From 06fe5fecc5b3e8aa8ee5b8c0bf3f1d1691e4fe06 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Thu, 2 Apr 2026 07:39:09 -0700 Subject: [PATCH 01/25] initial benchmarking and inference layer --- .gitlab-ci.yml | 4 +- CHANGELOG.md | 14 + Makefile | 2 +- README.md | 3 + physicsnemo/__init__.py | 5 + physicsnemo/cfd/bench/__init__.py | 9 + physicsnemo/cfd/bench/metric_registry.py | 30 + physicsnemo/cfd/bench/metrics/l2_errors.py | 2 +- physicsnemo/cfd/evaluation/__init__.py | 1 + .../cfd/evaluation/benchmarks/__init__.py | 6 + .../cfd/evaluation/benchmarks/__main__.py | 6 + .../cfd/evaluation/benchmarks/engine.py | 338 ++++++++++ .../cfd/evaluation/benchmarks/report.py | 125 ++++ .../evaluation/benchmarks/report_plugins.py | 37 + physicsnemo/cfd/evaluation/benchmarks/run.py | 45 ++ physicsnemo/cfd/evaluation/common/__init__.py | 6 + .../evaluation/common/checkpoint_compat.py | 33 + .../cfd/evaluation/common/interpolation.py | 58 ++ physicsnemo/cfd/evaluation/common/io.py | 165 +++++ physicsnemo/cfd/evaluation/config.py | 277 ++++++++ .../cfd/evaluation/datasets/__init__.py | 19 + .../evaluation/datasets/adapter_registry.py | 56 ++ .../evaluation/datasets/adapters/__init__.py | 10 + .../cfd/evaluation/datasets/adapters/ahmed.py | 29 + .../evaluation/datasets/adapters/drivaerml.py | 220 ++++++ .../cfd/evaluation/datasets/gt_alignment.py | 56 ++ .../cfd/evaluation/datasets/progress.py | 8 + physicsnemo/cfd/evaluation/datasets/schema.py | 78 +++ .../evaluation/datasets/vtk_ground_truth.py | 292 ++++++++ .../cfd/evaluation/inference/__init__.py | 23 + .../cfd/evaluation/inference/__main__.py | 6 + .../common_wrapper_utils/__init__.py | 19 + .../common_wrapper_utils/vtk_datapipe_io.py | 136 ++++ .../evaluation/inference/model_registry.py | 97 +++ .../cfd/evaluation/inference/progress.py | 8 + physicsnemo/cfd/evaluation/inference/run.py | 119 ++++ .../evaluation/inference/wrappers/__init__.py | 28 + .../inference/wrappers/domino/__init__.py | 17 + .../inference/wrappers/domino/inference.py | 631 ++++++++++++++++++ .../inference/wrappers/domino/scaling.py | 89 +++ .../inference/wrappers/domino/wrapper.py | 238 +++++++ .../inference/wrappers/fignet/__init__.py | 3 + .../inference/wrappers/fignet/wrapper.py | 230 +++++++ .../wrappers/geotransolver/__init__.py | 3 + .../wrappers/geotransolver/wrapper.py | 412 ++++++++++++ .../inference/wrappers/surface_baseline.py | 73 ++ .../inference/wrappers/transolver/__init__.py | 3 + .../inference/wrappers/transolver/wrapper.py | 335 ++++++++++ .../inference/wrappers/volume_baseline.py | 71 ++ .../inference/wrappers/xmgn/__init__.py | 3 + .../inference/wrappers/xmgn/wrapper.py | 194 ++++++ .../cfd/evaluation/metrics/__init__.py | 11 + .../evaluation/metrics/builtin/__init__.py | 19 + .../cfd/evaluation/metrics/builtin/forces.py | 142 ++++ .../cfd/evaluation/metrics/builtin/l2.py | 282 ++++++++ .../cfd/evaluation/metrics/builtin/physics.py | 116 ++++ .../cfd/evaluation/metrics/mesh_bridge.py | 112 ++++ .../cfd/evaluation/metrics/registry.py | 13 + pyproject.toml | 6 +- test/ci_tests/test_evaluation.py | 117 ++++ workflows/bench_example/README.md | 7 + workflows/evaluation_examples/README.md | 220 ++++++ .../evaluation_examples/benchmark_matrix.yaml | 37 + .../evaluation_examples/inference_config.yaml | 63 ++ 64 files changed, 5812 insertions(+), 5 deletions(-) create mode 100644 physicsnemo/__init__.py create mode 100644 physicsnemo/cfd/bench/__init__.py create mode 100644 physicsnemo/cfd/bench/metric_registry.py create mode 100644 physicsnemo/cfd/evaluation/__init__.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/__init__.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/__main__.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/engine.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/report.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/report_plugins.py create mode 100644 physicsnemo/cfd/evaluation/benchmarks/run.py create mode 100644 physicsnemo/cfd/evaluation/common/__init__.py create mode 100644 physicsnemo/cfd/evaluation/common/checkpoint_compat.py create mode 100644 physicsnemo/cfd/evaluation/common/interpolation.py create mode 100644 physicsnemo/cfd/evaluation/common/io.py create mode 100644 physicsnemo/cfd/evaluation/config.py create mode 100644 physicsnemo/cfd/evaluation/datasets/__init__.py create mode 100644 physicsnemo/cfd/evaluation/datasets/adapter_registry.py create mode 100644 physicsnemo/cfd/evaluation/datasets/adapters/__init__.py create mode 100644 physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py create mode 100644 physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py create mode 100644 physicsnemo/cfd/evaluation/datasets/gt_alignment.py create mode 100644 physicsnemo/cfd/evaluation/datasets/progress.py create mode 100644 physicsnemo/cfd/evaluation/datasets/schema.py create mode 100644 physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py create mode 100644 physicsnemo/cfd/evaluation/inference/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/__main__.py create mode 100644 physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py create mode 100644 physicsnemo/cfd/evaluation/inference/model_registry.py create mode 100644 physicsnemo/cfd/evaluation/inference/progress.py create mode 100644 physicsnemo/cfd/evaluation/inference/run.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py create mode 100644 physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py create mode 100644 physicsnemo/cfd/evaluation/metrics/__init__.py create mode 100644 physicsnemo/cfd/evaluation/metrics/builtin/__init__.py create mode 100644 physicsnemo/cfd/evaluation/metrics/builtin/forces.py create mode 100644 physicsnemo/cfd/evaluation/metrics/builtin/l2.py create mode 100644 physicsnemo/cfd/evaluation/metrics/builtin/physics.py create mode 100644 physicsnemo/cfd/evaluation/metrics/mesh_bridge.py create mode 100644 physicsnemo/cfd/evaluation/metrics/registry.py create mode 100644 test/ci_tests/test_evaluation.py create mode 100644 workflows/evaluation_examples/README.md create mode 100644 workflows/evaluation_examples/benchmark_matrix.yaml create mode 100644 workflows/evaluation_examples/inference_config.yaml diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index b328c7f..2c176c7 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -48,10 +48,10 @@ test: stage: test before_script: - - echo "Not implemented" + - pip install -e ".[dev]" script: - - echo "Not implemented" + - python -m pytest test/ci_tests/test_evaluation.py -v --tb=short rules: - if: $CI_PIPELINE_SOURCE == "merge_request_event" diff --git a/CHANGELOG.md b/CHANGELOG.md index 7a49f19..089535a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,8 +11,22 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Added +- `physicsnemo.cfd.evaluation`: config-driven inference and benchmarking with + metrics delegated to `physicsnemo.cfd.bench` (L2, area-weighted L2, forces, + continuity/momentum residual L2 chain). Mesh bridge attaches GT and prediction + fields using explicit VTK names from config (`output.mesh_field_names`, + `output.ground_truth_mesh_field_names`, and volume equivalents). +- `physicsnemo.cfd.bench.metric_registry` for named metrics shared with evaluation. +- Example YAML under `workflows/evaluation_examples/`. +- CLI: `python -m physicsnemo.cfd.evaluation.benchmarks` and + `python -m physicsnemo.cfd.evaluation.inference`. + ### Changed +- Benchmark metric registration for evaluation now uses bench-backed built-ins + under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated + NumPy-only helpers. + ### Deprecated ### Removed diff --git a/Makefile b/Makefile index 526bdc1..6c73df6 100644 --- a/Makefile +++ b/Makefile @@ -27,7 +27,7 @@ doctest: echo "Not implemented" pytest: - echo "Not implemented" + python -m pytest test/ci_tests/test_evaluation.py -v --tb=short coverage: echo "Not implemented" diff --git a/README.md b/README.md index 875566d..d785cad 100644 --- a/README.md +++ b/README.md @@ -39,6 +39,9 @@ from a Python interface, facilitating scalable deployment of trained models. against traditional CFD results using a broad set of built-in engineering metrics (for example, pointwise errors, integrated quantities, spectral metrics, PDE residuals). [Related publication](https://www.arxiv.org/abs/2507.10747) + - The `physicsnemo.cfd.evaluation` package runs config-driven inference and + uses the same `physicsnemo.cfd.bench` metric implementations as the workflow + scripts (see `workflows/evaluation_examples/`). - Utilities to extend and build custom metrics, analyze, and visualize the results of trained ML models, both mesh-based and point-cloud based models diff --git a/physicsnemo/__init__.py b/physicsnemo/__init__.py new file mode 100644 index 0000000..f378d3d --- /dev/null +++ b/physicsnemo/__init__.py @@ -0,0 +1,5 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""PhysicsNeMo namespace; CFD extensions ship in ``physicsnemo.cfd``.""" diff --git a/physicsnemo/cfd/bench/__init__.py b/physicsnemo/cfd/bench/__init__.py new file mode 100644 index 0000000..489d5e0 --- /dev/null +++ b/physicsnemo/cfd/bench/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""CFD benchmarking metrics, geometry, and visualization.""" + +from physicsnemo.cfd.bench import metric_registry # noqa: F401 + +__all__ = ["metric_registry"] diff --git a/physicsnemo/cfd/bench/metric_registry.py b/physicsnemo/cfd/bench/metric_registry.py new file mode 100644 index 0000000..e3875b5 --- /dev/null +++ b/physicsnemo/cfd/bench/metric_registry.py @@ -0,0 +1,30 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Named metric registry (used by physicsnemo.cfd.evaluation and workflows).""" + +from __future__ import annotations + +from typing import Any, Callable + +MetricFn = Callable[..., float | dict[str, float]] + +_REGISTRY: dict[str, MetricFn] = {} + + +def register_metric(name: str, fn: MetricFn) -> None: + """Register a metric by name.""" + _REGISTRY[name] = fn + + +def get_metric(name: str) -> MetricFn: + """Resolve metric function by name.""" + if name not in _REGISTRY: + raise KeyError(f"Unknown metric: {name}. Available: {sorted(_REGISTRY.keys())}") + return _REGISTRY[name] + + +def list_metrics() -> list[str]: + """Return registered metric names.""" + return sorted(_REGISTRY.keys()) diff --git a/physicsnemo/cfd/bench/metrics/l2_errors.py b/physicsnemo/cfd/bench/metrics/l2_errors.py index 447cb2e..bdb4d00 100644 --- a/physicsnemo/cfd/bench/metrics/l2_errors.py +++ b/physicsnemo/cfd/bench/metrics/l2_errors.py @@ -16,7 +16,7 @@ import numpy as np import pyvista as pv -from physicsnemo.utils.sdf import signed_distance_field +from physicsnemo.nn.functional import signed_distance_field def compute_l2_errors(data, true_fields, pred_fields, bounds=None, dtype="point"): diff --git a/physicsnemo/cfd/evaluation/__init__.py b/physicsnemo/cfd/evaluation/__init__.py new file mode 100644 index 0000000..d29191b --- /dev/null +++ b/physicsnemo/cfd/evaluation/__init__.py @@ -0,0 +1 @@ +"""PhysicsNeMo-CFD: Inference and benchmarking framework for AI-CFD models.""" diff --git a/physicsnemo/cfd/evaluation/benchmarks/__init__.py b/physicsnemo/cfd/evaluation/benchmarks/__init__.py new file mode 100644 index 0000000..cdb3f7a --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/__init__.py @@ -0,0 +1,6 @@ +"""Benchmark engine and reporting.""" + +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.benchmarks.report import write_report + +__all__ = ["run_benchmark", "write_report"] diff --git a/physicsnemo/cfd/evaluation/benchmarks/__main__.py b/physicsnemo/cfd/evaluation/benchmarks/__main__.py new file mode 100644 index 0000000..612aad0 --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/__main__.py @@ -0,0 +1,6 @@ +"""python -m physicsnemo.cfd.evaluation.benchmarks""" + +from physicsnemo.cfd.evaluation.benchmarks.run import main + +if __name__ == "__main__": + main() diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py new file mode 100644 index 0000000..99f42d1 --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -0,0 +1,338 @@ +"""Benchmark runner: config → model x dataset loop → metrics → results.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import Any + +from physicsnemo.cfd.evaluation.benchmarks.report import write_report +from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins +from physicsnemo.cfd.evaluation.config import Config, ModelConfig, DatasetConfig, OutputConfig +from physicsnemo.cfd.evaluation.datasets import get_adapter +from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.inference import get_model_wrapper +from physicsnemo.cfd.evaluation.inference.model_registry import get_inference_domain_for_model +from physicsnemo.cfd.evaluation.metrics import get_metric +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh + + +def _normalize_metrics_config(metrics: list[str] | list[dict]) -> list[tuple[str, dict]]: + """Return list of (metric_name, kwargs).""" + out = [] + for m in metrics: + if isinstance(m, str): + out.append((m, {})) + elif isinstance(m, dict) and "name" in m: + name = m["name"] + kwargs = {k: v for k, v in m.items() if k != "name"} + out.append((name, kwargs)) + else: + raise ValueError(f"Invalid metric entry: {m}") + return out + + +def _effective_inference_domain(model_config: ModelConfig) -> str: + dom = model_config.inference_domain + if dom in ("surface", "volume"): + return dom + return get_inference_domain_for_model(model_config.name) + + +def _call_metric( + fn: Any, + gt: dict, + predictions: dict, + *, + case: Any, + comparison_mesh: Any, + metric_dtype: str | None, + output: OutputConfig, + mkwargs: dict[str, Any], +) -> Any: + """Call metric with extended kwargs; fall back for legacy (gt, pred, **mkwargs) only.""" + extended = dict(mkwargs) + extended.update( + case=case, + comparison_mesh=comparison_mesh, + metric_dtype=metric_dtype, + output=output, + ) + try: + return fn(gt, predictions, **extended) + except TypeError: + return fn(gt, predictions, **mkwargs) + + +def _run_single( + model_config: ModelConfig, + dataset_config: DatasetConfig, + metric_names: list[tuple[str, dict]], + device: str, + output_dir: str, + case_ids: list[str] | None, + output_config: OutputConfig, + *, + allow_skip_mismatch: bool = False, +) -> dict[str, Any]: + """Run one model on one dataset; return results structure.""" + adapter_class = get_adapter(dataset_config.name) + m_dom = _effective_inference_domain(model_config) + d_dom = adapter_class.inference_domain_from_kwargs(dataset_config.kwargs) + if m_dom != d_dom: + reason = ( + f"inference_domain mismatch: model expects {m_dom!r}, " + f"dataset adapter {dataset_config.name!r} is {d_dom!r}" + ) + if allow_skip_mismatch: + print( + f"[benchmark] SKIP {model_config.name!r} × {dataset_config.name!r}: {reason}", + flush=True, + ) + return { + "model": model_config.name, + "dataset": dataset_config.name, + "skipped": True, + "skip_reason": reason, + "cases": [], + "metrics": {}, + "per_case": [], + } + raise ValueError(reason) + + dkwargs = resolve_dataset_kwargs_for_model(dataset_config.kwargs, model_config.name) + adapter = adapter_class(root=dataset_config.root, **dkwargs) + log_dataset( + dataset_config.name, + f"Listing cases under root {dataset_config.root!r} (split={dataset_config.split!r})…", + ) + cases = case_ids if case_ids is not None else adapter.list_cases(split=dataset_config.split) + if not cases: + return { + "model": model_config.name, + "dataset": dataset_config.name, + "cases": [], + "metrics": {}, + "per_case": [], + } + + wrapper_class = get_model_wrapper(model_config.name) + wrapper = wrapper_class() + wrapper.load( + checkpoint_path=model_config.checkpoint, + stats_path=model_config.stats_path, + device=device, + **model_config.merged_kwargs_for_load(), + ) + + per_case = [] + all_metric_values: dict[str, list[float]] = {} + + log_dataset( + dataset_config.name, + f"Loading {len(cases)} case(s) from root {dataset_config.root!r} " + f"(model {model_config.name!r})…", + ) + for cid in cases: + log_dataset( + dataset_config.name, + f"Reading case {cid!r}…", + ) + case = adapter.load_case(cid) + model_input = wrapper.prepare_inputs(case) + raw = wrapper.predict(model_input) + predictions = wrapper.decode_outputs(raw, case) + gt = case.ground_truth or {} + + comparison_mesh = None + metric_dtype: str | None = None + try: + comparison_mesh, metric_dtype = build_comparison_mesh(case, predictions, output_config) + except Exception as ex: + log_dataset( + dataset_config.name, + f"Warning: comparison mesh not built for case {cid!r}: {ex}", + ) + + case_metrics: dict[str, float] = {} + for mname, mkwargs in metric_names: + try: + fn = get_metric(mname) + out = _call_metric( + fn, + gt, + predictions, + case=case, + comparison_mesh=comparison_mesh, + metric_dtype=metric_dtype, + output=output_config, + mkwargs=mkwargs, + ) + if isinstance(out, dict): + for k, v in out.items(): + key = f"{mname}_{k}" if k else mname + case_metrics[key] = float(v) + all_metric_values.setdefault(key, []).append(float(v)) + else: + case_metrics[mname] = float(out) + all_metric_values.setdefault(mname, []).append(float(out)) + except Exception as e: + log_dataset(dataset_config.name, f"Metric {mname!r} failed for {cid!r}: {e}") + case_metrics[mname] = float("nan") + all_metric_values.setdefault(mname, []).append(float("nan")) + per_case.append({"case_id": cid, "metrics": case_metrics}) + + # Aggregate (mean over cases) + metrics_summary = {} + for mname, values in all_metric_values.items(): + valid = [v for v in values if v == v] # filter nan + metrics_summary[mname] = sum(valid) / len(valid) if valid else float("nan") + + return { + "model": model_config.name, + "dataset": dataset_config.name, + "cases": cases, + "metrics": metrics_summary, + "per_case": per_case, + } + + +def _case_ids_for_run( + dataset_case_ids: list[str] | None, + case_id_override: str | None, +) -> list[str] | None: + """If ``case_id_override`` is set, run only that case; else use dataset config.""" + if case_id_override: + return [case_id_override] + return dataset_case_ids + + +def run_benchmark( + config: Config, + *, + case_id: str | None = None, +) -> list[dict[str, Any]]: + """Run benchmark from config (single or matrix mode). Returns list of run results. + + If ``case_id`` is set, only that case is evaluated (overrides ``dataset.case_ids``). + """ + import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 + import physicsnemo.cfd.evaluation.inference.wrappers # noqa: F401 + import physicsnemo.cfd.evaluation.metrics # noqa: F401 — registers built-in metrics + + metric_specs = _normalize_metrics_config(config.metrics) + device = config.run.device + output_dir = config.run.output_dir + Path(output_dir).mkdir(parents=True, exist_ok=True) + + if config.benchmark.reproducibility.log_env: + env_log = Path(output_dir) / "env.json" + log_dataset("benchmark", f"Writing environment log to {env_log}…") + with open(env_log, "w") as f: + json.dump(dict(os.environ), f, indent=2) + + results: list[dict[str, Any]] = [] + + if config.benchmark.mode == "single": + case_ids = _case_ids_for_run(config.dataset.case_ids, case_id) + res = _run_single( + config.model, + config.dataset, + metric_specs, + device, + output_dir, + case_ids, + config.output, + allow_skip_mismatch=False, + ) + results.append(res) + else: + models = config.benchmark.models or [config.model] + datasets = config.benchmark.datasets or [config.dataset] + for m_cfg in models: + for d_cfg in datasets: + res = _run_single( + m_cfg, + d_cfg, + metric_specs, + device, + output_dir, + _case_ids_for_run(d_cfg.case_ids, case_id), + config.output, + allow_skip_mismatch=True, + ) + results.append(res) + + if config.benchmark.reproducibility.save_artifacts: + artifacts = Path(output_dir) / "benchmark_artifacts.json" + skipped = [r for r in results if r.get("skipped")] + log_dataset("benchmark", f"Writing artifacts to {artifacts}…") + with open(artifacts, "w") as f: + json.dump( + { + "config": _config_to_dict(config), + "results_summary": [ + { + "model": r["model"], + "dataset": r["dataset"], + "metrics": r["metrics"], + "skipped": r.get("skipped", False), + "skip_reason": r.get("skip_reason"), + } + for r in results + ], + "skipped_runs": skipped, + }, + f, + indent=2, + ) + + write_report(results, output_dir, formats=["json", "csv", "html"]) + run_optional_report_plugins(config, results, output_dir) + return results + + +def _config_to_dict(c: Config) -> dict: + """Serializable config for artifacts.""" + return { + "run": { + "device": c.run.device, + "output_dir": c.run.output_dir, + "seed": c.run.seed, + "batch_size": c.run.batch_size, + }, + "model": { + "name": c.model.name, + "checkpoint": c.model.checkpoint, + "stats_path": c.model.stats_path, + "kwargs": c.model.kwargs, + "inference_domain": c.model.inference_domain, + }, + "dataset": { + "name": c.dataset.name, + "root": c.dataset.root, + "split": c.dataset.split, + "case_ids": c.dataset.case_ids, + "kwargs": c.dataset.kwargs, + }, + "output": { + "mesh_field_names": c.output.mesh_field_names, + "volume_mesh_field_names": c.output.volume_mesh_field_names, + "ground_truth_mesh_field_names": c.output.ground_truth_mesh_field_names, + "ground_truth_volume_mesh_field_names": c.output.ground_truth_volume_mesh_field_names, + }, + "metrics": c.metrics, + "reports": { + "enabled": c.reports.enabled, + "plugins": c.reports.plugins, + }, + "benchmark": { + "mode": c.benchmark.mode, + "reproducibility": { + "log_env": c.benchmark.reproducibility.log_env, + "save_artifacts": c.benchmark.reproducibility.save_artifacts, + }, + }, + } diff --git a/physicsnemo/cfd/evaluation/benchmarks/report.py b/physicsnemo/cfd/evaluation/benchmarks/report.py new file mode 100644 index 0000000..43edbe8 --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/report.py @@ -0,0 +1,125 @@ +"""Aggregate results and export JSON/CSV/HTML reports.""" + +import csv +import json +from pathlib import Path +from typing import Any + +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset + + +def write_report( + results: list[dict[str, Any]], + output_dir: str | Path, + formats: list[str] | None = None, +) -> None: + """Write benchmark results to one or more formats. + + Args: + results: List of run result dicts (from run_benchmark). + output_dir: Directory to write files into. + formats: One or more of "json", "csv", "html". Default: all three. + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + if formats is None: + formats = ["json", "csv", "html"] + log_dataset( + "benchmark", + f"Writing benchmark reports to {output_dir} (formats: {', '.join(formats)})…", + ) + for fmt in formats: + if fmt == "json": + _write_json(results, output_dir) + elif fmt == "csv": + _write_csv(results, output_dir) + elif fmt == "html": + _write_html(results, output_dir) + else: + raise ValueError(f"Unknown format: {fmt}") + + +def _write_json(results: list[dict], output_dir: Path) -> None: + path = output_dir / "benchmark_results.json" + log_dataset("benchmark", f"Writing {path}…") + with open(path, "w") as f: + json.dump(results, f, indent=2) + + +def _write_csv(results: list[dict], output_dir: Path) -> None: + path = output_dir / "benchmark_results.csv" + log_dataset("benchmark", f"Writing {path}…") + rows = [] + for r in results: + model = r.get("model", "") + dataset = r.get("dataset", "") + if r.get("skipped"): + rows.append( + { + "model": model, + "dataset": dataset, + "case_id": "", + "metric": "_skipped", + "value": r.get("skip_reason", ""), + } + ) + continue + for case in r.get("per_case", []): + cid = case.get("case_id", "") + for mname, value in case.get("metrics", {}).items(): + rows.append({"model": model, "dataset": dataset, "case_id": cid, "metric": mname, "value": value}) + # Also summary row (no case_id) + for mname, value in r.get("metrics", {}).items(): + rows.append({"model": model, "dataset": dataset, "case_id": "", "metric": mname, "value": value}) + if not rows: + return + with open(path, "w", newline="") as f: + w = csv.DictWriter(f, fieldnames=["model", "dataset", "case_id", "metric", "value"]) + w.writeheader() + w.writerows(rows) + + +def _write_html(results: list[dict], output_dir: Path) -> None: + path = output_dir / "benchmark_results.html" + log_dataset("benchmark", f"Writing {path}…") + lines = [ + "", + "Benchmark Results", + "", + "

Benchmark Results

", + ] + for r in results: + model = r.get("model", "") + dataset = r.get("dataset", "") + lines.append(f"

{model} / {dataset}

") + if r.get("skipped"): + lines.append( + f"

Skipped: {r.get('skip_reason', '')}

" + ) + continue + metrics = r.get("metrics", {}) + if metrics: + lines.append("") + for mname, value in metrics.items(): + cell = f"{value:.6g}" if isinstance(value, (int, float)) else str(value) + lines.append(f"") + lines.append("
MetricValue
{mname}{cell}
") + per_case = r.get("per_case", []) + if per_case: + lines.append("

Per-case

") + all_metrics = sorted(set(k for c in per_case for k in c.get("metrics", {}))) + for m in all_metrics: + lines.append(f"") + lines.append("") + for c in per_case: + line = f"" + for m in all_metrics: + val = c.get("metrics", {}).get(m, "") + cell = f"{val:.6g}" if isinstance(val, (int, float)) else str(val) + line += f"" + line += "" + lines.append(line) + lines.append("
Case ID{m}
{c.get('case_id', '')}{cell}
") + lines.append("") + with open(path, "w") as f: + f.write("\n".join(lines)) diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py new file mode 100644 index 0000000..e1cc97b --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Optional post-scalar report plugins (streamlines, plots via bench.visualization).""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from physicsnemo.cfd.evaluation.config import Config +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset + + +def run_optional_report_plugins( + config: Config, + results: list[dict[str, Any]], + output_dir: str, +) -> None: + """If ``config.reports.enabled``, run configured plugins and write a small manifest.""" + if not config.reports.enabled: + return + out = Path(output_dir) / "report_plugins_manifest.json" + manifest = { + "enabled": True, + "plugins": config.reports.plugins, + "note": ( + "Plot/streamline plugins are optional; wire bench.visualization helpers here " + "when you add concrete plugin names and inputs." + ), + "runs": len(results), + } + log_dataset("benchmark", f"Writing report plugin manifest to {out}…") + with open(out, "w") as f: + json.dump(manifest, f, indent=2) diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py new file mode 100644 index 0000000..6a1b0d5 --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -0,0 +1,45 @@ +"""CLI entrypoint for benchmark runs.""" + +import argparse + +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.config import load_config + + +def _parse_overrides(args: list[str]) -> dict[str, str]: + overrides = {} + for a in args: + if "=" in a: + s = a[2:] if a.startswith("--") else a + key, _, val = s.partition("=") + overrides[key.strip()] = val.strip() + return overrides + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run benchmark from config.") + parser.add_argument( + "--config", + required=True, + help="Path to YAML/JSON config (merge on top of --base-config if set).", + ) + parser.add_argument( + "--base-config", + default=None, + help="Optional YAML merged first (e.g. inference_config.yaml); --config overlays it.", + ) + parser.add_argument( + "--case-id", + default=None, + help="Run only this case ID (overrides dataset.case_ids in config).", + ) + parser.add_argument("overrides", nargs="*", help="Key=value overrides, e.g. run.device=cuda:1") + args = parser.parse_args() + overrides = _parse_overrides(getattr(args, "overrides", [])) + config = load_config(args.config, overrides, base=args.base_config) + results = run_benchmark(config, case_id=args.case_id) + print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") + + +if __name__ == "__main__": + main() diff --git a/physicsnemo/cfd/evaluation/common/__init__.py b/physicsnemo/cfd/evaluation/common/__init__.py new file mode 100644 index 0000000..a0d9732 --- /dev/null +++ b/physicsnemo/cfd/evaluation/common/__init__.py @@ -0,0 +1,6 @@ +"""Shared utilities for I/O, interpolation, and mesh handling.""" + +from physicsnemo.cfd.evaluation.common.io import load_global_stats, load_mesh +from physicsnemo.cfd.evaluation.common.interpolation import interpolate_to_mesh + +__all__ = ["load_global_stats", "load_mesh", "interpolate_to_mesh"] diff --git a/physicsnemo/cfd/evaluation/common/checkpoint_compat.py b/physicsnemo/cfd/evaluation/common/checkpoint_compat.py new file mode 100644 index 0000000..43b19ea --- /dev/null +++ b/physicsnemo/cfd/evaluation/common/checkpoint_compat.py @@ -0,0 +1,33 @@ +"""PyTorch 2.6+ compatibility for loading full training checkpoints (not weights-only). + +``physicsnemo.utils.load_checkpoint`` uses ``torch.load`` without ``weights_only``. +Since PyTorch 2.6 the default is ``weights_only=True``, which fails on checkpoints that +contain OmegaConf objects and other metadata. Wrap those loads with +:func:`trusted_torch_load_context`. +""" + +from __future__ import annotations + +from contextlib import contextmanager +from typing import Any, Callable + +import torch + + +@contextmanager +def trusted_torch_load_context() -> Any: + """Temporarily set ``torch.load`` default to ``weights_only=False`` for trusted files. + + Use only for checkpoints from a trusted source (your own training runs). + """ + orig: Callable[..., Any] = torch.load + + def _load(*args: Any, **kwargs: Any) -> Any: + kwargs.setdefault("weights_only", False) + return orig(*args, **kwargs) + + torch.load = _load # type: ignore[assignment] + try: + yield + finally: + torch.load = orig # type: ignore[assignment] diff --git a/physicsnemo/cfd/evaluation/common/interpolation.py b/physicsnemo/cfd/evaluation/common/interpolation.py new file mode 100644 index 0000000..e99c6b2 --- /dev/null +++ b/physicsnemo/cfd/evaluation/common/interpolation.py @@ -0,0 +1,58 @@ +"""kNN-based interpolation from prediction points to full mesh.""" + +from typing import Union + +import numpy as np +import torch +from sklearn.neighbors import NearestNeighbors + + +def interpolate_to_mesh( + mesh_points: np.ndarray, + pred_coords: np.ndarray, + pressure_pred: Union[torch.Tensor, np.ndarray], + wss_pred: Union[torch.Tensor, np.ndarray], + k: int = 4, +) -> tuple[np.ndarray, np.ndarray]: + """Interpolate pressure and WSS from prediction points to full mesh via weighted kNN. + + Args: + mesh_points: Target coordinates [M, 3]. + pred_coords: Prediction point coordinates [N, 3]. + pressure_pred: Pressure at prediction points [N] or [N, 1]. + wss_pred: Shear stress at prediction points [N, 3]. + k: Number of nearest neighbors for weighting. + + Returns: + pressure_mesh: [M] or [M, 1] float32, pressure interpolated to mesh. + wss_mesh: [M, 3] float32, shear stress interpolated to mesh. + """ + if isinstance(pressure_pred, torch.Tensor): + pressure_pred = pressure_pred.cpu().numpy() + if isinstance(wss_pred, torch.Tensor): + wss_pred = wss_pred.cpu().numpy() + pressure_pred = np.asarray(pressure_pred, dtype=np.float32) + wss_pred = np.asarray(wss_pred, dtype=np.float32) + if pressure_pred.ndim == 2: + pressure_pred = pressure_pred.squeeze(-1) + pred_coords = np.asarray(pred_coords, dtype=np.float64) + mesh_points = np.asarray(mesh_points, dtype=np.float64) + + nbrs = NearestNeighbors(n_neighbors=min(k, len(pred_coords)), algorithm="ball_tree").fit( + pred_coords + ) + distances, indices = nbrs.kneighbors(mesh_points) + + if k == 1: + indices = indices.flatten() + p_mesh = pressure_pred[indices] + wss_mesh = wss_pred[indices] + else: + epsilon = 1e-8 + weights = 1.0 / (distances + epsilon) + norm_weights = weights / np.sum(weights, axis=1, keepdims=True) + p_mesh = np.sum(norm_weights * pressure_pred[indices], axis=1) + wss_mesh = np.sum( + norm_weights[:, :, np.newaxis] * wss_pred[indices], axis=1 + ) + return p_mesh.astype(np.float32), wss_mesh.astype(np.float32) diff --git a/physicsnemo/cfd/evaluation/common/io.py b/physicsnemo/cfd/evaluation/common/io.py new file mode 100644 index 0000000..ce45b6e --- /dev/null +++ b/physicsnemo/cfd/evaluation/common/io.py @@ -0,0 +1,165 @@ +"""Mesh I/O and normalization statistics loading.""" + +import json +from pathlib import Path +from typing import Any + +import numpy as np +import pyvista as pv +import torch + + +def load_global_stats(stats_path: str, device: str = "cpu") -> dict[str, Any]: + """Load normalization statistics from JSON (mean / std_dev keys). + + Returns dict with "mean" and "std" (std_dev in file) as tensors on device. + """ + path = Path(stats_path) + if not path.exists(): + raise FileNotFoundError(f"Stats file not found: {stats_path}") + with open(path, "r") as f: + data = json.load(f) + return { + "mean": { + k: torch.tensor(v, device=device, dtype=torch.float32) + for k, v in data["mean"].items() + }, + "std": { + k: torch.tensor(v, device=device, dtype=torch.float32) + for k, v in data["std_dev"].items() + }, + } + + +def surface_factors_from_global_stats( + data: dict[str, Any], + device: str, +) -> dict[str, torch.Tensor]: + """Build ``mean`` / ``std`` vectors for TransolverDataPipe from ``global_stats.json``. + + Stacks pressure (1) and shear_stress (3) using ``mean`` / ``std_dev`` in the JSON + (same layout as ``surface_fields_normalization.npz`` from training). + """ + mp = np.asarray(data["mean"]["pressure"], dtype=np.float64).ravel() + mt = np.asarray(data["mean"]["shear_stress"], dtype=np.float64).ravel() + sp = np.asarray(data["std_dev"]["pressure"], dtype=np.float64).ravel() + st = np.asarray(data["std_dev"]["shear_stress"], dtype=np.float64).ravel() + mean = np.concatenate([mp[:1], mt[:3]]) + std = np.concatenate([sp[:1], st[:3]]) + return { + "mean": torch.tensor(mean, device=device, dtype=torch.float32), + "std": torch.tensor(std, device=device, dtype=torch.float32), + } + + +def volume_factors_from_global_stats( + data: dict[str, Any], + device: str, +) -> dict[str, torch.Tensor]: + """Build ``mean`` / ``std`` vectors for volume ``TransolverDataPipe`` from ``global_stats.json``. + + Channel order matches training / ``volume_fields_normalization.npz``: + **velocity (3)**, **pressure_volume** (1), **turbulent_viscosity** (1). + + Uses keys ``velocity``, ``pressure_volume`` (or legacy ``pressure`` for volume pressure), + and ``turbulent_viscosity`` under ``mean`` / ``std_dev``. + """ + mean_block = data["mean"] + std_block = data["std_dev"] + pkey = "pressure_volume" if "pressure_volume" in mean_block else "pressure" + if pkey not in mean_block: + raise KeyError( + "global_stats.json must include mean/std for volume pressure as " + "'pressure_volume' or 'pressure'" + ) + for key in ("velocity", "turbulent_viscosity"): + if key not in mean_block: + raise KeyError(f"global_stats.json missing 'mean' entry for {key!r}") + + mv = np.asarray(mean_block["velocity"], dtype=np.float64).ravel() + mp = np.asarray(mean_block[pkey], dtype=np.float64).ravel() + mn = np.asarray(mean_block["turbulent_viscosity"], dtype=np.float64).ravel() + sv = np.asarray(std_block["velocity"], dtype=np.float64).ravel() + sp = np.asarray(std_block[pkey], dtype=np.float64).ravel() + sn = np.asarray(std_block["turbulent_viscosity"], dtype=np.float64).ravel() + + mean = np.concatenate([mv[:3], mp[:1], mn[:1]]) + std = np.concatenate([sv[:3], sp[:1], sn[:1]]) + return { + "mean": torch.tensor(mean, device=device, dtype=torch.float32), + "std": torch.tensor(std, device=device, dtype=torch.float32), + } + + +def resolve_global_stats_path(stats_path: str | Path) -> Path: + """Resolve ``global_stats.json`` from config ``stats_path`` (file or directory).""" + p = Path(stats_path) + if p.is_file() and p.name == "global_stats.json": + return p + if p.is_file(): + return p.parent / "global_stats.json" + return p / "global_stats.json" + + +def load_transolver_surface_factors( + stats_path: str, + device: str, +) -> dict[str, torch.Tensor] | None: + """Load surface normalization for ``TransolverDataPipe``: prefer ``global_stats.json``, else npz.""" + gs_path = resolve_global_stats_path(stats_path) + if gs_path.exists(): + with open(gs_path) as f: + data = json.load(f) + return surface_factors_from_global_stats(data, device) + + p = Path(stats_path) + norm_dir = p.parent if p.is_file() else (p if p.is_dir() else p.parent) + npz_path = norm_dir / "surface_fields_normalization.npz" + if npz_path.exists(): + norm_data = np.load(str(npz_path)) + return { + "mean": torch.from_numpy(norm_data["mean"]).to(device), + "std": torch.from_numpy(norm_data["std"]).to(device), + } + return None + + +def load_transolver_volume_factors( + stats_path: str, + device: str, +) -> dict[str, torch.Tensor] | None: + """Load volume normalization for ``TransolverDataPipe``. + + Prefer ``global_stats.json`` (same layout as surface), else ``volume_fields_normalization.npz``. + """ + gs_path = resolve_global_stats_path(stats_path) + if gs_path.exists(): + with open(gs_path) as f: + data = json.load(f) + try: + return volume_factors_from_global_stats(data, device) + except KeyError: + # JSON present but not a volume stats file (e.g. surface-only global_stats). + pass + + p = Path(stats_path) + norm_dir = p.parent if p.is_file() else (p if p.is_dir() else p.parent) + npz_path = norm_dir / "volume_fields_normalization.npz" + if npz_path.exists(): + norm_data = np.load(str(npz_path)) + return { + "mean": torch.from_numpy(norm_data["mean"]).to(device), + "std": torch.from_numpy(norm_data["std"]).to(device), + } + return None + + +def load_mesh(mesh_path: str) -> pv.PolyData: + """Load a VTP (or other PyVista-readable) mesh from disk.""" + path = Path(mesh_path) + if not path.exists(): + raise FileNotFoundError(f"Mesh file not found: {mesh_path}") + mesh = pv.read(str(path)) + if not isinstance(mesh, pv.PolyData): + raise TypeError(f"Expected PolyData, got {type(mesh)}") + return mesh diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py new file mode 100644 index 0000000..8ba7000 --- /dev/null +++ b/physicsnemo/cfd/evaluation/config.py @@ -0,0 +1,277 @@ +"""Config schema and loader for inference and benchmarking.""" + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import yaml + + +def deep_merge_dict(base: dict[str, Any], overlay: dict[str, Any]) -> dict[str, Any]: + """Recursively merge ``overlay`` into ``base``; overlay wins for non-dict values and new keys.""" + result = dict(base) + for key, val in overlay.items(): + if ( + key in result + and isinstance(result[key], dict) + and isinstance(val, dict) + ): + result[key] = deep_merge_dict(result[key], val) + else: + result[key] = val + return result + + +@dataclass +class RunConfig: + device: str = "cuda:0" + output_dir: str = "benchmark_results" + seed: int = 42 + batch_size: int = 1 + + +@dataclass +class ModelConfig: + name: str = "fignet" + checkpoint: str = "" + stats_path: str = "" + kwargs: dict[str, Any] = field(default_factory=dict) + # Optional override; otherwise taken from the registered wrapper's INFERENCE_DOMAIN. + inference_domain: str | None = None + + def merged_kwargs_for_load(self) -> dict[str, Any]: + """``model.kwargs`` plus ``inference_domain`` so wrappers can branch surface vs volume.""" + kw = dict(self.kwargs) + if self.inference_domain in ("surface", "volume"): + kw["inference_domain"] = self.inference_domain + return kw + + +@dataclass +class DatasetConfig: + name: str = "drivaerml" + root: str = "" + split: str | None = None + case_ids: list[str] | None = None + kwargs: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ReproducibilityConfig: + log_env: bool = True + save_artifacts: bool = True + + +@dataclass +class BenchmarkConfig: + mode: str = "single" # "single" | "matrix" + models: list[ModelConfig] = field(default_factory=list) + datasets: list[DatasetConfig] = field(default_factory=list) + reproducibility: ReproducibilityConfig = field(default_factory=ReproducibilityConfig) + + +@dataclass +class ReportsConfig: + """Optional Phase-B style plugins (plots, streamlines) after scalar metrics.""" + + enabled: bool = False + plugins: list[dict[str, Any]] = field(default_factory=list) + + +# Canonical prediction keys used by wrappers; mesh array names are configurable per dataset/convention. +DEFAULT_MESH_FIELD_NAMES: dict[str, str] = { + "pressure": "pMeanTrimPred", + "shear_stress": "wallShearStressMeanTrimPred", +} + +DEFAULT_VOLUME_MESH_FIELD_NAMES: dict[str, str] = { + "pressure_volume": "pMeanPred", + "turbulent_viscosity": "nutMeanPred", + "velocity": "UMeanPred", +} + +DEFAULT_GROUND_TRUTH_MESH_FIELD_NAMES: dict[str, str] = { + "pressure": "pMean", + "shear_stress": "wallShearStressMean", +} + +DEFAULT_GROUND_TRUTH_VOLUME_MESH_FIELD_NAMES: dict[str, str] = { + "pressure_volume": "pMean", + "turbulent_viscosity": "nutMean", + "velocity": "UMean", +} + + +@dataclass +class OutputConfig: + """Output / mesh writing options. + + ``mesh_field_names`` maps canonical surface keys to VTP array names (predictions). + ``ground_truth_mesh_field_names`` maps canonical keys to reference VTK names on the mesh. + ``volume_mesh_field_names`` / ``ground_truth_volume_mesh_field_names`` do the same for volume. + """ + + mesh_field_names: dict[str, str] = field(default_factory=lambda: dict(DEFAULT_MESH_FIELD_NAMES)) + volume_mesh_field_names: dict[str, str] = field( + default_factory=lambda: dict(DEFAULT_VOLUME_MESH_FIELD_NAMES) + ) + ground_truth_mesh_field_names: dict[str, str] = field( + default_factory=lambda: dict(DEFAULT_GROUND_TRUTH_MESH_FIELD_NAMES) + ) + ground_truth_volume_mesh_field_names: dict[str, str] = field( + default_factory=lambda: dict(DEFAULT_GROUND_TRUTH_VOLUME_MESH_FIELD_NAMES) + ) + + +@dataclass +class Config: + """Root config for inference and benchmarking.""" + + run: RunConfig = field(default_factory=RunConfig) + model: ModelConfig = field(default_factory=ModelConfig) + dataset: DatasetConfig = field(default_factory=DatasetConfig) + output: OutputConfig = field(default_factory=OutputConfig) + metrics: list[str] | list[dict[str, Any]] = field(default_factory=lambda: ["l2_pressure"]) + benchmark: BenchmarkConfig = field(default_factory=BenchmarkConfig) + reports: ReportsConfig = field(default_factory=ReportsConfig) + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "Config": + """Build Config from a nested dict (e.g. from YAML/JSON).""" + run = RunConfig(**(data.get("run") or {})) + model = ModelConfig(**(data.get("model") or {})) + dataset = DatasetConfig(**(data.get("dataset") or {})) + out = data.get("output") or {} + mesh_fn = dict(DEFAULT_MESH_FIELD_NAMES) + if "mesh_field_names" in out: + mesh_fn = {**mesh_fn, **out["mesh_field_names"]} + vol_fn = dict(DEFAULT_VOLUME_MESH_FIELD_NAMES) + if "volume_mesh_field_names" in out: + vol_fn = {**vol_fn, **out["volume_mesh_field_names"]} + gt_mesh = dict(DEFAULT_GROUND_TRUTH_MESH_FIELD_NAMES) + if "ground_truth_mesh_field_names" in out: + gt_mesh = {**gt_mesh, **out["ground_truth_mesh_field_names"]} + gt_vol = dict(DEFAULT_GROUND_TRUTH_VOLUME_MESH_FIELD_NAMES) + if "ground_truth_volume_mesh_field_names" in out: + gt_vol = {**gt_vol, **out["ground_truth_volume_mesh_field_names"]} + output = OutputConfig( + mesh_field_names=mesh_fn, + volume_mesh_field_names=vol_fn, + ground_truth_mesh_field_names=gt_mesh, + ground_truth_volume_mesh_field_names=gt_vol, + ) + metrics = data.get("metrics", ["l2_pressure"]) + bench = data.get("benchmark") or {} + rep = bench.get("reproducibility") or {} + base_model = data.get("model") or {} + base_dataset = data.get("dataset") or {} + model_list = [] + for m in bench.get("models", []): + if isinstance(m, dict): + model_list.append(ModelConfig(**m)) + else: + model_list.append(ModelConfig(**{**base_model, "name": str(m)})) + dataset_list = [] + for d in bench.get("datasets", []): + if isinstance(d, dict): + dataset_list.append(DatasetConfig(**d)) + else: + dataset_list.append(DatasetConfig(**{**base_dataset, "name": str(d)})) + benchmark = BenchmarkConfig( + mode=bench.get("mode", "single"), + models=model_list, + datasets=dataset_list, + reproducibility=ReproducibilityConfig(**rep), + ) + reports_raw = data.get("reports") or {} + if isinstance(reports_raw, dict): + reports = ReportsConfig( + enabled=bool(reports_raw.get("enabled", False)), + plugins=list(reports_raw.get("plugins") or []), + ) + else: + reports = ReportsConfig() + return cls( + run=run, + model=model, + dataset=dataset, + output=output, + metrics=metrics, + benchmark=benchmark, + reports=reports, + ) + + @classmethod + def _load_raw(cls, path: Path) -> dict[str, Any]: + """Load a YAML or JSON file into a dict (empty dict if file is empty).""" + if not path.exists(): + raise FileNotFoundError(f"Config file not found: {path}") + with open(path) as f: + if path.suffix in (".yaml", ".yml"): + data = yaml.safe_load(f) + else: + import json + data = json.load(f) + if data is None: + data = {} + if not isinstance(data, dict): + raise TypeError(f"Config root must be a mapping, got {type(data).__name__}") + return data + + @classmethod + def load(cls, path: str | Path) -> "Config": + """Load config from a single YAML or JSON file.""" + return cls.from_dict(cls._load_raw(Path(path))) + + @classmethod + def load_merged(cls, *paths: str | Path) -> "Config": + """Load multiple configs and deep-merge in order (later files override earlier).""" + merged: dict[str, Any] = {} + for p in paths: + merged = deep_merge_dict(merged, cls._load_raw(Path(p))) + return cls.from_dict(merged) + + def apply_overrides(self, overrides: dict[str, Any]) -> None: + """Apply CLI-style overrides (e.g. run.device, model.checkpoint).""" + for key, value in overrides.items(): + if "." not in key: + continue + parts = key.split(".") + obj: Any = self + for part in parts[:-1]: + obj = getattr(obj, part, None) + if obj is None: + break + if obj is not None: + attr = parts[-1] + current = getattr(obj, attr, None) + if isinstance(current, bool) and isinstance(value, str): + value = value.lower() in ("true", "1", "yes") + elif isinstance(current, int) and isinstance(value, str) and value.isdigit(): + value = int(value) + elif isinstance(current, float) and isinstance(value, str): + try: + value = float(value) + except ValueError: + pass + setattr(obj, attr, value) + + +def load_config( + path: str | Path, + overrides: dict[str, Any] | None = None, + *, + base: str | Path | None = None, +) -> Config: + """Load config from file and optionally apply overrides. + + If ``base`` is set, load ``base`` first then merge ``path`` on top (option A: + shared inference defaults + benchmark overlay). + """ + if base is not None: + config = Config.load_merged(base, path) + else: + config = Config.load(path) + if overrides: + config.apply_overrides(overrides) + return config diff --git a/physicsnemo/cfd/evaluation/datasets/__init__.py b/physicsnemo/cfd/evaluation/datasets/__init__.py new file mode 100644 index 0000000..61d1a65 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/__init__.py @@ -0,0 +1,19 @@ +"""Dataset adapters and canonical case schema.""" + +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, predictions_dict +from physicsnemo.cfd.evaluation.datasets.adapter_registry import ( + get_adapter, + list_adapters, + register_adapter, + DatasetAdapter, +) +import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 - register drivaerml, etc. + +__all__ = [ + "CanonicalCase", + "predictions_dict", + "DatasetAdapter", + "register_adapter", + "get_adapter", + "list_adapters", +] diff --git a/physicsnemo/cfd/evaluation/datasets/adapter_registry.py b/physicsnemo/cfd/evaluation/datasets/adapter_registry.py new file mode 100644 index 0000000..7191b28 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/adapter_registry.py @@ -0,0 +1,56 @@ +"""Dataset adapter base class and registry.""" + +from abc import ABC, abstractmethod +from typing import Any, Type + +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain + +_REGISTRY: dict[str, Type["DatasetAdapter"]] = {} + + +class DatasetAdapter(ABC): + """Abstract interface for CFD dataset adapters.""" + + @classmethod + def inference_domain(cls) -> InferenceDomain: + """Default domain when not configured via kwargs (subclasses may override).""" + return "surface" + + @classmethod + def inference_domain_from_kwargs(cls, kwargs: dict[str, Any] | None) -> InferenceDomain: + """Domain for this adapter given ``dataset.kwargs`` (used before instantiation). + + Adapters that switch surface/volume via kwargs (e.g. DrivAerML) must override this. + """ + return cls.inference_domain() + + @abstractmethod + def list_cases(self, split: str | None = None) -> list[str]: + """Return case IDs (e.g. run IDs or file stems).""" + ... + + @abstractmethod + def load_case(self, case_id: str) -> CanonicalCase: + """Load mesh(es), flow conditions, and optional ground truth into canonical schema.""" + ... + + def validate(self, case: CanonicalCase) -> bool: + """Check required fields and mesh validity. Override as needed.""" + return case.mesh_path != "" and case.mesh_type in ("point", "cell") + + +def register_adapter(name: str, adapter_class: Type[DatasetAdapter]) -> None: + """Register a dataset adapter by name.""" + _REGISTRY[name] = adapter_class + + +def get_adapter(name: str) -> Type[DatasetAdapter]: + """Resolve adapter class by name.""" + if name not in _REGISTRY: + raise KeyError(f"Unknown dataset adapter: {name}. Available: {list(_REGISTRY.keys())}") + return _REGISTRY[name] + + +def list_adapters() -> list[str]: + """Return registered adapter names.""" + return list(_REGISTRY.keys()) diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py b/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py new file mode 100644 index 0000000..4b12d6b --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py @@ -0,0 +1,10 @@ +"""Dataset adapter implementations.""" + +from physicsnemo.cfd.evaluation.datasets.adapter_registry import register_adapter +from physicsnemo.cfd.evaluation.datasets.adapters.drivaerml import DrivAerMLAdapter +from physicsnemo.cfd.evaluation.datasets.adapters.ahmed import AhmedAdapter + +register_adapter("drivaerml", DrivAerMLAdapter) +register_adapter("ahmed", AhmedAdapter) + +__all__ = ["DrivAerMLAdapter", "AhmedAdapter"] diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py b/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py new file mode 100644 index 0000000..d1f5943 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py @@ -0,0 +1,29 @@ +"""Stub adapter for Ahmed Body dataset (placeholder until implementation).""" + +from pathlib import Path + +from physicsnemo.cfd.evaluation.datasets.adapter_registry import DatasetAdapter +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase + + +class AhmedAdapter(DatasetAdapter): + """Placeholder for Ahmed Body dataset. Implement list_cases and load_case.""" + + def __init__(self, root: str, **kwargs: object) -> None: + self.root = Path(root) + if self.root.exists(): + pass # optional: validate layout + + def list_cases(self, split: str | None = None) -> list[str]: + """Return case IDs when dataset is implemented.""" + if not self.root.exists(): + return [] + # Stub: no cases until real layout is defined + return [] + + def load_case(self, case_id: str) -> CanonicalCase: + """Load one case. Raises until dataset layout is implemented.""" + raise NotImplementedError( + "Ahmed Body adapter not yet implemented. " + "Add mesh layout and GT loading in datasets/adapters/ahmed.py" + ) diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py new file mode 100644 index 0000000..f850823 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py @@ -0,0 +1,220 @@ +"""DrivAerML dataset adapter: surface VTP and/or volume VTU under each ``run_*`` directory.""" + +from pathlib import Path +from typing import Any + +import pyvista as pv + +from physicsnemo.cfd.evaluation.datasets.adapter_registry import DatasetAdapter +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.datasets.vtk_ground_truth import ( + DEFAULT_PRESSURE_NAMES, + DEFAULT_SHEAR_NAMES, + extract_pressure_wss_from_mesh, + extract_volume_fields_from_mesh, +) + + +class DrivAerMLAdapter(DatasetAdapter): + """Adapter for DrivAerML layout under ``root/run_/``. + + **Surface (default)** — boundary VTP per run aligns with the run tag, same idea as volume: + ``run_/boundary_.vtp`` (e.g. ``run_1`` → ``boundary_1.vtp``). Override with + ``boundary_vtp_filename`` (fixed name in every run dir) or ``boundary_vtp_template`` + (e.g. ``"boundary_{run_suffix}.vtp"``; supports ``{run_suffix}``, ``{case_id}``). + + **Volume** — set ``dataset.kwargs.inference_domain: volume``. Default + ``run_/volume_.vtu``. Override with ``volume_vtu_filename`` or ``volume_vtu_template``. + + Optional kwargs (from ``dataset.kwargs`` in config): + + - ``inference_domain``: ``"surface"`` (default) or ``volume``. + - ``gt_data_type``: for surface, ``auto`` / ``cell`` / ``point`` / ``from_model`` (see below). + For volume, passed to volume GT extraction as ``auto`` / ``cell`` / ``point`` only. + - ``align_ground_truth_to_model``: surface only; same as elsewhere. + - ``pressure_field_names``, ``shear_field_names``: surface GT array name overrides. + - ``turbulent_viscosity_field_names``, ``velocity_field_names``, + ``volume_pressure_field_names``: volume GT VTK array name tuples (defaults in + ``vtk_ground_truth``). Volume pressure is exposed in GT as ``pressure_volume``. + - ``boundary_vtp_filename``: if set, every run uses this exact VTP name (legacy e.g. ``boundary_1.vtp``). + - ``boundary_vtp_template``: custom surface filename pattern. + - ``volume_vtu_filename``: if set, every run uses this exact VTU name inside the run dir. + - ``volume_vtu_template``: format string with ``{run_suffix}`` (default pattern above). + + Deprecated alias: ``gt_prefer`` for ``gt_data_type``. + """ + + @classmethod + def inference_domain(cls) -> InferenceDomain: + return "surface" + + @classmethod + def inference_domain_from_kwargs(cls, kwargs: dict[str, Any] | None) -> InferenceDomain: + kw = kwargs or {} + dom = kw.get("inference_domain", "surface") + if dom in ("surface", "volume"): + return dom # type: ignore[return-value] + return "surface" + + def __init__(self, root: str, **kwargs: Any) -> None: + self.root = Path(root) + if not self.root.exists(): + raise FileNotFoundError(f"DrivAerML root not found: {self.root}") + self._inference_mode: InferenceDomain = self.inference_domain_from_kwargs(kwargs) + + self._gt_data_type = kwargs.get("gt_data_type", kwargs.get("gt_prefer", "auto")) + pn = kwargs.get("pressure_field_names") + sn = kwargs.get("shear_field_names") + self._pressure_names: tuple[str, ...] = ( + tuple(pn) if pn else DEFAULT_PRESSURE_NAMES + ) + self._shear_names: tuple[str, ...] = tuple(sn) if sn else DEFAULT_SHEAR_NAMES + + self._boundary_vtp_filename: str | None = kwargs.get("boundary_vtp_filename") + self._boundary_vtp_template: str | None = kwargs.get("boundary_vtp_template") + + self._volume_vtu_filename: str | None = kwargs.get("volume_vtu_filename") + self._volume_vtu_template: str | None = kwargs.get("volume_vtu_template") + + tn = kwargs.get("turbulent_viscosity_field_names") + vn = kwargs.get("velocity_field_names") + pn_vol = kwargs.get("volume_pressure_field_names") + self._nut_names: tuple[str, ...] = tuple(tn) if tn else () + self._vel_names: tuple[str, ...] = tuple(vn) if vn else () + self._volume_pressure_names: tuple[str, ...] = tuple(pn_vol) if pn_vol else () + + def _run_suffix(self, case_id: str) -> str: + return case_id[4:] if case_id.startswith("run_") else case_id + + def _boundary_vtp_path(self, run_dir: Path, case_id: str) -> Path: + if self._boundary_vtp_filename: + return run_dir / self._boundary_vtp_filename + if self._boundary_vtp_template: + return run_dir / self._boundary_vtp_template.format( + run_suffix=self._run_suffix(case_id), + case_id=case_id, + ) + return run_dir / f"boundary_{self._run_suffix(case_id)}.vtp" + + def _volume_vtu_path(self, run_dir: Path, case_id: str) -> Path: + if self._volume_vtu_filename: + return run_dir / self._volume_vtu_filename + if self._volume_vtu_template: + return run_dir / self._volume_vtu_template.format( + run_suffix=self._run_suffix(case_id), + case_id=case_id, + ) + return run_dir / f"volume_{self._run_suffix(case_id)}.vtu" + + def list_cases(self, split: str | None = None) -> list[str]: + """Return case IDs: run directory names that contain the required mesh for the mode.""" + case_ids: list[str] = [] + for p in self.root.iterdir(): + if not p.is_dir() or not p.name.startswith("run_"): + continue + if self._inference_mode == "volume": + if self._volume_vtu_path(p, p.name).exists(): + case_ids.append(p.name) + else: + if self._boundary_vtp_path(p, p.name).exists(): + case_ids.append(p.name) + return sorted(case_ids) + + def load_case(self, case_id: str) -> CanonicalCase: + """Load surface VTP or volume VTU and optional ground truth.""" + log_dataset( + "drivaerml", + f"load_case({case_id!r}): branch={self._inference_mode!r}, root={self.root}", + ) + run_dir = self.root / case_id + if not run_dir.exists(): + raise FileNotFoundError(f"Case not found: {run_dir}") + + if self._inference_mode == "volume": + return self._load_volume_case(case_id, run_dir) + return self._load_surface_case(case_id, run_dir) + + def _load_surface_case(self, case_id: str, run_dir: Path) -> CanonicalCase: + mesh_path = self._boundary_vtp_path(run_dir, case_id) + if not mesh_path.exists(): + raise FileNotFoundError(f"Surface mesh not found: {mesh_path}") + + log_dataset("drivaerml", f"Reading surface mesh from {mesh_path}") + mesh = pv.read(str(mesh_path)) + if not isinstance(mesh, pv.PolyData): + mesh = mesh.extract_surface() + + gt_dict, gt_loc = extract_pressure_wss_from_mesh( + mesh, + data_type=self._gt_data_type, + pressure_names=self._pressure_names, + shear_names=self._shear_names, + ) + ground_truth = gt_dict if gt_dict else None + mesh_type = gt_loc if gt_loc is not None else "cell" + + meta: dict[str, Any] = { + "dataset": "drivaerml", + "run": case_id, + "branch": "surface", + "boundary_vtp": mesh_path.name, + } + if ground_truth: + meta["ground_truth_location"] = gt_loc + meta["ground_truth_fields"] = list(ground_truth.keys()) + + return CanonicalCase( + case_id=case_id, + mesh_path=str(mesh_path), + mesh_type=mesh_type, + ground_truth=ground_truth, + metadata=meta, + inference_domain="surface", + ) + + def _load_volume_case(self, case_id: str, run_dir: Path) -> CanonicalCase: + mesh_path = self._volume_vtu_path(run_dir, case_id) + if not mesh_path.exists(): + raise FileNotFoundError(f"Volume mesh not found: {mesh_path}") + + log_dataset("drivaerml", f"Reading volume mesh from {mesh_path}") + mesh = pv.read(str(mesh_path)) + ugrid = ( + mesh.cast_to_unstructured_grid() + if hasattr(mesh, "cast_to_unstructured_grid") + else mesh + ) + + v_gt_type = self._gt_data_type + if v_gt_type not in ("auto", "cell", "point"): + v_gt_type = "auto" + + gt_dict, gt_loc = extract_volume_fields_from_mesh( + ugrid, + data_type=v_gt_type, + turbulent_viscosity_names=self._nut_names if self._nut_names else None, + velocity_names=self._vel_names if self._vel_names else None, + pressure_names=self._volume_pressure_names if self._volume_pressure_names else None, + ) + ground_truth = gt_dict if gt_dict else None + mesh_type = gt_loc if gt_loc is not None else "cell" + + meta: dict[str, Any] = { + "dataset": "drivaerml", + "run": case_id, + "branch": "volume", + "volume_vtu": mesh_path.name, + } + if ground_truth: + meta["ground_truth_location"] = gt_loc + meta["ground_truth_fields"] = list(ground_truth.keys()) + + return CanonicalCase( + case_id=case_id, + mesh_path=str(mesh_path.resolve()), + mesh_type=mesh_type, + ground_truth=ground_truth, + metadata=meta, + inference_domain="volume", + ) diff --git a/physicsnemo/cfd/evaluation/datasets/gt_alignment.py b/physicsnemo/cfd/evaluation/datasets/gt_alignment.py new file mode 100644 index 0000000..0b07538 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/gt_alignment.py @@ -0,0 +1,56 @@ +"""Resolve ground-truth mesh location (cell vs point) from dataset kwargs + model name.""" + +from __future__ import annotations + +from typing import Any + +from physicsnemo.cfd.evaluation.inference.model_registry import get_output_location_for_model + +# Keys used only for resolution; stripped before passing kwargs to dataset adapters. +_ALIGN_KEYS = frozenset({"align_ground_truth_to_model"}) + + +def resolve_dataset_kwargs_for_model( + dataset_kwargs: dict[str, Any], + model_name: str, +) -> dict[str, Any]: + """Return a copy of ``dataset_kwargs`` with effective ``gt_data_type`` when requested. + + **Precedence** + + - Explicit ``gt_data_type`` / ``gt_prefer`` of ``cell`` or ``point`` always wins. + - Otherwise, if ``gt_data_type`` is ``from_model`` / ``model`` (case-insensitive), or + ``align_ground_truth_to_model: true``, set ``gt_data_type`` to the registered model's + :attr:`~physicsnemo.cfd.evaluation.inference.model_registry.CFDModel.OUTPUT_LOCATION`. + - Otherwise leave ``gt_data_type`` unchanged (e.g. ``auto``). + + Alignment keys are removed from the returned dict so adapters only see concrete + ``auto`` | ``cell`` | ``point``. + """ + kw = dict(dataset_kwargs) + gt_raw = kw.get("gt_data_type", kw.get("gt_prefer", "auto")) + align = bool(kw.get("align_ground_truth_to_model", False)) + + explicit = {"cell", "point"} + if isinstance(gt_raw, str) and gt_raw.lower() in explicit: + kw["gt_data_type"] = gt_raw.lower() + _pop_align_keys(kw) + return kw + + from_model_aliases = {"from_model", "model"} + use_model = align or ( + isinstance(gt_raw, str) and gt_raw.lower() in from_model_aliases + ) + if use_model: + loc = get_output_location_for_model(model_name) + kw["gt_data_type"] = loc + _pop_align_keys(kw) + return kw + + _pop_align_keys(kw) + return kw + + +def _pop_align_keys(kw: dict[str, Any]) -> None: + for k in _ALIGN_KEYS: + kw.pop(k, None) diff --git a/physicsnemo/cfd/evaluation/datasets/progress.py b/physicsnemo/cfd/evaluation/datasets/progress.py new file mode 100644 index 0000000..a858856 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/progress.py @@ -0,0 +1,8 @@ +"""Progress messages for dataset I/O and benchmark outputs.""" + +from __future__ import annotations + + +def log_dataset(component: str, message: str) -> None: + """Print a tagged dataset/benchmark I/O line (``flush`` for piped/HPC logs).""" + print(f"[dataset:{component}] {message}", flush=True) diff --git a/physicsnemo/cfd/evaluation/datasets/schema.py b/physicsnemo/cfd/evaluation/datasets/schema.py new file mode 100644 index 0000000..135c719 --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/schema.py @@ -0,0 +1,78 @@ +"""Canonical CFD case schema for dataset adapters and model wrappers.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Literal + +import numpy as np + +# Surface vs volume inference (which manifold the case uses). Combined surface+volume is deferred. +InferenceDomain = Literal["surface", "volume"] + + +@dataclass +class CanonicalCase: + """Canonical representation of a single CFD case. + + ``mesh_path`` is the primary mesh: surface ``.vtp`` when ``inference_domain`` is + ``surface``, or volume ``.vtu`` when ``inference_domain`` is ``volume``. + """ + + case_id: str + mesh_path: str + mesh_type: str # "point" | "cell" — interpretation of field locations + ground_truth: dict[str, Any] | None = None # surface: pressure, shear_stress; volume: pressure_volume, … + metadata: dict[str, Any] = field(default_factory=dict) + inference_domain: InferenceDomain = "surface" + + def __post_init__(self) -> None: + if self.mesh_type not in ("point", "cell"): + raise ValueError("mesh_type must be 'point' or 'cell'") + if self.inference_domain not in ("surface", "volume"): + raise ValueError("inference_domain must be 'surface' or 'volume'") + + def get_ground_truth(self, key: str, default: Any = None) -> Any: + """Return ground truth field by key, or default if missing.""" + if self.ground_truth is None: + return default + return self.ground_truth.get(key, default) + + +def predictions_dict(pressure: np.ndarray, shear_stress: np.ndarray) -> dict[str, np.ndarray]: + """Build canonical predictions dict from pressure and WSS arrays (surface models).""" + return { + "pressure": np.asarray(pressure, dtype=np.float32), + "shear_stress": np.asarray(shear_stress, dtype=np.float32), + } + + +def build_predictions_dict( + *, + pressure: np.ndarray | None = None, + pressure_volume: np.ndarray | None = None, + shear_stress: np.ndarray | None = None, + velocity: np.ndarray | None = None, + turbulent_viscosity: np.ndarray | None = None, + **extra: np.ndarray, +) -> dict[str, np.ndarray]: + """Build predictions dict from optional canonical fields (omit None / missing). + + Use ``pressure`` for surface models; ``pressure_volume`` for volume-domain pressure + (distinct canonical key so surface and volume fields are not conflated). + """ + out: dict[str, np.ndarray] = {} + if pressure is not None: + out["pressure"] = np.asarray(pressure, dtype=np.float32) + if pressure_volume is not None: + out["pressure_volume"] = np.asarray(pressure_volume, dtype=np.float32) + if shear_stress is not None: + out["shear_stress"] = np.asarray(shear_stress, dtype=np.float32) + if velocity is not None: + out["velocity"] = np.asarray(velocity, dtype=np.float32) + if turbulent_viscosity is not None: + out["turbulent_viscosity"] = np.asarray(turbulent_viscosity, dtype=np.float32) + for k, v in extra.items(): + if v is not None: + out[k] = np.asarray(v, dtype=np.float32) + return out diff --git a/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py new file mode 100644 index 0000000..9bcaeca --- /dev/null +++ b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py @@ -0,0 +1,292 @@ +"""Load reference pressure / wall shear from VTK (VTP) for benchmarking.""" + +from typing import Literal + +import numpy as np +import pyvista as pv +from sklearn.neighbors import NearestNeighbors + + +# Common DrivAerML / external aero field names (reference CFD, not *Pred) +DEFAULT_PRESSURE_NAMES = ( + "pMeanTrim", + "pMean", + "Pressure", + "pressure", + "p", +) +DEFAULT_SHEAR_NAMES = ( + "wallShearStressMeanTrim", + "WallShearStress", + "wallShearStress", + "wallShearStressMean", +) + +# Volume RANS / LES-style names (reference CFD) +DEFAULT_TURBULENT_VISCOSITY_NAMES = ( + "nutMean", + "nut", + "turbulent_viscosity", + "TurbulentViscosity", + "nuTilde", +) +DEFAULT_VOLUME_VELOCITY_NAMES = ( + "UMean", + "U", + "velocity", + "Velocity", +) +# Volume pressure uses a distinct canonical key ``pressure_volume`` (surface uses ``pressure``). +DEFAULT_VOLUME_PRESSURE_NAMES = DEFAULT_PRESSURE_NAMES + + +def _find_array( + data: pv.DataSetAttributes, + candidates: tuple[str, ...], +) -> str | None: + for name in candidates: + if name in data: + return name + return None + + +def _pressure_to_1d(arr: np.ndarray) -> np.ndarray: + a = np.asarray(arr, dtype=np.float64) + if a.ndim == 2 and a.shape[1] == 1: + a = a[:, 0] + return a.reshape(-1) + + +def _shear_to_n3(arr: np.ndarray, n_expected: int) -> np.ndarray | None: + a = np.asarray(arr, dtype=np.float64) + if a.ndim == 1: + if a.size == n_expected * 3: + return a.reshape(n_expected, 3) + return None + if a.ndim == 2 and a.shape[1] == 3: + if a.shape[0] == n_expected: + return a + return None + + +def _extract_fields_at_location( + mesh: pv.PolyData, + loc_name: Literal["cell", "point"], + pressure_names: tuple[str, ...], + shear_names: tuple[str, ...], +) -> dict[str, np.ndarray] | None: + """Try to read pressure / shear at one mesh location; return dict or None if nothing valid.""" + data = mesh.cell_data if loc_name == "cell" else mesh.point_data + n = mesh.n_cells if loc_name == "cell" else mesh.n_points + if n == 0: + return None + + pkey = _find_array(data, pressure_names) + skey = _find_array(data, shear_names) + out: dict[str, np.ndarray] = {} + + if pkey is not None: + p = _pressure_to_1d(np.asarray(data[pkey])) + if p.size == n: + out["pressure"] = p.astype(np.float32) + if skey is not None: + w = _shear_to_n3(np.asarray(data[skey]), n) + if w is not None: + out["shear_stress"] = w.astype(np.float32) + + return out if out else None + + +def resample_cell_ground_truth_to_points( + mesh: pv.PolyData, + ground_truth_cell: dict[str, np.ndarray], +) -> dict[str, np.ndarray]: + """Map cell-centered GT arrays to mesh points using VTK cell→point averaging. + + Use when reference fields exist only on cells but predictions are point-based + (e.g. MeshGraphNet) so L2 metrics share the same dof count as ``mesh.n_points``. + """ + n_c = mesh.n_cells + m = mesh.copy(deep=True) + # Temporary names to avoid clobbering existing arrays + if "pressure" in ground_truth_cell: + p = np.asarray(ground_truth_cell["pressure"]).ravel() + if p.size != n_c: + raise ValueError( + f"pressure length {p.size} != n_cells {n_c} for cell→point resampling" + ) + m.cell_data["_cfd_gt_pressure"] = p.astype(np.float32) + if "shear_stress" in ground_truth_cell: + w = np.asarray(ground_truth_cell["shear_stress"]) + if w.shape[0] != n_c or w.shape[-1] != 3: + raise ValueError( + f"shear_stress shape {w.shape} incompatible with n_cells={n_c}" + ) + m.cell_data["_cfd_gt_shear"] = w.astype(np.float32) + + try: + m_pt = m.cell_data_to_point_data(pass_cell_data=True) + except Exception: + m_pt = m.cell_data_to_point_data() + + out: dict[str, np.ndarray] = {} + if "_cfd_gt_pressure" in m_pt.point_data: + out["pressure"] = np.asarray(m_pt.point_data["_cfd_gt_pressure"], dtype=np.float32).ravel() + if "_cfd_gt_shear" in m_pt.point_data: + out["shear_stress"] = np.asarray(m_pt.point_data["_cfd_gt_shear"], dtype=np.float32) + + if set(out.keys()) == set(ground_truth_cell.keys()): + return out + + # VTK filter sometimes omits custom arrays; map cell values to points by nearest cell center. + centers = mesh.cell_centers().points + pts = np.asarray(mesh.points, dtype=np.float64) + nbr = NearestNeighbors(n_neighbors=1, algorithm="ball_tree").fit(centers) + _, cell_idx = nbr.kneighbors(pts) + ci = cell_idx.ravel().astype(np.int64) + out_nn: dict[str, np.ndarray] = {} + if "pressure" in ground_truth_cell: + p = np.asarray(ground_truth_cell["pressure"], dtype=np.float64).ravel() + if p.size == n_c: + out_nn["pressure"] = p[ci].astype(np.float32) + if "shear_stress" in ground_truth_cell: + w = np.asarray(ground_truth_cell["shear_stress"], dtype=np.float64) + if w.shape[0] == n_c and w.shape[-1] == 3: + out_nn["shear_stress"] = w[ci].astype(np.float32) + return out_nn + + +def extract_pressure_wss_from_mesh( + mesh: pv.PolyData, + data_type: Literal["auto", "cell", "point"] = "auto", + pressure_names: tuple[str, ...] = DEFAULT_PRESSURE_NAMES, + shear_names: tuple[str, ...] = DEFAULT_SHEAR_NAMES, +) -> tuple[dict[str, np.ndarray] | None, Literal["cell", "point"] | None]: + """Read reference pressure and shear stress from mesh cell_data / point_data. + + ``data_type``: + + - ``auto``: tries **cell** first, then **point** (legacy behavior). + - ``cell``: cell first, then point (same as today for two-pass). + - ``point``: **point** first. If only cell reference fields exist, they are resampled to + points (``cell_data_to_point_data``) so array lengths match ``mesh.n_points`` — required + for point-based models vs metrics. + + Returns: + (ground_truth_dict_or_none, location_or_none). ``location`` is ``"point"`` or + ``"cell"`` describing where the returned arrays live (after any resampling). + """ + if data_type == "point": + pt = _extract_fields_at_location(mesh, "point", pressure_names, shear_names) + if pt: + return (pt, "point") + cell_gt = _extract_fields_at_location(mesh, "cell", pressure_names, shear_names) + if cell_gt: + try: + pt_resampled = resample_cell_ground_truth_to_points(mesh, cell_gt) + except Exception: + pt_resampled = {} + if pt_resampled: + return (pt_resampled, "point") + return (None, None) + + if data_type == "cell": + order: list[Literal["cell", "point"]] = ["cell", "point"] + else: + order = ["cell", "point"] + + for loc_name in order: + out = _extract_fields_at_location( + mesh, loc_name, pressure_names, shear_names + ) + if out: + return (out, loc_name) + + return (None, None) + + +def _scalar_field_to_1d(arr: np.ndarray, n: int) -> np.ndarray | None: + a = np.asarray(arr, dtype=np.float64) + if a.ndim == 2 and a.shape[1] == 1: + a = a[:, 0] + a = a.reshape(-1) + if a.size != n: + return None + return a.astype(np.float32) + + +def _volume_vec3(arr: np.ndarray, n: int) -> np.ndarray | None: + a = np.asarray(arr, dtype=np.float64) + if a.ndim == 1 and a.size == n * 3: + return a.reshape(n, 3).astype(np.float32) + if a.ndim == 2 and a.shape == (n, 3): + return a.astype(np.float32) + return None + + +def _extract_volume_at_location( + mesh: pv.DataSet, + loc_name: Literal["cell", "point"], + turbulent_viscosity_names: tuple[str, ...], + velocity_names: tuple[str, ...], + pressure_names: tuple[str, ...], +) -> dict[str, np.ndarray] | None: + data = mesh.cell_data if loc_name == "cell" else mesh.point_data + n = mesh.n_cells if loc_name == "cell" else mesh.n_points + if n == 0: + return None + out: dict[str, np.ndarray] = {} + pk = _find_array(data, pressure_names) + if pk is not None: + p = _pressure_to_1d(np.asarray(data[pk])) + if p.size == n: + out["pressure_volume"] = p.astype(np.float32) + nk = _find_array(data, turbulent_viscosity_names) + if nk is not None: + nut = _scalar_field_to_1d(np.asarray(data[nk]), n) + if nut is not None: + out["turbulent_viscosity"] = nut + vk = _find_array(data, velocity_names) + if vk is not None: + vel = _volume_vec3(np.asarray(data[vk]), n) + if vel is not None: + out["velocity"] = vel + return out if out else None + + +def extract_volume_fields_from_mesh( + mesh: pv.DataSet, + data_type: Literal["auto", "cell", "point"] = "auto", + turbulent_viscosity_names: tuple[str, ...] | None = None, + velocity_names: tuple[str, ...] | None = None, + pressure_names: tuple[str, ...] | None = None, +) -> tuple[dict[str, np.ndarray] | None, Literal["cell", "point"] | None]: + """Read reference fields from volume ``cell_data`` / ``point_data``. + + Scalar pressure is stored under the canonical key ``pressure_volume`` (not ``pressure``, + which is reserved for surface boundary pressure in this codebase). + + Uses defaults when a name tuple is ``None``. Pass ``()`` to skip reading that field group. + """ + p_names = ( + DEFAULT_VOLUME_PRESSURE_NAMES if pressure_names is None else pressure_names + ) + nut_names = ( + DEFAULT_TURBULENT_VISCOSITY_NAMES + if turbulent_viscosity_names is None + else turbulent_viscosity_names + ) + vel_names = DEFAULT_VOLUME_VELOCITY_NAMES if velocity_names is None else velocity_names + + if data_type == "point": + order: list[Literal["cell", "point"]] = ["point", "cell"] + elif data_type == "cell": + order = ["cell", "point"] + else: + order = ["cell", "point"] + + for loc_name in order: + got = _extract_volume_at_location(mesh, loc_name, nut_names, vel_names, p_names) + if got: + return (got, loc_name) + return (None, None) diff --git a/physicsnemo/cfd/evaluation/inference/__init__.py b/physicsnemo/cfd/evaluation/inference/__init__.py new file mode 100644 index 0000000..55ef180 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/__init__.py @@ -0,0 +1,23 @@ +"""Model inference and wrappers.""" + +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + OutputLocation, + get_inference_domain_for_model, + get_model_wrapper, + get_output_location_for_model, + list_models, + register_model, +) +# Ensure wrappers register themselves +import physicsnemo.cfd.evaluation.inference.wrappers # noqa: F401 + +__all__ = [ + "CFDModel", + "OutputLocation", + "register_model", + "get_model_wrapper", + "get_output_location_for_model", + "get_inference_domain_for_model", + "list_models", +] diff --git a/physicsnemo/cfd/evaluation/inference/__main__.py b/physicsnemo/cfd/evaluation/inference/__main__.py new file mode 100644 index 0000000..869dad7 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/__main__.py @@ -0,0 +1,6 @@ +"""python -m physicsnemo.cfd.evaluation.inference""" + +from physicsnemo.cfd.evaluation.inference.run import main + +if __name__ == "__main__": + main() diff --git a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py new file mode 100644 index 0000000..dc351a0 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py @@ -0,0 +1,19 @@ +"""Shared helpers for inference model wrappers (VTK/STL, datapipe tensors, etc.).""" + +from physicsnemo.cfd.evaluation.inference.common_wrapper_utils.vtk_datapipe_io import ( + build_surface_data_dict, + build_volume_data_dict, + read_stl_geometry, + read_surface_from_vtp, + read_volume_from_vtu, + run_id_from_case_id, +) + +__all__ = [ + "build_surface_data_dict", + "build_volume_data_dict", + "read_stl_geometry", + "read_surface_from_vtp", + "read_volume_from_vtu", + "run_id_from_case_id", +] diff --git a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py new file mode 100644 index 0000000..6152938 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py @@ -0,0 +1,136 @@ +"""VTK/STL → tensor dicts for CAE inference datapipes (surface and volume). + +Builds the same keys expected by ``TransolverDataPipe.process_data`` as in +``examples/cfd/external_aerodynamics/transformer_models/src/inference_on_vtk.py``. +Surface: STL + boundary VTP. Volume: STL + volume VTU. DrivAer-style ``run_*`` layout; +used by wrappers that feed those tensors into a datapipes-based model (e.g. Transolver, +GeoTransolver). +""" + +from pathlib import Path + +import numpy as np +import pyvista as pv +import torch + + +def read_stl_geometry(stl_path: str, device: torch.device) -> dict[str, torch.Tensor]: + """Read STL and return stl_coordinates, stl_faces, stl_centers for SDF/center of mass.""" + mesh = pv.read(stl_path) + stl_coordinates = torch.from_numpy(np.asarray(mesh.points)).to( + device=device, dtype=torch.float32 + ) + faces = mesh.faces.reshape(-1, 4)[:, 1:] + stl_faces = torch.from_numpy(faces.flatten()).to(device=device, dtype=torch.int32) + stl_centers = torch.from_numpy(np.asarray(mesh.cell_centers().points)).to( + device=device, dtype=torch.float32 + ) + return { + "stl_coordinates": stl_coordinates, + "stl_faces": stl_faces, + "stl_centers": stl_centers, + } + + +def read_surface_from_vtp( + vtp_path: str, device: torch.device, n_output_fields: int = 4 +) -> dict[str, torch.Tensor]: + """Read VTP surface: cell centers, normals, areas, dummy surface_fields.""" + mesh = pv.read(vtp_path) + surface_mesh_centers = torch.from_numpy(np.asarray(mesh.cell_centers().points)).to( + device=device, dtype=torch.float32 + ) + normals = np.asarray(mesh.cell_normals) + normals = normals / (np.linalg.norm(normals, axis=1, keepdims=True) + 1e-8) + surface_normals = torch.from_numpy(normals).to(device=device, dtype=torch.float32) + cell_sizes = mesh.compute_cell_sizes(length=False, area=True, volume=False) + surface_areas = torch.from_numpy(np.asarray(cell_sizes.cell_data["Area"])).to( + device=device, dtype=torch.float32 + ) + num_cells = surface_mesh_centers.shape[0] + surface_fields = torch.zeros( + (num_cells, n_output_fields), device=device, dtype=torch.float32 + ) + return { + "surface_mesh_centers": surface_mesh_centers, + "surface_normals": surface_normals, + "surface_areas": surface_areas, + "surface_fields": surface_fields, + } + + +def read_volume_from_vtu( + vtu_path: str, device: torch.device, n_output_fields: int = 5 +) -> dict[str, torch.Tensor]: + """Read VTU volume mesh: cell centers and dummy ``volume_fields`` (inference_on_vtk layout).""" + mesh = pv.read(vtu_path) + volume_mesh_centers = torch.from_numpy(np.asarray(mesh.cell_centers().points)).to( + device=device, dtype=torch.float32 + ) + num_cells = volume_mesh_centers.shape[0] + volume_fields = torch.zeros( + (num_cells, n_output_fields), device=device, dtype=torch.float32 + ) + return { + "volume_mesh_centers": volume_mesh_centers, + "volume_fields": volume_fields, + } + + +def build_volume_data_dict( + run_dir: Path, + vtu_path: str, + device: torch.device, + air_density: float, + stream_velocity: float, + run_idx: int = 1, + n_output_fields: int = 5, +) -> dict[str, torch.Tensor]: + """Build data dict for volume inference: STL + VTU + flow params (DrivAer-style run dir).""" + stl_path = run_dir / f"drivaer_{run_idx}_single_solid.stl" + if not stl_path.exists(): + stl_files = list(run_dir.glob("*_single_solid.stl")) + if not stl_files: + raise FileNotFoundError(f"No STL file found in {run_dir}") + stl_path = stl_files[0] + data_dict = read_stl_geometry(str(stl_path), device) + data_dict.update(read_volume_from_vtu(vtu_path, device, n_output_fields=n_output_fields)) + data_dict["air_density"] = torch.tensor([air_density], device=device, dtype=torch.float32) + data_dict["stream_velocity"] = torch.tensor( + [stream_velocity], device=device, dtype=torch.float32 + ) + return data_dict + + +def build_surface_data_dict( + run_dir: Path, + vtp_path: str, + device: torch.device, + air_density: float, + stream_velocity: float, + run_idx: int = 1, +) -> dict[str, torch.Tensor]: + """Build data dict for surface inference: STL + VTP + flow params. Finds STL in run_dir.""" + stl_path = run_dir / f"drivaer_{run_idx}_single_solid.stl" + if not stl_path.exists(): + stl_files = list(run_dir.glob("*_single_solid.stl")) + if not stl_files: + raise FileNotFoundError(f"No STL file found in {run_dir}") + stl_path = stl_files[0] + data_dict = read_stl_geometry(str(stl_path), device) + data_dict.update(read_surface_from_vtp(vtp_path, device)) + data_dict["air_density"] = torch.tensor([air_density], device=device, dtype=torch.float32) + data_dict["stream_velocity"] = torch.tensor( + [stream_velocity], device=device, dtype=torch.float32 + ) + return data_dict + + +def run_id_from_case_id(case_id: str) -> int: + """Parse run index from case_id (e.g. 'run_1' -> 1).""" + if case_id.startswith("run_"): + return int(case_id.split("_")[1]) + try: + return int(case_id) + except ValueError: + return 1 diff --git a/physicsnemo/cfd/evaluation/inference/model_registry.py b/physicsnemo/cfd/evaluation/inference/model_registry.py new file mode 100644 index 0000000..3934ffb --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/model_registry.py @@ -0,0 +1,97 @@ +"""CFDModel base class and registry for model wrappers.""" + +from abc import ABC, abstractmethod +from typing import Any, ClassVar, Literal, Type + +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain + +# Type aliases for model-specific inputs/outputs (opaque to engine) +ModelInput = Any +RawOutput = Any +Predictions = dict[str, Any] + +# Where the model's predictions are defined (mesh points vs cell centers) +OutputLocation = Literal["point", "cell"] + +_REGISTRY: dict[str, Type["CFDModel"]] = {} + + +class CFDModel(ABC): + """Abstract interface for CFD model wrappers. + + ``INFERENCE_DOMAIN`` is ``surface`` or ``volume``. ``OUTPUT_LOCATION`` is where + pointwise/cellwise predictions live (``point`` vs ``cell``) on that mesh. + """ + + OUTPUT_LOCATION: ClassVar[OutputLocation] + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + + @property + @abstractmethod + def output_location(self) -> OutputLocation: + """Whether predictions are defined on mesh points or cell centers (primary branch).""" + ... + + @abstractmethod + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "CFDModel": + """Load weights and stats; return self for chaining.""" + ... + + @abstractmethod + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + """Turn canonical case into model-specific input (tensors, graph, etc.).""" + ... + + @abstractmethod + def predict(self, model_input: ModelInput) -> RawOutput: + """Run forward pass; return raw model output.""" + ... + + @abstractmethod + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + """Denormalize and map to canonical predictions (e.g. pressure, shear_stress).""" + ... + + +def register_model(name: str, wrapper_class: Type[CFDModel]) -> None: + """Register a model wrapper by name.""" + _REGISTRY[name] = wrapper_class + + +def get_model_wrapper(name: str) -> Type[CFDModel]: + """Resolve wrapper class by name.""" + if name not in _REGISTRY: + raise KeyError(f"Unknown model: {name}. Available: {list(_REGISTRY.keys())}") + return _REGISTRY[name] + + +def list_models() -> list[str]: + """Return registered model names.""" + return list(_REGISTRY.keys()) + + +def get_output_location_for_model(name: str) -> OutputLocation: + """Return primary output location (``point`` vs ``cell``) without loading weights.""" + cls = get_model_wrapper(name) + loc = getattr(cls, "OUTPUT_LOCATION", None) + if loc is not None: + return loc # type: ignore[return-value] + raise ValueError( + f"Model wrapper {cls.__name__!r} ({name!r}) has no OUTPUT_LOCATION; " + "cannot align ground truth to model." + ) + + +def get_inference_domain_for_model(name: str) -> InferenceDomain: + """Return ``INFERENCE_DOMAIN`` for the registered wrapper without instantiating.""" + cls = get_model_wrapper(name) + dom = getattr(cls, "INFERENCE_DOMAIN", "surface") + if dom in ("surface", "volume"): + return dom # type: ignore[return-value] + return "surface" diff --git a/physicsnemo/cfd/evaluation/inference/progress.py b/physicsnemo/cfd/evaluation/inference/progress.py new file mode 100644 index 0000000..7a7b1da --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/progress.py @@ -0,0 +1,8 @@ +"""Uniform progress messages for inference (wrappers and CLI).""" + +from __future__ import annotations + + +def log_inference(component: str, message: str) -> None: + """Print a tagged inference progress line (flush for HPC / piped logs).""" + print(f"[inference:{component}] {message}", flush=True) diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py new file mode 100644 index 0000000..1b1e2ba --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -0,0 +1,119 @@ +"""CLI entrypoint for single-case inference.""" + +import argparse +from pathlib import Path + +from physicsnemo.cfd.evaluation.config import load_config +import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 +from physicsnemo.cfd.evaluation.datasets import get_adapter +from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.inference import get_model_wrapper +from physicsnemo.cfd.evaluation.inference.model_registry import get_inference_domain_for_model +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +def _effective_model_inference_domain(model_name: str, override: str | None) -> str: + if override in ("surface", "volume"): + return override + return get_inference_domain_for_model(model_name) + + +def _parse_overrides(args: list[str]) -> dict[str, str]: + overrides = {} + for a in args: + if "=" in a: + s = a[2:] if a.startswith("--") else a + key, _, val = s.partition("=") + overrides[key.strip()] = val.strip() + return overrides + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run inference for one model on one case.") + parser.add_argument("--config", required=True, help="Path to YAML/JSON config") + parser.add_argument( + "--base-config", + default=None, + help="Optional YAML/JSON merged before --config (shared defaults).", + ) + parser.add_argument("--case-id", default=None, help="Case ID (default: first case from dataset)") + parser.add_argument("overrides", nargs="*", help="Key=value overrides, e.g. run.device=cuda:1") + args = parser.parse_args() + overrides = _parse_overrides(getattr(args, "overrides", [])) + config = load_config(args.config, overrides, base=args.base_config) + + adapter_class = get_adapter(config.dataset.name) + dkwargs = resolve_dataset_kwargs_for_model(config.dataset.kwargs, config.model.name) + adapter = adapter_class(root=config.dataset.root, **dkwargs) + log_dataset( + config.dataset.name, + f"Listing cases under root {config.dataset.root!r} (split={config.dataset.split!r})…", + ) + case_ids = config.dataset.case_ids or adapter.list_cases(split=config.dataset.split) + if not case_ids: + raise SystemExit("No cases found for dataset.") + case_id = args.case_id if args.case_id else case_ids[0] + log_dataset( + config.dataset.name, + f"Reading case {case_id!r} from dataset root {config.dataset.root!r}…", + ) + case = adapter.load_case(case_id) + + model_domain = _effective_model_inference_domain( + config.model.name, config.model.inference_domain + ) + adapter_domain = adapter_class.inference_domain_from_kwargs(dkwargs) + if model_domain != adapter_domain: + raise SystemExit( + f"Inference domain mismatch: model {config.model.name!r} expects {model_domain!r}, " + f"dataset adapter {config.dataset.name!r} provides {adapter_domain!r}." + ) + + wrapper_class = get_model_wrapper(config.model.name) + wrapper = wrapper_class() + wrapper.load( + checkpoint_path=config.model.checkpoint, + stats_path=config.model.stats_path, + device=config.run.device, + **config.model.merged_kwargs_for_load(), + ) + model_input = wrapper.prepare_inputs(case) + raw = wrapper.predict(model_input) + predictions = wrapper.decode_outputs(raw, case) + + out_dir = Path(config.run.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + import pyvista as pv + + out_path = out_dir / f"inference_{config.model.name}_{case_id}{'.vtp' if model_domain == 'surface' else '.vtu'}" + log_inference( + config.model.name, + f"Saving predictions to file ({model_domain}): {out_path}", + ) + log_inference( + config.model.name, + f"Reading mesh file to attach fields: {case.mesh_path}", + ) + + if model_domain == "surface": + mesh = pv.read(case.mesh_path) + if not isinstance(mesh, pv.PolyData): + mesh = mesh.extract_surface() + names = config.output.mesh_field_names + else: + mesh = pv.read(case.mesh_path) + if hasattr(mesh, "cast_to_unstructured_grid"): + mesh = mesh.cast_to_unstructured_grid() + names = config.output.volume_mesh_field_names + + data_target = mesh.cell_data if wrapper.output_location == "cell" else mesh.point_data + for canonical_key, mesh_name in names.items(): + if canonical_key in predictions: + data_target[mesh_name] = predictions[canonical_key] + mesh.save(str(out_path)) + log_inference(config.model.name, f"Wrote mesh with prediction fields: {out_path}") + + +if __name__ == "__main__": + main() diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py new file mode 100644 index 0000000..d9737ec --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py @@ -0,0 +1,28 @@ +"""Model wrapper implementations; registration happens on import.""" + +from physicsnemo.cfd.evaluation.inference.model_registry import register_model +from physicsnemo.cfd.evaluation.inference.wrappers.domino import DominoWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.fignet import FIGNetWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.geotransolver import GeoTransolverWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.surface_baseline import SurfaceBaselineWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.transolver import TransolverWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.volume_baseline import VolumeBaselineWrapper +from physicsnemo.cfd.evaluation.inference.wrappers.xmgn import XMGNWrapper + +register_model("fignet", FIGNetWrapper) +register_model("xmgn", XMGNWrapper) +register_model("geotransolver", GeoTransolverWrapper) +register_model("transolver", TransolverWrapper) +register_model("domino", DominoWrapper) +register_model("surface_baseline", SurfaceBaselineWrapper) +register_model("volume_baseline", VolumeBaselineWrapper) + +__all__ = [ + "FIGNetWrapper", + "XMGNWrapper", + "GeoTransolverWrapper", + "TransolverWrapper", + "DominoWrapper", + "SurfaceBaselineWrapper", + "VolumeBaselineWrapper", +] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py new file mode 100644 index 0000000..2d7fa3d --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py @@ -0,0 +1,17 @@ +"""DoMINO surface inference: wrapper + colocated helpers.""" + +from physicsnemo.cfd.evaluation.inference.wrappers.domino.inference import ( + build_domin_surface_datadict, + domino_count_output_features, + domino_surface_test_step, +) +from physicsnemo.cfd.evaluation.inference.wrappers.domino.scaling import load_scaling_factors_tensors +from physicsnemo.cfd.evaluation.inference.wrappers.domino.wrapper import DominoWrapper + +__all__ = [ + "DominoWrapper", + "build_domin_surface_datadict", + "domino_count_output_features", + "domino_surface_test_step", + "load_scaling_factors_tensors", +] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py new file mode 100644 index 0000000..69e1cf7 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py @@ -0,0 +1,631 @@ +"""DoMINO surface inference helpers (aligned with physicsnemo domino ``src/test.py``).""" + +from __future__ import annotations + +import os +import re +from pathlib import Path +from typing import Any, Dict + +import numpy as np +import pyvista as pv +import torch +import vtk +from omegaconf import DictConfig +from vtk.util import numpy_support + +from physicsnemo.models.domino.utils import ( + calculate_center_of_mass, + create_grid, + normalize, + unnormalize, + unstandardize, +) +from physicsnemo.models.domino.geometry_rep import scale_sdf +from physicsnemo.models.domino.utils.vtk_file_utils import ( + get_fields, + get_node_to_elem, + get_volume_data, +) +from physicsnemo.nn.functional import knn, signed_distance_field + +from physicsnemo.cfd.evaluation.datasets.schema import build_predictions_dict + + +def domino_count_output_features(cfg: DictConfig) -> tuple[int | None, int | None, int]: + """Match ``test.py`` variable counting for ``DoMINO`` construction.""" + model_type = cfg.model.model_type + num_vol_vars = None + num_surf_vars = None + if model_type in ("volume", "combined"): + names = list(cfg.variables.volume.solution.keys()) + num_vol_vars = 0 + for j in names: + num_vol_vars += 3 if cfg.variables.volume.solution[j] == "vector" else 1 + if model_type in ("surface", "combined"): + names = list(cfg.variables.surface.solution.keys()) + num_surf_vars = 0 + for j in names: + num_surf_vars += 3 if cfg.variables.surface.solution[j] == "vector" else 1 + global_features = 0 + for param in cfg.variables.global_parameters.keys(): + g = cfg.variables.global_parameters[param] + if g.type == "vector": + global_features += len(g.reference) + else: + global_features += 1 + return num_vol_vars, num_surf_vars, global_features + + +def _find_stl(run_dir: Path, tag: int) -> Path: + """Resolve STL path (domino test uses ``drivaer_{tag}.stl``; DrivAerML may use ``*_single_solid.stl``).""" + candidates = [ + run_dir / f"drivaer_{tag}.stl", + run_dir / f"drivaer_{tag}_single_solid.stl", + ] + for p in candidates: + if p.exists(): + return p + globs = list(run_dir.glob("*_single_solid.stl")) + if globs: + return globs[0] + raise FileNotFoundError(f"No STL found in {run_dir} for tag {tag}") + + +def build_domin_surface_datadict( + cfg: DictConfig, + run_dir: Path, + vtp_path: str, + tag: int, + device: torch.device, +) -> dict[str, torch.Tensor]: + """Build batched (leading dim 1) surface ``data_dict`` like ``test.py`` (surface-only).""" + if cfg.model.model_type != "surface": + raise ValueError( + f"build_domin_surface_datadict requires model.model_type 'surface', got {cfg.model.model_type!r}" + ) + + surface_variable_names = list(cfg.variables.surface.solution.keys()) + stl_path = _find_stl(run_dir, tag) + + reader = pv.get_reader(str(stl_path)) + mesh_stl = reader.read() + stl_vertices = mesh_stl.points + stl_faces = np.array(mesh_stl.faces).reshape((-1, 4))[:, 1:] + mesh_indices_flattened = stl_faces.flatten() + length_scale = np.array( + np.amax(np.amax(stl_vertices, 0) - np.amin(stl_vertices, 0)), + dtype=np.float32, + ) + length_scale = torch.from_numpy(length_scale).to(torch.float32).to(device) + stl_sizes = mesh_stl.compute_cell_sizes(length=False, area=True, volume=False) + stl_sizes = np.array(stl_sizes.cell_data["Area"], dtype=np.float32) + stl_centers = np.array(mesh_stl.cell_centers().points, dtype=np.float32) + + stl_vertices_t = torch.from_numpy(stl_vertices).to(torch.float32).to(device) + stl_sizes_t = torch.from_numpy(stl_sizes).to(torch.float32).to(device) + stl_centers_t = torch.from_numpy(stl_centers).to(torch.float32).to(device) + mesh_indices_flattened_t = ( + torch.from_numpy(mesh_indices_flattened).to(torch.int32).to(device) + ) + + center_of_mass = calculate_center_of_mass(stl_centers_t, stl_sizes_t) + + s_max = torch.from_numpy(np.asarray(cfg.data.bounding_box_surface.max)).to( + torch.float32 + ).to(device) + s_min = torch.from_numpy(np.asarray(cfg.data.bounding_box_surface.min)).to( + torch.float32 + ).to(device) + + nx, ny, nz = cfg.model.interp_res + surf_grid = create_grid( + s_max, s_min, torch.from_numpy(np.asarray([nx, ny, nz])).to(device) + ) + + normed_stl_vertices_cp = normalize(stl_vertices_t, s_max, s_min) + surf_grid_normed = normalize(surf_grid, s_max, s_min) + + sdf_surf_grid, _ = signed_distance_field( + normed_stl_vertices_cp, + mesh_indices_flattened_t, + surf_grid_normed, + use_sign_winding_number=True, + ) + surf_grid_max_min = torch.stack([s_min, s_max]) + + global_params_reference: dict[str, Any] = { + name: cfg.variables.global_parameters[name]["reference"] + for name in cfg.variables.global_parameters.keys() + } + global_params_types = { + name: cfg.variables.global_parameters[name]["type"] + for name in cfg.variables.global_parameters.keys() + } + stream_velocity = global_params_reference["inlet_velocity"][0] + air_density = global_params_reference["air_density"] + + global_params_reference_list: list[float] = [] + for name, typ in global_params_types.items(): + if typ == "vector": + global_params_reference_list.extend(global_params_reference[name]) + elif typ == "scalar": + global_params_reference_list.append(global_params_reference[name]) + else: + raise ValueError(f"Global parameter {name} type not supported") + global_params_reference_t = torch.from_numpy( + np.array(global_params_reference_list, dtype=np.float32) + ).to(device) + + global_params_values_list: list[float] = [] + for key in global_params_types.keys(): + if key == "inlet_velocity": + global_params_values_list.append(stream_velocity) + elif key == "air_density": + global_params_values_list.append(air_density) + else: + raise ValueError(f"Global parameter {key} not supported for this recipe") + global_params_values_t = torch.from_numpy( + np.array(global_params_values_list, dtype=np.float32) + ).to(device) + + vtk_reader = vtk.vtkXMLPolyDataReader() + vtk_reader.SetFileName(vtp_path) + vtk_reader.Update() + polydata_surf = vtk_reader.GetOutput() + celldata_all = get_node_to_elem(polydata_surf) + celldata = celldata_all.GetCellData() + sf_list = get_fields(celldata, surface_variable_names) + surface_fields = np.concatenate(sf_list, axis=-1) # (n_cells, n_components) + + mesh = pv.PolyData(polydata_surf) + surface_coordinates = np.array(mesh.cell_centers().points, dtype=np.float32) + surface_normals = np.array(mesh.cell_normals, dtype=np.float32) + surface_sizes = mesh.compute_cell_sizes(length=False, area=True, volume=False) + surface_sizes = np.array(surface_sizes.cell_data["Area"], dtype=np.float32) + surface_normals = surface_normals / ( + np.linalg.norm(surface_normals, axis=1)[:, np.newaxis] + 1e-8 + ) + + surface_coordinates = ( + torch.from_numpy(surface_coordinates).to(torch.float32).to(device) + ) + surface_normals = torch.from_numpy(surface_normals).to(torch.float32).to(device) + surface_sizes = torch.from_numpy(surface_sizes).to(torch.float32).to(device) + surface_fields = torch.from_numpy(surface_fields).to(torch.float32).to(device) + + if cfg.model.num_neighbors_surface > 1: + ii, _dd = knn( + points=surface_coordinates, + queries=surface_coordinates, + k=cfg.model.num_neighbors_surface, + ) + surface_neighbors = surface_coordinates[ii][:, 1:] + surface_neighbors_normals = surface_normals[ii][:, 1:] + surface_neighbors_sizes = surface_sizes[ii][:, 1:] + else: + surface_neighbors = surface_coordinates + surface_neighbors_normals = surface_normals + surface_neighbors_sizes = surface_sizes + + if cfg.data.normalize_coordinates: + surface_coordinates = normalize(surface_coordinates, s_max, s_min) + surf_grid = normalize(surf_grid, s_max, s_min) + center_of_mass_normalized = normalize(center_of_mass, s_max, s_min) + surface_neighbors = normalize(surface_neighbors, s_max, s_min) + else: + center_of_mass_normalized = center_of_mass + pos_surface_center_of_mass = surface_coordinates - center_of_mass_normalized + + geom_centers = stl_vertices_t + + data_dict: dict[str, torch.Tensor] = { + "pos_surface_center_of_mass": pos_surface_center_of_mass, + "geometry_coordinates": geom_centers, + "surf_grid": surf_grid, + "sdf_surf_grid": sdf_surf_grid, + "surface_mesh_centers": surface_coordinates, + "surface_mesh_neighbors": surface_neighbors, + "surface_normals": surface_normals, + "surface_neighbors_normals": surface_neighbors_normals, + "surface_areas": surface_sizes, + "surface_neighbors_areas": surface_neighbors_sizes, + "surface_fields": surface_fields, + "surface_min_max": surf_grid_max_min, + "length_scale": length_scale, + "global_params_values": torch.unsqueeze(global_params_values_t, -1), + "global_params_reference": torch.unsqueeze(global_params_reference_t, -1), + } + return {k: torch.unsqueeze(v, 0) for k, v in data_dict.items()} + + +def build_domin_volume_datadict( + cfg: DictConfig, + run_dir: Path, + vtu_path: str, + tag: int, + device: torch.device, +) -> dict[str, torch.Tensor]: + """Build batched (leading dim 1) volume ``data_dict`` like domino ``test.py`` (volume-only).""" + if cfg.model.model_type != "volume": + raise ValueError( + f"build_domin_volume_datadict requires model.model_type 'volume', got {cfg.model.model_type!r}" + ) + + volume_variable_names = list(cfg.variables.volume.solution.keys()) + stl_path = _find_stl(run_dir, tag) + + reader = pv.get_reader(str(stl_path)) + mesh_stl = reader.read() + stl_vertices = mesh_stl.points + stl_faces = np.array(mesh_stl.faces).reshape((-1, 4))[:, 1:] + mesh_indices_flattened = stl_faces.flatten() + length_scale = np.array( + np.amax(np.amax(stl_vertices, 0) - np.amin(stl_vertices, 0)), + dtype=np.float32, + ) + length_scale = torch.from_numpy(length_scale).to(torch.float32).to(device) + stl_sizes = mesh_stl.compute_cell_sizes(length=False, area=True, volume=False) + stl_sizes = np.array(stl_sizes.cell_data["Area"], dtype=np.float32) + stl_centers = np.array(mesh_stl.cell_centers().points, dtype=np.float32) + + stl_vertices_t = torch.from_numpy(stl_vertices).to(torch.float32).to(device) + stl_sizes_t = torch.from_numpy(stl_sizes).to(torch.float32).to(device) + stl_centers_t = torch.from_numpy(stl_centers).to(torch.float32).to(device) + mesh_indices_flattened_t = ( + torch.from_numpy(mesh_indices_flattened).to(torch.int32).to(device) + ) + + center_of_mass = calculate_center_of_mass(stl_centers_t, stl_sizes_t) + + s_max = torch.from_numpy(np.asarray(cfg.data.bounding_box_surface.max)).to( + torch.float32 + ).to(device) + s_min = torch.from_numpy(np.asarray(cfg.data.bounding_box_surface.min)).to( + torch.float32 + ).to(device) + + nx, ny, nz = cfg.model.interp_res + surf_grid = create_grid( + s_max, s_min, torch.from_numpy(np.asarray([nx, ny, nz])).to(device) + ) + + normed_stl_vertices_cp = normalize(stl_vertices_t, s_max, s_min) + surf_grid_normed = normalize(surf_grid, s_max, s_min) + + sdf_surf_grid, _ = signed_distance_field( + normed_stl_vertices_cp, + mesh_indices_flattened_t, + surf_grid_normed, + use_sign_winding_number=True, + ) + surf_grid_max_min = torch.stack([s_min, s_max]) + + global_params_reference: dict[str, Any] = { + name: cfg.variables.global_parameters[name]["reference"] + for name in cfg.variables.global_parameters.keys() + } + global_params_types = { + name: cfg.variables.global_parameters[name]["type"] + for name in cfg.variables.global_parameters.keys() + } + stream_velocity = global_params_reference["inlet_velocity"][0] + air_density = global_params_reference["air_density"] + + global_params_reference_list: list[float] = [] + for name, typ in global_params_types.items(): + if typ == "vector": + global_params_reference_list.extend(global_params_reference[name]) + elif typ == "scalar": + global_params_reference_list.append(global_params_reference[name]) + else: + raise ValueError(f"Global parameter {name} type not supported") + global_params_reference_t = torch.from_numpy( + np.array(global_params_reference_list, dtype=np.float32) + ).to(device) + + global_params_values_list: list[float] = [] + for key in global_params_types.keys(): + if key == "inlet_velocity": + global_params_values_list.append(stream_velocity) + elif key == "air_density": + global_params_values_list.append(air_density) + else: + raise ValueError(f"Global parameter {key} not supported for this recipe") + global_params_values_t = torch.from_numpy( + np.array(global_params_values_list, dtype=np.float32) + ).to(device) + + vtk_reader_vol = vtk.vtkXMLUnstructuredGridReader() + vtk_reader_vol.SetFileName(vtu_path) + vtk_reader_vol.Update() + polydata_vol = vtk_reader_vol.GetOutput() + volume_coordinates_np, volume_fields_list = get_volume_data( + polydata_vol, volume_variable_names + ) + volume_fields_np = np.concatenate(volume_fields_list, axis=-1) + volume_coordinates = ( + torch.from_numpy(volume_coordinates_np).to(torch.float32).to(device) + ) + volume_fields = torch.from_numpy(volume_fields_np).to(torch.float32).to(device) + + c_max = torch.from_numpy(np.asarray(cfg.data.bounding_box.max)).to( + torch.float32 + ).to(device) + c_min = torch.from_numpy(np.asarray(cfg.data.bounding_box.min)).to( + torch.float32 + ).to(device) + + grid = create_grid( + c_max, c_min, torch.from_numpy(np.asarray([nx, ny, nz])).to(device) + ) + + if cfg.data.normalize_coordinates: + volume_coordinates = normalize(volume_coordinates, c_max, c_min) + grid = normalize(grid, c_max, c_min) + center_of_mass_normalized = normalize(center_of_mass, c_max, c_min) + normed_stl_vertices_vol = normalize(stl_vertices_t, c_max, c_min) + else: + center_of_mass_normalized = center_of_mass + normed_stl_vertices_vol = stl_vertices_t + + sdf_grid, _ = signed_distance_field( + normed_stl_vertices_vol, + mesh_indices_flattened_t, + grid, + use_sign_winding_number=True, + ) + + sdf_nodes, sdf_node_closest_point = signed_distance_field( + normed_stl_vertices_vol, + mesh_indices_flattened_t, + volume_coordinates, + use_sign_winding_number=True, + ) + sdf_nodes = sdf_nodes.reshape(-1, 1) + vol_grid_max_min = torch.stack([c_min, c_max]) + + pos_volume_closest = volume_coordinates - sdf_node_closest_point + pos_volume_center_of_mass = volume_coordinates - center_of_mass_normalized + + geom_centers = stl_vertices_t + + data_dict: dict[str, torch.Tensor] = { + "pos_volume_closest": pos_volume_closest, + "pos_volume_center_of_mass": pos_volume_center_of_mass, + "geometry_coordinates": geom_centers, + "grid": grid, + "surf_grid": surf_grid, + "sdf_grid": sdf_grid, + "sdf_surf_grid": sdf_surf_grid, + "sdf_nodes": sdf_nodes, + "volume_fields": volume_fields, + "volume_mesh_centers": volume_coordinates, + "volume_min_max": vol_grid_max_min, + "surface_min_max": surf_grid_max_min, + "length_scale": length_scale, + "global_params_values": torch.unsqueeze(global_params_values_t, -1), + "global_params_reference": torch.unsqueeze(global_params_reference_t, -1), + } + return {k: torch.unsqueeze(v, 0) for k, v in data_dict.items()} + + +def domino_surface_test_step( + data_dict: dict[str, torch.Tensor], + model: torch.nn.Module, + cfg: DictConfig, + surf_factors: torch.Tensor, + device: torch.device, + point_batch_size: int, +) -> torch.Tensor: + """Surface branch of ``test_step`` in domino ``test.py``.""" + global_params_values = data_dict["global_params_values"] + global_params_reference = data_dict["global_params_reference"] + stream_velocity = global_params_reference[:, 0, :] + air_density = global_params_reference[:, 1, :] + geo_centers = data_dict["geometry_coordinates"] + s_grid = data_dict["surf_grid"] + sdf_surf_grid = data_dict["sdf_surf_grid"] + surf_max = data_dict["surface_min_max"][:, 1] + surf_min = data_dict["surface_min_max"][:, 0] + + geo_centers_surf = 2.0 * (geo_centers - surf_min) / (surf_max - surf_min) - 1 + encoding_g_surf = model.geo_rep_surface(geo_centers_surf, s_grid, sdf_surf_grid) + + surface_mesh_centers = data_dict["surface_mesh_centers"] + surface_normals = data_dict["surface_normals"] + surface_areas = data_dict["surface_areas"] + surface_mesh_neighbors = data_dict["surface_mesh_neighbors"] + surface_neighbors_normals = data_dict["surface_neighbors_normals"] + surface_neighbors_areas = data_dict["surface_neighbors_areas"] + pos_surface_center_of_mass = data_dict["pos_surface_center_of_mass"] + + surface_areas = torch.unsqueeze(surface_areas, -1) + surface_neighbors_areas = torch.unsqueeze(surface_neighbors_areas, -1) + + num_points = surface_mesh_centers.shape[1] + subdomain_points = int(np.floor(num_points / point_batch_size)) + target_surf = data_dict["surface_fields"] + prediction_surf = torch.zeros_like(target_surf) + + for p in range(subdomain_points + 1): + start_idx = p * point_batch_size + end_idx = (p + 1) * point_batch_size + surface_mesh_centers_batch = surface_mesh_centers[:, start_idx:end_idx] + surface_mesh_neighbors_batch = surface_mesh_neighbors[:, start_idx:end_idx] + surface_normals_batch = surface_normals[:, start_idx:end_idx] + surface_neighbors_normals_batch = surface_neighbors_normals[:, start_idx:end_idx] + surface_areas_batch = surface_areas[:, start_idx:end_idx] + surface_neighbors_areas_batch = surface_neighbors_areas[:, start_idx:end_idx] + pos_surface_center_of_mass_batch = pos_surface_center_of_mass[ + :, start_idx:end_idx + ] + geo_encoding_local = model.surface_local_geo_encodings( + 0.5 * encoding_g_surf, + surface_mesh_centers_batch, + s_grid, + ) + pos_encoding = model.fc_p_surf(pos_surface_center_of_mass_batch) + tpredictions_batch = model.solution_calculator_surf( + surface_mesh_centers_batch, + geo_encoding_local, + pos_encoding, + surface_mesh_neighbors_batch, + surface_normals_batch, + surface_neighbors_normals_batch, + surface_areas_batch, + surface_neighbors_areas_batch, + global_params_values, + global_params_reference, + ) + prediction_surf[:, start_idx:end_idx] = tpredictions_batch + + if cfg.model.normalization == "min_max_scaling": + prediction_surf = unnormalize(prediction_surf, surf_factors[0], surf_factors[1]) + elif cfg.model.normalization == "mean_std_scaling": + prediction_surf = unstandardize( + prediction_surf, surf_factors[0], surf_factors[1] + ) + prediction_surf = ( + prediction_surf * stream_velocity[0, 0] ** 2.0 * air_density[0, 0] + ) + return prediction_surf + + +def domino_volume_test_step( + data_dict: dict[str, torch.Tensor], + model: torch.nn.Module, + cfg: DictConfig, + vol_factors: torch.Tensor, + _device: torch.device, + point_batch_size: int, +) -> torch.Tensor: + """Volume branch of ``test_step`` in domino ``test.py`` (volume-only models).""" + length_scale = data_dict["length_scale"] + global_params_values = data_dict["global_params_values"] + global_params_reference = data_dict["global_params_reference"] + stream_velocity = global_params_reference[:, 0, :] + air_density = global_params_reference[:, 1, :] + geo_centers = data_dict["geometry_coordinates"] + + p_grid = data_dict["grid"] + sdf_grid = data_dict["sdf_grid"] + if "volume_min_max" in data_dict: + vol_max = data_dict["volume_min_max"][:, 1] + vol_min = data_dict["volume_min_max"][:, 0] + geo_centers_vol = 2.0 * (geo_centers - vol_min) / (vol_max - vol_min) - 1 + else: + geo_centers_vol = geo_centers + + encoding_g_vol = model.geo_rep_volume(geo_centers_vol, p_grid, sdf_grid) + + volume_mesh_centers = data_dict["volume_mesh_centers"] + target_vol = data_dict["volume_fields"] + sdf_nodes = data_dict["sdf_nodes"] + pos_volume_closest = data_dict["pos_volume_closest"] + pos_volume_center_of_mass = data_dict["pos_volume_center_of_mass"] + + sdf_scaling_factor = cfg.model.geometry_rep.geo_processor.volume_sdf_scaling_factor + + prediction_vol = torch.zeros_like(target_vol) + num_points = volume_mesh_centers.shape[1] + subdomain_points = int(np.floor(num_points / point_batch_size)) + + for si in range(subdomain_points + 1): + start_idx = si * point_batch_size + end_idx = (si + 1) * point_batch_size + volume_mesh_centers_batch = volume_mesh_centers[:, start_idx:end_idx] + sdf_nodes_batch = sdf_nodes[:, start_idx:end_idx] + scaled_sdf_nodes_batch = [] + for j in range(len(sdf_scaling_factor)): + scaled_sdf_nodes_batch.append( + scale_sdf(sdf_nodes_batch, sdf_scaling_factor[j]) + ) + scaled_sdf_nodes_batch = torch.cat(scaled_sdf_nodes_batch, dim=-1) + + pos_volume_closest_batch = pos_volume_closest[:, start_idx:end_idx] + pos_normals_com_batch = pos_volume_center_of_mass[:, start_idx:end_idx] + geo_encoding_local = model.volume_local_geo_encodings( + 0.5 * encoding_g_vol, + volume_mesh_centers_batch, + p_grid, + ) + if cfg.model.use_sdf_in_basis_func: + pos_encoding_all = torch.cat( + ( + sdf_nodes_batch, + scaled_sdf_nodes_batch, + pos_volume_closest_batch, + pos_normals_com_batch, + ), + axis=-1, + ) + else: + pos_encoding_all = pos_normals_com_batch + + pos_encoding = model.fc_p_vol(pos_encoding_all) + tpredictions_batch = model.solution_calculator_vol( + volume_mesh_centers_batch, + geo_encoding_local, + pos_encoding, + global_params_values, + global_params_reference, + ) + prediction_vol[:, start_idx:end_idx] = tpredictions_batch + + if cfg.model.normalization == "min_max_scaling": + prediction_vol = unnormalize(prediction_vol, vol_factors[0], vol_factors[1]) + elif cfg.model.normalization == "mean_std_scaling": + prediction_vol = unstandardize(prediction_vol, vol_factors[0], vol_factors[1]) + + # Physical scaling matches domino ``test.py`` (DrivAer: vel, pressure, nut order). + n_out = int(prediction_vol.shape[-1]) + if n_out >= 3: + prediction_vol[:, :, :3] = prediction_vol[:, :, :3] * stream_velocity[0, 0] + if n_out >= 4: + prediction_vol[:, :, 3] = ( + prediction_vol[:, :, 3] * stream_velocity[0, 0] ** 2.0 * air_density[0, 0] + ) + if n_out >= 5: + prediction_vol[:, :, 4] = ( + prediction_vol[:, :, 4] * stream_velocity[0, 0] * length_scale[0] + ) + return prediction_vol + + +def domino_volume_predictions_to_canonical( + pred: torch.Tensor, cfg: DictConfig +) -> Dict[str, Any]: + """Map DoMINO volume output (physical units) to canonical keys using ``variables.volume.solution`` order. + + Assumes the usual DrivAer layout (velocity vector, pressure scalar, nut scalar) when names match; + other variables are passed through under their config key name. + """ + if pred.dim() == 3: + pred = pred.squeeze(0) + arr = pred.cpu().numpy().astype(np.float32) + + canonical_kw: dict[str, np.ndarray] = {} + extra: dict[str, np.ndarray] = {} + offset = 0 + for name, typ in cfg.variables.volume.solution.items(): + if typ == "vector": + chunk = arr[:, offset : offset + 3] + offset += 3 + nl = name.lower() + if nl.startswith("u") or "velocity" in nl: + canonical_kw["velocity"] = chunk + else: + extra[name] = chunk + else: + chunk = arr[:, offset] + offset += 1 + nl = name.lower() + if "nut" in nl or "turbulent" in nl or "viscosity" in nl: + canonical_kw["turbulent_viscosity"] = chunk + elif "pmean" in nl or nl.startswith("p_") or nl == "p": + canonical_kw["pressure_volume"] = chunk + else: + extra[name] = chunk + + return build_predictions_dict(**canonical_kw, **extra) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py new file mode 100644 index 0000000..bf7d3a0 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py @@ -0,0 +1,89 @@ +"""Load DoMINO scaling factors from training pickles (compatible with domino ``utils.ScalingFactors``).""" + +from __future__ import annotations + +import os +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict + +import numpy as np +import torch +from omegaconf import DictConfig + + +@dataclass +class ScalingFactors: + """Mirrors ``examples/cfd/external_aerodynamics/domino/src/utils.ScalingFactors`` for unpickling.""" + + mean: Dict[str, np.ndarray] + std: Dict[str, np.ndarray] + min_val: Dict[str, np.ndarray] + max_val: Dict[str, np.ndarray] + field_keys: list[str] + + @classmethod + def load(cls, filepath: str | Path) -> "ScalingFactors": + with open(filepath, "rb") as f: + return pickle.load(f) + + +class _ScalingUnpickler(pickle.Unpickler): + """Map pickles saved as ``utils.ScalingFactors`` to our local class.""" + + def find_class(self, module: str, name: str) -> Any: + if module == "utils" and name == "ScalingFactors": + return ScalingFactors + return super().find_class(module, name) + + +def load_scaling_factors_tensors( + cfg: DictConfig, + device: torch.device, +) -> tuple[torch.Tensor | None, torch.Tensor]: + """Return ``(vol_factors, surf_factors)`` as in domino ``test.py`` / ``load_scaling_factors``.""" + pickle_path = os.path.join(cfg.data.scaling_factors) + pickle_path = os.path.expanduser(pickle_path) + with open(pickle_path, "rb") as f: + try: + scaling_factors = _ScalingUnpickler(f).load() + except Exception: + f.seek(0) + scaling_factors = pickle.load(f) + + if cfg.model.normalization == "min_max_scaling": + vol_factors = np.asarray( + [ + scaling_factors.max_val["volume_fields"], + scaling_factors.min_val["volume_fields"], + ] + ) + surf_factors = np.asarray( + [ + scaling_factors.max_val["surface_fields"], + scaling_factors.min_val["surface_fields"], + ] + ) + elif cfg.model.normalization == "mean_std_scaling": + vol_factors = np.asarray( + [ + scaling_factors.mean["volume_fields"], + scaling_factors.std["volume_fields"], + ] + ) + surf_factors = np.asarray( + [ + scaling_factors.mean["surface_fields"], + scaling_factors.std["surface_fields"], + ] + ) + else: + raise ValueError(f"Invalid normalization mode: {cfg.model.normalization}") + + vol_t = torch.from_numpy(vol_factors).to(device=device, dtype=torch.float32) + surf_t = torch.from_numpy(surf_factors).to(device=device, dtype=torch.float32) + + if cfg.model.model_type == "surface": + vol_t = None + return vol_t, surf_t diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py new file mode 100644 index 0000000..c486c03 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py @@ -0,0 +1,238 @@ +"""DoMINO model wrapper (surface or volume inference; matches domino ``src/test.py``).""