTPUT_FOLDER": output_dir,
-        "SHOULD_CLASSES_COMBINE": False,
-    }
-
-    evaluator = trackeval.Evaluator(
-        {
-            **trackeval.Evaluator.get_default_eval_config(),
-            "TIME_PROGRESS": True,
-            "PLOT_CURVES": True,
-            "OUTPUT_SUMMARY": True,
-        }
-    )
-
-    dataset = TrackEvalDataset(dataset_config)
-    full_result, _ = evaluator.evaluate(
-        [dataset],
-        metrics_list,
-    )
-
-    trackers, _, classes = dataset.get_eval_info()
-    tracker = trackers[0]
-
-    tlap_by_seq = np.zeros((len(labels), len(classes)))
-    num_gt_by_seq = np.zeros((len(labels), len(classes)))
-
-    for i, seq_id in enumerate(labels.keys()):
-        raw_data = dataset.get_raw_seq_data(tracker, seq_id)
-        for j, clas in enumerate(classes):
-            data = dataset.get_preprocessed_seq_data(raw_data, clas)
-
-            num_gt_ids = len(data["gt_ids"])
-            tlap, precisions, recalls = calculate_TempLocAP(data)
-            tlap_by_seq[i, j] = tlap
-            num_gt_by_seq[i, j] = num_gt_ids
-
-    combined_tlap = np.average(tlap_by_seq, axis=1, weights=num_gt_by_seq)
-    referred_tlap = float(
-        combined_tlap[np.where(np.array(classes) == "REFERRED_OBJECT")]
-    )
-
-    return cast(Dict[str, Any], full_result), referred_tlap
-
-
-def _tune_score_thresholds(
-    labels: Sequences,
-    track_predictions: Sequences,
-    objective_metric: str,
-    classes: List[str],
-    num_thresholds: int = 10,
-    iou_threshold: float = 0.5,
-    match_distance_m: int = 2,
-) -> Tuple[Dict[str, float], Dict[str, float], Dict[str, float]]:
-    """Find the optimal score thresholds to optimize the objective metric.
-
-    Each class is processed independently.
-
-    Args:
-        labels: Dictionary of ground truth annotations
-        track_predictions: Dictionary of tracks
-        objective_metric: Name of the metric to optimize, one of HOTA or MOTA
-        classes: List of classes to evaluate
-        num_thresholds: Number of score thresholds to try
-        iou_threshold: IoU threshold for a True Positive match between a detection to a ground truth bounding box
-        match_distance_m: Maximum euclidean distance threshold for a match
-
-    Returns:
-        optimal_score_threshold_by_class: Dictionary of class name to optimal score threshold
-        optimal_metric_values_by_class: Dictionary of class name to metric value with the optimal score threshold
-        mean_metric_values_by_class: Dictionary of class name to metric value averaged over recall levels
-    """
-    metric_class = SUBMETRIC_TO_METRIC_CLASS_NAME[objective_metric]
-    metrics_config = {
-        "METRICS": [metric_class],
-        "THRESHOLD": iou_threshold,
-        "PRINT_CONFIG": False,
-    }
-    metrics_list = [
-        getattr(trackeval.metrics, metric_name)(metrics_config)
-        for metric_name in cast(List[str], metrics_config["METRICS"])
-    ]
-    dataset_config = {
-        **TrackEvalDataset.get_default_dataset_config(),
-        "GT_TRACKS": {"tracker": labels},
-        "PREDICTED_TRACKS": {"tracker": track_predictions},
-        "SEQ_IDS_TO_EVAL": list(labels.keys()),
-        "CLASSES_TO_EVAL": classes,
-        "TRACKERS_TO_EVAL": ["tracker"],
-        "OUTPUT_FOLDER": "tmp",
-    }
-    evaluator = trackeval.Evaluator(
-        {
-            **trackeval.Evaluator.get_default_eval_config(),
-            "PRINT_RESULTS": False,
-            "PRINT_CONFIG": False,
-            "TIME_PROGRESS": False,
-            "OUTPUT_SUMMARY": False,
-            "OUTPUT_DETAILED": False,
-            "PLOT_CURVES": False,
-        }
-    )
-
-    score_thresholds_by_class = {}
-    sim_func = partial(_xy_center_similarity, zero_distance=match_distance_m)
-    for name in classes:
-        single_cls_labels = _filter_by_class(labels, name)
-        single_cls_predictions = _filter_by_class(track_predictions, name)
-        score_thresholds_by_class[name] = _calculate_score_thresholds(
-            single_cls_labels,
-            single_cls_predictions,
-            sim_func,
-            num_thresholds=num_thresholds,
-        )
-
-    metric_results = []
-    for threshold_i in tqdm(
-        range(num_thresholds), "calculating optimal track score thresholds"
-    ):
-        score_threshold_by_class = {
-            n: score_thresholds_by_class[n][threshold_i] for n in classes
-        }
-        filtered_predictions = sm_utils.filter_by_class_thresholds(
-            track_predictions, score_threshold_by_class
-        )
-        with contextlib.redirect_stdout(
-            None
-        ):  # silence print statements from TrackEval
-            result_for_threshold, _ = evaluator.evaluate(
-                [
-                    TrackEvalDataset(
-                        {
-                            **dataset_config,
-                            "PREDICTED_TRACKS": {"tracker": filtered_predictions},
-                        }
-                    )
-                ],
-                metrics_list,
-            )
-        metric_results.append(
-            result_for_threshold["TrackEvalDataset"]["tracker"]["COMBINED_SEQ"]
-        )
-
-    optimal_score_threshold_by_class = {}
-    optimal_metric_values_by_class = {}
-    mean_metric_values_by_class = {}
-    for name in classes:
-        metric_values = [
-            r[name][metric_class][objective_metric] for r in metric_results
-        ]
-        metric_values = [
-            np.mean(v) if isinstance(v, np.ndarray) else v for v in metric_values
-        ]
-        optimal_threshold = score_thresholds_by_class[name][np.argmax(metric_values)]
-        optimal_score_threshold_by_class[name] = optimal_threshold
-        optimal_metric_values_by_class[name] = max(0, np.max(metric_values))
-        mean_metric_values_by_class[name] = np.nanmean(
-            np.array(metric_values).clip(min=0)
-        )
-    return (
-        optimal_score_threshold_by_class,
-        optimal_metric_values_by_class,
-        mean_metric_values_by_class,
-    )
-
-
-def _filter_by_class(detections: Any, name: str) -> Any:
-    return sm_utils.group_frames(
-        [
-            sm_utils.index_array_values(f, f["name"] == name)
-            for f in sm_utils.ungroup_frames(detections)
-        ]
-    )
-
-
-def _calculate_score_thresholds(
-    labels: Sequences,
-    predictions: Sequences,
-    sim_func: Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat],
-    num_thresholds: int = 40,
-    min_recall: float = 0.1,
-) -> NDArrayFloat:
-    scores, n_gt = _calculate_matched_scores(labels, predictions, sim_func)
-    recall_thresholds = np.linspace(min_recall, 1, num_thresholds).round(12)[::-1]
-    if len(scores) == 0:
-        return np.zeros_like(recall_thresholds)
-    score_thresholds = _recall_to_scores(
-        scores, recall_threshold=recall_thresholds, n_gt=n_gt
-    )
-    score_thresholds = np.nan_to_num(score_thresholds, nan=0)
-    return score_thresholds
-
-
-def _calculate_matched_scores(
-    labels: Sequences,
-    predictions: Sequences,
-    sim_func: Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat],
-) -> Tuple[NDArrayFloat, int]:
-    scores = []
-    n_gt = 0
-    num_tp = 0
-    for seq_id in labels:
-        for label_frame, prediction_frame in zip(labels[seq_id], predictions[seq_id]):
-            sim = sim_func(
-                label_frame["translation_m"], prediction_frame["translation_m"]
-            )
-            match_rows, match_cols = linear_sum_assignment(-sim)
-            scores.append(prediction_frame["score"][match_cols])
-            n_gt += len(label_frame["translation_m"])
-            num_tp += len(match_cols)
-
-    scores_array = np.concatenate(scores)
-    return scores_array, n_gt
-
-
-def _recall_to_scores(
-    scores: NDArrayFloat, recall_threshold: NDArrayFloat, n_gt: int
-) -> NDArrayFloat:
-    # Sort scores.
-    scores.sort()
-    scores = scores[::-1]
-
-    # Determine thresholds.
-    recall_values = np.arange(1, len(scores) + 1) / n_gt
-    max_recall_achieved = np.max(recall_values)
-    assert max_recall_achieved <= 1
-    score_thresholds = np.interp(recall_threshold, recall_values, scores, right=0)
-
-    # Set thresholds for unachieved recall values to nan to penalize AMOTA/AMOTP later.
-    if isinstance(recall_threshold, np.ndarray):
-        score_thresholds[recall_threshold > max_recall_achieved] = np.nan
-    return score_thresholds
-
-
-def calculate_TempLocAP(
-    data: dict[str, Any],
-) -> Tuple[float, NDArrayFloat, NDArrayFloat]:
-    """Calculates temporal localization average precision."""
-    # Return result quickly if tracker or gt sequence is empty
-    if data["num_tracker_dets"] == 0:
-        if data["num_gt_dets"] == 0:
-            precisions = np.array([1, 1])
-            recalls = np.array([0, 1])
-            TempLocAP = 1.0
-            return TempLocAP, precisions, recalls
-        else:
-            precisions = np.array([0, 0])
-            recalls = np.array([0, 1])
-            TempLocAP = 0.0
-            return TempLocAP, precisions, recalls
-    if data["num_gt_dets"] == 0:
-        precisions = np.array([0, 0])
-        recalls = np.array([0, 1])
-        TempLocAP = 0.0
-        return TempLocAP, precisions, recalls
-
-    TEMPORAL_IOU_THRESH = 0.5  # iou
-    MATCHING_DIST_THRESH = 2.0  # m
-
-    pred_tracks: dict[int, Any] = {}
-    gt_tracks: dict[int, Any] = {}
-
-    # Accumulate predicted and ground truth tracks from data
-    for t in range(data["num_timesteps"]):
-        for i, gt_id in enumerate(data["gt_ids"][t]):
-            if gt_id not in gt_tracks:
-                gt_tracks[gt_id] = {}
-                gt_tracks[gt_id]["xy_pos"] = []
-                gt_tracks[gt_id]["timestamps"] = []
-                gt_tracks[gt_id]["category"] = data["gt_classes"][t][i]
-
-            gt_tracks[gt_id]["xy_pos"].append(data["gt_dets"][t][i][:2])
-            gt_tracks[gt_id]["timestamps"].append(t)
-
-        for i, track_id in enumerate(data["tracker_ids"][t]):
-            if track_id not in pred_tracks:
-                pred_tracks[track_id] = {}
-                pred_tracks[track_id]["confidence"] = data["tracker_confidences"][t][i]
-                pred_tracks[track_id]["category"] = data["tracker_classes"][t][i]
-                pred_tracks[track_id]["xy_pos"] = []
-                pred_tracks[track_id]["timestamps"] = []
-
-            pred_tracks[track_id]["xy_pos"].append(data["tracker_dets"][t][i][:2])
-            pred_tracks[track_id]["timestamps"].append(t)
-
-    # 1 to 1 match of predicted and ground truth tracks
-    pred_ids_by_conf = sorted(
-        pred_tracks.keys(),
-        key=lambda key: pred_tracks[key]["confidence"],
-        reverse=True,
-    )
-
-    # keys are gt_id, values are list of corresponding pred_ids
-    matched_ids = {}
-
-    # keys are track_ids, values are the iou of the timestamps of the matched predicted and ground truth timestamps
-    unmatched_track_ids = []
-
-    for track_id in pred_ids_by_conf:
-        track_stats = pred_tracks[track_id]
-        track_confidence = track_stats["confidence"]
-        track_traj = track_stats["xy_pos"]
-        track_timestamps = track_stats["timestamps"]
-
-        max_similarity = 0
-        best_match = None
-        for gt_id, gt_stats in gt_tracks.items():
-            if gt_stats["category"] != track_stats["category"]:
-                continue
-
-            gt_traj = gt_stats["xy_pos"]
-            gt_timestamps = gt_stats["timestamps"]
-
-            intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
-            iol = intersection / len(track_timestamps)
-
-            if iol < TEMPORAL_IOU_THRESH:
-                continue
-
-            distances = []
-            for timestamp in track_timestamps:
-                if timestamp in gt_timestamps:
-                    distances.append(
-                        np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)]
-                            - gt_traj[gt_timestamps.index(timestamp)]
-                        )
-                    )
-
-            similarity_score = iol * np.maximum(
-                0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-            )
-
-            if similarity_score > max_similarity:
-                max_similarity = similarity_score
-                best_match = gt_id
-
-        if max_similarity > 0:
-            if best_match not in matched_ids:
-                matched_ids[best_match] = [track_id]
-            else:
-                matched_ids[best_match].append(track_id)
-        else:
-            unmatched_track_ids.append(track_id)
-
-    merged_predictions = {}
-    for gt_id, pred_ids in matched_ids.items():
-        merged_traj = []
-        merged_timestamps: list[int] = []
-        merged_confidences = []
-        merged_catetory = None
-
-        for pred_id in pred_ids:
-            track_timestamps = pred_tracks[pred_id]["timestamps"]
-            track_trajectory = pred_tracks[pred_id]["xy_pos"]
-            track_confidence = pred_tracks[pred_id]["confidence"]
-            track_category = pred_tracks[pred_id]["category"]
-
-            if len(merged_timestamps) == 0:
-                merged_timestamps.extend(track_timestamps)
-                merged_traj.extend(track_trajectory)
-                merged_catetory = track_category
-                merged_confidences.extend([track_confidence] * len(track_timestamps))
-                continue
-
-            for i, timestamp in enumerate(track_timestamps):
-                if timestamp not in merged_timestamps:
-                    insertion_index = 0
-                    for merge_timestamp in merged_timestamps:
-                        if merge_timestamp > timestamp:
-                            insertion_index += 1
-
-                    merged_timestamps.insert(insertion_index, timestamp)
-                    merged_traj.insert(insertion_index, track_trajectory[i])
-                    merged_confidences.insert(insertion_index, track_confidence)
-                else:
-                    insertion_index = merged_timestamps.index(timestamp)
-                    if track_confidence > merged_confidences[insertion_index]:
-                        merged_confidences[insertion_index] = track_confidence
-                        merged_traj[insertion_index] = track_trajectory[i]
-
-        merged_predictions[-gt_id - 1] = {
-            "xy_pos": merged_traj,
-            "timestamps": merged_timestamps,
-            "confidence": np.mean(np.array(merged_confidences)),
-            "category": merged_catetory,
-        }
-
-    for unmatched_track_id in unmatched_track_ids:
-        merged_predictions.update({unmatched_track_id: pred_tracks[unmatched_track_id]})
-
-    merged_ids_by_conf = sorted(
-        merged_predictions.keys(),
-        key=lambda key: merged_predictions[key]["confidence"],
-        reverse=True,
-    )
-    matched_gt_ids = []
-
-    # Compute precision and recall at all confidence thresholds.
-    tp = np.zeros(len(merged_ids_by_conf))
-    fp = np.zeros(len(merged_ids_by_conf))
-
-    for i, track_id in enumerate(merged_ids_by_conf):
-        track_stats = merged_predictions[track_id]
-        track_confidence = track_stats["confidence"]
-        track_traj = track_stats["xy_pos"]
-        track_timestamps = track_stats["timestamps"]
-
-        max_similarity = 0
-        best_match = None
-        for gt_id, gt_stats in gt_tracks.items():
-            if (
-                gt_id in matched_gt_ids
-                or gt_stats["category"] != track_stats["category"]
-            ):
-                continue
-
-            gt_traj = gt_stats["xy_pos"]
-            gt_timestamps = gt_stats["timestamps"]
-
-            intersection = len(set(gt_timestamps).intersection((set(track_timestamps))))
-            union = len(set(gt_timestamps).union((set(track_timestamps))))
-            iou = intersection / union
-
-            if iou < TEMPORAL_IOU_THRESH:
-                continue
-
-            distances = []
-            for timestamp in track_timestamps:
-                if timestamp in gt_timestamps:
-                    distances.append(
-                        np.linalg.norm(
-                            track_traj[track_timestamps.index(timestamp)]
-                            - gt_traj[gt_timestamps.index(timestamp)]
-                        )
-                    )
-
-            similarity_score = iou * np.maximum(
-                0, 1 - (np.mean(np.array(distances)) / MATCHING_DIST_THRESH)
-            )
-
-            if similarity_score > max_similarity:
-                max_similarity = similarity_score
-                best_match = gt_id
-
-        if max_similarity > 0:
-            matched_gt_ids.append(best_match)
-            tp[i] = 1
-        else:
-            fp[i] = 1
-
-    tp = np.cumsum(tp)
-    fp = np.cumsum(fp)
-
-    recalls = tp / len(gt_tracks)
-    precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
-
-    assert np.all(0 <= precisions) & np.all(precisions <= 1)
-    TempLocAP = get_ap(recalls, precisions)
-
-    return TempLocAP, precisions, recalls
-
-
-def _get_envelope(precisions: NDArrayFloat) -> NDArrayFloat:
-    """Compute the precision envelope.
-
-    Args:
-        precisions: A set of precision values <= 1
-
-    Returns:
-        The monotonically non-increasing precision values
-
-    """
-    for i in range(precisions.size - 1, 0, -1):
-        precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
-    return precisions
-
-
-def get_ap(recalls: NDArrayFloat, precisions: NDArrayFloat) -> float:
-    """Calculate average precision.
-
-    Args:
-        recalls: Array of recall values
-        precisions: Array of precision values
-
-    Returns:
-        float: average precision.
-    """
-    # first append sentinel values at the end
-    recalls = np.concatenate(([0.0], recalls, [1.0]))
-    precisions = np.concatenate(([0.0], precisions, [0.0]))
-
-    # get envelope (maximum precision for each recall value)
-    precisions = _get_envelope(precisions)
-
-    # to calculate area under PR curve, look for points where X axis (recall) changes value
-    i = np.where(recalls[1:] != recalls[:-1])[0]
-
-    # and sum (\Delta recall) * prec
-    ap = float(np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1]))
-
-    return ap
-
-
-def plot_precision_recall_curve(
-    recalls_list: list[NDArrayFloat],
-    precisions_list: list[NDArrayFloat],
-    ap_values: Union[list[float], None] = None,
-    labels: Union[list[str], None] = None,
-    colors: list[str] = ["blue", "green"],
-    save_path: Union[str, None] = None,
-) -> None:
-    """Plot precision-recall curves for one or two sets of data.
-
-    Args:
-        recalls_list: List of recall arrays to plot
-        precisions_list: List of precision arrays to plot
-        ap_values: Optional list of AP values to display in the title
-        labels: Optional list of labels for the legend
-        colors: List of colors for the plots (default: blue and green)
-        save_path: Optional path to save the plot
-    """
-    plt.figure(figsize=(8, 6))
-
-    if not isinstance(recalls_list, list):
-        recalls_list = [recalls_list]
-    if not isinstance(precisions_list, list):
-        precisions_list = [precisions_list]
-
-    if labels is None:
-        labels = [f"Curve {i+1}" for i in range(len(recalls_list))]
-
-    for i, (recalls, precisions) in enumerate(zip(recalls_list, precisions_list)):
-        color = colors[i % len(colors)]
-
-        # Prepare data for plotting (add sentinel values)
-        plot_recalls = np.concatenate(([0.0], recalls, [1.0]))
-        plot_precisions = np.concatenate(([0.0], precisions, [0.0]))
-        plot_precisions = _get_envelope(plot_precisions.copy())
-
-        # Plot the curve
-        plt.plot(
-            plot_recalls,
-            plot_precisions,
-            color=color,
-            linestyle="-",
-            linewidth=2,
-            label=labels[i],
-        )
-        plt.fill_between(plot_recalls, 0, plot_precisions, alpha=0.1, color=color)
-
-    # Set title
-    if ap_values:
-        ap_text = ", ".join(
-            [f"{label}: AP = {ap:.3f}" for label, ap in zip(labels, ap_values)]
-        )
-        plt.title(f"Precision-Recall Curves ({ap_text})", fontsize=16)
-    else:
-        plt.title("Precision-Recall Curves", fontsize=16)
-
-    # Set labels and limits
-    plt.xlabel("Recall", fontsize=14)
-    plt.ylabel("Precision", fontsize=14)
-    plt.xlim([0.0, 1.0])
-    plt.ylim([0.0, 1.05])
-    plt.grid(True)
-
-    # Add legend if multiple curves
-    if len(recalls_list) > 1:
-        plt.legend(loc="lower left")
-
-    # Save if path provided
-    if save_path:
-        plt.savefig(save_path, dpi=300, bbox_inches="tight")
-
-    plt.show()
-
-
-def _xy_center_similarity(
-    centers1: NDArrayFloat, centers2: NDArrayFloat, zero_distance: float
-) -> NDArrayFloat:
-    if centers1.size == 0 or centers2.size == 0:
-        return np.zeros((len(centers1), len(centers2)))
-    xy_dist = np.linalg.norm(
-        centers1[:, np.newaxis, :2] - centers2[np.newaxis, :, :2], axis=2
-    )
-    sim = np.maximum(0, 1 - xy_dist / zero_distance)
-    return cast(NDArrayFloat, sim)
-
-
-def filter_max_dist(tracks: Any, max_range_m: int) -> Any:
-    """Remove all tracks that are beyond the max_dist.
-
-    Args:
-        tracks: Dict[seq_id: List[frame]] Dictionary of tracks
-        max_range_m: maximum distance from ego-vehicle
-
-    Returns:
-        tracks: Dict[seq_id: List[frame]] Dictionary of tracks.
-    """
-    frames = sm_utils.ungroup_frames(tracks)
-    return sm_utils.group_frames(
-        [
-            sm_utils.index_array_values(
-                frame,
-                np.linalg.norm(
-                    frame["translation_m"][:, :2]
-                    - np.array(frame["ego_translation_m"])[:2],
-                    axis=1,
-                )
-                <= max_range_m,
-            )
-            for frame in frames
-        ]
-    )
-
-
 def load(pkl_path: Path) -> Any:
     """Loads a pkl file as a dict."""
     with open(pkl_path, "rb") as f:
@@ -940,19 +93,6 @@ def load(pkl_path: Path) -> Any:
     return data
 
 
-def yaw_to_quaternion3d(yaw: float) -> NDArrayFloat:
-    """Convert a rotation angle in the xy plane (i.e. about the z axis) to a quaternion.
-
-    Args:
-        yaw: angle to rotate about the z-axis, representing an Euler angle, in radians
-
-    Returns:
-        array w/ quaternion coefficients (qw,qx,qy,qz) in scalar-first order, per Argoverse convention.
-    """
-    qx, qy, qz, qw = Rotation.from_euler(seq="z", angles=yaw, degrees=False).as_quat()
-    return np.array([qw, qx, qy, qz])
-
-
 def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Sequences:
     """Convert the unified label format to a format that is easier to work with for forecasting evaluation.
 
@@ -977,15 +117,13 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
         avm = log_id_to_avm[log_id]
 
         for frame in tracks[log_prompt_pairs[i]]:
-            timestamp_ns = frame["timestamp_ns"]
-            city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)]
-            translation_m = frame["translation_m"] - frame["ego_translation_m"]
+            translation_m = frame["translation_m"]
             size = frame["size"]
             quat = np.array([yaw_to_quaternion3d(yaw) for yaw in frame["yaw"]])
             score = np.ones((translation_m.shape[0], 1))
             boxes = np.concatenate([translation_m, size, quat, score], axis=1)
 
-            is_evaluated = compute_objects_in_roi_mask(boxes, city_SE3_ego, avm)
+            is_evaluated = compute_objects_in_roi_mask(boxes, avm)
 
             frame["translation_m"] = frame["translation_m"][is_evaluated]
             frame["size"] = frame["size"][is_evaluated]
@@ -1016,6 +154,36 @@ def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Seque
     return tracks
 
 
+def compute_objects_in_roi_mask(
+    cuboids_city: NDArrayFloat, avm: ArgoverseStaticMap
+) -> NDArrayBool:
+    """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI.
+
+    Args:
+        cuboids_city: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`.
+        city_SE3_ego: Egovehicle pose in the city reference frame.
+        avm: Argoverse map object.
+
+    Returns:
+        (N,) Boolean mask indicating which cuboids will be evaluated.
+    """
+    is_within_roi: NDArrayBool
+    if len(cuboids_city) == 0:
+        is_within_roi = np.zeros((0,), dtype=bool)
+        return is_within_roi
+    cuboid_list_city: CuboidList = CuboidList(
+        [Cuboid.from_numpy(params) for params in cuboids_city]
+    )
+    cuboid_list_vertices_m_city = cuboid_list_city.vertices_m
+
+    is_within_roi = avm.get_raster_layer_points_boolean(
+        cuboid_list_vertices_m_city.reshape(-1, 3)[..., :2], RasterLayerType.ROI
+    )
+    is_within_roi = is_within_roi.reshape(-1, 8)
+    is_within_roi = is_within_roi.any(axis=1)
+    return is_within_roi
+
+
 def referred_full_tracks(sequences: Sequences) -> Sequences:
     """Expands the predicted partial tracks to the whole length of the track_id.
 
@@ -1060,7 +228,7 @@ def referred_full_tracks(sequences: Sequences) -> Sequences:
     return reconstructed_sequences
 
 
-def evaluate_mining(
+def compute_temporal_metrics(
     track_predictions: Sequences, labels: Sequences, output_dir: str
 ) -> tuple[float, float]:
     """Calculates the F1 score.
@@ -1069,41 +237,64 @@ def evaluate_mining(
     the ground-truth and predictions sequences contains no tracks
     or both contain at least one track corresponding to the prediction.
     """
-    gt_class = np.zeros(len(labels), dtype=np.int64)
-    pred_class = np.zeros(len(labels), dtype=np.int64)
+    scenario_gt = np.zeros(len(labels), dtype=bool)
+    scenario_pred = np.zeros(len(labels), dtype=bool)
+
+    timestamp_tp, timestamp_fp, timestamp_fn, timestamp_tn = 0, 0, 0, 0
 
     for i, description in enumerate(labels.keys()):
 
-        for frame in labels[description]:
-            if len(frame["label"]) > 0 and 0 in frame["label"]:
-                gt_class[i] = 1
-                break
+        timestamp_gt = np.zeros(len(labels[description]), dtype=bool)
+        timestamp_pred = np.zeros(len(labels[description]), dtype=bool)
 
-        for frame in track_predictions[description]:
+        for j, frame in enumerate(labels[description]):
             if len(frame["label"]) > 0 and 0 in frame["label"]:
-                pred_class[i] = 1
-                break
-
-    tp = np.sum(gt_class & pred_class)
-    fp = np.sum(~gt_class & pred_class)
-    fn = np.sum(gt_class & ~pred_class)
-
-    f1_score = float(2 * tp / (2 * tp + fp + fn))
+                timestamp_gt[j] = True
+                scenario_gt[i] = True
 
-    print(f"GT scenario matches: {gt_class}")
-    print(f"Predicted scenario matches: {pred_class}")
-    print(f"F1: {f1_score}")
+        for j, frame in enumerate(track_predictions[description]):
+            if len(frame["label"]) > 0 and 0 in frame["label"]:
+                timestamp_pred[j] = True
+                scenario_pred[i] = True
+
+        timestamp_tp += np.sum(timestamp_gt & timestamp_pred)
+        timestamp_fp += np.sum(~timestamp_gt & timestamp_pred)
+        timestamp_fn += np.sum(timestamp_gt & ~timestamp_pred)
+        timestamp_tn += np.sum(~timestamp_gt & ~timestamp_pred)
+
+    scenario_tp = np.sum(scenario_gt & scenario_pred)
+    scenario_fp = np.sum(~scenario_gt & scenario_pred)
+    scenario_fn = np.sum(scenario_gt & ~scenario_pred)
+    scenario_tn = np.sum(~scenario_gt & ~scenario_pred)
+
+    scenario_f1 = float(2 * scenario_tp / (2 * scenario_tp + scenario_fp + scenario_fn))
+    timestamp_f1 = float(
+        2 * timestamp_tp / (2 * timestamp_tp + timestamp_fp + timestamp_fn)
+    )
 
-    _plot_confusion_matrix(gt_class, pred_class, output_dir)
+    _plot_confusion_matrix(
+        scenario_tp,
+        scenario_fn,
+        scenario_fp,
+        scenario_tn,
+        title="scenario",
+        output_dir=output_dir,
+    )
+    _plot_confusion_matrix(
+        timestamp_tp,
+        timestamp_fn,
+        timestamp_fp,
+        timestamp_tn,
+        title="timestamp",
+        output_dir=output_dir,
+    )
 
     num_correct = 0
-    for i in range(len(gt_class)):
-        if gt_class[i] == pred_class[i]:
+    for i in range(len(scenario_gt)):
+        if scenario_gt[i] == scenario_pred[i]:
             num_correct += 1
 
-    acc = num_correct / len(labels)
-
-    return f1_score, acc
+    return scenario_f1, timestamp_f1
 
 
 def _relabel_seq_ids(data: Sequences) -> Sequences:
@@ -1150,12 +341,10 @@ def evaluate(
         tlap: Temporal localization average precision calculates how well predictions are temporally localized,
             with some built in give for ambiguous annotations
     """
-    output_dir = ""
-    if out:
-        output_dir = out + "/partial_tracks"
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
+    output_dir = out + "/partial_tracks"
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
 
-    res, partial_track_metrics, TempLocAP, f1_score = evaluate_scenario_mining(
+    res, partial_track_metrics, timestamp_f1, scenario_f1 = evaluate_scenario_mining(
         track_predictions,
         labels,
         objective_metric=objective_metric,
@@ -1167,10 +356,8 @@ def evaluate(
     full_track_preds = referred_full_tracks(track_predictions)
     full_track_labels = referred_full_tracks(labels)
 
-    output_dir = ""
-    if out:
-        output_dir = out + "/full_tracks"
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
+    output_dir = out + "/full_tracks"
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
 
     _, full_track_metrics, _, _ = evaluate_scenario_mining(
         full_track_preds,
@@ -1185,10 +372,12 @@ def evaluate(
     full_track_hota = full_track_metrics["REFERRED_OBJECT"]
     partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
 
-    print(
-        f"F1: {f1_score}, HOTA: {partial_track_hota}, HOTA_full: {full_track_hota}, TLAP: {TempLocAP}"
+    return (
+        partial_track_hota,
+        full_track_hota,
+        timestamp_f1,
+        scenario_f1,
     )
-    return f1_score, full_track_hota, partial_track_hota, TempLocAP
 
 
 def evaluate_scenario_mining(
@@ -1216,7 +405,7 @@ def evaluate_scenario_mining(
     Returns:
         Dictionary of per-category metrics.
     """
-    classes = list(constants.AV2_CATEGORIES)
+    classes = list(AV2_CATEGORIES)
 
     labels = filter_max_dist(labels, max_range_m)
     track_predictions = filter_max_dist(track_predictions, max_range_m)
@@ -1228,18 +417,19 @@ def evaluate_scenario_mining(
     track_predictions = _relabel_seq_ids(track_predictions)
     labels = _relabel_seq_ids(labels)
 
-    score_thresholds, tuned_metric_values, mean_metric_values = _tune_score_thresholds(
+    score_thresholds, tuned_metric_values, _ = _tune_score_thresholds(
         labels,
         track_predictions,
         objective_metric,
         classes,
-        num_thresholds=3,
+        num_thresholds=10,
         match_distance_m=2,
     )
     filtered_track_predictions = sm_utils.filter_by_class_thresholds(
         track_predictions, score_thresholds
     )
-    res, tlap = evaluate_tracking(
+
+    res = evaluate_tracking(
         labels,
         filtered_track_predictions,
         classes,
@@ -1248,10 +438,12 @@ def evaluate_scenario_mining(
     )
 
     if not full_tracks:
-        f1_score, acc = evaluate_mining(filtered_track_predictions, labels, out)
-        return res, tuned_metric_values, tlap, f1_score
+        scenario_f1, timestamp_f1 = compute_temporal_metrics(
+            filtered_track_predictions, labels, out
+        )
+        return res, tuned_metric_values, scenario_f1, timestamp_f1
 
-    return res, tuned_metric_values, tlap, 0
+    return res, tuned_metric_values, 0, 0
 
 
 @click.command()
@@ -1282,14 +474,14 @@ def runner(
     track_predictions = pickle.load(open(predictions, "rb"))
     labels = pickle.load(open(ground_truth, "rb"))
 
-    _, _, mean_metric_values, _ = evaluate_scenario_mining(
+    partial_track_hota, full_track_hota, timestamp_f1, scenario_f1 = evaluate(
         track_predictions, labels, objective_metric, max_range_m, dataset_dir, out
     )
 
-    pprint(mean_metric_values)
-
-    with open(out, "w") as f:
-        json.dump(mean_metric_values, f, indent=4)
+    print(f"Temporally-localized HOTA: {partial_track_hota:.2f}")
+    print(f"Full-lifespan HOTA: {full_track_hota:.2f}")
+    print(f"Timestamp-level F1: {timestamp_f1:.2f}")
+    print(f"Scenario-level F1: {scenario_f1:.2f}")
 