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, ClassVar, Literal, Optional + +import torch +from omegaconf import DictConfig, OmegaConf + +from physicsnemo.distributed import DistributedManager +from physicsnemo.models.domino.model import DoMINO + +from physicsnemo.cfd.evaluation.common.checkpoint_compat import trusted_torch_load_context +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain, predictions_dict +from physicsnemo.cfd.evaluation.inference.common_wrapper_utils.vtk_datapipe_io import ( + run_id_from_case_id, +) +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference +from physicsnemo.cfd.evaluation.inference.wrappers.domino.inference import ( + build_domin_surface_datadict, + build_domin_volume_datadict, + domino_count_output_features, + domino_surface_test_step, + domino_volume_predictions_to_canonical, + domino_volume_test_step, +) +from physicsnemo.cfd.evaluation.inference.wrappers.domino.scaling import load_scaling_factors_tensors +from physicsnemo.utils import load_checkpoint + + +class DominoWrapper(CFDModel): + """DoMINO inference using Hydra-style YAML + checkpoint from domino training. + + **Config (``model.kwargs``)** + + - ``domino_config`` (str): Path to ``config.yaml`` (same schema as domino training / test). + ``model.model_type`` must be ``surface`` or ``volume`` (``combined`` is not supported here). + - ``point_batch_size`` (int, optional): Subdomain batch size (default 256000). + + Set ``model.inference_domain: volume`` for VTU cases, or omit it to follow ``model.model_type`` + in the DoMINO YAML. + """ + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def __init__(self) -> None: + self._model: Optional[DoMINO] = None + self._cfg: Optional[DictConfig] = None + self._surf_factors: Optional[torch.Tensor] = None + self._vol_factors: Optional[torch.Tensor] = None + self._device: str = "cuda:0" + self._point_batch_size: int = 256_000 + self._inference_mode: Literal["surface", "volume"] = "surface" + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "DominoWrapper": + kw = dict(kwargs) + cfg_path = kw.get("domino_config") or kw.get("config_path") + if not cfg_path: + raise ValueError( + "DominoWrapper requires model.kwargs.domino_config (path to DoMINO config.yaml)." + ) + self._device = device + self._point_batch_size = int(kw.get("point_batch_size", 256_000)) + + if not DistributedManager.is_initialized(): + DistributedManager.initialize() + + log_inference( + "domino", + f"Loading DoMINO config from {cfg_path}; checkpoint from {checkpoint_path}", + ) + self._cfg = OmegaConf.load(cfg_path) + _cfg_p = Path(cfg_path).resolve() + _sf = self._cfg.data.scaling_factors + if _sf and not Path(str(_sf)).is_absolute(): + OmegaConf.update( + self._cfg, + "data.scaling_factors", + str(_cfg_p.parent / str(_sf)), + ) + + mtype = self._cfg.model.model_type + if mtype not in ("surface", "volume"): + raise NotImplementedError( + "DominoWrapper supports DoMINO config model.model_type " + f"'surface' or 'volume' only; got {mtype!r} (combined is not supported)." + ) + + dom = kw.pop("inference_domain", None) + if dom in ("surface", "volume"): + self._inference_mode = dom + else: + self._inference_mode = "volume" if mtype == "volume" else "surface" + + if self._inference_mode != mtype: + raise ValueError( + f"model.inference_domain is {self._inference_mode!r} but DoMINO config " + f"model.model_type is {mtype!r}; they must match." + ) + + dev = torch.device(device) + vol_f, surf_f = load_scaling_factors_tensors(self._cfg, dev) + self._vol_factors = vol_f + self._surf_factors = surf_f + if self._inference_mode == "surface": + if self._surf_factors is None: + raise RuntimeError("Surface scaling factors missing.") + else: + if self._vol_factors is None: + raise RuntimeError("Volume scaling factors missing.") + + num_vol, num_surf, num_glob = domino_count_output_features(self._cfg) + self._model = DoMINO( + input_features=3, + output_features_vol=num_vol, + output_features_surf=num_surf, + global_features=num_glob, + model_parameters=self._cfg.model, + ).to(dev) + + ckpt = Path(checkpoint_path) + # ``physicsnemo.utils.load_checkpoint`` expects a checkpoint *directory*, not a .pt path. + checkpoint_dir = ckpt.parent if ckpt.is_file() else ckpt + if not checkpoint_dir.is_dir(): + raise FileNotFoundError( + f"Checkpoint path must be a directory or a file inside one; got {checkpoint_path!r}" + ) + log_inference("domino", f"Loading checkpoint from directory {checkpoint_dir}") + + ckpt_args = { + "path": str(checkpoint_dir), + "models": self._model, + } + + loaded_epoch = load_checkpoint(device=dev, **ckpt_args) + self._model.eval() + log_inference("domino", "Checkpoint loaded; model ready for inference.") + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + if self._model is None or self._cfg is None: + raise RuntimeError("DominoWrapper: call load() first") + log_inference( + "domino", + f"Reading case inputs (case {case.case_id}): mesh {case.mesh_path}, " + f"run dir {Path(case.mesh_path).parent}", + ) + run_dir = Path(case.mesh_path).parent + tag = run_id_from_case_id(case.case_id) + dev = torch.device(self._device) + + if self._inference_mode == "volume": + data_dict = build_domin_volume_datadict( + self._cfg, run_dir, case.mesh_path, tag, dev + ) + return { + "data_dict": data_dict, + "cfg": self._cfg, + "vol_factors": self._vol_factors, + "point_batch_size": self._point_batch_size, + "mode": "volume", + } + + data_dict = build_domin_surface_datadict( + self._cfg, run_dir, case.mesh_path, tag, dev + ) + return { + "data_dict": data_dict, + "cfg": self._cfg, + "surf_factors": self._surf_factors, + "point_batch_size": self._point_batch_size, + "mode": "surface", + } + + def predict(self, model_input: ModelInput) -> RawOutput: + if self._model is None: + raise RuntimeError("DominoWrapper: call load() first") + dev = torch.device(self._device) + if model_input.get("mode") == "volume": + log_inference("domino", "Running forward pass (predicting volume fields)…") + with torch.no_grad(): + pred = domino_volume_test_step( + model_input["data_dict"], + self._model, + model_input["cfg"], + model_input["vol_factors"], + dev, + model_input["point_batch_size"], + ) + return pred + + log_inference("domino", "Running forward pass (predicting surface fields)…") + with torch.no_grad(): + pred = domino_surface_test_step( + model_input["data_dict"], + self._model, + model_input["cfg"], + model_input["surf_factors"], + dev, + model_input["point_batch_size"], + ) + return pred + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + if self._inference_mode == "volume": + assert self._cfg is not None + log_inference( + "domino", + "Decoding volume outputs (canonical keys from variables.volume.solution)…", + ) + return domino_volume_predictions_to_canonical(raw_output, self._cfg) + + log_inference("domino", "Decoding outputs (pressure + WSS to numpy)…") + pred = raw_output + if pred.dim() == 3: + pred = pred.squeeze(0) + pressure = pred[:, 0].cpu().numpy().astype("float32") + wss = pred[:, 1:4].cpu().numpy().astype("float32") + return predictions_dict(pressure, wss) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py new file mode 100644 index 0000000..406fccf --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py @@ -0,0 +1,3 @@ +from physicsnemo.cfd.evaluation.inference.wrappers.fignet.wrapper import FIGNetWrapper + +__all__ = ["FIGNetWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py new file mode 100644 index 0000000..5a57135 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py @@ -0,0 +1,230 @@ +"""FIGConvUNet (fignet) model wrapper for DrivAerML-style inference.""" + +from typing import Any, ClassVar, List, Literal, Optional, Tuple, Union + +import numpy as np +import pyvista as pv +import torch + +from physicsnemo.models.figconvnet import FIGConvUNet +from physicsnemo.models.figconvnet.components.reductions import REDUCTION_TYPES +from physicsnemo.models.figconvnet.geometries import GridFeaturesMemoryFormat + +from physicsnemo.cfd.evaluation.common.io import load_global_stats, load_mesh +from physicsnemo.cfd.evaluation.common.interpolation import interpolate_to_mesh +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain, predictions_dict +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +class FIGConvUNetDrivAerML(FIGConvUNet): + """FIGConvUNet variant for DrivAerML; in_channels remapped to hidden_channels[0].""" + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + hidden_channels: List[int], + num_levels: int = 3, + num_down_blocks: Union[int, List[int]] = 1, + num_up_blocks: Union[int, List[int]] = 1, + mlp_channels: List[int] = [512, 512], + aabb_max: Tuple[float, float, float] = (2.5, 1.5, 1.0), + aabb_min: Tuple[float, float, float] = (-2.5, -1.5, -1.0), + voxel_size: Optional[float] = None, + resolution_memory_format_pairs: List[ + Tuple[GridFeaturesMemoryFormat, Tuple[int, int, int]] + ] = [ + (GridFeaturesMemoryFormat.b_xc_y_z, (2, 128, 128)), + (GridFeaturesMemoryFormat.b_yc_x_z, (128, 2, 128)), + (GridFeaturesMemoryFormat.b_zc_x_y, (128, 128, 2)), + ], + use_rel_pos: bool = True, + use_rel_pos_encode: bool = True, + pos_encode_dim: int = 32, + communication_types: List[Literal["mul", "sum"]] = ["sum"], + to_point_sample_method: Literal["graphconv", "interp"] = "graphconv", + neighbor_search_type: Literal["knn", "radius"] = "knn", + knn_k: int = 16, + reductions: List[REDUCTION_TYPES] = ["mean"], + pooling_type: Literal["attention", "max", "mean"] = "max", + pooling_layers: Optional[List[int]] = None, + ): + super().__init__( + in_channels=hidden_channels[0], + out_channels=out_channels, + kernel_size=kernel_size, + hidden_channels=hidden_channels, + num_levels=num_levels, + num_down_blocks=num_down_blocks, + num_up_blocks=num_up_blocks, + mlp_channels=mlp_channels, + aabb_max=aabb_max, + aabb_min=aabb_min, + voxel_size=voxel_size, + resolution_memory_format_pairs=resolution_memory_format_pairs, + use_rel_pos=use_rel_pos, + use_rel_pos_embed=use_rel_pos_encode, + pos_encode_dim=pos_encode_dim, + communication_types=communication_types, + to_point_sample_method=to_point_sample_method, + neighbor_search_type=neighbor_search_type, + knn_k=knn_k, + reductions=reductions, + pooling_type=pooling_type, + pooling_layers=pooling_layers, + ) + + +class FIGNetWrapper(CFDModel): + """Wrapper for FIGConvUNet: cell-center points, pressure + WSS output.""" + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def __init__(self) -> None: + self._model: Optional[FIGConvUNetDrivAerML] = None + self._stats: Optional[dict] = None + self._device: str = "cuda:0" + self._max_points: Optional[int] = None + self._interpolation_k: int = 4 + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "FIGNetWrapper": + self._device = device + self._max_points = kwargs.get("max_points") + self._interpolation_k = kwargs.get("interpolation_k", 4) + log_inference("fignet", f"Loading normalization stats from {stats_path}") + self._stats = load_global_stats(stats_path, device) + log_inference("fignet", f"Loading checkpoint from {checkpoint_path}") + model = FIGConvUNetDrivAerML( + aabb_max=[2.0, 1.8, 2.6], + aabb_min=[-2.0, -1.8, -1.5], + hidden_channels=[16, 16, 16], + in_channels=1, + kernel_size=5, + mlp_channels=[512, 512], + neighbor_search_type="radius", + num_down_blocks=1, + num_levels=2, + out_channels=4, + pooling_layers=[2], + pooling_type="max", + reductions=["mean"], + resolution_memory_format_pairs=[ + (GridFeaturesMemoryFormat.b_xc_y_z, [5, 150, 100]), + (GridFeaturesMemoryFormat.b_yc_x_z, [250, 3, 100]), + (GridFeaturesMemoryFormat.b_zc_x_y, [250, 150, 2]), + ], + use_rel_pos_encode=True, + ) + checkpoint = torch.load(checkpoint_path, weights_only=False) + model.load_state_dict(checkpoint["model"], strict=False) + model = model.to(device) + model.eval() + self._model = model + log_inference("fignet", "Checkpoint loaded; model ready for inference.") + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + if self._model is None or self._stats is None: + raise RuntimeError("FIGNetWrapper: call load() first") + log_inference( + "fignet", + f"Reading mesh (case {case.case_id}): {case.mesh_path}", + ) + mesh = load_mesh(case.mesh_path) + mesh = mesh.compute_normals() + mesh = mesh.compute_cell_sizes() + coords = torch.from_numpy(mesh.cell_centers().points).to( + self._device, dtype=torch.float32 + ) + coords = coords.unsqueeze(0) + n_total = coords.shape[1] + if self._max_points is not None and n_total > self._max_points: + idx = torch.randperm(n_total)[: self._max_points].to(self._device) + coords = torch.index_select(coords, 1, idx) + vertices = (coords - self._stats["mean"]["coordinates"]) / self._stats["std"][ + "coordinates" + ] + self._last_mesh = mesh + self._last_coords_denorm = coords + return { + "mesh": mesh, + "vertices": vertices, + "coords_denorm": coords, + } + + def predict(self, model_input: ModelInput) -> RawOutput: + if self._model is None: + raise RuntimeError("FIGNetWrapper: call load() first") + log_inference("fignet", "Running forward pass (predicting fields)…") + with torch.inference_mode(): + pred, _ = self._model(model_input["vertices"]) + return pred + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + if self._stats is None: + raise RuntimeError("FIGNetWrapper: call load() first") + log_inference( + "fignet", + "Decoding outputs (denormalize + interpolate to mesh cells)…", + ) + # model_input was prepare_inputs output; we need mesh and coords_denorm from there. + # We don't have it in decode_outputs - we only have case. So we must either pass + # model_input through to decode_outputs or re-load mesh in decode_outputs. + # Plan says: decode_outputs(raw_output, case). So we need mesh in case or re-load. + # CanonicalCase has mesh_path, not mesh object. So we re-load mesh here to get + # cell centers for interpolation target. We have raw_output (pred) and stats; + # we need pred_coords (where pred was computed) - that's not in case. + # So the design has a gap: decode_outputs doesn't receive the model_input that + # had coords_denorm. Options: (1) Change API to decode_outputs(raw_output, case, model_input) + # or (2) Have prepare_inputs return a ModelInput that we pass to predict and then + # the engine passes the same model_input to decode_outputs. Plan says decode_outputs(raw_output, case). + # So we need to stash coords_denorm and mesh somewhere. Easiest: have decode_outputs + # accept optional model_input (for interpolation). Or stash in raw_output by + # having predict return a dict { "pred": tensor, "model_input": model_input }. + # That would require the engine to pass model_input to decode_outputs. Let me + # re-read the plan. "decode_outputs(raw_output: RawOutput, case: CanonicalCase) -> Predictions" + # So we can't change the signature. Then the only way is to re-run prepare_inputs in + # decode_outputs to get mesh and coords_denorm again (wasteful but correct), or + # store the last model_input on self (stateful). I'll store last model_input on self + # so decode_outputs can use it (we're single-threaded per model). + mesh = getattr(self, "_last_mesh", None) + coords_denorm = getattr(self, "_last_coords_denorm", None) + if mesh is None or coords_denorm is None: + # Fallback: reload from case (will not have subsampled coords; wrong for subsampled run) + mesh = load_mesh(case.mesh_path) + mesh = mesh.compute_normals() + mesh = mesh.compute_cell_sizes() + coords_denorm = torch.from_numpy(mesh.cell_centers().points).to( + self._device, dtype=torch.float32 + ).unsqueeze(0) + pred = raw_output + pressure = pred[..., :1] * self._stats["std"]["pressure"] + self._stats["mean"]["pressure"] + wss = pred[..., 1:] * self._stats["std"]["shear_stress"] + self._stats["mean"]["shear_stress"] + target_points = mesh.cell_centers().points + p_mesh, wss_mesh = interpolate_to_mesh( + target_points, + coords_denorm[0].cpu().numpy(), + pressure[0], + wss[0], + k=self._interpolation_k, + ) + return predictions_dict(p_mesh, wss_mesh) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py new file mode 100644 index 0000000..ae041f2 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py @@ -0,0 +1,3 @@ +from physicsnemo.cfd.evaluation.inference.wrappers.geotransolver.wrapper import GeoTransolverWrapper + +__all__ = ["GeoTransolverWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py new file mode 100644 index 0000000..a52245b --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py @@ -0,0 +1,412 @@ +"""GeoTransolver model wrapper for surface or volume inference (TransolverDataPipe + VTK). + +Surface: cell-centered pressure + WSS on boundary VTP (``inference_domain: surface`` or default). + +Volume: velocity + pressure + turbulent viscosity on volume VTU (``inference_domain: volume``), +aligned with ``examples/cfd/external_aerodynamics/transformer_models/src/inference_on_vtk.py``. +""" + +from pathlib import Path +from typing import Any, ClassVar, Literal, Optional + +import numpy as np +import torch + +from physicsnemo.cfd.evaluation.datasets.schema import ( + CanonicalCase, + InferenceDomain, + build_predictions_dict, + predictions_dict, +) +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference +from physicsnemo.cfd.evaluation.common.checkpoint_compat import trusted_torch_load_context +from physicsnemo.cfd.evaluation.common.io import load_transolver_surface_factors, load_transolver_volume_factors +from physicsnemo.cfd.evaluation.inference.common_wrapper_utils.vtk_datapipe_io import ( + build_surface_data_dict, + build_volume_data_dict, + run_id_from_case_id, +) + +# Optional physicsnemo imports (required for real inference) +try: + from physicsnemo.distributed import DistributedManager + from physicsnemo.datapipes.cae.transolver_datapipe import TransolverDataPipe + from physicsnemo.experimental.models.geotransolver import GeoTransolver + from physicsnemo.utils import load_checkpoint + + _PHYSICSNEMO_AVAILABLE = True +except ImportError: + _PHYSICSNEMO_AVAILABLE = False + + +# Default GeoTransolver surface config (from geotransolver_surface + model/geotransolver.yaml) +DEFAULT_GEOTRANSOLVER_KW = dict( + functional_dim=6, + global_dim=2, + geometry_dim=3, + out_dim=4, + n_layers=20, + n_hidden=256, + dropout=0.0, + n_head=8, + act="gelu", + mlp_ratio=2, + slice_num=128, + use_te=False, + plus=False, + include_local_features=True, + radii=[0.01, 0.05, 0.25, 1.0, 2.5, 5.0], + neighbors_in_radius=[4, 8, 16, 64, 128, 256], + n_hidden_local=32, +) + +# Volume training defaults (geotransolver_volume.yaml) +DEFAULT_GEOTRANSOLVER_VOLUME_KW = { + **DEFAULT_GEOTRANSOLVER_KW, + "functional_dim": 7, + "out_dim": 5, +} + +def _global_fx_to_bnc(fx: torch.Tensor) -> torch.Tensor: + """GeoTransolver requires ``global_embedding`` shape (B, N_g, C_g). + + With ``broadcast_global_features=False``, ``TransolverDataPipe`` may stack ``fx`` as + (1, 1, C) before ``__call__`` adds another batch dimension, yielding (1, 1, 1, C). + Squeeze singleton middle dims until 3D. + """ + out = fx + while out.ndim > 3: + for d in range(1, out.ndim - 1): + if out.shape[d] == 1: + out = out.squeeze(d) + break + else: + raise ValueError( + "Cannot reshape global ``fx`` to 3D for GeoTransolver; " + f"shape={tuple(fx.shape)}" + ) + return out + + +_DATAPIPE_KEYS = frozenset( + { + "include_normals", + "include_sdf", + "translational_invariance", + "scale_invariance", + "reference_scale", + "broadcast_global_features", + "include_geometry", + "return_mesh_features", + } +) + + +def _surface_datapipe_static_kw() -> dict[str, Any]: + return dict( + include_normals=True, + include_sdf=False, + broadcast_global_features=False, + include_geometry=True, + translational_invariance=True, + scale_invariance=True, + reference_scale=[12.0, 4.5, 3.25] + ) + + +def _volume_datapipe_static_kw() -> dict[str, Any]: + """Defaults aligned with ``data/core.yaml`` + ``geotransolver_volume.yaml``.""" + return dict( + include_normals=True, + include_sdf=True, + translational_invariance=True, + scale_invariance=True, + reference_scale=[12.0, 4.5, 3.25], + broadcast_global_features=False, + include_geometry=True, + return_mesh_features=False, + ) + + +class GeoTransolverWrapper(CFDModel): + """GeoTransolver: VTP+STL (surface) or VTU+STL (volume) via ``inference_domain`` in load kwargs.""" + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def __init__(self) -> None: + self._model: Optional[GeoTransolver] = None + self._datapipe: Optional[TransolverDataPipe] = None + self._datapipe_geometry_effective: Optional[int] = None + self._surface_factors: Any = None + self._volume_factors: Any = None + self._geometry_sampling_requested: int = 300_000 + self._datapipe_resolution: int = 100_000 + self._device: str = "cuda:0" + self._air_density: float = 1.205 + self._stream_velocity: float = 30.0 + self._batch_resolution: int = 2048 + self._inference_mode: Literal["surface", "volume"] = "surface" + self._datapipe_user_kw: dict[str, Any] = {} + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "GeoTransolverWrapper": + if not _PHYSICSNEMO_AVAILABLE: + raise RuntimeError( + "GeoTransolver wrapper requires physicsnemo (GeoTransolver, TransolverDataPipe, load_checkpoint)." + ) + kw = dict(kwargs) + dom = kw.pop("inference_domain", None) + self._inference_mode = dom if dom in ("surface", "volume") else "surface" + + self._device = device + self._air_density = float(kw.get("air_density", 1.205)) + self._stream_velocity = float(kw.get("stream_velocity", 30.0)) + self._batch_resolution = int(kw.get("batch_resolution", 2048)) + + checkpoint_dir = Path(checkpoint_path) + if checkpoint_dir.is_file(): + checkpoint_dir = checkpoint_dir.parent + + dp_user = {k: kw.pop(k) for k in list(kw.keys()) if k in _DATAPIPE_KEYS} + + if self._inference_mode == "volume": + log_inference("geotransolver", f"Loading volume normalization from {stats_path}") + self._volume_factors = load_transolver_volume_factors(stats_path, device) + if self._volume_factors is None: + raise FileNotFoundError( + "Volume inference requires ``global_stats.json`` (with velocity, " + "pressure_volume, turbulent_viscosity) or ``volume_fields_normalization.npz`` " + f"next to stats/checkpoint (looked under {stats_path!r})." + ) + self._surface_factors = None + model_kw = dict(DEFAULT_GEOTRANSOLVER_VOLUME_KW) + else: + log_inference("geotransolver", f"Loading surface normalization from {stats_path}") + self._surface_factors = load_transolver_surface_factors(stats_path, device) + self._volume_factors = None + model_kw = dict(DEFAULT_GEOTRANSOLVER_KW) + + self._geometry_sampling_requested = int(kw.get("geometry_sampling", 300_000)) + self._datapipe_resolution = int(kw.get("resolution", 100_000)) + + if not DistributedManager.is_initialized(): + DistributedManager.initialize() + dev = torch.device(device) + + self._datapipe = None + self._datapipe_geometry_effective = None + self._datapipe_user_kw = dp_user + + self._model = GeoTransolver(**model_kw) + # with trusted_torch_load_context(): + ckpt_args = { + "path": str(checkpoint_dir), + "models": self._model, + } + + loaded_epoch = load_checkpoint(device=dev, **ckpt_args) + # load_checkpoint(path=str(checkpoint_dir), models=self._model, device=dev) + self._model = self._model.to(dev) + self._model.eval() + return self + + def _move_reference_scale_to_device(self, dp: TransolverDataPipe) -> None: + """``reference_scale`` is often created on CPU; mesh tensors live on ``self._device``.""" + if dp.config.scale_invariance and dp.config.reference_scale is not None: + dev = torch.device(self._device) + dp.config.reference_scale = dp.config.reference_scale.to(dev) + + def _make_datapipe_surface(self, geometry_sampling: int) -> TransolverDataPipe: + dp = TransolverDataPipe( + input_path=None, + model_type="surface", + resolution=None, + surface_factors=self._surface_factors, + volume_factors=None, + scaling_type="mean_std_scaling", + return_mesh_features=True, + geometry_sampling=geometry_sampling, + **_surface_datapipe_static_kw(), + ) + self._move_reference_scale_to_device(dp) + return dp + + def _make_datapipe_volume( + self, geometry_sampling: int, user_kw: dict[str, Any] + ) -> TransolverDataPipe: + merged = {**_volume_datapipe_static_kw(), **user_kw} + merged["geometry_sampling"] = geometry_sampling + dp = TransolverDataPipe( + input_path=None, + model_type="volume", + resolution=None, + surface_factors=None, + volume_factors=self._volume_factors, + scaling_type="mean_std_scaling", + **merged, + ) + self._move_reference_scale_to_device(dp) + return dp + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + if self._model is None: + raise RuntimeError("GeoTransolverWrapper: call load() first") + log_inference( + "geotransolver", + f"Reading case inputs (case {case.case_id}): mesh {case.mesh_path}, " + f"run dir {Path(case.mesh_path).parent}", + ) + run_dir = Path(case.mesh_path).parent + run_idx = run_id_from_case_id(case.case_id) + device = torch.device(self._device) + + if self._inference_mode == "volume": + data_dict = build_volume_data_dict( + run_dir=run_dir, + vtu_path=case.mesh_path, + device=device, + air_density=self._air_density, + stream_velocity=self._stream_velocity, + run_idx=run_idx, + ) + n_stl = int(data_dict["stl_coordinates"].shape[0]) + safe_geo = max(1, min(self._geometry_sampling_requested, n_stl)) + user_kw = self._datapipe_user_kw + if self._datapipe is None or self._datapipe_geometry_effective != safe_geo: + self._datapipe = self._make_datapipe_volume(safe_geo, user_kw) + self._datapipe_geometry_effective = safe_geo + else: + data_dict = build_surface_data_dict( + run_dir=run_dir, + vtp_path=case.mesh_path, + device=device, + air_density=self._air_density, + stream_velocity=self._stream_velocity, + run_idx=run_idx, + ) + n_stl = int(data_dict["stl_coordinates"].shape[0]) + n_surf = int(data_dict["surface_mesh_centers"].shape[0]) + safe_geo = min(self._geometry_sampling_requested, n_stl, n_surf) + safe_geo = max(1, safe_geo) + if self._datapipe is None or self._datapipe_geometry_effective != safe_geo: + self._datapipe = self._make_datapipe_surface(safe_geo) + self._datapipe_geometry_effective = safe_geo + + batch = self._datapipe(data_dict) + return {"batch": batch, "datapipe": self._datapipe} + + def predict(self, model_input: ModelInput) -> RawOutput: + if self._model is None or self._datapipe is None: + raise RuntimeError("GeoTransolverWrapper: call load() first") + log_inference("geotransolver", "Running forward pass (predicting fields)…") + batch = model_input["batch"] + datapipe = model_input["datapipe"] + fx_bn_c = _global_fx_to_bnc(batch["fx"]) + N = batch["embeddings"].shape[1] + batch_res = min(self._batch_resolution, N) + indices = torch.randperm(N, device=batch["embeddings"].device) + index_blocks = torch.split(indices, batch_res) + preds_list = [] + use_full_fx = "geometry" in batch + # if "geometry" in batch: + # geo = batch["geometry"] + # g_pts_dim = 1 if geo.ndim >= 3 else 0 + # n_geo = int(geo.shape[g_pts_dim]) + # if n_geo > 1: + # geo_perm = torch.randperm(n_geo, device=geo.device) + # batch["geometry"] = geo.index_select(g_pts_dim, geo_perm) + + with torch.no_grad(): + for index_block in index_blocks: + local_embeddings = batch["embeddings"][:, index_block] + local_fields = batch["fields"][:, index_block] + local_fx = fx_bn_c if use_full_fx else fx_bn_c[:, index_block] + local_batch = { + "fx": local_fx, + "embeddings": local_embeddings, + "fields": local_fields, + } + if "geometry" in batch: + local_batch["geometry"] = batch["geometry"] + if "air_density" in batch: + local_batch["air_density"] = batch["air_density"] + if "stream_velocity" in batch: + local_batch["stream_velocity"] = batch["stream_velocity"] + local_positions = local_embeddings[:, :, :3] + outputs = self._model( + local_embedding=local_embeddings, + local_positions=local_positions, + global_embedding=local_fx, + geometry=local_batch.get("geometry"), + ) + preds_list.append(outputs) + # import pdb; pdb.set_trace() + predictions = torch.cat(preds_list, dim=1) + inverse_indices = torch.empty_like(indices) + inverse_indices[indices] = torch.arange(N, device=indices.device) + predictions = predictions[:, inverse_indices] + predictions = predictions.squeeze(0) + # import pdb; pdb.set_trace() + + if self._inference_mode == "volume": + predictions = datapipe.unscale_model_targets( + predictions, + air_density=batch.get("air_density"), + stream_velocity=batch.get("stream_velocity"), + factor_type="volume", + ) + return predictions + + predictions = datapipe.unscale_model_targets( + predictions, + air_density=batch.get("air_density"), + stream_velocity=batch.get("stream_velocity"), + factor_type="surface", + ) + # predictions = predictions * batch.get("air_density") * (batch.get("stream_velocity") ** 2) + return predictions + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + pred = raw_output + if pred.dim() == 3: + pred = pred.squeeze(0) + + if self._inference_mode == "volume": + log_inference( + "geotransolver", + "Decoding outputs (velocity + pressure + nut → canonical volume keys)…", + ) + u = float(self._stream_velocity) + rho = float(self._air_density) + dynamic_pressure = rho * (u**2) + velocity = (pred[:, 0:3] * u).cpu().numpy().astype(np.float32) + pressure_volume = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) + turbulent_viscosity = (pred[:, 4] * dynamic_pressure).cpu().numpy().astype(np.float32) + return build_predictions_dict( + velocity=velocity, + pressure_volume=pressure_volume, + turbulent_viscosity=turbulent_viscosity, + ) + + log_inference("geotransolver", "Decoding outputs (pressure + WSS to numpy)…") + dynamic_pressure = self._air_density * (self._stream_velocity ** 2) + pressure = (pred[:, 0] * dynamic_pressure).cpu().numpy().astype(np.float32) + wss = (pred[:, 1:4] * dynamic_pressure).cpu().numpy().astype(np.float32) + return predictions_dict(pressure, wss) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py b/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py new file mode 100644 index 0000000..431362a --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py @@ -0,0 +1,73 @@ +"""Surface baseline wrapper: zeros on boundary VTP for pipeline / smoke tests.""" + +from __future__ import annotations + +from typing import Any, ClassVar + +import numpy as np + +from physicsnemo.cfd.evaluation.common.io import load_mesh +from physicsnemo.cfd.evaluation.datasets.schema import ( + CanonicalCase, + InferenceDomain, + predictions_dict, +) +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +class SurfaceBaselineWrapper(CFDModel): + """No trained weights: zeros for ``pressure`` and ``shear_stress`` on the surface mesh. + + Uses **cell**-centered counts (same as many surface wrappers here). Pair with + ``drivaerml`` default surface branch and ``align_ground_truth_to_model`` / GT + location as needed for metrics. + """ + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> SurfaceBaselineWrapper: + log_inference( + "surface_baseline", + "No checkpoint to load (baseline stub).", + ) + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + log_inference( + "surface_baseline", + f"Preparing inputs (case {case.case_id}; mesh read in decode step).", + ) + return None + + def predict(self, model_input: ModelInput) -> RawOutput: + log_inference("surface_baseline", "Running forward pass (no-op for baseline)…") + return None + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + log_inference( + "surface_baseline", + f"Reading surface mesh and building baseline fields: {case.mesh_path}", + ) + mesh = load_mesh(case.mesh_path) + n = mesh.n_cells if self.output_location == "cell" else mesh.n_points + p = np.zeros((n,), dtype=np.float32) + wss = np.zeros((n, 3), dtype=np.float32) + return predictions_dict(p, wss) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py new file mode 100644 index 0000000..1588d03 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py @@ -0,0 +1,3 @@ +from physicsnemo.cfd.evaluation.inference.wrappers.transolver.wrapper import TransolverWrapper + +__all__ = ["TransolverWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py new file mode 100644 index 0000000..ce271af --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py @@ -0,0 +1,335 @@ +"""Transolver model wrapper for surface or volume inference (TransolverDataPipe + VTK). + +Surface: cell-centered pressure + WSS (default). Volume: velocity + pressure + nut on VTU when +``inference_domain: volume``, matching ``inference_on_vtk.py`` / ``transolver_volume`` training. +""" + +from pathlib import Path +from typing import Any, ClassVar, Literal, Optional + +import numpy as np +import torch + +from physicsnemo.cfd.evaluation.datasets.schema import ( + CanonicalCase, + InferenceDomain, + build_predictions_dict, + predictions_dict, +) +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference +from physicsnemo.cfd.evaluation.common.checkpoint_compat import trusted_torch_load_context +from physicsnemo.cfd.evaluation.common.io import load_transolver_surface_factors, load_transolver_volume_factors +from physicsnemo.cfd.evaluation.inference.common_wrapper_utils.vtk_datapipe_io import ( + build_surface_data_dict, + build_volume_data_dict, + run_id_from_case_id, +) + +# Optional physicsnemo imports (required for real inference) +try: + from physicsnemo.distributed import DistributedManager + from physicsnemo.datapipes.cae.transolver_datapipe import TransolverDataPipe + from physicsnemo.models.transolver.transolver import Transolver + from physicsnemo.utils import load_checkpoint + + _PHYSICSNEMO_AVAILABLE = True +except ImportError: + _PHYSICSNEMO_AVAILABLE = False + + +# Default Transolver surface config (from transolver_surface / model/transolver.yaml) +DEFAULT_TRANSOLVER_KW = dict( + functional_dim=2, + out_dim=4, + embedding_dim=6, + n_layers=20, + n_hidden=256, + dropout=0.0, + n_head=8, + act="gelu", + mlp_ratio=2, + slice_num=512, + unified_pos=False, + ref=8, + structured_shape=None, + use_te=False, + time_input=False, + plus=False, +) + +DEFAULT_TRANSOLVER_VOLUME_KW = { + **DEFAULT_TRANSOLVER_KW, + "embedding_dim": 7, + "out_dim": 5, +} + +_DATAPIPE_KEYS = frozenset( + { + "include_normals", + "include_sdf", + "translational_invariance", + "scale_invariance", + "reference_scale", + "broadcast_global_features", + "include_geometry", + "return_mesh_features", + } +) + + +def _surface_datapipe_kw() -> dict[str, Any]: + return dict( + include_normals=True, + include_sdf=False, + broadcast_global_features=True, + include_geometry=False, + translational_invariance=True, + reference_scale=[12.0, 4.5, 3.25], + scale_invariance=True, + return_mesh_features=True, + ) + + +def _volume_datapipe_kw() -> dict[str, Any]: + return dict( + include_normals=True, + include_sdf=True, + translational_invariance=True, + scale_invariance=True, + reference_scale=[12.0, 4.5, 3.25], + broadcast_global_features=True, + include_geometry=False, + return_mesh_features=False, + ) + + +class TransolverWrapper(CFDModel): + """Transolver: boundary VTP (surface) or volume VTU; set ``model.inference_domain: volume`` for VTU.""" + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def __init__(self) -> None: + self._model: Optional[Transolver] = None + self._datapipe: Optional[TransolverDataPipe] = None + self._device: str = "cuda:0" + self._air_density: float = 1.205 + self._stream_velocity: float = 30.0 + self._batch_resolution: int = 2048 + self._datapipe_resolution: int = 10_000_000 + self._inference_mode: Literal["surface", "volume"] = "surface" + self._datapipe_user_kw: dict[str, Any] = {} + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "TransolverWrapper": + if not _PHYSICSNEMO_AVAILABLE: + raise RuntimeError( + "Transolver wrapper requires physicsnemo (Transolver, TransolverDataPipe, load_checkpoint)." + ) + kw = dict(kwargs) + dom = kw.pop("inference_domain", None) + self._inference_mode = dom if dom in ("surface", "volume") else "surface" + + self._device = device + self._air_density = float(kw.get("air_density", 1.205)) + self._stream_velocity = float(kw.get("stream_velocity", 30.0)) + self._batch_resolution = int(kw.get("batch_resolution", 2048)) + self._datapipe_resolution = int(kw.get("resolution", 10_000_000)) + + checkpoint_dir = Path(checkpoint_path) + if checkpoint_dir.is_file(): + checkpoint_dir = checkpoint_dir.parent + + dp_user = {k: kw.pop(k) for k in list(kw.keys()) if k in _DATAPIPE_KEYS} + self._datapipe_user_kw = dp_user + + if not DistributedManager.is_initialized(): + DistributedManager.initialize() + dev = torch.device(device) + + if self._inference_mode == "volume": + log_inference("transolver", f"Loading volume normalization from {stats_path}") + volume_factors = load_transolver_volume_factors(stats_path, device) + if volume_factors is None: + raise FileNotFoundError( + "Volume inference requires ``global_stats.json`` (with velocity, " + "pressure_volume, turbulent_viscosity) or ``volume_fields_normalization.npz`` " + f"next to stats/checkpoint (looked under {stats_path!r})." + ) + pipe_kw = {**_volume_datapipe_kw(), **dp_user} + self._datapipe = TransolverDataPipe( + input_path=None, + model_type="volume", + resolution=None, + surface_factors=None, + volume_factors=volume_factors, + scaling_type="mean_std_scaling", + **pipe_kw, + ) + self._move_reference_scale_to_device(self._datapipe) + if self._datapipe.config.scale_invariance and self._datapipe.config.reference_scale is not None: + self._datapipe.config.reference_scale = self._datapipe.config.reference_scale.to(dev) + model_kw = dict(DEFAULT_TRANSOLVER_VOLUME_KW) + else: + log_inference("transolver", f"Loading surface normalization from {stats_path}") + surface_factors = load_transolver_surface_factors(stats_path, device) + pipe_kw = {**_surface_datapipe_kw(), **dp_user} + self._datapipe = TransolverDataPipe( + input_path=None, + model_type="surface", + resolution=None, + surface_factors=surface_factors, + volume_factors=None, + scaling_type="mean_std_scaling", + **pipe_kw, + ) + self._move_reference_scale_to_device(self._datapipe) + model_kw = dict(DEFAULT_TRANSOLVER_KW) + + self._model = Transolver(**model_kw) + log_inference("transolver", f"Loading checkpoint from {checkpoint_dir}") + ckpt_args = { + "path": str(checkpoint_dir), + "models": self._model, + } + loaded_epoch = load_checkpoint(device=dev, **ckpt_args) + # load_checkpoint(path=str(checkpoint_dir), models=self._model, device=dev) + self._model = self._model.to(dev) + self._model.eval() + log_inference("transolver", "Checkpoint loaded; model ready for inference.") + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + if self._datapipe is None: + raise RuntimeError("TransolverWrapper: call load() first") + log_inference( + "transolver", + f"Reading case inputs (case {case.case_id}): mesh {case.mesh_path}, " + f"run dir {Path(case.mesh_path).parent}", + ) + run_dir = Path(case.mesh_path).parent + run_idx = run_id_from_case_id(case.case_id) + device = torch.device(self._device) + + if self._inference_mode == "volume": + data_dict = build_volume_data_dict( + run_dir=run_dir, + vtu_path=case.mesh_path, + device=device, + air_density=self._air_density, + stream_velocity=self._stream_velocity, + run_idx=run_idx, + ) + else: + data_dict = build_surface_data_dict( + run_dir=run_dir, + vtp_path=case.mesh_path, + device=device, + air_density=self._air_density, + stream_velocity=self._stream_velocity, + run_idx=run_idx, + ) + + batch = self._datapipe(data_dict) + return {"batch": batch, "datapipe": self._datapipe} + + def predict(self, model_input: ModelInput) -> RawOutput: + if self._model is None or self._datapipe is None: + raise RuntimeError("TransolverWrapper: call load() first") + log_inference("transolver", "Running forward pass (predicting fields)…") + batch = model_input["batch"] + datapipe = model_input["datapipe"] + dev = batch["embeddings"].device + N = batch["embeddings"].shape[1] + batch_res = min(self._batch_resolution, N) + indices = torch.randperm(N, device=batch["embeddings"].device) + index_blocks = torch.split(indices, batch_res) + preds_list = [] + with torch.no_grad(): + for index_block in index_blocks: + local_embeddings = batch["embeddings"][:, index_block] + local_fx = batch["fx"][:, index_block] + local_batch = { + "fx": local_fx, + "embeddings": local_embeddings, + "fields": batch["fields"][:, index_block], + } + if "air_density" in batch: + local_batch["air_density"] = batch["air_density"] + if "stream_velocity" in batch: + local_batch["stream_velocity"] = batch["stream_velocity"] + outputs = self._model(fx=local_fx, embedding=local_embeddings) + preds_list.append(outputs) + predictions = torch.cat(preds_list, dim=1) + inverse_indices = torch.empty_like(indices) + inverse_indices[indices] = torch.arange(N, device=indices.device) + predictions = predictions[:, inverse_indices] + # import pdb; pdb.set_trace() + predictions = predictions.squeeze(0) + + if self._inference_mode == "volume": + return datapipe.unscale_model_targets( + predictions, + air_density=batch.get("air_density"), + stream_velocity=batch.get("stream_velocity"), + factor_type="volume", + ) + + predictions = datapipe.unscale_model_targets( + predictions, + air_density=batch.get("air_density"), + stream_velocity=batch.get("stream_velocity"), + factor_type="surface", + ) + # predictions = predictions * batch.get("air_density") * (batch.get("stream_velocity") ** 2) + return predictions + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + pred = raw_output + if pred.dim() == 3: + pred = pred.squeeze(0) + + if self._inference_mode == "volume": + log_inference( + "transolver", + "Decoding outputs (velocity + pressure + nut → canonical volume keys)…", + ) + u = float(self._stream_velocity) + rho = float(self._air_density) + dynamic_pressure = rho * (u**2) + velocity = (pred[:, 0:3] * u).cpu().numpy().astype(np.float32) + pressure_volume = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) + turbulent_viscosity = (pred[:, 4] * dynamic_pressure).cpu().numpy().astype(np.float32) + return build_predictions_dict( + velocity=velocity, + pressure_volume=pressure_volume, + turbulent_viscosity=turbulent_viscosity, + ) + + log_inference("transolver", "Decoding outputs (pressure + WSS to numpy)…") + dynamic_pressure = self._air_density * (self._stream_velocity ** 2) + pressure = (pred[:, 0] * dynamic_pressure).cpu().numpy().astype(np.float32) + wss = (pred[:, 1:4] * dynamic_pressure).cpu().numpy().astype(np.float32) + return predictions_dict(pressure, wss) + + def _move_reference_scale_to_device(self, dp: TransolverDataPipe) -> None: + """``reference_scale`` is often created on CPU; mesh tensors live on ``self._device``.""" + if dp.config.scale_invariance and dp.config.reference_scale is not None: + dev = torch.device(self._device) + dp.config.reference_scale = dp.config.reference_scale.to(dev) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py new file mode 100644 index 0000000..583a6b3 --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py @@ -0,0 +1,71 @@ +"""Volume baseline wrapper: zeros (or shape-correct) predictions for pipeline tests.""" + +from __future__ import annotations + +from typing import Any, ClassVar + +import numpy as np +import pyvista as pv + +from physicsnemo.cfd.evaluation.datasets.schema import ( + CanonicalCase, + InferenceDomain, + build_predictions_dict, +) +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +class VolumeBaselineWrapper(CFDModel): + """No trained weights: zeros for ``pressure_volume`` and ``turbulent_viscosity`` on the volume mesh.""" + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "volume" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> VolumeBaselineWrapper: + log_inference( + "volume_baseline", + "No checkpoint to load (baseline stub).", + ) + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + log_inference( + "volume_baseline", + f"Preparing inputs (case {case.case_id}; mesh read in decode step).", + ) + return None + + def predict(self, model_input: ModelInput) -> RawOutput: + log_inference("volume_baseline", "Running forward pass (no-op for baseline)…") + return None + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + log_inference( + "volume_baseline", + f"Reading volume mesh and building baseline fields: {case.mesh_path}", + ) + mesh = pv.read(case.mesh_path) + if hasattr(mesh, "cast_to_unstructured_grid"): + mesh = mesh.cast_to_unstructured_grid() + n = mesh.n_cells if self.output_location == "cell" else mesh.n_points + return build_predictions_dict( + pressure_volume=np.zeros((n,), dtype=np.float32), + turbulent_viscosity=np.zeros((n,), dtype=np.float32), + ) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py new file mode 100644 index 0000000..8a39f0d --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py @@ -0,0 +1,3 @@ +from physicsnemo.cfd.evaluation.inference.wrappers.xmgn.wrapper import XMGNWrapper + +__all__ = ["XMGNWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py new file mode 100644 index 0000000..96f6c3e --- /dev/null +++ b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py @@ -0,0 +1,194 @@ +"""MeshGraphNet (xmgn) model wrapper for DrivAerML-style inference.""" + +import os +os.environ.setdefault("PHYSICSNEMO_FORCE_TE", "False") + +from typing import Any, ClassVar, Optional + +import numpy as np +import torch +from sklearn.neighbors import NearestNeighbors +from torch_geometric.data import Data as PyGData + +from physicsnemo.models.meshgraphnet import MeshGraphNet + +from physicsnemo.cfd.evaluation.common.io import load_global_stats, load_mesh +from physicsnemo.cfd.evaluation.common.interpolation import interpolate_to_mesh +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, InferenceDomain, predictions_dict +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, + ModelInput, + OutputLocation, + RawOutput, + Predictions, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +def _build_pyg_graph( + points: np.ndarray, + normals: np.ndarray, + node_degree: int, +) -> tuple[PyGData, torch.Tensor, torch.Tensor, torch.Tensor]: + n_points = len(points) + nbrs = NearestNeighbors(n_neighbors=node_degree + 1, algorithm="ball_tree").fit(points) + _, indices = nbrs.kneighbors(points) + src = np.repeat(np.arange(n_points), node_degree) + dst = indices[:, 1:].flatten() + src_bi = np.concatenate([src, dst]) + dst_bi = np.concatenate([dst, src]) + edges = np.unique(np.stack([src_bi, dst_bi], axis=0), axis=1) + self_loops = np.stack([np.arange(n_points), np.arange(n_points)], axis=0) + edge_index = torch.tensor(np.concatenate([edges, self_loops], axis=1), dtype=torch.long) + coordinates = torch.tensor(points, dtype=torch.float32) + normals_t = torch.tensor(normals, dtype=torch.float32) + disp = coordinates[edge_index[0]] - coordinates[edge_index[1]] + disp_norm = torch.linalg.norm(disp, dim=-1, keepdim=True) + edge_features = torch.cat((disp, disp_norm), dim=-1) + graph = PyGData(edge_index=edge_index) + return graph, coordinates, normals_t, edge_features + + +def _prepare_node_features( + coords: torch.Tensor, + normals: torch.Tensor, + stats: dict, + device: str, +) -> torch.Tensor: + coords = coords.to(device) + normals = normals.to(device) + coords_norm = (coords - stats["mean"]["coordinates"]) / stats["std"]["coordinates"] + normals_norm = (normals - stats["mean"]["normals"]) / stats["std"]["normals"] + ndata = torch.cat([ + coords_norm, + normals_norm, + torch.sin(2 * np.pi * coords_norm), + torch.cos(2 * np.pi * coords_norm), + torch.sin(4 * np.pi * coords_norm), + torch.cos(4 * np.pi * coords_norm), + torch.sin(8 * np.pi * coords_norm), + torch.cos(8 * np.pi * coords_norm), + ], dim=1) + return ndata + + +class XMGNWrapper(CFDModel): + """Wrapper for MeshGraphNet: mesh points + PyG graph, pressure + WSS output.""" + + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "point" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def __init__(self) -> None: + self._model: Optional[MeshGraphNet] = None + self._stats: Optional[dict] = None + self._device: str = "cuda:0" + self._max_points: Optional[int] = None + self._node_degree: int = 6 + self._interpolation_k: int = 5 + + def load( + self, + checkpoint_path: str, + stats_path: str, + device: str, + **kwargs: Any, + ) -> "XMGNWrapper": + self._device = device + self._max_points = kwargs.get("max_points") + self._node_degree = kwargs.get("node_degree", 6) + self._interpolation_k = kwargs.get("interpolation_k", 5) + log_inference("xmgn", f"Loading normalization stats from {stats_path}") + self._stats = load_global_stats(stats_path, device) + log_inference("xmgn", f"Loading checkpoint from {checkpoint_path}") + model = MeshGraphNet( + input_dim_nodes=24, + input_dim_edges=4, + output_dim=4, + processor_size=15, + aggregation="sum", + hidden_dim_node_encoder=512, + hidden_dim_edge_encoder=512, + hidden_dim_node_decoder=512, + mlp_activation_fn="silu", + do_concat_trick=True, + num_processor_checkpoint_segments=3, + norm_type="LayerNorm", + ).to(device) + checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) + state_dict = checkpoint["model_state_dict"] + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + model.load_state_dict(state_dict) + model.eval() + self._model = model + log_inference("xmgn", "Checkpoint loaded; model ready for inference.") + return self + + def prepare_inputs(self, case: CanonicalCase) -> ModelInput: + if self._model is None or self._stats is None: + raise RuntimeError("XMGNWrapper: call load() first") + log_inference( + "xmgn", + f"Reading mesh (case {case.case_id}): {case.mesh_path}", + ) + mesh = load_mesh(case.mesh_path) + if "Normals" not in mesh.point_data: + mesh = mesh.compute_normals(point_normals=True, cell_normals=False, flip_normals=True) + points = np.array(mesh.points) + normals = np.array(mesh.point_data["Normals"]) + n_total = len(points) + if self._max_points is not None and n_total > self._max_points: + idx = np.random.choice(n_total, self._max_points, replace=False) + points = points[idx] + normals = normals[idx] + graph, coords, normals_t, edge_features = _build_pyg_graph( + points, normals, self._node_degree + ) + ndata = _prepare_node_features(coords, normals_t, self._stats, self._device) + self._last_mesh = mesh + self._last_pred_coords = points + return { + "graph": graph, + "ndata": ndata, + "edata": edge_features, + } + + def predict(self, model_input: ModelInput) -> RawOutput: + if self._model is None or self._stats is None: + raise RuntimeError("XMGNWrapper: call load() first") + log_inference("xmgn", "Running forward pass (predicting fields)…") + graph = model_input["graph"].to(self._device) + edata = model_input["edata"].to(self._device) + edata_norm = (edata - self._stats["mean"]["x"]) / self._stats["std"]["x"] + with torch.inference_mode(): + with torch.autocast("cuda", enabled=True, dtype=torch.bfloat16): + pred = self._model(model_input["ndata"], edata_norm, graph) + return pred + + def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: + if self._stats is None: + raise RuntimeError("XMGNWrapper: call load() first") + log_inference( + "xmgn", + "Decoding outputs (denormalize + interpolate to mesh points)…", + ) + pred = raw_output + pressure = pred[:, 0:1] * self._stats["std"]["pressure"] + self._stats["mean"]["pressure"] + wss = pred[:, 1:] * self._stats["std"]["shear_stress"] + self._stats["mean"]["shear_stress"] + mesh = getattr(self, "_last_mesh", None) + pred_coords = getattr(self, "_last_pred_coords", None) + if mesh is None or pred_coords is None: + mesh = load_mesh(case.mesh_path) + pred_coords = np.array(mesh.points) + target_points = np.array(mesh.points) + p_mesh, wss_mesh = interpolate_to_mesh( + target_points, + pred_coords, + pressure, + wss, + k=self._interpolation_k, + ) + return predictions_dict(p_mesh, wss_mesh) diff --git a/physicsnemo/cfd/evaluation/metrics/__init__.py b/physicsnemo/cfd/evaluation/metrics/__init__.py new file mode 100644 index 0000000..2d4bc00 --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/__init__.py @@ -0,0 +1,11 @@ +"""Metric registry and bench-backed CFD metrics.""" + +from physicsnemo.cfd.evaluation.metrics.registry import ( + get_metric, + list_metrics, + register_metric, +) + +import physicsnemo.cfd.evaluation.metrics.builtin # noqa: F401 - register built-in metrics + +__all__ = ["register_metric", "get_metric", "list_metrics"] diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py b/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py new file mode 100644 index 0000000..cc68de0 --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py @@ -0,0 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Register built-in evaluation metrics backed by physicsnemo.cfd.bench.""" + +from physicsnemo.cfd.evaluation.metrics.builtin.forces import register_force_metrics +from physicsnemo.cfd.evaluation.metrics.builtin.l2 import register_l2_metrics +from physicsnemo.cfd.evaluation.metrics.builtin.physics import register_physics_metrics + + +def register_all_builtin_metrics() -> None: + """Idempotent: register all default metric names.""" + register_l2_metrics() + register_force_metrics() + register_physics_metrics() + + +register_all_builtin_metrics() diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py new file mode 100644 index 0000000..4c1ced0 --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py @@ -0,0 +1,142 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Force coefficient metrics via physicsnemo.cfd.bench.metrics.aero_forces.""" + +from __future__ import annotations + +from typing import Any + +from physicsnemo.cfd.bench.metric_registry import register_metric +from physicsnemo.cfd.bench.metrics.aero_forces import compute_drag_and_lift +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh + + +def _resolve_mesh( + predictions: dict[str, Any], + *, + case: Any, + comparison_mesh: Any, + metric_dtype: str | None, + output: Any, +) -> tuple[Any, str] | tuple[None, None]: + if comparison_mesh is not None and metric_dtype is not None: + return comparison_mesh, metric_dtype + if case is not None and output is not None: + return build_comparison_mesh(case, predictions, output) + return None, None + + +def _scalar_drag_lift_error(ground_truth: dict, predictions: dict, key: str) -> float: + g = ground_truth.get(key) + p = predictions.get(key) + if g is None or p is None: + return float("nan") + gt = float(g) + pr = float(p) + denom = abs(gt) + if denom < 1e-14: + return abs(pr - gt) + return abs(pr - gt) / denom + + +def drag_error( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + coeff: float = 1.0, + drag_direction: list[float] | None = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + dd = drag_direction if drag_direction is not None else [1.0, 0.0, 0.0] + if mesh is None or output is None: + return _scalar_drag_lift_error(ground_truth, predictions, "drag") + gtp = output.ground_truth_mesh_field_names["pressure"] + gtw = output.ground_truth_mesh_field_names["shear_stress"] + prp = output.mesh_field_names["pressure"] + prw = output.mesh_field_names["shear_stress"] + try: + cd_gt, *_ = compute_drag_and_lift( + mesh, + pressure_field=gtp, + wss_field=gtw, + coeff=coeff, + drag_direction=dd, + dtype=dtype, + ) + cd_pr, *_ = compute_drag_and_lift( + mesh, + pressure_field=prp, + wss_field=prw, + coeff=coeff, + drag_direction=dd, + dtype=dtype, + ) + denom = abs(cd_gt) + if denom < 1e-14: + return float(abs(cd_pr - cd_gt)) + return float(abs(cd_pr - cd_gt) / denom) + except Exception: + return _scalar_drag_lift_error(ground_truth, predictions, "drag") + + +def lift_error( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + coeff: float = 1.0, + lift_direction: list[float] | None = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + ld = lift_direction if lift_direction is not None else [0.0, 0.0, 1.0] + if mesh is None or output is None: + return _scalar_drag_lift_error(ground_truth, predictions, "lift") + gtp = output.ground_truth_mesh_field_names["pressure"] + gtw = output.ground_truth_mesh_field_names["shear_stress"] + prp = output.mesh_field_names["pressure"] + prw = output.mesh_field_names["shear_stress"] + try: + _, _, _, cl_gt, _, _ = compute_drag_and_lift( + mesh, + pressure_field=gtp, + wss_field=gtw, + coeff=coeff, + drag_direction=[1.0, 0.0, 0.0], + lift_direction=ld, + dtype=dtype, + ) + _, _, _, cl_pr, _, _ = compute_drag_and_lift( + mesh, + pressure_field=prp, + wss_field=prw, + coeff=coeff, + drag_direction=[1.0, 0.0, 0.0], + lift_direction=ld, + dtype=dtype, + ) + denom = abs(cl_gt) + if denom < 1e-14: + return float(abs(cl_pr - cl_gt)) + return float(abs(cl_pr - cl_gt) / denom) + except Exception: + return _scalar_drag_lift_error(ground_truth, predictions, "lift") + + +def register_force_metrics() -> None: + register_metric("drag_error", drag_error) + register_metric("lift_error", lift_error) diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/l2.py b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py new file mode 100644 index 0000000..98213bf --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py @@ -0,0 +1,282 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""L2-style metrics delegating to physicsnemo.cfd.bench.metrics.l2_errors.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from physicsnemo.cfd.bench.metric_registry import register_metric +from physicsnemo.cfd.bench.metrics.l2_errors import ( + compute_area_weighted_l2_errors, + compute_l2_errors, +) +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh + + +def _resolve_mesh( + predictions: dict[str, Any], + *, + case: Any, + comparison_mesh: Any, + metric_dtype: str | None, + output: Any, +) -> tuple[Any, str] | tuple[None, None]: + if comparison_mesh is not None and metric_dtype is not None: + return comparison_mesh, metric_dtype + if case is not None and output is not None: + return build_comparison_mesh(case, predictions, output) + return None, None + + +def _l2_pressure_numpy( + ground_truth: dict, + predictions: dict, + mask: np.ndarray | None = None, + **_: object, +) -> float: + gt = np.asarray(ground_truth.get("pressure", []), dtype=np.float64).flatten() + pred = np.asarray(predictions.get("pressure", []), dtype=np.float64).flatten() + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return float("nan") + if mask is not None: + m = np.asarray(mask).flatten() + gt = gt[m] + pred = pred[m] + denom = np.linalg.norm(gt) + if denom < 1e-14: + return float(np.linalg.norm(pred - gt)) + return float(np.linalg.norm(pred - gt) / denom) + + +def _l2_shear_numpy( + ground_truth: dict, + predictions: dict, + mask: np.ndarray | None = None, + **_: object, +) -> float: + gt = np.asarray(ground_truth.get("shear_stress", []), dtype=np.float64) + pred = np.asarray(predictions.get("shear_stress", []), dtype=np.float64) + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return float("nan") + gt = gt.flatten() + pred = pred.flatten() + if mask is not None: + m = np.asarray(mask).flatten() + if m.dtype == bool and m.size * 3 == gt.size: + gt = gt.reshape(-1, 3)[m].flatten() + pred = pred.reshape(-1, 3)[m].flatten() + else: + gt = gt[m] + pred = pred[m] + denom = np.linalg.norm(gt) + if denom < 1e-14: + return float(np.linalg.norm(pred - gt)) + return float(np.linalg.norm(pred - gt) / denom) + + +def l2_pressure( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + mask: np.ndarray | None = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + return _l2_pressure_numpy(ground_truth, predictions, mask=mask) + gtn = output.ground_truth_mesh_field_names["pressure"] + prn = output.mesh_field_names["pressure"] + try: + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + return float(d[key]) if key in d else float("nan") + except Exception: + return float("nan") + + +def l2_shear_stress( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + mask: np.ndarray | None = None, + **_: object, +) -> dict[str, float]: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + v = _l2_shear_numpy(ground_truth, predictions, mask=mask) + return {"magnitude": v} + gtn = output.ground_truth_mesh_field_names["shear_stress"] + prn = output.mesh_field_names["shear_stress"] + try: + return compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + except Exception: + return {"magnitude": float("nan")} + + +def l2_pressure_area_weighted( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + return float("nan") + gtn = output.ground_truth_mesh_field_names["pressure"] + prn = output.mesh_field_names["pressure"] + try: + d = compute_area_weighted_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_area_wt_l2_error" + return float(d[key]) if key in d else float("nan") + except Exception: + return float("nan") + + +def l2_pressure_volume( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + gt_key: str = "pressure_volume", + pred_key: str | None = None, + mask: np.ndarray | None = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + pk = pred_key if pred_key is not None else gt_key + if mesh is None or output is None: + gt = np.asarray(ground_truth.get(gt_key, []), dtype=np.float64).flatten() + pred = np.asarray(predictions.get(pk, []), dtype=np.float64).flatten() + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return float("nan") + if mask is not None: + m = np.asarray(mask).flatten() + gt = gt[m] + pred = pred[m] + denom = np.linalg.norm(gt) + if denom < 1e-14: + return float(np.linalg.norm(pred - gt)) + return float(np.linalg.norm(pred - gt) / denom) + gtn = output.ground_truth_volume_mesh_field_names[gt_key] + prn = output.volume_mesh_field_names[pk] + try: + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + return float(d[key]) if key in d else float("nan") + except Exception: + return float("nan") + + +def l2_velocity( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + **_: object, +) -> dict[str, float]: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + gt = np.asarray(ground_truth.get("velocity", []), dtype=np.float64) + pred = np.asarray(predictions.get("velocity", []), dtype=np.float64) + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return {"magnitude": float("nan")} + gt = gt.flatten() + pred = pred.flatten() + denom = np.linalg.norm(gt) + if denom < 1e-14: + mag = float(np.linalg.norm(pred - gt)) + else: + mag = float(np.linalg.norm(pred - gt) / denom) + return {"magnitude": mag} + gtn = output.ground_truth_volume_mesh_field_names["velocity"] + prn = output.volume_mesh_field_names["velocity"] + try: + return compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + except Exception: + return {"magnitude": float("nan")} + + +def l2_turbulent_viscosity( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + gt_key: str = "turbulent_viscosity", + pred_key: str | None = None, + mask: np.ndarray | None = None, + **_: object, +) -> float: + mesh, dtype = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + pk = pred_key if pred_key is not None else gt_key + if mesh is None or output is None: + gt = np.asarray(ground_truth.get(gt_key, []), dtype=np.float64).flatten() + pred = np.asarray(predictions.get(pk, []), dtype=np.float64).flatten() + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return float("nan") + if mask is not None: + m = np.asarray(mask).flatten() + gt = gt[m] + pred = pred[m] + denom = np.linalg.norm(gt) + if denom < 1e-14: + return float(np.linalg.norm(pred - gt)) + return float(np.linalg.norm(pred - gt) / denom) + gtn = output.ground_truth_volume_mesh_field_names[gt_key] + prn = output.volume_mesh_field_names[pk] + try: + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + return float(d[key]) if key in d else float("nan") + except Exception: + return float("nan") + + +def register_l2_metrics() -> None: + for name, fn in [ + ("l2_pressure", l2_pressure), + ("l2_shear_stress", l2_shear_stress), + ("l2_pressure_area_weighted", l2_pressure_area_weighted), + ("area_wt_l2_pressure", l2_pressure_area_weighted), + ("l2_pressure_volume", l2_pressure_volume), + ("l2_velocity", l2_velocity), + ("l2_turbulent_viscosity", l2_turbulent_viscosity), + ]: + register_metric(name, fn) diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/physics.py b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py new file mode 100644 index 0000000..e3bcfcd --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py @@ -0,0 +1,116 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Continuity and momentum residual metrics (volume) via physicsnemo.cfd.bench.metrics.physics.""" + +from __future__ import annotations + +from typing import Any + +import pyvista as pv + +from physicsnemo.cfd.bench.metric_registry import register_metric +from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors +from physicsnemo.cfd.bench.metrics.physics import ( + compute_continuity_residuals, + compute_momentum_residuals, +) +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh + + +def _resolve_mesh( + predictions: dict[str, Any], + *, + case: Any, + comparison_mesh: Any, + metric_dtype: str | None, + output: Any, +) -> tuple[pv.DataSet, str] | tuple[None, None]: + if comparison_mesh is not None and metric_dtype is not None: + return comparison_mesh, metric_dtype + if case is not None and output is not None: + return build_comparison_mesh(case, predictions, output) + return None, None + + +def continuity_residual_l2( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + true_velocity_field: str | None = None, + predicted_velocity_field: str | None = None, + device: str = "cpu", + **_: object, +) -> dict[str, float]: + mesh, _ = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + return {"Continuity_l2_error": float("nan")} + gtn = true_velocity_field or output.ground_truth_volume_mesh_field_names["velocity"] + prn = predicted_velocity_field or output.volume_mesh_field_names["velocity"] + try: + m = mesh.copy(deep=True) + m = compute_continuity_residuals(m, gtn, prn, device=device) + return compute_l2_errors(m, ["Continuity"], ["ContinuityPred"], dtype="point") + except Exception: + return {"Continuity_l2_error": float("nan")} + + +def momentum_residual_l2( + ground_truth: dict, + predictions: dict, + *, + case: Any = None, + comparison_mesh: Any = None, + metric_dtype: str | None = None, + output: Any = None, + true_velocity_field: str | None = None, + predicted_velocity_field: str | None = None, + true_pressure_field: str | None = None, + predicted_pressure_field: str | None = None, + true_nu_field: str | None = None, + predicted_nu_field: str | None = None, + nu: float = 1.5e-5, + rho: float = 1.0, + device: str = "cpu", + **_: object, +) -> dict[str, float]: + mesh, _ = _resolve_mesh( + predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output + ) + if mesh is None or output is None: + return {"Momentum_x_l2_error": float("nan")} + uv = true_velocity_field or output.ground_truth_volume_mesh_field_names["velocity"] + pv = predicted_velocity_field or output.volume_mesh_field_names["velocity"] + tp = true_pressure_field or output.ground_truth_volume_mesh_field_names["pressure_volume"] + pp = predicted_pressure_field or output.volume_mesh_field_names["pressure_volume"] + tn = true_nu_field or output.ground_truth_volume_mesh_field_names["turbulent_viscosity"] + pn = predicted_nu_field or output.volume_mesh_field_names["turbulent_viscosity"] + try: + m = mesh.copy(deep=True) + m = compute_momentum_residuals( + m, + true_velocity_field=uv, + predicted_velocity_field=pv, + true_pressure_field=tp, + predicted_pressure_field=pp, + true_nu_field=tn, + predicted_nu_field=pn, + nu=nu, + rho=rho, + device=device, + ) + return compute_l2_errors(m, ["Momentum"], ["MomentumPred"], dtype="point") + except Exception: + return {"momentum_residual_failed": float("nan")} + + +def register_physics_metrics() -> None: + register_metric("continuity_residual_l2", continuity_residual_l2) + register_metric("momentum_residual_l2", momentum_residual_l2) diff --git a/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py new file mode 100644 index 0000000..660896c --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py @@ -0,0 +1,112 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Build a PyVista comparison mesh with GT and prediction VTK array names for bench metrics.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import pyvista as pv + +from physicsnemo.cfd.evaluation.config import OutputConfig +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase + + +def _assign_field( + mesh: pv.DataSet, + preference: str, + name: str, + arr: np.ndarray | Any, +) -> None: + data = np.asarray(arr, dtype=np.float64) + if preference == "cell": + n = mesh.n_cells + if data.ndim == 1: + if data.size != n: + raise ValueError( + f"Field {name!r}: expected {n} cell values, got shape {data.shape}" + ) + mesh.cell_data[name] = data + elif data.ndim == 2 and data.shape[1] == 3: + if data.shape[0] != n: + raise ValueError( + f"Field {name!r}: expected ({n}, 3) for cells, got {data.shape}" + ) + mesh.cell_data[name] = data + else: + raise ValueError(f"Unsupported cell array shape for {name!r}: {data.shape}") + else: + n = mesh.n_points + if data.ndim == 1: + if data.size != n: + raise ValueError( + f"Field {name!r}: expected {n} point values, got shape {data.shape}" + ) + mesh.point_data[name] = data + elif data.ndim == 2 and data.shape[1] == 3: + if data.shape[0] != n: + raise ValueError( + f"Field {name!r}: expected ({n}, 3) for points, got {data.shape}" + ) + mesh.point_data[name] = data + else: + raise ValueError(f"Unsupported point array shape for {name!r}: {data.shape}") + + +def build_comparison_mesh( + case: CanonicalCase, + predictions: dict[str, Any], + output: OutputConfig, + *, + mesh_override: pv.DataSet | None = None, +) -> tuple[pv.DataSet, str]: + """Load case geometry and attach GT / prediction fields for ``physicsnemo.cfd.bench`` metrics. + + Parameters + ---------- + mesh_override + If set, use this dataset instead of ``pv.read(case.mesh_path)`` (e.g. tests or in-memory + pipelines). ``case.mesh_path`` is still accepted on ``CanonicalCase`` but ignored when + override is provided. + + Returns + ------- + mesh + PyVista dataset. + dtype + ``\"cell\"`` or ``\"point\"`` for ``compute_l2_errors`` / ``compute_drag_and_lift``. + """ + if mesh_override is not None: + mesh = mesh_override.copy(deep=True) + else: + mesh = pv.read(case.mesh_path).copy(deep=True) + gt = case.ground_truth or {} + + if case.inference_domain == "surface": + mesh = mesh.point_data_to_cell_data(pass_point_data=True) + preference = "cell" + gt_map = output.ground_truth_mesh_field_names + pred_map = output.mesh_field_names + pairs = (("pressure", "pressure"), ("shear_stress", "shear_stress")) + elif case.inference_domain == "volume": + preference = "point" + gt_map = output.ground_truth_volume_mesh_field_names + pred_map = output.volume_mesh_field_names + pairs = ( + ("pressure_volume", "pressure_volume"), + ("velocity", "velocity"), + ("turbulent_viscosity", "turbulent_viscosity"), + ) + else: + raise ValueError(f"Unknown inference_domain: {case.inference_domain!r}") + + for canonical, _ in pairs: + if canonical in gt_map and canonical in gt and gt[canonical] is not None: + _assign_field(mesh, preference, gt_map[canonical], gt[canonical]) + if canonical in pred_map and canonical in predictions and predictions[canonical] is not None: + _assign_field(mesh, preference, pred_map[canonical], predictions[canonical]) + + return mesh, preference diff --git a/physicsnemo/cfd/evaluation/metrics/registry.py b/physicsnemo/cfd/evaluation/metrics/registry.py new file mode 100644 index 0000000..9dfa66e --- /dev/null +++ b/physicsnemo/cfd/evaluation/metrics/registry.py @@ -0,0 +1,13 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Re-export bench metric registry for ``physicsnemo.cfd.evaluation``.""" + +from physicsnemo.cfd.bench.metric_registry import ( # noqa: F401 + get_metric, + list_metrics, + register_metric, +) + +__all__ = ["register_metric", "get_metric", "list_metrics"] diff --git a/pyproject.toml b/pyproject.toml index 263cbbb..427cb95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,7 @@ requires-python = ">=3.10" license = "Apache-2.0" dependencies = [ "httpx>=0.27.0", + "pyyaml>=6.0", "numpy>=1.22.4", "pyvista>=0.45.2", "tqdm>=4.67.1", @@ -31,6 +32,9 @@ classifiers = [ dynamic = ["version"] [project.optional-dependencies] +dev = [ + "pytest>=7.0", +] # dependencies for GPU-accelerated functionality gpu = [ "cupy-cuda13x>=13.6.0", @@ -38,7 +42,7 @@ gpu = [ "warp-lang>=1.7.1", ] all = [ - "nvidia-physicsnemo-cfd[gpu]", + "nvidia-physicsnemo-cfd[dev,gpu]", ] [tool.setuptools.dynamic] diff --git a/test/ci_tests/test_evaluation.py b/test/ci_tests/test_evaluation.py new file mode 100644 index 0000000..e8b2d8f --- /dev/null +++ b/test/ci_tests/test_evaluation.py @@ -0,0 +1,117 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""CI tests for physicsnemo.cfd.evaluation (mesh bridge, metrics, config, engine helpers).""" + +from __future__ import annotations + +import importlib.util + +import pytest + +# Bench L2 imports physicsnemo.utils.sdf at module level; skip whole module if missing. +if importlib.util.find_spec("physicsnemo.utils.sdf") is None: + pytest.skip( + "physicsnemo.utils.sdf not available (install/upgrade nvidia-physicsnemo)", + allow_module_level=True, + ) + +import numpy as np +import pyvista as pv + +from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors +from physicsnemo.cfd.evaluation.benchmarks.engine import _call_metric, _normalize_metrics_config +from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase +from physicsnemo.cfd.evaluation.metrics import get_metric, list_metrics +from physicsnemo.cfd.evaluation.metrics.builtin.l2 import l2_pressure +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh +from physicsnemo.cfd.evaluation.metrics.registry import register_metric + + +def test_normalize_metrics_config_strings_and_dicts() -> None: + specs = _normalize_metrics_config( + [ + "l2_pressure", + {"name": "drag_error", "coeff": 1.5}, + ] + ) + assert specs == [("l2_pressure", {}), ("drag_error", {"coeff": 1.5})] + + +def test_config_from_dict_merges_output_and_reports() -> None: + cfg = Config.from_dict( + { + "metrics": ["l2_pressure"], + "output": { + "ground_truth_mesh_field_names": {"pressure": "p_gt_custom"}, + }, + "reports": {"enabled": True, "plugins": [{"kind": "stub"}]}, + } + ) + assert cfg.output.ground_truth_mesh_field_names["pressure"] == "p_gt_custom" + assert cfg.reports.enabled is True + assert cfg.reports.plugins == [{"kind": "stub"}] + + +def test_list_metrics_includes_core_builtin() -> None: + names = list_metrics() + assert "l2_pressure" in names + assert "drag_error" in names + + +def test_build_comparison_mesh_surface_zero_l2_when_identical() -> None: + """In-memory surface mesh + synthetic numpy fields; no VTP read/write.""" + base = pv.Plane(i_resolution=6, j_resolution=6) + mesh0 = base.point_data_to_cell_data(pass_point_data=True) + n_cells = mesh0.n_cells + p = np.random.randn(n_cells).astype(np.float64) + wss = np.random.randn(n_cells, 3).astype(np.float64) + + case = CanonicalCase( + case_id="syn", + mesh_path="", # unused when mesh_override is set + mesh_type="cell", + ground_truth={"pressure": p, "shear_stress": wss}, + inference_domain="surface", + ) + pred = {"pressure": p.copy(), "shear_stress": wss.copy()} + output = OutputConfig() + mesh, dtype = build_comparison_mesh(case, pred, output, mesh_override=mesh0) + gtn = output.ground_truth_mesh_field_names["pressure"] + prn = output.mesh_field_names["pressure"] + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + assert abs(float(d[key])) < 1e-10 + + v = l2_pressure( + case.ground_truth or {}, + pred, + case=case, + comparison_mesh=mesh, + metric_dtype=dtype, + output=output, + ) + assert abs(v) < 1e-10 + + +def test_legacy_metric_call_without_extended_kwargs() -> None: + """Metrics with a fixed (gt, pred) signature fall back when extended kwargs are rejected.""" + + def legacy(gt: dict, pred: dict) -> float: + return 1.0 + + register_metric("_ci_test_legacy_metric", legacy) + fn = get_metric("_ci_test_legacy_metric") + out = _call_metric( + fn, + {}, + {}, + case=None, + comparison_mesh=None, + metric_dtype=None, + output=OutputConfig(), + mkwargs={}, + ) + assert out == 1.0 diff --git a/workflows/bench_example/README.md b/workflows/bench_example/README.md index 80a9c88..f47d9ff 100644 --- a/workflows/bench_example/README.md +++ b/workflows/bench_example/README.md @@ -19,6 +19,13 @@ provide [notebook](./notebooks/) versions of these that can be used to deepen the understanding of several metrics and perform the comparisons with more flexibility and freedom. +For **on-the-fly** evaluation (config-driven inference plus the same +`physicsnemo.cfd.bench` metrics as the scripts above), use the +`physicsnemo.cfd.evaluation` package and the example YAML under +[`workflows/evaluation_examples/`](../evaluation_examples/). The pre-baked +`.vtp` / `.vtu` workflows in this directory remain the reference for +post-processed file-based benchmarks. + To keep the handling of various models simple, these workflows take AI model predictions post-processed to `.vtp` (for surface) and `.vtu` (for volume) formats as inputs. Generating these `vtp` and `vtu` files depends on the model's diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md new file mode 100644 index 0000000..ca5eada --- /dev/null +++ b/workflows/evaluation_examples/README.md @@ -0,0 +1,220 @@ +# Evaluation examples (PhysicsNeMo-CFD) + +Example YAML and scripts for **`physicsnemo.cfd.evaluation`**: config-driven inference and benchmarking (same stack as the `nvidia-physicsnemo-cfd` package). Install the repo or `pip install nvidia-physicsnemo-cfd`, then run the CLIs below from this directory. + +## Inference and benchmarking layer (`physicsnemo.cfd.evaluation`) + +Configure model, dataset, metrics, and checkpoint via YAML; run single-case inference or a full benchmark with JSON/CSV/HTML reports. Metrics delegate to **`physicsnemo.cfd.bench`** when a comparison mesh is built (see `output.mesh_field_names` and ground-truth maps in config). + +### Quick start + +**Single-case inference** (one model, one case; writes prediction mesh): + +```bash +python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml +python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml --case-id run_1 +``` + +**Shorthand** (same entrypoints): + +```bash +python -m physicsnemo.cfd.evaluation.inference +``` + +**Benchmark** (model × dataset, metrics, JSON/CSV/HTML report) — merge shared defaults + matrix overlay: + +```bash +python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml --case-id run_1 +``` + +```bash +python -m physicsnemo.cfd.evaluation.benchmarks +``` + +You can also use a **single** full YAML (no `--base-config`) if everything lives in one file. + +**CLI overrides** (without editing YAML): + +```bash +python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml run.device=cpu run.output_dir=./out +# or with -- prefix: +python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml --run.device=cuda:1 --model.checkpoint=path/to/ckpt.pth +``` + +Optional **`--base-config`** works for `physicsnemo.cfd.evaluation.inference.run` too (merge order: base, then `--config`). + +**Minimal surface-only example** (placeholders; edit paths): `surface_benchmark_minimal.yaml`. + +### Config + +Edit `inference_config.yaml` (shared run/model/dataset/output/metrics and default `benchmark.mode: single`) and, for matrix sweeps, `benchmark_matrix.yaml` (typically only a `benchmark:` block). JSON works as well. + +- **run**: `device`, `output_dir`, `seed`, `batch_size` +- **model**: `name` (registered wrapper id: fignet, xmgn, geotransolver, … or your [custom model](#custom-models-adding-a-new-wrapper)), `checkpoint`, `stats_path`, optional **`inference_domain`** (`surface` \| `volume`) to override the wrapper default (required for **GeoTransolver** / **Transolver** on VTU volume cases: use `volume` with a volume-trained checkpoint and **`global_stats.json`** (preferred; must include `velocity`, `pressure_volume`, `turbulent_viscosity`) or `volume_fields_normalization.npz` beside the checkpoint, as in `transformer_models/src/inference_on_vtk.py`; **DoMINO** uses the same key with a volume `model.model_type` in `domino_config` and VTU cases), `kwargs` (e.g. `max_points`, `node_degree`, `interpolation_k`; GeoTransolver/Transolver: `resolution`, `geometry_sampling`, `air_density`, `stream_velocity`, `batch_resolution`; optional datapipe overrides: `include_sdf`, `translational_invariance`, `scale_invariance`, `reference_scale`, `broadcast_global_features`, …; **DoMINO**: `domino_config`, `point_batch_size`). The **baseline** names are optional no-checkpoint stubs; see [Baseline model stubs](#baseline-model-stubs-surface_baseline-volume_baseline) below. +- **dataset**: `name` (registered adapter id: drivaerml, ahmed, or your [custom dataset](#custom-datasets-adding-a-new-adapter)), `root`, `split`, `case_ids` (null = all cases), optional `kwargs` e.g. `align_ground_truth_to_model: true` or `gt_data_type: from_model` so reference fields are read at the same mesh location as the model (point vs cell) without separate dataset entries per model in matrix benchmarks. **DrivAerML volume**: same `root` and `run_*` dirs as surface; set `inference_domain: volume` and place VTU files per run (default name `volume_.vtu` for `run_`, e.g. `run_1/volume_1.vtu`). Optional `volume_vtu_filename` or `volume_vtu_template` (e.g. `"volume_{run_suffix}.vtu"`) if your naming differs. +- **output** (optional): `mesh_field_names` / **`ground_truth_mesh_field_names`** — canonical surface keys → VTP array names for predictions vs reference. **`volume_mesh_field_names`** / **`ground_truth_volume_mesh_field_names`** — volume keys → VTU names; includes **`pressure_volume`** (volume pressure, distinct from surface `pressure`), plus `turbulent_viscosity`, `velocity` (defaults often `pMeanPred`, `nutMeanPred`, `UMeanPred` for preds, `pMean`, `nutMean`, `UMean` for GT). +- **metrics**: list of names or dicts with `name` + kwargs, e.g. `l2_pressure`, `l2_shear_stress`, **`l2_pressure_volume`** (volume pressure / `pressure_volume`), `l2_velocity`, **`l2_turbulent_viscosity`** (optional `gt_key` / `pred_key`), `drag_error`, `lift_error`, `continuity_residual_l2`, `momentum_residual_l2`. Metric kwargs are passed through from the config entry. +- **reports** (optional): `enabled`, `plugins` — post-scalar manifest hooks (see `physicsnemo.cfd.evaluation.config.ReportsConfig`). +- **benchmark**: `mode` (single | matrix), `models` / `datasets` for matrix mode, `reproducibility`. In **matrix** mode, pairs where the model **`INFERENCE_DOMAIN`** does not match the dataset adapter domain (from `dataset.kwargs`, e.g. DrivAerML `inference_domain: volume`) are **skipped**: a line is printed, rows appear in CSV/HTML as `_skipped`, and `benchmark_artifacts.json` lists `skipped_runs`. + +### Baseline model stubs (surface_baseline, volume_baseline) + +They are **optional** registered models—not required for normal inference with trained checkpoints. + +**Purpose** + +- **Smoke tests & CI** — Exercise the full path (dataset adapter → `CanonicalCase` → `CFDModel.load` / `prepare_inputs` / `predict` / `decode_outputs` → metrics and, for inference, VTK write) **without** loading a neural network, **without** valid `checkpoint` / `stats_path`, and **without** a GPU-heavy forward pass. +- **Layout & domain checks** — Confirm case listing, mesh paths (boundary VTP vs volume VTU), `inference_domain` matching, and benchmark matrix **skip** behavior for incompatible model×dataset pairs. +- **Debugging** — Isolate data I/O and reporting when a real model fails early (bad stats, OOM, etc.). + +**What they are *not*** + +- They are **not** chosen automatically from a path like `*_volume_checkpoint`; only **`model.name: surface_baseline`** or **`volume_baseline`** selects them. +- They are **not** physics-accurate predictions: outputs are **zeros** (or shape-only placeholders) with the right **canonical keys** for the domain (`pressure` / `shear_stress` on surface, `pressure_volume` / `turbulent_viscosity` / `velocity` on volume). +- **`surface_baseline`** uses **cell-centered** counts on the boundary mesh; if your ground truth is **point**-aligned, align GT via `dataset.kwargs` (e.g. `align_ground_truth_to_model` / `gt_data_type`) or expect metric shape mismatches or NaNs. + +**Config sketch** + +```yaml +model: + name: "surface_baseline" # or "volume_baseline" + checkpoint: "" + stats_path: "" +dataset: + name: drivaerml + root: /path/to/data + kwargs: {} # surface default + # kwargs: { inference_domain: volume } # required for volume_baseline +``` + +If you never use smoke tests or matrix stubs, you can ignore these names; the rest of the stack does not depend on them. + +### Custom models (adding a new wrapper) + +Built-in models (fignet, xmgn, geotransolver, …) are **registered** `CFDModel` implementations. To add your own architecture or checkpoint layout, implement a **wrapper** and register it once; inference and benchmarks pick it up by `model.name` in YAML. + +#### Where to put code + +- Add a Python module under **`physicsnemo/cfd/evaluation/inference/wrappers/`**, e.g. `wrappers/my_model/wrapper.py` or `wrappers/my_model.py`. +- Keep imports under `physicsnemo.cfd.evaluation` (and `nvidia-physicsnemo` / PyTorch as needed). Do **not** add inference code under `physicsnemo/cfd/inference/` — that tree is reserved for the DoMINO NIM client. + +#### Implement `CFDModel` + +Subclass **`CFDModel`** from `physicsnemo.cfd.evaluation.inference.model_registry` and implement: + +| Piece | Purpose | +| ----- | --- | +| **`INFERENCE_DOMAIN`** | Class variable: `"surface"` (boundary VTP) or `"volume"` (volume VTU). Must match the dataset adapter domain for benchmark runs. | +| **`OUTPUT_LOCATION`** + **`output_location`** | `"point"` or `"cell"` — where your predictions live on the mesh (drives GT alignment and writing). | +| **`load(checkpoint_path, stats_path, device, **kwargs)`** | Load weights and stats; **`kwargs`** includes `model.kwargs` from YAML and `inference_domain` when set. Return `self`. | +| **`prepare_inputs(case: CanonicalCase)`** | Build tensors / graphs / batches from `case.mesh_path` and optional `ground_truth` / `metadata`. | +| **`predict(model_input)`** | One forward pass; return raw outputs (tensors, dict, etc.). | +| **`decode_outputs(raw_output, case)`** | Map to the **canonical predictions dict** used by metrics and the mesh bridge: **surface** — `pressure`, `shear_stress`; **volume** — `pressure_volume`, `turbulent_viscosity`, `velocity` (use `build_predictions_dict` / `predictions_dict` from `physicsnemo.cfd.evaluation.datasets.schema`). | + +If your wrapper’s class defaults to `INFERENCE_DOMAIN = "surface"` but you train a VTU volume checkpoint, users set **`model.inference_domain: volume`** in YAML so the engine matches volume datasets. + +#### Register the wrapper + +1. In **`physicsnemo/cfd/evaluation/inference/wrappers/__init__.py`**, import your class and call **`register_model("my_model", MyModelWrapper)`** so the module is loaded at startup (same pattern as existing wrappers). +2. Reinstall or run with the repo on **`PYTHONPATH`** / `pip install -e .` so the new module is importable. + +#### Use in YAML + +```yaml +model: + name: my_model + checkpoint: /path/to/weights.pt + stats_path: /path/to/dir_with_stats # or file, per your load() + inference_domain: surface # optional override if your class default differs + kwargs: + batch_size: 1 + # any options your load() / prepare_inputs() read +``` + +No changes to **`run.py`** or **`benchmarks/engine.py`** are required. + +#### Reference implementations (in-repo) + +| Wrapper | Notes | +| ------- | --- | +| `wrappers/fignet/wrapper.py` | Cell-centered surface | +| `wrappers/xmgn/wrapper.py` | Point-based surface | +| `wrappers/transolver/wrapper.py`, `wrappers/geotransolver/wrapper.py` | Surface, cell; boundary VTP + STL + `surface_fields_normalization.npz` | +| `wrappers/domino/wrapper.py` | DoMINO config + scaling | +| `wrappers/surface_baseline.py`, `wrappers/volume_baseline.py` | No checkpoint; smoke tests | + +### Custom datasets (adding a new adapter) + +Built-in datasets (drivaerml, ahmed, …) are **registered** `DatasetAdapter` implementations. To plug in your own layout (folder naming, file formats, splits), add an **adapter** and register it once; inference and benchmarks resolve it by `dataset.name` in YAML. + +#### Where to put code + +- Add a module under **`physicsnemo/cfd/evaluation/datasets/adapters/`**, e.g. `my_dataset.py`. +- Shared helpers (VTK GT extraction, alignment) live under **`physicsnemo/cfd/evaluation/datasets/`** (`vtk_ground_truth.py`, `gt_alignment.py`, …)—reuse them when possible instead of duplicating I/O. + +#### Implement `DatasetAdapter` + +Subclass **`DatasetAdapter`** from `physicsnemo.cfd.evaluation.datasets.adapter_registry` (also exported as `physicsnemo.cfd.evaluation.datasets.DatasetAdapter`) and implement: + +| Piece | Purpose | +| ----- | --- | +| **`__init__(self, root: str, **kwargs)`** | Store **`root`** (required dataset root) and options from **`dataset.kwargs`** in YAML. Validate paths early if you need to fail fast. | +| **`list_cases(split=None)`** | Return case IDs (strings). **`split`** is adapter-defined (`"train"` / `"test"` / `None` = all); document what your dataset supports. | +| **`load_case(case_id: str)`** | Return a **`CanonicalCase`**: `case_id`, **`mesh_path`** (absolute or root-relative path to the primary **`.vtp`** or **`.vtu`**), **`mesh_type`** (`"point"` \| `"cell"` — how GT fields in `ground_truth` align to the mesh), optional **`ground_truth`** (numpy arrays keyed for surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `velocity`, `turbulent_viscosity`, …), optional **`metadata`**, and **`inference_domain`** (`"surface"` \| `"volume"`). | +| **`inference_domain_from_kwargs`** (classmethod, optional) | If domain depends on kwargs (e.g. DrivAerML surface vs volume), return `"surface"` or `"volume"` so matrix benchmarks can **skip** incompatible model×dataset pairs **before** loading cases. | + +Ground truth in **`ground_truth`** should match what your metrics and **`mesh_bridge`** expect: canonical keys, with shapes consistent with **`mesh_type`** and the model’s **`OUTPUT_LOCATION`** (use **`dataset.kwargs`** like `align_ground_truth_to_model` / `gt_data_type` where supported—see `datasets/gt_alignment.py`). + +#### Register the adapter + +1. In **`physicsnemo/cfd/evaluation/datasets/adapters/__init__.py`**, import your class and call **`register_adapter("my_dataset", MyDatasetAdapter)`** so it loads with `import physicsnemo.cfd.evaluation.datasets.adapters`. +2. Reinstall or use **`pip install -e .`** / **`PYTHONPATH`** to the repo root. + +#### Use in YAML + +```yaml +dataset: + name: my_dataset + root: /path/to/dataset_root + split: null + case_ids: null + kwargs: + # adapter-specific: splits, filename templates, inference_domain, GT field names, etc. + inference_domain: surface +``` + +The engine passes **`dataset.kwargs`** into your adapter’s constructor (merged with alignment helpers for known keys—see DrivAerML). + +#### Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) + +- **`CanonicalCase`**: `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. Produced by adapters; consumed by model wrappers and metrics. +- **Predictions** (model output, not dataset): surface — `pressure`, `shear_stress`; volume — **`pressure_volume`**, `turbulent_viscosity`, `velocity`. Helpers: `predictions_dict`, `build_predictions_dict`. + +#### Reference implementations (in-repo) + +| Adapter | Notes | +| ------- | --- | +| `adapters/drivaerml.py` | **Surface**: `run_/boundary_.vtp`, GT `pressure` / `shear_stress`; optional kwargs: `boundary_vtp_filename`, `boundary_vtp_template`, field name overrides. **Volume**: `kwargs.inference_domain: volume`, `run_/volume_.vtu`, GT `pressure_volume`, `turbulent_viscosity`, `velocity` via `extract_volume_fields_from_mesh`; optional `volume_vtu_filename` / `volume_vtu_template`. | +| `adapters/ahmed.py` | Stub for layout / registration tests | + +See **`datasets/vtk_ground_truth.py`**, **`datasets/gt_alignment.py`** for resampling and naming. For **point** vs **cell** GT alignment with MeshGraphNet-style models, use kwargs such as **`align_ground_truth_to_model`** / **`gt_data_type`** as in DrivAerML. + +### Package layout (in this repo) + +Paths are relative to the repository root: + +- `physicsnemo/cfd/evaluation/config.py` — config schema, loader, and `deep_merge_dict` / `Config.load_merged` for multi-file configs +- `inference_config.yaml` / `benchmark_matrix.yaml` / `surface_benchmark_minimal.yaml` — examples in this folder (`--base-config` + `--config` where applicable) +- `physicsnemo/cfd/evaluation/inference/` — model registry; `common_wrapper_utils/vtk_datapipe_io.py`; `wrappers//` +- `physicsnemo/cfd/evaluation/datasets/` — canonical case schema and adapters (drivaerml, ahmed stub) +- `physicsnemo/cfd/evaluation/metrics/` — registry + `mesh_bridge.py` + `builtin/` (thin wrappers around `physicsnemo.cfd.bench`) +- `physicsnemo/cfd/evaluation/benchmarks/` — engine, reporting (JSON, CSV, HTML), optional `report_plugins.py` +- `physicsnemo/cfd/evaluation/common/` — mesh I/O, stats, kNN interpolation +- `physicsnemo/cfd/bench/` — shared algorithms (L2, forces, physics, …); not duplicated under `evaluation/` + +### Migration from external `physicsnemo_cfd` + +If you used the old standalone package name **`physicsnemo_cfd`**, switch imports to **`physicsnemo.cfd.evaluation`** and run: + +`python -m physicsnemo.cfd.evaluation.benchmarks.run --config ...` +(or `python -m physicsnemo.cfd.evaluation.benchmarks`). diff --git a/workflows/evaluation_examples/benchmark_matrix.yaml b/workflows/evaluation_examples/benchmark_matrix.yaml new file mode 100644 index 0000000..4aae4fb --- /dev/null +++ b/workflows/evaluation_examples/benchmark_matrix.yaml @@ -0,0 +1,37 @@ +# Matrix benchmark overlay: merge on top of inference_config.yaml (see README). +# Usage: +# python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml + +benchmark: + mode: "matrix" + models: + # - name: "fignet" + # checkpoint: "benchmark_models/fignet_drivaerml_surface_checkpoint/model_00999.pth" + # stats_path: "benchmark_models/fignet_drivaerml_surface_checkpoint/global_stats.json" + # kwargs: {"max_points": 250000, "interpolation_k": 4} + # - name: "xmgn" + # checkpoint: "benchmark_models/xmgn_drivaerml_surface_checkpoint/final_model_checkpoint.pth" + # stats_path: "benchmark_models/xmgn_drivaerml_surface_checkpoint/global_stats.json" + # kwargs: {"max_points": 250000, "interpolation_k": 4} + - name: "geotransolver" + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" + kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} + # - name: "transolver" + # checkpoint: "benchmark_models/transolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + # stats_path: "benchmark_models/transolver_drivaerml_surface_checkpoint/global_stats.json" + # kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} + # - name: "domino" + # checkpoint: "benchmark_models/domino_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + # stats_path: "benchmark_models/domino_drivaerml_surface_checkpoint/global_stats.json" + # kwargs: {"domino_config": "benchmark_models/domino_drivaerml_surface_checkpoint/config.yaml"} + datasets: + - name: "drivaerml" + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + case_ids: ["run_1"] + kwargs: {align_ground_truth_to_model: true, inference_domain: volume} + reproducibility: + log_env: true + save_artifacts: true diff --git a/workflows/evaluation_examples/inference_config.yaml b/workflows/evaluation_examples/inference_config.yaml new file mode 100644 index 0000000..ace6b00 --- /dev/null +++ b/workflows/evaluation_examples/inference_config.yaml @@ -0,0 +1,63 @@ +# Shared defaults for inference and benchmarks (load alone, or as --base-config with a benchmark overlay). + +run: + device: "cuda:0" + output_dir: "benchmark_results" + seed: 42 + batch_size: 1 + +# Default single-run model (overridden per row in benchmark matrix when using benchmark_matrix.yaml). +# Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. +# GeoTransolver/Transolver volume: inference_domain: volume; global_stats.json (velocity, pressure_volume, turbulent_viscosity) or volume_fields_normalization.npz next to checkpoint +# (same layout as transformer_models training / inference_on_vtk.py). +model: + name: "geotransolver" + inference_domain: surface + checkpoint: "benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "benchmark_models/geotransolver_drivaerml_surface_checkpoint" + kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} + +dataset: + name: "drivaerml" + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + case_ids: null + # Volume branch (same root; run_/volume_.vtu): use with a volume model, e.g. volume_baseline + # kwargs: { inference_domain: volume } + +output: + mesh_field_names: + pressure: "pMeanTrimPred" + shear_stress: "wallShearStressMeanTrimPred" + # Written on volume inference (defaults include pressure_volume → pMeanPred, etc.) + volume_mesh_field_names: + pressure_volume: "pMeanTrimPred" + turbulent_viscosity: "nutMeanTrimPred" + velocity: "UMeanTrimPred" + +# Surface: l2_pressure, l2_shear_stress, l2_velocity, drag_error, lift_error +# Volume: l2_pressure_volume (pressure_volume), l2_turbulent_viscosity, l2_velocity +# Optional dict form for renamed keys: { name: l2_turbulent_viscosity, gt_key: turbulent_viscosity, pred_key: turbulent_viscosity } +metrics: + - l2_pressure + - l2_shear_stress + - l2_pressure_area_weighted # alias: area_wt_l2_pressure + - l2_pressure_volume # volume pressure; needs volume case + output maps + - l2_velocity + - l2_turbulent_viscosity + - drag_error + - lift_error + - continuity_residual_l2 # volume: needs velocity on volume mesh + - momentum_residual_l2 + # Surface (VTP): l2_pressure, l2_shear_stress + # - "l2_pressure_volume" + # - "l2_turbulent_viscosity" + # - "l2_velocity" + +benchmark: + mode: "single" + models: [] + datasets: [] + reproducibility: + log_env: true + save_artifacts: true From ba7ed719c21e46cb1e53ea12158502b52a63c0d6 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Thu, 2 Apr 2026 08:56:04 -0700 Subject: [PATCH 02/25] adding visualization layer and improving readme --- .../cfd/evaluation/benchmarks/engine.py | 17 +- .../evaluation/benchmarks/report_plugins.py | 66 ++++- physicsnemo/cfd/evaluation/common/io.py | 23 +- physicsnemo/cfd/evaluation/config.py | 12 +- physicsnemo/cfd/evaluation/inference/run.py | 91 +++++- .../cfd/evaluation/reports/__init__.py | 15 + .../evaluation/reports/builtin/__init__.py | 18 ++ .../reports/builtin/surface_volume.py | 131 ++++++++ .../cfd/evaluation/reports/registry.py | 47 +++ test/ci_tests/test_evaluation.py | 30 +- workflows/evaluation_examples/README.md | 279 +++++++++--------- .../evaluation_examples/inference_config.yaml | 27 +- workflows/evaluation_examples/setup.sh | 2 + 13 files changed, 572 insertions(+), 186 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/reports/__init__.py create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/__init__.py create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py create mode 100644 physicsnemo/cfd/evaluation/reports/registry.py create mode 100755 workflows/evaluation_examples/setup.sh diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index 99f42d1..cf414a8 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -1,4 +1,9 @@ -"""Benchmark runner: config → model x dataset loop → metrics → results.""" +"""Benchmark runner: config → model x dataset loop → metrics → results. + +Comparison meshes are built in memory for metrics only; the benchmark path does not write +VTP/VTU files or run visualization plugins (see ``report_plugins.run_optional_report_plugins`` for +optional post-processing outside this CLI). +""" from __future__ import annotations @@ -8,7 +13,6 @@ from typing import Any from physicsnemo.cfd.evaluation.benchmarks.report import write_report -from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins from physicsnemo.cfd.evaluation.config import Config, ModelConfig, DatasetConfig, OutputConfig from physicsnemo.cfd.evaluation.datasets import get_adapter from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model @@ -182,7 +186,10 @@ def _run_single( log_dataset(dataset_config.name, f"Metric {mname!r} failed for {cid!r}: {e}") case_metrics[mname] = float("nan") all_metric_values.setdefault(mname, []).append(float("nan")) - per_case.append({"case_id": cid, "metrics": case_metrics}) + row: dict[str, Any] = {"case_id": cid, "metrics": case_metrics} + if comparison_mesh is not None and metric_dtype is not None: + row["metric_dtype"] = metric_dtype + per_case.append(row) # Aggregate (mean over cases) metrics_summary = {} @@ -290,7 +297,6 @@ def run_benchmark( ) write_report(results, output_dir, formats=["json", "csv", "html"]) - run_optional_report_plugins(config, results, output_dir) return results @@ -327,6 +333,9 @@ def _config_to_dict(c: Config) -> dict: "reports": { "enabled": c.reports.enabled, "plugins": c.reports.plugins, + "save_comparison_meshes": c.reports.save_comparison_meshes, + "comparison_mesh_subdir": c.reports.comparison_mesh_subdir, + "visuals": c.reports.visuals, }, "benchmark": { "mode": c.benchmark.mode, diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py index e1cc97b..69f1f18 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -2,7 +2,17 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Optional post-scalar report plugins (streamlines, plots via bench.visualization).""" +"""Optional visuals (plots via ``physicsnemo.cfd.bench.visualization``). + +The benchmark engine does **not** call this module: benchmarking stays scalar-only (JSON/CSV/HTML) +with in-memory comparison meshes for metrics only. + +The **inference** CLI (``physicsnemo.cfd.evaluation.inference.run``) calls +:func:`run_optional_report_plugins` after saving the prediction mesh when ``reports.enabled`` and +``reports.visuals`` are set: it builds a comparison mesh, writes ``{case_id}_comparison.vtp|vtu`` when +needed, then runs registered visuals. You can also call :func:`run_optional_report_plugins` from +your own script when you have ``results`` and want a manifest. +""" from __future__ import annotations @@ -12,6 +22,7 @@ from physicsnemo.cfd.evaluation.config import Config from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.registry import get_visual, normalize_visuals_config def run_optional_report_plugins( @@ -19,19 +30,50 @@ def run_optional_report_plugins( results: list[dict[str, Any]], output_dir: str, ) -> None: - """If ``config.reports.enabled``, run configured plugins and write a small manifest.""" - if not config.reports.enabled: - return - out = Path(output_dir) / "report_plugins_manifest.json" - manifest = { - "enabled": True, + """Run registered visuals when ``config.reports.enabled``; write manifest and optional legacy plugins note.""" + out_dir = Path(output_dir) + manifest: dict[str, Any] = { + "enabled": bool(config.reports.enabled), "plugins": config.reports.plugins, - "note": ( - "Plot/streamline plugins are optional; wire bench.visualization helpers here " - "when you add concrete plugin names and inputs." - ), - "runs": len(results), + "save_comparison_meshes": config.reports.save_comparison_meshes, + "comparison_mesh_subdir": config.reports.comparison_mesh_subdir, + "visuals_ran": [], + "visual_errors": [], } + + if not config.reports.enabled: + _write_manifest(out_dir, manifest) + return + + import physicsnemo.cfd.evaluation.reports # noqa: F401 — register built-in visuals + + visuals_list = list(config.reports.visuals or []) + if not visuals_list: + log_dataset("benchmark", "reports.enabled but no reports.visuals configured; writing manifest only.") + _write_manifest(out_dir, manifest) + return + + try: + specs = normalize_visuals_config(visuals_list) + except ValueError as e: + manifest["visual_errors"].append({"stage": "normalize", "error": str(e)}) + _write_manifest(out_dir, manifest) + raise + + for name, vkwargs in specs: + try: + fn = get_visual(name) + fn(config, results, output_dir, **vkwargs) + manifest["visuals_ran"].append({"name": name, "kwargs": vkwargs}) + except Exception as e: + log_dataset("benchmark", f"Visual {name!r} failed: {e}") + manifest["visual_errors"].append({"name": name, "error": str(e)}) + + _write_manifest(out_dir, manifest) + + +def _write_manifest(out_dir: Path, manifest: dict[str, Any]) -> None: + out = out_dir / "report_plugins_manifest.json" log_dataset("benchmark", f"Writing report plugin manifest to {out}…") with open(out, "w") as f: json.dump(manifest, f, indent=2) diff --git a/physicsnemo/cfd/evaluation/common/io.py b/physicsnemo/cfd/evaluation/common/io.py index ce45b6e..8516610 100644 --- a/physicsnemo/cfd/evaluation/common/io.py +++ b/physicsnemo/cfd/evaluation/common/io.py @@ -40,10 +40,25 @@ def surface_factors_from_global_stats( Stacks pressure (1) and shear_stress (3) using ``mean`` / ``std_dev`` in the JSON (same layout as ``surface_fields_normalization.npz`` from training). """ - mp = np.asarray(data["mean"]["pressure"], dtype=np.float64).ravel() - mt = np.asarray(data["mean"]["shear_stress"], dtype=np.float64).ravel() - sp = np.asarray(data["std_dev"]["pressure"], dtype=np.float64).ravel() - st = np.asarray(data["std_dev"]["shear_stress"], dtype=np.float64).ravel() + mean_block = data.get("mean") or {} + std_block = data.get("std_dev") or {} + for k in ("pressure", "shear_stress"): + if k not in mean_block or k not in std_block: + have_m = sorted(mean_block.keys()) + have_s = sorted(std_block.keys()) + raise KeyError( + "Surface GeoTransolver/Transolver needs global_stats.json entries " + "mean/std_dev for 'pressure' and 'shear_stress'. " + f"Missing or incomplete key {k!r}. " + f"mean keys: {have_m}, std_dev keys: {have_s}. " + "If you only see velocity / pressure_volume / turbulent_viscosity, " + "that is a volume stats file—use inference_domain: volume with a volume " + "checkpoint, or point stats_path at a surface-trained checkpoint directory." + ) + mp = np.asarray(mean_block["pressure"], dtype=np.float64).ravel() + mt = np.asarray(mean_block["shear_stress"], dtype=np.float64).ravel() + sp = np.asarray(std_block["pressure"], dtype=np.float64).ravel() + st = np.asarray(std_block["shear_stress"], dtype=np.float64).ravel() mean = np.concatenate([mp[:1], mt[:3]]) std = np.concatenate([sp[:1], st[:3]]) return { diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index 8ba7000..c14b70a 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -72,10 +72,15 @@ class BenchmarkConfig: @dataclass class ReportsConfig: - """Optional Phase-B style plugins (plots, streamlines) after scalar metrics.""" + """Post-scalar outputs: optional comparison mesh export and registered visuals (plots).""" enabled: bool = False plugins: list[dict[str, Any]] = field(default_factory=list) + #: When True, write each case comparison mesh to ``comparison_mesh_subdir`` for downstream visualization. + save_comparison_meshes: bool = False + comparison_mesh_subdir: str = "comparison_meshes" + #: Visuals to run (same list style as ``metrics``): strings or ``{name: ..., ...kwargs}``. + visuals: list[str] | list[dict[str, Any]] = field(default_factory=list) # Canonical prediction keys used by wrappers; mesh array names are configurable per dataset/convention. @@ -188,6 +193,11 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": reports = ReportsConfig( enabled=bool(reports_raw.get("enabled", False)), plugins=list(reports_raw.get("plugins") or []), + save_comparison_meshes=bool(reports_raw.get("save_comparison_meshes", False)), + comparison_mesh_subdir=str( + reports_raw.get("comparison_mesh_subdir") or "comparison_meshes" + ), + visuals=list(reports_raw.get("visuals") or []), ) else: reports = ReportsConfig() diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py index 1b1e2ba..b8d885a 100644 --- a/physicsnemo/cfd/evaluation/inference/run.py +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -1,9 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + """CLI entrypoint for single-case inference.""" +from __future__ import annotations + import argparse from pathlib import Path +from typing import Any -from physicsnemo.cfd.evaluation.config import load_config +from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins +from physicsnemo.cfd.evaluation.config import Config, load_config import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 from physicsnemo.cfd.evaluation.datasets import get_adapter from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model @@ -11,6 +19,7 @@ from physicsnemo.cfd.evaluation.inference import get_model_wrapper from physicsnemo.cfd.evaluation.inference.model_registry import get_inference_domain_for_model from physicsnemo.cfd.evaluation.inference.progress import log_inference +from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh def _effective_model_inference_domain(model_name: str, override: str | None) -> str: @@ -114,6 +123,86 @@ def main() -> None: mesh.save(str(out_path)) log_inference(config.model.name, f"Wrote mesh with prediction fields: {out_path}") + _maybe_save_comparison_mesh_and_run_visuals( + config=config, + case_id=case_id, + case=case, + predictions=predictions, + out_dir=out_dir, + ) + + +def _maybe_save_comparison_mesh_and_run_visuals( + *, + config: Config, + case_id: str, + case: Any, + predictions: dict[str, Any], + out_dir: Path, +) -> None: + """Build GT+pred comparison mesh, optionally save VTK, run ``reports.visuals`` when enabled.""" + rep = config.reports + want_save = rep.save_comparison_meshes or ( + rep.enabled and bool(rep.visuals) + ) + if not want_save: + return + + try: + comparison_mesh, metric_dtype = build_comparison_mesh( + case, predictions, config.output + ) + except Exception as ex: + log_inference( + config.model.name, + f"Skipping comparison mesh / visuals: build_comparison_mesh failed: {ex}", + ) + return + + sub = out_dir / rep.comparison_mesh_subdir + sub.mkdir(parents=True, exist_ok=True) + ext = ".vtp" if case.inference_domain == "surface" else ".vtu" + cmp_path = sub / f"{case_id}_comparison{ext}" + try: + comparison_mesh.save(str(cmp_path)) + except Exception as ex: + log_inference( + config.model.name, + f"Could not save comparison mesh to {cmp_path}: {ex}", + ) + return + + log_inference( + config.model.name, + f"Wrote comparison mesh for visuals/metrics bridge: {cmp_path}", + ) + + if not rep.enabled or not rep.visuals: + return + + import physicsnemo.cfd.evaluation.reports # noqa: F401 — register built-in visuals + + results: list[dict[str, Any]] = [ + { + "model": config.model.name, + "dataset": config.dataset.name, + "skipped": False, + "per_case": [ + { + "case_id": case_id, + "metric_dtype": metric_dtype, + "comparison_mesh_path": str(cmp_path.resolve()), + "metrics": {}, + } + ], + } + ] + log_inference( + config.model.name, + f"Running reports.visuals ({len(rep.visuals)}) under {out_dir}…", + ) + run_optional_report_plugins(config, results, str(out_dir)) + if __name__ == "__main__": main() diff --git a/physicsnemo/cfd/evaluation/reports/__init__.py b/physicsnemo/cfd/evaluation/reports/__init__.py new file mode 100644 index 0000000..1be3b68 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/__init__.py @@ -0,0 +1,15 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Report visuals registry and built-in plot hooks (``physicsnemo.cfd.bench.visualization``).""" + +from physicsnemo.cfd.evaluation.reports.registry import ( + get_visual, + list_visuals, + register_visual, +) + +import physicsnemo.cfd.evaluation.reports.builtin # noqa: F401 — register built-ins + +__all__ = ["register_visual", "get_visual", "list_visuals"] diff --git a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py new file mode 100644 index 0000000..f2c8f13 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py @@ -0,0 +1,18 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Register built-in visuals.""" + +from physicsnemo.cfd.evaluation.reports.builtin.surface_volume import ( + register_plot_fields_volume, + register_field_comparison_surface, +) + + +def register_all_builtin_visuals() -> None: + register_field_comparison_surface() + register_plot_fields_volume() + + +register_all_builtin_visuals() diff --git a/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py new file mode 100644 index 0000000..0b4ba69 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py @@ -0,0 +1,131 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Built-in visuals wrapping ``physicsnemo.cfd.bench.visualization.utils``.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import pyvista as pv + +from physicsnemo.cfd.bench.visualization.utils import plot_field_comparisons, plot_fields +from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.registry import register_visual + + +def field_comparison_surface( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + case_ids: list[str] | None = None, + canonical_keys: list[str] | None = None, + view: str = "xy", + **kwargs: Any, +) -> None: + """Plot GT vs pred field comparison for surface comparison meshes (pressure default).""" + out = Path(output_dir) + vis_dir = out / "visuals" + vis_dir.mkdir(parents=True, exist_ok=True) + keys = canonical_keys or ["pressure"] + output: OutputConfig = config.output + + for run in results: + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + path = row.get("comparison_mesh_path") + dtype = row.get("metric_dtype") or "cell" + if not path: + log_dataset( + "benchmark", + f"Skip field_comparison_surface for {cid!r}: no comparison_mesh_path", + ) + continue + mesh = pv.read(path) + true_fields: list[str] = [] + pred_fields: list[str] = [] + for k in keys: + if k not in output.ground_truth_mesh_field_names: + raise KeyError(f"output.ground_truth_mesh_field_names missing {k!r}") + if k not in output.mesh_field_names: + raise KeyError(f"output.mesh_field_names missing {k!r}") + true_fields.append(output.ground_truth_mesh_field_names[k]) + pred_fields.append(output.mesh_field_names[k]) + plotter = plot_field_comparisons( + mesh, + true_fields, + pred_fields, + view=view, + dtype=dtype, + **kwargs, + ) + safe = f"{model}_{dataset}_{cid}_field_comparison.png".replace("/", "_") + plotter.screenshot(str(vis_dir / safe)) + plotter.close() + + +def plot_fields_volume( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + case_ids: list[str] | None = None, + fields: list[str] | None = None, + view: str = "xy", + **kwargs: Any, +) -> None: + """Plot selected VTK fields on volume comparison meshes (``plot_fields``).""" + out = Path(output_dir) + vis_dir = out / "visuals" + vis_dir.mkdir(parents=True, exist_ok=True) + output: OutputConfig = config.output + field_list = fields + if field_list is None: + field_list = list(output.volume_mesh_field_names.values()) + + for run in results: + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + path = row.get("comparison_mesh_path") + dtype = row.get("metric_dtype") or "point" + if not path: + log_dataset( + "benchmark", + f"Skip plot_fields_volume for {cid!r}: no comparison_mesh_path", + ) + continue + mesh = pv.read(path) + plotter = plot_fields( + mesh, + field_list, + view=view, + dtype=dtype, + **kwargs, + ) + safe = f"{model}_{dataset}_{cid}_plot_fields.png".replace("/", "_") + plotter.screenshot(str(vis_dir / safe)) + plotter.close() + + +def register_field_comparison_surface() -> None: + register_visual("field_comparison_surface", field_comparison_surface) + + +def register_plot_fields_volume() -> None: + register_visual("plot_fields_volume", plot_fields_volume) diff --git a/physicsnemo/cfd/evaluation/reports/registry.py b/physicsnemo/cfd/evaluation/reports/registry.py new file mode 100644 index 0000000..a08ec5b --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/registry.py @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Named visual/report plugin registry (metrics-style) for post-scalar benchmark outputs.""" + +from __future__ import annotations + +from typing import Any, Callable + +VisualFn = Callable[..., None] + +_REGISTRY: dict[str, VisualFn] = {} + + +def register_visual(name: str, fn: VisualFn) -> None: + """Register a visual plugin by name.""" + _REGISTRY[name] = fn + + +def get_visual(name: str) -> VisualFn: + """Resolve a visual function by name.""" + if name not in _REGISTRY: + raise KeyError(f"Unknown visual: {name}. Available: {sorted(_REGISTRY.keys())}") + return _REGISTRY[name] + + +def list_visuals() -> list[str]: + """Return registered visual names.""" + return sorted(_REGISTRY.keys()) + + +def normalize_visuals_config( + visuals: list[str] | list[dict[str, Any]], +) -> list[tuple[str, dict[str, Any]]]: + """Return list of (visual_name, kwargs) from YAML (same rules as metrics).""" + out: list[tuple[str, dict[str, Any]]] = [] + for v in visuals: + if isinstance(v, str): + out.append((v, {})) + elif isinstance(v, dict) and "name" in v: + name = v["name"] + kwargs = {k: val for k, val in v.items() if k != "name"} + out.append((name, kwargs)) + else: + raise ValueError(f"Invalid visual entry: {v}") + return out diff --git a/test/ci_tests/test_evaluation.py b/test/ci_tests/test_evaluation.py index e8b2d8f..20d01f2 100644 --- a/test/ci_tests/test_evaluation.py +++ b/test/ci_tests/test_evaluation.py @@ -79,21 +79,21 @@ def test_build_comparison_mesh_surface_zero_l2_when_identical() -> None: pred = {"pressure": p.copy(), "shear_stress": wss.copy()} output = OutputConfig() mesh, dtype = build_comparison_mesh(case, pred, output, mesh_override=mesh0) - gtn = output.ground_truth_mesh_field_names["pressure"] - prn = output.mesh_field_names["pressure"] - d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) - key = f"{gtn}_l2_error" - assert abs(float(d[key])) < 1e-10 - - v = l2_pressure( - case.ground_truth or {}, - pred, - case=case, - comparison_mesh=mesh, - metric_dtype=dtype, - output=output, - ) - assert abs(v) < 1e-10 + gtn = output.ground_truth_mesh_field_names["pressure"] + prn = output.mesh_field_names["pressure"] + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + assert abs(float(d[key])) < 1e-10 + + v = l2_pressure( + case.ground_truth or {}, + pred, + case=case, + comparison_mesh=mesh, + metric_dtype=dtype, + output=output, + ) + assert abs(v) < 1e-10 def test_legacy_metric_call_without_extended_kwargs() -> None: diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index ca5eada..6b2f04a 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -1,220 +1,205 @@ # Evaluation examples (PhysicsNeMo-CFD) -Example YAML and scripts for **`physicsnemo.cfd.evaluation`**: config-driven inference and benchmarking (same stack as the `nvidia-physicsnemo-cfd` package). Install the repo or `pip install nvidia-physicsnemo-cfd`, then run the CLIs below from this directory. +YAML examples and CLIs for **`physicsnemo.cfd.evaluation`**: single-case inference, benchmarking, and optional PNG reports. Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then run commands **from this directory** (or pass absolute paths to `--config`). -## Inference and benchmarking layer (`physicsnemo.cfd.evaluation`) +--- -Configure model, dataset, metrics, and checkpoint via YAML; run single-case inference or a full benchmark with JSON/CSV/HTML reports. Metrics delegate to **`physicsnemo.cfd.bench`** when a comparison mesh is built (see `output.mesh_field_names` and ground-truth maps in config). +## Commands -### Quick start - -**Single-case inference** (one model, one case; writes prediction mesh): +**Inference** — one model, one case; writes `inference__.vtp|vtu` under `run.output_dir`. With `reports.enabled: true` and `reports.visuals`, also writes a comparison mesh and PNGs (needs GT on the case; see [Reports and plots](#reports-and-plots)). ```bash python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml --case-id run_1 +# shorthand: python -m physicsnemo.cfd.evaluation.inference ``` -**Shorthand** (same entrypoints): - -```bash -python -m physicsnemo.cfd.evaluation.inference -``` - -**Benchmark** (model × dataset, metrics, JSON/CSV/HTML report) — merge shared defaults + matrix overlay: +**Benchmark** — metrics, JSON/CSV/HTML; **does not** run `reports.visuals` or save comparison VTK. ```bash python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml --case-id run_1 -``` - -```bash python -m physicsnemo.cfd.evaluation.benchmarks ``` -You can also use a **single** full YAML (no `--base-config`) if everything lives in one file. +**Merge order:** `--base-config` first, then `--config`, then optional CLI overrides (`run.device=cpu`, or `--run.device=cuda:1`). Inference supports `--base-config` the same way. -**CLI overrides** (without editing YAML): +**Minimal surface-only example:** `surface_benchmark_minimal.yaml` (edit paths). -```bash -python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml run.device=cpu run.output_dir=./out -# or with -- prefix: -python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml --run.device=cuda:1 --model.checkpoint=path/to/ckpt.pth -``` +--- -Optional **`--base-config`** works for `physicsnemo.cfd.evaluation.inference.run` too (merge order: base, then `--config`). +## Config files in this folder -**Minimal surface-only example** (placeholders; edit paths): `surface_benchmark_minimal.yaml`. +| File | Use | +| ---- | --- | +| [`inference_config.yaml`](inference_config.yaml) | Shared defaults: `run`, `model`, `dataset`, `output`, `metrics`, optional `reports`, `benchmark.mode: single`. Use alone or as `--base-config`. | +| [`benchmark_matrix.yaml`](benchmark_matrix.yaml) | Overlay: `benchmark.mode: matrix`, `models[]`, `datasets[]` (Cartesian product; incompatible surface/volume pairs are skipped). | -### Config +--- -Edit `inference_config.yaml` (shared run/model/dataset/output/metrics and default `benchmark.mode: single`) and, for matrix sweeps, `benchmark_matrix.yaml` (typically only a `benchmark:` block). JSON works as well. +## Config reference (merged YAML) -- **run**: `device`, `output_dir`, `seed`, `batch_size` -- **model**: `name` (registered wrapper id: fignet, xmgn, geotransolver, … or your [custom model](#custom-models-adding-a-new-wrapper)), `checkpoint`, `stats_path`, optional **`inference_domain`** (`surface` \| `volume`) to override the wrapper default (required for **GeoTransolver** / **Transolver** on VTU volume cases: use `volume` with a volume-trained checkpoint and **`global_stats.json`** (preferred; must include `velocity`, `pressure_volume`, `turbulent_viscosity`) or `volume_fields_normalization.npz` beside the checkpoint, as in `transformer_models/src/inference_on_vtk.py`; **DoMINO** uses the same key with a volume `model.model_type` in `domino_config` and VTU cases), `kwargs` (e.g. `max_points`, `node_degree`, `interpolation_k`; GeoTransolver/Transolver: `resolution`, `geometry_sampling`, `air_density`, `stream_velocity`, `batch_resolution`; optional datapipe overrides: `include_sdf`, `translational_invariance`, `scale_invariance`, `reference_scale`, `broadcast_global_features`, …; **DoMINO**: `domino_config`, `point_batch_size`). The **baseline** names are optional no-checkpoint stubs; see [Baseline model stubs](#baseline-model-stubs-surface_baseline-volume_baseline) below. -- **dataset**: `name` (registered adapter id: drivaerml, ahmed, or your [custom dataset](#custom-datasets-adding-a-new-adapter)), `root`, `split`, `case_ids` (null = all cases), optional `kwargs` e.g. `align_ground_truth_to_model: true` or `gt_data_type: from_model` so reference fields are read at the same mesh location as the model (point vs cell) without separate dataset entries per model in matrix benchmarks. **DrivAerML volume**: same `root` and `run_*` dirs as surface; set `inference_domain: volume` and place VTU files per run (default name `volume_.vtu` for `run_`, e.g. `run_1/volume_1.vtu`). Optional `volume_vtu_filename` or `volume_vtu_template` (e.g. `"volume_{run_suffix}.vtu"`) if your naming differs. -- **output** (optional): `mesh_field_names` / **`ground_truth_mesh_field_names`** — canonical surface keys → VTP array names for predictions vs reference. **`volume_mesh_field_names`** / **`ground_truth_volume_mesh_field_names`** — volume keys → VTU names; includes **`pressure_volume`** (volume pressure, distinct from surface `pressure`), plus `turbulent_viscosity`, `velocity` (defaults often `pMeanPred`, `nutMeanPred`, `UMeanPred` for preds, `pMean`, `nutMean`, `UMean` for GT). -- **metrics**: list of names or dicts with `name` + kwargs, e.g. `l2_pressure`, `l2_shear_stress`, **`l2_pressure_volume`** (volume pressure / `pressure_volume`), `l2_velocity`, **`l2_turbulent_viscosity`** (optional `gt_key` / `pred_key`), `drag_error`, `lift_error`, `continuity_residual_l2`, `momentum_residual_l2`. Metric kwargs are passed through from the config entry. -- **reports** (optional): `enabled`, `plugins` — post-scalar manifest hooks (see `physicsnemo.cfd.evaluation.config.ReportsConfig`). -- **benchmark**: `mode` (single | matrix), `models` / `datasets` for matrix mode, `reproducibility`. In **matrix** mode, pairs where the model **`INFERENCE_DOMAIN`** does not match the dataset adapter domain (from `dataset.kwargs`, e.g. DrivAerML `inference_domain: volume`) are **skipped**: a line is printed, rows appear in CSV/HTML as `_skipped`, and `benchmark_artifacts.json` lists `skipped_runs`. +Keys are the same whether they come from one file or base + overlay. -### Baseline model stubs (surface_baseline, volume_baseline) +| Section | Contents | +| ------- | -------- | +| **run** | `device`, `output_dir`, `seed`, `batch_size` | +| **model** | `name` (registered wrapper), `checkpoint`, `stats_path`, optional `inference_domain` (`surface` \| `volume`), `kwargs` | +| **dataset** | `name` (registered adapter), `root`, `split`, `case_ids` (`null` = all), `kwargs` (e.g. `align_ground_truth_to_model`, `inference_domain` for volume) | +| **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume) | +| **metrics** | List of metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | +| **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | +| **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | -They are **optional** registered models—not required for normal inference with trained checkpoints. +**Model notes:** GeoTransolver / Transolver on **volume** need `model.inference_domain: volume` and stats that match volume training (`global_stats.json` with `velocity`, `pressure_volume`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** GeoTransolver/Transolver needs surface stats (`pressure` + `shear_stress` in `global_stats.json` or `surface_fields_normalization.npz`). DoMINO uses `domino_config` for volume. -**Purpose** +**Custom code:** [Custom models](#custom-models-adding-a-new-wrapper) · [Custom datasets](#custom-datasets-adding-a-new-adapter) · [Baseline stubs](#baseline-model-stubs-surface_baseline-volume_baseline) -- **Smoke tests & CI** — Exercise the full path (dataset adapter → `CanonicalCase` → `CFDModel.load` / `prepare_inputs` / `predict` / `decode_outputs` → metrics and, for inference, VTK write) **without** loading a neural network, **without** valid `checkpoint` / `stats_path`, and **without** a GPU-heavy forward pass. -- **Layout & domain checks** — Confirm case listing, mesh paths (boundary VTP vs volume VTU), `inference_domain` matching, and benchmark matrix **skip** behavior for incompatible model×dataset pairs. -- **Debugging** — Isolate data I/O and reporting when a real model fails early (bad stats, OOM, etc.). +--- -**What they are *not*** +## Metrics -- They are **not** chosen automatically from a path like `*_volume_checkpoint`; only **`model.name: surface_baseline`** or **`volume_baseline`** selects them. -- They are **not** physics-accurate predictions: outputs are **zeros** (or shape-only placeholders) with the right **canonical keys** for the domain (`pressure` / `shear_stress` on surface, `pressure_volume` / `turbulent_viscosity` / `velocity` on volume). -- **`surface_baseline`** uses **cell-centered** counts on the boundary mesh; if your ground truth is **point**-aligned, align GT via `dataset.kwargs` (e.g. `align_ground_truth_to_model` / `gt_data_type`) or expect metric shape mismatches or NaNs. - -**Config sketch** +Registered names (or dicts with `name` + kwargs). Example list from `inference_config.yaml`: ```yaml -model: - name: "surface_baseline" # or "volume_baseline" - checkpoint: "" - stats_path: "" -dataset: - name: drivaerml - root: /path/to/data - kwargs: {} # surface default - # kwargs: { inference_domain: volume } # required for volume_baseline +metrics: + - l2_pressure + - l2_shear_stress + - l2_pressure_area_weighted # alias: area_wt_l2_pressure + - l2_pressure_volume # needs volume case + output maps + - l2_velocity + - l2_turbulent_viscosity + - drag_error + - lift_error + - continuity_residual_l2 # volume: needs velocity on volume mesh + - momentum_residual_l2 ``` -If you never use smoke tests or matrix stubs, you can ignore these names; the rest of the stack does not depend on them. - -### Custom models (adding a new wrapper) - -Built-in models (fignet, xmgn, geotransolver, …) are **registered** `CFDModel` implementations. To add your own architecture or checkpoint layout, implement a **wrapper** and register it once; inference and benchmarks pick it up by `model.name` in YAML. - -#### Where to put code +| Name | Meaning | +| ---- | ------- | +| `l2_pressure`, `l2_shear_stress` | Surface L2 | +| `l2_pressure_area_weighted` | Area-weighted L2 pressure (`area_wt_l2_pressure`) | +| `l2_pressure_volume` | Volume pressure (`pressure_volume`) | +| `l2_velocity`, `l2_turbulent_viscosity` | Volume / surface depending on case | +| `drag_error`, `lift_error` | Coefficient errors | +| `continuity_residual_l2`, `momentum_residual_l2` | Volume residuals | -- Add a Python module under **`physicsnemo/cfd/evaluation/inference/wrappers/`**, e.g. `wrappers/my_model/wrapper.py` or `wrappers/my_model.py`. -- Keep imports under `physicsnemo.cfd.evaluation` (and `nvidia-physicsnemo` / PyTorch as needed). Do **not** add inference code under `physicsnemo/cfd/inference/` — that tree is reserved for the DoMINO NIM client. +--- -#### Implement `CFDModel` +## Reports and plots -Subclass **`CFDModel`** from `physicsnemo.cfd.evaluation.inference.model_registry` and implement: +### What runs where -| Piece | Purpose | -| ----- | --- | -| **`INFERENCE_DOMAIN`** | Class variable: `"surface"` (boundary VTP) or `"volume"` (volume VTU). Must match the dataset adapter domain for benchmark runs. | -| **`OUTPUT_LOCATION`** + **`output_location`** | `"point"` or `"cell"` — where your predictions live on the mesh (drives GT alignment and writing). | -| **`load(checkpoint_path, stats_path, device, **kwargs)`** | Load weights and stats; **`kwargs`** includes `model.kwargs` from YAML and `inference_domain` when set. Return `self`. | -| **`prepare_inputs(case: CanonicalCase)`** | Build tensors / graphs / batches from `case.mesh_path` and optional `ground_truth` / `metadata`. | -| **`predict(model_input)`** | One forward pass; return raw outputs (tensors, dict, etc.). | -| **`decode_outputs(raw_output, case)`** | Map to the **canonical predictions dict** used by metrics and the mesh bridge: **surface** — `pressure`, `shear_stress`; **volume** — `pressure_volume`, `turbulent_viscosity`, `velocity` (use `build_predictions_dict` / `predictions_dict` from `physicsnemo.cfd.evaluation.datasets.schema`). | +| CLI | Metrics | `reports.visuals` (PNGs) | Comparison VTK | +| --- | ------- | -------------------------- | ---------------- | +| **Inference** | — | Yes, if `reports.enabled` and `visuals` non-empty | Saved if `save_comparison_meshes` **or** visuals enabled | +| **Benchmark** | Yes | No | Not written by engine | -If your wrapper’s class defaults to `INFERENCE_DOMAIN = "surface"` but you train a VTU volume checkpoint, users set **`model.inference_domain: volume`** in YAML so the engine matches volume datasets. +Built-in **visual** names: `field_comparison_surface` (surface GT vs pred), `plot_fields_volume` (volume scalars). Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. -#### Register the wrapper +**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/{case_id}_comparison.vtp|vtu`. -1. In **`physicsnemo/cfd/evaluation/inference/wrappers/__init__.py`**, import your class and call **`register_model("my_model", MyModelWrapper)`** so the module is loaded at startup (same pattern as existing wrappers). -2. Reinstall or run with the repo on **`PYTHONPATH`** / `pip install -e .` so the new module is importable. +**Inference writes two meshes:** (1) `inference__.vtp|vtu` — **predictions only**; (2) `comparison_meshes/_comparison.*` — **GT + pred** (same as `build_comparison_mesh` for metrics/plots). -#### Use in YAML +### `reports` YAML shape ```yaml -model: - name: my_model - checkpoint: /path/to/weights.pt - stats_path: /path/to/dir_with_stats # or file, per your load() - inference_domain: surface # optional override if your class default differs - kwargs: - batch_size: 1 - # any options your load() / prepare_inputs() read +reports: + enabled: true + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visuals: + - field_comparison_surface + - name: field_comparison_surface + case_ids: ["run_1"] # must match the case you run (--case-id) + canonical_keys: [pressure, shear_stress] + view: xy ``` -No changes to **`run.py`** or **`benchmarks/engine.py`** are required. - -#### Reference implementations (in-repo) +| Key | Role | +| --- | ---- | +| `enabled` | If true, run registered visuals (inference calls this automatically). | +| `save_comparison_meshes` | Save comparison VTK even with no `visuals` (inference only). | +| `comparison_mesh_subdir` | Subfolder for `*_comparison.vtp|vtu`. | +| `visuals` | Ordered list: string names or `{ name: ..., ...kwargs }`. | +| `plugins` | Legacy manifest hooks (`ReportsConfig`). | -| Wrapper | Notes | -| ------- | --- | -| `wrappers/fignet/wrapper.py` | Cell-centered surface | -| `wrappers/xmgn/wrapper.py` | Point-based surface | -| `wrappers/transolver/wrapper.py`, `wrappers/geotransolver/wrapper.py` | Surface, cell; boundary VTP + STL + `surface_fields_normalization.npz` | -| `wrappers/domino/wrapper.py` | DoMINO config + scaling | -| `wrappers/surface_baseline.py`, `wrappers/volume_baseline.py` | No checkpoint; smoke tests | +### Field selection (built-ins) -### Custom datasets (adding a new adapter) +- **`field_comparison_surface`** — Uses `canonical_keys` (default `pressure`) and `output.ground_truth_mesh_field_names` / `mesh_field_names`. +- **`plot_fields_volume`** — `fields` = VTK array names, or default all `output.volume_mesh_field_names` values. -Built-in datasets (drivaerml, ahmed, …) are **registered** `DatasetAdapter` implementations. To plug in your own layout (folder naming, file formats, splits), add an **adapter** and register it once; inference and benchmarks resolve it by `dataset.name` in YAML. +### Line plots -#### Where to put code +There is **no** built-in **`reports.visuals`** entry for **`plot_line`** (needs polyline extracts + `field_true` / `field_pred`). Options: **register** a custom visual that calls `physicsnemo.cfd.bench.visualization.utils.plot_line`, or use [`workflows/bench_example`](../bench_example/README.md) scripts. -- Add a module under **`physicsnemo/cfd/evaluation/datasets/adapters/`**, e.g. `my_dataset.py`. -- Shared helpers (VTK GT extraction, alignment) live under **`physicsnemo/cfd/evaluation/datasets/`** (`vtk_ground_truth.py`, `gt_alignment.py`, …)—reuse them when possible instead of duplicating I/O. +### Other plot paths -#### Implement `DatasetAdapter` +- **Bench example:** run inference to get VTK, then `generate_surface_benchmarks.py` / `generate_volume_benchmarks.py` with a matching field map. +- **Programmatic:** `bench.visualization.utils` or `run_optional_report_plugins(config, results, output_dir)` with `per_case[].comparison_mesh_path` if you build results yourself. -Subclass **`DatasetAdapter`** from `physicsnemo.cfd.evaluation.datasets.adapter_registry` (also exported as `physicsnemo.cfd.evaluation.datasets.DatasetAdapter`) and implement: +### Troubleshooting -| Piece | Purpose | -| ----- | --- | -| **`__init__(self, root: str, **kwargs)`** | Store **`root`** (required dataset root) and options from **`dataset.kwargs`** in YAML. Validate paths early if you need to fail fast. | -| **`list_cases(split=None)`** | Return case IDs (strings). **`split`** is adapter-defined (`"train"` / `"test"` / `None` = all); document what your dataset supports. | -| **`load_case(case_id: str)`** | Return a **`CanonicalCase`**: `case_id`, **`mesh_path`** (absolute or root-relative path to the primary **`.vtp`** or **`.vtu`**), **`mesh_type`** (`"point"` \| `"cell"` — how GT fields in `ground_truth` align to the mesh), optional **`ground_truth`** (numpy arrays keyed for surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `velocity`, `turbulent_viscosity`, …), optional **`metadata`**, and **`inference_domain`** (`"surface"` \| `"volume"`). | -| **`inference_domain_from_kwargs`** (classmethod, optional) | If domain depends on kwargs (e.g. DrivAerML surface vs volume), return `"surface"` or `"volume"` so matrix benchmarks can **skip** incompatible model×dataset pairs **before** loading cases. | +- **`case_ids`** in a visual must include the case you run — use `--case-id run_1` if the visual lists `case_ids: ["run_1"]`, or omit `case_ids`. +- Use an **editable install** or **`PYTHONPATH`** to this repo so inference includes the visualization step (not an old wheel). +- Check **`report_plugins_manifest.json`** → `visual_errors` (headless PyVista often needs **xvfb**). -Ground truth in **`ground_truth`** should match what your metrics and **`mesh_bridge`** expect: canonical keys, with shapes consistent with **`mesh_type`** and the model’s **`OUTPUT_LOCATION`** (use **`dataset.kwargs`** like `align_ground_truth_to_model` / `gt_data_type` where supported—see `datasets/gt_alignment.py`). +--- -#### Register the adapter +## Baseline model stubs (`surface_baseline`, `volume_baseline`) -1. In **`physicsnemo/cfd/evaluation/datasets/adapters/__init__.py`**, import your class and call **`register_adapter("my_dataset", MyDatasetAdapter)`** so it loads with `import physicsnemo.cfd.evaluation.datasets.adapters`. -2. Reinstall or use **`pip install -e .`** / **`PYTHONPATH`** to the repo root. - -#### Use in YAML +Optional **no-checkpoint** wrappers for smoke tests, layout checks, and **matrix skip** behavior. Not real physics: zeros / placeholders with correct canonical keys. Set `model.name` explicitly; use `checkpoint: ""`, `stats_path: ""`. ```yaml +model: + name: "surface_baseline" # or volume_baseline + checkpoint: "" + stats_path: "" dataset: - name: my_dataset - root: /path/to/dataset_root - split: null - case_ids: null - kwargs: - # adapter-specific: splits, filename templates, inference_domain, GT field names, etc. - inference_domain: surface + name: drivaerml + root: /path/to/data + # kwargs: { inference_domain: volume } # for volume_baseline ``` -The engine passes **`dataset.kwargs`** into your adapter’s constructor (merged with alignment helpers for known keys—see DrivAerML). +--- + +## Custom models (adding a new wrapper) + +1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. +2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs` (canonical keys: surface `pressure`, `shear_stress`; volume `pressure_volume`, `turbulent_viscosity`, `velocity`). +3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. + +Reference: `wrappers/fignet/wrapper.py`, `geotransolver/wrapper.py`, `surface_baseline.py`, … + +--- -#### Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) +## Custom datasets (adding a new adapter) -- **`CanonicalCase`**: `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. Produced by adapters; consumed by model wrappers and metrics. -- **Predictions** (model output, not dataset): surface — `pressure`, `shear_stress`; volume — **`pressure_volume`**, `turbulent_viscosity`, `velocity`. Helpers: `predictions_dict`, `build_predictions_dict`. +1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. +2. Implement `list_cases`, `load_case` → **`CanonicalCase`** (`mesh_path`, `mesh_type`, `ground_truth`, `inference_domain`), optional **`inference_domain_from_kwargs`** for matrix skips. +3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. -#### Reference implementations (in-repo) +Reference: `adapters/drivaerml.py`, `ahmed.py`. Helpers: `datasets/vtk_ground_truth.py`, `gt_alignment.py`. -| Adapter | Notes | -| ------- | --- | -| `adapters/drivaerml.py` | **Surface**: `run_/boundary_.vtp`, GT `pressure` / `shear_stress`; optional kwargs: `boundary_vtp_filename`, `boundary_vtp_template`, field name overrides. **Volume**: `kwargs.inference_domain: volume`, `run_/volume_.vtu`, GT `pressure_volume`, `turbulent_viscosity`, `velocity` via `extract_volume_fields_from_mesh`; optional `volume_vtu_filename` / `volume_vtu_template`. | -| `adapters/ahmed.py` | Stub for layout / registration tests | +--- -See **`datasets/vtk_ground_truth.py`**, **`datasets/gt_alignment.py`** for resampling and naming. For **point** vs **cell** GT alignment with MeshGraphNet-style models, use kwargs such as **`align_ground_truth_to_model`** / **`gt_data_type`** as in DrivAerML. +## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) -### Package layout (in this repo) +- **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. +- **Predictions** (model output) — surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `turbulent_viscosity`, `velocity`. -Paths are relative to the repository root: +--- -- `physicsnemo/cfd/evaluation/config.py` — config schema, loader, and `deep_merge_dict` / `Config.load_merged` for multi-file configs -- `inference_config.yaml` / `benchmark_matrix.yaml` / `surface_benchmark_minimal.yaml` — examples in this folder (`--base-config` + `--config` where applicable) -- `physicsnemo/cfd/evaluation/inference/` — model registry; `common_wrapper_utils/vtk_datapipe_io.py`; `wrappers//` -- `physicsnemo/cfd/evaluation/datasets/` — canonical case schema and adapters (drivaerml, ahmed stub) -- `physicsnemo/cfd/evaluation/metrics/` — registry + `mesh_bridge.py` + `builtin/` (thin wrappers around `physicsnemo.cfd.bench`) -- `physicsnemo/cfd/evaluation/benchmarks/` — engine, reporting (JSON, CSV, HTML), optional `report_plugins.py` -- `physicsnemo/cfd/evaluation/common/` — mesh I/O, stats, kNN interpolation -- `physicsnemo/cfd/bench/` — shared algorithms (L2, forces, physics, …); not duplicated under `evaluation/` +## Package layout (repo root) -### Migration from external `physicsnemo_cfd` +| Path | Role | +| ---- | ---- | +| `physicsnemo/cfd/evaluation/config.py` | Config, `load_config`, `Config.load_merged` | +| `physicsnemo/cfd/evaluation/inference/` | Model registry, `wrappers/` | +| `physicsnemo/cfd/evaluation/datasets/` | Adapters, `CanonicalCase`, GT helpers | +| `physicsnemo/cfd/evaluation/metrics/` | Registry, `mesh_bridge.py`, bench-backed metrics | +| `physicsnemo/cfd/evaluation/reports/` | `register_visual`, built-in plots | +| `physicsnemo/cfd/evaluation/benchmarks/` | Engine, JSON/CSV/HTML reports | +| `physicsnemo/cfd/bench/` | Shared algorithms (L2, forces, …) | -If you used the old standalone package name **`physicsnemo_cfd`**, switch imports to **`physicsnemo.cfd.evaluation`** and run: +--- -`python -m physicsnemo.cfd.evaluation.benchmarks.run --config ...` -(or `python -m physicsnemo.cfd.evaluation.benchmarks`). diff --git a/workflows/evaluation_examples/inference_config.yaml b/workflows/evaluation_examples/inference_config.yaml index ace6b00..612c53e 100644 --- a/workflows/evaluation_examples/inference_config.yaml +++ b/workflows/evaluation_examples/inference_config.yaml @@ -13,8 +13,8 @@ run: model: name: "geotransolver" inference_domain: surface - checkpoint: "benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" - stats_path: "benchmark_models/geotransolver_drivaerml_surface_checkpoint" + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} dataset: @@ -54,6 +54,29 @@ metrics: # - "l2_turbulent_viscosity" # - "l2_velocity" +# Registered visuals (metrics-style list). Inference CLI: when enabled + visuals, builds a GT+pred +# comparison mesh, saves under comparison_mesh_subdir, runs visuals → PNGs in output_dir/visuals/. +# Benchmark CLI still does not run visuals (metrics-only). +reports: + enabled: true + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visuals: + # - field_comparison_surface + - name: field_comparison_surface + case_ids: ["run_1"] # case ids to visualize + canonical_keys: [pressure, shear_stress] + view: xy + - name: my_line_plot + case_ids: ["run_1"] # case ids to visualize + plot_coord: x + field_true: pMean + field_pred: pMeanTrimPred + # - name: plot_fields_volume + # case_ids: ["run_1"] + # fields: ["pMeanPred", "nutMeanPred", "UMeanPred"] + # view: xy + benchmark: mode: "single" models: [] diff --git a/workflows/evaluation_examples/setup.sh b/workflows/evaluation_examples/setup.sh new file mode 100755 index 0000000..a1af399 --- /dev/null +++ b/workflows/evaluation_examples/setup.sh @@ -0,0 +1,2 @@ +apt-get update +apt install -y xvfb From 35adf9d8493b19fa77ce389c509e2290ae3fa6fe Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Thu, 2 Apr 2026 09:01:32 -0700 Subject: [PATCH 03/25] adding line plot visualization --- .../cfd/evaluation/benchmarks/engine.py | 1 + physicsnemo/cfd/evaluation/config.py | 2 + physicsnemo/cfd/evaluation/inference/run.py | 60 +++++---- .../evaluation/reports/builtin/__init__.py | 2 + .../evaluation/reports/builtin/line_plot.py | 121 ++++++++++++++++++ workflows/evaluation_examples/README.md | 12 +- .../evaluation_examples/inference_config.yaml | 1 + 7 files changed, 170 insertions(+), 29 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/line_plot.py diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index cf414a8..8684ef8 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -308,6 +308,7 @@ def _config_to_dict(c: Config) -> dict: "output_dir": c.run.output_dir, "seed": c.run.seed, "batch_size": c.run.batch_size, + "save_inference_mesh": c.run.save_inference_mesh, }, "model": { "name": c.model.name, diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index c14b70a..06fe20e 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -28,6 +28,8 @@ class RunConfig: output_dir: str = "benchmark_results" seed: int = 42 batch_size: int = 1 + #: If False, inference CLI skips writing ``inference__.vtp|vtu`` (comparison mesh / visuals unchanged). + save_inference_mesh: bool = True @dataclass diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py index b8d885a..b45c433 100644 --- a/physicsnemo/cfd/evaluation/inference/run.py +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -93,35 +93,43 @@ def main() -> None: out_dir = Path(config.run.output_dir) out_dir.mkdir(parents=True, exist_ok=True) - import pyvista as pv - out_path = out_dir / f"inference_{config.model.name}_{case_id}{'.vtp' if model_domain == 'surface' else '.vtu'}" - log_inference( - config.model.name, - f"Saving predictions to file ({model_domain}): {out_path}", - ) - log_inference( - config.model.name, - f"Reading mesh file to attach fields: {case.mesh_path}", - ) + if config.run.save_inference_mesh: + import pyvista as pv + + out_path = out_dir / f"inference_{config.model.name}_{case_id}{'.vtp' if model_domain == 'surface' else '.vtu'}" + log_inference( + config.model.name, + f"Saving predictions to file ({model_domain}): {out_path}", + ) + log_inference( + config.model.name, + f"Reading mesh file to attach fields: {case.mesh_path}", + ) - if model_domain == "surface": - mesh = pv.read(case.mesh_path) - if not isinstance(mesh, pv.PolyData): - mesh = mesh.extract_surface() - names = config.output.mesh_field_names + if model_domain == "surface": + mesh = pv.read(case.mesh_path) + if not isinstance(mesh, pv.PolyData): + mesh = mesh.extract_surface() + names = config.output.mesh_field_names + else: + mesh = pv.read(case.mesh_path) + if hasattr(mesh, "cast_to_unstructured_grid"): + mesh = mesh.cast_to_unstructured_grid() + names = config.output.volume_mesh_field_names + + data_target = mesh.cell_data if wrapper.output_location == "cell" else mesh.point_data + for canonical_key, mesh_name in names.items(): + if canonical_key in predictions: + data_target[mesh_name] = predictions[canonical_key] + mesh.save(str(out_path)) + log_inference(config.model.name, f"Wrote mesh with prediction fields: {out_path}") else: - mesh = pv.read(case.mesh_path) - if hasattr(mesh, "cast_to_unstructured_grid"): - mesh = mesh.cast_to_unstructured_grid() - names = config.output.volume_mesh_field_names - - data_target = mesh.cell_data if wrapper.output_location == "cell" else mesh.point_data - for canonical_key, mesh_name in names.items(): - if canonical_key in predictions: - data_target[mesh_name] = predictions[canonical_key] - mesh.save(str(out_path)) - log_inference(config.model.name, f"Wrote mesh with prediction fields: {out_path}") + log_inference( + config.model.name, + f"Skipping inference mesh file (run.save_inference_mesh: false); " + f"would be inference_{config.model.name}_{case_id}.vtp|vtu.", + ) _maybe_save_comparison_mesh_and_run_visuals( config=config, diff --git a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py index f2c8f13..bf9d6d6 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py @@ -4,6 +4,7 @@ """Register built-in visuals.""" +from physicsnemo.cfd.evaluation.reports.builtin.line_plot import register_line_plot from physicsnemo.cfd.evaluation.reports.builtin.surface_volume import ( register_plot_fields_volume, register_field_comparison_surface, @@ -13,6 +14,7 @@ def register_all_builtin_visuals() -> None: register_field_comparison_surface() register_plot_fields_volume() + register_line_plot() register_all_builtin_visuals() diff --git a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py new file mode 100644 index 0000000..f4c2aa2 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py @@ -0,0 +1,121 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Line plot visual wrapping ``physicsnemo.cfd.bench.visualization.utils.plot_line``.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import pyvista as pv + +from physicsnemo.cfd.bench.visualization.utils import plot_line +from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.registry import register_visual + + +def _resolve_gt_pred_fields(output: OutputConfig, canonical_key: str) -> tuple[str, str]: + """Surface or volume VTK names for a canonical key.""" + if canonical_key in output.ground_truth_mesh_field_names and canonical_key in output.mesh_field_names: + return ( + output.ground_truth_mesh_field_names[canonical_key], + output.mesh_field_names[canonical_key], + ) + if canonical_key in output.ground_truth_volume_mesh_field_names and canonical_key in output.volume_mesh_field_names: + return ( + output.ground_truth_volume_mesh_field_names[canonical_key], + output.volume_mesh_field_names[canonical_key], + ) + raise KeyError( + f"Canonical key {canonical_key!r} not found in surface or volume output field maps" + ) + + +def _comparison_mesh_to_line_polydata(mesh: pv.DataSet) -> pv.PolyData: + """One point per cell with cell fields on points (for ``plot_line`` single-line branch).""" + if mesh.n_cells > 0: + return mesh.cell_centers(vertex=False) + return mesh + + +def line_plot( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + case_ids: list[str] | None = None, + canonical_key: str = "pressure", + plot_coord: str = "x", + normalize_factor: float = 1.0, + coord_trim: tuple[float | None, float | None] | None = None, + field_trim: tuple[float | None, float | None] | None = None, + flip: bool = False, + **kwargs: Any, +) -> None: + """GT vs pred line plot along ``plot_coord`` using cell-centered samples of the comparison mesh. + + Resolves VTK array names from ``output.ground_truth_mesh_field_names`` and + ``output.mesh_field_names`` for ``canonical_key`` (surface defaults). The mesh is reduced to + cell centers (or points if there are no cells), then passed to ``plot_line`` as a **single** + polyline-like dataset (values vs sorted ``plot_coord``). + + Extra ``**kwargs`` are forwarded to ``plot_line`` (e.g. ``true_line_kwargs``, ``pred_line_kwargs``, + ``xlabel``, ``ylabel``, ``title_kwargs``). + """ + out = Path(output_dir) + vis_dir = out / "visuals" + vis_dir.mkdir(parents=True, exist_ok=True) + output: OutputConfig = config.output + + field_true, field_pred = _resolve_gt_pred_fields(output, canonical_key) + + ct_raw = coord_trim if coord_trim is not None else (None, None) + ft_raw = field_trim if field_trim is not None else (None, None) + ct = (ct_raw[0], ct_raw[1]) if isinstance(ct_raw, (list, tuple)) and len(ct_raw) == 2 else (None, None) + ft = (ft_raw[0], ft_raw[1]) if isinstance(ft_raw, (list, tuple)) and len(ft_raw) == 2 else (None, None) + + for run in results: + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + path = row.get("comparison_mesh_path") + if not path: + log_dataset( + "benchmark", + f"Skip line_plot for {cid!r}: no comparison_mesh_path", + ) + continue + mesh = pv.read(path) + line_mesh = _comparison_mesh_to_line_polydata(mesh) + fig = plot_line( + line_mesh, + plot_coord=plot_coord, + field_true=field_true, + field_pred=field_pred, + normalize_factor=normalize_factor, + coord_trim=ct, + field_trim=ft, + flip=flip, + **kwargs, + ) + safe = f"{model}_{dataset}_{cid}_line_{canonical_key}_{plot_coord}.png".replace( + "/", "_" + ) + out_png = vis_dir / safe + fig.savefig(str(out_png), bbox_inches="tight", dpi=150) + plt.close(fig) + log_dataset("benchmark", f"Wrote line plot {out_png}") + + +def register_line_plot() -> None: + register_visual("line_plot", line_plot) + register_visual("plot_line", line_plot) # alias (same implementation) diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index 6b2f04a..0f8572d 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -42,7 +42,7 @@ Keys are the same whether they come from one file or base + overlay. | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size` | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (default `true`; set `false` to skip writing `inference__.vtp\|vtu`) | | **model** | `name` (registered wrapper), `checkpoint`, `stats_path`, optional `inference_domain` (`surface` \| `volume`), `kwargs` | | **dataset** | `name` (registered adapter), `root`, `split`, `case_ids` (`null` = all), `kwargs` (e.g. `align_ground_truth_to_model`, `inference_domain` for volume) | | **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume) | @@ -94,7 +94,7 @@ metrics: | **Inference** | — | Yes, if `reports.enabled` and `visuals` non-empty | Saved if `save_comparison_meshes` **or** visuals enabled | | **Benchmark** | Yes | No | Not written by engine | -Built-in **visual** names: `field_comparison_surface` (surface GT vs pred), `plot_fields_volume` (volume scalars). Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. +Built-in **visual** names: `field_comparison_surface` (surface GT vs pred), `plot_fields_volume` (volume scalars), **`line_plot`** (GT vs pred along `plot_coord`, cell-center sample of comparison mesh; kwargs: `canonical_key`, `plot_coord`, `normalize_factor`, `case_ids`, …). Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. **Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/{case_id}_comparison.vtp|vtu`. @@ -113,6 +113,11 @@ reports: case_ids: ["run_1"] # must match the case you run (--case-id) canonical_keys: [pressure, shear_stress] view: xy + - name: line_plot + case_ids: ["run_1"] + canonical_key: pressure + plot_coord: x + normalize_factor: 1.0 ``` | Key | Role | @@ -127,10 +132,11 @@ reports: - **`field_comparison_surface`** — Uses `canonical_keys` (default `pressure`) and `output.ground_truth_mesh_field_names` / `mesh_field_names`. - **`plot_fields_volume`** — `fields` = VTK array names, or default all `output.volume_mesh_field_names` values. +- **`line_plot`** — One GT vs pred line per case; **`canonical_key`** picks surface or volume maps; optional **`coord_trim`** / **`field_trim`** (two-element lists), **`flip`**, matplotlib kwargs forwarded to `plot_line` (e.g. `xlabel`, `ylabel`, `true_line_kwargs`). ### Line plots -There is **no** built-in **`reports.visuals`** entry for **`plot_line`** (needs polyline extracts + `field_true` / `field_pred`). Options: **register** a custom visual that calls `physicsnemo.cfd.bench.visualization.utils.plot_line`, or use [`workflows/bench_example`](../bench_example/README.md) scripts. +Built-in **`line_plot`** wraps **`plot_line`**: it loads the comparison mesh, builds a **cell-center** point cloud (or uses points if there are no cells), and plots GT vs pred vs sorted **`plot_coord`** (`x` \| `y` \| `z`). Set **`canonical_key`** to a surface key (`pressure`, `shear_stress`) or volume key (`pressure_volume`, …) present in **`output`**. For **centerline-style** plots like [`workflows/bench_example`](../bench_example/README.md), use those scripts or a **custom** `register_visual` that passes a list of polylines to `plot_line`. ### Other plot paths diff --git a/workflows/evaluation_examples/inference_config.yaml b/workflows/evaluation_examples/inference_config.yaml index 612c53e..08fcc58 100644 --- a/workflows/evaluation_examples/inference_config.yaml +++ b/workflows/evaluation_examples/inference_config.yaml @@ -5,6 +5,7 @@ run: output_dir: "benchmark_results" seed: 42 batch_size: 1 + save_inference_mesh: false # false = skip writing inference__.vtp|vtu # Default single-run model (overridden per row in benchmark matrix when using benchmark_matrix.yaml). # Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. From 480655cedbae2161287b69a0ccf4da1a6b96ed72 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Thu, 2 Apr 2026 12:23:55 -0700 Subject: [PATCH 04/25] fixing issues with visualization and merging workflows --- CHANGELOG.md | 9 +- physicsnemo/cfd/bench/visualization/utils.py | 23 +- .../cfd/evaluation/benchmarks/engine.py | 176 ++++++++++++--- .../evaluation/benchmarks/report_plugins.py | 32 ++- physicsnemo/cfd/evaluation/benchmarks/run.py | 2 +- physicsnemo/cfd/evaluation/config.py | 5 + physicsnemo/cfd/evaluation/inference/run.py | 205 +++--------------- .../cfd/evaluation/metrics/builtin/forces.py | 64 ++++-- .../cfd/evaluation/reports/__init__.py | 7 +- .../evaluation/reports/builtin/__init__.py | 6 + .../reports/builtin/design_plots.py | 163 ++++++++++++++ .../cfd/evaluation/reports/builtin/hexbin.py | 49 +++++ .../evaluation/reports/builtin/line_plot.py | 160 +++++++++++++- .../reports/builtin/streamlines_visual.py | 112 ++++++++++ .../reports/builtin/surface_volume.py | 21 +- .../cfd/evaluation/reports/context_helpers.py | 30 +++ .../cfd/evaluation/reports/registry.py | 3 + workflows/evaluation_examples/README.md | 85 +++++--- .../evaluation_examples/benchmark_matrix.yaml | 37 ---- .../evaluation_config.yaml | 139 ++++++++++++ .../evaluation_examples/inference_config.yaml | 87 -------- 21 files changed, 998 insertions(+), 417 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/design_plots.py create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/hexbin.py create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py create mode 100644 physicsnemo/cfd/evaluation/reports/context_helpers.py delete mode 100644 workflows/evaluation_examples/benchmark_matrix.yaml create mode 100644 workflows/evaluation_examples/evaluation_config.yaml delete mode 100644 workflows/evaluation_examples/inference_config.yaml diff --git a/CHANGELOG.md b/CHANGELOG.md index 089535a..9e30414 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,14 +18,19 @@ Versioning](https://semver.org/spec/v2.0.0.html). `output.ground_truth_mesh_field_names`, and volume equivalents). - `physicsnemo.cfd.bench.metric_registry` for named metrics shared with evaluation. - Example YAML under `workflows/evaluation_examples/`. -- CLI: `python -m physicsnemo.cfd.evaluation.benchmarks` and - `python -m physicsnemo.cfd.evaluation.inference`. +- CLI: `python -m physicsnemo.cfd.evaluation.benchmarks` (canonical evaluation entrypoint) and + `python -m physicsnemo.cfd.evaluation.inference` (compatibility wrapper forwarding to benchmarks). ### Changed - Benchmark metric registration for evaluation now uses bench-backed built-ins under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated NumPy-only helpers. +- Evaluation examples use **`evaluation_config.yaml`** and **`benchmarks.run`** as the primary + workflow; `evaluation.inference` forwards to the benchmark engine; the benchmark runner can write + **`inference__.vtp|vtu`** when **`run.save_inference_mesh`** is true. +- Matrix benchmark settings live in the same **`evaluation_config.yaml`** (commented template); the + separate **`benchmark_matrix.yaml`** overlay was removed from the examples folder. ### Deprecated diff --git a/physicsnemo/cfd/bench/visualization/utils.py b/physicsnemo/cfd/bench/visualization/utils.py index 9f2bdc5..9725098 100644 --- a/physicsnemo/cfd/bench/visualization/utils.py +++ b/physicsnemo/cfd/bench/visualization/utils.py @@ -610,6 +610,7 @@ def plot_design_scatter(true_data_dict, pred_data_dict, **kwargs): len(true_data_dict.keys()), figsize=kwargs.get("figsize", (5 * len(true_data_dict.keys()), 5)), ) + axs = np.atleast_1d(axs) r_squared_dict = {} for i, key in enumerate(true_data_dict.keys()): @@ -663,6 +664,7 @@ def plot_design_trend(true_data_dict, pred_data_dict, idx_dict, **kwargs): len(true_data_dict.keys()), figsize=kwargs.get("figsize", (8 * len(true_data_dict.keys()), 6)), ) + axs = np.atleast_1d(axs) spearman_coeff_dict = {} mae_dict = {} @@ -861,10 +863,9 @@ def plot_line( mean_true_fields = mean_true_fields[sorted_indices] mean_pred_fields = mean_pred_fields[sorted_indices] - max_error = np.max( - np.abs((mean_true_fields - mean_pred_fields)[sorted_indices]) - ) - mae = np.mean(np.abs((mean_true_fields - mean_pred_fields)[sorted_indices])) + diff_mean = mean_true_fields - mean_pred_fields + max_error = np.max(np.abs(diff_mean)) + mae = np.mean(np.abs(diff_mean)) if coord_trim[0] is not None and coord_trim[1] is not None: mask = (mean_points >= coord_trim[0]) & (mean_points <= coord_trim[1]) @@ -913,7 +914,8 @@ def plot_line( f"Mean Abs. Error: {mae:.2e}. Max Abs. Error: {max_error:.2e}", **kwargs.get("title_kwargs", {}), ) - ax.set_aspect("equal", adjustable="box") + # Scalar vs coordinate: equal aspect collapses the small axis (e.g. x ~ 1 m vs p ~ 1e3 Pa). + ax.set_aspect("auto") else: if plot_coord == "x": @@ -935,12 +937,9 @@ def plot_line( true_data = true_data[sorted_indices] pred_data = pred_data[sorted_indices] - max_error = ( - np.max(np.abs((true_data - pred_data)[sorted_indices])) / normalize_factor - ) - mae = ( - np.mean(np.abs((true_data - pred_data)[sorted_indices])) / normalize_factor - ) + diff = true_data - pred_data + max_error = np.max(np.abs(diff)) / normalize_factor + mae = np.mean(np.abs(diff)) / normalize_factor if coord_trim[0] is not None and coord_trim[1] is not None: mask = (pts >= coord_trim[0]) & (pts <= coord_trim[1]) @@ -976,7 +975,7 @@ def plot_line( f"Mean Abs. Error: {mae:.2e}. Max Abs. Error: {max_error:.2e}", **kwargs.get("title_kwargs", {}), ) - ax.set_aspect("equal", adjustable="box") + ax.set_aspect("auto") if kwargs.get("xlabel", None) is not None: ax.set_xlabel(kwargs.get("xlabel")) diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index 8684ef8..da0420d 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -1,8 +1,8 @@ """Benchmark runner: config → model x dataset loop → metrics → results. -Comparison meshes are built in memory for metrics only; the benchmark path does not write -VTP/VTU files or run visualization plugins (see ``report_plugins.run_optional_report_plugins`` for -optional post-processing outside this CLI). +When ``reports.enabled`` and ``reports.visuals`` are set, optionally writes comparison VTK (if +``reports.save_comparison_meshes``) and runs :func:`report_plugins.run_optional_report_plugins` with +in-memory meshes in ``context["comparison_meshes_by_run"]`` so PNGs avoid disk reads when possible. """ from __future__ import annotations @@ -13,7 +13,14 @@ from typing import Any from physicsnemo.cfd.evaluation.benchmarks.report import write_report -from physicsnemo.cfd.evaluation.config import Config, ModelConfig, DatasetConfig, OutputConfig +from physicsnemo.cfd.evaluation.config import ( + Config, + DatasetConfig, + ModelConfig, + OutputConfig, + ReportsConfig, + RunConfig, +) from physicsnemo.cfd.evaluation.datasets import get_adapter from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model from physicsnemo.cfd.evaluation.datasets.progress import log_dataset @@ -45,6 +52,51 @@ def _effective_inference_domain(model_config: ModelConfig) -> str: return get_inference_domain_for_model(model_config.name) +def _save_inference_mesh_if_requested( + *, + run_config: RunConfig, + model_config: ModelConfig, + output_config: OutputConfig, + wrapper: Any, + case: Any, + case_id: str, + predictions: dict[str, Any], + output_dir: str, + dataset_name: str, +) -> None: + """Write ``inference__.vtp|vtu`` (predictions only) when ``run.save_inference_mesh``.""" + if not run_config.save_inference_mesh: + return + import pyvista as pv + + m_dom = case.inference_domain + out_path = Path(output_dir) / f"inference_{model_config.name}_{case_id}{'.vtp' if m_dom == 'surface' else '.vtu'}" + log_dataset( + dataset_name, + f"Writing inference mesh (predictions only) to {out_path}…", + ) + try: + if m_dom == "surface": + mesh = pv.read(case.mesh_path) + if not isinstance(mesh, pv.PolyData): + mesh = mesh.extract_surface() + names = output_config.mesh_field_names + else: + mesh = pv.read(case.mesh_path) + if hasattr(mesh, "cast_to_unstructured_grid"): + mesh = mesh.cast_to_unstructured_grid() + names = output_config.volume_mesh_field_names + + data_target = mesh.cell_data if wrapper.output_location == "cell" else mesh.point_data + for canonical_key, mesh_name in names.items(): + if canonical_key in predictions: + data_target[mesh_name] = predictions[canonical_key] + mesh.save(str(out_path)) + log_dataset(dataset_name, f"Wrote inference mesh: {out_path}") + except Exception as ex: + log_dataset(dataset_name, f"Could not write inference mesh to {out_path}: {ex}") + + def _call_metric( fn: Any, gt: dict, @@ -79,9 +131,11 @@ def _run_single( case_ids: list[str] | None, output_config: OutputConfig, *, + run_config: RunConfig, + reports: ReportsConfig | None = None, allow_skip_mismatch: bool = False, -) -> dict[str, Any]: - """Run one model on one dataset; return results structure.""" +) -> tuple[dict[str, Any], dict[str, Any]]: + """Run one model on one dataset; return results structure and optional in-memory comparison meshes.""" adapter_class = get_adapter(dataset_config.name) m_dom = _effective_inference_domain(model_config) d_dom = adapter_class.inference_domain_from_kwargs(dataset_config.kwargs) @@ -95,15 +149,18 @@ def _run_single( f"[benchmark] SKIP {model_config.name!r} × {dataset_config.name!r}: {reason}", flush=True, ) - return { - "model": model_config.name, - "dataset": dataset_config.name, - "skipped": True, - "skip_reason": reason, - "cases": [], - "metrics": {}, - "per_case": [], - } + return ( + { + "model": model_config.name, + "dataset": dataset_config.name, + "skipped": True, + "skip_reason": reason, + "cases": [], + "metrics": {}, + "per_case": [], + }, + {}, + ) raise ValueError(reason) dkwargs = resolve_dataset_kwargs_for_model(dataset_config.kwargs, model_config.name) @@ -114,13 +171,16 @@ def _run_single( ) cases = case_ids if case_ids is not None else adapter.list_cases(split=dataset_config.split) if not cases: - return { - "model": model_config.name, - "dataset": dataset_config.name, - "cases": [], - "metrics": {}, - "per_case": [], - } + return ( + { + "model": model_config.name, + "dataset": dataset_config.name, + "cases": [], + "metrics": {}, + "per_case": [], + }, + {}, + ) wrapper_class = get_model_wrapper(model_config.name) wrapper = wrapper_class() @@ -133,6 +193,7 @@ def _run_single( per_case = [] all_metric_values: dict[str, list[float]] = {} + mesh_ctx: dict[str, Any] = {} log_dataset( dataset_config.name, @@ -150,6 +211,18 @@ def _run_single( predictions = wrapper.decode_outputs(raw, case) gt = case.ground_truth or {} + _save_inference_mesh_if_requested( + run_config=run_config, + model_config=model_config, + output_config=output_config, + wrapper=wrapper, + case=case, + case_id=cid, + predictions=predictions, + output_dir=output_dir, + dataset_name=dataset_config.name, + ) + comparison_mesh = None metric_dtype: str | None = None try: @@ -189,6 +262,22 @@ def _run_single( row: dict[str, Any] = {"case_id": cid, "metrics": case_metrics} if comparison_mesh is not None and metric_dtype is not None: row["metric_dtype"] = metric_dtype + if reports: + if reports.save_comparison_meshes: + sub = Path(output_dir) / reports.comparison_mesh_subdir + sub.mkdir(parents=True, exist_ok=True) + ext = ".vtp" if case.inference_domain == "surface" else ".vtu" + cmp_p = sub / f"{cid}_comparison{ext}" + try: + comparison_mesh.save(str(cmp_p)) + row["comparison_mesh_path"] = str(cmp_p.resolve()) + except Exception as ex: + log_dataset( + dataset_config.name, + f"Could not save comparison mesh for {cid!r}: {ex}", + ) + if reports.enabled and reports.visuals: + mesh_ctx[cid] = comparison_mesh per_case.append(row) # Aggregate (mean over cases) @@ -197,13 +286,16 @@ def _run_single( valid = [v for v in values if v == v] # filter nan metrics_summary[mname] = sum(valid) / len(valid) if valid else float("nan") - return { - "model": model_config.name, - "dataset": dataset_config.name, - "cases": cases, - "metrics": metrics_summary, - "per_case": per_case, - } + return ( + { + "model": model_config.name, + "dataset": dataset_config.name, + "cases": cases, + "metrics": metrics_summary, + "per_case": per_case, + }, + mesh_ctx, + ) def _case_ids_for_run( @@ -241,10 +333,11 @@ def run_benchmark( json.dump(dict(os.environ), f, indent=2) results: list[dict[str, Any]] = [] + meshes_by_run: list[dict[str, Any]] = [] if config.benchmark.mode == "single": case_ids = _case_ids_for_run(config.dataset.case_ids, case_id) - res = _run_single( + res, mesh_ctx = _run_single( config.model, config.dataset, metric_specs, @@ -252,15 +345,18 @@ def run_benchmark( output_dir, case_ids, config.output, + run_config=config.run, + reports=config.reports, allow_skip_mismatch=False, ) results.append(res) + meshes_by_run.append(mesh_ctx) else: models = config.benchmark.models or [config.model] datasets = config.benchmark.datasets or [config.dataset] for m_cfg in models: for d_cfg in datasets: - res = _run_single( + res, mesh_ctx = _run_single( m_cfg, d_cfg, metric_specs, @@ -268,9 +364,12 @@ def run_benchmark( output_dir, _case_ids_for_run(d_cfg.case_ids, case_id), config.output, + run_config=config.run, + reports=config.reports, allow_skip_mismatch=True, ) results.append(res) + meshes_by_run.append(mesh_ctx) if config.benchmark.reproducibility.save_artifacts: artifacts = Path(output_dir) / "benchmark_artifacts.json" @@ -297,6 +396,20 @@ def run_benchmark( ) write_report(results, output_dir, formats=["json", "csv", "html"]) + + if config.reports.enabled and config.reports.visuals: + import physicsnemo.cfd.evaluation.reports # noqa: F401 — register built-in visuals + + from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins + + log_dataset("benchmark", "Running reports.visuals from benchmark results…") + run_optional_report_plugins( + config, + results, + output_dir, + context={"comparison_meshes_by_run": meshes_by_run}, + ) + return results @@ -329,6 +442,7 @@ def _config_to_dict(c: Config) -> dict: "volume_mesh_field_names": c.output.volume_mesh_field_names, "ground_truth_mesh_field_names": c.output.ground_truth_mesh_field_names, "ground_truth_volume_mesh_field_names": c.output.ground_truth_volume_mesh_field_names, + "streamlines_vector_canonical": c.output.streamlines_vector_canonical, }, "metrics": c.metrics, "reports": { diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py index 69f1f18..063f2b8 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -4,18 +4,16 @@ """Optional visuals (plots via ``physicsnemo.cfd.bench.visualization``). -The benchmark engine does **not** call this module: benchmarking stays scalar-only (JSON/CSV/HTML) -with in-memory comparison meshes for metrics only. - -The **inference** CLI (``physicsnemo.cfd.evaluation.inference.run``) calls -:func:`run_optional_report_plugins` after saving the prediction mesh when ``reports.enabled`` and -``reports.visuals`` are set: it builds a comparison mesh, writes ``{case_id}_comparison.vtp|vtu`` when -needed, then runs registered visuals. You can also call :func:`run_optional_report_plugins` from -your own script when you have ``results`` and want a manifest. +The **inference** and **benchmark** CLIs call :func:`run_optional_report_plugins` when +``reports.enabled`` and ``reports.visuals`` are set. Pass optional ``context`` with +``comparison_meshes_by_run`` (list of ``{case_id: pyvista.DataSet}`` aligned with ``results``) so +built-in mesh visuals can avoid reading large VTU/VTP from disk. You can also call this from your own +script when you have ``results`` and want a manifest. """ from __future__ import annotations +import inspect import json from pathlib import Path from typing import Any @@ -29,14 +27,24 @@ def run_optional_report_plugins( config: Config, results: list[dict[str, Any]], output_dir: str, + *, + context: dict[str, Any] | None = None, ) -> None: - """Run registered visuals when ``config.reports.enabled``; write manifest and optional legacy plugins note.""" + """Run registered visuals when ``config.reports.enabled``; write manifest. + + ``context`` is optional runtime state (not serialized to JSON). Supported keys: + + - ``comparison_meshes_by_run``: ``list[dict[str, Any]]`` with the same length as ``results``; + each inner dict maps ``case_id`` to an in-memory comparison mesh (PyVista dataset) so visuals + can skip ``pv.read(comparison_mesh_path)``. + """ out_dir = Path(output_dir) manifest: dict[str, Any] = { "enabled": bool(config.reports.enabled), "plugins": config.reports.plugins, "save_comparison_meshes": config.reports.save_comparison_meshes, "comparison_mesh_subdir": config.reports.comparison_mesh_subdir, + "context_keys": list(context.keys()) if context else [], "visuals_ran": [], "visual_errors": [], } @@ -63,7 +71,11 @@ def run_optional_report_plugins( for name, vkwargs in specs: try: fn = get_visual(name) - fn(config, results, output_dir, **vkwargs) + params = inspect.signature(fn).parameters + if "context" in params: + fn(config, results, output_dir, context=context, **vkwargs) + else: + fn(config, results, output_dir, **vkwargs) manifest["visuals_ran"].append({"name": name, "kwargs": vkwargs}) except Exception as e: log_dataset("benchmark", f"Visual {name!r} failed: {e}") diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py index 6a1b0d5..0e036f0 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/run.py +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -26,7 +26,7 @@ def main() -> None: parser.add_argument( "--base-config", default=None, - help="Optional YAML merged first (e.g. inference_config.yaml); --config overlays it.", + help="Optional YAML merged first (e.g. evaluation_config.yaml); --config overlays it.", ) parser.add_argument( "--case-id", diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index 06fe20e..0741b8e 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -128,6 +128,8 @@ class OutputConfig: ground_truth_volume_mesh_field_names: dict[str, str] = field( default_factory=lambda: dict(DEFAULT_GROUND_TRUTH_VOLUME_MESH_FIELD_NAMES) ) + #: Canonical key for ``streamlines_comparison`` report visual (volume vector field). + streamlines_vector_canonical: str = "velocity" @dataclass @@ -166,6 +168,9 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": volume_mesh_field_names=vol_fn, ground_truth_mesh_field_names=gt_mesh, ground_truth_volume_mesh_field_names=gt_vol, + streamlines_vector_canonical=str( + out.get("streamlines_vector_canonical") or "velocity" + ), ) metrics = data.get("metrics", ["l2_pressure"]) bench = data.get("benchmark") or {} diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py index b45c433..2393b00 100644 --- a/physicsnemo/cfd/evaluation/inference/run.py +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -2,30 +2,18 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""CLI entrypoint for single-case inference.""" +"""Compatibility CLI: forwards to the benchmark evaluation engine (metrics, tables, visuals).""" from __future__ import annotations import argparse -from pathlib import Path -from typing import Any +import sys -from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark from physicsnemo.cfd.evaluation.config import Config, load_config import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 from physicsnemo.cfd.evaluation.datasets import get_adapter from physicsnemo.cfd.evaluation.datasets.gt_alignment import resolve_dataset_kwargs_for_model -from physicsnemo.cfd.evaluation.datasets.progress import log_dataset -from physicsnemo.cfd.evaluation.inference import get_model_wrapper -from physicsnemo.cfd.evaluation.inference.model_registry import get_inference_domain_for_model -from physicsnemo.cfd.evaluation.inference.progress import log_inference -from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh - - -def _effective_model_inference_domain(model_name: str, override: str | None) -> str: - if override in ("surface", "volume"): - return override - return get_inference_domain_for_model(model_name) def _parse_overrides(args: list[str]) -> dict[str, str]: @@ -38,178 +26,51 @@ def _parse_overrides(args: list[str]) -> dict[str, str]: return overrides +def _first_case_id(config: Config) -> str | None: + """Legacy inference default: first case when ``--case-id`` is omitted.""" + adapter_class = get_adapter(config.dataset.name) + dkwargs = resolve_dataset_kwargs_for_model(config.dataset.kwargs, config.model.name) + adapter = adapter_class(root=config.dataset.root, **dkwargs) + case_ids = config.dataset.case_ids or adapter.list_cases(split=config.dataset.split) + if not case_ids: + return None + return case_ids[0] + + def main() -> None: - parser = argparse.ArgumentParser(description="Run inference for one model on one case.") + parser = argparse.ArgumentParser( + description=( + "Compatibility wrapper for the benchmark evaluation engine. Prefer: " + "python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml" + ), + ) parser.add_argument("--config", required=True, help="Path to YAML/JSON config") parser.add_argument( "--base-config", default=None, help="Optional YAML/JSON merged before --config (shared defaults).", ) - parser.add_argument("--case-id", default=None, help="Case ID (default: first case from dataset)") + parser.add_argument( + "--case-id", + default=None, + help="Case ID (default: first case from dataset, matching legacy single-case inference).", + ) parser.add_argument("overrides", nargs="*", help="Key=value overrides, e.g. run.device=cuda:1") args = parser.parse_args() overrides = _parse_overrides(getattr(args, "overrides", [])) config = load_config(args.config, overrides, base=args.base_config) - adapter_class = get_adapter(config.dataset.name) - dkwargs = resolve_dataset_kwargs_for_model(config.dataset.kwargs, config.model.name) - adapter = adapter_class(root=config.dataset.root, **dkwargs) - log_dataset( - config.dataset.name, - f"Listing cases under root {config.dataset.root!r} (split={config.dataset.split!r})…", - ) - case_ids = config.dataset.case_ids or adapter.list_cases(split=config.dataset.split) - if not case_ids: + case_id = args.case_id if args.case_id else _first_case_id(config) + if case_id is None: raise SystemExit("No cases found for dataset.") - case_id = args.case_id if args.case_id else case_ids[0] - log_dataset( - config.dataset.name, - f"Reading case {case_id!r} from dataset root {config.dataset.root!r}…", - ) - case = adapter.load_case(case_id) - - model_domain = _effective_model_inference_domain( - config.model.name, config.model.inference_domain - ) - adapter_domain = adapter_class.inference_domain_from_kwargs(dkwargs) - if model_domain != adapter_domain: - raise SystemExit( - f"Inference domain mismatch: model {config.model.name!r} expects {model_domain!r}, " - f"dataset adapter {config.dataset.name!r} provides {adapter_domain!r}." - ) - - wrapper_class = get_model_wrapper(config.model.name) - wrapper = wrapper_class() - wrapper.load( - checkpoint_path=config.model.checkpoint, - stats_path=config.model.stats_path, - device=config.run.device, - **config.model.merged_kwargs_for_load(), - ) - model_input = wrapper.prepare_inputs(case) - raw = wrapper.predict(model_input) - predictions = wrapper.decode_outputs(raw, case) - - out_dir = Path(config.run.output_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - if config.run.save_inference_mesh: - import pyvista as pv - - out_path = out_dir / f"inference_{config.model.name}_{case_id}{'.vtp' if model_domain == 'surface' else '.vtu'}" - log_inference( - config.model.name, - f"Saving predictions to file ({model_domain}): {out_path}", - ) - log_inference( - config.model.name, - f"Reading mesh file to attach fields: {case.mesh_path}", - ) - - if model_domain == "surface": - mesh = pv.read(case.mesh_path) - if not isinstance(mesh, pv.PolyData): - mesh = mesh.extract_surface() - names = config.output.mesh_field_names - else: - mesh = pv.read(case.mesh_path) - if hasattr(mesh, "cast_to_unstructured_grid"): - mesh = mesh.cast_to_unstructured_grid() - names = config.output.volume_mesh_field_names - - data_target = mesh.cell_data if wrapper.output_location == "cell" else mesh.point_data - for canonical_key, mesh_name in names.items(): - if canonical_key in predictions: - data_target[mesh_name] = predictions[canonical_key] - mesh.save(str(out_path)) - log_inference(config.model.name, f"Wrote mesh with prediction fields: {out_path}") - else: - log_inference( - config.model.name, - f"Skipping inference mesh file (run.save_inference_mesh: false); " - f"would be inference_{config.model.name}_{case_id}.vtp|vtu.", - ) - - _maybe_save_comparison_mesh_and_run_visuals( - config=config, - case_id=case_id, - case=case, - predictions=predictions, - out_dir=out_dir, - ) - - -def _maybe_save_comparison_mesh_and_run_visuals( - *, - config: Config, - case_id: str, - case: Any, - predictions: dict[str, Any], - out_dir: Path, -) -> None: - """Build GT+pred comparison mesh, optionally save VTK, run ``reports.visuals`` when enabled.""" - rep = config.reports - want_save = rep.save_comparison_meshes or ( - rep.enabled and bool(rep.visuals) - ) - if not want_save: - return - - try: - comparison_mesh, metric_dtype = build_comparison_mesh( - case, predictions, config.output - ) - except Exception as ex: - log_inference( - config.model.name, - f"Skipping comparison mesh / visuals: build_comparison_mesh failed: {ex}", - ) - return - - sub = out_dir / rep.comparison_mesh_subdir - sub.mkdir(parents=True, exist_ok=True) - ext = ".vtp" if case.inference_domain == "surface" else ".vtu" - cmp_path = sub / f"{case_id}_comparison{ext}" - try: - comparison_mesh.save(str(cmp_path)) - except Exception as ex: - log_inference( - config.model.name, - f"Could not save comparison mesh to {cmp_path}: {ex}", - ) - return - - log_inference( - config.model.name, - f"Wrote comparison mesh for visuals/metrics bridge: {cmp_path}", - ) - - if not rep.enabled or not rep.visuals: - return - - import physicsnemo.cfd.evaluation.reports # noqa: F401 — register built-in visuals - results: list[dict[str, Any]] = [ - { - "model": config.model.name, - "dataset": config.dataset.name, - "skipped": False, - "per_case": [ - { - "case_id": case_id, - "metric_dtype": metric_dtype, - "comparison_mesh_path": str(cmp_path.resolve()), - "metrics": {}, - } - ], - } - ] - log_inference( - config.model.name, - f"Running reports.visuals ({len(rep.visuals)}) under {out_dir}…", + print( + "[evaluation] physicsnemo.cfd.evaluation.inference forwards to the benchmark engine; " + "prefer: python -m physicsnemo.cfd.evaluation.benchmarks.run", + file=sys.stderr, ) - run_optional_report_plugins(config, results, str(out_dir)) + results = run_benchmark(config, case_id=case_id) + print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") if __name__ == "__main__": diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py index 4c1ced0..8135301 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py @@ -2,7 +2,17 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Force coefficient metrics via physicsnemo.cfd.bench.metrics.aero_forces.""" +"""Force coefficient metrics via physicsnemo.cfd.bench.metrics.aero_forces. + +Each metric returns a dict that expands in the benchmark engine to: + +- ``drag_error`` — relative |Cd_pred − Cd_true| / |Cd_true| (or absolute if |Cd_true| ≈ 0) +- ``drag_error_true`` / ``drag_error_pred`` — integrated **drag coefficient Cd** (GT vs pred fields) +- ``lift_error`` — relative |Cl_pred − Cl_true| / |Cl_true| +- ``lift_error_true`` / ``lift_error_pred`` — integrated **lift coefficient Cl** + +Use the ``*_true`` / ``*_pred`` keys with ``design_scatter`` / ``design_trend`` in evaluation config. +""" from __future__ import annotations @@ -29,16 +39,36 @@ def _resolve_mesh( def _scalar_drag_lift_error(ground_truth: dict, predictions: dict, key: str) -> float: + rel, _, _ = _scalar_drag_lift_triplet(ground_truth, predictions, key) + return rel + + +def _scalar_drag_lift_triplet( + ground_truth: dict, predictions: dict, key: str +) -> tuple[float, float, float]: + """Relative error and (true, pred) scalars for ``key`` (e.g. drag/lift from case metadata).""" g = ground_truth.get(key) p = predictions.get(key) if g is None or p is None: - return float("nan") + return float("nan"), float("nan"), float("nan") gt = float(g) pr = float(p) denom = abs(gt) if denom < 1e-14: - return abs(pr - gt) - return abs(pr - gt) / denom + rel = abs(pr - gt) + else: + rel = abs(pr - gt) / denom + return rel, gt, pr + + +def _rel_and_pair(cd_gt: float, cd_pr: float) -> dict[str, float]: + """Relative error (``""`` → scalar metric name) plus Cd or Cl pair (``true`` / ``pred`` sub-keys).""" + denom = abs(cd_gt) + if denom < 1e-14: + rel = float(abs(cd_pr - cd_gt)) + else: + rel = float(abs(cd_pr - cd_gt) / denom) + return {"": rel, "true": float(cd_gt), "pred": float(cd_pr)} def drag_error( @@ -52,13 +82,14 @@ def drag_error( coeff: float = 1.0, drag_direction: list[float] | None = None, **_: object, -) -> float: +) -> dict[str, float]: mesh, dtype = _resolve_mesh( predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output ) dd = drag_direction if drag_direction is not None else [1.0, 0.0, 0.0] if mesh is None or output is None: - return _scalar_drag_lift_error(ground_truth, predictions, "drag") + rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "drag") + return {"": rel, "true": gt, "pred": pr} gtp = output.ground_truth_mesh_field_names["pressure"] gtw = output.ground_truth_mesh_field_names["shear_stress"] prp = output.mesh_field_names["pressure"] @@ -80,12 +111,10 @@ def drag_error( drag_direction=dd, dtype=dtype, ) - denom = abs(cd_gt) - if denom < 1e-14: - return float(abs(cd_pr - cd_gt)) - return float(abs(cd_pr - cd_gt) / denom) + return _rel_and_pair(float(cd_gt), float(cd_pr)) except Exception: - return _scalar_drag_lift_error(ground_truth, predictions, "drag") + rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "drag") + return {"": rel, "true": gt, "pred": pr} def lift_error( @@ -99,13 +128,14 @@ def lift_error( coeff: float = 1.0, lift_direction: list[float] | None = None, **_: object, -) -> float: +) -> dict[str, float]: mesh, dtype = _resolve_mesh( predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output ) ld = lift_direction if lift_direction is not None else [0.0, 0.0, 1.0] if mesh is None or output is None: - return _scalar_drag_lift_error(ground_truth, predictions, "lift") + rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "lift") + return {"": rel, "true": gt, "pred": pr} gtp = output.ground_truth_mesh_field_names["pressure"] gtw = output.ground_truth_mesh_field_names["shear_stress"] prp = output.mesh_field_names["pressure"] @@ -129,12 +159,10 @@ def lift_error( lift_direction=ld, dtype=dtype, ) - denom = abs(cl_gt) - if denom < 1e-14: - return float(abs(cl_pr - cl_gt)) - return float(abs(cl_pr - cl_gt) / denom) + return _rel_and_pair(float(cl_gt), float(cl_pr)) except Exception: - return _scalar_drag_lift_error(ground_truth, predictions, "lift") + rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "lift") + return {"": rel, "true": gt, "pred": pr} def register_force_metrics() -> None: diff --git a/physicsnemo/cfd/evaluation/reports/__init__.py b/physicsnemo/cfd/evaluation/reports/__init__.py index 1be3b68..cbd1706 100644 --- a/physicsnemo/cfd/evaluation/reports/__init__.py +++ b/physicsnemo/cfd/evaluation/reports/__init__.py @@ -2,7 +2,12 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Report visuals registry and built-in plot hooks (``physicsnemo.cfd.bench.visualization``).""" +"""Report visuals registry and built-in plot hooks (``physicsnemo.cfd.bench.visualization``). + +Pass optional ``context`` with ``comparison_meshes_by_run`` into +``physicsnemo.cfd.evaluation.benchmarks.report_plugins.run_optional_report_plugins`` so mesh-based +built-ins can skip ``pv.read(comparison_mesh_path)``. +""" from physicsnemo.cfd.evaluation.reports.registry import ( get_visual, diff --git a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py index bf9d6d6..26471d9 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py @@ -4,7 +4,10 @@ """Register built-in visuals.""" +from physicsnemo.cfd.evaluation.reports.builtin.design_plots import register_design_visuals +from physicsnemo.cfd.evaluation.reports.builtin.hexbin import register_projections_hexbin from physicsnemo.cfd.evaluation.reports.builtin.line_plot import register_line_plot +from physicsnemo.cfd.evaluation.reports.builtin.streamlines_visual import register_streamlines_visual from physicsnemo.cfd.evaluation.reports.builtin.surface_volume import ( register_plot_fields_volume, register_field_comparison_surface, @@ -15,6 +18,9 @@ def register_all_builtin_visuals() -> None: register_field_comparison_surface() register_plot_fields_volume() register_line_plot() + register_design_visuals() + register_projections_hexbin() + register_streamlines_visual() register_all_builtin_visuals() diff --git a/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py new file mode 100644 index 0000000..9387562 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py @@ -0,0 +1,163 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Design scatter / trend visuals wrapping ``physicsnemo.cfd.bench.visualization.utils``.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt + +from physicsnemo.cfd.bench.visualization.utils import plot_design_scatter, plot_design_trend +from physicsnemo.cfd.evaluation.config import Config +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.registry import register_visual + + +def _design_skip_hint(per_case: list[dict[str, Any]], pairs: list[dict[str, Any]]) -> str: + """Explain why design plots may find no finite (true, pred) pairs.""" + if not per_case: + return "" + m = (per_case[0].get("metrics") or {}) if per_case else {} + parts: list[str] = [] + for p in pairs: + tk = str(p.get("true_key", "")) + pk = str(p.get("pred_key", "")) + if tk == "drag_error_true" and pk == "drag_error_pred": + if "drag_error" in m and tk not in m: + parts.append( + "metrics only have scalar drag_error (missing drag_error_true / drag_error_pred). " + "Re-run the benchmark after updating drag_error to return Cd true/pred dict entries, " + "and ensure `python` imports that code (e.g. `pip install -e .` from the repo root)." + ) + elif tk in m and pk in m and (m[tk] != m[tk] or m[pk] != m[pk]): + parts.append(f"{tk!r} or {pk!r} is NaN (finite values required).") + if tk == "lift_error_true" and pk == "lift_error_pred": + if "lift_error" in m and tk not in m: + parts.append( + "metrics only have scalar lift_error (missing lift_error_true / lift_error_pred); " + "re-run with updated lift_error metric." + ) + elif tk in m and pk in m and (m[tk] != m[tk] or m[pk] != m[pk]): + parts.append(f"{tk!r} or {pk!r} is NaN (finite values required).") + if not parts: + return "" + return " " + " ".join(dict.fromkeys(parts)) + + +def design_scatter( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + pairs: list[dict[str, Any]] | None = None, + **kwargs: Any, +) -> None: + """Scatter of pred vs true per run, using ``per_case[].metrics`` keys from *pairs*. + + Each entry in *pairs* is ``{name, true_key, pred_key}`` (metric names in ``metrics``). + """ + del context, config + if not pairs: + raise ValueError("design_scatter requires non-empty ``pairs`` (list of name/true_key/pred_key).") + out = Path(output_dir) / "visuals" + out.mkdir(parents=True, exist_ok=True) + for run in results: + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + true_data_dict: dict[str, list[float]] = {} + pred_data_dict: dict[str, list[float]] = {} + for p in pairs: + name = str(p["name"]) + tk = str(p["true_key"]) + pk = str(p["pred_key"]) + true_data_dict[name] = [] + pred_data_dict[name] = [] + for row in run.get("per_case") or []: + m = row.get("metrics") or {} + if tk in m and pk in m and m[tk] == m[tk] and m[pk] == m[pk]: + true_data_dict[name].append(float(m[tk])) + pred_data_dict[name].append(float(m[pk])) + if not any(len(true_data_dict[k]) > 0 for k in true_data_dict): + hint = _design_skip_hint(run.get("per_case") or [], pairs or []) + log_dataset( + "benchmark", + f"Skip design_scatter for {model}/{dataset}: no finite true/pred points for pairs " + f"{pairs!r}.{hint}", + ) + continue + fig, _ = plot_design_scatter(true_data_dict, pred_data_dict, **kwargs) + safe = f"{model}_{dataset}_design_scatter.png".replace("/", "_") + fig.savefig(str(out / safe), bbox_inches="tight", dpi=150) + plt.close(fig) + log_dataset("benchmark", f"Wrote design scatter {out / safe}") + + +def design_trend( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + pairs: list[dict[str, Any]] | None = None, + **kwargs: Any, +) -> None: + """Trend plot; optional ``idx_key`` per pair selects a metric for x labels (else ``case_id``).""" + del context, config + if not pairs: + raise ValueError("design_trend requires non-empty ``pairs``.") + out = Path(output_dir) / "visuals" + out.mkdir(parents=True, exist_ok=True) + for run in results: + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + true_data_dict: dict[str, list[float]] = {} + pred_data_dict: dict[str, list[float]] = {} + idx_dict: dict[str, list[Any]] = {} + for p in pairs: + name = str(p["name"]) + tk = str(p["true_key"]) + pk = str(p["pred_key"]) + idx_key = p.get("idx_key") + true_data_dict[name] = [] + pred_data_dict[name] = [] + idx_dict[name] = [] + for row in run.get("per_case") or []: + m = row.get("metrics") or {} + if tk not in m or pk not in m: + continue + if m[tk] != m[tk] or m[pk] != m[pk]: + continue + true_data_dict[name].append(float(m[tk])) + pred_data_dict[name].append(float(m[pk])) + if idx_key: + idx_dict[name].append(m.get(idx_key, row.get("case_id"))) + else: + idx_dict[name].append(str(row.get("case_id", ""))) + if not any(len(true_data_dict[k]) > 0 for k in true_data_dict): + hint = _design_skip_hint(run.get("per_case") or [], pairs or []) + log_dataset( + "benchmark", + f"Skip design_trend for {model}/{dataset}: no finite true/pred points for pairs " + f"{pairs!r}.{hint}", + ) + continue + res = plot_design_trend(true_data_dict, pred_data_dict, idx_dict, **kwargs) + fig = res[0] + safe = f"{model}_{dataset}_design_trend.png".replace("/", "_") + fig.savefig(str(out / safe), bbox_inches="tight", dpi=150) + plt.close(fig) + log_dataset("benchmark", f"Wrote design trend {out / safe}") + + +def register_design_visuals() -> None: + register_visual("design_scatter", design_scatter) + register_visual("design_trend", design_trend) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py b/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py new file mode 100644 index 0000000..3ee66da --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py @@ -0,0 +1,49 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Hexbin projection visual (``plot_projections_hexbin``) from explicit mesh paths.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import pyvista as pv + +from physicsnemo.cfd.bench.visualization.utils import plot_projections_hexbin +from physicsnemo.cfd.evaluation.config import Config +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.registry import register_visual + + +def projections_hexbin( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + mesh_paths: list[str] | None = None, + field: str = "p_error", + direction: str = "XY", + grid_size: int = 50, + **_kwargs: Any, +) -> None: + """Aggregate hexbin over multiple meshes (paths on disk). *results* / *context* unused.""" + del context, results, config + if not mesh_paths: + raise ValueError("projections_hexbin requires non-empty ``mesh_paths`` (list of VTK paths).") + meshes = [pv.read(p) for p in mesh_paths] + fig = plot_projections_hexbin(meshes, field, direction, grid_size=grid_size) + out = Path(output_dir) / "visuals" + out.mkdir(parents=True, exist_ok=True) + safe = f"hexbin_{field}_{direction}.png".replace("/", "_") + out_png = out / safe + fig.savefig(str(out_png), bbox_inches="tight", dpi=300) + plt.close(fig) + log_dataset("benchmark", f"Wrote hexbin {out_png}") + + +def register_projections_hexbin() -> None: + register_visual("projections_hexbin", projections_hexbin) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py index f4c2aa2..2de493b 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py @@ -15,6 +15,7 @@ from physicsnemo.cfd.bench.visualization.utils import plot_line from physicsnemo.cfd.evaluation.config import Config, OutputConfig from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case from physicsnemo.cfd.evaluation.reports.registry import register_visual @@ -42,11 +43,159 @@ def _comparison_mesh_to_line_polydata(mesh: pv.DataSet) -> pv.PolyData: return mesh +def _combine_if_multiblock(ds: pv.DataSet) -> pv.DataSet: + if isinstance(ds, pv.MultiBlock): + if ds.n_blocks == 0: + return pv.PolyData() + try: + return ds.combine(merge_points=False) + except Exception: + return ds.get_block(0) + return ds + + +def bench_style_surface_centerlines( + mesh: pv.DataSet, + *, + y_slice_origin: tuple[float, float, float] = (0.0, 0.0, 0.0), + z_clip: float = 0.4, +) -> tuple[pv.PolyData, pv.PolyData]: + """DrivAer-style top/bottom strips: ``y`` slice at ``y_slice_origin``, split at ``z = z_clip``. + + Matches ``workflows/bench_example/utils.py`` (surface): top = ``z`` above the clip plane, + bottom = below. + """ + slice_y = mesh.slice(normal="y", origin=y_slice_origin) + slice_y = _combine_if_multiblock(slice_y) + if slice_y.n_cells == 0 and slice_y.n_points == 0: + return pv.PolyData(), pv.PolyData() + top = slice_y.clip(normal="z", origin=(0.0, 0.0, z_clip), invert=False) + bottom = slice_y.clip(normal="z", origin=(0.0, 0.0, z_clip), invert=True) + top = _combine_if_multiblock(top) + bottom = _combine_if_multiblock(bottom) + return top, bottom + + +def _mesh_for_plot_line_centerline(poly: pv.DataSet) -> pv.PolyData | None: + """``plot_line`` single branch expects point_data for scalar/vector fields.""" + if poly.n_cells == 0 and poly.n_points == 0: + return None + if poly.n_cells > 0: + out = poly.cell_data_to_point_data(pass_cell_data=True) + else: + out = poly + return out if out.n_points > 0 else None + + +def line_plot_centerlines( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + case_ids: list[str] | None = None, + canonical_key: str = "pressure", + plot_coord: str = "x", + normalize_factor: float = 1.0, + coord_trim: tuple[float | None, float | None] | None = None, + field_trim: tuple[float | None, float | None] | None = None, + flip: bool = False, + y_slice_origin: list[float] | None = None, + z_clip: float = 0.4, + **kwargs: Any, +) -> None: + """GT vs pred line plots on **top** and **bottom** surface centerline strips (bench_example style). + + Geometry: slice with normal ``y`` through ``y_slice_origin`` (default origin), then clip at + ``z = z_clip`` to separate upper (``top``) and lower (``bottom``) surface lines along the vehicle. + + Surface (``metric_dtype`` cell) comparison meshes only. Writes two PNGs per case: + ``*_line_centerline_top_*`` and ``*_line_centerline_bottom_*``. + """ + out = Path(output_dir) / "visuals" + out.mkdir(parents=True, exist_ok=True) + output: OutputConfig = config.output + field_true, field_pred = _resolve_gt_pred_fields(output, canonical_key) + + yo = tuple(y_slice_origin) if y_slice_origin is not None and len(y_slice_origin) == 3 else (0.0, 0.0, 0.0) + + ct_raw = coord_trim if coord_trim is not None else (None, None) + ft_raw = field_trim if field_trim is not None else (None, None) + ct = (ct_raw[0], ct_raw[1]) if isinstance(ct_raw, (list, tuple)) and len(ct_raw) == 2 else (None, None) + ft = (ft_raw[0], ft_raw[1]) if isinstance(ft_raw, (list, tuple)) and len(ft_raw) == 2 else (None, None) + + for run_idx, run in enumerate(results): + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + if row.get("metric_dtype") != "cell": + log_dataset( + "benchmark", + f"Skip line_plot_centerlines for {cid!r}: surface (cell) comparison mesh only", + ) + continue + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: + log_dataset( + "benchmark", + f"Skip line_plot_centerlines for {cid!r}: no comparison mesh", + ) + continue + try: + top_raw, bottom_raw = bench_style_surface_centerlines( + mesh, y_slice_origin=yo, z_clip=z_clip + ) + except Exception as ex: + log_dataset("benchmark", f"line_plot_centerlines slice failed for {cid!r}: {ex}") + continue + + for label, strip in (("top", top_raw), ("bottom", bottom_raw)): + line_pd = _mesh_for_plot_line_centerline(strip) + if line_pd is None: + log_dataset( + "benchmark", + f"Skip line_plot_centerlines {label} for {cid!r}: empty slice", + ) + continue + if field_true not in line_pd.point_data or field_pred not in line_pd.point_data: + log_dataset( + "benchmark", + f"Skip line_plot_centerlines {label} for {cid!r}: missing {field_true!r} or {field_pred!r} on slice", + ) + continue + fig = plot_line( + line_pd, + plot_coord=plot_coord, + field_true=field_true, + field_pred=field_pred, + normalize_factor=normalize_factor, + coord_trim=ct, + field_trim=ft, + flip=flip, + **kwargs, + ) + safe = ( + f"{model}_{dataset}_{cid}_line_centerline_{label}_{canonical_key}_{plot_coord}.png".replace( + "/", "_" + ) + ) + out_png = out / safe + fig.savefig(str(out_png), bbox_inches="tight", dpi=150) + plt.close(fig) + log_dataset("benchmark", f"Wrote centerline line plot {out_png}") + + def line_plot( config: Config, results: list[dict[str, Any]], output_dir: str, *, + context: dict[str, Any] | None = None, case_ids: list[str] | None = None, canonical_key: str = "pressure", plot_coord: str = "x", @@ -78,7 +227,7 @@ def line_plot( ct = (ct_raw[0], ct_raw[1]) if isinstance(ct_raw, (list, tuple)) and len(ct_raw) == 2 else (None, None) ft = (ft_raw[0], ft_raw[1]) if isinstance(ft_raw, (list, tuple)) and len(ft_raw) == 2 else (None, None) - for run in results: + for run_idx, run in enumerate(results): if run.get("skipped"): continue model = run["model"] @@ -87,14 +236,13 @@ def line_plot( cid = row["case_id"] if case_ids is not None and cid not in case_ids: continue - path = row.get("comparison_mesh_path") - if not path: + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: log_dataset( "benchmark", - f"Skip line_plot for {cid!r}: no comparison_mesh_path", + f"Skip line_plot for {cid!r}: no comparison mesh in context or path", ) continue - mesh = pv.read(path) line_mesh = _comparison_mesh_to_line_polydata(mesh) fig = plot_line( line_mesh, @@ -119,3 +267,5 @@ def line_plot( def register_line_plot() -> None: register_visual("line_plot", line_plot) register_visual("plot_line", line_plot) # alias (same implementation) + register_visual("line_plot_surface", line_plot) # alias: same as line_plot for surface comparison meshes + register_visual("line_plot_centerlines", line_plot_centerlines) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py b/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py new file mode 100644 index 0000000..b3f2cc1 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py @@ -0,0 +1,112 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Streamline comparison: ``streamlines_comparison`` supports volume (point) and surface (cell) meshes.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from physicsnemo.cfd.bench.metrics.streamlines import compute_streamlines +from physicsnemo.cfd.bench.visualization.utils import plot_streamlines +from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case +from physicsnemo.cfd.evaluation.reports.registry import register_visual + + +def streamlines_comparison( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + case_ids: list[str] | None = None, + canonical_key: str | None = None, + view: str = "xy", + **kwargs: Any, +) -> None: + """GT vs pred streamlines on comparison meshes. + + Dispatches on each case's ``metric_dtype``: + + - ``point`` — volume mesh; uses ``ground_truth_volume_mesh_field_names`` / + ``volume_mesh_field_names`` (typically ``velocity``). + - ``cell`` — surface PolyData; uses ``ground_truth_mesh_field_names`` / ``mesh_field_names`` + (e.g. ``shear_stress`` for wall-shear / skin-friction lines). + + ``canonical_key`` defaults to ``output.streamlines_vector_canonical`` (usually ``velocity``). + For surface runs, set ``canonical_key`` in YAML (e.g. ``shear_stress``). + """ + out = Path(output_dir) / "visuals" + out.mkdir(parents=True, exist_ok=True) + output: OutputConfig = config.output + ck = canonical_key or output.streamlines_vector_canonical + + for run_idx, run in enumerate(results): + if run.get("skipped"): + continue + model = run["model"] + dataset = run["dataset"] + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + md = row.get("metric_dtype") + if md == "point": + if ck not in output.ground_truth_volume_mesh_field_names or ck not in output.volume_mesh_field_names: + log_dataset( + "benchmark", + f"Skip streamlines_comparison for {cid!r}: canonical_key {ck!r} missing from " + f"volume field maps (ground_truth_volume_mesh_field_names / volume_mesh_field_names)", + ) + continue + gt_name = output.ground_truth_volume_mesh_field_names[ck] + pred_name = output.volume_mesh_field_names[ck] + fname_suffix = "_streamlines.png" + elif md == "cell": + if ck not in output.ground_truth_mesh_field_names or ck not in output.mesh_field_names: + log_dataset( + "benchmark", + f"Skip streamlines_comparison for {cid!r}: canonical_key {ck!r} missing from " + f"surface field maps (ground_truth_mesh_field_names / mesh_field_names)", + ) + continue + gt_name = output.ground_truth_mesh_field_names[ck] + pred_name = output.mesh_field_names[ck] + fname_suffix = "_streamlines_surface.png" + else: + log_dataset( + "benchmark", + f"Skip streamlines_comparison for {cid!r}: unsupported metric_dtype {md!r}", + ) + continue + + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: + log_dataset( + "benchmark", + f"Skip streamlines_comparison for {cid!r}: no comparison mesh", + ) + continue + m1 = mesh.copy(deep=True) + m2 = mesh.copy(deep=True) + try: + sl_true = compute_streamlines(m1, gt_name) + sl_pred = compute_streamlines(m2, pred_name) + except Exception as e: + log_dataset("benchmark", f"compute_streamlines failed for {cid!r}: {e}") + continue + plotter = plot_streamlines(sl_true, sl_pred, geometry=mesh, view=view, **kwargs) + safe = f"{model}_{dataset}_{cid}{fname_suffix}".replace("/", "_") + plotter.screenshot(str(out / safe)) + plotter.close() + log_dataset("benchmark", f"Wrote streamlines {out / safe}") + + +def register_streamlines_visual() -> None: + register_visual("streamlines_comparison", streamlines_comparison) + # Backward-compatible alias (same implementation; surface uses cell dtype + surface field maps). + register_visual("streamlines_comparison_surface", streamlines_comparison) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py index 0b4ba69..c763e4c 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py @@ -14,6 +14,7 @@ from physicsnemo.cfd.bench.visualization.utils import plot_field_comparisons, plot_fields from physicsnemo.cfd.evaluation.config import Config, OutputConfig from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case from physicsnemo.cfd.evaluation.reports.registry import register_visual @@ -22,6 +23,7 @@ def field_comparison_surface( results: list[dict[str, Any]], output_dir: str, *, + context: dict[str, Any] | None = None, case_ids: list[str] | None = None, canonical_keys: list[str] | None = None, view: str = "xy", @@ -34,7 +36,7 @@ def field_comparison_surface( keys = canonical_keys or ["pressure"] output: OutputConfig = config.output - for run in results: + for run_idx, run in enumerate(results): if run.get("skipped"): continue model = run["model"] @@ -43,15 +45,14 @@ def field_comparison_surface( cid = row["case_id"] if case_ids is not None and cid not in case_ids: continue - path = row.get("comparison_mesh_path") dtype = row.get("metric_dtype") or "cell" - if not path: + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: log_dataset( "benchmark", - f"Skip field_comparison_surface for {cid!r}: no comparison_mesh_path", + f"Skip field_comparison_surface for {cid!r}: no comparison mesh in context or path", ) continue - mesh = pv.read(path) true_fields: list[str] = [] pred_fields: list[str] = [] for k in keys: @@ -79,6 +80,7 @@ def plot_fields_volume( results: list[dict[str, Any]], output_dir: str, *, + context: dict[str, Any] | None = None, case_ids: list[str] | None = None, fields: list[str] | None = None, view: str = "xy", @@ -93,7 +95,7 @@ def plot_fields_volume( if field_list is None: field_list = list(output.volume_mesh_field_names.values()) - for run in results: + for run_idx, run in enumerate(results): if run.get("skipped"): continue model = run["model"] @@ -102,15 +104,14 @@ def plot_fields_volume( cid = row["case_id"] if case_ids is not None and cid not in case_ids: continue - path = row.get("comparison_mesh_path") dtype = row.get("metric_dtype") or "point" - if not path: + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: log_dataset( "benchmark", - f"Skip plot_fields_volume for {cid!r}: no comparison_mesh_path", + f"Skip plot_fields_volume for {cid!r}: no comparison mesh in context or path", ) continue - mesh = pv.read(path) plotter = plot_fields( mesh, field_list, diff --git a/physicsnemo/cfd/evaluation/reports/context_helpers.py b/physicsnemo/cfd/evaluation/reports/context_helpers.py new file mode 100644 index 0000000..59cd2f9 --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/context_helpers.py @@ -0,0 +1,30 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Resolve comparison meshes for report visuals: in-memory context first, then disk.""" + +from __future__ import annotations + +from typing import Any + +import pyvista as pv + + +def get_comparison_mesh_for_case( + row: dict[str, Any], + case_id: str, + run_idx: int, + context: dict[str, Any] | None, +) -> pv.DataSet | None: + """Return comparison mesh from ``context['comparison_meshes_by_run']`` or ``comparison_mesh_path``.""" + ctx = context or {} + by_run = ctx.get("comparison_meshes_by_run") + if isinstance(by_run, list) and run_idx < len(by_run): + m = by_run[run_idx].get(case_id) + if m is not None: + return m + path = row.get("comparison_mesh_path") + if path: + return pv.read(path) + return None diff --git a/physicsnemo/cfd/evaluation/reports/registry.py b/physicsnemo/cfd/evaluation/reports/registry.py index a08ec5b..3dfc962 100644 --- a/physicsnemo/cfd/evaluation/reports/registry.py +++ b/physicsnemo/cfd/evaluation/reports/registry.py @@ -8,6 +8,9 @@ from typing import Any, Callable +# Visual plugins: (config, results, output_dir, *, context=None, **per_visual_kwargs) -> None. +# ``context`` may include ``comparison_meshes_by_run``: list[dict[case_id, pyvista.DataSet]] +# aligned with ``results`` (see ``run_optional_report_plugins``). VisualFn = Callable[..., None] _REGISTRY: dict[str, VisualFn] = {} diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index 0f8572d..b271077 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -1,38 +1,34 @@ # Evaluation examples (PhysicsNeMo-CFD) -YAML examples and CLIs for **`physicsnemo.cfd.evaluation`**: single-case inference, benchmarking, and optional PNG reports. Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then run commands **from this directory** (or pass absolute paths to `--config`). +YAML and the **benchmark** CLI drive all evaluation: **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK (`run.save_inference_mesh`, `reports.save_comparison_meshes`). Everything is configured in **[`evaluation_config.yaml`](evaluation_config.yaml)** — `benchmark.mode: single` uses the top-level `model` and `dataset`; **`benchmark.mode: matrix`** uses `benchmark.models` × `benchmark.datasets` (see the commented template at the bottom of that file). Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then run commands **from this directory** (or pass absolute paths to `--config`). --- -## Commands - -**Inference** — one model, one case; writes `inference__.vtp|vtu` under `run.output_dir`. With `reports.enabled: true` and `reports.visuals`, also writes a comparison mesh and PNGs (needs GT on the case; see [Reports and plots](#reports-and-plots)). +## Commands (canonical) ```bash -python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml -python -m physicsnemo.cfd.evaluation.inference.run --config inference_config.yaml --case-id run_1 -# shorthand: python -m physicsnemo.cfd.evaluation.inference +# One config: metrics + tables + optional visuals (see evaluation_config.yaml) +python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml +python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml --case-id run_1 +# shorthand: +python -m physicsnemo.cfd.evaluation.benchmarks ``` -**Benchmark** — metrics, JSON/CSV/HTML; **does not** run `reports.visuals` or save comparison VTK. +**Matrix:** edit [`evaluation_config.yaml`](evaluation_config.yaml) — set `benchmark.mode: matrix` and fill `benchmark.models` / `benchmark.datasets` (template is commented at the bottom of the file). Then run the same command as above. Incompatible surface/volume pairs are skipped. -```bash -python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml --case-id run_1 -python -m physicsnemo.cfd.evaluation.benchmarks -``` +**Optional second config:** `--base-config` then `--config` still works if you prefer splitting shared defaults from an overlay; one file is enough for most workflows. -**Merge order:** `--base-config` first, then `--config`, then optional CLI overrides (`run.device=cpu`, or `--run.device=cuda:1`). Inference supports `--base-config` the same way. +**Merge order** when using two files: `--base-config` first, then `--config`, then optional CLI overrides (`run.device=cpu`, or `--run.device=cuda:1`). -**Minimal surface-only example:** `surface_benchmark_minimal.yaml` (edit paths). +**Compatibility:** `python -m physicsnemo.cfd.evaluation.inference` forwards to the same benchmark engine (defaults to **one case** = first listed case when `--case-id` is omitted). Prefer the `benchmarks.run` command above. --- -## Config files in this folder +## Config file | File | Use | | ---- | --- | -| [`inference_config.yaml`](inference_config.yaml) | Shared defaults: `run`, `model`, `dataset`, `output`, `metrics`, optional `reports`, `benchmark.mode: single`. Use alone or as `--base-config`. | -| [`benchmark_matrix.yaml`](benchmark_matrix.yaml) | Overlay: `benchmark.mode: matrix`, `models[]`, `datasets[]` (Cartesian product; incompatible surface/volume pairs are skipped). | +| [`evaluation_config.yaml`](evaluation_config.yaml) | **Full evaluation config:** `run`, `model`, `dataset`, `output`, `metrics`, optional `reports`, and `benchmark` (`mode: single` or `mode: matrix` with `models` / `datasets`). Matrix example is in comments at the bottom of the file. | --- @@ -42,10 +38,10 @@ Keys are the same whether they come from one file or base + overlay. | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (default `true`; set `false` to skip writing `inference__.vtp\|vtu`) | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (if `true`, benchmark writes `inference__.vtp\|vtu` per case) | | **model** | `name` (registered wrapper), `checkpoint`, `stats_path`, optional `inference_domain` (`surface` \| `volume`), `kwargs` | | **dataset** | `name` (registered adapter), `root`, `split`, `case_ids` (`null` = all), `kwargs` (e.g. `align_ground_truth_to_model`, `inference_domain` for volume) | -| **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume) | +| **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume); optional **`streamlines_vector_canonical`** (default `velocity`) for `streamlines_comparison` | | **metrics** | List of metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | | **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | | **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | @@ -58,7 +54,7 @@ Keys are the same whether they come from one file or base + overlay. ## Metrics -Registered names (or dicts with `name` + kwargs). Example list from `inference_config.yaml`: +Registered names (or dicts with `name` + kwargs). Example list from `evaluation_config.yaml`: ```yaml metrics: @@ -89,16 +85,27 @@ metrics: ### What runs where -| CLI | Metrics | `reports.visuals` (PNGs) | Comparison VTK | -| --- | ------- | -------------------------- | ---------------- | -| **Inference** | — | Yes, if `reports.enabled` and `visuals` non-empty | Saved if `save_comparison_meshes` **or** visuals enabled | -| **Benchmark** | Yes | No | Not written by engine | +The benchmark engine writes **`benchmark_results.json` / `.csv` / `.html`**, then optional **`reports.visuals`** PNGs (when `reports.enabled` and `visuals` is non-empty). Comparison VTK on disk exists only if **`reports.save_comparison_meshes: true`** (otherwise PNGs use in-memory meshes). **`run.save_inference_mesh: true`** adds prediction-only VTK per case. + +Built-in **visual** names: + +| Name | Role | +| ---- | ---- | +| `field_comparison_surface` | Surface GT vs pred (`plot_field_comparisons`) | +| `plot_fields_volume` | Volume scalars (`plot_fields`) | +| `line_plot` / `plot_line` | GT vs pred along `plot_coord` on comparison mesh | +| `design_scatter` | Pred vs true scatter + R²; needs **`pairs`**: `[{name, true_key, pred_key}]` matching `per_case[].metrics` | +| `design_trend` | Trend vs index; same **`pairs`**, optional **`idx_key`** per pair for x labels | +| `projections_hexbin` | Hexbin over many meshes; needs **`mesh_paths`**, **`field`**, **`direction`** (see `bench.visualization.plot_projections_hexbin`) | +| `streamlines_comparison` | Volume streamlines (point comparison mesh); uses **`output.streamlines_vector_canonical`** or **`canonical_key`** | + +Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. -Built-in **visual** names: `field_comparison_surface` (surface GT vs pred), `plot_fields_volume` (volume scalars), **`line_plot`** (GT vs pred along `plot_coord`, cell-center sample of comparison mesh; kwargs: `canonical_key`, `plot_coord`, `normalize_factor`, `case_ids`, …). Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. +**Runtime context:** `run_optional_report_plugins(..., context={"comparison_meshes_by_run": [{case_id: mesh}, ...]})` aligns with `results` so built-ins can skip `pv.read` for large VTU/VTP. -**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/{case_id}_comparison.vtp|vtu`. +**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; optional comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/{case_id}_comparison.vtp|vtu`. -**Inference writes two meshes:** (1) `inference__.vtp|vtu` — **predictions only**; (2) `comparison_meshes/_comparison.*` — **GT + pred** (same as `build_comparison_mesh` for metrics/plots). +**Inference meshes:** (1) `inference__.vtp|vtu` — **predictions only** (optional via `run.save_inference_mesh`); (2) optional **`comparison_meshes/_comparison.*`** — **GT + pred** when `save_comparison_meshes` is true. ### `reports` YAML shape @@ -122,8 +129,8 @@ reports: | Key | Role | | --- | ---- | -| `enabled` | If true, run registered visuals (inference calls this automatically). | -| `save_comparison_meshes` | Save comparison VTK even with no `visuals` (inference only). | +| `enabled` | If true, run registered visuals after metrics/tables. | +| `save_comparison_meshes` | Save comparison VTK to disk. If false, mesh-based visuals still run from memory when the runner built a comparison mesh. | | `comparison_mesh_subdir` | Subfolder for `*_comparison.vtp|vtu`. | | `visuals` | Ordered list: string names or `{ name: ..., ...kwargs }`. | | `plugins` | Legacy manifest hooks (`ReportsConfig`). | @@ -133,6 +140,22 @@ reports: - **`field_comparison_surface`** — Uses `canonical_keys` (default `pressure`) and `output.ground_truth_mesh_field_names` / `mesh_field_names`. - **`plot_fields_volume`** — `fields` = VTK array names, or default all `output.volume_mesh_field_names` values. - **`line_plot`** — One GT vs pred line per case; **`canonical_key`** picks surface or volume maps; optional **`coord_trim`** / **`field_trim`** (two-element lists), **`flip`**, matplotlib kwargs forwarded to `plot_line` (e.g. `xlabel`, `ylabel`, `true_line_kwargs`). +- **`design_scatter` / `design_trend`** — Require **`pairs`** in YAML; values must appear under **`per_case[].metrics`** (e.g. from custom metrics or extended metric dicts). +- **`projections_hexbin`** — **`mesh_paths`**: explicit list of VTK paths (same workflow as [`workflows/bench_example`](../bench_example/) projection outputs). +- **`streamlines_comparison`** — Volume cases only (`metric_dtype` point); uses GT/pred VTK names from **`output`** for the chosen canonical key. + +### Bench example ↔ evaluation mapping + +| `workflows/bench_example` / `bench.visualization.utils` | Evaluation visual name | +| --------------------------------------------------------- | ------------------------ | +| `plot_field_comparisons` (contours / utils) | `field_comparison_surface` | +| `plot_fields` | `plot_fields_volume` | +| `plot_line` | `line_plot` | +| `plot_design_scatter` | `design_scatter` | +| `plot_design_trend` | `design_trend` | +| `plot_projections_hexbin` | `projections_hexbin` (`mesh_paths` in YAML) | +| `plot_streamlines` | `streamlines_comparison` | +| `get_visible_point_indices` | (no built-in; used inside bench_example contour scripts only) | ### Line plots @@ -141,12 +164,12 @@ Built-in **`line_plot`** wraps **`plot_line`**: it loads the comparison mesh, bu ### Other plot paths - **Bench example:** run inference to get VTK, then `generate_surface_benchmarks.py` / `generate_volume_benchmarks.py` with a matching field map. -- **Programmatic:** `bench.visualization.utils` or `run_optional_report_plugins(config, results, output_dir)` with `per_case[].comparison_mesh_path` if you build results yourself. +- **Programmatic:** `bench.visualization.utils` or `run_optional_report_plugins(config, results, output_dir, context={...})` with `per_case[].comparison_mesh_path` and/or **`context["comparison_meshes_by_run"]`**. ### Troubleshooting - **`case_ids`** in a visual must include the case you run — use `--case-id run_1` if the visual lists `case_ids: ["run_1"]`, or omit `case_ids`. -- Use an **editable install** or **`PYTHONPATH`** to this repo so inference includes the visualization step (not an old wheel). +- Use an **editable install** or **`PYTHONPATH`** to this repo so the benchmark run includes the visualization step (not an old wheel). - Check **`report_plugins_manifest.json`** → `visual_errors` (headless PyVista often needs **xvfb**). --- diff --git a/workflows/evaluation_examples/benchmark_matrix.yaml b/workflows/evaluation_examples/benchmark_matrix.yaml deleted file mode 100644 index 4aae4fb..0000000 --- a/workflows/evaluation_examples/benchmark_matrix.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# Matrix benchmark overlay: merge on top of inference_config.yaml (see README). -# Usage: -# python -m physicsnemo.cfd.evaluation.benchmarks.run --base-config inference_config.yaml --config benchmark_matrix.yaml - -benchmark: - mode: "matrix" - models: - # - name: "fignet" - # checkpoint: "benchmark_models/fignet_drivaerml_surface_checkpoint/model_00999.pth" - # stats_path: "benchmark_models/fignet_drivaerml_surface_checkpoint/global_stats.json" - # kwargs: {"max_points": 250000, "interpolation_k": 4} - # - name: "xmgn" - # checkpoint: "benchmark_models/xmgn_drivaerml_surface_checkpoint/final_model_checkpoint.pth" - # stats_path: "benchmark_models/xmgn_drivaerml_surface_checkpoint/global_stats.json" - # kwargs: {"max_points": 250000, "interpolation_k": 4} - - name: "geotransolver" - inference_domain: volume - checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" - stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" - kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} - # - name: "transolver" - # checkpoint: "benchmark_models/transolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" - # stats_path: "benchmark_models/transolver_drivaerml_surface_checkpoint/global_stats.json" - # kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} - # - name: "domino" - # checkpoint: "benchmark_models/domino_drivaerml_surface_checkpoint/checkpoint.0.501.pt" - # stats_path: "benchmark_models/domino_drivaerml_surface_checkpoint/global_stats.json" - # kwargs: {"domino_config": "benchmark_models/domino_drivaerml_surface_checkpoint/config.yaml"} - datasets: - - name: "drivaerml" - root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" - split: null - case_ids: ["run_1"] - kwargs: {align_ground_truth_to_model: true, inference_domain: volume} - reproducibility: - log_env: true - save_artifacts: true diff --git a/workflows/evaluation_examples/evaluation_config.yaml b/workflows/evaluation_examples/evaluation_config.yaml new file mode 100644 index 0000000..5cd1653 --- /dev/null +++ b/workflows/evaluation_examples/evaluation_config.yaml @@ -0,0 +1,139 @@ +# Single evaluation config: metrics, tabular reports (JSON/CSV/HTML), optional PNG visuals, optional VTK. +# Run: python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml [--case-id run_1] +# +# benchmark.mode: "single" — uses top-level `model` and `dataset` below (one model × one dataset). +# benchmark.mode: "matrix" — uses `benchmark.models` × `benchmark.datasets` (Cartesian product). See the +# commented template at the bottom of this file; merge those keys into `benchmark` and set mode: matrix. + +run: + device: "cuda:0" + output_dir: "benchmark_results" + seed: 42 + batch_size: 1 + save_inference_mesh: false # false = skip writing inference__.vtp|vtu + +# Default model (template for matrix rows when `benchmark.mode: matrix`; ignored as a pair when using lists). +# Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. +# GeoTransolver/Transolver volume: inference_domain: volume; global_stats.json (velocity, pressure_volume, turbulent_viscosity) or volume_fields_normalization.npz next to checkpoint +# (same layout as transformer_models training / inference_on_vtk.py). +benchmark: + mode: "matrix" + models: + - name: "geotransolver" + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" + kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} + datasets: + - name: "drivaerml" + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + case_ids: ["run_1", "run_11"] + kwargs: {align_ground_truth_to_model: true, inference_domain: surface} + reproducibility: + log_env: true + save_artifacts: true + +output: + mesh_field_names: + pressure: "pMeanTrimPred" + shear_stress: "wallShearStressMeanTrimPred" + # Written on volume runs (defaults include pressure_volume → pMeanPred, etc.) + volume_mesh_field_names: + pressure_volume: "pMeanTrimPred" + turbulent_viscosity: "nutMeanTrimPred" + velocity: "UMeanTrimPred" + +# Surface: l2_pressure, l2_shear_stress, l2_velocity, drag_error, lift_error +# Volume: l2_pressure_volume (pressure_volume), l2_turbulent_viscosity, l2_velocity +# Optional dict form for renamed keys: { name: l2_turbulent_viscosity, gt_key: turbulent_viscosity, pred_key: turbulent_viscosity } +metrics: + - l2_pressure + - l2_shear_stress + - l2_pressure_area_weighted # alias: area_wt_l2_pressure + - drag_error + - lift_error + # Surface (VTP): l2_pressure, l2_shear_stress + # - "l2_pressure_volume" + # - "l2_turbulent_viscosity" + # - "l2_velocity" + +# Optional PNGs: builds GT+pred comparison mesh in memory; VTK on disk only if save_comparison_meshes: true. +# Built-ins: field_comparison_surface | plot_fields_volume | line_plot | line_plot_centerlines (bench-style +# top/bottom surface strips) | design_scatter | design_trend | projections_hexbin | streamlines_comparison +reports: + enabled: true + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visuals: + - name: field_comparison_surface + case_ids: ["run_1"] + canonical_keys: [pressure, shear_stress] + view: xy + # Surface comparison mesh (cell centers); shear_stress line uses |tau_w| vs plot_coord. + - name: line_plot + case_ids: ["run_1", "run_11"] + canonical_key: pressure + plot_coord: x + normalize_factor: 1.0 + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + # Top/bottom centerline strips (same y-slice + z-clip as workflows/bench_example generate_surface_benchmarks). + # Writes two PNGs per case: *_line_centerline_top_* and *_line_centerline_bottom_*. + - name: line_plot_centerlines + case_ids: ["run_1", "run_11"] + canonical_key: pressure + plot_coord: x + normalize_factor: 1.0 + z_clip: 0.4 + y_slice_origin: [0, 0, 0] + xlabel: "X Coordinate" + ylabel: "Pressure" + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + # streamlines_comparison: volume uses velocity maps; surface (cell) uses canonical_key (e.g. shear_stress). + - name: streamlines_comparison + case_ids: ["run_1", "run_11"] + canonical_key: shear_stress + view: xy + # --- drag_error / lift_error populate drag_error_true|pred (Cd) and lift_error_true|pred (Cl) --- + - name: design_scatter + pairs: + - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } + - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } + - name: design_trend + pairs: + - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } + - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } + # - name: projections_hexbin + # mesh_paths: ["/abs/path/to/proj1.vtp"] + # field: p_error + # direction: XY + + +# ============================================================================= +# Matrix template (optional): set `benchmark.mode: matrix` and replace `models` / `datasets` with +# lists like the following (merge into `benchmark` above). Top-level `model` / `dataset` fill in +# missing fields for each matrix row per Config.from_dict. +# ============================================================================= +# benchmark: +# mode: "matrix" +# models: +# # - name: "fignet" +# # checkpoint: "benchmark_models/fignet_drivaerml_surface_checkpoint/model_00999.pth" +# # stats_path: "benchmark_models/fignet_drivaerml_surface_checkpoint/global_stats.json" +# # kwargs: {"max_points": 250000, "interpolation_k": 4} +# - name: "geotransolver" +# inference_domain: volume +# checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" +# stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" +# kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} +# datasets: +# - name: "drivaerml" +# root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" +# split: null +# case_ids: ["run_1"] +# kwargs: {align_ground_truth_to_model: true, inference_domain: volume} +# reproducibility: +# log_env: true +# save_artifacts: true diff --git a/workflows/evaluation_examples/inference_config.yaml b/workflows/evaluation_examples/inference_config.yaml deleted file mode 100644 index 08fcc58..0000000 --- a/workflows/evaluation_examples/inference_config.yaml +++ /dev/null @@ -1,87 +0,0 @@ -# Shared defaults for inference and benchmarks (load alone, or as --base-config with a benchmark overlay). - -run: - device: "cuda:0" - output_dir: "benchmark_results" - seed: 42 - batch_size: 1 - save_inference_mesh: false # false = skip writing inference__.vtp|vtu - -# Default single-run model (overridden per row in benchmark matrix when using benchmark_matrix.yaml). -# Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. -# GeoTransolver/Transolver volume: inference_domain: volume; global_stats.json (velocity, pressure_volume, turbulent_viscosity) or volume_fields_normalization.npz next to checkpoint -# (same layout as transformer_models training / inference_on_vtk.py). -model: - name: "geotransolver" - inference_domain: surface - checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" - stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" - kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} - -dataset: - name: "drivaerml" - root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" - split: null - case_ids: null - # Volume branch (same root; run_/volume_.vtu): use with a volume model, e.g. volume_baseline - # kwargs: { inference_domain: volume } - -output: - mesh_field_names: - pressure: "pMeanTrimPred" - shear_stress: "wallShearStressMeanTrimPred" - # Written on volume inference (defaults include pressure_volume → pMeanPred, etc.) - volume_mesh_field_names: - pressure_volume: "pMeanTrimPred" - turbulent_viscosity: "nutMeanTrimPred" - velocity: "UMeanTrimPred" - -# Surface: l2_pressure, l2_shear_stress, l2_velocity, drag_error, lift_error -# Volume: l2_pressure_volume (pressure_volume), l2_turbulent_viscosity, l2_velocity -# Optional dict form for renamed keys: { name: l2_turbulent_viscosity, gt_key: turbulent_viscosity, pred_key: turbulent_viscosity } -metrics: - - l2_pressure - - l2_shear_stress - - l2_pressure_area_weighted # alias: area_wt_l2_pressure - - l2_pressure_volume # volume pressure; needs volume case + output maps - - l2_velocity - - l2_turbulent_viscosity - - drag_error - - lift_error - - continuity_residual_l2 # volume: needs velocity on volume mesh - - momentum_residual_l2 - # Surface (VTP): l2_pressure, l2_shear_stress - # - "l2_pressure_volume" - # - "l2_turbulent_viscosity" - # - "l2_velocity" - -# Registered visuals (metrics-style list). Inference CLI: when enabled + visuals, builds a GT+pred -# comparison mesh, saves under comparison_mesh_subdir, runs visuals → PNGs in output_dir/visuals/. -# Benchmark CLI still does not run visuals (metrics-only). -reports: - enabled: true - save_comparison_meshes: false - comparison_mesh_subdir: comparison_meshes - visuals: - # - field_comparison_surface - - name: field_comparison_surface - case_ids: ["run_1"] # case ids to visualize - canonical_keys: [pressure, shear_stress] - view: xy - - name: my_line_plot - case_ids: ["run_1"] # case ids to visualize - plot_coord: x - field_true: pMean - field_pred: pMeanTrimPred - # - name: plot_fields_volume - # case_ids: ["run_1"] - # fields: ["pMeanPred", "nutMeanPred", "UMeanPred"] - # view: xy - -benchmark: - mode: "single" - models: [] - datasets: [] - reproducibility: - log_env: true - save_artifacts: true From d6915d02890c2bdc8344aaa1c20609c7793eecc0 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Fri, 3 Apr 2026 10:40:00 -0700 Subject: [PATCH 05/25] refactoring nim evaluation --- CHANGELOG.md | 2 ++ README.md | 2 +- physicsnemo/cfd/evaluation/nims/__init__.py | 9 +++++++++ .../cfd/{inference => evaluation/nims}/domino_nim.py | 0 .../nims}/launch_local_domino_nim.sh | 0 .../notebooks/benchmarking_in_absence_of_gt.ipynb | 2 +- .../bench_example/notebooks/surface_benchmarking.ipynb | 2 +- .../bench_example/notebooks/volume_benchmarking.ipynb | 2 +- workflows/hybrid_initialization_example/README.md | 2 +- .../prepare_domino_assets.py | 2 +- 10 files changed, 17 insertions(+), 6 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/nims/__init__.py rename physicsnemo/cfd/{inference => evaluation/nims}/domino_nim.py (100%) rename physicsnemo/cfd/{inference => evaluation/nims}/launch_local_domino_nim.sh (100%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9e30414..7666583 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,6 +23,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Changed +- DoMINO NIM helper `call_domino_nim` and `launch_local_domino_nim.sh` live under + **`physicsnemo.cfd.evaluation.nims`** (removed top-level **`physicsnemo.cfd.inference`** package). - Benchmark metric registration for evaluation now uses bench-backed built-ins under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated NumPy-only helpers. diff --git a/README.md b/README.md index d785cad..08ecbc9 100644 --- a/README.md +++ b/README.md @@ -94,7 +94,7 @@ file. To get started, use the DoMINO NIM on a sample as shown below: ```python -from physicsnemo.cfd.inference.domino_nim import call_domino_nim +from physicsnemo.cfd.evaluation.nims import call_domino_nim import subprocess filenames = [ diff --git a/physicsnemo/cfd/evaluation/nims/__init__.py b/physicsnemo/cfd/evaluation/nims/__init__.py new file mode 100644 index 0000000..0e98d68 --- /dev/null +++ b/physicsnemo/cfd/evaluation/nims/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""NIM (NVIDIA Inference Microservice) helpers for evaluation workflows (e.g. DoMINO automotive aero).""" + +from physicsnemo.cfd.evaluation.nims.domino_nim import call_domino_nim + +__all__ = ["call_domino_nim"] diff --git a/physicsnemo/cfd/inference/domino_nim.py b/physicsnemo/cfd/evaluation/nims/domino_nim.py similarity index 100% rename from physicsnemo/cfd/inference/domino_nim.py rename to physicsnemo/cfd/evaluation/nims/domino_nim.py diff --git a/physicsnemo/cfd/inference/launch_local_domino_nim.sh b/physicsnemo/cfd/evaluation/nims/launch_local_domino_nim.sh similarity index 100% rename from physicsnemo/cfd/inference/launch_local_domino_nim.sh rename to physicsnemo/cfd/evaluation/nims/launch_local_domino_nim.sh diff --git a/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb b/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb index dad3e95..ab86e84 100644 --- a/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb +++ b/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb @@ -255,7 +255,7 @@ " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", - "from physicsnemo.cfd.inference.domino_nim import call_domino_nim\n", + "from physicsnemo.cfd.evaluation.nims import call_domino_nim\n", "\n", "stl_files = glob.glob(\"remeshed_variant_*.stl\")\n", "\n", diff --git a/workflows/bench_example/notebooks/surface_benchmarking.ipynb b/workflows/bench_example/notebooks/surface_benchmarking.ipynb index f3d2c98..08cdc30 100644 --- a/workflows/bench_example/notebooks/surface_benchmarking.ipynb +++ b/workflows/bench_example/notebooks/surface_benchmarking.ipynb @@ -224,7 +224,7 @@ " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", - "from physicsnemo.cfd.inference.domino_nim import call_domino_nim\n", + "from physicsnemo.cfd.evaluation.nims import call_domino_nim\n", "\n", "output_dict = call_domino_nim(\n", " stl_path=\"./drivaer_202.stl\",\n", diff --git a/workflows/bench_example/notebooks/volume_benchmarking.ipynb b/workflows/bench_example/notebooks/volume_benchmarking.ipynb index 1bb67e6..abb3f61 100644 --- a/workflows/bench_example/notebooks/volume_benchmarking.ipynb +++ b/workflows/bench_example/notebooks/volume_benchmarking.ipynb @@ -273,7 +273,7 @@ " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", - "from physicsnemo.cfd.inference.domino_nim import call_domino_nim\n", + "from physicsnemo.cfd.evaluation.nims import call_domino_nim\n", "\n", "output_dict = call_domino_nim(\n", " stl_path=\"./drivaer_202.stl\",\n", diff --git a/workflows/hybrid_initialization_example/README.md b/workflows/hybrid_initialization_example/README.md index ed96840..3937a47 100644 --- a/workflows/hybrid_initialization_example/README.md +++ b/workflows/hybrid_initialization_example/README.md @@ -62,7 +62,7 @@ export NGC_API_KEY="your_ngc_api_key_here" # Tip: For a more long-term solution, you can set this in your shell profile. # Launch DoMINO NIM (Note: This requires an internet connection to download model checkpoints) -./physicsnemo/cfd/inference/launch_local_domino_nim.sh +./physicsnemo/cfd/evaluation/nims/launch_local_domino_nim.sh ``` **Option B - Manual setup:** diff --git a/workflows/hybrid_initialization_example/prepare_domino_assets.py b/workflows/hybrid_initialization_example/prepare_domino_assets.py index f8fdc92..0c2035d 100644 --- a/workflows/hybrid_initialization_example/prepare_domino_assets.py +++ b/workflows/hybrid_initialization_example/prepare_domino_assets.py @@ -26,7 +26,7 @@ ) # Performs a DoMINO inference on the vehicle.stl file -from physicsnemo.cfd.inference.domino_nim import call_domino_nim +from physicsnemo.cfd.evaluation.nims import call_domino_nim output_dict = call_domino_nim( stl_path=example_dir / "from_domino" / "vehicle.stl", From 37e48edfed6c12b7afcc8e4bfca22f5d44689613 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Fri, 3 Apr 2026 10:55:35 -0700 Subject: [PATCH 06/25] adding headers --- CHANGELOG.md | 3 ++ physicsnemo/cfd/evaluation/__init__.py | 16 ++++++++ .../cfd/evaluation/benchmarks/__init__.py | 16 ++++++++ .../cfd/evaluation/benchmarks/__main__.py | 16 ++++++++ .../cfd/evaluation/benchmarks/engine.py | 33 ++++++++++++++++- .../cfd/evaluation/benchmarks/report.py | 16 ++++++++ .../evaluation/benchmarks/report_plugins.py | 26 ++++++++++++- physicsnemo/cfd/evaluation/benchmarks/run.py | 16 ++++++++ physicsnemo/cfd/evaluation/common/__init__.py | 16 ++++++++ .../evaluation/common/checkpoint_compat.py | 16 ++++++++ .../cfd/evaluation/common/interpolation.py | 16 ++++++++ physicsnemo/cfd/evaluation/common/io.py | 16 ++++++++ physicsnemo/cfd/evaluation/config.py | 26 +++++++++++++ .../cfd/evaluation/datasets/__init__.py | 16 ++++++++ .../evaluation/datasets/adapter_registry.py | 16 ++++++++ .../evaluation/datasets/adapters/__init__.py | 16 ++++++++ .../cfd/evaluation/datasets/adapters/ahmed.py | 16 ++++++++ .../evaluation/datasets/adapters/drivaerml.py | 16 ++++++++ .../cfd/evaluation/datasets/gt_alignment.py | 16 ++++++++ .../cfd/evaluation/datasets/progress.py | 16 ++++++++ physicsnemo/cfd/evaluation/datasets/schema.py | 16 ++++++++ .../evaluation/datasets/vtk_ground_truth.py | 16 ++++++++ .../cfd/evaluation/inference/__init__.py | 16 ++++++++ .../cfd/evaluation/inference/__main__.py | 16 ++++++++ .../common_wrapper_utils/__init__.py | 16 ++++++++ .../common_wrapper_utils/vtk_datapipe_io.py | 16 ++++++++ .../evaluation/inference/model_registry.py | 16 ++++++++ .../cfd/evaluation/inference/progress.py | 16 ++++++++ .../evaluation/inference/wrappers/__init__.py | 16 ++++++++ .../inference/wrappers/domino/__init__.py | 16 ++++++++ .../inference/wrappers/domino/inference.py | 16 ++++++++ .../inference/wrappers/domino/scaling.py | 16 ++++++++ .../inference/wrappers/domino/wrapper.py | 16 ++++++++ .../inference/wrappers/fignet/__init__.py | 16 ++++++++ .../inference/wrappers/fignet/wrapper.py | 16 ++++++++ .../wrappers/geotransolver/__init__.py | 16 ++++++++ .../wrappers/geotransolver/wrapper.py | 16 ++++++++ .../inference/wrappers/surface_baseline.py | 16 ++++++++ .../inference/wrappers/transolver/__init__.py | 16 ++++++++ .../inference/wrappers/transolver/wrapper.py | 16 ++++++++ .../inference/wrappers/volume_baseline.py | 16 ++++++++ .../inference/wrappers/xmgn/__init__.py | 16 ++++++++ .../inference/wrappers/xmgn/wrapper.py | 16 ++++++++ .../cfd/evaluation/metrics/__init__.py | 16 ++++++++ test/ci_tests/test_evaluation.py | 37 ++++++++++++++++++- workflows/evaluation_examples/README.md | 1 + .../evaluation_config.yaml | 13 +++++-- 47 files changed, 771 insertions(+), 8 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7666583..2093085 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,6 +23,9 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Changed +- **`reports.visual_case_ids`**: optional list limiting which cases get an in-memory comparison mesh for + PNG visuals (default: all cases). Same list is the default **`case_ids`** for visuals that omit it; + per-visual **`case_ids`** still overrides. - DoMINO NIM helper `call_domino_nim` and `launch_local_domino_nim.sh` live under **`physicsnemo.cfd.evaluation.nims`** (removed top-level **`physicsnemo.cfd.inference`** package). - Benchmark metric registration for evaluation now uses bench-backed built-ins diff --git a/physicsnemo/cfd/evaluation/__init__.py b/physicsnemo/cfd/evaluation/__init__.py index d29191b..82486b1 100644 --- a/physicsnemo/cfd/evaluation/__init__.py +++ b/physicsnemo/cfd/evaluation/__init__.py @@ -1 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """PhysicsNeMo-CFD: Inference and benchmarking framework for AI-CFD models.""" diff --git a/physicsnemo/cfd/evaluation/benchmarks/__init__.py b/physicsnemo/cfd/evaluation/benchmarks/__init__.py index cdb3f7a..6186e9e 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/__init__.py +++ b/physicsnemo/cfd/evaluation/benchmarks/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Benchmark engine and reporting.""" from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark diff --git a/physicsnemo/cfd/evaluation/benchmarks/__main__.py b/physicsnemo/cfd/evaluation/benchmarks/__main__.py index 612aad0..81508fe 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/__main__.py +++ b/physicsnemo/cfd/evaluation/benchmarks/__main__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """python -m physicsnemo.cfd.evaluation.benchmarks""" from physicsnemo.cfd.evaluation.benchmarks.run import main diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index da0420d..31b4de0 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -1,8 +1,28 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Benchmark runner: config → model x dataset loop → metrics → results. When ``reports.enabled`` and ``reports.visuals`` are set, optionally writes comparison VTK (if ``reports.save_comparison_meshes``) and runs :func:`report_plugins.run_optional_report_plugins` with in-memory meshes in ``context["comparison_meshes_by_run"]`` so PNGs avoid disk reads when possible. + +If ``reports.visual_case_ids`` is set, only those case IDs are stored in memory; omit it to retain +all cases (legacy behavior). Meshes for other cases may still be read from ``comparison_mesh_path`` +when ``save_comparison_meshes`` is true. """ from __future__ import annotations @@ -30,6 +50,16 @@ from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh +def _retain_comparison_mesh_for_visual_context(reports: ReportsConfig | None, case_id: str) -> bool: + """Whether to store this case's comparison mesh in ``mesh_ctx`` for report visuals.""" + if reports is None or not reports.enabled or not reports.visuals: + return False + allow = reports.visual_case_ids + if allow is None: + return True + return case_id in allow + + def _normalize_metrics_config(metrics: list[str] | list[dict]) -> list[tuple[str, dict]]: """Return list of (metric_name, kwargs).""" out = [] @@ -276,7 +306,7 @@ def _run_single( dataset_config.name, f"Could not save comparison mesh for {cid!r}: {ex}", ) - if reports.enabled and reports.visuals: + if _retain_comparison_mesh_for_visual_context(reports, cid): mesh_ctx[cid] = comparison_mesh per_case.append(row) @@ -450,6 +480,7 @@ def _config_to_dict(c: Config) -> dict: "plugins": c.reports.plugins, "save_comparison_meshes": c.reports.save_comparison_meshes, "comparison_mesh_subdir": c.reports.comparison_mesh_subdir, + "visual_case_ids": c.reports.visual_case_ids, "visuals": c.reports.visuals, }, "benchmark": { diff --git a/physicsnemo/cfd/evaluation/benchmarks/report.py b/physicsnemo/cfd/evaluation/benchmarks/report.py index 43edbe8..02130bc 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Aggregate results and export JSON/CSV/HTML reports.""" import csv diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py index 063f2b8..e437814 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -23,6 +23,23 @@ from physicsnemo.cfd.evaluation.reports.registry import get_visual, normalize_visuals_config +def _apply_default_case_ids_to_visuals( + config: Config, + specs: list[tuple[str, dict[str, Any]]], +) -> list[tuple[str, dict[str, Any]]]: + """Inject ``reports.visual_case_ids`` as ``case_ids`` when a visual omits it.""" + default = config.reports.visual_case_ids + if default is None: + return specs + out: list[tuple[str, dict[str, Any]]] = [] + for name, kw in specs: + kw2 = dict(kw) + if "case_ids" not in kw2: + kw2["case_ids"] = list(default) + out.append((name, kw2)) + return out + + def run_optional_report_plugins( config: Config, results: list[dict[str, Any]], @@ -36,7 +53,11 @@ def run_optional_report_plugins( - ``comparison_meshes_by_run``: ``list[dict[str, Any]]`` with the same length as ``results``; each inner dict maps ``case_id`` to an in-memory comparison mesh (PyVista dataset) so visuals - can skip ``pv.read(comparison_mesh_path)``. + can skip ``pv.read(comparison_mesh_path)``. When ``config.reports.visual_case_ids`` is set, + only those case IDs are present per run (others may load from ``comparison_mesh_path`` if saved). + + If ``config.reports.visual_case_ids`` is set, visuals that do not specify ``case_ids`` receive + that list as their default filter (per-visual ``case_ids`` still overrides). """ out_dir = Path(output_dir) manifest: dict[str, Any] = { @@ -44,6 +65,7 @@ def run_optional_report_plugins( "plugins": config.reports.plugins, "save_comparison_meshes": config.reports.save_comparison_meshes, "comparison_mesh_subdir": config.reports.comparison_mesh_subdir, + "visual_case_ids": config.reports.visual_case_ids, "context_keys": list(context.keys()) if context else [], "visuals_ran": [], "visual_errors": [], @@ -68,6 +90,8 @@ def run_optional_report_plugins( _write_manifest(out_dir, manifest) raise + specs = _apply_default_case_ids_to_visuals(config, specs) + for name, vkwargs in specs: try: fn = get_visual(name) diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py index 0e036f0..e0bd146 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/run.py +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """CLI entrypoint for benchmark runs.""" import argparse diff --git a/physicsnemo/cfd/evaluation/common/__init__.py b/physicsnemo/cfd/evaluation/common/__init__.py index a0d9732..c34b260 100644 --- a/physicsnemo/cfd/evaluation/common/__init__.py +++ b/physicsnemo/cfd/evaluation/common/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Shared utilities for I/O, interpolation, and mesh handling.""" from physicsnemo.cfd.evaluation.common.io import load_global_stats, load_mesh diff --git a/physicsnemo/cfd/evaluation/common/checkpoint_compat.py b/physicsnemo/cfd/evaluation/common/checkpoint_compat.py index 43b19ea..8626c1a 100644 --- a/physicsnemo/cfd/evaluation/common/checkpoint_compat.py +++ b/physicsnemo/cfd/evaluation/common/checkpoint_compat.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """PyTorch 2.6+ compatibility for loading full training checkpoints (not weights-only). ``physicsnemo.utils.load_checkpoint`` uses ``torch.load`` without ``weights_only``. diff --git a/physicsnemo/cfd/evaluation/common/interpolation.py b/physicsnemo/cfd/evaluation/common/interpolation.py index e99c6b2..83e33dd 100644 --- a/physicsnemo/cfd/evaluation/common/interpolation.py +++ b/physicsnemo/cfd/evaluation/common/interpolation.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """kNN-based interpolation from prediction points to full mesh.""" from typing import Union diff --git a/physicsnemo/cfd/evaluation/common/io.py b/physicsnemo/cfd/evaluation/common/io.py index 8516610..790b4f1 100644 --- a/physicsnemo/cfd/evaluation/common/io.py +++ b/physicsnemo/cfd/evaluation/common/io.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Mesh I/O and normalization statistics loading.""" import json diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index 0741b8e..05668e4 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Config schema and loader for inference and benchmarking.""" from dataclasses import dataclass, field @@ -81,6 +97,12 @@ class ReportsConfig: #: When True, write each case comparison mesh to ``comparison_mesh_subdir`` for downstream visualization. save_comparison_meshes: bool = False comparison_mesh_subdir: str = "comparison_meshes" + #: Case IDs for which an in-memory comparison mesh is retained for ``reports.visuals`` (saves RAM on large sweeps). + #: When ``None``, all cases that build a comparison mesh are kept (legacy behavior). When a non-empty list, only + #: those IDs are stored in ``comparison_meshes_by_run``. Also used as default ``case_ids`` for any visual that + #: omits ``case_ids`` (per-visual ``case_ids`` still overrides). Empty list ``[]`` retains no meshes in context + #: (use ``save_comparison_meshes: true`` if PNGs must load from disk). + visual_case_ids: list[str] | None = None #: Visuals to run (same list style as ``metrics``): strings or ``{name: ..., ...kwargs}``. visuals: list[str] | list[dict[str, Any]] = field(default_factory=list) @@ -197,6 +219,9 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": ) reports_raw = data.get("reports") or {} if isinstance(reports_raw, dict): + vci = reports_raw.get("visual_case_ids") + if vci is not None: + vci = [str(x) for x in vci] reports = ReportsConfig( enabled=bool(reports_raw.get("enabled", False)), plugins=list(reports_raw.get("plugins") or []), @@ -204,6 +229,7 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": comparison_mesh_subdir=str( reports_raw.get("comparison_mesh_subdir") or "comparison_meshes" ), + visual_case_ids=vci, visuals=list(reports_raw.get("visuals") or []), ) else: diff --git a/physicsnemo/cfd/evaluation/datasets/__init__.py b/physicsnemo/cfd/evaluation/datasets/__init__.py index 61d1a65..d780711 100644 --- a/physicsnemo/cfd/evaluation/datasets/__init__.py +++ b/physicsnemo/cfd/evaluation/datasets/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Dataset adapters and canonical case schema.""" from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase, predictions_dict diff --git a/physicsnemo/cfd/evaluation/datasets/adapter_registry.py b/physicsnemo/cfd/evaluation/datasets/adapter_registry.py index 7191b28..c757d65 100644 --- a/physicsnemo/cfd/evaluation/datasets/adapter_registry.py +++ b/physicsnemo/cfd/evaluation/datasets/adapter_registry.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Dataset adapter base class and registry.""" from abc import ABC, abstractmethod diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py b/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py index 4b12d6b..d3331d5 100644 --- a/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py +++ b/physicsnemo/cfd/evaluation/datasets/adapters/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Dataset adapter implementations.""" from physicsnemo.cfd.evaluation.datasets.adapter_registry import register_adapter diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py b/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py index d1f5943..00ba3bb 100644 --- a/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py +++ b/physicsnemo/cfd/evaluation/datasets/adapters/ahmed.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Stub adapter for Ahmed Body dataset (placeholder until implementation).""" from pathlib import Path diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py index f850823..44382be 100644 --- a/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py +++ b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """DrivAerML dataset adapter: surface VTP and/or volume VTU under each ``run_*`` directory.""" from pathlib import Path diff --git a/physicsnemo/cfd/evaluation/datasets/gt_alignment.py b/physicsnemo/cfd/evaluation/datasets/gt_alignment.py index 0b07538..ede65c3 100644 --- a/physicsnemo/cfd/evaluation/datasets/gt_alignment.py +++ b/physicsnemo/cfd/evaluation/datasets/gt_alignment.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Resolve ground-truth mesh location (cell vs point) from dataset kwargs + model name.""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/datasets/progress.py b/physicsnemo/cfd/evaluation/datasets/progress.py index a858856..9049ca8 100644 --- a/physicsnemo/cfd/evaluation/datasets/progress.py +++ b/physicsnemo/cfd/evaluation/datasets/progress.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Progress messages for dataset I/O and benchmark outputs.""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/datasets/schema.py b/physicsnemo/cfd/evaluation/datasets/schema.py index 135c719..eb47ae2 100644 --- a/physicsnemo/cfd/evaluation/datasets/schema.py +++ b/physicsnemo/cfd/evaluation/datasets/schema.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Canonical CFD case schema for dataset adapters and model wrappers.""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py index 9bcaeca..e465b84 100644 --- a/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py +++ b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Load reference pressure / wall shear from VTK (VTP) for benchmarking.""" from typing import Literal diff --git a/physicsnemo/cfd/evaluation/inference/__init__.py b/physicsnemo/cfd/evaluation/inference/__init__.py index 55ef180..dab8481 100644 --- a/physicsnemo/cfd/evaluation/inference/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Model inference and wrappers.""" from physicsnemo.cfd.evaluation.inference.model_registry import ( diff --git a/physicsnemo/cfd/evaluation/inference/__main__.py b/physicsnemo/cfd/evaluation/inference/__main__.py index 869dad7..9a59de4 100644 --- a/physicsnemo/cfd/evaluation/inference/__main__.py +++ b/physicsnemo/cfd/evaluation/inference/__main__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """python -m physicsnemo.cfd.evaluation.inference""" from physicsnemo.cfd.evaluation.inference.run import main diff --git a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py index dc351a0..071b966 100644 --- a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Shared helpers for inference model wrappers (VTK/STL, datapipe tensors, etc.).""" from physicsnemo.cfd.evaluation.inference.common_wrapper_utils.vtk_datapipe_io import ( diff --git a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py index 6152938..a6799c4 100644 --- a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py +++ b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """VTK/STL → tensor dicts for CAE inference datapipes (surface and volume). Builds the same keys expected by ``TransolverDataPipe.process_data`` as in diff --git a/physicsnemo/cfd/evaluation/inference/model_registry.py b/physicsnemo/cfd/evaluation/inference/model_registry.py index 3934ffb..6278afe 100644 --- a/physicsnemo/cfd/evaluation/inference/model_registry.py +++ b/physicsnemo/cfd/evaluation/inference/model_registry.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """CFDModel base class and registry for model wrappers.""" from abc import ABC, abstractmethod diff --git a/physicsnemo/cfd/evaluation/inference/progress.py b/physicsnemo/cfd/evaluation/inference/progress.py index 7a7b1da..01fcbaf 100644 --- a/physicsnemo/cfd/evaluation/inference/progress.py +++ b/physicsnemo/cfd/evaluation/inference/progress.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Uniform progress messages for inference (wrappers and CLI).""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py index d9737ec..67ccfed 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Model wrapper implementations; registration happens on import.""" from physicsnemo.cfd.evaluation.inference.model_registry import register_model diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py index 2d7fa3d..3174ba1 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """DoMINO surface inference: wrapper + colocated helpers.""" from physicsnemo.cfd.evaluation.inference.wrappers.domino.inference import ( diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py index 69e1cf7..4bfe144 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """DoMINO surface inference helpers (aligned with physicsnemo domino ``src/test.py``).""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py index bf7d3a0..d338d60 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/scaling.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Load DoMINO scaling factors from training pickles (compatible with domino ``utils.ScalingFactors``).""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py index c486c03..01457d3 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/wrapper.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """DoMINO model wrapper (surface or volume inference; matches domino ``src/test.py``).""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py index 406fccf..b6c43b0 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from physicsnemo.cfd.evaluation.inference.wrappers.fignet.wrapper import FIGNetWrapper __all__ = ["FIGNetWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py index 5a57135..a564cb8 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/fignet/wrapper.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """FIGConvUNet (fignet) model wrapper for DrivAerML-style inference.""" from typing import Any, ClassVar, List, Literal, Optional, Tuple, Union diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py index ae041f2..4d4054a 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from physicsnemo.cfd.evaluation.inference.wrappers.geotransolver.wrapper import GeoTransolverWrapper __all__ = ["GeoTransolverWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py index a52245b..6af33bd 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """GeoTransolver model wrapper for surface or volume inference (TransolverDataPipe + VTK). Surface: cell-centered pressure + WSS on boundary VTP (``inference_domain: surface`` or default). diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py b/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py index 431362a..a187a2b 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Surface baseline wrapper: zeros on boundary VTP for pipeline / smoke tests.""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py index 1588d03..ecc4bea 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from physicsnemo.cfd.evaluation.inference.wrappers.transolver.wrapper import TransolverWrapper __all__ = ["TransolverWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py index ce271af..542822d 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Transolver model wrapper for surface or volume inference (TransolverDataPipe + VTK). Surface: cell-centered pressure + WSS (default). Volume: velocity + pressure + nut on VTU when diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py index 583a6b3..82d09bb 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Volume baseline wrapper: zeros (or shape-correct) predictions for pipeline tests.""" from __future__ import annotations diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py index 8a39f0d..44940e7 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from physicsnemo.cfd.evaluation.inference.wrappers.xmgn.wrapper import XMGNWrapper __all__ = ["XMGNWrapper"] diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py index 96f6c3e..f3aba0b 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/xmgn/wrapper.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """MeshGraphNet (xmgn) model wrapper for DrivAerML-style inference.""" import os diff --git a/physicsnemo/cfd/evaluation/metrics/__init__.py b/physicsnemo/cfd/evaluation/metrics/__init__.py index 2d4bc00..4b4334f 100644 --- a/physicsnemo/cfd/evaluation/metrics/__init__.py +++ b/physicsnemo/cfd/evaluation/metrics/__init__.py @@ -1,3 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Metric registry and bench-backed CFD metrics.""" from physicsnemo.cfd.evaluation.metrics.registry import ( diff --git a/test/ci_tests/test_evaluation.py b/test/ci_tests/test_evaluation.py index 20d01f2..9effc84 100644 --- a/test/ci_tests/test_evaluation.py +++ b/test/ci_tests/test_evaluation.py @@ -22,7 +22,9 @@ from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors from physicsnemo.cfd.evaluation.benchmarks.engine import _call_metric, _normalize_metrics_config -from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.benchmarks.report_plugins import _apply_default_case_ids_to_visuals +from physicsnemo.cfd.evaluation.benchmarks.engine import _retain_comparison_mesh_for_visual_context +from physicsnemo.cfd.evaluation.config import Config, OutputConfig, ReportsConfig from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase from physicsnemo.cfd.evaluation.metrics import get_metric, list_metrics from physicsnemo.cfd.evaluation.metrics.builtin.l2 import l2_pressure @@ -40,6 +42,32 @@ def test_normalize_metrics_config_strings_and_dicts() -> None: assert specs == [("l2_pressure", {}), ("drag_error", {"coeff": 1.5})] +def test_reports_visual_case_ids_and_mesh_retention() -> None: + rep_all = ReportsConfig(enabled=True, visuals=["field_comparison_surface"], visual_case_ids=None) + assert _retain_comparison_mesh_for_visual_context(rep_all, "run_1") is True + rep_sub = ReportsConfig( + enabled=True, + visuals=["field_comparison_surface"], + visual_case_ids=["run_1"], + ) + assert _retain_comparison_mesh_for_visual_context(rep_sub, "run_1") is True + assert _retain_comparison_mesh_for_visual_context(rep_sub, "run_99") is False + rep_off = ReportsConfig(enabled=False, visuals=["field_comparison_surface"], visual_case_ids=["run_1"]) + assert _retain_comparison_mesh_for_visual_context(rep_off, "run_1") is False + + +def test_apply_default_case_ids_to_visuals() -> None: + cfg = Config( + reports=ReportsConfig(visual_case_ids=["a", "b"], visuals=[], enabled=True), + ) + specs = [("line_plot", {"canonical_key": "pressure"}), ("line_plot", {"case_ids": ["c"], "canonical_key": "p"})] + out = _apply_default_case_ids_to_visuals(cfg, specs) + assert out[0][1]["case_ids"] == ["a", "b"] + assert out[1][1]["case_ids"] == ["c"] + cfg2 = Config(reports=ReportsConfig(visual_case_ids=None, visuals=[])) + assert _apply_default_case_ids_to_visuals(cfg2, specs) == specs + + def test_config_from_dict_merges_output_and_reports() -> None: cfg = Config.from_dict( { @@ -47,12 +75,17 @@ def test_config_from_dict_merges_output_and_reports() -> None: "output": { "ground_truth_mesh_field_names": {"pressure": "p_gt_custom"}, }, - "reports": {"enabled": True, "plugins": [{"kind": "stub"}]}, + "reports": { + "enabled": True, + "plugins": [{"kind": "stub"}], + "visual_case_ids": ["run_1"], + }, } ) assert cfg.output.ground_truth_mesh_field_names["pressure"] == "p_gt_custom" assert cfg.reports.enabled is True assert cfg.reports.plugins == [{"kind": "stub"}] + assert cfg.reports.visual_case_ids == ["run_1"] def test_list_metrics_includes_core_builtin() -> None: diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index b271077..24d8f32 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -132,6 +132,7 @@ reports: | `enabled` | If true, run registered visuals after metrics/tables. | | `save_comparison_meshes` | Save comparison VTK to disk. If false, mesh-based visuals still run from memory when the runner built a comparison mesh. | | `comparison_mesh_subdir` | Subfolder for `*_comparison.vtp|vtu`. | +| `visual_case_ids` | Optional. If set, only these case IDs are kept in RAM for mesh-backed PNGs; also becomes the default `case_ids` for any visual that omits it (per-visual `case_ids` overrides). Omit for legacy behavior (all cases in memory). | | `visuals` | Ordered list: string names or `{ name: ..., ...kwargs }`. | | `plugins` | Legacy manifest hooks (`ReportsConfig`). | diff --git a/workflows/evaluation_examples/evaluation_config.yaml b/workflows/evaluation_examples/evaluation_config.yaml index 5cd1653..0d570cc 100644 --- a/workflows/evaluation_examples/evaluation_config.yaml +++ b/workflows/evaluation_examples/evaluation_config.yaml @@ -65,23 +65,29 @@ reports: enabled: true save_comparison_meshes: false comparison_mesh_subdir: comparison_meshes + # Retain in-memory comparison meshes only for these cases (saves RAM on large sweeps). Also the + # default ``case_ids`` for any visual that omits ``case_ids`` (per-visual ``case_ids`` overrides). + visual_case_ids: ["run_1", "run_11"] visuals: - name: field_comparison_surface - case_ids: ["run_1"] canonical_keys: [pressure, shear_stress] view: xy # Surface comparison mesh (cell centers); shear_stress line uses |tau_w| vs plot_coord. - name: line_plot - case_ids: ["run_1", "run_11"] canonical_key: pressure plot_coord: x normalize_factor: 1.0 true_line_kwargs: { label: Ground truth } pred_line_kwargs: { label: Prediction } + - name: line_plot + canonical_key: shear_stress + plot_coord: x + normalize_factor: 1.0 + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } # Top/bottom centerline strips (same y-slice + z-clip as workflows/bench_example generate_surface_benchmarks). # Writes two PNGs per case: *_line_centerline_top_* and *_line_centerline_bottom_*. - name: line_plot_centerlines - case_ids: ["run_1", "run_11"] canonical_key: pressure plot_coord: x normalize_factor: 1.0 @@ -93,7 +99,6 @@ reports: pred_line_kwargs: { label: Prediction } # streamlines_comparison: volume uses velocity maps; surface (cell) uses canonical_key (e.g. shear_stress). - name: streamlines_comparison - case_ids: ["run_1", "run_11"] canonical_key: shear_stress view: xy # --- drag_error / lift_error populate drag_error_true|pred (Cd) and lift_error_true|pred (Cl) --- From 08166df263ac35b201d12fa7c92e898ae331504e Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Fri, 3 Apr 2026 12:00:50 -0700 Subject: [PATCH 07/25] adding caching capability and updating docstrings --- CHANGELOG.md | 2 + .../cfd/evaluation/benchmarks/engine.py | 322 +++++++++++++++-- .../evaluation/benchmarks/metrics_cache.py | 325 ++++++++++++++++++ .../cfd/evaluation/benchmarks/report.py | 29 +- .../evaluation/benchmarks/report_plugins.py | 66 +++- physicsnemo/cfd/evaluation/benchmarks/run.py | 6 +- physicsnemo/cfd/evaluation/config.py | 40 ++- test/ci_tests/test_metrics_cache.py | 137 ++++++++ workflows/evaluation_examples/README.md | 4 +- .../evaluation_config.yaml | 4 + 10 files changed, 882 insertions(+), 53 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/benchmarks/metrics_cache.py create mode 100644 test/ci_tests/test_metrics_cache.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 2093085..3210ae7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Added +- **Metrics cache:** optional `run.metrics_cache` (`enabled`, `path`) stores per-case scalar metrics on disk + so repeat benchmark runs can skip VTK I/O and inference for unchanged configs; visualization is unchanged. - `physicsnemo.cfd.evaluation`: config-driven inference and benchmarking with metrics delegated to `physicsnemo.cfd.bench` (L2, area-weighted L2, forces, continuity/momentum residual L2 chain). Mesh bridge attaches GT and prediction diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index 31b4de0..a1bcf64 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -14,15 +14,23 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Benchmark runner: config → model x dataset loop → metrics → results. - -When ``reports.enabled`` and ``reports.visuals`` are set, optionally writes comparison VTK (if -``reports.save_comparison_meshes``) and runs :func:`report_plugins.run_optional_report_plugins` with -in-memory meshes in ``context["comparison_meshes_by_run"]`` so PNGs avoid disk reads when possible. - -If ``reports.visual_case_ids`` is set, only those case IDs are stored in memory; omit it to retain -all cases (legacy behavior). Meshes for other cases may still be read from ``comparison_mesh_path`` -when ``save_comparison_meshes`` is true. +""" +Benchmark evaluation driver for config-driven model-by-dataset runs. + +Loads registered dataset adapters and model wrappers, evaluates configured +metrics per case, and aggregates per-metric means. Can write comparison VTK, +tabular reports (JSON/CSV/HTML), and PNG visuals from the report plugin +pipeline. + +When ``reports.enabled`` and ``reports.visuals`` are set, comparison meshes may +be written under ``reports.comparison_mesh_subdir`` and/or kept in memory for +plugins. Use ``reports.visual_case_ids`` to limit which cases retain meshes in +memory; other cases may still load from ``comparison_mesh_path`` on disk if +meshes were saved. + +When ``run.metrics_cache`` is enabled, a valid cache entry skips per-case VTK +load and inference for that case. The cache stores scalars only and does not +replace mesh or visualization workflows. """ from __future__ import annotations @@ -32,6 +40,15 @@ from pathlib import Path from typing import Any +from physicsnemo.cfd.evaluation.benchmarks.metrics_cache import ( + metrics_cache_file_path, + metrics_cache_fingerprint, + metrics_from_cache_json, + output_config_to_fingerprint_dict, + read_metrics_cache, + resolve_metrics_cache_root, + write_metrics_cache, +) from physicsnemo.cfd.evaluation.benchmarks.report import write_report from physicsnemo.cfd.evaluation.config import ( Config, @@ -51,7 +68,21 @@ def _retain_comparison_mesh_for_visual_context(reports: ReportsConfig | None, case_id: str) -> bool: - """Whether to store this case's comparison mesh in ``mesh_ctx`` for report visuals.""" + """ + Determine if the comparison mesh for this case should be kept for report visuals. + + Parameters + ---------- + reports : ReportsConfig or None + Report configuration (must be enabled with visuals for retention). + case_id : str + Case identifier. + + Returns + ------- + bool + True if ``mesh_ctx`` should hold this case's comparison mesh. + """ if reports is None or not reports.enabled or not reports.visuals: return False allow = reports.visual_case_ids @@ -61,7 +92,24 @@ def _retain_comparison_mesh_for_visual_context(reports: ReportsConfig | None, ca def _normalize_metrics_config(metrics: list[str] | list[dict]) -> list[tuple[str, dict]]: - """Return list of (metric_name, kwargs).""" + """ + Normalize the ``metrics`` config section to ``(name, kwargs)`` pairs. + + Parameters + ---------- + metrics : list + Strings or dicts with a ``"name"`` key. + + Returns + ------- + list of tuple + ``(metric_name, kwargs_dict)`` for each entry. + + Raises + ------ + ValueError + If an entry is not a string or a dict with ``name``. + """ out = [] for m in metrics: if isinstance(m, str): @@ -76,6 +124,19 @@ def _normalize_metrics_config(metrics: list[str] | list[dict]) -> list[tuple[str def _effective_inference_domain(model_config: ModelConfig) -> str: + """ + Return ``surface`` or ``volume`` for this model, using registry default if unset. + + Parameters + ---------- + model_config : ModelConfig + Model configuration (optional ``inference_domain`` override). + + Returns + ------- + str + Either ``"surface"`` or ``"volume"``. + """ dom = model_config.inference_domain if dom in ("surface", "volume"): return dom @@ -94,7 +155,33 @@ def _save_inference_mesh_if_requested( output_dir: str, dataset_name: str, ) -> None: - """Write ``inference__.vtp|vtu`` (predictions only) when ``run.save_inference_mesh``.""" + """ + Write ``inference__.vtp`` or ``.vtu`` when requested. + + Predictions are written under VTK names from ``output_config``; ground truth + is not required for this file. + + Parameters + ---------- + run_config : RunConfig + Must have ``save_inference_mesh`` True to write. + model_config : ModelConfig + Model name and domain. + output_config : OutputConfig + Mesh field name maps for surface or volume. + wrapper : object + Loaded model wrapper (``output_location`` selects point vs cell data). + case : object + Case with ``mesh_path`` and ``inference_domain``. + case_id : str + Case identifier for the filename. + predictions : dict + Decoded prediction arrays by canonical key. + output_dir : str + Benchmark output directory. + dataset_name : str + Name used in log messages. + """ if not run_config.save_inference_mesh: return import pyvista as pv @@ -138,7 +225,35 @@ def _call_metric( output: OutputConfig, mkwargs: dict[str, Any], ) -> Any: - """Call metric with extended kwargs; fall back for legacy (gt, pred, **mkwargs) only.""" + """ + Invoke a registered metric, passing extended kwargs when supported. + + Falls back to ``fn(gt, predictions, **mkwargs)`` for legacy signatures. + + Parameters + ---------- + fn : callable + Registered metric function. + gt : dict + Ground-truth fields. + predictions : dict + Model predictions. + case : object + Canonical case object from the adapter. + comparison_mesh : object or None + PyVista mesh with GT and prediction arrays, if built. + metric_dtype : str or None + Element dtype label for mesh-based metrics. + output : OutputConfig + Output / field name configuration. + mkwargs : dict + Per-metric kwargs from config. + + Returns + ------- + float or dict + Scalar metric or dict of sub-keys (expanded by the engine). + """ extended = dict(mkwargs) extended.update( case=case, @@ -165,7 +280,41 @@ def _run_single( reports: ReportsConfig | None = None, allow_skip_mismatch: bool = False, ) -> tuple[dict[str, Any], dict[str, Any]]: - """Run one model on one dataset; return results structure and optional in-memory comparison meshes.""" + """ + Run one model on one dataset: load cases, infer, compute metrics, aggregate means. + + Respects ``run.metrics_cache`` for per-case skips. Lazy-loads the model + wrapper on the first cache miss. + + Parameters + ---------- + model_config : ModelConfig + Model name, checkpoint, and kwargs. + dataset_config : DatasetConfig + Adapter name, root, split, and case list. + metric_names : list of tuple + Normalized ``(metric_name, kwargs)`` pairs. + device : str + Torch device string for inference. + output_dir : str + Directory for artifacts and optional meshes. + case_ids : list of str or None + Cases to run; ``None`` uses ``adapter.list_cases``. + output_config : OutputConfig + VTK field name mappings. + run_config : RunConfig + Device, inference mesh export, metrics cache, etc. + reports : ReportsConfig or None + Optional mesh save and visual retention policy. + allow_skip_mismatch : bool, optional + If True, return a skipped result when surface/volume domains disagree. + + Returns + ------- + tuple of (dict, dict) + Result dict with ``model``, ``dataset``, ``cases``, ``metrics``, ``per_case``, + and ``mesh_ctx`` mapping case id -> comparison mesh for visuals. + """ adapter_class = get_adapter(dataset_config.name) m_dom = _effective_inference_domain(model_config) d_dom = adapter_class.inference_domain_from_kwargs(dataset_config.kwargs) @@ -212,14 +361,33 @@ def _run_single( {}, ) - wrapper_class = get_model_wrapper(model_config.name) - wrapper = wrapper_class() - wrapper.load( - checkpoint_path=model_config.checkpoint, - stats_path=model_config.stats_path, - device=device, - **model_config.merged_kwargs_for_load(), + cache_root = resolve_metrics_cache_root( + enabled=run_config.metrics_cache.enabled, + path=run_config.metrics_cache.path, + output_dir=output_dir, ) + fingerprint: str | None = None + if cache_root is not None: + fingerprint = metrics_cache_fingerprint( + model_name=model_config.name, + model_checkpoint=model_config.checkpoint, + model_stats_path=model_config.stats_path, + model_kwargs=dict(model_config.kwargs), + model_inference_domain=model_config.inference_domain, + dataset_name=dataset_config.name, + dataset_root=dataset_config.root, + dataset_split=dataset_config.split, + dataset_kwargs_resolved=dict(dkwargs), + output_dict=output_config_to_fingerprint_dict(output_config), + metric_specs=metric_names, + ) + log_dataset( + dataset_config.name, + f"Metrics cache enabled under {cache_root} (fingerprint {fingerprint[:12]}…)…", + ) + + wrapper_class = get_model_wrapper(model_config.name) + wrapper = None per_case = [] all_metric_values: dict[str, list[float]] = {} @@ -231,6 +399,47 @@ def _run_single( f"(model {model_config.name!r})…", ) for cid in cases: + cache_file = ( + metrics_cache_file_path(cache_root, fingerprint, cid) + if cache_root is not None and fingerprint is not None + else None + ) + if cache_file is not None: + blob = read_metrics_cache(cache_file) + if ( + blob is not None + and blob.get("fingerprint") == fingerprint + and blob.get("model") == model_config.name + and blob.get("dataset") == dataset_config.name + and blob.get("case_id") == cid + ): + cached_metrics = metrics_from_cache_json(blob.get("metrics")) + if cached_metrics is not None: + for mkey, val in cached_metrics.items(): + all_metric_values.setdefault(mkey, []).append(val) + row_cb: dict[str, Any] = {"case_id": cid, "metrics": cached_metrics} + md_b = blob.get("metric_dtype") + if md_b: + row_cb["metric_dtype"] = md_b + cmp_b = blob.get("comparison_mesh_path") + if cmp_b: + row_cb["comparison_mesh_path"] = cmp_b + per_case.append(row_cb) + log_dataset( + dataset_config.name, + f"Metrics cache hit for case {cid!r} (skipped I/O and inference).", + ) + continue + + if wrapper is None: + wrapper = wrapper_class() + wrapper.load( + checkpoint_path=model_config.checkpoint, + stats_path=model_config.stats_path, + device=device, + **model_config.merged_kwargs_for_load(), + ) + log_dataset( dataset_config.name, f"Reading case {cid!r}…", @@ -309,6 +518,23 @@ def _run_single( if _retain_comparison_mesh_for_visual_context(reports, cid): mesh_ctx[cid] = comparison_mesh per_case.append(row) + if cache_file is not None and fingerprint is not None: + try: + write_metrics_cache( + cache_file, + fingerprint=fingerprint, + model=model_config.name, + dataset=dataset_config.name, + case_id=cid, + case_metrics=case_metrics, + metric_dtype=row.get("metric_dtype"), + comparison_mesh_path=row.get("comparison_mesh_path"), + ) + except OSError as ex: + log_dataset( + dataset_config.name, + f"Could not write metrics cache for case {cid!r}: {ex}", + ) # Aggregate (mean over cases) metrics_summary = {} @@ -332,7 +558,21 @@ def _case_ids_for_run( dataset_case_ids: list[str] | None, case_id_override: str | None, ) -> list[str] | None: - """If ``case_id_override`` is set, run only that case; else use dataset config.""" + """ + Resolve which case IDs to evaluate for one benchmark invocation. + + Parameters + ---------- + dataset_case_ids : list of str or None + Cases from dataset config (or ``None`` for “all cases” upstream). + case_id_override : str or None + CLI ``--case-id`` override. + + Returns + ------- + list of str or None + A single-element list if override is set; otherwise ``dataset_case_ids``. + """ if case_id_override: return [case_id_override] return dataset_case_ids @@ -343,9 +583,23 @@ def run_benchmark( *, case_id: str | None = None, ) -> list[dict[str, Any]]: - """Run benchmark from config (single or matrix mode). Returns list of run results. - - If ``case_id`` is set, only that case is evaluated (overrides ``dataset.case_ids``). + """ + Execute the benchmark from a loaded ``Config``. + + Writes JSON/CSV/HTML under ``run.output_dir``, optional artifacts, and runs + report plugins when configured. + + Parameters + ---------- + config : Config + Full evaluation configuration. + case_id : str or None, optional + If set, only this case is evaluated (overrides dataset case lists). + + Returns + ------- + list of dict + One result dict per model×dataset pair (or single pair in ``single`` mode). """ import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 import physicsnemo.cfd.evaluation.inference.wrappers # noqa: F401 @@ -444,7 +698,19 @@ def run_benchmark( def _config_to_dict(c: Config) -> dict: - """Serializable config for artifacts.""" + """ + Convert ``Config`` to a JSON-serializable dict for ``benchmark_artifacts.json``. + + Parameters + ---------- + c : Config + Active configuration. + + Returns + ------- + dict + Nested mapping suitable for ``json.dump``. + """ return { "run": { "device": c.run.device, @@ -452,6 +718,10 @@ def _config_to_dict(c: Config) -> dict: "seed": c.run.seed, "batch_size": c.run.batch_size, "save_inference_mesh": c.run.save_inference_mesh, + "metrics_cache": { + "enabled": c.run.metrics_cache.enabled, + "path": c.run.metrics_cache.path, + }, }, "model": { "name": c.model.name, diff --git a/physicsnemo/cfd/evaluation/benchmarks/metrics_cache.py b/physicsnemo/cfd/evaluation/benchmarks/metrics_cache.py new file mode 100644 index 0000000..92f6974 --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/metrics_cache.py @@ -0,0 +1,325 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +On-disk cache for benchmark metric scalars. + +Stores one JSON file per case containing float metrics only. Does not store +meshes, vector fields, or anything required for PNG/visual generation. +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +CACHE_FORMAT_VERSION = 1 + + +def _stable_json(obj: Any) -> str: + """Serialize ``obj`` to a stable JSON string for hashing (sorted keys).""" + return json.dumps(obj, sort_keys=True, separators=(",", ":"), default=str) + + +def metrics_cache_fingerprint( + *, + model_name: str, + model_checkpoint: str, + model_stats_path: str, + model_kwargs: dict[str, Any], + model_inference_domain: str | None, + dataset_name: str, + dataset_root: str, + dataset_split: str | None, + dataset_kwargs_resolved: dict[str, Any], + output_dict: dict[str, Any], + metric_specs: list[tuple[str, dict[str, Any]]], +) -> str: + """ + Build a SHA-256 fingerprint for cache lookup and invalidation. + + Any change to model, dataset, output field maps, or metric definitions + changes the digest so stale entries are not reused. + + Parameters + ---------- + model_name : str + Registered model name. + model_checkpoint : str + Checkpoint path. + model_stats_path : str + Normalization stats path. + model_kwargs : dict + Extra arguments passed to the model wrapper. + model_inference_domain : str or None + ``"surface"``, ``"volume"``, or ``None`` for registry default. + dataset_name : str + Registered dataset adapter name. + dataset_root : str + Dataset root directory. + dataset_split : str or None + Dataset split, if any. + dataset_kwargs_resolved : dict + Adapter kwargs after alignment for this model. + output_dict : dict + Serializable ``OutputConfig`` mapping (see ``output_config_to_fingerprint_dict``). + metric_specs : list of tuple + Normalized ``(metric_name, kwargs)`` pairs from the benchmark config. + + Returns + ------- + str + 64-character lowercase hex SHA-256 digest. + """ + specs_serializable: list[Any] = [] + for name, kw in sorted(metric_specs, key=lambda x: (x[0], tuple(sorted(x[1].keys())))): + specs_serializable.append([name, {k: kw[k] for k in sorted(kw.keys())}]) + payload = { + "fingerprint_schema": CACHE_FORMAT_VERSION, + "model": { + "name": model_name, + "checkpoint": model_checkpoint, + "stats_path": model_stats_path, + "kwargs": {k: model_kwargs[k] for k in sorted(model_kwargs.keys())}, + "inference_domain": model_inference_domain, + }, + "dataset": { + "name": dataset_name, + "root": dataset_root, + "split": dataset_split, + "kwargs": {k: dataset_kwargs_resolved[k] for k in sorted(dataset_kwargs_resolved.keys())}, + }, + "output": output_dict, + "metrics": specs_serializable, + } + return hashlib.sha256(_stable_json(payload).encode("utf-8")).hexdigest() + + +def case_id_cache_filename(case_id: str) -> str: + """ + Map a case id to a safe JSON filename. + + Parameters + ---------- + case_id : str + Dataset case identifier. + + Returns + ------- + str + Filename of the form ``".json"``. + """ + safe = "".join(ch if ch.isalnum() or ch in ("-", "_", ".") else "_" for ch in case_id) + return f"{safe}.json" + + +def metrics_cache_file_path(cache_root: Path, fingerprint_hex: str, case_id: str) -> Path: + """ + Path to the cache file for one case. + + Parameters + ---------- + cache_root : Path + Root metrics cache directory. + fingerprint_hex : str + Full fingerprint string (hex). + case_id : str + Case identifier. + + Returns + ------- + Path + ``cache_root / fingerprint_hex / .json``. + """ + return Path(cache_root) / fingerprint_hex / case_id_cache_filename(case_id) + + +def output_config_to_fingerprint_dict(output: Any) -> dict[str, Any]: + """ + Build an ordered dict of output field mappings for fingerprinting. + + Parameters + ---------- + output : OutputConfig + Evaluation output / VTK name mapping configuration. + + Returns + ------- + dict + Serializable mapping including mesh field names and streamlines key. + """ + return { + "mesh_field_names": {k: output.mesh_field_names[k] for k in sorted(output.mesh_field_names.keys())}, + "volume_mesh_field_names": { + k: output.volume_mesh_field_names[k] for k in sorted(output.volume_mesh_field_names.keys()) + }, + "ground_truth_mesh_field_names": { + k: output.ground_truth_mesh_field_names[k] + for k in sorted(output.ground_truth_mesh_field_names.keys()) + }, + "ground_truth_volume_mesh_field_names": { + k: output.ground_truth_volume_mesh_field_names[k] + for k in sorted(output.ground_truth_volume_mesh_field_names.keys()) + }, + "streamlines_vector_canonical": output.streamlines_vector_canonical, + } + + +def _metrics_to_jsonable(case_metrics: dict[str, float]) -> dict[str, Any]: + """Encode metric floats for JSON (NaN becomes null).""" + out: dict[str, Any] = {} + for k, v in case_metrics.items(): + if isinstance(v, float) and v != v: + out[k] = None + else: + out[k] = v + return out + + +def metrics_from_cache_json(metrics_obj: Any) -> dict[str, float] | None: + """ + Parse the ``metrics`` object from a cache JSON file. + + Parameters + ---------- + metrics_obj : Any + Object loaded from JSON; must be a dict with float-coercible values or nulls. + + Returns + ------- + dict or None + Metric name to float value, or ``None`` if parsing fails. + """ + if not isinstance(metrics_obj, dict): + return None + out: dict[str, float] = {} + for k, v in metrics_obj.items(): + if v is None: + out[str(k)] = float("nan") + else: + try: + out[str(k)] = float(v) + except (TypeError, ValueError): + return None + return out + + +def read_metrics_cache(path: Path) -> dict[str, Any] | None: + """ + Load and validate a cache file. + + Parameters + ---------- + path : Path + Path to a ``.json`` cache file. + + Returns + ------- + dict or None + Parsed payload if the file exists and matches the expected schema; otherwise ``None``. + """ + if not path.is_file(): + return None + try: + with open(path, encoding="utf-8") as f: + data = json.load(f) + except (json.JSONDecodeError, OSError): + return None + if not isinstance(data, dict): + return None + if data.get("cache_format_version") != CACHE_FORMAT_VERSION: + return None + if "fingerprint" not in data or "metrics" not in data: + return None + return data + + +def write_metrics_cache( + path: Path, + *, + fingerprint: str, + model: str, + dataset: str, + case_id: str, + case_metrics: dict[str, float], + metric_dtype: str | None = None, + comparison_mesh_path: str | None = None, +) -> None: + """ + Atomically write a per-case cache entry (via a temporary file and replace). + + Parameters + ---------- + path : Path + Destination cache path (see ``metrics_cache_file_path``). + fingerprint : str + Full hex fingerprint for validation on read. + model : str + Model name. + dataset : str + Dataset adapter name. + case_id : str + Case identifier. + case_metrics : dict + Scalar metric name -> value. + metric_dtype : str or None, optional + Dtype string recorded when the comparison mesh was built, if any. + comparison_mesh_path : str or None, optional + Path written when ``reports.save_comparison_meshes`` saved a mesh for this case. + """ + path.parent.mkdir(parents=True, exist_ok=True) + payload = { + "cache_format_version": CACHE_FORMAT_VERSION, + "fingerprint": fingerprint, + "model": model, + "dataset": dataset, + "case_id": case_id, + "metrics": _metrics_to_jsonable(case_metrics), + "metric_dtype": metric_dtype, + "comparison_mesh_path": comparison_mesh_path, + } + tmp = path.with_suffix(".tmp.json") + with open(tmp, "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + tmp.replace(path) + + +def resolve_metrics_cache_root(*, enabled: bool, path: str, output_dir: str) -> Path | None: + """ + Resolve the metrics cache root directory. + + Parameters + ---------- + enabled : bool + Whether caching is turned on in config. + path : str + User path, or empty to default under ``output_dir``. + output_dir : str + Benchmark ``run.output_dir`` (used when ``path`` is empty). + + Returns + ------- + Path or None + Absolute cache root, or ``None`` if caching is disabled. + """ + if not enabled: + return None + p = (path or "").strip() + if not p: + return Path(output_dir).expanduser().resolve() / ".metrics_cache" + return Path(p).expanduser().resolve() diff --git a/physicsnemo/cfd/evaluation/benchmarks/report.py b/physicsnemo/cfd/evaluation/benchmarks/report.py index 02130bc..dabdf4a 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report.py @@ -14,7 +14,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Aggregate results and export JSON/CSV/HTML reports.""" +""" +Aggregate benchmark results and export tabular reports. + +Writes ``benchmark_results.{json,csv,html}`` under the run output directory. +""" import csv import json @@ -29,12 +33,22 @@ def write_report( output_dir: str | Path, formats: list[str] | None = None, ) -> None: - """Write benchmark results to one or more formats. + """ + Write benchmark results in the requested formats. + + Parameters + ---------- + results : list of dict + One entry per model×dataset from ``run_benchmark`` (with ``per_case``, ``metrics``, etc.). + output_dir : str or Path + Directory for output files. + formats : list of str or None + Subset of ``"json"``, ``"csv"``, ``"html"``. Default is all three. - Args: - results: List of run result dicts (from run_benchmark). - output_dir: Directory to write files into. - formats: One or more of "json", "csv", "html". Default: all three. + Raises + ------ + ValueError + If an unknown format name is given. """ output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) @@ -56,6 +70,7 @@ def write_report( def _write_json(results: list[dict], output_dir: Path) -> None: + """Write ``benchmark_results.json``.""" path = output_dir / "benchmark_results.json" log_dataset("benchmark", f"Writing {path}…") with open(path, "w") as f: @@ -63,6 +78,7 @@ def _write_json(results: list[dict], output_dir: Path) -> None: def _write_csv(results: list[dict], output_dir: Path) -> None: + """Write long-form ``benchmark_results.csv`` (per-case and summary rows).""" path = output_dir / "benchmark_results.csv" log_dataset("benchmark", f"Writing {path}…") rows = [] @@ -96,6 +112,7 @@ def _write_csv(results: list[dict], output_dir: Path) -> None: def _write_html(results: list[dict], output_dir: Path) -> None: + """Write a simple HTML summary with tables of metrics and per-case values.""" path = output_dir / "benchmark_results.html" log_dataset("benchmark", f"Writing {path}…") lines = [ diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py index e437814..5744bdb 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -2,13 +2,13 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Optional visuals (plots via ``physicsnemo.cfd.bench.visualization``). +""" +Optional PNG report visuals using ``physicsnemo.cfd.bench.visualization``. -The **inference** and **benchmark** CLIs call :func:`run_optional_report_plugins` when -``reports.enabled`` and ``reports.visuals`` are set. Pass optional ``context`` with -``comparison_meshes_by_run`` (list of ``{case_id: pyvista.DataSet}`` aligned with ``results``) so -built-in mesh visuals can avoid reading large VTU/VTP from disk. You can also call this from your own -script when you have ``results`` and want a manifest. +Benchmark and inference entrypoints call ``run_optional_report_plugins`` when +``reports.enabled`` and ``reports.visuals`` are set. Optional ``context`` (see +that function) can supply in-memory comparison meshes so plugins avoid reading +large VTU/VTP files from disk. """ from __future__ import annotations @@ -27,7 +27,21 @@ def _apply_default_case_ids_to_visuals( config: Config, specs: list[tuple[str, dict[str, Any]]], ) -> list[tuple[str, dict[str, Any]]]: - """Inject ``reports.visual_case_ids`` as ``case_ids`` when a visual omits it.""" + """ + Use ``reports.visual_case_ids`` as the default ``case_ids`` for each visual spec. + + Parameters + ---------- + config : Config + Root configuration (reads ``reports.visual_case_ids``). + specs : list of tuple + Normalized ``(visual_name, kwargs)`` pairs. + + Returns + ------- + list of tuple + Specs with ``case_ids`` filled in when absent and a default list exists. + """ default = config.reports.visual_case_ids if default is None: return specs @@ -47,17 +61,32 @@ def run_optional_report_plugins( *, context: dict[str, Any] | None = None, ) -> None: - """Run registered visuals when ``config.reports.enabled``; write manifest. - - ``context`` is optional runtime state (not serialized to JSON). Supported keys: - - - ``comparison_meshes_by_run``: ``list[dict[str, Any]]`` with the same length as ``results``; - each inner dict maps ``case_id`` to an in-memory comparison mesh (PyVista dataset) so visuals - can skip ``pv.read(comparison_mesh_path)``. When ``config.reports.visual_case_ids`` is set, - only those case IDs are present per run (others may load from ``comparison_mesh_path`` if saved). - - If ``config.reports.visual_case_ids`` is set, visuals that do not specify ``case_ids`` receive - that list as their default filter (per-visual ``case_ids`` still overrides). + """ + Run registered report visuals and write ``report_plugins_manifest.json``. + + Parameters + ---------- + config : Config + Must have ``reports.enabled`` and a non-empty ``reports.visuals`` list to run plugins. + results : list of dict + Benchmark results (same structure as ``run_benchmark`` output). + output_dir : str + Directory for the manifest and plot outputs. + context : dict or None, optional + Runtime-only data not written to the manifest beyond key names. + + Supported keys: + + - ``comparison_meshes_by_run``: list of dict, same length as ``results``; + each dict maps ``case_id`` to an in-memory PyVista comparison mesh so + mesh-based visuals can avoid ``pv.read(comparison_mesh_path)``. If + ``reports.visual_case_ids`` is set, only those IDs appear per run; + other cases may still load from disk when comparison meshes were saved. + + Notes + ----- + Visuals that omit ``case_ids`` receive ``reports.visual_case_ids`` as the + default filter when that list is set; per-visual ``case_ids`` overrides. """ out_dir = Path(output_dir) manifest: dict[str, Any] = { @@ -109,6 +138,7 @@ def run_optional_report_plugins( def _write_manifest(out_dir: Path, manifest: dict[str, Any]) -> None: + """Persist plugin run metadata to ``report_plugins_manifest.json``.""" out = out_dir / "report_plugins_manifest.json" log_dataset("benchmark", f"Writing report plugin manifest to {out}…") with open(out, "w") as f: diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py index e0bd146..42a4d40 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/run.py +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -14,7 +14,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""CLI entrypoint for benchmark runs.""" +""" +Command-line entrypoint for benchmark runs. + +Loads YAML/JSON config, applies overrides, and calls ``run_benchmark``. +""" import argparse diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index 05668e4..84ab950 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -38,6 +38,27 @@ def deep_merge_dict(base: dict[str, Any], overlay: dict[str, Any]) -> dict[str, return result +@dataclass +class MetricsCacheConfig: + """ + Optional on-disk cache of per-case scalar metrics from benchmark runs. + + When enabled, cases with a matching fingerprint skip reading VTK, running + inference, and recomputing metrics. Meshes and report plots are not cached. + + Attributes + ---------- + enabled : bool + Whether to read and write metric cache files. + path : str + Cache root directory. If empty while ``enabled`` is True, defaults to + ``/.metrics_cache``. + """ + + enabled: bool = False + path: str = "" + + @dataclass class RunConfig: device: str = "cuda:0" @@ -46,6 +67,7 @@ class RunConfig: batch_size: int = 1 #: If False, inference CLI skips writing ``inference__.vtp|vtu`` (comparison mesh / visuals unchanged). save_inference_mesh: bool = True + metrics_cache: MetricsCacheConfig = field(default_factory=MetricsCacheConfig) @dataclass @@ -169,7 +191,23 @@ class Config: @classmethod def from_dict(cls, data: dict[str, Any]) -> "Config": """Build Config from a nested dict (e.g. from YAML/JSON).""" - run = RunConfig(**(data.get("run") or {})) + run_raw = dict(data.get("run") or {}) + mc_raw = run_raw.pop("metrics_cache", None) + if mc_raw is None: + mc_raw = {} + elif not isinstance(mc_raw, dict): + raise TypeError("run.metrics_cache must be a mapping if provided") + run = RunConfig( + device=str(run_raw.get("device", "cuda:0")), + output_dir=str(run_raw.get("output_dir", "benchmark_results")), + seed=int(run_raw.get("seed", 42)), + batch_size=int(run_raw.get("batch_size", 1)), + save_inference_mesh=bool(run_raw.get("save_inference_mesh", True)), + metrics_cache=MetricsCacheConfig( + enabled=bool(mc_raw.get("enabled", False)), + path=str(mc_raw.get("path") or ""), + ), + ) model = ModelConfig(**(data.get("model") or {})) dataset = DatasetConfig(**(data.get("dataset") or {})) out = data.get("output") or {} diff --git a/test/ci_tests/test_metrics_cache.py b/test/ci_tests/test_metrics_cache.py new file mode 100644 index 0000000..2a6ec5a --- /dev/null +++ b/test/ci_tests/test_metrics_cache.py @@ -0,0 +1,137 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Unit tests for benchmark metrics cache (no heavy dependencies).""" + +from __future__ import annotations + +import math +from pathlib import Path + +import pytest + +from physicsnemo.cfd.evaluation.benchmarks.metrics_cache import ( + case_id_cache_filename, + metrics_cache_fingerprint, + metrics_cache_file_path, + metrics_from_cache_json, + output_config_to_fingerprint_dict, + read_metrics_cache, + resolve_metrics_cache_root, + write_metrics_cache, +) +from physicsnemo.cfd.evaluation.config import Config, OutputConfig + + +def test_resolve_metrics_cache_root_disabled() -> None: + assert resolve_metrics_cache_root(enabled=False, path="", output_dir="/tmp/out") is None + + +def test_resolve_metrics_cache_default_dir(tmp_path: Path) -> None: + root = resolve_metrics_cache_root(enabled=True, path="", output_dir=str(tmp_path / "results")) + assert root is not None + assert root.name == ".metrics_cache" + assert root.parent.name == "results" + + +def test_resolve_metrics_cache_explicit_path(tmp_path: Path) -> None: + p = tmp_path / "my_cache" + root = resolve_metrics_cache_root(enabled=True, path=str(p), output_dir=str(tmp_path)) + assert root == p.resolve() + + +def test_fingerprint_changes_with_checkpoint() -> None: + base = dict( + model_name="m", + model_checkpoint="a.pt", + model_stats_path="", + model_kwargs={}, + model_inference_domain=None, + dataset_name="d", + dataset_root="/data", + dataset_split=None, + dataset_kwargs_resolved={}, + output_dict=output_config_to_fingerprint_dict(OutputConfig()), + metric_specs=[("l2_pressure", {})], + ) + fp1 = metrics_cache_fingerprint(**base) + fp2 = metrics_cache_fingerprint(**{**base, "model_checkpoint": "b.pt"}) + assert fp1 != fp2 + fp_same = metrics_cache_fingerprint(**base) + assert fp1 == fp_same + + +def test_fingerprint_metric_spec_kwargs_order() -> None: + out = output_config_to_fingerprint_dict(OutputConfig()) + base = dict( + model_name="m", + model_checkpoint="c.pt", + model_stats_path="", + model_kwargs={}, + model_inference_domain="surface", + dataset_name="d", + dataset_root="/r", + dataset_split=None, + dataset_kwargs_resolved={"xyz": 1}, + output_dict=out, + metric_specs=[("l2_pressure", {"foo": 1, "bar": 2})], + ) + fp_a = metrics_cache_fingerprint(**base) + fp_b = metrics_cache_fingerprint(**{**base, "metric_specs": [("l2_pressure", {"bar": 2, "foo": 1})]}) + assert fp_a == fp_b + + +def test_write_read_roundtrip(tmp_path: Path) -> None: + fp = "a" * 64 + path = metrics_cache_file_path(tmp_path / "root", fp, "run_1") + write_metrics_cache( + path, + fingerprint=fp, + model="geotransolver", + dataset="drivaerml", + case_id="run_1", + case_metrics={"l2_pressure": 0.5, "l2_shear_stress": float("nan")}, + metric_dtype="float64", + comparison_mesh_path="/abs/mesh.vtp", + ) + blob = read_metrics_cache(path) + assert blob is not None + assert blob["fingerprint"] == fp + m = metrics_from_cache_json(blob["metrics"]) + assert m is not None + assert m["l2_pressure"] == 0.5 + assert math.isnan(m["l2_shear_stress"]) + + +def test_read_missing_returns_none(tmp_path: Path) -> None: + assert read_metrics_cache(tmp_path / "nope.json") is None + + +def test_read_bad_json_returns_none(tmp_path: Path) -> None: + p = tmp_path / "bad.json" + p.write_text("not json {", encoding="utf-8") + assert read_metrics_cache(p) is None + + +@pytest.mark.parametrize( + "cid, expected", + [ + ("run_1", "run_1.json"), + ("a/b", "a_b.json"), + ], +) +def test_case_id_cache_filename(cid: str, expected: str) -> None: + assert case_id_cache_filename(cid) == expected + + +def test_config_from_dict_metrics_cache() -> None: + cfg = Config.from_dict( + { + "run": { + "metrics_cache": {"enabled": True, "path": "/tmp/mc"}, + }, + } + ) + assert cfg.run.metrics_cache.enabled is True + assert cfg.run.metrics_cache.path == "/tmp/mc" diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index 24d8f32..36713c5 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -38,7 +38,7 @@ Keys are the same whether they come from one file or base + overlay. | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (if `true`, benchmark writes `inference__.vtp\|vtu` per case) | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (if `true`, benchmark writes `inference__.vtp\|vtu` per case), optional **`metrics_cache`** (see below) | | **model** | `name` (registered wrapper), `checkpoint`, `stats_path`, optional `inference_domain` (`surface` \| `volume`), `kwargs` | | **dataset** | `name` (registered adapter), `root`, `split`, `case_ids` (`null` = all), `kwargs` (e.g. `align_ground_truth_to_model`, `inference_domain` for volume) | | **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume); optional **`streamlines_vector_canonical`** (default `velocity`) for `streamlines_comparison` | @@ -46,6 +46,8 @@ Keys are the same whether they come from one file or base + overlay. | **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | | **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | +**Metrics cache (optional):** Under `run.metrics_cache`, set `enabled: true` to store per-case metric scalars under `path` (or under `/.metrics_cache` if `path` is empty). When the fingerprint matches (model checkpoint/stats, dataset root and kwargs, `output` field maps, and `metrics` list), that case skips reading VTK, inference, and metric recomputation. **Plots and meshes are not cached**; cached rows do not populate in-memory comparison meshes for `reports.visuals`. For a clean recompute, delete the cache directory or set `enabled: false`. + **Model notes:** GeoTransolver / Transolver on **volume** need `model.inference_domain: volume` and stats that match volume training (`global_stats.json` with `velocity`, `pressure_volume`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** GeoTransolver/Transolver needs surface stats (`pressure` + `shear_stress` in `global_stats.json` or `surface_fields_normalization.npz`). DoMINO uses `domino_config` for volume. **Custom code:** [Custom models](#custom-models-adding-a-new-wrapper) · [Custom datasets](#custom-datasets-adding-a-new-adapter) · [Baseline stubs](#baseline-model-stubs-surface_baseline-volume_baseline) diff --git a/workflows/evaluation_examples/evaluation_config.yaml b/workflows/evaluation_examples/evaluation_config.yaml index 0d570cc..d635004 100644 --- a/workflows/evaluation_examples/evaluation_config.yaml +++ b/workflows/evaluation_examples/evaluation_config.yaml @@ -11,6 +11,10 @@ run: seed: 42 batch_size: 1 save_inference_mesh: false # false = skip writing inference__.vtp|vtu + # Optional: cache per-case metric scalars to skip VTK read + inference on repeat runs. Not used for PNG/VTK visuals. + metrics_cache: + enabled: false + path: "" # empty = /.metrics_cache; or set an absolute directory (delete that folder for a full recompute) # Default model (template for matrix rows when `benchmark.mode: matrix`; ignored as a pair when using lists). # Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. From f3ba6d55b0f5ddffe3118196cb7ef57df0921ca3 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Mon, 6 Apr 2026 06:08:18 -0700 Subject: [PATCH 08/25] refactoring code --- CHANGELOG.md | 29 +-- README.md | 4 +- .../evaluation/benchmarks/report_plugins.py | 2 +- physicsnemo/cfd/evaluation/benchmarks/run.py | 2 +- physicsnemo/cfd/evaluation/inference/run.py | 6 +- .../evaluation/metrics/builtin/__init__.py | 2 +- .../cfd/evaluation/metrics/builtin/forces.py | 6 +- .../cfd/evaluation/metrics/builtin/l2.py | 6 +- .../cfd/evaluation/metrics/builtin/physics.py | 8 +- .../cfd/evaluation/metrics/mesh_bridge.py | 2 +- .../cfd/evaluation/metrics/registry.py | 2 +- .../cfd/evaluation/reports/__init__.py | 2 +- .../reports/builtin/design_plots.py | 4 +- .../cfd/evaluation/reports/builtin/hexbin.py | 2 +- .../evaluation/reports/builtin/line_plot.py | 4 +- .../reports/builtin/streamlines_visual.py | 4 +- .../reports/builtin/surface_volume.py | 4 +- .../__init__.py | 4 +- .../geometry/distance.py | 0 .../interpolation/interpolate_mesh_to_pc.py | 0 .../metric_registry.py | 0 .../metrics/aero_forces.py | 0 .../metrics/frontal_area.py | 0 .../metrics/l2_errors.py | 0 .../metrics/physics.py | 0 .../metrics/streamlines.py | 0 .../visualization/utils.py | 0 pyproject.toml | 1 + test/ci_tests/test_evaluation.py | 2 +- workflows/bench_example/README.md | 2 +- .../generate_surface_benchmarks.py | 2 +- .../generate_volume_benchmarks.py | 2 +- .../benchmarking_in_absence_of_gt.ipynb | 10 +- .../notebooks/surface_benchmarking.ipynb | 20 +-- .../notebooks/volume_benchmarking.ipynb | 14 +- workflows/bench_example/utils.py | 12 +- workflows/evaluation_examples/README.md | 170 ++++++++---------- .../conf/config_surface.yaml | 113 ++++++++++++ .../conf/config_volume.yaml | 93 ++++++++++ .../evaluation_config.yaml | 148 --------------- workflows/evaluation_examples/main.py | 73 ++++++++ 41 files changed, 434 insertions(+), 321 deletions(-) rename physicsnemo/cfd/{bench => postprocessing_tools}/__init__.py (61%) rename physicsnemo/cfd/{bench => postprocessing_tools}/geometry/distance.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/interpolation/interpolate_mesh_to_pc.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metric_registry.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metrics/aero_forces.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metrics/frontal_area.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metrics/l2_errors.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metrics/physics.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/metrics/streamlines.py (100%) rename physicsnemo/cfd/{bench => postprocessing_tools}/visualization/utils.py (100%) create mode 100644 workflows/evaluation_examples/conf/config_surface.yaml create mode 100644 workflows/evaluation_examples/conf/config_volume.yaml delete mode 100644 workflows/evaluation_examples/evaluation_config.yaml create mode 100644 workflows/evaluation_examples/main.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 3210ae7..cd48b9a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,38 +11,45 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Added +- **Hydra workflow** for evaluation examples: `workflows/evaluation_examples/main.py` with + `conf/config_surface.yaml` (default) and `conf/config_volume.yaml` (`hydra-core` dependency), consistent + with other workflows (see **Removed** for dropped flat example YAML). - **Metrics cache:** optional `run.metrics_cache` (`enabled`, `path`) stores per-case scalar metrics on disk so repeat benchmark runs can skip VTK I/O and inference for unchanged configs; visualization is unchanged. - `physicsnemo.cfd.evaluation`: config-driven inference and benchmarking with - metrics delegated to `physicsnemo.cfd.bench` (L2, area-weighted L2, forces, + metrics delegated to `physicsnemo.cfd.postprocessing_tools` (L2, area-weighted L2, forces, continuity/momentum residual L2 chain). Mesh bridge attaches GT and prediction fields using explicit VTK names from config (`output.mesh_field_names`, `output.ground_truth_mesh_field_names`, and volume equivalents). -- `physicsnemo.cfd.bench.metric_registry` for named metrics shared with evaluation. -- Example YAML under `workflows/evaluation_examples/`. -- CLI: `python -m physicsnemo.cfd.evaluation.benchmarks` (canonical evaluation entrypoint) and - `python -m physicsnemo.cfd.evaluation.inference` (compatibility wrapper forwarding to benchmarks). +- `physicsnemo.cfd.postprocessing_tools.metric_registry` for named metrics shared with evaluation. +- Example Hydra configs under `workflows/evaluation_examples/conf/`. +- Library CLIs remain for scripting: `python -m physicsnemo.cfd.evaluation.benchmarks` / + `python -m physicsnemo.cfd.evaluation.inference` (flat YAML path, no `${...}` interpolation). ### Changed +- **Breaking:** `physicsnemo.cfd.bench` was renamed to **`physicsnemo.cfd.postprocessing_tools`** + (same submodules: `metrics`, `visualization`, `geometry`, `interpolation`, `metric_registry`). - **`reports.visual_case_ids`**: optional list limiting which cases get an in-memory comparison mesh for PNG visuals (default: all cases). Same list is the default **`case_ids`** for visuals that omit it; per-visual **`case_ids`** still overrides. - DoMINO NIM helper `call_domino_nim` and `launch_local_domino_nim.sh` live under **`physicsnemo.cfd.evaluation.nims`** (removed top-level **`physicsnemo.cfd.inference`** package). -- Benchmark metric registration for evaluation now uses bench-backed built-ins +- Benchmark metric registration for evaluation now uses postprocessing_tools-backed built-ins under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated NumPy-only helpers. -- Evaluation examples use **`evaluation_config.yaml`** and **`benchmarks.run`** as the primary - workflow; `evaluation.inference` forwards to the benchmark engine; the benchmark runner can write - **`inference__.vtp|vtu`** when **`run.save_inference_mesh`** is true. -- Matrix benchmark settings live in the same **`evaluation_config.yaml`** (commented template); the - separate **`benchmark_matrix.yaml`** overlay was removed from the examples folder. +- Evaluation **workflow docs** center on **`workflows/evaluation_examples/main.py`** (Hydra) and + **`conf/config_surface.yaml`** / **`conf/config_volume.yaml`**; `evaluation.inference` still forwards + to the benchmark engine; **`inference__.vtp|vtu`** when **`run.save_inference_mesh`** is true. +- Matrix benchmark settings are edited in the **`conf/*.yaml`** files; the old **`benchmark_matrix.yaml`** + overlay was removed earlier from the examples folder. ### Deprecated ### Removed +- **`workflows/evaluation_examples/evaluation_config.yaml`** — evaluation examples are driven by Hydra **`conf/config_surface.yaml`** / **`config_volume.yaml`** and **`main.py`**. + ### Fixed - Fixed a bug in GPU kernel for gradient computation. diff --git a/README.md b/README.md index 08ecbc9..eb73709 100644 --- a/README.md +++ b/README.md @@ -40,8 +40,8 @@ from a Python interface, facilitating scalable deployment of trained models. metrics (for example, pointwise errors, integrated quantities, spectral metrics, PDE residuals). [Related publication](https://www.arxiv.org/abs/2507.10747) - The `physicsnemo.cfd.evaluation` package runs config-driven inference and - uses the same `physicsnemo.cfd.bench` metric implementations as the workflow - scripts (see `workflows/evaluation_examples/`). + uses the same `physicsnemo.cfd.postprocessing_tools` metric implementations as the + **`workflows/evaluation_examples/`** workflow (run **`python main.py`** there; see that README). - Utilities to extend and build custom metrics, analyze, and visualize the results of trained ML models, both mesh-based and point-cloud based models diff --git a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py index 5744bdb..5304d0a 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py +++ b/physicsnemo/cfd/evaluation/benchmarks/report_plugins.py @@ -3,7 +3,7 @@ # SPDX-License-Identifier: Apache-2.0 """ -Optional PNG report visuals using ``physicsnemo.cfd.bench.visualization``. +Optional PNG report visuals using ``physicsnemo.cfd.postprocessing_tools.visualization``. Benchmark and inference entrypoints call ``run_optional_report_plugins`` when ``reports.enabled`` and ``reports.visuals`` are set. Optional ``context`` (see diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py index 42a4d40..d2e3718 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/run.py +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -46,7 +46,7 @@ def main() -> None: parser.add_argument( "--base-config", default=None, - help="Optional YAML merged first (e.g. evaluation_config.yaml); --config overlays it.", + help="Optional YAML merged first; --config overlays it.", ) parser.add_argument( "--case-id", diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py index 2393b00..3ca7718 100644 --- a/physicsnemo/cfd/evaluation/inference/run.py +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -40,8 +40,8 @@ def _first_case_id(config: Config) -> str | None: def main() -> None: parser = argparse.ArgumentParser( description=( - "Compatibility wrapper for the benchmark evaluation engine. Prefer: " - "python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml" + "Compatibility wrapper for the benchmark evaluation engine (flat YAML only). " + "For Hydra + OmegaConf, use workflows/evaluation_examples: python main.py" ), ) parser.add_argument("--config", required=True, help="Path to YAML/JSON config") @@ -66,7 +66,7 @@ def main() -> None: print( "[evaluation] physicsnemo.cfd.evaluation.inference forwards to the benchmark engine; " - "prefer: python -m physicsnemo.cfd.evaluation.benchmarks.run", + "prefer: workflows/evaluation_examples (python main.py) or benchmarks.run with flat YAML", file=sys.stderr, ) results = run_benchmark(config, case_id=case_id) diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py b/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py index cc68de0..b4366be 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/__init__.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Register built-in evaluation metrics backed by physicsnemo.cfd.bench.""" +"""Register built-in evaluation metrics backed by physicsnemo.cfd.postprocessing_tools.""" from physicsnemo.cfd.evaluation.metrics.builtin.forces import register_force_metrics from physicsnemo.cfd.evaluation.metrics.builtin.l2 import register_l2_metrics diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py index 8135301..23dea9f 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Force coefficient metrics via physicsnemo.cfd.bench.metrics.aero_forces. +"""Force coefficient metrics via physicsnemo.cfd.postprocessing_tools.metrics.aero_forces. Each metric returns a dict that expands in the benchmark engine to: @@ -18,8 +18,8 @@ from typing import Any -from physicsnemo.cfd.bench.metric_registry import register_metric -from physicsnemo.cfd.bench.metrics.aero_forces import compute_drag_and_lift +from physicsnemo.cfd.postprocessing_tools.metric_registry import register_metric +from physicsnemo.cfd.postprocessing_tools.metrics.aero_forces import compute_drag_and_lift from physicsnemo.cfd.evaluation.metrics.mesh_bridge import build_comparison_mesh diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/l2.py b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py index 98213bf..693168d 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/l2.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""L2-style metrics delegating to physicsnemo.cfd.bench.metrics.l2_errors.""" +"""L2-style metrics delegating to physicsnemo.cfd.postprocessing_tools.metrics.l2_errors.""" from __future__ import annotations @@ -10,8 +10,8 @@ import numpy as np -from physicsnemo.cfd.bench.metric_registry import register_metric -from physicsnemo.cfd.bench.metrics.l2_errors import ( +from physicsnemo.cfd.postprocessing_tools.metric_registry import register_metric +from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import ( compute_area_weighted_l2_errors, compute_l2_errors, ) diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/physics.py b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py index e3bcfcd..1bd3beb 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/physics.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Continuity and momentum residual metrics (volume) via physicsnemo.cfd.bench.metrics.physics.""" +"""Continuity and momentum residual metrics (volume) via physicsnemo.cfd.postprocessing_tools.metrics.physics.""" from __future__ import annotations @@ -10,9 +10,9 @@ import pyvista as pv -from physicsnemo.cfd.bench.metric_registry import register_metric -from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors -from physicsnemo.cfd.bench.metrics.physics import ( +from physicsnemo.cfd.postprocessing_tools.metric_registry import register_metric +from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors +from physicsnemo.cfd.postprocessing_tools.metrics.physics import ( compute_continuity_residuals, compute_momentum_residuals, ) diff --git a/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py index 660896c..745fc78 100644 --- a/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py +++ b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py @@ -63,7 +63,7 @@ def build_comparison_mesh( *, mesh_override: pv.DataSet | None = None, ) -> tuple[pv.DataSet, str]: - """Load case geometry and attach GT / prediction fields for ``physicsnemo.cfd.bench`` metrics. + """Load case geometry and attach GT / prediction fields for ``physicsnemo.cfd.postprocessing_tools`` metrics. Parameters ---------- diff --git a/physicsnemo/cfd/evaluation/metrics/registry.py b/physicsnemo/cfd/evaluation/metrics/registry.py index 9dfa66e..4b297a6 100644 --- a/physicsnemo/cfd/evaluation/metrics/registry.py +++ b/physicsnemo/cfd/evaluation/metrics/registry.py @@ -4,7 +4,7 @@ """Re-export bench metric registry for ``physicsnemo.cfd.evaluation``.""" -from physicsnemo.cfd.bench.metric_registry import ( # noqa: F401 +from physicsnemo.cfd.postprocessing_tools.metric_registry import ( # noqa: F401 get_metric, list_metrics, register_metric, diff --git a/physicsnemo/cfd/evaluation/reports/__init__.py b/physicsnemo/cfd/evaluation/reports/__init__.py index cbd1706..f767e7a 100644 --- a/physicsnemo/cfd/evaluation/reports/__init__.py +++ b/physicsnemo/cfd/evaluation/reports/__init__.