 
 if __name__ == "__main__":
diff --git a/src/av2/evaluation/scenario_mining/utils.py b/src/av2/evaluation/scenario_mining/utils.py
deleted file mode 100644
index 49605583..00000000
--- a/src/av2/evaluation/scenario_mining/utils.py
+++ /dev/null
@@ -1,177 +0,0 @@
-"""Tracking evaluation utilities.
-
-Detection and track data in a single frame are kept as a dictionary of names to numpy arrays.
-This module provides helper functions for manipulating this data format.
-"""
-
-import os
-import pickle
-from collections import defaultdict
-from itertools import chain
-from typing import Any, Dict, Iterable, List, Union, cast
-
-import numpy as np
-
-from av2.utils.typing import NDArrayInt
-
-from ..typing import Frame, Frames, Sequences
-
-
-def save(obj: Any, path: str) -> None:  # noqa
-    """Save an object to a file using pickle serialization.
-
-    Args:
-        obj: An object to be saved.
-        path: A string representing the file path to save the object to.
-    """
-    dir = os.path.dirname(path)
-    if dir != "":
-        os.makedirs(dir, exist_ok=True)
-    with open(path, "wb") as f:
-        pickle.dump(obj, f)
-
-
-def load(path: str) -> Any:  # noqa
-    """Load an object from file using pickle module.
-
-    Args:
-        path: File path.
-
-    Returns:
-        Object or None if the file does not exist.
-    """
-    if not os.path.exists(path):
-        return None
-    with open(path, "rb") as f:
-        return pickle.load(f)
-
-
-def annotate_frame_metadata(
-    prediction_frames: Frames, label_frames: Frames, metadata_keys: List[str]
-) -> None:
-    """Copy annotations with provided keys from label to prediction frames.
-
-    Args:
-        prediction_frames: list of prediction frames
-        label_frames: list of label frames
-        metadata_keys: keys of the annotations to be copied.
-    """
-    assert len(prediction_frames) == len(label_frames)
-    for prediction, label in zip(prediction_frames, label_frames):
-        for key in metadata_keys:
-            prediction[key] = label[key]
-
-
-def group_frames(frames_list: Frames) -> Sequences:
-    """Group list of frames into dictionary by sequence id.
-
-    Args:
-        frames_list: List of frames, each containing a detections snapshot at timestamp_ns.
-
-    Returns:
-        Dictionary of frames indexed by sequence id.
-    """
-    frames_by_seq_id = defaultdict(list)
-    sorted_frames_list = sorted(frames_list, key=lambda f: cast(int, f["timestamp_ns"]))
-    for frame in sorted_frames_list:
-        frames_by_seq_id[frame["seq_id"]].append(frame)
-    return dict(frames_by_seq_id)
-
-
-def ungroup_frames(frames_by_seq_id: Sequences) -> Frames:
-    """Ungroup dictionary of frames into a list of frames.
-
-    Args:
-        frames_by_seq_id: dictionary of frames
-
-    Returns:
-        List of frames
-    """
-    return list(chain.from_iterable(frames_by_seq_id.values()))
-
-
-def index_array_values(array_dict: Frame, index: Union[int, NDArrayInt]) -> Frame:
-    """Index each numpy array in dictionary.
-
-    Args:
-        array_dict: dictionary of numpy arrays
-        index: index used to access each numpy array in array_dict
-
-    Returns:
-        Dictionary of numpy arrays, each indexed by the provided index
-    """
-    return {
-        k: v[index] if isinstance(v, np.ndarray) else v for k, v in array_dict.items()
-    }
-
-
-def array_dict_iterator(array_dict: Frame, length: int) -> Iterable[Frame]:
-    """Get an iterator over each index in array_dict.
-
-    Args:
-        array_dict: dictionary of numpy arrays
-        length: number of elements to iterate over
-
-    Returns:
-        Iterator, each element is a dictionary of numpy arrays, indexed from 0 to (length-1)
-    """
-    return (index_array_values(array_dict, i) for i in range(length))
-
-
-def concatenate_array_values(array_dicts: Frames) -> Frame:
-    """Concatenates numpy arrays in list of dictionaries.
-
-    Handles inconsistent keys (will skip missing keys)
-    Does not concatenate non-numpy values (int, str), sets to value if all values are equal
-
-    Args:
-        array_dicts: list of dictionaries
-
-    Returns:
-        single dictionary of names to numpy arrays
-    """
-    combined = defaultdict(list)
-    for array_dict in array_dicts:
-        for k, v in array_dict.items():
-            combined[k].append(v)
-    concatenated = {}
-    for k, vs in combined.items():
-        if all(isinstance(v, np.ndarray) for v in vs):
-            if any(v.size > 0 for v in vs):
-                concatenated[k] = np.concatenate([v for v in vs if v.size > 0])
-            else:
-                concatenated[k] = vs[0]
-        elif all(vs[0] == v for v in vs):
-            concatenated[k] = vs[0]
-    return concatenated
-
-
-def filter_by_class_thresholds(
-    frames_by_seq_id: Sequences, thresholds_by_class: Dict[str, float]
-) -> Sequences:
-    """Filter detections, keeping only detections with score higher than the provided threshold for that class.
-
-    If a class threshold is not provided, all detections in that class is filtered.
-
-    Args:
-        frames_by_seq_id: Dictionary of frames
-        thresholds_by_class: Dictionary containing the score thresholds for each class
-
-    Returns:
-        Dictionary of frames, filtered by class score thresholds
-    """
-    frames = ungroup_frames(frames_by_seq_id)
-    return group_frames(
-        [
-            concatenate_array_values(
-                [
-                    index_array_values(
-                        frame,
-                        (frame["name"] == class_name) & (frame["score"] >= threshold),
-                    )
-                    for class_name, threshold in thresholds_by_class.items()
-                ]
-            )
-            for frame in frames
-        ]
-    )
diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb
index 179f1534..1eb100a5 100644
--- a/tutorials/map_tutorial.ipynb
+++ b/tutorials/map_tutorial.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "f25e1e66",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "f919b29b",
    "metadata": {},
    "outputs": [],
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "15a8196d",
    "metadata": {},
    "outputs": [],
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "3a02b3b8",
    "metadata": {},
    "outputs": [],
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "8d5ae748",
    "metadata": {},
    "outputs": [],
@@ -127,17 +127,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "4e27fe09",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "single_log_teaser(args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "56a98bd9",
    "metadata": {},
    "outputs": [],
@@ -179,17 +190,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "2bdc3926",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8vNz6vf/++wMwfCvE8+TbeXtLly7Ftm3bcM455zj6r+s6pk+fjuXLl6O1tTWw7XyeYfe4lw3u8i+//DIYY/jRj37kaLO+vh4HHnig1ebee++N6upqXH311XjooYewevXqjLrfeOMNAMjw8j/llFNQXl5e0JibL9544w0ccMABGD9+vGP7ueeeCyKy+swxY8YMqKpq/Q56rwcMGID169d3Qq97JorqVFhbW4vS0lLPC//UU0+hra0NGzduzBiIAaCsrAyVlZWObVu3bkV9fX3GYDVgwABEIhFs3bo1777269fP8TsejwMA2tvbrbYjkUhGufr6+lD1X3vttSgvL8eTTz6Jhx56CKqq4vDDD8dtt92GcePG5dXngQMHhi67YMECnHrqqTjllFPw85//HPX19YhEInjwwQfxhz/8wSq3ZcsWqKoaeF6bNm0CAHz729/23K8o4XhltmteSDv8Wairq3Ns97qHYfuTK/hxJSUlGfv+85//WAMTEeHhhx/G73//e4twHnnkkZaA4qirq8OWLVtCtT1ixAgAsJxLx4wZg5/+9Kf48MMPAdjnunXr1oxrtG3bNtTU1ATWv2XLFofw9sKmTZuwY8cOhyOxiG+++cbx2+u+xOPxvK8/h/tceH/8tnd0dACwnz/3BETEtm3bHGTDjVyfYa9xLxvc48CmTZtARBn3lWP48OEADEe6JUuW4Oabb8Z1112H7du3Y+DAgbjgggvwi1/8AtFo1Br3+vfv76iDMYb6+vqCxtx8sXXrVs/w0kGDBln7ReTyXpeUlBT8vO1OKCohUFUVRx11FBYuXIiNGzc6HtwDDjgAADzjiQHvGUq/fv3wzjvvgIgc+zdv3ox0Om15YfMBOJFIOI4vlDCk02ls3brV8YA1NjaGOj4SiWDWrFmYNWsWduzYgddffx3XXXcdjjnmGKxbtw5lZWUoKSnJ6DOQOXBy5OIJ++STT2LYsGF45plnHMe52+vfvz80TUNjY6Mv4eDX+U9/+hOGDBkSug+5opB2+D3atGkT9thjD2s7v4ddAd6Hbdu2ZexLpVLWc7plyxa0trbi4IMPtvarqprhXf3111+HijRwIxKJ4OCDD8azzz5rbRs1ahQA4F//+pf1LgLG9fn4449x+umnB9bZv39/fP3114Flamtr0a9fP7z66que+ysqKsKewi4Bv9b33nsvDj30UM8yfkLXXUfYZzgf73b3MbW1tWCM4f/+7/8s4SdC3DZq1CjMnz8fRISVK1fisccew0033YTS0lJcc8011ri3ZcsWBykgIjQ2NvoSHcB/HPYbz8KiX79+2LhxY8b2DRs2AEBe7wjHtm3bCjp+d0PRTQbXXnstNE3DxRdfjFQqVVBdU6ZMQUtLC1588UXH9scff9zaDxgvaUlJCVauXOko9+c//znvtidPngwAmDdvnmP7U089lXNdffv2xcknn4zLLrsM27Zts0jR0KFDsXnzZmtWAQDJZBKvvfZa3v3mYIwhFos5Bo/GxsaMa8JDzR588EHfuo455hhEIhF89tlnGDdunOenGCikncMPPxwA8Mwzzzi2/+lPf0I6nc67T7nMWGOxGIYPH47PPvssY9/gwYPxn//8B4AxU41Goxnk+IsvvrC+L168GE1NTZgwYULOfe7o6MDbb7+Nvffe29p2yCGHYODAgRlJlv70pz+hpaUF3//+9wPrPPbYY/Gf//wnQz0r4vjjj8fWrVuhaZrnvdtvv/1yPpdCtTa5YNKkSejbty9Wr17t+/xxrYJfv7rqXRFx/PHHg4iwfv16z/Y4GRTBGMOBBx6Iu+66C3379sX7778PwB5Tn3zySUf5559/Hq2trdZ+L/BZvHsc/t///d+Msrnc1ylTpmD16tVWHzkef/xxMMassToffP755w6C3NtR9DwEkyZNwv33348rrrgCBx98MC688EKMGDECiqJg48aNeP755wEglJrs7LPPxv33349zzjkHa9euxahRo/DWW2/hlltuwXHHHWfZM7n97A9/+AP22msvHHjggXj33XfzEt4c06ZNw+GHH46rrroKra2tGDduHP7xj3/giSeeCHX8d7/7XYwcORLjxo1D//798eWXX+Luu+/GkCFDsM8++wAAfvjDH+KGG27Aaaedhp///Ofo6OjAf/3Xf0HTtLz7zXH88cdjwYIFuPTSS3HyySdj3bp1+NWvfoWBAwfik08+scp95zvfwVlnnYVf//rX2LRpE44//njE43F88MEHKCsrwxVXXIGhQ4fipptuwvXXX4/PP/8c06dPR3V1NTZt2oR3330X5eXluPHGGwvucyHtjBgxAqeffjruvPNOS1O1atUq3Hnnnaiqqgpt1nCDz6ieeeYZDB8+HCUlJZ4DLMeRRx5p+WWImDZtGubPn4+TTjoJkUgE3/ve93DVVVdh4MCBGDx4MP7whz9g+fLl2GuvvfCnP/0Jl1xyCW6++eass+qJEyfihBNOwP7774+qqiqsXbsWDz74ID777DO88MILVjlVVXH77bfjrLPOwkUXXYTTTz8dn3zyCa666ipMnToV06dPD2xn5syZeOaZZ3DiiSfimmuuwfjx49He3o4lS5bg+OOPx+TJk3Haaadh3rx5OO644/DTn/4U48ePRzQaxddff42//e1vOPHEE/G9730vyxV3YuTIkQCAhx9+GBUVFSgpKcGwYcN8zUCFoE+fPrj33ntxzjnnYNu2bTj55JMxYMAAbNmyBR9++CG2bNliEWf+DNxzzz0455xzEI1Gsd9++3XZuyJi0qRJuPDCC/HjH/8Y7733Hg4//HCUl5dj48aNeOuttzBq1ChccsklePnll/HAAw/gpJNOwvDhw0FEWLBgAXbs2IGpU6cCAKZOnYpjjjkGV199NZqbmzFp0iSsXLkSc+bMwZgxY3DWWWf59qO+vh5HH300br31VlRXV2PIkCFYvHgxFixYkFGWX7/bbrsNxx57LFRVxejRoz3NTT/72c/w+OOPY8aMGbjpppswZMgQ/OUvf8EDDzyASy65BPvuu29e123r1q345JNPcMUVVzi2cyL96aef5lVvj0ZneSv+85//pB//+Mc0bNgwisfjVFJSQnvvvTedffbZGXGfPB7XC1u3bqWLL76YBg4cSJFIhIYMGULXXnutI/yFyM4ZUFdXR+Xl5fTd736X1q5d6xtlsGXLFsfx3PNc9BresWMH/eQnP6G+fftSWVkZTZ06lT7++ONQUQZ33nknTZw4kWpraykWi9HgwYPpvPPOy8gi+Ne//pUOOuggKi0tpeHDh9N9990XmIfAC35RBr/5zW9o6NChFI/Haf/996f//u//9qxb0zS66667aOTIkRSLxaiqqoomTJhAL730kqPciy++SJMnT6bKykqKx+M0ZMgQOvnkkx0x2l7wywzo55Ucpp2gPAQDBgygkpISK/64qqqKfvazn+XVn7Vr19K0adOooqIiVB6CxYsXEwB69913Hds/+eQTisfjVt2NjY102GGHWZEB3/72t+nCCy8kADRs2LCM0FA/zJ49mw488ECqqqqiSCRC9fX19L3vfY/+8Y9/eJZ/6qmnaPTo0RSLxai+vp7+3//7f7Rz585QbW3fvp1++tOf0uDBgykajdKAAQNoxowZ9PHHH1tlUqkU3XHHHVY8fJ8+fehb3/oWXXTRRY6Yf79n1iv73d13303Dhg0jVVVD5yEQIcani+D3+7nnnnNsX7JkCc2YMYNqamooGo3SHnvsQTNmzMgod+2119KgQYNIUZSM5ybMMxw07nnBb+zi+MMf/kCHHHIIlZeXU2lpKe2111509tlnW+GnH3/8MZ1++um01157UWlpKVVVVdH48ePpsccec9TT3t5OV199NQ0ZMoSi0SgNHDiQLrnkkqx5CIiINm7cSCeffDLV1NRQVVUV/ehHP7KiG8Qog0QiQeeffz7179+fGGOh8hCcccYZ1K9fP4pGo7TffvvRb3/7W988BG54jdmPPPIIRaNRKyyTY8iQIVnf890VjIioi7iHhESXY+nSpZg0aRLmzZsXOkKkUIwePRqTJk3KMMPceeeduPnmm7FgwQIrB/zXX3+Njo4O7L333ti0aROSySQaGhq6pJ8SEr0Z3/nOdzB48OAMs3BvhiQEErsNFi1ahGXLlmHs2LEoLS3Fhx9+iN/85jeoqqrCypUrPb3/OwOvvvoqvve97+GTTz7JSK98++2345e//CVOOeUUnH322RgzZgwqKiqwefNm/OMf/8Cjjz6KQYMG5bygkoSERHj8/e9/x7Rp07B69WorCkNCEgKJ3QjvvPMOZs+ejdWrV2Pnzp2ora3FMcccg1tvvTWnkM1i4L777sOBBx6I73znOxn7Vq5ciZtvvhmvvPIKdu7caW0fNmwYfvzjH2PmzJnd3iNfQqIn44UXXkAqlcrIW9LbIQmBhMQuQiqVwtdff42dO3eirq4ua0ibhISERGeiyzIVeuGBBx7AsGHDUFJSgrFjx+L//u//dmV3JCS6FNFoFMOGDcPo0aMlGZCQkNjl2GWE4JlnnsHMmTNx/fXX44MPPsB3vvMdHHvssfjqq692VZckJCQkJCR6LXaZyeCQQw7BwQcf7PDE3n///XHSSSfh1ltv3RVdkpCQkJCQ6LUoemKiMEgmk1ixYgWuueYax/Zp06Zh6dKlGeUTiYQjHaau69i2bRv69euXV+pPCQkJCYmeCSLCzp07MWjQIEfCsY6ODiSTyaK0EYvFuiwqqTthlxCCb775BpqmZdhN6+rqPNcKuPXWW4ue3UtCQkJCoudi3bp1VlhvR0cHhg3pg8bNhWd5BYysi1988UWvIwW7hBBwuGf35FrEiOPaa6/FrFmzrN9NTU0YPHgw1q1bl/NKYRIS3RnJZBJ/+/AkVNetgwICA5n/nTCSuwFEme+LDgYCoMLbGqiwwqyEOgC42uV12n3ODiYct33DvThk/DEF9Uuid6C5uRkNDQ2O0NxkMonGzRq+WDEElRWFucY179QxbOyXSCaTkhB0BWpra6GqaoY2YPPmzZ7e1vF43HMVr8rKSkkIJHYrJJNJlJVHUF6hQDHJQK6EgGCQAj9CwFhmfblA92jTSQgA5tFnr2OYWSpZnvsywBK9G16Tx8oKpWBC0JuxS65cLBbD2LFjsWjRIsf2RYsWYeLEibuiSxIS3RZ+olUFGUK1wBl/MUCuX7zHzPxkP0ZConBopBfl01uxy0wGs2bNwllnnYVx48ZhwoQJePjhh/HVV1/h4osv3lVdkpDoVggzi1dAxupIzHvm7gVjXp6fOM52FIPHLINRhokBMLQbVKC2QkJChA6CXiDVLPT4noxdRgh++MMfYuvWrbjpppuwceNGjBw5En/9618xZMiQXdUlCYnuAXPGn01o62BQhDKMkTE3NzcRfEiF747s8DJRZD9IinyJroEOHYXO7wuvoedilzoVXnrppbj00kt3ZRckJLodchGfulDa+EYhrPf5cYKgYZKIeZoueu9cS0Ki52GXEgIJCQlvMNhCO1/1fhDyMhuENknkrYCQkCgIGhG0AnPtFXp8T4YkBBIS3RT5CO3OGsp67xAp0ZMgfQgKg4zPkJDopshrlh3WXt97xzwJCQkfSA2BhEQ3RaHagUKiCfIBIXdzQVf3UWL3hg6CJjUEeUNqCCQkuhmYJVpzgzsCYFcOa0H5BxwwO7m9+aVO7I1EbwE3GRT66a2QhEBCohsimzANG/4XNLTlMuzl0h7lMuc3IxPiZZ9jFy28KiEhYUISAgmJbohc/QfyEaVhUwLlRBwEMhDmOBmNIFFM8CiDQj+dgbVr1+K8887DsGHDUFpair322gtz5sxxrND44Ycf4vTTT0dDQwNKS0ux//7745577sla95FHHgnGmONz2mmn5dxH6UMgIdENEWaOLdrr80oYFBL5kQ27b8yMoZTzf4nOho7gfBlh6+gMfPzxx9B1Hb///e+x995746OPPsIFF1yA1tZW3HHHHQCAFStWoH///njyySfR0NCApUuX4sILL4Sqqrj88ssD67/gggtw0003Wb9LS0tz7qMkBBIS3QxKSNFJ1EXueDmSDUP+286C0hIgIQFMnz4d06dPt34PHz4ca9aswYMPPmgRgp/85CeOY4YPH45ly5ZhwYIFWQlBWVkZ6uvrC+qjNBlISOxmIMf3rlXKy4gBiV0JzYwyKPQDGMssi59EIlH0/jY1NaGmpqbgMgAwb9481NbWYsSIEbjyyiuxc+fOnPsjNQQSErsdbBIQKJ5DxAjm7HgorMNgmTN8yityYSOJIkMj41NoHQDQ0NDg2D5nzhzMnTu3sMoFfPbZZ7j33ntx5513+pZZtmwZnn32WfzlL38JrOvMM8/EsGHDUF9fj48++gjXXnstPvzww4wVhbNBEgIJiW4ERVHQ0TIIwFe7uit5gayky8ZiSwyGTbYzfRwkJDiK6UOwbt06VFZWWtvj8bhn+blz5+LGG28MrHP58uUYN26c9XvDhg2YPn06TjnlFJx//vmex6xatQonnngibrjhBkydOjWw/gsuuMD6PnLkSOyzzz4YN24c3n//fRx88MGBx4qQhEBCohshEomgXJkB4O2sZRmjUIJ2V60twBc88tMU6MSMfAUeiyJJSOxqVFZWOgiBHy6//PKsHv1Dhw61vm/YsAGTJ0/GhAkT8PDDD3uWX716NY466ihccMEF+MUvfpFTvwHg4IMPRjQaxSeffCIJgYREbwADgJCkYFfC1hlkQi6EJFFM6GDQCnyi9ByPr62tRW1tbaiy69evx+TJkzF27Fg8+uijUJRMN75Vq1bhqKOOwjnnnIObb745p76IdaRSKQwcODCn46RToYSERFEhCn8Gc5CRWgCJLoBOxfl0BjZs2IAjjzwSDQ0NuOOOO7BlyxY0NjaisbHRKrNq1SpMnjwZU6dOxaxZs6z9W7ZsscqsX78e3/rWt/Duu+8CMHwRbrrpJrz33ntYu3Yt/vrXv+KUU07BmDFjMGnSpJz62KM1BLquQ9e9LUZezEtCoqeBBcQJhE1wvCvWCyC4+tfNtRgSEp2NhQsX4tNPP8Wnn36KPffc07GPZ+l87rnnsGXLFsybNw/z5s2z9g8ZMgRr164FAKRSKaxZswZtbW0AgFgshsWLF+Oee+5BS0sLGhoaMGPGDMyZMweqqubUR0Y9MF9oc3Mzqqqq8Mai76BPuXHC7gGoqXkKyssPAGCrJIcNH4+6OkOFkuuFkpDoKrz+t8dRNfyXAIycBL6ilJGnoNU9xL/qQwhYFk9/PUdBrjAS+kxQzW1+joXM3P/12pGYcthrYEwSB4lg8PG/qanJsvHzbe+sqkefisImgy07dRwyotFRf29Bj9YQDN7zP6ioUBxEgEDQCWjAx1Y51RxkNm8ehPVf9QEDsKP5uygpHQIA2Hffw9Cvtj8YY1KzINFtkHWBIB7mV4BjYWdrD4K0GJwMSEgUC1oRfAgKPb4no0cTAg1mSJMw5IgDkO3dbGzp33+9VW5P/AcKAAYFmzYMwvovS9C0swFK5HgwxrD/AVNQVdUX0Wi0C85EQsKGuIxwoNMds+3zvGyuM3qYfMKbLITrq9AdaxuztgTXokM6FUpIdBf0aEKQ9lmIwj3AeDuJEDQAgIbq/usAAAPwCRh7A4wYNqwbhC8/L8X2plMQi/VDZdUQHHDAeDDGJEmQ6HQYiwTZtCCMDiDfND88PNBZW7jcAeJiRorQYw4dABMSFrnbkL4FEsWETix3UuxRR29FjyYEhcBLlclg5F1nINQOWA8GYI89fgMFQFtbH6z5V3+QrqJDvx7jDzmmy/ss0fsQpCFwq/qzz8eLCz9zAPP5HlSPhEQxIE0GhaFHG8zDelm7j+HZrMj10cX9VgiK4RBVWtaC+vovsMegz9C8Y6Gjzvb2drz4xgt4f+V76OjoQDqdLuzEJHo9GAznPL7QkW/6X6scspYNQrGEsuj3YEVIZHFc7L3Dr4RE90KP1hCEHcTI9T0jJMqEe2CyHBWJoDBbjStaKZqbm/Hg4oewvuGfqO1I4h8rCBVNwzEgdgAaBo7G3sMPAGMMJSUl4U9MoteDCf9Fn4JQB3q8GNmcB93155PsyHkEOc5BQqIroEGBVuA8VytSX3oiejwh4E5JQQupeBECr338d4YPAgxTggICMUBnaTQ1NeGvb7+Kt/SlYHtuRZ9oAtFIOxS1HZEBX0NTX8WGtig2r44CyRKkW05BLNoXYw8+AaqqIh6PIxLp0ZdfopPh5bDnhg4maAaY71LDWcmzj50/bP/cUIT9FGDMkKmLJYoJKoIPQXfP/NmZ6PESKWghCz+SYM/8PbKqMb9IBZMsEKDt+TZu++f/Q6I2gfJIGlGmoURJIcI0KMxw7kqTipLyNpRXJFDGUoizW6BrEfz78zsBMHzTeDgi6t4YOuQI7DFoGBRFQVlZWf4XQmK3g9thzw9WhEHW+oJn62JkQ/a+ZXd1FE0H/JigchIShUL6EBSGHk8IguAegPiAGTSL0skgBeI2kRwwABVlzaivW4eUHkGaVDBGUKAjyjQo0M3BmUEjBWlSkGYKotARUVOo6Wekqazp9wwAoKXlfvz7sxhaW2qRaP0u4vEBOHjM8QCA8vJymUCplyCVSqG9vR0AoGkdmSp8H5U/36aDFTyz8Yo28C0bchvfnjX5EdORTqdlBI+ExC7Ebk0IvJAtL2OQvZbvK1MSGBhpQhvFkNQjSJEhtBVGiDBjaQydGHTGDJsWGaxVZZme4X367AAA9K3ZDGA10qkY1nz5GwDAlvXHQFUGYu+9jkO/fvWIRqMoLy/P/+QlcgIRobm5GUSEDz7+AF9u/Qwq06Eygso0RKBDYbqZz8K4r26Nkp2zzzmj5t8NkqlD0z9DxcClYABKBnVktfm7oYCgMwSG8YWpM0y4YZB2gIT/bkITVH7AoE/wzw//D98ed1Rg2xISQdBIgUYF+hD0YgtWryEExkAXXMawZ2ZXYZayJAZEmtGix9HG4uigKNJkJGxVoEOFDmY6IerEoDEFmumQ6FW36AMRiyZR3ddYyKKm75MAGFpbf4/mryPYsX1PJNqOQmnpUIweZayPXVXVV2ZXLBI6OjrQ1taGDz9ZjZUbPkEaKbwX/w+isQ7ES9tRPqgVFZEOVKrtqFTbUaF0oFzpQJwZZiOV2euk6WSSQTDopEAHM7VGKtJQoBOzhGYJS6FGbYcC3YgYYGRpmYJMBUQMxAwnKGZGGgQ5RIUyBRTBfpqPg6IaSUPTkgW3LdG7YbxnhY2Hei8OhO0VhICbArIiBBkAgBjT0FdpRxQaIkxDVI8hSRGkzQdRhWFCMNq2545+MdvuNt3e2uXlzQCAyqptAFYimYrj0w03Ia0r+OadGVBZFSrK98UB+x8Bxhj69q2WJCEkOjo6sKOpCQveXowPW9bjP+VNYLEUlL5JlEZSKI2kUKImEY8kEFdSiLE0IswQ3Iw5KR4Rg8KM+64wAKRDBcCUtDGDh4IOPYIERaAxxRq4GCPHM0KWRM3+0NoaLWZqABBAPXOIVsjSZvY+2QmVghx+JSQkug96NCHIEPSswMQKBN80riJUBpRBg6IkAR1QFIJKOtKkQoMCBQTVClK0Z3JuB8ZcBmaHL0M0iZJIGhoYhlQ9iQgj6KkYvtpUCl1TsOnd48FYH1RWjMZ++04AYww1Nf3kwjEmEokEtu/YgQVvL8bK1g1YVdqEVIkGdUAaJWoa8UgaJWoKJWoapWoKpWoSpWoKJUoaMUUTnEdNLZD54Zn6+OI+3JxQwtKIMw0aGFoQA3QgBRUakalVsp8Mt8nK1hJ4i1M++3Y/W7tC+IpRBQ5KwvMQUFevuSjR2yCdCgtDjyYEuitKAMTVp07kkq/AyFQYbDpgMEhBHDo0JQXSjW1J6GYMLDcdkDWYZ9pUw2dxY+DJlBg0YkgJfgmGECJEIkmUVCSgAKjo+0ejP8kSrNsSRzodw9vLZ4ChBIyVYfTI0xCNxtCnT0WviWxIJBLYtn07Xnx3MT5q2YCPynYgVaKDlWmIKhpKVA1xRUNMTSOuphFXjE+JSQTiSgolSgpRS0NgmwfInJ3bXgR8xT/Dp6SEpRFlukEelCRSUAAdUJkOHczSOHhZ2t3RLoWi0MWMwkQXOMsRGDGQDC2U6AIUx4eg9z6rPZoQ+KHQ2ykSAyVg7FNAxsyPMWug1cyhnZn7FUamP4FhV1aErG1BpMDOpGgM3xoxpMGMqAUYD71utalDBUPEFEQRZmgoYrEOxGOGx3rfvo8ZbZKCb1oeBoih8V9joKX2BQD073cEBg8eAQCorOyL0tLSQi5ht8A3W79BMpXCy8sXYU3Heqzusx2JMh3ooyPKCKWKjgjTEVE0xBQNMdUgB3EljZiSRpyZ/4XvMZZG1PQZUGDTAJsU2Mp/416TpUHgiJgRKbp5jArd0hIwH3ErquCzIahUkNNsGOS7XoJcs0BCovujRxMCd+x1kC3ez36frX6vwVMkDCqAKNOhw7ATp825o6E6huFgaNbCBbwYlx1kX9XBkDad09LEkIZiEQJdYMFpEFToiJgfHTqi0BFzkQ8AYExHWVkLAGD4vn8Hw99NbcKT2NJkPA7//Nd4aOkhYADqBkzHwIF7AQBqamoRj8ezXLVdi61bv8HadZ/h3a/+jv+Ufo6muIb2GoYUYyglhlLYy+6qTEdEMYRzVDE+MZa2SYEp+GOKZpGAGBO2szRU7k8g6Af4Neemg7TpWEgwQlEZCFFmuP9xk4HKnFkEvJ45v+dVVNXblMQf+WoJ/A0XHLaxQhG2AKafhCQFEp0MN/nOt47eih5NCNxwD1ZhnJ+y2fJ1MswH4kFOe60xI+e6gYhACAA717zlT+CuP0v/0lCQMklAmhTTT4EJc0pAIYIKBTo0+4VgMH0ZeB8yB3RRsRaLdVjf997vDfPcgHTqKTS1GGGV/1p1GNLpOhCAPQZ+D7W1e6AkXora2v4BZ9F5+OabLehIGLH7H676X7SlN6C9+p9o79OBnXvHUJ4uBdOjKNWjSOmq6dlv3EzFFOQRRTccQ5luaQC4diDKDH8BlRllIkxHFAY5iCtpRMzYEZUZhEw1TQmWZojfd1O7wwcabiLgAlIx81hwHUAuolrUIgG5qPNz1xKEciYUHCutXklzgUQXQS9C6mIZZdCDkTEDFvaFCrPyOdYZNuV/vAJDBcwYMz3JdWuAJtOJyhjwKSv5cPbL8BfQLQ2BQQa4uUAUAjoAnenGYkyMWSs2KiCA6Y7ZGhOOyWafZgyIxRLW732+tdBoAwzNiefx6c4SbPhqMNrenoBWLY7mVAl+MPoYHLDvt0KeZW5Y/OaTaOv4p/W7rPodVFY3QgdDv33SqGAK2vUYduoliOhliDANJVocHWauCI1UaMRM/xAycwroiDIdUZZGjGkmGUghppihhCa9UxgZGhhTMxCx9nHTkOEzwkMP+R0S/QyY9QyY8QDMqU0A7FTcbnibEfwJRKG+An5t5XIEf97CHKVI0iAhscvRowmBONhkG0TJ9d/TDOD6HXYw47Nw1QwV46YMring5gMly6pvIgyBbXqwk6ER4DZn9+Bs+CcohoAxNRoKCArZ3u6qazgPw6F5+JvXtVJjSZCqQun/DZqq3sK6RA3WttRi2Yod+C39ECP2Kz4paGr/F/Y64BkojKCZAo9rPnQwREhBVDVm9WL2yLgeRZIiht+FOStnpp8Fn/1HmWb6CqQQZ6YDITSQDiTbVMT7GJEFKgxtQsTyIYChFWB2ZAkXxPyeEwBFeLg4KRDBle1e+8QyIvzIgG2y8icFuRKGXH0HDMdc+/4A/gS99ypoJYoN6VRYGHo0IcgFQcRB3C6q8N2q1aCBy/AnsB8kAkCMHOaDoME+G5ggbBTGoJPTVsw1EjpjpgOiYmkmVJczY1hYvhLMSSB0AElSkSBj5k1gUJmO0kgS22pbMfOd53EPfoADikwKjhj/cyz56N8YOPRfpmOebqWK5rP4KDQoih30pjDDXp+kiJE8SrBj2xoC0wzA0iYZMP4r0JFOKUhuA8oqk5YmwDDF6Nb1tc1CJpjzPjMgQ22eIdzJzCHAnBZMP7u7m8CKR4kCOMjvIKzZILvvgDfE94aE/0FlJSQKATfaFVaHJAQSJvij5OWP4Gd/94KtLs3u5OXXD672V5luCAamg5HxwOpMmItyocFsj3e+loIGQpqRNVvMa+AlowkiIAWGDpMMJLiQhWEXL1GTqCxpw9YBKn66/Dn8qu04jB05umj56fv1q0VLyxhsS3+BvmorIpatmgsUg4DFmIYyloSuGGesQEfM7Cv3veB2/ggM3wBDM5A2FqkytynQES8ByoclbTMAs31CFKEep3kgc0ad7brbyYlyT+Qjks7uBLcJLqiP3bH/Ej0PGhkTokLr6K3ImUr9/e9/x3e/+10MGjQIjDG8+OKLjv1EhLlz52LQoEEoLS3FkUceiVWrVjnKJBIJXHHFFaitrUV5eTlOOOEEfP311wWdSLHBXB8FouDhJgH7EzTzye/x4up+7rCmObzc4+b/mOnkFuG59bnvAMy8BfxDPIFO7oOvYZIwcjykSEGCVKSIB8sZvhNRpqFUTaEimkDf0la09m/FxZ+/hEuevB9vf7ACqVQqr6vgho7+2KGVoYOinrNnBjPkkqVRwlIoYwmUK0mUKkmUKUmUKQmUKwmUKUmUswRKlSTKlQ6UKwn0UTpQxhIoYSnzGmtmUiHhAw1xpiHGjJBFHmXAnw/eC/f1C/pwsxCZH53s334QtQB+9zPbzD6sA2O+oYahvbU9zFISEhJdj5wJQWtrKw488EDcd999nvtvv/12/O53v8N9992H5cuXo76+HlOnTsXOnTutMjNnzsQLL7yA+fPn46233kJLSwuOP/54aFpQJvZdDzc5EGH7DYQnC9lgO7IRYtARM4VRCUujhKVRylIoVYxPiTnD5Tn1RTW2TpwY2ELAznMQvn9pGEmRrNBHU+ugmh76ZWoKFZEE+sY6UFPaiqq+Lfiwbgsu/uxlXDLvPix9fzkaN23K82oAK1evxOfl76IDUStVtCb4aQB2/oeIaSowrlMSZSxhkYFyixQk0EdJoIwlUWpevxgzBT4005SgI2Z+opwEmKRDdREB0W/E+i0Ier9Prg+IqIHILqyDSUU2UpCPuYDrS3QyZlvi8+bVu6D1GiQkcoFmRhkU+umtyNlkcOyxx+LYY4/13EdEuPvuu3H99dfj+9//PgDgj3/8I+rq6vDUU0/hoosuQlNTEx555BE88cQTOProowEATz75JBoaGvD666/jmGOOKeB0ug6cHGRbg76Q+lUYSY1UM5wQ8LYbc00ADzm0V9KzVdnGLD8zMRKE736+EjoMMpAkFSkyXhjdtHmrpuCNKymUqYbfvcIIEUVDSSSF9mgCHSVRrK5ox/9b/2f0Wx3HvqjEGQdMRHlJGepq+6NuwIDAa7Fu/dd4ecWr+E/5cvSt2wGNFCQogg6KCAmCMhGFDihJKKRDNZ0KxWvDnS25diVq/leZrWXJ1QlUvJ5hk/GI9yHbEaKADiOss/kJ8LMMcj7MB/bVI+tZ8YSMLpAoInRy5mjJr47e+0wW1Yfgiy++QGNjI6ZNm2Zti8fjOOKII7B06VJcdNFFWLHCUB+LZQYNGoSRI0di6dKlnoQgkUggkbDD35qbjcV+3APirlA7esX3FwuioyJP/eq0xdokQWOCJoCEHAWMe8Jnh5c3uJ0umSFJhnaAe/Ea6ZkNM0apopiOcYYHflxJIaFFkYhEkNRVJHXzf6WKj/XtuG7r10hpKgasimEPrdwK0eRnFVF5siAd2/tsBhvUhKpoG0qVJNzZ/xTmDA3lToZxphlOhqa2RMwiyX0BVJjZCgUnQT8zj9d9ZtZ2JxkQ1f32dfWKE7CPz2Znd9fVWWTUq71CjjecKg3Z735fe+9cTEKi+6GohKCxsREAUFdX59heV1eHL7/80ioTi8VQXV2dUYYf78att96KG2+80XOfe5YLhIsKKCa4ACkGMRBVuN7Z78SydmsKYC6FS9DgXO3OXmwn/HyPCxwdxmIfopmA16IyQpyloSsKFDIc9GJKGqV6CkmKIKkaUQhpgUhownedGPRqBdsF4ckYWZEBUcVQ21eoRhgg9wGIsrTjmrgJPXf2M7QrBEO/YeZoEK6J4bBpRgxAdAD18wFgjt+OPsCpFRAFt/8zISYUynx+3XD3IQzEvmbvReax+cJ9fUCZ9TGpHZAoMoqh8td6sQmrU6IM3KvqEVHWlfaCylx77bWYNWuW9bu5uRkNDQ3+dbn+u1Xk+Tv6BaMYxMBW8TtV/xxBs0IGQgSw8iFkIhwhEO3gRMxSh3N7r8p0RM0mmLnSY5IiiDMVpZREUokYSZRIQRqq6SSnCDkVuI3Z/O/qlRHXDyv7Hw8LjCmGo2CpkjJT/3LnSZ+ZJhlXJQICmOYQjmKkgOdh8CECrpl/0H0O61Tnnu2LhNaL8OaOYN1DMZMY2dfESa+CHB95GQmJQqGj8CiBrtC8dVcUlRDU19cDMLQAAwcOtLZv3rzZ0hrU19cjmUxi+/btDi3B5s2bMXHiRM964/G4Zw79XFWOIjHwu+nFIAvFJAb5HltMGL4CupmTwMy9DwVRphmzfaYYmQAhaAOgmpENhpreSAhkkwEuXMVES27HQJ5bQHEQA81aS0CBYZ8mxqALwp0LffEqRKx8Bf7w0gKId8FQfZval06cRHiZbjoTflqEfNq376uhAQgyhGS7HxISEl2LohKCYcOGob6+HosWLcKYMWMAAMlkEkuWLMFtt90GABg7diyi0SgWLVqEU089FQCwceNGfPTRR7j99tuL2R1fuIV+GEetfJRQQeSis/wOigHFNDkQDLMAg7leAuO2ez6rZSBmJAMxSAAzV2E0vf9JnP0zh+aBEwSbEGQKX9EpUkwIxIkBz0Ogg5m+HMHpobPfi+ziifeLF3VrDcRyRirrnivy8qek4vGZS39zdJamTqL3ojiJiXqvZ0vOhKClpQWffvqp9fuLL77AP//5T9TU1GDw4MGYOXMmbrnlFuyzzz7YZ599cMstt6CsrAxnnHEGAKCqqgrnnXceZs+ejX79+qGmpgZXXnklRo0aZUUdFAtuU0FQOT+CwBFGjZTLAOdVtjNIglvQuvvgJwpFDYWxJgKzMng5TArmb52Z5gBmtydqAwA4HAfFsDndtY2X5TZm7iJpZQVktjOgnS44P+Hid31sLY+4oHEmGIyZsN6NSUGYmXix1z5gLHuWA/EeS0gUA8VJXdx7CUHOZ/7ee+9hzJgxlgZg1qxZGDNmDG644QYAwFVXXYWZM2fi0ksvxbhx47B+/XosXLgQFRUVVh133XUXTjrpJJx66qmYNGkSysrK8NJLL0FV1dw6z8J/chmSuSmCf8I74hWWh4C3W1zx4VaB2x8eNy9+vIZxPivmsfdR8AWBjPj8EqahlGkoVdIoYxrKWBplLI0+LIVylkIf81OhJFGpJFGhJNHH/FQoCVQqCVSwJCpZwv4oHagwv/dhSfRhSZSxFMqUFMpYCiVMQwx6Ri6AMBDPPdss2H4W7AyFnuV8BBsnBd0dxSajbofLMGje+WUReyAh0b2wdu1anHfeeRg2bBhKS0ux1157Yc6cOUgmk45yjLGMz0MPPRRYd7GS/TGinhd02dzcjKqqKqxcPQAVFeE5jY5Mj/R8UMzBM+zMttA27WOdIs2vTltln2877u3hnfEsG70jZ4KxTRS8BBZ4P3MxBeQCt88Dhx7gW8A1Kd77Oj+EkEdRBJdx5qjIRbNhmG2M48XsjYD/c8QXP+L4eu1oHP2dV0O3KdE7wcf/pqYmVFZWOrb914pDUdqnMEt4e0sa/2/s2476i4FXX30VzzzzDE4//XTsvffe+Oijj3DBBRfgrLPOwh133GGVY4zh0UcfxfTp061tVVVVKC0t9a37kksuwUsvvYTHHnsM/fr1w+zZs7Ft2zasWLEip4n2brWWAXP9dwseHppn2H0La8evjVzhd7ybKIQxawRBFMfuesStToc25qFuDiYJ/vZ7u7fZBI1oow/a5nX+wSQgu2khDInwt4x3Xze5zjQbOMxC5n/3s+uG9B+Q6Ax0Z5PB9OnTHUJ++PDhWLNmDR588EEHIQCAvn37Wk762VDMZH+7hbFEVLWLaw54qeCtbUUajfI1L2SD2/zgpcZXPD65tm9fJ5+V++A2gTDPD3l8vPYVshCPw9xBhnbA8mGAUxi5zydzvQH/6xFmNu3dQf/agzUiwT1yeknk94QF+ZIUCnf/nWTV/+wkIZAoNoqZuri5udnxEZPjFQtNTU2oqanJ2H755ZejtrYW3/72t/HQQw9B1/11iNmS/eWCHq8hcA+TtjNYZjnxkioAdLNgZ9pOi1V3LpoE+JQNA/s6Ood0dzieV//8kGtf3FoLcaOff0Ox4fUMBcHfVOJtXsi2j5+VV8ZDZvnE5Hbe2bQEFKKM3zHOjczkRj3OGikhYcGd62bOnDmYO3du0er/7LPPcO+99+LOO+90bP/Vr36FKVOmoLS0FIsXL8bs2bPxzTff4Be/+IVnPfkk+/NDjyYEzOfD93mZDNykgPIkBb7JcDz6V2yHLRH5mA7cat1sx4tC18/EUGyQT+VeSvpOaz9wv8uQ4qMdCPK1996XSQQyjrOiNYwyCiveE8Z9AcLXltlPfqzxjhgJijJKyegCiU4AXym00DoAYN26dQ4fAq9cOAAwd+5c30y6HMuXL8e4ceOs3xs2bMD06dNxyimn4Pzzz3eUFQX/QQcdBAC46aabfAmBH8IkBHRjtyEEohmAWcKKLLWyKATJVQe5N/pAMevXybuubP3M5jjmpdYt5gzYrUURt4cVKW4NguhrUKwh3upfFqFhkIaQDzxz3vVsPiS5aDy8VebBd83LdGI4SeY+mOnEhGffq0div8L5EhTHL4YnjMr0TZDmAonOgF6E1MU8D0FlZWUop8LLL78cp512WmCZoUOHWt83bNiAyZMnY8KECXj44Yez1n/ooYeiubkZmzZtylgWAMgv2Z8fdgtCwMkAM6mALaiNgVI3VQAEbyEuOhsCyNAYMKt+V7uM27O9iQYYHIInG4EQhT8XGNZ5uPbnA3J9F18bL+t0GCHPBRCDt5DrTIikIYyAsbQbAZ3MZmvnPguO8sL6BWGEaTHJgHU8PzZLdsAwNLYophIrx4B3e7maJiQkuitqa2tRW1sbquz69esxefJkjB07Fo8++igUJTt5+eCDD1BSUoK+fft67i9msr8eTwhEMiA6jYmzXsWcqbj3uetiwg9R0LvJgOL67a6PzGO4nwIXQLnYpd0JbTpD1HppLArxR7BVzcG9LeaZhCUCXJsQPGsPrtFNBoxt7t8h++JAYWTAUT8xIIdFrApqK6sWxCadGQSAGKiL+inRe1Cc5Y87x9d+w4YNOPLIIzF48GDccccd2LJli7WPRxS89NJLaGxsxIQJE1BaWoq//e1vuP7663HhhRdaJov169djypQpePzxxzF+/PiiJvvr8YRAJAMKmIcQI0tA8+VXwwhmBr5IkKs9j+MZAzSXloDIW5udKynoariJTa4QNQbZ2vAqVawzJvF/gUSAl/E8qxw77PX8ADBm05TtyoUDWSaEXTgzZ8FRL1JDINEZ0GAsAV9oHZ2BhQsX4tNPP8Wnn36KPffc07GPpwOKRqN44IEHMGvWLOi6juHDh+Omm27CZZddZpVNpVJYs2YN2trarG133XUXIpEITj31VLS3t2PKlCl47LHHck7216MTE6359wBUVqgOMmCQBAYiMmd0BiXQAehE4Gvk5ZMIxkutDrOuBDGkiJlEgqwMehzk+u+GOztAbx0sbdtzvseGIwEc2XIikPXJ1NZ4rYLo7V/gt8/L9yKzTudeker6993P0dDQqhXnlXcnVOLhmjy9tG3SI8823ctxy8REEmEQlJjoV+8ehZICExN1tKTxy/FvFD0xUU9AD9cQODUDiulZZdiWDb2/YpIB+xj7f77DIgEGsTArSIGhg1SkSDEX4DHS+0aYHf/Ojwuqz3leThOHWLaY4ALRaSroWo6YSxbDzGP9hXE2hCEdvE6v9RLINOsYJJQsIiJC7Jnbx8Be6lrcblMI8xG2zs9rfQD/6AZv8koB+3JFkJeC/ez6E5Ns9UhI5IrubDLoCejRhCDZUYJkVDXt9YZLYSyShhpJG8OQuegMd8YTzQaFIEUMrWQs72uoqBSkyPBuVUCIkLEiXxyake8fhAjja3V71egMNVMFj3j3oymaRIoxkBrOgJmz1MxBvfgI64QXWEdITYAbYcwEQHBoKZ/9OkgVI18h7aQtzuWemVmPS9ybdQZoC3z2BaMQOlw8WM8yMVOjISFRGDQUrvLXshfZbdGjCYFa8hewEmPRJD7b+9d//oZ08t8AjBlcTd930b/f19BBIKYjGu8AH2hzHYL4zCoFFc16HClSkYICjVRLwDCQSQbS0JECkEaUaabwsLP9uVcU4Fu4M6KfeYJD3F9IHnxmaTCccznxO4/eEI8JgjtHgT0LLg7COgl6ISwRMEqJCv9wx3JtAe8n4BTYtg+BHZnBwdXqblU8JxrFMiN1pf0+yIeA90OSAQmJ7oEeTQgGDWrIsPHsscc5jt/bt29De3sbVAA7dmzGV588BwCorvk7KvtuQizeAVXNjRMmSMV2rQxJiiAFFRoZQ7kCQGE6oiyNEpaGxhhIMcwaZPLWtClmVIiDpT0j5yvq5aK08iM37sE4SMVrk6RM+72fnTssCh3uRQKQa33OvmcT5hRYyn2dDV8UZ+kwfXOX4WYH/7b8yYC7TzwNUJBjYWfBTTSyPSVy+WOJYkOaDApDjyYEYVBdXYPqaiNX9KBBe+KAAw4GYBCFZDKBf/3rRaTSG1Bd8zoqqr5BSUm7L0HgA1ySItihl6FDjyJNqpUIQwEhwjREmYYkSyGtKEhDhc4YSpQ0VFN8GE5XonOaAcWxul/mgMrtyrprDA0W9M7f2bQJTNBW7Eq4Z9e5CNowBCCb8Pc+xqidYJiK3PZ9sb/uPvHv9roEpu+ISSbVDMHo/K2Y2zKdIMlKmGX3M/NKuGsuVEsQztST2zUuK9+Cr776AoMHDyugZxK9Gd15caOegN2eEPiBk4S6uksAANu3/wzJZBIr//U8NG0zqvu9ij59dqC0tBWKSRD4LD5FEezQytGqxZCkCHQyErSqTEeEaYgraZQqSUODQBGkFBWllEKcpRFjaUSYDkUQzV6zTltb4NznNQqHNX/4kQGvWWbXuxa6BLqPOcBf1AWJHW8HTb8++JVRTMfBtJkeVbT5Mw87vugjoZuzdpEU6GDQTB+XONLOs2GZDonclJR57ruavjmhI9PkJRJdP9T034iNGz+RhEAib/D3qtA6eit6LSFwgxOEqXVGvOeOHTORTCbx4co/QdO2ol/tyygva0ZZeQsSehSbkxXYqZUgoUWhw5jhRU0yUKKmkFCiSCoJa/WsNFOgKcZwziMf3DM7wJ69KfAWTn6mAbed3uuRdq/l4He8OBMuNjyFvMcCPu7fYeeaIo0I23u7DfvMveYIXCPABXwaCojsVKeib4odUsgsMiCu/qhbvxWoMIgk7zOD4ZjINULOcyoWWSvMsTCcxsbVBiOETjUtISHR5ZCEwAd9+1YDAKYezQnC/0MqlcSHHz6L5lQCg9PlSOkqCEBKT2NZ9HPoVa0ojSXQJ5JAHzWBZESFzl3FFCP6IM7S4A6GgHPmpMNpV3WX4eW84Lbzu7+HGYbd5grFgxRkEwSiGj2oTNY6HD3KhnA+F+5Zqii47f3+dbmJixFlolhqf4DBTQZEIqBbRMA4RoOCNCkoYYZGiZMBZhltTAEq+AIwOOlAJmkz7l6YdTOKLZrDmq7c0M1nXlIFiUIhTQaFQRKCkOB5pI8++vKMfUSEM7ZsxtNvvYbX2cfY2a8ZfUtakSbVFCIEFQYZ0GGEWClm8iLA6ZjmOSgymxzo5K8lCLJC5+JnINblNRf1Tqgj1OETeseJTqDneY4zSOYhEu19zr6Idn5+zd3n4EXU3HUa2htDsKdIRRqGIxN3yOQaAMPXwNingRkOT7BJhEaGj0maJVBFrbaZiJFlHnCbjbzOPxOd6wcSdO/9ypp3wHO/dCyUKBaKudphb4QkBEUAYwx1A+rw0++dhdM2b8af330DH2/+D3ZWb0JpBQP6JAAoZhKlzGGRx7KrAMg1OPLJYZhnlJsZvIZXvk9MwWvbpe1ZMU/XbGgr/EwMTudHsT7LVp/HIM9gnL+dW0CcCXuTl+AKeRw/OfrIa/SiYWKvVTOPhQ64ztfUEIAhTSpSUJE2Z/x85k+C9oCTgbT5O01G+RRFkNIjqOrQURUx/FAUxj0MCGo0gUhZB6LQEIEGMIJSsIVUPI98aYN/D3id/H7ZGhNOI53X2u1rICEhsesgCUERwRhDfV0dLvru6UgkEkgkEvj3v99B8/qPEVWXobb234BSgoqqVuiMHMsoR5lzDXpxkNQpM7LAtw/wH2AZeLgjAQwZq/6JS2czhMtv4FZhA06CkKu4sWb1IcuGr5ccSYycmgByEBu3dsCLiESgIwYNxLjYi9haATI0BylSLcfShB5BkiLo0KNo16Jo12JoTpegqakPbhp4FvYZundGn198awE+3uPP2CO+A7WRnahWW9GHJRBl/mGyzrDRYFc+vvhQLtcx9IqW5uJFhvnC1AxRZnil6GcgiYFEoeA+W4XW0VshCUEnIR6PIx6P45BDpgKYikTiQiSTSezc2YTVq/8XOmlQIm+gqvoLRCJpVPfdljHUWo6FHsI7H9hOagZ4hkL3dsBoL98mg0L63Hb7oL7m1mYW/4qAFL/O/7bbnpfqUDcFrtNBkBOACBJkCP6EHjW+68a2Dj2GDi2CNi2GVi2GnakSbO8owz6b6zFm6kGey6BGlBIrrFXUTHjBT1DruyQLINcI2L/51gyQk4hKSBQCaTIoDJIQdBE4QaioqMCgQZcCABKJC5FOp7Fjx1as/vdfwRigqq+gouprRCIJ1FRvQz5i2etxdgt8BiPKQZwF+0UhFBNeGoXQs84sdfo5QPqRBVErkLnPqNFrriA6CfLU1ZpICvQoOvQoOijq0Ai0mZ+WVAw7kyXY0VYKpNSsa6LzBYP8tD9emoxMw5P3Fe4MbwO3ecdNZrzdICUkJHY1JCHYheAkoby8HHvscTEAIJH4MTRNwzffbMIna14HACjRF1HeZxOi0Q5U9t0aWCcX9NnKcBERJoKhK1BIP7g+QkXmegBBmoHsfu3egtTWcnATgU0KkqaZIEFRtOs2EWjXYmhNR9GajqM1FUNLIob2RBSkBZABy/lUh8p0q0WvXjoXSuIRDnZ/i+N3kJ06ZGtLCn6JzoSR4aXATIXSZCDRXRCPxwEAgwcPw+DBFwAAkslzoOs6Ghu/xmef/B0AgUX+hLKybQAj1NQ0Qo2kLNV/EPxmmRnJZBgCF4ISVexi3WHELD8+09EvfxgOk7bA0gEErXUQzkfB6dQoznL5d8NvQLHyEmjcd0BXkdQNn4EOzdASdGhRdKQj6EhFkExHoKVUQPe/WgywMl8ajoV6xv21nDiRuTbCrkDO91KGG0oUERoZBL3QOnorJCHoAYjFYgCAoUP3xlDT+SyZPAtEBF3XsWLFK0imdoIojWhsPmIlO1FW2ow+lduNCljwqn0AADOiQGHBs7ggHwA+U+fEIng+GW62CWSfVXqZDBQYEQuWv4THssRhZ7viokbcH4EISKej2PnNHpbPQJIiSOtR6BRFw85xqCkZZOUesEIRiUFngB5joCjD4GEDAs6LjERXSgolSgoxpnleNb6ts809EhISuzckIeih4CQBAA477PvW91TqbBARvli7Bl9+thIA0J5Yjurq5QCAyj7bUFG5I6M+7iwfpLr3WvjIC3z27PZgDwqLzDie7CA1Ltizq6K5O6C4rRBByRyOlwCgpaP4ZsueaNt2BPpVjsd3x07z9AGIRqNgBXrLMUYoUVIoZUmUsSRiLO1Rypkd0QtBiwjx+xFWq1N0mNEIvXdOJlFMSKfCwiAJwW6GaDQKANhv31HYb99RAABdPx2aZoSqffTR29i27XLU1Gx2HJdhbvB2CC8Ytu+CtxgSyYDVJn9BswgOr1m/43j39hAgGJqAbVv2RNu2SaipmogjDp6GWCyW1RmwUCgAypQkSpQUVOZPa8L5Q3QtRALHzRlhHSIlJPIFFWG1Q5KZCiV2ZyiKYgmvMWO+g4ULz0bfvneBKVpOgjHIHl+sAZ187P5csAclPAqjfeDIZjLY2VSNtpZ+aGsaj5rKw3DYwdMQj8cLnvXnAgZCqZJEhdKBqOkh4SX8uXZEgxJwH8IYSYoHUfOQzVNE7HNb25bO7JbEbg7NjPwptI7eCkkIeiHGjDkLX339NPr3X+85TDPzT5BTIQdXrxGQNd69UOLgp97mDoV+HvjwSaUsQtcVfNM4FOlUOaLamdhz0EHY64D9oShKl5IAEYwRypUESlkKCtMtTYfXVVYYQSEyk6p4R0Z05VnwKAeeZIqZhCRbP9LsaQBndkkfJSQknJCEoBeif/8B+OCD09Gv312A4p/1ToQYqsgRFFvuB1HxXew5a5DaPKMtUrB5wzCk0+WIaGegNF6DCQdORklJSaebAsKCAYiytCOk0MgXwZ0InREeRtglCaVzaan4uQh4pIfBp7zvjtuUwAJMIxIS2WBkdS3Uh6BInemBkISgl+Kgg36Er9Y/jdr+6x3bc7XR5wJxdmg5DOZwfLboh2zHbdqwF1p2jEZN1XQwpuCQUYejvLx8l2kAwkD103rAJgYAD3804KlFyeJYmO0K5GKOsY9h0AAwYgDToXY7TweJ3Q16EXwICj2+J0MSgl6KAQPqsHHjvdj2zSWoqd1kbQ8S0nxpXlEr4Ffey4mMv2b28Zl58hgjz/wBzNzHXNvE+nxBDJ9/fAy+c+jdqKyszFa620AnZuX/91w90vzPTSY8D0PnrHOYvyjPanaCf34MCQmJrkPvpUISOPDACdi86QzouurYnj28z4bCjOV63bNPdzm/Ot3Ci9v83dvcZCBbPzlad1bhs9Xf63FkAADSpKBJK8sw07jhTsrUHRGYMbIXh3lJFBdiWvFCPr0VkhD0chx5xP/DmtU/hK4bj0I21TDzUPu68w34JSViDN5rKniQAiZ+9wk3FBdR8pphtu7si9TOuzFj+n09jgwAwPj9DsWmbQOxXS9DEipSMJZQdlvZ+blnX7kwQCgXob92fU7fBgabMHZn0iLR88EzFRb66a2QhKCXo6SkBEce8WtsXHc/tm0d4FTf51ln4FzVY1e2mb9XP+zli42Ff+wP0NpSiU9X/wDlytM49JBjQve7u6F+QD22dfTFV8l+2KL1QYseR5qMBaxF2GaC7FEeXQl+T1TG3RwLSyUtISHRuZA+BBIoLS3FpEkn4cMP67D2819hyLAPjVA9n2Haa2VBjqBZqvcSzsz8yxztKYws27LXEbZ2wC6x/qv90brzAOw9/EIcf8won170LOxIleKbdAViShpMBSJMR9xVhgHWkszi3Dw3F77iRxowOENRg50XuzZPgsTuCelUWBgkIZCwcOCBE9De/jzef38hktpDaBi60rTne6v0/X3WvcHTI5MpuDZv3gPNW09F/YCJ2Ljp/8Ciy6EoSewx5EOoquatvmJAR1sZNm0YDRCQStVjj7ozAABjRo5EVVVVHmfePaGqKqItVWjWSlGlt6GMJVHKktAZ8xD3zDK/+PkTFBJpkE1UG3kHmEkAchPs2RIXSUiEBV87pNA6eiskIZBwwNAWnIi2tqn45NOV+HrDi4jEPkMkuhMNQ1a6SntlzPMY2pmhOdi2eSB27BgOTY+huupcDB60H/YcOxgAcBAmAQCSySQ+XvNPpHX//AjxeBmmTT4w73PsKYjH4xhfOgIr0p+jIxpDUlE91e6WnV7Y1t3ysetgGb4iHMxMHEVZEltJSEh0LiQhkPBEWVkZDhx9KA4cfSgAoKWlBV98sdpRxkswBaF+QB3GHTwksEwsFsPoUeNz7O3uC50UtGlRJPQINKiWWUC81gojixDoxOCjWzGRu2o+lzm/mE2SzP4ojCwy4PWM6JR5ThIS+YCKECXQm7Nl5GQsufXWW/Htb38bFRUVGDBgAE466SSsWbPGUYaIMHfuXAwaNAilpaU48sgjsWrVKkeZRCKBK664ArW1tSgvL8cJJ5yAr7/+uvCzkeg09OnTB6NGjXd8Rrs+7v3uz557BpMBiUzoxNChR5EiFWnynmMrIKimB0a28MMgp75wqw147xdDB/OZ5wetUSEhERZ8tcNCP52BtWvX4rzzzsOwYcNQWlqKvfbaC3PmzEEymbTKPPbYY2CMeX42b97sW/eRRx6ZUf60007LuY85EYIlS5bgsssuw9tvv41FixYhnU5j2rRpaG1ttcrcfvvt+N3vfof77rsPy5cvR319PaZOnYqdO3daZWbOnIkXXngB8+fPx1tvvYWWlhYcf/zx1op8EhISBogYEloEKVItO31GGfOjkQINzDO7oVifP/zDEgP76FMLJycaH2h78cxLomvAnQoL/XQGPv74Y+i6jt///vdYtWoV7rrrLjz00EO47rrrrDI//OEPsXHjRsfnmGOOwRFHHIEBAwYE1n/BBRc4jvv973+fcx9zMhm8+uqrjt+PPvooBgwYgBUrVuDwww8HEeHuu+/G9ddfj+9///sAgD/+8Y+oq6vDU089hYsuughNTU145JFH8MQTT+Doo48GADz55JNoaGjA66+/jmOOyQwTSyQSSCQS1u/m5uacT1RCoieCiCGlRZAm1UxPnClUuX1eMbUE2Vdr8zYb6AAUOHNHBoUK8rrcJMPhy2BFkZCMcZbo1Zg+fTqmT59u/R4+fDjWrFmDBx98EHfccQcAw4ertLTUKrNlyxa88cYbeOSRR7LWX1ZWhvr6+oL6WNA72tTUBACoqakBAHzxxRdobGzEtGnTrDLxeBxHHHEEli5dCgBYsWIFUqmUo8ygQYMwcuRIq4wbt956K6qqqqxPQ0NDId2WkOgxmD52IkraIubs388tz4DKCFGmZ32pgxZv0WFTBcMeGwy/zImZ27p3JkWJ3QPFNBk0Nzc7PuKktFhoamqy5KcXHn/8cZSVleHkk0/OWte8efNQW1uLESNG4Morr3Ro5cMib0JARJg1axYOO+wwjBw5EgDQ2NgIAKirq3OUraurs/Y1NjYiFouhurrat4wb1157LZqamqzPunXr8u22hESPQnVVX0BToQnaAbe9n4ghTQrSxJAkBemsKs9gFz6xnWz1ZJggKP8AQuO8COSdsEJCIiuKmbq4oaHBMRG99dZbi9rXzz77DPfeey8uvvhi3zJ/+MMfcMYZZzi0Bl4488wz8fTTT+PNN9/EL3/5Szz//POWlj4X5B1lcPnll2PlypV46623Mva5V48joqwrygWVicfjiMfd6VgkJHoHuKmA/3ebDQhAGgo0Uiy7vVKAr3QhayNkLlcVDpoZaVA3aA1W/usdK7pFQmJXYd26dY6U534yaO7cubjxxhsD61q+fDnGjRtn/d6wYQOmT5+OU045Beeff77nMcuWLcPq1avx+OOPZ+3rBRdcYH0fOXIk9tlnH4wbNw7vv/8+Dj744KzHc+RFCK644gr87//+L/7+979jzz33tLZz+0VjYyMGDhxobd+8ebOlNaivr0cymcT27dsdWoLNmzdj4sSJ+XRHQmK3BldjGjNobzHv3qpncS7Mhl01R4/EEmjd0Zq9oISEB4oRJcCPr6ysDLUGyuWXX57Vo3/o0KHW9w0bNmDy5MmYMGECHn74Yd9j/ud//gcHHXQQxo4dG67jAg4++GBEo1F88sknnUcIiAhXXHEFXnjhBbz55psYNmyYY/+wYcNQX1+PRYsWYcyYMQCMRDNLlizBbbfdBgAYO3YsotEoFi1ahFNPPRUAsHHjRnz00Ue4/fbbc+mOhESvgGjTD1qaOsJ0aGAhTAa7BkGGCgUy9FCicBSTEIRFbW0tamtrQ5Vdv349Jk+ejLFjx+LRRx+Foni/qy0tLXj22WfzNlOsWrUKqVTKMTEPg5wIwWWXXYannnoKf/7zn1FRUWHZ/KuqqlBaWgrGGGbOnIlbbrkF++yzD/bZZx/ccsstKCsrwxlnnGGVPe+88zB79mz069cPNTU1uPLKKzFq1Cgr6kBCQsIbfkOVCoLKNCRJRVpwDQrKENjVCAx49FnRUkJid8GGDRtw5JFHYvDgwbjjjjuwZcsWa587OuCZZ55BOp3GmWeemVHP+vXrMWXKFDz++OMYP348PvvsM8ybNw/HHXccamtrsXr1asyePRtjxozBpEmTcupjToTgwQcfBGAkQRDx6KOP4txzzwUAXHXVVWhvb8ell16K7du345BDDsHChQtRUVFhlb/rrrsQiURw6qmnor29HVOmTMFjjz0GVVVz6ryERG8Az0ToJ9h5JkC+oiAHCf/dwjbb2gWdAbHN7kFRJHY37AoNQVgsXLgQn376KT799FOHqR1AhiPtI488gu9///sZzvcAkEqlsGbNGrS1tQEwsrsuXrwY99xzD1paWtDQ0IAZM2Zgzpw5OctURj3Qpbe5uRlVVVVoamrqkevcS0iERWtrK85b8kvsOehrDC/ZjMGxrahVWxAVsg0wa3lhoIOMJEacQPBUx15kQmHZggqzwXvwtQmKs02VEeLwTj6mCBqCnZuewLfHTSmwbxK7K7zGf75t6l8vQrQ8VlD9qdYkFh33+14pX7qnsVFCQsKCvVaBvxeBYib+EZeG5sh9/cFwyLVOBpu4ZNRlZjLscbMTCYndCHJxIwmJbo6IokNlurm0sD+CzAp+R3S18j5oLQW2K+wYErsVeMhsoXX0VkhCICHRjZFIJKAqOiJMDxT2fBB0JCzqVOnqva6C3bZHKCQx+B3CgKyER0IiG7qzD0FPgCQEEhLdGH95502kqjoQYVqgzZ8vNQxA8B0gIetgZsKgzpqQi2GSoY0AvXcMligiJCEoDNKHQEKiGyNNScQjKcSUNFT4awkYDKe9CHTEmJaRlMjPbp8vwoh5P21F7x1uJSS6N6SGQEKiG0NlhLJIAiVKClEWvLwRn9moIGiMoLkEvrdGIB8/gmBzgdieW0uQb2pjCYkwkBqCwiAJgYREN4bKdFRGOlCuJBBn6axmAx6H0F1ELichnAT49ks6FEoUAZIQFAZpMpCQ6KZIpVJY0/4f9Iu0oEptQ7mSMM0BmX7UosDVQ87g80U2smG0La7MyMw+ekMmK5KQ6B6QGgIJiW6KVCoFVH+BhthWDIjsRAyGY6GX2UAUurprm1imcMfC3MmGDmPmESTwGSOpJJAoGESFk+HOJNPdHZIQSEh0Y+xfugEDI00oV5IOa784aBExECNLb8BgZi8k71TGIohYFy0qFOyrQMRk2KFEwdDBCs5DUOjxPRnSZCAh0Y3RL9JiORPy9QqyLYBsZwQMO7CFHwDzEdnch8CvFRI+ANDevj2PViQkJAqFJAQSEt0YEabnZGPvLnMbd19ZwD43OrQni9wbid4C7lRY6Ke3QpoMJCS6MXwHJ0EDb9vfg80DfPuuGO7I9T2wDyzduZ2R2G0hfQgKg9QQSEh0Y2hQoLmWNtLBMiS+34qGYRD+KIawa6O6Iw3kwkUSEt0fUkMgIdGN0abHUEIRxKFBB0MaChToUAWbPI8eyDf/QG6Ohb139iTR/SHzEBQGqSGQkOimiMfj6NhyEj5vr8dGrQo79FJ0UAS6+7X1keXdZU4eRnthmD1670AsURxwk0Ghn94KSQgkJLopVFXFiUdehHXr9se6VA22aeXo0KOeMxgiFhCBsGshRhn4hnSRTGksUTioCA6FkhBISEh0S5SVlWF05Fhs7ahEqx5HGqrpU+BE1+QSyB/ZVkzo3r2XkOgdkD4EEhLdFIlEAs8uvh6IfIOh0W2oUFtQypKOUEQOt8D1y0Pgt7iQToqjlMKM/5TjQkZG297Kf8Zg5lPIhMKkwUCicBAQ2vE1qI7eCkkIJCS6KTRNg97vI/Sp3YQYS6OE+a94aHjy24OZn/o93GBn04uw6lMSIgq8jiDAdITszcOtRGdDB3OE3+ZbR2+FNBlISHRjGHZNxfr4iVQxzI/Dq2RnDXW8Ld4Lw4Qhhb+ERE+C1BBISHRj8NzsYnpfnpMgU+BnTzsUZoYuCnOFUagwLFs/ELSff6OMfb13TiZRTMjERIVBaggkJLopSkpKULLtMAcJCBqsVEYOc0JXD2t+gp37FPTeYVaiqyBTFxcGSQgkJLopFEVBn2idj3OgrT2wNAhku/NlW1AoCIU6ZYkQTQhBfg265x4JCYmuhDQZSEj0AIhe/EQM7uzFDIBWpDk4c1RDYKwwNaq9+iLMXAReM5FsgYkSEtlBVIQog178GEoNgYRED4FICrz2FQteIY2FwPaB4DWxTI2AcAL96/+DVavfL7BVid4ImamwMEhCICHRg2D7E7i3M5fGgPKS5MWODrAcIcntdujsr9hqaVkL2tubitYHCQmJcJAmAwmJHgQx0sBNC8Rt5LEiYhgE6x/ymzkRGDReDTPyEfDt/K90Oty9oeu5e4lomoYVH7yJdKrNsb21tc3nCBllUCgkIZCQ6OZQQjoHcrKggCcpKt7AZvgR5HcsJzC6y5cAru86JDHoChBRXgK6vb0d76/8G3Rdc9cYeM9a2taDyv7X4eRqLePtMic5tFxMx6C6z1AR64AipOaONvs/iDoxMLnaYd6QhEBCoocg2zClCsOp3xAdLgmx93GFGhIIDLqgJbDjIYylm2EOxN19XYZCoWlugZofVq5chqaWRs99Rogqv2fOu7619RMk+iy1klm5jUR8q8J0g4zylNNKCpV166EoabsMyLpf3omwCFWAVU5c5IqIOfxLuNmLL8fNAKgsBcAW0tmeDelUWBgkIZCQ6DEgj2+ZSEGx1J4M3WNZYTvCgIERQMLaBWLUQXfPb6hpWsbsetPmjfj407ctkRZ0tXU9iaT6NKJRf7V3WFRUN6JvP7seHQwaMaRIRRvF0EERpCgCDQpAzll4FIoRc2+GrcL8bhABQoRpUKFb/1VTmGsg6KSAwUxaBYJCXvN7Z7gpAVAIcKcVtrVH5kqDPIzWVP1HFQ0qI6vmCOkZ7fQknHDCCfjnP/+JzZs3o7q6GkcffTRuu+02DBo0yCrz1Vdf4bLLLsMbb7yB0tJSnHHGGbjjjjsQi8V8600kErjyyivx9NNPo729HVOmTMEDDzyAPffcM6f+SUIgIdGNwcAXBPJO7uOOPAi7GJFnW4Gzr8L8CNw1ceEjaglyga7rSKfTocqqqgpVVXPspYF0Oo2tW7/Be6vfQpoYljV9gOaSFkSYhr7RdgyObUVZSTOqGxpRqiRRwtKIs7SgAbFhq8mLDwKgE5CGgiRF0KrH0EExpEg1rzXMpFUEHQo0UpAiFZpJDDRzm8J0xJiGEpZChGnGEYyByJjL85BUBToYmf8Bh0qfgwFQSDcX4yJL2yA+SQw8E6aCNBSrHymKIA0F0IFyJQkFQIoUpBFBEv733dAQFOpDUNDhgZg8eTKuu+46DBw4EOvXr8eVV16Jk08+GUuXLgVgEM4ZM2agf//+eOutt7B161acc845ICLce++9vvXOnDkTL730EubPn49+/fph9uzZOP7447FixYqcnn1JCCQkujlEpa4iDKfFnitlG0YVZgidfOoVMyhyTwJRMLj9CjZteQOL//aVZ30EoC3xGUqq3zBVzsKsErYamreV3D4KfeNjEVFLMP7g6SgrK8s6SKbTaSz/4C2s3PosklUbkaxrw8ZkJTb3qYCmK4iraVTGdqCqfJ0lODVSkIIKBQTi6nbHGXcOjLYZUlCRIhUJiqCN4mjXY0ibq1gqjBAxgz3TUNCux9CuR5EmFWlSkdAjIDDEWBrlagKkAqUAVKZDc98cAAwqGAgq06HCLAMnOVWgo5RpKGUpqNxjgBHWp6rRV21DuZKwcmq0URRfpWoxKLLDIgpJiqCJVNRjJ1ToIKZDIyVQ4Hd3p8Kf/exn1vchQ4bgmmuuwUknnYRUKoVoNIqFCxdi9erVWLdunaU1uPPOO3Huuefi5ptvRmVlZUadTU1NeOSRR/DEE0/g6KOPBgA8+eSTaGhowOuvv45jjjkmdP8kIZCQ6MYgSltLA2dGFXRjEMwMR2T/FHYRMdNlwKYFBHNpZGLYa8QjntUas1nzY37XBfW3U/1s1lv3NYBXoGsRLP73fUhuHYcRQ8/Ct/YdlUEMiAhvv/cmVm2dD2WP/0AdnkKUVKT1GOJKGjGmIaUAB5RuwAFlG0yBqVjmEEMAG+IvAtsGL8JLc5BXRknzOmowzARJqNBIQRIqEnoUSYoYTnYMYKRBY4amIE0qmrVStGhxo7weQVJXoTBCqZqCynSUKUkQM233TBEc7ZjjjkVIh8p0qz8aKdChQDHNDFGmIWKSBvv+M8SYhggIZGoWypHC4Mh29I/sNAifHkNHOopWvQTr030xMNKECHSQYD7obDQ3Nzt+x+NxxOPxotW/bds2zJs3DxMnTkQ0GgUALFu2DCNHjnSYEI455hgkEgmsWLECkydPzqhnxYoVSKVSmDZtmrVt0KBBGDlyJJYuXSoJgYTE7oCVq95Hc+UL6GPadcM423UXkmCHFBqCnky1tZi1kAsrxRLd3NrsLSQt1bTpLMcFFjNL86ONmai94BKf8SlqClX9NwL9X8JXO5dg9cIj0Cc2wmqPiKEjvQMdgxagZK+kqVpnSCGCNCmIMg1lahJVahsm9fkEjBFSFAGf//L2LULC1ePkpHKewp8F+x54X2Pjw7UCOgzVP3fKi0CDBsUwOTEyzBiMEIWGMiWJVi1uXVPjehGiLI1KtR1lShJRpoFM4sXvKQm2fgbb14CZvgVpGKtyAgSVETr0KEpYGhVKwur1kOhWq/8cKtMxINJsXZ8SlkJftQ06FGzTytBPbQHMhbbSAelziuGDwo9vaGhwbJ8zZw7mzp1bYO3A1Vdfjfvuuw9tbW049NBD8fLLL1v7GhsbUVdX5yhfXV2NWCyGxkZvB9LGxkbEYjFUV1c7ttfV1fke4wdJCCQkBLS1tYEKNCKu+HAJmlo3wPKaFlTaXv9V6ChRU+bRpsAkDc2VC1Def5PlrJYdTnVp7jbrbG3klsbYUAdzKsCFtVN5TsSgMzu0kgt0+1gDzPoQwIAIiX4IhgAEcZc1ggo7jBGuRZ8AIFa5DVWVLwB4AQQgDRVJUqGQCoWiSJGhOYgqGlKUQF+lFfFYGl+r1YizNCLmrBhMs4iAvQpl5oJO/A6Ky0Q7r2weWgKP+6CAEGE64iyFGEubGgTFbNPeV8KSqFFbrGezVYujVEmiX8TwjyBiSFIECdMp0SABiuCEaKj/iWD6HpCpHbDJAo8osWke70V2MEYoZ0m0syTADJNIEqpZg3/IZDFNBuvWrXOo6P20A3PnzsWNN94YWOfy5csxbtw4AMDPf/5znHfeefjyyy9x44034uyzz8bLL78MxngkRWb/ichze/B55H6MJAQS3RKJRAKpVCp7QQFbtmzEfz5d6NiWi2gnSiJWtQDRmOG57TVD9Xq9dABJUpCgCNKkgtW0guo0y6abpAgSehQJ83+7HkWHHkWHHkOHHkGF2oFvlW00bLLQoZgCrMy0QyuWqHGelzs1kZgNMCyFyBW52sLdgt0RbcDPyZpN2xoF3haE48StjBEYmWTApY/gIkgFD59zEgLRts9r1KEhzox7qJCONKmIMA1xlkarVoKKSDuSegTbtT1RylLWdVCgI2KaPhhgCUVTZlqkRPRpIHjkliB/MceYMyzTDWP2r0Mx61egQ1F0lCtJMBDaKWZoEIghwjSLsPDrwkBQYzqisD36OZFoJ8P00K7HkDa1DToxpJiKEpZC1KwvYhIjrvXh17mPkkCJSUyIZdKBIHEVZ2nUR5oAOENq3eSus1BZWelps3fj8ssvx2mnnRZYZujQodb32tpa1NbWYt9998X++++PhoYGvP3225gwYQLq6+vxzjvvOI7dvn07UqlUhuaAo76+HslkEtu3b3doCTZv3oyJEydm7b8ISQh6AYgILS0tec98P1y5EO1t64vcq2DBoqvLUF37b49jjPhqsMyBIaKmMORbO+w6kH02m8sM2i+kTAeQIBUdFEXK9JLWoSBKaWhMRYoMz/MERdDONESZhriSRolubDPUs4YilEG3VLuKOSdTGJ932ep0Im+B35lkIF+QJST4TBqWALeFo0gaTI2Cqfp21GXu14gTAH5fTKJgluJKZWYKS163Fa3hUtErMOwQUWggBkRg2Maj0FCttgIAIkoSR/T5GFFTOyDWCbMfxHSAjDA/nTGLsNjnJYaChkw4FeREBwYFhq2e94kYUArbkS/G0gCczpa8rOBp4ewLM65FCUsZ11tlhgZGIAwq04XrH9RD62tGyaDnlJPhzLIBRxXTZhASXMDn1ZQ5JicShkllwoQJuPnmm7Fx40YMHDgQALBw4ULE43GMHTvWs46xY8ciGo1i0aJFOPXUUwEAGzduxEcffYTbb789p/7kRAgefPBBPPjgg1i7di0AYMSIEbjhhhtw7LHHWid344034uGHH8b27dtxyCGH4P7778eIESOsOooVL7m7QtM07Ny5M1TZ9es/w7r1Szz3ia+dTgnU1D6PSCzpW9YtXJk1xAID92hGNJooqpAxbMjey966By6vY8MMpOKs1L9M+Dh9d33MtUcBIWrFbGsA47HhCpKkop1iiMAI64pRBCVKCkk9griSQsycYfH7YM3ceMgWc55z0DXoTmTAC25dh2Wbtr7b2RaN2HU+i7fn9Pw7IVPQWr4GjvbshDgmN8noE4FBYYSoGSrnhbhihBSKpIa3DbPfYAJxM/dyMOs4UaMBV6lwEM0QkYC7bptR8nsyrPqZY2NG/d7mANtc5DosoD1YNTr/M8d/TxRjcaJOijJ499138e677+Kwww5DdXU1Pv/8c9xwww3Ya6+9MGHCBADAtGnTcMABB+Css87Cb3/7W2zbtg1XXnklLrjgAktbsX79ekyZMgWPP/44xo8fj6qqKpx33nmYPXs2+vXrh5qaGlx55ZUYNWqUFXUQFjkRgj333BO/+c1vsPfeewMA/vjHP+LEE0/EBx98gBEjRuD222/H7373Ozz22GPYd9998etf/xpTp07FmjVrUFFRAaB48ZLdAYlEAm1trRnbvR7+f//7LTTv/HfGAOKGTltQv+crGceLdfMjo9EU9trfmzzksmqVMYB6H8+HxbDEO0gIF+s1C1sPny0FdZzPcJxzPncZuwJnOXvgVwBEmG4JCkXYR8xQJUfIcMCKkoaYGe6VUlTETNW0ONRxQgCTDFjWWWYPvLtGD5CbH4ETtnBwi8G06YjGt2muEvw2ilde1JsQYGkh7BL2t0whImoUnKVV8z46S9pwPyt2TaJLZCbEGbndGhO+90xkGnjc+3M7Ry723fcsy6tsHEXotpkKS0tLsWDBAsyZMwetra0YOHAgpk+fjvnz51v+Caqq4i9/+QsuvfRSTJo0yZGYiCOVSmHNmjVoa7OTUt11112IRCI49dRTrYn2Y489lrNMZVSgB1VNTQ1++9vf4ic/+QkGDRqEmTNn4uqrrwZgCMy6ujrcdtttuOiii9DU1IT+/fvjiSeewA9/+EMAwIYNG9DQ0IC//vWvocMjmpubUVVVhaamplA2HgDYuXMnkslE1nKNm77EV+sWhmOyymoMHLQCgD1YMQDb9TIoTEcl67DKlpS0IxrvMFWdmZnZ3AI8W173oEzkuS5haahQAwgI2d/D1OVXVpzlFqIhyAWG8164sl6CQ3QOI8AMc2Pe202nNrc6mvejQ4+gjWJIUgRJMymMRgoiTEcfpcNqz27b7hkXJtxbXIG/sYMLSy/4zRJ5RrkwyD/5EbmIZzHuclA/uqeIZfAmBxL+4NeLwLBzp46jRn3tGP+5TBj26PVQykoKaktv68AXP745J/myuyBvHwJN0/Dcc8+htbUVEyZMwBdffIHGxkZHLGQ8HscRRxyBpUuX4qKLLso7XjKRSFg2FsCOD33nnVeg659klPd6zcrLl6Cm39ee9evEB1EgEklhvxGtBa0LXYHtGf3hAtCvr2L6VsDJhr2GPK5SdSO/YRoWo3Efb+dDDwfeJ09yQbAcjrhWwqt+mwjZKuR8wefSVh8Cy/JBJ7Mv1szcIkeZXuOGmtm7v1z9b7hm6YgyFRoxEGOWrTrwPJhtSsh2R0SiEhadoyTNbMV+B2wPaFvlHHysGzZZ9dHsZDmpsCYYu0RxIJoS3PdKfOeZo2wm3BOG7kowONHO/u6Jv92aFFglAilgN09M1N2RMyH417/+hQkTJqCjowN9+vTBCy+8gAMOOMBKvej2hKyrq8OXX34JIP94yVtvvdUzrGPAgJ+jtp/TE53bFkVh6Ra0gPMlEwW2KByDEPSSOvriU9Brs9gs89gWpn1LfepzXBiIA5H7OuXSDzcUGNeYzxJFUgCIZEKcJdukIFcBZ8/e4XBOy/bCe+3liW6MZ8mONw/rJGUcS2ZCFh266WPA75XKdCu+2+tYEW4PdVGzIToedhZCqW6Fsn61WO+GFZooHimUQ36DdLZj3C0FCizhmeV3zYvIhr3y4vvpZa4K87zbZSj0++4Xq+LtcCiOJc4YEC9NGkztGWCnNLbJQPZ74dU+kPksBToCE0PBPgCSEITHfvvth3/+85/YsWMHnn/+eZxzzjlYsmSJtd8d9xgmFjJbmWuvvRazZs2yfjc3N6OhoQHxWAKiuA+zoKf4kAP+Qi6IE/jN9nOB14zAb4By9yWXWUMQRDOBu34gU1AXCj+SwAWjWIBfD3HOzV2wwlx6e8g2h2nBYz3X2bN4j7h9mZsH+H6vQVIEA/ea1szxRjwXc1Bm9sBpaE68RYz7XokDZO7pbYoP3ifee69ny7GNbBqjMOYsS11/TjzXQeb2THANhVfSKC9NjfhM2g6tLO8ld5lHSKcX3DN0/twpHvv4fuf4k02om46h5numQ3GS+ZzPz/m+AvY41FnrQUjkQQhisZjlVDhu3DgsX74c99xzj+U30NjYaIVLAEYsJNca5Bsv6ZcyUvQ0VuCvRhdnzASvl9R7NszV2VxwBpGIfCGmbbEHUbtfXq+hnzoxFzLg9lp3w0vTUiyIWgKv++E1sInCJayPgTOnvUkK8u+20U9iQp/5UAnHPfQ71km+xLIiUTF7LKwCE0hMu+HgmBnlb89jBYrm0XPmS7Szaxy8jjDaFuca7uPd74/Ya+NZ47/EHrjfUi7wyFIliGftFLjM8Zc/l7mOKdaTQ84XOUhDlXm08/3ONjmxw0e967fyYAhpowuBmFfDa7v3MfDVyoZvt7DjezIKMZUDMGb3iUQCw4YNQ319PRYtWmTtSyaTWLJkiSXsxXhJDh4vmWsCBcB4mEVS4Ce8xPtrxHrb8BOiom2byBBimvkp5IHJnO05rdEaGalCxXPzgzHrtD+5vH5kngtvx30tc/UdCNWm0E6ajI8m/Bf7oMH/fuYyfDIYIWh8PYCwGga//hdybDHKOMt7PcldQxDCOyDCym5Hrm1hz5drX3TXx+t+MPNeW2YpF/EVy+umvdkWMGJmRKN221zFZ8DGcbrjOPtoXh8vo5HiaMe9nwtP97kFfch9PXl9WY6xz8H7I/bN3V+HsA/Y3xUITOFNRfr0UuSkIbjuuutw7LHHoqGhATt37sT8+fPx5ptv4tVXXwVjDDNnzsQtt9yCffbZB/vssw9uueUWlJWV4YwzzgCAosZLAk4SIM40ve6ne7vo/CbO3DpDCHp9N35zBmyEvWmCl7rdTzETm4GCWRycgj+oTGegkHr54JwrinVvyfE/uB+Osq6Uwvy/eD7u2ar/c5xZXje3Z2TAc9VZHJBDg+HuX1joZn8BH1IOW6vgVb/tNyFudL4vQYLKbbaAcEqa+SM45U54FKsesbz7fu8+znDBWp3u6jy5OyAnQrBp0yacddZZ2LhxI6qqqjB69Gi8+uqrmDp1KgDgqquuQnt7Oy699FIrMdHChQutHARA8eIlAXvGbghR/0EFsEPcxHC/CDNmouQqWyxkU7e7FYmqOYsV+2j8d4oH9wCZy7yws861K5CPc6EInu8+nwE4jFMUV9972YP92vT2vvaz6Xr3nc/8sj0D3WEgFRMPAbaZLIj8iM+4+/4HvfPZwE1y/LufQA26/7viitrve2d7V/Arb7YbYnEtwH8sytDmZG07d8gog8JQcB6CXQEec/r+qgGorPCeLxuqYnsW7Fa/cxU738YZabGuRj6CK5d0pkEIspP2NPDhuliaPD0EIcickYa7I4bJQ1CvWnX42ELBSYRTe+BeoChbyBZXCStZSgZpD4zMfuGvcFAugiBzgL34Da8lvHYgDLiQD3ukXd4fXs+g2G9xm/i9eHCeS2eIK+5IWUwhnQ9Z87qmbrTu1DFl1DrPPASDH74BSmmBeQjaO/DVhTfJPAQ9DdleYnGG7jXwOAb+IkvNfKor1gyuJxIAX0emLprX5mqKENX9to+8bRO2fgvl3XfYTpoUPDMN0iCIfehKDUC+JhjnLL/4PXbOaYP7kT0Yzi4b9HS4PTl2NXKJkrAR/k7kcn29W3ESZJH+hiEE3UHTtbuiRxOCbAgr5IvtO5CrZ76tRu3+A3o2uGfF7ja9XmZRx+O2Efurkr0RZD7xctIMow72U9OLx3Mi4CQBztBCMg/QPUSMHYKW2b6bAHhdxe4giMJAVP13Zv3B+7tDcGbx4A7P64yrK17XQgSySAWy3ad8IE0GhaFHEwLrIfVQdXFTQUb5AANXmMx/fIAXM68X8voZHsN2Pfk8ikHth7XnBdXtZZ/2Oz6oXuP8WAbxyVwi1ilMDUHqJyw94JE62F1HNo2AHrA/m1rf6ZkOhwnBO+bby/4vLocbPATn4kNSPJDvmgZe5EY8zggP7px5XjbjEhdF+baemeCKPMeUbNrLYpj1vA1RxUPX+SoUEblZvvzr6KXo2YSAZebHF5FxXz2Ig24W9Avx87eF5ge3IPKySYaFHmJIcNeZ6+yoUEe+zPr4+ZNre6Y3ORemubTNBX/Qvcx2Ptn2eYabCd8dsd0O4pCrGApXPuwd6spBPVgLUCxvmcw2w6mzw90FO+mPd/mwTnaZ7YfbtqvArE9PlIzFoMfd6W50LXo0IeAQbx9/hN0L5yh8p+teKzAmjl4L+BTjsXCr0INm1mKIYTaEnWFllnEOxf51FJcIiAhaWZAjHzIgHhsEPmToXg+EsMWrns5Qd/vRFwX20sD5aIF2FfiTni1zY7GvZbYnNhdzQa5OlrsTei4ZkCgUuwUh8Fqi10tbkKGWJvG4zOGEIdOu797vFvjuwTDX2a2oPvaroRD3MbcNuje/9obq2t9Q46f6ZowC7IxifYXPhDkdEM0QIsIsdgR0PWnILvAN04FWpJ4FRVDw/oTx0cll1cfdFfmaLrsFpMmgIPRoQmBkDfOe1dv2ZzvESQPAyB6o/NS8EOpwp4b1a8u5v0CnliLWFVR/d4bhG0K+6v9iwY8UiIp+t6hxCzqROLgFIRO0T8G29WC4k1OJ28MeX2xkm+FnP1/yzaUfvg88dDG4hjAteC1b3RvBx72udHIuGiQhKAg9mxAAgYvGirN+xXzV/QS/fx357etMZJsJZUOh/c6mUg+DMO+tRQr4L+qca56NFHi2aZIVXs5BAhyC0NYS8Fmqm2RmN9+IZTvrqcvPOOPnWAjA93ydZYSsgtaWcMiWd0HsR7bn0ksz0BnmoZ4CezLVtVcgmwnTKCNpW2ehhxMC5hhsvNT5/rN4b3iVL8bj5+XnkM+xxXgdivU6FVKPLQTCetAT5wS+98bt+Z8LgkiB10zXvZCWW0sg7ndqCbwFDc/H11nD764cQrMLVnLcZ3dIZ2Z94bQCufZRhAL7nnbGAl89AUZm184lBW4hH6alwDJy+eOC0KMJgXuG4L6NfjODXUEIvNroLLhzN3bXAc0WtuFX7Qua7TlCwsxPLiaHYE2Bh/o7i5bAmbJI9DPxNkN4ttHNkd1sAOR6Xnad3uQsVwEVZrbrvuthyGr3g0CkXELN2xyS/ew6w58gM2FXEJytG2Zi/9Vc5GqHhaFHE4IwqkC/44pRJp82i6Guz7VvXq9PMUxtxYLfrLkYdYKRNUCGqd9rCW2/exfkS+DeH05L4L+Ed2ci6MqEmt0HmA14HeHvL/ONoLEFuy3cw4psvo6JXy/54kY2hesuyP62GyuTBpfzev7t9V+CiZKXP4FgyMswtGS7dpn7/YlM2BokioMeTwi4sCvmIJov0Qhbd3dArucYlkCEqdc7AVTnqcwZjBmSjnADjt8MPkjg22XEbW6jlXisTwRD1rls7shuP/c/jvsAZNMCZPcHCaclCPOciaYEW8hnv2JcsIUlBeHgXZtO/vu6C2ySwAJzLogaPPFp5k907s+qUUunXKNizHR6Md/o0YRARDGWBN5dwKw/Jqi4mgmvdy6X6+83C2chhE8hKLYWojPQGZqbQq9oV163XNuxc3ewUMQgDCkwCJJfPYK6ezeyNWcjB34qfrfBlsw/u8z0JX0ICkKPJgSM2bMbt/3PEzkIRqc9OLOBXSlYMvrj+pnxODNb0ATZxzJmij7nmWkvzx1+qvEwM9J84Oeg5lc2n3LBxznb9k+LXPzBqPAaO8OS7NVKfqsMcJs0g00sg5CNFBjLV3cfoRDmeRTt8qI2y8ss5RVN4ajLJAfOJeW9R4Iw5oowyHyXvJO0dSdT5+6IHk0IFHjPTH0fT1YAIRBQLAKZbzX5+k0wOPvuHua9zjOQQOTRD/fxPsNMUQVjWFMBrP5klg1LKMjx3U/oe593Z2pHCkF2H4DsfgRhUAxNLw/vtEW6d626VS7/Jy2X/nalg2LQs+q3TyQKliZQ8DvI9DdgJnHK3hexjezl7ZU78xrnCMgzo7Sjjt6KHk0I8kEuD5lf2e41f8gNgZoEAbuKiecykw9bXzEYXNhZmu++LBX09plP/iLAr67sGgMngSDrWAQeU1zB7nfO+ZppOJlmcEbu8IyWfn4rTr8Mj5k5uelCceHlJKp4NEWURQMkfQgKQq8jBBLZwU0LnfleeL23xYj5FrUexsJVeSyO5LHNM2grgGg4z88p7LwDwHoqxey+sGziwi+/cu7rn+f81Hz+nOQis71gRbxRU27aTLt1eybPzXJ2TgW7XqeJgVnlAcpqUvEDr1dxPPlCPwMzQYY7W2NBuyBCIH0ICkGPJgR+JoPePtsKC4ejEGVuz7fObBZnb6FbOBlwE4D8zsPbXJBtmxe58UNuvgcimOVEZ2/pvCc917qD1njIbnboSRDXlbCXcwb4s5+NgmTCKyxQcTn3+WssyUUKeM+4s6WY9posc4luleZHOvNr5p7vgTl/ucxI3dMgJiGiRxMCP+w+A0/x4bBxd4IWIF9hXNhy0shZE+BXl3f94Wb0XvZSexZmzxq9BvYwhIIcw32wSUusMSgvfbCDmfNNcg73mW2GIU6dDbcAzV00h4NtlmDQzdm3vc/dn2BNAECA4MAnCvcgk4dfUjY+gzbeC2cGTVtLYBMZ+521qUwYYs8hahxE8e94drrKMC9NBgWhRxMCHc6HdnfRDASdQz5qvIzflL2drkTYLIVeMMwbYVT3+bXhK+SyXLyM+ZbQvOI61k06CMz3mvBzZYwyjrPqz5irBfSTOYWKH3HITEyTqdHxuw/hs1B6te1P9LjwyvSLCal+Dl3SDb4ok0j0gvroIuLCPrG/hMxzytVyz5jziSfGHO+70wfCSQrc5xDGdOCu0znhcNKDYiBrPZIQFIQeTQiAricBBT9rBEs4+AmWMITAK5mMV32dpQEoRv3kMxAVC34CM/yxPjP1vG2MLOux2ciApUQmU6CEnHmFne3tKmRzKRRnrPx/NuHPbeJejoBc0LkFWlAfMpXqmYQgGzHwakMkAUGp2MOAJ1cSf4sagMz+OdNqu5+TMKYDvzGhM563XiyruwQ9nhAUC34Pmts7vBgPZCG5sq2XuxPejM5U+YoDTjHq5P4C3m0VphWw6ynseHFWrZviRGEIDNdyChkmiB9T+JN9bpwA5quOdR+Xi5q462DfSbdKPQjizJY71Bm1OePbmWt/tvqzqe+D7oRfBEOxr7cYERDmfXNqL5weEEHPhB9hFrMadvkSylJDUBB2+wR/5Pro4AtkhPu4j+/s/nl9Oqs93fXJt20eMsY/Ohg010dsI99+G/fOiH/mg57X7IQc4iM/cEHhJQAURhnClA9+/OPugTC3t463Pq4z4HWoXt+ZDoXx/7onGQh7jYsriDKvSVgUfrec8FJz8zb8VOAMxekHC/i4n41in7cb4d830VyVicJjA7KVC7pqOV4lHmVQ6KeTcMIJJ2Dw4MEoKSnBwIEDcdZZZ2HDhg3W/g8//BCnn346GhoaUFpaiv333x/33HNP1nqPPPJIMMYcn9NOOy3n/vVoDYGfQBA3BjP23NraVRBVcLk+qvmSikwx1fXXgNzfBfs5hN/u2V0Y90JxxugHmy1nrsiogGAsnJSpbmbmESJpYRnzVLsenTEkSBVa8w9cEzUGDk2BR5ls27zKhH++iqNLsImX855ku4PcHGD3xp7Z5tsr/lyJ19gP3T1tMScgYfw33InA3HfWS0vgpx1w12uVJ+ZDXrOb0QwSbT8h3fvKB2Py5Mm47rrrMHDgQKxfvx5XXnklTj75ZCxduhQAsGLFCvTv3x9PPvkkGhoasHTpUlx44YVQVRWXX355YN0XXHABbrrpJut3aWlpzv3r0YSAz+L94EkWioDsj6WXU5ezfPZsfB7JQWC/mNleimxe++4BoDtoyYJVtQBcDnAwFywSlyAWQUI5azgRyhrn7aoT3gMdA6B6XCUCed4TAqAzsw3KvNsMhqaBq1T5/xQUpMmIHHfb1fkgzOvi+9wZDvMJGcsVvP1srWQr51bfG/BfXMrYy+C+S/mer1NDk6n18T/O/u4mpJk1uYic6GznJSQ9li726k+2tMGcFGQjWAxeoZPZ4X9PnffVLp8ZkBmOWDmfoKCz6e6ZCn/2s59Z34cMGYJrrrkGJ510ElKpFKLRKH7yk584yg8fPhzLli3DggULshKCsrIy1NfXF9S/Hk0IClWr5zKXyK2dsA95uP224owPiME1ZHh/m6XFAVU8n7AzQ/4yi7ZQ8VW1hWzmzN15Lpl7vASHOAT4ERyx34owuOvCPsd/4W3XASjmgMSP1YlBCzkkKq774R62LSHA/AcZfj0iTDfVyUZBzWPNd17WOZPOFI9dFe/tvl9+gl8MSBP3B6nvFY/nzFlf4VBYYaOH3R/nO2oTt0wwOMmEuxS59rvbctTF/Imf450k77TYdn+D9FD8m7OtYCLiHTTLibGYgVARlicPi8BxuxizG/P45uZmx+Z4PI54PF5g5Ta2bduGefPmYeLEiYhGo77lmpqaUFNTk7W+efPm4cknn0RdXR2OPfZYzJkzBxUVFTn1qUcTAsBLC+D1KHb9DNjrkfWaSfjtd2/namdRCIepg6u6RaHvzKdmv8B+r5n3Fc38ZQtj8imZvQ0ezuU8P+85r1VWmEEpjrMKhmIe67g2jEySYAt7vwHITxiGJaoEQDPvDScAZJ4DMR26Byngbdk1GC0rwq/wQ2thcEee+5fLJLN8e5hjig2/pX7ta+d8P3zrsf57aRa85sNwXAUvrZL7WzCcFNRNSHgJZrIC3eXiJ/q18PHFPvP8r734LgFc2Nu9VIQ+hlkPwToP4XtXoKGhwfF7zpw5mDt3bsH1Xn311bjvvvvQ1taGQw89FC+//LJv2WXLluHZZ5/FX/7yl8A6zzzzTAwbNgz19fX46KOPcO211+LDDz/EokWLcupbjyYEOpgl7JyztGB4vYSZ6i3/48SBIEgoZ2tHFOxcOHgJdNH+5jxb/7bt385ZFgOfPRNUV1mVZfY5yCQDGPnG3T0Sj8lHOHmdodc2/ls1z43PgnJpUxxkvGYqQVfby+ziJhAkdtQF3fKBEIdm+3iFkXAtM89K1NhkC9vrLDivD/kucuT2+8i5Hbe5SEDQOxxEZt0C2VCd80x+5HvfOUQthjfxdQpsr2ejEOEmaiUytxvbxOgWIm8zZRgSKZL0bP0Wr7v3+xOOcDmOMMMpu/IZX7duHSorK63fftqBuXPn4sYbbwysa/ny5Rg3bhwA4Oc//znOO+88fPnll7jxxhtx9tln4+WXXwZzqYZWrVqFE088ETfccAOmTp0aWP8FF1xgfR85ciT22WcfjBs3Du+//z4OPvjgwGNF9GhCANgvHp9LZVsxzjmL9IdXjDy3tWV7KL32u4mAuLSoW/B6MWHxZWSubUHInL3799ndbtgZkt9AIa4oFxb+/TMIjFsVS8I2g+gUN7dB2Hp0+GinfJP1+GmzXIljzEFCJ++7zZ9nt8kgjGNeMZDdF4aXAzhhML45ISZccgtpp2HKr27+zZ84AHZefz+IrbvDRt1tOqlAOI1COJs+70t2ZGtVFMzZ/Irs58Y7+TK/D7lQT68JhT1JQU712U66/uUZULgPgfm/srLSQQj8cPnll2f16B86dKj1vba2FrW1tdh3332x//77o6GhAW+//TYmTJhglVm9ejWOOuooXHDBBfjFL36R8zkcfPDBiEaj+OSTT3oPIVBcrxZ/SVv1CDQoKGMpRATHrVzgZNjcq9kecBRmz/74IOf18Cse+5zrjGey6Qy/dSbajf2JgN8M2mufFzQKrzHhfeXn5TVk53Ldw5Z0z4QNLVHu9XjX7T/Ty2f+nX3Q93aecw/D2fRC3rNEEp7dcFclX22O2Ldg0Q1rr7st/g55J/pxP5XON8AqS3B4o3v3lTK+iyOI8z3zTjNt7898Z7N73nPBa5+PWJezX/azzYmMHeMf9p7a2jPfMoxMIeqt7eDgfQ77jvmRYQd5ZQB8NBd5YRcsbsQFfF5NmQllEomEtW3VqlU46qijcM455+Dmm2/Oq95Vq1YhlUph4MCBOR3XowkBhzgEEICdFIdODGVqChHYTiwEmA422Qcu/jJqxKAyQpzZ3uSAHZImPjtedXozZOeAKM4x3BqIsKo577LioBnsee6eVYuDnV/5oN9dgWJqAiwwyhgQsgs5fzLBmI/qmbiQ95oxemsOrL0Biwg5ayniQNvpCKI7BnRLiyAKG3KV4SQ884o6V+HLjG7gb7g4lnDzQQaBEfblqv622/QmR2If3Xu5hqHQuypqBMXv2VCM9826jy5iFBbdPeTTD++++y7effddHHbYYaiursbnn3+OG264AXvttZelHVi1ahUmT56MadOmYdasWWhsbAQAqKqK/v37AwDWr1+PKVOm4PHHH8f48ePx2WefYd68eTjuuONQW1uL1atXY/bs2RgzZgwmTZqUUx97PCGwBLOwrVJJgAGIMnJ59EKY7fvPbGx+bpABbmtXhHK5vhiOdghIB+znrYdZ8Ceb4HbXnwt2hZAPg2xqw/zrhO/swKm6zizD4AxLJNj99KrRcrgCT4DFkIYCIjt1sXuw5ImSFIS7N1wIhr9S+d3xbM9fLgmLRJENQFiASyC3jnvkRZ48NBAh2nZrbfjvIG1cZxIuL4Ju9Kt4byYz/VR2DaHvhGtXjFlCJ12M0tJSLFiwAHPmzEFraysGDhyI6dOnY/78+ZZ/wnPPPYctW7Zg3rx5mDdvnnXskCFDsHbtWgBAKpXCmjVr0NbWBgCIxWJYvHgx7rnnHrS0tKChoQEzZszAnDlzoKpuT7FgMKLOSILbuWhubkZVVRU+WDUAFRVOT2x+Ml6DZtDAlU34WiTC9QyLV8+r7nwFsaji48+4SGQUYZjkJd3n4Pan8Iqj70ngAraYZ6GDWTfJ/9nwJwLZINYpzmntu2cIoBQp6KAIkhSB7kq7w2eoKnREmYYI0639jjpznDkpzPnE5EYebGSSVybM5oOCUMUjnGRA9D4PqxERy7ufErfvQFBkTTY/pFwQrGWzdQB+Y4eejqC1uQaMyCMc0Ymyih2IRJO+baVJgSaQHVFjQgA0KNa44zbH5oJ8n6OwaN2pY9roL9HU1GTZ+LlMGHLLzVBKSgqqX+/owJfXXe+ov7egx2sI3BAHSDcCZzFZ9lt1uAp1itrahXxfLrFfIplw7/Nrz88ckV25G65P+cCL5PHt3gO8rdL1atvtgMfV/IAhYHnaZedMPfw9cV9DBvF4riUw/Fw0KEhSxNAUCAOzkbrYnaY4M9ERY9xE1dUqVTdZcsaaZ1eN2/dBF8rbWoLin48YfVMYGFp21EBL+8eRi9C1KFI7vg9VKQ9VPhLpg0PHngBFyZ5lfvG7P8LA4e977uvKqUDYxFheZFli12K3IwS5QhQUdqRCePDB3X8WkDu86izcZmj6J5gV++U5dw87XnPjQvsS9prYKnfjCFGVzuuIwD3z866Ja108CYXgN8CY7cehmTNTDYpFBJmQt4DXFnZBkEw1tu3wpYMhSRF0UBQpM40xgzGLj0IDGEGBbbH2voZGVAJPmxzYlyKmY/O75nxfkOrdWY9hntPBTKfd3DQDIhKtFehotZOyiPfL2sYytyXaq6AkfgDGQt5VAkbsPw011eGcyhhjKCkpyQgxKwqEvBX86rvzEoj7nRqm4vUntycr7FQsZG2Ebp2psLujxxMCUTjwwUcU6EGzOS4U/fbzmSNPpOGeOQcJbO64GIZkhHkldGJmZjXny5yrIluBkVLXq0FL0DHXYOEyixSisch2nuJ58RkjmQLZXrfdVlGrTHM4Ylp9Y85+GjNn0UvbNSi6nEYBWPZ83rax0Q7JUmDcEx0UmhTY3SMzdTGQ0CPYpFWiTY8hQVHLWU2FDtU8EeMZFPMv+oEc95CfR2b7XYP88yMQtm8cikjbuXkd279mXxwyamzORzLGipqNrivBFxVzjk/BBhs/M1wh73lYhDEleSHwiGKobCUh6Jkw7F4ZQXrQwCylq9ueJT4vJLw6biLhZtAOZJHgJPzPppr3U8uL0IX9Xv0J+/zaoV3eGgiRYHg5a4q/xe1ufwe4jud2S75TTDFst2+r7nk/dDCkiNs+FUs481hpHUBcSSDCNOscFJ9RjJ9fZq42537eTR32s+V9DZglohWwnEiBW0i2UQyN6SpHXxXoBqkhg8boYEZEVUCt7l5aJIl5Lbnsmi171FAcZL4sYeeEybb+OO6oczuhT7s37OfTfsK9rnlQKCI53obOAScj7nwPQSTSS9shUTz0aEIAOAU+t/dy9biXE51bceY1g7dnm7ZfucO0QE6thPt78V+hTNIjIlt7YYmHtZ/sGaZX2KQOhjTsmae13DHZr7IYS62ZV9JaHpjI0nYA9j10XjuGNDGkoCJBqmFbJ9UgBqYaP00q+kS3oUTR4JO3J+M6uOmAl7XTOG97u+Gg6lw5zq2RyFTIerfvKGcucpDQo9iRLkNMSSPKNMSYZi+Ty8hxoD0gOs9EN7NZdk06olzgvM/2Vn9DgyjQJPKB06nTQOfP+XPxy/AT7EGTHnsiEKBvlRqCglAQIbj11ltx3XXX4ac//SnuvvtuAAAR4cYbb8TDDz+M7du345BDDsH999+PESNGWMclEglceeWVePrpp9He3o4pU6bggQcewJ577plT++JMFuApbJlFCIKOE/8DdlRC5szVi1nbIJ/vYeBFKjoDFqEhcwU+j8bcffEKReIvZZoYEuaqfBoUaKQgDQaNFDiX6OHt2oOTCoICHaq5sIxX5jjjpTc87xMURQdF0aHHkDC98Dv0KJJbq4HmgaipWo2+Q1ZlJHvyg/tauzPHic+AFeLHTSjk2m9qOrRUDFu37OFDDoL7wgA0Ne2D/WMzrPrcHt7ftH2I2N4vg0Uz+xjuLN10smtGPC+tg6iFcmpnvG3dEvnAuMKF+onkQiH4OytOooKO5fccCNZU8H7YZIAFlpc+BIUhb0KwfPlyPPzwwxg9erRj++23347f/e53eOyxx7Dvvvvi17/+NaZOnYo1a9ZYKy/NnDkTL730EubPn49+/fph9uzZOP7447FixYqc4yZFeJkIcj3e77vXA+5Wubu3hYFIaN2CybMhoWwucyjjZSLrYfc6UjzHDIIAQwNjCGkVCYogQRGkoCJFxsdS7ZMt0kSbPeMaAmFlP3cSGZ0YdJNsGMI/hlY9hvZEGfSt/UAd1fh2n+9g9N4HY9DAQXjjzTOsPne0l2HrVm9S6a32t//v3DEKfcoOd5UKd4XjsQocPnZK3o5iiqIEepE3Nx+DxZ+8CbWmPVBDZKx5YYkDMy9/JjrDny0XeGtqJIoHb20R80i6VSw40zx5vzXiOCcmzfIyZTi1hqYvj1k+n8yzEuGQFyFoaWnBmWeeif/+7//Gr3/9a2s7EeHuu+/G9ddfj+9///sAgD/+8Y+oq6vDU089hYsuughNTU145JFH8MQTT+Doo48GADz55JNoaGjA66+/jmOOOaagE+qMx93Pvurelkt0ghe8tF35asC86smmifCazSnMSGmcBkMHqeigCNp1Y9aeoCjaWivQsa3eIAtQBa98/hI76+PEgMFQhTMQWDqOfaLHIqLazlx8OCPTDFESK8dhRx6WITwH1P4UX6/9IQCgrKwWh347t8xcHIqidI7ndxHAGEMfJYEYS/s+h34C1uued6ezdJMB/ozIIb8QFIdghdUQeDslOicszmRb2XsoLlpnax+EscO3M6xw0tNDMyEWA3kRgssuuwwzZszA0Ucf7SAEX3zxBRobGzFt2jRrWzwexxFHHIGlS5fioosuwooVK5BKpRxlBg0ahJEjR2Lp0qWehCCRSDhyPfN1qvnMFcgM/ep8i5mzrc4awJyaApaDPqs4Z8/P7fNVl6CyYjQIDCoYygCUEsPgPrUYc/j4gtsJE2fthQMPnFhw2z0BfZQUypQkEqQGqkzFaJRCCWpYZFP5eiEoMRAgCUE+KKXjAKyA0xMn3GTAC2EdC93GKbejoFe5cO3bZAAIQQbsgwpDL374ciYE8+fPx/vvv4/ly5dn7ON5l+vq6hzb6+rq8OWXX1plYrEYqqurM8rw49249dZbsy4v6SYAbqtpZ8Lv+UmnYmjcuDd4fHBl9XpUVm7POIb7QvC89s7Kdx1b5X4HKTDUVI/HhEMK095I5A8FhBKmIQIdHSYpsMMw3WDWs+SllC0mWc7FjVFUBWdtvxcPyvmitnpvpFzbPE2QRYX33dQBK+V7fm3zkONMMhDomyB9CApCToRg3bp1+OlPf4qFCxeiJCA9pFv1SkRZ1bFBZa699lrMmjXL+t3c3IyGhgbn8Qhn93ejra0Ptm7eK0spn2NbR6Om+ijf/RE1hu8ccrjlF/HX136KihF/yhjEORnw8lUv1rMZph7enu03YDgQtlJUjs/dAAoIEQaUQEMCCiCkorXKmKt7MpAVNuleTaGrKGYh7YR1EpUIBz8TEidofumas42hYcaoXGNfCE5dgOgkzv3EJDoHORGCFStWYPPmzRg71k74oWka/v73v+O+++7DmjVrABhaAHHZxc2bN1tag/r6eiSTSWzfvt2hJdi8eTMmTvRW/8bjcc9kIdsbH0KyxZ0CNDeP6rKyGhz5nW8HlikWMnO+8+3+/gP+dRXfLCJesSQpSJghfzv1EpTL4XmXgz8PCiMoBGiBZW1nTgDmCoHF75Nf6KbRh0ySHv4pIkSiO7Fz507LGVkif/iNMWGOy/fNz0c74U8GKBwZkCaDgpATIZgyZQr+9a9/Obb9+Mc/xre+9S1cffXVGD58OOrr67Fo0SKMGTMGAJBMJrFkyRLcdtttAICxY8ciGo1i0aJFOPXUUwEAGzduxEcffYTbb789p86PGzdlt1l8wusZVOBMMZwLCfBaOtW539/ZTAdDEgra9ShaKYZmvRRlvdjRprvAb6blvpc26RRtyeKxXkaG/BHGPp1L9A/3QK8dvAaffPohDh5zWAG9601wU67sks1y3g3YH7Yur2Nz0Q2IkQRAHmTAKFq4yl8SgnCoqKjAyJEjHdvKy8vRr18/a/vMmTNxyy23YJ999sE+++yDW265BWVlZTjjjDMAAFVVVTjvvPMwe/Zs9OvXDzU1NbjyyisxatQoK+qgt4E7z3jCFSoU3vPXpzr4607476S58l4bxdCil2CnVoo6qSHYpQjjGMZYOAVubtuzIxv5NEJNyZGPoqeuad/dISboyh4I2LmT6ewWf7FsEciARMEoeqbCq666Cu3t7bj00kutxEQLFy50qP3uuusuRCIRnHrqqVZioscee6ygHAQSbni/iP5e3QwaDC/1DlLRoUfRpnNCUCJfx26C7FHeWY7vJDns1S+b6JoOjqyQ+aZEGBj3d9deXVEzEM53yU0GYJEB43cO5yNNBgWhYELw5ptvOn4zxjB37lzMnTvX95iSkhLce++9uPfeewttXqJoIOhQkICReKiDominKFr1GHbqJR658CW6GsFjXf7R+7nZ9nNvnWCqcVnx/V4knMi8vkKys273DjuTl/EtIhnIOdGcJAQFIb/gb4keDa8XjCf90M31A3iWwDYtjuZ0Sa6vpUSRUajQ7g5jnCiQgtLqymctfxjEzM7yoBP/BF9zDi/iH2imyrOXXmmICyYDEgWjxy9uJJEHfKZz/OXTSEHK1BK06TE0p0vlq7mLwQkbv0te6X556mKvm9sZEQY5Q4YSdjJMNb250JVTuLOsz4CoSSh0HYQgnZFXiKObDORLgGUegsIgNQTdHMUeQAkwlxD22A6+cqGCJFQk9AhatTjamitQXVFT5J5I5IJgT/DOq7sYYDBCJdWMpa8yy0nCUAic5gFyzcCD77H/ihL5hCra/iPu3CqZ6cwVDzIgnQh3DSQh6EIUX7jnVmO28CK+rLChIYggQVG0pEtwXGoKxow8sOD+SnQObG/yAA8Ds9CusiPzQZ87jPn1IlR6WglfiEuSG/qkwq9lyNySDoiZNHkdOpSMJcTt+y3JQHeANBnsItiJN7qmLW5bFNN/ul87DQwaMaTN1QYTegSRzf0wfeLULuilRBCcaYa8kWvcd1fC7ezmF6ooTVPFQT7PgRgBEj5nROZYIpoc+P10r3fhJAP2toLJgHQqLAiSEHQRWltboca+tn47XiDzfzHVNUHPtI7M9CUEgxDoYNBIQZpUtOtRTFQPQ1VVVRF7JpEP7AHUuHNez0pQHoJd6UPATVHhlzmWpKBQkINy8eDPoOtqT1Fy1zsC4j0zzJLmc8oy12fxIwPFILPSh6AwSELQRfjmm0bsMeR9AMbDr8LOyJY9uYe4gYV6Yv0Yu1+9ulmOq/XSpCLd0gf77rFf1rYkOh9E/jk68psLdt9Rr/v2rOfBXx/ohk3XMrU52Y70qt3UDBCs1Td5K6K/AC9ZVDOBfIDyhvQh6CKs/vgpxKNJK5RGAUFF9hchG6cv3rNvW/LIdCyMNldi9P6jitaCRH4oLy/HN40nAvB6Hpg1GzP+exsXFGHDrhovDbsyzI93eJscy4sD54JAlOMM3CuTQXAGDPEIhZmBj0y3yICX8yCsbfKudxdIDUEXoLW1FZHYB1CZkWVeVKK51yvYdbBDlnQyzAZVHQOzrlIp0flQFAWq0heAbXsX6Zu3LT5XdK7WgCzNlnyeugI8P6RNB8IZDPJDZguMOTUAXs6inZJnQPoQFARJCLoA27d/gz0aPrC9wQXvnZyfvRzNBeK2oJfPyfIZ0qTgwH4HS0LQzeA1hCoZMef51Nt5iY+sx50MkaDItQw6BYwx6LoCRTHWwRQd/ghsl8zEvUwEQP5kgPuj+LYnfQgKgjQZdAE0LW2ozJj5gXnhzefazZzFnADZB3qv/XkOtjw0DYCmq2BMri3RncBV7n5BYKJTYUbSos7tWk71Bz3TkibkjwP2H4et65w+P841LoODPQsB81H9214JNgrRDMig1M6FJARdgNX/fhKxWDJTm+XxZFvOfeSTWcw1mPppA4L2h3kZI99UY+JBk0KUlOhK2DM+G2EGSPFZKvbsvFgDNHP9l8gNJSUl0LW4Y5soesPdp+JdfS8zQSHJp0JZA6hIn14KaTLoAhBaAKYb+cQBgGUO7Fxly4Tv7kE/7IuULwmwj2eAHkFZWVkOR0l0NvgMLFBlisxAsFCEAYWYDbLlR8i1XvFtkMgXYWWbJQcLJIo8LNb9/LnL5INspgKrfmkyKAhSQ9DFcJNQI1WwYbPXTM2A+FTzl8DvGc2cLXq/NOJLGuZ513vxS9HTwADH0sJ+TlyZR0nsrggrQH2Pz5MciInP3DXkS/EKPReJ8JCEoAth+QYQoJGRCChFDClSoItliFkOWB1UuBJHJANG+I//i8lDhqTA6Emw07/aZCAfe2t+97yY3FHssSSl+SMXK30umqQwddkhjm5zQe4t5EwGurnJ4IQTTsDgwYNRUlKCgQMH4qyzzsKGDRscZRhjGZ+HHnoosN5EIoErrrgCtbW1KC8vxwknnICvv/468BgvSELQBTAeauOjgSENhhQYkkKaYMCY5SnMmTm8lKXMb9liBMLD1ylN+K6RfDS6M/zvDll/g8a1bL4n3QGK5KR5ITeZxrIK3NxcAMlzAatctQO2bM7xIejmhGDy5Ml49tlnsWbNGjz//PP47LPPcPLJJ2eUe/TRR7Fx40brc8455wTWO3PmTLzwwguYP38+3nrrLbS0tOD444+Hpmk59U/6EHQRyPQQB3j0gDNRiwJmvzSMjJeU+AsR/ITae+02/KCa75d79uVm9TXte8uQw+4MblmC9zws25hWnCVuzbry3OcH25emu1GUnoFchGhYH4Pc8lTkf984Cdhd7/zPfvYz6/uQIUNwzTXX4KSTTkIqlUI0GrX29e3bF/X19aHqbGpqwiOPPIInnngCRx99NADgySefRENDA15//XUcc8wxofsnp4FdBDcZENf4MsIQXa+A+dOZUNQbXK0WRAasbHa8PebP2AnA8KqRkhB0UxTTD7xQFDu1jFzcqDDkOsEVfU0688qH0Vi5V0jkYK5PELj7VaEfAGhubnZ8EolEfifvg23btmHevHmYOHGigwwAwOWXX47a2lp8+9vfxkMPPQRd9x/dV6xYgVQqhWnTplnbBg0ahJEjR2Lp0qU59UkSgi4ACf+JnOow2yHMORQyl+mg4JeVGfMtjfy9vhUQoiDEmY5kqhmbN2/C5s2bsGXzJmzfvq3QHkgUAAKQhmqYmFykUoRf1sswz08na0s928v8TY5+JFNtXdij3QFhRwomfCt2+mDvPngJ+0wi4BT7hs8TZXx8UUSTQUNDA6qqqqzPrbfeGvYCBOLqq69GeXk5+vXrh6+++gp//vOfHft/9atf4bnnnsPrr7+O0047DbNnz8Ytt9ziW19jYyNisRiqq6sd2+vq6tDY2JhT3yQh6AIwACrI+DBChJH1O2J+3DeCAZY/Af8N2P4IbihmG2GGAy40IszZvz6Khj0ibRgX34zRQ+ci2XQk9KYpYM1H47OVl+V0zhLFRRtF8XGyFmtTfdGYLscOvQSpHF7fsMoeHvUSZFcms4wWwkQVhEzhYK/nyFvf2Px4AS1IBKO4pgUOY3zJrNs9w3eaB5hpVjWeK15SJIfG+ghZxrgiEoJ169ahqanJ+lx77bWeTc6dO9fTEVD8vPfee1b5n//85/jggw+wcOFCqKqKs88+GyQkCvnFL36BCRMm4KCDDsLs2bNx00034be//W3QWXtfCqKctbzSh6AL4DXT5zZ7cm0PBY8VD8n1373dijBgTnLh7qcKQGU6oiXtUFkHIlCgMgWRSEsuPZQoMggMGqnQmAYNCnToKDyPZLAZyk+TVCz4ObYaZi0zEz9LdmIPejPcnifFBF/H1b9lUbvFiSC5yriP7+r0y5WVlaisrMxa7vLLL8dpp50WWGbo0KHW99raWtTW1mLffffF/vvvj4aGBrz99tuYMGGC57GHHnoompubsWnTJtTV1WXsr6+vRzKZxPbt2x1ags2bN2PixIlZ+y9CEoIuBh/wGCMwEtYe93h7cnGu4S9Z5g6XBwIButkJ9+zOttOZxxBBYwRGOrTd1cunByL3Vev8yufiKGajWI9CVntwSI2XRDCc6YIykd9TEFSbPXY5Uydnmg24VsANHcyYnECHynRLi2CkvQ44F8qYK+V+BjkezwV8PuCagSD/hA8++AAlJSXo27ev5/6xY8ciGo1i0aJFOPXUUwEAGzduxEcffYTbb789p/5IQtBFsIiApRQVFnlhmYM8WX/s3wUNjsRAjKDB9k736qPCmJWnwCAZhDQAKkg5LFEoxGHW0wnV4wj+DDFWXN+A4goQJ2ztAH8jJBPtbFhaRGHRKfd4k41U5NoWr9PPNMU1qNxsSgizrguK4wjTSY/cu+++i3fffReHHXYYqqur8fnnn+OGG27AXnvtZWkHXnrpJTQ2NmLChAkoLS3F3/72N1x//fW48MILEY8baanXr1+PKVOm4PHHH8f48eNRVVWF8847D7Nnz0a/fv1QU1ODK6+8EqNGjbKiDsJCEoIuhE0K7P9BoV/ul0fUAeRDEMR3xet4LvIjwquvk9GmJgfmXQ47R4XtfuUN5+DJQN7rYmRFZ4p+f+zOYWedDa/rFu5qitFMduCnu6Zsz4NzjGFCnZmtBZEBxRrxjNwtu8PqmKWlpViwYAHmzJmD1tZWDBw4ENOnT8f8+fMtYR+NRvHAAw9g1qxZ0HUdw4cPx0033YTLLrN9uFKpFNasWYO2Ntvh9q677kIkEsGpp56K9vZ2TJkyBY899hhUNTfDoiQEXQSnIC+Mb4f1O/D2J/B/qXkEBDMzwuhE0M3hOT+BIlE82JZWxsJ7hQcRTp0YlCyEtBjDMGWxKXu1K5EfuOkwt9GF+ToCFtoXnmPF6Rron31QfK51ABDNqryGgGe2O69lMGrUKLzxxhuBZaZPn47p06cHlhk6dKjDCREwFra69957ce+99xbUR0kIugiMGQJXNB10hYU0SCPgfu6NJEnGVgUMaehIm8OL1BDsWtjOqGHmzwSFhU39G0AQEe65KTbE1MsSuUMHN/vZd8pphhHhHfufL9z3TEzIxtsL8mhxaxREXwQGw+E5sLfd2GTQEyAJQRdBjDTgg6wC7xDCUPCINMi5CuE7FwspACkyXsMUcc8B6UHQHZCLhzUDQS2C70D+uqxsDo1+RxlhuQrIDD+TyAVeV0yBbQ7IJFn++kYxm6UfOczeuqiZ9D5CDDz0PlaMypLPRGdC5iHoAtTWTEUqFbNiwZ2zva4FOT5MSApi9CRNwE5dRwvpSABIA9BgmA8kdh0sU4E5QBsDtfc94TZY4UjfesNZl23kMpvMl0YosPN0yOE/N2jJGsc1EycifrCoG4Up7Y9cJ+c8yVA2rZdVjvFWAvpIRfr0UkhC0AUYNGg/aJoiEIHsKPZQKM7XvFKEihnudBgZDXXxU9TeSOSKklhfUFoJRSL5PSaYfiHEfBMTZXPW4sQRyMXZz3Bq5Ct28nq8684ET06Toq6MOt89MLT2dNj+Jk6tklNLWXyqlSsZCLPqBtdu2M9vcLQBK9Knt0ISgv/f3pmHWVGd+f976vbt2wvQNjT0Is0iwQ2IbJEI+QUUhHFAk4eJBsEEHh0eGUUlQjToZOz4KBhn4oYzzsQwCILBZBDHjGEEXMj4IJGAGBaDKCiLdNoFaNZebp3fH3VP7eutulv3+3megu6qU1Wnuk6d8553O/mKj6/L05Js+nC8G7pWwugtTPbcXHPF8CloP9UlLcc8z+xuGXAm01/bGBthLGfdx5DkDC1cSdNMBISx1Iw6NbNmShy/qlmyUeD7Ne5E1QfYtwYrEpTMroxxyJypG5E5yIcgS6jagZRzoRvmjtLOMdDZ8mc8TnQMGGOIMQ4poK4myvWp/GoH9IKoWUNgFlG54ajysxh4KPgwfRQfElnVEHAoaWzFUutCLAj+N3Z2QvUbpOpfz5TqC4MIAeRUGAoSCPIQu5mUgvbJ+VFthW3XND/LL4ogp4SCIG/WS3T04ywWxFzgfg0Zbl4NRuc3mgumBwMUzYDurYnQTyk121b2ZV49LgZzptNQ+GlJRr8G/+Rz2GEhQAJBDnF3C8vMvdyubP5QZZBNLZ8oYkkU+fC9t5upufVxem9y2+Me5wdBmJ/04bcCvQAqwuSI4KirAjLjigGA8q7bkZ1JsOJDogWQisgqX+fC3pwEOOcwcDwxKCQQELlAnxuMQ/tYrCYFm5AgxyPGawerj33MeSd3vM0bhIbA7W0IRzJLznjPF+hX4RsNju2XaQYDxrmroEJYURzwNIdCEaonnPNkyJB0vv1R3tmag8BaIkgbcxr4qUVkDtIKZxtuP3vTe2RHdZ/gp2jRB/TR5SEewgCQvjYnqqhSS9t2UflKNkOSWMY7Btmw/DfhH9GX6J2BhSMfg/1kwa8J0kmjKdsec7qimnPTUBfrYKQ5pAqS7XF8+eF0fxVNd+vEkECQZYye+6nfM+w5q97LV4iZJhjot07+nRQcQU090Qy9LJIOVeQhoFYXHPENJ7mEVh5DG5eUDcrWbvBA0X/x6Wtj9IKHG0pfIhn6FeNx63X1eVKS7XGc+GgWrrlyvuM9hA9B2K2zQiaDHKC5BYpGnwXnnpTyUE5lOHSTBLnpf/PPRP4j5layzSzL7aywb9puYNCbB8RwZKcO5lDWV2hnNE8Jg5x632JhMsa4uny55lBofNf64FB93gnrKqzmCJBgpgf91fyGH2qTFEDmSUsefyI6SCDIEeqgy5k6WPvpBr0cA91QFhoBwJWPy21hG6IwMbcPlvInyCXmQUQvGJjLKZos/T4iHZTZtuI7AI8EUQIJANctgWyH3tdJ/O79jpwETS/zl9InCsFEKmpHxcCVWPfWKeeTyKkwFIFE8YaGBjDGDFtNTY16nHOOhoYG1NXVobS0FOPGjcPu3bsN12hpacEdd9yBqqoqlJeX47rrrsPhw4ejeZoCw9juMtdpaxK8sWPoxO2+w6MtH8t8+wdkbuJlnEO6qZg5gGRKtZ2E1CGWvc0l5nyA7omCrdkDzX9/yXzcZz2My3GLSAhvFKFA03PFitpReeFvnMuTySAUgXVzgwYNwtGjR9Vt586d6rFHH30Ujz32GJ5++mls3boVNTU1uPrqq3Hy5Em1zLx587B27VqsXr0ab7/9Nk6dOoUpU6YgmUxG80QFgNmHRU5tgR0LQ3SW1M0WHkEGR73fh9/kRP5NC8ERtfHySRHfg/ifWmo4hB1e/7tzWeX9GzRMptExTI+j5iSwETyczzLXkBxNM0lgk0FRUZFBKyDgnOOJJ57A/fffj6lTpwIAli9fjurqarzwwgu49dZbceLECSxduhTPP/88JkyYAABYuXIl6uvrsXHjRkyaNCnk4xQ2MgApghmR39m/+U7ZDTwjskW+vlfnlRT9qbgJ/9ibjdT0P+oet74jbM/EmPdCRnr0UShCiBQaA0fIZBCKwBqCffv2oa6uDv3798e0adOwf/9+AMCBAwfQ2NiIiRMnqmUTiQTGjh2LzZs3AwC2bduGtrY2Q5m6ujoMHjxYLWNHS0sLmpubDVtHg3vMnAJdK02hgpIQdTzMjqFebSxM+wt6rr0XAdQFmbTENkSU6POfWI6ZVjw0p6EO2z8x3b/+yit3tAtRtS1PJoNQBBIIRo0ahRUrVuC1117Ds88+i8bGRowePRpffvklGhsbAQDV1dWGc6qrq9VjjY2NKC4uRmVlpWMZOxYvXoyKigp1q6+vD1LtvEcfIxwUc9v1+mipey1cgqj09apis1Dguhxyhmz2bj4DAtXnwaeKm0gHZvopuD+A/dXsMF5R5gxJmxUs3Vo1t9SXyCSBBIJrrrkGf/d3f4chQ4ZgwoQJePXVVwEopgEBMxksOeeWfWa8yixcuBAnTpxQt0OHDgWpdt5h/gAUJxvA6ZNkho/WXWI3d+jGDlf3W2cWgzsBhqS1XNun/J/9usiwFwqYqRyH0W5dXPoFvvji84zXsbNgdDDUC2BBlPn+sHvbmpbKnDDJ2jqs2iwfkyazg1a6WyclVMBveXk5hgwZgn379ql+BeaZflNTk6o1qKmpQWtrK44dO+ZYxo5EIoFu3boZto6AJhg4CQLwXrrWZmU5w2EnCdtmJmifbUyhtPQrNDZ+5lYTIgso7yfIXElrZblO9OPXr0X/dJV1n+DQ4X0ZqlHnwzzwakmC/JwbsOUxOxFDMQXJJgFEizwwaRVMZTw1qSQQhCKUQNDS0oIPPvgAtbW16N+/P2pqarBhwwb1eGtrKzZt2oTRo0cDAEaMGIF4PG4oc/ToUezatUst01Gxc+ATA74E+4buvY69glkl7Kec3e9u96qqOYSDh3a7lCAyDUdQlX5qDfk8UbZ61UKf0JZIj+7n1eD0ie6Bz9PP2J3LBB8rGeOQmKxuSsYV6PKvGN94jClZKvXYCQWO9yMfglAEijJYsGABrr32WvTp0wdNTU146KGH0NzcjJkzZ4Ixhnnz5mHRokUYOHAgBg4ciEWLFqGsrAzTpyu5pysqKnDLLbdg/vz56NGjB7p3744FCxaoJoiODNP9r9hKOcwhPm7n6eG6/dyjnPF3LQEStzmnE38HBYM1vayxJYhOW1ngyGg6yLTTqGP6Gc5SoY/UwjLN1wZcjD+/WY/yiq98nyNWlszG2xGTHM4VYVViWptVtQSMA9woCDivwkBESSCB4PDhw7jxxhvxxRdfoGfPnvjmN7+JLVu2oG/fvgCAe+65B2fPnsVtt92GY8eOYdSoUVi/fj26du2qXuPxxx9HUVERbrjhBpw9exbjx4/Hc889h1gsFu2T5RHl5V3x6cF+OL/PXyy9ZvZDwrTkpF7L3hL5BYeU6kStiX6YriXpl4m1t9tnucX5WJSJyB769MTK7wDgvdy0nUCptLDgLYqlMiLKnKWslwxn5WIAQFfpHGIpvZbR70XzQXAkiqbWiZtqIIFg9erVrscZY2hoaEBDQ4NjmZKSEixZsgRLliwJcuuCprKyO5qbh+F8/CWS66X3CRKFThuPIQkJEtyTeMk6U4FoK3pHslzNsty0WURuEAmClME+3V4lvVYlhAKRkE0IKm08BjDld732wpcPCudgIVNuhj2/kKFVRHKMaOhKtkL7jyo3vuFEvpEUSVwNseLG7pKnhAGlrKRz3sr8MOzVQoM7RBKZwKktpCsO+MoP4HB9Q0QJa0ecJcGhtfUw9SKCQ4sb5RitK7d+VkLu9uxIUysYUt73jo3MlVhumTHbxYGEB7d9/nqfiV0ybELKpYai82L2ONLgumMc2qw92NXdLfz60ELNkV9nsmAcSJkvGZd1GQ3TMKmSySAUpCHIM9Jti0HO8xuVQOQPiUQC57640HVY94qayplLFgecxABqf5lHmXE7a4nEcslhWocELWzQLKzq4wiYrox+5i8EUX1klfAXCNJuKcogHCQQ5BhhP9N72jqUdD0iB10YiSgoEokEyvhQ13csZlQS7NuRn641EyID+YbnHr2RyZrux2Xg9T06GoMIjVFVNqYCg2BgvhIMpi7q17IHmQzyAgfpnDNwh1wEflSvfmy61FUXGtqaF3ZGJokBMueQ1IVqzUan3A7P1N6yjzZAu08qjL+FC/NTwl693RRF3ewyWcqpftEti6sFMhmEggSCnGFs/HZe4BxQhAKb8kTnw18L4JAYA+ccki6+QKJW1Mnhup/8tYJwbYUHGMiVtirbeLoIbYHfa0Wh8ieTAZF1zG3Oyf4rA77Sitrj/UlzMjUUEFb7rBN6R64spZxBJ47W6piEHhmDeTYJPwQ7E0JHaVrXXXcd+vTpg5KSEtTW1uIHP/gBPvtMSwn/3HPPgTFmuzU1NTled9y4cZby06ZNC1w/0hDkAUK1q4/GBcIpd/1b/qB6+JoRGQ0BmlnmAwxQUsD6MAYxBkjcI4lLpCgLlAUVCpTZn3NLz5e0yx0H97+nQY+Uil6KGnP71URWY7okbirjqyXkucngyiuvxH333Yfa2locOXIECxYswPe+9z1s3rwZAPD9738ff/M3f2M4Z9asWTh37hx69erleu3Zs2fjwQcfVH8vLS0NXD8SCHKI02JCfgSBTNv/9c5Aoj4tLSczeEfCCwYlz7tf50CWSgEbhPTblT9hIJ3rH2z8A4ahY691ki08x0vXkMPwo61TtkPjegV6zxdNYEjCKdxWd608Nxn86Ec/Un/u27cvfvKTn+C73/0u2traEI/HUVpaahjIP//8c7zxxhtYunSp57XLysrURQbThUwGeUB67S99ccCPSk7vJSzc0862LU/7nkR4GAOKWBIxX6mAlTISS+WGD2BqSA8Ot1XO9T4ygcLIwNFe9Oe0a0UEgzv8DEQzAXEyX0mqz4F297RMXjyiDUBzc7Nha2lp8V8PH3z11VdYtWoVRo8ejXg8bltmxYoVKCsrw/e+9z3P661atQpVVVUYNGgQFixYgJMng0/gSCDIEnaSsRmvjjIKwTXdnHUMgCS5p8wlMo//JK6C1BCccc27f3OBv2IdxWpcyETbaLz6QBGKaKdBEPuzaUCqr69HRUWFui1evDiS6957770oLy9Hjx49cPDgQfz3f/+3Y9n//M//xPTp0z3V/zNmzMCvf/1rvPXWW/jpT3+KNWvWYOrUqYHrRgJBnuDkVGg+bt6XCZxih8mcm3vc8gx4nZd50ruLnAql1PsLSNTWOiVCI2knFPjVFESVlOjQoUM4ceKEui1cuND2fg0NDY6OgGL705/+pJb/8Y9/jPfeew/r169HLBbDD3/4Q3Abafqdd97Bnj17cMstt3g+8+zZszFhwgQMHjwY06ZNw3/9139h48aN2L59u6+/mYB8CHKIvtEL5z53A1bgRJ5EB0SfKjb9CBR7Mu2b4nZfOygGJlrcVic0rpGh7RMOx2EXVQtyrpRyrdYLib7O5xyhw11S53fr1g3dunXzLD537lxPj/5+/fqpP1dVVaGqqgoXXnghLrnkEtTX12PLli244oorDOf86le/wtChQzFixIjAjzB8+HDE43Hs27cPw4cP930eCQRZRp8tjKU8Z9Wc3R7eLEJLEKbD9rOuuPgQ/Ya4EblBUaVGKxRwjoyaFzSfFLcyfldeILJPepOSdNT9oq3ke6SJGODTQWgGzP4Jp06dwm9+85u0zRS7d+9GW1sbamtrA51HJoMsoh/87fZH+TLcPlm7ztbOHGFW4xK5x/zuWMoZKzr7ambft/YNBHFyJME0CIm28R4llL+nnOPF0BjTnF7FZg5/9iNAGsqHNBdkci2Dd999F08//TR27NiBTz/9FG+++SamT5+OAQMGWLQDL774Itrb2zFjxgzLdY4cOYKLL74Y7777LgDg448/xoMPPog//elP+OSTT/D73/8e119/PYYNG4YxY8YEqiMJBDlCFQJSK3s55fV2I+gsys2D2C7cSGgkVMGA+uWc4uRnom8/Vm/tdMjMQGFMluQFNbZ0qep+ietxBs08oF/ZMNxKl8HdlTVhT9sYFKFAX5dAQkGEUQZRU1paipdeegnjx4/HRRddhJtvvhmDBw/Gpk2bkEgkDGWXLl2KqVOnorKy0nKdtrY27N27F2fOnAEAFBcX4/XXX8ekSZNw0UUX4c4778TEiROxceNGxGKxQHUkk0EO0M/mnLQGToQxGbie69IZcNP/RO7w9tQG7N80N5TIBVqX7we9boC0VEEQ2kYnU5J+4Na3lDDLp4t3yyztjOuO+3uTarJ2pmkxhFBQyKakIUOG4I033vBVViQqsqNfv34GJ8T6+nps2rQpdP0AEgiyBmM9wbkExmTFRss1W6mUSiCj/2jscnoHvKPrHvPHWbifGeGPbAdt2ZPOBEwqOo0zZ86grKwsE1XqkAhTklkAE78b59/RiPp6oUDzQwrjGKos1sWRcm5U7+IMk5UtDGHPL2TIZJAlLrnk+2hvKwZg1AqoGxO5vO3tpsFMCfb7uEkEMHQUObYnEj7g/rpuO78POUCfnElNkLUdepVn6NF3Dz76eFfmKtVBcffV0N6BzFlq+XRv3yInNC2i80QmvUkOV/tHwIf5II9NBoUAaQiyBkNrqrEJ6Vnf7mJQGn0SygfqV273r4YDuOmK4jypMy/vVWj4eOHmt6xoF/UWW+Zq8eWpwYExLwW/cm1fHgG6AUdOzfzsOvd2SDgrF+MML8ZpOYGzcjHOyXGMygPtRqFi7kuswn/Yv62mfRLtS1Z/N67QYjZRKBEtXv1PaiUDxnWaAiITkIYgi8imTS+MSlDy1EtwD/uyqgDtythjTg2qhz6y/OfCfhNw7nSXwOfZzcz8DeJe1w0229ewN19wKMJwGyS08CKckYtxSi7BiWQZ3HPsE06YtQT62XZUmCfVQSKT/NeE+6p7PkcZFAIkEOQB6Svq7M/2JgpPdCLbVFf3hpy0z3muJ7qhM3N+B24tXEqJvRLjkCAjzihldhiMQpu9i7Cd1tJ6najrAttkSM4h2D6EApGYKOzWSaFxIS9JP5zQD/qXzpj/u8WLT+L48eMB70ZkGzt9QL7h5bMiQihjTEaMyaTCShO7t+/2pxRCgVOLSaclmaMa/FzD2aTlbsYiDUE4SCDICdYuWwZT/AeQHQFVb6qwO2ZXhZreH2HfR8FyYxPZx9hhGmdgQcNcM4G2AqNHOciqUEBEh9OAKvYaVf7hW4qXaakTj795BwkEOcDJ7t8OLVFIJuNtk2A4yYvR7iiDCycwuyNELpE5SwmPzFPF64bf8zL1xr3S2DAIwUFGkepxQ/hFTDKCYneOXpuT7pqD+vPM7TbSCRCPaOukUJRBlrFrb84DsIZwDtK8dvXnGxcd8YpQaOUxnJXjSEgyJCZbE5iIuugWNiHygyQYklyR42PMu8sP+/aiXttAYrKqlRIigXk+KkRi4Wib9PGchBFNse79lxM9ij7xj7mP0a6r5RcQpfy8HdHv2Ze0T2JkPeJNFCr/ztzlkYYgy9jH+lqFAbFHdIpR6Qw4gLZUtgO3jl4cI2VtfiFmzn6EAeg0Tmb8T4ScZoQszZmdcUEvO6SUQ6HiWCgjRq0wEPp368tez7R00pKPv3XQ6wtk6ENPvaKllHL6lNxE5iENQRZxVHxy649qrDhLfUKuTlj+PhYhocchg0mtkJBEEo7dPTgT3TZ9jPlCDDzUABmtRjQNlTRX1ocXdWFwXlJX71hIRIv+qzasZwDrTN5uZq/PSOjvfu7GBnNOArvr+mptES5/3BkhgSCLOPkOcMPPpmbvYUoArB+K1xBewtrV8B27DgAw5hCXGamS8oWOkERK1gkBos2bW7mUEoaLIGfUn6YzIr5988JG+gRWAa7kWUpbtM34jsUS63qzgNlEoF+G3Y+WgEwG4aB+PseYQ3LMx+zUc/4WiHFSwll/99I9ePk3ENkjWv+r/Bho7ZxXtRUcZcRBeQiixdijWJccjq5lSJZezFwTrW/hNumT7Zwj86PVdkxIQ5BDuPqPIL2mbjfD8rqv/k4yoDoQOn26Qe9BZIZ80WaGqQbnSvILs5ZAHaBgNBfkOkyyEJHAU5oY/+p35wkCSzmXaleKyiFfe+OpFmCTzljvHOlsUDBVLHylOiUkEOQQ/0vB2p3r1UlaJW3zXv3MX0QV+Ls2kSvsokwyf8/o72dnMxZZ6oWTWwxJgHPwzqzDTQMhQEmqmMV8eZ0wddVVf287qtgPfz4LPh0kyWQQCjIZ5AizdsCHdSzYtV326+10FpMEs8rgIuSRyD2yzzCvSAfwLL96Bo44kihmYmvPfiUKmPMqqnH6RKWLMdIZRQuQ/b+1PurAznSgh1pC5iCBIAcEscsbP09/H4mzQOBnmBBaArLbdQQklw4+N12/gv3AozmPxZiMYtaOYtZOPgQBuXDgIJw51lv9XWgL/GMTU5CFabPZnyCtXkfm0WydFDIZZBGx7jhg70Boh94/N6pm6qYC1tuoZZDEmI8E1RVJjKl9nNVpKx3fEOf8Br5rZvIjUK/MOBjXeZazJJI0JwyNEAq8tEth32sYLL5NXGm7gXo+8iEIBQkEWcbeYS//0Dt8if9JS5B7gsybtK6UQ2JaKGlQonz3Uspx1b7Rm9e6V8SXWIRe750ZRSgwJhvLN1Og5vUAaLlZ/cMQgQ9BuNMLGpoAZglJkpCUY7bH/JoO3I/rm7E/d0MGpDLC2ZcRHYjw/s2vrqMz4+9N+DMj+fVfia6b1JY2NnqSWx3fNCdDIiq0v6XE/JoC7DVC/oKfg7Ubi+Y0XdMBkRYkEGSJ2treOPrJVQ5Ho2vwXg6F4n/RGTDAIlLrnQ9ZamU6EZJI5J505stRhitGcS3mGWOj5eInUTT/8BqmNQHC/d2J8FJjGLRXWmPXG0ezdVJIIMgSsVgMslxqSV8cZdNzn+1ZfReUWZndp60P/9KfQJJ6LikpKcGpzy9M40z37tuPgKF3bA2jwNcLm051EolsRGIiymMfNZn34A/qwmgW+rwSFjlei0ezdVYCCwRHjhzBTTfdhB49eqCsrAxDhw7Ftm3b1OOcczQ0NKCurg6lpaUYN24cdu/ebbhGS0sL7rjjDlRVVaG8vBzXXXcdDh8+HP5pCgKlccup2GB9Q7db60Ab5P102s4dP9eVEdfU7LPG9KFO53fi7yQvKCkpQVFyuK+y4n3qP/BQyyXzaIJP9S3U6VqiXRYxGbGUaaGtrSXknQmB07csB1LP+9MYOol85mgmcyQER0RRB0QgAgkEx44dw5gxYxCPx7Fu3Trs2bMHv/jFL3DeeeepZR599FE89thjePrpp7F161bU1NTg6quvxsmTJ9Uy8+bNw9q1a7F69Wq8/fbbOHXqFKZMmYJksmOHF1ntpMyw306JKmbx1v32KyR6ddkirY3MmfoRGpOCKIKKsswuQ3tqkzlDkj7I3MP9zeiNy2G7t4kgwl5QL3RNCyXOF23MqVapoUANS+QAAz79fHmg+xLOGCYAnEHmEmQuvIX8nC8GDrfyzlFTSTBVMykGfnGGOUGRHiVSxqOOPKKtkxIoyuDnP/856uvrsWzZMnVfv3791J8553jiiSdw//33Y+rUqQCA5cuXo7q6Gi+88AJuvfVWnDhxAkuXLsXzzz+PCRMmAABWrlyJ+vp6bNy4EZMmTYrgsfIXbvKc1XeYQhMgg2fMlqPdlyHJrVYAVQARqYx1/zuu1khkjSD9lWhD6ftsGwkapaCf5QmNFIeSBldiMOw11Fs9pl2Jxc6mXW9C+0sb9TPpaXy0Nqhd1amcdZ9+EiRSK2s6UHMbNS6A5L2+IuMcLKQPQNjzC5lA484rr7yCkSNH4vrrr0evXr0wbNgwPPvss+rxAwcOoLGxERMnTlT3JRIJjB07Fps3bwYAbNu2DW1tbYYydXV1GDx4sFrGTEtLC5qbmw1bIWNQhaX+lzlLDbq62VTqf/1nG7apmhdTErkR9AONcCZE6v8kl9AGCW2IgdR2hYMlxh8u+Scy8F6d9FWibTnfk+tmoUQUiHcR5ULSHhkNdP8a66AvITJv6jNwmq+brj8BEZxA39z+/fvxzDPPYODAgXjttdcwZ84c3HnnnVixYgUAoLGxEQBQXV1tOK+6ulo91tjYiOLiYlRWVjqWMbN48WJUVFSoW319fZBq5x2ug7qNSlhR5lnPsjMveN3P/IEK1a3Zr6BIv6UWmMll0hIiPNnUhprvZWjTqQOuXuqIdvDq7Fi1Q0y1y/syNTpEIqWLOhHRDfBCT2Ae8vVmBcCjH5Ij2jopgQQCWZYxfPhwLFq0CMOGDcOtt96K2bNn45lnnjGUY8z4wjjnln1m3MosXLgQJ06cULdDhw4FqXaHwk3i9vIANw78ItyH6z44+3MlcMQgp9LgEoWCcFz1Q3aFBZbyJSATVLYwu4Tq//bmAdeK9S0xuGuctGPWEpqDoF4UYUhyCa3cOVeLn7YiTAZht85KIIGgtrYWl156qWHfJZdcgoMHDwIAampqAMAy029qalK1BjU1NWhtbcWxY8ccy5hJJBLo1q2bYStUjB+hBhN5AZj+E7H5nCKepSsJYvyVi0HG0S82RXp/InOoPil50L/ZaQy8NE6kj4oeBqCdx9AqF0H7C7v/pZ0nAs7DtFvCI3MfKHOGVh7D7nPn490zF6BZLrVNsRwmSobwRyCBYMyYMdi7d69h34cffoi+ffsCAPr374+amhps2LBBPd7a2opNmzZh9OjRAIARI0YgHo8byhw9ehS7du1Sy3RkRCcoJHJhDtBL3Hq3H6E6jcpuZlAa+hQGRDSCBA4p8X4k9SCyib0Xt5lc2GbthQK9CpmIAqEFSELCKbkEH7f08qk98jAlhKqTMFkyfNHeFUfbKtGcLMVHLdVIcvueyVMoiCLCoBM3vEACwY9+9CNs2bIFixYtwkcffYQXXngBv/zlL3H77bcDUEwF8+bNw6JFi7B27Vrs2rULs2bNQllZGaZPnw4AqKiowC233IL58+fj9ddfx3vvvYebbroJQ4YMUaMOOjpmCVnfEevDADUvbWZ7fljU+YHHVx2lQEIQeuxmkp24P84oMpRQ4hKpFbXFx1Ohhma8R0RPwZI7pzrWOywLnyUGIAkJSTCcFzuDQSVHUMScQ9Bd+6ICyVTY0tKCoUOHgjGGHTt2GI4dPHgQ1157LcrLy1FVVYU777wTra2tnteLIrdPoLDDb3zjG1i7di0WLlyIBx98EP3798cTTzyBGTNmqGXuuecenD17FrfddhuOHTuGUaNGYf369ejatata5vHHH0dRURFuuOEGnD17FuPHj8dzzz2HWMzeftTRUFZ6E5Z8DS8rnqZdiEadaow3sL+i4mMjIhFIKCgU6F0RZvSW+67SObOLoUE4EzH/TtkE3IQGccTuXCm1hmoSeu0kx3mxMzgvdgZ9ir80CAN2KzS6DddRZBrMhqvUPffcg7q6Orz/vlHjmkwmMXnyZPTs2RNvv/02vvzyS8ycOROccyxZssTxevPmzcPvfvc7rF69Gj169MD8+fMxZcoUbNu2LdC4Gni1wylTpmDKlCmOxxljaGhoQENDg2OZkpISLFmyxPUBOzpisSCjfc4lECti3wG/V+OpWYXQVNDsLbdwAEkuWRYHsi+pRHmLlQ7zTRtqSY0t9udVLTsSHDEmA9yYjswsDOjL2+1nDj8L9H2aXfo00TbFIlcMQDlrwTfKDgAwtu+O6PC/bt06rF+/HmvWrMG6desMx9avX489e/bg0KFDqKurAwD84he/wKxZs/Dwww/b+s9FmduHQn0LkbBr0cNLxjeXY2hPqfSI3ONHMEvnTWVTYNCEAfcBh0iPWOt42/0sJRQI8V5ismHQ17sZOglr5mwCQQQ40Z9IjCOWclSOpdariLEkRHhkukt1R2kyMOe+aWkJnz77r3/9K2bPno3nn38eZWVlluPvvPMOBg8erAoDADBp0iS0tLQYlgjQk05uHydIIMgjnNO0OOPnU+SWn5nq3aup9+yvJJIlJblESUHyAn0Mij9yM992dhY0LrltZ2smoSAsVeddYvjd/DcVEU169ItJ2R0X1zHjP+mx8TriXvr7SmrK6vRgcjQbANTX1xvy3yxevDjtegEA5xyzZs3CnDlzMHLkSNsyjY2Nlmi7yspKFBcXO+bpSSe3jxOBTQZEtPjxBxBKtvTvYWeDU65qjnpwrgO54OYDHJqntV9fEtF+/K1qGM1yw25XkLmSnpinHsBcKzs1MQkI4dHs8Yr93myb1zszB8PNa8D5HLccBhLT3KlzZTY4dOiQQUWfSCRsyzU0NOBnP/uZ67W2bt2KzZs3o7m5GQsXLnQta5ePx08unyjOIYEgj4jKWdD1HmJtAohc4h4exYwjxmWAZad+hDv6BDOF+i6UPop7RrcYIUE0CoRKXmgMooxY8nst4731U43U0ms6LQELKqJGESWQOt9vzpu5c+di2rRprmX69euHhx56CFu2bLEIFiNHjsSMGTOwfPly1NTU4I9//KPh+LFjx9DW1uaYp0ef20evJWhqagocyk8CQR5hXvgoc/fRu5w5302UKWIcjMvqYiRELuGIwcuh0Eg+JCYKA7W56NAGY2fx3mlwdzPl+H1HZh8EP01TtHVfiYmiUGIGPL+qqgpVVVWe5Z566ik89NBD6u+fffYZJk2ahBdffBGjRo0CAFxxxRV4+OGHcfToUdTW1gJQHA0TiQRGjBhhe119bp8bbrgBgJbb59FHHw30LCQQ5BjzZ2lc3cv7vHSECO2T9NAOQPmAZShaBca9cxYQmYUBiDE5kECgnZsNcdMbRUsl6uNN7mvcsXBqB9rqg064jbbcc4JhrIP+inY6L6E5SAm/DKGdqXNNnz59DL936dIFADBgwAD07t0bADBx4kRceuml+MEPfoB//ud/xldffYUFCxZg9uzZqrbiyJEjGD9+PFasWIHLL7/ckNunR48e6N69OxYsWJBWbh8SCLJITY8pSLa/glhRu7ZTzUmg26X+7z17D4IausiV2yoRwc6I60vibiEdfojwlMSrkGyPI1bUkpYjV5Rq4jAIR7KkD9GmsA0k+Yeb87Jbn+P1BiQAzumEjNcRQonIVmhGVk2bKWGBC+2Cx7ULfPnjWCyGV199FbfddhvGjBmD0tJSTJ8+Hf/yL/+ilmlra8PevXtx5swZdV9UuX1IIMgi1b36469tErxeEbfpthnjkeciUD5G7/TF4gOWwCgsJcd8Y/h12HpoCYoq2hDz6W7FhAOfT8L6iviNfHF3K9OuRGJo9OgHZW2fNssXUUjBFjSzd1a0lhLvPiUQcL1njCYAanoDLbW7Xd9ovHh0PgSZpl+/fuA29+rTpw/+53/+J9B5UeX2IYEgTzF3lgwAQgoFdkKF5wdG5B12keCAc+hokDccheOo8/2UvVLKqdUpr4WIQZeYdg4RHLdJhHkvgyYkAP5V/3bX9dvehNAh7qplR1CuxDhXjEpMW0uFAp8zCwkEuSZHSwqLaAPA23QA5I+qmTDGfStaHvf3p39v2fAj8GMC8KOYpqiWcLDUYOpnEiFm7Xq3YfNZXD3q9ob9vzUx+OuFAS2jJgMQQ3OyBADQs+gkilK+M66uhcrFwtGJOzoSCHKJ36WHUy00l5kCGeD9MRJZQb9ENlKmnHx6L17Co14YdUM4H3bqHjokbkKBXUifQXi08W3yM3nwrhNs76n3nTorF6M5WYodZ/qCgWNSxU50YS2e4myh+xDkGhIIcoRTHnfXc6D7kHTOiNmaSdFsLT/Qh3+5zdhEe8lH7Y51KPKeexLp4fb+RbIi/393o39HOnUxa6nszKMS4ziv6AwGlR5G19g5xFkyNSHys/xxWB+CcKcXMiQQ5AA7YcBJlStCsySITyH9YTlqp0QiNwRfZoqnHAv9q3Izo3HQhiZt9p/yAzO1a3F3Jashtdtw8JQ/hhgvjaKiEh2Qnb+xFmZoDTWUwMGZUqYEbYizJCoSZ6F6OnEGmdlHJRDRQAJBlnHKMSDmR9aOUclLYOeV7Uc88Ofck49zSMIJnoZomM03HDQqQHWKZPaptAs9sVKukKAL29MNvyKcT1tAKKgIGPaFpAQCGwFVqbMiwMQgQ2IyVKdDdS0Vl5ZfQFEG+QgJBNnGoS17xQCLMubf/Q4K4Ry9iEIlHUEgG/4IYsU9DuaovdAc3UgoSAfNCGN01ITNYGynobTz9fBqG966K2ZYi0O7JlP1BmLxK/G7nCrlq39S5IdwdMQ1l31CAkGOMcbYWp14nBeJJTov/vxPnDRLfq6eSaFAmMy4qZO3N2kwKIviUgaMdDBb3WUoUSnpRJp4zM0B+Jt4mMvodUJW52XtroyJlN3UG2YKEgiyjP4DNX9gdh+bCMlxw4/K360ExfYWFsG0Qul0/CG9tOHe3hRbcOpncFsNrX6lOxFaSaSP5jUgxDB3nSDnVrOUGIrdW1RQQ5apD7T4Vxn7Sq++iqIMwkFid5aRdFvQrtoyg9L93nmbMOGENpCyQCr3bIiHIt7cCabbOGdgJcfx1bGvMl6vjgIDAzhTE/pIhoE16IoD/u9qNWtaIwiCXlX/DJ4LewkfgrBbJ4UEggIh3Sbq5+PzowYUW6L0SzQ1/TXN2hBREHRhIwbzUsPCOms/NAitVDDHQONmh2w6poZOMv0+oxWagaOIyajq/SGOHNkfoEadm0GXfhNfHhlgq61R2oOzUMC51i6CRCb5ay9ay5OYDKbbJGbVherbqKh3sHTKRBDIZJBluJz52ZcjjGtaBaa6F5nsdUbE4CA8knvUfopDh/+CXr3s1+YmMk8s1XF6dYtC6aslttL2ymona28iEK3Dr1jgNxBS+CdIhjXvdQ5mTLs75wwxxhFHG0rRbnM1wokuXbpAbk8Y9ukNBUr+Ae+sleEdl617deKG5zXl9jg+//hbYLwU8bKP0b3uI7S3ubQFijIIBQkEWeT8uj7Y9ea30ffi9ZFf2+nDNTRtnbQvBgJJ566lt9PqbbecM7RDSqlvO+/HUmiITjfobD+TiLA3bSEdYxtVFtSB4Vg6ambCiDmsOXt/z3ABr8n2Inzr6w2orT0fx44fw/FjX+Bk7BSA4fYnkEAQChIIskg8HgeXy3NdDQBaJ2tnMzJEN3BmWHSEOuf8wG+XFa47zhx6zYR9fg2eEgzI4TUqfHsOuCyK5KYx8NvWxPlB22XleZWoPK8Szc3NAc8k/EICQR4TRUfudo0gtmgOEZcMz+WbicwTfKAPLsr58yoPClejDJxt2EgJA7JOcCCxIAxewXp26xo4ldSumB76KCunDJVpQ3kIQkECQQ4Q6Yj9BPCE/UBsZwXMyZ3MiFA1Cx8COeW5HKPOOQ/wN0h6OfoJbVC2jQpySri0v682VIjMnvmo5ehoGJMTBRfC/L4jkX1V3FN/flh9EIUdhoOiDHKAUNV7zdDNOQvsCBN9IDEtK5jdNTkHZEho4zG08iK08iK0U5PJKSWJEpxoGohszZijVtgb4tp5qoXbNOLw81BCLwya/fe9HVLdrxnsLH/XiMQcSWGHoaDePcd4J/rMAFzLDQ44dxgcDG1cwmlejFM8gXM8jhZehLb2lkzUivBBWVkZ0Doy8ECZSfHBSx3thsyddFWaf0Hn7Z7D4R5a6m/4NZdwOsv9PTnd364kve1cQgJBFpFlGYy1BjonjPXU7dPiKQutbHIeEtEFnCspY1t5EU7JJTieLMdJuRTneBE+a30MR44cSrNWBOGNZcAgK1Va2AlrToO3XnRgLJiY5ywo2F/Fvl7W9x5YQJB5NFsnhQSCLLJ//17U9n3Ld3ntA/GeJ6Wj2tV/lGIREfM65a28CGfkBM7IxTgrx3FOjqPL+Z9i267fBL4fkRu8E085lwgz+48Krv5DZBr9aqxBBvh0Woqxr7EmwxI+JOdazga4KJkMwkACQZaQZRkffPRLlJSdVve5OdGIzytTEyNu+t/pPkkwJLkEGRLaeQztiCHJGVD5CmkJCgDzynJ2ROHM5Zd0uloORVtFZAPuuER79HfSTJVOd4yXnMP7f3kxC7UhABIIssaBAx+ipt86yBArjrn7+YtOWvs/ux2i6LglCMfDlE03ZfPt0rMR23bSh1oohGk9ucgE0HnnaPmFk7o/yus7Jc5SHSL5uWBXDK0d6LytjwSCLLFn3wokdNoBb3x67LoscOSnI/cqEWMcRUgiBhkxJuvyiHO0J7agra3NVz2J3ODXczvMoB/sXL9lSSOQK7yGRLfEROneL51j9ieQySAMJBBkgePHj6O4fDs4uK8Gnq6Myk0/m3/Xo6WGdfZOYFCEgWLWjmKpHUUsCUmn2+hRvxMf/GVHGjUlson/tuRuvgp6LCxey34TmUEIkUEXEuq8w2jHgRITZYFTp06gsvpjdfAV+QXcJG1l8ZHMdohKpAG3XYpZYhwxzlHMkkhI7WiTY+C65VQZ4ygqSkLmyYzWkQiHaEeaKJqez0oukhcJdXLQ1R2JsGiuzEHIiwRScgQiKkUZEJnkzJmTqTAeBdHRJcFSPgX+m3BUMzLtGvr0RzDsjTEZCdaOSuk0ylkLElIbEilNQSzVXRz87I0IakNkkkwO5NnyL2CM45Mj1NaiIEjURpDlj/0gJjsZg8vRbJ0UEgiywAcf/xpScZut84xwGBQCgptqVuDshOP/4+Vc8z5Pqvc2xwADRUxGudSKmqJmdJdOo5ydQzETPgVJyPFdvu9J5Bbm0TwyFX6od4oNeg2tpXMkpZ1p1oAQcHVLX0x0i4zyB5mC8hUyGWSFNuXz8xlH6BYGZmdG0F/W6RZ2kj7nLLWugftgwAAUsyQkqQXtKflemA5InZv/CJuwvzflv6Q/IUETBvSGCyL7iOXMZe4diipgjj4E6RkIMu7DT8sfh4IEgizjnSTG+L+636fqzqfMYbiu80evIOYSMSanPhatgydZP/9RZ4Wp1+yVpz5qPwJ9iwkibFDbig7hCixzIRi4/3UL9m9PPgShIIEgy8hwt9PkYoh1qxNLOR2KDlpiPA88h4jgKHoCTXGfrXbGdcKAf5huI8JhTE4lxHjh4Gz/MXuFAlpbUJ68LdIQhCKQD0G/fv3AGLNst99+OwCAc46GhgbU1dWhtLQU48aNw+7duw3XaGlpwR133IGqqiqUl5fjuuuuw+HDh6N7onyH5+HyHT60D8IZSIKiRhQbSQcdi0B+KL7KWvVIfkNv82B46VBw3WJm7l+uzufDpm+wH/qdr0Y9hJWWlhYMHToUjDHs2LFD3f/+++/jxhtvRH19PUpLS3HJJZfgySef9LzeuHHjLOPytGnTAtcrkECwdetWHD16VN02bNgAALj++usBAI8++igee+wxPP3009i6dStqampw9dVX4+TJk+o15s2bh7Vr12L16tV4++23cerUKUyZMgXJJIWvBcEtx0DY6zmhD5fMk/kA4YtohFD9IOJXFAxzX2vkCw0tYbCaBp2/Yj8aAuu1HO/sUbMIEbaxUFvmq3nPPfegrq7Osn/btm3o2bMnVq5cid27d+P+++/HwoUL8fTTT3tec/bs2Ybx+T/+4z8C1yuQyaBnz56G3x955BEMGDAAY8eOBeccTzzxBO6//35MnToVALB8+XJUV1fjhRdewK233ooTJ05g6dKleP755zFhwgQAwMqVK1FfX4+NGzdi0qRJgR+gEKipmoi29pcQLwq20qE7OntsyjnQD8Jh0Y/9364DzotYY8I3Zv8Br3cn1MpRveV0r2IXDEsCaHo4vU0/EU1+NTV5oyssAJPBunXrsH79eqxZswbr1q0zHLv55psNv19wwQV455138NJLL2Hu3Lmu1y0rK0NNTU2ouqUddtja2oqVK1fi5ptvBmMMBw4cQGNjIyZOnKiWSSQSGDt2LDZv3gxAkX7a2toMZerq6jB48GC1jB0tLS1obm42bIXE+bUDwZPZjfD0kvAt+xzMBtQJFzbcoNPxe4779fT/u6PdN+yAQe0wfdJx/hXlnTIVdgZ3YvOY09LSEvqaf/3rXzF79mw8//zzKCsr83XOiRMn0L17d89yq1atQlVVFQYNGoQFCxYYNPN+SXuUevnll3H8+HHMmjULANDY2AgAqK6uNpSrrq5WjzU2NqK4uBiVlZWOZexYvHgxKioq1K2+vj7daucFaX1KTJu7uaHFFzuvceC0z3JLm/vlxSyASAN/7Sczdw1/HpkLgtPe0tPxmFeYcVDy5u3IcjQbgPr6esO4s3jx4lBV45xj1qxZmDNnDkaOHOnrnHfeeQe/+c1vcOutt7qWmzFjBn7961/jrbfewk9/+lOsWbNG1dQHIe0og6VLl+Kaa66x2EGYKfsJ59yyz4xXmYULF+Luu+9Wf29ubi5YocArxM/xPAj1HfeUzv0lHbFeyUs9KMwNRG4pjveEnCyCFGv3eQaHxJjvaCqv8MKs+A9wRmqBkFR3mwbgTfV3rQ/RYgzs4boz/BGVkUlvKkordXuEJoNDhw6hW7du6u5EImFbvKGhAT/72c9cL7l161Zs3rwZzc3NWLhwoa9q7N69G9/5znfwT//0T7j66qtdy86ePVv9efDgwRg4cCBGjhyJ7du3Y/jw4b7uB6QpEHz66afYuHEjXnrpJXWfsF00NjaitrZW3d/U1KRqDWpqatDa2opjx44ZtARNTU0YPXq04/0SiYTjyygoIu7jnD5p8uLu2Iwc+h3sOvokyrqe8CyrddTBO3k7gugZ8mbW2FnRTbKUdqB89UYjkv1b8jfAR+9RJOqWD22nW7duBoHAiblz53p69Pfr1w8PPfQQtmzZYhnLRo4ciRkzZmD58uXqvj179uCqq67C7Nmz8Y//+I+B6z58+HDE43Hs27cv8wLBsmXL0KtXL0yePFnd179/f9TU1GDDhg0YNmwYAMXPYNOmTfj5z38OABgxYgTi8Tg2bNiAG264AQBw9OhR7Nq1C48++mg6VSkovLL6+Zn9C8wDejoDPAkFHR/9TJAxf5MnL8dCf5211eCk1zwwpnX8UefLJ6yw1F9fYhwyZzoBwR2nxGVCS6SlKIuijhGQA6fCqqoqVFVVeZZ76qmn8NBDD6m/f/bZZ5g0aRJefPFFjBo1St2/e/duXHXVVZg5cyYefvjhQHXRX6Otrc0wOfdDYIFAlmUsW7YMM2fORFGRdjpjDPPmzcOiRYswcOBADBw4EIsWLUJZWRmmT58OAKioqMAtt9yC+fPno0ePHujevTsWLFiAIUOGqFEHHRW3T0YydMGZX+VQX6sgsrjIRUDL0hYWEpTkU0HedhTCoqtzImdgTE7VyelOev0GtbmwmLUEUfoR+IlZ8qtzCEUeZyrs06eP4fcuXboAAAYMGIDevXsDUAbyK6+8EhMnTsTdd9+t+tbFYjE1yu/IkSMYP348VqxYgcsvvxwff/wxVq1ahb/9279FVVUV9uzZg/nz52PYsGEYM2ZMoDoGFgg2btyIgwcPWsIjACW28uzZs7jttttw7NgxjBo1CuvXr0fXrl3VMo8//jiKiopwww034OzZsxg/fjyee+45xGKxoFUpKJwaumRjx/eL31SyTjMwo5Rvf9x8TAgF4jiRG4LMyZjvlqKRSe2RUhsZMVEjxpHkWi2ZWk6rAzkVRkFKGGAcULUE3mdwzhyjDUQpN8HOq58R0DsGfvvb3+Lzzz/HqlWrsGrVKnV/37598cknnwAA2trasHfvXpw5cwYAUFxcjNdffx1PPvkkTp06hfr6ekyePBkPPPBA4HE1sEAwceJEcAeVCmMMDQ0NaGhocDy/pKQES5YswZIlS4LeusOh7/zEDM65rHcood944bDQXC33pGMeCuZHEM6S69VWJaa1f1l1l1UGFQk6c0JKSCCtVHj0RhzRX0Q1BLtdK1jQazg4l8FDLl8c9ny/9OvXzzKWeo2fdufV19dj06ZNkdSJlj/OKVqOcV+RAcyqTVDPcdECEB2PoO81vQ45/W7cKdRVtGCZM8iQ0A5JXXhHIFtauUJl7fv4eP/etOtEpPNGnfOT6Hss70Hf2iIkaBrSyAQGzhWVf5iN1jIgMkmvXrVo/OT/WfZzaJ2fWETICa/jTmTKYUtfdyIXBPvba86F/p1W0+8W7e+j3yuDoZ1LSHJJXY7XfH/AeJXSLidx7tzptGtFCK1MJtYhcb6eXhA07we0pdTtzJOBhYXQaYtJICAyTHFxMXiy0vaYmBt5vQi97d6J4LPG9Fy1SBjIPeloCLJlo3UfariPMlaotUVLkIE27N/efC8/fZlor17liGih5Y+zRKLoAsjJGKSYcREnqwnA/+eXq05SfKzk+V04BG1bgH+nVfM5bs5lemuznPIXcBIujYMItbcoCSKQ2fknmd+lt4hhvJsfATWtty3LAAvpA5AlH4J8hDQEWWLQxZPR1hY37LNTk7mhfXpG251X+XRx82vQ2/+I/IKDQU5tXLcv8zDXNqPUw4iTMKAII+KqRD6iHzajNkAYCXBlMhmEggSCLFFTcz4aD3xb7Zjt5lFOjljmY05hil7+AppUb0/Qjjeb3sOEPxRBQOug9YKB1mkHeWtBtQqZgdpZNPhLa+4X7a14CYFhBNLSnm/iiy8+T/t8wj8kEGSJeDyOC86fg/bWuK2DoNKJ2ztiAfaq2HQ+7KB2W+qICwfjLE17e0IIEAKjKmT6ECCDtBc/ZY3t2J8XS2Znn50HnhIWtbbg/Le3KvjdCOaNFFQkSZQ3oz3pb90OLsuRbJ0VEgiyyMUXDcXRj8dBypNh1o96lzriwkEEcHHOlLA+rnXUdkamXLxbp2gCu0HFLtKASA9NGAh+pldLCdOOGDgkxtNe9M1aGTIZhIEEgiwSj8cx6Gs/xhdH+1qOBcnixQ2devrdZbRyP5FLjGYBXftICQU8Fdqn10RF+W49hUtu1FCI8loYIiz5CMzmMsY4PmvcGWGtOz6aP4kg6liT9FuRJgjw6AUDIi1IIMgyAwZcjFPHL7PsF6E4To56+g43qk+GPr2OgdY2mK0ZQBuIg3fewdqIc3SBWYjVfBu0DbAKBcarc7QnVuKrr74MVKvOjPAnEXB4m4oAfcpo/W9GwmoQ7XRCDAgnGIRNSiS2TgoJBHmE2errh0yq9Uk7UBj4MfvINjNDv4OD3zo41cPsu2A+zxxpoBdwzPQ6fx+2v/9q+hXt1NgLjG54+ZsILU/QViQx7jABCumCyDnA5ZAbCQREgeDVVN2iDtyiGNKpiXeQGZEJ2tvbAd0Myqw5MkYXuIuY2Xl71gBC1cfBTqPh8DMAMCbjdNvrkDux41d6KH/vdM7zdzxsS0orLyERMSQQ5CFhPgm32ViwOthL6SJ7mAT6dHPFtvd/i5IycwpfvZ+AMcwwymVunXGYQZqFUsPvSiuTuWQjGDiLmr36bsGBAx+mXdPOh7swYDbbmI96E6y34an6JCEhKcxHNo6w4spB9AVc5pFsnRXKVJgTtJm1nWpU+BP4nX+bM8rJYIElArPNTp9JjJn2E7klKZ+GJCkZL/UaAKfO0y2EzNtW62cG6NxOrWYK0Tat2iv9Hlmot5l2nrhLUXEL2lpbPepNAFAdSp2OivRiRtW/PvYj9W482okEq7+CuR7i2qoZi9vnK+SppZmT4owgfRmXgTTjKYzX6JyQhiAH6JVjykzbaoG1m+f7lpQDfEBWZa7xmAQgxri6SebN/62ICDh37hySsd0AjEKAGk3gejYz+RJkIweAnYBi39rMJgS9OCoiJACAMeDg4a2hatVZcPcZMPY82t/Y7MDs3AKMuTCdy2m+IprzqAymaQhMW9JvX2e+D2kIQkH9eR4gBl7hZONczv5YaG9fZvUHEJ2D08yg88rQueXM2TM4r2YnhKd+UqwY6Ng6xCyMGWZwmVoFU0/we2ghkgDUgUHTgqRmskzGmfZ3oq1sB6S6Z3+cPFaltg1rOmtre+C6QVrBv7nJyeggc71Qp5knzFEmFOScewrSZMBTOqTm5uYc1yQ9zpxuxamTzkOqmPXJSKnNOIOUGpiTXP+xavhZV8CcDVENdUx5/JpVzrGUgBKzEQqEivH06baCfQ+FSHNzM06dkpFkstJ5c556c0bE4Gn/5kRkldau3NCUyk7Yz+g499ZqiTqaZyYMHEU6T3SJaUMGB7U7P/ToUY0te88DLxJpf/XvkEEW3zznqkmGCRMNN2oH7NqJXbvgsC5WpQgEHJKP8d7umq1nkzhZfBJlpeUAtH6f29gS2nlLaJV/O9pCnV/IMG73V81zDh8+jPr6+lxXgyAIgsgRhw4dQu/evQEoprT+/fujsbExkmvX1NTgwIEDKCkpieR6hUJBCgSyLGPv3r249NJLcejQIXTr1i3XVUqL5uZm1NfX0zPkmI7wDEDHeA56hvwhX5+Dc46TJ0+irq4OkqTpls6dO4fWiJxNi4uLO50wABSoyUCSJJx//vkAgG7duuVVY00Heob8oCM8A9AxnoOeIX/Ix+eoqKiw7CspKemUg3iUkFMhQRAEQRAkEBAEQRAEUcACQSKRwAMPPIBEIpHrqqQNPUN+0BGeAegYz0HPkD90lOcg/FOQToUEQRAEQURLwWoICIIgCIKIDhIICIIgCIIggYAgCIIgCBIICIIgCIIACQQEQRAEQaBABYJ/+7d/Q//+/VFSUoIRI0bg//7v/3JdJZU//OEPuPbaa1FXVwfGGF5++WXDcc45GhoaUFdXh9LSUowbNw67d+82lGlpacEdd9yBqqoqlJeX47rrrsPhw4ez9gyLFy/GN77xDXTt2hW9evXCd7/7Xezdu7egnuOZZ57B17/+dTXL2hVXXIF169YVTP3tWLx4MRhjmDdvnrqvEJ6joaEBjDHDVlNTU1DPAABHjhzBTTfdhB49eqCsrAxDhw7Ftm3bCuY5+vXrZ3kPjDHcfvvtBVF/IgvwAmP16tU8Ho/zZ599lu/Zs4ffddddvLy8nH/66ae5rhrnnPPf//73/P777+dr1qzhAPjatWsNxx955BHetWtXvmbNGr5z507+/e9/n9fW1vLm5ma1zJw5c/j555/PN2zYwLdv386vvPJKftlll/H29vasPMOkSZP4smXL+K5du/iOHTv45MmTeZ8+ffipU6cK5jleeeUV/uqrr/K9e/fyvXv38vvuu4/H43G+a9eugqi/mXfffZf369ePf/3rX+d33XWXur8QnuOBBx7ggwYN4kePHlW3pqamgnqGr776ivft25fPmjWL//GPf+QHDhzgGzdu5B999FHBPEdTU5PhHWzYsIED4G+++WZB1J/IPAUnEFx++eV8zpw5hn0XX3wx/8lPfpKjGjljFghkWeY1NTX8kUceUfedO3eOV1RU8H//93/nnHN+/PhxHo/H+erVq9UyR44c4ZIk8f/93//NWt31NDU1cQB806ZNnPPCfY7Kykr+q1/9quDqf/LkST5w4EC+YcMGPnbsWFUgKJTneOCBB/hll11me6xQnuHee+/l3/rWtxyPF8pz6Lnrrrv4gAEDuCzLBVl/InoKymTQ2tqKbdu2YeLEiYb9EydOxObNm3NUK/8cOHAAjY2NhvonEgmMHTtWrf+2bdvQ1tZmKFNXV4fBgwfn7BlPnDgBAOjevTuAwnuOZDKJ1atX4/Tp07jiiisKrv633347Jk+ejAkTJhj2F9Jz7Nu3D3V1dejfvz+mTZuG/fv3F9QzvPLKKxg5ciSuv/569OrVC8OGDcOzzz6rHi+U5xC0trZi5cqVuPnmm8EYK7j6E5mhoASCL774AslkEtXV1Yb91dXVka2DnUlEHd3q39jYiOLiYlRWVjqWySacc9x999341re+hcGDB6t1FHVyqmM+PMfOnTvRpUsXJBIJzJkzB2vXrsWll15aMPUHgNWrV2P79u1YvHix5VihPMeoUaOwYsUKvPbaa3j22WfR2NiI0aNH48svvyyYZ9i/fz+eeeYZDBw4EK+99hrmzJmDO++8EytWrFDrKOrkVMd8eA7Byy+/jOPHj2PWrFlq3URdnOqWT/UnMkNBLn/MGDP8zjm37Mtn0ql/rp5x7ty5+POf/4y3337bcizfn+Oiiy7Cjh07cPz4caxZswYzZ87Epk2b1OP5Xv9Dhw7hrrvuwvr1612Xdc3357jmmmvUn4cMGYIrrrgCAwYMwPLly/HNb34TQP4/gyzLGDlyJBYtWgQAGDZsGHbv3o1nnnkGP/zhD9Vy+f4cgqVLl+Kaa65BXV2dYX+h1J/IDAWlIaiqqkIsFrNIo01NTRbJNh8RntVu9a+pqUFrayuOHTvmWCZb3HHHHXjllVfw5ptvonfv3ur+QnmO4uJifO1rX8PIkSOxePFiXHbZZXjyyScLpv7btm1DU1MTRowYgaKiIhQVFWHTpk146qmnUFRUpNYj35/DTHl5OYYMGYJ9+/YVzLuora3FpZdeath3ySWX4ODBg2odgfx/DgD49NNPsXHjRvz93/+9uq+Q6k9kjoISCIqLizFixAhs2LDBsH/Dhg0YPXp0jmrln/79+6OmpsZQ/9bWVmzatEmt/4gRIxCPxw1ljh49il27dmXtGTnnmDt3Ll566SW88cYb6N+/f0E+hxnOOVpaWgqm/uPHj8fOnTuxY8cOdRs5ciRmzJiBHTt24IILLiiI5zDT0tKCDz74ALW1tQXzLsaMGWMJvf3www/Rt29fAIX1TSxbtgy9evXC5MmT1X2FVH8ig2TbizEsIuxw6dKlfM+ePXzevHm8vLycf/LJJ7muGudc8Qh/7733+HvvvccB8Mcee4y/9957aljkI488wisqKvhLL73Ed+7cyW+88Ubb0J7evXvzjRs38u3bt/Orrroqq6E9//AP/8ArKir4W2+9ZQhTOnPmjFom359j4cKF/A9/+AM/cOAA//Of/8zvu+8+LkkSX79+fUHU3wl9lAHnhfEc8+fP52+99Rbfv38/37JlC58yZQrv2rWr+s0WwjO8++67vKioiD/88MN83759fNWqVbysrIyvXLlSLVMIz5FMJnmfPn34vffeazlWCPUnMkvBCQScc/6v//qvvG/fvry4uJgPHz5cDYfLB958800OwLLNnDmTc66EJz3wwAO8pqaGJxIJ/u1vf5vv3LnTcI2zZ8/yuXPn8u7du/PS0lI+ZcoUfvDgwaw9g139AfBly5apZfL9OW6++Wa1jfTs2ZOPHz9eFQYKof5OmAWCQngOEc8ej8d5XV0dnzp1Kt+9e3dBPQPnnP/ud7/jgwcP5olEgl988cX8l7/8peF4ITzHa6+9xgHwvXv3Wo4VQv2JzMI45zwnqgmCIAiCIPKGgvIhIAiCIAgiM5BAQBAEQRAECQQEQRAEQZBAQBAEQRAESCAgCIIgCAIkEBAEQRAEARIICIIgCIIACQQEQRAEQYAEAoIgCIIgQAIBQRAEQRAggYAgCIIgCAD/H3Fsyn9rHs8WAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "visualize_raster_layers(args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "f45e82da",
    "metadata": {},
    "outputs": [],
@@ -258,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "4f7dec27",
    "metadata": {},
    "outputs": [],
@@ -320,10 +352,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "ac070630",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n",
+      "Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "argoverse2_sensor_dataset_teaser(args)"
    ]
@@ -339,7 +390,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "av2",
    "language": "python",
    "name": "python3"
   },
@@ -353,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