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Report visuals registry and built-in plot hooks (``physicsnemo.cfd.bench.visualization``). +"""Report visuals registry and built-in plot hooks (``physicsnemo.cfd.postprocessing_tools.visualization``). Pass optional ``context`` with ``comparison_meshes_by_run`` into ``physicsnemo.cfd.evaluation.benchmarks.report_plugins.run_optional_report_plugins`` so mesh-based diff --git a/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py index 9387562..82ebc9d 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Design scatter / trend visuals wrapping ``physicsnemo.cfd.bench.visualization.utils``.""" +"""Design scatter / trend visuals wrapping ``physicsnemo.cfd.postprocessing_tools.visualization.utils``.""" from __future__ import annotations @@ -11,7 +11,7 @@ import matplotlib.pyplot as plt -from physicsnemo.cfd.bench.visualization.utils import plot_design_scatter, plot_design_trend +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_design_scatter, plot_design_trend from physicsnemo.cfd.evaluation.config import Config from physicsnemo.cfd.evaluation.datasets.progress import log_dataset from physicsnemo.cfd.evaluation.reports.registry import register_visual diff --git a/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py b/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py index 3ee66da..a859780 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/hexbin.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import pyvista as pv -from physicsnemo.cfd.bench.visualization.utils import plot_projections_hexbin +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_projections_hexbin from physicsnemo.cfd.evaluation.config import Config from physicsnemo.cfd.evaluation.datasets.progress import log_dataset from physicsnemo.cfd.evaluation.reports.registry import register_visual diff --git a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py index 2de493b..bc81074 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Line plot visual wrapping ``physicsnemo.cfd.bench.visualization.utils.plot_line``.""" +"""Line plot visual wrapping ``physicsnemo.cfd.postprocessing_tools.visualization.utils.plot_line``.""" from __future__ import annotations @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import pyvista as pv -from physicsnemo.cfd.bench.visualization.utils import plot_line +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line from physicsnemo.cfd.evaluation.config import Config, OutputConfig from physicsnemo.cfd.evaluation.datasets.progress import log_dataset from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case diff --git a/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py b/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py index b3f2cc1..7361ba2 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/streamlines_visual.py @@ -9,8 +9,8 @@ from pathlib import Path from typing import Any -from physicsnemo.cfd.bench.metrics.streamlines import compute_streamlines -from physicsnemo.cfd.bench.visualization.utils import plot_streamlines +from physicsnemo.cfd.postprocessing_tools.metrics.streamlines import compute_streamlines +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_streamlines from physicsnemo.cfd.evaluation.config import Config, OutputConfig from physicsnemo.cfd.evaluation.datasets.progress import log_dataset from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case diff --git a/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py index c763e4c..f5aafad 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/surface_volume.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Built-in visuals wrapping ``physicsnemo.cfd.bench.visualization.utils``.""" +"""Built-in visuals wrapping ``physicsnemo.cfd.postprocessing_tools.visualization.utils``.""" from __future__ import annotations @@ -11,7 +11,7 @@ import pyvista as pv -from physicsnemo.cfd.bench.visualization.utils import plot_field_comparisons, plot_fields +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons, plot_fields from physicsnemo.cfd.evaluation.config import Config, OutputConfig from physicsnemo.cfd.evaluation.datasets.progress import log_dataset from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case diff --git a/physicsnemo/cfd/bench/__init__.py b/physicsnemo/cfd/postprocessing_tools/__init__.py similarity index 61% rename from physicsnemo/cfd/bench/__init__.py rename to physicsnemo/cfd/postprocessing_tools/__init__.py index 489d5e0..4eb7c81 100644 --- a/physicsnemo/cfd/bench/__init__.py +++ b/physicsnemo/cfd/postprocessing_tools/__init__.py @@ -2,8 +2,8 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""CFD benchmarking metrics, geometry, and visualization.""" +"""Post-processing metrics, geometry, interpolation, and visualization utilities.""" -from physicsnemo.cfd.bench import metric_registry # noqa: F401 +from . import metric_registry # noqa: F401 __all__ = ["metric_registry"] diff --git a/physicsnemo/cfd/bench/geometry/distance.py b/physicsnemo/cfd/postprocessing_tools/geometry/distance.py similarity index 100% rename from physicsnemo/cfd/bench/geometry/distance.py rename to physicsnemo/cfd/postprocessing_tools/geometry/distance.py diff --git a/physicsnemo/cfd/bench/interpolation/interpolate_mesh_to_pc.py b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py similarity index 100% rename from physicsnemo/cfd/bench/interpolation/interpolate_mesh_to_pc.py rename to physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py diff --git a/physicsnemo/cfd/bench/metric_registry.py b/physicsnemo/cfd/postprocessing_tools/metric_registry.py similarity index 100% rename from physicsnemo/cfd/bench/metric_registry.py rename to physicsnemo/cfd/postprocessing_tools/metric_registry.py diff --git a/physicsnemo/cfd/bench/metrics/aero_forces.py b/physicsnemo/cfd/postprocessing_tools/metrics/aero_forces.py similarity index 100% rename from physicsnemo/cfd/bench/metrics/aero_forces.py rename to physicsnemo/cfd/postprocessing_tools/metrics/aero_forces.py diff --git a/physicsnemo/cfd/bench/metrics/frontal_area.py b/physicsnemo/cfd/postprocessing_tools/metrics/frontal_area.py similarity index 100% rename from physicsnemo/cfd/bench/metrics/frontal_area.py rename to physicsnemo/cfd/postprocessing_tools/metrics/frontal_area.py diff --git a/physicsnemo/cfd/bench/metrics/l2_errors.py b/physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py similarity index 100% rename from physicsnemo/cfd/bench/metrics/l2_errors.py rename to physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py diff --git a/physicsnemo/cfd/bench/metrics/physics.py b/physicsnemo/cfd/postprocessing_tools/metrics/physics.py similarity index 100% rename from physicsnemo/cfd/bench/metrics/physics.py rename to physicsnemo/cfd/postprocessing_tools/metrics/physics.py diff --git a/physicsnemo/cfd/bench/metrics/streamlines.py b/physicsnemo/cfd/postprocessing_tools/metrics/streamlines.py similarity index 100% rename from physicsnemo/cfd/bench/metrics/streamlines.py rename to physicsnemo/cfd/postprocessing_tools/metrics/streamlines.py diff --git a/physicsnemo/cfd/bench/visualization/utils.py b/physicsnemo/cfd/postprocessing_tools/visualization/utils.py similarity index 100% rename from physicsnemo/cfd/bench/visualization/utils.py rename to physicsnemo/cfd/postprocessing_tools/visualization/utils.py diff --git a/pyproject.toml b/pyproject.toml index 427cb95..9b1d4bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,7 @@ requires-python = ">=3.10" license = "Apache-2.0" dependencies = [ "httpx>=0.27.0", + "hydra-core>=1.3.0", "pyyaml>=6.0", "numpy>=1.22.4", "pyvista>=0.45.2", diff --git a/test/ci_tests/test_evaluation.py b/test/ci_tests/test_evaluation.py index 9effc84..67b1c32 100644 --- a/test/ci_tests/test_evaluation.py +++ b/test/ci_tests/test_evaluation.py @@ -20,7 +20,7 @@ import numpy as np import pyvista as pv -from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors +from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors from physicsnemo.cfd.evaluation.benchmarks.engine import _call_metric, _normalize_metrics_config from physicsnemo.cfd.evaluation.benchmarks.report_plugins import _apply_default_case_ids_to_visuals from physicsnemo.cfd.evaluation.benchmarks.engine import _retain_comparison_mesh_for_visual_context diff --git a/workflows/bench_example/README.md b/workflows/bench_example/README.md index f47d9ff..733807b 100644 --- a/workflows/bench_example/README.md +++ b/workflows/bench_example/README.md @@ -20,7 +20,7 @@ the understanding of several metrics and perform the comparisons with more flexibility and freedom. For **on-the-fly** evaluation (config-driven inference plus the same -`physicsnemo.cfd.bench` metrics as the scripts above), use the +`physicsnemo.cfd.postprocessing_tools` metrics as the scripts above), use the `physicsnemo.cfd.evaluation` package and the example YAML under [`workflows/evaluation_examples/`](../evaluation_examples/). The pre-baked `.vtp` / `.vtu` workflows in this directory remain the reference for diff --git a/workflows/bench_example/generate_surface_benchmarks.py b/workflows/bench_example/generate_surface_benchmarks.py index b1e4cff..7f8b8a6 100644 --- a/workflows/bench_example/generate_surface_benchmarks.py +++ b/workflows/bench_example/generate_surface_benchmarks.py @@ -27,7 +27,7 @@ import json from functools import partial -from physicsnemo.cfd.bench.visualization.utils import ( +from physicsnemo.cfd.postprocessing_tools.visualization.utils import ( plot_design_scatter, plot_design_trend, plot_line, diff --git a/workflows/bench_example/generate_volume_benchmarks.py b/workflows/bench_example/generate_volume_benchmarks.py index ff29488..53ef0cb 100644 --- a/workflows/bench_example/generate_volume_benchmarks.py +++ b/workflows/bench_example/generate_volume_benchmarks.py @@ -32,7 +32,7 @@ vtk.vtkObject.GlobalWarningDisplayOff() -from physicsnemo.cfd.bench.visualization.utils import plot_line, plot_fields +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line, plot_fields from utils import ( load_mapping, process_volume_results, diff --git a/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb b/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb index ab86e84..49248b9 100644 --- a/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb +++ b/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb @@ -251,7 +251,7 @@ "source": [ "import numpy as np\n", "import os, time\n", - "from physicsnemo.cfd.bench.interpolation.interpolate_mesh_to_pc import (\n", + "from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import (\n", " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", @@ -342,7 +342,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_fields\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", "\n", "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", "plotter = plot_fields(mesh, \n", @@ -392,7 +392,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_fields\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", "\n", "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", "plotter = plot_fields(mesh, \n", @@ -443,7 +443,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_fields\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", "\n", "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", "plotter = plot_fields(mesh, \n", @@ -737,7 +737,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.geometry.distance import chamfer_distance, hausdorff_distance\n", + "from physicsnemo.cfd.postprocessing_tools.geometry.distance import chamfer_distance, hausdorff_distance\n", "\n", "sedan_1_vs_aerosuv = chamfer_distance(sedan_1.points, aero_suv.points)\n", "sedan_2_vs_aerosuv = chamfer_distance(sedan_2.points, aero_suv.points)\n", diff --git a/workflows/bench_example/notebooks/surface_benchmarking.ipynb b/workflows/bench_example/notebooks/surface_benchmarking.ipynb index 08cdc30..5d81dc5 100644 --- a/workflows/bench_example/notebooks/surface_benchmarking.ipynb +++ b/workflows/bench_example/notebooks/surface_benchmarking.ipynb @@ -220,7 +220,7 @@ "source": [ "import numpy as np\n", "import time\n", - "from physicsnemo.cfd.bench.interpolation.interpolate_mesh_to_pc import (\n", + "from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import (\n", " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", @@ -300,7 +300,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors\n", "\n", "l2_errors = compute_l2_errors(\n", " mesh,\n", @@ -342,7 +342,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.l2_errors import compute_area_weighted_l2_errors\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_area_weighted_l2_errors\n", "\n", "area_weighted_l2_errors = compute_area_weighted_l2_errors(\n", " mesh,\n", @@ -383,7 +383,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.aero_forces import compute_drag_and_lift\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.aero_forces import compute_drag_and_lift\n", "\n", "forces_true = compute_drag_and_lift(\n", " mesh,\n", @@ -417,7 +417,7 @@ "id": "0a091429-cc27-476c-bcbf-d69addfeb682", "metadata": {}, "source": [ - "When computing drag and lift across many cases, you can use visualization utilities such as `physicsnemo.cfd.bench.visualization.utils.plot_design_scatter` and `physicsnemo.cfd.bench.visualization.utils.plot_design_trend` to compute metrics (e.g., R2 scores), compare design trends, and measure the ability of models to capture directional changes. The workflow scripts ([../generate_surface_benchmarks.py](../generate_surface_benchmarks.py) and [../generate_volume_benchmarks.py](../generate_volume_benchmarks.py)) use these functions to create plots like the examples below:\n", + "When computing drag and lift across many cases, you can use visualization utilities such as `physicsnemo.cfd.postprocessing_tools.visualization.utils.plot_design_scatter` and `physicsnemo.cfd.postprocessing_tools.visualization.utils.plot_design_trend` to compute metrics (e.g., R2 scores), compare design trends, and measure the ability of models to capture directional changes. The workflow scripts ([../generate_surface_benchmarks.py](../generate_surface_benchmarks.py) and [../generate_volume_benchmarks.py](../generate_volume_benchmarks.py)) use these functions to create plots like the examples below:\n", "\n", "![Sample R2 score plot](img/design_scatter_plot.png)\n", "\n", @@ -461,7 +461,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_field_comparisons\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons\n", "\n", "pv.start_xvfb() # might require `sudo apt install libgl1 libglx-mesa0 xvfb`\n", "\n", @@ -526,7 +526,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_line\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line\n", "\n", "# create a slice along xz plane\n", "centerline = mesh.slice(origin=(0, 0, 0), normal=(0, 1, 0))\n", @@ -563,7 +563,7 @@ "id": "35889e15-2649-4895-a56a-d980b5d5af7f", "metadata": {}, "source": [ - "Streamlines provide another way to visualize the flow fields and gain deeper insight into flow behavior. For surface results, we can create streamlines of wall shear stress. Below, we create streamlines using `physicsnemo.cfd.bench.metrics.streamlines.compute_streamlines` and overlay them on the geometry STL for clarity." + "Streamlines provide another way to visualize the flow fields and gain deeper insight into flow behavior. For surface results, we can create streamlines of wall shear stress. Below, we create streamlines using `physicsnemo.cfd.postprocessing_tools.metrics.streamlines.compute_streamlines` and overlay them on the geometry STL for clarity." ] }, { @@ -594,8 +594,8 @@ ], "source": [ "import numpy as np\n", - "from physicsnemo.cfd.bench.metrics.streamlines import compute_streamlines\n", - "from physicsnemo.cfd.bench.visualization.utils import plot_streamlines\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.streamlines import compute_streamlines\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_streamlines\n", "\n", "true_streamlines = compute_streamlines(mesh, field=\"wallShearStressMeanTrim\")\n", "pred_streamlines = compute_streamlines(mesh, field=\"wallShearStressMeanTrimPred\")\n", diff --git a/workflows/bench_example/notebooks/volume_benchmarking.ipynb b/workflows/bench_example/notebooks/volume_benchmarking.ipynb index abb3f61..039ac4e 100644 --- a/workflows/bench_example/notebooks/volume_benchmarking.ipynb +++ b/workflows/bench_example/notebooks/volume_benchmarking.ipynb @@ -269,7 +269,7 @@ "source": [ "import numpy as np\n", "import os, time\n", - "from physicsnemo.cfd.bench.interpolation.interpolate_mesh_to_pc import (\n", + "from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import (\n", " _create_nbrs_surface,\n", " _interpolate,\n", ")\n", @@ -387,7 +387,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.l2_errors import compute_l2_errors\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors\n", "\n", "l2_errors = compute_l2_errors(\n", " mesh,\n", @@ -425,7 +425,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.l2_errors import compute_error_vs_sdf\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_error_vs_sdf\n", "import matplotlib.pyplot as plt\n", "\n", "# determine query points for sdf\n", @@ -538,7 +538,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_field_comparisons\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons\n", "\n", "pv.start_xvfb()\n", "\n", @@ -698,7 +698,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.visualization.utils import plot_line\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line\n", "\n", "centerline_bottom = y_slice.slice(normal=\"z\", origin=(0, 0, -0.2376))\n", "\n", @@ -838,7 +838,7 @@ } ], "source": [ - "from physicsnemo.cfd.bench.metrics.physics import (\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.physics import (\n", " compute_continuity_residuals,\n", " compute_momentum_residuals,\n", ")\n", @@ -970,7 +970,7 @@ ], "source": [ "from physicsnemo.metrics.cae.integral import surface_integral\n", - "from physicsnemo.cfd.bench.interpolation.interpolate_mesh_to_pc import interpolate_mesh_to_pc\n", + "from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import interpolate_mesh_to_pc\n", "\n", "# Define a box smaller than the overall bounding box\n", "integral_box_bounds = [-2, 5, -1.5, 1.5, -0.2, 1.5]\n", diff --git a/workflows/bench_example/utils.py b/workflows/bench_example/utils.py index eab0bcc..6950310 100644 --- a/workflows/bench_example/utils.py +++ b/workflows/bench_example/utils.py @@ -22,20 +22,20 @@ import warnings import numpy as np -from physicsnemo.cfd.bench.metrics.aero_forces import compute_drag_and_lift -from physicsnemo.cfd.bench.metrics.l2_errors import ( +from physicsnemo.cfd.postprocessing_tools.metrics.aero_forces import compute_drag_and_lift +from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import ( compute_l2_errors, compute_area_weighted_l2_errors, ) -from physicsnemo.cfd.bench.metrics.streamlines import compute_streamlines -from physicsnemo.cfd.bench.interpolation.interpolate_mesh_to_pc import ( +from physicsnemo.cfd.postprocessing_tools.metrics.streamlines import compute_streamlines +from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import ( interpolate_mesh_to_pc, ) -from physicsnemo.cfd.bench.metrics.physics import ( +from physicsnemo.cfd.postprocessing_tools.metrics.physics import ( compute_continuity_residuals, compute_momentum_residuals, ) -from physicsnemo.cfd.bench.visualization.utils import ( +from physicsnemo.cfd.postprocessing_tools.visualization.utils import ( get_visible_point_indices, plot_field_comparisons, ) diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index 36713c5..d9a8ced 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -1,74 +1,87 @@ # Evaluation examples (PhysicsNeMo-CFD) -YAML and the **benchmark** CLI drive all evaluation: **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK (`run.save_inference_mesh`, `reports.save_comparison_meshes`). Everything is configured in **[`evaluation_config.yaml`](evaluation_config.yaml)** — `benchmark.mode: single` uses the top-level `model` and `dataset`; **`benchmark.mode: matrix`** uses `benchmark.models` × `benchmark.datasets` (see the commented template at the bottom of that file). Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then run commands **from this directory** (or pass absolute paths to `--config`). +This workflow runs **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK (`run.save_inference_mesh`, `reports.save_comparison_meshes`) using **[Hydra](https://hydra.cc/)** and **OmegaConf** — the same pattern as **`workflows/domino_design_sensitivities/`**. + +Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then use the commands below **from this directory** (`workflows/evaluation_examples/`). + +Configs live under **`conf/`**: **[`config_surface.yaml`](conf/config_surface.yaml)** (default) and **[`config_volume.yaml`](conf/config_volume.yaml)**. OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). --- -## Commands (canonical) +## Commands ```bash -# One config: metrics + tables + optional visuals (see evaluation_config.yaml) -python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml -python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml --case-id run_1 -# shorthand: -python -m physicsnemo.cfd.evaluation.benchmarks -``` +# Surface benchmark (VTP / wall metrics, default config) +python main.py -**Matrix:** edit [`evaluation_config.yaml`](evaluation_config.yaml) — set `benchmark.mode: matrix` and fill `benchmark.models` / `benchmark.datasets` (template is commented at the bottom of the file). Then run the same command as above. Incompatible surface/volume pairs are skipped. +# Volume benchmark (VTU / volume metrics) +python main.py --config-name=config_volume -**Optional second config:** `--base-config` then `--config` still works if you prefer splitting shared defaults from an overlay; one file is enough for most workflows. - -**Merge order** when using two files: `--base-config` first, then `--config`, then optional CLI overrides (`run.device=cpu`, or `--run.device=cuda:1`). +# Hydra overrides (examples) +python main.py case_id=run_1 run.device=cuda:0 +python main.py --config-name=config_volume run.output_dir=my_volume_run +``` -**Compatibility:** `python -m physicsnemo.cfd.evaluation.inference` forwards to the same benchmark engine (defaults to **one case** = first listed case when `--case-id` is omitted). Prefer the `benchmarks.run` command above. +**Matrix mode:** edit **`conf/config_surface.yaml`** or **`conf/config_volume.yaml`** — set `benchmark.mode: matrix` and fill `benchmark.models` / `benchmark.datasets`. Incompatible surface/volume pairs are skipped automatically. --- -## Config file +## Config files -| File | Use | -| ---- | --- | -| [`evaluation_config.yaml`](evaluation_config.yaml) | **Full evaluation config:** `run`, `model`, `dataset`, `output`, `metrics`, optional `reports`, and `benchmark` (`mode: single` or `mode: matrix` with `models` / `datasets`). Matrix example is in comments at the bottom of the file. | +| File | Role | +| ---- | ----- | +| [`conf/config_surface.yaml`](conf/config_surface.yaml) | Surface: **GeoTransolver** + drivaerml (`inference_domain: surface`), wall L2, Cd/Cl, surface visuals. `run.output_dir`: **`benchmark_results_surface`**. | +| [`conf/config_volume.yaml`](conf/config_volume.yaml) | Volume: **GeoTransolver** + drivaerml (`inference_domain: volume`), volume L2 + residuals, `plot_fields_volume`, velocity streamlines. `run.output_dir`: **`benchmark_results_volume`**. | --- -## Config reference (merged YAML) - -Keys are the same whether they come from one file or base + overlay. +## Config reference (YAML keys) | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`** (if `true`, benchmark writes `inference__.vtp\|vtu` per case), optional **`metrics_cache`** (see below) | -| **model** | `name` (registered wrapper), `checkpoint`, `stats_path`, optional `inference_domain` (`surface` \| `volume`), `kwargs` | -| **dataset** | `name` (registered adapter), `root`, `split`, `case_ids` (`null` = all), `kwargs` (e.g. `align_ground_truth_to_model`, `inference_domain` for volume) | -| **output** | `mesh_field_names`, `ground_truth_mesh_field_names` (surface); `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names` (volume); optional **`streamlines_vector_canonical`** (default `velocity`) for `streamlines_comparison` | -| **metrics** | List of metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`**, optional **`metrics_cache`** (see below) | +| **model** / **benchmark.models** | `name`, `checkpoint`, `stats_path`, `inference_domain` (`surface` \| `volume`), `kwargs` | +| **dataset** / **benchmark.datasets** | `name`, `root`, `split`, `case_ids` (`null` = all), `kwargs` | +| **output** | `mesh_field_names`, `ground_truth_mesh_field_names`; `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names`; **`streamlines_vector_canonical`** (default `velocity`) | +| **metrics** | Metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | | **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | | **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | -**Metrics cache (optional):** Under `run.metrics_cache`, set `enabled: true` to store per-case metric scalars under `path` (or under `/.metrics_cache` if `path` is empty). When the fingerprint matches (model checkpoint/stats, dataset root and kwargs, `output` field maps, and `metrics` list), that case skips reading VTK, inference, and metric recomputation. **Plots and meshes are not cached**; cached rows do not populate in-memory comparison meshes for `reports.visuals`. For a clean recompute, delete the cache directory or set `enabled: false`. +**`case_id`:** optional top-level key in the Hydra config (`null` = use dataset case list). Override on the CLI: `case_id=run_1`. + +**Metrics cache (optional):** Under `run.metrics_cache`, `enabled: true` stores per-case scalars under `path` (resolved; default pattern `${run.output_dir}/metrics_cache`). Delete that directory for a full recompute. **Plots and meshes are not cached.** -**Model notes:** GeoTransolver / Transolver on **volume** need `model.inference_domain: volume` and stats that match volume training (`global_stats.json` with `velocity`, `pressure_volume`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** GeoTransolver/Transolver needs surface stats (`pressure` + `shear_stress` in `global_stats.json` or `surface_fields_normalization.npz`). DoMINO uses `domino_config` for volume. +**Model notes:** GeoTransolver / Transolver **volume** needs matching stats (`global_stats.json` with `velocity`, `pressure_volume`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** needs surface stats. DoMINO uses `domino_config` for volume. **Custom code:** [Custom models](#custom-models-adding-a-new-wrapper) · [Custom datasets](#custom-datasets-adding-a-new-adapter) · [Baseline stubs](#baseline-model-stubs-surface_baseline-volume_baseline) --- +## Advanced + +The Python API **`physicsnemo.cfd.evaluation.benchmarks.engine.run_benchmark`** and **`Config.from_dict`** remain for scripts and tests. The modules **`python -m physicsnemo.cfd.evaluation.benchmarks.run`** and **`evaluation.inference`** accept a YAML/JSON path **without** Hydra interpolation — use only if you supply a **flat** file (no `${...}`) or materialized values. + +--- + ## Metrics -Registered names (or dicts with `name` + kwargs). Example list from `evaluation_config.yaml`: +Registered names (or dicts with `name` + kwargs). Examples match **`conf/config_surface.yaml`** / **`config_volume.yaml`**: ```yaml +# Surface-oriented metrics: - l2_pressure - l2_shear_stress - - l2_pressure_area_weighted # alias: area_wt_l2_pressure - - l2_pressure_volume # needs volume case + output maps - - l2_velocity - - l2_turbulent_viscosity + - l2_pressure_area_weighted - drag_error - lift_error - - continuity_residual_l2 # volume: needs velocity on volume mesh + +# Volume-oriented (see config_volume.yaml) +metrics: + - l2_pressure_volume + - l2_turbulent_viscosity + - l2_velocity + - continuity_residual_l2 - momentum_residual_l2 ``` @@ -87,27 +100,21 @@ metrics: ### What runs where -The benchmark engine writes **`benchmark_results.json` / `.csv` / `.html`**, then optional **`reports.visuals`** PNGs (when `reports.enabled` and `visuals` is non-empty). Comparison VTK on disk exists only if **`reports.save_comparison_meshes: true`** (otherwise PNGs use in-memory meshes). **`run.save_inference_mesh: true`** adds prediction-only VTK per case. - -Built-in **visual** names: +The benchmark engine writes **`benchmark_results.json` / `.csv` / `.html`**, then optional **`reports.visuals`** PNGs (when `reports.enabled` and `visuals` is non-empty). Comparison VTK on disk exists only if **`reports.save_comparison_meshes: true`**. **`run.save_inference_mesh: true`** adds prediction-only VTK per case. | Name | Role | | ---- | ---- | | `field_comparison_surface` | Surface GT vs pred (`plot_field_comparisons`) | | `plot_fields_volume` | Volume scalars (`plot_fields`) | -| `line_plot` / `plot_line` | GT vs pred along `plot_coord` on comparison mesh | -| `design_scatter` | Pred vs true scatter + R²; needs **`pairs`**: `[{name, true_key, pred_key}]` matching `per_case[].metrics` | -| `design_trend` | Trend vs index; same **`pairs`**, optional **`idx_key`** per pair for x labels | -| `projections_hexbin` | Hexbin over many meshes; needs **`mesh_paths`**, **`field`**, **`direction`** (see `bench.visualization.plot_projections_hexbin`) | -| `streamlines_comparison` | Volume streamlines (point comparison mesh); uses **`output.streamlines_vector_canonical`** or **`canonical_key`** | +| `line_plot` | GT vs pred along `plot_coord` | +| `design_scatter` | Pred vs true scatter + R²; needs **`pairs`** matching `per_case[].metrics` | +| `design_trend` | Trend vs index; same **`pairs`**, optional **`idx_key`** | +| `projections_hexbin` | Hexbin; needs **`mesh_paths`**, **`field`**, **`direction`** | +| `streamlines_comparison` | Uses **`output.streamlines_vector_canonical`** or **`canonical_key`** | Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. -**Runtime context:** `run_optional_report_plugins(..., context={"comparison_meshes_by_run": [{case_id: mesh}, ...]})` aligns with `results` so built-ins can skip `pv.read` for large VTU/VTP. - -**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; optional comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/{case_id}_comparison.vtp|vtu`. - -**Inference meshes:** (1) `inference__.vtp|vtu` — **predictions only** (optional via `run.save_inference_mesh`); (2) optional **`comparison_meshes/_comparison.*`** — **GT + pred** when `save_comparison_meshes` is true. +**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; optional comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/`. ### `reports` YAML shape @@ -119,77 +126,54 @@ reports: visuals: - field_comparison_surface - name: field_comparison_surface - case_ids: ["run_1"] # must match the case you run (--case-id) + case_ids: ["run_1"] canonical_keys: [pressure, shear_stress] view: xy - - name: line_plot - case_ids: ["run_1"] - canonical_key: pressure - plot_coord: x - normalize_factor: 1.0 ``` | Key | Role | -| --- | ---- | +| --- | --- | | `enabled` | If true, run registered visuals after metrics/tables. | -| `save_comparison_meshes` | Save comparison VTK to disk. If false, mesh-based visuals still run from memory when the runner built a comparison mesh. | -| `comparison_mesh_subdir` | Subfolder for `*_comparison.vtp|vtu`. | -| `visual_case_ids` | Optional. If set, only these case IDs are kept in RAM for mesh-backed PNGs; also becomes the default `case_ids` for any visual that omits it (per-visual `case_ids` overrides). Omit for legacy behavior (all cases in memory). | +| `save_comparison_meshes` | Save comparison VTK to disk. | +| `visual_case_ids` | Optional; limits in-memory meshes for PNGs; default `case_ids` for visuals that omit it. | | `visuals` | Ordered list: string names or `{ name: ..., ...kwargs }`. | -| `plugins` | Legacy manifest hooks (`ReportsConfig`). | - -### Field selection (built-ins) - -- **`field_comparison_surface`** — Uses `canonical_keys` (default `pressure`) and `output.ground_truth_mesh_field_names` / `mesh_field_names`. -- **`plot_fields_volume`** — `fields` = VTK array names, or default all `output.volume_mesh_field_names` values. -- **`line_plot`** — One GT vs pred line per case; **`canonical_key`** picks surface or volume maps; optional **`coord_trim`** / **`field_trim`** (two-element lists), **`flip`**, matplotlib kwargs forwarded to `plot_line` (e.g. `xlabel`, `ylabel`, `true_line_kwargs`). -- **`design_scatter` / `design_trend`** — Require **`pairs`** in YAML; values must appear under **`per_case[].metrics`** (e.g. from custom metrics or extended metric dicts). -- **`projections_hexbin`** — **`mesh_paths`**: explicit list of VTK paths (same workflow as [`workflows/bench_example`](../bench_example/) projection outputs). -- **`streamlines_comparison`** — Volume cases only (`metric_dtype` point); uses GT/pred VTK names from **`output`** for the chosen canonical key. ### Bench example ↔ evaluation mapping -| `workflows/bench_example` / `bench.visualization.utils` | Evaluation visual name | +| `workflows/bench_example` / `postprocessing_tools.visualization.utils` | Evaluation visual name | | --------------------------------------------------------- | ------------------------ | -| `plot_field_comparisons` (contours / utils) | `field_comparison_surface` | +| `plot_field_comparisons` | `field_comparison_surface` | | `plot_fields` | `plot_fields_volume` | | `plot_line` | `line_plot` | | `plot_design_scatter` | `design_scatter` | | `plot_design_trend` | `design_trend` | -| `plot_projections_hexbin` | `projections_hexbin` (`mesh_paths` in YAML) | +| `plot_projections_hexbin` | `projections_hexbin` | | `plot_streamlines` | `streamlines_comparison` | -| `get_visible_point_indices` | (no built-in; used inside bench_example contour scripts only) | ### Line plots -Built-in **`line_plot`** wraps **`plot_line`**: it loads the comparison mesh, builds a **cell-center** point cloud (or uses points if there are no cells), and plots GT vs pred vs sorted **`plot_coord`** (`x` \| `y` \| `z`). Set **`canonical_key`** to a surface key (`pressure`, `shear_stress`) or volume key (`pressure_volume`, …) present in **`output`**. For **centerline-style** plots like [`workflows/bench_example`](../bench_example/README.md), use those scripts or a **custom** `register_visual` that passes a list of polylines to `plot_line`. - -### Other plot paths - -- **Bench example:** run inference to get VTK, then `generate_surface_benchmarks.py` / `generate_volume_benchmarks.py` with a matching field map. -- **Programmatic:** `bench.visualization.utils` or `run_optional_report_plugins(config, results, output_dir, context={...})` with `per_case[].comparison_mesh_path` and/or **`context["comparison_meshes_by_run"]`**. +**`line_plot`** uses **`canonical_key`** (surface: `pressure`, `shear_stress`; volume: `pressure_volume`, …) and **`plot_coord`**. Centerline-style strip plots: see [`workflows/bench_example`](../bench_example/README.md) or a custom `register_visual`. ### Troubleshooting -- **`case_ids`** in a visual must include the case you run — use `--case-id run_1` if the visual lists `case_ids: ["run_1"]`, or omit `case_ids`. -- Use an **editable install** or **`PYTHONPATH`** to this repo so the benchmark run includes the visualization step (not an old wheel). -- Check **`report_plugins_manifest.json`** → `visual_errors` (headless PyVista often needs **xvfb**). +- **`case_ids` in a visual** must cover the cases you evaluate; set global **`case_id=run_1`** or per-visual **`case_ids`** consistently. +- Headless PNG: **`report_plugins_manifest.json`** → `visual_errors`; often need **xvfb** (see `setup.sh`). +- Use an **editable install** so the installed package includes your local `evaluation` / `postprocessing_tools` changes. --- ## Baseline model stubs (`surface_baseline`, `volume_baseline`) -Optional **no-checkpoint** wrappers for smoke tests, layout checks, and **matrix skip** behavior. Not real physics: zeros / placeholders with correct canonical keys. Set `model.name` explicitly; use `checkpoint: ""`, `stats_path: ""`. +Smoke-test wrappers: **`checkpoint: ""`**, **`stats_path: ""`**. Set **`model.name`** in **`benchmark.models`** (matrix) or top-level **`model`** (single). ```yaml model: - name: "surface_baseline" # or volume_baseline + name: "surface_baseline" checkpoint: "" stats_path: "" dataset: name: drivaerml root: /path/to/data - # kwargs: { inference_domain: volume } # for volume_baseline ``` --- @@ -197,27 +181,23 @@ dataset: ## Custom models (adding a new wrapper) 1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. -2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs` (canonical keys: surface `pressure`, `shear_stress`; volume `pressure_volume`, `turbulent_viscosity`, `velocity`). +2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. 3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. -Reference: `wrappers/fignet/wrapper.py`, `geotransolver/wrapper.py`, `surface_baseline.py`, … - --- ## Custom datasets (adding a new adapter) 1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. -2. Implement `list_cases`, `load_case` → **`CanonicalCase`** (`mesh_path`, `mesh_type`, `ground_truth`, `inference_domain`), optional **`inference_domain_from_kwargs`** for matrix skips. +2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. 3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. -Reference: `adapters/drivaerml.py`, `ahmed.py`. Helpers: `datasets/vtk_ground_truth.py`, `gt_alignment.py`. - --- ## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) - **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. -- **Predictions** (model output) — surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `turbulent_viscosity`, `velocity`. +- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `turbulent_viscosity`, `velocity`. --- @@ -225,13 +205,7 @@ Reference: `adapters/drivaerml.py`, `ahmed.py`. Helpers: `datasets/vtk_ground_tr | Path | Role | | ---- | ---- | -| `physicsnemo/cfd/evaluation/config.py` | Config, `load_config`, `Config.load_merged` | -| `physicsnemo/cfd/evaluation/inference/` | Model registry, `wrappers/` | -| `physicsnemo/cfd/evaluation/datasets/` | Adapters, `CanonicalCase`, GT helpers | -| `physicsnemo/cfd/evaluation/metrics/` | Registry, `mesh_bridge.py`, bench-backed metrics | -| `physicsnemo/cfd/evaluation/reports/` | `register_visual`, built-in plots | +| `physicsnemo/cfd/evaluation/config.py` | `Config`, `load_config` | | `physicsnemo/cfd/evaluation/benchmarks/` | Engine, JSON/CSV/HTML reports | -| `physicsnemo/cfd/bench/` | Shared algorithms (L2, forces, …) | - ---- - +| `physicsnemo/cfd/evaluation/reports/` | `register_visual`, built-in plots | +| `physicsnemo/cfd/postprocessing_tools/` | Shared metrics / visualization algorithms | diff --git a/workflows/evaluation_examples/conf/config_surface.yaml b/workflows/evaluation_examples/conf/config_surface.yaml new file mode 100644 index 0000000..3aace80 --- /dev/null +++ b/workflows/evaluation_examples/conf/config_surface.yaml @@ -0,0 +1,113 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Surface benchmark (VTP / cell metrics, field comparison, wall quantities, Cd/Cl). +# Run: ``python main.py`` (default) or ``python main.py --config-name=config_surface`` + +defaults: + - _self_ + +hydra: + run: + dir: ${run.output_dir} + output_subdir: hydra + job: + chdir: false + +case_id: null + +run: + device: "cuda:0" + output_dir: "benchmark_results_surface" + seed: 42 + batch_size: 1 + save_inference_mesh: false + metrics_cache: + enabled: true + path: ${run.output_dir}/metrics_cache + +benchmark: + mode: "matrix" + models: + - name: "geotransolver" + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + datasets: + - name: "drivaerml" + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + case_ids: [run_1, run_11] + kwargs: { align_ground_truth_to_model: true, inference_domain: surface } + reproducibility: + log_env: true + save_artifacts: true + +output: + mesh_field_names: + pressure: "pMeanTrimPred" + shear_stress: "wallShearStressMeanTrimPred" + +metrics: + - l2_pressure + - l2_shear_stress + - l2_pressure_area_weighted + - drag_error + - lift_error + +reports: + enabled: false + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visual_case_ids: [run_1, run_11] + visuals: + - name: field_comparison_surface + canonical_keys: [pressure, shear_stress] + view: xy + - name: line_plot + canonical_key: pressure + plot_coord: x + normalize_factor: 1.0 + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + - name: line_plot + canonical_key: shear_stress + plot_coord: x + normalize_factor: 1.0 + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + - name: line_plot_centerlines + canonical_key: pressure + plot_coord: x + normalize_factor: 1.0 + z_clip: 0.4 + y_slice_origin: [0, 0, 0] + xlabel: "X Coordinate" + ylabel: "Pressure" + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + - name: streamlines_comparison + canonical_key: shear_stress + view: xy + - name: design_scatter + pairs: + - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } + - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } + - name: design_trend + pairs: + - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } + - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } diff --git a/workflows/evaluation_examples/conf/config_volume.yaml b/workflows/evaluation_examples/conf/config_volume.yaml new file mode 100644 index 0000000..3d826a7 --- /dev/null +++ b/workflows/evaluation_examples/conf/config_volume.yaml @@ -0,0 +1,93 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Volume benchmark (VTU / point metrics, volume fields, PDE residuals, velocity streamlines). +# Run: ``python main.py --config-name=config_volume`` + +defaults: + - _self_ + +hydra: + run: + dir: ${run.output_dir} + output_subdir: hydra + job: + chdir: false + +case_id: null + +run: + device: "cuda:0" + output_dir: "benchmark_results_volume" + seed: 42 + batch_size: 1 + save_inference_mesh: false + metrics_cache: + enabled: true + path: ${run.output_dir}/metrics_cache + +benchmark: + mode: "matrix" + models: + - name: "geotransolver" + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + datasets: + - name: "drivaerml" + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + case_ids: [run_1, run_11] + kwargs: { align_ground_truth_to_model: true, inference_domain: volume } + reproducibility: + log_env: true + save_artifacts: true + +output: + volume_mesh_field_names: + pressure_volume: "pMeanTrimPred" + turbulent_viscosity: "nutMeanTrimPred" + velocity: "UMeanTrimPred" + streamlines_vector_canonical: velocity + +metrics: + - l2_pressure_volume + - l2_turbulent_viscosity + - l2_velocity + - continuity_residual_l2 + - momentum_residual_l2 + - drag_error + - lift_error + +reports: + enabled: false + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visual_case_ids: [run_1, run_11] + visuals: + # Omit ``fields`` to plot all ``output.volume_mesh_field_names`` arrays on the comparison mesh. + - name: plot_fields_volume + view: xy + - name: line_plot + canonical_key: pressure_volume + plot_coord: x + normalize_factor: 1.0 + true_line_kwargs: { label: Ground truth } + pred_line_kwargs: { label: Prediction } + # Volume point mesh: uses ``output.streamlines_vector_canonical`` (velocity) when omitted. + - name: streamlines_comparison + view: xy diff --git a/workflows/evaluation_examples/evaluation_config.yaml b/workflows/evaluation_examples/evaluation_config.yaml deleted file mode 100644 index d635004..0000000 --- a/workflows/evaluation_examples/evaluation_config.yaml +++ /dev/null @@ -1,148 +0,0 @@ -# Single evaluation config: metrics, tabular reports (JSON/CSV/HTML), optional PNG visuals, optional VTK. -# Run: python -m physicsnemo.cfd.evaluation.benchmarks.run --config evaluation_config.yaml [--case-id run_1] -# -# benchmark.mode: "single" — uses top-level `model` and `dataset` below (one model × one dataset). -# benchmark.mode: "matrix" — uses `benchmark.models` × `benchmark.datasets` (Cartesian product). See the -# commented template at the bottom of this file; merge those keys into `benchmark` and set mode: matrix. - -run: - device: "cuda:0" - output_dir: "benchmark_results" - seed: 42 - batch_size: 1 - save_inference_mesh: false # false = skip writing inference__.vtp|vtu - # Optional: cache per-case metric scalars to skip VTK read + inference on repeat runs. Not used for PNG/VTK visuals. - metrics_cache: - enabled: false - path: "" # empty = /.metrics_cache; or set an absolute directory (delete that folder for a full recompute) - -# Default model (template for matrix rows when `benchmark.mode: matrix`; ignored as a pair when using lists). -# Smoke tests: model.name surface_baseline or volume_baseline, checkpoint/stats_path "", matching dataset domain. -# GeoTransolver/Transolver volume: inference_domain: volume; global_stats.json (velocity, pressure_volume, turbulent_viscosity) or volume_fields_normalization.npz next to checkpoint -# (same layout as transformer_models training / inference_on_vtk.py). -benchmark: - mode: "matrix" - models: - - name: "geotransolver" - inference_domain: surface - checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" - stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" - kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} - datasets: - - name: "drivaerml" - root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" - split: null - case_ids: ["run_1", "run_11"] - kwargs: {align_ground_truth_to_model: true, inference_domain: surface} - reproducibility: - log_env: true - save_artifacts: true - -output: - mesh_field_names: - pressure: "pMeanTrimPred" - shear_stress: "wallShearStressMeanTrimPred" - # Written on volume runs (defaults include pressure_volume → pMeanPred, etc.) - volume_mesh_field_names: - pressure_volume: "pMeanTrimPred" - turbulent_viscosity: "nutMeanTrimPred" - velocity: "UMeanTrimPred" - -# Surface: l2_pressure, l2_shear_stress, l2_velocity, drag_error, lift_error -# Volume: l2_pressure_volume (pressure_volume), l2_turbulent_viscosity, l2_velocity -# Optional dict form for renamed keys: { name: l2_turbulent_viscosity, gt_key: turbulent_viscosity, pred_key: turbulent_viscosity } -metrics: - - l2_pressure - - l2_shear_stress - - l2_pressure_area_weighted # alias: area_wt_l2_pressure - - drag_error - - lift_error - # Surface (VTP): l2_pressure, l2_shear_stress - # - "l2_pressure_volume" - # - "l2_turbulent_viscosity" - # - "l2_velocity" - -# Optional PNGs: builds GT+pred comparison mesh in memory; VTK on disk only if save_comparison_meshes: true. -# Built-ins: field_comparison_surface | plot_fields_volume | line_plot | line_plot_centerlines (bench-style -# top/bottom surface strips) | design_scatter | design_trend | projections_hexbin | streamlines_comparison -reports: - enabled: true - save_comparison_meshes: false - comparison_mesh_subdir: comparison_meshes - # Retain in-memory comparison meshes only for these cases (saves RAM on large sweeps). Also the - # default ``case_ids`` for any visual that omits ``case_ids`` (per-visual ``case_ids`` overrides). - visual_case_ids: ["run_1", "run_11"] - visuals: - - name: field_comparison_surface - canonical_keys: [pressure, shear_stress] - view: xy - # Surface comparison mesh (cell centers); shear_stress line uses |tau_w| vs plot_coord. - - name: line_plot - canonical_key: pressure - plot_coord: x - normalize_factor: 1.0 - true_line_kwargs: { label: Ground truth } - pred_line_kwargs: { label: Prediction } - - name: line_plot - canonical_key: shear_stress - plot_coord: x - normalize_factor: 1.0 - true_line_kwargs: { label: Ground truth } - pred_line_kwargs: { label: Prediction } - # Top/bottom centerline strips (same y-slice + z-clip as workflows/bench_example generate_surface_benchmarks). - # Writes two PNGs per case: *_line_centerline_top_* and *_line_centerline_bottom_*. - - name: line_plot_centerlines - canonical_key: pressure - plot_coord: x - normalize_factor: 1.0 - z_clip: 0.4 - y_slice_origin: [0, 0, 0] - xlabel: "X Coordinate" - ylabel: "Pressure" - true_line_kwargs: { label: Ground truth } - pred_line_kwargs: { label: Prediction } - # streamlines_comparison: volume uses velocity maps; surface (cell) uses canonical_key (e.g. shear_stress). - - name: streamlines_comparison - canonical_key: shear_stress - view: xy - # --- drag_error / lift_error populate drag_error_true|pred (Cd) and lift_error_true|pred (Cl) --- - - name: design_scatter - pairs: - - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } - - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } - - name: design_trend - pairs: - - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } - - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } - # - name: projections_hexbin - # mesh_paths: ["/abs/path/to/proj1.vtp"] - # field: p_error - # direction: XY - - -# ============================================================================= -# Matrix template (optional): set `benchmark.mode: matrix` and replace `models` / `datasets` with -# lists like the following (merge into `benchmark` above). Top-level `model` / `dataset` fill in -# missing fields for each matrix row per Config.from_dict. -# ============================================================================= -# benchmark: -# mode: "matrix" -# models: -# # - name: "fignet" -# # checkpoint: "benchmark_models/fignet_drivaerml_surface_checkpoint/model_00999.pth" -# # stats_path: "benchmark_models/fignet_drivaerml_surface_checkpoint/global_stats.json" -# # kwargs: {"max_points": 250000, "interpolation_k": 4} -# - name: "geotransolver" -# inference_domain: volume -# checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" -# stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" -# kwargs: {"batch_resolution": 60000, "geometry_sampling": 300000} -# datasets: -# - name: "drivaerml" -# root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" -# split: null -# case_ids: ["run_1"] -# kwargs: {align_ground_truth_to_model: true, inference_domain: volume} -# reproducibility: -# log_env: true -# save_artifacts: true diff --git a/workflows/evaluation_examples/main.py b/workflows/evaluation_examples/main.py new file mode 100644 index 0000000..6ec584d --- /dev/null +++ b/workflows/evaluation_examples/main.py @@ -0,0 +1,73 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Hydra entrypoint for config-driven benchmark evaluation. + +Run from this directory:: + + python main.py + python main.py --config-name=config_volume + python main.py case_id=run_1 run.device=cuda:0 + +Default Hydra config is ``conf/config_surface.yaml``. Use +``--config-name=config_volume`` for the volume benchmark. Same layout as +``workflows/domino_design_sensitivities/`` (Hydra + ``conf/``). +""" + +from __future__ import annotations + +import hydra +from omegaconf import DictConfig, OmegaConf + +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.config import Config + + +def _config_dict_from_hydra(cfg: DictConfig) -> dict: + """Turn composed DictConfig into a plain dict for :class:`Config`.""" + raw = OmegaConf.to_container(cfg, resolve=True) + if not isinstance(raw, dict): + raise TypeError("Hydra config root must map to a dict") + return raw + + +@hydra.main(version_base="1.3", config_path="conf", config_name="config_surface") +def main(cfg: DictConfig) -> None: + """ + Load benchmark configuration with Hydra/OmegaConf interpolation and run. + + Parameters + ---------- + cfg : DictConfig + Composed user config (``conf/config_surface.yaml`` by default, or + ``config_volume`` via ``--config-name=config_volume``) plus CLI overrides. + """ + raw = _config_dict_from_hydra(cfg) + case_id = raw.pop("case_id", None) + raw.pop("defaults", None) + raw.pop("hydra", None) + if case_id is not None and case_id != "null": + case_id = str(case_id) + else: + case_id = None + config = Config.from_dict(raw) + results = run_benchmark(config, case_id=case_id) + print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") + + +if __name__ == "__main__": + main() From 336f3c163d5e2e0478c9db9358d562c59314be1e Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Mon, 6 Apr 2026 06:47:12 -0700 Subject: [PATCH 09/25] adding distributed calculation and cleaning up --- CHANGELOG.md | 4 + .../benchmarks/distributed_utils.py | 169 ++++++++++++++++++ .../cfd/evaluation/benchmarks/engine.py | 78 ++++++-- physicsnemo/cfd/evaluation/config.py | 4 + .../wrappers/geotransolver/wrapper.py | 16 +- test/ci_tests/test_distributed_utils.py | 150 ++++++++++++++++ workflows/evaluation_examples/README.md | 17 +- .../conf/config_surface.yaml | 6 +- .../conf/config_volume.yaml | 4 +- workflows/evaluation_examples/main.py | 9 +- 10 files changed, 413 insertions(+), 44 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/benchmarks/distributed_utils.py create mode 100644 test/ci_tests/test_distributed_utils.py diff --git a/CHANGELOG.md b/CHANGELOG.md index cd48b9a..98be4a4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,6 +14,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). - **Hydra workflow** for evaluation examples: `workflows/evaluation_examples/main.py` with `conf/config_surface.yaml` (default) and `conf/config_volume.yaml` (`hydra-core` dependency), consistent with other workflows (see **Removed** for dropped flat example YAML). +- **Multi-GPU benchmarks:** `run_benchmark` / Hydra `main.py` can run under `torchrun` with PhysicsNeMo `DistributedManager` — per-rank case sharding, gather/merge on rank 0, and optional `run.distributed` (default true) to disable sharding for debugging. +- **Tests:** `test/ci_tests/test_distributed_utils.py` covers merge/shard helpers and `_case_ids_for_run`. - **Metrics cache:** optional `run.metrics_cache` (`enabled`, `path`) stores per-case scalar metrics on disk so repeat benchmark runs can skip VTK I/O and inference for unchanged configs; visualization is unchanged. - `physicsnemo.cfd.evaluation`: config-driven inference and benchmarking with @@ -35,6 +37,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). per-visual **`case_ids`** still overrides. - DoMINO NIM helper `call_domino_nim` and `launch_local_domino_nim.sh` live under **`physicsnemo.cfd.evaluation.nims`** (removed top-level **`physicsnemo.cfd.inference`** package). +- **GeoTransolver** checkpoint load uses ``trusted_torch_load_context`` (trusted checkpoints). +- Matrix benchmark domain-mismatch skip logs via ``log_dataset`` instead of ``print``. - Benchmark metric registration for evaluation now uses postprocessing_tools-backed built-ins under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated NumPy-only helpers. diff --git a/physicsnemo/cfd/evaluation/benchmarks/distributed_utils.py b/physicsnemo/cfd/evaluation/benchmarks/distributed_utils.py new file mode 100644 index 0000000..dbefe3d --- /dev/null +++ b/physicsnemo/cfd/evaluation/benchmarks/distributed_utils.py @@ -0,0 +1,169 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Helpers for multi-GPU benchmark runs using PhysicsNeMo ``DistributedManager``.""" + +from __future__ import annotations + +import os +from typing import Any + +import torch.distributed as dist + +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset + + +def try_get_distributed_manager() -> Any | None: + """ + Return an initialized ``DistributedManager`` instance, or ``None`` for single-process runs. + + Raises + ------ + RuntimeError + If ``WORLD_SIZE`` > 1 but ``physicsnemo.distributed`` is unavailable. + """ + try: + from physicsnemo.distributed import DistributedManager + except ImportError as e: + if int(os.environ.get("WORLD_SIZE", "1") or "1") > 1: + raise RuntimeError( + "Multi-process launch detected (WORLD_SIZE>1) but physicsnemo.distributed is not installed." + ) from e + return None + if not DistributedManager.is_initialized(): + DistributedManager.initialize() + return DistributedManager() + + +def shard_tuple(dm: Any | None, distributed_enabled: bool) -> tuple[int, int] | None: + """Return ``(rank, world_size)`` for case sharding, or ``None`` when sharding is off.""" + if not distributed_enabled or dm is None or dm.world_size <= 1: + return None + return (int(dm.rank), int(dm.world_size)) + + +def effective_device_str(dm: Any | None, run_device: str) -> str: + """Use ``DistributedManager`` device when available; otherwise ``run.device``.""" + if dm is None: + return run_device + return str(dm.device) + + +def merge_benchmark_result_shards(shards: list[dict[str, Any]]) -> dict[str, Any]: + """ + Combine per-rank benchmark ``dict``s for the same model×dataset into one result. + + Recomputes aggregate ``metrics`` from the union of ``per_case`` rows (mean over cases, + ignoring NaNs per key). Sorts ``per_case`` by ``case_id``. + """ + if not shards: + return {} + if all(s.get("skipped") for s in shards): + return dict(shards[0]) + active = [s for s in shards if not s.get("skipped")] + if not active: + return dict(shards[0]) + model = active[0]["model"] + dataset = active[0]["dataset"] + for s in active[1:]: + if s["model"] != model or s["dataset"] != dataset: + raise RuntimeError( + f"Distributed merge mismatch: expected {model!r}×{dataset!r}, " + f"got {s['model']!r}×{s['dataset']!r}" + ) + per_case: list[dict[str, Any]] = [] + for s in active: + per_case.extend(s.get("per_case") or []) + per_case.sort(key=lambda r: str(r.get("case_id", ""))) + all_metric_values: dict[str, list[float]] = {} + for row in per_case: + for k, v in (row.get("metrics") or {}).items(): + all_metric_values.setdefault(k, []).append(float(v)) + metrics_summary: dict[str, float] = {} + for mname, values in all_metric_values.items(): + valid = [v for v in values if v == v] + metrics_summary[mname] = sum(valid) / len(valid) if valid else float("nan") + case_ids = sorted({str(r["case_id"]) for r in per_case if r.get("case_id") is not None}) + return { + "model": model, + "dataset": dataset, + "cases": case_ids, + "metrics": metrics_summary, + "per_case": per_case, + } + + +def merge_mesh_context_shards(shards: list[dict[str, Any]]) -> dict[str, Any]: + """Merge rank-local comparison mesh dicts.""" + out: dict[str, Any] = {} + for s in shards: + out.update(s) + return out + + +def gather_merge_benchmark_outputs( + dm: Any, + results: list[dict[str, Any]], + meshes_by_run: list[dict[str, Any]], +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + """ + Collect per-rank ``results`` / ``meshes_by_run`` on rank 0, merge, and broadcast to all ranks. + """ + world = int(dm.world_size) + payload = (results, meshes_by_run) + if world <= 1: + return results, meshes_by_run + + if int(dm.rank) == 0: + gather_list: list[Any | None] = [None] * world + dist.gather_object(payload, gather_list, dst=0) + merged_results: list[dict[str, Any]] = [] + merged_meshes: list[dict[str, Any]] = [] + first = gather_list[0] + assert first is not None + n_runs = len(first[0]) + for j in range(n_runs): + res_shards = [gather_list[i][0][j] for i in range(world)] # type: ignore[index] + merged_results.append(merge_benchmark_result_shards(res_shards)) + mesh_shards = [gather_list[i][1][j] for i in range(world)] # type: ignore[index] + merged_meshes.append(merge_mesh_context_shards(mesh_shards)) + out_payload: tuple[list[dict[str, Any]], list[dict[str, Any]]] = ( + merged_results, + merged_meshes, + ) + else: + dist.gather_object(payload, None, dst=0) + out_payload = None + + dist.barrier() + if int(dm.rank) == 0: + obj_list: list[Any | None] = [out_payload] + else: + obj_list = [None] + dist.broadcast_object_list(obj_list, src=0) + merged_pair = obj_list[0] + assert merged_pair is not None + return merged_pair[0], merged_pair[1] + + +def log_distributed_context(dm: Any | None, shard: tuple[int, int] | None) -> None: + if dm is None: + return + if shard is None: + log_dataset("benchmark", f"Distributed: rank {dm.rank}/{dm.world_size} (no case sharding).") + else: + r, w = shard + log_dataset("benchmark", f"Distributed: rank {r}/{w} (case sharding enabled).") diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index a1bcf64..d625c98 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -31,6 +31,13 @@ When ``run.metrics_cache`` is enabled, a valid cache entry skips per-case VTK load and inference for that case. The cache stores scalars only and does not replace mesh or visualization workflows. + +Multi-GPU: launch with ``torchrun`` (or any launcher that sets ``WORLD_SIZE`` / +``LOCAL_RANK``) so ``physicsnemo.distributed.DistributedManager`` initializes. +With ``run.distributed`` true (default) and world size > 1, cases are strided +across ranks (``cases[rank::world_size]``), results are merged on rank 0, then +broadcast to all ranks; JSON/CSV/HTML, artifacts, and report plugins run on +rank 0 only. Inference uses ``str(dm.device)`` per rank when ``DistributedManager`` is active. """ from __future__ import annotations @@ -40,6 +47,14 @@ from pathlib import Path from typing import Any +from physicsnemo.cfd.evaluation.benchmarks.distributed_utils import ( + effective_device_str, + gather_merge_benchmark_outputs, + log_distributed_context, + shard_tuple, + try_get_distributed_manager, +) + from physicsnemo.cfd.evaluation.benchmarks.metrics_cache import ( metrics_cache_file_path, metrics_cache_fingerprint, @@ -279,6 +294,7 @@ def _run_single( run_config: RunConfig, reports: ReportsConfig | None = None, allow_skip_mismatch: bool = False, + shard: tuple[int, int] | None = None, ) -> tuple[dict[str, Any], dict[str, Any]]: """ Run one model on one dataset: load cases, infer, compute metrics, aggregate means. @@ -308,6 +324,8 @@ def _run_single( Optional mesh save and visual retention policy. allow_skip_mismatch : bool, optional If True, return a skipped result when surface/volume domains disagree. + shard : tuple of (int, int) or None, optional + If set, ``(rank, world_size)`` — keep only ``cases[rank::world_size]`` for distributed runs. Returns ------- @@ -324,9 +342,9 @@ def _run_single( f"dataset adapter {dataset_config.name!r} is {d_dom!r}" ) if allow_skip_mismatch: - print( - f"[benchmark] SKIP {model_config.name!r} × {dataset_config.name!r}: {reason}", - flush=True, + log_dataset( + "benchmark", + f"SKIP {model_config.name!r} × {dataset_config.name!r}: {reason}", ) return ( { @@ -349,6 +367,14 @@ def _run_single( f"Listing cases under root {dataset_config.root!r} (split={dataset_config.split!r})…", ) cases = case_ids if case_ids is not None else adapter.list_cases(split=dataset_config.split) + if shard is not None: + rank, world_size = shard + if world_size > 1: + cases = cases[rank::world_size] + log_dataset( + dataset_config.name, + f"Distributed shard: {len(cases)} case(s) for rank {rank}/{world_size}.", + ) if not cases: return ( { @@ -556,7 +582,7 @@ def _run_single( def _case_ids_for_run( dataset_case_ids: list[str] | None, - case_id_override: str | None, + case_id_override: str | list[str] | None, ) -> list[str] | None: """ Resolve which case IDs to evaluate for one benchmark invocation. @@ -565,23 +591,27 @@ def _case_ids_for_run( ---------- dataset_case_ids : list of str or None Cases from dataset config (or ``None`` for “all cases” upstream). - case_id_override : str or None - CLI ``--case-id`` override. + case_id_override : str, list of str, or None + Hydra ``case_id`` / CLI: one case, a list (same for each dataset in + matrix mode), or ``None`` for ``dataset_case_ids``. Returns ------- list of str or None - A single-element list if override is set; otherwise ``dataset_case_ids``. + Effective case list for the run. """ - if case_id_override: - return [case_id_override] - return dataset_case_ids + if case_id_override is None: + return dataset_case_ids + if isinstance(case_id_override, str): + return [case_id_override] if case_id_override else dataset_case_ids + out = [str(x) for x in case_id_override if x is not None and str(x) != ""] + return out if out else dataset_case_ids def run_benchmark( config: Config, *, - case_id: str | None = None, + case_id: str | list[str] | None = None, ) -> list[dict[str, Any]]: """ Execute the benchmark from a loaded ``Config``. @@ -593,8 +623,9 @@ def run_benchmark( ---------- config : Config Full evaluation configuration. - case_id : str or None, optional - If set, only this case is evaluated (overrides dataset case lists). + case_id : str, list of str, or None, optional + One case, a list (reused for every dataset in matrix mode), or ``None`` + for each dataset's ``case_ids`` (or all adapter cases). Returns ------- @@ -606,11 +637,15 @@ def run_benchmark( import physicsnemo.cfd.evaluation.metrics # noqa: F401 — registers built-in metrics metric_specs = _normalize_metrics_config(config.metrics) - device = config.run.device + dm = try_get_distributed_manager() + shard = shard_tuple(dm, config.run.distributed) + device = effective_device_str(dm, config.run.device) + log_distributed_context(dm, shard) + is_rank0 = dm is None or int(dm.rank) == 0 output_dir = config.run.output_dir Path(output_dir).mkdir(parents=True, exist_ok=True) - if config.benchmark.reproducibility.log_env: + if is_rank0 and config.benchmark.reproducibility.log_env: env_log = Path(output_dir) / "env.json" log_dataset("benchmark", f"Writing environment log to {env_log}…") with open(env_log, "w") as f: @@ -632,6 +667,7 @@ def run_benchmark( run_config=config.run, reports=config.reports, allow_skip_mismatch=False, + shard=shard, ) results.append(res) meshes_by_run.append(mesh_ctx) @@ -651,11 +687,15 @@ def run_benchmark( run_config=config.run, reports=config.reports, allow_skip_mismatch=True, + shard=shard, ) results.append(res) meshes_by_run.append(mesh_ctx) - if config.benchmark.reproducibility.save_artifacts: + if dm is not None and dm.world_size > 1 and config.run.distributed: + results, meshes_by_run = gather_merge_benchmark_outputs(dm, results, meshes_by_run) + + if is_rank0 and config.benchmark.reproducibility.save_artifacts: artifacts = Path(output_dir) / "benchmark_artifacts.json" skipped = [r for r in results if r.get("skipped")] log_dataset("benchmark", f"Writing artifacts to {artifacts}…") @@ -679,9 +719,10 @@ def run_benchmark( indent=2, ) - write_report(results, output_dir, formats=["json", "csv", "html"]) + if is_rank0: + write_report(results, output_dir, formats=["json", "csv", "html"]) - if config.reports.enabled and config.reports.visuals: + if is_rank0 and config.reports.enabled and config.reports.visuals: import physicsnemo.cfd.evaluation.reports # noqa: F401 — register built-in visuals from physicsnemo.cfd.evaluation.benchmarks.report_plugins import run_optional_report_plugins @@ -722,6 +763,7 @@ def _config_to_dict(c: Config) -> dict: "enabled": c.run.metrics_cache.enabled, "path": c.run.metrics_cache.path, }, + "distributed": c.run.distributed, }, "model": { "name": c.model.name, diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index 84ab950..fb81521 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -68,6 +68,9 @@ class RunConfig: #: If False, inference CLI skips writing ``inference__.vtp|vtu`` (comparison mesh / visuals unchanged). save_inference_mesh: bool = True metrics_cache: MetricsCacheConfig = field(default_factory=MetricsCacheConfig) + #: When True (default) and launched multi-process (e.g. ``torchrun``), shard cases across ranks via + #: ``DistributedManager`` and merge before reports. When False, each rank runs the full case list (debug only). + distributed: bool = True @dataclass @@ -207,6 +210,7 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": enabled=bool(mc_raw.get("enabled", False)), path=str(mc_raw.get("path") or ""), ), + distributed=bool(run_raw.get("distributed", True)), ) model = ModelConfig(**(data.get("model") or {})) dataset = DatasetConfig(**(data.get("dataset") or {})) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py index 6af33bd..4f83efb 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py @@ -231,14 +231,12 @@ def load( self._datapipe_user_kw = dp_user self._model = GeoTransolver(**model_kw) - # with trusted_torch_load_context(): ckpt_args = { "path": str(checkpoint_dir), "models": self._model, } - - loaded_epoch = load_checkpoint(device=dev, **ckpt_args) - # load_checkpoint(path=str(checkpoint_dir), models=self._model, device=dev) + with trusted_torch_load_context(): + _ = load_checkpoint(device=dev, **ckpt_args) self._model = self._model.to(dev) self._model.eval() return self @@ -341,13 +339,6 @@ def predict(self, model_input: ModelInput) -> RawOutput: index_blocks = torch.split(indices, batch_res) preds_list = [] use_full_fx = "geometry" in batch - # if "geometry" in batch: - # geo = batch["geometry"] - # g_pts_dim = 1 if geo.ndim >= 3 else 0 - # n_geo = int(geo.shape[g_pts_dim]) - # if n_geo > 1: - # geo_perm = torch.randperm(n_geo, device=geo.device) - # batch["geometry"] = geo.index_select(g_pts_dim, geo_perm) with torch.no_grad(): for index_block in index_blocks: @@ -373,13 +364,11 @@ def predict(self, model_input: ModelInput) -> RawOutput: geometry=local_batch.get("geometry"), ) preds_list.append(outputs) - # import pdb; pdb.set_trace() predictions = torch.cat(preds_list, dim=1) inverse_indices = torch.empty_like(indices) inverse_indices[indices] = torch.arange(N, device=indices.device) predictions = predictions[:, inverse_indices] predictions = predictions.squeeze(0) - # import pdb; pdb.set_trace() if self._inference_mode == "volume": predictions = datapipe.unscale_model_targets( @@ -396,7 +385,6 @@ def predict(self, model_input: ModelInput) -> RawOutput: stream_velocity=batch.get("stream_velocity"), factor_type="surface", ) - # predictions = predictions * batch.get("air_density") * (batch.get("stream_velocity") ** 2) return predictions def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predictions: diff --git a/test/ci_tests/test_distributed_utils.py b/test/ci_tests/test_distributed_utils.py new file mode 100644 index 0000000..c150242 --- /dev/null +++ b/test/ci_tests/test_distributed_utils.py @@ -0,0 +1,150 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Unit tests for benchmark distributed helpers and ``_case_ids_for_run`` (no GPU / torchrun).""" + +from __future__ import annotations + +from types import SimpleNamespace + +import pytest + +from physicsnemo.cfd.evaluation.benchmarks.distributed_utils import ( + effective_device_str, + merge_benchmark_result_shards, + merge_mesh_context_shards, + shard_tuple, +) +from physicsnemo.cfd.evaluation.benchmarks.engine import _case_ids_for_run + + +def test_merge_benchmark_result_shards_empty() -> None: + assert merge_benchmark_result_shards([]) == {} + + +def test_merge_benchmark_result_shards_all_skipped() -> None: + shards = [ + { + "model": "m", + "dataset": "d", + "skipped": True, + "skip_reason": "x", + "cases": [], + "metrics": {}, + "per_case": [], + }, + { + "model": "m", + "dataset": "d", + "skipped": True, + "skip_reason": "x", + "cases": [], + "metrics": {}, + "per_case": [], + }, + ] + out = merge_benchmark_result_shards(shards) + assert out["skipped"] is True + assert out["skip_reason"] == "x" + + +def test_merge_benchmark_result_shards_two_ranks_sorts_and_means() -> None: + a = { + "model": "m", + "dataset": "d", + "cases": ["c2"], + "metrics": {"x": 0.0}, + "per_case": [{"case_id": "c2", "metrics": {"x": 2.0}}], + } + b = { + "model": "m", + "dataset": "d", + "cases": ["c1"], + "metrics": {"x": 0.0}, + "per_case": [{"case_id": "c1", "metrics": {"x": 4.0}}], + } + merged = merge_benchmark_result_shards([a, b]) + assert [r["case_id"] for r in merged["per_case"]] == ["c1", "c2"] + assert merged["metrics"]["x"] == pytest.approx(3.0) + assert merged["cases"] == ["c1", "c2"] + + +def test_merge_benchmark_result_shards_ignores_nan_in_mean() -> None: + shards = [ + { + "model": "m", + "dataset": "d", + "cases": ["a"], + "metrics": {}, + "per_case": [{"case_id": "a", "metrics": {"x": 1.0}}], + }, + { + "model": "m", + "dataset": "d", + "cases": ["b"], + "metrics": {}, + "per_case": [{"case_id": "b", "metrics": {"x": float("nan")}}], + }, + ] + merged = merge_benchmark_result_shards(shards) + assert merged["metrics"]["x"] == pytest.approx(1.0) + + +def test_merge_benchmark_result_shards_model_mismatch() -> None: + with pytest.raises(RuntimeError, match="Distributed merge mismatch"): + merge_benchmark_result_shards( + [ + {"model": "m1", "dataset": "d", "per_case": []}, + {"model": "m2", "dataset": "d", "per_case": []}, + ] + ) + + +def test_merge_mesh_context_shards() -> None: + a = {"run_1": "mesh_a"} + b = {"run_2": "mesh_b", "run_1": "mesh_b_wins"} + assert merge_mesh_context_shards([a, b]) == {"run_1": "mesh_b_wins", "run_2": "mesh_b"} + + +def test_effective_device_str_no_dm() -> None: + assert effective_device_str(None, "cuda:0") == "cuda:0" + + +def test_effective_device_str_uses_dm() -> None: + dm = SimpleNamespace(device="cuda:3") + assert effective_device_str(dm, "cuda:0") == "cuda:3" + + +def test_shard_tuple_disabled_or_single() -> None: + dm = SimpleNamespace(world_size=4, rank=1) + assert shard_tuple(dm, distributed_enabled=False) is None + dm1 = SimpleNamespace(world_size=1, rank=0) + assert shard_tuple(dm1, distributed_enabled=True) is None + assert shard_tuple(None, distributed_enabled=True) is None + + +def test_shard_tuple_multi() -> None: + dm = SimpleNamespace(world_size=4, rank=1) + assert shard_tuple(dm, distributed_enabled=True) == (1, 4) + + +def test_case_ids_for_run_none_uses_dataset() -> None: + assert _case_ids_for_run(["a", "b"], None) == ["a", "b"] + assert _case_ids_for_run(None, None) is None + + +def test_case_ids_for_run_string() -> None: + assert _case_ids_for_run(["a", "b"], "z") == ["z"] + + +def test_case_ids_for_run_empty_string_fallback() -> None: + assert _case_ids_for_run(["a", "b"], "") == ["a", "b"] + + +def test_case_ids_for_run_list() -> None: + assert _case_ids_for_run(["a", "b"], ["x", "y"]) == ["x", "y"] + + +def test_case_ids_for_run_empty_list_fallback() -> None: + assert _case_ids_for_run(["a", "b"], []) == ["a", "b"] diff --git a/workflows/evaluation_examples/README.md b/workflows/evaluation_examples/README.md index d9a8ced..100cd33 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/evaluation_examples/README.md @@ -22,6 +22,15 @@ python main.py case_id=run_1 run.device=cuda:0 python main.py --config-name=config_volume run.output_dir=my_volume_run ``` +**Multi-GPU (optional):** install **physicsnemo** so `DistributedManager` is available, then from this directory launch with PyTorch distributed, for example: + +```bash +torchrun --standalone --nproc_per_node=4 main.py +# or: torchrun --standalone --nproc_per_node=4 main.py --config-name=config_volume +``` + +Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmark_results.*` and optional artifacts; all ranks return the merged result list. Set `run.distributed: false` only if debugging (each rank would run the full case list). + **Matrix mode:** edit **`conf/config_surface.yaml`** or **`conf/config_volume.yaml`** — set `benchmark.mode: matrix` and fill `benchmark.models` / `benchmark.datasets`. Incompatible surface/volume pairs are skipped automatically. --- @@ -39,15 +48,15 @@ python main.py --config-name=config_volume run.output_dir=my_volume_run | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`**, optional **`metrics_cache`** (see below) | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`**, **`distributed`** (default true: shard cases under `torchrun`), optional **`metrics_cache`** (see below) | | **model** / **benchmark.models** | `name`, `checkpoint`, `stats_path`, `inference_domain` (`surface` \| `volume`), `kwargs` | -| **dataset** / **benchmark.datasets** | `name`, `root`, `split`, `case_ids` (`null` = all), `kwargs` | +| **dataset** / **benchmark.datasets** | `name`, `root`, `split`, optional per-dataset `case_ids` (`null` = all), `kwargs` | | **output** | `mesh_field_names`, `ground_truth_mesh_field_names`; `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names`; **`streamlines_vector_canonical`** (default `velocity`) | | **metrics** | Metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | | **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | | **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | -**`case_id`:** optional top-level key in the Hydra config (`null` = use dataset case list). Override on the CLI: `case_id=run_1`. +**`case_id`:** optional top-level Hydra key: `null` uses each dataset's `case_ids` (or all adapter cases); a **string** runs one case on every dataset; a **list** runs those cases on every dataset in matrix mode (preferred over repeating `case_ids` on each dataset). CLI examples: `case_id=run_1`, `'case_id=[run_1,run_11]'`. **Metrics cache (optional):** Under `run.metrics_cache`, `enabled: true` stores per-case scalars under `path` (resolved; default pattern `${run.output_dir}/metrics_cache`). Delete that directory for a full recompute. **Plots and meshes are not cached.** @@ -156,7 +165,7 @@ reports: ### Troubleshooting -- **`case_ids` in a visual** must cover the cases you evaluate; set global **`case_id=run_1`** or per-visual **`case_ids`** consistently. +- **`case_ids` in a visual** must cover the cases you evaluate; set top-level **`case_id`** (string or list) or per-visual **`case_ids`** consistently. - Headless PNG: **`report_plugins_manifest.json`** → `visual_errors`; often need **xvfb** (see `setup.sh`). - Use an **editable install** so the installed package includes your local `evaluation` / `postprocessing_tools` changes. diff --git a/workflows/evaluation_examples/conf/config_surface.yaml b/workflows/evaluation_examples/conf/config_surface.yaml index 3aace80..908d682 100644 --- a/workflows/evaluation_examples/conf/config_surface.yaml +++ b/workflows/evaluation_examples/conf/config_surface.yaml @@ -27,10 +27,11 @@ hydra: job: chdir: false -case_id: null +# Top-level: one id, list (shared by all matrix datasets), or null for per-dataset case_ids / all cases. +case_id: [run_1, run_11] run: - device: "cuda:0" + device: "cuda:1" output_dir: "benchmark_results_surface" seed: 42 batch_size: 1 @@ -51,7 +52,6 @@ benchmark: - name: "drivaerml" root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" split: null - case_ids: [run_1, run_11] kwargs: { align_ground_truth_to_model: true, inference_domain: surface } reproducibility: log_env: true diff --git a/workflows/evaluation_examples/conf/config_volume.yaml b/workflows/evaluation_examples/conf/config_volume.yaml index 3d826a7..2c352d2 100644 --- a/workflows/evaluation_examples/conf/config_volume.yaml +++ b/workflows/evaluation_examples/conf/config_volume.yaml @@ -27,7 +27,8 @@ hydra: job: chdir: false -case_id: null +# Top-level: one id, list (shared by all matrix datasets), or null for per-dataset case_ids / all cases. +case_id: [run_1, run_11] run: device: "cuda:0" @@ -51,7 +52,6 @@ benchmark: - name: "drivaerml" root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" split: null - case_ids: [run_1, run_11] kwargs: { align_ground_truth_to_model: true, inference_domain: volume } reproducibility: log_env: true diff --git a/workflows/evaluation_examples/main.py b/workflows/evaluation_examples/main.py index 6ec584d..b7649dd 100644 --- a/workflows/evaluation_examples/main.py +++ b/workflows/evaluation_examples/main.py @@ -22,6 +22,7 @@ python main.py python main.py --config-name=config_volume python main.py case_id=run_1 run.device=cuda:0 + python main.py 'case_id=[run_1,run_11]' Default Hydra config is ``conf/config_surface.yaml``. Use ``--config-name=config_volume`` for the volume benchmark. Same layout as @@ -60,10 +61,12 @@ def main(cfg: DictConfig) -> None: case_id = raw.pop("case_id", None) raw.pop("defaults", None) raw.pop("hydra", None) - if case_id is not None and case_id != "null": - case_id = str(case_id) - else: + if case_id is None or case_id == "null": case_id = None + elif isinstance(case_id, (list, tuple)): + case_id = [str(x) for x in case_id] + else: + case_id = str(case_id) config = Config.from_dict(raw) results = run_benchmark(config, case_id=case_id) print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") From 15296e397a001e1a39a418786469daafb15a7aa6 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Mon, 6 Apr 2026 07:39:33 -0700 Subject: [PATCH 10/25] renaming example and adding matrix evaluation configs --- CHANGELOG.md | 10 +- README.md | 2 +- physicsnemo/cfd/evaluation/inference/run.py | 4 +- workflows/bench_example/README.md | 2 +- .../README.md | 18 ++- .../conf/config_matrix_surface.yaml | 110 ++++++++++++++++++ .../conf/config_matrix_volume.yaml | 110 ++++++++++++++++++ .../conf/config_surface.yaml | 0 .../conf/config_volume.yaml | 0 .../main.py | 0 .../setup.sh | 0 11 files changed, 240 insertions(+), 16 deletions(-) rename workflows/{evaluation_examples => benchmarking_workflow}/README.md (78%) create mode 100644 workflows/benchmarking_workflow/conf/config_matrix_surface.yaml create mode 100644 workflows/benchmarking_workflow/conf/config_matrix_volume.yaml rename workflows/{evaluation_examples => benchmarking_workflow}/conf/config_surface.yaml (100%) rename workflows/{evaluation_examples => benchmarking_workflow}/conf/config_volume.yaml (100%) rename workflows/{evaluation_examples => benchmarking_workflow}/main.py (100%) rename workflows/{evaluation_examples => benchmarking_workflow}/setup.sh (100%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 98be4a4..d81270c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,9 +11,6 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Added -- **Hydra workflow** for evaluation examples: `workflows/evaluation_examples/main.py` with - `conf/config_surface.yaml` (default) and `conf/config_volume.yaml` (`hydra-core` dependency), consistent - with other workflows (see **Removed** for dropped flat example YAML). - **Multi-GPU benchmarks:** `run_benchmark` / Hydra `main.py` can run under `torchrun` with PhysicsNeMo `DistributedManager` — per-rank case sharding, gather/merge on rank 0, and optional `run.distributed` (default true) to disable sharding for debugging. - **Tests:** `test/ci_tests/test_distributed_utils.py` covers merge/shard helpers and `_case_ids_for_run`. - **Metrics cache:** optional `run.metrics_cache` (`enabled`, `path`) stores per-case scalar metrics on disk @@ -24,7 +21,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). fields using explicit VTK names from config (`output.mesh_field_names`, `output.ground_truth_mesh_field_names`, and volume equivalents). - `physicsnemo.cfd.postprocessing_tools.metric_registry` for named metrics shared with evaluation. -- Example Hydra configs under `workflows/evaluation_examples/conf/`. +- Example Hydra configs under `workflows/benchmarking_workflow/conf/`, including matrix templates + `config_matrix_surface.yaml` and `config_matrix_volume.yaml` (multiple models × dataset blocks). - Library CLIs remain for scripting: `python -m physicsnemo.cfd.evaluation.benchmarks` / `python -m physicsnemo.cfd.evaluation.inference` (flat YAML path, no `${...}` interpolation). @@ -42,7 +40,7 @@ Versioning](https://semver.org/spec/v2.0.0.html). - Benchmark metric registration for evaluation now uses postprocessing_tools-backed built-ins under `physicsnemo.cfd.evaluation.metrics.builtin` instead of duplicated NumPy-only helpers. -- Evaluation **workflow docs** center on **`workflows/evaluation_examples/main.py`** (Hydra) and +- Evaluation **workflow docs** center on **`workflows/benchmarking_workflow/main.py`** (Hydra) and **`conf/config_surface.yaml`** / **`conf/config_volume.yaml`**; `evaluation.inference` still forwards to the benchmark engine; **`inference__.vtp|vtu`** when **`run.save_inference_mesh`** is true. - Matrix benchmark settings are edited in the **`conf/*.yaml`** files; the old **`benchmark_matrix.yaml`** @@ -52,7 +50,7 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Removed -- **`workflows/evaluation_examples/evaluation_config.yaml`** — evaluation examples are driven by Hydra **`conf/config_surface.yaml`** / **`config_volume.yaml`** and **`main.py`**. +- **`workflows/benchmarking_workflow/evaluation_config.yaml`** — the workflow is driven by Hydra **`conf/config_surface.yaml`** / **`config_volume.yaml`** and **`main.py`**. ### Fixed diff --git a/README.md b/README.md index eb73709..9076e14 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ from a Python interface, facilitating scalable deployment of trained models. metrics, PDE residuals). [Related publication](https://www.arxiv.org/abs/2507.10747) - The `physicsnemo.cfd.evaluation` package runs config-driven inference and uses the same `physicsnemo.cfd.postprocessing_tools` metric implementations as the - **`workflows/evaluation_examples/`** workflow (run **`python main.py`** there; see that README). + **`workflows/benchmarking_workflow/`** workflow (run **`python main.py`** there; see that README). - Utilities to extend and build custom metrics, analyze, and visualize the results of trained ML models, both mesh-based and point-cloud based models diff --git a/physicsnemo/cfd/evaluation/inference/run.py b/physicsnemo/cfd/evaluation/inference/run.py index 3ca7718..a0e076f 100644 --- a/physicsnemo/cfd/evaluation/inference/run.py +++ b/physicsnemo/cfd/evaluation/inference/run.py @@ -41,7 +41,7 @@ def main() -> None: parser = argparse.ArgumentParser( description=( "Compatibility wrapper for the benchmark evaluation engine (flat YAML only). " - "For Hydra + OmegaConf, use workflows/evaluation_examples: python main.py" + "For Hydra + OmegaConf, use workflows/benchmarking_workflow: python main.py" ), ) parser.add_argument("--config", required=True, help="Path to YAML/JSON config") @@ -66,7 +66,7 @@ def main() -> None: print( "[evaluation] physicsnemo.cfd.evaluation.inference forwards to the benchmark engine; " - "prefer: workflows/evaluation_examples (python main.py) or benchmarks.run with flat YAML", + "prefer: workflows/benchmarking_workflow (python main.py) or benchmarks.run with flat YAML", file=sys.stderr, ) results = run_benchmark(config, case_id=case_id) diff --git a/workflows/bench_example/README.md b/workflows/bench_example/README.md index 733807b..50a0f4d 100644 --- a/workflows/bench_example/README.md +++ b/workflows/bench_example/README.md @@ -22,7 +22,7 @@ flexibility and freedom. For **on-the-fly** evaluation (config-driven inference plus the same `physicsnemo.cfd.postprocessing_tools` metrics as the scripts above), use the `physicsnemo.cfd.evaluation` package and the example YAML under -[`workflows/evaluation_examples/`](../evaluation_examples/). The pre-baked +[`workflows/benchmarking_workflow/`](../benchmarking_workflow/). The pre-baked `.vtp` / `.vtu` workflows in this directory remain the reference for post-processed file-based benchmarks. diff --git a/workflows/evaluation_examples/README.md b/workflows/benchmarking_workflow/README.md similarity index 78% rename from workflows/evaluation_examples/README.md rename to workflows/benchmarking_workflow/README.md index 100cd33..b0773bf 100644 --- a/workflows/evaluation_examples/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -1,10 +1,10 @@ -# Evaluation examples (PhysicsNeMo-CFD) +# Benchmarking and Inference This workflow runs **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK (`run.save_inference_mesh`, `reports.save_comparison_meshes`) using **[Hydra](https://hydra.cc/)** and **OmegaConf** — the same pattern as **`workflows/domino_design_sensitivities/`**. -Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then use the commands below **from this directory** (`workflows/evaluation_examples/`). +Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then use the commands below **from this directory** (`workflows/benchmarking_workflow/`). -Configs live under **`conf/`**: **[`config_surface.yaml`](conf/config_surface.yaml)** (default) and **[`config_volume.yaml`](conf/config_volume.yaml)**. OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). +Configs live under **`conf/`**: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). --- @@ -17,6 +17,10 @@ python main.py # Volume benchmark (VTU / volume metrics) python main.py --config-name=config_volume +# Matrix: multiple models × multiple dataset blocks (see conf/config_matrix_*.yaml) +python main.py --config-name=config_matrix_surface +python main.py --config-name=config_matrix_volume + # Hydra overrides (examples) python main.py case_id=run_1 run.device=cuda:0 python main.py --config-name=config_volume run.output_dir=my_volume_run @@ -31,7 +35,7 @@ torchrun --standalone --nproc_per_node=4 main.py Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmark_results.*` and optional artifacts; all ranks return the merged result list. Set `run.distributed: false` only if debugging (each rank would run the full case list). -**Matrix mode:** edit **`conf/config_surface.yaml`** or **`conf/config_volume.yaml`** — set `benchmark.mode: matrix` and fill `benchmark.models` / `benchmark.datasets`. Incompatible surface/volume pairs are skipped automatically. +**Matrix mode:** use **[`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** or **[`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** as templates, or set `benchmark.mode: matrix` in any config and fill **`benchmark.models`** × **`benchmark.datasets`**. The product runs every model on every dataset entry; incompatible surface/volume pairs are skipped automatically. Edit checkpoint paths for your layout; comment out any **`- name:`** model block you do not want to run. --- @@ -39,8 +43,10 @@ Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmar | File | Role | | ---- | ----- | -| [`conf/config_surface.yaml`](conf/config_surface.yaml) | Surface: **GeoTransolver** + drivaerml (`inference_domain: surface`), wall L2, Cd/Cl, surface visuals. `run.output_dir`: **`benchmark_results_surface`**. | -| [`conf/config_volume.yaml`](conf/config_volume.yaml) | Volume: **GeoTransolver** + drivaerml (`inference_domain: volume`), volume L2 + residuals, `plot_fields_volume`, velocity streamlines. `run.output_dir`: **`benchmark_results_volume`**. | +| [`conf/config_surface.yaml`](conf/config_surface.yaml) | **Surface**, `benchmark.mode: matrix`, **GeoTransolver** × **drivaerml**. Top-level **`case_id: [run_1, run_11]`** (no per-dataset `case_ids`). **`run.device`**: `cuda:1`. **`run.output_dir`**: **`benchmark_results_surface`**. `reports.enabled: false` (visuals listed for copy/paste when enabling reports). | +| [`conf/config_volume.yaml`](conf/config_volume.yaml) | **Volume**, `benchmark.mode: matrix`, **GeoTransolver** × **drivaerml**. **`case_id: [run_1, run_11]`**. **`run.device`**: `cuda:0`. **`run.output_dir`**: **`benchmark_results_volume`**. | +| [`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml) | **Matrix surface:** **GeoTransolver, Transolver, XMGN, FigNet, DoMINO** × **one** drivaerml row; **`case_id: null`** (all cases unless overridden). Checkpoints under `.../benchmark_models/_drivaerml_surface_checkpoint/`. DoMINO **`kwargs.domino_config`** points at a YAML next to the checkpoint. **`run.output_dir`**: **`benchmark_results_matrix_surface`**. Optional commented baseline stub at bottom of **`benchmark.models`**. | +| [`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml) | **Matrix volume:** same five model names in **volume** mode × **one** drivaerml row; **`case_id: null`**. Paths use `.../_drivaerml_volume_checkpoint/`. **`run.output_dir`**: **`benchmark_results_matrix_volume`**. | --- diff --git a/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml b/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml new file mode 100644 index 0000000..537b4ef --- /dev/null +++ b/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml @@ -0,0 +1,110 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Matrix surface benchmark: every ``benchmark.models`` entry × every ``benchmark.datasets`` entry. +# Incompatible model/domain pairs are skipped automatically (see engine ``allow_skip_mismatch``). +# +# Run: +# python main.py --config-name=config_matrix_surface +# +# Edit ``ROOT``-style paths and Transolver checkpoint paths for your machine. Uncomment or add +# more models (registered names: geotransolver, transolver, domino, fignet, xmgn, surface_baseline, …). + +defaults: + - _self_ + +hydra: + run: + dir: ${run.output_dir} + output_subdir: hydra + job: + chdir: false + +# null = use each dataset's ``case_ids`` below (or CLI ``case_id=run_1`` / list override). +case_id: null + +run: + device: "cuda:0" + output_dir: "benchmark_results_matrix_surface" + seed: 42 + batch_size: 1 + save_inference_mesh: false + metrics_cache: + enabled: true + path: ${run.output_dir}/metrics_cache + +benchmark: + mode: matrix + models: + - name: geotransolver + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + - name: transolver + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + - name: xmgn + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { "max_points": 250000, "interpolation_k": 4 } + - name: fignet + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { "max_points": 250000, "interpolation_k": 4 } + - name: domino + inference_domain: surface + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_surface_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_surface_checkpoint/global_stats.json" + kwargs: { "domino_config": "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_surface_checkpoint/config.yaml" } + # Uncomment when you have a Transolver surface checkpoint + stats next to it (same layout as training). + # - name: surface_baseline_model + # inference_domain: surface + # checkpoint: "/path/to/surface_baseline_model/checkpoint.0.501.pt" + # stats_path: "/path/to/surface_baseline_model/global_stats.json" + # kwargs: { } + datasets: + # Same adapter twice with different ``case_ids`` illustrates a multi-dataset matrix without a second layout. + - name: drivaerml + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + kwargs: { align_ground_truth_to_model: true, inference_domain: surface } + reproducibility: + log_env: true + save_artifacts: true + +output: + mesh_field_names: + pressure: "pMeanTrimPred" + shear_stress: "wallShearStressMeanTrimPred" + +metrics: + - l2_pressure + - l2_shear_stress + - l2_pressure_area_weighted + - drag_error + - lift_error + +reports: + enabled: false + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visual_case_ids: [run_1, run_11] + visuals: [] diff --git a/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml new file mode 100644 index 0000000..e5280c1 --- /dev/null +++ b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml @@ -0,0 +1,110 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Matrix volume benchmark: every ``benchmark.models`` entry × every ``benchmark.datasets`` entry. +# +# Run: +# python main.py --config-name=config_matrix_volume +# +# Edit paths for your machine. Uncomment Transolver when a volume checkpoint is available. + +defaults: + - _self_ + +hydra: + run: + dir: ${run.output_dir} + output_subdir: hydra + job: + chdir: false + +case_id: null + +run: + device: "cuda:0" + output_dir: "benchmark_results_matrix_volume" + seed: 42 + batch_size: 1 + save_inference_mesh: false + metrics_cache: + enabled: true + path: ${run.output_dir}/metrics_cache + +benchmark: + mode: matrix + models: + - name: geotransolver + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + - name: transolver + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } + - name: xmgn + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { "max_points": 250000, "interpolation_k": 4 } + - name: fignet + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { "max_points": 250000, "interpolation_k": 4 } + - name: domino + inference_domain: volume + checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_volume_checkpoint/checkpoint.0.501.pt" + stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_volume_checkpoint/global_stats.json" + kwargs: { "domino_config": "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_volume_checkpoint/config.yaml" } + # Uncomment when you have a Transolver surface checkpoint + stats next to it (same layout as training). + # - name: volume_baseline_model + # inference_domain: volume + # checkpoint: "/path/to/volume_baseline_model/checkpoint.0.501.pt" + # stats_path: "/path/to/volume_baseline_model/global_stats.json" + # kwargs: { } + datasets: + - name: drivaerml + root: "/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune" + split: null + kwargs: { align_ground_truth_to_model: true, inference_domain: volume } + reproducibility: + log_env: true + save_artifacts: true + +output: + volume_mesh_field_names: + pressure_volume: "pMeanTrimPred" + turbulent_viscosity: "nutMeanTrimPred" + velocity: "UMeanTrimPred" + streamlines_vector_canonical: velocity + +metrics: + - l2_pressure_volume + - l2_turbulent_viscosity + - l2_velocity + - continuity_residual_l2 + - momentum_residual_l2 + - drag_error + - lift_error + +reports: + enabled: false + save_comparison_meshes: false + comparison_mesh_subdir: comparison_meshes + visual_case_ids: [run_1, run_11] + visuals: [] diff --git a/workflows/evaluation_examples/conf/config_surface.yaml b/workflows/benchmarking_workflow/conf/config_surface.yaml similarity index 100% rename from workflows/evaluation_examples/conf/config_surface.yaml rename to workflows/benchmarking_workflow/conf/config_surface.yaml diff --git a/workflows/evaluation_examples/conf/config_volume.yaml b/workflows/benchmarking_workflow/conf/config_volume.yaml similarity index 100% rename from workflows/evaluation_examples/conf/config_volume.yaml rename to workflows/benchmarking_workflow/conf/config_volume.yaml diff --git a/workflows/evaluation_examples/main.py b/workflows/benchmarking_workflow/main.py similarity index 100% rename from workflows/evaluation_examples/main.py rename to workflows/benchmarking_workflow/main.py diff --git a/workflows/evaluation_examples/setup.sh b/workflows/benchmarking_workflow/setup.sh similarity index 100% rename from workflows/evaluation_examples/setup.sh rename to workflows/benchmarking_workflow/setup.sh From fca40cd7232fbb0e9da56983d1919b747388a058 Mon Sep 17 00:00:00 2001 From: ram-cherukuri <104155145+ram-cherukuri@users.noreply.github.com> Date: Thu, 9 Apr 2026 17:43:34 -0400 Subject: [PATCH 11/25] Revise README for model evaluation and benchmarking Updated the README to clarify the purpose of the workflow and its usage. --- workflows/benchmarking_workflow/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index b0773bf..4509f04 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -1,8 +1,8 @@ -# Benchmarking and Inference +# Model evaluation and benchmarking -This workflow runs **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK (`run.save_inference_mesh`, `reports.save_comparison_meshes`) using **[Hydra](https://hydra.cc/)** and **OmegaConf** — the same pattern as **`workflows/domino_design_sensitivities/`**. +This is an opinionated workflow to help you get started with evaluating and benchmarking pretrained models. It stitches together the model inference, calcuating built-in metrics and generating artifacts for analysis and visualization. We use a configuration based approach to allow you to extend this workflow to add your datasets, models, custom metrics etc. -Install from this repo (`pip install -e .`) or `pip install nvidia-physicsnemo-cfd`, then use the commands below **from this directory** (`workflows/benchmarking_workflow/`). +# Running OOB Benchmark Configs live under **`conf/`**: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). From e8769ad2dbe9dd4ec7e52e841593d5e96f3c743d Mon Sep 17 00:00:00 2001 From: ram-cherukuri <104155145+ram-cherukuri@users.noreply.github.com> Date: Thu, 9 Apr 2026 22:56:29 -0400 Subject: [PATCH 12/25] Revise README for OOB Benchmarking section Updated section headers and improved clarity of config descriptions. --- workflows/benchmarking_workflow/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index 4509f04..0e0581b 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -2,9 +2,9 @@ This is an opinionated workflow to help you get started with evaluating and benchmarking pretrained models. It stitches together the model inference, calcuating built-in metrics and generating artifacts for analysis and visualization. We use a configuration based approach to allow you to extend this workflow to add your datasets, models, custom metrics etc. -# Running OOB Benchmark +# Running OOB -Configs live under **`conf/`**: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). +We have configs under **`conf/`** for surface, volume evaluations for a model and a matrix run for comparing multiple models: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). --- From 4d4703b79477e11a16858b8f318c948ff9381e8c Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Thu, 9 Apr 2026 21:37:16 -0700 Subject: [PATCH 13/25] domain-scoped metrics, aggregate volume visual, and naming cleanup --- .../cfd/evaluation/benchmarks/engine.py | 2 +- physicsnemo/cfd/evaluation/common/io.py | 12 +- physicsnemo/cfd/evaluation/config.py | 4 +- .../evaluation/datasets/adapters/drivaerml.py | 2 +- physicsnemo/cfd/evaluation/datasets/schema.py | 10 +- .../evaluation/datasets/vtk_ground_truth.py | 8 +- .../inference/wrappers/domino/inference.py | 2 +- .../wrappers/geotransolver/wrapper.py | 6 +- .../inference/wrappers/transolver/wrapper.py | 6 +- .../inference/wrappers/volume_baseline.py | 4 +- .../cfd/evaluation/metrics/builtin/forces.py | 20 +- .../cfd/evaluation/metrics/builtin/l2.py | 126 ++++++---- .../cfd/evaluation/metrics/builtin/physics.py | 8 +- .../cfd/evaluation/metrics/mesh_bridge.py | 2 +- .../evaluation/reports/builtin/__init__.py | 2 + .../reports/builtin/aggregate_volume.py | 224 ++++++++++++++++++ .../reports/builtin/design_plots.py | 14 +- .../interpolation/interpolate_mesh_to_pc.py | 26 +- .../postprocessing_tools/metric_registry.py | 40 +++- test/ci_tests/test_evaluation.py | 6 +- workflows/benchmarking_workflow/README.md | 16 +- .../conf/config_matrix_surface.yaml | 4 +- .../conf/config_matrix_volume.yaml | 8 +- .../conf/config_surface.yaml | 12 +- .../conf/config_volume.yaml | 12 +- 25 files changed, 423 insertions(+), 153 deletions(-) create mode 100644 physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index d625c98..0e74354 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -501,7 +501,7 @@ def _run_single( case_metrics: dict[str, float] = {} for mname, mkwargs in metric_names: try: - fn = get_metric(mname) + fn = get_metric(mname, domain=m_dom) out = _call_metric( fn, gt, diff --git a/physicsnemo/cfd/evaluation/common/io.py b/physicsnemo/cfd/evaluation/common/io.py index 790b4f1..ecf3cee 100644 --- a/physicsnemo/cfd/evaluation/common/io.py +++ b/physicsnemo/cfd/evaluation/common/io.py @@ -67,7 +67,7 @@ def surface_factors_from_global_stats( "mean/std_dev for 'pressure' and 'shear_stress'. " f"Missing or incomplete key {k!r}. " f"mean keys: {have_m}, std_dev keys: {have_s}. " - "If you only see velocity / pressure_volume / turbulent_viscosity, " + "If you only see velocity / pressure / turbulent_viscosity, " "that is a volume stats file—use inference_domain: volume with a volume " "checkpoint, or point stats_path at a surface-trained checkpoint directory." ) @@ -90,18 +90,18 @@ def volume_factors_from_global_stats( """Build ``mean`` / ``std`` vectors for volume ``TransolverDataPipe`` from ``global_stats.json``. Channel order matches training / ``volume_fields_normalization.npz``: - **velocity (3)**, **pressure_volume** (1), **turbulent_viscosity** (1). + **velocity (3)**, **pressure** (1), **turbulent_viscosity** (1). - Uses keys ``velocity``, ``pressure_volume`` (or legacy ``pressure`` for volume pressure), - and ``turbulent_viscosity`` under ``mean`` / ``std_dev``. + Uses keys ``velocity``, ``pressure`` (or legacy ``pressure_volume`` for backward + compatibility), and ``turbulent_viscosity`` under ``mean`` / ``std_dev``. """ mean_block = data["mean"] std_block = data["std_dev"] - pkey = "pressure_volume" if "pressure_volume" in mean_block else "pressure" + pkey = "pressure" if "pressure" in mean_block else "pressure_volume" if pkey not in mean_block: raise KeyError( "global_stats.json must include mean/std for volume pressure as " - "'pressure_volume' or 'pressure'" + "'pressure' (or legacy 'pressure_volume')" ) for key in ("velocity", "turbulent_viscosity"): if key not in mean_block: diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index fb81521..e7491d2 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -139,7 +139,7 @@ class ReportsConfig: } DEFAULT_VOLUME_MESH_FIELD_NAMES: dict[str, str] = { - "pressure_volume": "pMeanPred", + "pressure": "pMeanPred", "turbulent_viscosity": "nutMeanPred", "velocity": "UMeanPred", } @@ -150,7 +150,7 @@ class ReportsConfig: } DEFAULT_GROUND_TRUTH_VOLUME_MESH_FIELD_NAMES: dict[str, str] = { - "pressure_volume": "pMean", + "pressure": "pMean", "turbulent_viscosity": "nutMean", "velocity": "UMean", } diff --git a/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py index 44382be..f6a8d50 100644 --- a/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py +++ b/physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py @@ -52,7 +52,7 @@ class DrivAerMLAdapter(DatasetAdapter): - ``pressure_field_names``, ``shear_field_names``: surface GT array name overrides. - ``turbulent_viscosity_field_names``, ``velocity_field_names``, ``volume_pressure_field_names``: volume GT VTK array name tuples (defaults in - ``vtk_ground_truth``). Volume pressure is exposed in GT as ``pressure_volume``. + ``vtk_ground_truth``). Volume pressure is exposed in GT as ``pressure``. - ``boundary_vtp_filename``: if set, every run uses this exact VTP name (legacy e.g. ``boundary_1.vtp``). - ``boundary_vtp_template``: custom surface filename pattern. - ``volume_vtu_filename``: if set, every run uses this exact VTU name inside the run dir. diff --git a/physicsnemo/cfd/evaluation/datasets/schema.py b/physicsnemo/cfd/evaluation/datasets/schema.py index eb47ae2..e9582f3 100644 --- a/physicsnemo/cfd/evaluation/datasets/schema.py +++ b/physicsnemo/cfd/evaluation/datasets/schema.py @@ -38,7 +38,7 @@ class CanonicalCase: case_id: str mesh_path: str mesh_type: str # "point" | "cell" — interpretation of field locations - ground_truth: dict[str, Any] | None = None # surface: pressure, shear_stress; volume: pressure_volume, … + ground_truth: dict[str, Any] | None = None # surface: pressure, shear_stress; volume: pressure, velocity, … metadata: dict[str, Any] = field(default_factory=dict) inference_domain: InferenceDomain = "surface" @@ -66,7 +66,6 @@ def predictions_dict(pressure: np.ndarray, shear_stress: np.ndarray) -> dict[str def build_predictions_dict( *, pressure: np.ndarray | None = None, - pressure_volume: np.ndarray | None = None, shear_stress: np.ndarray | None = None, velocity: np.ndarray | None = None, turbulent_viscosity: np.ndarray | None = None, @@ -74,14 +73,13 @@ def build_predictions_dict( ) -> dict[str, np.ndarray]: """Build predictions dict from optional canonical fields (omit None / missing). - Use ``pressure`` for surface models; ``pressure_volume`` for volume-domain pressure - (distinct canonical key so surface and volume fields are not conflated). + Both surface and volume models use ``pressure`` as the canonical key for + pressure predictions. The domain (surface vs volume) is determined by the + case's ``inference_domain``, not the dict key. """ out: dict[str, np.ndarray] = {} if pressure is not None: out["pressure"] = np.asarray(pressure, dtype=np.float32) - if pressure_volume is not None: - out["pressure_volume"] = np.asarray(pressure_volume, dtype=np.float32) if shear_stress is not None: out["shear_stress"] = np.asarray(shear_stress, dtype=np.float32) if velocity is not None: diff --git a/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py index e465b84..d543f93 100644 --- a/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py +++ b/physicsnemo/cfd/evaluation/datasets/vtk_ground_truth.py @@ -52,7 +52,7 @@ "velocity", "Velocity", ) -# Volume pressure uses a distinct canonical key ``pressure_volume`` (surface uses ``pressure``). +# Volume pressure uses the same canonical key ``pressure`` as surface (domain disambiguates). DEFAULT_VOLUME_PRESSURE_NAMES = DEFAULT_PRESSURE_NAMES @@ -256,7 +256,7 @@ def _extract_volume_at_location( if pk is not None: p = _pressure_to_1d(np.asarray(data[pk])) if p.size == n: - out["pressure_volume"] = p.astype(np.float32) + out["pressure"] = p.astype(np.float32) nk = _find_array(data, turbulent_viscosity_names) if nk is not None: nut = _scalar_field_to_1d(np.asarray(data[nk]), n) @@ -279,8 +279,8 @@ def extract_volume_fields_from_mesh( ) -> tuple[dict[str, np.ndarray] | None, Literal["cell", "point"] | None]: """Read reference fields from volume ``cell_data`` / ``point_data``. - Scalar pressure is stored under the canonical key ``pressure_volume`` (not ``pressure``, - which is reserved for surface boundary pressure in this codebase). + Scalar pressure is stored under the canonical key ``pressure`` (same as surface; + the inference domain disambiguates surface vs volume). Uses defaults when a name tuple is ``None``. Pass ``()`` to skip reading that field group. """ diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py index 4bfe144..8c35d6c 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py @@ -640,7 +640,7 @@ def domino_volume_predictions_to_canonical( if "nut" in nl or "turbulent" in nl or "viscosity" in nl: canonical_kw["turbulent_viscosity"] = chunk elif "pmean" in nl or nl.startswith("p_") or nl == "p": - canonical_kw["pressure_volume"] = chunk + canonical_kw["pressure"] = chunk else: extra[name] = chunk diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py index 4f83efb..6c59096 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py @@ -208,7 +208,7 @@ def load( if self._volume_factors is None: raise FileNotFoundError( "Volume inference requires ``global_stats.json`` (with velocity, " - "pressure_volume, turbulent_viscosity) or ``volume_fields_normalization.npz`` " + "pressure, turbulent_viscosity) or ``volume_fields_normalization.npz`` " f"next to stats/checkpoint (looked under {stats_path!r})." ) self._surface_factors = None @@ -401,11 +401,11 @@ def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predicti rho = float(self._air_density) dynamic_pressure = rho * (u**2) velocity = (pred[:, 0:3] * u).cpu().numpy().astype(np.float32) - pressure_volume = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) + pressure = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) turbulent_viscosity = (pred[:, 4] * dynamic_pressure).cpu().numpy().astype(np.float32) return build_predictions_dict( velocity=velocity, - pressure_volume=pressure_volume, + pressure=pressure, turbulent_viscosity=turbulent_viscosity, ) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py index 542822d..aa77db4 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py @@ -185,7 +185,7 @@ def load( if volume_factors is None: raise FileNotFoundError( "Volume inference requires ``global_stats.json`` (with velocity, " - "pressure_volume, turbulent_viscosity) or ``volume_fields_normalization.npz`` " + "pressure, turbulent_viscosity) or ``volume_fields_normalization.npz`` " f"next to stats/checkpoint (looked under {stats_path!r})." ) pipe_kw = {**_volume_datapipe_kw(), **dp_user} @@ -330,11 +330,11 @@ def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predicti rho = float(self._air_density) dynamic_pressure = rho * (u**2) velocity = (pred[:, 0:3] * u).cpu().numpy().astype(np.float32) - pressure_volume = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) + pressure = (pred[:, 3] * dynamic_pressure).cpu().numpy().astype(np.float32) turbulent_viscosity = (pred[:, 4] * dynamic_pressure).cpu().numpy().astype(np.float32) return build_predictions_dict( velocity=velocity, - pressure_volume=pressure_volume, + pressure=pressure, turbulent_viscosity=turbulent_viscosity, ) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py index 82d09bb..79b8033 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/volume_baseline.py @@ -39,7 +39,7 @@ class VolumeBaselineWrapper(CFDModel): - """No trained weights: zeros for ``pressure_volume`` and ``turbulent_viscosity`` on the volume mesh.""" + """No trained weights: zeros for ``pressure`` and ``turbulent_viscosity`` on the volume mesh.""" INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "volume" OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" @@ -82,6 +82,6 @@ def decode_outputs(self, raw_output: RawOutput, case: CanonicalCase) -> Predicti mesh = mesh.cast_to_unstructured_grid() n = mesh.n_cells if self.output_location == "cell" else mesh.n_points return build_predictions_dict( - pressure_volume=np.zeros((n,), dtype=np.float32), + pressure=np.zeros((n,), dtype=np.float32), turbulent_viscosity=np.zeros((n,), dtype=np.float32), ) diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py index 23dea9f..7d31cd9 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/forces.