From bb35d5b9f629c975e5d3357363d0b74aba8194d3 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Sun, 9 Mar 2025 23:34:33 -0400
Subject: [PATCH 19/22] linting

---
 src/av2/evaluation/scenario_mining/eval.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 8f48e6d3..689ff66f 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -45,7 +45,12 @@
 
 
 def _plot_confusion_matrix(
-    tp:int, fn:int, fp:int, tn:int, title: str = "scenario", output_dir: Union[str, None] = None
+    tp: int,
+    fn: int,
+    fp: int,
+    tn: int,
+    title: str = "scenario",
+    output_dir: Union[str, None] = None,
 ) -> None:
     """Plots the confusion matrix for scenario mining.
 

From ce4dbf410b922a4fe94cfd54e83c669ef953e7b5 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Wed, 12 Mar 2025 00:55:29 -0400
Subject: [PATCH 20/22] Updated formatting and docstrings

---
 src/av2/evaluation/scenario_mining/eval.py    | 90 +++++++++++--------
 .../evaluation/scenario_mining/test_eval.py   | 12 ++-
 tutorials/map_tutorial.ipynb                  | 81 ++++-------------
 3 files changed, 78 insertions(+), 105 deletions(-)

diff --git a/src/av2/evaluation/scenario_mining/eval.py b/src/av2/evaluation/scenario_mining/eval.py
index 689ff66f..d227e840 100644
--- a/src/av2/evaluation/scenario_mining/eval.py
+++ b/src/av2/evaluation/scenario_mining/eval.py
@@ -54,8 +54,19 @@ def _plot_confusion_matrix(
 ) -> None:
     """Plots the confusion matrix for scenario mining.
 
-    A true label indicates that the scenario matches the description.
-    A false label indicates the scenario does not match the description.
+    A true label indicates that the scenario matches the prompt/description.
+    A false label indicates the scenario does not match the prompt/description.
+
+    Args:
+        tp: The number of classification true positives.
+        fn: The number of classification false negatives.
+        fp: The number of classification false positives.
+        tn: The number of classification true negatives.
+        title: A string description indicating the granularity of classification.
+        output_dir: The directory to save the confusion matrix plot.
+
+    Returns:
+        None
     """
     if output_dir is None:
         return
@@ -90,14 +101,6 @@ def _plot_confusion_matrix(
     plt.close()
 
 
-def load(pkl_path: Path) -> Any:
-    """Loads a pkl file as a dict."""
-    with open(pkl_path, "rb") as f:
-        data = pickle.load(f)
-
-    return data
-
-
 def filter_drivable_area(tracks: Sequences, dataset_dir: Optional[str]) -> Sequences:
     """Convert the unified label format to a format that is easier to work with for forecasting evaluation.
 
@@ -239,8 +242,19 @@ def compute_temporal_metrics(
     """Calculates the F1 score.
 
     F1 is a binary classification metric. A true postive when both
-    the ground-truth and predictions sequences contains no tracks
-    or both contain at least one track corresponding to the prediction.
+    the ground-truth and prediction sequence/timestamp contain no tracks
+    or both contain at least one track corresponding to the prompt.
+
+    Args:
+        track_predictions: Prediction sequences.
+        labels: Ground truth sequences.
+        output_dir: The directory to save the plotted confusion matrices.
+
+    Returns:
+        timestamp_f1: The F1 score where each timestamp counts as a prediction to evaluate.
+        scenario_f1: The F1 score where each log-prompt pair counts as a prediction to evaluate.
+
+
     """
     scenario_gt = np.zeros(len(labels), dtype=bool)
     scenario_pred = np.zeros(len(labels), dtype=bool)
@@ -294,19 +308,21 @@ def compute_temporal_metrics(
         output_dir=output_dir,
     )
 
-    num_correct = 0
-    for i in range(len(scenario_gt)):
-        if scenario_gt[i] == scenario_pred[i]:
-            num_correct += 1
-
     return scenario_f1, timestamp_f1
 
 
-def _relabel_seq_ids(data: Sequences) -> Sequences:
-    """Turns the (log_id, prompt) tuple format into a string for HOTA summarization."""
+def _relabel_seq_ids(sequences: Sequences) -> Sequences:
+    """Turns the (log_id, prompt) tuple format into a string for HOTA summarization.
+
+    Args:
+        sequences: The 'sequences' where each top level key is a tuple of (log_id, prompt)
+
+    Returns:
+        Sequences where each top level key is a string of '(log_id, prompt)'
+    """
     new_data = {}
 
-    for seq_id, frames in data.items():
+    for seq_id, frames in sequences.items():
         if isinstance(seq_id, tuple):
             new_seq_id = str(seq_id)
             new_data[new_seq_id] = frames
@@ -340,16 +356,15 @@ def evaluate(
         out: Output path.
 
     Returns:
-        F1_score: The F1 score for if the scenario matches the description
-        full_track_HOTA: The tracking metric for the full track of any objects that the description ever applies to.
         partial_track_HOTA: The tracking metric for the tracks that contain only the timestamps for which the description applies.
-        tlap: Temporal localization average precision calculates how well predictions are temporally localized,
-            with some built in give for ambiguous annotations
+        full_track_HOTA: The tracking metric for the full track of any objects that the description ever applies to.
+        timestamp_f1: A retrieval/classification metric for determining if each timestamp contains any instance of the prompt.
+        scenario_f1: A retrieval/classification metric for determining if each data log contains any instance of the prompt.
     """
     output_dir = out + "/partial_tracks"
     Path(output_dir).mkdir(parents=True, exist_ok=True)
 
-    res, partial_track_metrics, timestamp_f1, scenario_f1 = evaluate_scenario_mining(
+    partial_track_hota, timestamp_f1, scenario_f1 = evaluate_scenario_mining(
         track_predictions,
         labels,
         objective_metric=objective_metric,
@@ -364,7 +379,7 @@ def evaluate(
     output_dir = out + "/full_tracks"
     Path(output_dir).mkdir(parents=True, exist_ok=True)
 
-    _, full_track_metrics, _, _ = evaluate_scenario_mining(
+    full_track_hota, _, _ = evaluate_scenario_mining(
         full_track_preds,
         full_track_labels,
         objective_metric=objective_metric,
@@ -374,9 +389,6 @@ def evaluate(
         full_tracks=True,
     )
 
-    full_track_hota = full_track_metrics["REFERRED_OBJECT"]
-    partial_track_hota = partial_track_metrics["REFERRED_OBJECT"]
-
     return (
         partial_track_hota,
         full_track_hota,
@@ -393,7 +405,7 @@ def evaluate_scenario_mining(
     dataset_dir: Any,
     out: str,
     full_tracks: bool = False,
-) -> Tuple[Dict[str, Any], Dict[str, Any], float, float]:
+) -> Tuple[float, float, float]:
     """Run evaluation.
 
     Args:
@@ -408,7 +420,9 @@ def evaluate_scenario_mining(
         when the description applies.
 
     Returns:
-        Dictionary of per-category metrics.
+        referred_hota: The HOTA tracking metric applied to all objects with the category REFERRED_OBJECT
+        timestamp_f1: A retrieval/classification metric for determining if each timestamp contains any instance of the prompt.
+        scenario_f1: A retrieval/classification metric for determining if each data log contains any instance of the prompt.
     """
     classes = list(AV2_CATEGORIES)
 
@@ -442,13 +456,17 @@ def evaluate_scenario_mining(
         output_dir=out,
     )
 
+    referrred_hota = tuned_metric_values["REFERRED_OBJECT"]
+
     if not full_tracks:
         scenario_f1, timestamp_f1 = compute_temporal_metrics(
             filtered_track_predictions, labels, out
         )
-        return res, tuned_metric_values, scenario_f1, timestamp_f1
+    else:
+        scenario_f1 = 0
+        timestamp_f1 = 0
 
-    return res, tuned_metric_values, 0, 0
+    return referrred_hota, scenario_f1, timestamp_f1
 
 
 @click.command()
@@ -476,8 +494,10 @@ def runner(
     out: str,
 ) -> None:
     """Standalone evaluation function."""
-    track_predictions = pickle.load(open(predictions, "rb"))
-    labels = pickle.load(open(ground_truth, "rb"))
+    with open(predictions, "rb") as f:
+        track_predictions = pickle.load(f)
+    with open(ground_truth, "rb") as f:
+        labels = pickle.load(f)
 
     partial_track_hota, full_track_hota, timestamp_f1, scenario_f1 = evaluate(
         track_predictions, labels, objective_metric, max_range_m, dataset_dir, out
diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index b84c2dd5..74670ea7 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -4,8 +4,9 @@
 from pathlib import Path
 from typing import Final
 import sys
+import pickle
 
-from av2.evaluation.scenario_mining.eval import evaluate, load
+from av2.evaluation.scenario_mining.eval import evaluate
 
 
 matplotlib.use("Agg")
@@ -25,10 +26,13 @@ def test_evaluate() -> None:
     dataset_dir = TEST_DATA_DIR
     out = str(TEST_DATA_DIR / "eval_results")
 
-    predictions = load(pred_pkl)
-    ground_truth = load(gt_pkl)
+    with open(pred_pkl, "rb") as f:
+        predictions = pickle.load(f)
+    with open(gt_pkl, "rb") as f:
+        ground_truth = pickle.load(f)
 
-    evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
+    metrics = evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
+    print(metrics)
 
 
 test_evaluate()
diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb
index 1eb100a5..0f343752 100644
--- a/tutorials/map_tutorial.ipynb
+++ b/tutorials/map_tutorial.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "f25e1e66",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "f919b29b",
    "metadata": {},
    "outputs": [],
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "15a8196d",
    "metadata": {},
    "outputs": [],
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "3a02b3b8",
    "metadata": {},
    "outputs": [],
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "8d5ae748",
    "metadata": {},
    "outputs": [],
@@ -127,28 +127,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "4e27fe09",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "single_log_teaser(args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "56a98bd9",
    "metadata": {},
    "outputs": [],
@@ -190,38 +179,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "2bdc3926",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x1000 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_raster_layers(args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "f45e82da",
    "metadata": {},
    "outputs": [],
@@ -290,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "4f7dec27",
    "metadata": {},
    "outputs": [],
@@ -352,29 +320,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "ac070630",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n",
-      "Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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UlJSI+++/37kNadrLs/Gz9dRTTzm/Y0877TTx0ksviZdfflmcffbZQlEU8dprr6U9HhFRLmHoJqIjQk9hra2tTSiKIr773e92ettvfvMbAUBs3rxZCCHE8OHDxSWXXNLpfrfffnufQve2bdvERx99JF588UVxxRVXCJ/P12lNNADx6U9/utP72n/c7t+/P+X26upqsXLlSvHvf/9b/OxnPxN+v19cf/31PZ7Lhx9+KFRV7XaN7j333CMAiGeffbbT26ZMmSIuuOCCTrfboTs5DI0bN04cd9xxne5rf43ee+89IYQQe/bsEYWFheLBBx/sdJ/kr6NpmkLTtJR/XZk7d64YMmRIyvrmvp7XddddJ4LBYNqPX1lZmXLxwQ4GdXV1KffbvXu3ACB+9atfCSGsizbJ55+8Bt72ySefiBUrVojHH39cfOpTnxIjRowQGzZs6PKxLl68WPj9fvH5z3++01pvn8+X9uLDD3/4QwFAbN++3bntV7/6lcjPzxe//OUvxZIlS8SLL74oFi5cKIYOHSpWrVrl3K/j1yD5mP/4xz/EqaeeKgoKClKCY1efx2S///3vBYC0F6Vsn//854XX6xXV1dXdfqzuQrd9McD+lxxce/tzuHDhQjFt2rS0x/b7/eJb3/qW8/qrr74qCgsLxTe+8Q3x6quvijfeeEPccMMNwuv1iocffjjlfa+55hpRXl4u/vCHP4h33nlHPPPMM2Lu3LliwoQJYs+ePc79HnroIQFAfOc73xEHDhwQ+/btE9dcc43weDwCgHj66aeFEELce++9AoD4+OOPU46jaZrwer1pQ/dzzz3X6TGNGzdOLFiwoNPno2Po/vSnPy3mz5+f8j3RVejO9M+WfSFh6NChoqWlxblfe3u7GDVqlDjttNPSHo+IKJewvZyIjgqNjY0QQmDkyJGd3jZq1CgA1hRp+78jRozodL90t3VnypQpmDdvHj7zmc/gH//4BxYsWIDvfe97ME0z5X4dW1WTb7PPKfn2uXPn4rzzzsNdd92FX/7yl7jnnnuwevXqbs/lxBNPRGVlZcoWWh099NBDGDZsGD772c92etuQIUM6nQsAZx1xeXl5nx/T9773PRx33HG4/PLL0dTUhKamJmetZltbG5qbmwFYywJ8Pl/Kv3Tr6tetW4ePP/4YX/nKV9K24Pb2vIYMGYJIJJJ2TXZDQ0Onx+r1ejFkyJBuP+bVV1+dcv4LFizo9LHnzJmDk08+GVdeeSWWLFkCIUTKNOxk//73v3HZZZdh4cKFeOKJJzqt9bbP5/zzz0+5/cILLwQArFq1CgCwefNm3Hrrrbjjjjtwyy234KyzzsJnPvMZvPLKKygtLcWPfvQj5307fg3+/ve/AwD++c9/4gtf+AJGjx6Nxx9/HCtWrMDKlStx9dVXIxKJpD3/ZPY0/66+N+vq6vDSSy/h4osvTvv1660FCxaknH/HNf+9/d5I93PQ3t6OWCzmfG8IIXD11Vdj/vz5ePjhh3HBBRfg3HPPxd13340vf/nLuOGGG9De3g7AatV+6KGHcP/99+OHP/wh5s+fjy984Qt444030NDQkLJu+uqrr8Zdd92Fxx57DGPGjMHYsWOxadMmp1199OjRKefb8TGl+17t6fGne7zJnnvuObz22mv47W9/i+bmZudnGbDa4ZuampztwLLxs2W//dRTT0VRUZFzv/z8fJx55pnO9zoRUS7jlmFEdFQoKyuDqqopg4Zs9hZVQ4cOBWD9EXfo0KFO96upqRnQOZx44ol47bXXUFtbmxLg031c+7au/kBO/piAtYfzCSec0O19hRBdDp1avXo1Vq9ejRtvvNEZ7JTs+OOPx1NPPQVd11PWddtrSI877ri055/uNvsxbdiwAXv37kVZWVmn+5599tkoKSlBU1MTPvWpT2HlypUpb7cvlCR76KGHAKDToK2+npe93nT9+vU46aSTUu5XV1fX6bHquo76+vqUr1XHj3n77ben7FmcHA7SKSoqwrRp07Bt27ZOb/v3v/+NSy+9FGeeeSaef/55+P3+TveZOXNm2scqhACQGD62du1aCCE6ba3m8/kwa9YsvPPOO85tHb8G9pr5xx9/HBMmTMAzzzyTEv6j0Wi3j7Gjrr43H3vsMcRisW6/rr1x//33o7W11Xnd/nm39fZ74+mnn0ZNTU1KSO34c3Do0CFUV1fjW9/6VqePOW/ePDz66KPYs2cPjj32WKxZs8a5PVlpaSkmT57caZ3zz372M/zwhz/E9u3bUVRUhHHjxuFb3/oWCgoK8KlPfSrlfGtqapwgDiS+V9Pp6vFPnjw57f1tGzZsgK7rOPnkkzu97YEHHsADDzyAF154AZdeemlWfrbs9fTpdPc7j4gol/A3FREdFQoKCnDSSSfhn//8Z8rQJtM08fjjj2PMmDGorKwEAJx55pl4++23UVdXl3K/Z599tt/HF0LgnXfeQWlpaacg/dZbb6WEfMMw8Mwzz2DSpEkYM2ZMtx93yZIlANDjH8YffPABtm/fnvYPYyARWO1BSR197nOfQ1tbG55//vmU2//+979j1KhRKX9AA9bE4bVr16bc9uSTT6KoqAhz5swBADz99NNYsmRJyr+f/exnAIC//OUvePnllwFYAXTu3Lkp/zoGzWg0iscffxwnnnhipz/c+3peF1xwAYLBIB555JGU+z3yyCNQFAWXXnppp4/bcd/pJ598EgBw1llnAbAmPyefv733dlfq6uqwfv36Tl/X119/HZdeeilOP/10LFq0qMuK/uWXXw4AePXVV1NuX7x4MVRVdQKeffGiY5U5Go1i1apVKd9/Hb8G9vexoijw+/0pgbumpqbX08vtinl335ujRo1yqvT9NXXq1JTzHz9+fMrbe/Nz+NnPftbZbzvZI488gry8PGf/8LKyMgSDwbTV+xUrVkBVVafrpquvQX19PbZt25b2d0AgEMBxxx2HcePGYd++fXjmmWdw3XXXIS8vD0Di+67j9+U//vEP6Lqe9vPT8b7Lly/H3r17nY/Vla9//eudfo7t30uXXnoplixZgtNPPx1Adn62Ro4ciVNOOQXLli1DS0uLc79QKIR33nmny+8rIqKcIq+znYio9+x1ib/5zW/Es88+2+lfe3u7WLp0qfD5fOKkk04Szz77rHjxxRfF+eefLxRFcdZCCiHEmjVrRDAYFDNnzhTPPPOMeOmll8RFF10kxo0bJwCIvXv3dnsun/nMZ8Qtt9winn/+ebF06VLx5JNPivPOO08AEH/+859T7gtAHHPMMWLGjBniqaeeEi+99JK44IILUtZnCiHErbfeKr71rW+JJ554QixdulQsWrRIfPvb3xYej0dcccUVKR9z5syZ4re//a3417/+Jd544w1x5513itLSUjFq1ChRVVXV6XzD4bAoKyvrcZ/fhQsXirKyMvHXv/5VvP32287gp46DocaNGydGjx4txo4dKx5++GHx6quviiuvvNL5+nSnv4PUnn76aQFA/PWvf+3yPn05r1//+tdCURTxi1/8QixdulT87ne/E4FAQFx33XUp97vqqquE3+8XY8eOFXfeead4/fXXxe233y68Xq+48MILezzvpqYmMW/ePPGHP/xBvPzyy+Ktt94S9913n5g2bZrIz89P+Ty89957Ii8vT4wfP168/fbbYsWKFSn/mpubnfvGYjExZ84cUVJSIv74xz+KN954Q/zsZz8THo8nZQaAYRhi3rx5IhgMiltvvVW8+eab4vnnnxdnnXWWACAee+yxHh/Dww8/7Kwzfuutt8QjjzwiJk2aJKZMmSKS/4x49913xfnnny/+8pe/iNdff1289NJL4jvf+Y7weDzinHPOSTtI7YMPPhAAxC9+8Ysuj9/e3u78nN94440CgLj99tvFs88+KxYvXtzj+QvR+59DIYS49tprRSAQEL/73e/E0qVLxS9+8QuhKIq48847U+73ox/9SAAQX/3qV8XLL78sXn31VfGtb31LABDXXHONc7/W1lYxbtw4UVZWJn7/+9+Lt99+WzzxxBNi9uzZwuPxpKxPX79+vbj99tvFyy+/LN544w3x+9//XgwdOlTMnTs3ZUCkEEJ85StfEYqiiJ/+9Kfi9ddfF//7v/8rRo0aJYqLi9Ou6T7mmGPENddcI1577TXxwAMPiOHDh4vRo0en7D3e1Zrurj6n6fY+z8bP1rJly4Tf7xcnn3yyeOGFF8SiRYvEGWecIXw+n1i+fHmP50pEJBtDNxEdEew/HLv6Zw8/e++998Q555wjCgoKRF5enjj55JPFv/71r04f77333hMnnXSSCAQCoqKiQvzkJz9xBq41NTV1ey6/+c1vxLx580RZWZnweDxiyJAh4vzzzxcvv/xyp/vaf5jee++9YtKkScLn84lp06aJJ554IuV+L730kjj33HPFiBEjhNfrFYWFheLEE08Ud999d6fBYl/84hfF5MmTRUFBgfD5fGLcuHHi29/+dtrALURiEFHH4U4dtba2iu9///uioqJC+P1+MXPmzC6nhF988cXiueeeE8cee6zw+/1i/Pjx4n//93+7/fhC9D90L1y4UBQUFKQMUhroef3xj38UlZWVzh/+t912m4jFYin3ueqqq0RBQYFYt26dOOuss0ReXp4oLy8X3/nOd0RbW1uP5x2JRMS1114rpk+fLgoLC4XX6xVjxowRX/nKV8TGjRtT7msHnq7+dRweVl9fL771rW+JESNGCJ/PJyorK8Xvfve7TuG2qalJ3HTTTWL69OkiPz9fDB8+XJx11lm9DqxCCHHXXXeJ8ePHi0AgIKZPny4eeOAB53xt27dvFxdddJEYPXq0CAQCIhgMiuOPP17ceeedaQffCWEN3lIURezcubPLY9uDtdL9GzduXK/Ov7c/h0JYFzRuu+02MXbsWOH3+0VlZaW4++67O93PMAzxwAMPiLlz54rS0lJRXFwsTjjhBHHPPfd0+j6qrq4W119/vZg8ebIIBoNi1KhR4uKLLxYrVqxIud/WrVvF/PnzRXl5ufD7/WLy5Mni5ptvTvu9Fo1GxY033iiGDx8ugsGgOPnkk8WKFSvEuHHj0obu119/XXz1q18VpaWlIi8vT1x00UUpA/eEyEzoFiI7P1vvvfeeOPPMM0V+fr7Iz88X55xzjli2bFmP50lElAsUIeILwIiIBrnzzjsPe/bsSbvOloiIiIioPzhIjYgGpR/96Ec44YQTcMwxx6ChoQFPPPEE3njjDWftMxERERFRJjB0E9GgZBgGbr31VtTU1EBRFMyYMQOPPfaYs70REREREVEmsL2ciIiIiIiIKEu4ZRgRERERERFRljB0ExEREREREWUJQzcRERERERFRlnCQGhERZcWGDRtwxecvd/WYP/rRjbjum99EW1sb5s39lKvHtv34Jz/BNddci9bWVpw4b66Uc7j88sM4d2ETQiEVN/5ospRz2LR5CxRFwWc/c4kr2/D98e67cd555yMcDiMvLy/rxyMiIuothm4iIsqKYCDg+jHD4TAAwOPxuH5sm67pAABFUaSdg6Zbx1ZVebNS77j9dkRjUezfv9+V48ViMQDWzgRERES5hKGbiIiyIhAMun7M9lAIAKCq8lZPaZqWA+egxs9B2ingmWeedvV40ShDNxER5Sau6SYioqwISKx0ywy8ui6/0h2LKfFzkHYKrtPilW7TNCWfCRERUSqGbiIiygoZoTsUagcgO3RblW6p7eUxNX4O8trL3RaNRgEwdBMRUe5h6CYioqwIymgvb2sDILnKrMkP3THrFAZVpdte0y0EQzcREeUWrukmIqKskFPpttZ0S60y58AgtWjUEz8Haafguphmt5cPnuo+0dHIMAxnNgaRbD6fLyPDWRm6iYgoawKBgNP264a2eKVbJiMn2ssH35pu+/tMCIZuoiOREAI1NTVoamqSfSpEKUpLS1FRUTGg53WGbiIiyppgMCgldCuKAr/f77QcuykWkx+6o9FBlLbjEu3lDN1ERyI7cA8fPhz5+flSf4cSAdbzSSgUwuHDhwEAI0eO7PfHYugmIqKMMwwDHo8HfpdbzNvb252XCwoKpIRuLd7mLDd0D76RLbH4lmH8M53oyGMYhhO4hwwZIvt0iBx5eXkAgMOHD2P48OH9bjUffM/KRESUdfa2WUGXQ3dra6vzcmFhoavHtuXEIDVnejmgqoNjsJiMCyxElBn2Gu78/HzJZ0LUmf19OZBZAwzdRESUcU7odnmCeSwWc9qLCySFbj0HBgBFIonA7/cPjnZrJ3SzJZXoiMWWcspFmfi+ZOgmIqKMMwwDAFxvL08mq9Kt5VClGxg8oTsas2YH8I92IiLKNQzdRESUcWY8dLvdXg4kBmkVSGpTjOZAm3Mkknh6DwQHV3s5QzcREeUahm4iIso4w7SCXsDl9nIgKXQXFLh+bADQciB0m6YKe4h3wD9IQnd8Sr6q8k8bIqLe2LNnDxRFwZo1a7q8zyOPPILS0tJef8zx48fj//7v/wZ8bkcbPjMREVHGmfHQHQy4H7rtY0sL3TmwpjtZcJBVuhm6ichNX//616EoSqd/F1xwgexTy4j/9//+H7Zt2yb7NI543DKMiIgyTtiV7oDf/WPHS7x5+XJCd65N0R4sa7oZuolIlgsuuAB/+9vfUm4LSJxpkkl5eXnOtlnUf3xmIiKijLNjnoz2cnuIW56EYwOpa4tlBkC7vdw/SNrLo/H28v7uoUpEOUYIoL1dzj/Rt4uVgUAAFRUVKf/Kysqct2/ZsgWnn346gsEgZsyYgTfffBOKomDRokXOfdavX49zzjkHeXl5GDJkCL75zW+ira2ty2P+53/+J04++eROt8+cORO33Xab8/rf/vY3TJ8+HcFgENOmTcO9997b6X127dqFs88+G/n5+Zg1axZWrFjhvC1de/lLL72EuXPnIhgMYujQobjsssu6PM/m5mZ885vfxPDhw1FcXIxzzjkHa9eu7fL+dsv7008/jVNPPRXBYBDHHnssli5dmnK/d955ByeeeCICgQBGjhyJn//8587OKQDw3HPP4fjjj3c+n+eeey7a29v79HnJJIZuIiLKOLvaLKO93A7dsvZ7jUYTlW6ZlY7BFrpjMautn6Gb6CgRCgGFhXL+hUIZeximaeLSSy9Ffn4+PvzwQ/z1r3/FTTfd1OGhhnDBBRegrKwMK1euxLPPPos333wT119/fZcf98orr8SHH36InTt3Ordt3LgR69evx5VXXgkAeOCBB3DTTTfhzjvvxObNm/Ff//VfuOWWW/D3v/895WPddNNN+PGPf4w1a9agsrISX/rSl1ICbLJXXnkFl112GS6++GKsXr0ab731FubOnZv2vkIIXHzxxaipqcHixYvxySefYM6cOViwYAEaGhq6/bz95Cc/wY033ojVq1fj1FNPxWc+8xnU19cDAA4ePIiLLroI8+bNw9q1a3HffffhoYcewq9//WsAQHV1Nb70pS/h6quvxubNm7F06VJcdtllzt8mvf28ZBLby4mIKGtktJfba7pltcPpemJNdyAQQDgclnIeidA9ONrL7S3DGLqJyG0vv/xyp20qf/azn+GWW27B66+/jp07d2Lp0qWoqKgAANx5551YuHChc98nnngC4XAYjz76qDOP5J577sEll1yC3/zmNxgxYkSnYx533HGYOXMmnnzySdxyyy3Ox5k3bx4qKysBAL/61a/wP//zP04lesKECdi0aRPuv/9+XHXVVc7H+vGPf4yLL74YAHDHHXfg2GOPxY4dOzBt2rROx73zzjvxxS9+EXfccYdz26xZs9J+XpYsWYL169fj8OHDzkXo3//+91i0aBGee+45fPOb3+zyc3r99dfj8ssvBwDcd999eO211/DQQw/hpz/9Ke69914cc8wxuOeee6AoCqZNm4aqqir87Gc/w6233orq6mrouo7LLrsM48aNAwAcf/zxzsfu7eclkxi6iYgo4+xtm2S0l9tX5wNBOVVmu80ZALw+n5RzABKh2zdIKt321Hivl3/aEB0V8vOBbtqrs37sPjj77LNx3333pdxWXl4OANi6dSuOOeYYJ3ADwIknnphy382bN2PWrFkpA0BPO+00mKaJrVu3YsSIESmh/itf+Qr+8pe/4Morr8TDDz+MW265BUIIPPXUU/jhD38IAKitrcX+/ftxzTXX4LrrrnPeV9d1lJSUpBx/5syZzssjR44EABw+fDht6F6zZk3Kx+vOJ598gra2NgwZMiTl9nA4nFKhT+eUU05xXvZ6vZg7dy42b94MwPp8nXLKKSlbRJ522mloa2vDgQMHMGvWLCxYsADHH388zj//fJx33nn4/Oc/j7Kysj59XjKJz0xERJRx9hOhjPZyO3TLqnQnD1IL+N2v9NuEUACIQVPp5iA1oqOMogCSdqHoq4KCAkyePDnt24QQKeGwr/exb0/e1qu4uBgA8OUvfxk///nPsWrVKoTDYezfvx9f/OIXASS6vh544AGcdNJJKR+zY0eQL+kCsX08+/076stzq2maGDlyZKf12AD6tA1Zx3NL9/myW8cVRYHH48Ebb7yB5cuX4/XXX8ef/vQn3HTTTfjwww+dpWe9+bxkEp+ZiIgo4+zg45ewptkO3TICP5Aauv0SQ7f995LPJyd0/9d//Tc2bd6CN996y5XjJXcYEBHlimnTpmHfvn04dOiQc9vKlStT7jNjxgysWbMmZdDXsmXLoKqq0yo+efJk59/w4cMBAGPGjMH8+fPxxBNP4IknnsC5557rtKKPGDECo0ePxq5du1Led/LkyZgwYUK/H8/MmTPxVi9/r8+ZMwc1NTXwer2dzmHo0KHdvu8HH3zgvKzrOj755BOn8j5jxgwsX77cCdoAsHz5chQVFWH06NEArPB92mmn4Y477sDq1avh9/vxwgsvZO3z0hNWuomIKOPs0B2U0OJt75MtI/Db7Kvwcgep2ZVuOe3l9l61fr87n4Nc26qNiAaPaDSKmpqalNu8Xi+GDh2KhQsXYtKkSbjqqqvw29/+Fq2trc4gNbtae+WVV+K2227DVVddhdtvvx21tbW44YYb8NWvfjXteu5kV155JW6//XbEYjH84Q9/SHnb7bffju9///soLi7GhRdeiGg0io8//hiNjY340Y9+1K/Hetttt2HBggWYNGkSvvjFL0LXdbz66qv46U9/2um+5557Lk455RRceuml+M1vfoOpU6eiqqoKixcvxqWXXtrlADYA+POf/4wpU6Zg+vTp+MMf/oDGxkZcffXVAIDvfve7+L//+z/ccMMNuP7667F161bcdttt+NGPfgRVVfHhhx/irbfewnnnnYfhw4fjww8/RG1tLaZPn561z0tPWOkmIqKM83jioVtCtdkO3bmwR6rM9cVOpdsrp9Idc3mwmWmazteeiMhNr732GkaOHJny7/TTTwdg/Q5ctGgR2traMG/ePFx77bW4+eabAQDB+NyT/Px8/Pvf/0ZDQwPmzZuHz3/+81iwYAHuueeeHo99xRVXoL6+HqFQCJdeemnK26699lo8+OCDeOSRR3D88cfjzDPPxCOPPDKgiu5ZZ52FZ599Fi+99BJmz56Nc845Bx9++GHa+yqKgsWLF2P+/Pm4+uqrUVlZiS9+8YvYs2dPjxcT7rrrLvzmN7/BrFmz8N577+HFF190quOjR4/G4sWL8dFHH2HWrFn49re/jWuuucb5vBYXF+Pdd9/FRRddhMrKStx88834n//5H1x44YVZ+7z0RBGijxvRERER9aCtrQ2FhYX417/+hZ/+5MeuHvv5f76AGTNmYPXqVfjyl77k6rFtmzZvgaIo+PKXv4TVq1ZJOYff/m4HiopMvP9+MZ54vKLnd8iw//zPX+BrV12FlpYWnHTiPFeO+fEnq1IGERHRkSESiWD37t2YMGGCE0SPZsuWLcPpp5+OHTt2YNKkSbJPJ6fs2bMHEyZMwOrVqzF79mzZpwMgM9+fbC8nIqKMsyu8QQnVZrva6fPJW09tkzlIzTSttkVZa7ojkQgAdwebaVoMAEM3EeWWF154AYWFhZgyZQp27NiBH/zgBzjttNMYuAcRhm4iIso4O3TL2DLMbmuWOcTMXtMtM/gbRjx0S2ovj0at0O3mvtnRKNd1E1HuaW1txU9/+lPs378fQ4cOxbnnnov/+Z//kX1a5CKGbiIiyjg7aMlYVx2JWKHbJ3GPbJvPJ3FNt2H91+OVM0gtHHa/0s1hakSUi772ta/ha1/7muzTOCKMHz8eR+PqZw5SIyKijEvs0+1+6LYr3TJDt/0Hg8xzcCrdktrL7Up3T/vTZvaY3DaMiIhyD0M3ERFljYz28mi80i1zcrgTuiW2uOvx0O2V1F4eCocBsNJNRL1nmnI6c4i6k4nvS7aXExFR1sjYp9se4CWztdvml1jpttvLZV17CIdCANytdGsM3URHJL/fD1VVUVVVhWHDhsHv97v6u4MoHSEEYrEYamtroarqgGbFyP+LhIiIjlp+v7zQ7fHIr3R7vPJCt13p9njkVLq3bduG9957Fy3NLa4dMxpfWmAPsiOiI4OqqpgwYQKqq6tRVVUl+3SIUuTn52Ps2LED6txi6CYioqyRsd9qOGK1Nbs5NbsjO3TLrHTrutzQvXv3bnzzuutcPabdXm4YhtTlBUTUd36/H2PHjoWu6zAMQ/bpEAGw/pbwer0DvpDLZyQiIsoaGdPLw6HcCd25MEhNVuiWwd4yjKGb6MhkbbXoy4ndJ4gyiYPUiIgoa+RsGZZDodsv7w9HTbNDt7RTcF1ypZuIiChXMHQTEVHWeL1e1yuO7SH3p2Z3ZIdur8R15Ybk9nIZNIZuIiLKQQzdRESUVW5Xu0OhdgDuTs3uyN5exCtxgrpT6VYHT+i29+lm6CYiolzC0E1ERFnlduhub7NCdy5Uun0yp5fr1uNXB1N7uWZVurnXLxER5RKGbiIiygpN0wC4P8E8tyrdEtd0x9vL1UFY6WboJiKiXMLQTUREWaHrOgAgEHA3dLe1tbl6vHQSa7rllZnt9nKJBX/X2YPU7M8/ERFRLhhET8VEROSmROj2u3pcO3QriiKtxTwnKt2xwVfp1mJWd4VgpZuIiHIIQzcREWWFPczK7fby9vZ25+X8/HxXj21zQrfEvaJjWnxNt7wue9fZ7eWD5zIDEREdCRi6iYgoK+zg6XZ7eWtrq/NyUVGRq8e22Y/dJzF025VuZRA909vt5YzdRESUSwbRUzEREbkpEbrdbS8Ph8POml7Zodvjlb+mW1EGTwCNxuKV7sHzkImI6AjA0E1ERFnhhG6X28uTFRQWSjmu017ukVfpjtqV7kHUXm5XugfTYyYiotzH0E1ERFlhV5uDLreXJ8vPL5ByXNO01rPLXNOtxayn+MEUQBPt5YPoQRMRUc5j6CYiouywQ3cw4Pqh7UpzYYGsQWrWY/dIDN2DstIdH6SmDqYHTUREOY+hm4iIssJeVusPuB+67Sp7QYHcSrdH4j7d0ejge4p32ssH0+bkRESU8/isREREWaHEq40y2svtSndenpxKt2HEB6lJDN12e/lgEo1aoVvW/uxERETp8FmJiIiywg7dAYnt5fmS2svtPcplrumORJLXdJvSzsNNdqWboZuIiHIJn5WIiCgrnNAtob3cDr2yKt3OlmFS28sT65r97u7aJk1MY+gmIqLcw2clIiLKCk88+MhoL7dDd1DSdmX28T0eeU+zyWu6A4FBUumOD1KTebGDiIioI4ZuIiLKCnuYlYz28kSlO8/1Yycf3yNxn267vRwAgkE5oft///B/WLZ8BW6+5RZXjme3l3sZuomIKIcwdBMRUVbY1UYZ7eW6rgMA8qRXuuWFP9NU7V3b4PfLCd2TJ01CeXk5pkyZ4srxonalW+JaeiIioo4YuomIKCvsIWIBCe3lduiWsV0ZkBtbhiULSKp0a5oGAPD53FlU7lS6GbqJiCiHMHQTEVFW2IEzKKG9XI+HPdlrunNloFfAL3q+UxZo8YsfPp/PlePZoZuIiCiX5MZfA0REdNSxg1bA737ojsVDt4zWdgDQ9dyodMtuL7dDsFuh224vJyIiyiUM3URElBWqM0jN/Wqz3dbsl7RXlt3e7pFc6bZDdyAgp9Ltdui2v+5ERES5hKGbiIiySkZ7uR32ZIVue59uNUfWdPt8ctd0u7nGmtVuIiLKNQzdRESUVVLayyWHbl23wqbsNd2J9nI5lW47ALsZurmum4iIcg1DNxERZZWM9nL5oTs3BqmZpgJA3ppuLf51cHNtO0M3ERHlGoZuIiLKqqCEYWZ2hdUnaesow17TnSOD1Lw+OZXuiIRKN9vLiYgo1zB0ExFRVsmodEciEQCA16UBXh0ZZm5VugOSKt07d+xAfX0ddu7c4doxNVa6iYgox8gpARAR0aAhY9uuqB26JVW6dc2qdCuKIuX4tvg8N3i9cirdjzzyNzzyyN9cPWY0xko3ERHlFla6iYgoqwKBgOvhU0ZbczItPkhN9pZhduj2SWovlyEW47ZhRESUWxi6iYgo69yudofDYQDy1lQ7lW7podu62DG4Qjfby4mIKLcwdBMRUda5H7pDAOSFbsPIjTXdhmGFblmD1GSwB6kJMXgeMxER5TaGbiIiyho7fLoduu1BarJCt6bn2Jpuz+AJoPYgNft7j4iISDaGbiIiyho9Hj6DLk8wD7VblW5ZlWZd06Qe3zaYK9329x4REZFsDN1ERJQ1dvBxu9LdHmoHIC/02oPUZFe6ndAtaXq5DDGNlW4iIsotDN1ERJQ1ifZydyvd4ZBV6ZYVeg09R9Z0x4u9gyp0s72ciIhyDEM3ERFljR18gkF3K91tktvLtXi1VXalW49Xuj2DaE13NMrQTUREuYWhm4iIssY0reDjd7u9vL3N1eN1FNNyY5Ca014+iEK3Xek27SlyREREkjF0ExFR1pimFfaCLreXh9qtNd2yQq+eK5VuPV7p9ko9DVfF7C3DGLqJiChHMHQTEVHW2MHH7fby1tZW5+W8vDxXjw0AWnxNd86E7sFY6eY+3URElCMYuomIKGsErODjdnt5cuguKipy9dhADq3pjoduVR08ATQai1e6GbqJiChHMHQTEVH2xHOP2+3lbW2JNd2FUkJ3bmwZpmmDt9JNRESUKxi6iYgoe+KhM+ByezmQqHQW5Oe7fuxYTHP9mOkYdnv5IHq2Z+gmIqJcM4iehomISBa3K93JCgsLXT+moedGpTumWU/z6mCqdMcHqYHt5URElCMYuomIKGtUu9Lt8ppuIKnSLSF0R3Ok0q1r9ppuySfiIqfLQPIFDyIiItsgehomIiK3KfG0J6O93N6nOV9Ce7kWH+Ylv9KtxM9j8FR9o9Hc+NwTERHZGLqJiChr1HjoltFeble68/MLXD92TMuNSrc2KCvduTE5noiIyDaInoaJiMhtHo8HgJz2crvSXZDv/j7deq6E7phd6ZZ8Ii6KxbdrUwfTlQYiIsppfEYiIqKscUK3hPZywzAAAMGg+6E7VyZox2LxQWoS9+m+44478NDDf8Mpp5ziyvFibC8nIqIcw9BNRERZ4/V6AchpL7dDd56ESnc0mmhxlllxjeVApfviT1+CU089FfNOPMmV49kXPOwLPkRERLIxdBMRUdbYodsvob3cCd1SKt1R52W/3+/68Z3ziG8ZJjN0JzoO3LnwYg9SY3s5ERHlCj4jERFR1tjVxqCE9nJd1+PHdr/KrsWPDcgN3dGo/Eq326GblW4iIso1DN1ERJQ1irNPt/vBV2bottcVA4DP53P9+DYtJv9p3jSt0B1w6eKDHbrtLgsiIiLZ5D8bExHRUU/G9HI7dMsY4pY8SE1upVv+07yux0O3S98DUVa6iYgox8h/NiYioqOejPZyLR6+ZFTZU0O3+4/dltpebko5B123tk9zK3RrDN1ERJRjGLqJiCjrZARfe111IOB+pdk0TQghpB3fFktqL5fVbW1Xun0uVfyjSa39REREuYChm4iIsk5Ge7ldbZZZaQbkri0OhxNP88Gg3Eq3W232ubJHOhERkY2hm4iIsk5O6LYqnjLXVANytkuzJVe6A5JCt6ZZHQduDZQzDMOZmE5ERJQLGLqJiCjrpEwQj1kVVpnTwwHAL/H4ug7Eu9wR8MsJ3eFwGEIIGIbe850zhC3mRESUS7ifBhERZZ3H44HP54Omaa4d0w5eskO3T2p7u/z28i9/6YuuHzMWiyE/P9/14xIREaXDSjcREbnC7TbvSCQCQF7otgep+by5MUXb5xeyT8E1XNdNRES5hKGbiIhc4XaLub2mW9YgMyd0Sx7kJru9XAaGbiIiyiUM3URElFV2S3nA5dAdDocByJ0eDgB+v9z2dps/wEo3ERGRDAzdRESUVbq9X7bLFd9I2Govl17plrym3K50+wdRpZuD1IiIKJcwdBMRUVbZoTsYdDd025VuVZXzVJdY0y230m6Hbp+PlW4iIiIZGLqJiCir7D2TZbWXezxyBpnlSqXbNBUAQCAweCrd9np+IiKiXMDQTUREWWWaVthzu708FA4BkB+6vT53p7Z3Pg/rvz7v4Kt02997REREMjF0ExFRVpnxSrfb7eWhkBW6FUVx9bg2O3T7fXLby+3cOZjWdNuh217aQEREJBNDNxERZZUZD5+ut5fHQ7fsNd0ej+zQbV10GExruqNRhm4iIsodDN1ERJRVdvh0u728vb0dgLxKt93a7JO8ZZhd6ZY8z81VdqXbnidAREQkE0M3ERFllR263W4vb2uTG7oT08tlh+54pXswtZfHtwzjmm4iIsoFDN1ERJRVduZ1u728PdTu6vE6SgxSkx26rf96B+UgNVa6iYhIPoZuIiLKsviWVW63l7e1WUdXFPj97k8Qt6usXknT022GYX3+B1Xo1uzQPXgeMxER5S6GbiIiyiq7vTvgcnt5a2ur83JRUZGrxwaSQrfkxdR26B5cg9TYXk5ERLmDoZuIiLJKVa3QFwy4217eFq90A0BBQYGrxwYAYYduyVuG2aHb4xk8odtuL7db/ImIiGRi6CYioqxSFOupxu013bquO6GrsLDQ1WMDia3SZA9Sswd4D6r28ihDNxER5Q6GbiIiyipPfE1zIOD+umqblNAdr3SrXNPtOrvSDYZuIiLKAQzdRESUVXbodru9HEhUOvPz810/trNPt+Q13bo+eNvLiYiIcgFDNxERZZVT6Xa5vRxIhO4CCZVuI97X7cmZ0C31NFwVjVmD1CBni3YiIqIUDN1ERJRV9vRuGe3ldrU5P1/CIDV7n25vbrSXy6x0X/75z+OrX/uaax0HiUo3UzcREckn9/I7EREd9ezQLaO93A7deXl5rh/bqXR75D7Vapr80P2rX/0aiqJgzerVWL9+fdaPF4tvGWZvV0dERCQTK91ERJRVqipnejmQCN0FMtZ0O6FbbqU7l9Z0u3Xxw650qwzdRESUAxi6iYjIFTLay+1qs5RKt71Pd46s6VYlPuO7PdDODt2KzAdNREQUx2cjIiJyRUBCe7kTuvPdD92mE7rlVrq1mPVUr6ryKt1uh+5ofJ9ulaGbiIhyAJ+NiIjIFUEJ7eWGrgOQG/hVVXZ7OeLnIe8c7NDt1veAXemW3dpPREQEMHQTEZFLAn7328u1eOLMkxH4c2RNdzQHKt2JgXYuVbrjW4bJ/twTEREBDN1EROQSGYPUdF0DIKnKniOhW9fkr+lOhG53vg6aU+nmnzlERCQfn42IiMgVMoKvHq90+wMB149tmrkRurX4IDWZg7zt0O1Wm3+ivZw7oxIRkXwM3URE5Aq/3+/6vsmxmFXpljE5XdftNd1yn2qj0dxpLw+6VOmOxvfp9vl8rhyPiIioOwzdRETkmvLyIa4er6KiAgAwffoMV48LABMnTAAAjBs3zvVjJxtZYa9vlncOdqt9wO9Ox4Hs4XVERETJ2HdFRESuCQbdbfO2q8wyKp5qPOXKbi83TIl95XH/8cMfoKCwEOvXrXfleJFI2JXjEBER9QYr3URE5JrWtjZXj3fw4AEAQHV1tavHBYBdu3YBABoaGlw/drLdu9xfS9/RihUr8OYbb+DQoRpXjhcOM3QTEVHuYOgmIiLX6Jrm6vFaW62Qbw/WclNDfT2ARGu1LM3Ng6+pTfbnnIiIKBlDNxERucYeqOXe8eITxCUMM9PiFxhkt5dHooPvqV4IweBNREQ5Y/A9ExMRkTRuh27DsI6nStivOWdCd9g6vqIAquru5992xx13YPHiV/G9713v2jEZuomIKFcwdBMRkWuEcHfbKjt4KYr7T3cxO3RL3jIsEkkcPxiUE7rnzjsREyZOxAlz5rh2THuPdiIiItkYuomIyDXut5fHK90y2svj68hVyZXucDjx2PPy5YRuOwD7/e5NkWfoJiKiXMHQTURErnG70i0zdEft0C250q3rKuxPezAgKXTHq/5er3tD3dheTkREuYKhm4iIXOP+mm4reEmtdEsO3cny8iSF7vjXwetlpZuIiAaf3PlLgIiIKMNMZ023+8eO5WDoDkiqdNtD5Xw+VrqJiGjwyZ2/BIiI6KgmIwQZdnu5lEFquRO6nfZySYPUEpPc3QzdrHQTEVFukP+XABERDQput5YDSZVuCcE3EokAyK3Q7ffLDd1urunWdVa6iYgoN8j/S4CIiAYFt4eoAXLXdMeiVqVbkdHb3kGi0u3+1wCQE7rZXk5ERLmCoZuIiFwhM3TLCL7RWFTasTsyTescZFW67fXtHhe3TzM4SI2IiHIEQzcREblCSnu5s2WY+8E3Fs2l0G391+eXU+k+VFODcDiMuro6146ps9JNREQ5wr0+LyIiGtRkhG7dqXRL2Kc7h9rLrUq3QEBSpfuuu/4bd931364ek1uGERFRrmClm4iIXDHY2ssjkbC0Y3cku9ItA6eXExFRrmDoJiIiV8gJ3VbwkhO6o64fsyumYT1+WZVuGTi9nIiIcgVDNxERuULOlmH2mm4Z7eUR14/ZlfinAV7fYKp0M3QTEVFuYOgmIiJXCCFzTbeMSndE2rE7MnTrHHy+wVPptqeXy7jYQ0RElIyhm4iIXGGag6u9PBwOOy8Hg0HXj5/MMOzQPXgq3fYFF4ZuIiKSjaGbiIhcIWVNty6/0g3ID936IAzd9gUXhm4iIpKNoZuIiFwhI/wYprzQnVzpDgQkh+54e7nXO4hCt85KNxER5QaGbiIicoWcSrfM6eU5VOm2Q7dn8IRutpcTEVGuYOgmIiJXyAg/usQJ1qZpOhca8vLkhm5Ns0K3xyv1NFyl6xoAORd7iIiIkjF0ExGRK6S0l8cr3TK2DEsWzMuTeny2lxMREcnD0E1ERO6Q0V5u5EbgCgYCUo/vVLoHYXs5K91ERCQbQzcREbnClBB+dIlbhgGJwBeQHrqtp/vBFLo5vZyIiHIFQzcREblCyppuiVuGJfNLnl4eiw2+SrfdXs5KNxERycbQTURErhBS1nRrrh8zmX2hQfogtXjolry03VV2lwNDNxERyTaInn6JiEgmGe3lhiG30u20l/v9Uo5vi8asp3tVHTwB1OCabiIiyhEM3URE5AoZ4UfPkdDt98te0z34Kt1sLyciolwxiJ5+iYhIJilrujW57eWJ0C230h2L2pVuqafhKraXExFRrhhET79ERCSTjDXdmuRBavaFBtmh224vV5TBE0BZ6SYiolzB0E1ERK6QsabbjLeXy2KH7kBQ7iC1aMS66CB5iLurdF2XfQpEREQAAK/sEyAiosFBzpZhuTG93O/3ST2PaNSudMs7h5/89GcoKirCfff+GdXV1Vk/HtvLiYgoVzB0ExGRK6QMUotXO6W3l/vktpdHciB0f/3rX4eqqli65G1XQrfdXk5ERCQb28uJiMgVQkhY0y15kJodun2y13RH5D/dO0PlAu5McjdY6SYiohwh/1mYiIgGBdOUsU+3+0E/9fhWtVX2Pt2RSHKlW87nxA6/QZfWt+uS1/MTERHZGLqJiMgVg3FNtx26fT65a7rD4cTTfTAoO3TnuXI8u718EM2OIyKiHMXQTURErhiU7eXx0O2VHLrtQWqAvNBtX3QJut1e7srRiIiIusbQTURErpA5SE0Ww17T7ZM7tzQSSdR7g0E5MdTZPs2l0G23lw+mbdKIiCg3MXQTEZErZKzpTgQvOcnLrrb6JE8vB1TY1zxkV7r9AXc+F4np5UzdREQkF0M3ERG5QshY053UXu71ul9ttivtstd0JwtIDt2uVbolr+cnIiKyMXQTEZErTMlrumUEXz1ebZUR+DuyK915gyV0S+5yICIisjF0ExGRK2S0l2tJa7plhG4jfvxcCt1+v5zQbU9y97u0fZr9uWfkJiIi2eT/FUBEREc1IQQURZEySM2QHLrtamsuhW5Zg9Q+c8mnAQCtra2uHM8O+ZykRkREssn/K4CIiI5qTuiWsKY7ub3c4/FIO77s6eUAIIQCQEirdNfX17t6PLu1n+3lREQkG9vLiYgoq+wKt4w13brsSnd8mJfHIz9029c8AgE5odtt9uR4hm4iIpKNoZuIiLLKCd0S1nTHYjHnZa/X/dCtafE13RKq7B3ZoVtWpdttHKRGRES5gqGbiIjcIWFNt5nU0i6jxduutHtyYE23aVrh0++Xs6bbbQbby4mIKEcwdBMRUVbJbC9PPr6Ufbo1u71c/tOtYVjh0zdYQne8vVxV5X/uiYhocOMzERERZZVdbZbRXp5MRui229tzaU23zzc4Qjfby4mIKFcwdBMRUXY5a7rlriX2ShikpjmVbvlruu1Kt983SNZ0xz/3rHQTEZFsfCYiIqKssuuqMrYMSyal0m2H7hwIfnbo9g6SSrfBSjcREeUI+X8FEBHRUc1eUy0gub1cxj7d8fZyNQcq3boeX9M9SEK33V7OSjcREcnGZyIiIsoqmVuGJfNI2DIsaofuHAh+8bli8HoHR+g2dA5SIyKi3MBnIiIiyrJ4pVtSe7kd+n1eiZXuHAh+WrzSPWhCNyvdRESUI/hMREREWWVvzy1ryzCbjEFqsRwK3fogC906K91ERJQj+ExERERZJbu93D6+jGFmMS13QremWecwWEK3XenOhcnxREQ0uMn/K4CIiI5qziA1IXmQmoRKdzRqhe5cmKCta9Y5eDyDK3QTERHJxtBNRESukL2m2+txf8uwaCQCIEcq3frgCt12ezkREZFs8v8KICKio5rTXi55TbfH4/5TXjQWBZAblW4tZj3+wdJtzdBNRES5gqGbiIiyKmfWdEvYMiwWzZ3QHYu3l6vq4Kh0s72ciIhyBUM3ERFl2eDdMiyX1nTHYnZ7ueQTcQkr3URElCsYuomIKKvs+WmyB6l5vO6v6Y5EwgByJXRbT/mKMjgq3UIImJIu9BARESVz/y8QIiIalGSt6U5sGeZ+iTcSibp+zK7YlW5ZM93y8/MxZcoUAMDatWtdOaau6/D7/a4ci4iIqCsM3URElFXOlmGS13R7pbSXR1w/ZleiUStty1rTffGnL8Evf/lLaJqGmccf58oxua6biIhyAdvLiYgoy+xBapIr3RK2DIvEtwzLhfbyaNQ6B1mnImOoHEM3ERHlAoZuIiLKKnspt+z2cq+ENd3RaKK9XHabczRqVfplhW4Z69s5TI2IiHIBQzcREblC1iA1+7iqhH26Q6GQ83J+fr7rx08Wk1zpllH1Z6WbiIhyAUM3ERFlVc6s6ZbQXh4Oh52XA4Gg68dPFo7IfcqPsr2ciIgGKYZuIiJyhez2co+EQWp2dRcAgkG5oTuaErrd/1okX4Bwi65prh+TiIioI4ZuIiLKKjv0yh6kJqPSbZqmc/y8PLmhOxKx9+kGJCxvlxO6WekmIqIcwNBNRESukN1ersraoDoumJcn9fjhcOLx5+W5fwEkeU23W0Pt2F5ORES5gKGbiIiyy17TLXmQmsfjfnt5smAgIPX4kaT28qCE0B1KqnS7NVTODt0a28yJiEgiCQ1mREQ0mNhRW9aabrut3SthTTdghX5FURCQHLpNU4UQVnt5MOh+BTgWjeLRRx+FrusYPnw4Wlpasn7MwsJCAMC2bdtw7LHHZv14RERE6TB0ExGRK2Sv6VYlrOlO5pc8vRwA9u07HYYRwLChh7B/X8zVY7e2tmL37t0AUgfMZRMr3URElAsYuomIKLskbxlWXV2Nqqoqaet7n3/+ebS2tqK0pFjK8ZMdOHAqdD0fZWX/BlDv6rGT2/vdGnD24IMPwufzYc6cOa4cj4iIKB2GbiIiyio7asta0/3qq68iFAph+LBhUo5fVVWFpqYm5Lm0jrk7imJ1G0QiBZAZuj0udR3YFfX29nZXjkdERJQOQzcREWXVu+++i6qqKvhk7FMFa1o2AGi6LuX40WgUALB7124px08WixVAUYDWtjIA+1w9dvL0eJ9L6+vtJQ0M3UREJBOnlxMRUVaFw2E0NjbCkLSmu6mpCQCwZ4+c0GsPDGt2YXBYb/m8PtePmVLpdun4pmm1scvYI5yIiMjG0E1ERFlVW1sLANi7d6+U4yf26ZYzvdyutvr97gfdrri1T3ay5NDt87m1T7f1ube7DYiIiGRg6CYioqyyB5jFYnKCjx16k9ub3T2+9fi9EqrLHdkXILw+989FTQrdboX+xPeeu5PaiYiIkjF0ExFRVtkBS1bodSrd8bXdbrOrrX4JQbcrUirdqrzQrUtaz09ERAQwdBMRUZb54mFTdnu3Iin028FPRtDtyL4Akdzq7RaPR0162d3QbRgM3UREJA9DNxERZZU9PVyRVGmW3V5uBz6vS+uYe8MrJXQnVbpdOr5d4ba7DYiIiGRg6CYioqxKtPbK2afbqXRLCv26blVb3arudsdptZcQutWUfbrdOb4R/96TtUc8ERERwNBNRERZJns9rRlvMZYXuuOVbpf2pu6O3PZy90O3pmsAGLqJiEguhm4iIsoqp9IMSYPMpFe6rdCdU5VuCa32yYPU3Arduia3y4KIiAhg6CYiIpfIG2Qmt9Kua1a1Vdaa8mRSQ3dS0HarvV1z2stdORwREVFa8v8CICKio5rMoAckJljLqnRr8dAto6W7I5lD5dTk6eUuHV/TrP25ZX3tiYiIAIZuIiLKMtmhW/aa8lgOVrplhNDk9vLkAJ5N9gUPIiIimeT/BUBEREc1mUEPSEwPl1bpjiWqrbL36jZN63Mhvb3cpT3b7c89wGFqREQkD0M3ERFllexKt6HLrXZqScf3+XwSzwQwTXkXQJKr2259L8SSLnjYywyIiIjcJn+UKhERDQqy1jTHYnJDt5kU9nw+H8LhsPRzkdJenlLpzkzoVqDCAw9UxQMPvPH/euBRvFDhQbFRjoJwORShoGVbDH6vApiAMAChA8J+2QBKTlCh+rn2m4iIMo+hm4iIsiq5vVxRFNfbfDXJa7r1lNDtl3gmgGH2L3Sr8MCjeJL+60153QMfVEWNv+5Nut0bv92LCdoMDGscDUWo0CM6Ti6IQoXq3N8Ozl7FZ/2DDx7Fet2j+OCNv80DLzyK9U9x/qdChQIo1kuI3+rz+jBs13AACg4+aMLr0axJ5mb8gcU/DUIDWtepGHud3K8PEREdnRi6iYgoq5JDtsfjcX2wWfK6Xhk6Vrq7k1yt7Vi9TX7dCy8URY1XdX1QoTq3q04Y9jqh1n6/iQ3HokQrhWKquKjEBw9Sw61X8UFNCrdexQrXQCLMqlCgKGp83/V43FUS4bfj7fZXvyhchODBgPU5MQVKCycCimJtoa1YMVnY/xMCAmb8NTPpNpHmNhMmDBgwrdeT3lfVVRT58yEAeIYYCOT5ED+9lAsPsXoTsTrrfTnpnIiIMo2hm4iIskp26I51CN2K3ZScVJHt6vVE2PVAjb9uh1nVCbMqklua7XBsV4TLlHJM2j8VilBR4J+IUFnICrmwKrgeeKywq3iSKreJYJscZq1w2+H2eOAFAAHhhFj7dfttAFDSWgp/1KrmevLLYJpGF0E2OfQKCJEIwIlwa3bxvsm3J772I3wVKMwrAmBV3Kv03Vn8qltU4YGpWhc9TNWE4kkfqFWfAjMiYLQD3sKsnxYREQ0yDN1ERJQVQggIDfAYXii6B4pQUeodipge69Se3P3rSe3KduCNV2EVqPAoHngVv1Od9Sl2mLUqtkWiGKN3jIFiqhgybHa8EblzaO022Dq3KylBEugQbIWwqrdO0BXwqh6UNpdBKAIetRAhf3sXAdV0Aq8BI36xQsCMh1vEa7rp3q+3TO9IFORZqfKQsQ+G4d4FECVpHbdbteTkCz5GNxd7FD9ghAG9RcBbyEo3ERFlFkM3ERFlRbRG4MDfNRy39SIrjAoFFSVzYRQYXVdy47dbEoHJzrJW+y8AoTjJzQrCJsx4SI1HU+dlj/DCHyuAUAT8SgCmMOEE2Hi47blqawffvvMrAfjyrJeb22vRpDf17xOaAckhVFVVuDnQW01q21Zcit1CJC5IdDe9XPUBZswK3RjlxpkREdFgwtBNRERZETkgEDscD6yKgFAEoggjZka7aF0eWLjtihc+FAaCAIBWNMIw3W1vTw26cia421LOxeW1yylrpV09tNVvb+/Xno7iUQAhoLe4d1ZERDR4MHQTEVFWhPeagApovjDsrBdDCBERcfU8kiucXo/H1ZZqoHN1Wabkc1FcPhdZA8rsJe69mSWgt7g7WZ+IiAYHuc/+RER0VBKGQPsOE5681NvdDnpAaouxKmGv8FwN3W6fS2ql28UAHn/Mmt7zfu1aM0M3ERFlHkM3ERFlXKRaQG8R8BQoSMp5Erdjsk7CIzv0Sw7dyJH2cjePbD/knirdqh+I1TJ0ExFR5jF0ExFRxkUOmDAjgBpMvV32HsiqR0LoNpOCroRKezJzULaXW4+5u+nlAKD4AK1BpHy9iIiIMoGhm4iIMi6814Sidg5aqio3dCcmo7sneTCc7Ep3yp7pXncvAEi74BIP0R33a+9I9Sswo9Ze3URERJnE0E1ERBllagKhHQJqvn1LUnVVdqVbUui3W8xzKXT7vD5Xjy3jggeA+BZxgKZ1v6ZbsbcNa2Wlm4iIMouhm4iIMipaLaC3Wuu5O5IVvBLHlxW6rf/mVOj2uR26U15z7biGYab8tyuqDzA1TjAnIqLMY+gmIqKMCu83YcYANdD5bbIr3a5OzU7iVLolP/7k0O31urlrqNLpgotbF2BMM7FlXHfbxVl7dYN7dRMRUcYxdBMRUUaF95iAJ33AlhM6FdiVVVmh1w67iqLKrfZLCt1pvxdcqvqbZqLCHYn0vEc8K91ERJRpDN1ERJQxZlQgtEvAk99FuJWwpjr5iLIq7aaZG8PUkuOkoqjweNwJ3mlDt0uT5JOr+6FQqMf7x5oYuomIKLMYuomIKGMiBwWMNgFvgX2LQHLsVWVUeZP3h5bcXg5IDt0iNVD6XKp2Jz7vSdPT3WovT3rMra2t3d5X9QMa9+omIqIMY+gmIqKMiRwwYWoCit96vWN8kRF6lZTQLedpLznsSh2m1iF0e10apmZ/DZKP7trnIam9vKdKt+JToDVyr24iIsoshm4iIsqY0G4TikdJBN0OIU9+6Ja7phvInfZyAPD65FW6FbfWdCd97mM9bBum+sG9uomIKOMYuomIKCOMiEB4r5myVViHzC1lkFnyIQd96O7UXu5Opdv5uicd3q0905MfszBNiE6XHhKcvbo5TI2IiDKIoZuIiDIickBAbwM8BYnbOgUcKYGTle6ueF0K3UqagO3W58FeTy/i8wVi0WiX91X93KubiIgyL3ee+YmI6IgW2W9C6AKqPxGwkrdrAmRVuuWHbjNHQnenSrdr7eVq/PjJt7lV6Y7/N/692N22YYpq7dWtca9uIiLKIIZuIiLKiPZdJhRvapCyq4qGoQOQtaY7+WU5oTt5mJdHYuju+Ojd2qs78WlPuvjg0lA7O2zbFxxa29q6fweFlW4iIsoshm4iIhowo10gsj91PTcAhMNh6+2GASB9m3H2sdJt8ySFbAHh2l7dTqU7+TaXB6nZ/23vKXQD0JrMHu9DRETUWwzdREQ0YOEDJoz21PXcANAWDzi6Hq90S3jayYX28pQ13R6JodvjcV62W//d2Ks73TR799rL7f5y67/dtZcDgOoDtLpsnxUREQ0mDN1ERDRgkQMCwhBQfekr3Vp8qya3JlYnS8l2uRC6JVa6k9vJzXj3gRt7dacL2O5NL0+tWuu60e39FT/36iYiosxi6CYiogFr32FC8XUOUdH4mm49HrqlrOlObi/vtKrZHamh29PNPbMrXaXbjb26033dFdfWdHcOz/aMgXRUH2BGrEn8REREmcDQTUREA6K3CkSrO6/nBgDDsIKd5oRuCU87Ke3l7h8eSK22qh55oTul0h2/EODGXt321Hohob3cfpyKosCIL3PorsWc24YREVGmMXQTEdGAhPeb0NsAb4f13IaZaOON2aFbSnt5yvhy148PpG6V5cYa6q4kV7rtqd6BQCDrx7WHpqUMUnN5TbeiKIhErbBdV9f1om3FB5gxhm4iIsochm4iIhqQyAEBmOh6uzBTT0wvh4LOG1dll9LFy24SSVuGqapH2rru5Eq3XfX1BwJZPx/nYouMQWpmInS3t7cDABobGru8v32uemv2z42IiAYHhm4iIuo3IQTat5tQ/Z3fZrfwajFdSltx0gHlHTvOdGq81n+9LrR0d6Sqakp7vwCgaTEoUBAM5mX32PaWYUnFY9fWdMOMH88K3QIChmkiFot2+36sdBMRUaYwdBMRUb/pzUDsUPr13G1tVlVR07XUNc0uB19ZQTtZx2Febqyj7sjej9v+WiiK4kyXz8vLcuiOV48Fki++ZPWQDtOpdKswTRORiPWYu2sxhwJoDdyrm4iIMoOhm4iI+i1ywIQe6rw/NwCEQlbo1rUYgKSw5/K67txY050aut2YGN6R3VpuTy1PCd352Q3dTlU7pePBpUp3hy6L9vjFoO5Ct+oDYvWsdBMRUWYwdBMRUb+F95vWem5P19uFaZq1djgx0Mrdpx75de7k4Gedjc+FvbE78nitIWr2RPnk0B3wB7O6lZm9Zjw1AGftcCmSuyyS13Vrmtbl1mGKX4HemH67MSIior6SN0KViIiOaM567i6GX+vxQV2aU+lODLRyVfKabneP7EgOfkDqQDO3eD12pTs+1E5RYRg6YrEo/P4A8vLy0N6enc2p7a95X6eXK1ChJP9PsV9SgS5uT77Nvk8gWgAFKoZ6K2CaAr62fPi8fjRsb0dRUREg4kX4+D9hAEoZYLQD3qIsfEKIiGhQYegmIqJ+0RqAWK1Iu55bCDMeshWn0m0KAQ+4phuQU+n2qh5AKDB1AcVU4VE88Cl+aCEdeZ5CFAdLYYREr0Jsx9uT/+fcR1Gd18u0UnjhhS8aQ0BYU/dMw8RInxK/EJL6NVKgQEBACIHE/0zAfrnD7SKems14ahYQMIUJUxgwYCDqaYfiBRrEIUS0MCJaM8pLyxH1NOLEmXOheAF4FKg+QPFYnRu+csBT6OIXiIiIjloM3URE1C+RAyaMEOAr6/w2q7VcgWmaTgtv8tZNbsrm4XpbifXBD58eBIQVMr0iiBLPEPQUYlOruYkQ69SMhf2qgFDsc+r8gBUoKFKK4I8E4NECCMaC8OpBxDwmvJEA/Hl5KPF5EVX1XoVYQ+gwoMMQBkzo0IUOU+jQocEQOnTo1n2EBgMGZkyYgWB+ADWHazB8xHAIxURUi+LDPcthChMGDJjCgAnrn/VxTZhCj//XfpvpHDP1/exzs87fiN/f9tnTP4e8vDy8tm0xmpqaUCbKcP7cCyGEwKf/3zwpF0GIiGjwYOgmIqJ+Ce8zAdHDem5dc25LDFLrek13ptqJk28vFIXIi5Q4YXSEd2wXIVaxeox7GWL7Uon1QIUqPM70bqvoLKAbsU4h1oBmhcakEGvAgC40J8Ta4dcUJgSMlNBqJIXQ5BB7wqQ5KCsvxY5d2zFx/EQ0tzbh3e1L4fF5sHDOQkAFFu9+GeFIqMcQ21cFwz6LgoICrK9Zh+OHzQQAxGJRfBJa0u+P2Rf2PvGqx1q33tjYiPb2dhQUFGDHjh2YPn26K+dBRESDE0M3ERH1mRAC7TtMmDGByEGz05rYcLOJwnA5fNEYRvm8gBAojpbBZ/owQvEg6oulfLxMtxPbIdYUBjS1GEpBDKZiQigGtu1fB83UugyxJlKrq12FWLNDlTXl9vjHsj+2L+jDRadfBBMmoAoIxcRre15FY0ODa1+zyrJjoBZGsC+2BcMKitBmNKNG3wvoQHVkL8rKyhEc6sXhfY0ZP7a9hj0UCjm3uTlQzw7dXk9iWNzBgwdQWTkVy5cvZ+gmIqKsYugmIqJ+KZ6lwleuQPUA8AKqV4HitdbE7li5A7XBQzhYfRB7WnfBhInp46ajpLQEm1s2oqa5JtE63I8Qm/y2niqxs8eegGnjE6Hq36sXpYQ/N/h1P3SvdaEh4PcjGjNQUFDgaui29+K2J5YnTys/dOgwysrKMXzEcOzbtzfjx7ZDd3t8GznA3WUGduj2JIfuA1boPnDgAEzTdCasExERZRpDNxER9ZmiKBh6TtfrYHftWIlmTwtW7vkIO9t3AAD8wQhGDxmNHZ7V2BXe6dapwh+wBncJIaAoipT1u3boA4Ci4mJE6+pQWODelC6v15tUbbZCd3IAPXyoBtOmTcOIERUZP7aiKM6x2tvaUm53S7rQffjwYcRiMfj9fuzfvx/jxo1z7XyIiGhw4WVdIiLKKNM00dzSAgBobW1xbrcHqiUHHzf4ff6U12Vs15UcuocMGQIAKCgocO34dpU7Fos5W7glfx1qa2thmiaKioqQn5+f0WMnf77D4bCzlZzs0C2EQFXVQQDAihUrXDsXIiIafBi6iYgoo/bs2QPAGqZWV1fn3J4u+LjBrnTbZE2qth//iBEjAAAFhe5Vuu3QHYmEYZrWQLvkdmpN05xW9+HDR2T02B6vvT+4CdM0oWnWcD3ZoRsA9u/fDwDYsmULIpGIa+dDRESDC0M3ERFl1LvvvgsAOHBgvxPwAMDQJYVufyDlddmhu6zM2mNNRqU7HI50GUAPHT4EABg+YnhGj+31WKHbPq492d5NiS6L1C6HqoMH0dzcDEVR8Prrr7t+XkRENDgwdBMRUcYYhoG9e61BXPv27u30NgDweN0O3Yk13dbx5YRu07Qev92+7WboDiYNUbO/DoqipFS7Dx+yQnem13V7419vPV7hjkbdrygbhnXxp+OFBiEENqxfBwBYtWqV6wP2iIhocGDoJiKijNm50xqQFolEcPjw4ZS3GaasSne8vTweun0uh36bHfzy8vIghIDX60UgGHTl2CUlJQCAUKg9pfsgOXTX1tbCMKyp6pm8IOCNX+TQ42Hfnp7upu7mCezfvx+NjY1QFAWvLl7s9qkREdEgwNBNREQZ8+477wAA9u/b51SWbTLayz0ejzPIS8CudMvZuMOuMCdvT1XoQrVbURSMHj0aAFBTXZ0y1C35a2EYBhoa6gEk1p1ngv35tweo2dXkjpX2bOppnsD6eLV7/YYNaG9vT3sfIiKi/mLoJiKijNB1HQcOWtOg0+31LGOQmi9e5baGeFmh2yupvdx+/LquIxivcBe4sG3YsGHDEAgEEY1GUFtbm3IundZ1H7LXdWeuxdxpL7dDd1KodWuSfE/fe1UHD6K+vh6KouBf//qXK+dERESDB0M3ERGl1brBQPs2o1PFuitbt26FoigIhUJOuEsmI3QH4qE7Fos5j8Prcnu7LTl0l5QUAwAKCrNf6R495hgAwMGDB53PgX0uHSvNiXXdIzI2XdwO1kY8dCdXkt0aatebLov169YCsCaZt7S0dHk/IiKivmLoJiKiTtp3mKj+p4aqf+i9fp/33rOmlu/bty/t2xOh2732bn+a0O32IDebGX/8mqY523K5MUxtzJgxAIADBw4kzsVMP1isrq4O0WgUeXl5GDVqdEaOb7fza2lCt1tdB848gW6+9jU1NTh8+DAURcGLL77oynkREdHgwNBNREQpQntMVD2jIXJAAL0rciMWi6GmxqqSdpxabpNR6U4O3XbQ9LoY+pPZwS8WizlBuDDL7eVlZWUoKCiArus4VFOTOBe70t3ha2GaJnbu2AEAmDptWkbOoWOlO3mQWiAYSPs+meZUutXuv/fWr7PWdu/cuRONjY1ZPy8iIhocGLqJiMgROWii6mkNWqNAYGTvnyI2bdoERVHQ1tbmDOPqKDFB2r2nHnuP7uTQ7fb0dJsddCORCIYNGwYAyM9ypXtMvLW8uroqZYCacwEkzSCzbdu3wTAMDB8+HGXl5QM+h7w8a4s0e39uTdOcroOC+PZp2dbbCz61tYdRU1MNRVHwwgsvuHFqREQ0CDB0ExERACBaY+LgkxpihwWCoxU4S3p7Ue1+//33AaQfoGaT0l4esCvdUent5fbjj0ajKC0tBWC1l2dzgvfoNK3lQNft5QAQCYexP75EYOrUgVe7y+KPtampqdPb7ECebX3ZI96udu/btw/19ekvIBEREfUFQzcRESFWawXuaLVAcIwCRe39EK1oNIq6ujoAXbeWA5Lay33x0B2NOWuqe2oxzpbkSndJSQkURYHH48GIisxNCk9WWFiE0tJSmKaJqvhU+Y7n0rG93LZ16xYAwNixY5GXlzeg8ygtKwUANDV1btfOyx/Yx+6tvnzv1dfX4+DBg1AUBc8//1y2T42IiAYBhm4iokFOaxQ4+JSG8IF44Pb0bWr1unXroCgKWlqa01YzbVJCdyCxptuQ3l5uHT8ajUJRFEycOBEAMPaYsVk53phjrCr34UOHoGlaytvMbtrLAaCxsRGHDx2CqqqYUlnZ73PIy8tDIBCEaZpobm7u9PZgINjvj90Xfe2ysPftrqqqRnV1ddbOi4iIBgeGbiKiQUxrsQJ3aLeJvNF9D9xCCCxduhQAsG9v+qnlNjmD1Kw13dFY1GmpVl1cU57MDrqxWAwAcPrppwOwWsCz0WJur+c+cGB/p7f15gLElni1e/LkKf3eT7u0tAwA0Nra4nz+ATit/vZFkWzr6zyBpsZG7Nu7F4qi4G9/+1unixZERER9wdBNRDRI6W0CVU9pCG03EBytQvH2fV/mnTt3IhQKwTAM7Ny5o9v7ytynW4sl2stVWe3lZmroHjvWqnD7/X6MGDEio8cK5uVh6NChAIADHVrLgZ7bywGg6uBBtLa2wu/3Y/yECf06D6e1vLEp5XY7dAdytNINAJ988jHC4TA0TcOTTz6ZrVMjIqJBgKGbiGgQMkICVf/Q0L7FQGCUCtXX98AthMCiRYsAADt37kjZCirtMSWEbl88dEdjscRezVkcXNYdI2mfbgBQVRUT4mH2mAy3mI8Zbe2xXVdXh0iar0tP7eU2e213fweq2ZXuxg7rue3Q7fO5M1SvP9970WgUH6xYDiEE9uzZg48//jhbp0dEREc5hm4iokHGiAhUP6ehbb2JwEgVqr/vgRuwqtzt7e0wDAObN23q+bhJwUdR+nfMvrIr3bFozFlTrcgO3fFKNwCcccYZAKwW80x+TrprLQcS7eXdVboBYM/u3YjFYigqKsKoeJDvi9IuJpc7e6Z7fX3+mP3R3ws+hw4dwqaNGwEAL7/8MqeZExFRvzB0ExENImZMoOYFDS2rTfgrFKiB/gW9vla5AaTsE53NbbKS+QP2Pt3RREu17NCt685t48aNAwAEAoGMtZj7fD4Mj3+sgx22CrOZvQyhuq5j5w5r2UBfq90ejwdFRUUArDXSKcd3QnfuVrptGzasR+3hw1AUBQ8++GDK9zEREVFvMHQTEQ0Spi5Q86KO5pUm/MMVeIK9DNxp9unua5UbSA3dbrSYK4oCvz9penl8mJbah+3QMskOunpS6FZV1QnemWoxHzVqNFRVRXNzE1pbW9Pex+jD9mnbtm2FaZoYMWIESsvKen0eJSUlUFUVkUgEkUgk5W2mmQjBPl/2q90DCd1CCKxYsRzRaBSRSATPPdf3bcRMvReb3RMR0VGLoZuIaBAQhsDhl3U0fWDAN1SBJ7//wbM/VW77/dxc150c5mKxGAzdOraiyKp0W9VdIyl0A4kW8zHHZKbFfMwYa6uwA/vTV7mB5Pbynj8X4XAY+/dZk+n7Uu1OtJZ33p/b/lxY9+t9kO+vgV7wCYVC+PDDDwAAW7ZswYYNG3r9vpEqE7t+G8Ohf2nQmhi+iYgGI4ZuIqKjnDAFal/T0fC+AV+ZAm/BwIJdf6rctv5Mke6v/Px8AEA0GoEQAnqOtJfrHdqT7WFqgUAQw4cPH9AxPB4PRo4aBaDr9dxA79vLbfb2YePGjUMwL69X72OH6Y6TywFAJG0fVtaH6nl/ZaLLourgQWew3HPPPYeWlpZevV+sXiBWJ1D/to4998RQv1SHEWb4JiIaTBi6iYiOYkII1L2po36pAW+xAm/RwAJ3f6vcNqet2Jv9SndhYSEAoLW1DUCirdutIW4d2cGv45pgVVUxdqw1+GygLeYjKirg9XrR3t6OxsbOFeaO59Lb7dMaGxpQe/gwVFXFlClTevU+znZh6SrdLoduIYSzjnwgXRZr16xBQ0ODs747ee/xrtiNFcGxKsyIwKGXdOy9N4bmTwy2nRMRDRIM3URERykhBOqX6qh7y4CnUIGvZOBhcyBVbgBOi3dv1hIPVGF8iFdbmxW67aApLXTHLzikC2pnnDEfADDmmGMGdH5Oa3kXA9Q6nounF+3lNrvaPXnylF4F18R2YU2d3mZffAHQp3XiA5GJpQ2maWL5smXQNA2tra14+eV/9fxOSuKff5iKwGgF0cMCVU9r2P+ghrathrOFGhERHZ0YuomIjlKNyw3U/duAGlTgKx140BxolRtItFa7sabbrnS3xYeJ6fH9sWWFbrulO13otlvMg8Eghg0b1q+PrygKRse39TrYTWu5dS59r/pWHTyIttZWBAIBjI+fb1fy8/Ph9/thGAZa07Rhm0lruu2Ba9lmD9Ib6PdeW1srPl75EQBg1arVWL9+fbf3V1RYoTueq1WvguAoFf5hCtp3mDjwNw0Hn9AQOdhz1ZyIiI5MDN1EREehppUGDr+iQ/EB/vLMhMyBVrmBpGqjK+3lHSvdudFenq6q6fF4nCr1MWP712I+bNgwBAJBRKMR1NbW9upcetteDljnvXXbVgA9D1Szq9ctLS1pLzIkV/1VVUVJSUmvz6O/nC6LDMwT2Lt3L3bu2AFFUfD8889j+/btXd/Z/nbr8GVXAwryjlHhKVbQssrE3r/EcOhFDVojq95EREcbhm4ioqNMyxoDh17UrHbWoZn5NZ+JKjfQ9wFeA1FkV7rbrEq3Zle6s37k9LoL3UDSFPMx/WsxHz3GWhd+8ODBHtuVk7fs6ovdu3YhFouhuLjYqc6nU2YPUUvTWg4kPhd2IHejxdzo52Puyscfr8SBAwegKAqeePxx7N27N+39FFVBd19Ob4GC4FgFildB3VIde+6JWsPWQgzfRERHC4ZuIqKjSOtGAzX/1CAMwD8sMy3lALBy5coBV7mBRNjyZjl0q6qKvPj08tZ4pVvT4lt15WClGwAmTZoEQCAvLw9Dh/a9xby367mtc7Hby/v2Z4Cu69i0cSMAYNbs2V3usd3ddmFAoupsfy7cnGCeqS4LIQSWL3sfNTU1UFQVf/vb31BdXd35jvaa7m4ytKJYS0Dyxqowo8Chl3TsuTeGpo85bI2I6GjA0E1EdJRo22qg+jkNRhTwj1AG3kYdf/fa2josXrwYALB506Z+V7mBxJpuNcuhu6CgAKqqQtM0RCOR+LH1Ht4ruzpOLe/I4/Fg9ChrTfYx8WnmvVVWVoaCggJomoZDNTW9Ppf+DLTbtm0rWlqaEQzm4djjjk97H7ty3dTFBHW76uxq6M7CED/TNPH+e++irrYWiqLgr3/9K+rq6lLuY08v7010VlQF/mEqgqMVxOoEqp/WsP8BDW1bOGyNiOhIxtBNRHQUCO0yUf0PDUYbEBiZgcAdJ0yBhx9+CIqi4NChQ9i4ccOAPl4mJkj3Rsf13ACgxbSsHrMndnVZUZQut5o67fTTAQDHjOlb6LYHm9VUV/cY7oFEe3l/Ln6YpolVn6wCAFRWVqK4uDjl7V6v1xli12V7uZ56jvak82zK1jwBXdfxzjtLnS3a7r33XjQ3Nyfu0ItKd0eKV0FwpAr/cAWhXSYOPKLh4OMawgc4bI2I6EjE0E1EdIQL7zNR9bQGvRkIjM5c4AasVuVYLIZIJIIVK5YPuNrmXuhOXc8NAJoWA2CFXjemZXeUvE1WV8F4ypQpEEIgLz+/1y3mwWAQkyZNBmANu+uNxNehf5+HmppqHDiwH6qqYs6n5qa8raSkFIqiIBwKIRqNpn1/Penx67oOn8/nXCjJlmx+72mahqVL3kZLSwuEELjnnnsSF3yU/s8RUAMKgmNUeIsVtKw2se/+GGpe1BBrYNWbiOhIwtBNRHQEi1SZqHpKQ6xeZDxwNzU2oj3UDmECH6xYjsgA2sptprOme+ATpLvTcY9uwAp3Np/Pn9Xjp2N0CJrpeL1ejBo1CkDvp5hPmz4dXq8XdXV1qKlJs6Y47bnE13QPoNV69apVMAwDFRUVGJNUmS8tKwWQfn9um/34FUVxqsLZbjHP9gWfaDSKpUveRnt7O3Rdxz333INwONxpy7D+8NjD1nwK6pfq2PvnKOre1jhsjYjoCMHQTUR0hIoeMnHwSQ3RQwLBMQoUNXOBOxqNoOaQtTZ48+ZNqOnFOuHeSKzpzu7TT2KP7q5Cd/oBYNkkhHDayrsK3QBwut1ifkzPLeaBQACTJ08BAGzc0P1+0ckG0l5ua29vx+bN1lC9E+bMccJsqTO5PP16bgAwkkK3ve77SA/dABAKhbDk7bcRDocRjUbx5z/fg0jUmikwkNANxIetlcSHrcWAwy8b1rC1lRy2RkSU6xi6iYiOQLH6eOCuynzgFsLE5s2bASiIRqLYuGFg67iT2XtlZ2Kv5O4UFXVuLzdN02mP9/mye/yu2MGvu9BdWVkJIQTy8/MxZOjQbj+eXeWur69PPzm7h/Pob3u5bfOmTWhvb0dBQQGmT58BAChzJpc3dfl+yZXuxsYGANnfNsytpQ1tba1YuuRtRKNRtLeH8OBDD6A16ftwoBRVgX9o0rC1f2jY91cNbZs5bI2IKFcxdBMRHWG0JoGDT+oI74sHbk9mt8DauXMnDMOEaepoaKjP6B/ybgQfRVFQUGCH7ra095FR6QYSj9/eMzwdr9eLkRUVAIBjjum6xTwQCGDKlEoAwIY+VLmTz0Md4CRvwzCwerU1VG36jBkoKSlB+ZAhAICG+vou38+eJK8oChoarEr3sGHDsvp9obu4R3xzczPefvst68KDAhyuPYzt27d3e7Glr5xha8MUhHebOPB3DQcf0xDez2FrRES5hqGbiOgIorcKVD2lIbTLyErgrqurRXNzCwCgoaGhV5Ow+8KNfbrz8vLg8XhgGAZCoVDa+3i9ckK32YtKN5A0xbybFvOp06YlqtxVVX07D9Pep3vgX4cD+/fjUE0NPB4PTjzpJKiqipaWli4veACAHr/ooABoaKhHW2srfD5fr9ex94fphG53uhyam5rw+r9fw9btWwAhEA5FsHbtWjQ0dH0xoj9Shq2tiQ9bW2TNeSAiotzA0E1EdIQw2gWqntbQts1AcLQK1ZvZwB2NRrB3714A1nrcSHx/60xyo9JtD1Frb2/vVKW3X/fKai/vxZpuAJg6dSqEECgoKHAqx8n8/kSVuz/t/4lKt5qR4XuffPIJTNPEkCFWO3xVDxcBNC3++OPH3rXLmro+aeKkAZ9LV9xqL09mmia2bN6Muvp6p7th9+492LJ1i7PUIlOcYWt+BfXvxIetvaXBaGf4JiKSjaGbiOgIYEQEqv6hoW2TgeAoFaovs4FbCBNbtmwBoCASCaO+m9bggXDCXjZDd2H3reWA1cItQ2/WdANW+/uIESMAAGPTtJhPmzYNPp8PDQ0NqKo62O/zAJCR7dNaWpqxfds25/WaHtaX63pqe/2u3bthmiaGDR/ead/vTNGdeQLu/ukjYELToqipqooPlxNob2vH2rVrnfXsmZIybE0DDr8SH7b2kQFTY/gmIpKFoZuIKMeZMYHqZzW0rjPhr1Ch+jMbuAFg165d0HUDhmng0KEaDHjUchfcaC9Pt0e3LTFITe6a7t6s7T3ttNMAdG4x9/v9mFLZ/yo3kGgvBzJX+T148IDzck+TyJ1Kd1wkHHaq4xOzVO02XG4vt5kwIWD9RNXX1+HggQPQtBiEAHbt2o2tW7dmvOrtDFsbo0Crty7Y7X9QgzAYvImIZGDoJiLKYWZMoOafGlpWm/CPUOAJZj5wV1UdRFOTtVfy4UOHMjrsqSNDz36Lb1FhfI/u1s6Vbqe93MUW42R9Cd3Tp0+3WswLC522bQCYOtWqcjc2NqYE3b5I3r4sU10Hw4ePcF6eceyxCObldXlfTYsBsCqzdqV9106rxXz8hAkZqb53ZEpoLwcAEY/c9k9uJBrB/v37nap3W1sb1q5d2+0Wa/2leBQERqrwFiqI1prgcHMiIjkYuomIcpQwBA69pKPpIxP+YQo8eZkP3IcO1aC62tqDu7GhHqFQe8aPkcww3VjT3XOl2yOpvdwOftFotMf7+nw+jBkzBgBw3PHHAbCq3JVTpwLo277c6TiV3wwF3FGjRgEAWuND0WbPmt3lfVP3TPcDAKqrqxAOhRAMBjFq9OiMnFMyN7os0jGFEQ/eiZ9fIUyr6n3woFP13rlzF7Zt2+r8jGSUfWgONicikoKhm4goBwlD4PDLOhpXGPANUeDJz3zgrq+rxYEDVqW0ubkRDRleX5qOO2u645XuNGu67equ7DXdvQndAHDZZZdBCIGRI0dh+PDhqJw6FT6fD01Njc7Xrr/MDF4ACQaDzsC3jz9eCSEExk+YgKFd7DOeGrqtVn8hBHbt3gUAmDRp8oDPqdMxXfjeS0fEl2qkG1gXiYSxf/9+NDc3AQBaW9uwds0aHD58yHm/jFCQrRUjRETUCwzdREQ5RpgCta/raHjPgK9Mgbcw84G7sbERe/buBaCgpaUZdXV1Xd5XQeaOb7eXZ6vaGAgE4PP5IIRIG7qdSres9vJ40O3tZPjy8nJMmTIFADB79gmorLSq3Bv6uZY75VyMzLWXj4xXuevr63GopsZpFf/U3Llpw6Zpmknr6xMXQOz3q6ioQEFBwYDPK5mM6eWAFbq7C9BCmKirq8XBgweg6RqEAPbvP4A1q1ejrq4WGUvLAhCsdBMRScHQTUSUQ4QQqFuio/5tA55iBd6izAfulpZm7Ny1E4CCtrZW1NbWdnnfTAZuAM7AqGwFH7vKHQqFUoaF2TK5P3V/9LXSDQCf/exnIYRA+ZAh8Pv9aGpqwoH9+wd8Ls4aZ3Xgn4tRo6x2cHuS+rp1axGLxVBWVo6p06Z1+77JQ+3a29tRU1MDRVEwYcLEAZ9XMnmh2wBEzz9LkUgY+/ftQ319HQzTgGkK7N27D6tXr4nvJsBSNRHRkYqhm4gohzQuM1D3ugFPgbX1T6a1t7dh+/btUKCgPdSOw4cOwc0/5u3qarbWVNtTs1tbO6/nBgDhhO4jo70csKaxH3/88c7rmzYOvMoNJK+vH9ifAqqqoqKiAkBif+5oNIo1a1YDAGbOnIVhw4Z1+f5eb+okebvaPXHixIzsIW6Tt6ZbdFrT3RUhTDQ1NWLvnj1oaKiHaRowTRN79uzBmjVr4luM9f3nVeGabiIiqRi6iYhySGinCWEAvrLMB+5wOOTsxR0Oh3Copiaz60Z7IduV7pEjRwJAfNuzzkxhh245T3/2RYe+hG4gNSh6M7TdWabay4cNGwafz4dwOIzGhsRcgF07d2LPnt1QVRWnnnY6gsFgyvs5k+R9qRdADhzYj2g0ivyCAlRUjBzQuSWTV+k2kTy9vFfvI0w0NjZg7949aGysh2maMAwTu3btxhpn0nk/fnZZLCcikoKhm4gohyjepKpUBkWjEWzatBmAgkgkgurqaggJCzyzGXxUVcXwEda2VdXximvn48ttL7dburVYrNfvE41GsXrNGuf14447PiPnn6n2cru1PN3nfOVHH6GpqQl5eXk45dTT0lauOw61syq7uwEAEydlbs9ue56AjEFqva10d2SaJhoa4uG7qQFCmDB0Azt37sLatevQ0tLcuw8UH6TGLcOIiORg6CYiyiGKN/N/GMdiUWzcuBEAEI1FUV1dJSVwA4nQDWQ++CZXXJuamtLex1nTnYF1zP1hP/6YpvX6fd58800oijXwrr29Hfn5+ZhSWTnwczHtEDqwPwXsIWr2eu6UYxgGlr3/PjRNw4gRI3D88TOdtzmV7jRLDewW89GjR3eqkPdXtrssuiLiPd0DmY9gmgYa6uuxd+9eNDU1QggTuq5j+/YdWLduLVpbW3pzIqx0ExFJwtBNRJRDFK+S0T+MdV3Dxo0bIQSgaTFUV1U5W0XJkM3QPXKkFf6qq9NXuYHENlluVztt9uPXehm6o9EoPv54JQBg44aNWL9+HQBgxoxjUwaQ9e9cBl71LyoqQnFxMQzDQE1N+pb+1tYWfPTRhwCAGcce61TGnenlaUJ3c7M1UV9VVYyfMKHf55fMkLRdnCnMjC3jMAwd9fV12Lt3L5qbmyCECU3TsW3bdqxZs9raaizdBbV43melm4hIDoZuIqIconiRsdCtaTFs2LABpimg6xqqqg461T5ZhBBZmyA+cpS1/re6qrrL+5j2OmZV0ppus2+h++2334a1rVsL9u3bi7179qCpqQl+vx/TZ8wY0Llkor3crnLX1h5O2Xu7o/379mHb1q0AgJNPOQUFBQWJ7du6CMG7du4AYO3ZnYmBakbS+blZ7bbbyzO69Z6ho66uFvv27UVLS7PVdm6Y2L//AFatWo1du3chFkudGyCc/yMiIrcxdBMR5RA1Q0W49vY2rF+/HoZhwjB0VB082G0o6k4mJ0gD2VnXnZ+fj5KSUpim2eUQNSCppVpW6I4/9t58LWKxGD766CMA1sRyIQSEEFi3di0AoLJyKvLy8vp/LhloL09sFdZ1d4FtzZrVqKurg9/vx2mnn5G0Z3r6b/p9+/YhGo2iqKgIM2Yc2+9ztCV3Wbj59RfOyPDMD2vQdR21tYexd88e1NfXQdetizmNDY1Yv349NmzcgOaWZkARbC8nIpKIoZuIKJeoyoD/Nq+trcXmLVsghBXcDh44AE3v/RribMtG6LanltfX1yPWzZAy+9iyQrfZh9D96uLFAKztz/bu3evcXlV1ELW1h+H1enHcccd39e69OJeBdRx4vV5nK7Cqgz2HbtM0sXzZ+4hGIygvL3e+Bl5v+uPruo5Vn3wMADj2uOOc7eD6K6XLwsUWcxFPu5m+eJXMMA1rq7G9e1FTXY1wOARAQTQSxY7tO7B50ybU19f3eWo+ERFlBkM3EVEOUQb0W1lgz57d2LdvHxQoCIXacfDg/pwK3EC2QnfP67mBpAnW0irdZvy/3a+r//DDD7EmXtFev36dUxW2rY1PM58wcSKKior6eS4Day+vqBgJj8eDlpYWtLWl3xe9o1AohBXLV0AI4ayt7u74e/fuxb59+6CqKk4+5ZQBf93k7dXt1uBCgfZQG6qqDmLfPmvdt2maME2BhoYG/OmPf8Kjjz6Kw4cPu3Q+RES5wTAMRCIRtLa2orGxEXV1dag9fBiHDh1CTU0NDh2qwaFDh1BbW4uGhgY0N1vDS6PRqHPBdiDcnSZCRETdUvqZBUzTwJYtWxAORwAATU2NqK+vRy72k2Y6dKuqihEVFQCA6uqu13MDudBersf/23Xo3rZtG1599VUoioItWzZjX1KV21ZXV4eDBw5g9JgxmDlzFpYte7/v5zLA9vLRY8YA6F1rebKammps3LABxx1vVemDed1PJ/945UoMGzYMJSWlmDlzFtasWd2v8wWsz7vP53N9grkJM6NruntD02Koq6tFfX09hhQOw5CCEVAUBbt378Z9992HwsJCLFy4EMcdd5y0nwciIptpmtBiMWi6Dl3XYRhG/KKhCYj4xouKAlVVoaoqPB4PvF4vvF5vr36vezweeDyeAe+IEQ6HEYlEEA6FEAqHEQ6HcfzxPXedMXQTEeWSfvztG4tFsWnTJhiGCSFM1NbW9m4LIUkSoTszT0FDhw6Fz+dDJBJGY0ND98eOt3Vns9W32+PHK91dXTU/dOgQnnzySSiKgn379mLN6q4D5rp1azFq9GgcM3YsyjeXo6GHx97RQNrLS0pKMG7cOADWkLS+2rhxA6ZNnw6v14shQ4bC6/V22XIfi0Wx8qMPMf/MszB12jQcPGi11/dHNveJ706mB6n16djCRFtbK0RIwfK6ZRg+pRyjR49BW1sbXnjhBSxatAgzZ87EqaeeimHDhkn72SCi3CaEgKZp0DQtKRQbMA0zfnlfAFCcYNwxFPe0c4SqqggEgwgM8DxjsRhCoZAVjMPhlHAcif/Xej2ESDiCUDgUv591WzgSSbwcv3/yv46dZwCwecvWHs+LoZuIKIf0tb28tbUF27ZtB2BVUWuqqxGJRrJwZpmTCD6Zqa4lWsu7HqBms4OdqsoK3dZjTxe6W1pacP/990NRFNQePowPVqzo9mM1Nzdjz+7dmDBxImbNmo0lS97u17n0p718zpxPQVVV7N+/D/X1dX1+fyEEQqEQiouL4fP5cOJJJ2H5smVd3r+qqgo7d+zApMmTcdLJJ+O1Vxf3azCgrNBtCnnb9AH2unIF9XX12H5oE/Lz8zFp8mRMmjQZwWAQa9euxdq1a+HxeDBjxgyceuqpGDFiBAM40RFECAHDMBCLxeKhWIdhWJViIYS1Z6CipFSLk0NxT9tQKooCv98Pv98/oPPUdT1NmA0hHLLCcCRiB+AwQvFgbN8nFA/HkUg8OIc6B+Oelm/JwtBNRJRDrPby3rWE19RU4+DBgwAURKMR1NRU93tCeTrZqs7ZLdaZqnRXxIeo1fSwnhsA9PiTsTKwxfP9Zrd0dwzd0WgU9957L4QQaGlpxnvvvdurNWTr16/D2HHjMKKiAhUVFV3uld3dufS1vXzMmDEYUVEBwzC6rcT3xP5eFUJg7NhxaGhowJbNm7u8/+rVqzCiogKFhYU44YQ5WLnyoz4fU1roltBe3pECBUq8lSYUCmH9unXYuGEDjjnmGBwzdpwzjHD9+vVYv349PB4V06dPx6mnnoaKigoGcKIM0HXdqRQnt1Dbu1MoSvxnNV4ptqvFPp+vx7CrKIoTogfCNM2UEBtxKsOJcByORBIvdxGOU6rKSeG4t1tmHm0YuomIckhv1nQLCOzauQtNTU0AFLS1teLw4cMQrg1rGhh7mFkmgk9eXh7KysoghOhVpVt2e7k9vTy5Pc0wDPzlL39BNBpFOBzGO0uXdjuBPVkoFMKO7dsxddo0zJw1GzU1r/XhXPreXu7xeDD7hDkAgC2bN6O9vb3X79vp+PHQHwqFUFBQgNmzT4DP58P6devS3l/XdXz4wQc4Z8ECTJo8GQcPHujzenJ57eUmsrFlWO+PLwAF6Phtb5om9u7di71798Lr9WL06NEYc8zYeAD3YsOGjdiwYSMAYNasWfD5fLjooosYwOmoIoRwQrCu6zBNw6kQJ9YTC6iqB/n5+fB4PGhtbe3UQu33+3ucj5CJUAwgfet0vEKc2jodTgnHYaeKbIfgiPNyKH47dznIDoZuIknsNqBM/PKlo4gKdPfHuWHo2Lx5M6JRK5Q1NNSjsbFva3n7LsP7dJuZCz52da6hvh6xWM9/KGha5joB+iPR9hZfAScEHn30UTQ1NUHTNLz7ztI+B9mNGzdi4qRJKC8vx9ix47BvX+fBa92dS1/ay6dOm4bCwkKE2tuxadPGPp1nR3bo1zQN69atxcyZs3DsscchLy8PKz/6KO26udraw9i6ZQumTZ+OeSeehFcXL+7V192W6XkCvWXCyHrkVhTVaRlV4+2jiqpC9agIIA8FaiGmDZ2B9sAo+Hxe+LxWO6nX54PP54U3/rrP50sbqteuXQtFUXDRRRehtbUVoVAIXq8XJSUlfB6jATMMw2mJ1nWr+tuxCpwYpgUkrx1Wk6rCyZXh3lIUpVft1cl6GsYVi8VS257j4Ti1dTo5KCfWFSdap5PXGKe2Uaf7/Ui5jb8laVCxf4nbLT3JkxGFacK0f7EjUYlS4utfFKXzL3h7PYz9S74v7DYgwKrgNDY2QlVVlJaWul6FodyheLr+0zwSCWPz5s0wTQEhTBw6dAjt7W0unl1mZLLaWOGs5+5+arlNN2QPUrMr3dbrL730Evbt2xffw3oZGhsb+/wxY7EotmzejONnzsTxM2fiwIH9vWpNT7SX9+7rkJeXhxkzjgUArFm7ZsDr5pInyW/auBGRcBhz552IiRMnIRjMw7L330t7jHXr1mLkqJEoKSnF3HnzsLwPk9szPU+gt0xhranuyAnKigpVTQrKaf8p8ft54vdJPB8pqtpt+7pqeBHQC1A5cSpCwaY+n7/93FRQUADTNFFUVNRpqzrDMNDc1ARN15GXl4eioiJWxI8gdiEgte3ZiA/oFBCmNSwr3d9G6YJvX3cJsN8PAx6j1ZlpmohGo4hGo4jFYtbLkQiisSiiUev1WDSaeN15WxSx+Nuj8Yt7lVMq0dbWig8++KBz63Q8HEcikYwu9aKjA0M3ZV3nkGs4+4YmX8EUQiB+8RL2FczkX+bJQx+Sf7H3hf0x+nI1s7/sX+72L3gt/nJMiyEatV42DQNTKisxbNgwDBs2LOX9m5qaEItGUVxSMuDtDejIkW6psd0C2tBQD0CBrmuorq7uU4Uvl2SqvVxRFFTEtwqr6sV6bgDQ42vJZIduRVHw3nvvYU18v+2PP17Z4x7j3dm6dQsmT5mCoqIiTJw4CTt2bO/xfRLt5b0LoLNnnwCv14vaw4fTbmPWV/Ykd7sdc9euXYhEIjj1tNMxatQonHPOArz77judWh1N08QHK1Zg4XnnY+zYsTh4YBz29vJ8EvME+v+95/F4rOqw16oQW1N57apxfCCRN1FB9nl9GNkyEiX6MOQHfL0Oyv0jUp9fTRMCAh7TB4/pR1trG6IijEAg0GUbbDgcRktzMxqbGtHc1ISm5ma0NDc737v3/vkeTKmsRGVlJaZWTkVlZSUqp05FcXExyocMSfvxWltboShAUVExn8+6YZomNE1z/l5KLgykrPsFnL+VVEVxLtSkKwb0dju4TK0H7oqmaU7wtf4+Sg28kWjE+tvICb9R53XrbakBOBb/OJFo4mXrbbHE2yIRxGKxQbuGmHILQ/dRyL5aaeg6DNNw2nSS/9ktOvb97VYdVVVTwq4VbO3/ep1f6H3Rn3DcH/bExlj8F25MiyEW05zQG4vFnOAbjSUCcSymWfd3Xk+83bl/NPG2mBbrfP8Or/f1F/wxxxyDOXPmYPYJczBnzgmYPHkKSktLnbfruo6mpiYMHTo0w581yjkdfrwaGxuwe/fueGVUQSjUjsOHDzvh4UiUqUr30KFD4ff7EY1GetwqzJY77eXAW2+9BUVRsHHDBuzauXNAH1fXdWzcuAFz587Dsccdh927d/VYie5Le/nQocMwbvx4CCHwyapPBnSuzvGdSfKJb/qqqiosefstzD/zTAwZOhQLzl2Id5Yu6dRy39jYiI0bNuD4mTPxqbnzcPjwYYTD4S6PZQdl+2JLSUkpRo0abbVVOwE6EZqdVmtv/O3xf30JMcnyd+fBH/bB8Ker4gknJCe6rsxOz9tm/HZhmjAN0ek+AJxhS/5AAIH4lGFV9UA1PfDFfCgqLIKaZ33eNU1Dc3OzFaybmtDc3ISmpuYeL+a1t7djzerVnYboVVRUYOrUqZhSWYmpU60wPmHCROTl5SEvL6/Tx2lubkYkEoHX60UwGEReXp70/cKTq73pgq/T5gwRHwTdRbVXVeGJB9i+hFhVVREIZL7Sa4tEIklV3c4VXye89lDxjUWjiERSw280Gkt6WySlqhyLxXJ2ojSRWxi6M8QewmD/sk5Zi9LPtuXk4Qx9ke2rlck0TUsJm50Crma/rqUGYju8dgrESSFZi3V+n+QQrCWH4ugR/Qt9//792L9/P1588UUAQFFREWbNmo05c+bgpJNPxpw5czB06FCYpon6+noMHTqUbXtHKcUDQADRWBQ7tm9HJGL9AazrGurq6qS0k2e6Gues6fYOLHQnppbX9Hp9m+yWv+S2b0VRsHv3bqxfn35wWF/t2rkT06ZOQ2FREaZNn46NGzZ0e//etpcrioI5n/qUc4ymfrTAd3f8jr/L6uvr8eYbb+Css85GcXExFp53Plat+gThUDilsmyYBkKhEPLz83H+BReirq7Wamt1qsw+Z21nxzA3pbISUyorB3T+9n619iRiTdOgaxo0XYOm6UkvayjRpmGYoaJeVHcK0n0dgKhAgc/vRyAQcLbv8fsDXT7nCwhrMF8kgq1btmBXyxY0NzcNaAheOjU1NaipqcE777zj3Obz+TBhwkQnhFfG/1tRUYGSkhKUlJR0+zGt4VARaJoGYZpQFCWxt4MQKZ1xyR1xyWt7c6naaxiGU32NRCKpLc1JLc6RSDSl4msH3kg0/j7xgOu8TzSW9LbUAGxXfFntJZLriA7dpml2Crrp2nAAJBbQpYRcxboCnPRLOrl1uS/6M4ShvzpWVTuH2KSKa6eqbgyxaIeQnPR2LaVqqzlv12KdQ3UsFuMghyxpbW3F+++/h/fffw+4+4+YOXMmrvvmt3Duuec6bei1tbUoLy/n+u+jjAkDDQ0NqKndAygKBASamhrR2NAoaTp5FrYMy1B7eWJ/7t63ZWuaNYBO1kWrvLy8eHeRgsOHD2PlRx9m7GObpol169bh1NNOw7HHHof6+nrUdLPW3ezl+uYJEyeivLwcsVgM69at7fa+yUOMemrBHjLE6twJBPw499yFXQblYDCIU089rdvjBoNBjBlzTLf3AazPkaqqiEajaGtrs4KxpkFztvGJh2hNj98eD9Jah7f38eLNp8oaUOwfirAe6tP7+bw++DuEa5/f1+WFMF3XrPbaWDT+XB+FFtPggRfl3grsatiNKu1gn85hIDRNw7ZtW7Ft29aU20tKSpwQPn78eAwpH4Ky8nIMKS9HWXk5ysrK4PF4uqySD0RXLc7ReHtz6vpe6/OYrr3ZaYvuseIbix/nyC4OENHADDh026049pNQcitOylrd+B84HdfmBgIBJ6j29Y8gVVUHvEF7b6S0LdtPZkmV25T1uikhVutcpdXS3D+aeFv6qm5SKOaVykFp3bp1uOH672HKlCm49rpv4uKLL3bCd11dHUpKSly54EPZtWXLFrzx+DJMaT8dCCoIh0Oora11guLRIhPt5cXFxSgvLwcAVPdhb+rk36Fer9fVyrfP58P8M89ynus++Xhlrwae9cW+fXtRMbICEydOwmmnnY4333gdzc3Nae9rJG0ZZj8Xe+Mt1fbLwUAQx8+cCcBq6Z41a3a3Ldj9aQ9WVQ+GdphpkcwOykIItLe1ob29PSUoezwqxo4dB5/Ph3A4jPXr16G9rc2qNuup1ehZs2dj+vQZ2L1rF9as6f8e430lRPf7dHs8XidYBwJWuPb7/V3uJ2+YRoe/Fayw192FOUX6TuEJzc3NWLlyJVauXJn27aqqori4BEOGlKO8vBzl5UNQHg/jhmFYoTeSaIXuGHij8fbm5MBr/y3FYgERyTDg0J2tVpx0bcspAberqm6HtuREwB2cbct0dNm+fTt+9tOf4J4/3Y1rrr0Wn/vcZc467/r6egxJM8SGcl9TUxOeeuopHD58GCWiAoZp4vDhQ2hta5F9almRidA9d948AMDBAwcQjUR6/X7JIdvv97sWuv1+P86YPx8lJSXOheiB/PFvD4RMrgzbwbehvgHDhg1HUVERFpy7EFVVB+MXuu21yfHtouIXrYuKivG5yy7v8ZgjRowARozo1fl13WqdeLm8fAgqKipgGAaWL1/mVJQ7BmVVVXHSyadg3LhxKCwqwvYd27F1y5aU423etAlnn30OCgoLceyxx2HpkrfR2tra6bwyuUd8XxgwYLVBq85a6+QKdldbmAlhpl7wj8YQjcX6PNPBnuKidBwakaNM00RTUyOamhqxc4DzDoiIckGvk7K9/iTxL4xIJNrpv9FIBOFIBNHu7huNIBKOIBKNwBOveDc2NqKhocEKyZqW8av/REeT/fv34/bbbsOf//xnXHfddfjyl6/EkCFDnL0b8/PzZZ8i9YKmaVi8eDFWr14NRVFgmiZ2792F8oYZaI9ldr1lLhnoBOkJEyZi+PAR0HUdq/o41EvXdSf0utUdUlpaitPPmI/CwkKn0hYIBDBk6FAUFBamVos7TL12Xk5uz44vg+oNv9+P8eMn9Oq+iaBsVZCFEBgyZAgURcHBAwfQ3NKcaLV22q6t+zsvxycv9+aCwrRp0+LT5wUOHjjQ5f1M08SK5csQCYcxddo0nHDCHOTl5aUM8mpra8Obb76Bs84+ByUlJVhw7rlYsmQJmpuaUj6Wc8FngPMEuuL3+5FfUICC/Hzk5xcgvyAfBfkFGB+egGGx4Sgq6Gpyt4gXGxLBOtHdlpnKrJJTtW4iosGl16F71szjs3keRNQPtYcP47/uvBOLXngBv/zVr3DssccBsKre5eXlHLaWo4QQWLNmDf71r385AfDQoUNYteoTFLcPwwnlFx3Vfxwn2pr73iEVCAQw+4QTAAAb1q8f0DCo3nRo2RVlu5rcca1yT0E5GMxDIBBwfhaTl0SddNLJ/T53W3dDvBRFwZhjjoHH40F9fT12bN+W0pbt9/lx1tlnQ9M0/PP55zoF5TPPOhuKouDAgQN4/713B3yunc5dt7vIeve9vnr1KoTCIZxwwhxMmzYdecE8fPjhB85F+nA4jLffehNnnnU2ysvLsWDBuXhn6VLU19c5H8MZ4tePCz6qqiI/P9/6V1CAgvwC5+X8/HwUFBR0+T2Vty8IVbeOaRh6PFindtpld2ZDYoArERG574gepEZElk2bNuH/feEL+MpXv4rvf/8HGDJkCDRNQzgUQnEP02HJXTU1NXjiiSfQ1mZNIW9vb8fq1atwYP9+AECBrxwC3a//PNIlKt19b3WdPfsEBAIBNDY2YuvWLWnvk2ilTgrKSUO8bNOmT0csGktqzfamtGB7vb6stCHbF1ra29sRjUTSTrvWUwZ3pVaWnZAdr9p3Z8SuETjzrLMxZMgQHDyQj927Nzpvszti7PXSyUaNHo2RI0fCMAysWb0q458DADD0vu+ZvnXLFkQiEZx00skYN348AsEA3n/vPWeZQDQadbYcGzZsOM46+2y8/967OHToUPyYXW+TFggEUgN1gVWtLojf1tuBXuFwGKFQCKH2drSHQgiF2uENjQREAapqdsM03V+yZjeXZ2MwIhER9Yyhm+goYRgG/v7II3jzjTdx6223Yf78+fCVlKCxsRFFRUWubCFHXQuHw/jnP/+J7du3Q1EU6LqOLZs3Y/PmTSlzI0xhABDWAKWjdN5PYk134nuyY1DuOOna5/WhtLQUEyZOhBAC0UgEZ8w/s8M+yn0LysccM7bX52wH3Y4t2OmmXQsBTJo0CWVlZQCAHTt2YNOmjdBiMei6joULz0P5kCH4eOVHqO5mungmHDp0CJ98vBLzTjwJM2fNQmtrC/bHL/B0tbZeVVWccMIcAMDWrVucC0SZpsWDcl+rr3v37EE0EsFpp5+BioqRWHCuVdGOxNf2a5qGpUuW4PQzzsDIkaMw/8yzsGL5MjQ1NaGgsAAAUFJainknnhgP11aluje/I3VdRyjUjlB7CO3x/4ZC8XDd3o5QKJR2eVxlyWnQg5qUwG2xfpmoR8iabiKiow3/Cic6yhw8eADf+uZ1uOjii/GLX9yEIUOGwDRNNDQ0ONOeyT2maeK9997DkiVLnO0K9+3bhzWrVyEU6rx9kAkDJsQRUenuTVB2XvcmJmPnF1jBp7S0FJd+7jL4fH2rKCuK4uzR3Z2UoJzUgj1q9GgoioKqqirU1dWmqSynDvTqTUXZVlpWhjNOPwMFhYXQNA0fffiBE3JtA2lx7o+dO3eiuLgEU6dNw0knn4L29nY0NDSkXOxRVdUJizNnzUZRURHCoRA2bdzY1YcdMGMAQ+xqamrireRnoaysHAvPOx/r1q6BYRjxEF0AXTcQjUYRCARw+hnzU96/qKgIRUVFnT5uOBRyqtOdgnV7CLFYtF/nawpDamu3AOK/U3L/9woR0dGIoZvoKLX4lVew7P338ZOf/gyXX345ysvL0draGt+SJiD79I56Qgjs2LEDzz33HGKxGBRFQVNTE1Z98jEOHz7c5fuZMACRvdCtQIGStHWj9U+x/qtYr9tvz0cRPKoXp089A0pAdNpHeaCh0ePxdPoYya3TTlVZ01BQWIDS0jJr39+tW52hm93to9xVUL7iC/8PHo8H1VUHsX379gE9hmTjxo3HvBNPhNfrRWtrK95/792023Ulb9XlljVrVqOwqAijR4/GGfPPxBuv/9upDNvnYpompk6bhmnTpgEAPln1SVanu/d2C0yPx9N5LXW89VvXraFtBQUFOKWHvbwBOCE8Eolg27atSYG6HeFwOGtDXE2YkBt47Uo3QzcRkQwM3URHsebmZtx80y/wr5dewh133IFx48cDAOrr6jAkvtUYZZZpmli3bh1ee+01RKNWVSwajWLD+vXYsWN7jxVTQxgQHSrdnYNy+pDc8Z9zu6Ikbuti3990fFoeVCgYOWoUDE/XAamroJzcgp3cfh3wW8PQIpEIlrz9Vko1Ot3np6ioCBdceBEA4KOPPsT+fft6/RjSsY/hydCSC0VRMHv2CZgaD6tVVVVYsXxZl6HSrjCrLoZuIQRWLF+GcxcuRGlpGc6YfybeevMN5+2qx4Nxo0c7beVrVq925gxkS0xLv6bb6/Vi+PARqKiowIiKCpT0Yi6FvU4esHZbqao6iPq6eqf1e9KkyZgyZYpzwTESiWS1it+RKQzpcZeVbiIieRi6iQaBDz/8AJ/97Gfw7e98B9dccy2GDB2KSCQCwzBQEG/1pYHRdR0ffvghlixZ4oQqXdexb+9e7Nq1C4DAiIqKTlOvk9uxvV4fipVyDKkbihJPEUyf1ueg3FtCmDDNDv+EgGmaEPHXA0YUMBWsXPkRQnpbmhbsvrVe24qLizH7hBOgKEraSnBHc+fNg8fjQVVV1YADN5AI3V7vwLcMCwQCOPW00609rAFs3LABGzas7/ZzYhpdD/PKJl3X8e477+C8889HWdn/Z++/wxtJs/sM9K2EnJjBTHazmx3YcTrniZu9SbvalSXtam1LtiU5B8myrGhfW7Z0ZdmWbVmSpZUVrdW1pE2zk6d7pnPOkexmbGYQBJEq3D8KAMEMZvbM9/LBAxCoqq+qCID1+87vnFPEgYOH0HUdVVUJh8O5aup37tzmzp3by78/eZMSZWVlOaFdUlqKLE98z6fT6ZzFe2yS5XtszI5SV1VV8dyevbjdburrG0gkEjx69BDDMLh44TzJRIKWbXYnlkKLoi0Vqx3ptrBAYlm+SwQCgUAwN0J0CwQfEpLJJP/513+db3/72/zSL/4SO3buBER7scWQTqcZGRnh1KlTtLe3E41Gcz2KJUlCVVXWrV/PuvXrC96mI+1Bi6hIsomuTBZuVk7M2Xf5v4/fT/da5pHtMrUTPKcgSxKyqiBl/jU4DReypbCuch26nLZXybxPso8TiQTR6AjRkah9H41Om6uez0wFvKajoaGRioowuq5z8cL5OZcvhJzoXmSkuaioiCNHj+H1ekmn05w9c5qOWfpNZ5nP8S81Y2NjnHz3XV548SVqampy+7Jnz15kWaatrXVC/+vlwufzUVVVlfv9xZdenvB6NBqlp6eHpz3d9PX15Vwjs9HR0UFvby+7du+msXEdmzZtprq6hnNnz9LX18uNG9dRVZVNmzfjdDrZs2cvFy9emPek0UIwMVj9KLMk7OUCgUCwSgjRLRB8yLh/7x4/8ANf5ktf+hL/+J/8U0pKStB1nVgsVpCN88OGYRhEo1GSySSyLOPz+XJRMk3TKCkpwel0MjAwkFtnMRMYFqYdlZr24ljKbXsl5ki0tBMZmZKS0lnt5VA14Tdd14lGo5mbLcSzojyVSqFnhN5c1aIdDkeuJ/fNGzcW1ZM7n3F7+cJFb0NDI3v37UNRFHvi5eS7jIyMFLTuuOhenajjwMAAZ8+c4dDhwznhr2ka3d3dnDt7dlnGdDgclFdUUBmupCIcxufzTXg9lUrxtKeHnp4eenq6F/y3TqVSnD1zhiePn7B37178fj8vvvQS9+/d4+rVKzx69JBNmzdjWRZNGzbgcDo4f+5cwfnlC8XCQlrlbgR502UCgUAgWGGE6BYIPoSYpskf/dEf8cYbb/Cz/+bnePHFF3OCe2BgAFmWCYVCH4rot67rxONxWwzqOpIk4fF4cqJAURRCodCU9bo6O2lra+Px48f0PH2Kpmncu3uXgYEBdMPAMHTMTNQ7d6ErMSFTe7rnnJKbamkfBmnijE5YdMYLZmnyq3P93TKRb2vic9nfs0978COj8F7vSVIkya5k39nH5fa4M5WgA/j9fnw+H6qqUlRUlGuZlU8ymWA0Ot6CqraujpGREUYzLoF8du7chcvlYnh4eEntztliWaoy/3+BkiSxa9duNjY3A9DZ2cmZ0+/PS7SNVy9fvX/BT548pqi4iM2btwAwOjrKe6dOLlkhMVmWKS0tpSIcJhyunOKmMQyDgf5+ysrLkSSJd995m/7+/iUZG6C7u4vvfOfb7Ni5k6amDWzYuJGq6mquX7sK2O8BSZKoq6unpKSUs2fP0Jvp5b0cWJaFtQa+TkXLMIFAIFgdhOgWCD7EPH36lJ/48b/Pyy+/zI987Wvs2LGTkpKS3OvxeJxoNEooFMLhcKzinhZOVkQnk0lbxFkWsqLgcDhwuVxTKrerqjpt6yCA/v7+nLBua2vlcVsbbW1tPHnypCC760JwSC7GSscwMRg1l6c/cqFIsoKCSnd/N0krXtg6koTX67VFeMCfE+QBvx+P14vT6cLpdOWWP3z4SO5xLBYjGh3JiPDRnC3//PlzS2oBzgrL+dq7nS4Xhw8fprzczt++cf06N25cn/f444XUVk8AaZpGODzeds3hcOB2u4lGowveZiAQJFxpi+zy8vIpToZIZDgTye6hr7cXXdf5/i99GVieCYh0Os2F8+dpf/KEvfv24/P5OHDwUGY8hddf+x77DxzE7/fzwgsvcu/uXa5m2o4tNeMOltVmDSh/gUAg+BAiRLdAIOC1117jtddeIxQq4siRIxw7fpyjR48QChVNKDg0NjZGMpkgndYxTRNZlnMX68vVhkzXdcbGxkilUpmLYQtZnltEzySkwb4YHxoaYmBggKHBQQaHBhkcHGJocJD2jnZbaLe1MTq68qLXwoRnpE/3dFiWxejoKKOjo3R3T3xNUZTc3+bQ4SNIksTAwAB+vx+Hw4HX68Xr9U4Qg4ODg6SSqSXfR5ifvby4uJgjR47iyeRvnzn9Pp2dnQsa31yFlmH5yLLM0aPHKCoqmvA5PnrsOK+/9j1SqcLOt8vlykSyw1RUhPF4PBNeTyTiOZH9tKeHeHzmiRtVW77LkadPn/Ld73yb7dt35BwKAE6ni1e/+x127NzJhg0b2djcTLiykrNnTk9IF1kKTMw18Yn+MLiXBAKBYC0iRLdAIMgxPDzEN7/513zzm3+NLMts37GD48ePc+zYcbZs2ZLrlbuSqKpKIBCYdZlUKsXg4GDmNpAT0INDgwwNDjIwMP54cHBwUdG85Wa6lmGrxVLH5QzDYHh4mOHhYXRdR9M03n/vFLFYDIfDiT/gp7Kyis2bN+cEaXFxMZ/45CcZGBigtfURTx4/LlgUzsR8q4c3Nq7LVVAfGYlw8t2TRKOF5W9Ph7FK1cvBFl0HDx6ivKKCdDpNLBYjFAqRSCQIBAIcPnKEt996a0ZngSzL1NXVsb5pA2VlZRNe03Wdvr4+enq6edrTw/Dw8Jz7ky06qC1R+7aZ0HWdS5cu0tHRzgsvvgTA0WPHaGtr49LFi3R2dLBv/wECgQAvvvQyd27f5saN60tmt7cwl/4DNe99WBvfKwKBQPBhRIhugUAwLaZpcuXyZa5cvsx//vVfJxAIUF5eTklJKSUlxfZ9aQmlJaUUZ34vLSnB6/NlLtgtTNPKVNO2sEzTLpydbUtlWWBZmFbeMpaFZZnYL1nE42MMDg4xODCQiUYPMjQ4lCeu7eeWqsDWWsDEFt3yB7y1j2kagJazFadSSVS1iE2bNqEoCn19fTx8cJ/a2joqq6ooKSmhpKSEXbt209XVRVtrK93dXQsSRUaB9nJZltm1azcbNm4E7OrYZ06/j67r8x5zwvirWL189+7nqK2rwzAMTr77Tq6F1u3bt2hp2UZFRZg9e/Zy/vy5Cev5fH6amppoXLdugrtkcHAwUwDNrjI+37/HuOtg8e3bCqG3tzcn9E3TpKGhgXA4zMWLF/jOt7/Fc8/toaGxkS1bt1JZVcXZM6cLmjyYi+x34uoiIYmcboFAIFgVhOgWCAQFMTJi59o+ePBgtXflA49pGajS2vl6Xo7omKEb4By3eNfU1HDwkF1Nu7u7m1Mn38UwDNra2nA6ndTXN9DQ2EhxcTG1tbXU1taSTCbsfPvWVgYHBwseOysMZ8updrlcHD5yhLKycgCuX7vGzZs3FnHE46yW6N66tYUNGzdiWRanT79Pb28vRsbqnkgkeP/99zh69Bjrm5oYGRnh3r27VFfX0LRhA+FwOLedWCzGwwcPeNT6iMQslvHCsIWotohK8vPFMAxUVeW9U6fYtn07oVCIw4ePMDQ0xK2bN+jo7GDPnr0UFRXx8isf4eaN69y+fXtRdQUszJVpOTDHXqz2HggEAsGHlbVzVScQCD6UOE0T1bKIyfIauChdGxgYH/iIlJ5nsW5sbGTvvv3Iskz7kyecPv3+hIhpMpnk3r273Lt3l2AwSENjIw31Dbg9HjZubGbjxmYikQhtra20tbXOmjsM4/ZyeQZ7d0lJCYePHMXj8ZBKpThz+n26urqW6MjHq5fLKyi6161fz7bt2wG4eOECHe3twESrfXdXF1euXGb37ufYuWsXW7ZuzUW1Lcuiq6uLBw/u09PdvWSF7bKbUZbZXp6PYeioqko0OsL3Xv0uW7ZspXnTJoqKijh85CiRSISbN65TEa6kpqaG7Tt2UlVdw9kzpxecmmJirnqcG/jAf68IBALBWkWIboFAsOJUptN8cTjC5yMRyjIX/QlJ4lW/jz8Khbjmcj0TAjxoGHxqZIR1qRQO02JYUXjL5+Wi272o/TcsnbVTZXh59iMb7a2vr8/Ztx89fDhnpfJIJMLVK1e4dvUqFRVhGhsbqa6pIRgMsmPnTrbv2MHTpz20tbbS0dExrRU8J3rliQJEkiTWNzWxa9duFEUhEolw6uS7S14DwFzhSHd1dTV79uwF7H7nDx7cz72Wa1+mKlRWVlFRXpGzXzudTpLJJA/u3+fhwweMjY0t+b5Zi2jftlByLgtFwTRNbty4zr17d+0JnOZmgsEgz+3Zy2g0ysOHD6mtraW0tJSPfPRjXL1yhfv37817TAtrfIZhtbBEITWBQCBYLYToFggEK4dl8RMDA/zdgUHissxfBvxcdbkxJKhNp/l8ZIRPj7TzrtfDP62sZHSVqjvPRamu84/6+vlENIpsWTxwOklJEhW6zt8aGuKew8H/LCnm23MUgJsJA/0DX/AoK7qzgvvOnTtcuXyp4PUty6Knp5uenm5UVaWuro6GxkbKyysIhysJhytJp9N0tLfT2tY6oQdz1lKdL7rr6utpadmWK9rX3t7O2TOnF52/PR3j9vLljzqWlpZx8NBhZFnm4YMHXL9+bdrlWlq2TcjVTiaTOJ1OVFVlcHBgWQQ3gJkRopPbiy0nOZdFntBPpVLcuHGdO3dus2HDRpo3bcLn9+Pz+4mPjTGSKTT33J491NTUcPbsmXmdE8syV1/wSiLSLRAIBKuFEN0CwTOEZpqciMWoSusoWAwoKm/5vIysUXE6mZ/q6+MrQ8P815Ji/ndxMWOTIo2/VVzMi6Oj/HLPU363o4Ov1tZOWWa1qU+l+N32DhyWxW+WlPCNYIDBjGCQLIsDY2P80NAwv9rdw4Zkiv9cVjrvMQzL+MCL7vwq+IvNl9Z1nUePHvHo0SO8Xi8NDY00NDbi9/tpXLeOxnXrGBkZ4fy5c/T19Y73yZZlampqadm2jVAoBEAymeDmjZvcu3d3Ucc3GytVvTwYDHLs+HFUVaWjo4MLF87nXisvL6epaQO1tXUAuah2a2srDx/cJxaLcfDQYWprazl85Chnz57hcVvbku/jQtq3LRYzL7o/GV3XuX37Fvfu3WV9UxObN23G7fHgxhbmqqpSEQ7zsY9/gksXL9La+qiwMbFWvU+3JXK6BQLBhwBZNvF4TNxuE5fbwOUycbtMXC4Ll8vE6TRwOC2cThOHw8LpMNEcFppmomkWmmahquP3imKhKhayAopiIcsWigySDLJsFWxsFKJbIHgGCOkGXx0a4vsiEUoMg5gkYUgSAdMkLkl8K+Dnd4qLaXM4VntXZ+SVaJSvDA3zS+Xl/FFRaNplLEnidb+fTk3jD56081O9vfybvAJOq03AMPitjk4SssTfrKmlR5tYcdmSJE57vZz2ePja0BD/vK+fflXhD4uK5jWOQXr1o2LLhCRJ7N23Lye6W1tbl6xAGdhFvm7evMHNmzcoKS2lsbGRurr6TCuol7h3717O0ux2uzly9ChgC6o7t29z797dZYlu55MT3csoND0eD8dPPI/D4aCvr5fT77+Hqqo0rltHU1MTgUBwwvIdHR2cfv+93L4BvP/eKfbt20/junUcPHgITdUmWNOXgmzu/kq2TzP0ue39hmFw7+5dHty/T+O6dWzZvAWvzwfY+6xpGvsPHKCmpobz58+RSCRmHdNiaVqPLQZJVC8XCAQrgmkLXXdW+GZEr9vE5TRxukycTguHw8TpMHE4LRxaVvjmiV/VQlGzwpeM8LUfZ8Vu9j57g7WbnShEt0CwxqlLpfhfHZ0UGQb/v0CAPw0FeZSxgZbqOp+PRPj+4QgfjY7yk1VVnPGubB/tQvnhoSHOeNwzCu58brtc/PeSEn5yYIBfKy1jeAWjYLPx/cMRynWdTzY0TBHcE5Akfre4mNpUmp/sH+AbwSCJeUTsP6iRblmWOXjoELW1dbm84f7+vmUbb6C/n4H+fq5cvszOXbtoatrAxo0bx6uXyzLpdJq7d+9w984d0un0su1LPll7+3IJzVAoxLFjx/F4PAwPD3P6/ffZsWMn69avz9m40+k0bW1tqIpC47p1RKMjEwQ32FHos2fPkE6n2djczJ69e9EcGrdv3Vqyfc2J7hWuXg6FnX/TNO1K7Q8f0tDQwOYtW3MpCJZlUV1TQ1lZGY8ePaLtcRvDQ0PTbsfCRLJW9zNtWcJeLhB8ODBRVTLRXgO32xa6HreJ02XgdFq2+HVaOJwmDs0WwA6HiZqJ9DoctthV1fFo7/jNFruyvPZFb7aURqZLLZYFpilhmuP3hiHZN11CNyR0HXRdIp2WSKdl0imJVFomlZJIpySSSZlkSiaZzDxOyJw4Mfe+CNEtEKxhinWd3+7oRAc+01BP1ySh16+q/M+SEv6gqIhf7+ziv3V28kN1tdxyuVZnh2egOZHkuXiCf1BVWfA63wgG+cmBAT4fifA7JcXLuHeFIVsWXxoe5lt+P52OwnoK/05xEV+MRPh4NMpfBINzr5DBtD54Od2qqnLkyFHClZUYhsHAwADl5eUrUkxM13WePH5MaUkpoaKiXC63aZp859vfWrZ85ZlYzurllVVVHDp0GE3TiEQi3Lt3l5dffgV3xlkwNDTEgwf3edzWhq7rbNtmVzSfTYBeunSRVCpFy7Zt7NixE4fm4OrVK0uyv2aBPdOXEn0BTgPLsmhtbaWtrY3a2jq2bN2aS0lwOJ1s2ryZTZs3E4lEeNzWxuPHbcRisQnrI62uvVxibV0MCwQfZGQ5E+312FFet8fIWZydLjvC63TaN81h4XRYaA4TTbMtzxMszood8c0K3qzF2Ra9E4VvlrXyWc+vH5kvei0TjIzwtUUvE4RvOi3lhG8qlRW/Msl84ZsVvwmZREIiHpeJxxXiCZn4mEwiIWOaa2eiUYhugWAN83cGB/EbBp+fRnDnMybL/IPqKv7wSTv/qreXH6yrW8G9nBnZsvCYJi+MRklJ8FRV2Ts2htc08ZgmHtMaf2yZuEwLp2XitCw0yyIqy3xtcJATsRgOy8RpWjgtC0fmdRn7QlLOfKtL2d+x86uzz1lAWpLQJQlDAh37sf2c/buR+T0pScRliTFJJi7L9mNZpiStU6Xr9KgqL0WjRBSFqCwzoiiMyDIxWcaa9F+uw+HglNfDpyMj8xLdOuk1JroXty+lpWXs27+fQCBAOp3m1Ml3qa9voLy8HHWZxVZJaSnbt22nIpOmYBgGqVQKt9uNLMu8+NLLnDt7hqd5hdaWm+WqXr5h40Z27dqNLMv09fai6zr79u0HYGRkhIsXzk85zlz18jn25caN66TTaXbt3s3mLVvQHBoXL1xYdOuw1bCXL+b8W5bFkyePefLkMdXV1WzZ2kJJSUnutWAwyPYdO9i+Ywd9fX08bmvjyZMna6JlmCVZItIt+FAiyyZer4nHY+D1GhMtzy47Cux251mdM1Hf/Iivplm4nAY+v/2dlUjIH5hob07wZm66Pn6zo7220E2lZPuWlkgl7UhvInOfTCgkEnJG+Nrid2xMxs7WEt87IES3QLBmcZkmn42M8H9DwVkFd5aELPNbJcX8elc3GxNJ7rmcc64zGcmycFu2EPYZJl7Tvvny7j2TnhsX0JnHGaHtMU3cky7I//RJ+7z3CaB4jr7LBbFE7Xp+fHBw2ucNmCDCo4pCRJYpS+tU6jo/MDRMv6owqCj0qyoDGdE+3X/m1WgZ5tDjbO17n4pYG5qZJKl46CnaS3vx4QVvU1EUduzYyYaNG5EkibGxMd47dZKBgQGqa2ozyyzPv6Gi4mK2bdtOVVUVYIvthw8fcuvWTRob17Fjxw4sy8Lr9fL8Cy/y8MEDrly5vCIWc2OJRbckSeze/VyuEnx/Xx+hoiI0TcMwDG7fusWtWzcn9D6fvC9yAZXU7969QzqdYs/efTQ1bUDTNM6cPr0o4Z2z+q9AJfcsS3X+Ozs76ezsJByuZGvLVsrKyie8XlZWRllZGbufew6ttQjXUxeSLmNZq5PfLWV+BILVJl8Eu91mzgadzQN2uswJ9mdbDI8XucpGgSdan6e3PMPyCGCPp/DP8XSi17Imil7TsCO8hmFbmw1dIp0vfFO2vTmVlkklJZIZAWwLXtkWvwk7ujs2Ni5+U6m1Fe39MCNEt0CwRnk5OorfNPnTOSKk2Wiy1zRp1TQGFZmfHOjnrwIBPJPEcf7jqc/Z21mOr2YDe56zW1EYUxTGMtHjMcmOEI9lbklJIiHb0eaUJPH5yAiKZfGbpSWkMs8lJYmkJJOWJEzJjmKP3ySsKc/ZY6uWhQJoloVqWagWKFgTflexcJkWbsvEnYnEZx9vTCTZm0hwyuPGa1r4TZOgYRAw7ci8AoRMk9A0wgbgZ3t7pzyXkiT6FIVeVeWpqtKbuVWlL1DiU0kpKSLOUkx5+b6qHfoYx5/8OS29p1DNNE+99aQUJ+XpdnY9fYuYo5h40MVvFXkx53HlUl5ezr79B/Blik89eviQy5cv5UStYdjFypZabIVCIVq2baempgawRV3ro0fcvHkjZyPXM/tgmiYPHz5k48aNrG9qorKyknPnz9HT3b2k+zSZpRTdmqZx8NBhqqqqsCyLeDxOaVkZAL29vVw4f46RkZEZ1zfnmV/+6NEj0uk0Bw4eor6+AVXVeP+9U1PywQvFzFWSX4Wc7iWa9Mi2ristLaO6ppqKijBFRUW5YoiyLFNUUkQgHqSxopFEPMHw8HDm/bhy8W+7erkQ3YKp5Ko9e0y8OSFsi+DJxa+cGRGsOaxcNHgtiOBCyRfApjkufvMtzvmiNxfpTcuYhkVFOE0qJXP7todkMiOAkwqJhG15Ho/2KsTj9u+6LkSvQIhugWBFkC0LV0Ycu00Tt2nhscYfu61MZDgn8iz2j42RlCT+Uf+AvZyVEYGZCLI7sy3vNFGml0ZjvDQam2ZPCkMHRjNCOCbLjGbuJzxWZGLS+PO2cJZyAjr7XEySaEkk+KP2Dn62Msz7Xm9B++AxTf5x/wB/UBTie37/go9lqdg3Nsbvt3fwn0vLuOGemDPvME2Cpok/I8KDhoHfNAkYBj80NIzHNLnidlNi6JTqBsWZ1x2WRbWuUz25Wnb/nwJ/CoCJRNRZwpCrnIizjCFXOcOucoZdZQy7ykmqhZ3P6fCkInz/rV8hkBzkXNXHuFpxnFHneP583dggO3pe5Sd6vsumuI9/VlWJPsfVkqqq7Ni5iw0bNgB2NfHz587S09MzYblx4bM0/4YCgQAt27ZRV1cP2IL68eM2bt64wejo6KSx7fMtSRKXLl6go/0J+/btx+f3c+LE81MmCJaa7LFLkoQsy9NGoAvB6/Vy9NhxQqEQpmn3gfZ4PKRSKa5cvsyjRw/n3pcF5Je3t7ej6+9y+MhRqqurOX78BO+++86Cqr7nt29bKfRlsvf39/flCgM6HA7Ky8upqAgTrqwk2zhQQsbt9uB2e7AwSSZSREciRKPRZW8pJiLdzw7ZfGCv15xoh85Eg7M3p9PKRYEdmZZH+fnA+QWwVHXti+DJtmddZ4rlOT/HN5XK2JwzEd9EJtJrC1+FRCbaG4spxGJrL8dX8OFCiG7BhxK/YVCbTuPI5Ac7zcy9ZaJZdt6w07JwZHKIs885MvnGjsxrDms8xzi7fv72sq+7FmG//EQ0WtByKSCmyDhNuznNXZczJ4inCObM47EZBHVyckWORXLZ7eauw8GXh4cLFt2fGhnBbZr82TxyoZeTC243XarKlyLD/Gv3xDZmKVmmT5bpUyd+pVan0vxMbx8/V1HBn4cmHofTNCkxDMp1nTJdp1zXqcjcb7JKCBsW7kQ3DjNFMNlPMNk/7X7FVS/DTluID7nKGXRX0uutZdBdiSHPnJYgmzqfv/PreNJR/s+2n2XAUzVlmQFfI+82/SS/pdzjP3Y+4mee9vIL4YoZtxkOh9m7bz/ezN/4/v37XL1yeVoxlhVbi83p9vn8tGxrob6+AUmScjm3N67fIBqdPsKbFdPZd3hvby/f+c632b5jJxs3bmTd+vWEKys5f/4c3V1di9q/6ciPCi9UdJeUlHD02DFcLjemaeZE6+O2Ni5dvkRyjhZWWcbzm+d3Idrd3c07b7/F0WPHKa+o4PkXXuSdt98mlUrOazvjOeUraS/XM2MuX3Q9lUrR0dFBR0cHALuCw5QXbWXMGsXt8iDLMhIyLpcLl8tFWXk56XSasfgYiXiCeDye28+lwu4YIATHQsgXwW63gdtjV4J2e/JEsNPM9f515ongnCVatVC1iRWg7YJYz44Izo8ET875TaXsKs+2AJ5oe07Es7ZnO/obG1OIj9k2aCGCBR8WhOgWfCg5Ehvj15bZQjodJhCX7GhwXJaJS1KmWJcdJY5LduGuuCyzLpniwNgY/9/SUkYylmx7nfHiXnFJYlRWiMkS6cxF9zfaHvPA6eBfVhZeKXzZkST+sCjEzz/t5cXoKG/4fbMuXpVO8+P9A7zh883emmsFMSWJPw0F+XsDg/xmSUlBefZ/a2iQUVnmW4GpkfqkLNMly9Nu5wX/Fzjg/RhdqQd40xFCiT5CiV77lszcJ/rwpSO49RhuvZXKWOuEbRiSwqArTJ+3lj5PDb2eWvq8tUQdxSBJNA9eoGr0EV/f9m+mFdxZJOAdf5BfKS/jp3v7+J3iIjom9YPXNI1du3azbv16AEZHRzl37iy9sxQnW6zF1+v1srWlhYaGxpzgbG9v58b1a0QikVnXTaczYibvytYwDC5fumhHvfcfwO/3c/z4CVofPeLSpYtLGvXOF9mKosw7QlxbV8eBAwdz506WZUZHR7lw4fy8rfGLaV/W19fHW2++wfETz1NSUsKLL73I22+9RXweNRiy469kpDtnqV+megLTEU/GSSQS9ETtv4+mOQgGg3i93kwbN8l+TnMQDGTXsjBNC9M0SOs66VSKZDJJIpEgnU7PPzdc+iBFusft0B6PgScjgl0uw7ZDu+yiV07XzHboiT2AJ4pgScre26M9iyI4nR6PAk8oepWxP2ejwdn837ExOxosRLBAsDwI0S34UBJVZHpUlWRexeqUJJGUpbzcYTmXQ5ySJFKyNPH3/Nfl/Oezj+Xc9rLiOjGPCHJlOs1rj1oZVeQpUdKZ2JJIsCWZ5L+Wlizm9CwLfx4Mcigz2fFvzHL+KhCYUu0bYHMiwX/p7CIhy/xiRfk0W1o9/jQU4vsiI/xWRyd/q6aap7MI768ODvLl4Qj/rryM+DwFhWllIqGSRMwRIuYI0RnYMGU5zUjkBHgo0UtR4iml8U7KYh24jDHK4p2UxTsnrJNQPPR5awjFexlwhZGwkE19hrzxcYfGnweD/Hj/AF8ajvCfystyz1dWVbF37z48mZZU9+7e5dq1q3MKyfECXoWLPUmSKCsro66+nsbGdTnR2dnZyY3r1xiaoUfyZPRZIoh9fX189zvfZtv2HTQ3N9O4bh3hcJjz58/T1dU543rzRdd1VFWd96TDli1b2b5jR+530zS5e+cON25cX1Be9WLblw0NDfHG66/z/AsvEAyGePGll3nrzTcmtMuadfyc1X/lI93LXTl/IhPzqdPpVM6OLkkywVAQv8+PqmrIcrYPg4Qs2ykIqqrhdrknbtEyMU0T3TDQ02lSySTJZJJUOo2u61NEubSsOd2ZSLAvI4LdZsYSbeB2WZm8YANHpj+wI9MeyZktjpWJAqtqfnukiXbotSuCwTIljGn6/WbbHtl26Lzqz5lCWPH4ePGreMKOCMdiihDBAsEHECG6BR9KTnm9PL9+3Wrvxqx0axpv+bz84NAwfxEMzplLC/CVwSG6VJV3CrRwrySWJPEvqir5xZ6n/Puep/y9gUH+OBTimsuFIUFtOs0XhiPsj8e563DwYzXVDKhr6ysqoij8aE01v9Pewf99/ISvFxXxjWCAoex+WhYHxuL84PAQL47G+J/FxfxBppfvfDApLIKVVlz0eevo805qEWdZ+FODlI11UBZrp2ysg/Kxdorj3biMMWpH7mU2AD90/ZdIyxrdvnV0+DfSEdhIp7+JlOqZsMmELPNXwQCfGhnhP5WX4XA42L37ORoaGwG7JdX5c2fp6+sraN8LjXS73W4qK6uorKokHK5Ey5vo6Onu5vr1awwMDBQ0ZpZ0avaotWEYXLl8ifb2J+zff4BAIMCx48e5fevWqvWnlmWZQ4cPU5Op+g4wMNDP+XPnGB4eXvh+LEF+czQ6whuvv8aJ51/A7/fz0ksv89ZbbzEyMrvjAFY3p3s5+qTPhDlLVNqyTIaHhhjOmzRSFBW324XT6cLhcNgTNKqasaXb/wskSUZRZBRFxelw4vX6pmzXNE1Mw8TCIpAqZlfTbkqLNSRJztUUkCRpyi1L/mNVTWJZMuvWfZuKiisTRLC97GLP0sKYSwRnq0Jno8ATLNFJOy842+83kZBJJiQSSVv4xvOiwPZNFMUSCAQLY21d0QoEggn8r+Ji/uBJO7/c08PPhMMYs1zV/NDQEH8jGuXfVJTPq8r0SpKWJH46XMGfBYN8aXiYf9LfjyMv3/2C280/rQzzmt9Peo0eQ5vDwZfq6/hHff38xMAAPzkwwH2Hg6QkEdbtXt73HQ7+RTjMX4/7ROeFYRmLa3Em2cXXos4SHhWNR0VlU6ck3sXGgQsc6fhL2v0bKIn34NGj1I3cpW7kLnTaMbleTy3doe10h57Da5gkZHjgcFBqGGzb2sK6DRtwu+184nt373L9+rV5RVoNffqcbkmSKC0to7KqksrKKoqKiia8nkgk6Onu5uHDBwUL/Mmk06ncWLPlVA/09/Pqd7/Dtu3b2bRpM5u3bMHt8XDu7JkFFz/LMh97vcPh5IUXXySUmcCxJwUu8+DB/UX3yV4qe3csFssJ71AoxIsvvcQ7b7/F4Awt9rLo+nhRuZViqauX52NHpVUcDgeapqFpGqqqUeGowIWLkCdkC11ZRpIl5Iz4nfA4X/xmLOHZ02OZFkj233z6c5YvmLOi3P5dMVRcLs8UcV4ohmEXkEynA8w2HzpTT2DDyNzr4yJ4Ojt0KpMXbFuiZeLxcRGciMuMZcRwLKYKESwQCJ4ZhOgWCNYw19xufroyzH/o7qFc1/kfJSWcc7snhBSakkm+OjjE50dG+J2iIv7vAiKrK4okcdnj5rLHzS9lCompFgyqdv/qZ4E+VeVnKsP8x/Iy/kZkhMZUCpdlcc3t4g2fjwuT/kbzxcJYljbdpqzS561DM1Mc6fhLvrfuK/R7aihO9FAzco+akXtUR+9TnHhKxdgTKsaesLPrm7yMxL2iEGYwiARs27IFU1WJRCKcO3tm3pFmyC+gpeByu6mqtEV2RTiMIy9n3LIsBgYG6O7qoru7a04RVwj51ndNc5BMzlx0LCtwh4eH2bdvPw0NDbjdLk6dPLmoPO9C7fV+v58XX3oZl8sWPIODg5x895155U3Puh/m0gnQRCLBm2+8zrHjJygtLeX5F17k5Lvv0DtNu7zc+NnWcUsY6dYcDjRVRdMcaI5x4aupKqqmUpZpqVZUVMTefftRFQVFVTMCVUFRFGRZQcmIYznvNjk6DMwYGc4nFK3C99gHrrJl+WwXgoSU1evzwjTtiLnP14UkxXnwIMLp98vtwlhxyS6ONZa1QytCBAsEAsE0PBtXuALBh5hvBwIMKwr/qreP32/v4IHDwS2XEx2JhlSK3YkEfYrCL5SX8ydFodXe3XkxqiiMrmhe5dIyrCh8vbho7gXnicEiI91zEHXYbcHKx57Qn6l0Puiu5FrFcQC8qWFqo/dpjrVSM3QVX6yDLUNDkLG//tNf+RXOFhfzLVkm6XHDPP+GkiThz/TvDoZCfOYzn53weiKRoKenOyO0e+ZdEXsuJopubVbRnaWttZVEPM7hI0epqAhnIrlvL1j85qKtsxQwKy8v59jxE5lCW3ahuPffO7Xo6HY+S2EvzyeVSvHuO+9w9OhRysrLOX7ieW7evMHIyIgtfhUVVdNQVQVVVSktLcuNf+z48YmiV1HyBK+EJMnzskPPhc/ny/WRXw7y/06mZWBZVi7P2rIyDcIsK/O8ZUeFsfJj1ZmK1pmoeJ61fMYxsQuvmYaJYZiYpoFh2GNrhpfoSJREIJGbxMnHMAwikQhDQ4MMDw3b98PDM9RnCC3gjAgEAsGHFyG6BYJngPe9Xj7Z4GFvPM7nIxGq0zqKZdGvqvzjykre8PvWrB1bMH9Ma3ki3VmizmLaglvY1fMWt8oO556XkHB7PPgqwqS8z3HX8HDfUnhc8n8ov3aGz506RRrwpFI839PD80AaOO/x8IbPx6t+34x5+C63m8pMNDucF81WVRXLshgcGKC7u5uuri6GhgaXVFhOxjTNTPskCU0r/N9gT08Pb7zxOsePnyAUKuLll1/hnXfenrNa+vT7MLvYbWhoZP+BAzkReefOba5cvlzQtmVZtqO8moamZSK+WlbsamiqgqpqKKqdNwx2X+mjx46jKgpyJuIry8qEKO90kV5JysjAGYSvoihs376DuZBlmaqq6oKOr1Dy30PZx9l7RVEwTZNUKpWL5JqmgWlaGIaBaRgYGcGq6waGoWPoBrquoxs6hq6TTuvoejrv3r6lUil0XZ+QgtDo2EJJ0VZ69Ccoqmz/bVT7b2I/1lA1tYCK6ha6rpPW0+hpe+z8x+NtxiRcTidujwePx4PT5cIZdxIIBBhxuTAMg8GBAQYHBxkaHmJoaIiRSGRZP3cCgUDwYUaIboHgWUGSOO/xcN7jmXtZwTONXUhteSdRLodf4LN3/yu1kTv0VezC5/fj8/kmRF51PU0iFuPPHpzl0223kSyLTzU2UGYYvDA6ygujMdanUhwaG+PQ2Bj/qreXsx4P3wr4ebuoCKWkJFcEraioeML4qVQSh8NJMpnk29/6Jsnk0kazC0WbZ0u64aEhXvveqxw/8TzBYJAXX3qZUyffnWKhntHirKlomobD4QRg48aN1NXXocjjFme/P5CLwFqWRSqVoq6unvr6Blv8SpKdEzxPi/NsKIpCdfXSil4gN7mRfWxZVm7SY3ziQ8OyLEZHo5norGmL3ozw1XUD09DRdcOu1K3bojcndHUdPa1nRG8qJ3xnyruvCId5/vkXiEQivPrd7yz5MUuShMfjIRAI4A8E8PsDNDo2UzxcjMerzvnRNkwjI6LtKuTp9Lio1vU0FtMLY1mW7c+xx4vb40Ge5KLQdYOn3b28e/ttent7592qTiAQCAQLR4hugUAgWGPkWoYtE5Ik8yR8iO6+t/n8/d/kZP2vMxyoAewc22g0ymg0iqo70XBytOsJ/6i/n68XFdHpcNAlSdwrKeEPvF42AMeGhznS3cP6gYGcANf7+nnQZHG9soq7/gC6ZTE4OEh3Vxdd3V0Yus7HPv4JwFoWwa2qtrgdF78Z4ZuxOGfZ2LyJuvqGXF6vmrU3K/KsFmdZkrAsC4fDwfMvvDjp/BYueqvmELqSJOF0Oud38HlMjvZOd3M6nRnRO5qL+k6N9urjUd9MtFefEOGdPtoL0Ni4jp27duJ0upAkidbWR1y9csWeTKiv59Chw1iWxbe++c0FH+d8yBbxW6ylXtO0nLAO+AP4A378/gB+v3/KtgOxYpRRBQkZ09InnK9stDz7eD5F+mRZwef14vX5cLvdE1qvmabB2NgY8bExxuJjlEt1PBl9TNdo16KOWyAQCATzR4hugUAgWGMYLE50S0i5qKqaq6Cs5kRnNpp9ruI/cfSNf8yJV/8edxs+ysXy5+mWiuzoo6pQn3hIS8e3aexp49T69Vx65WU+5fPjdrsnFL5qy9xCg4O03LhBy40bVPT2sunuXTbdvUvUofFaeQWvVoaJFhdTUlyMO+PYUFWNPXv2oqgKimL3rZ7O4jyxqJWUa3lUiMV5NmpqahZ1rucaayaLc9amret6zoqcbQ2VXWZ4eJhkIpGL9k5ncc72ZM5GRtO6TjqVykVJ5xJwmqbx+e/7ApIk8e1vfXNZ7MWtrY/o7Oxgx86drF/fxPr1TdTU1HDlyhWSCXvCZUWrl5vTV86fDkmS8Pn8BAL+cXHttx9PlxedG8MwiEajREdGGImO4BkpoVzfQW+qg5Qxdw2B2VAUFa/Xi8/nw+V2T8jzTqWSxGIxYrFYZjJr/O8paRISosiZQCAQrAZCdAsEAsEao5BIt6KouZxdW2DbOaGaqmWE2xwFlyyTuOLhtRO/RsvNr9P06Ntsffj/iPprSWteXIkhPGNPGfXV8d2P/g3O7t9FaZ4wmk6cDRUVcfLoUU4ePUp5Xx/brl1jx9WrBKJRPtfRwec6OugOh7m0ezdX1zeRxo42Nm3YMN9TNC+mi/Zm85LzRa9lmpiWOaPFeaLoNdD1NIauU1VdTWVlFQBtbW3cunmDVCo1q8X58JGj1NbWcvnyJdpaWzl2/AQVFRUAJJNJ3nj9NUZGRpb1vAAT9k9RlGWzHKdSKc6fO0fro0fs2buXUKiI/fsPMJTXm3qlyEW61XHR7XS5CPjzhHXAFtc+n2/WyupjY2M5YR0diWbuRxgbG5vwvqvRmkgXp/JyrueHqqp4vXbhN1vsj38Wk6kEsdEYo6OjuXZ402Kt7OSGQCAQCMYRolsgEAiWgWxep8fjwe1243K5cblduJxOVM2BMxPVHG9VpOZsy2Wj6yjtKsPlliZGbyeOsMj9k1FVGdQgt/f+JHd3/yg1j98iNHgXVU+Q1nwMlB9ioPwQ1zZ+F6TkpPVnH7+vvJw3X3qJt154gXUPH7LzyhU23blDZU8Pn/j2t3nxjTe4smsX5/bupcfnyytmlWdvzlVhtnN5DWN6i/N4Ianpo73T8YUvfj+KonD9+jXu3rmzqHN5//59mps3sWv3bhoaGlAUmTOnT88aZc5WDXc4HLz8ykdyPbhjo6O8/vprS9YSbC7ye6svp+jO0t/fz6vf/S4bm5tpadk2oQ+7oijz6vW+UGTZfu+qqsbxE89TXFw8q4U/nU7bUetJwjoajRZ8vizMTGXy+X1uNc1BUXFxptr/+LqJRIJYbJTR0VF0vcDWdRLzHl8gEAgES4MQ3QKB4EOFqpp4PCZer47Xa+LxGLg9Jh63ictt4nIaOF0WLqeJ02nicJo4HBaalrmpFqpmoiigqhaybKEocO/eF4hE6tB1N6YpLyqipCSy1umlsoJObxnOPWtZ6JJKa/1LWHUv2sWuLAuH7kJOp+nt6SEuxfKivXomQmygG3pO3E5ncU6lxiNvwcYGPjUywg8MDdOYTHLgzBn2nznDSZ+P3wsFOe3xLKq/+XzIRiELsRgXwt27dxiLj3HgwEFqa+twuVycfPfdCcefT1Zcbt68JVfJfWhoiDffeH1R/b8XgmEYudz1lcCyLO7euUP7kyfs3befyspKJEni45/4JJcuXqCzs3NJxwsEApSWllJSWkppaSmBQBCwRX5lZWVun2KxGCMjIxlxPcJIxh6+FBMgZu7TVtj72xbbRfh8/pxQjifixEZHicVGFzQ5shDRLxAIBIKlQYhugUCwJphODLtcJm6Pidtt4nZNFMOaw8LhyBPE04hhWSZ3n9Vyy6XpTFMlnfYtyRiWlImQWuRdo08nnAsdaPrlJOwLcV3XSeWJ5HQ6RSqVxoUXh+Tk1J2TjJmj8zqG6YgoCv+nqIg/DIU4PDbGP3d72Hj/PsdGRzk2Osp1l5P/VVzM6z4f1jKL71zrqBlanC2E9idPSMQTHD12jLKycl56+WXeefttYrHYlGVlxRa4WcHd3d3FyXffnVcRraXCNM1cf+yVZGxsjDOnT/PZz30OAK/Xy9Fjx+ns6ODSpYvTnre50DSNkpKSnMAuKSnNnePpuHjhAv39fUQikWU995aViXTbH7oZ0dSM2PaPi+1YbJShwUGSS9CvXhY53QKBQLAqCNEtEAhmRZZNfD5bBHu8hn3vniiGHU4LlysTGZ4khlV1/KYo9m0lxfBMZNMtLcu+mSaYpoRpShgG6LqUu6XTEum0TColkUpJJJNy7haPyyQSMsVF3VhEGRhwMxJRSaWSJJMpkslkJqqZKaJFVgJnCoDJ48XAFEXB7XLTqJrUJ5Mk5YRtQVfmztG2j8UkkUySiMdJJBJYpmlvHzk3jqaqdkVvh4ZDcyBJ2Z7OU4WJnNKwUvBc/V76RnuIjtgW22yV6wWfe0nilNdL+AtfpDwSoexP/oRP9/ezLZHkN7q6eeTQ+J3iYv46EFi2/vPZ/Z+7L/L86Ovr5fXXXuP4iRMEAkFeevkV3nrzTUZGxnt5FxcXU1dXn/v90aOHnDt7dkn3Yz4YhoGmaSsuuoEJOcj37t5lfVMT1TU1VITD3Lxxg7t378z6XgsEArbALrEj2cFgcIrLRNd1BgcG6B/op7+/n6HBQT79mc8C0NbWuiLOArvN14RZtAloqkZRcfGyie3sXohCagKBQLA6CNEtEDyjyLKJ12vi9U4Sw1mbtGv8li+GczZp7dkQw5YFpiFhTBLDRkYMp9ISqZQ8RQwnEjKJuC2Kx+IKY2OyfYspxGL26yzpBejSWWJjDoOWopfoGejEwkRCQlEVVMXOAbeFuII66bEs26Ld7XIDVibvM0ZsNEZan97mrGZ6STscDrt/dKavtKpqKLKM7FCpb2igWq3MraPrOj093XR2dtLV1UUysbBqzIZhMFhSwv9pqOc3An5+aHiYHxgaZl0qzb/tecrfGxjgN0tK+KtAAGOJ34g5e7m69EJzZCTC6699j2PHT1BUVMSx48f53qvfJZVKEQ6HOXrseM7K3d/fv6qCG8at7itlL88n27NbkiQePnzIgwf32bNnL+UVFezYuZOGxgYunL9AX18vqqpOENilpdNHsUej0ZzAHujvZ3h4eErhv1wFeUWBFRHdZkZyT3wf22K7CJ8/MC62x2K22E4ursr51H0AWVr5iRWBQCAQCNEtECw5WTHs8Ri2IPbYtmm32xgXw848Mex8NsQwTBTC42IYDEPCMDKR4fREMZxO54nhhEw8TwwnEjJjY7YIjo0pxEaXQww/e5h5RZfs+JiVq7LNLEEvTXPg9XnxeXw4XS67eJvLTUlJ6aRWQuMX89ntxuNjE7YlSTIBRxEezcutmzdxBNVcdWdN06ipqaWmphbLshgYGKCzs4Ouzk4ikcjk3ZqRrNhTFIUhVeU3Skv5naIivhiJ8CODQ9Skdf5dz1N+bGCQ/1ZSwrcCfswleuOPR7qXR4TE43HeevMNXvnIR/H5fBw6dJhHjx6x/8CBCWMOD6989e7JLPe5KBRNU+nv7+fNN9+goaGRnbt2EQyGePGll0ilUmiaNn0Ue3AgJ7D7BwYKmgQyDMOetFKU2T5SS4aJCdZ4TrWqahRPEttjYzEGl0FsjyNyugUCgWC1EKJb8KFBlrM5wxPFsMuVEcSTI8NOC02bWERrLYthsC3StlXajgpPEMM5m/TUyHAqKZFIKMTjEvG4YkeH88RwPBMl/rCL4ZXCtEwWcoGcTqcYHkoxPDSU6+Xr9fpwe9w4HE4cDidFRcXouk4sNkosFssUiZqaZGpZJul0mrge5077bcbMaO61UChEdXUN1dXVFJeUUJqJOu7YsZPR0dGcAO/t7Z2173O+6M4SUxT+d3ExfxwK8aXhYf7O4BD16TS/0tPDjw4O8itlZZz0eWfcpsc02RmPEzBMErLEfYeTToc29fiWyV6eTyqV4uTJd3n55VcIV1ZSEQ7nROPAwAAlJSWrLnRh+r/DaqCqWq64WWVVVa5nOYznvicSCXp6emyB3d83bRS7ELKie6WO2bIsLCwUVaU8VI4vsJJiO7sPIqdbIBAIVgshugWrTr4Ydrsz95lq0vli2Om08qpJTxLDSkYMq8+2GM5GhVOpbGR4XAwnkrZFOj5m38cyNmkhhj94mOiYGVv5QjEMnZGRCCMjEWRZxuPx4vV68Xi8qKpKMBgiGAxhmgaxMduCbvcWnjtXe3h4mOHhYW7evIHb7aaqqpqq6mrC4TA+n4/m5k00N28ilUrR3d1FV8aGPjl3djaxl5Blfq+4mD8LhfibQ8N8bXCQplSK3+rs5JTHw38oL+NBXpunhlSKHxga5jMjI/gn5QCf9Hj446IQb3m9uS8C08qK7uX97ESGh+nu6qK2ri4nuGOxGE8eP15zoluWV3dftmzdSlHRETRtfJIkNjpK/0A/JSWluR7VPq+XO7dvMTw8vOCxVnqiwe1xEQwGKQ4UYSr252AsYyNPLLPYHsdawo4IAoFAIJgPQnQLZkSWbbHr9Zl4M1bp8dZKRp5NeqIY1jQLh8MWwZo6vRiWJMimD65FMWzoEumMIE6lJorhVFImkZBIZPKG4/FJYjjPJm2a4gJHMH9MMpFuSZq10nHB2zNNRkejjI5GkZBwezyZKLgXRVHx+wL4fQEsy2QsHp9XPmk8Hufhwwc8fPgARVEIhyuprrZFuMvlor6+gfr6BkzTpK+vLxcFHx0dLUj4jMky/6ukmD8JBfmxgUF+aHiYI2NjHGh7zNeLivhvpSUcjsX4le4eYrLMH4ZC/FUgQJ+q4LYsDsVifHk4wm92dvEXgQA/F65AlyQMY/kt1W63mz1791FdXT3h+Zs3rpMtqKWsstAFMM3s32Hlvq+yEe3avIJy5eXlgC20n7Q/of1JO4ODA4DdF37Dxo1s27ad0rIyXvnIR2lrbeXu3TvzSmnIslKi2+FwsmPHDjbVbMfd6iFNkrGxMYYGB1ZQbI8j7OUCgUCwOgjR/QwyWQx7PCbu7P0kMZwVwk7nVDGs5NmkFWXtimEzmzOcjQrnRYftatK2EE5OEsOJuMJYXCaejQoLMSx4RjAsY9l66lpYjI3FGBuL0dcn4XI58Xp9eL1eOyfc48Xr8RKNRogPpucl+g3DoLOzg87ODiRJori4JCfAQ6EQFRUVVFRUsHv3c0QikZzgKUT4RBWF/1Rexp+EgvyLvn5eHh3la0NDfDYSIWCavOr38a/CYZJ5xcBGgb8MBvnLYJBPjozwb7t7APiZcMV4HvMyid6mpiZ27NyFpmkYhoFhGDmL9JatLdy+dcsef01EulcmpztfaFdVVU2IaAP09HRz7eq1nNDOx7Is7t29S/uTJ+za/Rx1dXWsW7+edevX09PTw727d+jq6ip4X1ZCdAeDQY4eO47P50NOSSSTCfpGeogkBpdtzNmwsIS9XCAQCFYJIbqXlKxN2szlDGfFsMtp4HZbuDK9hp2TxPBMOcNrTQxPbK00W87wuBhOpSRbBCfyIsMZMRyPZ23SdpRYiGGBAEyyonu5Pw92hfNEIsHAQD8Oh4NQKITfH8TvDxJyOhgbGUMemP9+2AXW+hkY6Ofatat4vd5cHnhZeTnBYDC3bE1tLV1dXTkhNBsdDgf/oLqKo6MxfubpU+p1HYCQblCm63TM0JP5m4EAsgX/oaeH7wT8ueiuvMSiy+/3s3ff/lzUtr+/n2QySXV1NSMjI8iyjM/no2nDhmUZfyEsp718NqGdjWhv3NiMoig87Xk6reDOJx6P8/57p7h7t5Tm5k3U1NQQDocJh8OMjIxw/949Wlsf2UUHZ2G5RXdVVRUHDx1G0zSi0Shnz56nWGohZU7fRWClEC3DBAKBYHX4kIjuiWLY7bYrS7szOcP5YjgrhCe3V/qgiOFcEa2kNN5WKSHb0eH4uFV6PGdYiGGBYKUxLSNTHGplv1RSqRS9vb1EIiOUlZXhVZwEAkFefOllzl49RU9Pz4K3HYvFuHfvLvfu3UXTNKqrq3luz140TaO+voGy0jJu3LhBa+ujggpjnfR5+W9GCb/S85QUcCge5/+1PebXysr441AQa5ov5L8K+PnK0BA/MDTMn2eiu0vVJkuSJDZv3szWlm0oikI6nebatask4nEOHzmKaZqcOXMaQ9d5+ZWPUFRUBKyNSPdS28sLEdr51vGmpg0oioKiFn5JMtDfz/v9p/B4PGzYuJH165sIBAI8t2cP27Zv59HDh9y7d5exsbFp1zcMPbevS82mTZvZsXMnkiTx9OlT3jt1EofugVKWLGVkYVjIIqdbIBAIVoVVE92yLKOoKpqq4vVKlJWDy5lCkgZyYtjpsnC7TJyZatITxLCaEcPazGI4/7bSLFgMpyaJ4USeGI5LjI0pmT7DdhEtIYYFgg8e+S3DVoNkMkFHRztlfp2SQBkBf4ATz79AR0c7ly9dIhaLLWr76XSatrY2Kquqqa+vJ5lM4vF62bd/P5s2b+LatWt0tLfPuZ3PjYxw1u3mZ8MV/HLPU/bF4/xsby+vRKP863DF1Ki3JPHHoRC/8PQp3xuJQEXFkojuouJi9u3bnxPS3d1dnD93DtM0+djHPw7A7Vu3GBywRebZM2c4fOQIAC6Xa9HjL5asvXwxUff5Cu18cj3TFzD+2NgYV69c4eaNGzQ0NrJxYzOBQIBNmzezsbmZjo4O7t29Q39//4T1xi31S3cZJMsye/fuo3HdOgAe3L/PxYsXsCwLTXZjrZ7aBsj0CRfXDAKBQLAaFPzf5sTzz6PICrIiI0myLZrzHkuSlLvPvwETHmeZ/DtAMNjGzp2/t7gjmoNCxfCUnOE8MZxKSsQTdlul+JjMWKa9khDDAoFgKTAsnbXQUzcWG8WIm9yT7lO/qZqamloqK6u4c/s2t27dLMgOPhvZaOPdO3fQDZ0tW7YSCAQ5cuQogwMDXL12laezRNebkin+sChEu8PBV2tr+PJwhH/a18f+eJy/bHvMfyyzc8DzZ17f93qQgbLBQaIsLtKtKAot27bR3LwJWZZJJhNcunSJx21tABw9egyn08XQ0CA3b97Irdfe/oTW1kc0Nq7D4/EQCoUWVYl7sZhZq/UC7OVut5tt27dTW1s3L6GdT050zyPSPRld13lw/z4P7t+nsqqK5uZmwuFK6urqqKurY2BgwM4Jb3+CaZp5ke6l+V/tdDo5cvQYZWVlmKbJ5UuXuH//Xu51CwOs1S5ktvrfKQKBQLBaSJKEy+XK3ZwuFy6nc+JzThculzPzmguX2753upyZexfu7LouZ2b5wibPC/4PFw5XLvggC0GS7ItMWwiPi2HTtAWwYYCelknnieF0WhovoJVpr5QvhhOJvF7DMZWxMRldF2JYIBCsbcxMrHt1raiZfbFMbly/yu3Wq+x+bg/hcJitLS00NDZy5fJl2tufLHjbhp7NJZa5d+sujx4+pHnTJjZt2kxxSQnPP/8CT3t6uHr16rTCzWlZJDOC2pIk/qgoxLteD/82E/X+ud5ejsZi/OtwBUMZQZfKLK+k0rmxF0J5RQV79+7D7/cD8LitjUuXLpJMJgFobFxHdU0NhmFw5vTpXOG2LPfv3aOxcR2SJHHk6DG+9+p3SaVWJ9/XMOef3yzLMhs3NrO1pSUntucjtPPJFbVTl8bq3d3VRXdXF8FgkI3NzTQ0NFJSUsLBQ4fYMbaTB/fv5SbglyLSHQyFOHbsOF6vl1QqxfvvTU3FMDN9ulc6ZSQfC9GnWyAQrB0kScLpdOJyuXG5nBnR68wJ4py4zRO94yLYFr2517LbcOaJabcblzMjoF2uXDHT1aLg/zYjkQiGaWKaJqZp2PeGiWEaGIaJmanOqhtG5rGObhjouoGhp+3H6cy9rtuPdT1zSzHeZ3jj8hypQCAQPCPYUbHF9eleWiRGRiK8/dab1NTUsGvXbrw+H4ePHOHp06ecPXN6xtzZ2ciJvYzY0nWdmzdu8OD+fbZs2UrThg1UhMO8Eg7T3t7O9WtXGRkZya0/IsuUTyqY1ZGJev/Q0DD/tL+fF2Ix/rLtMT9dGeY9rze3fDQjwucrujVNY+euXaxf3wTAWCzGhQvnJ1TO9ng87H7uOQCuX782bUsrPTPhYJomPp+PQ4eP8M7bbxWUz77UjNvLCzsX5RUVPPfcnlwxvL6+Pq5euTzFwl0oy1VJPhKJcP7cOa5dvcr6piY2bNiIx+Nh+46duTE9Xu+ixqiurubAwUNomsbIyAgn332HaDQ6ZTkr2wZwUaMtFtGnWyAQTESWZVRVRdM0NE1DVdXc76pqPzdd5HeuaHBO9Oaiwfa92+0eF9ZO56oddzpPh2Y7jJimiWVZuRvYEwOyJCFlHN6KoqIoSu4czecaomDR/e1vf2v+R1Qw4p+AQCAQZFnOlmGLpaOjg+7ubjZt2szmLVuoqKjg5Vc+wsmT7+ZylgslG+meHGFNJpNcvnyJu3fv0LJtGw0NjdTW1lJdXU1bWys3rl9nbGyMt31ePjES5dfKykjnWcgtSeLrxUWc8Xj41e5umlIpfrujk/9cWkKRbjCgKDwIBWlk+lSnmaipqeG5PXtxu90A3L9/j6tXrkyplL3/wAE0TaOvr4+7d+5Mf+wZe7NtdTYIh8Ps2LGTK1cuF7w/S0Wh9nK3283OXbupr7d7aycSCa5euUxra+vixl/iSPdkkskkt27e5M7t29TW1dHcvIni4mIANm3aRCgY5O7du3R3F95yDGDzli1s374DSZLo6enh/fdOzehWsFj9SDeInG6BYKnJCjBbhDnQtHzRqtrC1aGhTRKzqqqiOTL32eU0DTVvfS273KTnVdXeXm4MbdJ2J6wzVUxnf5+vaFxObAGctoO1WRFsGLZLKHOz/11LuZRmO9VZQVEU+xxqWsGOrex5WEk+JNXLBQKB4Nlh5VqGFcJUkWAYBjdv3qCtrZUjR49RVFTECy+8yNkzZ+ZlNzfmEHtjY2OcO3uWO7dvs237Dmpra1m3bj319Q08uH+fv7h0kb85HOGVaJRvBQJT1r/ncvJ99XX8VG8fX4pE+If9A6SBrxcVkcjMYssFiG6Xy8Vzz+2htq4OgJGREc6fO0tfX9+UZTds3EhFRRhd1zl75vSMkWvDHK+e/v7773HkyFE2bd7M0NAgjx8/nnOflhJjjvZpkiSxsbmZlpZtaJqGaZo8eHCf69eukU6nFz2+tcw907OYpsnjtjYet7Vx8NBh6uvrsSyLcGUl4cpKRkYi3Lt7l9bW1lnrFciyzL59+2lobATsVIFLly7O6lKwI92rm9O9VifyBILFks3VdTqdeVFUF06nY0KersPpnBCldTqcGWty3uNMpNbpdEyI2jqcTrxeb27CTtd1VFVdM6J1qTAzruZs5DcrgLO/50eDpxfBdjQ4K/ILrdUxn2WfVT7YRycQCATPIKa1utXLCyUWi/HG669x8NBhqqurOXzkCNeuXuHWrVsFrZ8T3XNEOEdGRnjv1EmKS0rYsWMHFRVhmjdtIr1+PRd+53f42ae93HU6eTCNVS0py/xCuIK7Tgc/29uHBhyNxTifsQBLc1wwrVu3jp27duNwODBNk9u3b3Hzxo0pOdpg9+jesWMnAFcuX2Z0dHTG7ZrGeD57Z0cHt27eZMvWrezdt59IJLKihdXGe1ZPPRfl5eU8t2cPwWAIgP6+Pi5cvMDw0NDSjZ8V3SvYPm008/dvbW0llUyybv16AoEge/buY/uOnTx88ID79+9NSZtwuVwcOXqM0tJSTNPk0sWLPHhwf87xsp/p1Ua0DBMsN6qqZsTtAsSvMyOc5xC/E3J/nc5VydWdacx8oWrm0nLHxepkC/OEyTrLysxzTyxELWH/r8ovWp0VuLKs5CK+2dcWQ1Y8f9AF8GogzqhAIBCsMbKR7mfhAlnXdU6dfJedO3fRvGkT23fsxO8PcP78uWmFaT7z7ZU8ODDAW2++SUU4zI7tOyguKeH1r3yF8G//Nn/4pJ1/V17Gd/x+UvlC2rLYnkjwiegoOpCQZTamUvyHN97kG+FK2mpqph3L5/Oxd+8+KsLh3Njnzp2dUQxLksSBAwdRVZWe7u45hVh+JFVRFK5fv0aoqIiqqiqOHj3Gq6++SiqVLOi8LBbTmBppdrnd7Nq5i/qGBiBrJb9Ca+ujpR8/F2lfufd79vxbpsmVK5e5ceM6jY3r2NjcjN/vZ/OWLTRv2kRHezudXZ2MRCLIssyhw0fwer0kk0nee+8UvU+fFjRe1l6+uoXMVnt8wUpj5+O6JkSBc3m4eeJ3WsG7APHrdDpXXayN5+nqOauyaZpYpolpWYBla9s8MTvZqpyNui5kIjC7rZW2LgvWPkJ0CwQCwRrDvjw3eVa+oi3L4vLlS0SjI+x+bg+N69bh9Xk5dfLkrBW5F9or+WlPD9/r6aG2to69+/bxf/723+YTf/7n/PuHD/mXfX181++nT1FxWhZHYjG2JpO0axpfra3lqabyXzq72JJM8pXf/32++bGP8X/ztl1SUsqGDRuoratDURR0Xef69Wvcu3t3Vvvw5s1bKCktJZVKcfbsmTmPIX9CQpbtcU6//x6vfOSj+P1+du3exdkzc29nKci3l0uSxMaNzbRss63klmXx4P59rl+/tmzV1bOiX15me3k+k10Wuq5z//497t+/R1VVNc3NzVSEw9TV11OXyWHPkk6n6ehox+f1YZQajEQic9rss/byVa1evuotyz68jFdpnix+7eeykeFcpeYZxW9mXed45WZb/I63LnI6HZnosnPVrc+Ti1VNjvrmF6vK3vLFb74ALvRYPgw2ZcGziXhXCgQCwRpEt3Q0afUqe2aZzyX6gwcPGB2NcejwYcrL7QJr777z9rTVnCHf1rwwsdXe/oSRkQjHj5/gL37oh3i1s5Pqv/5r9g8OEDBMErLEPYeT3ygt4ZTXi5mx3f1gXS3/eSTK0adP+fS3vsVoURF/sn8f65ubKSoqzm2/p6eHC+fPzWoTBwiFQmxtaQHg0sULxOPxOffdsiwMw8hcWNoXk+l0mtPvv88rH/kIjY3ruHfvHkODgws6N/Mha3V3u9185KMfIxQKAdDf38/FC+cZWkIr+XTkqtivoECY7b3X1dVJV1cnwVCIdevWU1FRQTAYzNk2NU1j/fqmXAV7gHg8zsjICCORiH0/EiESiZBIJAAy1nKLRTo/F43Eyk1srFUURckJ3rnFr2N6IVyI+HU41kyrItM0SafTdpehSQLYsiws0xwv8ydNzdGV5fFqzUpmcq4QVqNYlUCwVhGiWyAQCNYgpmU8k1Gpnp5uXn/tNY4fP47f7+ell1/hvVMn6e3tnbLsfO3l0xGJRHjtte9x/MQJqK7m5t/6W/zWqZNT+iTnE5dlfmHLZn5myxaef+st/ubQEM+1tvHnz+0hqes8efKYB/fvM1iA4JVlmQMHD6EoCu3t7bS1tRW87+Oie/z4BwcHaGttpaGxkV27dvPmG68XvL2FknUclJSUIEkSyaRtJX/0aOmt5NOPbwvgufLrl2PM2VwWkeFh2tpaWb9+PZIk0dbWxqOHD/D7/QSCQQKBIIFAAK/Xi9vtxu12U1FRMWEbqVQqJ8a9/T5IS8RSGrq++AJ088VaA6J/Mln7c76Azeb45rckyhe/jkx+cE78OrJ9eAsTv6stAguxP9sKeDyvNz9nV1VVVEVBmUdEV5blVW3PJBAIhOgWCASCNYmJsUaql8/fkjoyEuF7r32Po5mCU8dPPM+F8+en5AMvNtKdJR6P88brr3PkyFEqwmGOHT/BuXNnaZumlZUkSVRX17Bl61beLS6mt6yMz3/jG2y6e5fP/I//wY9WlDM0S+XqybRs20YoFCKRSHDh/Ll57Xc2l3ny8V+7dpWa2lrKy8upqamlo6N9XtudL8GQ3W9bkiQet7Vx8eKFZbOST0euT/iqiO6Zx/R4PBw7dhxVVenq6spVo588gaSqKoFAwBbhwUBOjPt8PhwOB6WlpZSWlhIyQiiGhs/hxrJMUukU6VSaVCqVuxmGkbHdzl4PYaHIKxjp9nq91NTU2O3+Mvc11TXU1NYSDodxu91r3v6MZeXp32z0V0KWx63PiqLMq/WSsD8LBB9OxKdeIBAI1iC6pT+Tke4syUSCt958g337D1BfX8/+Awfw+/1cu3Y1t8xSiW6wL57feedt9u3fT0NDIwcOHMTj8XDr5k3ALgy2ft161jc14fF4cuvd3ryZf9aylV++foOWgQH+1+goP1pTzWABF8UlpaVs2rQZgPPnzpFMzq/w2XhO+8TjHxsb486d27S0bGPnzp10dXXOWZRuoVRUVOSOIZlMcvr0+8syzmxkHQ8rK7qzLovp/86apnH8xAncbjdDQ0O8/96pGXP6dV1ncHBwijNClmU7Kp4R4036CTyKD0szkSQZp8OF0+GacR8ty458WqY1w+NMNeS8x5ZlYprW1MemiYaGU3Hi8/nQDQMjT2guBE3TqKyspKbGFtJZQV1TU0NNTQ1FRUUFb8s0zZz41XV9QssiyzKx9e/E/F9ZkpCmKX6lqqqwPwsEgjWHEN0CgUCwBjHQ15wVdL4YhsHp999jNBpla0sLW7ZuxePxcObM6dzrsHStokzT5Mzp04yNjbFly1a2b99BSUkJhmFQU1ObE3WJRJz2J+1s2LgRSZJ4NZGgvbaG3+roZGsyye+1d/DV2ppZhbeiKBw4cBBZlmltfURnZ8e893e2479z+zbr163H5/ezYeNG7t65M+/tz0VpaRlHjx3PjZ/NP15pDD1jL1/BN/xMEx5gi+UjR44SDIYYGxvj3XfeRtf1eY9hmiaRiJ3bTTscKunBpwQZMvrQVA3NoeFwOHI3TXNMmHiQJBlFgqUyvLiSPgKah09u+BtT9jMX5TUMjDwBny92Gxoa8Pv9NDQ0sClTaG6uz+5YLEZsbAzTNFFVFZ/PN63NWZbl3HkQCASCDyJCdAsEAsEaxLD0NWMvXyzXr18jGh1h3/4DNDQ2MhYf49rVq0suurPcvnULn9dHbV0d1dXjLcH6enu5/+A+He3tOBwONmzcCNh2z1suFz9YV8vvtbezIZXid9s7+GptLcMz9BDfvn0Hfr+fsViMSxcvLmg/s8cvT3P8uq5z7dpV9h84yNatLbS1ts47kj4bRUVFHDtuW6f7+vooKytbNauvntezfKWYrZ7Anr17qQiHSafTvPvO2wUVxisEExO7VJVFWk+R1lOMjcUmLTUxipv/OD+vd7pbfg9f+yYjSeORYQsLLGlCxBjGWxzNxfDwMMPDw+zevZuq6mrAdphEo1HS6TSSJOHxePB6vblte7xePF7vkpw/gUAgeJYRolsgEAjWIIaVZjXbCy01bW1tkOllvWXLVmKjozzN9DheKtEdCoVoatpAfUNDzjJqWRaSJDE8PMyp906RzERz8yOXDocDXddpczj4am0tv/+kneZUit/t6OBHamuITNo/VVVZ32RXrj5//tyc7aJmIjfpMEOrrNbWVjZsbKa4uJiWbdu4eOHCgsaZTDAY5MTzL+BwOOjtfcrVK1d5+ZVXlnzyo1Cyf4uVjXRPP+GzZetW1q1bj2mavP/eezP2ZZ8NSZImWJ3tm4bb5cIjuzGkQE5ES7KELNl5wpKUJ6Bzz8uZ5yUW/X2Q15ZpMkae3dwwTSRJwuFwTLBql5aW4vf7J6yvaRrFxcVTticQCASCiQjRLRAIBGsQg2ezevlstLW24vV62bZtO8/t2cvZjM18oWJPkiR8Ph8lpaWsX7+esrLy3GuRyDD3799nJBLh0OEjhEIhXn75Fd55+y2i0Si6rucEuZqX05kT3u3tbE4m+d12W3iP5O1jXV09qqoyMhKhu7t7gWdjvFXXbMd/+dIlXnzpJdavb+L+vfuMjEQWPB6Az+fnxPMv4HQ6Gejv59133snluM9WVGw5yVUvXyXRnRWXdXX1bN++A4BHDx/gcDhYv74JVZsonrXsY83+farAnv7SquJhGFfSj8u5uBzibJ6zaZqYlollWnnFv+w87vznrcxyPrOIuBnj1YffmZA/nf0sABQXF7Nz127Ky8c/S36/nxdffJGWlpZVm5gRCASCZx0hugUCgWANYqyBQmqWZS15sP3mjRv4vD4a161jz959ADlL7EyFqsCOqIVCIUKhosx9iGAoNEHgmKZJR3s79+/fp69vvML066+9xvETJzItzF7m3XffZaC/f3zbk0RSq9PBj9TW8PvtHWxJJvntjg5+pKaGWEZwrFu3DoBHDxfXUsuYoXp5Pn19vbS3t1NbW8vOXbt49523Fzyex+Ph+RdeyBUHeyeTq5wtpCXPEHFfbvSMU2Au0T2hWJZmi10tTwyPC+FJ4ngGgQzgcrn43Oe/b8pYTRs20rRh46KOK1ccLJ0mrevEk2PIKQcxI2YL4bw2UZMFdFYom6Y1YZnFVDVXFTcpMzVt33WPx8P2HTtpaGgA7M/+5s2befnll0UkWyAQCJYAIboFAoFgDaKz+qI7y1Lvx/nz5/B4PFSEw7nnFEXJ2Yx9Pr8trItskV0UCuH1+abdlq7rRCIRujo7efjoIYlp8m9HR6O8/tr3OHbsOCWlpTz//AsTqnRPV734odPJj9TW8HvtHWxLJPkvXV38WHU17lCI0rIyTNOkrW1qS7L5UEjbKoCrVy5TVVVFVVUV4cpKehYQXXe5XDz/wot4vV5GRiK8/fZbubZgS51bL0mSLZC1ScJ3kjDWMs9lRZ0syxw7fmKCMNbyBPZyRsKzrgdd1xkdHc2LAmdaSqXHo8JpfbzNlP28/Xs673H+ZEaW0uIWKjSTfn3h7ohFHSPWlDoRqqqyZetWmps35f7+1dXVfPGLXyQQCKzGbgoEAsEHEiG6BQKBYA1iWOkVtduuJKZpcurUSV56+RWCQbtH9J69+/D5fASDwRlb+MRisUwxpyGGh+z70dHRWSPkWZLJJG+++QaHDh2muqaGI0eO5l5TZxjvvtPJ36mp5utP2jk4FudXunv4/Z07Aejq6lp0te9cf+o5xO7o6Cj3799j06bN7Nq1i+/29BR0zFkcDicnnn8Bv9/P6Ogob735Zi63PVt8C2zRW1xcPB4p1maOIk+IMs8QRZ4vkiRRVVU153L5vZWnE8ZpfaL4HRfM6dzrEhKvfOQjE8bu7+/nrTffyE1CLDXjhdRWDznznSJJEuvWr2fbtu24XHbbMr/Px/d/6UtUZ4qkCQQCgWDpEKJbIBAI1iDPevXybCEml8uFy+XCmbl3uVz4/QGKQiF8fn9u+aytFezIayRTKXloeCj3OBuZXSiGYXDq1Emee24PTRs25J5X1ZlzbG+5XPxEdTX/s7OTj46OEnrwkFe3bKX10cNF7Yu9PzNX0J7MzRs3aGxsJBgMsXnzZjo7O22hOyFqPFUgOxwOwuFKHA4HhmFgWSYvvfxKThxPHvuVj3x00ceVZWLv5fQkUTwugp0OJw2NjViWxbmzZ8ZfnxQ5zt6WgsnHPTo6ysl331k2wQ1gWuYqu1csQKKoqIi9+/bnHAaKovDpT3+alpaWD+xEn0AgEKw2QnQLBALBGsRYQ/byLIqi4HQ6pwpp52Rh7cThcBbUhihr6wV4+vQpFy+cJxqNziuSOx8sy+LChfMgQVOTLbxdrql9g/M54/XwL8NhfrW7mwOXLzMQCvGn3V2zrjPBUq2p09qsAwE7yl9TU0vAH5g1/1hV1dz53L5jJ9t37Jz3sSuKgt8/u2U4FovNKpCnixrPZLOebK+eidKyMhoaGwG7YvtKkC+uU6kU77z99pK2ZJsOk+UT9IUhEQoV8fKujyArMpZlcfz4cY4dOyYKpAkEAsEyI0S3QCBYETRNw+fz5W5er9e+9/nwefOe93lzr/m89utejwfdMEgmkySTCZLJFMlEgkTe42QqSTKRJJlMkkgmSCWTJBITH9vLJDLbySyb9/tyRrnmi2EZLI/snIjd+9eOeKqKgqza94qi4pa8OCUPL+/+CJLPmNH2PRvJZML+OyQS9t8skWA0NmrbxIeG+djHPobb48E0TSoqKqitrePmzRvLcKQTuXzpEuvXNyFJEvX1DTxua5tGII9HjdtVjb8aGuIzb73Fx956iwevvMztxnWTco/H15kPpaWllJaWzmsdXddJxON5oniqMC6vqKCkpATLMnn++RcIh8M4HI5pb7/0S7+EJEm89tr3ps2LX07SqYW1XFsM69atzz2+cP4c0ejIso9prWKk2+v1UREKo6gyiqxQWVXJl7/8ZXwz1EoQCAQCwdIiRLdAIFgyPF4vBw8c4OixY2xr2YbP78+JaYfDsdq7NyfDw0O8/977vHvyXU6dPMnAwMCq7YuFCYuS3eO9grVJkVMlI6oVxe4BPBOq7kA1Hfh8PlLaGGBHCO0Jj4lCOpFMTPw9M5kxV8Q6O9Fx584dtmzZwrbt24nFRu2+3pOQZXlSrvHEaPK4ANYmRY0nHn9WIGcpKiri05/57Jxn9KplURuJ8NylS/zdt9/ht5s20F9WNuPyE+3VU23W/kCAoqIiBgb66ejomBJFzkWS86LIZWXlHD9xAkmSeOutN4nFYtOO7fP5ad60CYC/+Td/kA15dvrZUApwJyw1uj5evVyW5YIj5AvF6/Wya/fu3O9DQ8PLOl6W1cjpVlWV0tIyvF4fSlpBAr74xe9n05bmFd0PgUAg+LAjRLdAIFgUGzZs4OjRYxw9dozdu3fPKa6TySSpVGpChd+sxViWJGRFQdM0NE3D4XBMsT1aloVhGLn1DcPANA1M034ey7Klaq7dlb1dSZZRFBlZVjKth5QpUclQqIiPf+ITfPwTnwDgxo0bvPvOO5w8+S7Xrl1bdjGQj4E+q+aWJHmioNYmtklSVLXgqJppGhhG9qajGyamrqPoTlTTwcl33qE/0UMikSCdLiwqqaoqTqdzWmGsTYgi29HzUChIJBIhGAyy/8BBtmxtAawJYrkQu/pCsSyLdDo9qUjXuED2+nyUlpbyjRMncD5+TMvAAJ/73/+bn9jzHIOynGezTk94b87G1pYWioqKGBwc4vatWwXtZ3d3F93d3VRWVrJj5y7ef+/UtMtt274dWZYpKioqSHBnLf6rYTPOf09pmoNkcnEF6uZi//4DaJqGYRiZCaiVmWgwMVZQckuEQkGKi0syE2sWwWCAcDjMhibXiu2FQCAQCGyE6BYIBPPm+PHjPP/CCxw9emxKteHh4WF0Xae4uHhakeR0OnE6Z8+hnQ1JGo/gLjWWZREZHsYwDUpKSmlpaaGlpYW//+M/zvDwEKdOneLkuyc5deokg4ODSz5+PoqqoGoqXqdvYqQ681gpoKeyZU0tZJXOiMGswDYME0myRbwsy7kJClmWccu20A2GQrg1xxRhP10U2Z4ImL8NvapqvGKyJEmztiuaKXI83/zjl15+BYfDQTw+htvtITI8zJtvvjFtdP5jH7cnYq7evs1boRB/OjJCXTzOP7l4iR+tqcZcQAGqQluGTebK5UtUVHyMuro67t8ro6+vb8LrRUVF1NfXA/CFL3xhXtueq5L6cpBfHE3TVJYztXpjczPlFRWk02lSqRRerxdFWZlLIdNamUi30+mirLwcp8P+ntU0lQ0bN6ImXfZkpEAgEAhWHCG6BQJBwdTX1/MLv/hL7N+/P/dcOp1maGgIn8+Lx+MlFAqt3g4uEkmSCBUV5X5PJpMMDw8TDAYJhYr45Cc/xSc/+SlM0+TGjRucPPkuJ999l+vXr88rCi7LMm63G6/Xi8frxevxZh578GQe1wxtpainCM0980WyaZoYho5pmBimgWWamJaFZVlYmUrFcsaya9uynXjljKjOPF+IvXx7046cvXw+WJaVi/5OzD0eF8LhcCUej4fHba30DwxgGgbNzZsIBINEo1FOn36fdCqVsVmnMxW4l0Y4ZEVvf18/4cpKysrL2bRpM7dvT4w6l5SUEgwG0XWdJ08eo6sKP1FdxZ88fsLhsTF+bGCQ/15asuDx5xtdjkQiPHr4kKYNG9i1azff+96rE17fvmMHAJWVlVRWVha0TVmSMC1rVezl+W6XhUzYFIrfH2D7dvvcXLlymQ0bNgL2BNdKYEe6l0902y3fSjJt+CTAoqamhoqKCkAinbTAAmvlDDsCgUAgyCBEt0AgmBNVVfmRr32NH//xn8DpdJJMJolGoxQXF6NpGuXl5au9i8uC0+nMXLDaAnJ4eBjTNCkpKWH79u1s376dH//xn2BoyI6Cnzr5LidPniQajRIIBPD7/fh8/py4drtcuNxuHA7HnK15TEz7AjlzhWwvP3EdWZaRZQcsgU6xsGzRbpqYppWJkpuohkl7+xNGzeG8POOZeyCP5yGnCypMd+LE83g8Hrq7u3N53B0dHXz0Yx/H7/dTW1vL1StXFn+A05CdKLEsi0sXL7D/wEG2bd9OT083Q0NDueXWrV8HQPuTJ7mo7H2nk1+sKOff9zzlJwYGuOx2c8brmd/4CxTdANevX6O+oYHikhIaGhpy5668vJzKyiosy+L7vu/7Ct6eJMuQsVuvJssluiVJ4sCBA6iqSnd3Nw8fPGB9ppjaSh2zndO9PDidLsLhcK79ndfnpWn9+lnb4QkEAoFg5RCiWyAQzEpLyzZ+6Zd/mU2ZokwDAwOUlJQsyiL+LCJJdn/bLMlkkkhkmECmGNanPvUpPvWpT2FZFm1tbXz9619f5IAWVsb2PRnLyohjKyuUrfHnJjxvZp637MeZ18xJr1mmmYmMT8Qt+XDJHi7cO0/UHJry+lKgZ4Rnvq05mUxy/tw5jh0/zqZNm+nq7JxioV4KshFzRVVobW2lqqqa2ro6Dhw8xPde/S6GYaCqKnV1tlX70aTe3H8ZDLInHuf7IiP8x+5uPtdQT9880h4MwxZhCxF9yWSSmzdvsHPnLrbv2El7ezuGYeRaiTU1NeX6MBeCLMsYhrEq9vJ8liNtBGDzli2UlJaSSqU4d/YMsHCnwUIxLWNZ+mAH/AFKy8qQJBlJhvXrmwhm2tHlI0mZMhEi0i0QCAQrjhDdAoFgWtxuNz/5D/4BP/zDX0FRFMZiMSRZpqRk/jbaDyJOp5Py8olR8FQqRUVFxYQc1elIpVJEIhGi0ZFpI8ZpXWejkSAkr6en/0lOKGdF8uKqms+P5W5xNJPw6erq5NHDh6xbv579Bw7y3e98e87zOl9ykeZMfvz58+coLbWt5Dt27uTSxYvU1taiaRrRaHRa4f/L5eW0JBJsSqb4le5uvlZTg1WgsDImjT9f7t29S1PTBnw+H00bNjAaHaW0tBTLsvjMZz4zr21l6y+shr0cxvu1q8sQ6Q6FQrS0bAPg4sULxDMt0VZcdC9x9XIJiZKyspzAdrtdNG/aNPv7aWW/PgQCgUCQQYhugUAwhcNHjvDzP/8L1NTUAOPRbcH0ZKPgIyMj3Lx5E5fLxec+9zmGh4fp6+tjcHCQaDRKPB4nlUrhcDgoKyvD6XRy985tWltbp+SEF7vrSQfTJNPLWFWqQJZTeM9msb506SIVFRX4fD527trFhfPnl3RsI3PO5Uwhs1QqxdmzZzjx/Ats3NhMd1cXjRkL8uQod5akLPOPq6r4RttjDozF+eGhYX6/uGjaZaeOPzXKPx9M0+TWzRvs23+ApvVNmJnI/Y4dO+bdfzl7/lc90r3ERc1kWebAwUPIskx7+xMe57Wi01cj0r1E21IUlXA4jMvlBizKysqoq6tjVlGfeUnUUhMIBIKVR4hugUCQIxQq4qd+6qf4dCZKNjIygtvtFoK7QAKBAFu3bp11mdHRUb73ve9x7do1AoEAe/ftZ9v27dy7d48H9++TSqUAu+jSh/+/7F8AAJcaSURBVIGs8JlObOm6ztmzZ3jhxZdoatpAZ0cH3d3dSzZ2dqIjX3T19PRw9+5dmpub2X/gIC6XC9M0aWttnXE7bQ4H/6G8jF942ss/7u/nPa+HBwWkXyxFpPXJkyfs2v0c/ky1d8uy+NjHPjbv7WT3YbVyurNW//z+6UtBy7ZthEIhEonElEmbxeTUL4SlinS7XC7C4cpM1XWLdevWUVRUYCqBiHQLBALBqrA6PjKBQLDm+OQnP8W3vv1tPv2Zz2CaJkNDQwQCgWWtJvxhxOfz8bnPfY6f/umf5siRI0iShMvlZvv2HXzqb3yaXbufw+v1YlgfDtFt5HK6p/931Nvby907dwDYt2//nH3g50NWdMmT7LjXrl4hEhnG5bL7Gfd0d+csyTPxZ8Eg73i9OC2L/9Ddg1ZAOHEpRF+2onqWQwcP5vZ7PuRE9yraywG0JczpLiktZdOmzQCcP3eO5KReZCtvLzdYrOgOBIJUVdegKCqKLNPS0lK44BaRboFAIFg1RKRbIPiQU1VVxc/9/C9w7NgxgJzYzi8aJlh6nE4nL774IidOnODSpUu8/vrrADQ3N7NhwwaM+07UXg3Sq7yjy4xh2Hnas/VKvnbtKuHKSoLBIM/t2cPp999fmrGz9u5JQtMwDM6cPs0rH/kokiQRi8Xm3pgk8bPhCv6yrY0tyST/rLePiKLwkWiUYsNAlyTaNY1vBIN8x+8jlSlcBosXfem0/SaxLIsDBw8uaBvZAmarZS/PFbVbInu5oigcOHAQWZZpbX1EZ2fHlGVW2l5uYSEtUPBKSJSVl+H32/nbHo+b5ubmKRNGc+6DhSikJhAIBKuAiHQLBB9SZFnmh3/4K/z1X3+TY8eOkU6nGRkZoaioaNXbBn2YUBSFvXv38lM/9VN8+ctfxu/3I8sy5ZUVFIWKqAxX4XTOP3L5rGDk7OUzv+cMw+DsmdOYpkl9fQO1dXVLNHYmp3ua6K6mablK0w2NjQXlSPepKr+YaZ/3Q8PD/O3BQW64XPxRKMQ3ggHSksS/7+nh7UeP+PjISJ7oXvi/YlVVaWhoBOzaAhcvXlzwdux9WS3RnbH6L1Gku7l5E36/n7FYjEsznJOVjnRbloW1ANWtqirVNTUZwW1RUVHO5s2b5y24l7kmokAgEAhmQUS6BYIPIRs3NvNLv/zLbN++HbALpRUXFxPI5IUKVh5Jkti4cSP/5J/8E7q6unjz987AYwuPx+7xHRuLMTgwQCq1+oXVlpJChc/g4CC3bt6kZds29uzZS19vL4lEYknGnk50B0MhAJKJBE6XiwMHD/HG66/lIrLToVoWnx6JYmLPaLdpGv8mXIGRV828IZXiJ/oH+NXuHv4/moMUU+3t82HTps24XC7i8Thut5vTp09z4sSJebemyqaRLLSS+mKxq/IvjQCWZZkNGzcCcPXa1ZwTYDKFuCyWEmsBIebJ+dtNTU0Eg6EFjS/ZO4ElIt0CgUCw4ohIt0DwIcLhcPAP/+E/4s+/8Q22b99OPB4nFotRUlKyLP1jBQujqqqKz3zu01TVVOH1eQELr8dLbW0d4XDlkuY1rzbziTbevHmDwcFBnE4n+/btX/TYZkZ0yfLU937Ab09APWl/QiqVorS0lA0bNs66vX/U18/hWIx/VhkmIstsTqX4ytDE/uZtDgf/rDLMH4RC/IsnT6h9/HjBQtPpdNK8aRMAV65cRtd10uk0HR1TrdRzkRXdM+XWLzdWpqjdbI6HQmloaMDtdjMWi/Hk8eMZlzONpRuzEMx5im6Xy0VlZbWdv63IbNu2bcGCewIip1sgEAhWHCG6BYIPCXv37uX//eVf8Xf/3t9D0zT6+/txu914vd7V3jXBNEiKhKZpNG9opqWlBY/HA1h4vT5qa+upqAijacstvpf/6ny8kNrcwseyLM6cPo1hGFRVV7Nu/fpFja3r9tiSNPVfYbYa+ODAAFevXAFg85YtMwpkr2HwpeFhfrukmO8EAvxKeRkAP9k/QF2mIn0OSeLfl5fxyOnk4OnTKIqyoEmvzZu3oGkagwMDPG5ro739CQCvvfbavLeVi3Svkr3cXEJ7eXOmeNrde3dndSbouUj3SuV0mwV/pFxOW3DLsozT6WD79h04HHNXxJ+V7FtMVFITCASCFUeIboHgA47f7+cXfvEX+fof/B8aGxsZHY2STCYpLS1d7V0TzIKkAJJ9fex0uti8eTNbtm7F7bbzu30+P3V1dZRXVKCpyyu+l9MFUUhOdz4jIxGuXbsKwK5duxc1aaTPYi/PplqMjERpbX1EbHQUt9vN+qamabf16ZEoTsviT4J2oau/CAQ47fHgsix+/mnvFKFjShJ/XFxE8927BCKRafdhNhRFoXHdOgCu37gOwMMHdi/xJ0+eTKnUPRdZ98RirO6LYdxevrjLksqqKoLBIKlUiocPHsy67HwmfJYCewJgbsHrdLqorKpClmUcDgdbtmyd9/tj5n0oaBcEAoFAsMQI0S0QfIB5+eWX+ea3vs0Xv/j9gJ0X6/P5cRbQQ1iwysjYkam8C2S3y82WLVvZsmVzpi2UhN8XoLa+jtLSMqRnsFLSQopZ3bt7l97ep2iaxr79BxYxth3pnDypoKpqxlkA0egIpmly89ZNwI4uT7evx2OjvO/10JttsSdJ/FxFOUlJ4uDYGC+Njk5Z5698fgCaHjyYd7S1trYWp9NJbHSUnkzv8v7+PiKRCJIkcX5ST+q5yIruxYrehZLrmb5I0Z9tEfbw4QN0XZ912RUvpIY5ZzEzp9NFVVUVsqzgcGhs3bplyQS3aBkmEAgEq4cQ3QLBB5DKykp+4zf+C7/xX/4r5eXlDA8Po+s6xcUF9nMVrDqSDNIk0Z3F7fawdetWNm/ehNPpQEIiGAxRXV2Tq0L9rJATPmrhwseyLM6eOYOu61RUVFCeqRg+X8bt5ROVkN9vi+FEIkEqYw1va22dNdodNEz6Jp37doeD/51pvffP+/pxmBNzemOyRNLpxB2Pzzvamt2Hhw8fTrBQP3pkR7tPnTo1r+05c6J7tSLdmUryixD9RcXFVFRUYJom9+7enXvMebosFouJiTWL6nY6nDnBrTk0tm7duvTOAxHpFggEglVBiG6B4APEjh07+NVf/TW+99rrvPzKK+i6zvDwMKFQ6JkTYx92svby2S6QPR4vLS3bWLeuEbBwulzU1NTidnuWZB9W4trc0LMW7/mJi1gsRuujR4Cda70Q9ExV6ymiO2Mtj46M5J6bK9qdlCQc5tQz9r9KiulVFOrSaX54aHjii5aFmk6TVlWUeUQzA4EgZWXlmKbJo9ZHE15ra23FMAySySTdmQh4ITgy7pfVspcvRdR5U6ao3OPHj4nH43Muvxp9umcKMzscTiqrq23BranLI7hzOd1Lu1mBQCAQzI0Q3QLBM46qqnz8E5/gT/70z/iTP/0zPv6JT6CqKv19fSiKQijT+kjwjJGxlxdiBS0qKqalpQVFkVEUlcqqKkKZCOvSsHZyuvO5c+c2pmlSWbmw453JXp6tXD4SHZnw/GzR7vtOB/viY6iT/mBjssyvltlF1f7uwABleZbn3fEEmmHQV14+L+GXHbuzs5PEJHGZTCbpzFQvf/311wvepp2usIqRbmNhky9ZvF67uj/A3Tu3C1pn5XO6zWnrIzgcDqqqq1BkBVVV2drSsnyt22bW/QKBQCBYRoToFgieQRwOB1tbWvixH/u7vPb6G/zqr/4aO3bsIJ1O09/fj2VZlJaViTZgzzDSNDnds+F0uti+fQd+vw8JiZLiUsLhyqXLB10mcr2S52EvzxKLxWh/Ylfs3rx5/tHubP/myZ+S8Uh3dMLzs0W7/ywYokI3eGGa3O2/Dvi56nLhtSz+QX9/7vkvDw/TV1REa0NDwWJXURQaGxsBZiwU9vDhg8z9wxl7VE8mW+dhtXK6DTMruhf2nbVxYzOyLNPT3c3w8HBhY+orG+k2sexodx6a5qCqqhpFVlFVhZaWrcsmuCUR6RYIBIJVQ/hNBYI1jtPpZNOmTWzZstXO492yhQ0bNuRa/ACMjo5iGAbBYFBUJf+AICkz53TPhCzLbNzYTE9PN52dnXi9PmpqHPT0dOdyk9caRqZXsqIs7N/R7du3qG9ooLa2lus+H6PTiN6ZSKczUefJke6AndM9OdINdrR765ateH0+1jc15XKH77mcXHC7+cn+Ad73eBjNE3JWpkXYHz9p57OREX63qJiQYfBKNMp3TpwAWS5Y+NXW1eFwOBgdHaWnZ3r7+NOnTxkdHcXn83H58mX27ds353ZXu3p59n2wkPE1Tcu1j7tTYJTbHnNlc7qtSX26Nc1BdbXdh9sW3C0L/hzMYyeE6BYIBIJVQIhugWARaJqG0+m0by4XLqcTl8uF0+nC5co+58Lpctr3Tuf4Y5e9zITXXS6cTkdmfRdut5uqqqpp87HHxsYYHY0SDATx+XyrcPSC5URSpILt5ZMJhyvx+/3cvXsXTXNQU1NLb28vo6PRuVeeuicLWKdwjEX2Sh4eHqarq4uqqiqaN23m4oXCq3Zn+zRPxu+fmtOdJRvt3rdvP5s3b+Hhgwc58fbzFeX80ZN2fqujkx+vrmIo73N7xe3mdZ+Xl0Zj/PzTpzQnk1x1uznZ0oKfwo9//XrbWv7o4eztsB49fMj2HTt45513ChLd2e+Y1bKXG7O0b5uL9U1NaJrG8PAQPT09hY9pzr+I32KwMJEs+/OUL7iVlRLc2f0w515GIBAIBEuLEN2CDwyKotii1eGwhe8cItjpnCx4pxPDE0WwM7c9+36lLlBjsRijo6M4HA4CgQCKouDxeHJtjQQfQOZpL5+M1+tj+/Yd3Llzm2QyRUVFGJfLxUB//xSL62qSL7YkSZpQibtQbt+6SVVVFevWrePmjeskEomC1kunplqvPR4PqqpimuaMUfOZot0PnU7+Vk0N/72zk9cetfLXgQB/HgrSoWmolsVZt4cXR2Psi8e54XTy96urOJiJsheSVxwMBikrK7MLqD16NOuyj1of0bJtG2NjY/T398/pgBkX3atkL58hv34uZFmmeWMzAHdu35nfmDl7+QqJXcsCyUJTHVRXZQS3ItOydfkFt2VYJHstHEUSsmtZhxIIBALBNAjRLVgWJEnKiV2n0zGt8HW5nLnnHBkBbIvl6UWwMxMpdmWjyrn17efz7darQTqdRtd1dF3HMAwMw8A0TSzLyggJi2zUUJYlJElGluVc8StVtW+Koky58PR6vXi93pU/KMGqsRB7+WSyRZket7UxMDBIMBjC6XTR09OdEzmrTVZ0gz1xNldv5eno6+vLCcuNzc1cu3q1oPV0fbx6uSzLmKaZy+ceHY3OOAEwW7T7htvFZxvq+eJwhC9GhvlSJDJh3UFFodgwGJVlRhVlvGp3AbbqXAG1jo45JxYS8ThdXV3U1NTw7jvv8LnPf37W5bOie9Xs5frCIt21tXW4PR7GxsZ48uTx/MbMfAZWqu6BiYkiq1SFq1HUjOBuaVn2zhKmbpHstHCGJSq/qOEsW9t1HgQCgeCDiBDdHxKykVlHRrROFcF5UeAJFmh72Ski2JkRyhPWzYphVy4/cLXIit/JAjgrgm1sEWxfcNsiWFEUFEXJCWBVVQuOvGiaturCX/DBYb6F1GbcDhINDY0EAgFaW1txuVzU1tbytKeHeGLutkrLzVKIbrCj3UePHaepaQO3b90qqIBY/jKa5iCZTIxXLh+Z3Yo/U7QboF9V+c3SEn6rpJid8TjFhkFakmjXNMZkme88auVAPM5zY2O5qt1zWZwVRaGhwS6g9mAOa3mWjo52ampquHfv3pzLZl07q2Uvz7bvmm+ku6GxAbCLx5nm/HzT+W3KFuqymA9Ol4NgKERcHUaWs4J7ef9nmEmLZLeJe51C1ferOMuF4BYIBILVQIjuVUDTtFx0drbo77gIdkzNDXZNWjZ/W5Os0NlWMKuFYRjo6TS6YeREsGmaWKaJaWX6l2QutCRJykWd5ExxoXwBXGhEIru8QPDMMo+WYYVQXFyC1+vl9u3bgEpldTV9vU+JRufO85aWPa/byE14LZTOzk4ikQjBYJD1TU3cuT13Qa18ga9pGslkAn+miNp0+dz5zBbtzm1fkrgwTQrIXwSDfCkS4e8ODPL1AiPdddkCatEoTwvMW84ul0gmicfjuN3uGZdddXu5Pv+os9PloqIiDMDjtrb5j7lEEz6FoCgKLdtbUAYVZFmiZdvyC25jzCLVa+HbqlD5BQ0tKLpZCAQCwWrxoVclqqrmCmFNF/2d/JzT4Zw1+ut0zW6BdjqdqxZJAPtCMZ1OY+g6ei4CbGCaViZKYGUCwJnL7IwAzl4Q54vg+bS4URQF5zIel0DwQUNSWJJIdz7ZtmL37t8jNhqjvLwCCWnaKt0ryVKIbrArmR84cJDm5k3cu3t3zshn1vkiSRKaZv87nKlH93TMFu2ejd8uLub7IhGOjI3x9tMejJqaOY99fdMGwG4DVijxeJxIZJhgMMT9+/