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/forces.py @@ -7,9 +7,9 @@ Each metric returns a dict that expands in the benchmark engine to: - ``drag_error`` — relative |Cd_pred − Cd_true| / |Cd_true| (or absolute if |Cd_true| ≈ 0) -- ``drag_error_true`` / ``drag_error_pred`` — integrated **drag coefficient Cd** (GT vs pred fields) +- ``drag_true`` / ``drag_pred`` — integrated **drag coefficient Cd** (GT vs pred fields) - ``lift_error`` — relative |Cl_pred − Cl_true| / |Cl_true| -- ``lift_error_true`` / ``lift_error_pred`` — integrated **lift coefficient Cl** +- ``lift_true`` / ``lift_pred`` — integrated **lift coefficient Cl** Use the ``*_true`` / ``*_pred`` keys with ``design_scatter`` / ``design_trend`` in evaluation config. """ @@ -62,13 +62,13 @@ def _scalar_drag_lift_triplet( def _rel_and_pair(cd_gt: float, cd_pr: float) -> dict[str, float]: - """Relative error (``""`` → scalar metric name) plus Cd or Cl pair (``true`` / ``pred`` sub-keys).""" + """Relative error (``"error"`` sub-key) plus Cd or Cl pair (``true`` / ``pred`` sub-keys).""" denom = abs(cd_gt) if denom < 1e-14: rel = float(abs(cd_pr - cd_gt)) else: rel = float(abs(cd_pr - cd_gt) / denom) - return {"": rel, "true": float(cd_gt), "pred": float(cd_pr)} + return {"error": rel, "true": float(cd_gt), "pred": float(cd_pr)} def drag_error( @@ -89,7 +89,7 @@ def drag_error( dd = drag_direction if drag_direction is not None else [1.0, 0.0, 0.0] if mesh is None or output is None: rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "drag") - return {"": rel, "true": gt, "pred": pr} + return {"error": rel, "true": gt, "pred": pr} gtp = output.ground_truth_mesh_field_names["pressure"] gtw = output.ground_truth_mesh_field_names["shear_stress"] prp = output.mesh_field_names["pressure"] @@ -114,7 +114,7 @@ def drag_error( return _rel_and_pair(float(cd_gt), float(cd_pr)) except Exception: rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "drag") - return {"": rel, "true": gt, "pred": pr} + return {"error": rel, "true": gt, "pred": pr} def lift_error( @@ -135,7 +135,7 @@ def lift_error( ld = lift_direction if lift_direction is not None else [0.0, 0.0, 1.0] if mesh is None or output is None: rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "lift") - return {"": rel, "true": gt, "pred": pr} + return {"error": rel, "true": gt, "pred": pr} gtp = output.ground_truth_mesh_field_names["pressure"] gtw = output.ground_truth_mesh_field_names["shear_stress"] prp = output.mesh_field_names["pressure"] @@ -162,9 +162,9 @@ def lift_error( return _rel_and_pair(float(cl_gt), float(cl_pr)) except Exception: rel, gt, pr = _scalar_drag_lift_triplet(ground_truth, predictions, "lift") - return {"": rel, "true": gt, "pred": pr} + return {"error": rel, "true": gt, "pred": pr} def register_force_metrics() -> None: - register_metric("drag_error", drag_error) - register_metric("lift_error", lift_error) + register_metric("drag", drag_error, domain="surface") + register_metric("lift", lift_error, domain="surface") diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/l2.py b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py index 693168d..55c4aa9 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/l2.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/l2.py @@ -79,7 +79,11 @@ def _l2_shear_numpy( return float(np.linalg.norm(pred - gt) / denom) -def l2_pressure( +# --------------------------------------------------------------------------- +# Surface pressure L2 +# --------------------------------------------------------------------------- + +def l2_pressure_surface( ground_truth: dict, predictions: dict, *, @@ -105,7 +109,11 @@ def l2_pressure( return float("nan") -def l2_shear_stress( +# --------------------------------------------------------------------------- +# Volume pressure L2 +# --------------------------------------------------------------------------- + +def l2_pressure_volume( ground_truth: dict, predictions: dict, *, @@ -113,24 +121,43 @@ def l2_shear_stress( comparison_mesh: Any = None, metric_dtype: str | None = None, output: Any = None, + gt_key: str = "pressure", + pred_key: str | None = None, mask: np.ndarray | None = None, **_: object, -) -> dict[str, float]: +) -> float: mesh, dtype = _resolve_mesh( predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output ) + pk = pred_key if pred_key is not None else gt_key if mesh is None or output is None: - v = _l2_shear_numpy(ground_truth, predictions, mask=mask) - return {"magnitude": v} - gtn = output.ground_truth_mesh_field_names["shear_stress"] - prn = output.mesh_field_names["shear_stress"] + gt = np.asarray(ground_truth.get(gt_key, []), dtype=np.float64).flatten() + pred = np.asarray(predictions.get(pk, []), dtype=np.float64).flatten() + if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: + return float("nan") + if mask is not None: + m = np.asarray(mask).flatten() + gt = gt[m] + pred = pred[m] + denom = np.linalg.norm(gt) + if denom < 1e-14: + return float(np.linalg.norm(pred - gt)) + return float(np.linalg.norm(pred - gt) / denom) + gtn = output.ground_truth_volume_mesh_field_names[gt_key] + prn = output.volume_mesh_field_names[pk] try: - return compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_l2_error" + return float(d[key]) if key in d else float("nan") except Exception: - return {"magnitude": float("nan")} + return float("nan") -def l2_pressure_area_weighted( +# --------------------------------------------------------------------------- +# Shear stress L2 (surface) +# --------------------------------------------------------------------------- + +def l2_shear_stress( ground_truth: dict, predictions: dict, *, @@ -138,24 +165,28 @@ def l2_pressure_area_weighted( comparison_mesh: Any = None, metric_dtype: str | None = None, output: Any = None, + mask: np.ndarray | None = None, **_: object, -) -> float: +) -> dict[str, float]: mesh, dtype = _resolve_mesh( predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output ) if mesh is None or output is None: - return float("nan") - gtn = output.ground_truth_mesh_field_names["pressure"] - prn = output.mesh_field_names["pressure"] + v = _l2_shear_numpy(ground_truth, predictions, mask=mask) + return {"magnitude": v} + gtn = output.ground_truth_mesh_field_names["shear_stress"] + prn = output.mesh_field_names["shear_stress"] try: - d = compute_area_weighted_l2_errors(mesh, [gtn], [prn], dtype=dtype) - key = f"{gtn}_area_wt_l2_error" - return float(d[key]) if key in d else float("nan") + return compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) except Exception: - return float("nan") + return {"magnitude": float("nan")} -def l2_pressure_volume( +# --------------------------------------------------------------------------- +# Area-weighted L2 pressure (surface) +# --------------------------------------------------------------------------- + +def l2_pressure_area_weighted( ground_truth: dict, predictions: dict, *, @@ -163,38 +194,27 @@ def l2_pressure_volume( comparison_mesh: Any = None, metric_dtype: str | None = None, output: Any = None, - gt_key: str = "pressure_volume", - pred_key: str | None = None, - mask: np.ndarray | None = None, **_: object, ) -> float: mesh, dtype = _resolve_mesh( predictions, case=case, comparison_mesh=comparison_mesh, metric_dtype=metric_dtype, output=output ) - pk = pred_key if pred_key is not None else gt_key if mesh is None or output is None: - gt = np.asarray(ground_truth.get(gt_key, []), dtype=np.float64).flatten() - pred = np.asarray(predictions.get(pk, []), dtype=np.float64).flatten() - if gt.size == 0 or pred.size == 0 or gt.shape != pred.shape: - return float("nan") - if mask is not None: - m = np.asarray(mask).flatten() - gt = gt[m] - pred = pred[m] - denom = np.linalg.norm(gt) - if denom < 1e-14: - return float(np.linalg.norm(pred - gt)) - return float(np.linalg.norm(pred - gt) / denom) - gtn = output.ground_truth_volume_mesh_field_names[gt_key] - prn = output.volume_mesh_field_names[pk] + return float("nan") + gtn = output.ground_truth_mesh_field_names["pressure"] + prn = output.mesh_field_names["pressure"] try: - d = compute_l2_errors(mesh, [gtn], [prn], dtype=dtype) - key = f"{gtn}_l2_error" + d = compute_area_weighted_l2_errors(mesh, [gtn], [prn], dtype=dtype) + key = f"{gtn}_area_wt_l2_error" return float(d[key]) if key in d else float("nan") except Exception: return float("nan") +# --------------------------------------------------------------------------- +# Velocity L2 (volume) +# --------------------------------------------------------------------------- + def l2_velocity( ground_truth: dict, predictions: dict, @@ -229,6 +249,10 @@ def l2_velocity( return {"magnitude": float("nan")} +# --------------------------------------------------------------------------- +# Turbulent viscosity L2 (volume) +# --------------------------------------------------------------------------- + def l2_turbulent_viscosity( ground_truth: dict, predictions: dict, @@ -269,14 +293,18 @@ def l2_turbulent_viscosity( return float("nan") +# --------------------------------------------------------------------------- +# Registration +# --------------------------------------------------------------------------- + def register_l2_metrics() -> None: - for name, fn in [ - ("l2_pressure", l2_pressure), - ("l2_shear_stress", l2_shear_stress), - ("l2_pressure_area_weighted", l2_pressure_area_weighted), - ("area_wt_l2_pressure", l2_pressure_area_weighted), - ("l2_pressure_volume", l2_pressure_volume), - ("l2_velocity", l2_velocity), - ("l2_turbulent_viscosity", l2_turbulent_viscosity), - ]: - register_metric(name, fn) + # Surface metrics + register_metric("l2_pressure", l2_pressure_surface, domain="surface") + register_metric("l2_shear_stress", l2_shear_stress, domain="surface") + register_metric("l2_pressure_area_weighted", l2_pressure_area_weighted, domain="surface") + register_metric("area_wt_l2_pressure", l2_pressure_area_weighted, domain="surface") + + # Volume metrics + register_metric("l2_pressure", l2_pressure_volume, domain="volume") + register_metric("l2_velocity", l2_velocity, domain="volume") + register_metric("l2_turbulent_viscosity", l2_turbulent_viscosity, domain="volume") diff --git a/physicsnemo/cfd/evaluation/metrics/builtin/physics.py b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py index 1bd3beb..3d3f6bf 100644 --- a/physicsnemo/cfd/evaluation/metrics/builtin/physics.py +++ b/physicsnemo/cfd/evaluation/metrics/builtin/physics.py @@ -88,8 +88,8 @@ def momentum_residual_l2( return {"Momentum_x_l2_error": float("nan")} uv = true_velocity_field or output.ground_truth_volume_mesh_field_names["velocity"] pv = predicted_velocity_field or output.volume_mesh_field_names["velocity"] - tp = true_pressure_field or output.ground_truth_volume_mesh_field_names["pressure_volume"] - pp = predicted_pressure_field or output.volume_mesh_field_names["pressure_volume"] + tp = true_pressure_field or output.ground_truth_volume_mesh_field_names["pressure"] + pp = predicted_pressure_field or output.volume_mesh_field_names["pressure"] tn = true_nu_field or output.ground_truth_volume_mesh_field_names["turbulent_viscosity"] pn = predicted_nu_field or output.volume_mesh_field_names["turbulent_viscosity"] try: @@ -112,5 +112,5 @@ def momentum_residual_l2( def register_physics_metrics() -> None: - register_metric("continuity_residual_l2", continuity_residual_l2) - register_metric("momentum_residual_l2", momentum_residual_l2) + register_metric("continuity_residual_l2", continuity_residual_l2, domain="volume") + register_metric("momentum_residual_l2", momentum_residual_l2, domain="volume") diff --git a/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py index 745fc78..6f95981 100644 --- a/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py +++ b/physicsnemo/cfd/evaluation/metrics/mesh_bridge.py @@ -96,7 +96,7 @@ def build_comparison_mesh( gt_map = output.ground_truth_volume_mesh_field_names pred_map = output.volume_mesh_field_names pairs = ( - ("pressure_volume", "pressure_volume"), + ("pressure", "pressure"), ("velocity", "velocity"), ("turbulent_viscosity", "turbulent_viscosity"), ) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py index 26471d9..c759af4 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/__init__.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/__init__.py @@ -12,6 +12,7 @@ register_plot_fields_volume, register_field_comparison_surface, ) +from physicsnemo.cfd.evaluation.reports.builtin.aggregate_volume import register_aggregate_volume def register_all_builtin_visuals() -> None: @@ -21,6 +22,7 @@ def register_all_builtin_visuals() -> None: register_design_visuals() register_projections_hexbin() register_streamlines_visual() + register_aggregate_volume() register_all_builtin_visuals() diff --git a/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py b/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py new file mode 100644 index 0000000..98e2e2e --- /dev/null +++ b/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py @@ -0,0 +1,224 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Aggregate volume error visual: resample to structured grid, compute mean/std error, slice and plot.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import numpy as np +import pyvista as pv + +from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import ( + interpolate_mesh_to_pc, +) +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields +from physicsnemo.cfd.evaluation.config import Config, OutputConfig +from physicsnemo.cfd.evaluation.datasets.progress import log_dataset +from physicsnemo.cfd.evaluation.reports.context_helpers import get_comparison_mesh_for_case +from physicsnemo.cfd.evaluation.reports.registry import register_visual + +DEFAULT_BOUNDS = [-3.5, 8.5, -2.25, 2.25, -0.32, 3.00] + + +def _build_structured_grid( + bounds: list[float], voxel_size: float +) -> pv.StructuredGrid: + """Create an axis-aligned structured grid from bounds and voxel size.""" + x = np.arange(bounds[0], bounds[1] + voxel_size, voxel_size) + y = np.arange(bounds[2], bounds[3] + voxel_size, voxel_size) + z = np.arange(bounds[4], bounds[5] + voxel_size, voxel_size) + xx, yy, zz = np.meshgrid(x, y, z, indexing="ij") + return pv.StructuredGrid(xx, yy, zz) + + +def _resolve_volume_field_pairs( + output: OutputConfig, + canonical_keys: list[str] | None, +) -> list[tuple[str, str, str]]: + """Return (canonical_key, gt_vtk_name, pred_vtk_name) triples for the requested fields.""" + keys = canonical_keys or list(output.ground_truth_volume_mesh_field_names.keys()) + pairs: list[tuple[str, str, str]] = [] + for k in keys: + gt_name = output.ground_truth_volume_mesh_field_names.get(k) + pred_name = output.volume_mesh_field_names.get(k) + if gt_name is None or pred_name is None: + log_dataset( + "benchmark", + f"aggregate_volume_errors: skipping canonical key {k!r} " + f"(gt={gt_name!r}, pred={pred_name!r})", + ) + continue + pairs.append((k, gt_name, pred_name)) + return pairs + + +def _interpolation_device(run_device: str) -> str: + """Map ``config.run.device`` (e.g. ``"cuda:0"``) to the ``"cpu"``/``"gpu"`` flag used by ``interpolate_mesh_to_pc``.""" + if run_device.startswith("cuda"): + return "gpu" + return "cpu" + + +def aggregate_volume_errors( + config: Config, + results: list[dict[str, Any]], + output_dir: str, + *, + context: dict[str, Any] | None = None, + case_ids: list[str] | None = None, + canonical_keys: list[str] | None = None, + bounds: list[float] | None = None, + voxel_size: float = 0.03, + y_slice_origin: list[float] | tuple[float, ...] = (0, 0, 0), + z_slice_origin: list[float] | tuple[float, ...] = (0, 0, -0.2376), + save_vtk: bool = True, + cmap: str = "jet", + lut: int = 20, + window_size: list[int] | None = None, + plot_vector_components: bool = True, + device: str | None = None, + **kwargs: Any, +) -> None: + """Resample volume cases onto a common structured grid, aggregate mean/std errors, and plot slices. + + For each case's comparison mesh, fields are interpolated onto a regular voxel + grid. Point-wise ``|GT - pred|`` is computed per field, then mean and std are + taken across cases. The aggregate grid is optionally saved as VTK and two + slices (y-normal and z-normal) are rendered via ``plot_fields``. + + When a CUDA device is available (via ``config.run.device`` or the explicit + ``device`` kwarg), kNN interpolation uses cuML/CuPy for GPU acceleration. + + Parameters + ---------- + canonical_keys : list[str] or None + Volume field canonical names (e.g. ``["pressure", "velocity"]``). + Defaults to all keys in ``output.ground_truth_volume_mesh_field_names``. + bounds : list[float] or None + ``[xmin, xmax, ymin, ymax, zmin, zmax]`` for the structured grid. + voxel_size : float + Grid spacing (metres). Smaller = finer but heavier. + y_slice_origin, z_slice_origin : tuple + Origins for the y-normal and z-normal slices. + save_vtk : bool + Whether to write ``aggregate_resampled_volume.vtk``. + device : str or None + ``"cpu"`` or ``"gpu"``. When ``None`` (default), inferred from + ``config.run.device`` — CUDA devices map to ``"gpu"``. + """ + vis_dir = Path(output_dir) / "visuals" + vis_dir.mkdir(parents=True, exist_ok=True) + + interp_device = device if device is not None else _interpolation_device(config.run.device) + log_dataset("benchmark", f"aggregate_volume_errors: interpolation device={interp_device!r}") + effective_bounds = bounds if bounds is not None else list(DEFAULT_BOUNDS) + field_pairs = _resolve_volume_field_pairs(config.output, canonical_keys) + if not field_pairs: + log_dataset("benchmark", "aggregate_volume_errors: no valid field pairs; skipping.") + return + + all_vtk_names = [] + for _, gt_name, pred_name in field_pairs: + all_vtk_names.extend([gt_name, pred_name]) + + error_arrays: dict[str, list[np.ndarray]] = { + f"{gt_name}_error": [] for _, gt_name, _ in field_pairs + } + + template_grid: pv.StructuredGrid | None = None + case_count = 0 + + for run_idx, run in enumerate(results): + if run.get("skipped"): + continue + for row in run.get("per_case") or []: + cid = row["case_id"] + if case_ids is not None and cid not in case_ids: + continue + + mesh = get_comparison_mesh_for_case(row, cid, run_idx, context) + if mesh is None: + log_dataset( + "benchmark", + f"aggregate_volume_errors: no mesh for {cid!r}; skipping case.", + ) + continue + + grid = _build_structured_grid(effective_bounds, voxel_size) + grid = interpolate_mesh_to_pc(grid, mesh, all_vtk_names, mesh_dtype="point", device=interp_device) + + if template_grid is None: + template_grid = grid + + for _, gt_name, pred_name in field_pairs: + error = np.abs( + grid.point_data[gt_name] - grid.point_data[pred_name] + ) + error_arrays[f"{gt_name}_error"].append(error) + + case_count += 1 + + if template_grid is None or case_count == 0: + log_dataset("benchmark", "aggregate_volume_errors: no cases processed; skipping.") + return + + log_dataset("benchmark", f"aggregate_volume_errors: aggregating over {case_count} cases.") + + fields_to_plot: list[str] = [] + for key, arrays in error_arrays.items(): + stacked = np.stack(arrays, axis=0) + mean = np.mean(stacked, axis=0) + std = np.std(stacked, axis=0) + template_grid.point_data[f"{key}_mean"] = mean + template_grid.point_data[f"{key}_std"] = std + fields_to_plot.append(f"{key}_mean") + fields_to_plot.append(f"{key}_std") + + if save_vtk: + vtk_path = vis_dir / "aggregate_resampled_volume.vtk" + template_grid.save(str(vtk_path)) + log_dataset("benchmark", f"Wrote {vtk_path}") + + win = window_size or [1280, 3840] + + y_slice = template_grid.slice(normal="y", origin=tuple(y_slice_origin)) + plotter = plot_fields( + y_slice, + fields_to_plot, + plot_vector_components=plot_vector_components, + view="xz", + dtype="point", + cmap=cmap, + lut=lut, + window_size=win, + **kwargs, + ) + y_png = vis_dir / "aggregate_volume_y_slice.png" + plotter.screenshot(str(y_png)) + plotter.close() + log_dataset("benchmark", f"Wrote {y_png}") + + z_slice = template_grid.slice(normal="z", origin=tuple(z_slice_origin)) + plotter = plot_fields( + z_slice, + fields_to_plot, + plot_vector_components=plot_vector_components, + view="xy", + dtype="point", + cmap=cmap, + lut=lut, + window_size=win, + **kwargs, + ) + z_png = vis_dir / "aggregate_volume_z_slice.png" + plotter.screenshot(str(z_png)) + plotter.close() + log_dataset("benchmark", f"Wrote {z_png}") + + +def register_aggregate_volume() -> None: + register_visual("aggregate_volume_errors", aggregate_volume_errors) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py index 82ebc9d..d47ac5b 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/design_plots.py @@ -26,20 +26,20 @@ def _design_skip_hint(per_case: list[dict[str, Any]], pairs: list[dict[str, Any] for p in pairs: tk = str(p.get("true_key", "")) pk = str(p.get("pred_key", "")) - if tk == "drag_error_true" and pk == "drag_error_pred": + if tk == "drag_true" and pk == "drag_pred": if "drag_error" in m and tk not in m: parts.append( - "metrics only have scalar drag_error (missing drag_error_true / drag_error_pred). " - "Re-run the benchmark after updating drag_error to return Cd true/pred dict entries, " - "and ensure `python` imports that code (e.g. `pip install -e .` from the repo root)." + "metrics have drag_error but missing drag_true / drag_pred. " + "Ensure the 'drag' metric is listed in config and returns a dict with " + "error/true/pred sub-keys (pip install -e . from the repo root)." ) elif tk in m and pk in m and (m[tk] != m[tk] or m[pk] != m[pk]): parts.append(f"{tk!r} or {pk!r} is NaN (finite values required).") - if tk == "lift_error_true" and pk == "lift_error_pred": + if tk == "lift_true" and pk == "lift_pred": if "lift_error" in m and tk not in m: parts.append( - "metrics only have scalar lift_error (missing lift_error_true / lift_error_pred); " - "re-run with updated lift_error metric." + "metrics have lift_error but missing lift_true / lift_pred; " + "ensure the 'lift' metric is listed in config." ) elif tk in m and pk in m and (m[tk] != m[tk] or m[pk] != m[pk]): parts.append(f"{tk!r} or {pk!r} is NaN (finite values required).") diff --git a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py index 6408e47..0d1843b 100644 --- a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py +++ b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py @@ -93,24 +93,26 @@ def _interpolate( return field_interp -def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell"): - """Interpolate mesh results on a point cloud using inverse weighted kNN +def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell", device="cpu"): + """Interpolate mesh results on a point cloud using inverse weighted kNN. Parameters ---------- pc : - Point Cloud to interpolate values on (PyVista Dataset) + Point Cloud to interpolate values on (PyVista Dataset). mesh : - Mesh for the source values (PyVista Dataset) + Mesh for the source values (PyVista Dataset). fields_to_interpolate : - List of fields (str) to interpolate (must be present in the mesh dataset) + List of fields (str) to interpolate (must be present in the mesh dataset). mesh_dtype : - Whether the mesh fields are of point or cell type. Default cell. + Whether the mesh fields are of point or cell type. Default ``"cell"``. + device : + ``"cpu"`` for scipy cKDTree or ``"gpu"`` for cuML/CuPy accelerated kNN. Returns ------- - _type_ - Point cloud with interpolated values + pv.DataSet + Point cloud with interpolated values. """ k = 5 @@ -120,19 +122,17 @@ def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell"): cell_centers = mesh.cell_centers() source_points = cell_centers.points - nbrs_surface = _create_nbrs_surface(source_points, n_neighbors=k) + nbrs_surface = _create_nbrs_surface(source_points, n_neighbors=k, device=device) if mesh_dtype == "point": - # TODO: Possible to vectorize for field in fields_to_interpolate: pc.point_data[field] = _interpolate( - nbrs_surface, pc.points, mesh.point_data[field], n_neighbors=k + nbrs_surface, pc.points, mesh.point_data[field], device=device, n_neighbors=k ) elif mesh_dtype == "cell": - # TODO: Possible to vectorize for field in fields_to_interpolate: pc.point_data[field] = _interpolate( - nbrs_surface, pc.points, mesh.cell_data[field], n_neighbors=k + nbrs_surface, pc.points, mesh.cell_data[field], device=device, n_neighbors=k ) return pc diff --git a/physicsnemo/cfd/postprocessing_tools/metric_registry.py b/physicsnemo/cfd/postprocessing_tools/metric_registry.py index e3875b5..8d51efb 100644 --- a/physicsnemo/cfd/postprocessing_tools/metric_registry.py +++ b/physicsnemo/cfd/postprocessing_tools/metric_registry.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Named metric registry (used by physicsnemo.cfd.evaluation and workflows).""" +"""Named metric registry with optional domain scoping (surface / volume).""" from __future__ import annotations @@ -10,21 +10,37 @@ MetricFn = Callable[..., float | dict[str, float]] -_REGISTRY: dict[str, MetricFn] = {} +_REGISTRY: dict[tuple[str, str | None], MetricFn] = {} -def register_metric(name: str, fn: MetricFn) -> None: - """Register a metric by name.""" - _REGISTRY[name] = fn +def register_metric(name: str, fn: MetricFn, *, domain: str | None = None) -> None: + """Register a metric by name and optional domain. + When ``domain`` is ``None`` the metric is domain-agnostic and acts as the + fallback when no domain-specific variant exists. When set (e.g. + ``"surface"`` or ``"volume"``), the metric is scoped to that domain and + only selected when the engine resolves with a matching domain. + """ + _REGISTRY[(name, domain)] = fn -def get_metric(name: str) -> MetricFn: - """Resolve metric function by name.""" - if name not in _REGISTRY: - raise KeyError(f"Unknown metric: {name}. Available: {sorted(_REGISTRY.keys())}") - return _REGISTRY[name] + +def get_metric(name: str, *, domain: str | None = None) -> MetricFn: + """Resolve a metric function by name, preferring a domain-specific variant. + + Lookup order: + 1. ``(name, domain)`` — exact domain match. + 2. ``(name, None)`` — domain-agnostic fallback. + """ + key = (name, domain) + if key in _REGISTRY: + return _REGISTRY[key] + fallback = (name, None) + if fallback in _REGISTRY: + return _REGISTRY[fallback] + available = sorted({n for n, _ in _REGISTRY}) + raise KeyError(f"Unknown metric: {name!r} (domain={domain!r}). Available: {available}") def list_metrics() -> list[str]: - """Return registered metric names.""" - return sorted(_REGISTRY.keys()) + """Return sorted unique metric names (without domain qualifiers).""" + return sorted({name for name, _ in _REGISTRY}) diff --git a/test/ci_tests/test_evaluation.py b/test/ci_tests/test_evaluation.py index 67b1c32..796caaa 100644 --- a/test/ci_tests/test_evaluation.py +++ b/test/ci_tests/test_evaluation.py @@ -36,10 +36,10 @@ def test_normalize_metrics_config_strings_and_dicts() -> None: specs = _normalize_metrics_config( [ "l2_pressure", - {"name": "drag_error", "coeff": 1.5}, + {"name": "drag", "coeff": 1.5}, ] ) - assert specs == [("l2_pressure", {}), ("drag_error", {"coeff": 1.5})] + assert specs == [("l2_pressure", {}), ("drag", {"coeff": 1.5})] def test_reports_visual_case_ids_and_mesh_retention() -> None: @@ -91,7 +91,7 @@ def test_config_from_dict_merges_output_and_reports() -> None: def test_list_metrics_includes_core_builtin() -> None: names = list_metrics() assert "l2_pressure" in names - assert "drag_error" in names + assert "drag" in names def test_build_comparison_mesh_surface_zero_l2_when_identical() -> None: diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index 0e0581b..cb38ee2 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -66,7 +66,7 @@ Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmar **Metrics cache (optional):** Under `run.metrics_cache`, `enabled: true` stores per-case scalars under `path` (resolved; default pattern `${run.output_dir}/metrics_cache`). Delete that directory for a full recompute. **Plots and meshes are not cached.** -**Model notes:** GeoTransolver / Transolver **volume** needs matching stats (`global_stats.json` with `velocity`, `pressure_volume`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** needs surface stats. DoMINO uses `domino_config` for volume. +**Model notes:** GeoTransolver / Transolver **volume** needs matching stats (`global_stats.json` with `velocity`, `pressure`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** needs surface stats. DoMINO uses `domino_config` for volume. **Custom code:** [Custom models](#custom-models-adding-a-new-wrapper) · [Custom datasets](#custom-datasets-adding-a-new-adapter) · [Baseline stubs](#baseline-model-stubs-surface_baseline-volume_baseline) @@ -88,12 +88,12 @@ metrics: - l2_pressure - l2_shear_stress - l2_pressure_area_weighted - - drag_error - - lift_error + - drag + - lift # Volume-oriented (see config_volume.yaml) metrics: - - l2_pressure_volume + - l2_pressure - l2_turbulent_viscosity - l2_velocity - continuity_residual_l2 @@ -104,9 +104,9 @@ metrics: | ---- | ------- | | `l2_pressure`, `l2_shear_stress` | Surface L2 | | `l2_pressure_area_weighted` | Area-weighted L2 pressure (`area_wt_l2_pressure`) | -| `l2_pressure_volume` | Volume pressure (`pressure_volume`) | +| `l2_pressure` (volume) | Volume pressure (domain-scoped, same name as surface) | | `l2_velocity`, `l2_turbulent_viscosity` | Volume / surface depending on case | -| `drag_error`, `lift_error` | Coefficient errors | +| `drag`, `lift` | Drag / lift coefficient errors (expands to `drag_error`, `drag_true`, `drag_pred`, etc.) | | `continuity_residual_l2`, `momentum_residual_l2` | Volume residuals | --- @@ -167,7 +167,7 @@ reports: ### Line plots -**`line_plot`** uses **`canonical_key`** (surface: `pressure`, `shear_stress`; volume: `pressure_volume`, …) and **`plot_coord`**. Centerline-style strip plots: see [`workflows/bench_example`](../bench_example/README.md) or a custom `register_visual`. +**`line_plot`** uses **`canonical_key`** (surface: `pressure`, `shear_stress`; volume: `pressure`, `velocity`, …) and **`plot_coord`**. Centerline-style strip plots: see [`workflows/bench_example`](../bench_example/README.md) or a custom `register_visual`. ### Troubleshooting @@ -212,7 +212,7 @@ dataset: ## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) - **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. -- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure_volume`, `turbulent_viscosity`, `velocity`. +- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure`, `turbulent_viscosity`, `velocity`. --- diff --git a/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml b/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml index 537b4ef..f828d90 100644 --- a/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml +++ b/workflows/benchmarking_workflow/conf/config_matrix_surface.yaml @@ -99,8 +99,8 @@ metrics: - l2_pressure - l2_shear_stress - l2_pressure_area_weighted - - drag_error - - lift_error + - drag + - lift reports: enabled: false diff --git a/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml index e5280c1..18cb219 100644 --- a/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml +++ b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml @@ -88,19 +88,19 @@ benchmark: output: volume_mesh_field_names: - pressure_volume: "pMeanTrimPred" + pressure: "pMeanTrimPred" turbulent_viscosity: "nutMeanTrimPred" velocity: "UMeanTrimPred" streamlines_vector_canonical: velocity metrics: - - l2_pressure_volume + - l2_pressure - l2_turbulent_viscosity - l2_velocity - continuity_residual_l2 - momentum_residual_l2 - - drag_error - - lift_error + - drag + - lift reports: enabled: false diff --git a/workflows/benchmarking_workflow/conf/config_surface.yaml b/workflows/benchmarking_workflow/conf/config_surface.yaml index 908d682..dd683dd 100644 --- a/workflows/benchmarking_workflow/conf/config_surface.yaml +++ b/workflows/benchmarking_workflow/conf/config_surface.yaml @@ -66,8 +66,8 @@ metrics: - l2_pressure - l2_shear_stress - l2_pressure_area_weighted - - drag_error - - lift_error + - drag + - lift reports: enabled: false @@ -105,9 +105,9 @@ reports: view: xy - name: design_scatter pairs: - - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } - - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } + - { name: Cd, true_key: drag_true, pred_key: drag_pred } + - { name: Cl, true_key: lift_true, pred_key: lift_pred } - name: design_trend pairs: - - { name: Cd, true_key: drag_error_true, pred_key: drag_error_pred } - - { name: Cl, true_key: lift_error_true, pred_key: lift_error_pred } + - { name: Cd, true_key: drag_true, pred_key: drag_pred } + - { name: Cl, true_key: lift_true, pred_key: lift_pred } diff --git a/workflows/benchmarking_workflow/conf/config_volume.yaml b/workflows/benchmarking_workflow/conf/config_volume.yaml index 2c352d2..e38527a 100644 --- a/workflows/benchmarking_workflow/conf/config_volume.yaml +++ b/workflows/benchmarking_workflow/conf/config_volume.yaml @@ -59,19 +59,17 @@ benchmark: output: volume_mesh_field_names: - pressure_volume: "pMeanTrimPred" + pressure: "pMeanTrimPred" turbulent_viscosity: "nutMeanTrimPred" velocity: "UMeanTrimPred" streamlines_vector_canonical: velocity metrics: - - l2_pressure_volume + - l2_pressure - l2_turbulent_viscosity - l2_velocity - continuity_residual_l2 - momentum_residual_l2 - - drag_error - - lift_error reports: enabled: false @@ -83,7 +81,7 @@ reports: - name: plot_fields_volume view: xy - name: line_plot - canonical_key: pressure_volume + canonical_key: pressure plot_coord: x normalize_factor: 1.0 true_line_kwargs: { label: Ground truth } @@ -91,3 +89,7 @@ reports: # Volume point mesh: uses ``output.streamlines_vector_canonical`` (velocity) when omitted. - name: streamlines_comparison view: xy + - name: aggregate_volume_errors + bounds: [-3.5, 8.5, -2.25, 2.25, -0.32, 3.00] + voxel_size: 0.03 + canonical_keys: ["pressure", "velocity", "turbulent_viscosity"] From 362ee52298bb5c0a66ad83a3d314f8882aae241a Mon Sep 17 00:00:00 2001 From: ram-cherukuri <104155145+ram-cherukuri@users.noreply.github.com> Date: Fri, 10 Apr 2026 09:19:44 -0400 Subject: [PATCH 14/25] Revise README for clarity and customization options Updated README to streamline configuration instructions and extend workflow customization guidelines. --- workflows/benchmarking_workflow/README.md | 51 ++++++++++------------- 1 file changed, 22 insertions(+), 29 deletions(-) diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index cb38ee2..8fa0227 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -3,12 +3,9 @@ This is an opinionated workflow to help you get started with evaluating and benchmarking pretrained models. It stitches together the model inference, calcuating built-in metrics and generating artifacts for analysis and visualization. We use a configuration based approach to allow you to extend this workflow to add your datasets, models, custom metrics etc. # Running OOB +We use configuration files to setup the model evaluation pipeline. These configs are under **`conf/`** for surface, volume evaluations for a model and a matrix run for comparing multiple models: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). -We have configs under **`conf/`** for surface, volume evaluations for a model and a matrix run for comparing multiple models: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). OmegaConf resolves **`${run.output_dir}`** (e.g. `run.metrics_cache.path`). - ---- - -## Commands +As a first step, you can this benchmarking example (link) to do an out of the box run. ```bash # Surface benchmark (VTP / wall metrics, default config) @@ -26,7 +23,7 @@ python main.py case_id=run_1 run.device=cuda:0 python main.py --config-name=config_volume run.output_dir=my_volume_run ``` -**Multi-GPU (optional):** install **physicsnemo** so `DistributedManager` is available, then from this directory launch with PyTorch distributed, for example: +**Multi-GPU (optional):** ```bash torchrun --standalone --nproc_per_node=4 main.py @@ -37,6 +34,25 @@ Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmar **Matrix mode:** use **[`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** or **[`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** as templates, or set `benchmark.mode: matrix` in any config and fill **`benchmark.models`** × **`benchmark.datasets`**. The product runs every model on every dataset entry; incompatible surface/volume pairs are skipped automatically. Edit checkpoint paths for your layout; comment out any **`- name:`** model block you do not want to run. +# How to Extend (Add Your Own Data/Models) +To extend this workflow for your own custom evaluation or benchmarking, you will likely want to customize three things: + +## Custom models (adding a new wrapper) + +1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. +2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. +3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. + +## Custom datasets (adding a new adapter) + +1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. +2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. +3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. + +## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) + +- **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. +- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure`, `turbulent_viscosity`, `velocity`. --- ## Config files @@ -193,29 +209,6 @@ dataset: --- -## Custom models (adding a new wrapper) - -1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. -2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. -3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. - ---- - -## Custom datasets (adding a new adapter) - -1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. -2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. -3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. - ---- - -## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) - -- **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. -- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure`, `turbulent_viscosity`, `velocity`. - ---- - ## Package layout (repo root) | Path | Role | From cb8f8accd54b469a063d98881370d977de93eca3 Mon Sep 17 00:00:00 2001 From: ram-cherukuri <104155145+ram-cherukuri@users.noreply.github.com> Date: Fri, 10 Apr 2026 09:23:22 -0400 Subject: [PATCH 15/25] Update README for benchmarking workflow sections --- workflows/benchmarking_workflow/README.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index 8fa0227..933bd93 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -2,7 +2,7 @@ This is an opinionated workflow to help you get started with evaluating and benchmarking pretrained models. It stitches together the model inference, calcuating built-in metrics and generating artifacts for analysis and visualization. We use a configuration based approach to allow you to extend this workflow to add your datasets, models, custom metrics etc. -# Running OOB +## Running OOB We use configuration files to setup the model evaluation pipeline. These configs are under **`conf/`** for surface, volume evaluations for a model and a matrix run for comparing multiple models: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). As a first step, you can this benchmarking example (link) to do an out of the box run. @@ -34,22 +34,27 @@ Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmar **Matrix mode:** use **[`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** or **[`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** as templates, or set `benchmark.mode: matrix` in any config and fill **`benchmark.models`** × **`benchmark.datasets`**. The product runs every model on every dataset entry; incompatible surface/volume pairs are skipped automatically. Edit checkpoint paths for your layout; comment out any **`- name:`** model block you do not want to run. -# How to Extend (Add Your Own Data/Models) +## How to Extend (Add Your Own Data/Models) To extend this workflow for your own custom evaluation or benchmarking, you will likely want to customize three things: -## Custom models (adding a new wrapper) +### Custom models (adding a new wrapper) 1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. 2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. 3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. -## Custom datasets (adding a new adapter) +### Custom datasets (adding a new adapter) 1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. 2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. 3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. -## Canonical types (`physicsnemo.cfd.evaluation.datasets.schema`) +### Custom Metrics (adding a new metric) + + +### Canonical types + +The schema is defined in `physicsnemo.cfd.evaluation.datasets.schema` - **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. - **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure`, `turbulent_viscosity`, `velocity`. From d0c37f48d152140bac03905aa12dab328327aa96 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Fri, 10 Apr 2026 12:22:10 -0700 Subject: [PATCH 16/25] update api --- .../cfd/postprocessing_tools/metrics/l2_errors.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py b/physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py index bdb4d00..b49e4c5 100644 --- a/physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py +++ b/physicsnemo/cfd/postprocessing_tools/metrics/l2_errors.py @@ -16,6 +16,7 @@ import numpy as np import pyvista as pv +import torch from physicsnemo.nn.functional import signed_distance_field @@ -229,12 +230,15 @@ def compute_error_vs_sdf( - "bin_edges": List of SDF bin edges - "mean_errors": List of mean errors for each SDF bin """ - stl_vertices = stl_mesh.points - stl_indices = np.arange(0, stl_mesh.points.shape[0]) + stl_vertices = torch.tensor(np.asarray(stl_mesh.points), dtype=torch.float32) + stl_faces = np.array(stl_mesh.faces).reshape(-1, 4)[:, 1:] + stl_indices = torch.tensor(stl_faces.flatten(), dtype=torch.int32) + query_points = torch.tensor(np.asarray(data.points), dtype=torch.float32) - sdf_field = signed_distance_field( - stl_vertices, stl_indices, data.points, use_sign_winding_number=True + sdf_field, _ = signed_distance_field( + stl_vertices, stl_indices, query_points, use_sign_winding_number=True ) + sdf_field = sdf_field.numpy() true_fields_list = true_fields pred_fields_list = pred_fields From 0124bee59a642f68b176c6e56603538ed59cfae2 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Fri, 10 Apr 2026 12:22:34 -0700 Subject: [PATCH 17/25] remove xmgn and fgnet volume, because they don't exist --- .../conf/config_matrix_volume.yaml | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml index 18cb219..f88131c 100644 --- a/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml +++ b/workflows/benchmarking_workflow/conf/config_matrix_volume.yaml @@ -56,16 +56,6 @@ benchmark: checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_volume_checkpoint/checkpoint.0.501.pt" stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_volume_checkpoint/global_stats.json" kwargs: { batch_resolution: 60000, geometry_sampling: 300000 } - - name: xmgn - inference_domain: volume - checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_volume_checkpoint/checkpoint.0.501.pt" - stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/xmgn_drivaerml_volume_checkpoint/global_stats.json" - kwargs: { "max_points": 250000, "interpolation_k": 4 } - - name: fignet - inference_domain: volume - checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_volume_checkpoint/checkpoint.0.501.pt" - stats_path: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/fignet_drivaerml_volume_checkpoint/global_stats.json" - kwargs: { "max_points": 250000, "interpolation_k": 4 } - name: domino inference_domain: volume checkpoint: "/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/domino_drivaerml_volume_checkpoint/checkpoint.0.501.pt" From eb0be99760e297371e65da6204aa04e21b145f50 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Fri, 10 Apr 2026 12:22:58 -0700 Subject: [PATCH 18/25] add notebooks after validation --- .../notebooks/surface_benchmarking.ipynb | 807 ++++++++++ .../notebooks/volume_benchmarking.ipynb | 1370 +++++++++++++++++ 2 files changed, 2177 insertions(+) create mode 100644 workflows/benchmarking_workflow/notebooks/surface_benchmarking.ipynb create mode 100644 workflows/benchmarking_workflow/notebooks/volume_benchmarking.ipynb diff --git a/workflows/benchmarking_workflow/notebooks/surface_benchmarking.ipynb b/workflows/benchmarking_workflow/notebooks/surface_benchmarking.ipynb new file mode 100644 index 0000000..8234edd --- /dev/null +++ b/workflows/benchmarking_workflow/notebooks/surface_benchmarking.ipynb @@ -0,0 +1,807 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "bb31b8c6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:07.788497Z", + "iopub.status.busy": "2026-04-10T06:31:07.788418Z", + "iopub.status.idle": "2026-04-10T06:31:07.790616Z", + "shell.execute_reply": "2026-04-10T06:31:07.790221Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"WORLD_SIZE\"] = \"1\"\n", + "os.environ[\"RANK\"] = \"0\"\n", + "os.environ[\"LOCAL_RANK\"] = \"0\"\n", + "os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", + "os.environ[\"MASTER_PORT\"] = \"12355\"" + ] + }, + { + "cell_type": "markdown", + "id": "2ade0467-02ea-433d-8a27-cc19970076c7", + "metadata": {}, + "source": [ + "# Surface Benchmarking of External Aero Flows\n", + "\n", + "In this notebook, we walk through the steps required to generate a sample model inference on a simulation mesh and to compare the model's predictions with ground-truth data across several metrics relevant to external aerodynamics. This notebook uses the surface results from the [DrivAerML dataset](https://caemldatasets.org/drivaerml/). Specifically, we use sample ID `202` to compute all metrics and comparisons." + ] + }, + { + "cell_type": "markdown", + "id": "f6a0b332", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "For running this notebook, ensure that you have installed the library using `[gpu]` optional dependency group. This can be done using:\n", + "\n", + "```bash\n", + "pip install .[gpu] --extra-index-url=https://pypi.nvidia.com\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a856a38e-51df-4c54-8a7c-012428a33578", + "metadata": {}, + "source": [ + "## Compute model inference on the benchmark simulation\n", + "\n", + "The benchmark results are saved in `.vtp` (surface) and `.vtu` (volume) formats. For this notebook, let's start by loading the `.vtp` file and inspecting the available fields. We will use the [`pyvista`](https://pyvista.org/) library for this purpose." + ] + }, + { + "cell_type": "markdown", + "id": "34449906-7936-42d4-b727-e57662fde348", + "metadata": {}, + "source": [ + "### Download the benchmark solution\n", + "\n", + "Let's download the solution from the [DrivAerML dataset](https://huggingface.co/datasets/neashton/drivaerml)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e4fea01c-f4fa-44c3-8b8e-9130834eb420", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:07.791608Z", + "iopub.status.busy": "2026-04-10T06:31:07.791526Z", + "iopub.status.idle": "2026-04-10T06:31:07.794207Z", + "shell.execute_reply": "2026-04-10T06:31:07.793849Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drivaer_202.stl already exists. Skipping download.\n", + "boundary_202.vtp already exists. Skipping download.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filenames = [\n", + " \"drivaer_202.stl\",\n", + " \"boundary_202.vtp\",\n", + "]\n", + "urls = [\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/drivaer_202.stl\",\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/boundary_202.vtp\",\n", + "]\n", + "\n", + "for filename, url in zip(filenames, urls):\n", + " if not os.path.exists(filename):\n", + " !wget \"{url}\" -O \"{filename}\"\n", + " else:\n", + " print(f\"{filename} already exists. Skipping download.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7f45afee-0a6e-45f4-9c02-4053184d5ce9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:07.795113Z", + "iopub.status.busy": "2026-04-10T06:31:07.795030Z", + "iopub.status.idle": "2026-04-10T06:31:12.134983Z", + "shell.execute_reply": "2026-04-10T06:31:12.134474Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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You can adapt the code to your specific model inputs/outputs.\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "20d2f20d-b77d-485f-84d8-9beadb47fbd1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:12.136495Z", + "iopub.status.busy": "2026-04-10T06:31:12.136341Z", + "iopub.status.idle": "2026-04-10T06:31:53.822071Z", + "shell.execute_reply": "2026-04-10T06:31:53.821610Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warp DeprecationWarning: The namespace `warp.context` will soon be removed from the public API. It can still be accessed from `warp._src.context` but might be changed or removed without notice.\n", + "Warp DeprecationWarning: The symbol `warp.context.Device` will soon be removed from the public API. Use `warp.Device` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/library.py:357: UserWarning: Warning only once for all operators, other operators may also be overridden.\n", + " Overriding a previously registered kernel for the same operator and the same dispatch key\n", + " operator: flash_attn::_flash_attn_backward(Tensor dout, Tensor q, Tensor k, Tensor v, Tensor out, Tensor softmax_lse, Tensor(a6!)? dq, Tensor(a7!)? dk, Tensor(a8!)? dv, float dropout_p, float softmax_scale, bool causal, SymInt window_size_left, SymInt window_size_right, float softcap, Tensor? alibi_slopes, bool deterministic, Tensor? rng_state=None) -> Tensor\n", + " registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926\n", + " dispatch key: ADInplaceOrView\n", + " previous kernel: no debug info\n", + " new kernel: registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926 (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/core/dispatch/OperatorEntry.cpp:208.)\n", + " self.m.impl(\n", + "/usr/local/lib/python3.12/dist-packages/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py:57: ExperimentalFeatureWarning: You are importing from 'physicsnemo.experimental'. The APIs in this namespace are experimental, under active development, and may change without notice. Expect possible back-compatibility breaking changes and only partial test coverage.\n", + " from physicsnemo.experimental.models.geotransolver import GeoTransolver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading DoMINO config from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/config_surface.yaml; checkpoint from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/surface/DoMINO.0.535.mdlus\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading checkpoint from directory /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/surface\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[93mCould not find valid checkpoint file, skipping load\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Checkpoint loaded; model ready for inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference took 27.479 sec\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
NameFieldTypeN CompMinMax
CpMeanTrimCellsfloat321-4.619e+001.039e+00
pMeanTrimCellsfloat321-3.493e+037.859e+02
pPrime2MeanTrimCellsfloat3213.644e-011.568e+06
wallShearStressMeanTrimCellsfloat323-3.807e+013.822e+01
pMeanTrimPredCellsfloat321-2.620e+038.415e+02
wallShearStressMeanTrimPredCellsfloat323-2.875e+013.176e+01
TimeValueFieldsfloat3213.000e+003.000e+00
\n", + "\n", + "
" + ], + "text/plain": [ + "PolyData (0x7fffb62f9c00)\n", + " N Cells: 8883343\n", + " N Points: 8902069\n", + " N Strips: 0\n", + " X Bounds: -9.240e-01, 3.886e+00\n", + " Y Bounds: -1.061e+00, 1.061e+00\n", + " Z Bounds: -3.176e-01, 1.074e+00\n", + " N Arrays: 7" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.domino.wrapper import DominoWrapper\n", + "from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase\n", + "\n", + "CHECKPOINT_DIR = \"/lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint\"\n", + "\n", + "wrapper = DominoWrapper()\n", + "wrapper.load(\n", + " checkpoint_path=f\"{CHECKPOINT_DIR}/surface/DoMINO.0.535.mdlus\",\n", + " stats_path=\"\",\n", + " device=\"cuda:0\",\n", + " domino_config=f\"{CHECKPOINT_DIR}/config_surface.yaml\",\n", + ")\n", + "\n", + "case = CanonicalCase(\n", + " case_id=\"run_202\",\n", + " mesh_path=\"./boundary_202.vtp\",\n", + " mesh_type=\"cell\",\n", + " inference_domain=\"surface\",\n", + ")\n", + "\n", + "start_time = time.time()\n", + "model_input = wrapper.prepare_inputs(case)\n", + "raw_output = wrapper.predict(model_input)\n", + "predictions = wrapper.decode_outputs(raw_output, case)\n", + "\n", + "mesh.cell_data[\"pMeanTrimPred\"] = predictions[\"pressure\"]\n", + "mesh.cell_data[\"wallShearStressMeanTrimPred\"] = predictions[\"shear_stress\"]\n", + "print(f\"Inference took {time.time() - start_time:.3f} sec\")\n", + "\n", + "mesh" + ] + }, + { + "cell_type": "markdown", + "id": "55f8bd44-6e6d-4d04-8c5b-961171593dc4", + "metadata": {}, + "source": [ + "Now that we have both the predicted and true fields on the same points (cell centers to be precise), we can start with comparing these two solutions." + ] + }, + { + "cell_type": "markdown", + "id": "796c655f-f956-4c32-ad2c-4c4c3390c3b3", + "metadata": {}, + "source": [ + "## L2 Errors\n", + "\n", + "Let's compare the L2 errors for the pressure and the wall shear stress fields. We will use the `compute_l2_errors` function from the library. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "856740d4-9321-4db6-9f0b-b187ce093c56", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:53.824104Z", + "iopub.status.busy": "2026-04-10T06:31:53.823527Z", + "iopub.status.idle": "2026-04-10T06:31:53.960139Z", + "shell.execute_reply": "2026-04-10T06:31:53.959682Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pMeanTrim_l2_error': 0.10618807,\n", + " 'wallShearStressMeanTrim_x_l2_error': 0.13462158,\n", + " 'wallShearStressMeanTrim_y_l2_error': 0.17281345,\n", + " 'wallShearStressMeanTrim_z_l2_error': 0.17960995}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors\n", + "\n", + "l2_errors = compute_l2_errors(\n", + " mesh,\n", + " true_fields=[\"pMeanTrim\", \"wallShearStressMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"wallShearStressMeanTrimPred\"],\n", + " dtype=\"cell\",\n", + ")\n", + "l2_errors" + ] + }, + { + "cell_type": "markdown", + "id": "6a6c4bbc-9e2f-49ea-8f7d-4479ee3bad5d", + "metadata": {}, + "source": [ + "This provides a numerical comparison of the model's overall performance.\n", + "\n", + "Meshes are typically refined with smaller triangles in areas of high geometric detail. We can compute area-weighted L2 errors to see how the error relates to cell size using `compute_area_weighted_l2_errors`. Note: this metric only works for `cell` dtype." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dcb8c5e1-9ffa-4e60-8ff3-a48158f41fc4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:53.961577Z", + "iopub.status.busy": "2026-04-10T06:31:53.961487Z", + "iopub.status.idle": "2026-04-10T06:31:56.028319Z", + "shell.execute_reply": "2026-04-10T06:31:56.027813Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pMeanTrim_area_wt_l2_error': 0.07522427231349582,\n", + " 'wallShearStressMeanTrim_x_area_wt_l2_error': 0.07893754107934493,\n", + " 'wallShearStressMeanTrim_y_area_wt_l2_error': 0.16301201776720795,\n", + " 'wallShearStressMeanTrim_z_area_wt_l2_error': 0.13930543469879025}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_area_weighted_l2_errors\n", + "\n", + "area_weighted_l2_errors = compute_area_weighted_l2_errors(\n", + " mesh,\n", + " true_fields=[\"pMeanTrim\", \"wallShearStressMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"wallShearStressMeanTrimPred\"],\n", + " dtype=\"cell\",\n", + ")\n", + "area_weighted_l2_errors" + ] + }, + { + "cell_type": "markdown", + "id": "7e4ddb73-3b34-48f1-a758-ecacc8fdcf87", + "metadata": {}, + "source": [ + "## Computing drag metrics\n", + "\n", + "Drag force is an important consideration for the efficiency and performance of the vehicle. We can use the `compute_drag_and_lift` function to compute the drag and lift coefficient / forces" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b087438c-64f6-4a1b-b0bb-5673bf559a79", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:31:56.029843Z", + "iopub.status.busy": "2026-04-10T06:31:56.029741Z", + "iopub.status.idle": "2026-04-10T06:32:07.501093Z", + "shell.execute_reply": "2026-04-10T06:32:07.500631Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drag Force (D): True: 482.3444254302631, Pred: 470.91008068498274, Error (%): 2.3705767377907625\n", + "D_p: True: 435.0610781379878, Pred: 423.5345596397642, Error (%): 2.6494023661127746\n", + "D_f: True: 47.28334729227524, Pred: 47.37552104521856, Error (%): 0.19493914500926843\n", + "Lift Force (L): True: 37.28053920122983, Pred: 46.80885802714559, Error (%): 25.558425468270684\n", + "L_p: True: 34.24916887927558, Pred: 43.60398851369594, Error (%): 27.314004808102155\n", + "L_f: True: 3.03137032195425, Pred: 3.204869513449652, Error (%): 5.723457481880723\n" + ] + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.metrics.aero_forces import compute_drag_and_lift\n", + "\n", + "forces_true = compute_drag_and_lift(\n", + " mesh,\n", + " pressure_field=\"pMeanTrim\",\n", + " wss_field=\"wallShearStressMeanTrim\",\n", + " coeff=1.0, # Set it to 1.0 to get raw forces. can be replaced 2 / (rho * u * u * ref_area) for coefficients\n", + " drag_direction=[1, 0, 0],\n", + " lift_direction=[0, 0, 1],\n", + " dtype=\"cell\",\n", + ") # returns F_drag, F_drag_pressure, F_drag_skin_friction, F_lift, F_lift_pressure, F_lift_skin_friction\n", + "\n", + "forces_pred = compute_drag_and_lift(\n", + " mesh,\n", + " pressure_field=\"pMeanTrimPred\",\n", + " wss_field=\"wallShearStressMeanTrimPred\",\n", + " coeff=1.0, # Set it to 1.0 to get raw forces. can be replaced 2 / (rho * u * u * ref_area) for coefficients\n", + " drag_direction=[1, 0, 0],\n", + " lift_direction=[0, 0, 1],\n", + " dtype=\"cell\",\n", + ") # returns F_drag, F_drag_pressure, F_drag_skin_friction, F_lift, F_lift_pressure, F_lift_skin_friction\n", + "\n", + "labels = [\"Drag Force (D)\", \"D_p\", \"D_f\", \"Lift Force (L)\", \"L_p\", \"L_f\"]\n", + "for label, f_true, f_pred in zip(labels, forces_true, forces_pred):\n", + " print(\n", + " f\"{label}: True: {f_true}, Pred: {f_pred}, Error (%): {abs(f_true - f_pred) * 100 / f_true}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "0a091429-cc27-476c-bcbf-d69addfeb682", + "metadata": {}, + "source": [ + "When computing drag and lift across many cases, you can use visualization utilities such as `physicsnemo.cfd.postprocessing_tools.visualization.utils.plot_design_scatter` and `physicsnemo.cfd.postprocessing_tools.visualization.utils.plot_design_trend` to compute metrics (e.g., R2 scores), compare design trends, and measure the ability of models to capture directional changes. The workflow scripts ([../generate_surface_benchmarks.py](../generate_surface_benchmarks.py) and [../generate_volume_benchmarks.py](../generate_volume_benchmarks.py)) use these functions to create plots like the examples below:\n", + "\n", + "![Sample R2 score plot](img/design_scatter_plot.png)\n", + "\n", + "![Sample design trend plot](img/design_trend_plot.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9e24e2e5-0aa6-461e-818c-df313053a784", + "metadata": {}, + "source": [ + "## Plotting fields\n", + "\n", + "So far, we have examined numerical metrics, which are important for analysis. However, visualizing the flow fields is often critical for understanding flow behavior (e.g., separation regions and stagnation points)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ee8375bc-ee00-44f0-95ee-dcfbef3050e3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:32:07.502534Z", + "iopub.status.busy": "2026-04-10T06:32:07.502444Z", + "iopub.status.idle": "2026-04-10T06:33:18.538323Z", + "shell.execute_reply": "2026-04-10T06:33:18.537821Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/physicsnemo/cfd/postprocessing_tools/visualization/utils.py:26: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2326229/65658671.py:3: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb() # might require `sudo apt install libgl1 libglx-mesa0 xvfb`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-09 23:32:14.120 ( 66.274s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-09 23:32:14.204 ( 66.358s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons\n", + "\n", + "pv.start_xvfb() # might require `sudo apt install libgl1 libglx-mesa0 xvfb`\n", + "\n", + "plotter = plot_field_comparisons(\n", + " mesh,\n", + " true_fields=[\"pMeanTrim\", \"wallShearStressMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"wallShearStressMeanTrimPred\"],\n", + " view=\"xy\",\n", + " plot_vector_components=True,\n", + " cmap=\"jet\",\n", + " lut=20,\n", + ")\n", + "plotter.screenshot(\"./sample_202_surface_comparison.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_surface_comparison.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac187de5", + "metadata": {}, + "source": [ + "The visualization reveals that the AI prediction captures the larger structures in the flow field for most variables, except the x-component of wall shear stress, where it struggles with the flow structures on the hood and roof. For other variables, larger errors are observed near the mirrors and sharp edges.\n", + "\n", + "Such visualizations do not show how predictions look across the entire validation dataset. For that, one can project errors from different samples onto fixed planes and build aggregate visualizations. The [`../generate_surface_benchmarks.py`](../generate_surface_benchmarks.py) script provides this capability.\n", + "\n", + "Below we show the error distribution across the entire DrivAerML validation set.\n", + "\n", + "![Aggregate Errors](img/hexbin_sample.png)\n", + "\n", + "Overall, errors are typically higher near the intersections of the front windshield with the roof and the rear windshield with the roof. Errors are also higher near the front of the vehicle.\n", + "\n", + "Such analysis is especially useful when geometric differences between samples are modest (e.g., DrivAerML), where such projections enable meaningful aggregate visualizations." + ] + }, + { + "cell_type": "markdown", + "id": "87751582-0117-41cd-8cba-82974b13beb8", + "metadata": {}, + "source": [ + "We can also create line plots for more detailed visualization and comparison. Here, we plot the pressure along the centerline of the car." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "553298a7-5e35-46ce-9f89-1238c996c1f3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:33:18.559901Z", + "iopub.status.busy": "2026-04-10T06:33:18.559306Z", + "iopub.status.idle": "2026-04-10T06:33:19.555251Z", + "shell.execute_reply": "2026-04-10T06:33:19.554717Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line\n", + "\n", + "# create a slice along xz plane\n", + "centerline = mesh.slice(origin=(0, 0, 0), normal=(0, 1, 0))\n", + "centerline = centerline.clip(\n", + " origin=(0, 0, 0.4), normal=(0, 0, 1), invert=False\n", + ") # Select only the top half\n", + "centerline = centerline.cell_data_to_point_data(pass_cell_data=True)\n", + "\n", + "fig = plot_line(\n", + " centerline,\n", + " plot_coord=\"x\",\n", + " field_true=\"pMeanTrim\",\n", + " field_pred=\"pMeanTrimPred\",\n", + " normalize_factor=(38.89**2) / 2,\n", + " flip=False,\n", + " xlabel=\"X Coordinate\",\n", + " ylabel=\"p / U_ref^2\",\n", + " true_line_kwargs={\"color\": \"red\", \"label\": \"True\"},\n", + " pred_line_kwargs={\"color\": \"green\", \"label\": \"Pred\"},\n", + " figsize=(6, 4),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "95ab0bfd", + "metadata": {}, + "source": [ + "Aside from the sharp peaks, the centerline pressure matches the ground truth well." + ] + }, + { + "cell_type": "markdown", + "id": "35889e15-2649-4895-a56a-d980b5d5af7f", + "metadata": {}, + "source": [ + "Streamlines provide another way to visualize the flow fields and gain deeper insight into flow behavior. For surface results, we can create streamlines of wall shear stress. Below, we create streamlines using `physicsnemo.cfd.postprocessing_tools.metrics.streamlines.compute_streamlines` and overlay them on the geometry STL for clarity." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "14d73c3b-74d0-46c8-8559-0206baae71b4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T06:33:19.556483Z", + "iopub.status.busy": "2026-04-10T06:33:19.556391Z", + "iopub.status.idle": "2026-04-10T06:34:37.727016Z", + "shell.execute_reply": "2026-04-10T06:34:37.726487Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from physicsnemo.cfd.postprocessing_tools.metrics.streamlines import compute_streamlines\n", + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_streamlines\n", + "\n", + "true_streamlines = compute_streamlines(mesh, field=\"wallShearStressMeanTrim\")\n", + "pred_streamlines = compute_streamlines(mesh, field=\"wallShearStressMeanTrimPred\")\n", + "\n", + "stl_mesh = pv.read(\"./drivaer_202.stl\")\n", + "# Shrink the STL slightly\n", + "center = np.array(stl_mesh.center)\n", + "scale_factor = 0.999\n", + "stl_mesh = stl_mesh.translate(-center).scale(scale_factor).translate(center)\n", + "\n", + "plotter = plot_streamlines(true_streamlines, pred_streamlines, stl_mesh, view=\"xy\")\n", + "plotter.screenshot(\"./sample_202_streamlines.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_streamlines.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "4b17db24-be5c-47a0-ac06-49586cfbdda6", + "metadata": {}, + "source": [ + "That completes the surface benchmarking notebook. For volume benchmarking, see [`volume_benchmarking.ipynb`](./volume_benchmarking.ipynb). To run these metrics across multiple geometries or cases, refer to [`../generate_surface_benchmarks.py`](../generate_surface_benchmarks.py)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflows/benchmarking_workflow/notebooks/volume_benchmarking.ipynb b/workflows/benchmarking_workflow/notebooks/volume_benchmarking.ipynb new file mode 100644 index 0000000..f7fb0d1 --- /dev/null +++ b/workflows/benchmarking_workflow/notebooks/volume_benchmarking.ipynb @@ -0,0 +1,1370 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6edb7ff5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:28:50.581439Z", + "iopub.status.busy": "2026-04-10T17:28:50.581363Z", + "iopub.status.idle": "2026-04-10T17:28:50.583528Z", + "shell.execute_reply": "2026-04-10T17:28:50.583194Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"WORLD_SIZE\"] = \"1\"\n", + "os.environ[\"RANK\"] = \"0\"\n", + "os.environ[\"LOCAL_RANK\"] = \"0\"\n", + "os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", + "os.environ[\"MASTER_PORT\"] = \"12355\"" + ] + }, + { + "cell_type": "markdown", + "id": "b210208e-6aa5-43a1-a4c0-08505276f1e9", + "metadata": {}, + "source": [ + "# Volume Benchmarking of External Aero Flows\n", + "\n", + "In this notebook, we walk through the steps required to generate a sample model inference on a simulation mesh and compare the model's predictions with ground-truth data across several metrics relevant to external aerodynamics. This notebook uses the volume results from the [DrivAerML dataset](https://caemldatasets.org/drivaerml/). Specifically, we use sample ID `202` to compute all metrics and comparisons." + ] + }, + { + "cell_type": "markdown", + "id": "07d9e313", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "For running this notebook, ensure that you have installed the library using `[gpu]` optional dependency group. This can be done using:\n", + "\n", + "```bash\n", + "pip install .[gpu] --extra-index-url=https://pypi.nvidia.com\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7f15e290-ecfe-4f31-89aa-0aa499927df8", + "metadata": {}, + "source": [ + "## Compute model inference on the benchmark simulation\n", + "\n", + "The benchmark results are saved in `.vtp` (surface) and `.vtu` (volume) formats. For this notebook, let's start by loading the `.vtu` file and inspecting the available fields. We will use the `pyvista` library for this purpose.\n", + "\n", + "---\n", + "**NOTE**\n", + "\n", + "The volume files in the DrivAerML dataset are large (each file is ~38 GB). Ensure you have sufficient memory to load them.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b92172c0-267d-4a95-8903-94b1da58a7b2", + "metadata": {}, + "source": [ + "### Download the benchmark solution\n", + "\n", + "Let's download the solution from the [DrivAerML dataset](https://huggingface.co/datasets/neashton/drivaerml)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5b0ba7c-cb5e-47e6-8b6b-a1ee5e0605f0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:28:50.584535Z", + "iopub.status.busy": "2026-04-10T17:28:50.584455Z", + "iopub.status.idle": "2026-04-10T17:28:50.589339Z", + "shell.execute_reply": "2026-04-10T17:28:50.588962Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drivaer_202.stl already exists. Skipping download.\n", + "volume_202.vtu.00.part already exists. Skipping download.\n", + "volume_202.vtu.01.part already exists. Skipping download.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filenames = [\n", + " \"drivaer_202.stl\",\n", + " \"volume_202.vtu.00.part\",\n", + " \"volume_202.vtu.01.part\",\n", + "]\n", + "urls = [\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/drivaer_202.stl\",\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/volume_202.vtu.00.part\",\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_202/volume_202.vtu.01.part\",\n", + "]\n", + "\n", + "for filename, url in zip(filenames, urls):\n", + " if not os.path.exists(filename):\n", + " !wget \"{url}\" -O \"{filename}\"\n", + " else:\n", + " print(f\"{filename} already exists. Skipping download.\")\n", + "\n", + "if not os.path.exists(\"volume_202.vtu\"):\n", + " !cat \"volume_202.vtu.00.part\" \"volume_202.vtu.01.part\" > \"volume_202.vtu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6ee18bd3-feff-4ded-9ce9-b20b92603ccd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:28:50.590268Z", + "iopub.status.busy": "2026-04-10T17:28:50.590187Z", + "iopub.status.idle": "2026-04-10T17:34:13.497773Z", + "shell.execute_reply": "2026-04-10T17:34:13.497196Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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N Points165122607
X Bounds-4.000e+01, 8.000e+01
Y Bounds-2.200e+01, 2.200e+01
Z Bounds-3.176e-01, 1.968e+01
N Arrays21
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NameFieldTypeN CompMinMax
CpMeanTrimPointsfloat321-4.286e+001.035e+00
CptMeanTrimPointsfloat321-4.286e+001.301e+00
magUMeanNormTrimPointsfloat3211.462e-042.020e+00
microDragMeanTrimPointsfloat321-2.409e+004.420e+00
nutMeanTrimPointsfloat3215.102e-116.102e-02
pMeanTrimPointsfloat321-3.241e+037.829e+02
pPrime2MeanTrimPointsfloat3210.000e+006.405e+05
UMeanTrimPointsfloat323-6.739e+016.879e+01
UPrime2MeanTrimPointsfloat326-1.944e+024.149e+02
turbulenceProperties:RMeanTrimPointsfloat326-2.458e+018.858e+01
CpMeanTrimCellsfloat321-4.619e+001.039e+00
CptMeanTrimCellsfloat321-1.744e+001.618e+00
magUMeanNormTrimCellsfloat3211.438e-052.092e+00
microDragMeanTrimCellsfloat321-3.446e+004.820e+00
nutMeanTrimCellsfloat3219.542e-126.847e-02
pMeanTrimCellsfloat321-3.493e+037.859e+02
pPrime2MeanTrimCellsfloat3219.291e-061.568e+06
UMeanTrimCellsfloat323-7.398e+017.424e+01
UPrime2MeanTrimCellsfloat326-2.800e+024.767e+02
turbulenceProperties:RMeanTrimCellsfloat326-2.622e+011.136e+02
TimeValueFieldsfloat3213.000e+003.000e+00
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" + ], + "text/plain": [ + "UnstructuredGrid (0x7fffb63061a0)\n", + " N Cells: 146754704\n", + " N Points: 165122607\n", + " X Bounds: -4.000e+01, 8.000e+01\n", + " Y Bounds: -2.200e+01, 2.200e+01\n", + " Z Bounds: -3.176e-01, 1.968e+01\n", + " N Arrays: 21" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pyvista as pv\n", + "\n", + "mesh = pv.read(\"./volume_202.vtu\")\n", + "mesh" + ] + }, + { + "cell_type": "markdown", + "id": "c170b571-5f5f-4223-bc96-4a71573d77b4", + "metadata": {}, + "source": [ + "We observe that the mesh contains `165,122,607` points and `146,754,704` cells. The pressure, velocity, and turbulent viscosity variables are stored as `pMeanTrim`, `UMeanTrim`, and `nutMeanTrim`, respectively. We'll denote the model's predictions as `pMeanTrimPred`, `UMeanTrimPred`, and `nutMeanTrimPred`. We compute the inference using the DoMINO model wrapper with a pretrained checkpoint. Unlike the surface notebook, we will perform all computations on point data, and we will store model results on point data as well. You can adapt the code to your specific model inputs/outputs.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a63a430-3904-42e9-9adb-ce52f2b68b08", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:34:13.499196Z", + "iopub.status.busy": "2026-04-10T17:34:13.499040Z", + "iopub.status.idle": "2026-04-10T17:43:17.825110Z", + "shell.execute_reply": "2026-04-10T17:43:17.824494Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo/physicsnemo/nn/functional/interpolation/_torch_impl.py:69: SyntaxWarning: invalid escape sequence '\\('\n", + " \"\"\"\n", + "/workspace/aero-benchmarking/physicsnemo/physicsnemo/nn/functional/interpolation/_torch_impl.py:92: SyntaxWarning: invalid escape sequence '\\('\n", + " \"\"\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warp DeprecationWarning: The namespace `warp.context` will soon be removed from the public API. It can still be accessed from `warp._src.context` but might be changed or removed without notice.\n", + "Warp DeprecationWarning: The symbol `warp.context.Device` will soon be removed from the public API. Use `warp.Device` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/library.py:357: UserWarning: Warning only once for all operators, other operators may also be overridden.\n", + " Overriding a previously registered kernel for the same operator and the same dispatch key\n", + " operator: flash_attn::_flash_attn_backward(Tensor dout, Tensor q, Tensor k, Tensor v, Tensor out, Tensor softmax_lse, Tensor(a6!)? dq, Tensor(a7!)? dk, Tensor(a8!)? dv, float dropout_p, float softmax_scale, bool causal, SymInt window_size_left, SymInt window_size_right, float softcap, Tensor? alibi_slopes, bool deterministic, Tensor? rng_state=None) -> Tensor\n", + " registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926\n", + " dispatch key: ADInplaceOrView\n", + " previous kernel: no debug info\n", + " new kernel: registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926 (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/core/dispatch/OperatorEntry.cpp:208.)\n", + " self.m.impl(\n", + "/workspace/aero-benchmarking/physicsnemo/physicsnemo/utils/logging/launch.py:327: SyntaxWarning: invalid escape sequence '\\.'\n", + " key = re.sub(\"[^a-zA-Z0-9\\.\\-\\s\\/\\_]+\", \"\", key)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py:57: ExperimentalFeatureWarning: You are importing from 'physicsnemo.experimental'. The APIs in this namespace are experimental, under active development, and may change without notice. Expect possible back-compatibility breaking changes and only partial test coverage.\n", + " from physicsnemo.experimental.models.geotransolver import GeoTransolver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading DoMINO config from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/config_volume.yaml; checkpoint from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/volume/DoMINO.0.615.mdlus\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading checkpoint from directory /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/volume\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[93mCould not find valid checkpoint file, skipping load\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Checkpoint loaded; model ready for inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Reading case inputs (case run_202): mesh ./volume_202.vtu, run dir .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting volume fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding volume outputs (canonical keys from variables.volume.solution)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference took 493.826 sec\n" + ] + }, + { + "data": { + "text/html": [ + "
HeaderData Arrays
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UnstructuredGridInformation
N Cells146754704
N Points165122607
X Bounds-4.000e+01, 8.000e+01
Y Bounds-2.200e+01, 2.200e+01
Z Bounds-3.176e-01, 1.968e+01
N Arrays24
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NameFieldTypeN CompMinMax
CpMeanTrimPointsfloat321-4.286e+001.035e+00
CptMeanTrimPointsfloat321-4.286e+001.301e+00
magUMeanNormTrimPointsfloat3211.462e-042.020e+00
microDragMeanTrimPointsfloat321-2.409e+004.420e+00
nutMeanTrimPointsfloat3215.102e-116.102e-02
pMeanTrimPointsfloat321-3.241e+037.829e+02
pPrime2MeanTrimPointsfloat3210.000e+006.405e+05
UMeanTrimPointsfloat323-6.739e+016.879e+01
UPrime2MeanTrimPointsfloat326-1.944e+024.149e+02
turbulenceProperties:RMeanTrimPointsfloat326-2.458e+018.858e+01
pMeanTrimPredPointsfloat321-1.538e+041.544e+03
UMeanTrimPredPointsfloat323-4.149e+028.817e+01
nutMeanTrimPredPointsfloat321-1.667e-022.379e-02
CpMeanTrimCellsfloat321-4.619e+001.039e+00
CptMeanTrimCellsfloat321-1.744e+001.618e+00
magUMeanNormTrimCellsfloat3211.438e-052.092e+00
microDragMeanTrimCellsfloat321-3.446e+004.820e+00
nutMeanTrimCellsfloat3219.542e-126.847e-02
pMeanTrimCellsfloat321-3.493e+037.859e+02
pPrime2MeanTrimCellsfloat3219.291e-061.568e+06
UMeanTrimCellsfloat323-7.398e+017.424e+01
UPrime2MeanTrimCellsfloat326-2.800e+024.767e+02
turbulenceProperties:RMeanTrimCellsfloat326-2.622e+011.136e+02
TimeValueFieldsfloat3213.000e+003.000e+00
\n", + "\n", + "
" + ], + "text/plain": [ + "UnstructuredGrid (0x7fffb63061a0)\n", + " N Cells: 146754704\n", + " N Points: 165122607\n", + " X Bounds: -4.000e+01, 8.000e+01\n", + " Y Bounds: -2.200e+01, 2.200e+01\n", + " Z Bounds: -3.176e-01, 1.968e+01\n", + " N Arrays: 24" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import time\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.domino.wrapper import DominoWrapper\n", + "from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase\n", + "\n", + "CHECKPOINT_DIR = \"/lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint\"\n", + "\n", + "wrapper = DominoWrapper()\n", + "wrapper.load(\n", + " checkpoint_path=f\"{CHECKPOINT_DIR}/volume/DoMINO.0.615.mdlus\",\n", + " stats_path=\"\",\n", + " device=\"cuda:0\",\n", + " domino_config=f\"{CHECKPOINT_DIR}/config_volume.yaml\",\n", + ")\n", + "\n", + "case = CanonicalCase(\n", + " case_id=\"run_202\",\n", + " mesh_path=\"./volume_202.vtu\",\n", + " mesh_type=\"point\",\n", + " inference_domain=\"volume\",\n", + ")\n", + "\n", + "start_time = time.time()\n", + "model_input = wrapper.prepare_inputs(case)\n", + "raw_output = wrapper.predict(model_input)\n", + "predictions = wrapper.decode_outputs(raw_output, case)\n", + "\n", + "mesh.point_data[\"pMeanTrimPred\"] = predictions[\"pressure\"]\n", + "mesh.point_data[\"UMeanTrimPred\"] = predictions[\"velocity\"]\n", + "mesh.point_data[\"nutMeanTrimPred\"] = predictions[\"turbulent_viscosity\"]\n", + "print(f\"Inference took {time.time() - start_time:.3f} sec\")\n", + "\n", + "bounds = [-3.5, 8.5, -2.25, 2.25, -0.32, 3.00]\n", + "\n", + "mesh" + ] + }, + { + "cell_type": "markdown", + "id": "d8bd7b94-b6c0-4a77-b31e-5403f6b241c2", + "metadata": {}, + "source": [ + "Now that we have both the predicted and true fields on the same points, we can start comparing these two solutions." + ] + }, + { + "cell_type": "markdown", + "id": "eb48cbe2-8d76-46d2-a6c3-95bb39ee053d", + "metadata": {}, + "source": [ + "## L2 Errors\n", + "\n", + "Let's compare the L2 errors for the pressure, velocity, and turbulent viscosity fields using the library function `compute_l2_errors`. We pass the `bounds` argument to restrict the computation to the region where model results are available." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f3f3e60b-c523-4527-84ce-de99d48a6a66", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:43:17.826907Z", + "iopub.status.busy": "2026-04-10T17:43:17.826309Z", + "iopub.status.idle": "2026-04-10T17:43:29.033815Z", + "shell.execute_reply": "2026-04-10T17:43:29.033286Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pMeanTrim_l2_error': 0.10848417,\n", + " 'UMeanTrim_x_l2_error': 0.08351647,\n", + " 'UMeanTrim_y_l2_error': 0.18684481,\n", + " 'UMeanTrim_z_l2_error': 0.19030663}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_l2_errors\n", + "\n", + "l2_errors = compute_l2_errors(\n", + " mesh,\n", + " true_fields=[\"pMeanTrim\", \"UMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"UMeanTrimPred\"],\n", + " bounds=[-3.5, 8.5, -2.25, 2.25, -0.32, 3.00],\n", + " dtype=\"point\",\n", + ")\n", + "l2_errors" + ] + }, + { + "cell_type": "markdown", + "id": "634097ea", + "metadata": {}, + "source": [ + "L2 errors provide an aggregate sense of model performance, but they do not reveal the distribution of errors. Below, we compute the distribution of absolute errors as a function of distance from the STL." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "75f0b8a1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T17:43:29.035122Z", + "iopub.status.busy": "2026-04-10T17:43:29.035024Z", + "iopub.status.idle": "2026-04-10T18:18:19.341432Z", + "shell.execute_reply": "2026-04-10T18:18:19.340978Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.metrics.l2_errors import compute_error_vs_sdf\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# determine query points for sdf\n", + "bin_edges = np.linspace(0, 2, 50, endpoint=False)\n", + "bin_edges = np.concatenate([bin_edges, np.linspace(2, 8, 51)])\n", + "\n", + "stl_mesh = pv.read(\"./drivaer_202.stl\")\n", + "\n", + "output_dict = compute_error_vs_sdf(\n", + " mesh,\n", + " [\"pMeanTrim\", \"UMeanTrim\", \"nutMeanTrim\"],\n", + " [\"pMeanTrimPred\", \"UMeanTrimPred\", \"nutMeanTrimPred\"],\n", + " stl_mesh,\n", + " bin_edges,\n", + " bounds=[-3.5, 8.5, -2.25, 2.25, -0.32, 3.00],\n", + " dtype=\"point\"\n", + ")\n", + "# print(output_dict)\n", + "keys_to_plot = [key for key in output_dict.keys() if \"histogram\" in key]\n", + "fig, axs = plt.subplots(1, len(keys_to_plot), figsize=(18,6))\n", + "for i, key in enumerate(keys_to_plot):\n", + " ax = axs[i]\n", + " bin_centers = output_dict[key][\"bin_edges\"]\n", + " bin_centers = [(a + b) / 2 for a, b in zip(bin_centers, bin_centers[1:])]\n", + " errors = output_dict[key][\"mean_errors\"]\n", + " ax.plot(bin_centers, errors, marker='o')\n", + " ax.set_title(key)\n", + " ax.set_xlabel('Distance from Surface of Vehicle')\n", + " ax.set_ylabel('Mean Error per point')\n", + " ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "354c4054-026d-4e4a-b264-a18638aa6fb5", + "metadata": {}, + "source": [ + "We observe that for pressure and velocity, error is higher near the STL surface, while for turbulent viscosity the trend is reversed. This can inform model architecture or loss weighting choices. Below, we show this distribution computed across the entire DrivAerML validation dataset.\n", + "\n", + "![Aggregate Error as a function of SDF](img/error_vs_sdf.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8b5cd20-a027-4186-b86f-1c85cd1c2ac8", + "metadata": {}, + "source": [ + "## Plotting fields\n", + "\n", + "Much of volumetric data analysis relies on visualizing the flow field for specific features of interest. Let's create a few slices and use `plot_field_comparisons` to generate visuals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "96d18409-8204-46c7-be69-408caaac2e33", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:18:19.343570Z", + "iopub.status.busy": "2026-04-10T18:18:19.343164Z", + "iopub.status.idle": "2026-04-10T18:20:11.765338Z", + "shell.execute_reply": "2026-04-10T18:20:11.764780Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-10 11:18:47.642 (2997.005s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:18:47.844 (2997.208s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:18:58.234 (3007.597s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:18:58.565 (3007.929s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:19:15.461 (3024.825s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:19:22.654 (3032.018s) [ 7FFFF7C73540] vtkPolyhedron.cxx:2025 WARN| A cell with a non-manifold triangulation has been encountered. This cell cannot be contoured.\u001b[0m\n" + ] + } + ], + "source": [ + "# Create a slice that runs along the centerline\n", + "y_slice = mesh.slice(normal=\"y\", origin=(0, 0, 0))\n", + "y_slice = y_slice.clip_box(bounds, invert=False)\n", + "\n", + "# Create a slice to visualize the wake of the car\n", + "x_4_slice = mesh.slice(normal=\"x\", origin=(4, 0, 0))\n", + "x_4_slice = x_4_slice.clip_box(bounds, invert=False)\n", + "\n", + "# Create a slice normal to z axis to visualize the wheel wakes\n", + "z_neg_0_2376_slice = mesh.slice(normal=\"z\", origin=(0, 0, -0.2376))\n", + "z_neg_0_2376_slice = z_neg_0_2376_slice.clip_box(bounds, invert=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7367d63a-9f67-42d8-b8b7-c400f4653ccb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:11.766857Z", + "iopub.status.busy": "2026-04-10T18:20:11.766764Z", + "iopub.status.idle": "2026-04-10T18:20:19.133419Z", + "shell.execute_reply": "2026-04-10T18:20:19.132930Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/postprocessing_tools/visualization/utils.py:26: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1583154/306130833.py:3: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-10 11:20:17.934 (3087.297s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 11:20:18.006 (3087.370s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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12kY8Y2An7fCMwaG3JtcRv2Wao+s6ztVujNvNMM2Ao6RssMMtMTlBVjncIncZcMTz6GP6q/uK7pS5SZEjR7/Oz5faqtsoS7ZFEwPHulPoBMTA4qlBcHS5r2cdMQOm/+fAIAAGAAAAkJSSs+mkdquUAYOJoIAYiXgR0M7g9pfo/ArDbxxAjHWX6iiX4L2ZZhubiEib+Nqnv7og2b4rWqsYOFyXUVvR1ck2xzP6dS5iP9gRt0R3GHUdQesyYCU19JwKq5tq+hCAo3IVdce83s6jkfOu1PvLcm2/yq5a+qpftEl/vZ0O47d0kbASA7d95KJf6JhmwCJSnllR21KwocXdmNP+xKLa4hd1p/EOU0/nzX+BhCMABgAAAIAoMDAwrCTnQwxBFbujHU3MDADW1Na0GyUvT6oSvTkxZNMg2BgDh7KbvDU+i0ULUdHkfM4jNyd5m036K4Ysc4sU5ssmzTxlhm/p34nIuIXLpF93qd4oeW4XJeIxBnZ4on3vLF3J1B2eUF3Qaz8ur7/ts5WlmZ5ct8MDt3+kwP8wOCetqjyt3UXMydMDvnX/Hl30G4fWrh2rETDNf4OEABgAAAAAouOhz2oEHulvIPHABwCf+JhpRQxa4jbYsD37GDiaRszRRL/OB3B1lQTbt362T3/VaXW2SsmvlHwfx5rVbZX2WQSbb0VOsBzHwM4TpuhLr2nWq7arNj2bEQ+1cQbn0a86p8PYPmIMLO4bBKvTMe0d2uGP2vdRwJ2LZ9LZsTJgBAYBMAAAAAD4gWQISAU88AEgaunpldqXse6U2IrDAX1didgNr1UM7G0wUc/RbzRhqqvGl7oNaJDsLDE/5hGjOF1f0J7pVmQcmThaQ8wzYA9hmy/pr5MZlLPp+fDap79Wn0bs81k3s+2nXpJgiXEY7OrBDrV4xCElddX0PPoO9nXrTfIYmOa/AUMADAAAAAA+cZsM9RTZEKttQSLR/DfwiIEB+McmFBQH/QnbpyzGCE03c0LaB9vHwFqudk18auzrhHFpTrI3V+mvMU13G+/Zr0V7bJc3ltmXBHepldoUOIq+eX0smfaNgMVRTlzR9q0yxyutcLyBll/Rrs6mKbBmHqddW5t9167ZujceMlTnKWlmTnNjrbt4y68Bp6ORzE2BSX+DhwAYAAAA6LCWkDMlJVcnpUBkjYiIbI3JtiAx+G2mCIdnmZwYgC2b9Fdh03mywioDzuvUoEzYBDD+NwB1LOJ+iZsN87Gxr1V0FzF7a5vNRQK3XkpqJcvq04gtql2tSPuysrmXboaIGbBr7dPfrYsLTeMltZSKSFH62mhW6Nfh6iKNykSJ/KL7yJjC6kpLrIcBjhgDR5MBty3B5yTYQx/vugbBVsFkpm1U7CrOjOc1sEM0BbYXZb/9iBsCYAAAAKADWqKZIGfquIiFAozfJlTGksBvH4CIuOz82T4uLc+saApnr24q1r5Z3ZpV7ewevY8xsGkIZxNKOYmB7Vkdxj6yPku2RfpuhZsVbZ/ZYRIsIuuk9zZDvtsg2Wukp/GLtS0FG1rydWch+lDTpuGvjv8ZsAPVrTuOj1pc1c3wq6PyPrJ+jRTr3rTqVXubZLbNUKa+qS0qNims8qaPMbDF6L+WGyB+ZMBty/EtCfbcnXLEEDf6RquJGiU92WJgtyNzl2dWGJ8jQbIhAAYAAAA6miWGl+RMHQWpT0rhtwkr2oLBZQFIVcZUr4s0GMNC02+Z5jEZobooW2XFtDWw/dC5nmNg03DUvtVv9OGcsoR10ltNCk1Z7aw2/XVyviJmwLrjZjWwq5N1qRmwbubo+601jZfCdRm1FV2NM1Q299IGw1reymfERwG0lkm58ekBJW0tlJpusn0M7/5SsVmKLJZQJn6UNJuM2Z5fGXDb0vxJgpOt2Wiiol+teA5+7Lsom+wjDgiAAQAAgI5mIIFBcuPsQKUUBmJg2EjIJZ2/I0ASMKZ6EdNflc3AwMkWsSjUppb2uVTEAY+N85u96edYvyKSLU11kqF70yKZs4vcjLvm/Ewp3zXur9XgwTnSoO1Z2kORMBakWDRYDGU3mb5vlf6actj7t7HsmRYVZR61ubBunk3SdZN01b0Tsc9wV0mwTb7rfARiifTIhTfRJ8HJc4FymP56brvsVmIbBDP6b1ARAAMAAAAAEEtLRPqJdE70ZiDZJCqC5YkEIGlYpXo2M4tIjjQUy7rYZcDR9wNsul9KEOV7OuvBMilzFcg5SX/dRr/eOO8O2jT9tRqF1xVvTYFt1ptTVhPKbtq6uNCXzbOnzYCNRdH0JDopt8byoCtgkfoMr3Aym6v0V7Nw/2Ngcf+sRkdnGgPbXye9XYQTEgO7/d0lcOR4uEUADAAAAHRA2sZb3MoHkp9yt5ZfKyTRTW8HaiZoBAwkBzVHcdi+s1aylCDQPgNWJhLY2M40rYxPUBqR2wxY9103M/u8R9qjGnHh2rPvMFJyEupE3x20ltIIWFmmkww4ygcUlAzYYfqr+7SLNJTIL07W4qoLaCclykmEbDNP7FoDBywGtuo+XeGq4PVM27KhJd/bZsQzBtb94tSVOvklxmiT4CMCYAAAAKBjGsgIo+jIlmqmd03YVgBxlfDAdaDhZcI3CUhJ2bLZ9H1XUYrDxqD+JsE2CzGNRpy3WPUgyiV7yICtAjZjrmZ6Kv3KS5wcVQ/Rb+zokiSrFurKbOG6DKt+oV2xabPrNv1VbZMs0zmt1qUtMHF44EBZxWYp0nVSrZnBz4GB25bpOgZOkl6gjU8S6H5WTnbN5qmdnLSq8rQq7ercbmEcYmCr9Fd86jAACUcADAAAAHRYpL/oEJZGPYMiADkxQwIjgUwLHhkwkCBqbFAtBRslz9tClCDQYfQS6zbBVu0yY5oBx1PEHM7mRDg/5g6bGJoe1aZwdkaoTrcubaITMc7Rrd1m9FPnjYBzymqczKZykv7qCluUZSz6TNTZ0MJlxjcjpsIeun3uJpXdpNLqi/QIraX8ZOzn0TW491zSPI8l7G+De92StS+Na1HeIQbu0AiAAQAAAMRSlUhBorcBcbZUZFfHsa6rxZrqcMEwbfcRfxQ5ICk5j0y0sUHPtC05bQ3LXGXAitglwQHOgL0Nvyqx7CXVeNJXNxVrX7oNjayyZ8/BlZhFR8rS+masV4I354t11R7Rl6GmjUfY8XDdlsMMG+Ysc79djth3Ch2jpsDrpUQ7+HTy06a/EU+uzQztx5a2uyAnQ+f8iojpr/YjMuCOiwAYAAAAQCxtENmgeRn4EGJNojcgURqk3V0R39NfG8Z1KXddkvkGFBlwCkpgQ1v7wkYjYCARjAmB8zxgQ0u+tOSrQYKHDFgRiyiiY2XADnuBtk/pTIO0pnC2Lo4VkXBdRm2FSd+8voQr2pNotSIbEVseG7vtjaZhohq8qeutbO5V3WpSdWtcmddYuz3C0AZRNiMBd5EG51tilYOa/qC0b/Zp+xdOllg2IVUW3kfWi0iWbHO+Va7YlGGbGDhGTYE7EG35MXT+vD25d9Xjt7e1O7n8+t4I2Hn6q5uBJLjDIQCGiMTm1gP/gAQAAICRWksMXvql7FpugrfCT7pg1bShbTyD3oj6JXfoq5Pw7qAJoVOEk7OcK7I15hsCQFUn3bTPx3nLX7UBmOcMWOEwinC4nR0rA47IYRtN3cE37VdW7dzYKkRx2P9zRL6MoeuEh1zKKujVctgY0fgTUKaVkXqdpHQ28afyg9J1/ytuegD2JSZ0ol66dLZNl+1jYB8z4I7V/NeK9sTpkmD7gaUdNgLWifOIyMaLj6ufsKsu5ZEMCIBTVRz+nW9cBZEwAAAAVAELnwJW1zWNdZMq61V0uM6fjWIXAzv5iQXsZwgjh+eX9BeIr2zZrEw4vO8frstQIz37ACzKYTj9CiBtMmCJYhDNaDhMBD0s1uoj01zHYWRi07zVsyjTGr8yKpv012EQ5SQDFj/OuHZprgqt2/XaN0PXBbemM9t3+Gw/Tyy6g44ozqmn6Qao0+1PtPm5a58K7/iuL514OzkavjQCzsxp1r0TzTLpGrpDIABOGcnwr3rtNgTsBhkAAABM2df6AhM+UbmNvwBEv1oxioHJgFMcZxZIVnlSYwxrrW7E5w7eFMpu0n7auDIvs7RamfY9A/aLTYSp3ULfw2Cb7M1DIuit+a/KVcqlG2pXnfA9CfaXfS7lMCJym0L5kgHHYBBcP3Nft7NJFDFwqnUHbdP5sz37c+G5EbBEN9L2dotEhkSYRf3DoYg+Ud66uFCGR7kMxBYBcNAl7b/3CIMBAAAgSdALbpSMVdkANKRLwpa+WgGLfrV8/DksaT9hv8wUyYDrNdOVbRN94rV2/tkLwKC/rJBMWd5YZn8X3vhpY216puZlMmTALZXZaUX6To+dNGP10LOu/fxbpDBfNin/NVtdeR9Zrx2NVek+17QT3Yjpr7+BWWIbRCaKSeFfZPuFtogrygzYr3PnrZGx80zXw3KijIEVSRsGx7TVqfFsqgdKe6j7S4V66LTTItJFGra19YPt4VLssWF0pch6ERFZJFIuku3oS/4OKoykRQAcUB3rX+/q1vJPYgAAgNTUQfMnqq9xFuDoV8t5uXL+qzH+xNZEmiHJ1Yt0bj8RkXa2Pm3H2Xi0Y3Qc+km8ezztWCcUSDGFUqe0FdueAQ/20gJSNJ366gKw7lK9UfJ83eTIGyMbso3BjPOujN0mJabNiBsku1KkUvJFxDQAFpE1UqzGPGqo4yH9Nd0So0R1dWtsmGuTnEU8R/a74K1zWv1X7HNf7WzuM2DtArpIg9tNNeUh+vUr93WyKIcxsG4GdaRb3di3vvP2owjXZUjbeTd96MT52q1Ki5Z6kE0nlklZvXT5RXqq8+uOVYPDJLY9myuG5Q+tqC0AFj/SX/WXGKk9MToEAuAACcC/7pRd4D4aAABAMLiq13Ws/CnAVdbkbP6bItGvW55/NQEowJ3bWvQ6TH+NBsYx/RWRzvH9B28HupwCqaqLNKr9hZZnVmzPgE0zMF3+orm/rx0bWBtpbJS8/rKiWgqUab+2uW/G+tVNxTYzmEaMyxvLeqZtyUmr8mszjIzNiG3COYe5XcT8bJ30Vlca0ybXsRgPOJ7UqCm7Z52a2LXLn0yL/WIRESkWMQZV7jNgLbWBZjSKZEvEeQqlRkS67eh1xB/Z0lQnGREHBlZn0LVedUKNgV1+y+mvwPMjEaHsJjUEtU9/7XtUdvKD0h007cui5bWV5TlKBqz8t0G6rBH9tTFL6kwLYcQDFaOnRtRfimX6W6kJkkXzw1T2rMj3LUI8EAB3fMH7dx29QwMAAASDq7SjY1Vr49+YL8UtJQO24G+v0R3rZ+g5+lXpMuD47H6se8DqWCcRSGEl8ouS0LSLD122uFLTXzGLNPKkSv1vRE7Cm4xQnWmsouQKSqiga5qs2NCSLy35phsZN/1leYN00Xb+7CQVU+cxJsHOc0Qno3vmdWqobjVfoMODZgyNcgdv0j4iYL95HubRrk5bBqx4aa9ptTwlmhoi0r7I6TJgZcLDONMRv1sp+UpDc5uUdJN0Vf9r1EUat0mmhzbBdZJhfNPqSQXlfW1/xc5XtEzKnbeWdpj++pJr2nQBrSuonpNUXYfP6nTR8lp1QpcBawcAjrTw7YXK9KDZbLD578u2wa7psXLUklhrPa2BOzAC4I4sFf5dR+/QqcBJKxPu9wEA0HGZNrMzztOxRJ88AT5ykt0qP8PcQAxT7SP1OZX4X4WcXBs9LBNACjDe09fFHh4SL1ct1dTV6SJANVQwjYHFLNuwSh+jabpnxTT9LZTaTZKjnc1ieNTtneXqUuS2TyM3ArbPoorS1yrt63TzuNpfY9Jsn/7qFt5dqo2PC9jsl2nkbJMBq611fRt8tH1TYGUD1E0yJsEeGL+r+6VoMz9XTWa3Saa46Wncnk0L4Ihv2nD4lIPNEa5tKVBb/zvPYm2eh7Bi9Usx/iis5jT2DK+dVqNfke3jj5hmwMr1IWISbHXErA6R5U9G8ySEllVGzri/KYgAuANKzX/RkQQHhrdOBY3fIhIGAKADsc85UrN+C/hriUiuSJ+2lz1FNpjNZp/+drhGwH6JYc+dcZSa5w7o4LSNxhwOpGrT+q1tmSvav9wRSrkdztOQdekvl7okWLcLVjGwli6YiRgRRdkfsjEn6ya/dpNfTeesl66/mHV76nlUVCdNgZ3vndVIzw5bPWpXZPPEgPGjailQ1+shA464YY7Utz2O2T4A0/UILTFodK78CiwemPDSc7Iv/AqS3a/Xrgq1vWy05LtdbHVrlmkGrH0zr1NDUfpahwu0KQaGC2aFbrpd9Ku9Qb1r20flOzJg5ekQXRnQXTG0B029oLlu+Cv6wm8zwne0Da9p/tuREQB3HPxbTkES3BHFYiQ53TLJgwEAwVMfrGamVt1Bd9xabixa7yWDXZN1GGBEtNWPMpmaGXBCLrb+XkBS8KwBHZwusHFyj169v68NM7QN7MQ2/TW+1GxMtD2XqttvzP/UlxGja4cphS4DzpGGWskSs0xO6WW3beMrxMD0TaVhn4h0lpr+UqO8qQt4zBbldCRg500SbSg7mydVpiuNGCnZp79KObEqFXlSpV2v2wzYH8tFyjV/vg09QoumU+goV2V6dmz6iI74a+oj67WlyDQ2Nl2IcZ6EhM2aDbAs8NEf9urWLG3RUs9CUbxGxW6X/ur+caT8O71thJqi5bVSvmN7jGG89gelHjR117xEvxI5/fXtENmmv9k93ffrjvgiAO4I+IecKVdDyiEh4nnrUF0XSTAAIDDqImUSHTFMTciImwAQIzkitZHnMuHjv2Sjv5Dyz2og7jZJtvZlxOwwp6xG7ctXd1tfm/56puQTDdJljRQ7m98kCbbJgBUOk2DT79p8pVay1Ay4bfN0sXeFOp0tTb1kjdWitAN8iogSA6sL8TDYpw0nrYFNGTJ+y8Kji2ZNAyGbJwaM4WL77o5XSFsBMM2AJdb9zSpbN1jzjqE1sDh47CDyegwZpJaHkYZNf2VOSpRxHiffikVIvF5Kah2PgR0NpWgZR/b1fUWmFw2T9Ff3L1lNBqzrDlq7NPU0KaXF4a8+QsNfMUl/7cfqdvR7HKJZvgNeBvZGfBEAJzFuhzlBDJyEEttkRFl7rkjvhG4GAADR26DpwVVXM7Sq/ESTrcatwbGaAVPdTU6bE70BpnomegNSBz/MiLqLaG+gbRLZmLBtIf0Fgscmr1Jv7ltlfpGa/1ZoXy6TMm0DWRHJkm32mZ/VGo2NQXdkwHXSPuwWaZ9D6PbXW2RYK1mm8VuJ/KLZ1Aqrr7fr4lV21IqNMbBmaSYZcB/ZsMZllcUqyuou1cqE2tlyjjQUyzrTmdV913bObL983RcdZoTGlsFqUSzPrKhtKdjQvrNfH5oCq6Wj0OxNEVksUtz+U7MY2FR2zzpX8ZUapPVM22J88MKma+jE8r1jakfdPvtB6d4gDo19VcpVQp/+VhnGVdks0q2t1xzlRvSuOzJgdS4lBtY2/I02+hXL9DdWR4nOnzs+AuCkxD+53Qpq/3sdTvL0Frh1xx9gAACCwENVR/2Kad1yjfVQoHGoiwaguhvUymfyVOd0NliMaBsRtUG3UrMLaFc2Rp34+nUBIf0FOqxq6WqayjhMf8VBzmSf/uresRlA1FnPtCaNQbeHf9kii+xSBO2grfZjx9o35dTGb2rXuOp+6drk6Y6GEvHuiIEtnonUNe8zpqFZUmfTS7ZzOdJgHNy3VrLcLjNGg+Ba2WA21KvVWWt3roe0PSigbXo42PAdVWH7xNerug3ZsiHbuJER26BvaMnXjmurPcLOz1Ef2ZAlO+LnaAqMyuayEH2bdSdb6GP6K5rjHIcyrFw0tFeMyvKcouW12wfHKWg/t/Zp5rZ/ayiXkbbQt0xdrFinv8ZHYSI8MGHR7bPu+PSXFWoTbXfNf3VIfwOBADjJFPKPba8G2t7HREwl7Y1CIQYGAMD9/X2b5rkBG5bYrWAnJclco4sGA4V4sKT9I7YOeyAwUr+Y4peOGIny1kG9JF8jJSAVaVMZq2GA7YdrNeW2tZ8aiNokwaZLtmoM2m5HhuhzC52tiwszc5oba9tuU2tTwCHtZlMDj4jRprphxiRY2u+muu/6GLiNOiSwGDJgMYuB295v1zbalSg719WVouWNZXmdGorS17paSKHUbJKu0WyGPX3cpTQTV0+3WcPx7SL2T+s+tXKSjRnn0ZVGI/vY0m1jcSd05S365sgOC7B97ms8dO1+7+2ZPjHgrQybagpnZ4TaNfteJv10B2rH9UEZ2be8/WVhqaYOZp3+2nT7bN9DvqVI6a92L+LTQTc6BAJgBEifoN+VS0Id5UYhMTAAoMNJeK3GNPtZ4T5yCEBvzwk/F3HQUSp10dDuI9XCiJZYTHteiKpDXw2SR/SHkfQXSKg66abrMNaqV2fTm/vaFNbZIKAVNi9N09CISbB2SzRpa7sMeEcPwEoGbOi8VGWe/hooS1NHRDYOzmqW5ZgnwdI+DI4YA2u/ZRzm05dRge3ZZGxWjw6oqluzqg0DqdrrJpXdpDJiMYiy3aoSYoXrMpQTqsuDzZuGm+a71kUrdmx6MlfYn5S+Get1SaTvPIxPrPuivYjtfa1CTav0V/sV3SFVyrAy7aQk68YFV2WE6qwGddZeK/S/8fLtc25vE6yhPh1iTH8dRr+RORj01/4U71iv7gJr+nsZEqHbBnQsBMAIFoYEjqcOd6NwKTf7AABwT01wl7S99BA8+NipbNyaEqZIlbLD1eh8od3rfsIj8nFl3zs9IurnxzWw3ocNAeAL+9BXZTNWa8TGvvbpr5g1itXN5rBZcFun1rYZsMI0XTCNfs3mbPo1O7O0SfuOtkGw8ZCq/UIbNrhMF3g7jIHVDFg0B8e0KbBVru+WfdKm+zRcl1FbsaPxrrGhajwHVVUZV6puTyi7SS0tosmDVdos0DxCi3VYZWzC7nikYZUu0VzdVKxO53VqqG41qY9GPFMOz6nVj8JmBofrtRHl8M82+bqTvS6WdRF3Sr1uuNj9tiRYy2G3z+YHZJFIubP27o7TX3V3Ip8m06turkix2fvomAiAEUQMCRxrHfdGIRkwAADeRF+5ipgB61ah7TzW+EBz7KKjlKpGdtxKnY+MpYvqYnxY/dbiHwxXGYZ2i6koLzK+PAFD818g0Zqbi6w6MTaO5qiZdtexszhIf40fGeNemxbD7WdbHiED1lpkFqeZMmTAjbXpjYsLxSwW0h09pbtXYztIdQBmXe7rtjWwmIwurI+Bo8yA1QhHF+s6F3H4ZCP1COj2zsiqnaUqr1ODiJh23qs2XC7PrMiRBuXcLW8sU/Jg7ZyVzb3UfFS3L40r82yak/pjkWHC+JGV9n2Y6z5U98U0/RU3Q+pG7D1ex0OZdL4xjgaytWKW5bsqw066VbDfFw8Hx77bZ5MDUimyvm1ava47vCSKiJv013USv1Wk1N03kMwIgBFQNAWOkQDcJaQ7aABAUmnugDUWH5vzapdptRbTtMPfJsUpmIU0pOReO0T74MRSrwa+NHW1p/78Nxg+Uu6eMXQxgBj5JVe5wmhziPLMCtPhfrtIQ4n80vbmjhmM1klvEdkmmaYz615m1Ifz1m4TkepeXUSkqXNIN9ta6VMnGca12ISCXaRhm2SJVfQypH2qoTR926YJQlzaurhQ7Q5aYZXrKN3t6pLgZVKuNgXuIo3qQV4mZdnS1EvWKC8ry3Py1jXVdUuX9kepXrpWSV6dZChHbJ30Vo+8ehzU1a2XEg+jctpFOGrnwVZtBzW2muXlWuukt7r7osn4u0hjF2lU9ksJt7pIg7p32lNsdeSVaLM60uMO6sExzqNNf7VaKrPrNmSLSGZOc2ZptRiOknagWZtBZ+3Uid1DFxG70+hsFuMpEWG2SPsNzimrERFd0+coqSfFc3fTzkNfsQ8aIyblxjnbt4s1/th1RSVHGoplnW5hTh6/sNrHpoxsEVGOm9q3fIN0yZJtpvMrP5BqKdgoeZaLrRPZJlLU9j+tRbZHydD8V7PeHZe16rZHGpW1O31qhN6eA40AGIFGU2B/BSD9VSn7wh09AAA8cJW8autjnlPbmFbqUjPgoQoUEc8LJpyHQcfd6mx9eYnd2r1d0OgxGwgc41CXyi17Y1SjjRK1vRbrGENZ3cy6l0qWqQ5gab8o+/fbPi3XTG/PJOwaAYvIcpEh3gNg540Cle52jUMFqxnwNsnUdOZcsVZKtOF3/5IKcXxYjIMBe2sBHKEBn4Pc137Jas+3/WXFNsnSxOG6XqzbDW+sjX5NIy5twdZ15hzNMMY24aLaIlykXc/MPrQMzjY8taDl7R8RFifO3+hXy1uP37619/VsuT6YrK3oqv3J61r810qW5zGPTZleNNZIsWg6YzD+2POkSgmAzWVb/3KVnXWclJuOgK6bxyT9JetNPQTACDqaAvslSOmvSqkPcHcPAACHosktPHzRPjD2sREwNUZIB6wT+lJue5o1fk24OIed8c9W+xma4Gu3wXhOSX+BQNO1zlzdVKyOZavtstiq/2frxriNaqhp38Wx/aIiDgCsbJtp+uvISpFcka0uvuELbQasfV/tF3qT6LMTm0NhzIHUtTjZGKuwTfeUgG5UVEfBW92OwKlxZZ7SUla7Um0MbHUSjXvhpHdZq49MY/uIR0DPPifTdRteJ2LynINj6qKcN2O1X057bjvodi6m0W+scl9VFH1Bay+e0dAdQNNfihNOf63iJaA1JsG1LfEc2gTJjgAYqYGmwFEKZPqrYmBgAEACpWvu7NtUVxJxX6ydgYYJb0uot3hY3qq2tqTt0zhU51ItBlbqP8Gu5tnouNW/gbYvPbC5RxT/n0OKJJ2dbfdUey1KkQMCQJOm9N5tx2ippjGG0pA3Yi6rdkcshtzX2DLY+HUnua86vUUKNe9H7ha4Ha9VXA+xma7JoBrnaOexiXYipj7tM/t2oakyGrHVVpm+v3VxoRLfqnuq22X1pVp4dnR0XGcysGi7lrJtS1CPiWlWvbyxrLKTSQ/M7dIs7W5lt73cJtKl7R2b7zqnLlbbXtyqE2Zj38vdvaxTz2UbzXbfMhOxDNsnuLqS4y3utVqaldjmvg6ydm0GbBz2W2X/7EXEFufq8rUve6ZtyUmrMl6WnTzn4SID9kq3tTFP6NFBEAAjZZABe5YKtwXJgAEAycB4o980LnVVpbEKD5wvxN/4wVtXacrW6poqeulLz4EUjIFTobKnSvizFJ4lJAg0rjR2Pw2STh0OCBBs1r0vFKWvNX1f2wg4YjprsYQyY1NgQ/fFJkvWDntpGoJWSn7bp5HSX5sOdd0wJmcRh7nVbZVNv6lGSuTjcE6rlZomVdqjpA9sKkWKTPZIuxzl68o8Sn/L2yNebd/FiywzSGWNywdb749IdWuWZZJkHCJ3SFviu7ztpV+yRSoNvYU7/peFD91Bq6LeKZtS6irEjTLxVSU4+jU9no4Psk0G7LsNLfnSkq82mncr+gzY2BOA7iPAiAAYqYQM2K2UuhtIBgwASEJWjWVVnpuImX5liYN5Ekh7qzTW25ZSMXDqNAXuoJW9pPol2vdY4GRT1S8m1X6ljhS5rAEdgmawUlV2T30jUW99mSpBr+mowGLoEdp0TnHQ4FWrWgq0I1/atUXzKQM2Xb7DHmLFesTl2paCnLQq7TzbZbZ7acycTD+1j9bsOlJun/6WZ1ZoNyxHGmolS7ciZRBZ85DJ5oCXew32Fonktn9niMW0X4pEijR5tsNSlEyDnvoV/caTP7mv8VcfxXnR/cyNT3XElGnk7HAbosyAPfS4HmtbFxfK8ISsGU4RACPFpNS9vCilwk1AHTJgAECH42+CotaUEhLMOB+LNG6bl1KPDwagKbCxIrfU+qMOIZkj0ih7g0ecpc6lDOjg0orMewlW2IwE7Ip9F9DOc19tOG2V/prHEv4FeMblO2wKrFjdVGzybku+cYHzpd1iTZNdpQ2u1ac6dunvIpEh29el5kk5aVU50lAs65SX66WkVrKUd5ZJP2U2tUFw48q8xtp0R1l7NAVK26tK3HLWIYYJncq2nNhokUixSJGj9ezoT9urzJzmtJwmEanbkC3WZTKx0a9NWY0qU7QpeH4UFdNHPeLWGtjmoEX87cehL+gdKp2WdgQYATBSUkrdy4MrZMAAACQqnomY/iawC1yqjknOqv7WUep1ph1TE5TCF1y+gGRmFoRUNvfS9QLtthGwNty1YtoFtGn0q3Z2ajq6rXHbHHYnK+J1RNX2vHV8at9+V0sX1ThMbpz3RG0VkOvSX2UIYaXVr0KZrpUs9XRodyGjR11jbVdlUS6O8GKRriJ9Hc8viW9faxKnKYlXdB0LK8c/oy6jtqKrtD+hztO7xtp00UTIusyycWVeZmm10w3qWKyKXKJLS5KISQZc19b7uu7gEwCnPAJgpCoyYHsdvf1HNMiAAQCIP5uKWTLEYMo2rOmww8cGXoNo7ogCaM+04+6OOx42EAw9TbIQJ1mmX42A25ZWpi7W8JF+kEurYS/t82l1pywDjyFmY7tqmfWVrdCNhuuq8Z8uzzYOrKuwGXTTlM1JVEcCjpD+iuSU1aibpB0/OEcaRHMitL3O6paZmdO8Y3EOM+DFItI+/c0VKXXwxUTIHbyppTJbaTGvO+bqUVVjNm0hND2hVmctlN1k/Mh0de2aC6tRnEJTgLXrVc6yW0q59TYGram8Tg3VrebVaLeFfwfToNeudwPXrE5ZPIcE1nLV8YD4ngFbXSRjn7gz9nDyIwBGCiMDtpLK6a+CDBgAgHjqKFWyPh1nU1NKv4Cmv4nqjB0dV5VIQft3dKXIvkR5vr4pi82OMBcAh6pbs+ybbKkZsJLa2uTBuga+NgvUTJsES0rzU9slmDRC1bFLgovaN1Nb6eIJle5SrfQ+raQ+zptKd5GGbZJl/IquO2VXnGQh6mJbKrO139LGltI+/VWp7X117ysv7fIk0wxYSXwHi4jI6raJ7RsRbfTrPTt0sCjlnbSiOl3Up/aArbwsz6xYrt0pzUf28Zs2UHeyeds73FZYZe3GEXAbvP/pjD4GVn/URelrq2131reo0sHOWmX5CWT6aIiampv2EBDXTHSR9csYp79Evx0FATBSGxkwrJABAwAQa06qYQRgcdNxHwFcQbUNKUx3IdUFwK4uoaYNhR1+BYCvjI3YdK1sbYbpddL/c/slW6a/phmYumGeW9pFjgZLzVK0ReZ5Rp5U5UmV7k3PjaSXSXl/WaFtE7y8sSxGKZQyLqxKm6aoB7Zn2pactCoRUcf61c7QFM7WjmGsy2MyRUQk3NaJsUmH24PbMmAxdPusSX9zymqUgY2VEY7tj4YSFjrMWX2nBrfqAbSJcq22zUOp9jhUsPvnF3UPK6hl1S3tUXISdfsY50dci/07Tuj2yOqEmpYN+7Ov/bS67WmDKIch98LqIYOVmmnSX7QhAEbKIwPW6bj3/nxHBgwAQIw4qX3RPWk8NSR6A6LkVw022ep+NAJOTQn/96mTJJiSCUSp99bczj60dYumO+h10nubJoNy0opX96kxA3bedFLND9q1nlTZ9lpszB6Mx8FtEL5Oem/bnpl65CoRsW9+quR81VLQP22Fdtf0uxmShswua6RYDBoke430FJGmjOzVg4t3rFQXAw82fFOTG+n2SImBI+6mMRJr1z2yUV2EtqGmAZuyGdrCZl/2yjMrKpt76fo6zh28SUm1tW96aPndjrPgLTOnOT9na1H62ihX5yED9rzG2MX5MU0T7R9kifimlYjXOmObdR0Xx9PhMN7qv51Jf6FBAAyQAWuQ/gIAgCSxVSQ30duQcNFELK5GLHbaXWLQqZXhOCfBPOuA5KS7BC2xeB+AH3y5pW5MPbtIozHX3CKFlZKvvtQGSMZIQx1p1SGrIUWtmhtmllZnmgaiDkeubVMoNd2kUn3pKgBeK31K5JdlUtZfliuNgEVkmfRrt7UWTZBFJDOnObO02sW2amj7K1Ym+suKLtLQXypK5BdxsCP10vWXdj1obx/aebAsWibly0I7erTe0Y7T2BpY9HtnUyBtRo01lVla3WgTdCmhdrFmHGhlS2yDYW074+2L0ZysyuZeRelrRaQpnK0eWNO+jnXprzdKI2m1DNsE3uo253VqyEvbLG5CR6vuzZWRoWudNSjWdmJc2dzL4apVsciAE5ImOmwlLIbBwnXfUn5WEY+JsU2wi8Po5jIoEtv0V3ey+mastx8dAMmAABgQEZF+3PaCGRoBAwDgu3rHcyZbKrYm7mtcItJPpLOn7/YREZcxMFRKEpwMowsTuaUa51dIq697u2I4QSEE/JOeXhl5JveWSZmSFxqH/l0m5eqot2IWZhij2R0RxYZsq5BG1wjYpkmckskpDTFFxNgW0yQRsegPpnFlXmZpdV6nBhFZLyXFsk5E6iVLRLJlR56XUR+22hitps4hZUIJy5WjpB6fxpV5Im19q1pEko216Y2OO311Evxskywltu8vFRlbwiKSUd/a1LlT3i/bdHNW9+6SIdUDs7bHz1uk55bOmbvJj8uln4h0kYb+smK9lNRKltoENrtn3fbep5XMVekCvG2/bHZB6Y+6srlX51C9dDKZQT2nSsva2oquavfROWU123ui1qmTHX2Ql2ve1E4YjrmSsG5dXJhTVtP0a7Yxfa9uzcoLZ2eE6nTplNvoWkTCdRnqtFVabGwbHbEteXVrVnXrjqbbEWPgHGnQ/Wbdtv3V/TbdHgdVhPbcbiRDQ9K8Tg3KswI6OYb+kbTv6OJ2h8dk6+LCHb8+J9xGv1EP3R1h8aS/HRMBMCAiIp1pB0zzXwtkwAAA+Mt5OOFlTKuYSVRFcYXj0KVKZIPXtai1HSqESVvxozvojsvVuYsmvo1DCYlpwAykkubmIhGT1EHhbWxdxWYp2iRdTQcJ1uaaWmoypLaqtBrVUswym+WNZU5yterWLGP7S+PydzAdBlhEhmwPXLeKVA/OEpHazO2r1rZ1XigD+neusN8kLSUyV46bcpR2jP5r3fbXdC+MaZB60CL2ZlyeWaE9R8ukbHtTbe1/TTa+vP3Lfrp0sLalICetSmkCm1ZUl1tUF67LqJWuSsth0xxOHWY4I1SnjfmVtGyD2eCpnVsKNrRs38RQdpN2sbqXVgdBifZNh1DVPWegnunM0u2hrFII1aKobLZ4Cn21fGkiHJH6G7T67Rsb+DpMf6Pt0VrD37a/yZD+Stt1yXjYa62flVGpTaidJ+JO01+30a8ixk/96prdKwOQR/PXCvFBAAxokAEDAAAkjySJGRJeP4znBiR5Eqz0Ct5bRNq2MMqxopM27jWlKwmeW4cjpkx/sNo3I8a03v5ZGuv0l8boQGwYe7I1ZdX7q5GS/tqHQzaNfR32ZSrt85toAjbXqZImjlW+u3ywgxaX1pQOsdXm0fr0t851l6rGNMj5Pu44NZn65EnNYh0tp60JuBJ/qiHNjrQmU2SPyMtRWvgZMx7T1EdJf9UgVtoKhhrBarNYZQZlENzcwZu291NdWlaeWbF8cLtV6Ar/kvCg8syKHGnQZqLLG8uK0tcqHWFXN5bldWpQD5TDwlmeWaHE5E5m1vExZNWNauzXoqIUz+hX3Wvt6TAON66+b/y6hx1Xlu/jEYuKt/RX/W6MBwDWSZaDBmsEwEB7Ud5C6riS8x5fkqARMAAA8ZckGUPC099E0VZ+fhGR5KgkK9uw1PBOrmFCRzeedG//Ny0B1NuhuSJFhMEdh5Mk1TQDHtj+68aPYiFlr4FArP2Su3W5eaZizDmcp7+6BqzqAm3u0fs+oqepcF2GsSWl3artI5D2IcfWxYVKZKiLgZWGsFbLMGbkaqa4I/0Vu2FoTSj9oW4T6eLii9pOhlu3pXfq0iwiS1b+dkeQrHazqgyXazpYgFINKBbp0vZOtuSU1VSbbYaxe1sdhwPKaukCV23iq0aw2ha60r5RuNKOXA1u1UK7e+YS3ZnStUhWqC9NOxi3ooTQ0tas2Vv6q669tqWgPry9NhbNIxEqdSRjVxwGckof3bos1qYPAF84jH4V2tNhlYX72PxUPW7KkVGmrTZYe3wyc5q3T/kSvkaT/hpVSvvxwZGKCICB9vqk5D9xSX8BAEA82bdvI/pNBh2rfrjVMGE1g0K7dwF4zm9r2w4myW8nlbm6bigzW521gRYz6F763jd4il/6gLiz6edW4SH9VZIMNaLYHo7ahDqxDAm8JElOIhDtPEM0TYHbq+zUSxvFbW9mGikh855+ZWv+64z54MfGxda1pb9i/ciXIXxSQ3clXlXTRA/5rlZTOFvp/TWi6tYsbZwmtlU27TvzZfsx+TZnv8ba9JyyGhFp3Zau9qA7XwqVrrYHDvhpTbr+AGofOFA3QBmQWNu/9I4o2rZIqL/KaBo7Gs+y2lW4Vcpo32t6lFuleyDDeR8Arjjs5zkW3Qi77frbdMedHI12Lf511y5jHuxvvmtKt4r1baN9DzHMo3snvk2HETcEwIABHUEDAIC4aY48S8fgsPpkGlQkVWqV4vXABnF8nzkodFF3h86DGSc4nvy6Vtj0Du3kbPpyxl3tS5VIgR8rBVJc762y3IdRMJdJeX9ZrkyYjFwrO8JRPWNI4PLuv82owMZ5YkvZl3KzEXbbv1RjxZios4x+je0s1fcdHSInoVGk06emiVZhm7ZZrdKOVtf81GHQGO0etd8RJV3TRsi6j1Zu6ZNWVCciLZXZacrYxm0NW3VjCavpr81GmhZmJzvuoairwaGTn5K3rYoodr/QzJzmzNLqiLOpjbD9ohyWnmlbctKq7HN9VeQByIdYvK+Va/ZmHOJeJ9aLiPWo6rqXtlcSh2MWIKkQAAMp75dEb0CHQC/QAIDYiXjnPTkTHW/hh/Zbyr+T+/iwLdGqT73U01THavUbI+pB6KB1P91PLBl+X8EQn0dD7NsEe7AmBp23bxDZ0DadnH+egA7CeBvdW/NfZQhbk/R3k0gXNwnEIpFylz0ei0hbfKK00RSLrM4RpaPj5RHm2q7e0BDW4RftRdMGznDorJIS9X1t58+Wokh/TdMam+BQ2wtuKLvJYX4m0Ye+VrOVi0jkWF3ZWiX9DWU3qe8rE+G6jOyedZ26NIfrMpR3Wiqz1ZbEOlY7orbWjSldx8JOAtSk1Vib3mh2MHUFcnuH4Zr+6n1Ze8TevNU24o0r87a/ZVM+nRTdZBgrxxeR/hCQAXc4BMCAmZRqBByYP1EAAASVkwEj48bHOtJWiwXGfzdJf4X012CpSG4HHy14a5JdPTq0eP4L0aZNsJVEPcXie2INpIz09ErdO547f1bS3x3dPiv9OQ/xrUtPt/f6vTcIViIHYz+lpqy6QY6G2eGKmMPZxyG606oLX7VppThpiShmybdxNk1+4yiatW67rNLto5Kf+Zn77liTSGmEtau2H1517OdMs5kyZXl2mfaNtKK6nC7Nrp5UiCr9XWl26zXSb9MqQFV10BBOW2YcPp1g02ZdLLrCrmzuZd//s5L+7tiYOpt5U4/6MI31kyXSYUtgCiIABiykVAYMJ2gEDABIuMTeak/m8CP61aVy3Y/o10qQHpQkCY5eQq4SujUONFwhlX6/YxGEOEff44Anvgwsqjb8FTXMUEfz1dy7t79Tbx/J2DAGM/b7EnHh+kDRSc+rURpit3AnrTBNd8oqsrLPg/U9Qus2bHHbhGGoYz1jfrOoLVj1cCSHiLjqKzjKk2Wb/moHdu2bsV5EmsLZGaEd8Z22I2vT0uhoR9TI1sODFB4aOus4W12UvTcrR1XtK9vqt9m4Ms9F+B1xBNz2HF557K8qpp86Gf233QF03/NBSrAt/MTAHQUBMGAtFe4Dcr8PAdDTehyyKk0PdQAQGLG+1W5cfgJrRPHJvFMzuqAe6ASPAEIr4f9CXGL2Mhny1yUiRyd6G4AOJVs2q9NWYeEy6WffCFib/or7G/E7cjLbQNFVp6zlmRW1LQUbWvK1b2pDO3vqLpgnwb7HwEMsptvYpL/KIKM2y7Y6d8pZU5geLnOLI89iQnvEtjo4gMpaykW2iaxvS5qNzYt/FcnRPGrg/LyYNl+ORDtEbnVrlppWVrd0q24tVvvyVedckpOX0aPOpIHvogh5v4lFmv+KoZAouXO2RRtfz5xvYRRN/LWhr82lI7O0WmlWrQyubLk40202fTM5GpXu6PkZ9iINDLx1caEMj9O2wBsCYABA0vN8S6vAOhtWBf45DwCB5GMsanoZTLZGsTGKgZWbUAkPThKC9Ne5gGXANAVGjNB9IuCSGhBqE0Edqwy4Wgo2yvYAI5pRM/vLimXST1lCUzh7dVNxxKVFzKRz0qrKDclokenc7Wlb8pknwf7GwLpIw9DWLXfwJm24qGV/lLSHqH9ba9xl28ez1Z935XBpU/wdu6xNKyO2+tVyG3Oq1LV0FjFtX5orIoZGus4bajtJf7WBU/u+qdUjo0xsFU0Eq1l7o6Q3ikVUGWXhiV1LdG+i6+bd1WCuEbor96O3+cycZh+W4oy+WbOHhwNSSqQYGMmMABiwlTz3PZEMAnb7L2nF+XakcXX86gEEj+5xe4cXumS7HkYfA9eLiOZQJLbT1MTalQzYjV86+GDAphi91Zvk/BfikmTdMAAW8qTm/9n78/hG0vu+E3/jRgEgCBIkeHXzmmn2sOdqzWg00kiyzh55fduyHSWxs47zcrL+Jdnd7PoVd9bZZLNxNjPJ2jmcaze/XzZyEme8tmJbthS5W7LGkkbXaGZ6Znqa3ewekmDzJkGCAHFfvz8eVLFQKFy8utj9vP/oBguFqgeFQuGp5/N8Pl/tsVAE1xlMYWKTnWPMT057aoQND5kgO0Ga2U9zKJ425mX4yO3rkbaxRrpmm9WItTWbSNpNME3uNVGC2ywSrJFVH6R1omYbSoapJdFwiMTHYfryyf0g5v0/NRmY2mMlNiveeEMNuB0mOiyl3CZtCj9HKKGZhglLfa6eIyr13SmNlGNtzkSnCdXtxK03Z8K9cMA4/Zh6DJucZlldsvr0Ab3spx4pA59OpAAskbRC3khLJCeApQYfrRN8KpFIJI3o9LKp3aBmaXsEz6ocRrXS36jvNE2JCNxf9V9NmYK4rJXQHnv370RAK6QHnxa0Y2XN60MzJehEkPXzJJJD0EQxLVSUlK36uJEQK8TFXXozeNN4gHpV0kdukJWWLWmpHBt0TQNC4GwuA/eRqFevDcHI4kG9EtwiGrr++nxhX1jam+nVBIwaXVkzm6paml6CNbgkU6Uekfxc/1mYHplJFuYY16+j14DFdtKV8JotgE7BMmrAqC00XGkXdWZc8dZqV6hup7lg0yi+uPGrlEjGEa6eJCafSDsy7XYDh3EjTMshT8M2rKsLNfvywcQ50/cb023/YEy08fuof3cH6GM0eflBtbozrnV9ZWUDhrrL+hhzzTHfxFtcrw27/cV8yplv5UhuJPGKPPaDq7+GgIILkNHJveKQemFCt/Bo1V/Dt+bAEylOxsRsaKrE8kgBWCKRSCT3iNMy2ij1YIlEYjUOc/1snnhs/UvcEf52bDQVgC2o7hwt0v7bPvel7qun/ot/WjppJ8atugcW1ICPbzJHm29WRkBLJJ2QoMugj2rqxTPut7R4Z0ATWkzVX73o2M12rFZcrF+nJR4yhoYZ9qtKvObb1PtcRb503Qom76J9x3AzGRjIwGhVTNKvP+FeiBWHKuPJFF3USlP7UqtSIwxrDzQ12jTYGeglGSKWxeclXd/gSRYmWYjTv42/0ZuasSmTxMLsvsoTn3D/KXBl9fsNStjeTK+JjthA6jO8QdOd7h/DUdPnm5HZUNhorGoazMcDuEeLxsTdBojYbVFrVnw02qdZ/aSESK8JXb2qkDygU2qfakMGC9S+8ZjusVDEm2hpLdVr7Xezfee04AaM1pUcFn8OtCdFG36v2yngqqI/VZYKA23s7IAYbP36L6zWHv94Up++rn0NNQ24Pq7ANMAgVhwyLUC+/8UPAyiRTGZD2b+8KGYR8d4Ok9jrqXcYX6h7oP1ZP+GDBgv16FXqg9HmmSaQpvzTgBSAJZI2uF9NwCc18FfocEeu+36U7QHntA8pau2/Ly8LEonEUpz8BdN0j1a43B35oRBvahlGjvQNttnnqe/q5DALfZRI7hHtfylOe78Os6LIVrjo3XPE0dCSEuQxkUiOmgyhSV6dY0xU3hW5qXszvf7x5JY7WO+gban+akt0tWY70AHmzJRj9amx+gbMMdFk+1ozJonqyxV3qv4KsUcfj6xhoo9SlUbyKWdgejtozyXKHk0TCjtXE8q4qRraPORZa7P+/U6yoP0LuLKVgtemPeuLl9IhR3W/8ymACUJsmr7Hz1d+8jHb9yZZ+DIfmyQ6yfyX+ajdV9S/61JMqfEEa6iiUf37apTmrS82LB60FdWr91hrumm9FmVmphQ7ctcublRcmdpas+JBXmthrGrpzi86TYoEr1f3uP9vE3XKIHuH655abPza9VYCsBBxG36lGqNvvGEhED6o3mZQlBtgeia0XyRYj/70a27M1au/erTTI2jPhZ2rjTZed10yXkzCztVEgwYY3lpANbWbX1sMZ1SjI9noA7rQ4LEZ1WOintiG0suNjlgVRZ270E5qumGuxuHPNIklsQGVSuVeN0NS5ebvvLh7/fK9boWkAfffTe/xCMCdyr3tYxVh2CLNOHXcB+ODptwHYarHwLMvya6FdbHZZPfPWuwtRd/5pfGaRZa9YJ58X+iYDsXRvpHcIX4ItDGKe6L+rhyxhbGSp7hwlBs0xTneei/OcQCbu8VqB0H2A1ti2SuYQFSCPHznTbzNZd2SPQjU/nkqCMBIq3WW23o7Z//qleEPXzqKNkmOBdkDtBq/duPzn43Pah5ZoZHszfSSIfDU9oR7Yb80L2Ntqr+HQROANQVFKNNNtJaWDWgiKuvW6axacCMxSR/UrImLjURQVF9g0J4TfxoUJj+5AdbQvUeh9SoUhlgGXNlKcDVd/U3RlEJNUzRoh2K5FyA24QdWGQHmGbvO+TnGfoQradxf5qOf5OU1hq8zJQ6+eHV+MegeTTSSafVGSdMPq/4Ia8dQ89o2o0m8dnWvgKrC1knCwlhZ31SN/GLQXM3Sa8nt6Zcd0E7d3I4EsKMOwtUft4YYWmgwbjZP7zh0gxsJw/V6rUb9l7fJGVhvwdfQF94eYX0Zc7NyDmWZCOqlTFt+xrV/mOqNzobdiQtFB/WM9dnR7aR/qzQR2rVD2sjNrMfY1IyZIbidhpm+UI8oijwBXiq/8fFWm5PcM2yy/2c1pABsaaQA3JTj032bcG8kYTnw1xEWHwQ8Wu6/q8RBkQKwlZHdP6tRIwCfimvmCVzrTuA4HMf0nZZ9IQt2IY6h/yb6hK6p450UqO3l5IhA6AR3d39wKq5pTZBdu06QArDFkT1Aq/HiW6/8p71voctJFuqIUPsAvQZs4GilX0G9AGxQa4QucoQacKfSr6FVpnWCDZgKwM39iKYaqkEG1oy/VXevihB3xXLtsVYAOE54m65deoFXeQLwk7uePw+cca