fZvn37jMuutr18pqJ2s1FXW4csywz098+ran2WlRTdu3bvxuPxwiC0tLSgLbPg1kcs0sMWwX0y4c9qKC4huAUCgWA1WVOiW1EUgsEgpmnZhazyC1dNskDn5wHPVg3atkCPC998C7TT6VzVaKhpmlMs0JPzgLN2t+zsf34EeHIUuBBkWbb7sTqFBBYI1jJ2Tre05JWGZVlmU3MzDx48IBIZoay8AiSJkZHI3CsvE/k238Xw5PFjtm3bjtfrpbGxcV4CNZsaMt6je27RXUi0ezo6HRp/HQjw2ZERPn7lKn/93B7kWSLMLreb0tJSu4Ba6+wF1CbT09NDMBji4sWLBYnuVaters+/kFp9QwMAjx/PL5d7wrhLNOEzG9XVNTQ1bcAcNikrK0PTljf9KjVoYcYtSk4olH1MRVaF4BYIBILVpmDF2dDQMEEAOx3OKVZo+7mJhbDybc9zVYNe7TzgyYWwTNPENAzMPAFsXw9kLNCShKwoE2zQmqZNWwhrOmRZxuFwrPpxCwSCNYjEktrLJ2+8qamJhw8fMTw8TFlZOZIkEYkML8dgc7JUots0Te7evcPu3c+xafMWHj16VHCerqra393ZgoVz5XRnWWi0+7dKivn0yAi729s519Mz67GXFBdn9ilCssDK7Fl6enpobt5ER0fHrMuttujO5tcXKrp9Pl9uImK+BdTyWW7R7XK52LffbtnW0FiP+/HMFv/FYlm2nRyg/BMqxUcVJFkIboFAIFgLFCy6v/PdV5dzP6Zgi980um5MXwjLsjIu6Gw1aHmKDTorgAvNEROFsAQCwVpBkiTbYp4ar3q/xCOwfv06WlvbGBwcpLS0DEmWGB4aWoaxZmepRDfY/am3bm3B7/dTU1NDe3v7rMtn7eWqpuL32/ncyWSCVCpZ0HgLjXa3ORx81+/n49EoB99/nzebN864bHFxCQCDA4MF7VM+fb29uf0ZHBykOCPgJ5PvlspWcl9J5ltIrb6+AYDep08LbhE3HUv53puO/QcO5jpvHD16lK62ud8bC8EyLZLdFoobKj6tEdglz7sonUAgEAiWj4JFdyIRL0gA5xfCygpeUQhLIBAI5o+ksKQ53dOMQGNjA7Is09/fT0lxKTISg0PzF3eLYVz4LP47X9d17t+7R8u2bWzesnVO0Z1FVdWctbzQKHeWCdHu9U3cu1dYtPt3iov4eDRKy40blDbUz7hccYktugcGB+a1X2Cfj4GBfsrLK7h58yZHjx6ddrl80akoysqL7gKqzedTnzlfbY/bFjXucorujc3NVFZWYlkWX/va11B6ZWDpRbdlWCQ6LRwlEuHPq/iaV69ujEAgEAimp+ArHJdr+SxRAoFAIJiKpLLMohtAor6+HlmW6O3to6i4BGSJwYH5C7yFku2XvFSVs+/du8emzZspLi6mIhyetdp31n6uqmqu00N0noXlTNPk9u1b7Nm7j8Z1jQWL7lsuF7dKS9nS389HHzzkrRmWy0anBwcXNhnS09NDeXkFV65cKUh0y4oC8xTBi6WQFm9ZioqKCASC6LpOR4GTKjOxXKI7GAqxY8dOAJ5//nnKy8uJ9tljZd0VS4GZtkh2WbiqJSq/X8NdI1qCCQQCwVpEfDsLBALBGkWSlyuneyq1tXVUhCsAKAoVU1pSujIDs7SRboBUKsmjTBG1devWzbpsVnRrmZoc9vqpeY/55MkTTNOkqKg4lxdeCH+13t6/Fx48wD1NdNnr9eJ0OjEMg8jw8Lz3C2zRDTA4ODBjBDu/T7Uyj7ZdS0V6HoXUstbyrq7ORVccXw7RrSgKBw8eQlEUSktLOXbsmP1Cpk7DUmEkLJKdJp4mmZqvCsEtEAgEaxnxDS0QCARrlJWJdI9TU11DZVUlAMFQEUWZCOtyZ4Yahi0El1L4ZCtaV1ZWzSrkckJTVZEl+1/iQqzVqVSK3t5eAGpqawte72x5OYNFRfhSKT4dmRphz1rLh4eHF2z5HhoczEwkSHR3d8+5/GoUU0vnTXTMllomSRJ19ba1/HHbwguoZRl3WSzdMW/fsZNQKATAV7/61dz7T5JZsjaAeswi9dQisEOh5oc1HCXick4gEAjWMuJbWiAQCNYoalDCjEN62Cq4Cvdiqaqsorq6CrDw+wO2eFjmgkzLIXwGBvpJJOI4HA7Ky8tnXC4rZBVFRcpEeC1zYec6a3WuqSlcdKdNk7P79wPwg8PDU6wNJbkiagu3+1uWxdOndrT78uXLMy6XK0y6GqI7z14+m+guKy/H4/GQSqXo7u5a9LhL7bIIV1bS3NwMwBe+8IWJrofsx2iRH+V0xCI9aBE6qFD1ZQ3VJwqmCQQCwVpHiG6BQCBYo1R+QaP0FQUrZZHssLD0lRHe4XAltbW1gIXH42H3rt3LWgl5ufJqOzs7AbtP8kyM53QryJn2SguNKHd02m25ysrKcLkLq4NiGgZXdu0ioWmsT6XYG49PeL24JJvPvbgc+6zF/Pbt2zMuk/0bK/LKi+58m7g2SxvN+kyUuz1j518sS+mycDqd7N9/AICNGzawZcuWCa9LsrSo+SvLskj1mxgxi9KXFCo/pyI7heAWCASCZwEhugUCgWCNorgkyj+mUf0VDWeVRLzDRI+ujPAuL6+guroay4Ka2joOHjq0bMLb0JdJdHdkRHfNzKJ7PNI93l7StBYm5hLxOP19fYBt1S8EwzBJOZ2c37ABgC8NR3KvSZJEUdHiiqhlyRaTi8ViM+as50T3EhW0my/j+fXTt+6UZZna2joAHi+yanmWpXRZ7Nu3H7fbjaZpfN8XvjB1gWxO9wI+wpZlkeyxwILwp1XKPqIiKUJwCwQCwbOCEN0CgUCwxvFtVKj7UQfFR1WMUYtEp4llLL/4Li4qJlxRAZZFXV09hw8fWRbhnYt0q0srup8+7UHXdbxeby7HdjKWNW4vX0xOd5b2jozFvMC87uyxv9/SAsBL0Sglmaiv3+9H0zTS6TQjI/OrqD6Z0dFRRkdHkSSJ1tbWaZfJTjqshr08n2xBu8lUhMM4HA7GxsZy+fOLZalcFtXVNVTX1GBZFj/yIz8y7TFk3l7z1tyWaZHstFDdUPlFjaJDqujBLRAIBM8YQnQLBALBM4Dqkwh/VqXqBzQcJRKJdhNjbPmFtz/g55Of+hSmaVJTW8vGjc1LPoZhLk+k2zAMenrswmEzWczHI91yLqd7MaK7s8O2mJeXl+OYxSadv48AXeEwV10uHMBnMwXVskXUhoaGliSnPxvtvnDhwrSvZ0X3atjL81G16fOrs/nt2b/pUrBULott27cBsH37diorK6dfaAGRbku3SLRbaCUS1T/kILBd9OAWCASCZxEhugUCgeAZQZIkAtsV6n7MQXCPQnrQIvnUXHDhr0LZuGEDL730EgDbd+zA7w8s6faXy14Oc1vM83N6sznd1iJE9+joKENDQ8iyTHV19ZzL50da/yQUBOALkQhYFsXZImqLzOfOkhWrbW1t076ePf/yKtvLZyqkVlRUBNiTEEtFbsJnES6L2to6QqEiLMviYx/72MwLSvPrBGCmLOIdJu56iZqvaHjWiUs2gUAgeFYR3+ACgUDwjKGFJKq+rFH5BQ3FIxF/YmEklld4HzlyhKKiEIqicODAgSW1ty5XITWwezlblkVxcTEej2fK67lIt6zk2csXdy7nU8U8P8r/qt9PTJKoS6fZHU9QUrw0+dxZnj59imVZ6LpONBqd8nq2P/dqtAyDfNE9vb08K7qHl1J0L/K9J0kSLdvsKPdzzz2He5YCevNpGWbELRJdJv7NCjVfceCqFJdrAoFA8CwjvsUFAoHgGUSSJUL7FOr+joa/RSbVa1c2XvLWYpnNSZLEV7/6I1iWRUlpKZs2bV6yIZZTdCeTSfr77eJmVVVTI89mRvTKijJuL19gIbUsHZm87nBl5aztr8CuXg72scdlme/6/QB8NjpCKCMyF9MuLJ9UKpUT8Hfu3JnyupLZ19W2l2vTRJ0dDieeTPutJY10Z10WCzzm+vp6gsEglmXxyiuvzL5wgS3D9KhFqtci+JxC9Q9qaCGRvy0QCATPOkJ0CwQCwTOMs0Km5isaFX9DQZIh0W5hppYn6h0IBPjIRz4CQMu2bQSDwSXZ7lL3Sp7MbBZzM2Mvl2U5l9O8GHs5QCQSYWRkBEVRqKysmnXZyRMOfxm0rfsfjY7iNE2SySSjo6OL2p98snndly5enPJadoJgte3lyjQTFUXF9gRENBqd0F5ssSymiJ8kSWxtsaPc+/fvx+l0zr58NtI9C+khCz1iUXxcoeqLtpNFIBAIBM8+QnQLBALBM46sSpQc16j92w4862WSXRbpIWvpo97AgQMHKC0tQVEU9h84uCQ28+WMdAN0do4XN5tcVTpr784X3UvR/7mjwCrm2WPPjn3B7aZTVfEZBs137jC0RNbyLNm87p6M1TyfrOheNXt55ryr00y+jOdzL+35WMx7r7FxHf6MM+HFF1+ce4VMIbXpPpaWZZHsNTETFqUfVaj4lIrsEIJbIBAIPigI0S0QCAQfENy1MrVf0yj9iIKlWyQ7LCx9EcJ7mmt+SZL44R/+Si5PesvWrQvffobxXsnL8y8pGo0yMhJBURTC4YmVpfNF77joXvxkRUe7LfSrqqpy252O7PhZwWtJUi7avePqVQaWqIhalv7+/lykuC/TUzyLusr2cjOX0z11/OUoogYLd1nIsszWTJu3w4cPF1SpXprhbWBZFsluC0mB8Oc0Sl9QkWQhuAUCgeCDhBDdAoFA8AFCcUmUf1Sj5isOnNUS8Q4TPbpwEWkxNQXV7/fziU98AoCtW1tygmihLLe9HGa2mGdzemVZRs5E7Reb0w12xfGxWAxN0wiHwzMulz327D4A/GXAFt3rHz5Ez9jBlwrTNHM9rq9fvz7htawLYC3ay0Oh5Rbd85toWLduPV6vF0mC48ePF7bSNC3DLMNuCab6Jaq+pBHap4ge3AKBQPABRIhugUAg+ADi3SBT96MOio+pGKMWiU4Ty1g6u/mePXuoqChHlmX2Hzg4azR3LpbbXg7Q0Tkeec4XNfn2cmmJcrqztGd6ds9mMc+3smeP/4nDQVdFBbJlsTtTCX0pyRaWu3///oTns6J7tezl+ZXk81EUhf9/e3ceJll21nf+d2/cG1uuVZW1b72pW2p1t5qWRGtvrSAZMGAsIXlYNGYxYBtkBjzD2AY8Hgz2gIHBxoABG2RGAi02y4AZIRASIAm1dqSWWr2pq7qruvaqzIztbvPHvSciMiurKpfIc25kfD/Po6eyqzLz3IjIRzd/8b7nPbPFGxGjD90b77KoVCp6btHh8cADD1y1ZeGa/JXNI2mUqXMyU+2ApyPfGmrmTs7gBoCditANADtUMOXpwDcEOvQ/haru8dQ5kSpZHk3w9jxP3/qt3yZJmp+f77faboaN0H3h/Hl1Oh1Vq1Xt27dvsHbRau153kj3dEuDo8MOHz5yzeplmg4mzpvHX6lU9NCdd0qSvvvkU/ovT57Qr584of/z1Gm9sNVae1PwBly6eEmSdH7VVHTTIu2svdyE7lXt5fPzu+R5ntqtlrqdzkjX3EyXxa233aZGsynP8/TSl75s3V83fGRY2s3fCGve7OvIW0M1jvPrGADsZPy/PADsYJ7nafbuio79g6rmXlhRdDFT95lU2Qj2LU9NTenrv/7rJUnPec6d2r17z6a+j43QnWVZf4jYnoWF/t/Hpr18RegezRsT586dVafTUa1WWxH0V1v9+P9Ou60XPvigJGkhjnW5UtHFSqD72m395omT+v0nvqyXLi9v+rouXcqrxVEUrZgEbkK3q/byfuhe9XOwXfu5pY3/7Hmep+cUx+W95jWvueGRcKu+Ol9zOVPnVKaZuys6/O2hanv5VQwAdjr+nx4AJkA4n+8ZPVgcQ9R+MlPS2Xq4vPfee/vDwu5/0Ys2FZxthG5pENrm5+f7fxcX7cXeiPd0S3nQf8q0mB+5dov5YJhbRd91/oL+j8ef0ImjR3Vubk6+pD+dntb/cuig/tbNN+mtR47obBDol04+pa+7fGVT19VqtdTr9eR5ns6dO9f/+36lu2Tt5f3Qfcl96N63b58azaakfJL/RngVSZ6ULEu7XuTr8N8LFc6yfxsAJgGhGwAmhOd7mn9hRce+O9TM3b56ZzL1zqVbPlrsW77lWyRJc3Nzuvvuezb89bZC96VLlyQNhnJJK9vLR72nWxo6OmyNM8L711A8/q+6dEk/eO6c/uutt+jdb3yjPvKsZ+V/v7io4iL10ammvvPIYf3u7Kx+4vRp3ddqb+q6LhVvQJwY2jNuzpl23V6+utJepkr38eM3FX8e3/DPq1+V6od97XlNRQe+KVSlTuAGgElB6AaACVPb5+vIt4Xa//WBvIrUeTJT2tt88G40Gvqmb/omSdIdz362Fhb2bujrh4PPdk5uNqF7ZmamH5ii+Orp4aPa0y1JzzzzjHq9nhrNpvbsWVjzc9IkkbJM3/XUU3r/9JTee9fdkufprw4dkiS9rNXS1NCU89Tz9GMH9utLtZr+wSaPFLtYPBcPP/xw/+/qReh2114+qPgbnudpruhMuOQ4dPu+3x+K98pXvnLDa1Wano59d6h9XxPIDwjcADBJCN0AMIH8wNOeVwQ6+h1VNW/z1T2VKbqYbbrqfdddd+nYsWPyPE/3v+hFG5pmvtaxWduh2+mo02nnQW5uTpIUx5Gk1YPURjflPU1TPf3005KkI0fXrnYnaaKbHn9cx9tt/cauXWpM5e3Lj1RDPRGGqmaZXtxqrfwaz9Pbd83rFcstHe31NnxdZl/3qVNP9/+uVq9LctdePnxmujE7N6dKpaJer6elpaVtW3M9j/ngoUOqVqvyPE/Hjx/f1Hqe73EkGABMIEI3AEywxlFfx74j1MJXVZTFmbonM6XxqtC5zgz6lre8RVJeSb7pppvWfQ3Dodtei/m8JCmOBqHbhKFsRHu6jaeKFvODBw+t+e9Jkuq5n/ucTjYa+lijoUYjD93tdkcfnJqSJL1suXXV1/3RzIyWPU9fvbjxMGqqxsvLrf4bLa6nlw8C8OBXE9Nafmkb9nOvXPPGXRamtfyOO+4gOAMANoTQDQATzq952vf6UEe+varaEU+dk6niKxuv9tbrdb34xS+WJN1+x7PX/XVZllnf1z1X7OuOt7m9XJLOns3PxZ6dnV3z8SVJrKnlZT3dbEiep2ajIUlqt1v6iyJ0v3x5+aqjwrq+r2fCQHuS+KrveSOXL1/uP87FYs+4OW/ad1bpLvZ0e4NfTebn5iVJF4tjzka/5vq6LIIg0KGi3f8Vr3jFtlwLAGDnInQDACRJU8/ydfy7qtr9ikDJcqbuMxsP3q94xSuUZZnm5+e1f//+dX/dZs5L3gwTuncVle6oqHRL2xe6O52OWq2WfN/X7t27r/r3JEmV+r6C4uk207Fb7bY+1myo63k6FMe6dY028komxZuouqZpqsXFfPr56dOn8+/VPyfcza8GydAkecM8F8vLo28tz9dcX5fFkSNHFQSBKpWKDhw4sC3XAgDYuQjdAIC+ypSnA98Q6PC3hKof9OSHG/v6er2u2269VZJ0++13rPvrzBCtSmCr0j0vSYqiPMgOt5ePOnRL0oXz+cCz3XuuPss8SRJd3LVLty4uqpplqhd7q9utljq+r78uKt8vX3U290Ic61AU6WS4wRepcKmoHn/pS1+SpP6Z076z9vKi0j0cus1z0d7clPYbWW+XxfGb8j3cz3ve82gtBwBsGKEbALCC53mauauiY99d1ZFvq6q6d2Mh4w1/629Jkg4dPqzp6Zl1fU0S22kvv1K0VddqNTUajRWVbmNbQncxZXz37qtDd5ok+tRXfIXmokhf2+nK932laapOpyNJQy3mK/d1/93LlxV7nv5wZn3P8Wrm3OsnnnhC0iB0uxukNji+zagXbzh02p1tW7d/Pvg1uixqtZr278+r2y996Uu37ToAADsXoRsAsKZw3lPzFn/Dlb09e/Zofn5enufp9ttvX9fXxKbauM1V1jRNdeVK3lY9P79LcXz1fuitnlu+lvMXLkiS9qzVXp4mOr+woE8vLOjvnz2rsNdbUdn9UBG6X9Buq1kExIU41t+7eEl/MDujxU2GZFP1N+df90P3Nk6Qvx7zWqyodJvQ3dmeSrc0CPvX6rI4euyYfN9XrVZbc3sAAAA3QugGAIzc3yqq3Tffckt/QNf19Ft8t7m9XJIuD00wj+P4qpC9ne3l0zMz/Snhhqny/+bdd+lgr6dvfuc7lV6+3P/3x6uhToaBqlmmr2y1tBDH+pWTTymT9ItrtKuvl5lgHsexoigatJe7qnQXz4N5k6dSqfR/drarvXx43Wu94WOmlr/whS/ctmsAAOxshG4AwMjddtttCoJAYRjqlltuveHnp5aml0tXHxu22nZUuqMo6lfYV7eYJ8V+9if37NFPvPCFOvzUU/qR3/otfdf5C9odx5Ln6SPFQLHvOX9Bv/fEE9qdxPrOo0d0epP7uaV8wFun05HneTpz5ozz9vJ4VXu5qXLHcbxmR8Lo1r32z97U1JT27t2rLMv0lV/5ldt2DQCAnY3QDQAYOc/z9MADD0iSnnX77TdsUTeV7sBm6N41v+1rDbtQtJjv3rOyRXl4kNcjx47qV7/ru/TZffv0fefP64OPPqa/fORRveFKfqzXXZ2Ofn92Vm86dkxfqtW2fE3m/Osnn3yyHzqvd3TWdoqjlaHbDJTrbGOVW7p+l8Wx4/kAtZmZGc1scu88AACEbgDAtrj//vslSdPT0zp0+PB1P9dUG220NpugOTMzazVgXmuYWpqYQV4VNRtNnV9Y0M/fc49eeest+tED+/Wbu+b1jvm5/HMl/ezCgs5socI9zEwwf/jhh4eml298H/8oxPHKoXZmiFp7G/dzS9fvsjCt5S95yUvW9b22o0sCADD+tvdAVADAxArDUHffdZc++zd/ozvuuENPnTx5zc9dz7FNo9Jut9XtdlWr1TQ7N6csy6yETLOve/UwtWRoiJw5l7rdbulypaL3zuVh+3Cvp7dcuqypLNNHH3lEkTydDEO9d25O/31uVlc2+byZCebPPPNMP3RL+euwnS3da4lWVbob9c1NLvdVUcULVFFQ/Fn8d/F3wdDHFS/UwtJx7aku6NmVF2hv45b+1zSqTT23d7+8056OnnqeTr03UtbLlEZSFklZLKW9LP+z+DtJOvwtoRrHqGkAAAYI3QCAbfPa171On/nsZ7Vv337t2rWrPyl7NZuhW8pbzPfv36/5+Xlr1cmLFy8qTVM1mk01Go3+cLDhx272Mbda+fFgYZbpR595Rn/n8hUlxff5SKOpD081dXenqx86e1ZvO3dOP7t3QW/ftWvD12SGqbVarRXPfX5W9+ZCtydvEHhXhN/B3wWr/t33KlpI92rvxVvkpZ5e2Hytbgpu0eEzxxVe2qfqzAEFChR6NQVeVaFXVeCF+f+U/3fFCxR6oTz58uTlf3qefPPfnt//t/zv8n+fuzCv6nJV+7271ZnrKFMmZVK1WtP8mTl5nq8rf+FLXiJ5kudrxZ/m4yyT0rYUL1LtBgCsROgGAGyb2dlZHTx4UKdPn9btd9yhj37kI2t+Xtrf023ntuQidCdJosuXL2vXrl3avXuPnnrqZP/vpby1vtmvdLcVZJn+/VNP6UWttn5i314FmfQjZ88qkPRfimr5QrxX333+gv73M2c1k6T6xYW8dX2toFtRRYEXquIF/Wpw0Ak1e+mAAi/Uqb+4ooULN8lXRS9ovlpJmK76PpVB6FVVoReq4oUKvUHoDRTK9yqrgq6JuXnI9bQq/HqeMmUKgkC7ntqlTNLU7HHNxrOqn2loV3tJx5r3KPMyZUqVZZlSpfnHypRlafHR8H+nypQoy/LPz79i8O+ZBl+TBAc03ZjRueUzuhINpsbPT+9S0EhUr9fVOH7jynV8JZPvZ6ofpsoNAFiJ0A0A2FZf8zVfo1/7tV/TsWPH9elPfUqdztXtwtebIL0dzLFhc3PbE7rzUFu5KvxGZ6VmfZeeNX+3/DNNVbxAB5Nj2nXliBRPaWFpr7zM13N0v9506aN6Sauj//u2b9JTs7fqaPu8dPZX9fxOrG+c/U5V/LoCr6on94X64FN/pH/85Lt1fN936cSeFw8F3ZUVXs/z+3HXfM7uJ/YoCAKdekeqW869SJ6k2alblMaJtKrtfhBah4JsNgi8w/+dKlGaXe/zzd/nz3+oqsJG/vGp6HGpdkhxY0pnFp/RYnxl5K9R/zEVr7+/6rHWikF1MzPT6/o+yXKm+lFPwdxorw8AMP4I3QCAbXXkyJF+O/Vttz1Lf/M3n73qc5LiuKhRDVIzLc6+V8n38CpcEX6by/OaWd6rij+l+lJD1UpNXubLzyq6p/GyNfb+DtqiB+3NQ23OGmp39sKrWphN1J06P625eF7PSnq6vPuyPPmqp3XNfnlOURwpbIVK01TzjX36qvO/qYeOvEV7D36P9nlSNpWpG7xTtXhJL45ndG7m9n6Y/eKx79SzLj2kFzz9/+rphZcV1eBEiakMZ8Mhd2W1OFFbM40ZNacqWl7OJ6w/kzypKO6N5LVYr+Hz0X2/0p8mbn42tosJ3Z63skJtzlOfm1tfik57UvNWN0PoAADlRugGAIxclmXKknzYlBLptS/5av3xH71Pdx+/T2e/eEle5q9oe97fu0W7Lx/RTUmgtNGQP9QKXVk1GCtvcR7s5Q28UKEXKvCqCpSH3hu1OAeVUHuf2Kssk5R6Q1PMM83O3SplUuZlkjzlf3jypDVDa179XauFOVOiaEWLc6/bVi0MlVZinT37lDKlamRN+Y1YvairarWmbq+rPU+/U0HS0of2PV+L8WP95/XEzK267eKn1bz4QZ1pNFc853994AF9/cO/qHDx0zrXPLLu1youwnWrtSzPy/cmr6762mC2GEiS73v9rQZxnFzrS0ZiELoHj9mTp7AI3abl/7rfI8kkX2ocobUcAHA1QjcAYNOyLNOZP4jVfjJVFklpXEx2TqQszfIzrjJpKr1D9zyaVy5vO/yAuu3uihbnqe6Upk5M62inrTvnFld8/zwLFROrTEPymi3Lg7bl9DotzplSpbHUrFbleVKcRKpUgvzbS3o6ekzbpdPztNdbkBf48gNfURznoc+TvCL4J3Gsmy/9jU7M3qHF2srjxU7O3KHbLn5aR648rAcPvX7Fvz28+/mK/KpuvvQ3GwrdZmp4t9vt/53n2w/deZt5/kaH71f6XQ9Jsr2hO83yCvtw6K5Wq/KKH4hwHcezJS0paEp1QjcAYA2EbgDA5qXS8pdStR7LFMwVW4D9IrR5XvFx/uHUQiOfXt6MdXHpzIpq8Ww2p6yxV0vJop6JTlu59CjtKQzCQZ63IlO321O9XletXlO01FNWtFWb6nIcx6rHy2qFV7c1n5y9XZJ05MrDeUl6KCimfqB2MK1a3NrQFUVR1F/X87zijQ634TEIAnnKB6xtd+jO0qLSPXRme7XYzx2Ggdbzw5EsZ6ofZj83AGBthG4AwKZ5FU9zz/fVOZmouuBdt0K6/+iCzl05o3A6VHI2UZoNwlSmq6uN2y2Oojx0W9btdlSv11Wv1bS0tHhVe3OapooqVQVp96qvPT19XIl8TcWLeuDLv6Ol6i49M3VMJ2fvkCSFaVeRX93Q9ZjQPQjbds4tv54gzH89yVvOt3e6/FqD1MwQtfW0lktS2pWat7GfGwCwNkI3AGBLZu6q6Pz7E8WL0hrF2b56vSHf95Wmqaanp3TlymAiddoPPvYqrHEcWVtrmGnjrtXrkgaPfbiieqZ5TPedfr/CpKuokgfAmy9+Vi98+o9UKd6g+Ipn/lR+lihMI51rHNKj8/eoES/r7NTRDV1PksTKsjTf+15cguvw2N/Pvc1D1KS193RvZIga+7kBADfCHQIAsCXVPb6mnuMrvnzjiuTCwoIkaXp6ZsXfD1p87YU9s5d59bFY280cmZZXU72h0Jf/e5Zl+vT+B1RL2nrOuQ9Lkl588vf0pod+WrWkrS/PPluS9IkDr9G/u/8/6R3P/d90rnlYX3nqfyjyqzoxc/uGrymOVz4XrkK3+QkyR8clcXrtTx7Vmmvt6S4q3dPTNz4ujP3cAIAb4Q4BANiyuXsr8ipS2r1+8N63f58kqd5o5MPLCmtVG7ebCZqevQ3dkqQo6ilN88pytVodnBNePPYsy3SlvleP7HqeXnLi9/SiE7+nVzz5Hn3o6Dfq7Xf/qB7a+2JJ0sGlJyTP05Nzz9GDB79KqXx5WaKvfeRXpGxjYdW0mMvB67BCtnJ/dbbBx7EZq7ssgiBQxa9IylQvuhGuJ1nOVF1gPzcA4NoI3QCALWs+y1ftgKfo4vVDd61aU6WSTy0friK6CN0maLrIl91uXu2u12qDx97/1/y/33fLt6mSxXrFiffoswsv0V8d/QbJ83Rq6mZJ0oGlx6Us0/FLn9Pffehn9dTs7fr9Z32Pbr/wCd1y8TMbup7hfd2S+/Zyf+gNiO22usuiWs2r3L7vr2ugXNrNf/5dP2cAgPIidAMAtswP8oFqaXcQYq5l79682r0idKcOBqmZPd0OwpLZ112t14equSZo5v+1WNujz+57mSTpzvMf0dd/8d/rpkt/o55fU6xA9aSlb/vMj+vNn/+3OjV9s97z7Lfp4T0v1Kmpm3Tf6T/d0PXEJQndg6K/qXRbCN2rHrMZolZbR5W7v5/7KL9OAQCujUFqAICRWO9AtX379ur06VOq1xsKgkBxHDsapJYoU2a9vVySer2eJKkahle9STEcNJ914ZP64u4X6MTcs3Xf6ffrmz//f6343CDt6b/f/o/08J77lHn5PuhPHXi1Xv/of9Z094KWarvXdT1mf3u+tuewalv8HBQ/Bi5CtxmiNjszc82vMZKWVGlK9cOEbgDAtRG6AQAjYQaqXX4wVTh37dAWhlUFQag4jjU9Pa1Lly4Ngo/FQWpSlh8bFm7siK1RMO3cQRgqW3Uk1vB/z3fO6pMHXq1PHHydPnHgtdrbOqGp6IruPfUnuuPiJ/X4rnv0xYUXrvj6p2Zuk6dMc91z6w/dqya5+44HqfUr3dt8XJh0dZeFqXTPzs7e8GuT5Uy1g56C+W27PADADsBbswCAkVn3QLV9psU8ryauNUHahv7Ubsv6oTsIlE8wHxoYZqq7WSY/S5Sa6r/n6ezUMT0xf5ce2fMCSdKBpSeu+t5pUfH2h85BX+/19HeWW33zY8jq9vYbbFUYhasGqYX52e2NRuPGX9uTptjPDQC4AUI3AGBk1jtQbe/eBUmZarW6wmDQYr2ewVWjNAibdvXPxpanMAhWtFEPju321ApntKtz5qqvP9PMz+Let/xlLSyfVLM3OPN8vvj8VnjjSu1gzVTJ0JnYvoOW+/w6Vobu7Y/cWtFl4Xl+f7uBObbsml+X5F/HUWEAgBuhvRwAMDJmoNozv5soS7NrtosHQaiwWlXUizQ9M6NLly71/83zfCtHRUnuKt1SHvir1VreYj4cuoei5hf2fKWee/av9MFjf1eJn1dgDyw9ruefep8ySfWkre/49D+TJJ2YvV2f3P9q3XH+YzrXOKTzjUMbvJ54cIyb7yZI9h/50Jnl277mUJeF33/c2dDHa2M/NwBgvbhTAABGauauioJpKV68/ucd2LdfUj7FfDhcuTg2zIWoCPxhGPZbnCUNlbrzoWhT0RXdefbD8rJEX/Xof9G3f+bHdfTyF9QO8unv77v5f9LvP+u7lcrX3/7SL+n2Cx/X5xdetOGp7PHQvm5X3eWmndyTxenlQ10WK4P29Z+EpJWputdTuGsbLw4AsCMQugEAI2UGqsWXrx+Y9izskZSpWq0pHBoo5uTYMAfMMV3hqgnmwznzfPOQHtpzv177+Nv1TQ/9rO4580H98S3frl9+/k/rqZlnFZ/l6fN7X6rfu/0f6mJtr1L5et4zH9BU79KGrmf4DQjbbf5G2v8ZyP/bRugefsOj0g/dN/4ZTDvS1G3s5wYA3BihGwAwcusZqFapBP1J0TMz0/3gaXNydpLYaWNfy/AwtUyD61gdNP/wtu/QYnW3br30WT28+z49vPv5yjxf55qHJUl7l5/Uc8/8hb71s/9SYdrTbz/3n8pXptc/+p83dT2Su3O6hw7qLv7T3pFhkuQX+7hv9PCzJJM89nMDANaHPd0AgJEzA9V6ZzPVDlw7wRw4cEBf/vKTmp6eKfbW+laPDUtT96F7daV79fiwuFJTt9LQlepu3XbxU7r9wif0xNxdCtO2JOnus3+pe898UI/N360/vuWtulJf0IeO/h294dFf11znjC7X963reuIShO7+ULNV/73Nq/bPa69U8hB9w/3cbfZzAwDWj7sFAGDkzEC1tKtVgXKlXbvyDbFhWB2aXG3v1lSa0L3W9PLC/qUndGj5cb3vlm/VLz7/5/Tnx98kT6nqcUuSlMrXr9z7U3rXnT+kK/UFSdJDC/erW2no3tN/tv7ricsTuocmqdlZ13RZ+KbSfYPQvZypusdTuL5j0AEAE47QDQDYFusZqNaflj3EZuDLsnTFtHCbzOR0E/SM1dXdg0uPKpWvR3c9T51wWh879Hq9684f0tvv/jFl8hRmkTrh1MrvXanp8fm7dWjpsQ1cz+Bcb9/Vnu5s5Z7u1NJrYyaYmz3dlRtUutNO3s3Bfm4AwHoQugEA22K9A9WCYGXotLmnW5IyR9XuLEuVrHVk2arQXU26iio1Zd7K5ymuVLVYzTsF1jrLuxvUFSadjVzR4PgsR+PLr3otrtMlMdJ1M1PpLtrLK9f+9cjs526wnxsAsE7cMQAA22Y9A9XMMDXTUmy7ephaCnZrMS3dw4959dV0Kw2FSUeV9OpJ65dqeTv5sUuf00z3/IrA3oiW1as0NnQ9gxZ/1+3l5r/truuZSnelcs3P7e/nPkKVGwCwPgxSAwBsm/UMVJuamtbycqvfUuzdoLV31Ex114UoilSvN1aG7lVJ8+TsHfKV6fbzD+qhvS+WJB1YfEwPPPkuHV18WJL0wIn36oET79VyMKMHD75On933Ct186bP668Nv2ND1pGkq3684OzIsWz29XHZeG9PW3m8vD67961GynKm2z1O4m9ANAFgfQjcAYNuYgWrP/G6iLM3WbFuenp7WmTNn+sHTdpU1SVKFodUl+8zE8OuF7vPNQ/ry7HN03+n366GF+/W3H/6Pes75v5YkdSoN1ZO2ztf263J9r45ffkivOPFevezk78rLMn16/ys3dD3Zqj3Vtq1+A8RapTtdWekOrlPpTjtSk/O5AQAbQHs5AGBb3WigWr1RlzQIPPbbyx1WuvvD1K5/O37w0FfpyOKX9Pc/9c/07PN/rfP1A/rP9/y4/vjW/1mS1KrO6V3P/WH93P3/UQ8eeJ38LJGnVM3elQ1dj3kuXFW6Tau/3SPDBmHfvA7BNd6FydJiP/dRfn0CAKwfdw0AwLa60UC1WjXf0+0VUcu3PMQrS5Mbf9I2iaKepJWhe62g+cju+/TY3F3a235aS+GcfvOeH9WZqZtUTfKzuvctP6lXPv5OvezJ9+q55z6sy7UFZZLe/Pl/s6Fy8SB0b+FBbUF61Z5uu4PUzBs+YbB26E5aUqXBfm4AwMbQXg4A2HZz91Z05ZOp0m4mv7YysPi+L88bZEPbVdbU6Z7uvNLt3SB0S9Ke1tNK5WkquqJ/9OAPqOfXNVW0D1TTjl5w+v9TJUuUyNdDC/erkka698wHdduFT+qRPfet63oGobsc08tdVbrDcO1fj5LlTNW97OcGAGwMoRsAsO1uNFAtCIJBAJ2g6eVJEivL0hu+0bBn+YRmowt6ZNe9Wgx36b4zfyZvKJB6kh6dv0dfWLhf+5ee0L1nPqAr4W5lkl5+4j0bDt35d/R09Sz17eUqdKerKt3Xml6edqQp9nMDADaI9nIAwLYzA9XS7mBo1bBard7/eJL2dEv5BPNhawXNO899RJ6kS9UF3Xfmz/T+m/6efuZFv6p/d/8vazGckyR95MjX6aG9L9YHbn6L/uvd/0L1pKXYC7W7fXrd1zL8XLgIlqvby21NUusPUises79G6Daf0zjGr04AgI2h0g0AsGLmrorOvz9RvCgVObFvanpKS0tLkrTmhPPt5Dx0x7Gq1Vr/v9cK3Y0of27ufeYD+tjBr9KDh746/9pKXYu1PZqJLmu6d7H/+eeaR/Se57xN3/6ZH1em9T+faTLY3+55nrXp4f31V70WV4XwdfDly5Mv3yv+VGXo49V/78lTRXPxnJrtaSnNH3d6OlS7miqv9BfPXyoFu9jPDQDYOEI3AMAKM1Dt8oOpwrmVwWV6akrPFB/7E1bpjqOepKnBX6wRNHtBQ5LkK9FHD3/Nin9brO6W9JhmexdW/P0z0zer59dVTTv591zH8zq8v327Kt1eEXm9NcJwPW2oGjXlZZ68zNNuf7/k6arAnPXb3geh2JOnTPn+7FSpMiVKlSrNUmVDfyZZoihrK8q66mVdRVlXuyrz2jU/r9jrSUGqO169T7PzgbxQ8kJPfiB5oVQ/7Km6h0o3AGBjCN0AAGuuNVCtXh9uL7c8SM11pbvYy26sVdt9ZNe9uv/pP9JSdbeWq/Mr/u1KbY8kabZ7fsXf+0mkMO3Kk7TQOqlzU0dveC1pmklZHmADL5TvVVZUjn0V/+35q/5+5efIxOKhQrGZT29CcR6MV4biPGGnSvxUqR/rbPy0uklbvayT/y/tqqeO4ixSkkWKsp7iLFasnqKspySLFJv/yfz70N9lPcWKrnrcdx2+W3cduVtZlsnzPO193es0Pc2vSACA0eCOAgCw5loD1Wq1mkxCu9GZ1aPmPnRfHQJXOzn3bKWSpqNLUpbK84J+5Xe5fkCStKt7WVP+XP/vX3DyHfKLCH808VQJjg21XXt5G/uqYnY9rWu6M6PMy7QniBQpGgrJSf/jPPAOKsVR2lXXBOOssyL4xllP0XDo7f/94L+jrKdEkaqNql7/8jdIXn7d7/rEbytJtv9IN7OGqe5Xq9VtXxMAMDkI3QAAa8xAtWd+N1GWZv3923l1+/qTo7fLdoRur99EbUJuZdV+4sHfh0lVjc5c/jWZp0Phzf1W6X7dO5NSr6og6+ktD/9nfeDOf6XMy5QqVdy4WZI017ugQKEypZpaPqGvOPFORV6oMIt0LjqpL3U/pV7WVTdt90PuykpwT3N75nX38buUeok+dPLPdOHKhTVDcqL4Wg99S+Ko2g/ckr03RIaDfZZlCsO1z+kGAGAzCN0AAKuuNVCtElSUxIm1SrdfDNMK0kCVJJSX5cdkTftz8lW5bgu1J79olS6+2VDF2FSR02JP8YoW6uLjVInidBB6m82KUj9WUon0qRMPqpO0V1WCI72y2tCuKNOx8x/UnZ98o37qyL26EEi3dC7otZL81mP65bP/TC9ZuqB/9/QJ+VmmX9gzr7edP6/3dP5fff7S+2/4nOyL9unozG5J0hnvhM7H52/wFaMVx4Mwn6aptSPDkmTlmwgcCQYAGCVCNwDAqmsNVKuGodpDodtUhK8Ovav3Ga/9OXm1+OrQZqrIaZYoUyrf81SNG8q8TJkyVRQU4ThWZ6iFupd21Mu6RQt1/ndmP/GK1umh/cRJEZiH9xObz0k1aGl+02ve3A96f/LRd6vX61113e+eCfS95yJ9oVrVC5bO6He+8P/pE426/qrZlCTtTiL96Zc+o11p/p1/8OABfdXSkh6tVvX5Wu2q77eW4VZ32x0H0iBoe55nte0/SdwelQYA2NkI3QAA68xAtfaX06JKnGmms6BKu6UszXQ49FdWh5Uqy4rK8dAe4yjtqJf1FGX5n72snQfjtKNIvRWV4qv3E+d/51U9vfb+1yj1E6Veond9+p1F+/X2tFCvlmWZoqjXPzbsWpX+98zN6R+eO6/3TU/rN8NA/+DCBb2g3dEL2p386yRNpan+cGZaP7OwoFDSvz11Wv923951TS6XVlaafd9+6B5mN3QPP26mkwMARovQDQCwrvksX3seqEi+Jz+UvKp06jOn9dgjDynKevrTR/5kMIBLw5OqTSU5/3itSvZGVdKK2vUX9v+7k7W2/D03qtu9ceg+HwT6jd279H0XLuifHjyg199yi473erqt29X/eeq05rNM337sqD7VaOhAFOmXTpzUyTDUf5udXfd1rKx0uw6f9g4JH97THTio8AMAdjZCNwDAOj/wtO9rVg6rCtXW2fOPK0kSPdz9pLVrSZKk39LsSjTUTn69SuvPLixoXxzrZ06d1otaLf0/8/N6/8yMvuf8Bc13uzoURXpeu6PvuHBBPc/TW48e0fIGQuRwpTtwPEwsTR2FboaoAQBGjNANACgFc1a3i/DrOnR3ut3+x9fbS515nn7kwAE9VKvrrRcv6o2Xr+iL1aoWitD4U6dOK/M8/Y/paf30vr06G2zsNh/Hcf+5qIZujs3qvxaWhqhJK0M3k8sBAKPmuncMAABJ7kO3S91up/9xcIOgnHmefmP3Lr32lpv1/QcP6pkgUL3Y/9zzPH2uVtPn6nV1N/k8mufCVfg069t8RYZDN2d0AwBGjdANACiFRqMhaUJDd2eo0r3O6vRXtNt62/lzekWrpU7Rkv5UGOp8UNEPnT2rDzz6mH7ozFlVNvjYzACzsOq24mvzNRkO3fV1TnoHAGC9CN0AgFKoDYUd2xOkbU7KXku3NwjdN6p0S9Irl5b0ayef0oVKoG87ekQ/vyc/W/upMNQ/PnxYr771Fv367l369osX9XNPP72h4G2eiyBwW+m2aUXoLt78AQBgVAjdAIBSaBbnTUv2z4h2Hbp7Q3u6g8r1Q/fN3Z5+5ulT+vOpKf39o0f0sWZTZ4tW8L3FILRzQaB/v7Cgf3T4kF65tKx/cvbcuq+lX+ne4H7wUXERuuOh0N0gdAMARozQDQAoheFK96SF7u5w6A6vH3a//eJFLVZ8/fDBA4qKVvwzRUDeF688W/zPp6f1S3t26+9duqTZoWB5PWnxeTe6jm1nMXynhG4AwDYidAMASmE4dNseZpWsM5Bul253cGTY9Srd00mir7tyRb8zN6/uUAu+mVK+J0nkrwqrvz0/r0qW6RsuX1nXtSRpcsPr2E79SrfFvf0JoRsAsI0I3QCAUhielj1pobvXW9+RYS9ptdTMMr13bnbF31+oVJRIqkjaveqxnAsCfXB6Sq9bWlrXtSRx/vWVwG63geGivTxN0/66hG4AwKgRugEApeB5Xj/4VKt2J0i7Dt3D7eXXm16+K0mUSDq96nMSz9OFIqyvbjGX8gFr8+t8jGZ/c8V1pduR9QyyAwBgIwjdAIDSqVo+ripeI6ja1OsN2suvV+WPPE8V5RXt1cy+7r1rPJZqlqm3znbtpPh62/vqDRO6bR8dZzotCN0AgFEjdAMASicI7baXR1Fkdb3Vhtubq+G133B4vHhent9uX/VvZ/uhe1VFO8v0/FZbX17nGxlxUo7QbVu9Xpfk7nEDAHYu3s4FAJRGlmXyPE9Vy5Oz49ht6JYGjz28Tjj+ZKOuR6pVvfnSJf310BFrknQ2WLu9/Pnttm7v9fRT+/au6zriKP9622elGwcOHNCVK1d04MABq+vec889ajab2rt3fc8TAADrRaUbAFAapqU4vE61dzv0euUI3ZIUBtd57J6nd8zP6bWLS7pnVbV7rfbyIMv0/efO6/Ew1EdWhfRrufW22yRJx48f38jlj4xpr7fdXn7rrbfqpS99qfbt22d1XQDAzkfoBgCUTuV6wXMbDO+pdsX38+DfnLp+e/W75+b0mUZdv/TUUyuC97li8NlC0R5eS1P9zNOndG+7rR87sF/ZOkPsgQP786+v2R1mZ9x000161atepfvuu8/quq63GAAAdi7aywEApTGodNu9PUWR+9A9N3dKV67crIMHL1z383q+r394+LD+w1NP6beePKE/mZ7WO3bN62Ilfx99Xxzre8+d15suX9Zckuhthw/pY+usckv5GxAve9nLND09vaXHs1mzs7N6+ctfrieeeMLqumXYYgAA2JkI3QCA0jDHhgWWK93drvvQ7fuRgqAt309v+LmXKxX9/SNH9MbLl/WWS5f1GydO9v/tnk5Xt3V7+oPZWf3Grnk9tsGK9fLSkl7zmteo0+ls+DGMgqk4254ibvayAwAwaoRuAEBp3HbbbTp48KA++YlP6C//4kPW1m02G3rjG9+oSqWid77j/7G27rA9e96t5z2vp0ceqUs6dsPP7/m+fmvXLv3W/Lzua3f0wlZLbzt/Xsuep1fdeosWNzmFu120rLsapGZCd8Xy+pHjY+MAADsXoRsAUBp33HGHnv/856uzxpFY22nxyqLuvPNOSVKz2VSr1bK6viQlSd5aX6ls8Mgsz9Mnmg09VqvqbefPayrL1N5CYF1ezh+7q9AdF6Hbt3x0lwn7Zoo8AACjwiA1AEBpNBoNSfYHm128ONhH7WovcxznQS8INndO9WXfl6nV7kqS637u9Sy3liW5C93mtbd9XnZcVLrT9Mbt/QAAbAShGwBQGvv355OzL1++bHXd4cp2o7H+oWOjZHLyZrNm5nm6VHzx7njzobtdPBeuqr1mqJ390J1XupMtvGEBAMBaCN0AgNIw1VVzVrMtaZr2z8memnITuqMof+wbbi8fcsGE7mTz+5OXlpY2/bWj0DPt5ZYr7WaQGpVuAMCoEboBAKVhglZoOXQPa05NOVnXzPHaUugO8tC9lfbyxSJ0u6p093puQrfZ052mVLoBAKNF6AYAlIZpKbZd6ZY0qHRv4EzrUUri/Ja8lax52c+fv/lk89Xa5aXl/sdNB89Ft9uV5KDSXbzrkWzhuQMAYC2EbgBAaQxCt91zuqVB6K7X69bXlqQo2uT08iFXKvltfXYL1drFxSv9j2dmZjf9fTYrKgapWa90M0gNALBNCN0AgNIoQ6W74ajSHRXTy31/86H7cvH8zW6hWjs8VG5q2n6rfa/nqtI9ODIMAIBRInQDAEojCAJJbkK3qXA2i2PLbIt6JnRv/nuY9vK5LU7gNsFzxsHxad2i0m17T/lgTzeVbgDAaAWuLwAAAMPV9HJJOnXqac3Ozun8+fPW15YG53RvpdI9ivbyYS7OLO92OpLc7emm0g0AGDVCNwCgdKqh/dD9+q/+autrDutFWx+kdqVf6d5atTbLMnme56TV3lWl2xwZllHpBgCMGO3lAIDScXlkmCumvXwrWbPl51+8J461K978Wd2mxXqqaX9Pt6l0Ww/dZpAalW4AwIhR6QYAlI6L9nLXetHm28tv6vX05kuX9E2XL0uSboki/dWjj+lT9breMT+v/zEzrd4GSuj90D1lv9LdKUK3bWZPNwAAo0alGwBQOhMZurv5LXlDBd4s0/edO68/evwJ/e0rV/QHMzOSpI6kHz54QMu+r39z+rT+4Ikv6+Zub93f1oTuhoOhcuacbuuD1GJCNwBgexC6AQClM4mhO4o2Hrp/4Nx5/ePz5/ULe/bolbfcop/bu1eSVJf0x9PT+s6jR/Q1Nx1Xx/P09hMndLS3vuDdD90O9nR3itBt22CQGnu6AQCjRegGAJTOJIbuXm9jld2vbLX0PRcu6KcXFvSLC3vU830tDrWQTxfB+bFaTd929KgWfV//16nT0jr2LCfFkWONxuS0l5tBagAAjBqhGwBQOrVazfUlWNfdYHv5t1y8pC9Vq/q13bv6f5d6npaLbzAzNIX7UlDRv9m3V8/rdHT3OkKtqfrWHbwO7VZbUt5ebs5tt2Gwp9tuWzsAYOcjdAMASicMQ9eXYF13A5XufVGkVy0t6R3z81el9KXirO7pVUdffXBqSk8Fgd586fINv/+g0m1/T/dwpbter1tbN2ZPNwBgmxC6AQClU61WrQ/Scq3bWf8t+a5OV4GkP5mZvurfFouzuleH7tTz9P7pad3bad/w+/cr3RZDr9HtDoVui6E/2sIRawAAXA+hGwBQSpNW7e71Bu3lQXD9YV5TRaBeXOMYsOXi72aSq7/HYsXvf+31mFbras3+3vpWq9X/uGG10k3oBgBsD0I3AKCUJm2YmtnTLUnV6vWDsWkhnyvawIeZID6dXv1vc0mqpaISfj1xEbprNfuV7t7QhHWb7e3mjYZJ67AAAGw/QjcAoJQmLXR3htrLa7Xrh+7P1OvqSfrqxaWr/q1VhO5munJKeZBlet3Soj6+jiDb64duNwPtsmLCus31zfRyQjcAYNQI3QCAUjFtvpMWuoeP0K7Xrx+6zweB3jczo7dcuiRv1RFgg9C98nu8amlJ++NE75ifW8e15Bfj+jWoORikRugGAIwaoRsAUCpmcrbrwGef3z9Cu1a78Vna/3XXvG6KIv3j8+dX/H3Lz0NjMxuE7r1xrP/tzFl9rNHQF9YRZKOoJKHbYnu7ebOH0A0AGDVCNwCgVCY3dA/cqL1ckj7VaOhnFhb0vecv6H89c0bTxfNmKt2Nor38nnZbv/XkCXmSfvjggXWt3+2a0O1mmJ1pL2807IVus6fb9wndAIDRClxfAAAAwwjdUniDQWrGr+7Zra7v6YfPnNUbL13W78/O9oer3d1u651fflLP63T0cLWq7z16RM+scyJ8t9uVJAWB29Btc0931K90U48AAIwWoRsAUCppsRd5EkN3luVHhtWqN24vN96+a5f+eHpaf/fyFb3p8iXtj/PQ/YJORx9qNvV9hw/pz6emlG6gbdqEblfHtvVDt8WfATNIzV/jGDYAALaCOwsAoFRMpTuc0NAt3fjIsNXOhKF+cWGPXnnLLfqJvQuSpD+bauq7jx7Rn01PbyhwS1Kn05EkBYGb9+ZN6K4zSA0AsAMQugEApZJlk13plqTqBirdK3ieLhRBuZ5t8ntI6nbaktyF7kG3g8X28mJPd6Vy43PMAQDYCEI3AKBU0mIAWDWcxNCdV1k3Wuke1r7GOd0b0emYPd1uK93VmsX28pj2cgDA9uDOAgAolX7gslzpvvvuu/ULv/Dv9RM/8a+trjvMnPIVbrbSLanlrX1O90a023ml21XV18W+/ojQDQDYJtxZAACl4ip0P+/ee/Xa171OX/t1X2d13WGmOB0GWwjd5pzuLYTuVrslyV0ANaHb5vTymPZyAMA2IXQDAErGTeg21V2Xlc40Ne3lWwnd+fVP7YRKt8UtBuzpBgBsF0I3AKBc+sPELIfuVl7ddTm92uTkINx86DZ7umtbGKTWapWj0m1zgr3Z0w0AwKgRugEApVSt2j0julWK0F1UusPNV6l7xfU3smwwDn2DlpeXJbl7LvrHxlk8J5zQDQDYLoRuAEC5FEHP9jndpQjdedZUZQt7ujtD11/dZOh2XfU3obvmYHo5AACjRugGAJSKCXqu9nS7lBSV7q0MUusOtYRv9qzupSW3lW7TXh4E9irdZk83AACjRugGAJSKq9C9PFTddbWXOUnyx76VPd2RpKJgvul93cvLS5tefxSSJK86V0O754QTvAEA24HQDQAoFWehu6juSlK9Xre6ttEP3VuodMvz+i3mtU1OMB/e0237dZCkOC72dFftHRmWr0uLOQBg9AjdAIBSMVVm66F7qLo7PT1tdW2j2Mq8tdCtQYv5ZtvLFxcX+x/PzMxs6Vo2w+zpDgK7lW5CNwBgOxC6AQCl0g/dFs9olvJBalmWKcsyNRpNq2sbptJdqWwtdPcr3Zve0z14A2JqampL17IZSRF+Q9rLAQA7gN27GQAAN+Cq0p2mqe58zrOtrrlaHBft5VsM3ebYsPom28vjOFaWZfI8z0nVPypCN5VuAMBOQKUbAFAqlUpFkv3QXQZJ/8iwrX2fjr+1SvcwF6E77oduu2e1x1S6AQDbgNANACgVE7ptn9NdBlGU35a32l7e9Yo93enmv09WBPZm036rvZlebrvSHVHpBgBsA0I3AKBUTNCaxEq3yXyu93RLg9A95bLSXbwBYwt7ugEA24HQDQAolYluL4/z2/JWjwnv9tvLN7enW8r3uEtSs2l/kFqvl4ffCnu6AQA7AKEbAFAqrs7pLoMoGtX08q23l5vQ3Wg0tnQtmxEzSA0AsIMQugEApTSRobuYXu777tvLTeiecrCnO456kqRKxe6vKXFMezkAYPQI3QCAUqpW7U6uLoOoZ0L31r5Pr2gvr2+hvTwpRqm7qHT328u3OsZ9g9jTDQDYDoRuAEApTWKlOx5ZpXvr7eUmdNfr9S1dy2b0IhO67Q5Si6O8vTwbwVFrAAAYhG4AQClVw8kL3b1oRIPURtBenhT7m+sOKt2m4lzZ6hOx0XWLx2zecAAAYBQI3QCAUprIc7qL9vIiM29aZwTTy00AbTiodJvQ7duudBO6AQDbwO5mKQAA1mkS28t70dbay/dHkd54+bK++dJlSdI3Xr6iZ3V7evfcnP7HzLR6G6gcm6FitVptU9eyFb1uV5Lk2650FwPczBA5AABGgUo3AKCUJjJ0d/Pb8kYr3WGW6cdPP6M/eexxvfXCRZ0M8yF0J8JQkefp35w+rT977HG94criur+nqfrWXOzp7uXh13boNo+Z0A0AGCVCNwCglCYxdEfRxkN3mGX6Dyef0jdeuaKf3rtXD9x6i/7b3Kwk6clqqO84ekRvuPkmfbTZ0L87dUpvvHRpXd/XTBCv1ey/Dr3IUeiOCN0AgNGjvRwAUEqVSkWVSmWi9tf2ehvfzP2DZ8/q/nZb/+DwYX1kKj9Tu7tqevkT1ap+8OBBXaic0Y89c0ZfqtX0qRsMSIuKarOLNz+odAMAdhIq3QCA0pq0and3g+3lM0mib750Wb+8e3c/cEuDQWr14enlnqd/vW+fHq9W9dYLF2/4vc2xXS5eg04n39PtbXWi3AaZAW6EbgDAKBG6AQClNXGhe4OV7m+4ckVBlul35udWfh9v7enlqefpHfPzes3SkvYVAfNaer08+Lo4us1VpTsqhsdxTjcAYJQI3QCA0jGVxokL3Z2N3ZYfWFrWX05N6VywcrdYp2gvr6VXh8ffnZ1RIOmlrdb1r6WYIB4UQ9ls6nU7kuxXumkvBwBsB0I3AKB0TPipVu0fV+VSrzdoLw+CGwe/2TTRM8HV41kG53RfHbqXKxUt+b7mbrBX3hzbFToI3Z2Oo9BdDFKTqHQDAEaHQWoAgNIxw9MmrtLdHbwXXq2miuPrvzfe9XzVs6vD+aVKRQ82Gjq1RiBXlqmWpv1ha9di9lW7CN3dbt5e7mpPd7ZGhwAAAJtF6AYAlM4gdNsNfDfffLPuvucenT1zRh/+8Ietri1JnaH28lot1Q06wPVItaoHlpdVyTIlQwH1iWpV33rs6Jpf88J2W6GkR25wFFi7qDYHQWV9Fz9Cna6bQWqmw4LIDQAYJUI3AKB0XO3p/v4feJte//rX69SpU3r1q15pdW1JKuaHSZLq9Ru3l79rfk5vvnxZr1xa1vtnpte1xlsuXdKj1ao+doMjw7rFvupKxf6vCmZt2xikBgDYDuzpBgCUjqv2cjM8rFKxX93N+TJ5r1a7cfD7fL2uT9br+v5z5zS1jvPMn99q6bWLS3rH/NwNzyVrt9uSpGCtFvVt1inWts1UugEAGCVCNwCgdFxVus1RVS6C5mphdX0TtH/swH4djGP9ysmntOs6ofH+5ZZ+8amn9fFmQ78zP3/D79suetttH9sluRukFvWPUaPSDQAYHUI3AKB0THuv/Uq3aal2VekeqNXWF7q/VKvpO44c1vEo0vsee1w/dvoZPbfT0XySaCGO9brFRf36iRP6LydP6rP1uv7RoUOK1hFm2213z4XpOJCker1ubV0zvZzucgDAKLl/Kx8AgFUyR5VuU2F1GbqzLO/8rlXXn/w+22joG246rm++dElvunRZb758ecW/f6Je1z89cEB/NDujeJ3V45bDSrcZ4iblobvTsbPH27SX262vAwB2OkI3AKB00qLUGNoO3UV110XQNEyVtbrO9nLjXBDoPyws6Jf37NG97bZ2JYkiz9OJMNSjtY2fd77cWpZkv8VbklrLy/2PazWLle5ikNqN9rsDALARhG4AQGnZr3TnA7xcV7olqbqBSvew2PP0YLO55etot/LnwuWebklqNq8/ZX2UzJ5uF280AAB2LvZ0AwBKx9We7k7HzfnQw7IsXzsM3W4sXl5ekuTmuUjTtP8zULO4pztiejkAYBsQugEApWU7dLfb7vYxG1nRVV5d5yC17bK0tOR0fcPFIDUq3QCAUSJ0AwBKx2Seamg3dLeK86Fdhq60KHCHgetK92BPt8s3IRo2Q3dM6AYAjB57ugEAJZSHHtuV7o9+5CP65//8n+nKlUWr6w5LU09Stuk93aOyOFTpnp6e1pUrV6yun2WZPM9TbRND4DYrivJz2n1CNwBghAjdAIDSMZVG26H74sWLes+73211zdWK09IUON7TvbQ4eONhamrKeug2bIbufqXbYWUfALDzcFcBAJSO7yh0l0Fe6XbfXt7pdPrDzGZmZqyvnxbvPtgcpEZ7OQBgOxC6AQClYyqNts/pLoM0yf8MQreD1IZNTU9bX7M/vdziOd1mernLPewAgJ2HuwoAoHRM6JnESndSkkq3NAi+zeaUs7VrNXs/A1EvP6eb0A0AGCXuKgCA0hmE7tDxldiXJHnodr2nWxoE3+mpprO1axbfeIljQjcAYPS4qwAASqdSqUia0Eq3Cd0lqnRPTdmvdJs93dWq/UFqhG4AwChxVwEAlM5kh+78z0rFfeg2wbfRsF/p7g9SczC93Pz8AQAwCoRuAEDpTHboLk+l2wTfpoP2crO2zWF6UZS3lxO6AQCjROgGAJROEASSpGo4eaE7jovQXYJKd1KU3V1Wum3u648i2ssBAKPHXQUAUDoTPb3ctJcHbq9DGoTuusWzslevbfNnwAxSAwBglAjdAIDSmsRzuqMovzWXYU/3oNLdsL52v9JtsdvB7OkGAGCUCN0AgNKaxEq3yX0V333oNiG04bDSHYY228updAMARo/QDQAorUkM3Umc35r9EszyiosQWrU4QdwwoTuwGLrNmgAAjBKhGwBQWpMYuqMoH6RWhvZyU+melD3dktTr9ayuBwDY+QjdAIDSmsjQXUwv90vQXt4rKt02z8o2+pXuwO5EOfZ1AwBGjdANACitiQzdPRO6HV+IpCjKq74uXgcTfm3u6ZbY1w0AGL0S3NIBAFjbJIbuOC5P6O71ij3dDkM3lW4AwLgrwS0dAIC12a5ylkGvODKsFO3lPXeV7iRxU+kmdAMARo3QDQAorWq1Ks/zXF+GVaa9vAwPu9vtSnLz5kccmz3ddse4xzHt5QCA0SJ0AwBKbdKq3b2oRIPUTKXbwWtg9lZXKnbby9nTDQAYNUI3AKDUbLc2/7f//rv6/ENf0H/61V+zuq7R6+a35jJUujudjiS7Z2Ubg0Fqlvd0R7SXAwBGi9ANACg126Hb9315nufkmCxJiqLy3Jq7JnRbHmYmOax0s6cbADBi5bmzAwAwxFQ6bYdul8dkSVKvRHu6O8Webheh27z+lYrtPd2EbgDAaBG6AQCl5Cp0m2OyQgdBU5K6JWovb7fbkuwHX2mwn9z22uzpBgCMGqEbAFBKSZJPr7Ydus30alcD3Lq9EqTtQrvdkuQmdMf99nKmlwMAxhuhGwBQSmmaSrIfurvdvMLqYniYJHU75bk1m0FqLkJ35Cp0F4PUzM8fAABbVZ47OwAAQ1xVuk1bs4t9zPn6g/Zy33cb/FrLeaXb9+3/utDrdZ2sbbY1ELoBAKNC6AYAlJIJPaH1Sre74WH5+oNbc73uNvgtt5YlOQrdjirdZl3zpg8AAFtF6AYAlJIJ3bWq3aO7eo5Dd2eovbxWcxu626280u05mOpmBtq5qnQTugEAo0LoBgCUUpZlkhzs6S7aml3sY5akortdkvtK95LD9vKoeCLsh+487NNeDgAYFTdv4wMAcAOuQvef/emfau/efXr00UesrjvgK8vyPd21WuboGnLLy0vO1jZt/uzpBgCMO0I3AKCUXIXuD3zgA/rABz5gdc1rCauuB6ktO1vb1SC1qGhrzzJCNwBgNGgvBwCUUz90uzm6qwxc7+leXFyUlO/pbjQaVtfuFu3ltveTDyrdbrsMAAA7B6EbAFBqtivdZVC836Ba1W3wM6FbkmZmZqyu3S3OCLcduiPaywEAI0boBgCUkombto8MKwMTup23lxfTyyVpamrK6truKt2mvZxKNwBgNAjdAIBSMmGLSrc7aZr2w+f09LTVtU2l2/qe7ojQDQAYLUI3AKCUJjt05489DMsT/KyH7mJ6uW1mTzehGwAwKoRuAEApTXToLrrKq44HqUmD8Nls2m0v7zja0x1HeegWoRsAMCKEbgBAKU1y6DaDs8PAffAzA8Wmpi2HbteVbierAwB2IkI3AKCUzF7eajiBoTst2ssd7+mW3Fe6bYuinpN1AQA7F6EbAFBK/dA9iZXuoqu8DHu6TaW7Ua9bXXf4yDCbw9Qi9nQDAEaM0A0AKKVKpSJpUkN3UekuQXt5kiSSpGazaXXdVqvd/7huMfCb9nIAAEaF0A0AKKVKJb9FTeI53WmecxWE7gepmdDdaDasrtvtDtrLmxbPCDeD1CzPbwMA7GCEbgBAKVX8ya10JyWsdNusNksr93TbbG0353RLpG4AwGgQugEApVQJAkkTGrqTPPBVShG688pvo2630j0cumu1mrV14zgP3URuAMCoELoBAKU0yXu6TeguwyC1qGi3tl3plgbDzJzs6aa/HAAwIoRuAEApDc7pDh1fiX1FR7cqFfeh24TQWs3dmx/1hr0qu5le7hG6AQAjQugGAJTaJFe6gxK0l5s9ztWa/Uq3UbO4tnm8vk/oBgCMBqEbAFBq1XDyQnccF6G7BJVuE0LrdXv7qo1+e7nNPd396eX8igQAGA3uKACAUpvMSnf+ZzHA3ako6kmSQgdvfpjQbbO1vT9IjfZyAMCIELoBAKU2ied0R1F+ey5De3mvl4duF29+uByk5vv8igQAGA3uKACAUnMR9t7znvfqwx/5qN74pm+2vrYkmQHaFd996O523Ydum/vJB3u6+RUJADAagesLAADgelyEvWPHj2t6elrHjh21vrYkJXEe+MrQXm4q3WFo/1eGNE0l2f0ZiPqVbtrLAQCjwdu4AIBScxG6k2JTtYuzqSUpivLAV4Yjw7qdjiQpCOwf3TZoL7c/SM0vwzseAIAdgdANACg1J6G7fza1o9BdTC/3S9Be3unmoTsM7YduF5VuM0iN9nIAwKhwRwEAlJrv+woCu63Ncb/Sbf+YLEmKeiZ0O1l+hU6nK0kKKvYrv/3QbXFyutnTXXHweAEAO1MJbucAAFyf7Wq3mWBdrboJ3XFcotDdbkuSKpbf+JBcVbqZXg4AGC3uKACA0rMdugft5W5Cd684MqwM7eVtE7pdVrotvg6EbgDAqHFHAQCUlgldts/qNhOsXexjlgbt5V4JBmh3ikFqTkJ30eZv83Uw7eUAAIwKoRsAUFr9Nm+Le3qlQfByMcRNknqRCd3uK93LrZYkN5XfmNANANgBCN0AgNIyR3fZDr/OQ3fXtJc7WX6FdmtZkpvQPRikZi90mzd6AAAYlRLczgEAWNsgdNtt837ooYd05swZffGLX7C6rhFF5bk9t4o93S5Cd5KYgXb2Xv80TfthHwCAUbA/ihQAgHVyMb1akn70X/xzq+ut1ivRnu7lpSVJkufgYpIkf/0Dy9sL4jh21uUAANh5yvNWOgAAq6SO2std6xbt5WUI3UvLeXu5i9BtWr1tn9POvm4AwCgRugEApeWq0u1at1eCtF1oFaHbhaQ/RZ7QDQAYX4RuAEBpTWzo7pTn9rw01F5u+3VwMb1cYpgaAGC0ynNXBwBglTTLj8yatNDd6w3ay33f7VCvxcXF/sdTU1NW1zYV50rFbqWb0A0AGCVCNwCgtLIidIcTFrrNnm5Jqtfdhu7lofby6elpq2ub6eXs6QYAjDNCNwCgxCaz0t0Zai+v1dyG7l6v13/zY8py6DYV59By6KbSDQAYJUI3AKC0iqw3caG71xt8XKtl7i5kFduV7ijKw2/Fduim0g0AGCFCNwCgtMwpVbVqze2FWOf333Bw3V4uDdr8p5pNq+sO9nRX7K5LpRsAMEKEbgBAieWpe9Iq3cPCaolCt+VBanGUl/yth24q3QCAESJ0AwBKy5xWPcmh2/WebmlwdJvt0N2L3AxSY083AGCUCN0AgNLyvMmtdJv28lrV/Z5uU+muNxpW1+0Vm9t93+6vK4RuAMAoEboBAKXlFWFr0o4Mkwahuwzt5UmSSJKajcnY0x3H+bqmwg8AwFYQugEApTWodIeOr8S+MlW6+6Hb0SA125Vus6553AAAbAWhGwBQWiZsTWZ7ef6GQxi6D92m4luv162uG/XcDFKLi73khG4AwCgQugEApWXC1kSG7qKzuVqCQWpmj3O9YTd0d7tdSQ4q3cXjpb0cADAKhG4AQGlNcqU7NXu6A/eVbhO6G7YHqUVuB6kRugEAo0DoBgCUVr/SHU5g6E6L9vIS7Ok2IbRarVld19X08qgI+4RuAMAoELoBAKVlzmeeyEp3kffKsKfbDBazvae7183DrxmoZwuVbgDAKBG6AQClNcl7uvuV7hK0l5vQXavZrXR3HO3pNoPUsozQDQDYOkI3AKC0fIfndP/HX/pl/cZv/qYOHjxofW1JSovB2UHgPviZ0G376LZetyPJZaXb/RseAIDxF7i+AAAAbsRFpfuBBx6Q53k6fvy4Tp06ZX39JC3PkWFmb7XtPd3dnpv2cvMmQ0Z7OQBgBKh0AwBKz0XozrI87DabTetrS1KS5EGzUoL28l4vb/O2/Tp02m4q3VFchG6rqwIAdipCNwCg9FyGbtvDwwwTustQ6e4WA83CwG6DnNnT7aq93PwMAACwFYRuAEDpuQzdDWeV7vzPSsV98OsWe6tt760369o2GKTm/rkHAIw/QjcAoPRchG5zXFSj0bC+tjSodAclaC/vdvKKc2C50t3tuAndZk83DeYAgFEgdAMASs/21GzJfeiO4yJ0l6LS7SZ0t9ttSe7ay8ncAIBRIHQDAEovDO1XupOiv9vdnu78T7/iZPkVTPi1Xukuwr5kt9shZpAaAGCECN0AgNJz2V5er7updEdRfosuQ3t5u5OH7krF7jsA7aH2cptT5CNT6QYAYAQI3QCA0gvD0HqLsal012r2A78kmdxX8d2H7k4Rfm2H7k5RYZekWs1ex8FgTzcAAFtH6AYAjAXrZ0R3OkrTVGmSWl3XSOL8Fl2K9vJWS5KD0D1U6bbZ5m+ml9t+owcAsDPZ3ZwFAMAmVavVFXt8t9urXvmAtbXWEkV54CvDkWHLrbzi7Pt236uP41hZlsnzPDUaFkN3TOgGAIwOlW4AwFhwsa/bpaiYXu6XoL3cVLpdhtC6xSnyZpAakRsAMAqEbgBAqZmq46SF7jgyodvxhUhabi1Lsl/pHlav1ayt1d/TTaUbADACJbiVAwBwbZMauqMyhe6lJUluKt1Zllf6azZDd/Ez5/uEbgDA1pXgVg4AwLWZKeKTFrp7xZFhnue+vbxVtJe7VLU4vXywp5tfkwAAW8fdBABQaua87HDCQnfUK0+le7EElW6rg9SYXg4AGKES3MoBALi2ya1054GvDJXupcXF/sfT09NW1zZvutQsvv5R1JPkdg87AGDn4G4CACg1E7omLXRHveKc7hLcqZeKSrckTU3ZDd2m0l2t2tvTHff3dJfgyQcAjD3uJgCAUuuH7nCyQnevV55b9PCe7pkZN6G7Xid0AwDGE3cTAECpDSqdkxa6TXu54wspmNdhembG6rqDTgcX08v5NQkAsHXcTQAApTapobvbNdPLHV/IKlPNptX1XAzSi3r5Od2EbgDAKHA3AQCU2sSG7l650rZ5HZqW93QPKt2htTXjOA/dlUrF2poAgJ2L0A0AKLd+6LYXusqg1y3XLdqE36kpN5XuWs3+nu4gCKytCQDYucp1RwcA4BomrtI91F7u+6njqxmE32ajYXXd/pFxoc1Kd2xtLQDAzkfoBgCUW7Gp2eae3jLoDlW66/UShW5Xe7otTq+PosjaWgCAnY/QDQAYC5NW6e50BrfoWs196DYV54bl0G3WDS1WugndAIBRInQDAErNKyrdkxa6e73Bx7Va5u5CCqbiXK/bbS9PTei2+PrTXg4AGCVCNwCg1CY1dEu+mSFXivZyE0Qblvd0xw4q3VmW9SvsAABsFaEbAFBq5qzkyQvdA2G1RKG7Xre67qC93O4kcVrMAQCjQugGAJSa7+eV7lrV3pFRZVOrum8vN+HX5tFd+bp52Lc5SE2ixRwAMDocQAkAKDVXle7Z2Vl97dd+rfxKRf/17W+3uraRZfnw9jIMUjOV35rlSncUuTkzO46pdAMARoPQDQAoNd+vSLJ/ZNiznnW7/sWP/piyLHMauqVytJf3Q7ejSrft0G3CPgAAW0V7OQCg1CqVPHTbrnQvt5atrrcWE7rL0F5uQrft1yGO87Z2+6GbSjcAYDQI3QCAUnMVulvLeej2PM964DOyLN/PHobuQ3evOMPMdqXb7K22315OpRsAMBqEbgBAqQ1Ct70joySp1W73P56enra6tpEVXeVhCSrdJnTbfh1MxZnQDQAYV4RuAECpOWsvX1rqf2z7bGojLbJ2NXS/p9uEbpvnZUuD0G1+DmxhkBoAYFQI3QCAUvO8vMXaduhuD1W6p6amrK5tpGnRXl6CSne325Xk7uiuwHLoZk83AGBUCN0AgLFQtRz2JCkrJpm5C935n2XY093pdCRJoe02b1Pptr4u7eUAgNEgdAMAxoLtSvewZrPpZN1+pTtwH7pNpdt2+DVt7bbbyyP2dAMARoTQDQAYC7bP6ZYGle5m01GlOz8tS0Hgfk93t5O327s6usv37f7KYtrazc8AAACbRegGAIwFF5VuE7gaTTeD1JK0PEeGtdt5e7nt0N2LHFW6i7CfJInVdQEAO4+bg0cBANggF6H7H37f90qSPv7xj1tfW5KSJA/dlRK0l5s93bbDr6v2cjO9PE3ddxkAAMYboRsAMBZchO4///M/t77msKTfXu4+dLfaLUn227x7PUft5cUgNUI3AGCraC8HAIwFl4PUXDGV7lKE7lYeuq23eReVbld7utOU9nIAwNYQugEAY6FWq7m+BOvKFLrNueX2K91dJ+sO9nRT6QYAbA2hGwAwNsIwdH0JVsVxEbor7kP38vKyJMnzPKvrmqPKrIfumPZyAMBoELoBAGPDxbFhLpk93b7dju41LS85Ct1Fe7ntdc0gNY4MAwBsFaEbADA2quFkhe4yVbrNnm77lW5XoZtKNwBgNAjdAIDSM2clV6uT1V4eRcWRYSUI3cvLS07W7Xbzo8qsh+5iejmVbgDAVhG6AQClNwjdk1XpTuL8Nl2G9vLFxUVJefit1+vW1u123IRuM0gto9INANgiQjcAoPQmNXSXq9K93P94amrK2rpmkJqz9nIq3QCALSJ0AwBKb2JDd7Gn2/fdB7+lpUF7+fT0jLV1XYXuqBikBgDAVhG6AQClN6mhO45M6HZ8Icorv2Z/88zMtLV120V7uW2m0s2ebgDAVpXgNg4AwPWZCdKTFrqjEoXuYc1m09paHUeh2+zplgjdAICtKdltHACAq01q6O5F+W3a88oR/EzVd3raXqV7eJCab/HdBzO9HACArSJ0AwBKL5vQ0B31ylXpNqG7aXGQ2nCl2+bUdNNeTqEbALBVJbmNAwBwbWaCdDhhobtXtJeXpdJtOg4aDXvt5e12u/9xo9Gwtm5cDFIrxzMPABhnhG4AQOmZCuvkVbpNe7njCymY0D3laE+3zUp3r9jTbXtqOgBg5yF0AwDGwGSG7l5JQ3ezaa/iPBy6ay7aywEA2CJCNwCg9MypTdVw0kK3aS93fCEFc3RbvW4vdEtDe8lttpcXg9SodAMAtorQDQAoPZN7Jq3S3e2Wq9JtQnfDYqV7WK1ms9Id3fiTAABYB0I3AGAM5KnTRej2fd/qXuJh3V5J0nbBtFzbrnQb9XrN2lrmnG6bx5QBAHYm7iQAgNIz0dN26P7+H3ibPvf5h/QXf/lXVtc1et1y3ab7obtmL/xKg/Zym2HfPFbaywEAW1WuuzkAAGswwcd26I56PUnuqp3D7eW+nzq5hmGm5dp25d+E7lrN3usfEboBACNC6AYAlJ5XhF7b53SbM6Jdh25Jqtfdh+6oGC5Ws9jmLQ0dGWexwm4Gqfk+oRsAsDWEbgBA6Q0q3aHVdV2H7k5nsG6tVobQnVe6bXcc9CvdVRd7uivW1gQA7EyEbgBA6ZnQazvsmTOiXbUYF93tkqRaLXNyDcNMu33VYviVhkK31XO689BNezkAYKsI3QCA0qtU8mqj7dDdclzplvzBGeVV95XubhG6a5ZfhzTNH7vNPd1mkBrTywEAW8WdBABQes5Cd6slqRzVzlop9nTnoTsM7bb5m9BdDe2H7gqhGwCwRdxJAACl1w/dFkOXJHWKSncZ1Kru28u73a4k+wPt+qHb4ro9s6e7wp5uAMDWELoBAKXnqtK93Fq2ut5aTHt5GQapdTtF6A4Dq+sO2sttTi/PQ3eF0A0A2CJCNwCg9Pqh22LokqTWch66Pc+zHvgNE7rDMuzpLirdQeCmvdxmW3u/vZzQDQDYIrtvVQMAsAmuppefPXtWf/SHf9gPmy70B6nZzblrahfT3IPAcqU7SSTZbWs3oRsAgK0idAMAxobt0N1ut/WDP/hPrK65WpZ5krJSTC/vdPI97rZDd+JgT7c5pxsAgK2ivRwAMDZctXi7lBVZOyzBIDVzbrntluukqHRXHbSXAwCwVYRuAMDYmMTQnfbby91XutvFEWr2Q3cegG3u6abSDQAYFUI3AGBsVMuwsdmyNM3PCC9DpbvVytvLfctnVydJ/oZDYPl8cKrdAIBRIHQDAMaG7XO6y6DYzqwgcB+6221HoTu2X+mWqHYDAEaD0A0AGBs2p1eXhal0V0P3oducW247dMf90G13gBuVbgDAKBC6AQBjYyL3dOczxBQE7vd0D59bblNcDFKzPTWd0A0AGAVCNwBgbExi6E7Mnu4SVLpbxSA166G7CL9BYHlPN+3lAIARIHQDAMZGEATWW5tdS5I84FZKsKd7aWnJybpxnIdf25Vu9nQDAEZhsn5zAQCMvUmrdif99nL3oXtxMQ/dnudZffNjUOm2HLppLwcAjAChGwAwViYvdOeV7jKEbjNITZKmp6etrRtFefi1fT44e7oBAKNA6AYAjBVCtztLi4v9j2dmZqyt66rSTegGAIwCoRsAMBbM/tpJC91xXOzptlvkXVOn01GW5eHfZuiOel1J9ivd7OkGAIwCoRsAMBaSYnPzpIVus6e7UnFf6R7WaDatrdVz1l5O6AYAbB2hGwAwFkzoDicsdJtKd1CS0G0q3dNT9vZ0m/BrPXRHtJcDALaO0A0AGAuTWumOItNeXq7QPTU9ZW3NbrcnyUF7ebGn2zxmAAA2g9ANABgLaZpKkqrhZIXuJM5v1X4J9nRLgwDabDSsrWn2Vts+o92sa97wAQBgMwjdAICx0A/dE1rp9v1yVFvN69CcslfpjnpuKt1mejmhGwCwFYRuAMBYmNjQHZervdwE0KbVQWp56LZd6TZ7yc3PHgAAm0HoBgCMBdPWXK2Gjq/Errhf6XZ8IQUTQOt1e+3l3W5+ZJj10F0MUiN0AwC2oiS3cAAArm8Quu1Wuv/lv/yX+tiDH9ev/KdftbquEZUsdPcr3Rb3dPeK9nLP86ytKQ32dBO6AQBbUZJbOAAA1+cqdB88dEjT09M6duyo1XWNXpTfqj2vHO3lZp9zo1G3tmav66q9nEo3AGDrCN0AgPHgKHT3itbmIAisrmtEvXJWums1i6G756a9PCJ0AwBGoCS3cAAArs/UeUPLoXtwRrSb0N0r2stLU+kuWq5rdXuhu9PpSLLfXs4gNQDAKBC6AQBjwQQu25XurvNKt2kvd7L8VUz1t24xdHcd7+k2WxsAANgMQjcAYKxYD92Ozog2emUL3UUQtfk6mDc+rFe6i+nlgz4LAAA2jtANABgLzirdRWuzu9Bt2sudLH+VqDgzu1arWVuz3XbVXm72dBO6AQCbR+gGAIyFfugO7YbujuPQ3e2Wq9Ld67modHesrTXMhG7RXg4A2AJCNwBgLJjJ1bYr3e12e8X6tnV7JUnbBXNmdhiG1tY03Qa29YqqPpEbALAVhG4AwFjwfbeD1FyF7l63XLdq83y42NNtW7/SDQDAFrgZxQoAwAa5qnQ/+uijeuqpp3T+/Hmr6xrD7eW+nypN3YZwE4BtVrpNt4HneapWq/1q+3YzoZvp5QCArSB0AwDGgu/ne6ptn9P9oQ99UK99zautrjmsO1TprtdTtVrlCN02j1BrD7WX1+t1e6E7otINANi6cvWsAQBwDWaQme1Kt2udzuBWXa2mDq8kZ/ZX2wzdnaLSLdk9H9wcj1auXfUAgHFD6AYAjIVJDd3DRd163X2bc9tF6B6qdDcaTWvrxnEeukszOh4AMJYI3QCAsTAI3fb2EpeD3z+xqgyVbhdHqMVx3N9X3WhYrHQzSA0AMAKEbgDAWJjUSvewWt196G63W5LcnVteq9WsrWUGqXlUugEAW0DoBgCMBRN8Jjp0V0vQXt6anNAd9Yo93YRuAMAWELoBAGOlGk5e6Dbt5WEZQnc7by+3fW75oL28YW1NKt0AgFEgdAMAxsokVrpN6K7VErcXImm5tSzJfug2qjV7e7rNIDVCNwBgKwjdAICxYvuc7jLoD1IrwQy55eW8vdxVpbtWs/f6U+kGAIwCoRsAMFYms9Jt9rO7H6TWKvZ02w6ig9BtcU93cU63q6o+AGBn4C4CABgrNkNXWWRF1i7Dnu7l5SUn66Zp/iTULbaXmyPDCN0AgK3gLgIAGCuTWOlOzSC10H2le2lxUZK7SrfN1z+m0g0AGAHuIgCAsTKRoTs17eVlqHQv9z+2OUncVLqthm4q3QCAEeAuAgAYK5MZuvM/g8B96F4sKt2SNDMzY23dfui2OEiNPd0AgFHgLgIAGDthWIIx3hb1K92h+9C9tDTY0z3tIHTb3NNvKt2VSsXamgCAnYfQDQAYO5NW7U6L47mDwP2ebmmwv3qq2bS2Zr/SHVqsdNNeDgAYAe4iAICxM2mhOykq3UEJKt3Dpqenra1lQrfNc9rNIDUq3QCArSB0AwDGRpLkJd+JC91JEbpLsKdbGqp0Wwzd5rWvWXztzZ5uAAC2gtANABgbkxu68z/LErpN1blpsb3cvPY29/ObPd0AAGwFoRsAMDYmN3SXs9LdbE5ZWzMtXvvAYug2P28AAGwFoRsAMDZche4f+ZH/Xe/87d/RN3/zm62ua5Q1dB/Yv8/amonZ0x0G1takvRwAMAqEbgDA2HAVul/z2tfoec97nl72spdZXXcgD7n1ejmml5sW73u/4j5ray4sLEiSvsLimhLVbgDA1hG6AQBjo39slOXQbfb2Vi2eET1s79684rqwUI7Kq6l025zq/eUnnpAknTnzjLU1JanT6VhdDwCw8xC6AQBjw4Tto8eOW1330qXL6nQ6ztqN49hzsu61XLlyRZL0xS9+wdqaZ8+dkyTVqnbf+GCYGgBgqwjdAICxYUL34UOHrK5bq1VVr9c1Pz9vdV3j8cfrkqTLl+ztZ76eJMmDaKPesLbmlcuX8zUtTkyXCN0AgK0jdAMAxoZp9b10+ZLVdXu9vMJt87iqYXGc3659e93c1xXHxZnZFtvtL166KEmq1+vW1pQYpgYA2DpCNwBgbLRaLUnShQsXrK4bx45Dd5S3l1cq5ZheboJovW4vdF84f16S3aAvUekGAGwdoRsAMDbMAC/bg9RMpTsI3LR3R0Xo9v1yhG4Xg+XOnstDt+3XgEo3AGCrCN0AgLFTDe2G7ijqSXJX6R6EbifLXyXq5c+Hzarz2WJquc2J6ZIUE7oBAFtUjoksAACsg1cM8bZe6e52JVHpNnr9NyHsvQ4nT55Uu922foQXlW4AwFYRugEAYyQPn7Yrzq7by3tRMUitJJVu83zULL758cgjj+i+r7jX2npGxJ5uAMAWleT2DQDAjZnTqm1XurtFpdt2a7NhKt1eSY7r7vXy5yO0/Dq4wCA1AMBWEboBAGPDK1Kn/UFqjkN3z1S6S9Je3nO7x90m015uhvgBALBRhG4AwNjwiv5q26G703G7p7vbK1elu1s8H2G483epmePi0jR1fCUAgHG18++WAIAdw1S6bbc1//qv/7o+/OG/0rlz56yua/SKSndpQnd/sNzkVLqTJHHW6QAAGG+EbgDA2PAdVbqfeea0nnnmtNU1h/W6JUnbhXYxQdxV5d+mOMr3dFPpBgBsFu3lAICxMQjdO7/COqxbukp3HronofI7XOkGAGAzCN0AgLFhQp7N86HLoNsp1+263W5LmpBKd0ylGwCwNeW6iwMAcB0mdE9cpXtFe7n78Ndp55VuvywHh28jU+kmdAMANmvn3y0BADvGIHRPVqV7eJBaGYrLrVZL0oS0l1PpBgBsEaEbADA2TDvzpIXudntwu67X3Yc/M0htEird5sgwzukGAGzWzr9bAgB2DFfTy10zlW5JqpUgdLday5IGR7jtZLSXAwC2itANABg71QkbpBbHvkyhtVZ1H/5Me/lEVLqLI8MkKt0AgM3Z+XdLAMCOE05YpXtYGdrLl5eWJE1apZvQDQDYHEI3AGDsVMPJml4+LKy6D39LReieBObIMLGnGwCwSYRuAMDYmcRKt8l89VoJKt1Fe/kkVbqJ3ACAzSJ0AwDGzqQNUpMGobsUle7Fxf7H09PTDq9k+0XF9HIAADaL0A0AGDuTGLqNMgxSG24vn5ra2aHbtJdzZBgAYLMI3QCAsTOJoducWFUtQXu5mV4uSTMzOzt0m/ZyAAA2i9ANABg7kxi6syzfP10Ny1FxNZXfqakpx1eyvcyRYROwfR0AsE0I3QCAsVOr1VxfgnWm0l2GPd3DbO/p/v4feJt+8T/+kl7+8ldYWW9Q6SZ1AwA2J3B9AQAAbEYQBIPjnCZAXunOVA3dt5dLeaXb8zw1mk2r677lLW/R/Py8Tp8+rQ996IPbvl5cDFIjcgMANotKNwBgLE1ai3m/0l2S9vK0uKCppt328k6nI0man5+3sl6/0k1/OQBgkwjdAICxNKmhOyhJ6DZ7um1Xus0Qt7m5OSvrmW6KSTiTHACwPQjdAICxYiqsoYPQ3Ww2nYX9JMlDX/kq3Q2r6y4vL0uyt5c8InQDALaI0A0AGCum8lgN7YbfBz/+CX38E5/U93zv91ld10jTInQH5QjdSZJIkup1u6G71+tJkqrV0Mp6pr3c9/mVCQCwOdxBAABjxYQ92xXnQcisW113sH7+Z1CSQWrm+WhYrnSbEFyp2JkFOzgyjEo3AGBzCN0AgLEyCN12Kp2GaaeuOWovT4v28qB0lW67b0KY0B0EdkJ3FOWVdSrdAIDN4g4CABgrJvzar3TnFU9Xle64dKE7fz4altvLbYdus52B0A0A2CzuIACAsWIqrKHlPd1x7Kat3Uj6odvJ8lcxz0etVrO6rtnTHVQqVtZjTzcAYKu4gwAAxoo5qsp2e7mpeNoOmYP18z8rlXJUuk0YrdftPh9R1FOWZbL1LJjXvWIp5AMAdh5CNwBgrLhqLzfhy8VRZdJwpbtcobtq+U2If/rDP6w7n/NsvebVr7KyXo9KNwBgi7iDAADGSuY4dLtqL4/jPHSXpdIdF2HUVeXflqhoZ6fSDQDYLEI3AGCsDNrL7YbffmXXdeguyZ27F5nzsnd46O4fUUboBgBsTklu3QAArI+p89pu8+63l4d295L314/KVenu9Yo3IRw9H7aY9nIAADarJDNQAQBYH1eV7t/57Xfq0UdfqI985MNW1zWiOH+f3C9N6O5KcrfH3RbTXg4AwGYRugEAY8l2xfld73qX3vWud1ldc1hUVLrLMs/LHN3lqvJvS0SlGwCwRSW5dQMAsD6el4dPV3urXRmE7nJUurudotId7vz373tUuwEAW0DoBgCMpUkN3WWZ59Xt5qE7CHZ2pVui2g0A2BpCNwBgrExqpTuO8lu255Wj0t3udCRJQbDzK91RRKUbALB5hG4AwFjph+5wskJ3t1euPd3dbh66J+EorahHpRsAsHkluXUDALA+fpE6J63SbdrLi/ccnGu325Imo9Ldo9INANgCQjcAYKz4/mS2l/e65Wov77TzSrdfltL7NmJPNwBgK3b+nRIAsKOYkLfTj6pardcrV6W71WpJor0cAIAbIXQDAMaK7+chb+Iq3T1T6XZ8IQUzSI1KNwAA17fz75QAgB2lUikq3RMWurvdcoXuVmtZ0mCw3U7Gnm4AwFYQugEAY6UyoZVuM728LJaX8tBNpRsAgOvb+XdKAMCOUimmZU9c6O6U65Y9SZVus6c7TVPHVwIAGEfluoMDAHADZnDXpIXu4T3dvu8+/C0tLbm+BGtMe3mSJI6vBAAwjgjdAICxYiqr1epkTS/vDFW663X3oXu5mF4+SZVuQjcAYDMI3QCAsRSGk1XpNoPUJKlWcx+6lxYX+x9PT087vJLtZ/Z0E7oBAJtB6AYAjKVJq3T3hgZo12qZuwspDLeXT03ZDd2vec1r9Q3f+I2an5+3sh7t5QCArSB0AwDG0qTt6ZZ8ZUXWLkN7eatoL5ekmRm7ofv//oVf0E/+5E/p/vvvt7Ieg9QAAFtB6AYAjKXqhLWXDwur5Qh/WfEuwNTUlNV1Tfit1xtW1jPt5VlWjucdADBeCN0AgLEUTlyle6AMe7qH2d7TbcJ+tVazsp5pL09T9239AIDxQ+gGAIylyWsvV7+9vAx7uqVB+G00m07WrdfthG7TXk6lGwCwGYRuAMBYchG6P/rXH9PHP/FJ3XzzzdbXlgahuxqWI/yZNu+ppqv28rqV9Ux7OZVuAMBmBK4vAACAzQhD+9PLZ2Zm5Hme5ixNzV6tH7onvNJtQnfNVnt5MTrePF4AADaCSjcAYCwFQSDfd3Mbm7IcMo009SSVsdJtZ6DZ6nVtDdMzlW4AADaD0A0AGFu2W8wHe4nthszB+vmf1Wo5Kq7m3Grbz4ftSvcgdJfjeQcAjBdCNwBgbDkL3Q07e4lXM8dEB2E5wp8J3Q3LlW6zrq3p5fv375fk7s0WAMB4I3QDAMaW7dBtKqzNhpvwZdrLayU7p3tubs7quiZ01yy9/rt27crXsxTyAQA7C6EbADB2TLuvq0p3zdLU7NUqlXz93XvKscfY8/I3Afbt22913YsXLqjb7fbX326PP/G4JKnb7VpZDwCwsxC6AQBjJzXtxY4q3Q1HlW6zl/vQoZ6T9VdbXFxUFEVqLS9bXbfeaKhWq2nWUoX90UcelTSYYg4AwEZwZBgAYOx4xdTyg4cO6YknnrC27tmzZzU3NyffUoV1tTSVfF9KEjfrr/bzP//zajab+vKXv2x1XRN+bb350em0JcnZtHwAwHjj7gEAGDtBkL9nvGfPHqvrzs/Pa3Z2VjOzs1bXNS5dyh/3M8/YP6N8LVmWV/5ttXkbps27Xrezx7rd6UiSKpWKlfUAADsLoRsAMHZM6Fq23NY8OCLLzZ5uU+EOStKnZo4ws10BNq9/rWondHfaeaWb0A0A2AxCNwBg7LSLEOQqdNdqbkJ3HOehOwzLNb3cdqX7s5/5jE49/bQ+9/nPW1mvU1S6g7K82wEAGCvcPQAAY8cMNKuGdgepxXEsSao7OjqqfJXuPHT7vt3Q/ZM/+a/1kz/5r62tZ97kCcNytPUDAMYLlW4AwNgxYc/29HITul2d11ws3z86zLV+6PZ29q8Tpp2dSjcAYDN29l0SALAjuQ7dVWeh21S6yxW6bbeX22Yq3QAAbAahGwAwdtyF7sjJuoP1i9Bdskr3Tg/dptINAMBmELoBAOOnH7rt7rGNonKE7rK1l3uW93S70Gq1XF8CAGBMEboBAGPLdviNory93NVArTjKb9t+SU6ucnVOtwtmgjkAABtF6AYAjB1T5w2th+6eJClwdF5zFJW00r3DB6lJg7O6AQDYKMZwAgDGjqms2q50/89vfaviOO4fWWabCd2+X5LQnU7Gnm5JalPpBgBsEqEbADB2XIXuXq9ndb3VBpVup5fRNymD1CSp06HSDQDYnJ3fDwYA2HFchW7Xur38tu155ah0pxO0p7vdptINANgcQjcAYOxMaugetJc7vpDCJFa6zVntAACsV0lu2wAArJ9fpM5qOFmhu9ctppeX5O6dTtCe7k5R6SZ0AwA2qiS3bQAA1q8fuies0t0rWXv5JB0ZZgapEboBABtF6AYAjJ1KMUls0kJ3t5eH27Jk3MmqdNNeDgDYHEI3AGDsVCr57cv2Od2uDSrdji+kkKWTV+lOksTxlQAAxg2hGwAwdir+ZFa6e91yVbr7g9QcX4cNZpCaqzPaAQDji9ANABg7lSCQNHmhu9st123bHBlWmncBtpEZpJYRugEAG1SuuzcAAOswqXu6O51y3bYn6ciwdlHpLscIOwDAOCnX3RsAgHUYnNMdOr4Su0yl2/OkIHBfcU0naE93v9KdEbsBABtD6AYAjK1JrnTX62UI3ZMTQE2lGwCAjSJ0AwDGVjWcrNDdbg9u27Wa+9Dtcnr5j/7Yj+sd7/xt/e2v/3or65lKt+/v/Ko+AGC0CN0AgLE1aZXuOJZMd3MZQrcZpOaiu/y1r32t7r33Xr3kxS+xsl63m4duz+NXJwDAxnDnAACMrUk7p3v4tl2u9nL7qTuKIklSvdGwsl67qHSbM+IBAFgv7hwAgLE1aZXuYbWa+/3ULtvLo6gnSWpYCt3mnG5zRjwAAOtF6AYAjK1JDN2lai93eGZ1r2tCd93Kev1Kd3FGPAAA60XoBgCMrUkO3dWq+9CdZe4q3Z1uV5JUq9kJ3abSHRC6AQAbROgGAIytSQ7dtbr79vIkcRf8u0XotvUz0O7kle4wnKyz4QEAW0foBgCMLd/3rVceX/qyl+v1b3iDpqenra5rZFleVS5DpTvN3AX/ThGCa7WanfXaxZ7uCnu6AQAbQ+gGAIw129XuX/mVX9HP/uzP6fnPf77VdY1Be7n7SneWJpIctZcXIbhatVN5NiEfAICNInQDAMaa7dCdFam30WxaXdcws8uqYQkq3Q4HqbWLPdZhaOf1j6JIcRxbWQsAsLMQugEAY8kEPttndZvQ3bR0VNVqaWray91XupPU3TVcOH9BSZKo3W5ZW5NqNwBgMwjdAICxZKqOVUuVTsOE/UbDbaU7CN2H7tRhe/lP/dRP6q7n3qnXf/VXW1uzXbS0AwCwEYRuAMBYSpI88LlqL6/X7QzwWi1Nikp3CdrLM4ft5S5Q6QYAbAahGwAwlgah2+4RTmbdet1Ne3lSqkr3hIVuKt0AgE0gdAMAxpIJfLYr3WbdeqNudV0jKSrdYUDotq1NpRsAsAmEbgDAWHLVXj6odLsN3WWodJvnYlJ0ionpmcPzyQEA44fQDQAYS64q3UkxwK1Wc1zpLsGe7kmrdHfaeaV70t5sAABsDaEbADCWTLXRduiO+5VuN4PUkuKo6CBwsvwKkxa6zdngURQ5vhIAwDgpwS0bAICNyxxVuv/Vv/o/tG/vXj344INW1zXiotJdqbhvcU6KqW4ujgxzwVS6zXF1AACsB6EbADCW0qLSHVoO3X/yvvdZXW+1OC72dJdikNqgzdr3/R1f+TaD1AjdAICNoL0cADDWbFe6XeuH7hJUuodDtu/v/F8pzCC1nf7mAgBgtHb+HRIAsCO52tPtWhSVqL18KHxWKju/ea7TYZAaAGDjCN0AgLE2aaE7KSrdfsXxhWiwr16SKpWd/yuF2dPNkWEAgI3Y+XdIAMCOZGZ3VcPJCt29KL91l6HSPby3eZLaywndAICN2Pl3SADADpWn7kmrdJv2ct8vV/DzKyUovW8zM0gNAICNIHQDAMaSOaZq4kJ3z4RuxxdSMFXfiah0F+3lk3JEGgBgNHb+HRIAsCP5kxq6S1rprkxA6G4X7eWEbgDARuz8OyQAYEfyipBn+5xu13q9/HGXLeNOUqV7Eh4rAGB0uGsAAMaSCT6TVunulay93PDLME59m5lBaoRuAMBGcNcAAIylQegOHV+JXabS7Xnlai/3J+DIsHZR6Q6CnX8mOQBgdHb+HRIAsCNVimnZk1bp7haV7rJtK/bLdkHbwFS6KxMwqR0AMDq8VQsAGEsTG7q7+eMuTcbNMsnznLRcv/zlr9Dz7n2e/uazn9UHPvCBbV/PVLrDcLK6KwAAW0PoBgCMpUkN3VHJKt2Z8hPTXZzT/U9+8Af1nOc8R5/85CeshG5T6Z60nzkAwNbQXg4AGEtmX201nKwA1O2WJG2v4qLS3enkledatWZlvXaxHgAAG0HoBgCMpUmdXt7plvPW7Xv2r6vb7UqSqjU7obtL6AYAbEI579wAAKzTpJ3T3ekM9nT7fur4aqQsy6eo+779Cvyg0m3nZyBJEvV6PStrAQB2DkI3AGCsTVqlu9sZ3Lrrdfeh23Cxp7vdzvdY23zjxawJAMB6EboBAGNt0kJ3Zyh0NxruQ7epdHsOJru1Wy1JUhjamwvbocUcALBBhG4AwFibtNA93N1cq7kP3UbFwSC1VjsP3UFg7wivDpVuAMAGEboBAGPNdug+fvwmfe7zD+nzD33B6roDvoricinay/uVbt9+e3lr2YRue5VuJpgDADaK0A0AGGu2Q3ev15Xv+/I8T41Gw+raq9VqmdP1h/kO2suXlpckDc5styGKGKQGANgYQjcAYKzZDt3Dg7Smpqetrm0sL+9RqzWvPXvcn9ltjm6bnZuzvvby0tKKa7Dh2LHjkqQ0dd9lAAAYD/b6sQAA2Ab1et3qeq1WS5/73Od04cIFNR1VuqemzsvzpOPHp/QXf+HkEvpM4J2amrK+9h/8wR/oi198WBcunLe2ZhRFkvLQbTPsAwDGF6EbADDWKpWKfN+3VnmM41jvec97lGWZZmdnray5WpoGqlRiNacSJ+sPi6NIYbWqLLNf+b1y5Yo+/vEHra75sY99TG94wxvU63Wt7iUHAIwvLzMTUAAAAAAAwEjRFwUAAAAAwDYhdAMAAAAAsE0I3QAAAAAAbBNCNwAAAAAA24TQDQAAAADANiF0AwAAAACwTQjdAAAAAABsE0I3AAAAAADbhNANAAAAAMA2+f8BaajNZ4XC0AcAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "argoverse2_sensor_dataset_teaser(args)"
    ]
@@ -404,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "undefined.undefined.undefined"
   }
  },
  "nbformat": 4,