0vFQaEzHNnddoRzggVx1QTaqRqNzpbDOhPNlSxvAN96+gITG83VH8FphLsBaPPuC0r843amOVAq+Brg5Ldjq3zhMnA5qFnmzUqL90oONosf1irsW3AcKIKK7/pt8/wwdVLv3rFVyC+kto3sVEAO7ovoCCHb5kBfVK9bs1q1IH+Glh/Uh3MEt0p4r0bai3T6vIlOPDXWUzRqHl5k2kQuiB0KQBbGZvNJiOgJRLJobgnum+jvVvFIiwRnPYhvwMgkwMlEslhOC2XzeMrjXGSR6C+FvLh39QB+iERGgxWnJR9cOrofcCOIcoZihvYQgCVOIAtVH1weIS790R7fcLWGTrBPd43tPO1Oq+uacFroNY2LFn0VyKR3BfMMeYnl8Ij1BH3aEKTPeq9v51Kv70kt2ll8WwPod+YxkE3QR9Jrad99Vcz4+pfMuFeEAdNv6ZBcCpUTDxuevnEVCaZV2t86t+sVvbYICYxsb9H/VNfnhhXF07oVSVxDFOlnvW9PrGCcMTOvXsun3LuZVhhCDAtrxtx7PodO36qluWJBh9BWj0mpqfKHBOiJZoUJxrQRIsVebNuf7Fm4WgCyC8GHf5CKeVy9WcKm0rNy2/oXIYNqpaay1SmK2er/unqloGweXTwvnFWrKb/7daa1ES11fauAIh3nU85q6ew4YjWbkd/iNyjCXE897dgQDTP8M3QDpdhYf0xEe7MAOZsw3rVmtkWNxoUPNaO4ajZ8aR6BPIpZ95svoK+Uq8W5K59SfVmVn28uUH9rffiUzcPA8NUjFnwwwjMwhTP8I5YXFDciUFX7Tt8eY7xLL4VItXtsBujW2v/fs1pqp+CVlO87mAdCwdQf6k9nqbPtnw5huBrzE6PezBpRHJA5ARAayEdwJbm/lN0Dqfd3lvptyXHOCwoZebmtJMj90Bx/1062kA6gK2M9H9YjX0HsAXFjyYc7cXNgu/9Abl6d9Kds2zfzzleTXuu5OGYkp81xh7Ias1ihFErmH0edmDjGHZ0sEuBX31wgC7otm6kNdVsxQflmnAIpAPY4sgeoNV48a1XfnV7VRMSNDmzUFFCtrjQPAQ+csAgK0225iOv/1O/8hrDaWMBViNrDGvCYb0DWO9GNagyPnLaviZZWGNY26Z+p/rt07n3V+xOvEqzxOkXtoM+BnZfHRE/AUJa0kUZa2HF2gfUqBKwXvc1uKjn8+M1CbdNYoczujaYaaWG8GRDyq7BH6n/UFDPDfFxiBbqTcD7LdTaMANCBddk14la7VP8qX8vmh5pUAoHauvyZiBdVyS4CS1DaGlwSA9h2NX8rIZ48FhxCBCa5f75I3YkksCbqK2mc8gO7x6O1WY+i5ZswRlVAwYGTkSo072Xen3xjGvdIGQK9HJmfjHo6s+Ik1w7vcWXTpzPPvKDrIgMdle2EpxXp9DGIAZRxNSIyu2aXWg9u2pXcQKbEyZACMFTAIkJX8Frm2N8jvE1hucZi1dCdxNnUjNdzKgf6wCECUxvGy6AAv0luvYaaHLPsM6g2jaPNp/DgEIuo75WP82lUQR0k4rapjSK6RbtSeHRPpr9eSGLZuew6qSv/KJ0AFsX6QCWSDrh+Pwupw3LDv/p0RopbcEnh5R+TdF7RyQSiaQRFlRAm3NUFzfLvvEHJNRh6lhSoE8Y05LAmip8xDyA6i+wZ/ZVjUBP7ZJ79WXRhve0k7lR/38btjrc+P19BZBIJJZBjPL7yWHLpfBouuYkUaEimPpoNTHSIPEajar7yyfqjaH14c/Uqb+oRXarHlY8wp2cxtPQHVu3fQ4a+6w/IAYlSZ/X2hz9C/cdckJtrZPiCptKPtUl3i9mbmCBaYQs+orO1IqRjTTgeVUrNVN/3f6iQd1JlD0Jnb1ymYjek22QnfTnhvbpCxMwMM949Wgoqg10WhUsNUXTcMrMq+9FcEFt9p7OSyq0Iu0HuqMivsLwutgqpVmg6VL6LTcy7LYq/asX2oXSps0ACDtXtS+F0Xzc0mirKWdHGBltKnJrEr7Gids067289XV2z7jW8+Wao5ZPOcU3DvX0Fl867SIzwro4kzO48DI5vYAw/o7AyP5HYBPnvui1LhPQa5Zioa6LWJ/TnsaTwuOyZWxKAQWe2l9ZU3+pFWVRv3EHKHmeanBfoV9ucP02svZ2FEzdqEiz2K9p8LV5Cs5ee+W0JfcaKQBLJJIOKG4cWXzfiVGYlRrwiWDZEXyL8IAICRKJ5EHjANPjTt3vxUlewMfAq9oIMqq6dtz7bS/YtpJvvY6lKC4cQw+wUdTefcxY7bCm9nXoMVm34be7/XP4qIKgDzytQbidTm9v7QBH72BvVgZiSySHZyWAdz8ZVRBiF0gRMeiOBjS1rx3JwUCjlzQy/urRa8DoSma2bInpu6gvb3kwRthYVhNcm6MvQdpO4VjxftGF2TYRm03CpTWJbht84K2L/NUQCooPrW2a8c7tLzr8hfxiEDVJWF9yVWvYOoN6S+IcE82N45pwLo7JviKuWXW1i3ygdu5UH9xUPaZAL3xDdQwL6/A31OVNBCl95vAFnTNYS2leBNR/BZodebtW1NxTf5LERtbhgmpH1vzHemtyA0RWc72NskXobv2vYRM38JHoZO24ojWmW6/SMRdq29C4PyC+PtqXSCNoz2mScI1FvhZ95rweMYtljvFJFuYm1CBo7Zv1qWZtF0KveG39csEk0QQ9KceuOHriWpcq9UQca6m6GAPdpdgY129Y0nKeSqrUs1Hqri+TfIT1uYP2nN5A3E495ipNvzstnpVYAJkAYy1kBLTVOb1jAaZ0ODJyKoy/TTiyQUApJxs4dUP5FuE+u57UIiOgrYwMALQae2vRd/6PcXM15TTS6OJ2v/5YLIMPNhoMfwTUYSk9HaVlnMyPRed9vEoem/vUTA08bCdQfILDLdY6HWhn4wmntrR5Jgd0QdOiQravgdh8Ao2xLFoi95GzrD4wvZoZ9rhsXE1GQFsc2QO0Gi9+6c1f9d5GFRj6SATZEU+JYrGmr2pZe1IvSFBbrLcdp2wT9VdDyDmiJK3prvU02mmq1GN4eacYUlgBIYKKhNVG1jpDGzZK3aZPiUhVocIa4pc1RESwJqjUxEqn1SKsauKxIVjYa8v6HTsiVVjP1kK/aSHe/S3Pm6nICv7xpHgomnrGtQ64bJkm1Zq1KFp0BnTtuIkKyrlU16bbh6oTi8MStOd2NkOllEs0tVp2dxH3tOqLzVRbZVJEVqCv6dtIQdSv0+hxm9Qpr6b1g/ebLR6kVUk+DSm1kabeYg3xA603LmcaFPE9QjKwCei0cEN7MPdSG+o6G58dTZRiSim1XzTX4S84wplmemS9oq/blygXLb4FezO94owVpaPrP476L53+0qc/q4WpfZfebra1B/r5KDmUHN4gO07s23RvERQv19zwPvKaS37NrLuvWerXGWznwoKapSwuUI1epf+66bOda1LZaXyy1ZWg1uaFUBdgriGuOV5bVrv0GYt/m0Y9a3tsoPVWfkNGQFsXGQEtkUja5bSrv6hvQbqBJVZBRkNLJBKN+0b95f4Vehuxp94kN8rFqn9Kq6LanBP7gThQH0+kKzsjEKGSNw9htg76fqxjCLu506Ax4hMUGzntPUnxXo7ke5rV+dTrOdgJvKd7YQ9swN4RXSFv1Xa99ErzqUN8iDvH8NshJN5Gh+WWjLSRSI6U4b29V/YNtVsENQFYQ6+1GKyo2nKDj9YguLYfudyO9KutEJje3ih1o1NPm7hjDS5ngVB/9RJIp2gx1IYl7W/B79ihgQBsUwracdA7FPVexh7y77yj7m4R+lWRTwFFVb8U46uEzJPAY7rrfMppFAu1hFVNehH1d2M1ApvWSLEv4bCs/1CaJ2bHK6H9+tM25hg7H7i5/1p3VMhkPnLpIZF5OwaMsDEbOb8x3Q24Mi7t0PnHkym6qi03OL01BVcYQfdU4+8NdWGqdv1pnQymV381MbheFQ5A/36tYr3EKAzWoDuGMUB3PIVkq//TULf4Qtua7ibsteX9bRnhq1lpjd9Qpb2s7DqaTTUADLogkHKy0fQ9B2AULVE8ML1diimO8P5nb/cVgUrGhTpTIZ/qMm2Jdj5rH9y83s2sC7yvP5+1k1z/IIcCmSA7GfW6oYnEPnKmxXob7UJcKg1ldPVTdiKOXRw7mMUq1K9jkhygodR9cTQ0p/58VfXPTzjzqf2Xi65iYHo7YTaXKIHH5Drf3Mg7IJ2+pxgpAEskktbcB+qvhpSBj5gHbaD/yJHjaBKJRHJa0C7UBwi+br416sattBFgfZnVOr/dYYnDRmevEMZfwSntH3as/hrQv+sIhA63tXvFEfY6DKdQpOOTygTNbHqwfqb4Nu3UtaT+XWuS8KmblheADdiA85CF6OH65NqslCZHYE+3pkQiOSKUSM3ofn2IKOzrf2K8Xi8DHwdtxo3qGyNo3iTtWVH7U6i/pmJko1xobQsGLflgRYUN2zTQPBRae1zpstcoIqPQXiVOo1c4rFMiNQUUdcmAbqFehjRIkrqNa22ony6gN5fnF4N945v6SNhKxrWkDLydecymFM641l22zBxjWsK2dnJ+fvcHJ7tvA3O758pp59szT1Ubo3kHB8BH6otd+/3bRonBIvY5UGtzXK97V+LZCaOvtIYb6grrOsH1AuhKPguX5N5Mb1U501Zbh0DtluuU++aYnzMx9c1qanHM2P72d6GtqX+JYb/mzuZGxMwOtUr+QoeaUQz8ABHHbuSxXWEw1au/gE0plGJKZkPB0HLt+NRJjPkBpzhi+vXf1oWAK5GMYS+aPDzHmJas4FGl1HglJE5sbfWWdcTNM5lrtVV9lvUevYFp85klupezd32idRC9UhuWjtmMEHGOmRmF9270Npt80MTsW0+jUyVwwPkHkpNEJsBYCxkBbXXuszve9obtTunoXjscUAaW4rEgcn9Z1izC/XKRkRHQVkYGAFqNvbXoO/96/OCvP6qSmfeQ4/CxHS3WvDifP5wkHG8t1Im6v5UcpVVsodOR9twcYf8tbuBsq1ZgY6Zg5X4Jhe6I8w0s7Ef4HdGGhg2T5CxyobPm1cCUekF3DKJqtvYxVPCVEdAWR/YArcav3fj8/7aR1+Q6odJNEhV5oUJy2JvprVFE1KH8JgpQmxhkJ6MYo9cS2itc2kjHCtpzQUc8ZIsr5IAtgsIQrFYOHqNO9BXJzIaAZT0tc7AFetlmwr1gCJ3WP6uJUh1QawA1qFAGd6B+pzVFT/W6V9sFYjv69A0rGz6mCffC5pvn13b97t6i62yGHHggB5C62TU0Fk8oDs0baVMKmnez6qAFkd+LqZ1UfXeaKrkvTy6qopE+NFikW2tar9kH4vYXXf2Zbn/Nb9jOZsjuK4rm1UQK6z+jmJrnPN/KNzyqrm+mNDc/nvWI0Ozm6wjaPKs15vPjhpKujejsEiE+CH355L3aB3omcPfVfPqB6e123nLrHGlqTeFN0J0q4vQQe29yPLVMgvn8uIiyL1QUrSyxoJJxldPOFpHXgHC6i2+0Jqhr9lyl9oFAP9ujzW+9XvQ1fBBZXeC89t3RX8AH1DO/0+D0dlDbX/lFGQFtXWQEtEQiacF9rP4ChVlpBT4oFhmDu/84EleZRCJ5cLil+/f0Xpml+tsm2kesDVdpJTk7beRKu/KPlu18H6i/QGmVkhiPPYwALHqPD5r6e77ugcYRfkcaXcesc30zffuN6hc24QAvad4Y05qIhtZm1SXaVdc61zeJ5MGjUvFA3rBQCKL76q9gm6rhrROZsEqDoNqGqoaZ3twODTc4vR12ZlJ4FHJC/UW8QTeAXnfRJBnxZxNl6wAG6OpLGgQ+d6z+6g15Zh7ERNnjrStynCr1oA+UNihbtR+uMK3q9UVDmq4oyttIgNQ+DsPnIup9CsnWPZq4decJ13hGSWceHpq5decJV3+msKkg9LxuEiEHUNhURPFX0Xi3vwhK1Uerbdag/gp9V/W85nGKOrX5PSeov4A3dBVqUZNslRZnXT7lBGVFPYbV8sPgH09q7dn3+/bp7LB6g7X4uRRfq6xuiT56upXBWuyipQZ8TOqv9hKtmU2+FObB0Y1Qaku97tU9qF3Z8NHXp9bXL2+Btvc2v5Q6J3c+5cynurSYBOFir39FxLGrTTrZKHUHK159/V3xkVW/p6ZXhpjOZX6DFF0mKrW25IIqqId1jtuwuoJ+Nf212vB4Qn1AXTltL3jVHwgtJVsfl90mByitLTk9yAmA1kI6gE8B99NNcitx9/5WfzWc4/thhm0hNWPrjMHdr5z+64x0AFsZ6f+wGodyAOsvF/LifEyc/DX5SD7KJs3OUR8waaCcVIXS+46OO371PIBdwfqg8va/F/p0Zf25fR+kF2jUR7gb7PUB3XQNK5NtfXFognQAWxzZA7QaL771yq9urxocwIL5/HhVLzHoCmYOYMHBfMA1NNjX4dFaKzRLbbnhzybk1XKk+sRUfa5vB7G3uvWbCGPVp17vrRERBeJANQ0+banL1mzKgNmnLNRfU5lQ6MoGc/Pe67373mKhsK+bf6bNj57ITKZOfq7H5DAuquVv2y8dOqDzUN6oc1Le0HmF0TkdLxgl+dZkat2lS3BG92zdhyuO0n6C9CFoLhgLU6/pBy3sqm57Vpsz0aYDWKOzlhtiyY8K/WfUZh1l0zbUftbaiaqnpaweKw5pR7v+WtTicGXMU5ePHsO3gE6OW6z2i9/kozyMAByg8g+lA9i6SAewRCJpyAOi/qJaW6QVuF3um6E6KyN9wBKJ5ADcT2rKA84hP8qWvyBtCDz2rmpU8v1h/NVTXMAWOnQE9AOFMOjUi5pt0uiEvJ+uV4Yhsx7LBxs0Qpo/JJKTZCWg/9IJKUI8Nld/m6JXlTS5tAOOSf1dhNF9HeVtejXLphLJzIfH0aks7fgU9c9qj/ffbDtH7ML++q33peAeTbgNK7dxcNpV2i6oCs0MTKsajKplahsR6mMgnGmil+9dn9iXcjWJWtgNteKdZg5yV3/GPVp1qYrYbSDoiC8VBvQp3EL9rWRcWvxy0BFPlELaSaudgeL0UyKZjCZSiZ3G1GrH641jMLRDYfBHovZG9Eteg17Yhs+plsf6E2CggZdXwe0v8ox6MmgSsngTWV3ucf++zzWfcrqbKrjtfO7aOv7xZL0/WBzPRh/0Rqlb72I/LvVXO4yNKwS3Rl+wVv+5HEBOHjBbWJtVkE8587VvMDC93ahwuEbYuRpWM+fbbczRyuEns8fjbvMxVBWRHC1SAJZITg/1oySHm6Mt0SPjoNti7F43QCKRSCRNkBqwpKX6m2t3S/fxXMBKnEIcDuwGftCq/4qR04OpvwZOe179/Y2cfSiR3FOMWk4rx1sTNM2SNmttHhWmG6zVabSw5cyGYohXPbix8qBvRO8nNjxVrXGb6VA2O4BwHoZ1Vb+c12W33qgJttUa2UTpz884a5QYgyqjV5c1JqpRt8JAmSh7qqJvKRS057y27OrGoCOcCdpzlbQrUfHYlIJYJ1H2JMoDmmCsR5x+pZiyXxtVe6fiX/EgA5t1jQyoabf11L8d7ZMxBN7OVN9atdzvOmRhC/p063jJ12siWbWWqkatD9g/nmzQOLX5TUpr15Fa6GpZQvioaPc0PuQ1IaB6vjUu6P498MZb6tD6IGUzmsRBA37HzoRjp150D0xvH2QyzUztnxOnZHbdqWik5KBIAVgisTxNhki8umeP9I69nSG/a6nW6+i56D9YW06O4oa0g7RC9glODGkClkgk7VB/rZAa8JFzHpZPfGrzwXSyo/jhuI9133oOGAPzQM1zj6jdv+ZnY0fnnpSBLYjsdkok9wKhyrSlMRzUkttE4zxi2lF3mq/TMqe00UHQL2+yi9oKu/pnjFKloahtDGisQmmbbaRc6miR3W0QMs1ijU0+xyZvWV951AsTOs1V6MHz1bhjYaD0jye3NoPu0YQIfF6ZGVIimb3Xe/eU/aRoJZJZ2ajmb++BEsmUUi4Rj2x+Gou42hikYBQWwQ+pBvr0nk4FF1ZgccB84IUZXZy1tk69PbS+EqoXHjHbo4jw7YKk+kB7ie6wm8YL62lkMJ1vXIq1ZaR2+9TXIdYK2Xac+XzYpjR9VvO7H5i2L4P1X7RG6q+G9iHqDcFiNkPNYWx+mTKovweoxSuRHANSAJZILExHwyJHpwRX8s2e7VT3NX2hNcXgShykANwIOUh38kgNWCKRHIzDlPB5YBGpKo3y6O4Vy8dQN7RVVpxjqFn137kCiTxBNwE7e+XqYwPiqUnX/pJUBb/toA0+foTm3ZkMPHu/VwLutOPXaH3TnozsVVoN2eGUSO4pJrJZU0ObqWuwZT1dk4DljoqzNqLex6l1RLPqkjb7pS0b08rnZ/JUrYaqP3T6I2ZTCvqn9o+SsCiHdf82QWlR3tWUwPT2HrUqneE4tKxua9C/JyC931q3v+h4b0EzXlctrTfAu1/pVt/svvE9YCsWLKVcZMhEFdKQJv+6E6/q4c6w941esYtMVEGBDPmYE596MmjVeQOwpTsftgBYhDScgSVIwrSq9QLb8Iz6kmmdzTcLWXXJlqq1b8OAbgXDaTYBM6rsPa+eouKcF8HOaegz6/mbnS3CD20otGx4HLTnws7V+fy4JiUaCnvrd9Km+tumgtvx9I5jzQRucsaGdd+jk81S1n8ujVYwLDE/qk2arVd/pfQrsRJSAJZIrMqBR0bOHzYaWpgh6jmw9NtkU1ZTglsHQd/fg32NkON09wqpAUskDwLFum+6yNtvNFgmRtOa/NBHW21BoueWTve1lPoL7MEtGDu5j7KcNFF/N0qsZGuWJPIkdI8NiKeu6ZYH3STyluv1GZBJMFUCRzrtQPYhJRKJpB30YtD8vjJXTyP1FzMbYn1tyxqrayMlI9CyuTqUuu6Tt+6BQN+XmNc91oyz3to1m3R+YkY5tqH9tIHrd8K9UKgoNBDODQe5FFMytWnVjdYXkchhZ+OZdCqFiuKyVWv6GrXnC6pfVn9gtbK+qOJlGOrfuHi/CmRw9xUd/oIQGt0pVz7lrJ5m4uBPVOVtJZIpxZSe/vjWQj+wknKSwe0o5pecCLlTaK6PgF9VT4E9COtOoQGIghdckFWV14DqPNYeiK09rZ7kZyALE6oiO6+qufUv6VPFYy/0wRisQxqehxugm3S436ufBy8MqKWCL8AiLAKQ052BF1SPcgYaf8j1n6mIFNaX7k6UPYn8OA20RrGkUFH0hZPrKcUUu68oHhtc6YdFfPr1sdvZ2u9jIzqNMq77khq5oHrT26fpZ6QnvxisN21r3/SIY7dR0d9KxoXpkdd/dwwc2PU70d7bOfmqw5L7DikASySW5JBjJd6j142OUP013ax1xgRlMWAjcuTu3iI1YInkAUSIu40uv1oca6OLg7xut484hlbTffUEjkH9HTOfQGAa/nwkPUAhEl9LnUR/T7iNN0pEHJ29sBKnEmq7JPB93Fds6XCSWJaWkkkjGlwTOqa9YVmJRGKC/uvTYdpzw/jZWl2zknG1qyftteE61VO/mqliob86NddIml/HzFolRFCRS6xfbpByI45dzcTpsmXEoTvjWl8q1OcI76MJcgbqxfh2pF9BohRKlBvvNNwgKVc/YXEdLlCVdYUuNQA+9VxScI8m8ovBqgA8msjP9KKoCijqSy6Q2VCUSGY3FchvOZWBjDtPPu3MR524oABp8MEZ2II9yMKA2jYhiQXUxqB6bb2q8RfVmOtXY5/7q21jAFJqJPUmoP4r3p0hksdbKwYDL8MEnIHPQRec0XmIRa1fv66arzgjxDm5DT51ZfFGtGzq2p8ww3SB+o9CRAqHnavBilJ//tRrwEIqjjh2m6i/1DqD2zSUa1/thtMgBOINjupk1zalX4G2ZpsC5/rh0p6btKGN65KINKfBMdwodVdrXZc9gKhpLZ4S0dkm1X+b9HAOYPbt6CJ/JFENkgcbKQBLJJ1wMjLMUY3bnocd2Gi8whTUjfTVj/0dk/QrCLoZcAKkKofajj5XcKNUzSTUYx2B+ZQhVQQrID4FKQNLJJJ2kNftB5k25wy1rf7OFQ7ZoBqCbq6l9vt+Ar+t2gnU9+Xa7xaKl99Omzy1AsCwtwMluJJrWwDOtQ7TPn3Iq8dp58CTRQ48e1ieMxLJyVIvZrSp/h6wAPCNg9YeNs1hPiQNWmI4JoHpba0Aaj36CF8NoQQ3CdA2BESb0jxdVk/BTC80oR3VR3xAis7Gugj9VbEqvxh09WeAvdd7UXSivnggxNrFqihbTjuVsYzdVyyVXIP+1Bp+d1cxv+bkJqzj/nAxn3VWNd0w7q4iPeRfdeKDXdzni/nvOhmFr0MfpMGlmoaFEiyiofthCc4B4FOLE+dUL29SrebrU0Vl0UKhQ6NuakyVb8XyZ1SleVpdB1iEXsjCF6ELfOCGPIxCCXZVYfg1OANi5DAGPgDUasemFtLJ2j50gp4tgtpMAlTPveYvN5xU+cXgxii08X0UbWh+1mk2Vv352agbW7NHTVz3qvnbHTFzmvKN6wskC/QyvEGS7zj5uSMOc0WVMrDkoEgBWCKxGEd7F90DPYfSjY5Q/dWP9/ltZEIEFNIe/LANniA5g+EgKFYFIAUJeiEFASeVIjYnZKrydiWPzU05ic1DcYGIg4hjPwQmVSFVrnkj53wtCtE1NAHfx24PU+SYjqWQVmCJ5EHj1tGV4ZQ04qh8b8dHp+7kdt6R2RRAW4hKvGbJITuBho6fMOZmR7F5qiYQ0QPMBSmFAYxjbEOwCjqZVXQJbWKbGdigkgfohqdzNcnVWsnhaylWsqyoGdSiSU06gaVV7C3NUWOnUPqNNJ0SerSZz5LTi+kviL7zeYJx9BLJA8HwXsBrdKw2oU31twPpt02JkYOKFvodofOeHiB5pUEqtdtf3NP5/ITYY1MKe2bmP+H5ixWHTDclnvXastlKzZWuuV9T0FL91QcF6xHtdPuLmtbo9hfRykK36fy7oVpmxXHerD7Op5z5VJd5ReGA6rj1Q5rMmwp7cA4lmMlvOdc2nfjJl5zuwWK+3+kOFPN7Thy4p4sOf6GUcjkihdKSy/8DycKmAvTac7s/URjsim08211ec4a6c7FC4NzzG9sZXzztydxV3IUiEzj8hZ/PJ74a83wsmPtq0XNnN5dPDSjnl7p7y2ed9qfTvBZ3EeBVXGxCARY8JP0Mp/D6ceIOFjlDrz3H0+DC5ims/mlIeTSTiSlDz8UTIUfqta6PfuT6N+cfCZf2VrMh/gieg1UYghzunmL+685qlRxgBgbABTsQgBT4IA2QV7WSvZleJZJxhDPz+XHhHZ/bfz1+cildjzBV6slWvOJTvrM6rWWGCylX8+bm9d/HGGCevCJW1r689d99Tf19xLWYs5UbnRriaiBijRuGGNeruR05g0+edmqB69AOoxLJ2H1FLQjBP57U1mmWjpA5uqNx+Atpp6HZx0rjUgUSqyEFYInEShxT4bGD6kYdDfwJ64XoMnW7GXIy9fdfwEPw6afwkemybbhCY90DBdxkFdKJsbEzB2mTjmh0CcAXtHv3HJSic3OePld/Pp1Zf82ZgF3mrl4ObxLKENpgOI/Nve9u2SgBHWcDHoQmY/EWF/OkimBBpBVYIpHoMfy+y+v2ARC+t6xVZeADyC1e3ZnQ5PeiNluvkj+g+pveHy7jjBcveB9i8i+/sONl7P1PlbpI4E4kGJ58hKRCIQOFo+kB2oOEMnZKXgrvJla6yHTnS6nF10lAltxvXnaCbZln9FWNXQCpChsl/PaGMnBhFud4Ax9wAIY7b24EetTHx/cL3vzr33y/1hlIklgQ+csikRwzQtppWWW2nnrRsd7D2kJdbt9PdmAZ+Kgsa3s65XJvvzFaRqv2TvVveW+mV6+taiVam5BoNcnLYC8O2nMi9rn+4OszZo3vpvZz0eJqD4I4LCKc+XXwqiHJ9Uc+sG8OdjuKDn8hM6+wDllIQy8skkHBz+BIKvlQ2aYUSsvKUDi++r0+UrBKfsbpfoj8lpNVJxPkr3fRB3t8/0DMa2P80nlnceeJT0xx10lvkfIjU/0hIOzbie2FxsbMS662TzS65OqtFOLdOKCQmZ3d5PEg8Hrvjs2vYOOfuLde/ncXhj6QWF0IDU6mbH890921GV0YnwzvvPPScD7pdD9fzLudVdlVFBUu7Odm+8eT3f69RNmjBQK7Ib8YtPuK5bRzbqNXiQyKfGbxoSvkrufPowsQrvlY1eMv5OR8E/1Fn5MsJjr4jaqwfsviiqGVsL1ZGG00/yBWHKppkrjAtOPO99aqwvUW4SU4bHf+KGj7uqS/DpgagmuKoxtQjiiE+ZDqLxCzTKdddzGRnApsQKVyuOhVydFx83de3L1++V63QtKY49ZdjvUG27TxTSOg68f+rkEQBtU/RY6LHz74wgsTH3oqMezw9z1S3i4ffmjvCIlGl+y9di+FxNbtwu3X2GX3ty5Ddai3kqe4ADqziOBQDuBOP0dr6nlyuMfiWPO0UXn2Jdm1sC42m+z+WYu9peg7vzRu/lxLZ552KZAX7SPk3l5gj/yjbKMH2Lz7B8ypDl19P/DZF1449+xTpSGWA4GhrvNjwYOOXR4P0c2Ey7u7nrjruvt1513mfvNybxZbDvZ0qnADTPqBEQi1ve92PsTlIx3EaendPMDLJZIOOfvDV4bfe+let0LSENkDtBovvvXKr27v/xo1F2tb2n87Vn85nKTRRMw4QsNcp7QpsWQOK120WZxV0PCDqD/+ov2iedqzA4eupRrYr4nrzhfz3U5ugAuS6lS+MdiGedw/XhwZXAvbE8vFSGwj5HAUMr+l8AlQGBreWl3p4wYXzsb/wnj4qaEJYGrU7qQ0MjbWsgknRjS65OstpbM9d/Jzby2u5ff6/2jr5ncL/SRx+AvdG+W1NT8e6IZd3PZi3uF0P1w8M3E3W/EGHfEh23oGT4jdHN6N4sTthZH8opPUvsG6yUdv/KBvGCdcdsaFFgJno/rWWsa4YbKC8MdrC2uKgre8FGw3Pgm7rCEGAwPmdmqNNr+2za6cB75mHl79PXwbmtPq6DWn8osfP7qmSI4Ym+z/WQ0pAFuaExgNvNcCcKPhv2vqAzHql4K/fPVKeYjy2aetNtjXJtHNhM+7s3znJe8Su1+4zHpVD9YPCJr4P1oKwIf5BC2l58lxwFOEpc4cFSkAWxnZ/bMazQRgMZzS3AO6o/MXSo6Dk7nMHusvr6nWqOv1NRGAr0EQusAPF1944bFnn1o72x929llqtl+bCO/IZmLO88635754ORytqSdSyVHOANgVsyBo4cloxwHcaaLyUZ1gAfBBTytHu3gje7KzJzl6pABscWQP0Gr8o+tf/ftbMf0SU+FB713T0NTf5tnCbXGEMvCBN6UlPOsDRZsLydmmPeQm2aQ3YFst+IrZRjqRarTo6XZfEKsV0rK6x/qWGITDeh1RW9LkOFyoBkq7+jNC89ub6XU7ivmSkxjcgV5YBx/0wSL4cD9TPDe6kfHnVt8ZzMwqnIElWNq++g8ft3uy50shS2m97RONLtHj++03b1VWfZe/ESMCUZTvz2RmFNZxP1PMp5wi7VlEK1cyrsKm4vAXQv4UYPMVUM3celU1aM8FHfGlwoAWyKxxXHVk605OkWMsmqRdFpoUtK5nv6mxVlMNmsjAE1YqEtH0K9ymDJxfDGoBA+a0+WkeofTbhEVdCHw9h57y0gRxPJMf/cnj2oHk0EgB2HJIAdjSnGr7r6D+LbQhAF8DIAj/nytXcqPuR89/5Jhadw+JJtdn7v6n8aXC7lcuV4fM4mbF0poLwEfyCVpEzJNjgqcRK0WYSgHYysjun9UwCsDyCmxBdpqWUD0Sjvtzr9eA4zVvSnQCUxVup6tLrgHwgy+88Ox7nyo8c1rn/DUhGt319caX77y08tnL4TJsQK7VL2k7YTAH+CgP3/3T73QH0q0cJ7KYq+QYkAKwxZE9QKthcAALTHUj/3hS7+RrSQd6ZBM0/fXwDlSBFiRbX3NUMNGeStFSd2lemfIwWkgGNtXsU1FRtfmmMrrH8zBhJmlr9VYNh0VoOVrwtWBP/bOlr7Q+ozUDCm5Hse+RzaAj3lsulh178Uro5kvT+HA/VOzdy60t+S88s3ljpv+vvvrHP/8v/qyyujn9ofvwqh5dSdy59Yev0RXfe+3X5h8hH37fbvwbsT4cMIG7r+geTUy4F/pIBNkRL5ljLFXq2Sh1a4HP6Ey3QKGiuGwZ7XGiFKIuADy/GESXW25ORnd2iRND/3FPAPsfa/3sEH3zNEQNYM34q11MjBpnm979+kRoGn+p7xVNLyYtr6g19uhGtHm4TkYAvhfoD6MUgK2MFIAthxSALc19IADTQgOuF4A34fkrV9JdQ5fe/9ixt80CRKNLn0++/QH7W6VvXWYR0B2xeOPov6P97KygAUv54bRzr88iKQBbGdn9sxp7a9F3/vX4vW6FpBXHel09gZ/dTkzAcwW+lufylSsPX7oPR/1MiSej3777Oe+1X1EyWRbVwxWvE/6bC8AHU1Xvs8hxyQOMFIAtjuwBWo0Xv/Tmr3pv1y83lW8115pB4DGsHJjerllyhMavw5sX9bqRqVykU0rc/mILnazNJrUpKneKptK1ufEbunenb/aMqq8PgP6TbOzkc/cV8ylndYOmRyCg04lHq6/yTyeDtlzSaw/acyvfG3L3FR3+AllKJZerP9Pt3/tZ5++lFj6aTrp+4YeeyN7IffTSR9t7Y6eb5Wj05t3Pfa/0kX8Wf3f13T6+wuCTe9sjXrogS98jm2Hnqp/cAGvAOoPxSkikKwu0CtCAn5y2PFVXSbpQUYBEKWRQZw0pzXpaTuMITG+byr1NEEpwaqHLeKHQaPNrvgRAErCY91dPK+W1pRW4LRm4+RG779Rf00QKpABsbaQAbDmkAGxp7g8BmLo30mDsb6PE+//oyvAnHtB7+OjdxO8lr4Zv/S9nPYvKTrZ6lEwdlsfxwd1r9U6OBt5X3IvTSQrAVkZ2/6yGFIBPB6ddABborcwrNZJwcYNKHMD2v74Q+Jn3PzlyHya+tEM0uf71u787sPY3g6tptmFLp52Lkb1G42ynug6I7PhJjgIpAFsc2QO0Gi/+5pu/OmoiAGOm/eiLdzZZzUjTGqKCtqRW/dYaocXDmoq72Tq3nGG1OtuuiC/Op5yaTmMSyqpv0lKDWqTCPtvEE1z/CnWPR+Ol1mM4hkIU16u/jT8sJZIppVyu/kxVlFqERViHaVX0NWjMAdzTxXzK6R9Pdvv3go64y5aZz493ZcvxlL8Uc4VH40mvPeLYzaS6bL7Cvx/6wKVzk0f4Xk8Ry9HoS3Pv/p9XFzkbXnvbTzeP/jdRly0LeB3bKTwjbMTp7icZYPs6U8INjKoB+8mF2O0hnsbjI7dHb4DtPMoyESBeCYVs8WJFcdoyG6VBIFvxem3ZbMUonIrPqFEjCxUlX/au7/Wh2nk1DF5kvUrdnOOKqrYITb9NjnDDQ62nLRnYND37mGaf3CMa6eVnXOsuW+at537phNsjaR8pAFsOKQBbl9NeAFhPYwEYKMziGGLs3zy40q+BaGL7ytLv2+MvDrEcXCu4MvmaI9a88MxhkHYQyTFxIqeWFICtjOz+WQ0pAJ8OjvXiefg8Xj+EqYaXNEH/Lmp7gJU8o//syswj/Z8YuXi4ptwPRO8mfnv3tbXErz3K7dHsUnA1XT1cjcLAD9NxOoGA8ZbIjp/kKJACsMWRPUCr8eJvvnk5FWviQqt395ouB10YqV7w0FScC20pwW3RRBlqYvCdgS7VL9hoHUGDFjYrtduOWNXyjd84xMExbUCjrTVp7QWo1+PrTMZufzE/46QfXlPL9+6pLxcr34Bt3I8X80Un69XN+seThU0ln3KSgTSoFd/c4aKjr/DGJc/5kU+2817ve6IriZfemv+nS4tr3/FT5Mf/7Exf4Ht36V8rTWxUgomy54xrPWSLAyk88/lxLV1ZM+OKiRr5xaDDX7D7ilXtMANKVcIX9YbtvqJexNXK9+rxk0vhmSa+iT1aGtsodeu/Ao28mG3SsMatyDmnjZjxU0F732vT63Bn8z/qv9onaf/VvvvHvHfDgdLOWykAWxmbzdbeJC+JRGJ9DJF0s+ZrAZyvHf6b2l+5nGTyv14Z/rC8b99nLNj7Cxd+Phr9yZd2335z8LODrExOLEyyEJ5Xu8xNDvUpRQ4C3t/Uf7733HgkkUgkDziZgwrAU3V/ZiG2P67XJjMOJv74m8MjHxg+UCvuP8bOBv/m2Y/Bx6Kbid/dfHnZ+3/XdP8Mfb9bcOsQKn7PvROAZSVgiUQiuYdEWpSQFK5fISA1U39vmD02VYJNnz0OhBisF32TZuvUa8ANhNgOlBixa8OWtUq6mlZav6MDa8D6Yr2GPdbvwhRdkVe9Juf2F/PUjttnyOPED/PgA9QXaqVh1bmA+bedpOEMLIKL1GoXOzCBO1bMh51uWzH/TeeP/NmNf/SxvvMjP9j+e73vGRsO/vLwk5+JTrwUvv2bqejv/dYj7nPnwsN7fefnzrPqIRunW6w5wkafO7HlDmqvDQNQcCmJUigxmgBKMVW9VwAyG9U/xQN9rrt40EcC8JD1kImQukvfRun8F/bO2ZRCxLHblS1rmmzz+OKIY1czKCfKHu1PPe7RhNv0m7XZQPqtrzwtvPX6utTaOsKBnIJRuKFWpNY2fkG9W/FD6piVZvGlq62dXM8ReP1POO255cSXJivcX9ZkSTvICYDWQjqArYtlHcDNS5HRQJ6sfzs5zv5PUvptQTS69NK12Hd6v1p2vDvJ/CQLvaRCbPriJWUnC7Dc8ZBrQ+6hJicFYIngECehdABbGen/sBrSAXw6OL7f5Qj0HPS19f1AkU2yDNT1SQyVLHKQIVbk8X975eyE7AE2I7qS+O3t176a+K/T3JhkYZIFV7YSXE2TbcN13SamRZqPA9nNkxwb0gFscWQP0Gq8+KU3/35lgbo01yYYVQrDEP8B8sk6FS3adAB3hKkVuE2Jov4IzDfYoGHjHFYCMUrymVo5v+VnYdC/hUKmQMtQbu0tiy3M6CKgJ3RtEEtc4IF3efTPr7zz7eGhj2zFFkO98RxlfvNHHr104QENfG6TaHRpJvX/Lq9e+6ZCJv3IoO+bAYJPx7+Z8Hpuex8KsrVCJIdvmQFgjjFRBniCqAiC3iEUp1urBxwrDjWq1ysCnEWxYR+5bKV/K3nu9WSPrb+AGrE7t3vOMBdELO/0TWnh1XpaeOtFmeqTETj18zAWYVT9Zg2YZSybct+V3a3hmNK56xL4m62rm3wgHcCnAhkBbTmkAGxdLCgAt5R+9dTLwLp3NJNl9O9f//jjj3bYggeXq1+5/ivffnvmg65n3G8NsjbIyiQLw8S8JIEjMwffEw1YDgtKDs8tKQBbGtn9sxpSAD4FtP+LXD87vvnKI503xt/Gq7YhYzYpTZ82nOPsr0i1pgOi0d2XFu78J9dXR9iYZGGCBS/pfTFYcOC5gCfZ6zvMnAOJpDFSALY4sgdoNV5865Vf3V7t9FUtHMAdcUilpM39tiMMt5RsT4BGRyOmWjs1FmvFkhtM/vS8UrG/886YtgRqyx6LKr9iyYSZNmwQjC+YHd5GovIARGEad6CY33C6I0W8uEcTXdly7M2Qw1fIrCjuoWK+6FQGMoP9sb7/3fVP/m7fBy/Jy3W7vHz15dR//pj30YGb5x4aKG6vh3pDueR28YM3wzFghkd62X2nMhVdGy+lXPkt52AkZR9NhJ2rgFCFNRm4UYFeUYk5kfXYQibTQbSgadSIaVTfsKgl7LVl/Y6dQkUxSMKpUo94VvyptwUbdtHBhUUz+wrEd0EER/fDPARUa6+2QkwtVSMeU/edEsnkQuK9XxRckdFdiiki7ru+iHsj2rUjH60Y3PZhN02BlgKwlZECsOWQArBFOZkRmY6Et47UX4GpGJnl7F+8MvyI7PkdhKtXr7++WhJleybcC5NEJ5kHJllQKAyxjF4MpnM9WArAklPLs39Pdi2si+z+WQ0pAFudY/05PsDPbqdTAPtgS7dkB3qkTnNwotH1l/507XIqNvzoati5Okl0hPUe4oOsiGAYYF8SFgNuLV3C96oMsIyAlhwp8sJicWQP0GrUC8CmdUCB+fy4YUkLK3CbHInQ0s6u29GAu6Cvw18lbbMG/djgIOz04FzQVd5t87Xb8Di8jfs9RSIA+S1nVf01vHdDU/UhuobljSYUXlDVsjCA2190+AuZqPLoe6PvfG0MH3wdzuhCd2/Ce2AXd28xv5m78tOPXjonjb8H4a0vv7wSfnN+ZcaZ++63Bz7ltNkXKTyy7vqGe2jp1tjaa36iFbpthGCnwoiNaVAITG8H7TkhBqMqso2swAKR2HzGtS6k4qA9t7MZAuy+Yjnt1NRE8biwqRgqAZvqu+iUY2BvpleJZLRI6n0Wa5OcjxzthBe7MPh6ByAF/ZBWE841zPz6/vFqsnz7IQoWQXxGWkA3qjZv4AiCqfUYUu7r6ou3Q332uBSArY8UgC2HFIBb0I7jQXC0ZVmtJgAfQP0V6A+Ln7M/IqXfIyAa3X3pDxYu345xAaEEA3oxWPsXvR7cPDPQENJ4kkgBWHIUSAHYysjun9WQAvAB6YMmN8VH0hU87h7gcau/Aq3L4YcUlRK+X/zyEyOf6HxDkn2i0aWX/kvs8o0YT+EfT0523xbBfWF2vWQ9pKntBFZ7gKbdvxMLf9YQg25S/ZUcNVIAtjiyB2g1hADcSPStxyAD12gDRyQA+8eT9VJKKaaYCEX1NG9DvQY8Xbv8ACZg7bUDtX1C8b5isK5KSuJ3VnPQmpYfPti+6tcR9k4fjKnilrbc8MIJXUlU4dsUh3kR/LBeq8PVfVhuf9HVnwFSM10o8B1wwS4MwAX848mud+3ksQ0V3q9c/7btkc+OvffSRSn9HpZ4MvqfZ19Pzf5Jouj7L97HJ0euf4Lv/K2bv5S5rvz4p78yZos62StRzDJ4fefC0s7Q8lo4n3USripnQtwVrtByuhr07QhnqIt09pMTvmHNxeu2Z/Nlr14/Fs5ggcEfDOzN9AqJVGRHVxGy3w2dFluvwoovjmbSvQETOofuUTFQ5wM2Q4lUj4lQvhutJmK0O22CaSD2gWnnYj5JVKSFD7CWQwE8ZOYYE8sbNeyIleADYWr81ZACsJWRArDlkAKwOQfWOzklw3+CNgcBD3M0VGJbdP3ktz488v4j2JYEEHaQ3167fCXGdLVrLsRg8buuF4NpUw++VxqwFIAlR4EUgK2M7P5ZDSkAt0VzudfAUU0EPO7f4k4joNvuBBYUtyuT3/9b+FC9DD8ny/0eJdHo0ku/vXX5yjbTMIB7tOgeTYjun0IuyA4wybyP/CArqD3A4FrBtZMnXevM1jixABgZBC05aqQAbHFkD9BqvPjWK/9p71sdvaShBnyvUqBb7lqvuXK4qOf6LdQnKjd6O4ZGLkHyEO0JqKqt5qi7ocpm9aWLF+HVBrKx1tr6lOkbteqvulm9BiOkvkrGlXqti9fgadjGPVbse89mouzJzwbD7MUygXzJ+T+M/peff+rnnph874HercSEaGL7c4vfez1xJfnme7+Lf3u38C++75/ejb9nNjn1NXv/iON2D/ay3dvr2vui/QkgtdDl9hffl48/Mr74fvtvfcX73utMFSrK3cQZvaip9wr7yYXYBZaJ+Mm5K44dm7OPxBZBsYJIkxZWYCAT1Z15SkN/p9tfBLQi09qf/vFkOe0spVyAw18Q3mLxlFhH/6oqGd0MhkPGEdfVoK13mgrqhd4+EqLLrRFmt5vtXXqBbraBXXpjNBR69crr4RGh3wOs6RdO1lQIr94RZHU25xURHVBtzwSQoEf/WSdKoebG8SboxVqRE16fKlFPo8xqg/Q7SRT4/ed+42Btk5wAUgC2HFIAruEolM4qhxkHPMkY3pbaW6tjEpvw6/+syR9WV5ge+72JEXlnfixcvfrd5z+9S8DBBfVeQhWDxWw+8dOo14O1tEAMn9e2blhQjNuejB4sBWDJUSAFYCsju39WQwrAJrQf+qJnWX1wsCKsphxrPzAC6bbfadNOYKbHW/DaC16bL14qeO2ubNmZLQOaDLyR8SrPvPHJJx85ZJMl9USjuy+9NHf5j3Z4BLxV283Z4JKwcej7fo9yu4dEVzYBKPHivkjfJBjmuGchyI7fydPmdJajTbQ6EaQAbHFkD9Bq/NqNz382fpCvujZ2XxWADym9HEwDNt1pVn2QBnQhru0ETqiT1Zpt1oC2sog7VuUuUXpTPM4vBquqlb7BejvveicysFbcdLT1uhoGyc2ozOn/jIFPtVoKg6awAqvDSkDQngs64kLC6cqWV2/0MYP7w0Ug/8dOpnGPFvNfd37ow6tJT+mHXK995PH/QWY+HxOvv/ny2u98LDzArb1Hhp07+d9bdwe5u8aFn2Dt8bF49+h1b28ve14yyf+4/d6v3ez9JLazEOLu8Diw0d2zTVcWTxp3Dt8OoTT7Cp+PnHjQQzyL10t2mYEwu0LIFOpgrDiUKHv2ZnrJUKswGiVVzS+uSXr6/OFsxRt2rqZKPaKQcL3WqFmNa8zEAmHQ3azLs9F/bdNqqvM6dMGZpofV7IrUxH4qXDfan5PGA9GMHL5lTEoyN1hZ8ZBpvs4I657q5Y9hNrykgWFigJdkcK2w3/nXrv1iFD9cvaAVFHdi0AXMMZ7Ft0LEcG60KVdPdnL/kENZ1inQhYrJ3AFDeekRNgDtgEgB2MpIAdhySAG4yhFKv3oOdhd9SgRgg/Rrytxa/yMPf+mTT8iRnuPl6tVrzz+/AD6GnNW+i5iUOlEtASI6K2Kemo9cD3FPtVuw4SWt1Q/2xUvKThYaDwseR2agPDskR4EUgK2M7P5ZDSkAGxltb6DwBNSRk+kEtpnE27gTmOnxpkMOw0KhBAMFry089oVnRz5ymDZKWhKNLr300sblfxDjjIPzaiFDdcT2MfctVDeAj5wwBIuOn0nlYI0mJ/lRCcMPTsdPyK7LMKJzpGl97L4GhuxD0ubVrB2al4+xBlIAtjiyB2g1fu3G5383/rbBKKbHdKBf79yqCsAZM/XlYNQbWA3EOo+BNXj7tEvZARpcZxPUa72NqPFJDxxFjO0FtbqwhrDwChGugaAumipqr4rI36o4ncHdV6zxVmZqHJxi/a5sWfEn/Y4dP7mN0qDbnk2UQrupQOqLXQAD4IM47jPF/C3nD/w3r0bdEd//U/jsX5qY/pC8LB8vV/80+i8+983xP/NGeD2RGiycZbH7e7endqLX+9+X3klM/vub3r81sDfove35pDdXPBvdDCtfTik9/swOGZzL+d3HB1L9PW97z+1h3ya4S08KzzBrwsM6xBrgJZfFI/5N4wZy+DShcY6JOcZixaGthf78ltNcixW9kYAqN/pw9+1LwkF7VWwWuq/4U68BVzIuIf0qkUwp5TJagTViQIOvWBa2VOc9DTzxWiq12YXIEFCv14D1YqcQgH3kxYHS8hebs8awWN8UzUy8xrA2fmtA29EwMS9JILhWQEzG1frzoveeU68YMSrb2NwQgZBuWxE4BxkYqd7lzTEuntEe6JptYgvW69CmjYwTDhETAvMkC8KLrHchawewHSZZ+GvPXW9zZcnJIwVgyyEF4OOSfjU6HSs8SfWXVkMwDQ5OS+lX/EI8M/Yv5djfSRKNrr/00ncuX/YSceCw7d8GaAlFuqRo1C6LNiaI7rdZ7xI2nzKGHAeUWAspAFsZ2f2zGlIA3qedfuD28eg09ZxwJ1DccTeK5BWDjGZKUpN+YJaulTXv+eE/vPT+xw7bPEl7RKNLL730+uXLZSJdPGEjAqM1I1liGFeL+JskqpUNNiRFC2o6fhrLDWzuB5sa+CAUAz5Y8exYJ3ECx30b24R27nDFdSJV+1i/8Cg4+2evDD8ilQbrInuAVqORA1ir/amnPrHzCPKfGxGo/TU5WL5rE2Nx+1trr0pxE5pVSt5ukMegpTo3QpOpqDtW9Wv6a+KdA9PbQvcNTG+L8N7BrthGqTv1Zhdu3H3F/JbTHSg6woXMhiJSeUP+lOJPAlOOWzm8QAZPCs/c7jlR/VcEzg3Z1it7wyj5xx3f+t3oT/3mo49K78dJEr2beOlr838ntf7jvVfO390Z/+y/uzBI4Xm3ayCfnBnIlh9afzTpHAptbj85N5y5nr5wJzk+ElnoSzmn/bcftr2Sq7znuZv/rmBzuyr5uxPjObrie135Pt7iYhKnkIS9ZCdYAAp0AZ4s217HKoMZvDGGVwmUi+FouerQDdpzu6kAUE47M1EFBRZ156oLCpCFPtU9b2pMF/+KFwZUP7op+i+C/guiZwYmamXgevN97Ve+/vteX2e3Xv2tfbZmfYVCBpdh+SojwoRjkFe1JcNsxAlpgqhhm8Yaf+KivgMb+z20wizANbXHlYY10E9dCap/XoRzPlJlIg5cU2r3UnyVp6D21m+O8UkW6ptt2s56snR5Scbpz+AWhwXdIepomz/w3JGbkyRHhhSALccDLQCf5D1z+zKw5QXgRqN+q4yIS/Yc47E1/89M/q1LFy8eun2SjolGl8A1O7sD3tff2AXwcvmNGHFd7Ik6JqhNX9VLwloPxlQPxjAyaDAHiD5HR8h7BMlRIAVgKyO7f1ZDCsDQnk+uI+l3VDVniM1afAqgHtPKrA0ysU37gVm6Vghn8T0z9s8+MvKBo2+hpBXRuwnKmdnZDQi+fjfOLih87k9uvPrQAKNGT494iagVosXD6C3C6MwEGsY6L4YzfAfSbYvB96sA7N/P07MI7SQ26amv5mNxzj4tBWBLI3uAVqNlBLSo8Vm/vEbUpHN1tl6aPVoJuWWmdDu7021En/6qL5JqwKCRVzKuwqayb1U07FQfBaEmdpjU4hU08lgPQMpsebb212diX0tz+4v5Raf/6f3f9IHAVq99F8iUem7fHJkeX5xNDpVSLoe/UIq5zkzfXd/rGwhsRRxrwAgbs5yNFYd27oZKd13u9ycm3AuiWOwTxLYoPLra/7vf6f43z3/fE5/8qOlRkhwrd5MrZRyz72T7zi7PLc0uB7qfjd1e+Oqve5M7q0//1GOrv+O6m898n1d5O3vnmccd3UVbtmfN607ScyP93gu+P7698RF/ZOVhtt6pTL9F/7l4dibYc9YRDREvUhZlg4HdZH/QGc+XvTalsLoxqBnERRu0PGdAK5ebKvVkUl3xlL+UcuW3nPs66RI8ogabt1R5NbR45ya9LNO5EeKrvK4zxuhs/do33fRrbppp7COXxiOK/uqXD7MBeGtdsL2kvGQNPep2EEKpfsn+MKy4hN+COGxQ3KAS51qKa53uowEj0A8X/QDOcWzuWkkY6APUvH39Na0psQm/K1speG3ttMGVrdRUrjGwDVtyANDSSAHYcjy4AvDJz5hucxzQ2gKw6SCCmPszx/gcEwl6fmXsJz4xcvFomic5UqLRJexBYHY+jovXb+8I988/3VrcLntc/Rkx0y3i2BWdNq27Uy8Ja6nRAqNZxHC2N/EK368jgJITR/b/rIzs/lmNB1cAPkChX9P+mx8U8LXxE9bIN2ngSEI14rVZXm0SUWff699Le+qv6AFm8a0znF6z/1jX5UuX3td5CyTHTjS6i902O7eIPRhbvB0N9OHh72ys941vJsoeoQQDfnIKuSA72gu14a1ekiFi+jn4NUph/XmuFVa81WA8zuLz/0Q+8z202HZOp1pvc3zxElAtDWNhpABscWQP0Gq8+NYrv7pdI3LUm9s0UbMUUzIbdaGo9YU/m9Bclz0qDbgj9Xemaf1ddVOB6e1Gum+hoogfTe1ACVut3Ve0KYW9mV7NcVtjfzTUG65vs26qVvWw6zVgg/plKrdoWdMTkIZwtRKwezRRiil2XzGSS6+Vgj39ca+turmIY00hN8GCl+yN2Me/kz+b2VDOn7uTL3tzXsd5561edpeJbJQGl2bO4sU9mgjac5W0ix3nY/23ftT+Rwtf/MjPfOr5Jy99tPExldwzosl1OyVgfm6z1Ff47hs2nE5bOLG0ceU9ytW+9d1ceWdl6JPBtfUrkY9N2Ff+jy/8hHIhE+pLKb3JTKqrl/xH7zpSSX4rGaYHx0gh5E99Mvgn4yTGeG2Ds9sEutn5SunTbsf29eK5rnzK6XYJQ/BIcdvtS8SKQ8DKO0NKJJOJKszUVtH2V0OJq2i1qE3Rn/NZdc0JALy13voszMO0rvA2OvVXNcNo9a3F13yEDVFcVovJMexf9IRNmzZMbIWw0HqptigJCL0zuFYoeu2tO1TL6s2XeC9CXNYmImu3irOUk5RWgaMUfU0RveCHvUQcOMcBCGIT2nkENtobzj2S0iSzMKrelauXymdflF0L6yIFYMvxIArA9+pO3poCMI3HX+oOlFYZXkO4foX0O8eY93uRf/DkU5c+IkP/TiXR6BKuIC5m310k6MOF3Zf4he++dW5gDlgloHWJ0EnCWnhgZ5KwRHLUSAHYysjun9V4EAXgA0i/R0jzH8GO1N/mmxKV+0wKM7XxKsPNuZjcrQ6mGDqBoge4xvB1zt9MP/4bA99/6fHaEnmSU0J1dqADXMxubtFVstkKr23v3L35lqtvadObi2EHeog7sXexLUbEhBKsJcT44iVntrzf8WtinW/DInDPOFVyr8bR6r56ToUbWArAFkf2AK1GcwFY72fVCnCa0KGbtiFHIgB3pDELQaiRAKxTf2kqjespxRQtWqOFT1rTqAIwWhV6lUhGKMcRx+7S/FktqNmmFPY/gsXGARsB1UmJTiqehvR+yVVNnC7FlMGuWLbstfkKXlvWbc8GyPnIj9kWQuwuVSa7bfEI2d105PPJZyL+7d7A4mpl4OZXp3kNJuAcbn+RLP/jY7998fbtV+0/trX9ys9e+BuXLn2oQeMklmY5Gi3ixafMvrs4oCQ27OGK1/VrX/zsrDe8vDzy7LPRPs9exrcHzJbOL71+Nn/H6X64+PGRm35PIap4nL5deynwrP3Ojq1copSj75Px627v9h97H0njSdBzsbJznfBd+oHecnHb7rz51jToXO/6nGeDr90g94peZSMbbZfZUwOQhr6acnjoEp5NQxC14U2BafiwL15SdrKZHq+JpqtZgsU3Xbtr89dNlNxRPfoihKmJEDALUMljc7M8Q8TBtRTp4x9hvQibOiuwS99JbtRhFrOKTfv59UegfmuztUla4vFs7Tq6KadyANDK2Gy2BlW7JZKT4R7e2E+1cYXuVP013eCxvUf9wJ/m+r3GU19986O8zpU/9/Cl/16O/Z1ixsaqCdFj/fsS/l+ZLV/+CQ94+FDw7UegD/d00T2aeNtx0e/YEbGB1zkPTDL/DufEqyZZYFB9IJgA04GkEyusKJFIJJIHmXsrZDS56W2H9m/xhZAcgDBk2jAEC2tm/Y26X831yoK3Rv2N079C/+tcvM7UndXpv/jK8l/9week+nt60bp/wFh/UDw4V1of/zNfgQhM82NOzlRHqxuOmoX2s6OByYkFbZtimMy4106L3R4JQicVZrZ27PsWJtPjTYccx72XxJAvuGp0wEgkktOOECn1EceYSZsN1V+aFujVZau24PDqb/N9Ndm+wYxrRtCe0/8pjo9R3xWtUI9kfjGI5t9drGvbklqQ60LVXgw8PDQD+MltlAb9jp3Jh7KJUkhUURWeXVGxa4/e6jz8tOqSTFGt75DeL7XuHizmS07SuAPFPE53oNhrz21vKkAp5gLFPZVYi4WFA9htz+Y2e3O7FHpTK3bP78anPtL/7ox/+JLtG7M+18/7/s3HZt+6av8br+z2k+LHf3EmE4iGKb9Lz6c35mfXCttvdv3IX/i+j178n5odRIm1GRkTM0BrBgDPBn2f6/79ieDDlcRbwYHoH/LffDB758xL/zjgJvGDvuCNdCHv/vXgr8divWFYTkVifiVf9roL2fnl8T8Mf2AqH9tIer32bLYYSHatLC2MJfvK5bjz5rYCKN2Z0p4rf8fJWxWesDEKd8GnnsM9sApp6AUvLOlk3YlaNdeAKACsv9vqr11BwT+eBM4Gl4CQLT7AmhZyIwYt9VpvcK2AOgAunC0FpVqLV5vsWO3cCguMmGS8XN0XvWpHVz9DzzBbT/tTtPm8bguCPcjtTxEuLpCqsJLlzgmaa/rhnA9Q7b8acdhQTcD6mB/9VTMAhsrpYxBVpx0LxKepabpTkNW9PX2pwSyxaT/ABOH5VLsJW5J7ipwAaC0eLAewFaZ1N79UtykAt3m9b/P9tuEA1qaWzzG+xnAa97f4wPX8+b3P9fI52Fu48rkpOe/v/iMa3R0f/45ugToGFOnCYeMCTMOFaiFhLTtFXydjUpcPpe9RGWbSHbvDQNzjLbZYS3KqkRMArYz0f1iNB9EBLLBmDEyrSd8HJ9KGBlw/WVt0+tRSpokhX8FrW2Vkh+44oa/wkW+/+QGuwNfKV17qu3Tp4uGaKLEiL7745cuXrwH7fT988ATAkJOHQJ0U6OrPdPv3go64KCcMiARpH7ke4sIurLdTaKZhgStbcWXLNe5hPQa78HG6h7WhvUYUvfaC1w60Wb3MgL7smRhY3H+qUYWzVhyf91fDla1YXAOWDmCLI3uAVuPFL715eT6mL3uZKBtjQ4yFbBtRr7C24/rt9LWGNTWxQXtg+sJDlCjWDo7mAN5Xf2+oa6oZpMadatvJqPa+TdXjqHc3XkCJZHr640FHXCwQitStyiNLhYEzrnXgbuKMTSmIMZb5/HjQnhO5zRulbiEeby3099pzu4o9s6FUg2nDuP3FfMpJDHz4p5OFu4ojXCilXGK4pof87ZsjeOkb36ykXfGYfzK8s437XNfcndLZ4eTO06F3t2zlzcrQlG1lpzK4bct2Xe13fHLtx1e28rNfe+1jH3zz7vvnFv3v61/8ayMflAOA9x/RaOLuK/0b470j82uFWbdrLs/jxM8O+Cs7O7ZJ3+2dzU/2zlQ+/oYt8FCFgq3gYj1OdxLns9m7N72BDN41BoFSxZfOBCZyO90eZSdnS/Ws95Mop/vmPf1nHJuxSlfItvJG6QlAqdi30/5ExVPedtp7i6mFrurJLCY6RMELAdzeIkkQk1KS9A7nKFbb3KsUqiZaAGx96V5fBnDYMg57xmHLjKnjgEHKeXJDbOzRH1D7omeyS4O5JQrgIjifpggZWAcx8VWfORwFqgV3HUPVEOa5AgE7EQdzBRJ5UP2yQKoC4Nf1GbNDBDwAlRw2D4DtHJV1CGNiltRfmFUTcHEBYKPEnezxysAX1QfDXgbUIuV2BftQ7XriyHsgqH46ABQibtdGtXNbeNjtSuVZhZ5qN77gd7tS+YLfXd2C2rdv1B+OTfjD8ynjrMRZOQBoaWQEtOV4gARgK6i/HFoAPsA1vuUbr0/tV1MXxOzyVUZWGdyma4YLbxWfXHlniN+CbyTgDnDlyk9dujRmslnJfYHNdqXFGr12PPbqrc50taKGkITR3TUdQBXWMPeO3ENknLUlkf0/KyO7f1ZDCsD3gEa/HTuw0eFLDkBzJTigRrF5YRQWKZx3J8ZdC0yt0JfDt1k5/2Z5fH1rYPWLIb5e4dYr/+SFh3/8539IM4xK7jOuXr32/PNfBuBhQA3m04wYQ9DPkJMQDMM0DFRPITFuHnHs+h07gJ8cMMAaunLCULULZ/F5SRtqiOhxZau/GgWvzZWtKPGiNjZUUNwH1k3bQWxfzH7Qa7fHSqcTIk9AAG6O0O/TIYex8ssJIgVgiyN7gFbDEAHdKP9ZYGp4rcGgsx5YAG7/hfXbuaB7TN2fTajdqd4SrT8shYqyVBioHoqW2zQ1JcfUsqZLqoWxNlRDEHHszl2fEInQIrS5nHb29Me78imbUnDZMrHi0G4qIF6y/9Fk1NcrsIjy3v1YaeB93a/dTDzW3bWZL3v9jp1xth5iyUM2jStILIfvNZ4QBYAfysbueD44tF12kP290vgvfHPmr6c/9H8+OvfKL0cfy/3bwl9ye/4g3/0ffvRf53/yn4+85+OPP9rqQEhOK9/5ezbOwJtwDfZgDM6CAmdgi8J598Z4ryvLd7yPD7N1h+FdejcZ3sMN9LKbwgN4yERI9ZJYpFch6yWbxQvE6B5hHQizF2RrhUgvSS+ZLMo2Xbv0drOdw+cjf52pHuJpPDkUYKEyBoRs8WJF2cNzN3Gm27+XKHv68+m1ZLiUcrn6M4VNxeEo2HuLQVsunvKH/Cnxr+JPuu3ZfNk75oieYcNFWnRHh9kYZlOblVjtySyDAksQg1jVZF+c491NgGt5Eoc4thcBGPaykuWiH1uISlyNVhZ3avEG92s58BhlYCE5H0cWtBBztTQoIOgmkSfoZsCJ30aqUv03VSZSl0fjHKe4gGuKitoxtLn3HxuwuXWeYA88pXMDN0cKwNZGCsCW40ERgC2i/goaXZ6PQ/0VNHn7mvrrhxEyPd6C177oPeMJ/fcZlMnup8rpIDtOkomph/uhIGLirl69+fzzf3rlyqemppz64DjJ/cfVqwvPP9/2mTfkBPbFYPVx/SxaGkvC1CnBjYRhjVNRoqwGKSEfA7L/Z2Vk989qPKAC8D3sDWabplDU9wDjjVXhA6DpuwaTj7jfDkOYxIRvl0jMO+AJ/cX0pueRyDOltS5garIbkqKzF43ujj/+MoP+K/9m6tLHZebz/Uw0uj4+/h+AquvXyFvqgy6YqJkLOFFTa00LiQFETswIGx4ygI+cvsqaoeiaQKEAKOT1puF7QpauLN5tY35fZ7Ts0GoIydkXL6VDDn2puYLizoScJ6NGH4bgWiEx6BJG5+MWhqUAbHFkD9Bq6AXg+hq3dCQDm6qhnUq5plbaNp9tZ+Umkq2ZANzomBgF4Bn1wbQu21ljQnul+iALaRjddy1Xk6LVXe99o1fz7yqRDCD8wUuFgUrGBZxxrSfdfk+25POWouUusbDbv7ezGSqlXA5/obTkyrudbn/R4S+E/Cmgl7zStZHMBIBb337YPVp0+AuDjoTPWxp23r2zMha0F9/87BnGt//ms2/02n5ozL4ZPtN7diB/dz3gGPC/byQQ23OIHuCfvPvOX/8vr/3nDz7RPdInBwDvb+6+c3XlS88TgjRsgZ/CGberkmcWltUSMxsUfsQtjMLLE4PlrPeudzjKk3YWc/h2CClkM3gDFANs+Mjbce5hF9vfpTdI2c+Gn9ImyhZn3s93t+kCtukaZmOFSA6fh3SA8hqDEZZy+M6wu0T3MpEAhbPsprAv0pvGE6TQxXaS3iLlQVYeZsVHcYkeYJLF4fia9r6U61lmdVnNr1NOYu8CKCf50h2Afk6itq6Bn/OzUarKqM5xbCKPRqQli1tXkavsqWlZOblvQZ50cS3FJg1mU7bHRUAVp8/5aozLGqkKt9Nc9HNNHX8Vdudrqf2X1KjaejSFW1GnrSjVc8mIeG3LpFIpAFsbKQBbjgdCALaU+isw/Uk5vgBA7WfDAzl1AtcU7/b4Hpr4u0SeTeMvxnqn+vooJNrsz129ek2G/j0IdCIAp/eniHXZCDh4SB1r1onBNJ5d274kbLrEFOsn17WmuWwgUZH9Pysju39W40EUgC3SGzT9RTU1AevKPh2WAAwDEKESJOWn+0dfuLtpH3r4Q3uV7spSeWro7NhYdztbikbXx8YGjqhZEktjC/4WyZWq2deETdA8ZKohGKp9vwFdkV0xDq7g9hf7xje9tmy24g07V/3kFHIesh4yPnJpPHqLsI+8l9w2Xb0ks3gM2nAvKS9ZL8ksXd59X7IRIZ0mhnxAwWsTemQmVJOydxgxdY7xdlZT1BpojYzOEoG+0y7i/tp5lRSALY7sAVoNIQCbypx6hO1V+9NEA27uhW0ey9xcJD5AbeD6WOaD2n+bCcAZnaArBODpxhvXGhAAP4ThRk1YmlBwBwJbc9cn3P5i/pqTeZgAD2zhfqyYX3PSX13f1Z8RnuCq6zFeyHodqzf6RJUGt7OY/46TpyEF3fifTEYcu257dv7WeK8ntzaTUx71Pntmaa9Q/Kh9ftD7gx8b7Y0t9+LlwkCliKPNAcC3vvzyE5/8aDtrSk41313+WuW/fAQHhZAbr24W1zZsqcU4+tSIZuAd2Gblmwz/eQoD7lsXH93w9r7NZJlAmCXx0h7i/RSSlOcZe4x33bs8XHn3dqhPocCuPxgv2gaSu163O8st79gwW9t0ZfEMswXc5HyEFaAL+xcqHzlrm8vgvc6Up2J/Z+18T39c1MyuptZnANx9xfyMEz/uSDG/4XSPFskyMr4GhP1bTlvGXXGct91ykTrDtouEj+LZ7GpPfLv6Zr8DPbAMezBblVqF5HntKI5wEC6qVUcGnASHsXfpUuKndEP3HrW88evqgz0qeXbnq2rrRomV7NG06qL6YNhbY+oVgq74FzXXGlgvVv3HPgi6WVNPE5+ueIyBJ32kygB+e7X9rilVhg+or9Si8rU7d0Nkqe6O/tk/L7sW1kUKwJbj/heALTLeZ6AjAfgw6u8YQKWf0E++4Hn0qQ3cqUTfQ91nx4ZlZJ+kXWzhL7Nd7uQV4sZoAtSAaHS3WNptklmhHXQ3XZpHRKCXhBvZRA48snb6PMTHx6l1J0sB2MrI7p/VeIAEYAv2A+svs8u1leE4CvU3AgqxMSZ/4AXbo0/v4t692zs11C/lW0mbXL167fnnvwOBBgKwgXlIwlA1MS7iwGFjWBdkN6GGXopEOQUtoNKmFM641l22apevj4RQhXMoHjJ6VVg4QoQ2nMXjJReqVooDtR8orLpCHj66g9GQNjVgPcPEVggPEwOOr5Fx+k39yse932OiSUddCsAWR/YArcav3fj8Z+Pt3u8Z3MA1MrAmcDaqzl6v8h6fAKwnUNenqqeuAY1mqGNq/22+Qa36bxrCVadvYHp7b6Z3+NFVry3rd+ykSj1jjugrdz7kcBTOjy4WKt7t5e7envTtxUhvLkcPa0n/8KOrIvw5UQp5bVm3PTv37jkgv+gkCtNMPjM/d32CGBNn18+MrA4qO9/YeE/PEtu4Pv3O3tlnpp/+lLuyEhjo2+h2DErbrqR9/tknbe9/jsJFdybk3PRGRApLnP4Qm+FvpHgdxqoBMYkh3y6Rm97RSRa7stmt3NjV7if6SMbpjhMSudAu7NmK90nbmylcDpwJQkNsdLEdIxBmTyE/mt3IenMJ+gZYWWd4jLkdgqO7S8vdD23h7ckWz+yuRL2Pb3Tns3iGsqkgW1/x/NieLdtLspvtVR5O4NzDrVScGwQypZ5ouSvi2H3YcXe5OJaxlbMVbw/5kiPtKPnszpif3AVuxeieZN5HfoIooJA3dB33K49kIQYp+A5AZYniAt9MHc2w2Qj44UkfaIJvD45Q9dm9HF1DVLYpxdnbITgMkFjhm+lqQeSjnVc4Asswpdp6Lup6kcLRW5itsfYWN7ArAPYuCrPYQjgjVPLY3BQ3AJwRyknKGaC6ZjlTIydDNU0aVWne90BrQriG5glWa1ZKAdjKSAHYctznArAFR/0E9b8VR6H+VlP1hwj93AvBp59a63KGgxfGuuRIn+RQXL268Pynb5Ps6Lr9lu6xGh44pFoumorBWv1gGvuDqbMI09gT3H7gXhOkQnzPaPsaKAVgKyO7f1bjgRCAj7kT2Lz6ZrNfDcNlTQwr6Acr483yn8V9NVRvrStxHEPYPKSC+MOE/vILrseejrr6HyqNt2nqlUgaYbP9X4Auy7Il87o6wQ/vB8NoPcCHoA/QpWWqxYMBJZJxhPdn/ulVYWEXDrIj/vSRU8iKQnEendPAdIKghkKhhwT3Qv6cY1ybpyik2SPpnTbZXfsr95K65wnbB0C7xkoB2OLIHqDV+Fc3fvNK/NXm68ztl2RsrAG3FIA1hCJ7QfeqiX2rrrj379hhfEgazEQ3paH6q9uI9vulfyP+8WRqoUvbxd7rvUDgqW2gknHZlELQnhPKrn1+KNK9cXN3ZG3Wzw7ux4vh/r1YMpBPOd2OYt8jm5W0a8Af85En4/IE1oALpNYozkSfy57Zm/tbYy/8dP9T7++xhTPnnCE51U9ySFZevnr3Xz2f+Tlv0hu65T0LeEmvMQw8xzfDMykWoY/Y0/44/XOMrjLwOk+u753NbPSsLfiV6UwmqqDw5NDSHbo/2bvqdM6UK4EcnmHb1iK9YVYUssAQa4OsDGZTZ1cXMj3ed0Ojo9kl1245PeDwxUskKZW6iyHeDQ1XdpVHE69v9Q8WvUqZzPDtNWepvDn43ndDhR0mH9l941b3mJfcLNNdbO8QWmMwgwdQyGXwbJQGgSD5kiM1wgbgISNGFEV/zLQvlKULWCEsgmdW1A6reEk1tmRWHcxfAR9sUNDd6F1L7XtkE3muwQj0wzkft9XeqwhPFsJqJY/tMf0nAR/TTRSehanqjeStd0jkgcMmP5tyEa7BRTMNuAMioKjJ1fVqbhNm1bxo7YEeXRXLZ/+m7FpYFykAW477WQA+EfW3yfBfC8XIMPxXLwC3J3uIccBYLxd//cp22NFVfFR2+CRHy9Wrbz//326yWuzwdWm4U7uksRJMbYBSq7Bo2tCDaSX9HsfQ2/2QOH1qkRMArYzs/lkNKQAfnuYCsMieNX9uGfQ9REMPcKWZc0VTf8XjbZj4716w//jTy9mhjz/0aDvNlkja58UXX7l8+XpDAVjYfAU1vcS36lZVe4BaMIxmDu4CH2gD5gFQq0uLIoiaJBxx7PodVQG4jwSg14MBU+m3lySgNwo3QqGQwTVM7NS5Y5sgIrJXqyXvyOASD3pJZXD3kDjVb1bca0sB2OLIHqDVaEcApiMNWAjAzSORBRfarul7rOpvPWaOZP1ARI0+vVjbT1Nf6x9P2pTC3kyvqOMLVCOjA7AFY2rA6TqUYBh8sAl+qiEXWXgYZSCTSSjVo3QDAlXVRHlvZiiyBnRlKpte5aLj3fTmyP8yfKl7ZGGgOCndvZKj5ctvftf/k8/a/xyFJ92Lg8NzjIaIxQlrJTkei7+rrGZj0/45xucYX2P45cQnYxshEbwMuLuKjr5C6Y5r8pO35753bnps7Yci/3G28mzSlsvhBZ7mLW82lPJuDbMxyd3eeNwZLxdDduV72ULAXUlSesKuXMsmv0b6zz3Xa//ezkRvZH6tEHOnBnpi5xQv2cjC9lrPsCfh3ThbVsjbUdayozkviwSHWM/isdN3g/5udoBNhs9z8zpT09wIUHaTArxkLnBDOJv3x/HEBW0WpkgM+bRCIWJqXRbfChFgl14v1Rs9EU4TIi7+HGZTmIld2QqgxIuunXx1m4JbEIBYrSAqajUCPbADPQCFJ92unbyQ28lAWPUiqzePlesUF6qPr6W4BkFIHPoE0EzAIg5635ULBCCs5lRpNYbi6txlMd1H/DvWtuIr3rvYwlirGUUyAvqUIAVgy3HfCsD3dNRPT0MZuLkA3Er9FaN+F//9laUhz/tGvq/NxkgkB8M2/CedC8Aa+nHArpphRFMlmA5iogWTdUGZpnow7Ym+xz32J0XiY0L2/6yM7P5ZjQdCAOa4eoOH6gQayrrrw58b9P1EvktxAVsIu0I5Q/hvvuD/4adu7vZfunix7VZLJB1z9eq1559f2Dfy6tGkXD17JbPAGK0f2AVilqqPLhsuGx47oWoidHWQyGw6oD4bhlolGOgjoSnB9Yyw7iGdwwd4GlYlA5hkIYvPW7fOYSqM3EOEz1iUH9Z0XyFyCyf0qZZ+NcLzKSkAWxzZA7Qa//HGv3w9/l8NC+fMJvo00oCNAvCM+kRz9VfQPPxZ44QF4HommgrV+uYFYLRaptemVCu+1xwinSLifryY33MCxCHEhWD8jjvg6s90+/dGnQvxSii6Nl5KuXrtOVtfJrYRIsvko7c/ZnvjzY33z+0NnOuK/W+Tz5S2ui59bBSJ5Dj5wzO2yF9i+VODWbxzjG7TBfjI7xAaYv0Cs6IXkaVrh+CbPPUqjynkVisDc18/Ry9npu9+n+P1NfzpSu8GXedsd3croRHb3CQLD+0myuRf7364gH+UuQvZjRWvY5hYOev1eTddu5Vkd9BL0hEPhN5dx0ehx52jJ7C8XuhyZ0adu0Ts3qw/Xsl7bUmvN07Puewt13q5MGC/7T1/Lnsr+L00DgpDagHj12BQ/TovwZvwFLioqrde6GPPPxBYWsdLwenOjfcE7qwDew8PAJuDvW52AC9JoXZPsjDDBVFtZJmBEdbFETM8DrPrJTvEOrDKwDQ39EORNXeI2oRhr/qnqoAWFHfRay947a5sWdnJZnq8yk62oLhd38kDzEIYXgcoJ7m9yICzaiwWc0vWzMTgIEzq/3TvPx5wVtOYAVsIwBFSpd8IACF1prL4Mw1jEIVhqpMwfXAGEpBQw3661Gwgn/oSIAxeVQgfqY2RGK29U8bkdl67E/+B51rG/UvuGVIAthxSAO6U9kf99JiMADYRgBurv5U8MYX3/IcrhfCzY/2yjq/khHjxH715+cXNDisBt40mAws6sQVTJwZjpgdzOEm40zWPFpk+3SZSALYysvtnNR4UAVhjtNVs4vboqBNobgI29PH0oV4GdEXsKnkqCmO/cWX+rPLBkQ+13waJ5DBcvfrd55+PN3za0H/TU6oAbJTa3ZM+I1oowV3qyJFQJRRomhGt4Sc3wFq7+4Uwuxm8TYKjadUDPHWmYeFiOXXNNsWVrTw6/PtSALYysgdoNYQAbKr4GsihLFeH+UGnARsjkeu9v41U3kb2X+Gpba65Ct+bfrRf6ya1KSo3R6/pZqnGtnrVHbGfTmFcGbiAfzxZ2FSEBlzJuAqbStUErCEkjT114w/hdhQZwhEuTNj3bsz3uwNFvOQXnYODKduZDBlnbDMQZu+hu8WcP/sP/vwHPmBzB86MIZGcCK+++GL5jcux/94/x7jw/u4QmmM8TncKz0XujDIXJpXCmcUD5PBdZ2q2dH59r2+y+7bojP00fwTMMWrLjuS88R52H87OFbyM/MEasPHRwYSnP4tzyxsp4Muyt0PoKd7M4Qe26fKSeTL7DlDw2uYYH9uN4aErG18JDcbpGWR1h6ADzxI9WTx2nMACoy7SvSTOsOsl002if23bs77jmstXUti8cINKAi5gex2guEEalHjNe3ddggA8DktkPuZ1VspAYtAl8q69ZOKEBlkRvalektt0+cincc8xEWY3RjeQoMdDNoc3yI4Qg8U0xGE2xMvFvrSZeSKEZoegNmkvWzv/coWI1h0VXThfvAQoO1m2YQt2VHn1dQiAD0JUrlcr8mo1d+vRV+Q1JaX+gPttXEsBBN0MOPlmWvi99xsqkq5FqLXYsl2htKrzEAvTsBYHLQTgJuhu4fW34auMZHD9teeuN32x5F5is9ka3yhKJEfFvTZ8mL62mYrTSv2t5Inm+PDnrhS6p98vA14k94SW6m+XDSDgMH/W3BQCwGqxZgxR3FCJGzn9hGJ1uTajVjhCtHtRbRxQm608woaH6sig/i5XLwYbyqQ1GeNrUlDtWLXhA1x5pGYskUgeXI6hE3iwHmA65EiH6rp/o5CGjJoCrQ1lTtX0AMtJhLmykmf0X1+Zn6zqvsMHaIdEclCmpsZQE+1q6LXjtJks1xDR0PUKcaPeoBYzk1ONxRfUTuDMvq6QuaFwYV8fuEmviNzUlkQcu7GKd5HxsHPVsAdRRTiDR19LGBDjdE20kEnm5xg3FBtG1/dbIbxfx7g9xEifth3Nrast5BgE2ixdWbwhNidZmGN8hfCkat9ZIdykKyvSEYFVRhTyQAa3Pk16m/0rpKiTJ3akvfCYqF5d5WVRIumQdtRfQLuPFky4FwxZ0Pu0o/7SVP0Fk9na+k2t1z2r9aD0cuyA2ZqN0G9/Qk1dBry1swbr3bbihSLheQAlkkktdPE6+XQXo2rDBnBHivm3nbggCVtwBvfjxfBo/JHg9VfufCg/4xxyxbsHV3e2+od64/QUY+shZTqjFPfWbgfppXf1jc/+1E9N+d0y4Vly8qxBJIIrW4l7wxsMp3ABy0RSpZ6IYy2N5+uVj4/Y5tJ4fOR2CAGTLGzZB8e6ox6y09zoJ9Of3Sh4bZfmF8hS6HG77uRR4E14nfgtCv987eEfXeMChYfdrut5EiR+wvc97/tdJCe5e3Z3FQ8x79iZtXeDL+eD77+Nl2LFrlzNPvTYAvGF+PTAyJ/OcpXpH4YCuOA7lGPYJ+AxeIPiqzhC7OVwTlGeZXWJgR8jXST4GCtfJLlJIk/adAD+96v/ByHxG9mf8+MYIvx0PkzqocACU2Qe8yo72Wd4Bygo7sSgS1TcgJdXGXmHc6JPFScsnNN6NB1XjC6Kxwp5ETYzRFJ7Ssuk0ZRmbUBSGJEJMcf4cGhjeGIzxKYvXkqHHED4YynNU2sbgxjO10HMH54FcZvZBRHYUKsL7U/12acwi2OIcoZUDL+dVJnbWYa93MmymSeR5yE37+bxwyY85CZgZyXLsJfbaYa9AP4dUjEGwnW7EGHXabOyR326ujC19+D6t2/SXIn1kBMArcV96AC2nvqrYRwE1NcAMCxRSSa58B+uvJPsvnTpfYdvgERyMF78R29e/uWm91JNXCCm6K0hEUeNR6RDQ7CguS24STxgI2ewboWF5is0wuKZgfdfErV0AFsZ6f+wGvebA9gyhT/qMfEBaynQWfYTK9TSv2VN8fkrL/R8+plHJj5+4F1LJIckGl0fH/8WrMNATRC0aa9PK+trimmep6kerJUWDum0vYGa8SB9qWC3vwjoM6I1gvZc2LlaqCguW0b827h9RvzkgHozcZjdbrYNC33kDUsGWYkTBrxk6mOl20QEON83cc1Hi3ZbLSOgLY7sAVqNf3vj330h/kY7axocwKgmYKMDWJ/eqbtDFxdnDaMjln0Btcbn1uLWvI6Jpi8x5LAYwmAC0A9KnTW5zULFKm5/Mf+q0+BIHhqMJ3vKZ1zrQKIUWnlniIzqz4sy/LOrDzvu3p0ZG/GXldHb1995wvZIZtixPmaLfiC7cvH8r7i2Et/3pCz0JrlnvPPiizu5v7Pzod6Yt3+VfuBtHlpjMINnhI1R3s3hm2P8MWY9pFcZFHO/prkZJzRBtIfEyGtrLIEfSuCHMLwJT1LIu12red6GABWF4nvcrtV84qM+12ZZiWUTE75Ct22DyVS2azQ311WJ3/WOKuQ9ZJNe70b2/ChRvPGRb6zFzw44Vwl0rwN8i73pgcC19cobJON8Y5neYbZXgJrxuAPXxw3CRTe2D7O9yfsewxYn9YmBAj3kIEhhGF9lpxDvcWV2GMKzsLMXmgzE51zFfKHP7UrlC3tugrhS+UKf27WVBwp+N0O4MvmCzY2XlLcnFbIBeXqWCGRRVhko4N8mmKj03KW/knHlM96NUjdQTjkpYPcXcRH05hIVzxnXujPlK/XtBsj12dZKFZ/Dlu4hfoFbXnUezzCbw/G1ldDgcHwNUHayyxODvfH4digEaA9EtLV4SZ4eNztznD2fvXvLe/YMe/7sTsrbA/izO754pejFmWVzsNpB71/bFonZeXqALE4vRTc7WbxxesrkqbM1ix5yVc+utjO2Q3CIZeHx1dZcYziNG90EpjnG3nrulw70eUpOAukAlhwzFlZ/aekDrmUmyfN/WL2hlTObJZamU/WXOmuIeLBXwmUjV66pKndDdyepc4Hs33NeAF2hHaEEa9OThRK8RXCLamS6ISC6kTNYt8K4YUmbknAGl+nctHsVJW2g4LW1c2WTZmKJRGI5xKUrfDSpzi0pKG5XtlLwNvU7NiYdcoC3RgPWKhvpf47UGdApuPBvr7xTGLp06bGD7VEiOWqSau3epoSaPqv15eLsxy0HHASgVCFTrirBXbZqF1GsGVdfuA7rMKF+6/fgRtWwJUSFvNoP1HuCE2VPQudXC9pzibKn9RvRcZ3z4oE2s3CudoVGidPaC4ER1pcZ8JFrHjStMcyGGBETI1+Zxg7jYWLCa9vOZoUfN0sXYHFF2fSS68pWRA28e9IkieS+IUGfvrgvuuuYWK7dLC/XmcKECTgwvb0308sF1byr74zporxMFF8DiplduH6J6fwhfZH4p6qaTiXj0q7/+cXqvb+xGTpxVz+D3PADYfp7sV/ZV0gqYuRggnzGqZ+WJFZYXQuxRv6x3YhjLW/zBqa3u7JlILYRykedWwv9u/2B8TPJ725E+Hw/j/JwrDC8ePOvXPifL106D61iUSWSY8ahUBy13/Ke9ZLeRPkWH4gmzsUWQ2em724wuOyIpPCMsLFMZIeQCDr2kVshEqCcw58lnznjVcgWwu7EuAvI0tU7FN8OhSJr26ThAxDDNkVmwukK5rO5oKsr/u7Z8f7sRjrbH2Yz7XUWCDkXt/2j9q7cRpbgQzcX3GfoXsx4HDs46P76+vqA278MSfb+lNy76xs7jMZJxvDDw8PcAe8Wy3lQpd+Dqb9AAr6Wh68AvP0WAF9cr0sb0P95U32Q1/1r/mAKnvOv+8E1BWNrExl4EsIwQmzCj40bXPD60nO+cR/5OcbT1fTk6hydPgAmfW/PMTYirt62zCTzc0zMM9bNdi9JIIP7G6H3hYhlQqMK+e2QX6GwEgqL+JmVUHiOcRFn3UtSxHoPsjLH08CKNzLMxneZnPQuiF2Xvd7ooJ/qVMWS6K9uDYq2hNcYBoReq7HGsNbyerRRWRGXDU8u1956iEjtZSKFigIsFdq4MZHca+QEQGtxvzmAj1oAFnkOR7vNfUFF+H3r7L8zSS68/KfvG5Ez/iRWoZkDuIn6axoAZXoL1xLDpqZbrdBGqWBqM6Lrnlr3tO3YOCpN1yLa8IG5t2qxdABbGen/sBr3iQPYAsbf5iFU+hDXfSuw5gCuLQBcTjL2764Mf1ha2SRWIRrdHR//t+pfQ9AP0GUzr/fRThXGOOYaaK5MQf2BUOz7GjCNk2Ba7dTtLzr8BUc4o5cEBKWYApRSrtb6hBmGHiaq1bjJS1pWJvaRS+NpEktzmEAaMTCnUDjtZmLTfqZ0AFsc2QO0Gr924/OfjZtVHWsbcx9wSw5WqTfWINK56cU/n3JWVWrdFXtfvq27jOcXg/qfA/Fs/YU9VeoRDjyB+HGJOHb1C/vz6U23rxRTunfKa+/68YMPFNz+Yn7LSRp3pJjfc37i2VdDtpUv7X688JbCNn/7sT/+wBM/+8knL7Y+IBLJiTDz2y8m9i4XFPdb44/+RuUv5VJdG6Xus8GlIdt6Bs/1/PlSTBntXh7zrSjEgMe45SUHhIihGj19FNM4fRTPv7Gy+Z7e0FolHaJ7Zifw5jpusMMOhcfdrmyeLHwJnoIRSMI8yet0iamwGTau06dgm8I2BauwQqULm5/Cw27X63mAfohS2eZr7zDoBng3b+Esvlb8nB9bCGcEpiAAH9sXJsSNp778sGmkv5h6CGizD+eYEF3N+pXnGJskqp8Y5CcXYpfaQgDi5aLIsfY4gzeNR9udnnUGUaN0tNlFc4w1GYPVN0k0I1XbYEMZguRHf7L5diT3EOkAlhwnxzAO2JH6Kwo41S833LFnerwmk5dnAWaSvOfll39u5CMdNlMiOV6eek+3+b1Xr91kYfO7uwtt3CjulYxji/rCwKhl4SZ0M45rDcE09gSjE4P185oNzuD6HkyTUbmjcvq2U83CyiJxE7FEOoklEsnRIwYwR4/SCtxS9BX1Mtvc2grhXtWflw45FEMtgr395Oezv35l+KNSw5BYi7GxbuiiKhn2V5cmKySLUDcF0NBVM5ADGqi/oiyIYgdqpF/9C8X4j+j+6WXgG7oMTx0ietQRzgAG9Vdb7ghn3LSmknGZbkRPc/UXSOHRj6wZ+pyAGJITw3OmRmF9rbiO0AL0MrhMzcRW7lhKJJIjp1gMz+fHTedGH5bt2qx+A81/I+rJNM12bmQLzpDPOFH2hwK0OUD1c3fEs6mFLoDYfhL13kyvfzyZUKqXy325whFNOHq0XDFhb+sjEXFUJYoQu8vuyGZ+3O4rrkW7/M8mU6tdQvrNrzoZwu0o5v/USTdfmXmGLtyu9YcHr//Tn/nZS48/3/ZxkUhOgu7ppxLfIDHtusn5RCm0cn0IhcSjobzNC3RlywlfsUvZU4iNsN5DPER8nvFp3TfTS7oMXvKDxAKh9cDMOjF6UnCL+B8ChM4DZL+dd4SwT8AY+CALIQjQ9ecAKECIyA/CFvSRecirrGYLPW6gmLPnurtTP2S7zZMX4tFYyBXMFsLe8kB818PuSLw7sLpeGHK7vplnl8JzblcyX3C6na/kgeR38PXx3RneMVbwuMcEYRLmCkzG4UMQqIYBuDJ5cKHOLR4mJu5GQ8QmWYgTDhHTQpIFhtFL/Z9rDPcQ1wY8RadUu9Al6BHl8yaZz+HTXDHDbKwQGWYDXX1ijWluiMIlCvmM2oz6DGfRDL1onUPJ4d2/ruoQ6m/D2vMSyyMnAFqL+8oBfNQCcPvJz53KNlUtRO8AnuXsv7oy/H459iexKFevLjz/fN1kYVP7b5v3dTl4t8FTq2YDi402Xm8HadCG+ru+Rre+9QNzdSt0Wp5Ie6H5Ho8EvdXsPqDTKsXSAWxlpP/DaljdAWza/2o0jeQoBODmXb6ORN96jD1A4QC+pRp//9wLw3/mF8fOmtz3SiT3nGh0fXz8PwD7DmCNjqzATXp9zQlBmBoDQBecqVttomHJRiWSEYpvI4L2HKClfbaZFB1x7GYr3pbSb3NG2KDWYKHRTlfTRz5EHLORuHvCMDHx4Fi7o4b+oXQAWxzZA7QaL771yj+PL9Rf5bT7YoM8bDr43tr+K34I6leo/YFw+4umFdxrLLkHyA9TfxH84zXXovqpPGdc61qUqP5di9EA/YycXXpjdAPrDKYaB5kWKorYYCXjKmwqQH7LSRx3uJi/4ySA8t7Mk0PX4pXQz8e+8qFH/84HHrrY+duTSE6C5S9fvXn7Z994cvRz/Oi33/yAEsk8PDQzSfSTfNWOe55RYIw7WXw97IqIEVGswRkv3xofV8iH2IzTP7q2kqMnsLzOEjiovMzum6R7qMQA7mQB3jPCHgyFsJ2hkqBSwObCNkDlNrZe6IFHANgFBXogDiHwQpzEh32x7rHB+Lxzvpx4jytOv5dsbzzujJdfHn//IpNP8OowscXsk1vesp/yB+Kv5Ha7/Z6dHD1AYHmdOZijvMKdFd5YOXhS9FExApf8IEKhpyAC52Br/563oLhdmTyQGPK5smWg4DVz5oAoqBGnP4NbyLHCnL1Sl/Av8KnZ1IOsCEEXUMiLwsBa786VbfibrpXwWFWD7DUZ2CBRrzE8yIqpg9lwmW2kAUsHsJWxyf6f1bh/BGBrq78a2iBgeD7FLGQhSveHX1B+6OfGxmSKvcTSvPjiG5cv15YZq9doH6LxDZEO0xs5ofvWU7+XNmVgszVNJ/82mQTdUgzmEHowJ2K86CXVZn24U80PPLfXeiXJPUJ2/6yG1QXg9jlc96/Nzl5HPT1TtAk64fkUyxCDKMxy9h9fGf6ElC4klubq1ZvPP/9H0EX9GI3pXL0Q+NSSwCsQb283uTKeutGrRr1K016foQRj/fNmPUBTIo5dv2MHSJV6xINjpY+EMFsYOEz3Ut3CwiG3cDDEoOHQ8ecvhudTUgC2OLIHaDVefOuVX90+yMwVfYQytBZ3TcjUzdSJNSpxXosIezC91RuAdd2zujZo1/z6O/0+EkCQHVFEU1tuuPEPs9tNdSNCpdCyHIQgIbYcKw4JQX3/EL2O8v2ZUsqVn3ECLIIP90NF9/sTD+Xjf8H5nyc2PvW+D/yZkbGaYswSidV45cUXC1P/258MfOz3+fh8fjzi2H3cce1DfGeYzQzudzj3KLdHs0uubFm5nq1O1fWDAuIaE4YUhTNu11fzTMIMDLL2X9ldJJFn2EvEwTdTpMAPKXjIzYATv41UhUAP1zcp5Lnox/Uhyu9ifww8ajBMlEoYWxg+CO/AKtWvpgKz0A+Ps/HoYGR+jUV2nnwkwNzi4PBD31go+N07E5N44w5vMjyf4i14gtiEv/pYzC9cgm9ReB0gVeF2mmsne9gvwkU/qQqh8+qt7nm4BU9BGLyql0w8pX8MZI0icabHmw45AF+8JB7E6Qe8ZBvNb55kYY7xSRZWGamvHuLKVjSVV/8YWGVEk3sNt8+mQq+eBD1bBFvGPuuRArCVkQKw5bhPBGA/6uSSo+FYBwTFrbgmAJcfeuEDf/mXD7AdieSEiUZ3x8e/Y1xqGPsbVof8mlN/x9hI/TXdi6BlYWDT1YDOZWDaU4J1K1vRInzCezlhpABsZWT3z2rcJwLwgdRf7Ta4TQ6v/gr2u39CAP4WgX/98qOPyqofklPAiy9+/vLlWXjC+IRp9wwIwbDa2Qs10ID3SgDJut+FiGM/CDoEwHDdayca+P5bacCAEsnYfcXmkc71HEtcKgB9JLYINulkjrDuOZzBt5dkSPXmnjzH3eecHvu9iREpAFsX2QO0Go0EYBF3LyinnVpwwn5IMo3NuGa32yKKv2Gp9Qyk1RpTAzoZWFyrUrBXu7zR3gdMJGSD9Gu4wOovqqaahNB9NcuvhtAnNDWiRhFfVBXobViHjzL03q3YRogsjnAhs6H4+5I/4/v2HSIjnp3vf83/Z39ZDgBKTgF7a9Fr35z69uCTK0Su8ZSHLDDJgof0JAtrDIsYkg9/+VUWASo3sI1SWWTlDYbOAOxlsI/i2wS4vQhq2nq2j+0Veod526w4iBjd1yaRBeFTPuw92EP4chBi9Toj01Ty2M5UCwDjo/wu9iHYgwj0Qw8o8CYVP3ixvR+WIEb+rNtdVqOf3wIPRKpr8izcgvOwA9+ACGxAhPjX8as901SFN9M86eN2mveMsLfD7fS+XltOkljhm2n6IehmLc/BKq5PwXN+XE+pb0eBj8OWqu86wKsLx5qCbdhSH6u0ec8bXCskBl3BtQIiazpb08cuKO6i117w2rXwFSGXaHKyHv1dc+3j/SutiJieY0xovZNEDRNxNLS5NRraVVfUeq/84sdbvjvJvUIKwJbjPhGAj9T+mxjy6eewNKLJgGCjp7QbYG0QsNv9wiM/JTt/ktNBWwJwO/nPnaq/He2r7URoGntBjlAJxvL+4BPe0fEhBWArI7t/VuM+EYDpuAfYfr6LnnY0YEOqlcYwG1o6674GvIbyg68+8ch7D9AYieTkefHFL1++vLdfIJHG0i9qaDMQa2z/zZXZLpss16u/Go36lo1m/gkah0IbcPuLrv5Mm5JwxLHrtmddtmaZ0ofETw4YYK3JOgfrW7ajBGfxaZcs/ePDoFA4PjewFIAtjuwBWo1fu/H5z8Zn6bCw4r7Y2YkG3D76u3Kj1VhPyzjoC/tbO+Nad9ky+jv3SeY1HWKE9ad4M4NL6+M1N6jNMVaoKC5bxigAa8WDtauykE+S8ATuvmJ+y8km7umiqz9TeFX5zY9c/zMf+Z9avQ2JxBLsxaJfevOj/8V7KY1H2N/95BRyD3PXT6FEaYq3z8W3/Gs7rlRVUi043a7ZPK8R66b7Lhtb7FXIb7NaZjsPHDBgeQoe8aH0kIyxVyZgrwrMNk81NZoEDFFZwNaP7cOQgbvgAg+Fj7ldX80zATnwUwi7XYt5MrADPQA4qXwb2wBMUbkDCVjFdg4C7L2BHyo5bKoQWclRzuAYJZXAD6U4dgVbP7tv0j1BcQHXFIVZbCHeWAZIQ4pD9YGecePro7IF8FA/9iHsCuyp+rfYh6iWIqTrNPigRzVWT4GZzJGlS1h7ffGSM1t27eSrPb6t6tOEIQwxUGDLeNMdm/D74iUtelrLmt7GD2TxaSnT2qVVS1AwxU9ugDUxz6b5AdGEYekAtjI2m63xvaJEcjCOOvy5HfV3tYHjuPkQoUhR0B4khnzJqR9+5CBtlEisQZPhv/Yxqr9v6R6b5Q02Z8msMlwD9mZ6TTVg7b7OVAnWOi5tKsH6m8lOB+zqLynHpNQ2v3bdZ9WFJRKJdTF06prPmj7qHuAB0N/fmrJCxEde1JCbY1yhsNg7fGbsp35Iqr+S04QCOr22UffPIAAMNxaAPXaG7DU9wEYVhUPtN7KW+g6XmVEMyKec+VSX9mfzpOiNUjelfU9Y0J7z2rJHKwmL7Dv9MJkYFNOvo+9b6qtUNmebrm26mvckvaSz+LIoIWJHVVpYk1jug+mGEsl9g7jPTZV6NkrdrdbVcaGBCnujdh1tSdtZXFr99WZisNhaTLUO729RrG2yrznGtHt2/ZXTQ/odzhmWN6nvq9fL92Z63f7iI0/MOEq+HdyJsidozyWe8uzN9BKDGfDifq6Il77xzeGH1r/31sX8jDNcXj+zHB3IywkrktNDAU8u2+ON7zDuJ3eBWxNEvWS8pBUK59cWil678m6W74ILzkIB116eu5RX6d2mkqMS443sEVTVXQNfGtEr2YQnvZQzAPYQJLCdoXJDVX+dVL5A0olyB8c09hL8Xj45RNc78DAUcMXyfAx2YQ/eB70Ati7wA9jCsKR2FwsEzkEUmxuguEElzrUU53zcXuain++l2ITnfKSW8Nv53jtc9FNO4hiitAoQdHMtX/9uOuPVPFov72vLTC1X47IDdgbiAM5JiEJAvRICG9CjWoRnoY8gaYMheIXwJEkgHXKAg0FXeD4F1QNSZRsUdY7LtlqHGIDwTApQvACJoeoM0RCbQgDW7o5zusmjk0SbaMApPHOMjbAxyQ7qpVvYhantFYedq4lOJjBJ7hVSAJZYmjatIVquvR5TBWWN4TQeVOFHu/uNZUf+wrV/faW03b5YJZE8ALxV+2et6LhXMh8c1NPhF6qRBiyYz483cQN3qgSj3l4exhN8YpKwnpXq9L+T3q9EInlQaCTlHrXEG55PHcAE3EtK3M3W4yUt6iQ1efkOIWEOFkpwytO1+8r0D32o01ZIJPcQRZc015hcXcneC03LAAshuVShWKFQoVQBjA7gOMQbVBiZga5OOn7rZnGjdTQyn5l2FxNlTwIPtfLJkedFp1TnjYa+55nGU29ca9LVbHS9Ehco8ayP6oClJgYfpN0Ndi2nFUok9xCFuH4U3u/YmXDs0MoQHJje3s+CvgAZs0k2GjdqH+suuUoko4VL118q9Rdm0R7twltzZTa5Ma0hvxh0jyaWCgPCBGx6zz7HhJB7Td+4aJve8rvfALWS8c3vTKNADPdoMTGaEK3NLwYZxT1aFby8tuzy2pkhf/yTP3zl4TuVxf/7/+YnpQAsOTUU/SS7vV/nfWJY7FWe8JIdYn2e8WluZELOaizwIDxSVWe5DVPYpyCGbZbhKf5MkvhrvJlmrXP7bxC6oB8u+nEMYfOwO89FG84nsGUpblBexREi/gVup3G5Kdxh2MsXs9WXj3wPhPv2Dj/loztPMo4P3v0O538cPPA7lFexd8FnYBZepzCLcxybm8IsGyVWsrxnhEqcb6aq05Ivwu+kAa6p/eLf0U2Wu5aCO/uNTxxa/a1HNGNZbHkZYGSZfgDSX+c5caD6sYn1xtSe+RYKWUWtnllQ3JODC6bbTwz5lHgxIfRg7bL3rFfZyRq1YfVPfTq0Vj8YWGMYmGQ+h2+ZAXQXYREBPcCaEHpzKB5qZlKOsKFN36mXjY+vMovkCJECsOTUY3rbXL/QcCtuEH5e9z5z5cnIpU88dhwtPFai0V1IQhd2Gw5qpPACs6Kwg/iNyUERykxNjY6NtYhxkFif2dnFmr8jdVpsfZE2A4b5ws28v2bo1d9D5z9rlGKKdiNaT3MNWCB6JCNsGHotjdc/rAxcu7Vx/Z/6uNFjpWXWvUQikbRL+/WRDi0JV2c3q7SjB4fYDLF5sO7f/n7ZzZGdIPoPvvQT3/6r59psrXWIRtehAEGogA0S1a6gV9f9A8QPuxO6i2wyNTU0NjZw71otORpuRt+ha5BkhS4bLrOopGFIw7tNt2Ja70NkPjts+8pxvdbbBX2Ni/76wafG3x0PotJYfjHo8Bea9BiBoD0HxIpDQNhpUmWzHUQEtIHmidB6fOSANYbrbcE+8mnczYOgDb04L+mT6VVKJJKTpH5U3XDDWy+L2pSCkGP3ZnpRdLfYN2otv4IBnUlXd3F2hDOVjEtE7s/nxyOO3Xr/8YR7wbD3ZqHQZuRTzvxML3CzRq/gbZ4SDzRdWWuMATECINTf6t41Q/M8XCC/6CSM21/M49xv4Q2YgE3yW72/dPELfdzuKy/hovvMzv+79vPf+muZX796ZfqTH+3ovViBaHQJoNfl3M5qC/29OHcBvjVzl66QQ0mUtoKU07YIlY381NT42Jh0upx63pqLZnA/xuyU+65bHem6wflJFma4MOcdn5xYYILep1NxesrkAeXpQg7/Q/GV0JfW+Slsb1IJkXqVRx9m9g4jHYYhi2jn5Xph9Vu6ldQtBvN8ysc30zzj5lFX1Yb7zRQ/4GX4PVQ2KcXpGsX2EOeHIQpl+AClUbc9lmcWRmAKVxYWYRZbiKEhBlapxNkoMTTMENxZ4Zrahi51z0HoG6a8BeCHTB9nqSY22/q4C8qWcRLldp0w3L40Pu5mO08CgnDejQLX8jzpZUC90iZWCD0GATgLXvCy/KHBLN5t/Fl884xrd6/pSu8GXbpNU8m4KGMbKFBBu8cdt0WBXMjrIeshM8I6kJ3weslq9dRz+Dxm3cUcvh1CgI9cGo/4F50SLB7Uj6N6yBjW0dDqB7d9zCT3BlkCxFqc+hrAR2oNaWcQ8KDq7xjqZWuS+cG12+/p+tylS6dS/R0f/yOGhoyimvar4eKFnwyL0P7LX4gxU332wrn4X3jsoc98cmJsWCrBp5WrVxeef143SN9rx2OvWaOR1GqaE9WW+vtE9X99Tbj2pd8mTdLRPOtP0P4Us/ZlYN1L1k17S0eCBeXYXlIhNo98s7IGsJWRFeCsxqmvAXzi3T/MOnuG/OdG0m8OJYc3yM6jbPzs0F+5e8d96dLp8/+++OLnL1/28kknPfCQunQLViAPbl740bBzMlG0F315bv322r+MD/AdeIgXPtP7mR+fpJCQQ4GnF9sHr/JN9QLeKKu5EaUKGyXzp/SbMui+9aJvYD93zlL4x/fNrC0LCWsZpwf2LrSfOqMhhuoMXc1ekl4ywAnru0feL5U1gC2O7AFajf9441/+bvym9mfzoox66iVhoy5ruOWvuw1v56a7nk7V3/bbI/CPJw2X7gn3gvD+AlUB2HQ0Q603nF8M5l91gmqJC6C8NwNkNpRHnpiJkPxUdmnnd/M/9hM/9cFLp+9iFY3u/s+v/eNeFhf3PvCjmbezZ+zB3Ode9fzcXCH4I9940zeaTH2xMPi3frqcOrMaT6W9v/O07eVXnP/tcOIbi7sP/0j4iaHnfmHsrBwAPK18691r/8/6P/s2j46wEad7gDXNw9BLcoi1SDbhJZH0BrN4h+NrBZvdlS0r6Wwh7nZl89yGdQpX+fcpgget/tsRI2o28vBD2B6DECxACFzwOIRhEbIUnnWTxbWUr5bnnYIUvKUGKfsofGPf4Hvt+Jt9HARhEJ7z47oEZ6GHzLPedMjxTZ5L455jIoeyTERc6woVBWhUzcRPLsSu9qd+tHOEdWHtrarCeGO0qCkg5im2WbvEgOFe+/ef+40DbERyMsgawJIHAv1VSd+l1h5/5tm/+b6R06f+ApBFxEt8pQjp2uorA+B74YX+X/7pJ8XfT/kWn/+VO3ggyY3XQ5czscuvxj40tfVD585/5umJsX7ZETx12OiykVRv4A3qLw2E3no6kn6pLTVXf+d2UOm3/VvQjgbplonQ4fCc6DNxdJ5gPQbFwgp68Db+RmGqGsckEkskkvuE5WqA1cFoPwW6ebZzk+q/CXq2qOnnbBGcYOeRiY8/0mFpe2vxVoVihS9VSC7Csvpj7QM+86/CY2PVHqDtb/8J6apOfPnL25eXtunnhYdin3lugrRUgk8hb+seJysoFWNQcz17pf0eYz29dpy2/Y3o1d8B8NX5fQPV+4+D4fZXe5751FGORQhncEvRV49Qf4P2XDsBM6asM9i+G7h5x/KeWHu1YECJRHKvmGS+0ZS1JohLll4GDkxv16izeh9w3eYb3XprWzBdoRRT9v9oT9A1RxVrxV8T7oVGxY/F2zQRQsRLRTlL8Qv1OnjZoxctJa0XLlQV5fxikAw3vzqde9/Sv3i39yt/cWL6fadP/QWcxD8T+k8esktK6vXukTMs/H//6O/9uR/87jOv3s1+Ivu3N//Sr/xy/qc+8hmx8vfe7H75t7f7P5C45vv0+33/9Q8qIf+1v3vzlv8zwwMf7f2RwGC7sw0kFiGFsk1wKzFKkD4SOZQZLvQQT+OB+TPsOLxJsozMr2V6vM5smRzKK1lcuLbylXf5kyhbK8eo+wZhEnq9jIVxTGNzQBo+RuaiN9ltL3htX+AHgB1CkyyESb3Koz3T8RC2R7LXdr2uyfEFIPxail6YhSfAA7NU1EvRtWNr+QkgzNOzKaZ+n+f8uD6EciurfJAf7r366sSjw2y8zkUPGafNXqSM2h8Xvw4dDUtqK+/S21L9BRSyhiXtdwvFmnHCQFY6gC2PFIAlR8e98H/UYxgcNFV/9R3lecb/WfD8wfZlFaoCns+ka49urCeXJgN9NdW5vjHb943Z2OUvxJ4ZXP/0uQuf+ZD0BJ8ebDYCDpJmCX4tMcn9a6X7ciDpt+mtYKfzjk+4tsTRRkM32MV4/UILjsS1IxJrWLD9EonkGDnqwsBHQqPJf/oe4JmIJZveLj4os1GCNPjgLJytXUFXFOS1VSJBwv7qvfki7HJ5Pnb53ZjbUfzfh2Kf+ZhUgk8VIvxZE3Qbqb+5Mtvl1ltrFOtSL/2KJbrxf4HbX3T1Z2jDcWt8YXurtWM4E0P87sYJok0QMrCIP/U7dtp/YUfzC5v3J8XAWZzwUdX3bZ8sXbIGsERySjHkM++HQms0uBkX65jX9G2wpMoiiJingPrAzFu8N9MrJuU0aole/QVM1d9mGFoXoJp1x77xV8RUFDYVUBz+AltOUqxthv+s/7XgwKc6252V6MpmPOxO735RqXwo4L1zI/g//u2vP0yRT3f/wU90vzKQ+4C2pm+jMvLFq8Ho1dTP/OxXvvupT6/NfvkDz3p3Zv7y0kgl89YHB/7xX5+YGj/zc9IKclpwe1KxvenV+b7ktL3gWgeWCgPiG+Qj5yNfIhchsTkReWh+oWBzOymzjDCIJtPkdOrvccjACaHRZmGZ4DKD4IP3zOEiq/wQPMOPTXxh0xuZY/R7PJFDubX32Foy7Ahn+lM/uGn3TbgXRtiYfHphmhvZCd8FboSnUsxiu4XLzdNJzq3U1Pc9pczCbAr+mO9zE/gDRn6B9/7Dd2x/gwvTN+bUOOg5JrRUwse4lcYtRtgUCgr5RsaMOP21Q3YLzWdOA4YqJAoFoIeE1i2M0w80Hwl8itea70ViEaQALDnFtLycNVF/a3q0jyr6pP1TRQEah7+tFtGH33p9vLbJa3G6evhEH4/gni4iZt/f4NW1gVfXYpe/HnvhmTBpPvODEzIcxuK8/nrbQ1Qmcu88JmM9XerYXi2a7hsyqyvcoe7bkeh7JIrvAdL59GiXkWNVgmv3OG66/LQIqy2vzBKJ5H5gtEER0A458IQ/A2sN6t7XT/7LLwbzKScZik93ONpoLXphC0QBCN0NXQiWhNdTp4F12cgkqeB+zJNfcbofjufzIVywRL7gvLwWu/xGDD8vjMZkcRDrE40mVMm2QZLzXgkw8/tqw2a+quUX6AG7jWHIQRCGwUvVDDAEIVB0NxMK1Mq9pZhi9xXRSb9nXOtAvux127M0zq/T8jzF43zZ22T0v6OuY6fqr56NUjdqM9oRg9cZpI16wFoZYIVsN+bvJY17jvFhNjpu9KFZIawVBT0tXU2J5H7lAJUUJ9wLseKQmMhS3ch4MrXQ1vBWW3nOGao3wZriW32xybpuf9E9mkC9brvN9qK/pIsAhvrt5BeD7tGE+EEBRCCqwD+eTBkG74QmPa3+eInBjH7IgEJ+y8k8g0/muiOp7YAn87LynR+atPXWZaedErodfDv0vu9/7Q/Lv0nkJ768+eyHf/yRd79wc/Tc45uv/MknB0I7E48MaCvvwJ8+/5ede/P87tOJUX589qlf+tk/nNhy9ZQddx/q/dq7H7+5nu7f/K3vTz5Dyfkzz4ZGxqQn2NJ8+2bu5etT/ukksFQYqGRc3f49oI+EKOwa5SGPd7eS3Yr3DITeWC/E3W8mGSjCCG9/lfVffORDV2++dadaIlerXNumGKwVuxWIu4WE+tiwHFiDi27SEJyk8ha2IsFY2v5wJj+44+U7r3NxIHB30+0rxZTlWDC/5JyfJmYfWnCMfd32vmd4a4XI8MTG5MTd3mfjymrWHiM0yy8swQaF2Woi9GaHNYzvOWLq8ZM+7D0kY3SFqXwP2xmYJZxL+YbeFVmEPvKA0H3XGNb3zfTqb3g+VVDcrkw+MeRT4kUGN5uE9q0xHCK+QsRHfpCVOGEvGX32zDCx+umAYoMZ3BlcCoWMfnqx2ml0ZSvB1TTAcwc5JpITQwrAkvuKRsKDufqrpdakM3BaR7teeKEPm8NkELYAxdJTT+13badG7QsLH9T+FD6PaHQJX3Dq//ft/HL1anD5j2P4uPy7MXI8w/qnf3iaSvmpp8LA1FQPAJmxsQEkVsNE5dWThju1S54wWWvI7EfBoOZ2GPLcctjujGtdFLpoNEp4MA6p+9Zz8kpwXQPGTZfL0TqJRHKiHKlvNrhWSAy6Wq/XmDWG07VOQu1y3az7lwWnk48cZs/3kqee8r3wjyMotqoI6Ia8+lwJ8jXS4MLb32eyCV/wpWu3L//JLouwDgNcfjd2+d/GeBeGeeGnewEq5aeesoNjakqUe01Kl7AFUJVdTeJtJwymy0YgCLWT+QyVfc3K+rr9Rfd6bTzMAACUQUlEQVS0ydhg0J4DwkMLpntzOVr06PRdPpct43JkJurU1lSpJ1vx6lWNk0QvBoueav06QqfRrjONep5pVc5J42kkAAM+8vckBVqPvqs5TAyQ5mCJ5FhJ0FXfaemUsHM1rJvoZlMKxjjog2EIed6r1YADJhpwPuWsj3bQRgPqExoSZU8l49qXq7ULbRpXv8vlziAurWpKRcgdT7k9PAkwnx8vxZTMhqL/5RJTlAqbitChga5smQlnLBlwKIV80ul+ptidzRa2bafUARIrOYZDP8Z7PvT2wNM7vpCva/tjDj72I1uZ8kjXmXju2sinvPuBrmempv/W1P+q/fkHf/cM/HfR6JKT0uQ//4O/MfJN999JOF4Yf/zGz/zGjZ/PXvsj28BO8evvP/M3Ph2PB/+8b+/dwYGHz/QUst3d/3/27jxOrqpOG/hza7+1dS29d6e3JJ0NskEAIzskIIKIiDIq6jgyjo4r44KjjqijI477Mm7j+sIIiOKCCgQIGHZISEKSTjpLL+m9u/bt1nrfP07V7eo11Ukn6RTP94980lW3qu+9Xcu55znndywBl4PZ8Omnqib0JWKyw77CX60PKTbLMsOBRowsxsDG0GPOP8ZhAeoQHYFyGEdeQng0VWeAvh4vPIkIkPnh/qcAAOGi9V5LnwrcnZrwY/hY/28ADqdg8EHngr0RaEd6iSnnigSwuAsNdmRW4oDTlPbXOl/qWIsYoglPFAitsC9y9j2D81ukHm1UXNptMvam0AD4ADuM7VjbCSAfAy/wDNgJrDWhrgqDo1jaBN1iAIALjl5I5wLxwjpKw0AL6uE7gkUWJAEE4OrAytZCX58CKxDXqrY4h9IAjIkUAJG/ertSANKySdwerrO2Wbq1acEuBAHUY2QA1SL9LTxnfgmSI1gEoA1HkR8aONm06a/229E5T+eLThoJgKrOvCAQnVr7f3tnaM/tp3svjsvpqP88NQ6Zqf6zaExP7v7z59fM7f5Z+6u8P6unp6/zcA5A+1IXconOzh6gGgi2t9d1Hkzu2D4GAEjcfvuzgANonTrn+JG7lmy6vKnwbOF77u9CFmjMrK+ugqoWkmMA4fHgGQ72JJ4Iqf5x4Ji57xTTRrzCTNN25zLHd/a416lLeg2Ds2xwIuY98S2RFclaDBx7u9PhNGbDH9y453T9ajomSWLzb2GJDvXs/WHL6d6Lkp38wskztQOnHQczXWtw5uYfgH35OoHffb/0oRsvO/G9PaP19IQ7e4NIo31xRWd3sL3F0tkZhC7dvqTpnnv2AHFAvv32l7761bNu/9pR+I1wVEPWYZGEAcAFVEL9++Xas935rV3iP+vXuJEDpHD7EtE4HB87eDQykPPn2Pw7blu2dG9+15EZmn/xCXVcHBIAyLp8hWfXxDouNdOV0JwYAMvVCb13+ij3pK7KMe2EsNPrmMfbgBEzjj2Q8ZiDCD2I+OE4le03MaVjvn7jiuYHWhvOyJU1XyXYAlxo/mffrx8JvoiZ09/jGCQ9+0fo9MGwyHpXTgl9Jyl5daeZPjPFguuT9lDskpg9XLwIsXgS7Ro/DPdY0bSNrlTL+LEkplmeQOxPqteZ2mYwXZSfmhzt8LTWDW9dd9GrvB3S09M3EBy1SwlPRQOAfd2RZa7BI8GqpS3ug51dg6b9w8NLa/t8ryjP+dobLUNSl69/cdYUuDp0ztBf3S7HtvDrP7DkxtVnnZt/ttFw4jc/hBe7GysqPRfJarq+rs0aD6SADPTiVPf39GTcDgTir/IzfyLW/uHXw1H3UMRma4lU2KKBUZdLl5S8CYuknK9/vhYDh3BWFQYsUK5WtjlGg/IuJbHBAjPkbiUdNRn+kFJH0Z2A2Yd9uQnTeY+DeCuKOs/1Fth0sEmIqbBJMLwGkh0wA68FPNjT2i4j9Xu8IYPcMGp7wkt9Iy5RH96+wt+Q8ddb+5wItKGrHiP1GHVhND+3dVcKCsY7+TqRLkoZRfqLM3Nt4LXAKFAFrLXB0IJMN4ztQDsgVqdsh5ja62u1icTX2xXztdq8XTGgKGoNYHLhmOLFLadcsKdlU8aiS1t0zsG4uNa2BrMGJSdS5EnEBmJ270wX5s6hdP6xnQBw/hfYtFi4JLb/FhoGwEIpAfAxOwGn1n8e7wEU7doOYB8wmFHVzSe0u68+W7bs37z5wfwU0joDgEfuXrLpsqbCvT2b33V4fOvi6wQRJdoBAFGgBvDCZMt80VZzzlqLmssPKRp7uWvYmgYAawZxA+LG2hy89UuRVOGIwGJtb3M1O094TOuZrKcn1PKa7cdIfydlvTNdsDkmLAs9jePKek+kZ/B0RbnzSFu040wx712NDIAXMjb/FhoGwNOatjV4zMF/OOb4vz4gAuzDI79esunKKRMeaQY9PX2A8Z57Xrr99iBQher8QEB1+AptG2nl4wgCWRVuoFFCEuOVLB1AK+ABYjCtyHh0SQTgcaffWbl4fbU2TBDdoRfVmA1xowOQUoABo9At99YBosFj3LSpuG/j1ejOO3fcfvtY0Q2isVHIfUVtZ700Ie5tna5gew2mG9+fnz41eyFlMf3XIilzWjF3WmlVDmdd2o/zMt/XqUtqzyN2db6eFsAsAxltSE66xYVQcSpsnbKBpngooRXT9MRNS8znOMF5wyIAljH5L168CFzpGAAvcGwBLjTf3ve7nwfHx4WIj8RpP7LmOpDal6kT/wnnzMWfisLkGHj23FczZZyQyZYBoE23FSb1A4hLe1EzH0AM5kk1q6MdHrGOe7U+pC0HMHtnwnjTLjG+PIG2J9q5siEZVF2hSNVgVyV6AWDleaPnqVt/8aYflXbAlLd375NnPZDFM/7/+tCf9BW5z259576N0uLL8p/2h5/acp/uzu3q5QlkvNLAoz2b3YM4ZLcvsUYDmbHFiJ2TwXeeX7TyomwiMXxu1Q4HpLOW3fgGWHpdrozP0+Q98uyukeHKxfZYV/pola25qxqh3JBtmWdxGsYo0gDWrGi3N76qZyEfjQz80/O/ylgTHoQ6sahazdZLXTnVYYBOLw1WwO+G6SgqNmK7CZH64SH5bwqGAR2wGogDPVD7gDDUJGLAQC8MOgB4ScnHwA1ApBDoLrVC2QDrIUgyHIuBNUistchxxbfCNogGPcx9cFchOQqzArOCehX2NHzdaHYiAyCTrRqGLOnHAtk6ACOq0yIpimpxpGJN8FXL/ippcC0CNUNH7N1HRMtI/2QqN4Q4MDQKPxApauf6UwDy1aqndTLWMz71RBnttcCyBtjrIFVDskHdB6kRaARGgeZC3GsvPGZqHX5RmGFSDKwAcaQbTMVBb8JtkQeV8asDrXCAD4hhNtrVeQPQP2FjBsALGQPgBYcBsHAqAuAOAPkAuLt7DWsaz9WWLTsBABbAAkjt7e7mZqd21+Z3TYwGtQSxeC5p0fWDfYXfqUtqHUnamNBG43BfesKfxqlLBkZdX1qR/beVb5jHwzmz9PSEWlqeh0OavMCblvhODd01s47bncksWe9cg94SZ0iUpdNVO/pkOGZgzAB4IWPzb6E5wwJgnLoMOFxnTVuk4lumNv8mlYDWmn/DqBUFWsfniEwY/zf4yCOr2tvr2AKck56e4c7OQUAPGIEwUFGcyEreP8FsH986q+JsaUKdYTtQBSDfXStXJ9xVQQBOfVDMcCqe6hTLukeyFSJ1W4z4tr8vNjVlDt1w1iLH9Os9vxps2dK9eXPR9Aet4eeaOLt36ti+iWd+wj0T23haTnCCc3y1P2Xx2o2pnAWAVthZTeSHforlhBMjU3ZunthX+LM+GcBME5qnEi+82TYovGhnN+klraiWudbCsSEpIwnAienjdi9CFignPu6wHiOzZ8mlDBZkALzAsQW40Ny5++n/9M+5PtZcP5+nnRM8IQMuMQAWputP0Fb/deqS2sejDclJq6QX98tpnLpkk6EbRUuqD6NWRnIMznwTTnyIygBga4mICs/5/fflZxRUtowCCIy6tM/5s0wHxH+UrGdf1+LUmOGicw9b/Zmt2yrGPvxaB9e7naO/7/q7EWpyRK1aIkmHUhXt7dqawQPPbTl3MGIxRJtdwaguUykNjam1lt6awxa5wTRgcId2PLPh3CW9zzzbdsH1z+7qXHuOGnx988MPZzY+8dt201mZ6zb8pQaDJph2oW0MzkVqfLk0sl+tDm9dduHSo9/sang0+e5R98/f9OG3n94zcBpFh3oueH7b8qoXKxB4Rj3/c9LX2+7rljIYeVPtgKU5Dv3LONuI+ABqV2BfPUbacNSCiFFRFaXJYul1DsbFJZW3K4ZOwAbEgADgArqAemAU0RcAJ+wrgRxy2yCZIZmAdqAaAOBGeo2pt7a+ShkxKjm/yyWqBIursKFCG9QMoxcxWUHOolQNB5N6U31kdwOGbX2KLwz1GYSeA4ADykKv2yxokXN44v+1u4pXPj6pObSzkM0DcJqwuAo6GTpHIfEVF+bVgDxxxKetUF96SsiiwIGJy3yI2cAAjP2p/EduZyFjTmLaGTpqIVDOdAPAhVE2LRYuBsALzpkaAM93P+ApDoCB3ar68RPaYyqyZcvOzZsLpSimRpJaHjkxAJ70JJOGuE66SPhStfTRlTfO1w6fcca7/0Qt7knndsol2TFX4Z3dnK4wy2Dy7mlXHjnxNzc+eLp3gWbE5t9Cc+YFwDhthaCPbwZwfoW5fYACdAEDgE/FSPaRR6o3bVo73zv+6iVJ9wLu8Umo9YALQFG14VZgNN/8k6sTOmtGzDSdpaVRvBCsmjB2Xri+3rHoZB/IwtTT09dydgcAyDoAOFfKd7itgMmeSUUNAGwrIk4p6TQEwxkXAKchCCCccYVVc4Ut6tQHRQQLwKQbXyZwUpBpQ9KFUBIWMxQAxzFuLwnZjESyKG0OoiKouoxSojgP7kvXaBkwgPFlICcRE7ymzSdmmMc8L06w/Xwijtn2PmZ724vQLOsNT/eEx/iNpfAgdlbz/65uuOLYm9JpwhbgQnPnQ7tu7/KVuPGkD6XiDwobkrHxmhvTmLbqct7UT1dluroRk/dmmmXjxUTeSbs3ad9mqlA99XMvn/5O2r1JfR3aF4Q4NyuAGOxX5U/UeuOhETiOhhudUtI34jI6E041e3nNM0Nq1aOv/dSxjpBKtW/LlsPpD9/tel2NaghI4UfDly9BfNszTe+96tkuGIw5/aX6R5vG9P808IFEXDbZM6k+A6KQVyfa6wc7I3VX1m1bhCNL0J/wvea3rsXDfq/kTqtxY4Vj1J5Qm60H6wPON48tv/hdr94AeGv/Sw889vv99Q4AaxCUu0ebZf1Yl65dig33QX9L76Kh0Rcf9xhyfgWBi21IJQJjY6mli7H/9Wd7lVHTsGrzB5Kqe2hldXP3AXRASbozLW7UwnAwYAkEwoa23EqP7Dps/nsgkoHdgpTXbT84HBmADdCtB0aQ60P2bJNxNJXeYIIPSaMbTmRa0etqNynotVQeQV0AujDcRuhcyCxCnzwGe9ZvUw9UJELeJ4dxBKH9GBrF3lILndD01gJLrbBJMIrrcS0AFjWVmsY/wLWr6WkDFNH2G68sfaDovk6gHeiEmoJkQroThhZIU9d4BwCkO2Fsx/k72LRYuCRJmnkxSKLTpMQFgE/UikIGDORLGdO8cU1ei2DaWaczp7+YUmBNXA9oVwsvBB8GXr0B8I4dA7jQgLUAxs9tKYvuHLeTFOueeNJZPM6jbBzHQZVHZkxEZ5LOCZeX8yUtm8K1xmNvN6s2dIkP0gaM9KO61dTdJS56tcXtgsivijrvB/BqZ86vOwtMSH+bgCbI1YlsnzFVlb/8TIzIoulSrQ/Fsm6TTpl2PqVNH7ABXiCtyi5TcDTsetUGwJ2dA7hIj2vzTb5WU3clwuZC1bZSY9p86W5YkYzDLP4jQ/GhQtyi/ViPIW1eaQieUtLE4trFlkK5Y6XQ7x+XTACGpHrx/ACMpoQ2e9+XqZNWpAFMMxVY/HRcNWxOxPQrZc7M1jI+kWL2GtrHNCkgaTQOT3p3TFoutA09InTXbvGhQpzkYrMsUKL1DJ5IEuzHKbmKJ3q1Eh9K2lW/WE9Xu1erQj97EjzZpHjVD3hKaxxFgX1A64TCEmIcj6ju1pVqEZ9dWjkWbbP8sLyJXoFHy4/VhDE9KqdihvGheygsZ7Bv4teBNjyoCaYV+SrQ0R0eyEACf4+fB8BUnYEl64kl/Yo8GDX8PnDpNct2l3JuqEQBwN1z5LrMo/dWXnO98sKo06lE21qbhje82PFP2+7e34vctRv3Xrn6yspthmxtF8yuFWlFH1mKo3ZkNtnHKqHcH7/2gBwzen2LsT9hW9UXq8nFDRWO0XXW7dcpD+1S3hh9+h14FQfA4UNPrv/9f8X/iAvPxVMvIYD8uhHDQBhY9XM8kkItcNG56NqDYeCAgms+DsSw6OOvWOIIVcLoQaJzONsxvBeo0iGaG66rGh4cRV0VjBtg6tuPPyJng74WHhlIwIRhOOE4H1CAHcAIdOuhi6SQhfHeFLwwmoeRg7oXZ4eGJQeWO6CaIYm39QHAgKHXNUctTtjxn+YP93S3JCplVKryDf2fWfbiu8M/CX0qlbPhj7tO51k9c+0E4nEAWCMi2wOoaAUAaSlgLozgKeE6PYgqF0bzP9iAZcDu/GTfXATZiRM68gsVa5oLC/10TrydFioGwFS2tO4/TaupO9/u1HoAFwMwHGMhVTpuYvrvtOWIS44ttejxCJq1DQJwve2Zf77QdeF1i658NVQC7Onp6zycQxU+s+8vHTVV1RtDZ180uQrc6Zp6e3qjx0m/vSzz4FKUcuAMiYlo3py0y7xjpr9t6J40hFlkTsVVoDXFcYh9hT/a4YG90BTZB3h009TDpROShL1QI81VuM0D04pMqteQ6JVhy8/XFJ28M81e0laLEEsGittFJ/LtPX95U+hwVSSzrvaa5uZJZY7LUE9PqNMX2hvc/T3jsMmqXPCZYAzmaZt82ozPIdTXYiAJq4j6xI9im1ly3Em3z/7jTIrfhtO+JUPw1GJAKxXYhh6tVHs4ZxZXanpvwl4o4FlKBGuyZVKx09mnYV/hVxPGE0x8Z3LMVT+1CquljADoR40VyeL1hqcSH7DzMiGYiEo1bYWDuYx6mT30TavyeFcYAK1RhKKeMZGzrpju8WLihHbXdKUXps79XYawKFwvBqwU78BMH5gTUuF9gL0QSK8oRBodQCuwD6gBrIVg2J6vU5rqMKAJep9sW5HfGXuf2S8WG67ODO1zAcAhJGD9q3kNXjvj6aK5clTjWdu6XKhhT3Tlnx9+bd2atA/2BotPacNYeOOT/7b25ei6jp1N1y19xRA3vsn8RL3JXJHaNlLhTinV3mSXyxz+ZKjyIauuCy0H9q3r2l3z7kVDh1cOvZxueka5ot/R2jG29G/XnPPC3W+8dN1F59je/Goo393f0yP1dSKHPz/w7TXP/dXghKzgrWvglLHik1C3QXICQOLDFnmPIkr1RpfVBK6KVimqLuhYnBh+vPXyavQl3mIEUA/f/Tj/fGVXHUbSFimrOJpGg+kq3bKuOHoRvsTq7IqnZZPRl0pUWuLNejElNC2Zelvqq5QR+fwMgHCtMYiqKmUkbZEUOEQJ6Hr4AnAmYNSuzpKw7rhoTQxmUfElnHWF02aXN+a+OmhWsl/IflnWpQ69/Zalb/zZI/ecnnN7Jlor/rVBcsHQBtQDQaBx4oq/lYB1PPQtXkdp2um/APyw+WFDazcAp5zOLxJsBjqhcyBbaH7GVACI5WArniJcSJ2zQajB/Da0kLECzMLCEtAoeQbwMUtAoygUSULuRzWKq0AD44Wge3Nq55XHu7+vRj09oXvu7YaaA9RC2R1A0gOABbc/78PwlMdMtxKtNoNh2t+i9WhocxQmjXYXG7zdtfS8xnc0O+c2SP+M0DMaPpLq7Dy042/GV2ba5pj9OK82Q6iPz2nUMxXMb0LMEtALGQsALjRnWAnoovWE5tFxN/8UWAfy61Np20xeBARAV6olP7OwF4gCfcBRPPJfDcVL2NIxbXmsZ8dLPsBfyM6tMOmQAxyADbff78NRAEC8sCRtK1AJjBX6Iwrd2baWSI19zKbPj2bT1kkVMXAbeqxItiKchOUQHCha0bkS4QTMDRipQOBdtvdevObiU3fwp0pPT6gz4DNU+f/S8/0BmNwIlrK8qzb71oKkArMFSRd8R9BiRcqFIIAgXHGYPIi44AMQhBeA+L+SX1YMlkJm7EJQWw42CK8FiWlXhw3Cq8DsQlCB7McMBZxnFoInAYvWbDuC5uJxAGIR6OLt5zolt3RydeI4ViA22cbHEIvFL+dRtT6kvUFmcYIDQGeZE6zRXjOluLj5OywBvZCxBbjQ3Hnfrtu3zfr+aj322u2YoX7ypA2m1l6er2WAS9mfqbs020e6tjNa7rti4i1TiVg6Aciwr/CLHdCGGfkydZfpd77ce25d1UDybw1rvfu/9+//MeNvpyl6RsNPj96d8zX4PVl3p2MgVOVuHXIcMpjOOhrpMu41/f5i6ZloYqldPvhizbp9WPq7565HB97/7ie6JMURd+yO1vRmGzYlImrbzpfC631bDfrV5vaqwRp7n22g8qFwa7t78JrKo/ckFl/z0rChyXBfrtkdGziUam9d1b0xph9QUz5jdrV5bHuuEZ2m/1D157/hwoZyjIGjfT1HBzr3J3bWb/mk2gGpCtgH1YFYEnZHfsVrtAM+wAq4ARvQB4wASaC90DXbCHjQ0Xp2FskKJRWymHZgDYAl6K/BwMtYM4zaJhyxIFmHoV1Y70JoLV62IKLAsRPrajG0SBlMWzAAbxoVWaTdCAVQMYz6OEwKLAB8qCiuOzKMWm2ZD7HUiLZ6CwoT+gEYqxIA3lbxx0NofBOebVb2VP1gSHJjy/+DKYXO03LGF6p2wAqstRUKLzcX6pYWLsMTbotByYnINuG2pC06Le6dZEpKMuFHMeBPRroO/RDloLW/xIEJq/+mp870FT8WtmcJ6IWMawAvOGdkAHw6FgBGaQFwCB6t+FVxDyCKmpsmW+bBurpNm84+vr19Fdqy5RXAuXnzAaCwBq2wElhRdDEw+yVEIQD+w9IlS5x1iMjIjlelA4AsACAXBvCOOx946vE6XOTB+vzY0uLh8BuweyOeN7s+PNy1+s2r1zQvcuLM0dPTB6uzc3BM8ih6KX4otBtA5NDd/ko9Cl14006hmMUpGLMvpm6IHRMdi5Yps0yGUD/XPT9xr9oZwKfeMdNiBsALGZt/C80ZFgCfjmF/mlKaf0lY+1FTuCvf/PNl6sI583h1WR9+U5+7+T0c/1eqLY/2QlV3vBy4/VMjcEiIqPlCL/VADVAFpAErxrMkK9AMWAEZ8CE/NLDQkf3IlS2SLr1Ursu39yyAmA6ULTw8F+7sDGz+QhdkO1ogX53IxoyNrUe1SKwBI2eh04NIpevtmxe9vtlRc0pOw7zpORru7O81N6b0UhzAQGjHjv33LpETqIglMD4PXoHVgriIYFEIaAEE4RWZnHjxFzfGFgKx29pM31kahJMyYADDqHUhBKB/4sCOmZaNTPU6T+8M4GIiBRGXmSZbxtQULv4/gNLnCp/4MsDHp5ThgB5EJl2hWJESlwAMgBc4tgAXmjt/vev2F2cNgKebB3x8K5QXd6H4MnUAREIzHsSeQAYsiME0cnVCXyjkULrJ9f8TmMPg5KKdKZ5jIBZKGFNrETTt7mi/auPjZz+Q+kN96w/OOvvSTZfOdQ9ftV7uf8YI5UMPPvcl8xd+6fpId5XXmNPb9EdbFUNL6LnGrS/tz7VtbD/yu3P/yQb/zpF1zdUHq0K51oqXhkMtpgrfk1h/cPjCgCyhy3DHsh9eZPqkc1F7xuhGFsgVfoc7UyFFYn4AyED/Ly3vWem84tyf/MXdP7p39aUJ675Fivo7y8aQ6rp0pNNTEzpn14OV3i/G7VLNqreeWUlwT0+fV8ruOtDpMcCfQca3o2oQj992e+sSWG6qsR0YRhgA/IPwrgPqgdWAH7ACQcAFxAEPoCDtNiVcBgBpi+QcSgPIWHQGJReuNWYVR8RiGUBVEK4AXAosIq8VO9CGbnGLF6EGjEWhS8KqwJKApRYDR9Aqpt/kYEsjF0RFUHWlchaTTglnXaGYPRc36KwZAPki7YBcnQCQjRkBpGKG2d/+1fpQtX6oFT2LkLVi9Ir9DxtHU9FFNfavD7/4MowmpFPYeVL/AAublvsa24F2YFn+utvXatOudsV/PIgUj7xsQ7doe096wuIFPqZeRGvlglDUkJu8MLBGTP/VVhoWHQIK0JuPis9/gE2LhYsB8ILDABgnFgBPvX3aWSD5X5Sp8xoG6xB9eOMdc9zHctPT0wddUW6ab4epgFLokANgBNDZObh5czeqHdBLWIzx7poVgB32q/wPLm147mUThg23P1i4kpmhPt8jb1uy6TVN09830ZZHezdv2g88DpyNujpcBlw43rJvQ4/WVWFFqnOofuPy625ovEzMCe7pCYndBiyAsmN0BEf1N998dnPzqciJe3r6AABGuI0Hw0N6Ka5D2hd6OQ71aPD3sULi/aqawjvTO/e4ccrvwvSHjd873btAM2Lzb6E5wwJgzNvqv/OS/k57e3HzLwz3GMa/9GNZNwCTTvle82VXNp7yZUUXmJ6jYaAwAi8L5NTC5JpJLcD0Pffsuv32NBw2AIioWGFAEFgMVALn4HWWJz721lt2PBa//Y9FHdkpwARECvOAbUAH0ItH7li86YqSeusk6Q/AIQDwrMXFOlTg3Ft3JqWcmCJciwEPIiG4zYjbkK10vf3qRVfn/PbmZmdPTx/g6Ox8BbADKSC1Y0cIevunPn7JPJ25Yyi0AJ0Hg/v1lToTlMOHtstVUvjQL1EZK94yAaMHMQsUCyJBVAFIwORGWIFFW1HVg9iZu7rqEbTUY2TSNH1N8XANzaT3bLG0Kvel5znsP3kTi2dxzIBk9gC4ASMofeHn42VFssQJ6Jq3Nn/i/IZT9C6j48AW4EIzYwA8c/Pk+NLfWZQaAItqzB3Tl3bTaONdjsmpS2pzBCfvidCbr+08DVH22Q4AqIJY8RfIT/8Vz6zNO0z1GpDCynWj/phVyqX/wbrvG2/+bCl7WMb6e3oyOrdo9RmgVNiRMMEIhBQ34mHAYUAw5zECOPzCyI/2fK1Sue4G6WPxpe6qnfszVaZMm+4e00evGDua2/Pomrf834HA4GFvVc3Rj++IvcXkGt2N+gtGIoPNusquZCCyqNdReZ3/t6Pnhq5r/vezGi4vZff+vuvv7z7S/W++/+n0bLzW9eNeLBt4Vj5nxzPONabwWKp39caGtiO/eOWLb2i6b9FTjx7W37ps/eKVr7+5ytEMINrb4zO4+/Y+bwYyK+0v37Wr+SLnhobXVp2SnDjf/NM5vWrgsL7bgrQjovqP7tAPIHTw9lhVDbYP2w4BNkhJRE2w9gAu6LyADLQCDUAWaAD8gB7YD8SBdkAGxgAgvcaUcBkOWPKXOaKCiwh6AViR6ke1G0EAZsT1MPjh1NJfMa5OVGYW+tI1YqHu4hu1u7R3qJowxjockGHSZ1JdRUPuxoAVwChQBcQnVIY32TJ6W1pnzRRXho9l3Yt1R/VSPIiK1+ORZmWoWgkBsB0MGF9JoROZIzg8ir+n5u3PcQZZCxhNaDGgohXSUqAdWD2e+2qza5KwAjAjniwU79F4EY1CZ0VKG59XnO+WqB4jWorsQUxbIdjbFfO12sJo8sMSgqcPHrEbHkTWY7vY5pqNM31e0+knSdJCGS1LdJIUrwQ8aaB0m6GnDV2rcBC44zTs2enQ0xMCIoBDlH8XOgeCm288lF8I2SFB1mGRBBfyw4lche1qC//54HQ9Mq2AjM+ppksaXnNJAwCsb+re/K4jWDndRQLw1aXe4vS3p2cYOnl8BnAWyCW0nsfN738QVy7HuZtNKzJA/opCq+pT/LRxmBprx3qDv/hO8Bdi2sEetPsydaGMPdbtyF9CdGSAw5/61LpSz9rMenr6YHTCkTgY8umluAmKP7S9Z/99Q7U2G7IODMtIJ2CUkUY/ABRP6XBg+kp5xd+ywlwT06nPcHwG0VC8w/Nl2qFnc3LKpvlOGjKyQGgrIxIRnTa9AE50COBc01/M5RukDV3a8CAnAmIVOgBhuBv0I2YkzsKBlT3teHUEwEXNvwh0TuREhTj1md+++Lb/lZBT4QPSfqyRASuaJAyrSAJmoE2qXR4b6rMhDdTIeL8MPSCSzQggpvnZ8e7a4V+894sANr0bN1/W17J+P1p1kAEzEAbMQBwYA2wwXZLpvOGC5qrxlmTPQBhp5JsbaRSPPuzsDgLDtR9r98uWVE5nWpExViUMUiKqusKS24qhEERPcQZAAuax4P/dFbx7q3rRov4j+7AMQK+5pc2H7TlXrT7ctbsGmfTNPeETHwLY09MnxvYd7joCr1EvxfceOtBQFXgx+Lc6JTZosdVjLA6DGyEAMlKWmAKg0hhBELbK2Z5ZtN9cAAALIsXNOfF/bdG1SerhE7X7LIjM6ViKH1L8/8FCnXcZqRPMnj2IBMcvJ6buwDRjSZwITBsAi3n8J7Iz05qUpqgJYy5uKL0idPHKl9NSE8YJC1sCtpZIrNthnyEAnj361ZbIOQXiMMcL8fz8LhRCRNOzA7OOkJ/f9HduJaDFtiumdO+IBxYqfKRihlSHx2TLGKsSs3w2ig868X02ucCDVjVkErFKsdYTMPVcxQEZ0Q5PSqwNnyharaIps+/lKpMzc6t9SA08e6xDLR89A2GkEwYoGbfDEIhkoAfQa+j7+jeOOFd0e9rV7Bb7rnTdqotGftHr/Xps69GjsK02/qXr/BsXP/q7x15nvmDIODi69dGL3/S+sWjkSvv9dwebl9s9R75v+s+3Wb/ua1rqHfjU8tWXLwcAPO3+n50PPPyn+MaPu741qDrWB5vsh/X/b43nsN103f7as58676yN4+lvf09PxuiGI+HxK2EgAz3cVgTyydOjP7jrtlUHtn1/9Wrrt/62G6v+iuWZFb/+6Bte6WtxrfPdZPjTR43/eeTp1vCqa8YufOtX/d+4/1Dj4796Er3qZSv7jmYWd67wVe6v+qfH3hr4G7y3XqPv3v7n9H+8p/kDJ34+ByJHAThTub7wYVsqCxO6ntzTK291/+LPvkuXVz69H044l5kMqdSQCchC0SOQQma9SU0hu9FiVgLGDHAJYEFaMtkTqai7Rm8JxV36I2gJwuuHYwj1bgStrakaDAyvra9AAIA211PEftrIOZHsJmHpyTabdAoAo5SIZd3FS0iIZVbEv2JxjUbjMIC+dE21PiQyYDG0ThQp0cZPBOKyzpoRoyjybyULYCu8PWuAKNAFKIXRGNbxevWpmMFWlUBR4RNRdWBIsqfVqlVSZydWdFma6yxDG/GM0Z5CA+CDAWgLvuoC4LViyu964DKgHWnZNFLrGYD3CFqKa5oCaMAwAJHpFie7og5QEC4rMIIGe+HyYVL660EEgAs+GWkAboQtiBgVFQCScA7EASCOLedcLMoODcDqh22Z0uUcjEPMCW7tdQIKHGsQ+Qn+EQDQ9SguFUnzNSfrDNH84ADAhYUzgHHCM4DFt+akG6eOLhcXsRdie8M9Qzsavrlk/Yb25atFANlcf8bUEO7p6bvnnh3r19e2ty8D0NxcMcvGW7Z0b97ciWo99IX01wUEASCf/uYv7Fvz24hOUa1xP6WuziTRDs8jFy7ZdHZT4de9svnOUayYvG6NeOwVw6OvXdJ+Xq01A/mzPX/RlvuaFG61oat4BBMmTvcsZdkqLSwUYZ64urjR+OJ7mt+aStoAqDkjLFlJyl+cSFK+ubHv0D49UsaqHIA0DACsSLy8/75mA5KVEJN3p1bbE4lvYee7Z9+3M9RxB8PHl/uepLh3YYa78+IklQScK84AXsg4/2OhOfNmABeb+2zghNsSd+mPvd0UM30BTdv2m6k+RBu62tBdD1/yLUOrfvl0xdImwAkrEA83N89QtGTh2bKlGwi2t9eJrHTqnvf09Gk3StJ34FiZr8681I7uGFps2JGFR4e0iogKAA4JAGolGHSQVSyWkAYqAQvQinxPQhVMlRmIyTSFlfaQAFJ45GrPprVrIZp/fxtFF9AIKIDIO22AF7WOmD9n/uLyivXuKiiGz7y4Jb00FYapXX8gCcsYnKIRaEXSDGMLjvbCtSrUP1jh8MMZgjuDnGi9i1J1FfCLOrS1GFBgVSAD0MrSip6II2hNQhYzHsTM7y979TUVZ+VgkFRJlab/DCxuAdqQzCEHYO/+/6tyWZIWxKCftvknI5WASUYKQAImADJS8zIg7zRS4ABQPBd5jg/PF6ObpQU49Rpt9um/4azrZGTAp0xxjdOZgpzTVfx5dl6EKjB5h6e20j/W/LZLGl5zqnaK5owtwIVm8hrAsw5Ls6/wRzs885IBT4h+i4st9wGRoi4gMcVWTOsSi+yWsM6XtrfF9xzzk01EU5MzafuUScDaGsA1+cBJK6iQn6eIfFtFJNCpXqc3Gx0M5L+Q/3vJs7/oq/qsJVO3fuXS9tYKM3xJ/RnU/Ovp6dvZ85A1uXztBYtSfsxe/Xjbrif/evTJ7xxc/u2HH/3q+y48rz9SuS/+8gVJn7P6mnjUYgo96az5WOwviYpDX332v3qWJN5hGk6+aFh97u/+e/gjX1I/I8U8f934+vf//TN/bf7S4pbf74nfeFmyI5XabT9woCaaGj6neSx38XLnLa/dtEn8uo6ntvzp27996Kbzb7U+H7CNVO2zq5FHD2y4MStLEUiujoFN0fNyq9YD+MOd/528znPF4AMWGTsvuup589lHRlqbag6d81zk71Kry+rbr9/gUQaTUUddvfSiw7bOdLA71eL3WZc2+tLO/vXYdUA9xyUNXBd6OVMxPKScI1vih+Ib/mhqGXtGvuiig/8QvjdtrhiyLKrGnn60Xz70xLcevfTHb/snnwPGfINWnzFYVNWkt8WSkYqoJ4ekYaxzR8aqmBw2XSRiqlIcY4YKU+Lwg//hqz639slnvJdCHUbf0mvcme1xj7v6uf2JZRboASeS5gpXYBh70R1e7nubDCAEjwUJJ8ZQmAQipupOmltZPKrvCFpEi0hkut1qMwCXFAQgI2mGgqKyH2G4nQhoS2YAsCGZQS4JOYgKrXNVzOsVBZxRSHwV1WKRlJFsBQCnLpl/wpxZ5L5OXTIw6srGjN7qoG/EJe7VCjtnfeMdu+Pj5BJAfEL6Oy0xWs6h5JY79wCIqK6l0v5LsDum2CyWgAuB+uCQ/FUFVsAFdQ92B/DCANqBTsAJlFRVYGYNwCYbAEgu6GRIZkgmAFBT+f8AyIxADQJATMXBOAAcAdqAnRN3QFRpFtYWWsf6OgDQFV2Mpjvzz7Nz1r2qAjasA+z59Feb8nsELVObWCUOxTuC1uKJcMXd5pPqPGvyBZ/9QAIQhYrEEAI3EABGgCCwBgDQDOzHczdf+nv1KrfUL4Zci+fkGnALGUtALzgMgEufDjJLV0LJs0O629DtfTiW+Rn2qjjwmS9+81urcehZwApUAJYN6LjxqzdBskuL4+vqG2AGdJn2hkqkgfSC6CXs6elradkH7AYA1AHAG6u/esGEmQG3f/nQVz+zZP16LxDevHmwsDibeIgVcACt+VXcANQX5h2IRn9NflhmcfN9lra7Dck3Ks831F7VEdzWBefshXlFVbFJnT5TOxe0rygUCl94EBHLns1uloKQxzeGfZZAt1yz3pnMKcqd08bzVc+5jPPdeXRSOxAZAC9kbP4tNGd2ADz3duBxzP3FsYYfKbAG4Spl7XmR/lbv90d24KgRDtl9m/urQz/JmS5Pp/a437Dm2UWHNiy6ogZ6GfWmDetkmJCNpNvdrdBJyC2I5h+AO7/+8u2f6IEjDhgAMyLqV7/aBISgdyEXhq7i9i/2fPULzdCHbr5+8R13/vmXP6wDAMSAQWCs0HexGJ4amHVYWZhSYwHsQBVQ1H9aYYuGc+bi6QKi80hRLU59EIBRSlyAfetdl/1t/7NSrV4EeKLXySjl+6psSAJwIZSERZuQLYhiztOWKbMiBcCCpGj4aQXQRIGy4lF3Gm312VJeDFN5ELEgUcpiulrd5tk3Mypq2iJp/zcqOYOSA5Cx6OSAkpbzOykWcgMgNi5+1Gk307TjWYgyPLP8CZKwBuCa1OSbqfEmyj5PLRZ6goqfsNE4fGqi5WiHxySmpgE4rgx4vhpvw6gF4EKoH9Vimcx5KTDDAHiBYwtwobnzoV23dx27Z+Mkln3GxEy3A2gtGtinBbqi9MsxC3xODLC13T5m+qvRZgNPU5xfmxnsL0xHVop21V6oQ1sIZ0z2TCpqyHf8FIpF39f60f/+5HuuuXZL8+BfX/HU1OtW3/eDRed/8/wdi/rWH22M6377Ls+NewcS6gXuC9MtI8urjb7gSldDCshgQeTEPT19d/zxnoddbcvCPW/q6whdoP/rrrb3XNoSgaNxsLu/x9O2dmjos78K3P7z852BtKPyZ6NbHjiyKvWS4WPvfCHV0fyD7pqfXPldv0tZ82xP1+JLHrS3XqDbUX+koyU0suM1V4Yw8rB6dSYnG5LZ3TvbYYSxOZHuka9c9fKIbLxBenZH4IKz3U/61OVZqfsXg+9CCOf4utfmfG9ZbN4afMHvPe91gW+9VHPTolce9a9szqSWGmwHh81t0fiSA5lkS3VnNforQvbPHv2X1K99N97ZZQnGLebUz56/CKrNVJ1BAt6m6Mrqg0diddeg/wc7zpeXJtbU7dyAPZfiiQ+OfidSoXMouWbnwd5MS71++KXn15r0mVTWINckEl0yegAHTIsz/7T2qYdzzTZ9oAEjNiTfpjxSsTNgRuCRC654Cudei905DJwd7H3F1VQJJYtkDjUAjsK+TOlzKmlTJGAdDsRr3LZYAECs1p11Rfcr5y9SRkddhn4sdaMrhlobhvQwZpEGIMYg1mEoAaNeWfaA+ewhyY7C5BYvQglYZCgALPkcd+pCrfmVd1GY1IuiuDcJ2YyECH3DcCdgjpXWY6atmjF7C0qb8ium6jp1yVDMLslpcYtDyflGXKamsLYBgFSvUxtgMWG2/XRhsMmW8VYHkTBI3oQaNzY7D7aiR6xksQ67UnA0KIedL8XRAGwFAPU5ZIOIA4PzUQ66AdhoRUUr1CR05wDLkF5jylh0aYsubZEUOI5gURuOirmwEYuzeshvDKTQCzQVKhgdAOxAQ2ERXI22Gm5l/hMpLZvCtUbnUNqYSKETCABxZB6CGsQvY5Nj7LWA04Q2I4ztwHqgYTwAPoKWqf2ixZ3Y4vooDpPoiFZgnbrSiuj91l5X4mpL3FXcoy5qCGmPyk8ILlyPBFElXrTiQkBG2o2wdlEgfnsQVS6MGhV10+UT1rihBYUB8IJz5gXAp2n6L47VDzh75iQ+p9rQ7e2KoRPYivQOjGQBoMYLw9sRvsr6jOWKNJQ4TCF4LFC0L2nxWFEzoUnpGw0iiKaNre+OmaXKivVxWBudDUYgDt2hvoDeGQaQgqXNuQj+9Lw3GXt6+lrW78ebdFgP+wp/o3HYJQVrMKRlq+LjXgwEu3bosV24dO/AWX690a83w4Li1VmcuqTXMDjzGTt2X0Mp83GL1WMEwExLgh0zUpWRrhOFlWdwkioYo+S4Nz+Kal6F66ynt0OwlLM6p17X457gO4zaEhu+NC/m2uHIAHghY/NvoTmDA+BTlf6ihOFEpYw3akO3GHTv3R7D41BTGHgckhcJH2Qv6j+MnReefwi1duRGYAnAJUOxIW1GohJKP9xNOFyP0cSYu8IYaGn+F0MSAByuc6ImY3ZMrW1bnFZtPV177JVZAFDcaUuo3rl0kaP++A55Fnf+4Mn/6BhI9deYrsykhg2tG/pqvEcBNGDk5ezq96QfG7BkhlHXoza/NheNjOZiuYr9ijcdsHdZzRVqzrWi98jhpZUto2Yla7ZFALikoPhWrSz0TjgRsCIJIA6ztzDDwIcK0chUYEnAoq0PakVKLD9WYlNQ67kocVSfMKmTYlqiq0LbrJRXhejOmGvx5GPKdwCdTMf9bpqTIKr8sE0btxebtvtpqjlN+Z1cF7RI1ifrrBkA+eKEgEhVteUnsz559kV2T73i6b/Fpo12jpmXzDW1Pb6Wsw3JGMylNwIZAC9wbAEuNCcjAC7OaWZS6rq/c1VCANyGntnHahd/8md9cjZmROETPs9XmKMWHf+NxWNr0AuMFRbcigAOmJoz+sZ0RSJ3dXXH3v213uXBxX+I4/JDcoduZHDveW+u2v/oqu/vXPexd/fWJw984u+bvrvkkd1LrZZtLuO6cMW+uiPhwJblLWqf/epa6UD3wTe1+BZdsqm7t279YncthpwVTWLR3H2dESPG0rBaEVp1QXvIP/+ZcU9P37/e+fF3tTsDbV2PX++vx44V3z7bZ1ztDQVyRx+VGtx+x5V9+91XNPz2sZ53vTBW/974r//n2g+31fZv/tHvom+qcCP2Su26NimaqBn7q3rpe//U9Un9a248L3t00OaXgzA7zjN2Dbp0Z1sfz8B5IL5SZx3zqfV/ffhCeVkiMZwvBWzSZzzWpORILK/aY0eqc6g1LFUNvugy1WZSg4aV5402VHekY7U5W/DF7rNrzb61uu7n9MuDGVutydf1YiOskJcmNlZ09ssmO5JXjhw67Kh7amCV3Bqw6QOiXktf1yK9Le2uClokpV1/YL0SH7KE92HZrsG1iR4539Ichu2aiDOYHTzsMjkyqUMGuHHZ1U/IqmGx1OFC6CmcOwZnLOs26ZR10q4uNO84tMFbHXy98ykThiLwrMMr64I74i69AodnOGgYyhk7UmgEZMAD+JE2mIzOVNBdY0YIwGFXUwAVCmQLEkG4LEj64ZhUsBCAHoYH1U0ieRW0178NSRlJAKLsjbjRjISIeItX6hWKB1Nqy/RqYyu1bbQKz8W3APBl6iySAkCb9evUJcUIzmnzYBH9iv9LclrM99XaWqkxg6myUFw9BYwAkcIIjEjh7Sam7BuRb2405Rfn1gaVtlUcFC2KpTi6OtRRrYZ0lkjE4vQEg/JPFQC5PkhNyDyMLTEAWGzCUAqds70nJh5CIWRdC6y1QV8HnQNoBrzAmnxMq4Ws2jDTqXWkZro8EZcMAERsLNYxGUCVBXFRjsgFnwJrG45WD/mhwNiXwrNIbxmfVbyzsHsAllphk2BcD7wG8CJxliXu0u/AOYOoEU1l8cKYtA/TVmSZ+jrUImQtAy7aOBmHubgDX1taWPwoSk+LMQcTV9WcMJNK6xgX/+EawAsZA+AFhwHwnDoyZu/Tmene4g+pfAB8AOgcL/ggSC4YqoF2oLnwrTbLwWrfSAoAoAf5Nq2l8KhOYDd8GbRd+9X8lhk4V60HgCzC8R35r6kUxj+0tS4jE5DK/3jkwfGXh7kLSSe8i7WlcieyA97CntsAe2G8pNhD7/i9UzPFkxedClOz22P+sRaykxH0niTHnR+X8pIooWt+blkvZ/GeoUSbngHwQsbm30Jz5gXANhRWCJ2z446sjjkN8ZhfQ+Jivg3d+WSuH+gDOoERpDsBwK/CDLguAmTAmx+LXSwtmyZEeuLqeyy/f0C+HjJMQCPgBTqBRvgOwxtFxYavAoCMQBjNZ62HBT27d7htOHDoP5at/2L+GVKFVh+KGoRRHNl1Ow7nfzIDyQS8SwrbyBhfQ9dSmA2jFLX0FCBeKNfsARSk3SZjIuVrtSlwOJRwr6XRjXAAzgAqAIiZtV1oFqWV6zE6bflfBVY3QvPbXCwMYy91IVvnUBpAxqITP4q64tZgVkyxBRCuPaHdswazAAxK7mTnuPMrLY/PbDi+ZxB/AgWOQFEuO+3fWsy3DsKrwIwZOtE0U9uBYoiqjOSkAHiW3HeaOWElmPfJc3NVym7PspOlT547vdrQwwB4gWMLcKEpMQBGCZ9jx/PxWEr6qxT+U1gubPLCH9OVrdbKMgslfohN/fCfMG5G5L4iYrDnV5cAYLKNF3wWJ0GsQCwelIsbANQ6fGZbpE4aXo2xzFMXRdZ3nLt96Cvpa69Zuf03gfNaB5P/0LDtK6YrL2vZHhloOnjUu/78l5f57D/oPEtfmc6mjKkRQ92q4GIlEXCr/nCowZDbG2xLHJRRhdqxXigYsjchpq5cNuZt678wvHtLzeprEgMvhatqsOvyZTf2d7aYI8GkYtj0+pwh4DAOBR7V+13DvmxfRdvSiNkde0penwk6mzuD/S0mSHC2HAzkWnVSSvJbu7d3qPsdP1hkNJmdDS2D33jin7953s+RG1liT7frHrQ4VzsT6QrbI4+a32jze4PZ6v3QbbD6QrG6l7NH00PLNy/f3WN2Xzj60Cpl5xt6f71mhy98c/gdif0Vo/VPeCwPKsvOb9uzQQqmDjcuMez/tPmK5pbunoMttV5fYth92co//6HrelhQVz00OFILwGWLhVVzhS0aitnTR2W9N91c23003BjrcIhpuKamjN6W1nsTVcPptY17aqXhGowAOIBaACLyrIBuJxY9E35tWDXn4ga9N6EmjEvtA8MxbzBmA+CuCoZz5my/rPNkFjn7lkpHV2NHAC4xLrMLLWNwdqVa8nNPhQRqq2NSZaLCMbomFx3Rx+oRPwy3jGRPtrlRN6qX4oNqjTYddpnhQAJmABuwpxoDg6gxI16PkUZEx6Crhy8Ap5xPB/NvgH1or8NQABUi+hUPmemVnIHjf7ve0dzSbZQSXakW7fXfgBFRorkNPWG4J43i0mJgUZljprFxAESqrf0YzrpErOs1DIrFMsTtFkkRiwEXx8MAKhFOwCyi6IjqihbSwWTMkbTotWw41ev0VgcHuyrl6kQ2ZlziCByKuI1ViVzc4AonfWm7uMrwJJMepANLM2qfjAQ8jfGELTkU8WZjRr0tXZHI+XOigjX0tnQ2ZjQuSqxwHpYTOBRrrKgcvV7aenXwL3tci8VVgPeHMaSBILAEL/4Ao0AEONeCl5RjVIEW0e/aiWWZdXVAM9BWOOxV1rRFEukvpiwXqNHWJSyeNat1TWvVvI+gZWp/tQexKmVELJqLTuD58cvMnbHx9HdnIQNea4OxHbAD9cAyhC+ZvIdhuAFMejGIukoykk4Epg2JAXgRKl4/GIXcV+THotDmLFUztXPSgJGppWK0JLj4DHxw456pu0ELBAPgBYcB8DwGwBptAapi+W8XEQAjnwED+Y9mAIaW8cUAJh+mVupB0z9DPRxtS1EsYuLIpXRnIWaed7P8XYKAjOnHfzdPbM3PuqrfaZyKemYEriWOUpv70oknYk7vrxPsai8l7p2XWbyz9BKecc6UDr5S7N748dO9CzQjNv8WmjMvAMbxZ8DH134QWVSJceO0X0+i4Sem/0KbndkJKMAuqH3IBscHAurrIJkheSBWoppAFAFTgJ5CRUEUdUdqrcFmwAdEgSSQAEby96qFJFEyYd40F7XrkoAPmNOU42UTf2wqqqBoAfpn+0OLCsbGREr8Wa3B7PGt7lxM1EmWA8qxNz2VxO6INpsfsAI+wFuoinncTn47MC2bEi7D7G+62Zt82hrbk6YXiDLOKMwRn9TNNInoSJqlQ1MrVDiTCV29cycWnzvuhx+f44urMfe9bTQOi77dWNYtenunOuYKytqTnIhvNV95RcPaE3wSOnnYAlxoTm4APDXfXVm4feUMG0xdc1cjlgcG4AAqJ3552YGmY+xw6RmwWGmi+MNqwuf/pH22Fy1RPFx0I4Am2Ff41YQRQPqoDAtW13euDA+f4+ryBHebBlZ/XVn/6arf3V35pj8fOjuVNWAUrZf1XdalPLev0rp5YHd/e+Xy0cCoS+9NOAZ11V5/GKahAa+rIumL2PW5dOKvMlYDVmA7Pvmehx7cd8G+3a66G8d8D7m8l0QbHAOWAfdTj5uxxIM4Vm4Y7crZsz6j1xsMjtoSu2XTqgx88MjJoU4bFCAHxIHlQBJ1l4wtrTwSzlUYktn9htrlsaGoSwJglBLmuKUjvSjW4RBLGsMGU3UGFqQ6DLaLI7Fuh8mWSW0zmDZk9Pq0y5NcWX3Ql3N6deF9kVZfrwsJpHoMSKPuyrFlasRSfeDJwUtq9eGkJ6f2y4OvuEzLM97qYIOjbxF8SZgrgobf9Z/buOJoIuZodh7csWfD0uX9sj4wkK1pMnSPZGsTMUfEokv1OlO9BqAwhDFQaCnVAS6sRDCwMiNZ0wDMSra/u9bbFPSNuPIPGQZqIK9IuGyxiEUHQE0Y00dlU3s465PXVR8y6BLhXEVWHzOruoFsjahOHO3wjK9dXQOTI6OvTK9qOYCEqdLaG842pHTZBqkLgBehENwxmJOwDKo14jSK70qRabkRjMN8LnYrMCdhPQedNiWgt0SOoMUOnROjYkicAouohgJgL5Z6EFEKje84TNo6JmImpciqn45fmDbrbfqAlr+KohqTXvPFN4oyPJNaSto8YF+mLpwzV+tD4ovel6nzGga1MFjEwGKa76TverHWr5gNbNIpYmnhSTKqnJTyAyhzGa/qszprDsVgrlazQ5GGNuceO/x2ZPxwVMTdddZnRtDghPpY9vzX6g+l4B+BfRi1Stbj1g+aofSj2pepq5bCbv1gAuaRbK1Jp7RIPW4Eu9AcVF3ViFwkPR6H6Qhaz8KBlTgoLtCOoGWZ0uV8KY4YYAN8gA/YATEvOrMHAHQydI78Irs2CcDEfnXRGb4s/3/RBVooqmwCsBPrtaVqAIjGp2gCFSflWu2TWRYQFAvHALAgrpUpyqcMgQmDjI+kEU4hXljSWFMLLLHApoOrsMNYBihA+4T5WlokjML0XBSqhQdRoS0FrdGmlTdgGIAZcW1Os7gi1tJrUeZHe3JtIeq46klJ2RoMTXpmLW8WE4i1SwMPIp/d+ORMJ4pOOwbACw4D4JMRAE+ljVKZEABj1tDOPsfetKmmpL+Cce7ncG6PFQN3jlm4q3m6VLh51m6pUxtezkYpdMCdwJykOSi9BMkJmqf31ymorD573DunubzlFOieDPPSPXcKMABeyNj8W2jOyAAYx98MOL5JwMexEOkkWvprVNT80Ox+wFdoIw1M6fSc1A0qaquID2B5YuY6baQyAFiB+IQn0erNSC7oZACQzEAhD1ZT8xoMT+3GbS7Elr7psm1NNSAf649rKxRgFH/MholL8SknrYnYX/R7Y0W7UbrjeMgpMN+N6lJyX8zc8BPDZ6c2/LRep2kNo3bqdN7pd0+VUznLSHb6p1ITxnwv9gkkvlOJJzyps4GjHR65OjFtnedx4jMknp+7Nr5y3szE/DZxNibNq5t3optY9B0fx8BEBsALHFuAC83sAfCcPq9KCoBnURziHl9d6OmmAqPkJHhqJ4A2nXfCd4EW+4kCJ5hhPWBbYShbHLXWmKc1HlCM1bpoSyy9bvkfo/EV5w31y/u6quLP/Omqf831tuw4O+oNmJ6LtvZ3eVI7jIjBdFEG9WhbfLBOGk7A/ErorBr7mE0fODS4IhszpsYMkAuTksXv6gBWAF2QL01cW/vcvsgyP0yWUFaJm+UlgcGRWpct5htxpV40ADBtyHirg0hi8MFKWAA7YATEgmyVgA3yioTLEItU6KpS8f6h2sbWo836njE4RVHfI3taMVjY3ghUANHxv6AplUkdMojuGbk5oU3bVeNGJAxBvxkWuHTJoMm8pm6nJeuI6jL9Iy1hi77F5j+4vwF+oB5iIm/0px54ADcQgLwqoWvOOLp1oWqd3ptoNXUfCS3N+Q21VT6zLWJH0p+rSMQckjWt+mTJmxjYW4cEMAokgXThD2SfOGiyZvwE2tf7AUT/5IE9XwXH5M7AgZWth8MwicizK9WS7ZcBwILESzKQP1fZmLGyZbTJ0A3ApOqbpe4MclF4ojCJaYvViB1FpZh9a0PShZAoQKItdyJDEWV1PYgMosaNYD3GQnBXo28QtaLmswvBAVRn4FiDHYOo9cOh5b4oRL8AxCIpcZgN0O1V2zGlaPNUU9dZmLQ6hoh+RbI76V+vYXDqsAk1YUyPytqSf9rGTn0wlbNMO1BMyz7FgLwNpt2Das0yaf8RNF+AvVEY3QiuwqE4DFZk/HC0KoEui9sPxxG0JiGLZBdARpUdUlArvp3KWcS/zfqeQbVGy55dCMVVTysCUUmpQEAPvR2j52KvScGQxSYjvTjYO16DZzewemI3rK1wBSfKO4uGfRNgyV9mDqIBwF4sRVHbVXRLxrLuSe3PSeshViLsRKDwp5kx/RXqMaItIWxUcvKTCnryuxpNof/g3JYxbgCuXoLsICBS7Q3AamArsBYYAV6LdIPJ2J+CB75Wm1h8VzxwB85xwVe8XIs2583XanMOpfMLG4vlijvzpyvcmh+WPYiGACq0lVxEDDyM2qkBsKgIXXxajqBVhMHf3PjgHA6VTi0GwAsOA+BTEABr3X/jE0lLzIDnT3r+fstx5MfHMPsTLpv1Xpp3x/X3ndP8qllj3envminuLTHoZcR7kiyoCcQMgBcyNv8WmjM1ABaO63vquAtBn8gqFeJK2KioAPIBsB8YK6rjMrV5NstUmFk0TzPwLhfJX88fk+QCAL0LahK6Qj3dec6GNaUMcJx9OCDNl8pCL/bxSsumUqpezz6WQpshIYTgqYA/CasCy9Todxi1AGYJfWcPesXyctmYcX6D3hKZbPlKlalep9ZJetxmnKC8r9DBPddPktZjB8PHQYtStMWSj5s2JFF0PYsOZQbACxxbgAvNLAHwnNLf6ZcYP44ct3ge7YkoLQyeZJqj0Mo+twJdRa0yUZZDZBCipVQ5pa1SU/TYOADUrQpeYBscHXJdj70WCb9fbFgTkGrkricttTt3XCg7Yl37a/LD2oaL8lRbJhUzjOfQCSCen7eqfZfaWiLpUVmvTyeek2EElkKWE1mdUZ9OJw7JiEK+IJEIy+gFooACrECtMxZy6xIdMmIw1WY8uuTQbhsswErIzkT2qLFy4yiAkN+eDsteR7SiehCF2Yr5b5x9U/5e2mn3wdSUyafUgCjhq9enXdZkMGdO9MiQIVcnVtUeeOn5tbXOmHXZiFh2NxszprYVvsuaIdclEiE5f579ReVhFKASqAJGgSrIzQm9N5H1yaKec4UtGs6Z839NkQGL1VJEVGoFxgpDJK1FVWdqAHMhTe8aT/FN9gws8Dqig0Mukz6TihpM9kwqaoCc/x53VwUBOPXBnqGW5tpuAFrouER/NI1cIFtn0fsBaBNtzaouKeXEdNtJdZhFHjxp8VTxHyuSbgS1fNeLUAKW4o0DcImnSha+v8W041TOoqgWpz6oJaxaJGxDMqPKldJQEhYnAqJZpe2n1ogSY7MCoy4AYlVsY1UiPSrnV+QF5ObC4LME5Ob8kydGZPsKv0h/1YQxFzfUVQ+JnFisFN5oHAYgqoM0GoctOd2I6gzF7OlOeUVb72iFDCCcMy/Rjy7W7wewV22vk4bPwsFeVL6SXauoFjVuBBCM2RIjslydELtnagpnfbK7KuhGKiHlFNWi+uR11Xuapd696hK9FHep2YuSY4csATeCAbjqMAzAD0cbumWkB1HbiEBL8KBByWUsOr/L1dCVjyEPt7ZUKSNazSGReoqazEG4AIjKNEfQ0o/qtCq7pGBQdYWzLjFxXNQD0P4j/l/0/pm8WnnxXeLP7UawHzUi/hSTX+sx4kZo2VC3cVcKAYhJvsHt2BVHDBMXPjwuWmnrI0Ab4DRhcdX4YGJDdaHggfhgXA/0QawunRmB4Wqoz0FNIjsIQwskT2GzauD8QpGndgDj86RRqHE9gOqp6woDaEP3EbSIIkC1GKjHiDZglAHwQsYAeMFhADzXDsHjyIAnTP8VigNgnNwMeB6j30nmOQku8dmYB588c5kLclLn+B5H3DuniPe46+OdYqd9AbnjdlqCYQbACxmbfwvNmR0A4/iDq1MWA2uD/4TxGcCTAmBhupm7J+jkNf/mbTGRaiA+92o3dsBawnThk6of8BYl7tOl7+Oq8xNZxv/FlB9LpC23jMIkzhM5CXNsxifcllmqbWvrKM/+TlFg1Qb7TzK17TdpMsq0Yln3pLqdmhnzUa0nfV6I7v4mYN/0JUlPheObOXfitBhAS45753wGTryt+8CSlVc2zpD80ALAFuBCM1MAXPqb8diX0uJDScGEmWwrCv8psWLHpPFwRdnnNHxTPtVrCpFqdLpsuPhj0w6MFe1SByAWBxgulJ5G4V4FGJsu99VWkLAAbtQtCfpgT91rQDNq5Zju+vBr9fuXSp0d0YtzEbmiovuR7g2NKw7uTzdlfbJIRk36TKrLAABNhYopombDvimnq3g+axXk5kTiaRnDgLVotqsf8BSOq3L8DNhaIgCcUrLCMXrk8NL80JxQJpUx5JsxDpiqM6nnDflj1KY1y4VKEoUq0PnEVFiZf+Zc3KCzZvIZofZHaS3MVB4txLdNwI5CCjsGNBZN1Z30zPaiGirRwt9UNBZEsustKss3PN2c7GlfM0VlMGRnQsxOjrhzqU5nKmoYb7xHJ74IJ46REjHnwN46FNZQcOqSWsXjSUskaBWVxWPFEqfasq/Fpq6uqkXC2gYAkrAkYA6qLq2GsAhu88c35vZF7FoYX7xktZZHivm7Yh1f7cm1mFb8mOp1psYMGIWpNgM3UjsNMANNhXFdCZjsmSXWaKI6Kslp8TxiqePh6KKkRV+vH/bnKozJbMRkA/Ba/f7tuZbhaKUkp5cbe5NSTpwfG5IeNZuTov2oBmBWdUiY9htqi0uzyNWJ7EGjuPRLjRjEa7LWERuK2PJnK4iV60aNtshIzinbIkv0Rx0IrMVOUWR7daijWgmNVVRXKiO2roBRSaEDaAeOAH4gXChQVA10AnLhZdMEZJFeYwJgDKQA+FbYxIjGevgADMAbhLcNvVXKyN8sl2nhZRJWkQdvwO4B1AIQf+4kLLUYCsAlSiivwL4h1HehuRU9tRgIwTOA2jZ0B+C6FluOoGkflrWh24XgGmXvQcuyOAznBl+Rn1cA4ACQRG4Pth06dcUiT8RaoN6Caj0gcgRRDbRQg1qs7wPgQG1LAkbtInrSxUXxbGNxQcEAeCFjALzgMAA+2QFw8RLlkwNgFGXAmP8Y+OT1/RWbsHTxCSrxj1tdaHud4r4/pdDwHZlyl+jLmlMv3gJRwjmfvcuvmFjOYZYNSo97Z8p6jxn0ninh7nw540LiSeVu5hED4IWMzb+F5swOgOdp8YXSv90Ecc3vQWz2bzoAMtJ1RSOwxQxgOZgxJlL5ABiFEdqTYmDMTwaspiYsMHxS6evGJw1PlYtAMh+roSgaUa6Sf6Xoj9OqRp948edjlmieVGsahd5DESU2FHX+irtmaRDOsrfFi/7O9MDjPljxmvWW+iSlVHbRFqib4d4Zs15R523au4ZRK4olTl2yDoWChOL/kyZVjPc4TyJmAk1dM3sBKp5RcTL4CwGJo7CsZjHRGJ+XN1Txc5Y8pfi4G7S/X7xm06Klx/dYOgXYAlxoTjwARqFsvn2FXwy1KX5s/mJcS1jFOr4OoPF497j4s3GWT5USh8JoX7XaZ91KYB/gB5oKk2XHCrlv88SlH7T8sriuvgggRdPOAdumSKzDge2AFZBgWpxJpQ0Xndt7jmN/Akmzv/6luNthTu9Wa3wRe2rAIC9NZH3G1CuG8W9FL3AQSBcNspn6sWwHomjd3Aczhka9iS4ZKCSglfmFQky1mdQhAwBEAG3O4QrIrYnEsAwUTZN1IF8DuQOwFo5aC48xnqabajOpiMHkyKQiBgAmRyY1VMitC9NAtRobJlsm1WHIB9Jixm1l0cTcSX02Wl5bXOMaAGCqzKTGDADyFbCtMNkzqS4DBoG68UOQz0kk9sj5B6bQur6vf6i2smU05Lc7LUmMGII2MwBtwm4i5lhj61VgSiUMZvuQrBp6sotq9SPPpxenOp1Lm0YOjlQDSPUaTNWZVNZgSmVSQwZUFf3pE8B2wFM0OzkOeUVChPoiFRaHICoha2GtWAkYwKR5wGKyryV/VzxUOEcV8A+hHhNnCVuRHEKtKCgdh1mbAdyPahuSvZkWEe5aJGU4Wllhi4Zi9lzc0FzbnezzmBv9diQHsjWiRjfkTH4nEgYAaszoz5lTMUNdbTAYN2ezxtSAARnUtsT8OXNqzGCyZ4zViRYpkvAkR7IVVal4i/2QEbpD2UUeXSiTk8MwVeuHRrK1I9kKkTSbB3VDBqcrlQRQ0zh8YKRJ700s0Y8ejNZX2KJj3eK05lPqs0wHesJLLTpFHrF3yea66qHBkVox3zfrM6aiBpM+kxoymGozqah+Y3NXrAK7drVhAK2XDDd6BxMy0jFnnb3zzcOHhyrMwxbVgEgzYssPdphNoay5IueKOPfH0VPoC+0FDhRNNCpujbcDuwFvUVP/WaC98MbXAw3jQzN9K2zGkBoyV1sto0ZFfd5ybht6XRgNouoImuowlIBxmdIF4ICl9azg4YjF5VCCaYsubZEUOBo68nOO026TMZHKrzkopugcBiz5xYnVHkg+ZEagk/HQoXmY73tqNABVwNrCFYOxvagjeiXSZ5kAiKMOt1rFJXPCbZEPK+kGkyjKnXBb5ICixTdiiWIGwAuZJEmnodoSlY95Lz68oLRPtxrc8To16S+ATDcA6OvyS8oJc46Eq2fo+JtTsFo58cfSc0BtNRcUWpDTshTaAWdc0Fvy1N5S+vuOOQtq9kESpc/wmCnrPf6It/ckdKud7ikHpZyNBRUSh3PmcGnTtRdUfWkiWkBihSmY2oSM44or5IAiF1akSssmYyKVlk0Zi06kwkZFFRfkFkS0Acht6FbgqIcyAG+Fkk5ZoH0haqOSPYgliqpXieexBrP5NaWsQCXQB6DwlaQt6GsttEamrgQ8dalgTP+NVlz2OabCJk34f0xF3A1rAED+R50bcnDyxrOTXOPRsqgdDVH1qxoA1BQASB6ofgCQqoBw/nbJNH4s0RTghN0AeAuHnyx0/C0C3EU5q/i/WG1LdCLrATfQB3gBGfAA/UgvMYnh+WJOSdqdX7BqAmVin+PEwFW8Bsaj3DiQABqAfsAGyIUmoqXwr+iCiE14kgn/1yJbb1HA7J05bNYeKHpItR5npTAXobIoeEbRysTFveHFxRKndrCKw8SExcFEl0q4zmpUclOHRChwBOCsQ39x3CvW6y3eLAivCz5RzLm4gJuo2AZAdFaKvssk5CAqAMyU9cZgFtNlROVJUUsQhQLO+QlGhaktedqUqdjEDuti0Sk5el/hpHmAjqKIQmSlK3BKnbzot2Pij1PTX0ycqCfOg3jUiZyErjm0k7WlN01NYS1binZ4RCM22uE58QrSRAQA9VG7ZXzyojYbUruoLL5yFCN3iwffCJKcFpuZmsKTOn7E7WqLMT0qp1405D9MrJiGVlRZFEwOAqbCF30EqCmaxrq+8MU3Kf1NFJ4B+NeG4R/sN6LPM/2nVmuh9sP68dXZnbqkG6msPu5aE0zsaKxZ35UNVIwZHU5d+Pl9datX+vr9ctBfi1gsW+0yuhPORFYNGKXadDAQTQxaEfFc6B17yu59g2f0hSbbUMhWG4sNbXMgCjTB1JpZcmjoNRvH7okufk1u3wu7l729csc2d+SZXeeYLBmvrTv1fK72QvNQhw0xwA/UwOTIiDaPflU68VwCPZ6VS4P7Aq6Va4KHgnYEgGakugwYVGGUTA2ZEUNFc0xJ/J8siqlsqBp+0V9Tm4oNRWwrPYf2+ZYgCwziDa8Zecjs0Vemz/WOvhSrchliiSdkdGFD7bB5uf6FhAsZNTVoNFVn3nbt0C8HG032zNVB/wseW6hK9/bGDr/P/ODQMm9DdLEx89SOSlNFZkkkuq/HhTG87RzfL39nq73VP/T8InikRJeMVmAUtatiSw71PmVYYWrNpF40IO5fuULnQeZQUq5ZH9jV2whf4S8SBwww1WQQUVMmI/wwNWVSHYZ/PLfLd9D2p3S1KZXR29J1SlTVGf29plTGsLI2FLBh0OaqbYkO5eyAalqW9cSS6EJiDLW22JrKvqRbOjBUneo1vNk48JLFpjvk9SdU1Z2raRo+Z3h0+0BrsyW+O2dAi6nB3uuTa91xi87q795dEVoeSx1xApAdI96EFaExtaIusijn6dD5h6OoqVmJ4L7trpUXBM/zJe+tdyY8cr65lYIpmEmZDIntMiKAGzE4ZGci6zMAMNfHQ/o6yBnZFlGTxmQwXFVveinb2q4/IMftPtmUkrIA7MjsVs9aKu0XjSUDdMswvBdVCVhCaqMs+ZIwL0O0V62qlgb9cHgQqsHwMOqGUb1uqGekVs7FvWZY1snhm2I/f8x5ycu9Z61qfiWhuGHDkmSozhE8MFq1rL5/kzL4/ci551Tv2mtbanIodiT7e1ss9uiK6u6esbZEc1KnWqqURBgmly7jC7lsayLpo/LQiM2UypjkTKrLkNpj33vUgbNhyma6Ap7+phqkpNRRw5E61Koxf6W5z70otc1gOnt4eGnlO417XnqhTT43eqjT7W2PHuhtApDqde7c74ZVilkdGIVpRSZ11LDCHMpIuuHhtmprOOlJuqqj/27Y/fPExvaMz2G0HDoi+xNY6Q52Gc2oMqRMhtqaWJfNs8QYllckXKuSQwHHosYeJOy2ABS58ffV9hZpTwhNZ2PPorGhh5ZeuTG4O+ZCVqlvbBmILJfTyiIPOm3ugLE91d9aC2AA3jZ0G0PqgYp8p2V9q0+BBUC+sNMKBFFVpYykV0gAFDgcSjhtkfI3VkhWjBoV9YCldT22B1EFwA/bemwXFaR7LY116G9Dd9ylP4q6pQinLZJRUWGJ+FbYxIQx4xMpnA9YCpcziVT6LJMxkUIMaIfkBRIwjEDdg2suQnoHfhnLF21esNYW1ZHW1yFeBaMIBpNIX2kyjqSMgVTabQKQqLM4u+KioS4HlEOLz7f/9Xn7IOxLIFsUAF7EEm7LHtdiZfovFVpYOABwYTnDZgDPdwCclk0J1zEGJShwpi0ITAyoigMw7aNHgWwpjOHSukVkpAGIiSDjBQD7gRigAL6i7r9i0y7LUugXUEte1D0bBJDvp4vN/Lab2uWn9ejpXZPvym8gWvr26cqqTDNkf4aNi/vLmqa7EQjXjX+yl77K7IkT83WK5f92J59WASO/J4mS/95Tnmfal/fU0yh697QfJ+W7CqzBonx+6qoMAIaKajjGp3sFTFpcRDP1ehJAqtcJYMbl2XoL/1nI0zhmYQeqTsoqaydIVAfSiAGYp2tnptL6f2cSufTNp2xnaK44/2OhOUUzgLWpuv3HmmFZ/BAAMUCPdKPJOJYCJiZeTUi7TcZACj6kK02wjH9LpmWTUU2lpfxI4eJgT/wn4PK4g/6AywMgbYG4YjcqasTidIyFYYRidqaTLpsa0FkiEYsTQE6x6CyKAktWMUUsNkcoFjZXSpbE47jobHSFkKvHyDDql2CoO36+1/osgGHUrw516CtSKThMiKTgaAke9Cs6hwvOwXi+n2gMANQ9kHLAImAEagz5UbItUJ9HcNNy94H9CCKwfrnrT/ulpVCjACDZAR9UMyQ9EIeagb8O5kEgDJsTsTCSgNeDsUF424EeZIPARUi8BBsQMKMiCP9yWMaQOwjnRcBhxIDsADL1MAPJxZD6oMowB5EELGbYN0J9BjEn7FXwjcLTDryEoepb6yp/ml5nMjyVkiJQF0GKA5cDvwXc8MXgNQBrgAHAjPRik3EwBTPgALKI1tbYB4fRjLRsMvalArbl7sP7E2ssBjWXjLntlmGRwibqLElUxLG4KvhSxqUbcNV6g4k+RYJlY8412qJsj6AJlmB4yIVaY4Ny+KBlmR+OViUgW0YDSp3ekgnBW6n40hbEYXAhMIqWKnSbFKQssA8pTimYrtCNWqp1kHNImBToLIpDCStwjlic1UrYj6X1ye0AjGouiQqdFAn5c7VyDkDCZUASTn8cIaAin56mZZOxPxVeZZWDGdGLYexKjayrdSjBkHK5F48aAylYEHTX2HoCfWsWV73YYTcibTHBgqTFbR8aTteaxDZp2WQMpPpblzcM7k/LpnyPjhMZi86g5ETInfBYDIkcLIil3bZYINFiEIMMDEouZnHrMhGjIZe26CLJprTZY8WR/BvBAh+WenEQiitsMToxGoCzejicrLCMWNy1GAzAmYAxjQqnklAgH7Gsk9G5HytEDb0YqsPQ1WEoCavWFLQiFYi3p6xhk2pSpaAFSgAuOzLdapNTCvSj2oXQIoz9PX6B2zoKQMl6wjAB8OhCA9maUMxeYYuq+2V5RWA4WokwcgaDuyqoxo2+EZfRmYAZubihVRftytmzPiMAb1VUFDY02TOpEQNSMFkzqVcM+aEV2kqN8UI9TKXQxTwGNAFpFX4J2px1EYUuAlLAcOFyzFxU/1OYNMddXJqIST8ZIAhUFkZveApTnbSylii6CxNHnTYBY0UrFGqb9RUyacE/JcgvHvXiL1rUUDyPCTha2O1I0XxfMdZEAtTCj4npBuDWFJ4qUih/Gp+yV3agd+Je2QvVMlH4j9bW9U08QDHOw1u43Qto6wQPwrSkMNOrML9qpTN4yGTHxOsCuTqhs2ZEM5UzgBc4tgAXmjvv2/Wlwijb9Iic8hvq1o45DUFxS1+6ZkLHVBqL6vsAqAmjyRpOq3I46wr5xVsdjfZh8TZM5SzD0Uok4bQkndkg0qiv7nMiEFUbn+tbFjuUwUEg5EEj5HMSuppMzm9w6ZK+tB2AR5cUdVzl5kR2yJjab7hw/eAbU0/qHl9629nnmBIZ70VBBAy+I/YP9+1K2Bt/v968xB1+l/L4Bw69I+UzwAhEYVqeubW/Y2vCvc/ZCAMwoF7Y6APQv1Zp6LV4lP5FvtEfdF+wAbEXHUbTMmdrXdeB/+eF5Prge3bvUa09gVQi7Dyvd0/sgvolePbHX37rFZ/Zk9I5HVXRR7eff3Xt81c+6fvUxs0XunY/+fw5a4Z3ne+usJhDnfpFLzYZ/8Gx65t/X/2vZ6Wfih/d9XLb29/w+98efPc5oe7a5cPBPdIqs7n6spfuDq56f9/z2VXLe/eMOTeM7uq8+aH+2s9WPdxv8TxibmvUH9n27OpNrbte/4L5/pXNh/zxRd3m2IbIoqas2e9+KKQuqfVd6U08ur2iaWfo8WWLP1b/xxV7zfsWV37t0PoNi+MXeg6lfte49SKvzRC75IXhv56zGOrYsqbhnbol7wm/8HN9a6Kn9d8W7d5ma3phzPXupj9k/mf5d69f4+nvW35e6tKjfburatp+euilm84OGAPrFEPo3DH38EAbenSPDj4gf3r9n576023nVmdDr1ve98uXr/xo6k//MXj5v5//5EpL7q4XVy2+YKv5lY0Rm2TKmr/1bN2HPri7tvu57bGLGq3ykv8LfOvGs7r+YPruiq5HvYbRler6zuwP7lp/2T88tvWOhu9+Y1en4dyoM/zLr7Z88B37nluZXDkgdWyvarvMsOjReOK1vT/YdcO/Ng4jrP71/Exld/pqZ+eO7T32NUv21C5f/JLhofjIW54bqvpIpWfwb3/Hxe3PVOMKw+Fn/b11iy8KfsZw7qe3+mwBc+wqs+8h/RLJMFgdClqDFeuzzr3r4yNbjVhner/z0337P6ycm1o++qcfBd63Zvkhh1/6we/OXtmic10x9JbvHOjcuOqJTKXt70fPe6sd6k7Dyqr7us76eOaldMue2z93de07Fl2d3eZqlL/990ve7dzzy45V37383kfDlz8Ucb+tcXh15c9+lnnvpbsNu1LS9e2HX3JZU/DZolZPtfpkHGGYbxxJPtFecU6yf/3RcPdSx2HZ4B5KJ5xmm3UgNZRdVOvbG98Qg8lvzbQGpFb37oPq0qXDo1LtkSNoCcDlUM2mhGre+3zbuc09w5k37Dz42Qv/cV1HpLXyqd1N5z40UH3bT/9n/wfOvuTx0UeWXz0aNG93tmw2b2vpXxS/+qlX9l/S4hqodOp0P2tLfvCIPOhXc62Vf//tj5z/MgS7vEQ6y7/rHes7dpkXDcTqHt+9fGnTnrNc0oMB57pRxxLXwC97Vn9x5Y8f6T33qZ0tV7y9b98rtsaAJeZT9sU8H2z1P271+lXT0CHbqtcP+F7OLDFY7V/reuh1baaNFefpA09t06PGvPK89L5uF3qwcnXQdl/yvPP6ft6ycl1Lz5H9btTZ3+9+aMRntkejZ7fsdmZ6XpHe7tNb/cqI7dlL6s5+enDveU9fG1us358bbl9R3fWdgYsuqN+f7ap8Yl/LpSu7o+5E32DzGmPfw/cte82qQ88qpg053YUXj6kO3x/1y/7rxf965tLLDF31zn0vJ19z1pNJ9fyca1X0wP7lwyvHhuL2WuPgzh0tN5wdiqmuZ+Qxk2JvahzdqQ5ufrktc679b/3KFR3ZNUE5cvneR+5edb3TOvSmhC+0tzKw0tqSGHslK69OvLyj2fPm7X987pxLq9D9o6EPfVH6TH94jW6Rt63rr//X/DFnOLj4d79YVIG/oMa8+a2V9tT5D/5cMbh7U4vVS7PLf/v86Pm3Olx/6n7/ML63fFnyiC/r3tp4/ZuDvzTuTAXMy932/b5NtnhX1tiR053nqfrVkM+PUTNwGH290N2A3APQVeLIwPx8TbTV55+qOFpuW40ju9FWj7HCbwkDbVcg+RgALHkrsofR9RI2XwlDEjkrJCNGX4b1cuy21yQ6h69oQqS95shw4FDLuxzRvfWXjVkU855XrBs29KS3qCOrbsjUpdr+fpexK+W76J89j/xEWgnbUVOq0d2/9uwVLz86sm559Z79R89uScDkrejfgXOWD/e++YauafaeFgaWgF5wokOzLFq1gPiSc6jRNw+MTmTn43mK91rL1MyFlEWaEq6oc1he7rRJnoR5/Kc3ZjoTzvo8OJGTPC9vh0ly8/t0J/61Ii3skXN0DM3Nx11ZjE46Nv8WoKhvcgswc6zqHQZtPFI6/9U5h4cUNs7fkkbGln8mOTbhizhhghFIA3HVgcApGnqV57YiaYAlAb8MK5AGHAn4AVhglWBJICLG9Yl1iMKwOse/W41AXIVOgh6wZxBIAzJ0gCuBoIxc4S5HAkEZ+vwh6yxRC9Jp6NJKBeKAO2OVInG/C+4sAAQMVk9AgTGnWpA0wJxByABXwohUGiYoMhTAlYBqNEoxK3IRGHOK3asL+EwWb0DJuBGHTvXnTB4oMOYKrVIrFAXGnGI3WkJW5NJAPOLWOaIAcopdZ4nm/Dm4rZDS4pnTqi3f9ksD9gwARA35/4QMcCV0yOYUuzicnGoxSrG0vwLuDACdpOQidqRV6AqDz8T+RypghM4SzQXtqCg0jEMGWArNFUdCh2wuYocRUJA/qwCiBqMjlN8lpWgWeA7wJADAb4FRAgB7BiEDcmHonLAA5gwCcRidSCdglPMvOUt+4KYO2Rz0CMpwJRCRdY6oOF3elOIzOgDkz784D5aEDlnxR/FmAz6rQ5wHKHL+LIk3iD2DgGH8tW0snMCoAcaiywEAUhqKLG6pkCIhv1vnieYidjgSAPJ3iVdmRIYRiIeNHjWtVOSfRLvLnslfXIgXqsgpxc6IIxXbRA1wJKDI2qmGIiMO6AA9kAZ0QC4BqwwA8QTcxvw+izgzBxiBbNHGk27U/mRZIKeK8d/iLOe3ROH1kEvAKE6aCkiwAvEw4MinlPltCl8cRglpFNp7CnRy4dkAhCEGMhqBdGGWtE7O/y6gsA+JwitGpKAKdDJy6gxTYunMwObfAscW4ELT3zOh+WcCjjnY3ARYAEmPcIXOiJycQqiEcdg5j1GPrBW5mD//JPAg7ndDB68aCKkAkLG6jZYQgDRMBr+CorwhA70BWZMHcb9ovTi89qAv6q7wBAwB+FQ9jE6kAXfGEIhkoAeccGe84Ygvq4fRaXSEjP5c3O0A4I1HfEkLjDLSKoySIR3Ib6+NsXJnDIEIAJMHccXtTQZCWVSYkbEhAqPOnwZQYQcS8GX1AKyebMoPyaNL+yUDshlYYJURD3v12VAWFXaoUUhm+NJu6KFzRHX+dAYuK4Jxj8WIVNpfAYTzzQK31RuOqFkxOshtQCADPdxWAGKXxHnIWN2Ihw3IZuDyIKh4EPdbjJ5U2i/B6kRchVUyWkJp1SYeJXY4FEXOY9T50xUS8iccegBGj+qM5ZCBL6v36rMAfFk9dE7kEkDagGwGeq8+m6hAXHGLxrA3pviSekOhV6jCjFCysG/QQ+c05AIAMnADEEdhQDZ/0nRu0YTTjghWJ4DCEekNyGbcjnybzW1FIC4eG4oiY3QjHdZ+b86Tb+gmTIj79VZkU4DNgxAsBr+Sgd4rZRNuxP16A7I5jzEXtFe4AiHVIV4kOo/OnUpnTIj58zsv/ugpf/7fCjMCNmPOn4PV6U0EfGqhP9djhD8NnRO5wmsGYcABq+TVBUJRVEjwGdxIqwYEMx6LN6X4km6tqalDNufXixeJB1kAfo8F/rTOo8tF7EiH4bZ60xGfyeLxK0GPMQc9FBnxMNxWRA1Ih40eNe2XAMDo9KQDfqsb8TAA8dfXeWLuWFr8RXQeXU6xi3u99qwvmn/75Js6VifiYXiM8CPf4BaHBuTfUEgAaQ+yYSADfYUnG8q/AZ3wJMRJ8EqBTAUKtwNuKwJ6rz4IQ77fXnvnes1ZNQlJQsBt1COb9jvzb0C3wyjFnKmcL+dGHPl3otVpjQficOX75YxOGOFNBHwGN5C/bqqwBEKKW5xVROT8e9nq9CYDPrPbmw349G7EE7DKVksg5Yd4cVa4AoaQ9iJXoZOMrpAzloMRhfNTRDsh40enXY06gAisTihALgyrM7/z4oUhrgrF9SAiHmT9RnehJRyB0Yl0OP8kOmli49MBd7bka95pl/FYmOa0q8ZpOqy1d5x43U7BFuBCxgCYiIiI6NWFzT8iIiKiVxu2AImIiIheVSRJ0p3ufSAiIiIiIiIiIiIiIiIiovnBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCIiIiIiIiIiIiIiIiKiMsEAmIiIiIiIiIiIiIiIiIioTDAAJiIiIiIiIiIiIiIiIiIqEwyAiYiIiIiIiIiIiIiIiIjKBANgIiIiIiIiIiIiIiIiIqIywQCYiIiIiIiIiIiIiIiIiKhMMAAmIiIiIiIiIiIiIiIiIioTDICJiIiIiIiIiIiIiIiIiMoEA2AiIiIiIiIiIiIiIiIiojLBAJiIiIiIiIiIiIiIiIiIqEwwACYiIiIiIiIiIiIiIiIiKhMMgImIiIiIiIiIiIiIiIiIygQDYCKiheuWW2654YYbpt5eVVX1gx/8QNtGkqR3vvOdMz1Jb2+vXq+XJGnv3r0na0en09raKk3x6U9/etotv/GNb5zKfSMiIiI6ediEK4U4A4LVam1ubn7DG96wZcuWE9j3GR0+fFiSpF27dp2MJyciIiI6cWxAEtG8M5zuHSAiohNls9nuu+++b33rW16vd+q9P/3pT2VZjsVip3ivHnjggWQyCeCtb33rpk2b3vve9wKor68/xbtBREREtDCxCXfxxRd/7Wtfy+VysVhsdHT0j3/84zXXXPPggw9eddVVJ+PXEREREZ3p2IAkotIxACYiOuNdeuml27dv/9WvfnXbbbdNuiuTyfz85z//h3/4h//93/89xXu1du1a8R9ZlhsbG88///yZtvzVr37V1NR0inaLiIiIaGFgE87j8RQ//z/8wz+87W1vu+22207xnBUiIiKiMwUbkERUOpaAJiI64xmNxn/8x3/80Y9+pKrqpLv+/Oc/+3y+d73rXadlx0p04MCBsbGx070XRERERKcUm3BT3Xjjjfv37z/101aIiIiIzghsQBJR6RgAExGVg1tvvfXQoUNbt26ddPuPf/zjG2+8saqqatLtqqr+5Cc/ufzyy71eb01NzXXXXffYY49Nfdq77rrrqquuqq6ubm5uvvHGGx9++OG9e/dKknTgwAEU1u34/e9/f+mll1ZUVKxbt+6Tn/ykoijH3FvxwO985zvnnHPOtm3bvvKVrzz55JPaXf/93/99xx13LF++vLKy8o1vfOO2bdsAbNu27aabbqqtrV28ePF73vMev99fymk57meb/cDFCfzRj350+eWXezyeFStWvOc97zly5MgjjzxyxRVXlLJjRERERGATbgq3253L5YLB4NRfV/rh33vvva973etqa2ubmppuuummabeZ6QDZdCQiIqIFjg3ImRzzSCftzNTWJoD/9//+3+bNm6uqqhYtWnTttddu2bJllmcocceITheWgCYiKgetra2bN28W/UrajV1dXY888sgTTzwxaeNkMnnVVVd1dHR86EMf+vjHPx6Pxx9//PFrr7323/7t3/7zP/9TbJPNZt/0pjc99NBD//Iv//LP//zP2Wz2iSeeuO66697ylrcUP9XPfvazSCTy0Y9+9FOf+tTevXv/+7//e8uWLU899ZTNZpt9h3/1q1/5/f5/+Zd/Wb58+aS7vva1ry1evPjzn/+8xWL5zW9+s2nTpq9//eu33XbbLbfc8uMf/zgQCHzjG98455xzXn75ZZfLdcwzM9dnK+XA0+n0Nddc89xzz33kIx/56Ec/GolE/vKXv7zmNa+5+eabn3nmmWPuEhEREZHAJtwkTz75pNvtbmhomPrrSjn8XC530003Pfjgg+9///tvvfXWdDq9devWa6655pZbbpn9uI77KNh0JCIiolOMDchplXKkU3em+MdMJnPDDTc89thjH/rQhz7wgQ8oivLwww9fddVVH//4x7/2ta+VcjhEC5FKREQL0jve8Y43vvGNU2+vrKz8/ve/P2mb3/3ud0ajcXBwUNvs9ttvX758uaqq+/fvB7Bnzx5x+4c//OGlS5cODw8XP+e2bdtkWb7vvvvEj1/+8pdtNtvLL79cvM3TTz9tMpkA7N+/X1XVlpaWqqqqgYEBbYPe3l6r1fqNb3yj+FHLly+/4447im9paWmprq4eGxvTfvz617+u/b+pqSmRSGgb33zzzQBuu+027ZZwOOx2u7/85S9Pf9Ym/qK5PlspB/75z3/e6/V2dXUVb3P33XcDsFgsx9wrotOLzT8iopONTTi1hEZX8VlKpVL9/f3f+973jEbjf/zHf0z760o5/K985Ss2m23nzp3F2zz77LMWiwXApNunYtORyhhbgERECxwbkOqJ9QGWcqRTd6b4xzvuuMPpdHZ0dBQ/w/333w/gL3/5y7QPIVrIwBLQRERl4w1veENlZeXPf/5z8WM6nf7FL37xvve9b9Jm6XT6Jz/5yZ133lldXV18+4UXXnjrrbd+61vfAqCq6te+9rU77rhj7dq1xdts3LjxYx/7WPEtb3nLW+rq6rQfFy1adMMNNzz++OPH3Nubb77Z6/VOe9cNN9wgOumEm266CcA73vEO7RaHw3H55Zfv2rXrmL9lrs9WyoGrqvrNb37zC1/4QktLS/E2b3vb297whjeUsktEREREmldzE+4Pf/iDJEmSJJlMpoaGhs997nMf//jHP//5z0/9dSUe/p133vn5z39+zZo1xdtccMEFn/vc5455aMdxFGw6EhER0Wnxam5ATquUI512Z7Qfc7ncN77xjS984QuT5vXeeOONb3zjG2d5BqKFjAEwEdHCJUlSLpebensul9PpJn+AGwyG97znPT/96U/FQ/7whz+EQqF3vvOdkzbbt2+foihvetObpCm++93vioU9Dh06FAqFNm3aNPVXX3nllcU/NjY2TtqgqalpcHDwmIc29YEareifIBYvqaysLL6xpqZmZGTkmL9lrs9WyoEfOnQoEolcfPHFU7e59NJLS9klIiIiKm9swqG0JtzFF1/83HPPPffccy+88EJHR8fw8PBXvvKV4lOk/boTPPyrr776mId2HEfBpiMRERHNFzYgcQJ9gKUc6bQ7o/3Y2dkZiUQuu+yyqU9+2WWXvfzyy6UcDtFCwzWAiYgWrsrKyiNHjky6MZvNBoPBSSPahPe+973/9V//9fDDD7/uda/78Y9/fNNNN3k8nknbiLbUXXfdtWTJkpl+r2g+ZjKZme7SGI3GSRvYbLZ4PD7jIRVMbbzOdJf4cdobSzGnZyvlwLPZ7EzbKIpS4l4RERFRGWMTDqU14Twez/nnn1/Kryvl8EWVs2l7TtVC/dtjYtORiIiITgs2IHECfYClHOksvxGAwWDADI1Go9EoWnSl7w/RAsEAmIho4WpoaDhw4EA6nS5uY+3fvz+Xy00aIie0tLRs3rz5Rz/60ZIlSx5//PFt27ZN3aa+vr62tnZ4ePjtb3/7pLt++MMfilou7e3tTqdzy5Yt55xzzqRtHnvssRM9qgWslANvb293OBxbt25dt27dpG1KqXtDREREZY9NuHlXyuEvXbrU5XI98sgj69evn7TNww8/fDL2ik1HIiIimi9sQJ6IUo50dosXL3Y6nU888cSkUtgAnnrqqak3Ep0ROFqBiGjhevOb3xwMBn/84x9rt6iq+tWvfrWtrW3Dhg3TPuR973vfX/7yl8997nMrV6587WtfO+02X/ziFz//+c+/8sorxTfed999H/zgB8WIOUmSbrvtti996UvFFU4APP/889/+9rdP8KAWslIOXKfT3XbbbV/4whc6OzuLt/nlL3+5devWU7arREREtGCxCXcylHL4t99++5e+9KUdO3YUb/Pyyy9/+ctfPhm7xKYjERERzRc2IE/QMY90dpIkfeITn/jCF75w8ODB4tsff/zxe+6554Mf/OA87y7RKcEZwEREC1dzc/MXv/jFj3zkI/v27bvqqqui0ehvfvObJ5544t5779Xr9dM+5Nprr62urr733nu/+93vzvS0t9566zPPPHPeeed98IMfvOSSS1RVfeCBB+66665vfetbWnvxM5/5zPbt2y+44IIPfOADF198sV6vf+KJJ77//e/fcsstP//5z0/K0S4MpRz4Zz/72RdeeGH9+vUf+9jHLrjgglgs9uCDD95///0f+tCHfv3rX5/e/SciIqLTjk24k6GUw//EJz6xc+dO7fCNRuOTTz75ve9979Zbb/3BD35wMvaKTUciIiKaF2xAnqBSjnR2n/70p59//vm1a9d+5CMfueCCC1RVfeyxx374wx++733vu/HGG0/2/hOdDAyAiYgWtE9/+tNnn332D3/4w/e+970Oh+O88857/vnnV61aNdP2BoPhn/7pn77xjW/ccsstszztL37xi+uuu+6nP/3pr3/9a71ev379+qeffrp4RKHBYPjTn/70f//3f7/61a/uueceAOedd9699967Zs2affv2WSyWeTzGBaWUAzcYDH/9619/8Ytf3HXXXf/zP/+jquprXvOap59+euvWrTU1Naf7CIiIiOj0YxPuZDjm4et0ut/85jd//OMff/KTn/zud79LpVIXXHDBb37zm/POO2/79u1Wq3Xed4lNRyIiIpovbECeoGMe6ez0ev2f//znu++++9e//vUvfvGLXC63du3a+++///rrrz+pu0108kiYYWlrIiIimtY73/lOh8MxdR7JddddB+DPf/7z6dgpolJJEpt/REREpw6bjrQQsAVIRERE9KoiSRLXACY6Rbq7u6+//nqPx9PW1vbv//7vyWTymA8JBoM1NTX/9m//dgp2rww8//zzUsHdd9896d5HH3309a9/fV1dXVNT01vf+tZnnnmmxKe95ZZbJEl68skn53t/y8qWLVsuu+wyl8u1aNGit7zlLYcPH55pyzmdz1I2lmY258Mo2c033/yjH/3oj3/8Y/GNP//5zx988MF3v/vdJ+/3TjX7B8vIyMi73vWuurq6urq6W265ZXh4+Lifiqa6/vrrJ73kfvWrX2n3lv6mAE8+Ufli8+8UYAvwdDmNzb8TUR5NR7YATyO2AInomGZvnEzCj4ITN6f23pw+qOmY5vRqF07keue0NOQWpjl9dBzHn4lKVOLFy2m8zGcJaKJTIZlMbtq0qbW19e677x4aGvr0pz8dCAR++MMfzv6oz372swaD4Y477jgl+3jGW7Vq1XPPPQfgsssum3TXj370o3/9139929ve9u1vf1uv1z/wwAOXX375XXfd9eY3v3n25/zb3/7G78Vjuv/++9/61rfefPPNH/zgB3O53I9//OPVq1e/+OKLK1eunLTlnM5niRtHo9E57/EJu+aaaz784Q/fcMMN73vf+zZv3my1Wrdu3fqtb33rH//xH09lWZjZP1hSqdTll19ut9t/+MMf5nK5//zP/7zyyit37NhhNBrn+lQ0rb179376058u/osvXrxY/Kf0NwV48onKF5t/pwZbgKfF6W3+nYgyaDqyBXh6sQVIRMc0S+NkEn4UnLg5tffm9EFNpSj91a45keud09KQW4Dm+tFxHH8mKkXpFy+n+TJfJaKT7P7777fZbKFQSPx4zz336PX6RCIxy0O2b9+u0+nuvffeU7KDZcVut991113aj4FAoKKi4rOf/WzxNp/61KdcLlckEpnlecLh8KJFi9773vcCeOKJJ07W7p7hUqlUQ0PDP//zP2u35HK5yy+//Iorrpi05ZzO58I/+blc7r777rvwwgu9Xm91dfU111zz05/+9BTvw+wfLPfcc4/NZvP7/eLHgYEBg8HwwAMPHMdT0VTxeFyn023dunXqXaW/KYRTf/LZ/CM6Ndj8O8XYAjxlXrXNvxMxj01HtgBPI7YAiWhOJjVOpuLn8AmaU3tvrh/UNCfHfLULvN6ZF8f90VHin4lKUfrFy2l82QNgCWiiU0GW5Y9//ONOp1P8uGzZsmw2Ozg4ONP2qqp+4AMfuPzyy9/ylrecqn0sW0899VQsFvvEJz5RfOMnP/nJYDD4xBNPzPLA22+/3Wg0fuELXzi5+3eG2759e39//yc/+UntFkmSbr311qeffjqbzRZvOafzufBPviRJN91007Zt28bGxoaHh//yl7+Ir/xTafYPlo6OjtbWVrfbLX6sq6trbGzs7Ow8jqeiqTo6OnK53PLly6feVfqbQuDJJypXbP6dXmwBnjyv2ubfiZjHpiNbgKcRW4BE9P/bu7fYqqp1D+BjUdrDpUCpSAtaqBoVxRPj5fgKmnrZCIFIwD7ZGB94IAENJhqJsJWYoKIhKIRggESMF0CM4QGJl/jAw1bkfjHGcqBQsJwQsC2UCkrPQ/du6lrQdraza5XZ3++tY801MhmZ8+M/1tfOFS+loIci5b2ohZrY2e/ERenoC7q4ecn5Ze8R0JANU6ZMmTJlStuPX3755ciRI8vLy691/Nq1a/fs2fPDDz8cOXLk1ltv7YffZBCjEydOjB49uu0/xVbFxcUjRozo4AuxduzYsXr16u3btw8dOrT3z/E6lp+fP2PGjLKysvaDgwYNam5ubmxsLCoqah2JtJ4Wv4s6Lizjx4+vrq6uq6srLS0NIZw8ebK2tnbixIndmIpMhw4dGjFiRElJyfHjx1OpVPtboIs3RRuLD0kl/uWWBNh7xL/ckgBzSAIE4qUU9FCkvBe1UBM7+524KB051/XNS84vew1gyJ7m5uYlS5YcOHBg27ZtmzZtutYNf+bMmZdffjmVSj300EOXL18uKipavHjx/Pnz/b/YPQ0NDcOGDcscHzFixLW+OqK5ufm5556rqqqqqKior6/v5RO8vj3wwANffPFF2uDWrVtvueWWtvQcaT0tflTXKixPP/300qVLH3/88VdeeSUvL2/JkiUPPvjgpEmTujEVmQ4dOpRKpe68885ff/01hFBaWrp06dKqqqrQtZsik8WHpBL/ckUC7D3iX18gAeaEBAj0BqWg2yLlve4VauJivxM7pSNXur556QuXvUdAQ/ZcuXLl4MGDR48eDSHs37//WoctWLDg4sWLb7755rFjx06dOvX6668vXLhw8eLFWTzTpLlqVR0w4JoF8LXXXquvr1+2bFlvnlRifffdd+vXr1+0aFHbSKT1tPhRXauwDBky5Pnnn9+/f39lZeWsWbMOHDgwd+7cwsLCbkxFpsOHD1+4cGHOnDk1NTW1tbUvvPDCnDlz3nnnnasenHlTZLL4kFTiXw5JgFkj/mWfBJgTEiDQG5SCnoia99rrSqEmLvY7sVM6cqXrm5e+ctln/8uHoZ/7/PPPCwoKVqxYkfnSL7/8kkqlNmzY0H5w/fr1+fn5R48ezdL5XefSvtB+6dKlEyZMyDysvLx8+fLlmeO7d+8eOHDgxo0bW3/8/fffQ2ff5U6bn376qaio6Nlnn20bibSeFr8n0grLihUrBg0atGLFirq6urq6umXLlhUUFKxcubIbU5Fp165dO3bsaD/y3nvvDR48+NSpU2lHZt4UHcvC4ot/kBPiX2+TAHNF/MstCTCbJEAgkrRw0il1OKqoea+9qIWajnV8tdvv9Kqul46oRYlMXd+89IXLXgMY4ldfX3+snYaGhqseNm/evPLy8szxNWvWFBUVpQ3+9ddfQ4YMUaC7KPPjvzvuuCPzsHHjxmXGwcuXL993333Tpk1rG/EhVNe1puennnrqzz//bB2JtJ4Wv+faCsuFCxeGDx/+1ltvtX910aJFI0eObG5ujjQVXXTp0qWCgoJNmza1H8y8Kbqitxdf/IPYiX99gQSYE+JfXyAB5pAECP1ZpwmwG70WdTiSSHmvve4VajrQ8dVuv9Pbulg6NIB7KNLmpS9c9sEjoCF2H3zwQXk769atO3jw4Pvvv5922OTJk48dO3bp0qW08ePHj99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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons\n", + "\n", + "pv.start_xvfb()\n", + "\n", + "plotter = plot_field_comparisons(\n", + " y_slice,\n", + " true_fields=[\"pMeanTrim\", \"UMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"UMeanTrimPred\"],\n", + " plot_vector_components=False,\n", + " view=\"xz\",\n", + " dtype=\"point\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[2560, 1280],\n", + ")\n", + "\n", + "plotter.screenshot(\"./sample_202_volume_y_slice_comparison.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_volume_y_slice_comparison.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be2cd971-e4dd-4f8b-88cd-a7c2a40cfe64", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:19.135974Z", + "iopub.status.busy": "2026-04-10T18:20:19.135883Z", + "iopub.status.idle": "2026-04-10T18:20:19.822086Z", + "shell.execute_reply": "2026-04-10T18:20:19.821531Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotter = plot_field_comparisons(\n", + " x_4_slice,\n", + " true_fields=[\"pMeanTrim\", \"UMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"UMeanTrimPred\"],\n", + " plot_vector_components=False,\n", + " view=\"yz\",\n", + " dtype=\"point\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[2560, 1280],\n", + ")\n", + "\n", + "plotter.screenshot(\"./sample_202_volume_x_4_slice_comparison.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_volume_x_4_slice_comparison.