From 23eac3f8bfac981883a50bf7279d73b0680bc482 Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Wed, 12 Mar 2025 01:05:41 -0400
Subject: [PATCH 21/22] Linting

---
 tests/unit/evaluation/scenario_mining/test_eval.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/unit/evaluation/scenario_mining/test_eval.py b/tests/unit/evaluation/scenario_mining/test_eval.py
index 74670ea7..6b99e3b7 100644
--- a/tests/unit/evaluation/scenario_mining/test_eval.py
+++ b/tests/unit/evaluation/scenario_mining/test_eval.py
@@ -31,7 +31,9 @@ def test_evaluate() -> None:
     with open(gt_pkl, "rb") as f:
         ground_truth = pickle.load(f)
 
-    metrics = evaluate(predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out)
+    metrics = evaluate(
+        predictions, ground_truth, objective_metric, max_range_m, dataset_dir, out
+    )
     print(metrics)
 
 

From e634ab8a9bd45989ec7d99c0740f6a8d025eafcd Mon Sep 17 00:00:00 2001
From: Cainan Davidson <crdavids@andrew.cmu.edu>
Date: Wed, 12 Mar 2025 10:33:55 -0400
Subject: [PATCH 22/22] Restored map_tutorial.ipynb to previous version

---
 tutorials/map_tutorial.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb
index 0f343752..179f1534 100644
--- a/tutorials/map_tutorial.ipynb
+++ b/tutorials/map_tutorial.ipynb
@@ -339,7 +339,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "av2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -353,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "undefined.undefined.undefined"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,