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ea38f899-c25e-4f82-aa57-d0180b51def9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:19.825735Z", + "iopub.status.busy": "2026-04-10T18:20:19.825640Z", + "iopub.status.idle": "2026-04-10T18:20:20.974855Z", + "shell.execute_reply": "2026-04-10T18:20:20.974310Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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3va/A+NredSXpZz/7mXkybwnI7Ozs2WefbV/4kpe8xFWmYAWDwb/6q78yl6IkVVRU/PKXv8y2MRMTE7t27bKr3bt3r7NZnMt//dd/dXR0mCV/53d+56Mf/ah5nLsExGhra7vjjjsyrvbZZ5+196lJOvPMM+fn57Ntg1FcCYi5HObxeN773vcGg8EcS+YoATGuv/76cDic/na/+c1v7CXd3bt3X3/99eax3+//6le/mnELb7rpJvt3d+vWraOjo7n3HQCAk5P5W+l6kuGfwfAv25Zs3uFfcRXAzjmbb7vttiKOibFK59709PQZZ5xhV3vWWWfl+Ci/973v2XPPnH72LQo+HgCATc/88s+xgK1dO+WUU8wfF9O2xBgYGHjFK15h/5p89KMfTV+DqxoyHA43NDRICgQCrvrCubm55uZmSZWVlePj4+mvdXnmmWfq6ursX7277rrL2Z73iSee2Ldvn/luQ0ND+tj1ox/96AUXXPDv//7vpnmM00MPPXTZZZfZIUe2qRzswTF3el1xxRX79++33z1+/PhrXvMae3AKmd+k6ApgUxL60pe+9IknnrDfnZqaGhkZsV8WWElp9uWaa65xHpZf//rX27ZtMwtcfPHF//iP/yips7PzzjvvtI1zx8fH7f6WlZXZaSbWBeftRj5vY0s5dTfCeVtIBfDCwkJ9fb1Z7KUvfWlxO7vMcyMWi33jG9+wn8UFF1zw5JNP2m8dPHjwoosusj8URrbdAVDaMv8KSL9MU1ZW9ulPfzrv6g4ePHjaaafZV9muCy4Zr8J8+MMfNk9u2bIlFAoVsvVvfvObzUvOPPNM+2TeK4B/8id/Yrfw+uuvn5uby/0ud9xxR0VFhVl+165d2S7uXHnllXa1V155Zd5dOH78uO0XYX/j570C2NjYeODAgRyrnZ+fv/rqq+3yX/ziF3NvRnFXACXV19fffffdeZfMfQXwxS9+cY55F5555hnXvVEej+db3/pWjo103p33sY99LOeuAwBwkjJ/KF1PMvxzYviXcUs26fCvuADYea/9Qw89lG13chwTY5XOvde+9rV2ta9+9aszBupOo6Oj6VMvEwADwEnF/PLPsYCNLiT19PRMTEy4Fpifn7dZWlVV1bFjx1wLpIdh9u+gK9Gxf2evueaabK91slnIpZdemrF369zcnM3D3vrWt7q+m+3vqbGwsGCnVMh2V6Lz4FxzzTXps8POzc3ZeSve9KY35Xg7o+gAWNIVV1yRe0RRYJAm6c1vfnP6Avfcc49dwOfztbW1HTlyxLVMJBKx/wT427/927zbv3o4bzfyeRtbyqm7Ec7bQgLg22+/3b7Rhz70oeJ2dpnnxuzsbEtLi/nuueeem97ePBKJuAb/2bYEQGnL/Csg/Qqg6QFYiKGhIdv3rKWlJeOFsIxXYY4cOWJbT3z+85/P+0bHjx+3rRv+4z/+wz6f+wrgz3/+c7tTz3ve8yKRSCE79fGPf9y+6u1vf3v6As4rRC996UvzFl4YwWDQtGWw8l4BvOmmm/Kudnp62t70dP755+deuOgrgP/0T/9UyJK5rwDawp1s/uIv/sK5/Mte9rLcy8/Pz7e3t5uFzzjjjNwLAwBwcjJ/KF1PMvxzYfiXviWbdPhXXABsM/WmpibX/fgFHpPYqp17tu+0pMsuuyz9Wl5Gs7OzL3jBC5wbTwAMACcV88s/xwLO6CLb3+tDhw7ZW7XM1PJO6WGY/VN40UUXOZe0dXg/+MEPsr3W+vWvf22+VV9f76xzdRkcHDQDy6qqqsnJyRx7ms5Oz7F79+6MC9iDU19fnx4xGl/60pfMMqeffnredyw6APb5fIcOHcq9cIFBWmNjY7aZX50TrGYbi/793/+9WcBVBLnGOG838nkbW8qpuxHO27wB8LFjx5zzTDvLzQvf2eWfG9/85jfNGjwezyOPPJLx5QcOHLD/1hYBMHCyUt45gCW95jWv+T//5//kXcw49dRTv/KVr5jHw8PDn/vc5wp8YVtbm72T3U7tlsMXv/jF2dlZSY2Nja9//esLfJe/+7u/Mw8CgcDNN9+crVO/y/vf//4rrrjCvm/6NHW2fqWlpeWb3/ymnTAst0Ag8J3vfMf5uzi37du3O8s7sqmpqXn3u99tHt9///1HjhwpcP2F6+rqsvP2Fe3iiy++8MILcy/zvve9z/kZ/emf/mnu5cvLy9/2treZx729vXaqDAAAsCQM/xj+uZxUw7+77rrr29/+tnn8yle+0rZldsl7TFbp3Pv//r//zzxobW298cYbs22eS2Vl5Xe/+90tW7YUsjAA4GR25plnZvt7vX379le/+tXmcd7p6iVdcMEFZr6Me++9d2hoyDw5NjZ22223SWppabn88svzruTrX/+6eXDddddt3bo122Lt7e2m4m12dvYXv/hF3tU6nXbaaaZX6tNPPz0xMZFjyWuvvdZ2f3V50YteZB4888wzdorQFfeKV7xi+/btK7Kqa6+9tqamJuO37E1j9fX12caiv/u7v2se7N+/f0W2Z5k4b3Msue7nrVbu1F2X83Zubq6/v/9zn/vc2WefbeaZlvTa177WOdWLU+6dXf65ceutt5oHL37xi88888yML9+9e3chJyqAkpfnekFFRcUnPvGJJa3x937v9+wfj0996lOFv/CGG24wD5588smf/OQnOZacn5+31xbf+ta3+v3+Qtb/xBNP3HXXXebxhz/8YedtQbl5PJ4vf/nLphvbwsLCF77wBed3f/7znz/00EPm8Yc+9KEcv7jT9fT02E6Geb3lLW+xHeFyc07h8NhjjxW+PQW65pprCryAlUPey3+SWltb7SxlPp/PXgvL4YUvfKF5EIvFent7l7OFAACcnBj+ieFfmpNn+Pftb3/bHkyfz/ehD30o25K5j8kqnXu/+MUvfvOb35jHH/zgB50ze+W1Y8eO97///YUvDwA4Ob3sZS/L8V37p/nRRx+dm5vLu7Y//MM/lBRzzFh58803m5zpDW94QyHjnP/93/81Dy655JLcS9ohhB2nuQwNDX35y19+97vf/frXv/6Vr3zlyxyCwaDZztw30l1wwQXZvrVjxw7zIBaLTU5O5t7Uotkh9/Kdd9552b7V1tZmHjz/+c/P9hnZZXJHj2uG8zbHW6z7eauVO3XX5rwdHBz0OPh8vq6urne+8532OJ999tlf/vKXs708984u/9ywNcQFnvYATmZ5/mK9+tWvds7rVqD3vve99913n6RDhw49/vjj9rdVbueff/7ZZ59tfqN9+tOffulLX5ptyVtvvfXQoUOSysrK3vGOdxS4VXfccYd54PF4rr322gJfZbS2tl5yySV33nmnpNtuu832i5Bkm7DV19c7Z/kq0Hvf+17XZZ1sst1VlK6rq6u2ttYUQOzfvz/3H4MinH766ctfibNjRg67d+9+8MEHJXV0dBQywLL9DyWNjY0VvXkAAJy0GP4ZDP+cSnX4t7CwMDs7Ozo6OjAw8Ktf/erb3/72Aw88YL/7oQ99KEdwm/uYrNK5Z1db3Ln3f/7P//mHf/gH2uQAAHLYu3dvId+NRCJDQ0N5B41vfOMb/+Zv/iYWi33961//q7/6K0lf+9rXzLdMxpZbzDH/wic+8Qk78UdGtiPu8ePHXd86ePDgu9/97jvvvDNm58PLIncG1tramu1btsmwpJmZmdzvUrQiRunZ2DlE09l9KWSZ1dvZJeG8zfHddT9vtXKn7rqft7W1te94xzv+7u/+zk5OlC7Hzi7/3Jifnx8YGDCPCzztAZzM8lxSufLKK4tY6SWXXOLz+cyNUXfeeWeBVwAl3XDDDaYk4gc/+EFfX1+2i0S2SeCVV17Z0dFR4MrNNRRJv/u7v2vvbyrcH/zBH5g1PP7448ePH7elHj/60Y/Mg8suuyzHr/5sdu3atWvXrqeffjrvkvYepUJ0dXU9+uijkkZHR5e6SXmt5RXAuro68yBbrxIX5xVArm0BAFAEhn8Wwz+rBIZ/d999t8fjKWSFxvve976//du/zbFA7mOySueeXe2ll15axLlXXV192WWXffe7313qCwEAJ4+mpqYCv1tI6efOnTv37dt3zz33HDx48Je//GVDQ4OpXTvzzDOzdS51mpycXFhYMI9/+ctf5l3evsr55X333feyl70sFAoV8tpIJJLjuwU2RMkb1xWttrZ2pVZVyL4svwFMusceeyxHSxI71Fkqztsc313381Yrd+quzXlbVVVl5ySW5PV66+vrd+zY8YIXvODSSy/Nuy85Flj+ueE8gQs/7QGctPIEwIU0aksXCASe//zn33///ZIKubZlXXvtte973/tOnDixuLj42c9+9p/+6Z/Sl3n44YdNfYmkJU1F9vjjj5sHzhZ5hbvqqqve9ra3mZnnDhw4YK/C2B0supfFi170okKOkvOerLzsX5oCBwpLkuNGqsIV2LDOXtgq8I93IBCorKw0455CmroAAAAXhn8Wwz/rpBr+dXd3//M//3PeKurcx2S1z73ifk4lXXTRRQTAAIAccv/9raystI9zZ07Wm970pnvuuUfS1772tYaGBvtkIa8tblSzuLhoH09PT7/2ta8166mpqXnzm9982WWX7dmzp7W11e/3214jHR0dg4ODRbzXGisryzOX38Y3NjZmb6ZcQZy3G9zmOnVbWlqKvhdBOXd2+eeG8wQu/LQHcNLK88s3R4+I3Oyd+MeOHSv8VVVVVW9961vN4y996UtmJgMXW//R09OTo0+gy/z8vC2GeN/73udZurq6OnMJRpKd7z0Sidh7cIqeyr7A2g6v11v4Ou3EeKvRvmNJxRPLXIldrPA3XZHNAwDgpMXwj+FfulId/pWXl9fW1jY3N59xxhlXXXXVX//1X//617/u7e0tpId2jjddpXNvdnZ2amrKPHbWPS9JEeXIAICTSu5uas7v2qYdub3mNa8JBAKSbrnlFjOjakVFxetf//pCXutsB3L//ffHCvPVr37VvurLX/7y8PCwpIaGht/85jef/vSnX/nKV+7atau2ttY50wQ95DY7zltsCss/N5wncOGnPYCTVq4AuLq6uuieCY2NjebBUnvQveMd7ygvL5c0OTn59a9/3fXd48ePf/Ob3zSPl1T/ceLEiRVsZGGvvDj3rsABRLoCu9stiTmGSr1FCAAAIDeGf9kw/NvsLrnkkvSLKfPz81NTU8PDw0888cT3vve9j3zkI+eee+7y32uVzj3nDMdFn3tbtmxZgW0CAJSu/v7+HN+1tyVJam5uLmSFNTU1ph/G+Pj44cOHJV1++eUFNhepqamxDVFGRkYKeYnLXXfdZR782Z/92Z49ezIuEwqFCmkLjBWxb9++HEFX0avlvMWmsPxzo7a21t74W/hpD+CklSsAnpmZsTeeL5UtjFjqJYb29vZXvepV5vFnPvMZ19/+L3zhC6bRQX19/Rvf+MbCV2t/M64IW1fhvHh34sSJ4ta2GvO0AQAAFIHhXzYM/1C4VTr3nD9ZRZ9CnHsAgNzMXKd5v7t9+/YCgzSlNc4tsI+ucd5555kHZqqRpRoaGjIPctzj9ctf/pL75zY7zltsFss8NyQ973nPMw8KPO0BnMzytIA+cuRIces9evSoeVBEF0Fb2/HUU0/9+Mc/ts/Pz8//v//3/8zjN7/5zTU1NYWvs76+3l6Iuf322wtssJDN+9//frOqQCBg+oFIevbZZ5e6p4a5jwwAAGAjYPjH8A/LtErnnt/vt/cfHDp0qLhts9cTAQDI6Pbbb884JYdx8803mwdLmo3+oosuam9vN48bGhquvPLKwl9r52W48cYb5+bmCn+hUchLvvKVryx1tdhoOG+xWSzz3JD04he/2Dy49dZbo9FoxmUWFha+9a1vFbeFAEpJngD4Zz/7WRErjUQiDz74oHnc1dW11JdfdNFFe/fuNY/tlG+Svvvd75qLZR6P553vfOdSV2v/Zu/fv3+pr82hs7PTPLj33nuLW0NxBxkAAGA1MPzLi+Ef8lqlc++0004zD4o+9/7nf/5nxbYGAFCKpqenP/7xj2f81s033/zYY4+Zx9dff33h6/R4PIODg+bGpvHx8crKysJf+9a3vrW2tlbSs88++7d/+7eFv9Boa2szD+67776MC/z85z+3s41g8+K8xWaxzHNDjmL04eFh57+dnb74xS8ODAwUu40ASkeeAPj2228vYqX33HOP7VR2xRVXFLEGWwVyxx13PP300+ax/Y12xRVXnH766Utd52WXXWYeFLdTeVd777332gm6Cvf4448XXTsCAACw4hj+Fb5ahn/IZpXOPfvDdc8990xPTy/15dPT0wTAAIC8Pvaxj914442uJ++///4/+ZM/MY/POeecSy+9dG02ZsuWLX/zN39jN+wv//IvzeQg6UKh0Fe+8pWLL77Y+eS+ffvMg3/7t3979NFHXS/59a9//fu///v00S0NnLfYFJZ5bkh6znOec9VVV5nHH/rQh2677TbXAnffffef//mfr+hWA9is8gTAt956axEThv/zP/+zebBt27azzjqriM267rrrGhoaJMVisX/7t3+T9NBDD/3iF78w373hhhuKWOfv/d7vmQf33nvvwYMHl/TaWCx21VVXPe95z3ve855nm7AZL3/5y82DcDj8mc98Zqlb9clPfnKpLwEAAFg9DP8Mhn9YjlU69175yleaB8Wde//2b/+WozsiAACSXve61y0uLl533XWvetWrbrzxxp/97Gff+9733vrWt774xS829x5VVlZ+4QtfKCvLc0VxBb33ve+95pprzON//Md/7OzsfM973vPtb3/7pz/96T333PO9733v7//+76+88sqtW7f+0R/9UW9vr/O1f/Inf2LmZQgGgxdccMF73vOe22+//X//939vvvnm66677oILLjhx4sTll19+6qmnrtnuYDVw3mITWc65YXz605/esmWLpLm5uVe96lWve93rbrnllp/97Gff+c533vjGN15++eWzs7PXXnvtmu4VgA2pIve3FxYWPvjBD95yyy2Fr/EnP/mJnbnthhtu8Hg8RWxWIBB485vf/C//8i+SvvKVr3z0ox+19R+7du26/PLLi1jnxRdf/JznPOepp56KxWLvfve777jjjsJf+6Uvfem//uu/zOPPfvazzm/t27fPrFbSP/3TP11//fXbt28vcLWPPfbYTTfdVPhmAAAArDaGfwbDPyzHKp1755133u/+7u/+6le/kvTJT37yTW96U+Hn3uHDh7n5AACQ19vf/na/3//Vr371+9///ve//33Xd71e77e+9a3nPe95a7xVN910U1tb26c+9SlJR48etbce5tXW1vYf//Efb3zjGxcXF2dmZv75n//Z9dq9e/feeOONz3/+81d+o7GGOG+xuRR9bhg7duz44Q9/eOmll05PT8disW9961uuGX8vvfTSj3zkI3b2awAnrfz3PX3rW9/6whe+UODqjh07dt1115nHLS0txdVqGO985zvN1cPp6elPfOIT9hfWu971ruKuKpaXl3/kIx8xj3/4wx8WfvljcHDwgx/8oHm8b9++Cy64wLXav/u7vzOPx8fHX/e61xU4f3soFLr22mvn5+cL3AwAAIC1wfCP4R+WaZXOPUn/8A//YB6Mj49fffXV0Wi0kNXOz8//wR/8wdjYWIGbAQA4mX3lK1/5l3/5l61bt7qeNzchXXnllWu/SeXl5f/6r/96//33v/zlL6+oyFzN0tPT84EPfOCnP/2p6/nXv/71P/jBD0477TTX8zU1NTfccMOvf/3rpqamVdlorC3OW2wiyzk3jN/93d99+OGH7bwzVl1d3Yc+9KEf/vCH2VYL4KRS0C+Cd77znRUVFX/0R3+Ue7HBwcErrrji2LFj5st//dd/DQQCRW/ZaaeddsUVV5ib5f/+7//ePFlTU3P99dcXvc7XvOY1L3vZy+68805J73//+71e75/92Z/lfskTTzzxspe97MSJE5Jqa2szdlq7+uqrv/CFL/zkJz+R9POf//yVr3zld77zHTOdezYTExOvfOUr9+/fX/S+AAAArB6Gfwz/sEyrdO5dfPHFb3zjG7/xjW9Iuv/++1/xild897vfraury7HamZmZq6+++n//938llZWVMWMcACCvd7/73e985zvvvffeZ555Znx8vKWl5bzzzuvp6cnxkq9+9atf/epXi3u7Al973nnn3X777cFg8L777hsaGhodHS0rK6uvrz/ttNN+53d+p7W1NdsLr7jiigMHDvzqV796+OGHJycnGxsb29vb9+3bV11dbRYYGBjI8b65v2vFYrFCFpPU0dFR+MKFb4Bx7733LnNVH/7whz/84Q/nXmapu7A2OG+d1v28LXwbtDHO2+WcDFriz6lR9LlhnHbaaT/60Y/6+/t/9rOfHT16tLa2dufOnRdffLE5QzbmDymANZYnAL7uuuv+8z//c35+/i1vecsvfvGLf/iHf2hubk5fLBaLfeMb33jf+943MjJinnnve9+7/EbzN9xwg6tb2pve9KbcVzdy83g83/zmN1/wghc8/fTTphvbfffd93//7//t6OhIXzgSiXzlK1/54Ac/ODExIam8vPzmm2/eu3dvxtXecsst5557bn9/v6S77rrrBS94wac//elLL70042bcfffdf/qnf/r0009Lqquru/rqq7/0pS8VvVMAAAAriOEfwz+siFU69yR94QtfOHDgwK9//WtJd99997nnnvsv//Ivdmpql/vuu+9P//RPH3/8cUm1tbV/9md/9tGPfnSFdhEAUMq8Xu+ll16abWyzjmpqal72spct9VXl5eUXXHBBemsNlBjOW2w6xZ0bVmdnZ2dn5wpuD4BSkicA/qu/+iuPx2NuMP/Sl770rW9969WvfvWVV175nOc8p6WlJRgMPvvss//zP/9zyy23OCckf/vb3/7xj398+Rt3+eWX79q1y1wpM971rnctc50NDQ133nnnK17xCjNt23e+853//u//Njt11llnNTc3j42NHThw4OGHH/73f//3o0ePmld5PJ5PfepT2a6qSGpqavrhD3/4ile84plnnpH01FNPXXbZZeeff/4111xzwQUXbN++3ev1Hj58+Fe/+tU3v/nNn/3sZ+ZVFRUV3/nOdzLO5Q4AALAuGP6ZVzH8w/Kt0rlXVVV12223XXXVVb/4xS8kHTx48BWveMW555579dVXX3jhhdu3b6+qqjp8+PBvfvObm2++2dSpSyorK7vxxhtnZmZWf78BAAAAAMA6yxMAezye//iP/6itrf3c5z4naXp6+mtf+9rXvva1bMtXVlb+3//7f5cz95vr3d/5zne++93vNl9eeumlz3nOc5a/2q6url/+8pdveMMbbr/9dklzc3O33HLLLbfckm35+vr6r3/963nnitizZ8+vf/3r173udXfffbd55v7777///vtzrPbLX/7ypZdeyhVAAACwcTD8E8M/rJxVOveam5vvueeed73rXV/84hfNMw888MADDzyQbflAIPCFL3zh937v93K8NQAAAAAAKBlleZfw+Xyf/exnf/SjH51++uk5FvN4PNddd92BAwdW6vKf8eY3v7mmpsY8XsE119fX/+AHP/jBD36Qra+a4fV6//iP//jxxx/PewnGaGxs/PGPf/xf//VfuVcr6SUvecmjjz766le/egkbDQAAsCYY/jH8wwpapXPP5/N94QtfeOCBB/I2OTznnHN+85vfvP71r1/CRgMAAAAAgM3Mo7TJ3p988kl7beKpp57as2eP/db9999/0003Pfzww4cPHz569GggEGhrazvttNNe/vKXX3nllW1tbWu56SsiFov95je/+f73v//Tn/7U7FRFRUVra+uePXuuuOKKK6+8cseOHcWt9sEHH/zBD35w7733Hj58eHh4eGFhobW1ta2t7cILL3zDG97gvPrz6KOP3nfffZICgcD111+/UrsGAACQzuNh+MfwD+tjlc49Sc8888wPfvCDH/3oR4ODg8eOHQuFQq2trdu2bTvnnHP+8A//8JxzzrF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dvr6M1ci//HxfHT2ZH5B9za3v4Vgi4HWFzXS39v3jljedvunFedYQ5cDlclEBDABlgGuFeQRXewUAACiK+ZuePwbO+LtPZrx22W+Z3237r912KSLt43xPkhSSQlIHGTAALMx5EX9qV1PtjtHeaKczHM0ff3ZrnzMDtuSMo5Yt/VVq63JlJMWmv5IG1daqQ87H92pLUDMhRxsKKz5ZIHZyBie1kjTV36RhSZp6sEl+qVaaSoZtU/lfnmP5Uxm9RKx8tCf1pvbLI7ukHVkWlhE35lwHWy64jKmblRH2Sx2OZwO2A6w/+f9Tu5pMnGlSXufq2R+3x6vJaXs0W1q5ltmb7cGI5Jd6FI14kic8ZsVGFB3xqFnR/lThtpW92UNZ89FnDf7teywo7ZI6FO3wJGd4QKpJvnX2NRzMuX1TPan9HHE8Z5ZgYlC/bQOzvoWkXbZmMGa2EWlQapWa5QvGTdwrqTfaWV81424OT/28KXlAZs0pwzn2cwYTPJv1tO9MK6HvlVqlmuQMU7uaeneoy9eX8d2Y56tS0ki83f5jxvGZ/RewABnfhyYGzhMhZ6S/abewZLUz/faF/vSnbHLdZrTd9aT9Rz/Nr9YIAmBgtXG9DwYZcC4haY/jQWqCAQCS1kvh1PSm1MQBpXrvrSpnDGy6cNVI6xwzmyuGqFS70ydqpU2paev6kr1n8tGQjJqrTllj4D3SNtvpn7nY7fyl3mab+ahiv5xqf/BoOGyAo8TBWvmVCHszy79SQZEzAy6w8jUjA85fQKbabImdJGUpaiyCo/TQbE7hCwhqRpI9zTWPZI1+lcpyMtLf0Oy6odlk3+HZkfQV6tfCrNM2f/qDkVT0VRTrO9we8mWNG0PpT6VSxuw5fUY1qtPhnB/xwjICOROqBaR+aYPtrQ+nJjpsf/oXSmcLP7rscyanTZFrqU6td9qOB7NAewyfdbebrbOOgWlpWqpJ3+pBRWs9CqbiWPNLEbD9u8ZalLOU1lrOlDQlNUlTtgdrsgW0o9K0NCl1ZXs2kgpNrUfMO9pvL6iTJm3ZaiQ9Eh5OLXxY2py+cGtiMPXfQUV3eqK7mnzBuDsYq6qJTwQ09eum+fd1Mke4+SzMp9BqOzO0f9D2WwrCqUcO2+aplZrTSrendjU9pqZg53yP5fx1txny3zyR5Ws8Jdg5mTWvzVig+dFUFbuDMXdzeOGgNyxNS6HUgZpKuyXbrQYq9Hff/HGp0YykWsVnFZcUlr9NBwt6PcoAATCwqkh/YUcGXDhzTfBou94HAMgwbJt23i1Ubsylh6zDRZH+HlWmbP8KMFfszWXKo7CD9Ijt1g3Z7t6w/zo7o9/10nBqnkVfuV67suYKpL9Axcl+iT9X1FewPHXA80XAJmKcSm8ea7P4Brb2+rOF2NuN2jkLec0j+Yv2jDyNW0tTM2r/Ns5VzujMJgv8Djevsi/Qv1DS2Z/32anFHlHOjbLXlWZ9xx7HxDKpKd1btGZblIldc9fRZsr1W2KWk7Hz7b9rO20T9tUw2Weuj9WZvEqqcazGdPqc9qw3Iu2QdtkesU+bV1lJrZmoS71kR+51sOuRdioa8ijk2XjcwMEn2jUljebeV3J8ELn2v0nrp1K/F61SIH1fZaTFKVm/b4u6MaXYlxcVMydvbgh5NOT4OrazvmADOf7JaeRdhsXcY2R9r3Zr37Sq92rLek3Ua8zeFhtljgAYWD2kv8ASWRcH6SIIAFgrRqQpqSW9DniLCivdQYUyBcG7j5oM2CretZhikay1+0EpkH63h1lC/guFALDGZSas6flHRhHwXm2xFwGbASxzXaC3Z8AZRcBpGbAl61CyebpG51FM1pg1/V2ErFXO82tuVbIuog+HvUg6a4tjpYa2tTOz5QopcxdeJ+1KFWuasM1Kha19O1JMPOlck/yf0XLHt4swkjfrku2TnS7yzKGo3Wi914LHT216kXQhMj4Ua3vzB/wW5yrV5J55vaT0SNjKg62Q1BQEWzZL0sKBoDm2nd8n5qAqyUldMHUyaXbLYO4PsbYE99NocV+DyyTb7TX21XMOMZ511Gf7bObL0/yZ6FXnZu/g/lirfH3DqpcUka/kG4HlQAAMrBLSX2R1NFzyKznqgAEAZSWSSnmzMo9PS0plwJwWIqNHtDkhHJMCFXqLmxX9Wolvnn6bofRg2HSHHuYMEEAli/bX+zomFv3yverqVm93qkyvqFKtLHmGI6myEoJShR/OLtD1VTOLWI6JKzZ7B72u8Ei8fWIus1Z4diQQtpfQ2XspO9PBwsOhcJbUNtASDg8F1GxrOavUgCDWwp15akdqgblkG/dXSiXQ4fTEaxGdkJ1jzVqPr0DJX467DZwCLeHZkDca8qSlv1lHivXn+HyXgzltyzo8c22qMnWJzUtKXqqeIesBYzZnV3o1sNIbRy9oKn3luyTp4PfbF3muaz+2u2z9zGvS97B1P0St7ZdrZ2nS33xr5yj/ra+aafYM2O9HydNL3xeM+zomFv52zX33hv1bOuPrtNkzMBJvrz9uwHxDZqxGtL/e/qO508isyZNqktS7Q5K6fH3x4u5iwKohAAZWA5f5kAcZcOG48AcAKEOF1PKatm9Dy74uWMOcY0VXpKKat5vBzDgDBFDpoiFPWmnVSJZ5nCMB59Kt3owMOE8RsBaKdTOCjWUqgJuYq85f1Wlf54w9sD/WmjFzIuyNHQ5kZi0ZI+mW+o6rcNZmrQvGsaaeNZA+4mkGE8I5I0Yr0zIDx2bkW4VXBmcNxnam/rvoANJZHet8ZLrQADj77s01iKxWsGuI+f1wbppp+GxF+wVH3ZmW8hE4FVKybLH6PI+mBja2v3Y6/cesKbid+bAW/XuX0cy5wzZtf2ZHONy8yJwyV+3s4kzMVU+kf9PmGes6GvJEl9Zfwb7yGbfCWKthhijOWI2MH6d2NZkRiDOW3Jv/w0U5IQAGAKxNXPsDAJQh7vPDUmy3FQGLmwJt1tPwGcBRY+NUrX9U9mw1R25nz4AzukBncGbA+VmBRyHhbnEZcMHDAOfKtp0tnc0jLe4jWZeTfd0yRtJ1qs32oJ09wHOmrYW8MGuMZ5rW5l+ayXczhkKwekF3pfLjDM2FBcALbojZM8EiOyRnBI0RaTjVOthucKF+zpLylk5mt5L9VJqkQak3b0IfLvKYyVBIBlxg8Xexe8ZsVFO2s7Km1KcckXrT68WLipmLZXbjgp3Al2BqV1Owc9KM2mtCU+vxPC8pSWy8dPm+nAu7CyHr79rUrqa5Y4JLWC+sHAJgYMVxWRALogg4P6JfACgf1ndyUWV8lWqM0XyxNPZ/KRyFZ4MZ15HsnZ9JfwEczex5z2ILBzMy4PxFwMUqNAO2Gv6n9xnOqDCzZGTA+VdyaLYhOUSlzcLpby5Thc1m1cXmGgPY2ZT4sCSpwzY8sHNU+57UyLXO2My8Klf5XW3uw6OQyHDB7sRWUXLhnaXNhtgD0VwVzAXLTKSydn5eRbk2zUp8A+lJsP1Ze/fvQPoj5sjJs+fNJ9iz0GwW6xgrqlV4ntp0f+q/1h5YnvL6+TWx2NJf0z95/kfTKtwSSO3SPN+ljkboob66HLPm5OylLCkR9ha9qFzdqq2v0zy/uWHpcO4P1wrmW6VQ+mzmb0X+vzXj1XpW3hlQHgiAgZVF+gssEekvAJSVPakhOaGjpmcvVkbl3RG4XhqWJAXTw12L9aD9zhLO/QAcxeaD1WKKBbtzpGF56oBNzpovYU2FJVkr24oeqzJdZstrm6KSaSv9zbI+JkQpvA1ygXoc0wtWZ1qlupasOy9XZtbsiGqcC5eUCpyKKM52Zt7OYYAXxzoed6XHwEWNIJtH+Q9Fmuv319oztaVo7NyRWtRU+kQu1s6fynYXQla1qfkL5C9sTYq1M+c42c5vJ1/HhDtj8O+sJfIZy1/yIMFZeykXVLluT6Z7bP+VbZXsR0uefRvI+6w/9bk7vxWXUqGOMkMADABlqfIu+ZUEVwABoAyR/tpxtx9KaKyy7ioIO3oG5EqCLZz7AUAOi+svas+A7UXARr4YOJA9A15i9GulLCXsmJpcJWdVaAlHTs1jKe9SSGvfXMWX1hIklWjU0tLsMX9q7FhT+LvL9lTWPsmLsDKf7LJaSj5qPnRrJ5hFdaWnywsuP8/vsb08dMpWeJqxAub7oSe9ENmw6lMX90lldJA2b2fLc61gNddh724O1zYnE+NCa3CXIf4stG95nnL2nuIHgc4/f57PfckROMoEATCwgrggCCwFVwABAMDRY03fC7gtb/HuAWmbNCoNS9ukSPrFRE75AMDG2V05I+TIGCg3V/mvfYb84wF3+fpCs+uGZhsy392WuBRUV1pgSlHSmKGIgtcyNFLAPCZLW2jfFlFuuDJKEvSuXVkP8p5FlcZmxL25ZlD6O1qjDlt16va8dipvTGgvD7Uv0xpw2gjML7l2x+hUCUfvsNJf27tbBe5mIqOFQIv7yNBsg/lujCUC9s7wrkCsdsdoRn/mQi22A39BS1a2hUdSE9ZO6HHUYdsGLA+0hGczWl4r74ebf3jm5dterKCy+TMAVDzSXxSLImA7LgUCAMpfZOFZgIWt6TPAjgLaOYZS8fBwaoIzPQDIzZ7C5k9/nWoUnc4MR+Y5i4CNoHtMsw1Fr+iiCzF7pJ2FFgEnwl5JrkDM/mBm7rtGS0IXbE+9YFJuCxrTQiBn1WYFWFufspW82i2i8Nd8xPZIzzxSm5oIS9NpY+ImY7yMt7aCvY5U+pgrJtyZY+W7spfhmgetkHX+66t0n5f1RWFN1FfNTMxVK3X/StA9trkq8x9m1rdlb7TT1zHhsw3TG+yctJcF57x5YlnTUGvh9g/CflJtdVC3HzZmK1Mf0Hyn657cH5zzkYXuwsn4Zl7b99kcfQiAAQBlj2uCAIDyx51bOJpZY4H7bY9k2JPjRwb6BYCFmBDFfhXeUfi7z/mqbiXn2avO9McXKAJejFKkOyZXyB8DL9y+tRxywUUPeppR35krwnFmdVYdZ1bBUg/CuhwKLAQ3G17q43eF2D+1RR8k9rjOHAkZYXAgfZ7e1JyWfltbZkkBx/DYGQeY83gLS4FUvWm/R83ydUxEU7mgFRD6gvGpXU0r8CvZ7BmwIu+ge0zS/lhrl68vlgjI8W3Z5evrjXbOjgSsdTY3lNi/eXyFN4teohGpOfWRGTtzhPFZy+itE+9ax1PWDQHK8suV9jVbZIF+8rWNM8W9DKuEABhYEZT/YnG4lCwuCAIA1g5O+bAU1llf/m5sZcvU/uZPc51PrdGNrSAjXUHng95IIuZ3ZZ3fG0nUD0wv80oByCJX+muPfjflqCHtVl9GBrwgk5EoWw/qLJZeYFpYI+gF1qTAnKk2NQj9lNSaXitZ+ELy2LmEHWLFvT3pK7Pg/smT/kpqtm1mOQTklkV0/7Yy8tYCaqadnCPUroxRx3irVvq7YFdnZ1Rsvy0g6z60HgxnS3NHHG9njhD78Wa9dcaEEZAkd3N4NuStfcno1K6mrEODz9fRmvXJP4K1ZaFPdmpXU6Al7E4N66tcI5dL+2Ot9VUzzZ4sc7qbw9H+el8w7uuYULb7TkyzaOcy59tHh6XD6VF6ITL2fNbe4BmPO48N57NT2Wab3+7kBibC3rlpj7s53OXrC2ompOqMV8QSAa8rLIdcexjljwAYAFDGSH8BAEDFM9f1rDv/1mIgauW4ea6CZT2vW4sbu/ZlDX3tcqW/5qkFXw5g6az8NetTuV5VraLvz8jVBdpUzhVq6dWltjzD2Qt6kblvrgrLqdR/Szf28DyzzI68WVdRpZ/OMDhrdaCz/m/BBZZExrZk/NjlKD9dyqii9j2g4tNfs26F7/lFV+hmlesQjth6OGd9u8JXw+xb+x42db32ilJTEtps23sZ9cRyHGlmhmwneGaoadluE0mEva5ALNg56QrE5n9trRF4O1KhqfJuVAGfbHgooKGA8jYMsL5Cc32Xmug3D/t3kflOTvsuCkgbJKUqqntyFOLn6sNcm3rWCvIzjnDl+NFaf9OqOmOwZ9vvlxX6mhLnLl+ffFKDlLpzqFlHRpTW7X+va8smDVUrLQOeUSBjJIFYIlBVFcq+WigzBMDA8qMWBFgc0l8AAHA0mJK2S7vTu7+srdLY6VS1k3X+dkDalGNmZxEMlp9JbZt7QxPtNau9LgAKYgazHFpoLN4ZBTIu1hdiwS7QWYvAslswTdwlqbguo5mjh9qFU5liflbFobJFTeaR/MNkLlGuMtOs1Y25/ujbV89ZnZnxdgsKp3eaLQnzvj2OH63S6lxDzxalJ++PBZpK5XMFvjy4IrXC5nPP8y55gtKMQzfj92Jn6kjrX2jbM+qD7fccFFmXb3omB1rCYXvz5FJVn2fko2EpkMxos9Yfm0f8rsiEquW4s2Szd3B/rNUKR+1mRwKmvDjYOWkeifbX792wNUtH6IBtX8n2xSLHnRZmutX2yJRtNnsrgtQ+t6+tfaPsGxLd1VS7YzQR9nY3PLX3yNZQX91zTngwlgiYDthmnm7fvr3aYu7pMblvt3qt4eEblJagW38autUraa+6zIT5K2M9KJekv8ncGyhLBMAAUN6Ozi7QRL8AgDVnO7f9oUi1tutx5pTvgO3ZNZT+Slmih01KXlByZr2kv8tppCvY3BsyEzXjs57I3ESb1/7s6q0agEJZF+6D7rEu95i9fK2QoX+LlVHvZRUEZ6l4sywiyMkTAztCJtNhNctbjzg6Nudiwk4rFs21wvlT1eWQtbrR/kc/o69vxiCvufTn6GhtV0hwXqxc0eygbUuLCtf7HWF2jyNLW4SdeYuP8xxX1sr3ZPsx68yLXtXFbWZGCWltqgDdvsI9qcrUkfRWz4WvSY/UlWUHzjd5Tv3C+oLx8L6AlG1vL/02i9TGBlrC4aHkG5hOztY6WDeOmE7RQ83Jx60019gfs4exadzN4fqqmYm5alcgZiasNtFWNpz8dsqzJ0eyPWj9UtgLu02hfPP8bjdvZIXTm72D3hP6eqOdm72DXle4d4eU+n6u3THa5evrVWdQMxc23HzjjnNjicB215ONvvFWHTKL36TBbvXudXU168izdTCkqjYdlBRRjV/TGzVy0Hb0H9LGs3WnGTLAGkW+W31NmhxVXbf6xtVco91tOlir1+feeJQRAmBgmXEdECgW6S8AADgaTNn+qzX+D4esTeAIeleDSX/tE5I318wAypDbnVnxZ7WDXjD9PaSN5sp+UTIKiM1irRg4SxnuKg4iaxWV5lmH5WjsXLhFj02bRyG5oDlqTOzq7ENrH9K1hG2NC7TgoKd2U47C04z5F7cJVv1l1qDX+WDWtNheM59/iNbFWfRyetJ3i305ZkPMMVnI/RMZcbI9RS6setsdjCm0nJFTj7RTVvqrVAgdTfWjNo2pzYNWp2ilqpMNK8p1lv8aE3PVsn33mhhYUlVNfL5oONfnZXbXSGq48Zps917YP6yu5Et8wbhOlq9jor5qRhtmJuaq66tmOjx91ki9ZkjjFveRodkGs2It7iMj8XZJIVU/lDihvmrG6wrv1ZbeaGfIV22+zMfUuE46XrslHZa/RlFJTQqFFQso6tdkQPWSwvJKatNBK/3dq04rA27USKNG7I9grSAABgCUDaJfAMDaFVntFUBlWOvdX/akzuisiYy2lmurtXVFMEnwRHtNnpF9AZQPl2vG+aBzSOCstb/TqZzAyH+xPn8XaDM2sHnf+WK7VYx+C5QrXCztwLf5LTH9Lazvbj72PrQZnJ2iy/8zNaxPME/6u0vqynuaYeLPHltdbMaExZ7YZdRkO2d2jtO88qYcE+aMy74hg4UdnM6tyFWE7TxQexTuCWTu1ZG875u+ECu7LW4Na6UORUMe86bJJTiLtlM5a/KmltQowvZ7XOyNl/86sMPU+x58on2BVXJuS3PyeKvdMToVsN1D05NaYeuTSn1G0Z0eSdFfN01tkAIKdk5OBPR4dP4fBlO7mmZbwmaVjKHZhkTYGzsc2LthqysQ2+wdTBv5ODVw7yYN7VCPpBklxwEZVXCvOjdqaFTZe8NEVJPnz0eTQgHFc+4HlBMCYGA5rem7+IEVRvoLALBYfxT2SJLWS8Orti6FKkELRiDVS3xNZ8AZ/OmD/pL+rohYwCfJG46K6BeoFBnlvyvJ3RzWUGHVf0VpzdFAYgXsSjWj3mXrSl2SIeoLqU+1lwjnGi6h5Fa3MNouI010hqlZFTIm9Y6F7sjsSY1xm/+9rGfNSvanf6a1jhVbhl+O4mS0ns5TnVy72IGNM8YJNo/0p2rNM3ZCv2Nw6Ix1mHKsc8oC6W+uJtv24Y3zbJ0VD1utsHemtZqfz4bNmu4YlTQ7ErB3nE5bGeco4+m7wgzQa2XMvmDcpLzJFzpXT/MfX6ivzheM+zomrEF/k8sZCthT6rlpj+lQLck+9K9h3SpkRvOt1vRGDZlHjtNTYXmbFGrUYVP1m3pJ2hICirXrQMbtRI06nG0DUI4IgAGg7FXYRUAnol8AgN22bNMZ18X2rNC6FIExgFESu23H0pa1HJfaa4zoBb1SYgGfGfG3/lBsos1L82cAS5dWfNxV0nFkrRDUOezrIhQVcO5wTCj3X6sCq4etsVezshpgmIFUrXFYm6T9jrd27pAVbt28s0Qj7+ZiX6w9Xs3zIRYYslrHZ65S4KwF1jsd88i2khl73t6k2qx8bWp4XXNIr8yH5WypUptambB86+PJctgMJVmxXAM/W9H4VN4jx6xDf3qpuvURFD7Od4Ey8ntrxTLKwSWlx89WyGoqbmttdbfzIbFzvGpbVwUz6rDpF22yW1/HhE+aHQmEld5TIf2r1SqDjoY86q9Xamzj5NPhtB+rauL1VTPNngFJI/H2WCLgdSVXdZOGajSzTuPVmja1v/YQt10HJA1oU2PqcSsGts82rg3WI9bjA9q0WVgbCICBZcMVQKAQpL8AgEXYVpYZMFAS1r8jzC37a+5GQNP8uT+9BbT9MmVJSqyQjTccbe6NTrTXmBgYAAx7dZdd/i7QdslAYpkqHXOHUtkL7yy5+veWSv7i1IxgKX/oZbG2xopXnUHKlCMDdu4ie2Pb/Ba3c+xlss5WuiVUeCRprUmuhHWXbbo3Pd2368k9GHDGG+Vhn2HK8WNRzEix1v0QecaQthes+20/Pmg7y5qSphTtzR36lPDjm0pPLhfc8P22o93MbG2s9Vs8uFAAnFGLXODw2EWyl9jmeTZzfPSdyZLfrEML2xNlX8gbDXmy/272KGoyu1ppSlF5zF0jUw82+dbHJSkwnwGb/s9HpIlAp6T6qrQRBGbkt0aFr9Z0RsobSbWDHlODP/Ws0tPf5t5Qs0IjXUHrhebZsLx1msuzi1A+CIABAKuE6BcAkBXhEGBZWx2hO7JV2/hTt2tY537D0jCngiVmrs0BqGC90U57b8+92pJ1GGDT59P68ZA2TlvjQC7KSLxdC/Zlzc805jUrlWsk+Bwhbvb0t5Ber0tnX5+ubBWoRQVL/vRlWmu+K0dOaU9/MzYzayyda1csJRoPO4a/zfNGK8MEfrn2/I70DDjXy7XYGlOzH0yP3568Sa3F/u+aXAf/VPoWZV2m9Vqzgaa+2R4G51q49dpaaTh9nlzHXp5F2Zno1/xem12xYOmz814H+8b2pGqIexyHnP3HYo/GrGtlFuL4vc6f+2ZZtm3+tCLd3GZHAuGhQO2O0agjPF5AQNGQx549m6TZ1xFT+hgBWf80ZOS73eob1wbTydlq72zNUzM+GxiLSAqv8wcGIs29IUne9l4zpEhzb0hdfcIaQQAMAFgNXPKrOCNdwZa61V4JAJVhuIAAeI8UXL1x47Ki+wuWSfmnv1ZFfr/jHM9+3dNUBh9I/xGFMfmuuQA30hVMdXgGcFSY78BcvAXT3wWLgJs9AxPRTmsMy0W2BfbnLse0FFjIW0hppn2Q0azsZYs7C+hCvEylz5HUf515W09qaNUCe27bY+CiQt+sBb7m3Zd1aNui+iRb46r2LKHBcquk1F5dkPUuzrcL2KLxBQPgJtvnW+C4Hlk3MOO19vrmrlQgLcfBbBZlvdafyo/z/zIWuJ69tlU1+yHP51JgqGwtIaMDubV19p7JuY7PjB0YTP/R/ttRTPqbMbau82vZennW8l+LuzlsukkXGDanNeGXJNVXzUzMVZuXb/YOmobP9tDXugfIXstrn/ZGEvUD082pf0s364ns771HgW0R61bOek1bzzT3hjzthaw+Vh8BMLA8uAKIEir/q35F4UrfGhde559udK/2WgCodM72zs64l/QXRwP7oVW254R7ck9vczxir1ONSNNUA6fJX8hrPbt86W+uPrFO3eqLqM6vyWVaEwALylUEXKwazUyr2vn4Jg0dUMvSl1+EYqPl2hyDB0+l0j67PP2cFzcCsbWEsC2BK/y1Zv48UZwzEU//MaP60DmDxRpSNN/65HiXLHMWG05nNWWroO1KpapOZt/ad29Gyugs+TXpvvOuhuZigm3rXcyxYY0KbHZOYKH7FezFwQXmqfa3zpoBW1vaJQ2npp1tyTty7KvlGJY4Y5kFprxZOVtAOX+hemzvYo69rKOS23+dw6khtxeSEcdmxL1OuWawwtoW95Gh2YYFl2MxX+bWt7r9673bt2+vtthn7tCMpFYdsr08e+hbfyjmDUfnX2ndf+n85/ZuqUVaJ0k6kPpwI5r/I2OaEgWlTZLknnEsAWWJABgAylvZXukrFtf11hoaCQIoL2UV9wIrrGJOCDP4Jb9SA5AdvVb+pMsEt+a/hSe+GVIvXFwbTQCLkdEFuiSOqCmgiDVOpGWvuqrTE7lg52Sor05aQglmyeWqF5Q0KIUWqgMulWIqZdOyWJOW5Rn9JCMD619MVm3ecb6G26nYXWSFoEVxHjZWRNqbO8svKllfsMo875aaHZX2AWUMhLzTVoucf6cV8i+XjPDSXsOd9ffLvnXHpubJyKHzDEQdSRXi5++SnUeultG53svOaoFurV7G8VD4AED2Jdt3YK1tn1j1wYGcdz/YfyPs6e+C37H2225MTGtPZ+0v73KP5VpIUDMhVWfcwWNCXFuU2zujmmpNH1FTjfZk3KbTnR59O0fwNW1jJEfWa//R3Glq/UNjSBpKn/mO9Mu5Zv5Q+qtQ3giAgWVACQhKpQL+mpL7lj2CXgAogfK5EooK0+K4EFOGrPO9PenT9metHzc5hgRedLFIObFaNMcCPm84av1oJryRRMzvMv9dldXLlvI22/4L4OjVoOxtSE1raJNtbPYO7o+1zvc17Si+VHcFOFdpyvZ47rjOZD85w9HCLVgUu1OS3MGYTBxlhWGFv/OULYBMrXPho5bW7hg1g4ZajyTC3mSovwLynypPpWeZhR9gXdlGwM2aUy7UY9zEhGlhof0lpqQ4YPuUdy70T4DWhf7MWgsfsT1SyLZnrRLO2HsZM/hTkbO9d3RRFkx/rQ8i/5jES5Q1hzY3EJhbVgLZb5WwflOi/fW+jgktlPvm769gviHP1l3FrXzKJg1W2zoqd6vPnKp1qzcjyt2rzgaNNmRZgb6M6fpDMUnecDR7dW8uB20TGyXZTtprJWnuAVWZLwnr1H13arRmrAUEwABQltZ69Lu+mH/DYPmR8gLAMuLmPywTc0K4bpXXIrv1Uo3UL8l2jc9+51/GXYB5nipLE+013shcscNepM64vOk/yuS+K5b+LrqoF0AZCs0W92ege0ltebMzY0yWWNYobqSAQVUtVpPnEcdTzpra3LWz0f56dzDfmJ1ZVm9nWhA7L5Bex5mhR9qp8FAg0BIOD6W/2Lk3cqWA9qGLJRUQXdsTzYzRSfMPVprTggnlosO/xQ0vLdvbZS2rXVTl9Pwq2ds+q7CUuti3s3LiwvdARga8U7Lqy+3T9gVmfDf408PUUakm9we3S2q1/VrtlyazBbF+ab8kzQ8QYZ/HWpmI44XOsajt7FuRNYe23UBQu2NUO+Z/L3wdEz5pdmT+N25z1zNBR23uek3Ua/5B+xdpkyYbNTKu5lHVOWPXJoW61RdRjT8V6I6rWZJfYfPIXnXaX2Vs1EhE/lEFmxRq1GGlB7paaKCNjJmTxb4muDX/MnVmwDZzk5odkHdbamIq+WBV6nCKpb98diC5TK91Dr+tsLG0UQYIgAGg/GxZeJZytBau5VU2Ul4AWB3byYCxDMr8dkBr8DlzBrh8dR6rwZxTxfzFpb+rjtwXqEhB95hmndVfhapRdFq+xb3WXgS86BWYl6vHr6XY8G8wlRVlZMYRx+ACud/aKgGsbQ5LmtrVlK9VcrMtqAvkyICVe0NSNXOZ6W9WrXmzcOcIxwsxm1b0y7Iyu93afOf22k8JVusMwezAEalZmpJGpJpFta3ucDQYz3OgpoeXVvNhK/LPkv3bFfsr4OgCPd/COqxoIbmPPUzNfyOBmTOSqu7NxUqUdzmWr1RiPSp1OBoJWB22s8paGJ3xO2LmadXUz5vsH4Speg8PBTQUML8CQ7MNSh+dt1v7ZmwHR8ZtNI0akeRXuNt2s4lVnjuqoK1+t2+vOhvT70lxpr+S/Jo8qGb7U1bbGPs85uU147PWLYneSKJ+YHqkK5g2uG/u6NeKck12G9sjV6OqUttqkt3YHnm3qaouOWFmjmWLkOeXJimaZQaUIQJgoNS4/IclWlttNAh9V9ZEe81qdQ4EAACLZC6b57r6aV0Yta6mrZUk1X4euFbWOa/yv5durzoDSlZrhVN1xgAqXpevrzfaqQIGp3Qyg/uW+AaRRZRpLpj+quDmt7XpQ/862f8kFTm6baAlnAg7vl0zgt6MktCd2Z7KasqxKKursFNz3qrQRfXvL2UGLNtq5/ng9qcqQQscOzaDFSJGpN5Ut+f8em0fQU36eL0mXBwpbO+1SjW2Vs9h20J60z84s8D0TzbQEnY3h6X5my/MHQay3WowPzZtWDrs6OdsTgv9qbA/426AVlsxerq0Ftb92TYtoz4+zxDUGevjT+3/aWlzjhjYaiudtUuzWU5T7rGKi0rozedo7Zl+24+pYmJfMG6anGfc2OH8LjVDnludmTdqSJIp4Y2oplvPHJQ2JpPg+arcjams10S5EdVZj0Tk9ytSF5mY9Nc71z2iuucdeipzg3pDscD8/Trz+a4UeFzalPphj5r3hCQpmBqO94A0lYx+40NKjDvfcD67TYxrdlyuRskR9Hq3KT4kT0tyOhFV3LafHg7pecHMpaH8EQCjnBSYJPEVg4q0Ze1cOCP0XU7lf+URAJDpwGqvAMrBogt2/Y6JcmPO/Q6krjGpEs4Gy+SMK6K6g0VexS+H3JdSY2DlWRlwfhmFaxljSVry/BZbpWyLsSt3RFdgR9ydBXSBzj+UbMbS0jnrL00mFOycNM2Qs1RnZl2fjMpFa6jXBTNs8/mYkGw6M4nMGKM02l8ftUYLltSf2vY8Q7c6371rPlSzlt/iPjK0hLLyLHJt+OZUdrtLqpM2256K2DoS70i/H855CPlT8+R6364cHbltZbjJGfL+1fUF45K8G8Ih1Znlm0fcLbHZkDca8iig2teNmvjWjCPr1OI+Ekn4mz0D1iO90U6TOIZm15k9H+ycnB99OZAqh+1KBti+YDw67DErnJzTudqpR+yhvjmefcG4gnIHY2GziIyhqZukUWkw1dK5kPS3VqqVhlMfjXlJ1t/0EyXZDgb77RpTqVcVFuebPZyzHD/iOOSmshwGpv9zcl12jNZXzdg/F2uUX+ubs1t9AcXadcAU2s4vaI82dRySnJl6aH4yKIVCGpPWSR3SiDQi+VW/bVqR9BdGlHznLZofaHiLtE/ejLpaE4KYE+97pH2p6XFpyGxVcsdaiezDIU3bXmdNWG0R6n2aiEohPW9cTib9VYvUKJfk9c0v+XlBhRIKpmpS5uayvBxliAAYq2Qp44M6LzcQCWMtKvO2fnZr/xpfmSiTS40AUHaCaf92LrEVOHUs/EIkKoN1MaulXAfoXYT1jq7OdqPSJmmPtC1v37+1wxtJLF9XFRPrWrUgJuINKLYq2a19UDqlUp+s3QgXRO4LrK5FlP/mkivlzf/lkExicvVoNX8denPUelpRTZ4C2UUUFufiKDjOaPVchELOUe0dp7scI61msPZAIG3F7EyymFbKKVvL3DxVks4RiHuz7PMl9hXP99ZZ8/Id0i5pMjWO7LTSBjY1B4yVBdoPoYxjyW87BzOhe/6Sa0nO3ajcvbtTM0dDdRmPKORRqoo0ayF1Rpl+JN4uKZYIeF1h8/hIvH1irtqafz79VWqIYnP3w05Jig57NJ1cSfuc9jLW2h2jzmp1M4N9nbP/WhWY+1qs7HZBGZ9I1n8l+QuqCI+GPNE841tnXRnH0R7tr1fqQ6zdMToxVz2RCuMl7dWW7vkANiks7151dvv7RrqCybF187yjXUjanWr8Y+LefdJ22789d6cuBVvvuU+aSf24xxbY2sT2zA++a5/OKM+V9HC2L6ttjo74E9H5Ql4jObLvttQ6mJVpzLK0oCutDhhrAgEwVtYyxUjWYlc9Cab/M7JaQ9W9kjrW1NqWE/JdAFgM5/mh/cb/Ys/uCvkrZr1jJEdjtKIs0+nfjO0Wb/tOqJU2Ls87onBWV8ZyO2XaljqqnTW7hrnZokOatl34s37janJvUZMUSS223La6MLGAL+6vCoxFwuv81jhqJTGgTVmT3YxwZSXT30PamH/Iz4x1a9JkRNWNGrdHxdac3eobV/Oo6gSg0plf+VzxcLISMZCj3NOEOqO2QUCzMi+sTZXr5c9KF8eWABXb8Xi+WtTIWsiY++0k6XDupdtiaVNXWtzKFJlcS7bSZCnYOZl31hJplkLp+8qcNuxI9XO2x8N1jmxKueubTQPhqdRn0Sz121Ln/N00rF03besLnTsGTrJGpU3Valv1tc4K4N5op6mr7o121lfNmKDRPGV+VKqwO7Ok1RpgeGdq5zRLgZzrZnJfkwqbsnWLGex2vrbYrHmXo790roPZOey0NURdUXe79uQe+Dn/gND2X7f8PQNaUweAc9160g57MyiymZ7a1WR+C3rVudk76HWFJc0oUK3wXnV1Z/syCq/zB8YikrQ+R88n68Gp1JrbA+UZxz8V7T8enF95MzSv+W3P2sP5voeSsWsoIe2eL8N1hr7mGDddp52/XhlMn2dJMoW/1j+Tt0kHk//qzGgTvbVGD4e0dcFFo2wQAGP5rWTtYPkkwTg6raGiXoOstzAkuwWaVNVqrwKAMrNNOpAqHMxqfbYH7X+b8pxJ2pe5uBNOf3rV46obT/Xysjj321R6KzAsXa3tYmLGIzXSkFRrG3OrrGQ9Bvy2x/Ostj/bdP7TwrV50jjRXmOv9J1uXNJJ3aqXwEZUczB5ia6UTLg7nWPJq77VwFEra8/njDrgjAq2XDGGU66U12TAttl696rLvG+vOtP61mbVJA3m7QVtmL+5Wdd0NLWcRWhdzOC4syOBqpq4KxDLksxZrN7LGetcOz/aaPaRXO1smZbJDqd2NTnv1snZ89ZKBBcse3U0wrWCcOv4sVLJLEwymjsftWpPsx8MHY6ew4a5LSBPBFgrBbMVEJtgcso2vTPVMNlI/9Azg3PZNiSQ+aA1sxUTzme0OyXJtz5uQsRoyGNaiPs6JtwjAXd6KXl91UzQPdblHuuNdjZ7BhRvl9TsGRiJtzd7Bvyz64ZmG8yHbop051fSHDlmW6wtGknr8GwdEqb42BWIWd2MR1JvJKlXnZIaglOuHTGl8mbzwmQ7cftnajqKWwMbm73REY8Oe+Y/u0Jy3y4FtoTDQ4H5ezvS73JIG+dY2eJnezNw873hHCHYiqKtGvdcjeJtNxBYDb29GyRbXt7iPmLSX6WG/q3RjFItEEwLaEnNvSGNpv6pmOeWX9u/1+xBqatRifFUcW1KfEjuxszKXeOhAwuMK/Sn0Hyg+3C+GefZZ3tethliqXpiDUlD0hbJ+lYw9xw7ipKDLiqA1xgCYCyb1b0mRRKM5bPmUl4uEGdT8uKPylbghb/NC88C4ChgSgyDUrMUSY+gSvsnqSRLMw3QyuSMcWjhWebN2P59jkWwLj9NSdtTPdky/qsyPvHjBC+30t66t/LxJ4ErgDzsqbAJ87J2MV0KkwHbv4vWa2JY9fVVMxOBVBxlBZBZB53tzd0L2i7rsK+5ot+FOv1myFX7a7XqtRJQd3N4diTgTi+mzM6Z/k6lt3QurErYlJDW7hgtrio3Y+Dh9ELn+QGDlTP9VaopsaTs6W/YVnuaI/0162zy8pw3BOS5CSH/bQEbsj1uoj6zt810T/q7mHTQ5I79igY98yHxTlsabeXiVmZvmi0HJFun6GTb4dQeTjWF9kgKDwWsz87s8Nodo7MjAXdzeLhvw0RHtZm2fklNEbD5r3nKinKj93nylZUr1bLF3BAwktoPO2UVHx98on04uMGsg1VtPLWrKWrLv6P2pDmDvcQ2lfebQY6zNBLPo1fhQGB+V9sOm7T3DdhOvDPk6Qht/56pTQ2sm2eM8J2S43ffea9DJOHPOFNcp3Frej79VY4bhW3/cszTh7k+pomonrcn7fF6nybyprx5LLHm9uEcGXBSS/aez5JUmwyJs5Ymo/wRAKPUyu1KhLNv3vKh/3PFKNsrfbkEy7UwZQVllHegcFxkBFBiodR/Q+lnYuV2lmjXUYpe0EtU7Pkq6e8S2S8/RaQt0ljqJND+33KzfrFFURVqoj15Nazk54HW2L2LcyjVqz1/K2YAKIQVGgU7J+1NX60xR+0ZsL0IOKOcN0ONorm+o6x/JJry32HVKxUZZtanmoSm3/aHNVecYw0SbJcx/quTIzq1amfNI1bYkwh7Y4cDSm/6mqG+aiaWCHT5+kKz6yRNpE6n7MlcPibbtseNixq32Ncx4Us17JW02Tu4P9aa7wVZC2r75zO8RNjr65iIphoU24O3jF0RnfNPJBrtedh8gak9Wmu1tUq2SeadqfR3btpjr08tAWsznYXC+atRp2yxrolLe2z/zVi40p+yGpJbdc8jjtemKo9NcDul+ZJcSUoFw2baepGpGJ5fyL5A2jpMpbbUvnpWlfmObEdXjyRF5YmGmqT0lUkVJEflsbetzkxhcxV2W1s9mIparXw6f1AtWzV2xpKtGunAohqYZzDr0CzVpH+UqVzffD1avZ3tmqVYImB+yzZ75wNk87XZrCPVmt6oIaWKgJPpr5XUWhf8U02b40OqCmh2QHtjmohKUr1Pkg5FtSfVfllRTThbNEfzbWJ9aqIux70QludJD9vmn8g9p91eqdtMxNTtlSRXY+q56VRJh/OzTj3iaZFaiIHXHgJglE45X9RbyRh45eXfrnL+XFZFeV7LW4QK/WRptlxCJLsAVp/9LGW0jIOrxfW2PVDkgFi5HCzRclAIq7HzFsfnXv4tjst48I6J9pr6geTwseZ0rv5QbKJt5Ua9LYlFhL6cbgFYJqazq9IDV3u1pRV89kY7TQa8SUOmnWmBjaDbdLBkX2IdBUSh/tQosEae+uAcxb72wj5nga8rEPN1xBJhbzRUFw15MpJt0y9Xqd0VdI9J0myDeXZ+J2eNWi328Y/Dmq+VNHlbrsLEHJtjZfkZ6W9myWZ/jhNF24NmUSaLzSj3dAfTKpuHUptszI4E5hPKGlsUOp3aLkdYaFofmy7QsyGvuzlcgrGKnYalGqmpsH9BmJE7Nki9UjjVPHnQlkpaB2fG8NUmy98gBdPrnptTn+Z+abMUSf14t9SUCpvtsbHJWVN9m80O8QXjpmJ4fufY423Db/uv9RuxIzVDnnbZPbZBka0tarW9S2vqg8gIfQOSs2Rcqa1Wqtu2s/+5YQ+DrY7rPVmOE18w7m6JzR9dgcx201J6G2qlgmR7lXZPet9169DaOX8viPnVtndBqNFMmw4qdf9KjWamVT3hWrfd9+Sg2gKakVSvMUnme7JGUTN/Wvq7x5b77pFs7Z1DCT2VPOe19Vi2JbsFxrFOE7aJrHXC9VaCu6g3mpAeNpeTo3o4qq01Co4rZvWpftBxqdkcY6ku0PEhScn0d2+MM+A1gwAYJbImsihH2/q1qqitWPrweGsUQW95INBdDlxqBLAmlW36u2ibStEApjLOTsuZCXrHJEnrUg+uyzl7OSrLs8GRrqC5QGad7GWc9a1u+pv1ZGmjRsZU364Diz6VWovnYCYHKnxYUABlIuf4rCkmszRFb2ac0QNqKbYXdK6RgA0Tn+zVFqWqjfPVeu4sIAPekZ4BZ7w8t4y417Rxtpo521mpqunT61yUvf4vY/lTu5rmYyo5NscaWzRjVa3mtBkLTs1mkiorbzPRqSt3x+n5MVPzf23vlKRAS9gksqbPba7G105mHdISymnH2MyO8utof713Q9gViJmGxr6O5Fak9YIOFHYw5Lc5NRr0oK02McPOtDLotBVuTh8YeKetabb5rwlre6RWW9dr+5FgXmK9r0mC7U14pxxHQuod7V2j50NWq9uQGfLWGiT7RKk/NXpOOPWUGUw6/21/zlx2MJXYZayYI51NdrHO+utsfXat2e5psL9prlvmrG7S2VpPmzGV55e20zYM88nzT9mntXO+lto6wlvcR4LuMfONZ53h1CjaqHG/ps3X2tm6c686TauDGQ3a5zSsBgnd6qsZnw2MRaTUv852z/8zzUS/Vi3vwzm2ewVMlOLd5//1OS1Jzwsqtic5YrF7UlXZerrH0v/FanbFKMPArREEwFiysrwekdOaLgVe+mpbS1hbn9qCKiDrXZufCOFuCa3F64kAUKg18Wdu5e8UXKNnpGXO3q3ODJBhSijKJPE1p06hBeZKUza/PrGAb6LNaw99y+dUsMDzKFPdW+xJ1xLS4q6FZyrSJg1WJ8uyksvvVq9pNz2duy98KsLJtz7d6p1RzQG15smJl2OLAOSXFrjaE7VUwBPqq6vdMToxV+2fXZesai1G1q84+y+7SX9jiYCkRNhb4n6/hXGmv/b/9kY7M8b4NFlRrijU2R7W+UbJbSx81GFnhW7qtdYyfR0T3lTumyf9nRfI23p3p2SKLJuTm9PsGbDyuJF4+4I3EFirNzsS8JmcsjmViVrMdFeyn3Aq0UwFRLbmxvMPLphb59Ga6u5rokdzoO1w9CK2/y2ypb+mJbWJtK0+4SaZrt0xmuj0SoodDpgAOxH2SlJn2mfR5esbibf7XZFEp9cViCU6vbHDgegujySdJgVsSW2KLxh3B2OzIa87GKuqiZuhkZPPjTiaFRt+6RXyrY9LMncG+M6bmD+GT0zONeUcvtfsW2tU49ZUIG1qfDvS3sQeyWcM1WwWa/9FzhzL2WTAzuMhqyILvr0bwr6O2NSuJoWlncn7IczxY1qjW7tiu+0Oj83eQW972Nz2oVTrZknd6jX1uxHV+DUtKaBYWN5u9Y2rOaIaK+KNqGZcjabS12I9O1/1q/noNyP3PZyjKnftetg0kU6N31TvU/eABvarxZ02m9Xm2v5CSS9dgVVEKRAAY2nK5npEcZbjAt/yDQC8HBcH13QSvNbj3rW2zxlbd9HIdAFgjVnEKeIWFVlpUzp7pFqlhhk9KpkSh01SRArnjnjLp2ey1cDZ3tBvVBqWgo5UeFtqzrIRbvSoDELflTm/KvZdZlRTrekjahpRw8JzL8EBZQ4SWapQ1loOKS9QDpyFrVnTX7ulp795fv3zhKbF2Z/j8RzvbCV58zP6+qxpEwIFfTOSxhONViNld3M4WBPPqLI1/Z8lxRKBjM0xyzTZUrLzdnp4HO2vtwLF+QrRDDlyslyFyLmkLTk90pt/iy7JNriv1d3a0uwZUN4MOBH2mljUFPKmjVPrZEWYzbIPLptM/npTbXszhtTNw16Pa5PcovWSFK3xmLpV82Bms+KUQEt4NuSNhjxmJ7sDsdrm+Q/XPNh9fK+kYMNYaHZdsGFMUiwRmKhqlO2oMLq1Tx51a9+WhnX7ZrdEgv4j9mxbUsd83ukLxk2WLGndhnFrb5s3TYS9sWAgGvJk/W01r20ITkmqP+Yprysc1Ewo/Uau0PFHhmYbNruPSIok/EdCtQ3BqYkTqyVF++u1Xr6OifqqmSOhWpNnm6WNHW50N4fNITFyXPuRUK2k1trhyHFTE3PV5peivmpmuG/DfK1tMO4KxDLvflDu3uOWnmy/ueHkMu0fmT1gbghOTcxVm7Te3Rzu8vXFvIH9O1pl++2uTwS8rnC39m3yDR1Qi1K/7Jt8yS73lhnVSIqo5qBaJG3UkDl3PqiWGkUjqvYr4FfYPGJG+ZW0USN+TZrp+eh3TBrKjH6nK/2W3Yn0LtYPR9UtPWIbgfjhLC9KmlzG9UIpEQBjCdZajpVmTbSDXoE13FPen+PazXrLea9KKoOLd2sOaS4AFK3s/xqWwKLDxZKc5k05lrNFuYsA16wFTwj95ZTyZsj6W2Bf26a83dGXf7vMOaG5+BUL+OL+qulGd3NvaAXOFcvt5Kqo9SElBbACkklMMa1092pLgV2g86e/pvA3g8m6rF6sWSzY+NdqGLorfWzX3uypdjTkieYOUM3GturQXm1xRtS5qmztc5oCYhMJm/DJJIITqUJDk8RbIbS1JoVXQlsvqa+aGZsOuHPX/pos1vm4CQtDfXUZvamj8qhL0eGmqYAmdiTP/zZ7BzNGFM5gwuy56WQi4G4OJ2thLTtTb7EzNVyr+UxN6BtI7yccsPVhLrxgWul9tnfamgZLMiPUhpK7dz5HNAW1AVt4HJZ1KJrtssftViHp0GzDZu+g+ShbJElB91iHZ9weuHZrnznmz9Zdkh7WuqB7rEUzoYbq3h2dm72DXlfYqjU3n9Rm7+AzE5tNpOp3RSZsS8s8NjJ+KVoVlScaqgupzheMHwy1m0buWffT/t5nWXsg1F+XvCfA7K5dTVPWDuxRaGddqht5QDtGzR0AJnYd2hGzr1I0PZ01v2VZVnjK8Rud6yPuklLt6M3m+zomNruPWANOd/n6dEJy3uQXlE8zDYFqhbvVK5f2+rpkb3Pismae71FvTVtlu9bjknaqR1JEdQfV3K2+dTrSrgOS9qpzy+CIu3XSr2n7C5O5r2xVvwelqbSq34dzbG4Fs/LgCqt1PsoRAGNRKuNy3jbpQJEd2FbMSobT5r1W9zNtKZu+fLmYK2Cmrcqm9H+olCsi3qzM+eJGjRzMOVwJAGDJtpVX5eLy2r6cnWCKtc+WAc84JsqfVTLSIgXKONZd0Hpp2FbFu0obEgv4vOGopPA6/3SjO8+cGeeNpT2NLLegV4tapcVV984oIMlUq8woUL1Qn8QJrRtWvaSMSqD1mqhX0bV9ANaoeLxZGlh4PklSsHMxVVj2r0F79DuotpDjpCE0u84EOQtEnkWN+dpVxB9H0wvXVFtaPWANe1BtDQmcf4TdzBWxLc35YNZhhufl/1IPJ7NSs7TQ7Lqqmnie1XM3h2dHAslo2ezMnZLkDsZMgabJHZPD09rfJTxfZ2xPf60yX/s7mjDbFEmbR8zCMz9c8xYmmM8R+80P5tqRfQZJwc7J2OGAeVMr4Z4dCZiy3WDnZLJM1lbQ7A7G7D+a6t5of31UydmiOz0aUWBHOHxXwHdyPDrs8a2PW+mvybPN9lql2/tjrebdzZGcmFpvdYF+Vv1+ryv82Ozztrl3e1S1V101mplMNMo193g0eROiSX+TtwhIoZZ1vqqIpO6Gp6wt7XKPhWaTjz/T6W0ITh0J1ZqgPWxl5mEpkKxrn9rVtPG4gYm56vVVM35XJOgeiyUCz0xsdgViLe4jvqrIvkOdVTVxRWx3G3QouQfkCbSEw/sCssbKtQ9vHNbUrqaEVXQbtt1K0iVJ0bBH06k03cj45bXOXW1Zu1UI7gvGo8MeBdJaptfuGE2WHcfbW47vHZptCLrHjncfMjO06pCkGs1I2qndfoXX6ciYGvya3qiRMdUfp6cktevAgDaZ4NYbScT8Lm8ksV29ZkLS9mztxesHpm0/hfxdk95I4phIv6SYv6rb36dWeSOJbn/ffOgr24X33ckfrWFuHw4djdEvKhgBMIpXGemvYQYGK7dS4FVZn9UtBZ5e7QDYyncl1Uj+hS7VrV76Gwv4JE20eRecs7INaFNYi98JpL8AsCzs5xJrNLozm1BuJ4dF2ZfqjWwfLM38t6xO481KNq+pQ8XZqDn/zOaWwdXYQJPgeiMJU9G7Au84oE3rNBGRPyxfWN6AYpKWcrZWrEPaOC2fpBpFzUSpFFvsm7Vszi6omVYdsvLgrPNnBDDDqjfBcOE2KbNNIoC1JZnZ9DuesKVxVgCTNcW0swbIzFP46/w6soaSNRFsvjcoKv0dTY1dWrBoyOPriEna7B20P26+Ua01N3mtM16tr5rJeCTjPhuzHPsMJgtfr4mgb3dI1SHbEMtWm+jkZ9SVbZxXs0NaFe2v7+3obHEfGZptCPXVmSFjTR1wi61E0kjmu0q+VqlRfs3617cN7o+1mg892l+fDF9TGV60v16SSRbtsWuuHtT2vWRem0Vrqg9wl3Q4dUqZag4bzRErmPa/tsGPY9bWmdW2+gPPjzsbUHSXJ9lgOeTxjXjdzbHZUPIswjr8fMG4DkpHpA6FdwXUJTNAb/SwJ7qrybcjPnV3k5oVaAnPHg4cNCMW72pKtoke9gQkM0KwOxgL99UFWsKNwdDep7d6N4Rba4cfjZ/Q4ekz71jnGj8Y326C87qxqt51nS3uI38d2NHecigR9tbWDlVNNXqDEx7NjCcaazUzpWpTWW6aOateXld4bHrHs9t3hWbXDTU3mI97YKhtS1tfdM4fdI/FnjvY7hqs19ig2rq0b1rVcqmxYbxRR9Zp/KHECe0thyIJv3/HcCBRtW+Hov31Vtfo4b4NsyFv7YnJPewNe5VqrRztr/d2huemPQ3BqbkWT3goYCLnZEg8Ml/J7QvG13ceHu7bICm605MlAzYeTJ3K7kwWYUfDHutzMTegBDsnE2HvREAT0c5E2DsRqN7sHQzNrotVRRpd41b6u07jEfnbdSgsb1heU5LrjSQO+ucv0LXrQLLM198n6bC/ZVRB82NEdRH5/Yr403sPj3QF6w/FrMukpo1NzJ88+zVBcszvqj9k+1rISH8zvx6AikIAjCKV1WWjUll6O+hSlX2s6auNhTNn5stxLayEx2dZXouMBXxrMfotw7KPo9ZyfxYnc14BHLXWxOAaR48px7T57wrc8Gdlz1ts15i0UmdW9oDW1ODK9qPJwkKpp8zMQSmQPqdzmQHbDYKb0qvb/amUN5I+vm8k9WDpmHLeompzY36Xdf1r0Qq/6y5su8GuwJdYgYSkveq0WvMt5YylVOnvIpo8W/W7+YVUvWBIvHQHkn0ukwpsCZvfCqw2gEwZY3BmS3/t1mtCUrcjkDyY/p1g2Ab/zvLbba98XaDjcVHpr6QmaZfUmn5vfY/UqgVv1d4fa7Wn3SFVD6ptwTesd4/bf7T6/RomDDaPrNeE/Zt8WPWxRCBZVjvbkFEQnMzVAlKrlBZMp8ZGDci7ISxpaLbB1PVGd3oU8tQ2hxNh796+LtM119qi3minqfQNK6AeadD0Cm6KBuO+jomJuVarw7N3Qzg5Nu1I8q2j8minoruaNKJQf502yOqinNEYudkzYN+KXJ+sLxiPjnjmq3s75h/3dUzMjgSqauJWHa0ZzzXaX+8Oxqpq4r6OWCLsnZv2mKJnVyBmqn69G8JSwB2MBYKx8FAgUB8OTyQT7MBJ4fBQILAlLKkhPDcaqjZRcXIM4GFPske0T762eFQeX0c8OuxpOyZ0KBb0BeNNVTOTzXNN0Sr5VbVhQhu0IRKOz/nHalzrFE1U1bhaYmM1ru4NQ9MRd23t0Fywra3uwKHZli3Pfqpesb/OPmu7Z7ckk8hK6vD0hRqqR+Lt/vURV8IfdI+d0H6oP95ZHxwfibe3BCbic4Enhrav2zDu9Qy44zVzqql3RQYnOqYiszX+2VDQv27DeHWiaiThr4vMTSSqB7W+veXQkckNk/6q+oTf74qMqX2ftkQS/n511kVD0am6Q7P1VTXxUF9dW11odK46OuzRtHyeePSAR1JUdSHVWQdb9L4mdUlR6YgkhTrqJKlHUdVJCtcGkt8eEUX9qQs1tdKgaROt6H2eg6H2+c97Z/qpu5nd9ov/nBMetJoBKNVh2/7LaFqpr/dNDKu+W/vklqQazUyr2nwjmdO8GQW7U/9u3KvObn+fdfoXUZ1fk00KNerwuDY06rD5n/WUP/ews6a6N7zOb6ZN85u0kl+L1d04Vftr93B5NgoFloALtShGRaa/RlG38y+T1b1suhz/li/tCL4VfPjlUIY9nM1wGqu9FhUuopqs/zgHgPK1zfbfo9DibgSsdVzSXRkHpY2lW5qJe2tytHLZVLo3sgRTCa6pHMqfK+dp2WKeMtdCa1LxsPXPAeufBuvTF+LP9qZN6Q/6c8+5WLGAL9zoifldKlFBrSmhMBfUMoxrw6hKfwpa4OnNKt41uIgOz4Nqa9SRA2vktM0ZdWSdLSMUAVBesqW/Js+zHi+8XXzW9Ddfu+OselInA8Wyqmbtf2cHbfmTuW3LNtxsriFyM77Qunx985GtJKm+asY+7u8mDWVkxvYlmMRXqaGCM3aI+dHUYs5Xr8r0BU4/tUuNahzqqwu0hN3N4eQW9Ug75zNXUxPc25H2Lslxbc25YkTqUbTWEw01bTxuwGzgSLx9IiDT7jikOg2mjo1UGCzHKMDmHWt3jE7MVU+kGmWbp0wjYqvPduxwQFI05PFuCK/vnDoSqlUq3zWhr/Vak/7Wu2ZcNbEjqjWvUsjjC8alZGNnqw+2EQ3V+YLx8FAg2DnpC8armuK+2bgk74bw3KhHYc2GvOs7Dx8J1a4PHh473CjJvKM7OJ8lJ8JeHU6myFPNM77+ancwNtU8Ux+Zc22LTcxV10t+V2QmOHNkqKmhbmBWmmj2Sa569/isVFsb3qQh1Q9t1KGNnt5GHanXyMlu+TUzqromTUa0e0BtPdrerX3dnn3W3V0BzWx0D06pusPTF1J1daJqS1tfo2vcl3CPeUaCmmnUkUjd3PPrHxlTo6RxNUh6rueRmXp/tSLjagipuqX+UKOOzMgvKazqdZoJqzqk6mp3Vbx6trpqdGK2UZ2alKK7PD53PDroSVZam1sMrQTXnKyam+g2pH6bkmMASz3JEXytYmv1yHdy3NcxUTdWNfCFRr1EkvRzqS41MvfO5G9foCW8bsO43xVpcR+S1H9c58Rc9Wbv4Ml6tMe9Peges8rlrRHHm3XkoNrkUrXCNZp5nh6cUU21pmsUbdNBSYe0sU0HA4qZ9s4R1dVFJmJ+V8ZovibfNSeoGaepfk2aDDjzyJa8kYQZ8URSYCxiTQSc34X2C++7pXFpSJJiezQ0q4MR6agc9xcVjwAYBavsi3qbVjV/LYeKmaWMDFeSoLeyD7CCLThC20qictfOVKiwTwBgXnB5Er5ysE06sMx3B65K+mved1xqXMISapf/c7fHsetT1bdahsF0rYvO1mJNU8r+1OG9ZzUH/rCk0t+SyZX+lvw8Z686mzQ5qrqFZ11tixjft1WHCik7K0O50l9R4wuUsxy1vxNz1c57tPeqy14EnOfr3fqtzx/9Zi8SNe2prVOa0YX+aNo7P0/bhgGuzTaIbCDzgfBQoLY5LGkk3m5l3lZ7A/voxV5XOJmSzlVLmpir9tsaOB9QS66mCHu1JaiZkO1Prin5tQ8rK8kdjM2nvybWPZx+alc7P1yr6cMc3VOfaniRZCVz0ZAnahu81hq3NblAs4um5AvG/a7IxGxjvVt+V2RC1ZLMELPzx0ZzjhLqVAprEtzY4YA7GDMxsy8YP5jalmjIYwJaU3ob6qsLheskKWB6/wY0neqekix3DminQv1189teKwUVHfEkP+iAfOvj0WFPcMekUnXA7uawN+xtcR853BGri8w1HLPfvPSIa8OWzj6vK1ydqGpuGJCkDTKBq9cVDjbMBDQjKaxq+RRqqK5OVHlc4aHZtqZj+j2ucEjV8qk6UbXRrTrXuFnm9tYnZxSYkd/rCVcnqupc49WKrNP4cGLjVldPRP4O7a2Sx6cZSU2abNehCa3fqT09mpHkVyQiv0dVbTrUqy1hVbe7hmKaO6CW0Oy66pmwtyYsaUh1XoUlrdN40DUzpsagZnwKb0w1FW/Q6IxqDqjVo6q45jyqkjQjf0jVgdSoEHvmtre4D8XiG5s9A0e01RWItXeNj0zWaqcUVnLIXvthNpg8MOb1pB6x+saPKCqPNZ50tN8THW6a2i8dKwWlKWmH7eUjkqSQwgpU1cQnAtWm2Hezd7DeLa8rfJ+eq+Qs1Xu1ZZOGrN+j+/Rca3pMjWNqrFa4WUek0b3qNGGwafSyV50Rc3AUcEpvv3fQLMRMb9TQuBoblfyU/f5pczdLQLGwvBuTW5LMjJXKlSWpK1korD2pS9m7JckrtexRS1APh/Q8SdJeaWLhFQTWBgJgFOZoCOdWq3Xhqqe/RX24i856V/wQypWkZm8AUgZWsd73aAs1S769JR9qDgDWhspuCWEyzuU4Tzu4eunvIpjLwua6YcnbOFvdlUdsgwGbG/ezXkFegT7S5n23pdZk9f4RFAv44v6qwFgkvM5fqvTXNNOzd1o2lnhqZB9811TP2J8t8/R3EX2eRUoKoMz0RjvzjAF8KFvrj4xvv8WkvyPp5zO70sOkrKalplRObP1NL6DtcwZ7xbPF3JRjv8Gl2TMwkdquodmGLvd8PaAJeht1JGOs9KxxcpevT7754ZBlVejKVnFrt1/arOSYtR0T83vPZKI7k6dV9qFwZd/J5qTL3sO7Szqs6H2ewQ3rJTU3DHjd4S73WG+0090crm0OT+1qCrSEq2ric9Oe+XWTkmPfpt5CYYX3BTQtX0s83B8whaRRecyYvr5oPOrzaFrREY+k6IhHIWlGakrljjsV2BIODwV80Xi0xhPcMRk7HIj2eyR1vWr/0GzD3LRndsTb3DG+qW6/xxUeHOqubTkQ1MzOZ+/u0XZJXdq3qX3ogFoO+dpqFX2h7gn72ntV71GVV9MvqH/8SW1/Ss/qdvW1a1BSr2fLEa1r1UC1pps0+bCe51N4k4ZmFZ9RzaCrvUFjO9273fIMJ9rnXIdqNLPeNTmsujE1btUzLTrYo+1jiU1R1+zJetTvigyq/YBa1mn8eNf9w9q8Xvsn1HysdlcrJGlGwXWaaNeBAW2KqDqmGklh+Ws0sytxrMcVbtWhx2afF0n4JW10D3pqwk9Gt7e4j/iqIps0NK6Gx7VtvSZ26MkxNUYVGFPjOo0HFDmkjSYV7tK+UdX3qrNJRzZoYoeevG32vEhie717/NiqA3sTzc+EmlyBuobgVL17fO/k1mi/RyG1nz48cP969Uq1qd+XHtux4Rx/2h4SW8dnT7IteeBvwsmDZDB1QJqlhZKDPQdawqG+OnOXScYXS8adE2ZY3wNq3aQhpTrP71XXJg2asNYtz6zi1Zq2n3n6bbdCmEhYUpNCVgcaazrjfHVczaOq26ghv6bbMu6nSB7gXkkH1dytvgFtCqu5W33j2pClbjhvBPBwzmeANYkAGAU4GtJfY+Uz4FVMf/N/rEUFveuXvTaihOFosYta7sB4xXLfCuvebOpxrQuOq6sc1gEAVtrRc364oO1FdoFe3fR3i6PvS61UkyosyNrJWamrtEFHVW6pSnLtJcUrM1pwIVZjTWIBn6SJtmRXxunGQk8Ux7XBr4ik/Od75oLa4hJfU7GRp5PztHxr6KRoEdHvjALVCpP+AlhdWYf+zZ8BZziSuoLj/EKzYsis75LGHnzuytJzONOu1ETG5aOazBlzCXZOypFIWWYUMJWU9s78ZmYTb9vrhiWFVN0ozSgwrobWVKWmUl/1rTpkNf41I5ta6a9SwaqkaNijrlSxcn+qY/NmSYrelxqR12KKnnvmB1u1MuBg56TVKTr5EaRyYl8wHr3PY5K/0K66wJbwSLDd74pI2uwdjM75Iwl/y/G9kYR/Yq7aHYjJFgCH9wV0WAqmuptI2iCNKrrPI698HfHo0x6tlyRfbTw65dFhBY4Pzz7jdW+IhZ8IaHPqcSXrSsM9AbUqOuiRFNpVJyXvFOy9cbNapRr51sdHhhrHQ8E294RqYtHp+lC1O+SulhTUzFOJY6dcTVPyNenIHj2rT68KakbSBk1OqeWg2iSZqtkBtY6pcZOGurQvoupp+QbU2qqBA2p93PaPkAPa3qgj42oIuGYmE111rvGHEidMzDa2uCYOuFtiiZfsj7XWV834XZH+RKdpKN0YDA0F2xIjNRG/e0v9U406cp+O36ShVg2s0/hBrd+o4VHVDWnThLzNUlSBac1tcvXWaEbSce6nqhSa0LomHYlrrsu3T9KUmp6lvoj8fkWqNd2gqphis4o0SGH5JfVqyyYNhRPNg66Z+xLP8brCw6qfizcH3GOSjoRqJwLV+0Y6Ze4tCGvqsA6qXU8mT9EHhtdLkl+akoJSONV3fSp1UMl2EG5I/Wgb0Dd5q0GPJIV7bcX1Zjk7k/N4N4RNh+3nnPCg+UqJJQLbXU+aec0eCCjSkBxDRUfUVK1pe7OBGdV0q7dGUUnT8m3RX03zZ/NsKo6NWIls/aHY9sbemN9VMz7b2Oj2RhLmrscNkaGY3+Xo+dy3V50bNeLXpPN2RsPMIKldB8a1IaK6rD1vkvebmv8m1y45+u/DWeYG1jYCYCzkaLu6t4gMeHEDv2mV0t+sH2j+uHfJx0AZjmVbuDwrX38oZo0zUZIFLoe1Ut274CVFAEAZOXrODws5MyzkVHA8OcTUqjHXd5wlQNZJoD36NW0Yp6WwrTA3q4Ij0ljAt4izpkWwv1HG9ESbt/5QTFK40VM/kKVuoBx4w9FYwFczPlvgmCBWazu/Ist9q99aP087pI3Tqq7RzPRiB79ZK8P9AoA9knHemmOa3jubP9srfbNX/doWodZUBmyS3cGFCgOy1gfXZmn1nJUVSFtRtzUQqWFqeasVXq+JsKpla3efKzOuVnhQbfaFWMuRVK+xeo3NKHDA1SJps3dwf6w1EfbGDgeiIU9yrmlpMFWUaQZeHZ4fUdX0f07bk+ZjMSldWMEdk65AzBv2NgSnzJWbusjcbEtyBarc8frE7MChxsAZ4Ybw3Ohcta9jor5qRpKvKiLJ6wp73eEtmhhWfVBSvH1irrp2x+jsSMAU/gZ3TGqH6iOzA7sbfV1xtzsWHgioWWqSQorGPYpJLulRNT175tBBj2IKPxNQv3xNUkzqSzQ9d2Z0vRRRVJ6dbeN/nauVFK3x+Nxx9+aYSSsDXeHG5pC/KrLZP7M/Ur299vGqRG2z6+CEvFWJ+hNdd0dUPabGQbWf4rrHLe9ftWlMja/R/ztWu+7XceZ2hO2aCCg6p7gkv2Y2a8ynyT/rlCb110UOHfF7x9U8oNaX6C81ik/Lc7deVKOZF+hhSQNq61XnVteTAUWaXEfinrkZBSTNuPyNvvGh2bage0yz69pbDkUS/hbXhN89qhY9R08/o/Uz8jfqSKsGJO1V5yYNHdDWbj3cqy0zCrRrd0w1Xk2bHHda1c/Vg3/SKfUaCymwUYfC8o+psVqhiPztOjSgtkG1N2miRQeUuhy3SfEm3XdALXHX4blE3fNdj5hF1Xj2RBIbnut++L6G50qqbquaUvWR4IbxULBxZ2hkqDG6waNdUpO0TsmhvWtTx9sG+dxxBaSwoj5PoCU8O+KNTnl8O+LzQy/bbq2Ihjy1O0anzK+oNW63OWJTlcFRJRuA+zomzHeCufXB+qIIaiagmWpF7KNmhOU3p1Wmu7X1uPkITDXwmBpNQfAhbWzUuF/T89F0m7rVF1GdGifHtWHUHzSxrkl/nb+z5tmI6rKmv0ovGrZHv+bUer7Cx3SJNv+Q2Zj8597zghqalSJkwKg0BMDI6+i5urfyVjj9zfgonYlvAZ91WQ1PWw4m2rySd7XXIrvyyX3LZ00AACi9AwXMU7qzvkRULl/yvwVpkaYduW/GIL5BW+tF+1C7fskR3IbX+QNjEaUaFEsKjEXM9HSju2Z81sw23ej2RhKSvJG5wFhkpCtYfyg20V7jjczF/FWSTP4aXueXFPNX2X/0RObMO06011hzTrTXZES21oqZklmTm0oKN3om2rzeSMIbmZtudGecqlmVtc4Flo+iRvwdU31Y3mVtyG5KN8rnjM5cTIzIv4hReyUtOv2dUSComTyj5wLAypja1bRweW5upv9B/vR3YTXZGs8ugnPo3xycWx1StTO+lVSvseECWjUENWN2grUrsg4MXK3wJg1ZNwC5AjEpkOzSbLXhNY12Tai2ef618+mvmb9ZGkwr7Y0dDng3JIcWjh0ORIc9oUBdcuZ+qUOhp6QmhUcC4eZkAbTfFfFVRRpd49bfI1OmbG/ivW7D+PHtT0l6PL41Me1tcUVOfenvtuvQbrU17ZiIqPrCyK/f/OA3oiOenacerjnuQCCs0bGWwAvHDu1vfv2xN/YOnnb37va2naFjjzmwO9Ry6vre+0a2PPe4p7e49q1PtD8wseOF41PeLbslNbUfOZxoPzK6vrV+v6TR6fU7ggNzUoNrf53GpaYO133rI1V+/6ExhTZrZFgds4rXKOrT9IhqH9IJG3Xw2Tr4/3SapB16sjVy5Fb/6VEFejV2auTxLn9/QyReH6l6VuRp6emn/TvkD7lV3aaBGsV71SHJr5nn68EmTZgbHRr0tFseUyUcVvV4orHFfSik/5+9/45zJM/v++B3AYVCFXJuoLun08SevLOz4XYv7F4OvDsxWKRIWhRJ6bFpWhZty+Y+lm056LF50vNItBX4kkjKFskjT2YQyTsejxf37nb3Ns5O7pmemU7T3ehGNxqxUIVCev74AWh07pnpSXv4/IFXoVAJVQXU9/f9fD+fr7NhOPyarthNE+SGJkvGciPhlorHGQdKKMKueZiZFbwp9gGjXJ1hxEEpjV/Gto+0l9k54gepRbkeRs/gnyNylDcGzYXX1VMZAlFKo1wz0ZL0AO/nbSf6t/hQifAxloJMZKVAkrjYYJDsnOScJ95Xo2zPG5LTQT3im9HcQUP32t0VlmXOwFXItIJzITHXAaywTApt1CAl13SHVZOVSLUpaWW1rqLd0bl4LtS8r2iZ+ggmONqxvIGVli295RDg9okO1jF7zm3PXLYOiwnxaaWhJaTFfOsO7CNVRvWRmWBQrwVP2M8Lo/UJBjt/WWFyafydFSrC3q8dyoqAU6OSaI2yRAeTzl+lSiE8qbfVNUKcs24csbH2VMvASqtptyCCRRVvGcCeoJYkZucm+LoNgLt4b6FLAHfRxQY8ACPoPdp+pbUdx1b0bef8TtJ3YBPFxmOt0+0CyBJt98zYczwiZstddNFFF1108Wjhgfg51wtIToDq1OpMKYA9sDUTHIMAAAGIdXTwFRmfCCzDoWb41/Zba2Ot5doaDnWdKXHnW1EpKNYVG6yodrGAYF4rarOUcF3YufbtGta2/dFmkapji2kqqtTe10YIenirTx8MjKAqUlT5hEucK1e2VlFtu6R+JxjSqBh7XYko+kRuDPkeFvu7lUvz3M5Oo3t7GIPA/WB/hbZmb7fZRRdddLENtmr9e2fsr7GW/R2FMe6m4fvRO1u8YTgEV6rXgoKF0nF2ulj3kdreqF98X0Fi6TiFmHjSGup3LHYqHekgg12UhfPtnBRrKokPMDkwBBQJrTkVnTHhUQDRmhewuarCWVfHC/SeTebrTuGvG5Cy1Ya2UvdbyzIllEjVQmYGlsENB5tyYXeiILaAB4dkzFSHAOFKLfZS0x3KQD5mz33C/gM3yzoRA2dFrod8uaO+G5cbT8Sk1BHzvIXbtej5l+Fftd6VtU8bV/8oSipKCmSUI9Wwt/i73/nrhDjasxQ5NPnyG09/+plXgOeiKxl805mDscDKj/i/m/J7ko2e0Iq0FHLdmDmUrnjGrUTcyEuDpZV6n7HijTXMcrSQkBa/Vf1Aw3JQYDB6A6iW/H5Nn6xHTtjP5wlOFQ843YUYBUuqvVs7edF2+qB6bYH4SnHglhJ42fGhQ1wfVlckNW/yFJBg4a8aPwt4pewwUyrmhcbpiDRfwhkmR1ON2jPAcpCshdbL9bTkP8jtG+w76HnlBvtGmAxRMHGG0Z2SUcfqJS1q6UxcI9zO4BshY6DUqJg4vaw4KamYZ7hwhYOD3AqS8/EGYMdpog4zHaJwUR0Nkp1gCLIznJax5XGU0ebomWBQXOU8wWTjA4BTdzrceVkyflB9OrMUSMQWYvYFDVK1eKrm73csuu2ZlOo3JjVKaIOGcVXDhAgUm1pzLWYEo9nla1EGqOkOLWYYKU3zGTXdYQ9XNCrGmEYYDChhzci4Ea2am7eo2erDDcpw1aqt9qJWItV1ranFDcYojWJEn/L6jpVT1lDMnkvV/DF7bloESPaMXgtm7eVsI7As+cScrX6GRkcyuozLSWme2EY5b6Kjxnb/ZOtTwYKLTLib8Bt6p8GSVjabut5eAMe4tWqAlF1jxVQvUDdoZAH0BjdKACVY6PK+XbxH0SWAu9gaP8zy3wffDHjXqGx2YNUUcqchWfvaHd4wZwui99Ep6t8lOsvBBNqdz9a2iHiP435fuMfuxuiiiy666OK+44cnRDRbZfIPCfUCteTmHwn2dw08a5W+W1T+dRq6mHibpsEqm7GJzUp8jQpg4BDLaFQ0rBXcIhhL0ifetj3ZzNbO5gn3khYd0VQKbVK57Vp8B+diT1FRJbCVht13187jDnakKUKRIJv1qmrbwO+uobp3abTTLvjbW/bXxGWiPQrVfmVcPHCKl5ZX4TYmz/dD+9tlf7vooou7Q1sO2+ZE7wW19O5cmNvYuPim9s47YmZXCmD3UEF8R5+t7LNnt/nn3Oo/XFTbVBrN407V/NSaHhKT1pDPVp6tNB86ggmm1QlYdA1wUQ6SdWKImYvEh5UpvRb0HStnlgKGpkFL49uBNosm2N+YPZfS/O6hgk8qh+VkGEaYLkuaA1tFymHD/ey5dDWxPBXVYgYxRtyZFZTkZEQ7axyLXzcKsStjvVrMSJbiAbc3q7vj3nTxVgjgBkZaS5xd/qjy7dMs/Fnj2RdQh6XX5jjUmwlWg8XlxtmPlL/1Hedn6vnqk75rwcFr15dC2o8ZtUmH9rOGMa0pZ6ujwZmxzIDqKX7whXG7VFopDpRQPvns17xUb5VGTjmtA/ar2UBfRBqfb5xwStmD0m05ZF+sx36u55sptXiVw/tK9av6sGwz3dEZqeYur3jf8vYcV667vXLK14xwXFqxAjH7wvXGEauuAlZdvdkIHJavD9qnaw3XudJZxZXv84wfJTvC1ARD+UYwKpmHuH2FoQmGRC/eINkMAQO1KtWdOISjS7uF8AwRGdsKfgUDWKbPRTlH8CS3NCwdp5u6j2WNZgQYJB8EEx0wcLixB8nkiagYR7kB1EwlrM7FzLzXzJbJFVTNT8ph1n2BpQUSJeQVfDkGx9lXLMYkrdLIOlI1v7doK4Tr9RU5YCtfuzUqwmpB01o1GVDsVUfMmPju8OzAPru7kogtNAzHbaNf0irWjM+dKOiG1xjToMX+mrAIOsaMZkQ1xVO1xmSisCyjYSxqilU1LmkMNxcTPzHlE1VoNru1JmUcdMZ61iUZsJBFyYKly6SxDFmYS+PGmvEBxbGQ4q4CmaWAkdKKRgiNIqGWhniYEhOLw7B5Vcd198n+4dtmQwXETzhPUPirOzHC5MZwCTZdxTDRAqST9AXJi8GCkPYKCyLMVuPe61BuktyNFWrZJqErD1E9hxGgnsEtoTfgndUjudFy/+nSvV38UKFLAHexBX54UnsPHndLLW9K/dIp/xUThzumO+jeTZm8PZR1btWA4T7BwLHZNxJfdlcmeA/4gPcQXdK3iy666KKLLh4Edt3gdg/RsGiUt+R9ASlAI4tNa2l/27XtbcbXhJPQsmvOJ1wF1SdYWBO10yxk+zjQhRUga6K12mQ1GUcDR3tC6FDFNkX8MMLUPGENRVDFGXzACu4RChl8iRYNbOJdKzJ+0BBEbLudh9AEN1NLmy7fMprefrNtU+uWATUtindvuqjcvyBNpZQlIGQxD50GfpDsr3Am3EYu1kUXXXTxsOAZXSmOhTjashq+uoZcEbpPm6sq3gpx6qYmxhuxSLzzrZG6QwJ4r2pXdmej0ma483Wn7w4fp53GyJvCmvHlBzangZp9ZNGEaa3QAQvT6bnGMPaMWU0Eo9mAW883nIA+5QWE5NdIaYJLMwxNuD3P4rO7K0ZKYwh/QwtIWcF+XW8ciVEAnA3b++Q3L/ceG3bdvF0dDdsXq0bo1Km3L1ZPlaX6jVQMA2NaSwxnJVfFP1uf0+P/y+F//e3qi9cDsX/k/r8HvvJldVhJjyTOymf75yu+8nzprLwkDaTSh//TL//qV3/q538y96ULno983XnyMM7kV8LDH02Zx+2NZc3QsLsrt7yBUK4ctuVTeAPUDthy01lnf9DUWIq4pFvYe7FGpB8MLy/HIvOtCxM27W+57P45wj/DH0y7DhzhB7cZtlGw2+21cO1m42gf00EpexiCZF/j2VPSxLnG8UHSUalQtCt4OMBtN5U8NsCQ1CddryzQq2EaqGVcB5hFmvWxXIFneO0KB0WVmAvLxBQKEBvmCCs5QoKhVzFdqBpmlbqG2UeqhGJDLaIWsRVRPdQt5CscDFHIEaxRLeOKYaZQEyym6F0gfQA5g/8i++u4Q41GXL2lcfgd9ehtx+B++7VhHP56uAABAABJREFUpjWsLzp+XGmY+VrAZ89OTw0ZixqA4BctCKBrNOPcGNro6k/VFqoqWqVhOCq3Nf1bXjxQwJjRJl4dRtzpHihimd5mbOKB5bWjkgEwsIoyAyj2qj1cqekOKy1byAzDJDigCCtoP2bYw4YQ7DYMhz0s21xVn1ROXo006xXav8GrrIcHa3KVNrKQAWNGW5URqx1bGGh1BBd/U1dXN0IRC3nirWE8MMB8OoELtGZhx7AyNdHq5z3Jh0a55qS0gpdmzjYMjASmANlUHIKz74fl1gjICSkkBXsAKUZlvOmWpGXR4bwO4FPIWwBLEKXb37eLH0Z0CeAuNkOX/WXXIuDr9/1A2Jr6tSeweeEQDIK66uMnckOdSb2t/NO2h4tynPmdl2vuYmjT+Y8sz9o+4Ef2CDfi/mX9uqRvF1100UUXXayB2UribIM9jQO30ft2opHFcQg8EIZDEIYwlaBSVW0rgYCgeIWaU8WYD8REpgxwUhJOazlCflbKuATNJtQtgOirqq52D6NEDFhpuTqGKIhpwQ0DKqUMfrXjTImIos0Qi5kaFUEVJ+kDNFTAQNFatPTdmbi0ueROefHdQVhGr3O33gzbiW47nbS3MaDeJdpa6vvX4KNjXy6VUoBslsDDZX8nGBYZ9ntHGS2LP0AOmCPmptye3rDTLvvbRRddPJYQxO3GfsCiv6YLa91fejsk2D0ESWPN+DotYfem+69AJ+20hSN0Z/ff2UqPz1YOyzsETOt4X2vG54gagkgWZtft76UM5IWKujan2fuM2UqPMIieIxYh78QYYWqOnj4WMwRkbMNMl3DKklFsBMJyMl1NaO4COvmGMzG83FjWJFsFrRqIl9MFj5WWFatqlWQsrJsyyJjohvf2SP9tZz/gk8oR30wGOV3t2U9pXN43oM0DVatid5U+63q9QWDZvnjx8kng6PCS4S6H3cvGfCzkTwf6ri2X+tCs/7Xxxfg3b7hh7olez7vqE+43XbFvvTnxj95Y1F7o+eqPLd4oPtGTbew31VeDzszZJe+/uPk3OCOlfC4aSP3lEXtKsZm2VCTjrhScSmKlft06eNR/dS4/nF4Oj0Zmsg6vzzN1s3H0O/pHz0Te+SiT81SdZmCE3LyzNyelDlLT6V8kfqph1qUb73B6hKlwI+CWLlRxm6iZ0oGCa36kkb1kPDlEJuWSh1mWyBcI+WhIFO0E3VROcsPEaSelYam47Zh5/KPMFonfRstAA0eAym2CuYbXKS0WCMlQwXW5MRKXFmIUHSwCQbIBpD5uGSgaloZVJ3uNUXH/z5CoUs80+lzSiogTJksHvFoRWKn7Q7ZcEadDMi59+wzAChRQ9letqowORc73HGYRHOCEZYgwX0wQRDtsAKgE0HGjVkxhkpyq+dcZiPjMmubJ4SHpjisnsIcrtRsOdHCCE4pQJH5IX8i1wr8iqCgnqk061o1ir1pLMsAAVkpmVhZtXxS5ai3JOKACJkd/ZGnarkazlUVnJFbNmXU174Iy6VRAGzSMCxoKyomq3V0xJjX0Fs28Aos0U8gtp2hMWG4xryVYbIn+V1qfFluMbBFmoAd0iK79ryiu+b3reBV39aZ71B42fLby5frh48r1c5zqI1Wl3sfiBEMiWyt6ADsMq8k6u1pbyzZl9+2cedvPWWBCaHw7qjfX2Eh20cUPDboEcBddPNLYTvUbg0NwivSoO0lfkvgKXkH0trMYlYbWtrLpRLNzydYQdaMlnNswxyO7i/rXMYuPEdv6SCFJ3563eaPL+3bRRRdddNHFVlB34f8sRLc70sA7lRVWU03Xsk2hN9DruG24JewJbMfBCWcwDqq3/QPX1cESSo6QgTrJoEbZR2aRuI4zXU347FnAIRmiW14fqSx+NjOzdVMWShTwb3UkC5SDNA+0tIXLogsLWFdBuI4PXjvhbu4fdmnishbhDa+raPta95IGVAoTDLWn73xfO2Cr9r2dxPDGdsvrIHhf4bB9p7Gf4HFp+TkDgZZ6e9OFTTSh93VhbXVBHwxEaQIwwuRd1K0uEtcoL+Pb+JHe+l46Tv2hfscuuuiiiz1HmxbtlP+2EzVx5k1c8x1/fc5WwVYPCzvWvrQ3Xktra9jf+4erO3DAgqnN1535Fr/b71icrfT0OxbztYBoiNuJhuGol2QjpSnuqpDnQtOrWZ/yuocKdnelltYEj67FDOF2mxogBm57ZhmfE3OOnjC5OXo6M2BhcmnJnydYsWtWXS2oNolKARv9ZSBmL7h9GX9Myw8F5q8kEvFsciHA0RYxVqKS1wBLl+sxGQbyDWdlSWvEsvlGzO2/XG3Eg64lA+etxrGgdHuYzM8c+Kfjzqe/tjT6Ly7871cVxvc9pVz7yNngypD1u/Xpf/LlyGcaZz7/owe/HvxexnH4nd+rfGox+imv0zjqnP4Pt39iSefEqX/1/lvTX9z/8anbT1UMZ/8zt0O2nFfKOjHdlOeI6Tj1SFFqqHP1UN5R94eWvjr5pBQxFqZ8kzNPYeAe6cNJvSS/Vn/+B66nozkj71TMutrnnE0XI2ZdzeruY/7Ji3XV716+rfdeVE815rSs4rSHDW/SllWcQWV/ZilQ0x0HbMVJzfl9V9XvLmaWAgP+OUmriEbIX+N9DslwUwayjYBDMiLkv8xTzoatLNUncgcBcUHdQ4V6SQZqusPurgSj2df0J8Sl8buLPntWrdsA1d68hxeL++YW4mF/FshW3WKt5u0gPJbncX+8gJOJd4aVgaq1LONC8VQtl+w5syKKBrRRo6Y7AGtapgcWQW2RpglQMVIak9QGHA6XkbXcgCtU09/2KpFqyFZ+ceTLMrZbxZOTWmTQPu0jY+57tpHWkgsB7aDBtEyJxNnl4Ky8giMn24aHZk2H2ljWcqM24SJuD2s13YGJY58ROVKcv5JQ3FV7rBK35ycnepSBqjUja2cN42UNBwxz9VaUaSZHvcCk5iUNiy1qNiVThArWorzKDalggguGW9MDrcbDwBmYgaMo9qqwsMZY+4O92lqy2JIFt+XFs1BobbYDli5r7krDcOQ1fLayjtNNuUrdRdmF5adg4uplKWqmfMmScDbiHViCCsxDkex1gBslJoCupXMXXWwBCWg0Gg/7MLrYGdf+8Au5yy89iD115b+d2FEEfBfKj91ZQG9H/R6Cw6Q/4Z5g6Ju8KAL3SWtIBCWwmXfH8CZtWjYWim5FDAs7jm2Oto9F584ymfV4dMjgR+dI2rhPjC976vvdRRfr8JOD/80zfR962EfRxQ6QpG7499iguDB95TeGHvZRrIUb+h72MewC6WF3eFK/162swPIuFstAaheLbRbabU/9AhMVRhwgIsBBeIL8+10F1fcKT04wLIjeSWuoltaaOSldFpnNduc5QFhEth0UY/acYjNFwnRdI70IecBHZptDEg4xQkWxY/gn5EchCiqGeuex4p5DoxIkb6IGWHrYx7IJhMThXoLALOEAafG6zWImriwBoMX+PiqR4QK9pTtstVtGK6Nuyv528cBw8bm//7APoYud0Y0AHyN84eKr/2gluUmGZy0/uj0B3M5ydJZ9t4ts2nkk8XZ1X5ttf/2nGzNO9wObkcEbs1jr0DAcQGVpZ9K63Vo4Zs8Bi8WI4A5Ft+DD0rUyWpCshplgQTwuadHqWQJJ4vPE54jptWAyFRcssmKvomLpsuKuhmzlhStuyrAI+6HSqnbTmxa+2qABBKPZRsnR5529eOt42Fv0x5IvsqgaxojrfCYXUvy5ktk78IMfFJOZg8lFgii3KB87Eq5fS9qU8R/7RH9xqnR93+jcN2f/+v4x89n3l/991tx/2x79F+rP/T3PnzVy+qh5/mXvTx6tfC1m5l7t+fDNxsFJSTnI7Sp1oBfLoHSbkSjzC6VjftctN+oirijzOYIqDuB2I5Kv+01b3V7w23wLk9ZQw3D4pLLmLgCisWu+7oxaJR/WeCFhDxvFsVBieFkQ2yGrnAvabK6qPuZVIlVMhGXxwVjqRiqmDOSBWloL2MrpgqcvvpCq+f3uJnOoSiag2Eyrrs5O7rN02T1UaFttC3PjxWJE0io+W7nXvliW6kCEvIFTxxkh78Qsoxo46Sh/rDS06YWheDU/lwuvuhwL4awELgiDgdZjBMJ6Y1bLabbEwMLs5D67u2K8qmlPGsbrWlvkqg0a9rDRMBx+dzFfd4oyBZ+tLAhaR9TwSeVB341ULX7Cfj5PMERuBf8yvgj5WsN1ozCSnIxA01ld6zFqusOalBmAGbSzhpHSSK/eNpTWVjy2m0+nWybMJspTVeVQXjTutZZlsYBir1qX5CZHK7Z2tWnR3FTWim/kpu0UoESqTd9yUNzVcCybbzjrJVlodsV1B3y2spgQJRSI8Qhrj6otJm6j9QN3DxUAYQct5owwnSc4yrVRrmpUgOOT4wDj4IZZuA1OGKeawtCYn2HBuut2i13cPX7+619//mMfe9hH0cUOkCSpqwDuYi267O9DwlZ0byekAPL74bO8NXzsXc68xckm6XsVTJiEMtzqWGE/q/mTsQ3FVkfXBPEijG5H/+uYYB3nBIN9pJxbNHtpd+rapSxY4NExYW5bizxImHiB+bsRu9wZujLfLrrooosu9gyPNvvbbtTqMBv5hMuXvDfG0QWRXXDAQWAXHPAgnY35dmn4POJAPon0SfLvd82p+y8yMMP+H1SfztwO1G45rNsyy3AbGg1cUjMH5AQvVkQGUMGDYWjQUQs4CjU/IHwOFZvZpoFFvsyJuTHkaztFl3Dm1qZwtqGBBbO4ghe8ggbOEth9h5HdQ6SHOk2nOznU9lsDB/gMHEJim2BuK+9oEaeJHsZB8vOEtWZnNgSF3OmVLbYgVuncmth4kr4g+Y3LtE2eBRstArY7NXxui31NnHHmTZxZwnSogQUW6BWu3VkCKuW2rfejw/7ehfD3vWTdLDT6YizWTqSyC9+mLrro4ocLm2lkN7K/LqzOR23nX337ad5eV/zzNPsNb4HOqrImPLvt4Hv32Iz2Fge5kQZuGA7BC64eqghkRPDTGdS0wiF9ytt7LKlKpgiETvgXqg2tYHgirkVDKi8SP8p1PyuASjnQckABguRoRTjLjfhEMWJMa1hwrWFVZCrgwnLIC5pMDCVeDX+gKIWNzM2acd6lHdUCYTNt9zr2GZW0hkpvfHHQXd5HLrb/9a9+//151X4h7fxk7zfqb56ct53iXfPDT76d9TB0efFGnBuf/MXw95xHOF//yJErPaM56kcu7Zt53vwL8xdfrx0JO4sl9WOT/sEY+pni0qJx8Hhj9s97PrgIivn8hH/Zdz1sHc6+j1s3OT7KUgEJcGE9kUvN+nmucmEZ2z7zVi+D19XIES5X2Bcyl3xq9q3y6ajiPNfwPs142laup53SgODOF2aqQz571mfHsqsFJFupWktrir2aN536hJdZFpDdHyvoY16txzAmNcKQkkkz4Q5auhxO2pLJgBKpZt3Y3ZWFQli0SRYs4/JMFFAG8jF7zu6uoMtA77Ek0Cg5QjZds+XO+idn0KoNrYjzJMksch5ntaE54YA0W0TOEBBaZxWHA2OacK+0MBQzrk/3nRwZn/P2plMeKti1rHE+onygSoYDaubw8WTMqJf1yMDI1xYdh3y2ZK1vuqzp4z/uVSUzMJz95vTHs3WnzV31NWrBWVs4knn7dvRseKmRdr1yJeJ9OqvYq5jo3/PqUW/SilAlORyPR9PXVfvym1Hrujz8H80qNjOru0mC0qRda8sOqyYTBQPtrFHTHRiwAv0AiqdqzcrIoKB4qlZNxgIL7aDRjPZNOINVk61zIW3QqOkObdAwUpoWM2q6g+Emf6zEqtak3GzWe3T1N6LEquGBbDoVsOZlpbcaGVrK152kNAywk04FmpWm01pt0LC5msJ6y73KEzcHIwZMtv4lxGun/XzrFy1Y/GFlqtLQEtKigVOjfJpzwDG+rmGJCDk8qWPCDJjwHSg2h1Hn9W5D3y662BW6BYCPDR6EArjL/m6K7anZe1MA74b3FXAcgs/y1ieOfZMXv2V9qMn7jgFwFZLVzVdLtGL0dsQ82prwwMCaZdfF0JumG0Y6M5db445o4LUrbrLTPYQQv64bDj2YXT8AFnadv1MXXTx4dBXAjwW6+o/HCI+iApjHIGKsaIoRkGlZ8grTXYfZcJh12aw7DGunDcBcB9U9B9triXcZDbaivoZFLbuD8BfQG/iOApT/Z/Ubgc9dITbXGL5WGSh+McQ8BKAE12GsCnBAIm8DsEsEINyqAvRCBNRWKy8N0uBCiVRF0zugrQwG1mmCI+S3VwMLiGyyi7LW4kT97CDQYW0j4R0XFlhHam6DCYbE9lVKIgbbGP4JNbCYVilsas7cbjCcwZdgTih0d9y7WEysBWhYQJswzuDTsATRu6PPc7ZVKSi8msX0o8Pa3iPugvRtrfjeoX7bmLSGtnIxFZSweBVsTb9jEVin4H+I6CqAHwt0I8DHCLtRAHfKf0WiRhizrXvebUxEbCUChk1EApt8tBv57yy4OhqFboVdsshrOW/FXRWy0U5sQgCvXQVY5ajaMNAGmy4pIijySWXJVXlKfl1ENTmCRxgDRERRoGeQCQMlT0RHDpB+lzN1vNforTa0GMXbkitQ9a8ge+V5YDp/MOTSB+23j0vTA2Z6Rg3/zsRfO+2eHPQm99U9N6f2TQ2VkiVbTFXeON+Lxo/Yr6cq1fI34qO/8M7z5/75157+Bx/S33hnWVV+5U8+8dsnsvaRg+pfLf6VueDvOfJB/qLnxY+8a/92deYPD//nv+j79v4bt6YPakHTFy1mSovqtHrg+YlfX3768FX/0RuNg6/MPBGsVN43PDdVliadPsBYDmbrTuOGRqrVV7V1wYdPL5peZ/JKgGkUV8UadKDBORhEiVWtmizOW8Ct++SspFWsujponwacmMuNuAsrI8npamJ5KoqJVZRZQvFXrVtyU5RydPWKJGILpenYQsENLbWop+WrTCt8tUBB6zGMRa35FrgFFRiAbAOPxDxUYLChxKvWOQdAAnRogAPldNXurtTSDru7cmzo+nHpVrDkmnfVSijJRqws1cM15fpsf7rgccQM/U2v0lO191eMMU0ZqNrrlWGrePVyVHvGONYz6df0sVr/gDxlKwZLmpRq+IKlRtg7e1vvXVgKx6PpVMmvJ71CQAwEwrrkqgSxNHsmRnG+Hq001Oflm1Ix/GW154D9tiqZ/Uy81ngmXwsAQkPsdxe9lq7WbTdr0RP+yxpluWFbqvfkUY7Zb2jIM2ipWjxkyyWklA19rjHslbJAstFT1r1zC3FH1KivyDXdYdXkTh1w+85XIq2ZnWRtu7ixXQJbaqWO20Jh0et3ufVRsXXJIq2fs1gsiHCZhtbGWz9k8d8ics5uyjrOEabD5IBhpoVp0AhT4TEdFcbhOmRpzJKbZKrKW7sYyXXxYNBVAD8W6CqAu+ji3mDuvMgmaPWB2w37a09g88Ih0v+F+9/w829Vn52/kliV/IrIe5X9FRkx1/qtbNpGZac4e9Ia2sgBixHCLmngu8DGwUkvqXs3DFy32RLKxlRgkr4Ec7vZwvZU8V0TvSYuIEvgvZHR66KLLrrooouHBYdhVVVbKWAXbwWrV1GlimoHuytrKwXsOxhEazAOA6BCH82q801x59Fgdao5oTdwS6RqxOyrn4qZjjMEeuFjfGP0g19s/PXbuX59yssMlGESsjApqN8SlMHJYolChJANAIlbcBTKtHSercwO4AYNa1m29BAt17U28rWAz77KKi3jE8X429PAnVqi3VC/bQjp6jyxXdbhbR8TZgl3sqQllM6+tu3wT2CEKQOHQTiELqjZrdjfLFEVM0jexBtgaSNPLIS8AZbEkqKDr4Fi4NCoZPAbzcNelfZ2dkHubNzb/oIiLNyqsO+xiBVzhDpF0m27oHtEGW3uvVLvWGlowmhUkL4+W9khGfl6DyCcFQVEJ++wbNAyfezKgrvo4ocOW3TGpeMPobP17x1tuy0CBkSf3dV2uR1YlQj3tDxdt0E/sMHxtY11X2dHRvnqmrU2sr9A+5jbvTBWrXFpejLbXFXBOwonYZ9UTk5GbK6qMBzO4QCyuvuQmT4WW5JYyRN+jq/lidwmOMFpYISptznmwkoSVzFf5RnBd+qSfHPh8NspTWxWcxemcsfrJdl4VTv44xOFrOfrvrMHjeVrqvbi8NsBGjcN37jHuBZUg3POo8H8X1zvtZL2v+VOjY9oP67+TvoTHFWyb536tWA9m/YdP5G4Mf/qz0zk8raK7RzPTfT+7fCnXv1to+ft2wemnrgwgu8f5H5/fKGyr3bpW2/854Mn0m9WBy4Pjnhv+Qof/S99qdT3jWDNk+jzzR92Lvz+zPsCYT2k6FeuDGoxw5jWlN6qpcgAWZQjVeuWHB/STa8zPeMBGMU+WCXlwEB5qoqKI2qICKZekvMNp6S48xVnw3CY7qYRdPuKWDO+5rUAZbhqTcpEWp8ZWGmZMAqkan76yx7KxbEQYXC18poiYzkGJXCBijGpNXvbicsaaQZW2odMY1rTnjQSsQXFZh6Ubi+d9QbIBckuEC80Ait1v/Cp9sWzotmwlxW7a66Xao9pg6v/p/yTy2ktphWf7Z+5/tbAVRMqvBh/Z2z2IMFKquQ/7Lt+4j+acq1EXkNJ675e9+KxmnbFk7EWDzwTu4g7mqm7ftR+8w9U9f2e10x3NBezX64ezCwF/Jn6E95r8/Vow3BoNtAYts3elILfrI0YerDPPWWXSiuNwM36hxSbGZaTbsqXcseB5anovJ4AtJhxoXS6pjvETQskS3FxFmu6IzXgvzhz3NJlLdby8plu3vTWsrdZsujGSsvN32y7OaCGpXdQwpv+JNtcb5v6HYTFFj0vGPoZCLXI4GVIQAYiEIUl6EeJNAsy1vz0bOWw3PRBancbVDGdlEycKsagmXaZtSb7K2LJVHMAVbHwdXv9dtHFHaJLAHfRwiMv5rgjpIc3MU/bg1Zw67BLGvQwsKU65LwO4FOaDd4E5CGkg3AIwqTf7/49fvrL1sebql+BVt0cVyEhtzhgF2ym+mWzusuthxA7YoLB7TngBXr3ytxvXeZrN/nB3VOwIhUoaOBt5BfrNijedh7JugWyhFfYZMj0mCJH6I6SuXeBrfQfdy0l76KLLrro4n7hzuxpHxAqmuIwLCOoahmzoin5+HaSylLA7jAbYpUtFwqBATMQgRCoW3tBbxUQeVoTxY45HeV351thaa/KTA0VfP14P0BgCCB7tmc24P//FP9BuWqf/8tEs7j+GrigDMUGSw1CNmQvKReUcHjwSsgS+yQ0OAwFcLKaazNbhyQYpA7/Q1oObEC+7hT8U9saWsep42w3WBXd1DbtCSJoYINeIEh2x/bAJZQ2kSniMfF2N8V/wvdYtLk1W7WPKoa56/axneGcMGR2ZWtaxjSCart0oO3eTMsLOtGyd267NwuiN0tYJUqrkk+0CQyQzaKZOFU0tXXGhAOzsMJWKQvGeq05p/XA+vKWcTkpiUivjCtDIM78BMPtAEx4fbfjQLF8+yOxlnDDzhAQ89d28PXv3aHeDe/bKWtbB0F4PCwmtdLQZivrGfF83ZlvHXBnDj1f7/HZyvl6z7Ayla4m2jnTLrro4r2NbQyZ16EzOeNiB3Fc59BbaO9Y6zwvaRXB9YpWppscT5jt2lhtyua2Q6BN01DtmVsxwT1r9tjpAt0wHGva/Xb2Q3Wt54BBs5ZlJVJV3FWfWUsuRBLxbEGrixIcv7uYWQoYKe1Cuv9C6md+5cNvx2Ivx3L5lH9fiIKd1G1GrjEaorCAXCSkYGTxD0rTry09X/DbarqDGfSrXt3jJRphFq6T+LvLudneqt82aJuQnfInzOQXrz+vhsofCI9pxYCvZyWnYtP033r1j4rDHu3cPzv4F9ijDD6tvPMr1t/4n/6r9Djlzz2X/gEnDq388+jfOjJb+VZMCp4tZVKnpUjlRbMQ/UZPMXhq+snXs7d8bx0Nn2pM/V7hgymXmi24a25HfelAKTIol2srTqehB9/+1lHlSDVoNW7Y+xSrChw+edMqes2YvaDazjhuZiR5eWBAtZmAvd/JmEwJTNyRAh4krdIODisNLSdF8w2nz55VJTXmXxANd69XD/vs2dv5fkmrOKIGaBYyYBVloi1WvtTidMHSZWssJKJQ0dy3yReGO1y7l1qjj3V3XcvR0EhpaNTSjiTxgK38Ngm0qle+9XL1rN+7ZNXVkC1XljJWyTfkmu5lISEtTGWejgRX3Kxcqh+Yd9n2V0tPSuNv2nsW055Pn371A4eVm85YbTZq+pzBqm0osbJ0ZWTOU/+PI28OSpGvf/mD2tPOMXfawLcy6frBwrM4eDqc/Y3MSK2qvLL8oXA0fcrBxYs+P/UVj/ObV5613rITkRKjWUzUaNFIB3scmYa/7KdyNX980HcD/IKZdiNLWsUz67QNLc1fSWC0CN1JAD3txQVLEIUS6FhjIfHLMq5qyomqNSk3f2jtX5wo16i1lLhLLcEu6y0hmz+fErhQnqpab8mrlaNtiH+aCJTBAy6YY7WpcBRAO2zUdAcqjtHmJVQGmn8j7aCrs73gCJOdkecw0wnmUNGSZlOho9LOLt8oQZf97aKLO0fXAeaxwf21gH782d9NGd9tcGdk8FZS3R0d/w5vscr4+o4Fp2EJDqtov//c78Ze+Hr+aX3MyyQUYLa1rhf6O6ybZ1pP9JW1BZjbeOx4aEZdm6Gd++vEjmmR7ZngR5PA6xzztI9whCmRBGzfG+KmStIHiNSe2Wx9t95EupMA3shl3uNJKONap5nYzQZzhNJ7l3R7jOCifD86C3axS3QtoB8LdA0AHyM8ihbQj1LQuC7829TCdxs4zMZ2TYJXOhhfsZ9Ng8e5zVxVhD2t2nqbgRSUYZrKOBMVznekZ585gPq5n5n9aPFfLfyNqz+IcqtI3MM3GxySSIAD9kMSvOCBGai0tBEF8MJ1cIEGESjAcEfBfvvgXa2kGwCiJZiYFjnTZmOwzVQ1PltZlUy3fXMFsJtygBywKSUs7BMFTbgjH7wVQhTadXUjTK2T+W6P9n47J4A5ekTAEKIQIN1LWqUgIsBUIm5XC1miUTN1XR2u4E/jDqPXqAi98gpecRiCxBVHskCvhtkZerkoA21PbCFrECRrkKyJ6mdlmxo7ccCdr0JQu45/Fa8TDItTncYv9guUcPaxKFhqcQnE/L1S4t4/lNEAJ8YicUDfNakPpKuJtlXyLlcRgx0hxr3jY90JbY3vbKWnU2O3h2h/2X7H4mylx2cr++xZq66aDXUdVdz5HSsNjZaDtJgWcEjG3XHMXQvoxwLdCPAxwhe+duGlyfT2y3hGVzZ2/20XUQlXie0zFZ12+rv5g9otJ72Rx71T+cHutrDG7dlgTZqk/azrZA1nwI0Sq6LSNr9VIk1HvaZBtAEllGDVysjDgcUDT1z/pdw33gm4k8QBAzVDoJcFAxUoEqo1XHHp5h8kf3LYVrxZCALWjCwarL7fvbzikqX9pWBJet4++7asyCvqX/3GIe0X5v5m9Upf8RXFdvzdKZ2Uy/Onv1n6pU+P2rJX5o55/vQ3gR/5ub9zu/cd+YkPpS42Dv3g1zXp75w7xWDt+56eTC70mVNT/xYYH3km9J03jJ/4dLJ0uzgc/ZPSj6bnws8dfKeYPzpYSX+zOJAJSn3e2RjFPmkymxpJNkacsdmYUfuuMhjE8srzOs59jdJ+acxFRSZfQhFxkQhyFukF3q59eMFmL+teYElx9TsWPUZj0HWjSr2O24Zex61gxNCBLLYF4sNM5QjqOEuNEJDCO1vpEVS93V2xuar1kiy8uMUV7DToXu/g3UHhb+Jj3LHMcGIRV2VhKTzQM9frmi00AsVW/KDWbaP2dz/Nla/xvjzys9x6tXS2tBDJxBov2m5/aeqsFK78pHKhqvj/YiXRp9QLc/OD1r6vOUOjfQs/mvruJ8f+7ZUXP/vu9MeX/NLxA99aahx4d/no/tuW7YnJ3GJkbHH4qhFQCtWQtyx5KvuvVxniQKU2pZSqHnWutzY4WXnDEx/Zlyrl7IPO0suXm0MpxVMND2SHzfxUSMvpnnpJ/lDiu9/PPa9/zyvGHYmzy+nrAWtBZgb2w62WpL7TIrvF0SoDG86bIM51lOGqlZKbo5hO32ZY85ew1BoyCEy2fkftbLNYuAQy2j6jVnOIbjL1khz3piWt0m9bSuF1SEaEPFBruFJ4PyO99SoHaSl9O5OZIgGrUUkw1zbdiZopoDlAE6n4L6/mz6Hb9PfRQtcC+rGA1I3/HiN0CeBNcae870bsigm+OwJ4I/vbsWLjMm9faT66fv7rXy/H3PsDA+AADRpQGB+fAkA5dOgIIJJ24+Opc+eqL/3TFU5ITaJ3eEtCtwkROm/o+LsN1vV6EdieCd69KfTDooS37zE2wqQggFNf1j/+419frjsSo8OlhpeMDIyPz4AXSUJqIBcOHR3AWUWq/NH3r2tDU69kX1kg3lbGbLbx++WY3cUjgi7r3EaXAH4s0A3/HiM8cgTwIxMx3nsE2IZvobKlFHgr52cT0lt00xDl8MEN8wUHPE72On9VIg8///WvO2JKT+B0y1WtAI7x8RsABA4dG6AC5MELhS996eWXfstSPt9vOWRFq1o2Gb3VbQsYaCV6ii2VjNFUBojUD25YbCVPjS1Dx02b6rURs+e2ooFpRTtltE2Z4Db6WATumgzeJTYWz3V8pJVR277WYXJ+Vj7Ky6dD/8FwSuHIwdxKkGCVjH18fAZ8UEDxUi0cOjBAsIpUuTVx64pj+hvZC7VS0HDpBs7F4r6yavfZswEpC2QbgYCU1SgbOIEAue3PyXsbm94Se2XjLOjee9+OwPZ3+F1ArwXd9kzb4XkPt3xHaDPEnaO5Nhks2gmvkyPH7DnBH3fyxGzRIUigSwA/FuhGgI8RdiSAO5tojjDdWdfeXmZ79jdPsDOJsZcE8L1jawK4kyBsTouYZxtXarGuOJ0u0JputM32tO23LX/aypJmLctim8qBqj1SeXb4DRE5LOMTzUoTLAwzncEfNKtvqKdMnOeqz2j2TBFnNFteCjgnrhw8UC7WzyTjut3uzl1dGP5C5Svv3rLOz/G//ugvVnAMHzpA0EXGLpMFboxPVmOuRso6dOykXGk+jG6MT05dmKvr/6pn6bWvHfnnUu/rT026Z299b2j8mr+XYIP8R3suR350QXN9Mfc8JjGtWI4W4ro9qzUCVN5vvxph6U8aTzmQLMNxxnXz24y4KQ9npFTA7pDSF6unDsvXQ+RW8DsxR5gSZWdAeyJDYJJBINsIDEnTWfw6Tr0WFN1niyhOTAe2CvX2436CwUpDS0iLy/j0WjBO8WKtT3wUs+cM3VtQbY2mkzRCwC0ugSAUBU8sTI/XXUktZthcVaBNIUOzwFHxVO3hinCBzhWiycmIe6gw6rslS0a2EXDqTs2zqDTsp6QLn752PpnwhxbD4Zlv3nzNeuZXvz5bTRw6FAhLtXTD7gg1xl9P22L58tKQJ7ZSrHMoOBx2ZtNu9Y9eHd/vnEvdeinq6Pvz1E8EZUf+GBdsJFyzlydOmXbnZwZf/p2bP0KK8P5iqF5qTLuuvu53/3xRiMtrsw5rWgaU3qo1LyuDVSstswy9JM4up1MBMli11lcutdywwzCDMlq1ZmT3k4X6ilyrORxRwyeV06mApcuKvRoeyBZUWy3dPCFrePSZ1iiAFpvrQRltujGvntnW6KD3WHL+SkKLGeL0NicM0BAu0zXdYXdXAm5dcxeGbcu6VNVxPsXFSYbiLABx5hsEyqgSeWEtUySWwi3+o0IUzvAOEJ7UhRtTRVMcGQtAhblWuW0r2V59hUYWvcGN0sOhfk+3FNcTMAJAiaZpdVeITJcAfkzQJYAfJ9xHAviRyeXdEfYw8bczB3zvBHDnSW4VMb31Lk/c1X/l9PTs0NA/gI+TSPAZiGwgd9cVP26KnShh91Ch0/BHoN+xuGN1/J3ynfePEt4q/ddZ6No6hukRJv/u4N8b7rvjy/GNb7zy8Y8vE/NyQqIHQqvntk2lPxR/OTfldjuNLt6TeAQV9l0C+LFAN/x7jPCoEMCPUqy4hxGggMNsaNnqlhzwChhrtb+Crt0qCPRAX8fbgQ4d8ByM03id37rC/zCXG+zdsnRsK3zjG698/OMXCO3jb3uwQbunmmB5ix2dutbB7JAFu1tfx725heOmljDsJAVm14FHW6vax2KGgIYp+uYK9apQxxqod1dZ1Rb4bpi/hnFMVxNAWE5GyP9CoOdnj/7yne5oenr2S394/aV/ArAaAQI9aKOGzVUVUbSQY9JBod0FhEWnuyXt3avoTghtRSaXDiPQzp1unH64uB8K2o2497hdHGfMnkvVHl0/njvSSe+Iwgs/sVeb6uL+oRsBPkZoEsCdPGiHBLb9pB5Wpjrlv7tnfzemRDb+wbbp3rbZ8sbewBvFA3tDEm8kgDvyV+tFopuuIgw42nVyG1ZU7FV7uGJzVQXdGIxmGyWH5KqIp/b0wlBTqDqtUeLoUDZTR40Xy6q9177YJ02e5aKFO4xRQZ9h/zLeZPHMuOp1mrUz7tk//s6z2rDx4tA7hcLAGzO9v9L4/iHf1Ynk4KdPfOpuEoBL+d/5s1+7bNZ+6krmW+9zPtGXO/7q7+bHeOVLeP+PZ0aO5647P33NcH+/fsA3w2IleCSRGg+p/6P8z6VF78WekzpmqtEzlTpsuMs9ntvVhlata6P2dzXMeeJl1DgLwp5ExuZlJUi2jOsyh0Tg5GzY5gr9+YbTJ5XzDWd9RTbymhYzgtFsZikg6NgR/41Ja0h0rxexol4Ltp+A4s4RVKLirjqihj7lFYxvOJbV3IXFYiRWLpVDdXEJxLOpYTj87mInGdm+iGFvUQobOd0D7PPN5lKJbN1ppDTSoKOEq1ZVVnqrykBeHNKAPJWqxU/ZJtJ4gX1Lxb9m++LV+qc/GPw7L3zo9J1ejm986/LLN3938rh64eLRD7uX5npXalfdsSfNWNb4g+IxzbCFo3OzlYQULk18Y1jxVe2RSs3msG7KAAkSw8vvl6+PG4mxmQHrq7LyYtW6LpOAIlRQ+qrWnCwMnOP79Nx+mzGmJc4u7zcqZmwR8NRlr30eUDCq1F2UZWwRZmfY7ydjoFpoCsYkgxrlZKOnrHvnFuKWLouT09QKz8jKQNURNcSFE6daGKgsT0UdUUPSKmJalESImNZr1iVXpVFyaO7CoH26XeZYRg2RUzCCZBMsruB1YYl2JybaCl5hSzDCVJbo/smp9LA7PKavltiuG1LNQ28zbV4ZB1aNMx8w+iC6oV1jG0KOvETTpvqHFl0C+LFAlwB+nNAlgNvY88SfwA408KYc8DYE8Gbsb/vIw5M64/iCvzb6K796J8e4ii984Tsv/WEDaPbbGm6lF8e2WEGUerWTUxuxhSfPpmpggd2Qweyp/nVHumt7mS8d45yNw5v/Kab810c/d3cH9oUv/NZLL30fgP3wLAmZoy0v7q2J9m3O7abYc13CQ0FXD/0g8eAZ4i4B/FigG/49RngkCOBHKVC8T0Egd6QDNjfr+xvbTPV7CMAIqhXVpmWrmDhuWlSpf+Dc+z72xN0dp+T7fRwxohJuCEq80CrqN1uhoOgM0g6I2gZuodZiIhnaJomHW4bSG8jgbQKV7Rky4fzmI5Mn6GPPQpcwORWTbQXEWxHAm0aAPlt51Er9d9FnPnzqubs7JEn6OfDCQRgkZmefm8Pg6mD9OwJskfRsrri2yHIbKk7E2zt2qxXnXGi53JSF/liwthHy7elHCsIYWSRnO7/+HhKT6xpY3gXujgx+MET1I4UuAfxYoBsBPkb4wp9deOmbaeiwYF2bsWkrgDsJYDo44DYB3Jkk2cj7trHpH9c6Drj9divLkL2UCO/kAr2eBu5c3oRSh19uscMERawbWeM8bHNV/e6iKpkx+0KqFvdhDdqv1nF/+cLHNZ9hC1X1Ka8wiw57iwdj45ZUC5Ab5WqO0Fuc1GtBY8WbvBRRglXHiFG5oFklOe7Uj71wdflm4rm+2xO3Qh9dulZ//iP/7Yt3mXH60j/+V3859cX3hwKx/DtTqcVL/x7gKYXT/xtvD//YgUtfGfvAi19xvng1dRBAqx6urLi03Fw9XEl7b64Ew94ikLxWV4Z8YbX4M9FXX9P3WwnzoHRN3DCin4XoKJEhMF47PGifdmJm8U/nD/q9SzEK+br//FcP4wcFbXC1WDBmz4kJka3a6H4hRL2AiIJEGYEoYqjNaUZecw8V/O5iu2AuQl5Ih1M1f6dHhRANV5a0kf03okZZdmXF/OnaYMiWu3jruN1eMRY1z5mV4ushbZ8BxH1pf0D32XIfkL79LT60WNx32HP5tFnsnYs85/M8+fn/9u4ux3f/2T97M/Zd//dj/e7f1OO/ePmJQKnQSPZIL+a+LfsTWdT/K/2pCxdG/t6pty44/alUzIgV5xbimPTF0saEp+f4YsrwNZa0cDBHtHLlLweF57NwDm/2nWkr2oMknsiiVYVs2u8uHpavj3JNcPZnuJBgLkv0W3zAz0qIgonzImeSjRjglbLX8seTVyNoKFbVKsgsoj1rGHlN8xnxaHrIc1OcQOAZ+xtih2/Unhm0Tw8z9RYngXQ1cVi+7iMjGnP0kfKTERbfvSwMMx0gvUBvGVcP8yqlLOEcQSelo9wIsFQyozbV7JtcYLzVUmccxlt1q0WqKRpZAClAI8tEhbyFTyFvMfGwVbanxaubVI2YvTlTHOd5nV6VefOH3ZK6SwA/FpAkaUPNVBc/bHiUknq7wf1L/KWH3XfWGPjOt08rCp8YZmR4yn8P6YgzZyLMLjELBbi6WXC8DvMQhsWOMGJdq+D2FtZG1SKC3zS+3+gVtik9uancdqfD3Rw78rvbrru+vc26wUk1dPeZpjNnjsDNVkr1IklInuSqDHAU3mott/acF6+2DmAXHXE8oyupmp97lhE8FC1yJ7YZc26Kro75XrCbn0w7n8799+TsoosuHj88SoHi/QsCt8PGum4VYpBaO7MEWgftR7P8Sxxzkj4j7tCoMIR/0Xru2chdH86v/Y9DL71qNsO/qTrzNnrBCQrcBq3lcDgPWQi07OPoiAAnwdsxf6WVIW27w7WY4HWRknB+s4eNhuGY7NAVAf2OxXwt0O4bKmjIzlcBQUwKSvguuOE0flpNdoV1M63muypmhkAJp4tyaQumU9CNnd+rCBxjf2DXXVLWYnp6ttWZ+QacI3WC1EkmbThtBCAMzlZpphdcWCHZ8rS0U1Fg1Yt7UzdxQHFXp90aYA8bm6bm21a9nedZX0v3dn6UriZEg9hHR5m6kevdlP0Vad+N4rN7gXuooE95t6/IbJ/23YTQ7Xusiy666OJe0f4jXLzzBrpb4E5H4p0ojoU6/y0tXVY2W2xzbe5dYF2Ca0NVvWd0pZbWPB1JqqKIZsSKakeH1Gjzmau4q8poHqiltaaiVFhA6zIGseO5lOI3dK9aMfMBblbPrJTcJ06du/TtM4peTQwvk5HzAfsR/3kDZw8Lx7l+k+MxkoF8ZBlX8lIECHuLjTEHkbIULsb04vfffiJkL//2+fgvPfenb73lfXpx+a7PR+8njp99qxD/n15LHkH9fnPmWxbOL5Gb/5PsU5wYPM8A9R597OpTz3nnpP7C2Mx+Q6v7+1eO9q/I+dq8N/jB/bdLKM9LN/vMiZ/wv94gWqbwDsdPc14nliTmoig6HB+3jwN+MiqG6etP4axRC9qz/81nb/zbzH+hB0vT+YOavaAueybrniSaPWz0VVfSSkKocv1uVTzKm5pgTy6lrUYdvceS+brTmvG1TYaBXvtiWaoLxxEn5giZsj2l2AbztQAicjPQegxbqHrmwFvDTHtc1UvsH2a6hPMZ+0KR0MEDfxShMD8cA6ofrIsuXT/gfeduPoXJjeihwZ6x456rvpLyTe2AuiyV5oef/PxdXo7g8y/Ozt9e/pu5CPFrZuEH9f122f217wz/SfjFJ47d1C8Ejw7Pvn9EaYTSxaw7E6kWFFfIVg5FS9lQ/YCRn9ckv3up3AhN1IK1aw7FXw25ywuTbmtFph/Rmtr96UJbJ51cCLiHCvW0HIxmgdeTz6TchzRv6rA0n2GqjHsFb4KFDIEkLqBCvSzV09XEfous7hajAyssE6b3g0mnWTs+fLVWCvpdt0o4n2HslP2f5hlQyOSJenKF551XDlXOX1eHn+TyyJRRjJs5WSnjBuaJ9LI8T+QjfF/FzBP1sWSinl74rsOwmlmludYwxAFFQjf0osaty4RtO9k4d+bjtyjKfcA4Dz4o6cQVYJUDPq9z2t0UAZ/utiXu4nFAlwD+4cajlNTbEQ8g67dnHPAG+W+b/e0owxzqcXznCHepAMbmY3aJQuttUvQsEZk815olvRIeO1nIAh2DB5GQWkcD04qY144xLF22OpKAmyZK1tGT2+iDdxx47KFIdCtro9WcZvv7zns4eZd7OXfuGkRorn+x+ZoEvCSHARLy6r56W7rtNsbAC/1bbN0DA7uqpd2+dZ/APSoSHjx/rOO8l5FqG13l8VbozKffO0w2TQV00UUXjycejUDxwfC++bgDHL6FCrBGCtwHK2tTEkCwpfcVamDxmDLWEsCt6QmGsoRX8ApxarinGMyne7377uIgp6cXpQGJ8zrX3RRrFBpQYkyFJbwxNBt2iXIdWWqukJWa4d9hVoNG1k53xhce0Jvt8TZCGPcZKc09VFj3kSC91Fpwe6sSQUy2+UjhHafjjJB3YorWvAFytHyJBWEsFl7HFqfxp1sPrxJO1kyvoozWtjhew/6mm13Q5kkwesde3Ks4cIibzo4TepEVIEESiDaD8FqDsATQ27Gi4DFdW3vzAGAhg0wPhNdcEiEmlrSK4HHzG6I74ddtNtR83SmkOW3GN1/fgaEUVGtbOFtLa5tagu+4BbGRtmWoSGLuDTGwRxAHVhwLbWV73ol2CO2zldu1Dm3sbTfiLrrooos1uLodBzzB4AjTEwwLEfDE2gqtzsW238mwMrUxV+AZXVmtmhoLtc14tyqdUQby1u5FwDvKGNrYwP4C9rDR6SHR5J6PwkxHXVWxua7irtrdleJYSDxABeloc1WVtretVjnAUsrl80u6aavvqxtTZig9czJxLJutOwGtP3PCfvspXs8QcFLKE06QmiRiyMVYvZoMRxLDy+nrgZCjvHJDsd5wH/6PUifj488XK+/c7vs3k5/5+33f+4tvf/2//qlf2PV3XoOw1PjqX7z40/+q3HP+gPNnJ/bnM2lXrG4LFNPe3vNfHXKSjTFnnPr0DefPKF/JRldu0P/C4PkcA38t98fX/ANvBt9XKapvzZ+IB/IXY9ll1XU9MzTvDTrN2hnPO3/W+Pxcod/vXZqt9DSf2gbu0UKPZ3mxGPFJ5azuTsQWzIaaW/HoE14gMZydndlnrcjkhJRTmzmrHY7NhP1JZ8Mm2u56KK/U/YBiM2MgYpKoVUrV/PWS7Igalu6lFV5eKR22h41BW8Emp6cagw7JEE3ow3Iybw0Jvt8Y03iZ13nf66PvUzzV8EAWHxrlPMRZCJLVUY8wVkKxI+cI9ZJ6issnDtwsEb7e6M1PHzC0csSTfr+Rrdw44vpg7u6uBTCb+e4fJt//i84/dzh5vX7m0tjR942+e3SwsjLrCKTNd4Phsbfjob6yVDiLJQdv0Kg7ns4Xp0cqI2+pKwcbH1phBS1t1DWKV1NRJVY9GLsRfbJ+nPE0vV4yK/i1hvzu8BG/d0n0M9anvInh5UbJcdA7EU+M1SgMM/0EF4AASwfNxpIaE9Mm3v1mekW1l2RlRt5/ymVcch3X8Sr2qlWU599OxGP6Fe2gqaiH0YeZMlC+xM/2sNDD/Cnzyoy/36BSwBdCDzB1e+iMjcUBcxaoqFKAjIq5z0x6zWwpYI8trDguWGRaw6I5SNOoImlwk3yK63P4e/HIfNu865P9kJGHPCxYjEDMDdDIctoNcNDFjZaAwvewxcpddLE9HqEBWBddbIMHpvm4fzrgjQ48LwYO3P3m6rdZBidcbbO/Fzs+TrSUBVBoUKgSs2OXoCUHaWNsMw6YLQXBAuvIyE2j/3X173dEHO6G8NuGz1skvpXN3fohTftrGpzp22jduFucOXMEvrJ23iUAvM2cYBLa9HKyxcqvw/psagujWw+NxK3USgmu4+l3xJ16ULNHjna7NA/fW2x1U3UVxl100UUXjzIesOo3H3e4sjVH5zPKhG0EG+lWY111LfsLQEVTTLzACt4FegU3WSJ6oHqXCuDBwZ7Gl25xzc1si/1d0aEAbgoNHI1msGeXVtcJAGtjDBGglcC1vmiwmTDdWhAr2Lu2IgHorDxL1fy+huqzZ3f5lG9Ha8v4aLHCeken3k4l6zK+EabLraDHyc67WNf3dw3aemiDwehdEsCDg/2/9rddL71UgwNws3mYRETYB0UKPRRcACmI2cm2rkugxbI7O45kU7T1rtNrbjBrWBZpU7ao/8vXnfnWqdtU7Cv8GHdLx6Y2qwi4QzxS1O86GCmt/R13rKfM150bGfcuuuiiiweMdZLcrbB7E7WtRvrCLEFMt1uxiozQOl/oO8Duqd/28h2JqeJYqN2XVDQuzdedq9zzQGv7HnA3nVEsZM1dcQ8VhBfxYHzq2sVR91ChYTh6PMumSwVqdn2AlcXivkEte2FlIEZpztDyqp0VsqY74puxUZphpEo9w1AZtZ/UxeJTis28/p0RcmR9bsuQF3Jy4tls8t3Aa2Mj9v5Kaa7o6DFry4GpJ3J/54Nn7vhEtVBddAy+MGuTg/XvfVX5MfXC2TPPvf3aBQZ7juF4neU01jufG31+rHCiMHRtLFcMxri4ovY++zt/aLyojvhxoIU9hQOHLt0snvjO6y9wGeIQQYlUzdjzgOYuxCh4qo23p45iQBb9q97ZAxpB8lGMaW1ibNj9ZKGS1xRPFZXklQDT4AUnDDfVJtdTA4nYwrLuPes7d1S6nsZ/0F6WsTkoTdqHmqV+irOH21lfwCEZ+vFgMhUHjGkNXWZMu+IKURpsRqdLsEyzOe4iuMALg4jmtcIJOc6ChbaCf4F4gLqfhQBZFQ1wUgqSW8J1mb44C7IUHBh8Z6oxmKPxpt7ztiv83Q/cvaeIcuCTn5h/48vV9yf8F/u47hwtf8L11Z/u/b+cYZNG4FDsyQvDI8Di9DGzP5MhiM7MYDZmWzTCBw3J9rV6MOZeSdW1dMGjjRoBt2540WvBjD3wDG+OMHuNQ39V/8hzvleBrLfYcGhA8u0IYbIx92B86qBUNdivmBf2XZmq9CmYqEOm18yb+Nxmwx+YreOPU8oROsLYQf/t+ZPDc3hu5/v1Ke/ClLt3QB2Qp5bxwdA88SDZGlUTbUmNaVhBbpuoQJaom9sTDBiqo5e0SiHAksNs+JIlTLRbEGq5OgMZmCV/mXoGW5DkErcsgIl58vN3faYfFeSFzHcDV1Bqzeuyv1084nh0h2Fd3Hc8GqqO3eDhOP7tiG0aAG+BCYZE/C24qN+6dv2z92Lm80oeXHCxxTUKnABW2V+BRMcvvdTKN7Uxu7X2dHfYzdhj43DiHlnAu1CFto9hkyGKxrnxzMdO3KUHIABJuAgnV0lfgMLaq9OefZJCdc2chLz5KOjoZn2d25z9jg1ehzcX8Qjc6VBtN/KI3WA35nibqivuB+5dYdzVFnfRRRfvTTzsQPFhhX+yWb+DpftgDgyYBg/0bbKIiQso4RRq1OuNwI3s/ODgXcdeE7yzACUotYhGYD/ASg/eEQo6XgmHG6cNIN0gLDV5xAgsd1C/rk1Ia+hIm+7EBAPrKs+KkIkFNkYL/Y4mz7nnsZ+bcoBcW+a7EW1X3rYUtckde5pJq+ml/F1zwMDaaKzYcVESrYQl4CUVACe48EqUJQBZwi5Ra0AHZ19rrOHvgf1wu6N8U8RQK6vSYWtYtpZD28R7GBuiQaPlFt5uFy0yWANbr3IXMHYRqd4Rhlt3bxudB7knx9yqp7yLKsn7CmvGtyOD3jZpfyBH1EUXXdx/eFqFWVtnjUSH+LYImI5mwHSwv1sNeHdT3r1NJ/UHQf1uuqKJPuOlSPEotE6S4q6uGlC3z1jr0SAK1+olOewtFrT6xK2DntGV2pxWqzkmpoZHjk+67Rk35UwpmtAykj2NM1521UlRL8n+Rt01mNJrQeypKnUP1RJGFr8de9i9nNYjFOEAtayDS2gfKaWXPKfOzKZqavKd8NU3lnr/oeNX9/36d179zErvV+Eum86WsD4UXXJdePf6//xLv/pbJ5XqyNnAmycyuUu3/bYnDq7EHQfM9CXdaS56PuVeHrFfkpnOoPxXP/Yv/+mf/PJ+79R+29TpMz2ZfaEfuJ3+Z5bzT/lvFHuFwDdmy5u2+kyqz3B7JVdFUwwjrcWjevHDZWu8FR1pUEL/Ey+DoKMdN3o/uOSzZ4ekaQc2DytHuQ4k6TFRJ31DZdS/nP5r6ZTH3l8xxjRAGzaGXUUzUpwd2xeOFgd7xnpYWLSXBxPTw0wZCdWG7xZBRPFfLZiq+YWbSL0k13SHNSPjgknQsZZla9lb75Gnogeu1Y9ndXdt1uE4ZEx7BhVbU2cqAr9aWgNqusMak0c+PTmtH0xejQwfnyXlOOVKvv57s8/+6vvu7nK4i3O/fyX+6z/6e9caz8ayzrmyI+3aV3ce2pe9lQsUPnz7nWf2nXun9NnLnkX/Yu17Hq9GMbAsORpagIkDPQuVUnBGPzBecvYP3wbMhvqxFfPd8IIPpUDIY479yMKXDwYnxv1PfHbh974X/9zPjfzb76vPrxz1+sn4qI9nTtwI7Nsnffc76lOZJz92lOui525Sjc8TR6Vd/ijsSbwZG9ATWXzSN2adqkUpHOByFMPAGSS3z5z3k1pSY/NEswTrWAVGHBR6WVIxVQpHMQGVQpaoYuJSl4ygqt0ycdFs65uBEixReYVkhQWL8fdQY7E+cENcYcTBRIURB0sBCkv0yCxWm+WrBatLAHfxqKNLAHfxSOOh5P7uazPgESbbUfgnjjx7bxv7DxvmnABvM5HTJn23Gi0Mb5H1YwfWcK8gYrIHxvPtjEh152V2wKUNE+s+PdGa7pRrn4S2jLuFtkS4c5yzzr67E5vKuNkp7yaGlLuuQuiUR+wIMcQS/oS73UEHdqmueOgtjbnDWoQuW9xFF108Hnio7O/DrfwzAvIaC+gdUxiiY67oCmy2gis3tKyk1dYmRDrGueK0ee4l5DAhT/NpMgg+OEDIgyxxVqIXln2r/X2BiEQJQq2H/hFwQxjSrZL16AZSjU3iwLZ/r8iudtr5rrP2rekOm6sqvH/bMcDG2q+YPbe9X/SOcFMGNMpODPCLvnEbF3NIhtAGSVqlmRrWWjRqmEQ8e9fs7/T07Eu/dRuKMNnB+7bROefEanVgIQHuVhNm14a1wNsigB0SThu3AJiH/WsVwyXoBxMmWxfXs7aZ8Lq3W6EzVtxlXr695V3uYq+wezr5nvtltsVtnbfxA4Aw3L4bTqWtzNvWnbWLLrp4bJDa+Q+2sxB/kbjwtdpR8nsXnl6dIuCdsWkGgw2PmJXWE00c71a5qU2hQhHMNdu0jm6W39YQttV2d0UEJ3nDIVGPDC3ldE+t5nBEjbC3tliMSJo/Zs/FXAt1KJRiGPLy+YClyNRk21DB1lAH5CngbO5Wxe84ZRaqqvLlxiddWHO6x/PjKw3D4ZPK9DOczV9IRBe/HwwdLDUSpU//V1N6yvd7zp/6dNHR5/nknXzJNSjDzOXya0d+ixn+xUe/5H31m7N/Zr34At+p4V6m8H1WLH7ql3uqh9TK0Mn42zf+5KMfyyL96rUvZo/3uK1M2R5MaX41K386+zt12/8yY2g5by6l+Xs8y2VbOV+IjrgzhrusNWz7D84XDlWD9uQyvsAR/825sObJ+I7P59KBhVGfzVX1u4uZicD82wnncO2C7azPnQ26vBbaHDGNcogcYKOk9WdGBpJl3Zt60l8vyUZKu7qoMRkFksVAcvJ9TKI8VY0MLRXsgSJOICEtFhqBCk5D98bcOdOtZpYCgCNqRIaKOd3jjdlW6k7g4NCcZumyK6fjlFyJYNQypDrgkAwRDcbsuVTNH4xmxRYSH1rWGjajTGI4uzAffkJOfv701WfyO8sStoJPsp99/tr3s0c+o30pfjn15In90YW3HXPWy8c+a1crk/uCOYK6K63To9tQK+ZILHl1YfiJeGoRpdTYX3VqsrbyRCg3aFuxcFpSfjmcShafs9nSuLilHv7O0FMurCD5SW3o1cYJTe2dIxYgN8dhjbIvOJsgeNScS6gTXjMPtPyfs6Ncm2RIlEUKD22frZwPOo8r1wGVynGuZgj0knJRPZu7bjp9wYWVtJZA5ZR5xWHW5Wxd15adzlwZv9vM6GrQDKjCWTpqpnLEskRrqin3rTgyFm64BFdoWEgKUoCeDAsWvIcskYW4Km9x3mo6P7syRJutTshbnIfTQLcTcBePNroE8A8r7kXo+KDwiAp/7xYjTNEyghYk0J9em/jPjv7Nu91eu9GmF4ZW7YUTawset+IFBbbVduwG9y4JFTzfA/AE7hzntFvXtE+UZ3TlK+f//Fc/9OTdbfzcuWutyU2pX9Z+emLtzA1kMC3j7k5sKhHehhLeiI03gxhS3lER7q7vGZEIbvsT7mrbD8SS+uFyxl22uIsuunjU8R6lfk286pa9Ftagoko7L9SJdspysGNab39YgLALy0VZOBj3hi/Ww8/d2S7W728aPkgoShhw8QQ0JE5KhOk9lly+FrWHKzXdYekyBlgovauUrYCly4RRBprErXt058TuquS3xQG7hwqODscZZaCyjirbnjZrqzTuGoLu7WEhT3Ar+a+AKpnCD3nVHzIMoMWMfNX+jdcvf+zZ43dxAIOD/dxMQ2HrNh5tTLHalLEIRRhusb8baOBCozXVINbh6S2Y4ECrl3ChFf6NtiK6dVTBVsxBu0yhnXzvgcXWa/utt2URtLKhUXFxw8T2MKG0k9m1wPYjl91j0+D2DgsfWStu29Eaun3/b+ya3P5I2JZWljRlIG/N+Ozuiuh9qAzkV/e1F4bbne6sXXTRxXsbQgS8zXNwkXjnwnexi53Z321SCm1bkQ2HxTBMbkb99uzukVHaZF2R6hEdf42UJqwR3EMFWmGJTyoXsGWWAkZKE8S2boCGZ3RlsRhJluLBaHZ5PmpdkZX9Va6hHKn6pHJYTgp+/U3/qbO8ElSXrnIwIM2fSz2rRTL5YqSypKXRgGQp8pz9WvIFz9XL/YqzWrdcZ4NXB1T3W/t7/u0N5R6EkYE/ln/5l3/n129BdencHzz7d2KfvzLxG691LrHwm4u3LQb/36ZtenHof7t28O/23CpxwoXDa016Pq9mfpCRR7429Jn+8uvnqmc/6Tk3SShI9o3aM8CVt3uVgWrIVp7uL1szvnAsDKRdlaziRPfOpgKAtSwDFadmXZOVE9XJyf7h47P5aqBQdWftAUCXjLeskyftc0mbfMh+faox6PGknI3CrNbjCRvrrCy0F4xEbGHQPg30kbE1fCpLx6UbOg7DpwILxJPxnnaeUPUEFZ/pqquKzbRJxqB8u0odiMj56dqgoXslV8UNGmUAe2bYngEGElMaZa0hv1s4gpP0LU+4t3hYk/5y+djEm//mfX/rV+/uYlwpLZ/qObeCL2becsrVRTOAenblmGFXF3678XMvSu+mcANu10JFC78ofXOe+C8kvlXFt89MLqp+t71ep5q0R5czJy47I15NnagdnliKLe3Tvps74jNr2bozEVtQbCa+FzyUi42wVVexAyzjc2I6Mc++/RphKkElH3cA80SBLAET9SjXr3JYl8jXmxY4l4zjPZ7lqP3GIgkF4xynP8J3/9T/GYAhhpka4faM2h8jr8bzgeSi4VRtasFxywq4FgNJKkElnFkAfOrU6lkQv75ecEIGJil2FHa+N9hfgfPwQYW8xUQFIN+qE/YplKAPlmDu4R1eF13sBl0C+IcVd1Re9zDwcNnfvRQBj8MhwpO6+EaCBhY4G79y11s9d26B2N/kOamZmmkngICelhhl13go2YGHyMPZw0ZnbsUzujKsTP3HJ+7+cuwd2mTwyfWfdEqE2/LurVyjN8U6krjtVL2NFnwjijsRxvcgudhG7rCHt+iOg95HQVUssHu2uNvJuIsuutgbPFT2N5/YTA15D5hYpdwEVsOjznhsHVzZ2uqblW0bALdhthS5MQi2Zo7DIVzZWm8gLWaUUHKEQuT7Kwt3/bx86XWFD/8IveHhvz7rD+iLsz3JbKD3WHJ/qZK3VavYB46/lW0EGobDqxXnCv2Sq6JKJh2NYBuGIzJUzNebmWJRUXinz1lls5nrVL+bot+xuJtOEJ3olPa6KWuUl/FFyANOTMBHxkdmm46/is2k9fW1mGFzVfUpr3uo4HcXT8oXfPsCd3Q8bUxPz25L/YpIS7x6OohescpYK/zqgaVWC7UNlHCqRsi2xik6C9mWI3QvlLcI8HaZPRdY7HhtTxQ6trxjo+JdFVfsAptWNO4VK7yu8FEUNe7aOFpYQ+92X1uTuIJHaW6qWS55J1u+E4jQut0ps4suunj8sFOwIETAbSNoNtQQd44o74793Q6Cm9uNPcPGgiEvqFv8w4chDFfXGk4MwExrU8Mtp5M2r9wqUheF/o6oUVnSxL+f16xndXcwms3XnTF7znSp/fasI2E44zajFpyIyfawYc34vBkbWhlbbf7tBBraSQNIfDwruSpeS+8jVUYro5Yx7TgW6b3GaPL2ibAz/fZXj7o/WLDGZO2sMRifmjh/8LWvHNZOmaeOT4wvJX7/1unPv6+wlOxz5Jd/+Wtf5+///V2e2nU4n19469ulPz8ZdyT7PN8613fxN0d6mYC+X1LmfsPywYlTXLrAvl/qmf7fF3/0v1Z+88v/7Mz3fnnfc0ek3772uy/9w32Vc7nqTz1z69URo3jJeTjgXP5e/oWT3ht/fP6jokuINmoYY9rCosyUG0iORdqBiXFTw44SqSqBqjUtE0N71vDn6z3HZ/IowEn5gpsyjZ7phvqUcrFaCgy4MsuNuFVXG3lHxWcftBWm6167vaLFmqx8AD2tBybeGu47nAsFJ8Pk0hIZAtGSx+8an+doqRF6wVi+VU6mg5abcqo0eKA8R6Bs2CtODFvDh8Rf5ysGSpHed+ypJZ9vjliloSHlU7W4YjMdklFpaFm0LOQK0XQqgEnIWX6u57vfrZ385frV7Kt378fjLJuVypFL10amHWXvE+f9kFQNVzb8PfXJZ6WrrxWfddjMuaVeXFW1bqYjvlzWvRyKD0rTr9WfO5paesfTV6mrfvdyGOsDrss3awfm0v5AWO+1Mk57baEerumOeLac1hyKKz9fTfjs2ffZrplYK/g1ygdLzo9Zf9T4BtJ+HFUrXLZ8n6qoQ6aJ6qKqUr7KYVEf2ae8OV47jIeYfcGshW7XBuP2yUCjEZRm3uVHnFyPYhooE5ycZOgDXL6u7utl6fqwe4Qp31SFEOlhtz3rCVxexAQ3BGAcgAosAVAEAylFQ8UTZDFNv8r4vRZ5Pkz4YKTzrcJpC+Cgiwsl4grAEhQgbjVPRhddPPqQgEajseNyXTx0XPvDL+Quv7Rnm3vYfd22x6Og/V1PAG/8X9+mB/DhtW8Hmhke8b1aIuCp8KTuX/w17cd/aXDwzqznvnFjYm7prxWpr2sqLLAu4t8mHdZuigYMK1OVhna/ZbgPF1sNeIaVqRGmR5j8m/YPBOM/c6dt+b7xjVc+/vHf3HaRRCuRd9ep7cT6vs7rP99dNc89G+LBPafh9uQYtsAPWynD/cNdi4//y8Gf/lDfXbbS6eKBQZK64d9jg+LC9JXfGHpw+3tPyH+T9Bk4dl5uCxrYt1BpWkCL5MXM7vaagRTNgKvNAR+ioilGQK6o0mr4N6ZzAeOz33nhYy/sbtOr+L0rf1xN/tWkpgxL31ukV6Usm9ai6jdQhpkpIbuozhMBksRD5OeILRD30SzIF7lLA6domktLRCuQribaXTkqDc2qqyJ9JsKnzoddO2IUrcUAn60MtEnl3SNmzwkp8F2HoBHy7S+4TeHUxiBQeFA/xcWfWv7jnvhfnHz6hTva7ze+8cq5c9deeun7ACSg2GJBEy1758iWJs9rIOjTRIdldJs5FujZciMhGz22piD47lDumL7jq/eQsFd8cCfuZ3T6iOB+B8mFF37ivm6/iz1BNwJ8jPCF37nw0lvp3fw7iV/3bsakd0QADytTnctvXqud3qlCaDfVSOZm9ehH10qHi62Jzl71tCp41jb6FbC7K8KMoWE4/O4iIIKcSkMLSNkAuTKqkKterPUVx0IYkEXprwobFUfU8EnlPu+sV8oKW+MjjJVQxjj63739f0wcf0ZXixOc/MuJz90seayirA0acW+6rNqDmcYL0XdfSY/+ROPtl+uHLU/D0PjJS9X5E7dHJry9joEn3/fTd5oAfPkbL//Fb/9vysHjjcG050u/s/wtPP/pz4y8/cXcfqZDPZGVxYl/z8hPIoWOPJW79lV/zxPHkW2Hrp8cOr74fcer8+EzfHPoJ20Lrudf/tfOiFIuBC+c+YU/iPZ+xPXynzVenHj7oDUtE0IZqFrnZUIdAa2r2WZC6a9SxarJWo8RcJWzdaffqOc0m81eraS0s6Njw/bbtwiqxfBZz9d6Wcrgv8rBKvX3ccNNbZxIFr+D0jzxINkY8pdrTxor3uStCCUogQxJSEAFHM0yQ23UqM06kLEKshKqWilZCVatgkwKKuDhk5/92nHG54gVCd1o7MvXAmE56aacbQREVDlpDRXHQpwDCY6gOKt2d8U/Xu//8PRTrvN/euHT/65+49CP3HEC8OKb5//DuW/8a/9+n2/5peC/+PXU38/nHOG+tF/TJ+sRR7k2J4eOK9f7SAE30yekQEW1r9hqnmP2828Wnz9C4YJb6aM4h+cEC4u4Z+tRoWBulBx50xlz5RaWwrWaAxNHzNAnvO7Rgt9dDGJ55flqQ/NK2anFA3/wlz9XeBdvGA6BRmVIqQ7aLgf2j3HUhbWCb5KhQiNQluoiqnfbM1bJJ2mVfC1wwH47bFh119ITTGfxuUmtq5rNEHBT7iGZYFHFyBIAelkGSsgqpV7SfZMLFUlBxZGxSMM4pKm+RSPLeR0eN0vkQ+CC027O602rZ0AKYNOoJXEcAqiM4zhENUUjCzBRaaqBzz+UI3408PNf//rzH/vYwz6KLnaA1I3/HiPsJQH8aLO/+YTrjs337gPuiQBmAwfcOufi2zU3Pg5f5q13cf3arx0+c8YWswfKtZpTARqyo+JXimXFcsqU7ThrTim/P10gx/lf+rg/ixyDQy2zwUOwRdq0/RTvfJzv2BWmjObE4A7tah8jdI5kxEhJEMAjTI0wFQv8D9klB8HPNJZU6u10pAog2QCkBoBNosHHf/YmbOjgSwlu3s9vsBMfvLrgndg87L/n3N/epuTuQzLugTHE70lWeBu0CeMuAfxYoBv+PUZ4oATwe4L93aD63Q4h9ECzfH0VDrPhS5bgDglggQxoa/OYrbNqBFXZrDsMi3G4Tv0d/vAmx3/t1w6fOeMEJYYrRbXXjousonkw6kuhlFcLqfP+Yk2bbVx++9zk//iSL4rtRYb8MAdDkIQnQKYo9TidGceiZTyrlvE7ya0EAibqCm6NSga/SmmBXpVyjqCTUqml4F2gt4QTKKMBgh4WrLDosLuppeRW3XY70VlWKJqQdb7d24rDzqKlPMH2V1iHjeEfcJzxEaaGmY5cmQqd/UcN9RPlnJNCDnxQBQ+YSC5sUGtgM7FpNDg3k3npH6eZrYFwbBZCJFdLDdHZ/dfbajhMixhu914Wwt/FVlZ7YxfhTeEFDyThJJQY9ZEFoLfVJ/gW9IIGQnmehf1gwPzuNt9GoLV6b2v6HjFP81AF9jzMa3tW3wve00zwfQ2DuwTwY4FuBPgY4Qu/c+ElPb2bJds/7e1HoHfK/m5ca7v25BuZ4E1bR23lJbYpB9yJoy2W92rrj3qm9SBl/Y5EowrFXXVEm8GGpFV8tnJTAdxQhTOK256ZyB0U7Wm1mGG8rSnxqpWWScIolFAGqpGhJZ89OyRNOzFclIESzqXagV+5/duleCCthr5a+lCy0Hv1ShSLxHPLkqvSa2W8rlS5FtZs5nFp+sLEk9c17+fiP7hpDFydHfxfzS9evHr4Q08e8w6d6teSizlHj1RZbDgAZ6xeTqk9sr5YdQAOKhUc9pi0/O8+/gNzpOfoBybHD5wLm+8P/NHRG5cm68rcb1gjHyERIT+G/Hc/fbvxwb4L//eFf3nt+Y+wr4dXXvz8cf6q51Y9mUN/zdr3qZ7y2KLTpAzSISp1Kj82dKn+479bfHJyokfxVK2irESq1rysUO3rT09e6Ynv11cUp7Usk4UKDKDkqlZOxo3irQKWIXMLalCBYShy9Gg2EybmXknVfTFbPo8y5Lk5XRtcLEYkreLN2PpiUxdvHRfu3O5EoZLXyGApsmKvOvYZ+pQXA8VTDbnKCwU3NwBwtsTfUQD3aKFyW7MmZWW0enL/5WFDj7uuvsyTzoZtLL9fMP0+e/Z2vr9ekm2uamVJ62yDEnYUs4rTP1/XDmf+E8/vmygT505+5kS/M2r4514kWt1vy2SMaE7N1Qy/00/FkiBXTg/1SGlCNUz5//7WK/+sOKjUnAeGMlevRhNHssls4Gh19qYzbuVl9qG4q5hEjix5LV2uumSbmUcZdU/N16NBe9KDFSBbJpJueONGweZaHmscmiv0p9sm2xpHTo7lCtG86axn5ZrNAYRjWdWsjUZvzNejNbvupvy3/ui1029/lbPUv4+tRt0OfdQ+qDiqVuWA8nL82RW8IQoz7HdQPMcpjbKPzKXa6VTNLxoki67Vfu9SWfd6PCl7zX2zFn2/8ub14vGyalclU7GZVl0FQrbcQelahEKNmpOSC2uY6STxAOle0l4z7zDrWsYUAxzKME1lnIkKC4+JQPa0eHWjN3BLTa63CTEdg9TadVpfrNKaEJz3xHvL+3o36BLAjwW6BPDjhB8eAnjP5b/bpwK38gBcQwBv+tTangBmAwdMx5kfb21hnMp481EB+BRG1kpW9Aa0qvNnOhqGiIokewKbkAcMrqXuOne9lh5eywQPsRZlXE5KZVxzq7mqPcA2KuQ7Qh8pJ7vNGC4S3zE7KSDyhmFyflba94NGJU8kjfsyhycYbBYPbjpi6XSrW5fbAmoNqg1W6lvv/97Z4g1+0dvDK+Gx3+Wu7pokfrS54QcsHX4P08P/bPCjH+k7/bCPoosd0A3/HiM8IAJ44CE3B9mr2C9LdIU72JRGJbGhZ9POJYDbIAMlCG84n4fAbHHJczRep5bl1hJpuGLhg9MKCxYHVGL2ZvZhpUG1zk0TvaUGFZVfB124JewJbImWL2KstVNar33ghjBGQq2otoLqUymYeE1UwEAxcJi4gPlWeCaYYBflUsdjvl0R2J5YN18gTxBwYoqZ4q2xNlzo9HBe93bdRxplweD2kZoj1kcqi/8u2hzsJvgUEWAfi0Gy8RZBakMxzeDb6v4F4q8nn6npDmtG5q/A2dLiXgPACU4od8R+vgbzdYBCA6+02sq3c3rj21UID81dmimfxCvRb18fdj5gBCAMtyAALtBa9LNQPt8p5cz94V+FoPpOA9Hh3VpDPwjMbHBP9UD0bo5Qixnr+hO3IfzbNxI8gkQRBu9izqaG0l0C+LFANwJ8jPCF37nw0nJ6Nz/zzpHspsPMTuO3NoR39LqZW41SBQ28HQF8d+jMrmzPAa/7TzZgqUMWLEyh2eRfUYsZNd0hKEBLl8VXtmZ8op2tNmgYL2tNo2lxhuYhBwMo8Soajn3GPt+sUAzXGq4FyQNUGtr0wtD/If0PxD3fabz43dqh+e8l4ujSWWPYthz2zFSov2I9/Zxy+eLrT/73Pb+Z6jn07RtPFKLGEzfdLzz9G7+U/NX/V+a6y5F7X+z/KeajJa91q376q+nnUornsDU9Ha79aO/Uf7h56J9Y//4ryvP1dO3MzXjySH0ofOnr9g8dr/7gO9c+KZ9aGQksLBf2rxQW3r//xmde+XcLJ3vLTu+k/hFbRbtY6supteOh6XdUd/BqdP5o/pOzkj0yM+vU/tYf/vrsyU+rpewXwv/Z53r++Jrz8O9deREDgvynvV97l/6Ya7qvUblN0CWVzVLiZt2foFT2LNcaruXJQ1enAoRbp/QtGQ/accO4qFFBiVWtlEwB5XCVGGRQTuVrc5qxqGk9hpFvXhjFXrVqzWshrqN7tCB4X3Ht3EMF/R0vMlTB1Uq1qbDSKqJzddwnHn7ms189wltXOfxu4xSQrwVEd2dxkIC9Xjl28Pp8radRcuQbzlHfLReWKplzSwdeiL5rY/5S+kOvZUZC8+VcLHPs8HI6Wf8H5d8uvc2y4xd6Dn1Fnrju/epiKkLh7/2KWit84I9/++2f/ny/ef5y7KMxafwSfcuNQwfzt8NO6wvyp+ozvvd5Fq4nw+bxzOyr+5D51NMTf/mdkZGP3ijr3o96XtYaakWyJPIW8UpDyxvuD7j+6gajX0t9ZC4fdkSNM45bN6v9yasRJVKNDC3ldE9lSQvHsuLrHvFdNnBmG4F/9MWvOv7dV129eJw0ylycQYODAxDA9iRkyf+ca8bfnyeSIHOTaIreZbwiwl8knqrFWdsaRp/yKu6qtSyzhBKvApYiazEjGM2qkplMxW2u6gn/5WwjEJCyGmUnZpBskCyQYDFA2oZy0LyuZasOw+IiXKB6uSkFvrv+uH0ARGECRmCpNQ5qv92Tnrudbs9ihCUFsAeQjkMZwtDX+msSr3PggtsQhuLqILGawqaRn2ex+kOnCe4SwI8FugTw44Q9I4B/mNjfXapANuWAdyCAd2R/29hIA3duYRw6ioZ2xPmOgzq94VSt8sFttC/34TVv153njSdqgd7S5v3dtkRbRPJoYis1c6dqZGTr9jU5Qmn8eYLL+OiohF0zClpHD29sY9a5QK1BqrZhicm966J24I4dp70ScPfc8F3n7PaQG97TvGHXUPou0CWAHwt0w7/HCA+CAH7YkeEexn53RABvDP9Wtb8Ccy2t5qaY6+BfRV5ro3d+Z0tggVb417CoZWlkm3TvVtAb6HXmW520fAoeG24bbml1RZGqaJSRnOBDEgt74FCri56ABiEAI6gCpYAdSNIHtB2zTVwqpa3i5zIuQSGn8YfJpfFvedyPBrZRAwu0g0AX5SBZJ6V1CwjXHCGPniM2aQ01DEdlSUOoNNphownLayO4cosebktve+F2A3vrYpfrwNoCQSEOLu4iFCzByc3DvJidagNZotrAaaPWwKjjkKg08NibbwHNhl2iWGty1Q5p9UjEW0CWVo/2oeD+iXHvKPLcw8PYqt+w0VHDsZHr3Qb3cGyr+fe10xvfCuwYFXcJ4McC3QjwMcIXvnbhpck7UwC3sW5E2aniFWTPVutuTwCvgzXj2/hfsf2xbUIhd2ZIZgGIbGCCO8TEirsq9KNazDBSGlfBAZWt+eN2F2HB7063FvM0rYaVWNWxzxBSSNH8Ijs2sFJ3Ro4sAV5LV+s2SasE7UnBveVrAVUyF4sR/Zw3cTSbrTuNae2oIzvZ63zGe3uf5/wi8QNcPcnMdzkZoXChcer2Su+Z8uJ0wv7C0uLvh07Wx32fD1y/JAfOVjNTyX2XYg2fS6/67YZU92YbhYB0/Lr7o9VbK73Smy67Vpfrml0tl3pd45M3E7XYwYw3VT838uZSYDR84cLicOKZOnVZChsD8lSUwl/PXv3/1j7nc8izpjMcTZ6qc6xy/XI9MSrN9kjfv+T85MzsoWt253guMXhkaoR0/o0jr2iR5om1VwBbqOoza8lvB45+eCmDI73kcewz/O6i19I/6PrBt2rvbxgOv6Y3DEfK8KUXA6xg5eRmICk6cjjACe5WZZgBSyjDVcAqyk0qrzVfFCxC8xGpuKvKQL54LkQJFqGnFV0PtiTm7ZvhKvSAxdkPXn3a9b1znALO3XxK3JOKuxqOZbO6u6Y7RvbfGJKms/gzpeiwM3/c/oqH+j/J/eJP+F5bkaRSI3iAhWWprqf3mwFO2+ZfyfZT4enY9xzFJ2+4yx+UXvujhR/5FF/xG+6rtU985sA/VP9/C/pnTgxkLqXiatV25ouDZ+PI3yPxy/zRP679nVDeuDh16GnX0s2Mb6XitHSZBZ578vzFwwMf1G+lAo5IvW6zleIYE/pg3Z11GM6rhcHkmwGKrZvZA27QYRkiEOXZZ38QIPf93PNnSpn/xPzCgYVr6JChckJBRdeCgT9YJA9nwA01UEmdjZ9Tn8rQ7+FmhsC3Mh+1il6Xv+b1TV/KHRfyaFHaJfTxmaVALe0Q/ueYWJMyUTxnVmL2nKF7s7o7EVtYLEZ6PMuG7o34ZvpIHeT2Mt4F4p/ma0FyGtY80aNcDU/qzTHUxaYaOFVjqc5b1tb/FB3wtSheNtgeCn9mOjqs3Ivitg+iUIK4Qt7iiT7sgeZHUj9orTszDU4YbN2K4zRTyx3jvoZFdWr17Xn9h0gK3CWAHwt0CeDHCT8MBPAeZgDvyACQzZKAe0YAC6yjgdurbzCO2FtsYl7ReTBrb4aN53/H03in5/kRJ4m3IYDbEEywmG6Tyts5I63L43SywusI4wfhI32HWuGNuCNPaSDA3bem2xNi+DHXCm/EI84QdwngxwLd8O8xQjE9feX/HLqPO3hP2D4L3Kn8lw3h3xoC2NzW/NncjO7dCM9aNbBYq7y6bruJ1PY4r+NTyFv0qk32tw0psH4L9gSSE0lU8Q2C0fLvPdxKj0YAKn2Kw7DaLYodZkN0YNk0tDNxze/ayiVHyM+KMJURb8V8A/V+B4GCx80QWLejReJiopMM7iwBFDYw2298gmEX5QwBISxOVxNAZikgpEUI2qzNCns6wr82PSxS7oWWYlgQw8IqBjZzixGhoLeDEvauaQncKSYO2ZpbCNlw2la3LHjczrrD9pJeCc22Zn6lsbrBmB27RLLaXEwQxmLioWBvI7o7CjI3NTLdJa6uvRn2HA/cs3qrSLhLAD8W6EaAjxHuhQAWdscbl9xUwrupg3T7EblpxmOrLQv2rnP+plbS649kK19ogQ3/cu6hgvBtJg3TENnsP9bTMogWDFKpgz5cBrVFUs7Ak4gGq732xSsLhxOxhcV0RJ/w9p5Nzl9JuIcKfncxahpLqhbEWim58w0noI95WQIHFCEEizDMqeMTY1MDZ46/JS8OFawakAhmi3r45uu+FZfzQCzzoly/dWTuu+Ojh/Ylj5ZSZ73p/z774bg3baY9g4Njjmrs5kR4eP9tJe0ZWi68qSR+5cC/1BddPX77d+snay5dY+nSuaff0A4ZVzSAfuLo2vFM2L38ZN36s+vHPnj0jTPmxT/XP5kI5MbGRjIDEoYcpHLzavAzPdNLs75XHFGlVgl5rFzIZlzUAIrgRDlRHR2aASLu9FitPybl4/bJqcYgsI+l+dpg2L5YQinizNcCjbTWF5uar/UIyhAVa1nmIrhQTldJYuVknCjhaihQXsi7Eb7NS5rdXrG5q/WaDBgpTdgyW7pMGlyMPDVp6N709YClyEdOjuVSiWzd6b9cXxl2NhfrRGdBlcHRxFIgnD7lOfeD2pM3a1FvxiaFjcxSQHCc/Y7F8lKoJzYxzHSGQDAT+PPKMZ9aPmxbcdlypbrf0us9sbkJfThV933K++6/zz/78eqsEZ4uNULpWs+n7edV6cZc4xTS3BnzVqBcuKw/Pd1r+9HF1/9PfvU/u/2vvB6WIrXJSMhher/rPPPajeMBWzl9xWPZZcVZtZblz/6NrydL/aVCdFpSfyp864z9917n6SV8y434fK0np3vqJRmTWs1xdPhWza4LO+un/e9crx5enooCB2Opj0wkP/Gt/9b1s6r2B2b+NVY0wqC5sZ9GWoB+8NFYYfwP2D+KTUc6iXQKTCpnFV0KWn7Jm8u+0vP0Cl4bEZV5k95LDF2uHgxixeTJN3NP6l/1QivMbHfXBiZJfDibLngwsS7JyomqPVwBjJc17QVjwD8XdC1VG1pEWniS80Cc+RGmwu/oLMMczFK9zLtzlHayVeqDuZYhM2slT/IQkrImba43uFFq+jbfaI3ezsPprdW3h2C8w/BZoJkzF0M28a95GDIQBLNFqAN2WIZgRzJfGHV2jAc77aB9Ct+ztjuY9wy6BPBjgS4B/DjhPU8AP5Teb51YlwTcYwJ4K2zY8v1ggh3rLnrn2w1m0Z3Y6qLsOTHciTIuYG89qHeD3bC/69BJBrNBZJyuJtrjrjWVtgaru9qUDxaCjM1x8U4Pchd44KywwN1lrIb3wqd0r5Nl2/jpPXg8XIa4SwA/FuiGf48RiunpK78/xPL92frDiwkrmpKPO3Ze7k5wFwTwOgto30LFYbSq001Ib6EAFmvcEaOzkQEahw0F4ztie7kwLTWw1PZwEaSt1lKjOteKkiPAqixYaILbSNLXVgavQ5YwYOIUbjEurN3YxogAT7DCQlkr+FoR8oXJicVUzI1KXIEcIRXTRG2/ig2K+RkCGiYgqNxdxpOuZv6G+O4Mi9uxX9sVpt3heF3gJ2mVNeqozv6IKx3TZUi3KOFyfdumIXeILb2m7xvEHtdJiu8Iuwwp9yqQuwtf6MmOVpTATIsb3tj/8kHiwdLAm3LAXQL4sUA3AnyMsEsCePfVyVsZOG9PANPKcmxPAIunXvutz1YGwvL63vadD8o1j8htOODW/1tTGzoWcg8V9Clv3KsvFNzMwAzNqGGddLing/RdV5I13NRZamcNY1obeWrSbKifl78zVjt5fSUeLMg3657mQYoCL3tVbNnS5aZngzikgZZfgogYB2hy0gkIEHfoCxX3sbPTKxm3FKwA899MfO7A3NfmYyD19afncuHRgZmUw/dR37fPVZ9xSKZw2vcuaFLvYs1p17Pup8IXv107pdjMiVsHw7ViuuKxUrLirfaNLpRVe699cVCaBn7a/LqXmbfUT9dMm6LmVMq9t9N/Fv7cU65XI2b6VfXpQWYj6cFsqHxVqmdqZ07kZy8FnGWccenmKNd8pr+qpmNm5po68MLC60CZ4GJcAxqmN6mqM+z3kT7DpVd4UhT2RXAYlG6w70L+7CnbApp1YXlE6KEpMbxvsTxcn/9mAkBBCVbDrmLa7gnlyoV4Xb/lJQdRtEHDeFtbcwUHUWJVqyijkYhns3WnzVXVp7ziogvZt6gAAGq6Q8jBgbhDPzH6jiqZRZTr1cNCpV0vyaJKLxHPZkvO4L5sIC1L4ZJmzwwajT5t8a+MAy9oY69Jfc6SevH6IbzY7ZXEwAKg2MxTunXBrUwvDAWssn8gOcr8R8pXFJPxAK5caGDl7bmeD7zt8t6uDf49+2/Ubh8sRPWS6i2kev+f8MnkTPxQNHnhlRGtxzDKGlmOPTe/grI/U3lHCRxIjMUateTkwZDdeCWZYBZCoKPEq45DRo9n+Zj9RoX693PPn/BfHm5Yf3vqHzc0v8dYxIQ5uAFRuNWSXD8JY/AxjBFVu2FW/hfso0gOikt4fwwg+2yPk9zVnlG/Wa2ofJ1ng2SnGseLKLP1qKF7j3onczgKWc/16wcUe9UqyE1Pb53m0Epv6rbFb8HurthcVZ9Ubixr/cM3ylLd3Qqnj3J9lGtBckHyoWxWe9tkGW7TWKS4xLmbAAsb1LGnWxM+hR55zWBHHqI6tSanXRlvvl1XRztRAZoOzJ0odXjm9KoU6/TIBA4DNCwkpWNE7Gl5TwOHYK5lAV1ik8F4JxO8WWnvN/S98al+xNElgB8LdAngxwl7QwA/quwve0QA3wvvuCUBfHcNgHeJLejeO00Ibg8pgLxRtrHuZtimY3EL21yj7c/8vVyXvcJEsy3MJrgL6ndTLNALrGue19l/bnOhcHu0s5U4eMts4P0ggzfFXTHEd8EKP0QT6fuZOHvoouF1uK8McZcAfizQDf8eIxTT01e+NsTKZsPOe8fDCws30o17gm04y02hUQmSV1vyyt1aQN97EDjfzEW2jaD3Cu1uIM2MhoBngxtHpzQ5sqahmhFUK6pNqIHbMPECJqroH7zprltRkLJLPvihoE1Ci7DwHiPAtsq50+FmY+/hdvgn7KNX893CJMZs1QWW4VZrnU7BbluJu/dYarnfta2nT7Z0Up6We+PDg/jiu0Rvq9DhjuCF/jtc5dFET4fZ+33GRoWfQJcAfizQjQAfI+xIAN/RGHOb9r0bCeCRtSYn24iA1xlKd25wN27Sq0e1aQ1Nxwi988v6bGWnWVtSXMVzodVOwO5WzCaKctq12Z3PNL0l/wVMlONVYUQceWKp1764UvcDs5P7lIH8EcdMwfDkC5Fs3SlayVozsjbaahg8A6GmUFKJNP8SrRmf3V0BAlYZFWxV1WbOJeNhtdiwOSR3xeddnpwYOpAr3qx7HCeNfseiVyvGKNalogMbMFM8oHkWny1Zb9Z7NFs54JrScYapv1Y8OTcVd+wz9CmvYm+KL5+I3Txif+cJzh02J3PEcqrSn829FThs4S5i82NL49Zx5AjqOLP4qyW/15X6Cb5yjcM6jgPMW7gcODXmDyxO1E2ny71kq3o95xdnRk8MzF9Kv8/tm6qk4iNeZt5Qzx41U6p6e4KhQTP9ZfVTo1x/i2MapoGaIXCQ21MMFlGu5Y+f9Z27Ujvow8q3QkFD92ruAqDYTMBDGUNJ132SVmlMuOaksDUjAyzCCsrTVSstUyG+Xy8eKPts5fm3E9qgAdjDhqgzaBgOv7uYrzsbhqOpBRcSiyAjH5sM2XJFnLlCdNB3o4/UHLFLueOiOqGW1oy3NdywAivggAjxqE6JhXk3NG1WFE/18PGLh7gN1BueC/URIGTLJaTU24tPH4yNPyHNfJuRYjHmqtu8vulqQ5uv9Ryw3w4blqxVJo2eoiaV9ebvYqEQDrh1YUZtd1cSsYUD9tuXqwfnrySG989KWiWZitd0hyNq+MxaciEwcnzSbKjzbyfeX1g++9FzNWk6wVKNygvXXk8dCV3lUIiCi+pQ9sZ8IA7Yzd7IK7eSZ5+uBqZyhP2kHSYHrlyaezIeW1hxzFlGRJ0aPBg2l26riUkGLnMoi19p2Eso1yoDTbfti3ASVpo8dKI/m172WMty0zqoB2Wgitl08FZGq3Z3pW26Lphgv3fpCekCoGH6WYliqJg9zPdmF7TLJj8AD0zTWCE3iVvivN6MPmmp9M/DobVxp3A/EhpfvU7M3qxzrU41BcG0BFRtP6RUjVhrfHle30RDvIpDrRy4YHkPbWkjX9GU1fpgmv8ezKy1gxqnYQHNNL7ofFx4z7lAn+6YPt+a6BLAjwW6BPDjhPc2AfzQ2V+gl7Ta0XNrOwJ4l75/O2Inse+eqIHXy3/b2JgNBAa3kFdutpG75oN3v8x7AOuI543Dp9VhT6dZ9FZk8HbpvwfGB3M3DYbvKIsncC+k7D1Swg9ESPGoEcPsBTfcJYAfC3TDv8cITQKYnWOGO8bDiwn31vm5DRPv/J1zICH0QLPP1Fr3F4FNqfcMpO7iAFsdYUurj/uGRaNMbb1CZleQAtg0JCewNqPRhpC8xFoW0BvRGfWJhGlHEJhPuIB1TPA6bM+4m7gAlVKng/Qu6WGxWPs1zvwCvY8srywg+OCN7U46HWIqDW22sipKbgaB7cR3O/wTsuDejulsxxaLNaCptd1S41vaIlRr+2Du2Gm47VgtIMoBJ0HooqKbrHH/sMuywv13yAHvSbeRRwcPUAe8MYjtEsCPBboR4GOEvSKAt6F+N25qUw54nQK4OBZqL19La8LLt9Oht/PA+h2LDmnVKGtLI+hOj7ROtP7WNn5ZQbgaNzRyLZFiGDq6mDcnOnMsPS3LaAE3uEgcXU5ejSSGs8k3A+5nCj6j5h9IWnU1ZMsZtWDJtC8Uws2uqEWZSZgFF4RWt6kMVB1RA/C7i42SI+TSNXumiPPaG6NMQo3PPZe6mVFC/YYcSWdx5Az3kOdmoRGISAtZ/BrlTkORXCEaM82qatPkbLrgM+uqz5/N34hoxzPJVHwwPpWQFk9zzoU1zHSCOdHCw2E2bqiH3+G4sEgZ5VqGEY1kjqCfzKs8U0a9lj/+Qd/rtxr9H5IunuXNZWy9pB0m0sJI9PJrlR5FXrJ4A6kMg/ACjEMY47j6buBMkEIGr45s4Z4jAgwzXSR6rnG8KtWdmMcZ/+Pij3k8KWfDBqzU/YLXtLsrx+LXgblCf9BomJGiDyvV8M3/aSJ+Rl+pO4WrcPPqLEIQJVxFw5qUlXg18sQSkK87a2nNHjZi9lyq5gcahqO+IttCVUmrNO+iG+AECfahuKsOnyHiAZ9ULqg2n60sDL2bmuCZ1u262Lox2oVwAyj9VdH2+LB83UdmhMkrnO1nIkQ+R+gm/dXqvoB860rtIGDo3oJq66uu5KuB9Ezg8PGLH2bCw813G+83pGo/qYVG/KY+kqr5ezzLqZr/gH3p0/aXb3Kw2FBSePpqrjl7ab7Wk687+/SV3PXEj+eLw+//Xs413WPmjt0+V9gXoMwr/k9/6vbvyyv1haHe8/6D+5fzIX3hxmDslHnlEs8+/+63bz8xZKlcY9RGxM+tMQ6f5vwVDohvmWAxRzBH8BIHUrW4D+vq5P7I0NLyVNRalpVI1VqW0eAqHEVxVx1Ro7KkRYaWMrcD/kZ9YcrNCjhbLYrFD0qkOacgBjKJ08vJyciRk2MJafHTfM2O4zAXTLx9YwukYRzS1C9TSzbZ3AsmUTgPfeCGcfBBHk5Dr8q82Wz322ZwUzUS/c0iVzrrXA+1xsgiON3UDb4IgxBeq+5tMbvpYbfDbADX1eERprJEAyxtTFa3XaMmGGpPhyd1I6hqb5gcgi8DzRrfyjgTFb63u57HjyD6Wg76nQrm0xsWE57eXQL4sUCXAH6c0CWAt8eesImdIuDtCOD7LP9t4x4J4C2pX3a6EzaqgXda8R7J4HtHW3fyAPa1EeLO2bFD3h2QwZ2kbzsb2DmzUxqyHg+SCd6IXcuF75QSfoiW0fdyAHcC91Ch0zvrEcS6MfxGdAngxwLd8O8xwnuPAL4f7O+dCn87sU4EvJ4D3kgA30sVoGDyNvOO3jTkW9ffd3NDl+1xqOX8vCnWtSh2d2RGNsP2um0Tr4m6vQt3mxJuz8kSVls6HXUL5+dtkCUcID3BkAsLCJDNEnh0eOJ1gV/bNVpArwVFHlMk0JtzhbdwWxxMq39wGyIarDWanX1h64AQmsUNQssrOgoDB2BxFwTwNmhzw95Wylaw2ltRzjchAcnWuoWOiV3Hje3exttg96HavWh/72tP33uBp+nQ+ED32WJlugTwY4FuBPgYYXsCeDfs7w7U79VNJLYbCeCN7C8dTgBrnl93eJC7bQO8lgMW7Y2b+zVoKoBZbVyquKtCiSu8f1ep5bYIuP0KuNB6jJrusIoycDSxJPWV9sljH+Hlc5zOEFioDTcMR0oPpZc8TQLYhAgsQwTcEF5tCuAZXRHe1z57drbSUxwLKe7q8wdeMXDqOPtIAd/PPd/jWVZspofySt1vNtSwnJzIHdTHvGgkhpcxZNVddKk1zZ75Eellb073OzP7F8ZeGXr6KocXiA8z5WfFhaVSFmJQhYIwaPl24/OGVBlhKkg2SVzFPMbNeSJJ4m9xUq8FgUH7tBOzH/Mwrx/MpuLJ6cYMUvr/z96/BbeS5/ed4CeBzEQmEneQIEiew9upOqd4qrqq+lLqUquklmT1WJJHnlk7Zndmdhyzjth98MbG7us+7O7TvkzEvk5M2BGOcMx6d8ersWdt2daobVktqdXq7uoqVVdXHda5FG/nkCBB4o5EJhK3ffhnJpMgSIKXc2EJ36g4BQKJzERmAvn7/7+/7/fL4AeQoLdNOAWzSDH4ZZh32wEfa/dayMA+czPs9sgcoh0M7jlSxUF30DeZmqd4OMiX+0mrHBeVp2401JAdbiSnjfJDM5+MH0TMSNtoO31NNyNl1NLDVPhWx1rTAUyvZJ1mdrla7Uesn+nLv/HsQI32Snp6ulozY7cTz+q9VL0fcS8e6yiqVp3qurRu9KiZQP1a1+nJs6lq4dMUeGJx8WAZWuirltB9Wo91urz5a1stO2z3tTcSny2z6YeSvMbubbsAPNVmgZxdearNPSV9yK06SpT2HnnLO8UzFNZZeotHPbq3qTxhvkLKQd8hVx2kao3pZPwgJVV12qt80cF4ynSVZGbQ+2Xpj5ftykPt1q/af6rYff0XdjMxU7mnA5GaFmdTf2w/+dbX5muPibCZer1N9I8Gf+M70r87ZGGK7X/H7+qU2ui3OXjC7a3e4sBS7sU+2xwsxmjHpapCSKf0/cJvi++sanTn83sbf3JLXMZ6zrK2dGD5rWcbP7yVv2uWa5Gprx1oki0UyUIu7+p2fV9ow6sBTVhAz1mzub3F8NZbPJZp/M3qz3qpZmKvozxx+Et8+/TOx0gpmhXX6tmX7YreVpfljUEUUrALc96J80+iGLDseA+ME6ZNO8fHOByNfIM5RDbxCgkLxfYqWI2WGFZotG0iFa+LVrgHtdG9uV83iWaW/fs8EgPJ7IbpDtgfMviM7iY/Mq9/BH853IW9wJ95iMInkIAV78nTWkdPQoizgU8mBPANwYQAvkm4BgJ4wv6ehxdKAI93H7gQB3zWzOD4Z/80EfDYazv3hL4K2l//Bn+VicI5imNOVp78yME5wQszwZwYI51vDLhxtZm+y+Ei3tHj88GpUeL1cfA8dB5/XbXCQQRZ4QkBfCMwKf9uEI4IYK6VA/5qyX+vQgBz3AMmWu3pFdt9Qfx/+8QbrlIHtkfwx8IIGo7oXil17E8CcyLHvJ3PxRknWkyspAPPLEArIGo5D/XZ6Bn6YJu4RuOKp+aKsIna6ClK4oFNRAT92kSvkSc+V9N8UhPs20T7HLCAP7GOlzvIA49ufHaijnsAcxxLLj4nQlhM7Fxvm2Dco4GDnG7wJZ/3HXNtpya2eItIxM5zjz+3NhNs9diX+rVhLZBJGcQ1diviTZIuXN8Kz93gapkJAXxDMKkAbxAuSgCPdGMegaFphBMS25GOUGKaYhwxsdvTFSCFRXrrqcsHFboiE2FozH7/2O4JKaees3qm4hzKR3SUCQuu6teXMLoE8MFxETCoua7Tk0Xvddzu1wcR4a789pufxaXq9/jROvd1DvJ0fzB4s4/+oLFc2Jhy7TqETe59KHmaYy8VNRVq17Xw7cSzlFQ1iazXXk8azZoZAxJS+43EZ4ckHn74NjpOURbvPTo44jjMuPvGNsavNWZih18LfyITylBvokzRSFCa43CXqXWWHPQFvmwTXeMNi0ixl8+EaovS1iNum710JlQr95NGuCJ432IvKUS04hjekx/GcJYH9RnpYLH2cKpaTLX2+QL2IA8LVG/NhJKNx9q9CGaBfIbGLlMVUjaaEMLGKNbIWGgztDZIt9HnB/aOpCmEnvYWB5aiGPVuX5dD1lr9znvhwrauAANLEdz2bm9GHBnXwxnycbPcjzjbMlE4gAWo8a1f/eSLzkIi1K6ZMXEwOwc6QJFb334aTPl1z2/pGAHsVlCCs59GDXedPZkmKKjvdrPxZl0Lix2YXT4cPNb3ZEOoh785vTYtFVSsFpE3KersKehxirlaJRKxP9PuKzRsdA0rSneDhRbqMptT2IImByKYOo6GXbFni1q6R9cmUiDfJ241X7sn/+J7m/+vH7zx/n/gu1u9RcAIVwza/wf7n/2F9ktfMLfKF3/76Z/JJg/fmLu3ubudX7r/w58U38t3IikLZcouNrr6F1MLGRo/5Ft/gz/9Q35bxdpgsTpIaf3QvpmVop3dn82qU91wuLNyuxiXd63mjB7bF70ID5tvfSP20eeDuymp2h3oa/U7r8d2HxYXFpI727X5lGGWiinxQZRpazXx5Vr9zkzs0B5oorlhMdTY6sfxwk2wcTZkmq6AWDhdfz386bt8YhO5z+O56p7+ExsDnoEOG1CHIuTof0koTr9xpO51sQgWpHDDhbfgG143Y9NbYAvuwcPj2b0Vb3QzBdbR88E21qHpWb8/9aTbkKjh59nfYYYTyFLTsNNU8+zeszcaWkKjEa329P/e5hHdIn+1w/qrZwT9Lnzi/es/4+9nkBKGYzbagDngcQsmFtA3DRMC+Cbhq0oAvzrsLwEC+FXwf+YU9vcsXW8Qi+N5oMXOUXucg6sxwSfxKnDDQwjqeoeCoi+Ba2CCz6CBBU4lg5/HxN/4uKBr9Jhefy8xNngkXpT53itLCf+LO+987/brL3svJjgHk/LvBuGrRAA/J+dnrk8BrNgDxe4fI4Bboyygry8DOAgx99EtEtLpFdyuvtMaAUUI1llMsCCBsudVg77pSxh6x2Ux894REAKLKJRGF43CQq2jH+2N31nvQ/DB/uMKCSBNXfTdAzqd4EnMYPpiYsHQ+xVUcMkM5hmxxKfBJjrUwLfHnEa7zNHkU5DWFfJi35J6jiKn65WrZIPrYRRD7Nd+J92hRb4d4Gwngo6awyVfsBrcPW4QPVZasK8GfjUxe77F9NmF4jhe0KvePP5zxdpxWnx8zFzfvj3/6jS2Wp4QwDcCkwrwBuGiBPDwXeMkTlPZjrJZ9mngC1C/I2GdaUtwcpeenbBnuA8gGF8/gLy5ljGWGkI1Kzbh904J9hdwidUhc+n7qLWu8o41EzsEBKGlSNb9Xu+18B8XyM9zWGcpQvsN+/NfRN57U/r+H/Lbj7j9pLB6ZB1setpfz2ja55KFuHOGPXF/n6f4afedQUtpaKG43deNxn5zyvwo7qYRr6JOeVz1miwKTvW9rtOUeQYduM/ynWdJ3cyHNwzaMxQAG61GukyyMJgJN5JyyP5STQlD7P/t7D81UQ6Yqw1SCpJlxn/emzfX4sD77/+lTlsh9KR32x5oh5vT2Vz1/5b4p3/x/V9LfUNLG49nnJ/NdHZrpNWemefZL7T3zcgUUNP0XwSmsHoDfa13K40zLTUemvnBM13Kdt4znZ9isMvenqHmuq8ZzXJa4SG1N0KpaHtQUjKdzoOPUyzyt+/s/Ku1eToA99+sVgwGprJXNO7nqtUF60jaK5ovFe792pMdOdP8cUad64aNTjhrNT/OAF97/2OOF05HbuSHshrrhrMdwSuL60RftHolJZztCLZYnDU9Zy0kd5y+ljUOX5e++KvBO7XirJS16v0IkAi1NckWucUDS0lHD4BltjZYTFEzaJtE0lT3yEew22h59lbY/Lb9i4fa7TS1PjOPyGywWOitlEOy6AmYot4YpG5T0qSD/0X1x47Gj7Q3bLQSyTY68E0+qZBqkOkP4t+VfrKw9SiSrDW0VPphWfm+g0XnP1MVzdmVvzP38Eed19T/eem7baIZGibhz7i32XxHMer3pN0SoUe9e5lQLYoTlcqF5t2c8SwhVX6Fnz1hfo03CoMZrR/6RvgvN1iyiGx3l7JywaD9mXNPXEvp6WoiXK01phtaKBeuWWa8ahrYLMzspKMHM+y1BtnPe6+/Lf+8jVYYzGxtLvV6CoCNcttaTXzZJCLsoFfsRm5vHQ3l587gCVILfhM+92rXe97AqgpFuAu7EPXSdvwRq3Cf9lGFdzxfpbYnPPFDfKe84dsCaJSWDdHgK4aiVabLGEPjDoEgAVwjUyLJ6Vhho010lQfACpuKPUgUWuyACX9Av8FHTyBAlN4gnM0Bf+KxJZ8AEwL4hmBCAN8kTAjgkbhevnAsAvhys35iPXeP/3k6+o0LBMKFZ090S3EFk+dLYIxL6/lN+47EK0gkD2FoD0fSwIwccY1kgrmQJvjlekRzMWXwOEzwVSa2ruIBeAaWX6gFn56zRD/vS8eEAL4RmJR/NwgTAvhs+M5dV1nJHCXApycTex3F8lKbdk5YinFlArh6VoRw59Gxuq7fAOhbI8TB5zhC+85pZ2PI98XT0LhPlr3eLUED+y/ZYwkWz7aMfk64lkvixDqjVVL5Y2LbcyAaCyokdJwhW+yg/thLMIkAbfQqSdPjLUvdWTEFiSewOJrZH9IzBcXBQTJYGERb/dNzgnkODjGXYzuDOLNlMBNCls7yjDm3JnxOhd9pWDt/kVNxvbv6nJXBg3/wm89r1RNcHyYV4A3CGQSw78AcxPmGz2fghBf0iJWfF9N7Kk7jgE/bpaEa48T6XdWsx/v6N0dxWAaWApibcT1nufbUxzek/7rLJcftvhTtaJKdaVtJ3bwvfZKiNk95pfaL7eStLukdVsI0f8Q3ir2Zx825zlPd2ZAxYAta0PLihLUjmfJ76qd+1oPZSy+Gtz503gZuKfsx2g0rtrG75CYTH7qyVDf3AYQy1f3Zb0HZNddV3+1OLR1k5cI8xTn2kpS3ubNDbr95O2I0nL5WKOZThqkbjf91+J9bqBFabaIf8XYMZ73+Vi9eW//89b/71v+YZ3cKvc7ggOifFH9nozCjGl2qg+xi7Xem//Use3McfvOHf8WAJ+99TSauaOuPeedzlg5ItNF6g6gj9Yq9vD3QgMpBKhTtmptx1eg6u7Ka6TpN2VUc+EbZwCH5t829Tw3/TxZhDTKwD0nvXDdglvycKS1Yhd0pNdwFMqH2rdXH5X4ygfPJh/eEybO60BX0ZFYumL20GrKfdWaEMbiznXC2ZffsfA39tmU91RGzYlHUcDd8q2MVdSHdNpYa+F0CNVDRF61QtDsTO7TMuLgw1JA9K+0nqMiEyiQTVKK08co2/3GddIZameRPa9/st+SV6eL601zI6M5kD9WQnZKqKWo75JY4DHmDijrpxiCVpv5L0o8qpCK0hOfwLSqKTZfUYu1D/YFNFaYofc7hP8OCXYd7Gs9sInNMl1l+i9Z7xPo0f20mrNfakWQv1dzk7gYLIvu5jd6jF6E1jWUSrpPt0cOLBBaFaJrqgPQeEaBEMkrbV5YDFVIrbFZI7TBTJx3DmWY3Qz1Ca5ktHWedhQIzFVIb3hymSeRJYdXa0tFdU/S35Z9HsObZj+Issw2Uia+wbaHOsiM6RKPVnmz3gaMhGJSWjcRex0rJerXb1ULlVCpu1/Vqt55XbOK5vXI9ryT2Ol0tJNt9KyUDYknZ7ot/gw2pNnFAoyEI4BO/OlFh/iz+HNmpGcQU9Qg2sMqDNtFlNoH7PHD9rp/BJ+x8yL+1R7771cL88axf4F3Xq9stx08SwJ8E/pwQwDcCEwL4JuGqBPAryf4Gzfcvh5tBAI89Y3vF0F9G6oPPPvXjGD6Pj4tcZmc7B75EXPSiupAseJQCeOn4n1ezhmZ8JviiNPD4kRBj4rppYK5D4nC9LnwCL0oWPISXohKeEMA3ApPy7wbheRHAwNSLdkB9TgTwrpBgXAE6nVlv5HssA9iG0igCuHIWgzsWRp3KbvGI5Q3PAuf3AoqQ4GOJWScxJhPMBWtCQRWPXfhZaU2v2FZaAy7KCiv24IoV4zj5xJfAHCUbzXJn0FwdeVDrPIQq04C/G2e4wgTTgkvdWUCQwecwwX5BOKQJBnqD05ngA5iGg4t4NV8j4l50rahLxzCMES7QIgX5JFLnBYVco7L2NJRH+TxfGs+PsY5dJx88IYBvBCYV4A3ChRTA5/g/n83+CgTal4cIZncK4tyVjDnmFU3LI7nkkZgJ9KKdqPiCbs/GUkMYAvuvulRr0wv1ADSYcfk/czNuLDVy4dpS7EmlN/u3wv9TCDlVa7+z9nkkXCneXSFZ/YIP9pD/Te+3gEIxf+Q7DZje/XfZFfJKekdkAGflgpjA+T31+3/g/Ee9kv7O7CcpaoeDfOlg1pbChd0pSqgL7rt6Jd2VFwufW0GZW9Byl1lJPp6nmKa6wmae3QymQ/xTFmpkdsmLT3Y4yP996Z9G6T7xnjHJbZCO0m4R+YCfzNq2pDVUm6fa3BPmd5iRCe2Qq1SmH/7wNQrwNu+//5fqIDwtFbYGi190FoBEqC1cf+OVUCPdz4Vr9kATMcxAyjBL2ykswrc6gKD98PvS17wTt3/8XjYTCI71tNSzy4eFjak85tI7n1Za0zty5oPwZijUKkvh7kDf7c0cbk4Lgv87yoOnesIXpxZ7yYGlCKEzBx6zvoBa66LjqLJQh4tk6MX8Zr2XSoSrTl8TTLbfbCdY5Fy4ZoQreDHY8+xHaHWJy15pJ8hU4QqTZ1en8zmvN8n9v4v/SUJrV00jFO1KekccqN+W/2iX/Os8/WJw75ekH7VQ216RozD1DOXv8K/WWZhlb4PF+zxOcZDY67Tt9NrS1O1qM8k+DfTPbet9Tf8ndv1HNKeYy7P7GaG3aO2xPM9ght43VfnfOuY3Zw5+O2Mj18gCLcImoQqpd9mLc1hGq5FZ47X3+WmKyjoLDsZtmgZPgTKvrZFOU81iljA+495bPExRtdFrpHt0ozjCdbxL3/c9TlGtkgJE4PQ+s2WSFhFApy1aH8SBfS38NCy1REyySWSFzQ5Gj56Flmc3Q2OWPQslOCLzi3+beKZa/Sx1Z4VN0eU5y84md/MUAMWmoyGI4a4WAjpaKFFoAR1d7WqhoUGHP3LU6YgezSHnHlGKi6yWEdOwFsZqI2k0s3Jhha02+q/wEw3rPg/cFKFHsAMf0y3yz3deOfPnhOfzfDfws0qg8g7+2QoQwJ+YvB51M5uFAvgT7y0TAvhGYEIA3yR8JQngFy//9d8ykrQ7nwB+tdnfIEY7RZ97GVyXMvgK19sL1gq/FJw0qxyHBva1IMf8nU5jgtvw5dBWRzLBN0cNPCYHzDURrjfcI/okXgwfPCGAbwQm5d8NwnMkgHnRxeG139/FLMBJheUZWGcpipOiGvTvDU43HGUAl0eZP3sbdj3HLoHqCPJ44NDdvNTaRkHwwVLkhEF0LLDPiyBmlc+gyvyUkPH0vu4U3tgUckdXrZR8ktkVMz7+ifCdpX1NgOggzG6YVlrraKFXs5twJIJM9lApOJQ6FqwDg2SwgD8hxVBNOCTPOsMmujeg2DtzZ8UXZP9qct64JzsteMxuy2up8B2er9ZfeHZxeHbRJSagr1jvncxmfgG49hp1BrJHeZaXw4QAvhGYVIA3CGcQwKcN604VAY9DAAuc9rM5/hrOWMlJbI+qpsQv0kjdcAmyLunbb8np6Wq9HxGcq4BP5rmUqrg/+lvxjBBiq+VeSU9PVwclPZkrADHaX5d+oWGrRCSqJlMS9QGJKqGdwUqRuOALDzennUPZbb0S1ZEorpZRp7rZXFWKdmpmTNI7A0uJl0K/svLvf7T/3boWTkjtqcR2Z6A7fS0X3vtF7a1+WQ5lukmjOWgpUrRz+MW0U5TZR33PjTFWF+q5cE1YEM9K+3n28uyKTAqNtoZVJVUgXyKZoNMiomL9F/YfPNVm2+gmcszWFRp/qf3y08FUC/X/2P6HBqE9TQcimNQMkmaB/H27WGnqG1NTPx1854m5Yje0QUHbqxm0JZqod7pOW4YjQpotWOXoCOAZw4ibuZi4MqGJ+rWu05QBX7krzlqvpKDh0vaHMngMcQw12XW6cuyD8mKoUUHNSfXiIJEIV0VOrdVLb5qZd2Kb/XCz0Z2roAoVcudAF8Sw6IHQc5b1WEeFZx7NHIFtyECM2N8sO48Szi9kZjC+6XYMhI1OKNoFJL1zS9lPSdVpGiFMwf7OUdwlN0cxTU3HCaGb9BbsZyLwVehKf8CvV0k+5rbIfhYEcC68N8Pet/g0bXdpkp7aTXFgEzdriR8n31lmc44DoEo6T6Ff027vbu5MvxFtkh58QY1OSmWLQQHlZ86jj7DgoXM+oZiAb2lM3yMRot+gbxHSCc0yKCNlGJSR3g98v24F2Pp5qukZR5M6mjgnKyF2Ney4XQdC1XgtlUarajTqLDxgScd5RmaWfQ1rl1ybaIRWk+mnTANpqg66SWSH3DzFNhpQGMw868wIs+7ZfLWR7gMDS7mdeCaU1mJH3uKhb7ozNG/vjw7EoNKdtxdDtruBkbIYkmyD4Y5lgoNQUYGvszRHUVjy1MgkKQPrLIvZ12NGjN5vkfjhXVY3RXPAb/Enc5Tidj1RaPEpbMEjBg4/+/wVtX1+14v+PXWBwHhaNBkXexghl/cVGGJ/mRDANwQTAvgmYUIAD+Gi7O/I5YduJ+cQwDeH/Q3i1Mzg3HhyEH/h9GX34JW59p4ftXxMMHSRHThbEHy2LzTHO9GOpvwuQwO/3Ci458AB82rTwALXKry48MafDx88IYBvBCbl3w3CEQH8POqEm0wAX6gOtInucswuWbTti8dBAtj1f7Y9O76R2LkI+1v1yq2qx6kF3jtwGLQvkPoxPkbng5yB2Hm6SYExVcIvtfYTmcR4smP/eV9/HK32Oq5KQBLmcj4vq9gD8fzQOoPcrfCp802MROjX5S7voct4SIWwx1wrkGTbRm+jBclgkX7n/ynm1ABKAQXqGWVhu0+5P96efgpxWPamaYEYFMYwfH4NzPMDfS+Bc2vCcepA8YGGGJMxOx64uL3zaQxKyvuhGOc76EOc+ej1WddcrXKeEMA3ApMK8AbhNAL47EHc6CTgC9G3BEapF32jj5krNZQMw5/ruO/9qYMn/5V0NxpUKDiPboUC4obo/7DHYAHhDq0arpnwoKV8J/EXUdq7g+WUtNtvZW/rezuSssCXOjGL5hr3qySrg1StOAsUNlLH7DeWXTNqIZu+pexv7S2J9f+Xr/3LHw/uC9JXaCJz4ZrS7tW7qdJ2yunJes5KGWZhY4oSREE/inYSMcxmL22EK52BviRtRbCy1Cw0HXuWPUB488qERH7E+3xRow+0iVZIVUhFsPbJVwepJWkrTTXPbhh5h1ya6jf4ORAm8jkrwBv29hfawmNu10kLKm6dxcP6ghays8bhF52F5lqGx949XwMb9a2u80QWNyM11xUEtnv2TU+Ja0DU5YCJosa6Tk92ueSsRyoH3zXtOXsfysZqo3Ogh8OdUKZrbsYFbf/O7CefOfdE7i8e0+9Ty+Fsx3qs0z3iOI2lRueR7jRkV7G9fNSKpq66+c1C0BlqpvTYPrDMlo4NJCn74tQMJpDiAMhumKLU9DNl56p7rVS4wHyFZNruFrT4I1Y/4bUp6oBFJEUtgiXU2Cts1MhYaECa6ioPxDjlNpW37E/0alepOPW5aOJftppPiJWpFzmED3eACyhKvzbHdJlGn12HOyp3fw0K1DZI3YMPaL47Ezvcx6STU5kFUH7ioNF5Ry3mM7m9sm+hnNjsKM8ct3nPK++Ls3lwpbcdTSow/zlHU0CnpecKxblJxOylC8V8r6Q4Tdnv84itlv3o8XmK4movkcxSE1e73yg5R1GjVSWboiQoWFGii0GcCKDxZ2h940m/kl9nKYMpuoeHPJ/FpKvZS+83p455KpTQVy2rqMdWywNLWUk+FhzwB/wEeMf+PFFo8Qg+BYvuZ/zFznMZuF8a7w79aQBuI2jOE0gHiYNukXAKSR3BU3xicnDcMnpCAN8ITAjgm4SvHgH8wuS/Zyx5DgF8Y+W/J3EqDSxwicvjclrhV+86PANnXKJjMr7jr/mEAnjozxERFCNoYE4RBJ8cuZ3qC/2yDAD/unLAQbzY2OAhGEsNf/B8FUwI4BuBSfl3g9AsbX3+j5dGuBBfC174TflaOOCR0U1nY0heKSCmD/BMwFIcCCYPOIsAtmHrItt+5B3n3RG0cbcIHDk/XyPkJQbtC3LAPhYJ0I6jIGylx2kNfOFO4y8LQ2SzgJAvWyk5UWiNDL4ZMoXmeEKwgNAlBJsCOZFJ5teEx1xASwGDyqBQ9WR3YLN3Zk7w+DjA/Sy+dHjWY3+vO0kkEyISOvXVcSrAqxd4glE4VwR8UQYldUEmmOu2ib5/embn6ZgQwDcCkwrwBuFyBPAIEfBp2b2nwZd1XhGX6nUWNOqxTzG0/0Jxe99dGFAX6kIELNTAxyzTfLtpnwAO3B18Arjej3zg7LZjh+XmQia2vczm49a7c9Fntyj3ORRsVmUwX+8n981s4cEUB8AwAWwsNRJ2r9qPzOb29ptTnQOdCv/1e//DU6Y2B4tOX7PMeNU03sw/zNH8s879t9SHPpHpPEqgIZh7//zeUvadvibsiAGDtknEoD3Dnnhmnn1gnSXxUhvtm3zaQq2Q0rFLJNdZ9D3kxIaEeLGPEcIUZGSUdtpzCBHhwbZ37i20Cilgr7dcR7UH2u7ns0fZEwp0PFG13+Xl5xkbEIWPIIOoX9Qpl201N+Ozy4dV07C2dH3Rsn6mHx1J2/Po5rjvt+eJrS9a4azVXMsIu+9QtNtvyeGsJT5d7Flkr2GIWGg9Z/VMZWrp4HBz2jcJn01Vq2pEmISv3Hn8xU9WxQqF0npW2hdMrWDKgQyNFCXhPCxyaoGOFtKrXWXHcfdwDxwwQHcr3tKy4Vd3a9z3Z/baaAkqQVsXgzaQoiZeaqMbtBf48jaVHp1bNG998aWy5RClk1CVP3G+/AOAbIgftYZDW8/Fu5BQuTON/QaxKcjBt+lIqvLEATiEpwBkIUrn11Vlx+kYKimUisM22HDLI/Uzx+L8hCGT0NRGcVqoI3UsgjEV1sruS7XX3QzpQM+KmJU6yQRr2JGAe5PAHEVAjObGD+YrMD/LToF5HednvCmeFF/zffKfOe5Mt/8rJOKixVzZwFL6LXk2t2eEKwbt+zxMUv4d+0+AxOctDun+YwZV/slzGrlfHL7n87uQUAHqDu8aSCnADRKSc6e+3YdPWIRn6RX4kXnEdUwI4BuBCQF8k3AlAnjhOaRaXhkvRv577pLB+8RZBPDzZH95/gSwwDk0MJedC74u4+hL78CrgLPVQoz+aBdigs+OB2achOBx44F5gdbQcY7PaZ6DCxHA3DQOmOtul74UriIOnhDANwKT8u8Gobm39fl/t/RcVv0yisNrIYAvpP0dklQGIaZ1xGNRASr2ILHRAmid6f88JgHsM753oT38roEDXKfzsw8p5XZtXxWibmmfTgZfNDNYfw5ksD+lG1RtliHjPbPjGVPbgX+vcbsXRHDKzEeBeeBkOEiGRvDqbRPd4UjvWydtETEDp8ef5x3WP40fFwI0eyjS2Mrgs3HdpO9J5MKjk4AF5sazO7pimXe2DvjS+jkfd85ryDiJ5ef28356XT0hgG8EJhXgDcJIAnhomObTJKPnBHxc/YfoJO5zpOM8YyvjDcZP9iKP7nEX1v3HdcCCCxSq2YGlAP2W7N4EH3iEcewoa1bIf8UbZ2KHmVDti87CG8p2jma1n3Ta4blGsjizLVSzQBstz95fDd6p91K7P5tFRw17MtaNox5uISQV/s+Cfs7Hzd9d+f0Nliwi1UGq3ksdbk6v3Hlc76WycuGwvlAfRDoH+nyktCcnALFXQtacNJqJcFWRrFJ3NisXzF5aH4QUya6j+pSwiCCNYPUxDoinqP0NfrDBUoRWjQywS17kmDbXMrHVcnMto+csIBTtzsQORQKuJtm58J6oJaaoC+2vUGoK0aogLEvdWfHR3PNS8uJ7cU25j+h2r/oQ5zQRalcOUuGsdUvZB551ZpbVzVJ3Fhi0lMKDqaPM4KE2Bf/EAdMBCXhA/w0o0xYgPLeBhNQuFVPu8bnz+Gn9Vi5c22tk30xuNHUJmJX2t3qLufDeZ86918IHdqi/JG096t3LhfeW2XLQl9lKUhEDhBU2j0XD+B/Qcj9+J64q/9Lhe1Ck+v5MqrBffCMP/Dvt13v0ojg75CqkqiQBnbagfjsDHVAky+ylAZGpnAvXAN/xe5lNkbMbQv5ba3/YSavKTxzuwu8zeMZGlac7JFU+dEZ9nU5AEM51eE9lZZpGifnv0i8R+j2oQAFidOZVZd1hC/4T+BC+B4/ovKNu5e/l+FJkr3y2fHfBfnag5ULoh4REz2KKapraBouiedGvXYMiFnFpics1SIEHf7hEC6P/Eyd+3AQBDLzFQ7H+c4neFTbXWZqjtHv6tJoYS2ZoAGXiosD2dyy4Vz7EdSvivcW34GvvfCw+1wf85D4Psn9kssPgx+x+yb8d7gh9VfBrKnXvmvENn12aIMfgGZJKvzG6gVhwFspdOo/4xJxkAN8wTAjgm4QrEcCvJKn2AgjgcRZ7FQjgF8P+CpzPAQu8dCb42vH8vgUXOn0ndmPkVOD10MDBTJ3L+EL7uEYyeGyl72m4KAEscHUa+EVywC8vJ/hsjEMMTwjgG4FJ+XeD8LwIYC8S6QVjpAjyohizCDyppDwJUfj5FtBu+WefTgBfyPzZLw/ujoj+vd7c3yHIS5clgIURdBWK42WFiOm5CzHBZ8PgSO8efHzR947/Fp8n9fQlzHvTfP4c38g3MorSDrYDBvfnePkn7OlOrvXktW0TtdF9GlhIc4aM9U5TAzM0gX5aWShwGj1wTon4auAMGjg1hppW+FhfrtKzvd+KkSLg6yVdXh0meFSlOiGAbwQmFeANwkkCOMiS+tSvj9H2YD6u/nMkFL3j2AMEtzWGx9XIMeapJmccxe6Cd6f2NuFSswS8hb3cX3cBTx4qCGOhss2F99RBOClVP+p+LVLrRTKNiBl51Jh9Z/YTk8gKW/vki728cIWde7MgGM2hnVQNNwO43o801zJYqFPd/+q1f/onzd9qa2Fflzy1dJAIV5/Wb83EDp9t3HZMeXb5sLAxJXjfzoEeNLUWpGm9l0qEq886M76+WTyvSBZg0NZpFwYzs9L+LYoz7O2Qy7Prc1qFwczT+i3BxdbMmLkZZ9s9lfqia4UdpFFvJ56JdIlEqJ0IV+u9lL/zYaPjJiv7F+a+R65vwZR3XnSwXLfnoHoyF64Z4YrgkoV+Fxgy2iUKG3A/UE0FC++Y96enug72DQgOuHOgTy0dAIJV9eltg3Z1kIrRjkvVQxLzFA8H+d3ejMgY1mkDy2xmqM+wK+J+72xsYkMJnnllvGjuFIrJIgMH6e/CFJ159Un+3se8+ZjbCqED4kLePRM7BIq9pDh3Qj8qJMhTSwc1M9Z5qoezHUAYWQOCm0+Eq7PS/ipfAA0yqzyO0n+n+mO9YLMDn7HzBwDNPn82HgfsIwH3VKZDzGQB5BXIQ51BD1SkWUhDA0x4neZrM0+Xcgm7U9H6Os4DXs+zu8dcxRsh+Kpcv0AVvs1AllqSst+8KNhfoTv3dcBDUSYEvlMjmeAobT/Bx0/zEc2+URyNNlAm7ps8BWETBYKRQG2igFDMi24Js5cu9twa+5ayL75iAkJnv+Es5cK1Yi8Zt/tStPNa+Gla2vkuH9+2d28XNvmcwb96dTOABd4F4PWom+x7jCC4C7uBkWAMoF9w+WAxdBXLCw54HeoTAviGYEIA3yR8xQjgV0T+K+DzvqcSwJdgf3kVCeAx7R2O4YW5Q79EXO4LcumzdmJzIyfELxEP7Cs/GMcX+uRLvYEbBHE+LpQZPHud2W+XI4C5Jlb1+SkqTuJVpYF9jHSNnhDANwKT8u8G4XkRwC/PG+ZVSAAR8BXAPgHsBgCXR7G/FW96cUz5b7BIyA2zvzxnAlhg3J4/H8LYOXWpjeXGc4T+CmPhTOE4MDUirnVkFyBgEx8SLpy8nocSgjnOBAdrQsYvC6t4E2vHcW0e0deHs7W/qfFclGeuT5V+0gi6DYzSWF8F9wOx4uPj6l2MseNT8BMC+MZiUgHeIAwRwH7E7Enq18c5HPBIRg1PVns2Th+ZCiHmcPBwKRDserq71RntxUcf4WwL6yAZ7MuRfRfiLH62rg9hDnxr+el+c+p24plPr76hbKumVv1p/kkuNr+019bCQCJcjdGO4jwu3i01YupCXbgNCz7PT1wW8l9AkHyC1FSd7q+898Ot3qI90LJyYb32urkWn/tWAdAkW+g+m2sZNdwV/s++F67gp8UmEqF23DEbqqFJtmXG64OI2FAiXAUUyVpha51FISoFcjSiUnmOvVn2PuPePnnBWvV29LvThQNNd3d7W4Yjk465bxXELiXC1VpjWhDSgDJt+Sa9RzJf3TvF2cC5Fiye6R15vMNuYaw2+i338khPVwX76zs5HyOAT4N/DYiteFeUf/0ET4dw9k4Zpkgv3u3NZOWCoMkPSYgHefb2yIs/xRqE3HmFDWCO4gpPgfmNPXdQUIEiPKLfoFcAMAck5gj9PdiGu1hvaeVU6hO+XiK2xhtbvUXrMF3X3Fa/hNRuaCFnO4GN05ODpsdCk52ergKDliJFO5pkG+HKFPVDEp2BPivt59kTM9Xf4UfZj0wewyNosbvO4x32YIVLMo534evzWBWyacIpypCsIv8GAwlpABFY4MvfWkpxIOKNo3Q/57Ug6StswwHB/kZpAzp2h2gX10umRaROOoIttLzCutxGE5yr00qo0TrHbWx8c++hHQ7++olTKYTFwkPbf0lwzwTGeiIwWIiDgRDqEo+qTO8yvcHSLPvAA+5VSAlmWkh711lcCQz8hiTCvg64M9B/R/rDD/goU63K/0vbHPD7w+zzq4IErEBCZUUBCM8SmgVgzqN+OV7yVeEWfOz9GTvigwUHDPza97//9oQAfuUhSdJlZ9InmOCVx4UcAv864MLzgAKPxoiCG4Lgy28QDXwuletPlO9cXFxyclUnoFgODBPAosgLXsbBZ0RtKmhgUZSss5iVC6IY3nCW/Gq4uZY5GrA9CMwBiSm/4Eth6Ri9epbmI3q6llekCF9Z6XsScYnYCLnMC8WL5EsevOoc8NBo7SrG0RNMMMELwqvXEfg8MGYFqLlWbkfWu10tpFgQPa7dFGa/QhE75ni+ffzPUe963uwv0Hl0wdrvorGjQRSheKNqv2tHUPirj2KCD0d8AUU2MNDVQkOa4CHzOlEE2kR97YLQPQQ7AsUssHgsakJ/Ni22Wj7yhRbVhaCBV8EOzK2nAuRiNUAGx8LoA2DsTsHng7Nzf4Oojnc9X92gWviNj4wBvl7qV0CcNXFexq8SRc1/lZzgIbrola9RJ5jgqwfB/p6B564APgE9Z4WiXdEQrA5tN+sRwBdkf32Rq7AsBtBh2ZPzDq/CY38PvNDZoY9pDu+DanRD0W56urrfnEoaTa0fIlyt91KJUHutfideCkW/W5y3mgmcLuGkVG9IHaubleVdKWs5eym2E4IDrqGIDx5bLbuUcLha76WAzoEOUCKz1K70ZgFNss1eeiX5uPNtl6Z91pnplXRrS+cAlnEOZRGRK/awZyqhaLeuu3RXJ6wnqDp9DZD0zuHmdNjo1LORW8p+Z6CvS4udga5I1oazlAi1n5q3gK8lPyuRrJMW4tdbyn5sqb3bm3G1vKZMFrbBZPb9w/mpZ7u9mcXw1lZvEagPInNvFur9SPPjjGPGsXAsGR1nWz46kqIzwafnZiCLrlrWJ7roJvQ/Djq+rHlgKbufzxpLDUHsGUuNzoEuJMKCIRbP90q60MgedRXoXptCFAIEOV7ws1jKX94q6j1DWbnz+IvOwrTTKmmzpmQXe8n31E8tIg+79wrhmRhtpH6xly/2kr2SvpjfVCRrncV5invM2egpSiyT08tK1PETd3sFNpLMfo3I39VCz2w011FJtvvzG3uZ9J+UtaX/EPlusZccaGHhmG0sNbDknhlRpq0lqWEZ7Wcbt4X8OhTtriQfm710oZgXCuAZqQEc7q+UstaCvFnspx8O7h3KiRaRefb/OX+Hb/L1b3787hePlYgzravToPzAafwh31pCatMv8PtPAH51nuaAP909ioB9T+VDhyWVaeg4ZDQe2qTmiELim3z5IXfeIgt8HbaQfhk0GNCZV4F/w++mqX7Gvbf5soMBVEjJhL7Ln2ywmKFRIx2htczWBos10hVSaarBJsU8e4IGzlDzBcGCalWjdcF2i8J1w1kSl0H4hNKAwK9cItRGLlQHqUPJZfEPWRQi3RW2SiQttDy7ZeIalkbLH/SJclqENKc4KGOs8sAmqtFaYfMj3n2LRy0iQsc8f7yBN4INCWFgDoivyTzFVenBHMVMtar/qd2f5UfjK2VeOOLwCbzrUAwxdwdgUEZS4ZEn/x36xS4et3CYI9QcFrCNdMCZ4BXEhACe4IbBPiXIbQgT9ncIl2R/BS7qNsbLmAEMZvJdoxWhwNnhvuNjavSOXUgO5betMYoGBtZZDKYBXYYJFhiXDB7C9HXqfYN46ewvsPYCvaBv2syaO0TPRrj9sndlggkm8PGcglcvi46uZjfM01SP14K9sWlM3xxMx51ikG0v9zTY7LUFMYiNUPGeivMKp8EF7dpeEM6I+x0HF/WYeTGwwXqx6mQTTJga63tXn40CJ78OGg1ghQZQYD4YDxzFCRqb+1Z7AkEOGMjKBc2zswtnrVjWOpqg92lg7URNKJCCVIAGFnJbvzhs949yguPSi9AHi023++NywOOQlD7zLSxenh2nSMunnMRgAnTmvAzg54cHcAcY+2vb8Hb1RfrZTDDBBC8WZzknn4Zz5b+nQDQVndoEfOYv8Mh33VL2g36wxzjgIPwfYdGbshF47OOE8ljQ1YBQ0Ep6R5PsOmpO2nvWnwFy4Vp2+XCnuKRJ7YNMGLCIqQObcKvSmj7cnRa8Yy5cI4Yasp91ZnxBMKBIVla26s6SMm05Zpwse0Uju6DZqIKkLDpLwhO4UMyHol2rqNOCJk5Tdj+gBZBdrgq1qDLNuvW6pHeEc7KDVrcjyWizvlAXXsdOXxN5wMKoVhw9sTON+qKeaPtJq4pktb3OMLOXZpXmWka4QBf+/VTj74aAh917Imy435JrxFwaexvagVuhn46BK/BVw11H0AolrKzOffeqc3TZNZoGZ1t2zKNZXHMzLkTYyrQVNjpih9NG07ctCWetcNYaWIq60Dm6nrPuCRWRz7lwTQ3Z9V5qWd00e2mRZyz0xIIGdg7lZwszwIEaTdC2B9qyurnVWxQVUb1/5KcNzOb2xIWXC9d2wlRJtoissGET1fIN8kq2ZfIXAPwj9a7lUIJndrDS62qhet6wiRvVyv9V+3/sq3MfKd/ZKd/5kZlIVHulTszZlenyeTQO4IBKSjbrRB5+9rbTk+feLPxybjsa3qqjvMnB3ow6y95HvJsId7LsxugWMT7mnRn25rDWuL/2xn0bbZY9YPk/39L/c2cL9U51W7b7f8fGTKVTn+3ziLt19jbIZ+l+xpcH/JrK1OtkEgwO6FvkdIwUoTt0f0juu1CERTiAJvycQQ/pG3SjoT/mVxP0C+SBHw9+6W1pc5fUXw3eidHel/Lbg+kYbeGkLcT0teJsI90fWMotZX9HzgCJUFuTbHHkxfdFWHOLQ9cZ6IeBKlicUN+H+SR6JT2ctTTJ9lXvplcGCfYXqJOGCtAmuktuhc00tQrJB9xf4alGo4yR4mCdpQxmGWOXnFAzr/Jgj7l98pXB/GvSA4t2kMYWCuasXKA7C9T7kQ/Un0awkoTu80D/ic0WTYtp/IaBVw5ixz6BT2ze/dyNAQ7PEoof54AjcM9Tdm3B4jEHLHmJ2gaPX1WV8wSnYUIAT/AScBX3vzOC3J8XXtX89jFxJer31cdId+4z7BlfrjZl1CzS2V+HIN0bfJJAl4OggX0nQF8QTMAjZSwmGFg7PlQbSQb7eDGxcJf2fA7ippGpMFFXTDDBBFfGSwr6PQOK5TCK7hofOh3rhGdGEHl2r6EL0FcAV0YbOJ+P4Luiw/OSkkp41jWRe67oFi+S/RG5Agd87b13l4aY22gedzAbOoMxyF5qh+3x3nVK7X25DOxZdvxLWqOVp0Ug4jpJWdjc+SlrwY5AwAhXlsMVPDWwqAZ9g0q30vDjgf2asOxRAilPE7wL1cBuRULMBojY2PGd7nl8cFhyqeJMCFmiOziijS8KQf2Oyf5eqIKKwyE0OFJTr8GyV7efPOla4PmyFyE8PnqDI/Pq4ONL4HLfVkGWvLCOxgkmmOByOO4m4hONpe5sIlwNhlP6OZpHbNnl9L5jeEEH3WuDJG6vdG4y8PkYSgPF85cGHGTuewTk9gm6dwgx2PcKOc+AzSrqYofr/Ujc7iei7UIxn56uNrpzzbXM8ux+VAtNSXs7+q0Vea8Tbj/p3V7A3O4b+80pwDFl3XD7Be2BVuwkxRnx7aAFDZkItWuiTLWghR6ubNbvCH0wpGoo658t6zmr35Jd52Tbpbcd06WBG1rIO4+6ulDvlfRiFnrJW8q+lOoIzjJpNIUFtIAQPtZ7KZFOCjwu5sqoU4ljOgbhJ0yYWyH72SqAs51wdNmnQtdrryeNZnq6Wu9HlGnUhc4xMa7uJSgvWq6hCDg9j71ueWdHnKYHsAwlMMEYjo7umUrY6AjVrziMNTN2MlvK2U4MPaPnLEnv+NrQrFyIDEImaJJNiJo3QFDD3alvHVQOUuKzCF/uur4EiCDkfkuuRRXfmHo/6rKS9kBTB7opQaCj9D4PiMI9uIfyrxwU6MAiPIYWzIOBjq1X6LQ7yqHjfCtf1azvSj9Z6P4/K9/J/I+D/6os2bHXHHugVUgAzzZezy8+e7B2x+nJwiK7Zsb+SH992ppfij35F4M3FMmaJ/8WjzZY3GBRp/1NPn2dp59xdwPdwHifv9xgqch8j94yW1XSZeJbqTuLfHmnum3Ylc5rqqI7/Jz870AV+Tvc+xHdzwiD9C2kNKE14rcgDyXk+8Q/g7uwBUuucZEUhl30RfvtxadJSodoVVJILLO1Cu9Jn69xL0n5bYl98rJszVNUZWuDRXJMSdas2gzR6vTuGeGK2Usvhrf2m99MGs1KSw9BUU8KCb74PorjvKxubjhLy+qmKFyHaGD/B1Dog4u9JKNI4lJoNisXSt3Z9+Qfr7O8wkYUZ4+5PLtpariEQhZYZ0mns8u0cNaJ0FpnWVimA0jtB9wTxuD+yvfJC9Z50FKAW/H9zcHikrRlsCe+h1T5q53LRwW+YHwCBybfiWJ4rs5Hu34XHkLMo4S33N5l6REsIm25+cGM7Y01wauACQH81wBfbf5vFK5Z/jtm2NsQXo1f/Wtgf1/lDOBLZDPfOHvqUV7QI5/Pez59JwXBjMkEcyYZHFxMYExqVsxt+TNcImn4Wmjdc3HtBOoLniwTR3tCA08wwQSXwyvG/gpcMQP4bPaXsYvAKEciXB3HJq7REPw0eJOGNkKccWG0jzOOowbHL4D9BQZVuozNAV+F/S29AtfbQ4+GbAb+HYkmzI/N5hJYcszlxSjAGKaZR0Z+nLnZU32PNFopgJSvBo7QChrD4JWC/mSW760HSHontloWU6uOKR8FlPhlXiZAfwpydA7mvIukfZ6/cZDUDFLFYenosSB0ewO6AyKhc9oKL1Q3XtTM3KdvgzGTh8dfGsK5NMkZ7It/cApdMiHXVTsuoUjj0tsCqQssOwJr1xqBPMEEE1w7jt+RfXqs3o9k5SNOwvdEPcKl3Z7HkAI7puxPJggpnvjXZwRH78z9YQpQcJxnRwiN8LL213D/+MccyikPpgJ721WNbq+kC0ZQC9kH/Wg+Xto7yB6WFGOpYUvh1xMP//TT73575emamtP6dr0f+RlzcbufNJqHm9PCOxrBPHk2xULY2oTYatkeaDUz5vs/C9ncTuNW50APG/Kz7MwtZX93cza2Wm5+7H0uv7oIBOv6rKdjysJxWvwpfKqBej+iSTae8BcwiXQGuuC9xDNho1MfRLRmTtI7Qim7IG8mpGZBEJCdGUEVK9NW2JAFA1fvR5JG01dqujHGfsCzBeCUZLIcnWvLO8hRL/G35JXQy7DhnRcDWl4xHIUNnJjMtBw8oarRDRsy0DOV4Txp/2LXwSMC7YG2X59KGs2a6TagdQ70sNERO6wu1Hc/n9Vzlni135J7pqJMA+yuZVwRs/evanTFJ3KMhDJt1fXIsrrph7+mqVWZZpWsZgJMeeWBaBVNe3ohoZ5MsfNGfp3bGyyt8cbyzBIQlbY/H3wtJVXvSw815gqDXPNWWwuXv/FWuTpIzUr7QJ69DRarSkpolBOhtimlPx28I2h+U7L+9eB7imTNUxS2yY+4PU9RIXRA/MngflhqvcvHT3hrh6nPU6+vsKnT2cgv3v/mYwt1wX62rd3Sf8uZtosPteUQahv9te+uNzQtbtufaffv8SD3VrmSysTtakcLJQotK63pFdtKa1+mFj7inkIrSXmN+3Ps/THfXWbLQVexPu/9RiPcMXtpoBpKuddeLxoJtwtSzOzdVkO2aIx42L1nbsZN4uIrLy7ywwM9bHT8oN9Sd9b1PB/oimQZ4QoB0TDHIX52/MfiDBpLjbpO3VlKhNrrLE5RX/eWD3rnCLEvAQkN3qzp0M+p2B+RBwyYRBTJMmj/ZuJPK6QqpObYW+WLFZ5m10z+B/oN9rhJ2IHHLVrwnUcod73woObxn9MszHGsV/EuCrz7iE/Ma4hSmeCFYUIATzCBGwj/svfi+iGlXvgmbxSx+hIgJgSPc+rZDZMxJsRHSoEZHRU8QhDMcSZY9NONZoIZRQb7QpAhMnKc4aWY5/Jnu4aShp8fbjr762MiBZ5gggkmAJv42TYwwXjUcxGcCNCwNZ/n8X1f7Mu2AHKCRj2hAAbkJXpVBtXLbuJ0uEZeMHCQ1POW9hG7CPubA/04G/ri2V9xpkT97h/e8dn6nbOCCYdxOXGz6QlfAgfn7KqvynSZcTskqqMIwGAksIBO2wyc2mV10w8GFt6VQWPGEfoqzXNFFjG3Yk2RQGXSBgt2Pb/o8SHIzrA0bC59dezCLty5VEODL+c9W9R7afbXhyC8fT10Y0Acyt0LHIfolW3b92H/gvXtCVfVCSaY4AUjF675j4N0xejo3+uCZ+d7bAIBEFzgSPb3wajHo0a1zbWMWO05H0Hc8fdPrBw4BC3gRdGEAwAMOIBp0AkbnZ6p1KIxSe9kjUO7ccu2Yr7bcKmY+sG/e9vp6D/p3O5FlHC289rsmtlLq3F7/cvXhRJXpNgKNtEnKfWcJUjlrFzQYmnb0A6/mAbQwKRUTDmmzKGs23xRX9VzVnMtIxhTNdd1yjIRsFCnumL3HOQg/emYsmIpgKR3/LBbfz5HCVuCKhNzO2LfguzsRitOmeXffvbbsT/aJR/BEsdIrOFWyHbCmpqw672UJtlGuCLUja5ryJttIZztt2QVHGQh/3VFwDkLEIy4IOeEjFhwdeLf3qIezlpxu18fRIB+S04ZZn0Q6S96O2kBqEZXJASLvQ5Fu+pCx9lOuKyzaMzymHKrpAumuWmBjkk8SOViynrOem12Tex/IuwGJxf1pMgMrvcjLuXs/WssNSS9c8tTTvuqiSk3OZfPeX2FzTIGy5uKPdD1rpJxIGDymwXTneKr55Vdsrvkdpip9GYPwwmzl86Eak5fM8ORDtFpdg+k+Hvqpwkq8+xHJaeNnqCUxXyPzwdtraEZD8P5DRanabTRir0ZoDgQhsMzpdCsf5CflFYFAxq3+1XT+DHfDkW7QtOctPrlfmRq6UCT7MXw1paymGMvQeeJcnuRrcJgZkna+v9Ffi9M66F8b26wPyu997X85gaJlNb/JX7682UtTW0rdeddeyNnV1taZGtw1+ql35A/ecA9YyB/Jt19e7Bfl/RCKVeMRBNOO6fXHzWWUqF24fPU7J1qqTOPjdOTfd/1RdseLB/Spl6NdIq6U/TypE2Zoi7M1Q83p+sLkUSo7avbxQWfSLje5s21jGp0xZfRKuoxjwAWESfB34N6P1J3llA3C4MZ0Sfhx6MMRaVw/FdUHFv/MhB5wG10YIWNefYjXlfvd3gwRwmYX9vj38EjOo/4J8EIoRuCTwCImrz7COWup/S9BwZ87Dk8TQGQOaZz8zngCW4KJgTwBDcJzynZt0z8q0cAvwTn55vC/j7X/RwSJY90RDydBuZMJlhwvSNnwE/SwEOCYI4zwb4fIMc1wZxGBmeOSwT8tLOziclLdx9fBc+JKx2eUH2BmLC/E0wwwVcIY7Y9ncS5ISDjs78CwhAMqJCY9dkezZvT0Y7LSS+K2DlvlNTrZ3+Har8LsL9chNTJvdhI3ZGwoXTZUwPEXiBjbY4o/MS3oKOrVkoOOqKnOEhxgKf9tdHKGKfZnvuVXpVs2dMK+yJgPxs4QUUIF+qkRRxgVi4kBroiWRvOkpACi/cei1pcCGxJzMAGw3EB25uWjUDEU6MGpbdV3LX5Y6wotC5IEl8al66dLmTmfBLnaqN95MKAK/8NPjmmKfTl6O2TuGh9KzjjSWk6wQQvFsHRuuGN4kfjOQ3AvZuCz9H6gQKCuhvB3foi3ZhLwZ6E/y73wXGV8Kmf5WSJJaZcBA08g0i3BWiB4RKErsrTjOs5ay16x5XqNsDGKcrqQnf+15u/+1ft/3ZtZvZXq5npWqk7q0l2vZdSpq3mWkZQnsJ/uGcqWG5aqoUeinYlvfPwydtho2MVdayjJqFsrlr40ykWsOq6nrCsv9C55b7q2LKrnTVcf+mw0dGNjrWlBw+CUOL65tUDS8km6Qx0wdc6fe1Zz/XNlvRO7yDwTh10iLLx+7fe//taBKt94hyoIRu4Jz8U5cGStAXskDNoIxcSA/0ZM2G9E85a4ZIuqFlgZHXpG/kKM3CxJz1TaSzU+yUZCGetBqFEpVdFFmG9vve1WO3RpyjpLgvu72/LO8vRgK1OiSP5obdkOOuS4gvyJmCGI52BTi8pnJ+HhKSq0RXPFHvJZXXTz5Sdoh7BJjBSAKpMp7QDUrJrF+Tyl57iOYAMjTb7hNknT7gyRTEacqKUc9i7aDPs7ZPPEGqQgTL08gxKZFVas1qxQBx4j88SlP6Y7yZCNUfqzQ2sbl8nlBAf4bXwwZekROOCpHcaWig06ApX8HY4fqBGG5ai6vWaGTt8Om0txBtaSNDbt5T9QxJtM/6p9g6gSXbNjNX1yBcslMKzkXBpD/6K/9UMex/wkz7dj7Xbbe7JhBelrRn5xzZakkpC6m8NFm3JaZJ8P/cnLSL75Lfqr4ei3Xllt/FBXwq1p7BWw8/WercON6d7ppJNVmfju1Y/206Hk3Tmpr8sryYLxbzfB2AVdYq60Ogn8vv+WZiXi1WSM+zNqvsJKuvveMTtLMweO+al7mxWLuDFnfhPiseCVBYRxb/ov2sPNNFO4S/pNhwAEFsti46KZXWzQipNNUtNxC3Pef5O93kAZNdMgEfwiOpDfnQz3ZDvQhTeNZCXIIckvgQ2PPRigNuwDLujJ9ITFxpsTvBSMSGAJ/iq4SokcZXplNsreLNxbezv+Ot5KezvvQu6QD/vgLqTOyMo15HzjKNoYMZggjUaKzQ43RSaUYJgTmGCOe4Ozcmu3pGRwENagTVG44zpoTOGpqkxJgdf/MTTS4w2nCiAJ5hggr/2GKe6m6N4IQ7YFwELdk2xvdRS3ZvNiV4q/RfYPT41OWolA8f12roW+Krfy6M0tnHuNcT8XRkazMPOZTngl+JW7Rd+ZbepTrEc33XcSmutVNhfVkjSNRrBgYkwKhehZUFKOEUpRSmogBe1n4YtUoEFBBPcRt8hJ/QQYrrTz1yMrZbFZGJwIgy8SL/ScdmrdjwwGGgdZ09T3oO540/6f/rq1bablXht3PBLrJrGJGVH+l3HwiOeHInrYn+B1qVK3ElpOsEELxBB9tcfuZ+K+8+HAz7+rfd36aQ16xGCkryRlYN13Hh/TNz31nyyAAj+mh0Ia2ZoelG7UwFiuE02Vy18MoVKPmHu7cq/lKz+8Itb/22znF8yq+1I6ZN59e364fa0y+mCVdSNaFfIW4c8is0/i7sMt/+82O4ChZ9N4QUnW3Wd172mqGC0xL44GPrcm4WaGXOdk49T5s21jLHU6Bzo6kJ9w1m6pewLt+cgjzVaP62Dxg/2f3tqZp1AXDReGq6kdxTVmqJ+SGKHnCBBu4TMflKoioeqAj1nWUVdz1k9U1GmLSE5FYcomA8toBpdodoMG26ccH3asjb1o1MfA+OYH/jAUo5EzH4Dmf/J9BPXUpDyzlm3lP22GVdilvA+6Qz0ei8FiNxfazMujrXPPSuWIlKcfQYRiGCL5FcdG88wsoyR4qCjSfXZaKLQcgtRERbj7Z5iD1a0zQdEI7SitGfYA9ZZnJLqaapPiItUDoP2DjmddgKljR4mvM2UTGiH35im0aH/mTdLuN2bFTsP9FuycFxfM12T8Gy8WcdNMq7rEUhpup0YtLVYzR5oGO3abaqmgUk4azXXMls5XehxO5t6ONwJz1t4bsw/I+EcunHOes76Pr89m9szwpXbg9Y62SVpy0az0BYot1C+If2wTHyGaIVUlHaKWirxswjWPvnf49MNFpfZ2mDxnvzw3msPE1RkQjvklqi8xrMm8h75ZiiymN98Gr2VsHvVfgQv2TcXrimSVerOvid/sc7iW+yJE7HMpoW2ErBmGlLxZuVC8NoW8Pndej9S78/0SnotGhMdFYBjdJ1D73LVj/1eGeHKrZA9T3GOvSRloIUq5lozmOJEu9fAjpuP+7jFdEAWfoOwBysA9KoMNpGXkFSvv0F8nrvetPbD49P+MYC6wwQ3BRIwGAzOXW6Cl44vfv+/qX32f77MO1+xDOCrZL+NM/13OQLYZ87EA5eEEzM1lwia5WIZwNc1/efjhRLAL134O84JehViic+gn887zud+a0677E8+f9IbcKh44kT0xeixxBkDy9P44BuNl+X/LHBzZtm+/8Fr3/vawvnLTfBSIUmT8u/GoLm39fl/t3TJN79iFaCPi5aC45d2Fy0CMzSEB4wo/6LVnl6xsWEbKpdlf/EILf/tp6iBBw7dzctuIgB3rH51LJ7HKokP8rzb6S6KMc9UDlqvQFCxjxMJwUHUZ6NBZfBJCIlwhcSQONgmaqOXA+HBbaJBGlhgqPYTWoeTWxlmggXEHNkZs/ZlaHk5eVHYx92dc8W1PiW8ex1k8Iuvnc6ojXsDij3iEo0z77zn+j+ngIuHHJ+N1YvkYfs4cXgH/+A3r2mHJniOmFSANwj/zad/8X8vF4LPjGR//ZH7sTH7tXPAsSMR8Mjg3lPdm0fuybkx6mdg5G/7doDunfHyF/x/CTxYAAs11nWKMiZqtpsJtfccgxof/FLhpwfTTlPmCSygv25ZWzot9FWX7ATMzbhqdMPhjvVj/eiD3HedhI8OgiA4FWJ/u9z8Ycbdk6zXSmVDHDoeLY1H91qoU10qOE9kd+XRYb5TGCYLzk+QeQmpLTyWOwf6iFu2COX9ktj/vjzsEY1LhcZWy7lwTUiK671UvR9xP0jp+DHEO5JCdyu8tYdwkun3a4b73gpFSSw+u0hrDiQBH/sIQxePl0GgCuouEOLrmLKx1EgaTV8Gqkm2EHoOLEWovd01l4553gjz4YGlCAUtXrKyzzWKjroojpACz1ESLXrukGHoYC8bwq6vSvYB9xz0MkngkESpO7sgb1YHKaev2QMNEEHOQC68J1hhnfZWbxGwB5pIdO5vJ6KLReswjd4tFVPgOpkr01a8FNprGL4Xt1BO+07XIuP5TrVVHMQEw9ozFWdb1pctNHqmks1VCw+mvvXtT3Z7M0C6NXhczIknj7xbsgjLZaGgXZK2qiTnKZpE5tjbYGmZzQPmYpQd9CSVDPWPeDvPnoN+n8clYussfW3QVqW1nw6+05CUb7NTQ5pi54/4wKANmERE8wGeilccB2HdPEX9O/y4hVojo2EPBfe20QU97DvciPaFZ50ZP568ZyrOoRz7hhs8LDzMp5YOdj+fpYS60HVMee7NAl4vhVDDz1OMYAnD5xU2baJzHKQ4SOx16nkl+5HJITyEKrTofMwnpmukfHPxvwkMzZW7DDSkBa+h+S4sBnKRxNS6mPd+xId/xde///1f+d73Xuz+TnBhSJI0UQBPMMFXDS+O/X3p1K9A8A40hBfvUniGKHnLW+AkRAfAwqmTL+caZp60gD7t+aGEYLzqNjgVKMaWvmWKP8Y7Ng84Uhks4HOlX0kmeIIJJphggsviEl2A1xv/0SYaOTKPwyZiE9VorbPk9wK6r6evQABHOBYtImbKLu1XfCakFN3N66j9zmV/CYTCvlJIe5376dM1wfeg8iqxv3i2gVMQHXFIE4VWR1e7WqijhUYywWL+UUezUIKCYI2WRkvDqpISGvcIrZG1n5ACiz+F1gEYEk+oC3VOcsBiovakJtiHnxtyy/sTb6r3EIAp2Aik7frwr8A5mIMqpODBxdOFBaqMSkl+bqiOIbw7m/2F8/2fq8/hQ12C/WUiAp5ggheN07S/y+pm0MfrmInX+DRw+Xje00kE7q1Bveb5OO2H4uTt4+yIccEjnuAXXcpwwVtGUJJZsFAXuoBjyrHVciLUrhyk0tNVYNBSqqahWszfLtlS5F60mNnIlSWlXJMzxbb0ZrMUizlNORTtqlPdlTuPn3Vm/LBbIcO19vWj3r776Dmrc6CrC53YalkYIAOWrvMTvhd+9j9lM+6H3YcZj58GOt5tUQMDIJ8zy/2I05OP7iYt2PBo5hmI4liyc3jU46VOdQuHcZexMz2SXpSgUU8GXYbfoPlx5qhzawaiqOGu05JF0VvsJeNmvKGF4nbfKSZcWw7cz6gudwHnQxktQLTjPT7p7z1S2/3geEOk5j3eOLpIjtUblrer+14mS9b1C3EeyOp7XSCc6wDhrKVCItT2JbxCD0ofQmC0B1K7oYWmQu16PyJmtFbe2hAsrJjvWkk+9jcryD/xeIj9xau+gFYq3NGiiUILsNKaIIOj1Z6dIoOp00lRWmdpAdZZTlBZkd3o2aLnR10j1jnQby0//cy5lwi1D1tuIdfQQolQe732ekJqhxbqxV6yuZcylhpho/Ot5MOPO3eSVn9vM57IV2eNaqkRCxudfktOG02m2/V+JBGuJsI4fW1Z3TzIzOpSRbMUSe9YZlxa6kzblqR39s1s3Y6889Z6jqYsW9VBKpSwfjPxaZVk8tsHMdpfdBZE/i6wIG8KOfUOuRW2dsiLPyPYD7in097k7jxFFa0MEWwdO001Te1D3lphc5fFPku/3f7zL7VsC6ONssHi+zxQaDbI1FHqpA3aJhFx1kwJk8istG8RSVDxBmjlKI4oa1fY9D2cxLBuj3aePUCXbKCkJh/m36j3Uon8vtPX9qenRMq4ulCfEkfejL355lZc3v34yXux1bJwh36ju7erpn32V6w/jCzGoRssrfIgkzfjtt2Z7ygZhwq0oYh58/upEvCJybsGUgo5ByCJXxLB/lbhUYAdENPd3nf5XYMrWlBN8MIwIYAnmOA5oP2yd+AF4BVhf328OvtzBgd89k5uA2fx7uPQwCMnyi+aEOxuTi6IIZjfVqwu1EWpNdzhe9oI86Rq9oZSwi9X/gtsH0/jm2CCCSb4a4DrpX4Fguwv0ELNH38GjpsWXg5DFtCt0azk9ch2Rae2c+W1WWCNxy09fDVigIPQPAbLp3grkD7ObAV3+HKM17VD6H5O2RPFchQL0lpHO9UZ2M8MBqpMl3FLRI2WuLCDvtB+7VcjUyIZwRK1n1BO+KbQHE9Q82s/TgqCs8cn68V8cfYUykF8TD9IOGgfLb6CJ/XBKSBAHlRHrXZo+euVxl4IqTO5lrBEJkS5f9Ya4tL56b/XaP4scJUSd8IBTzDBi4JPxoyEzwEDsdXy0VD9tG/oyR+r/fMI4BMQsbg9U1EX6qfKf8/AqH07tvNDOIX6HQ4sCDx2X7JwHiV2m7I61d0tzuoJq9dTnDVZXe3ukOVLftBI6b9iLe+27TvNvfL0fbM6/+ZnVNVuLLxfnan3Usvq5pPSaijaFVpSx3SdcrHdWHTrgc4yzmHGWG0c09fCo0ESAtJkYAs0iHnpCeLmuA+wpxvup/BPUNRrt4oFKHPBhhqQDTCmotfQD132y841eM97fsZjhUENd50nMjE4xFlIrNx5jEKjM9PQQu4H9BncaZyeDPANZvPV0nbMFSgrEAkwwcsBEbODY8vHyht/l4SU/IFr/uwS2yfnOvwjsB94ryC2TbjP1NJBzYwl7J4+VSn2krlwLfjuoKGdOGvKtFVDSRpNbfmpPdCE64n4Wt1SjjUjRE4MA1qowgUaKDCfpg5oNDqa5E/NtVJHc3RlDJ0OXjbNChttohVS7/FpSUouqVtttMNkwuyli3rSCFeWw5UNZykRdZW70kCr9yOS3mkQGpgxczOu5yxhXPznP3sXMJswQ6GUcjsJpmV1qlvxUoGdsKaGbDVkl7qzQtOMYTt9rT6IdDZTlXAnlOkmpHa/Jxf7ibqpzsSexqwBeqgqJWfYQwJ4S31Y7OWFKFynbXqVRxs9RW0Gq42+ygNByq5x/y0eJanUSFtou+R/hZ+YyKs82GMOqfYO2wusJ3m2weIC7QyNPs46SxVS8+xHae95pLKoRYFDEitsRWmL4+9z8KJtF8hgrrOgUdwll2dXzGqKNsd98kAiXFUkqz5I9Vuy361SM2NJowmE5FJ1kLr32qfAFPVZeT8hV6bQhfAXiNAS7HKKapXUCpsh1Gl7o6NJ9bySXXO4BUXIkWjQesKNhrCALvYwKhhV4Li5UAqKxzlgoHnEekSZ4GZgQgBPcGPw/Pyfrx/XOzKf4MYhSPRedJLx0ZVMO0/jgDlFJSy6GoO+0KcJgoNl9LH+Yh/3PfOlM+BPM9mXivx58Xjp1K/A89GNTTDBBF9xXO2Gcu2ozz7fEeIec357+PjwpxKAjhbSgexxs7uLog3R6/zdDs8CSJERftGiU7vfuHIAMNAamzx71SygR0LQvaft5yuy/xefMD8DKQ4s1CFHaI3WHEVfDSyQpJyk7Nd+Iht435t9A7JyITHQRQybzwQD6kI9XNJD0e6wh6SAflwcbI73LRByYeGDeJpHtCAa586zhq4GXr12ovRstM/bXG9wDvsLNAY0uue4QH953Zzr2oQDnmCCG4B6P3KGOHYIxlLDz7l8rhA3AucS7O8J+AyN/2BgKWd/CuHmOjqnQA/8e4DzkcwqzqGsTnWtz3R1tQuwS2aqXY5H8nnz3dk/b+Uzbz/N/KNq9/b8/uyXU//MyPY2lUyyXT2IHJrTeH7LR5sbusHpqOGuMIh2TFlslzegpfAgYPVc9h4cBmoA/1V//xXouObSqtNlAedQPqJLvUvB3Zb/Yf3ZlWZgzavQBBt/dkf/ltUzvRjjQ9T3us6hvGUsCffgykHKXW44NwwsCj9NuU4eLYhCx9uc4KQ9d2X1nfrU19tZuRBqpjpm/MFfTaMep3j9G4cQ9UYD5K6wmF6GAziERY8DLkCa/IIZvVe0B5qIxZWylqByjXDFX7fZSwMD66gcChsdSe8IV+SaFfOfr6FIesfpa0r4iPRdZzEgjVgW02Vl4mXicxShZbknIJvBtFAFH2yjadgVEjpOBlNwwDZkaNhEgDytoSw2Qf26B1vdNHvprFoA9muvE0jXVo0uNsZSw1yLi1NGHLbgEByYgwKOIoPMok6WdSuuTnWzuWpCrtbVVM2M5cI1Se/MxA4zidrO4a1k4kDrh5Lxg1pjWmzl7ehPd8m3BpmE1Jnn509Z6dDXw22LSBW9MHC9YZy+thnSU1J1BiuCtccckGd3lQc7zPXo7pHTsXuoD7iXpgro2O3BzI6UNrXVD3lrjr1Z9spgB2omA83y/txwlsShMMKVdRbf4tEUDXEW9pj7Dj8KHsBv28WOJmm0bKL+LOg6y7/MX+5Jcy0i6yxm5cLbsz9vo1lETCKiFp6nuEPunvRFnfQymxo2XovwHEV/VOhbQ1WZvrOxiU19OZrdMCmD5Q4Vq3+OIV0o/vFVxCfwayq7nqP5uwZAv0HfOk4Z+o7QAltMcLMwiQC5MZhkAD9XAvgrkwF8bf7PnHflvDqK268Ynlse8LnLnBsPPJQNzEXDfkbildUExwM6lVcBN2R+bZIBfCMwSYC7QbhSBjCvVhF4oQpw/IouqHE8A22vQTkoAvYzgIE5SnG7rle7iuWw43HAlygC2yeGxGKu6QQZdi0ZwFKKkH4dBHBuPPnvSyz/pjxx9lXo+VcB430rrbR2mv/zSZzxfdljbmRXhNABB5/xQ9R8nJYNLCwuraI+evIdz9Vm/DaIZ+clBF80zzIFvAxB8Gn7WeheYCVxidipsm8X18VwX73c9QrUSQbwjcCkArxBGDMDOIjgaP0cVe4VQ4KvaWQ60kdahJiefP6YC4Wo3YR087gLsbgxiexSoOf1LaUMsz6IJKR2YWPKjTCIcT9d3chFvjcoL8/vNoxqUwovDhoHUjc1iP+gv1hHFWmsD5+8DTiHskuy+prUGU/+drInKmeFot2E1C78m6mjmFvfG9mGXVhBaHBZdKnT2eXD+iASL4X2Ng1MaEPD61ob4omnA+vUjx8QAXFoWzDlrl81umGjE85ag6piFuIA25Akf8tsZPsiV1hww2q46/RktgOJvwQI5mArf7CFKBAULUKRhcQ2J9X3n80AhUJq5LE6Buv4AiVwWH7v2Uz26XZ3SXg4KwmLCEmjKXJbNcn2CWCRAlvvpfA0oPMUqyRFyC4get06A93paxxnjqeoAwkqHIfIgvX/PIqMCUCQvoBI5fCzOWyiaWobLGq0a6QjtArkgRJJmZCKJUSrddKARUSnLf49JDFFfau3aIQrZi8tOG9J7zjbCcSl2EJd6DrbsprrOk1ZnXLLjNdzxXhiq9jL3wodRKWyQihGWWxlnv0OMRgUiWWp9Ynf4bMCM1GcRZ6EUPMUPuHrKuY2d3I8+xHvi90QGcYiUFnSO+KHKD3oOlJP7OoSh48Ht3M0RMEpS1axl8+EalPSXprq48Ebs1Jxk6kp6oLr1WlHsNto4lObx/jgtk47QUVQ73MUV3h6lLv8pU0UWnAIU0ddFPXZaEeTCszrOGWMKtky8SjOZ9wDorRbRLLUaqS79IEVNsTkp9hKm6jwmg6e6DmK93nwMd/UsNLULJS3ql+2UmEEX/ATSEOFh/+QunODM4DfBeD1KIYU8JQSQ5VHXvrvXa+tPHY8T+chwO3/3ffn/sYkA/hVxyQDeIIJvmroPLpWDvg0TNjf54Qrs7+cKQI+740bQxzwCltDUuAhDvhUm6bxY4fEmOHcib8Xj8aVVRHXiBvC/k4wwQSvHHZesdzT8XChu5h20sN5FCKjFtM8nzedjjBzU7SQUvFEwPZxG70xMZKVuZogWEoxqB57EHx8DewvUDyPAI49t2vJ8DQfnCB3xZPB7e48n314YbhIlT4m+wvorgyHIQUwkKLKcQWwgNABE2CChRR4KBtYyFM6Az0YDxzOWkAsa/VKemyk+WewE+zsatC3gD4bqfFigO/jGl2/LC/o1Cn76et6z2WCx2F/uT4p8NWbHSci4AkmeFEQ8rgzFghSp6Mtu3ycm1l+NoTv17V+9/2PJqSQJ1vPh7fu49AzWDbBwEEGHFMekiZb6KrRLZhxgKhryCzda4UG4anYl7no533kp5W30ylzi2xO2plrpx933khI7U5cB8JGh0PZdXfz3Yyjns74OG2pGl1xo5RCHWaghb5ouc4ZMaiBBvdgA96AQ2jBAkxTeDDFAeahZxP9OqrRddZk8UY133X2ZBZQja4y7Vawrk46etxfzYYoaB5D5omGe6bi/4uFutx1erJkdJKGhYEm2YViXjc6PVPRDatnKK6ameN36hasegRz0O6uCdsuMy1CkQVnWWe6WU25qmj/xAnuvHWcQbe8f3XeXN6NJ7aqgxTwrDNz4NxrrmWMpYZzKDuHcVqY0bg61R3Em8V0BK9ZTTCU4nEijNlL/7C3JL4XuXBNDdl4PsOGV94EbYcZRQDvMBOlrWOLqik4SPEFvlXaoqNUlGHi3ypZDatCUqNtE2mhtlCTlGtkBCWpQpaahdbGjmAtUyuRjKBXSQquVOywGrLtntZvyT3hQI4r+Hb2ZfD+PZCZRl+0Pt+YU52c+k5dDdkKcqk7m5UL8xSB/zD47rPOzLK6OUV9nUWzl/4zvpEL7wH/vPl37L729fgXRWLlftIeaH7074fO2/7nFaS+OGhF4l/n5xVSK1T6GPekLxY4fMztOfZqpN8Lf/aY2wbtvxq8Myvtq1gG7d4gOi1VD4gfkpgCwS4Li2lfde3T7StszlE6on4rdkdX3et52z36HV1VLMdKax1Nsomnqe+Stb2ehRTVt3jYQm0TXaHaQi2R9Hll3zUqihPFWeXBCpu+L1SV7BwH6yyJLGcbPUOjnEqtc3uFpx29w6+j/Nwhzd3/mt6/ZX2HOjcJCVjxeF+EkOwuCJFocMR3zxvYivHL0EhQ8ALTz393J7gOTAjgCSaY4FVFcNZPzPfddM3HGbg+2v65GqGP5IA5bWw5/sDSn3561QTBrw4HPMEEE0xwCZivnBH0ubB5Eb6Fgc1FNVrHaDPNqzqsSxG3u6OeXBzhlDWm/DdI+gLhWXoFgEHVdYe+BohJzLNtbK+d/TVOWefZ2RnzYHtTPzcLF/kaXsIw6ST1K6DR0tDPsEYf0gGLbOAhDxhOkWSFs1avdLoOGLDG6KI4owVQXJMPxpOn45WdL5GPrF55DY0BsfOXAth9qZnHQUw44AkmeB7YjTU3MiM1sqchKxfqo4bqvZI+wrffx0Ujq3i++URBUaaAYPWO2S/7dxZ/57e8l4JZuQtQ8ohGE2daZg0WwYFD8qtmuR/NGbXdaKs9WHq/2v+e9N//uP03/u7+3j+bfvOt6INcabYoJ+v1W0CvpHAALW9zYgdEt1PriGEVcExZsZTOgc7SATLsYGV11eg6yDShA3OwD78KGzAFGoLWDRsdFrHWdFXuOkWZbdy3xHCdq707pk9z6jlrxMnVvD2MESSMzc24KoysBR3rgIqUtYCI3VMNOxTtuozyh7IrrW4FDqkwic14/4qDX4bVgFBYx5df40XwwnF+OuYtL+b3gqfMI9c/fzCnv58OZQKpExbmR/Gjd5k4ulwwU+yh5yw8T3JxTNLT1a29pZRhDgZKDWUmdqiG7JRUNYmYvbQgVgmEzgpMncLi6dga9h5zaaq2921JUi4TbxMV/s9lb/xyPI8mLp6pkNrhqJGuTjqC3UZvYeEFD4t6LIIlQnYTVEzJrbsS4Wo9GwFUCBsda0t3XbLxzhHQoldSRDJ3cy1Tf7MNKU2yS91ZwfwokvWW+rA6SFlSuzPQxUHYqr+uG42I0ZD6nT/r3M+Fa0a4YkB1kJqV9iPYZjhtDzQhtq73I/V+pN73PojyTkqq6mgWER21Sgf4kLc7A70sJTPUhNy2MJhRpBBQJO7QE2+1iIiUXwc9gi2SkiO0BAt735tGzG6YpWVDr9iAYjlAR1eVu46V1vQvbSXj+AWzRsMmnsH8GUsrbAriNowcpbXC5hr3w4SBDI0y8V1ywllaiGeiOIBIehbq7RSlFAcpDmziOp1ZdhR7cKDl3B2zUQYOU/BzBn+KnGNl54aJgFcgoWJIhGcJxb1Bii/0Er259wA6v6oqRWe0J9ZdgPb4+QQTvFRMCOAJJnj5GJrauwpehPz30lg4fWgxNJd3csngFOFLyVgwnif9PN5Zu7R9+vXiJAd8Fi7aXCzY1pdOAy+/MgGBAsFjOJlim2CCCW4mxBj+3MU0Gis0nmszk0CGBp6A2BdQynYgp3MsafEJzI0qVEblJPlU7tkIloiDKr3An73CNXHAzeOpTiMhRv5X94A5jff1cfL+OzQ3XbryPrwUXKQVI7HXqedHE7pDqDKd4uA09hewiZZPb6poExUylMAzOmDQDjryBeW/QQgv6FPZ39JxO0pvh9xv1tBL1eMs74MTr44DPyd4/Ezr68W5da+QAjd7NAbEJRoBJ95MiEjoAtuqep/x3ATiM3AJ4uckJqXpBBM8N4g2a58G3nCWBENzoZWEsxZnEMCXhnWene958JvIY6vloRmGU7XLwYiB4C+Y75PsP27CA4h50zhCKDzlmY4kocYgrejPYvXbd99KrO2kGv/G/AfzkSfhxWdLhD5svVuUkwm710j3HTPu1h7fBP0E55o9ir8F97HIAx6UdGoQAwvHkmlBDGwowzLqVNepylTgvkuaJkLtmhmLfVBurmXUua6zLbMGGdjGOZRdtnUBx5AdMyMI46M9OWk0vUzsG2XglrLv9DV7oEmrnYGlOGYcHZquWnr3Z7Ozs9WdTswxb6lGF8u7Tfs0vx9lshiw3fbxgVeY6a7xtbkZd9YywgXaPY/BuT7vlqEaXdfUesErJCyPU7dgAauuG5lGr6SIhR2fv8h6yxyHsdTot+Rw1pp2WjubeXWh3iB0W3kmWN7OQBd6YiNcEU7R7rtoC0fiSGCNUdp5dgUl6T+Zp4VnKSQ43Qgt8R9e1kyQ5R3COotCO+vJjhNi68AMe3XSwoUl4lHC++SBKeqHEkAi1E7k97f2lgBjtdE50B1ddr+DLfRVyyrqaKQMU5ruaJJtD7R78kOLiDoIFwdxIEejZSVT0Wp1kHL6mjgOyfiBIlkG7WooJUxfxAFZlj4Vhslvhh8/HtzOylapO5sL1wARDjKwlK3S0rOsJYy+Ncn+afObnQNdmbYSdm+98Xo2Vy1tp5TbVuepvpVd+k+TP/9JRJsNtWJS6Rv2JpDWwppdTW6XY8X1yrcyHY3cXll54lCis6AqRYcKtMj+gXl0BeZQWg5Z9KwtLgOR3tjR1XpeyVSresVOLR+IUaQgd1uo6yxFaM1R3GZqg8UkZVH9Amvc90+fCCdOUdVo7TGXwQTKGCtsPuD+fe3BzJ7VTw0aWoKlRvbfO/SgSSh+nZGOLwwHgAMKvQJSBKkNbwde9kdqNsq2czR+OTGQKS0b5+cwTfBqYEIATzDBy4eco1N92TsxhDOmqMZsSw9inMm+MSfFXtjN9dz9KcPh89/Kc8CFZtVHqkBO4lQvaC5iB/2ycLM0vhMyeIIJJrgobpoImIvEGdhEj/fajwubiB8ArOOIB6K7HLzYs+epAJYuzdkE0CvQK1xH/9841d0lKsAgDMheim0SmuyvgA1M+Siu7GwolpPYYxwOOOUKQC6JCK0hda2YeZzBCpZ/Yu54KA/YDaIbyf4OOT0K2HB4Qu/bDjD6I7874yBYDqUC/754tMdbLBZ2v01X/E6JmnDusgRw/Mrs76QQnWCCF4Lm2pEUuNhLLp9OAF+gV9tXW176d2Dj2n4EzkksHsLJPR/5WcSTfuehWGABCujLlvWZnml3ckqlOd/6IPfJR4O7f6/9oWqo9WLy084HKw8Sa+/enskeFlr5lG02ff6yBbpnoUwgfzcaSOFddp93DmVpyYKUS39OQxRV7jqGe990TFl9rescyrPLh0AmZN6W1zYTi7c5eHJnpdhLhjsyy1gPdWyPfF3wmvaiOMj4t+AgE69756WEs51QF+qii2tgKa66V+iGA+8pVFMA2zjTMs+gEyDXm4GoY78Sa0MHYmB4jtzLENDgqkZXUODHDj7uXhFFZNaKEsJdzHL9tIVdtuCPXwsffPLhPVo4UZnWiSLK47zFdvWcJZy3D9SokCALX3E3/VdCOCEDVSk5w15wTcLk2ULTsWfZE6bNiMMMgEa7THyO4i45oIWapipGH22iO8zMs3+S/V1n0e+oW6+97uu2favqaad1oEaL4bwRrggPakEPCy2y2UsTRjyo9yM185ZwXvEPnXsOo+4RcExZinYqB6nZ3F69HzkkEe4Z3ZAVo13uJ4sh0EmBYH8BIYkWu5SSqoDp5RBbaOKlHaYEgz5oKXaUQUuJ06+ahlXU9ZzVXMs4hnsqhdC835Ib2c63jCcm6v2VnfvRnyaSpRZqGPkf7/+R/EVfyTnF5XzuZ3uD/x7uIUXp3Fan/y97kkjeSdP9CKpON4Wco9+gbyHn4C7koBhoRdUASsuGTXyP2T6OnuqQwkIRtO4a91fYXOONBJV1FlfYEpz6h7wtHn/GXaFCFgnBFVLinIpx5QNe12gDD4hWSVWZTuUPQIrb9UShxR145FZx5uCSPcMvC/MwDe8a4EvIIlCB9IlF/Yn6wEgz2M+9ztItLtK8OMHLw4QAnmCCl49u8WXvwYVw0cnQc9nf8fEiW6tesTauMeW/L0AydRlciP19fvLfm0X0TjDBBBNcI24aBzz+7aw6Kt90HATfZaGmxIO0JnzGXIi5hgthbAXwmBbQJzHkHCMvMXCQLnMMAhinurui5aMJ+qUmmu2vBPsLWBf4JgoOGMaigVfYLDA/Uges0RJ+d6e9V8duneAP2+hT1MVcJKBIlhK2EgMt6AItZlfDI51FW8ctOs9ABOagein29xVkHyNwB758UZu74hGYBABPMMHNxLlhwGNBSC0fjPqtnoH9U77dV+9FPtOr/6xMAQGRN7ngdRrhsbxDH0Fwk344ZQuWXWGr8Y1GZ0vXv2U9+J9TD9ZT3/r7D4yW9n9y/sl663fuhn/0cCYTav6tmf/oD1Zb//Fa8zY2jWyIBfj4KP3X1fIKJbHQ2s4cL1RasAE2uffquwuzLkVqQBknIrsthgeg4FRklik8mDJWG41+KE4SeGKuFHtJ86M45nFi29/KjMv8icOlegycYECPHL+jOKbMdkIs4x9Y1ej6HLb7vAU66mrXOZTpeBtqwrR3nZQCqutDz1W74U2w+M1MJcge2ys4IdXdh2WcbVnsIbhCZDdNWQcQ7O+yuvnJP/oGcc95+/AEAfyR9yAjtqP7SnfRMCGaIXwaeIccHh+8T14QnELw2sYWbswtIiWSQh6aZ7fiNZQJnahgf9tE01QL5AnkaKyz5GuI66TxMm79fxkFSe/kqKkhu9SdTYSrimQJttUiAuTCeyaRzkA3wpXEQMNo1/sRQXIL12vgyLUbVKN7+MU0GvvNKWDdej0htRtaKBFqa5It+gDqoVQiXDV7acQlHK50Brop0RnoKalq9tKdkA58JkWAzkB/Wr8l6Z2BpfRbcliznGJCNAf4V5FzKPu9BJ0DPWx04nZ/gyndaPxS+Bcmyt7g29PSw7sclGdSlZnE0qNavFYljPQ+zMMnyP/Kkd6HRzBH/yPCCwx0QnGIEcIzKBa8bxpsOt9Wi/mM5nUUfszXWqjrLM+zv8YbW71FNWSLw/hD55cE154ItTf6S8CP135ZNbobC0siUnpDX1pWN/0oaBFFvM7iWzxqEVlhI4rjl9CusFjbLC0b2Q2TuxCGj0n9Kt/5IVHzxrhAD7O/ouGz6BHAdz0rhRKY3rBFeHbeBU91Laj3F7vjE1wJEwJ4gglePq7L/xnoPHr1XKDNwEzo0L69YiTrTcc40+VnLLMu+jaHnzxf/svZTbvjs7/PzsyBuyK+kuzvtjckm2CCCSY4F8F77ssoFS4UZDCmArhK9nLsL4GmfmCWHUCxB0cW0CakGZ14dAmMw4SNhyH2V0rR3bym2u9cbvKKakWBHU+v4BcOZ5vQXovhyisCMVn8aFwxtNCjK7bc0aRz152mbjE6hss+RR8qguuGMoAFxCSpj1J3VpPskxnAA0sZZn/FhKSwiAxe8yPlv1UAUqBD6uIBuhdlH1fhGUQD195JTbZfi86M8q9+pZC68hrGlqSPxoT6nWCCF4igCPg6EfwiBy2dT4t1XL6y8Pf09xpLDZ/KGgnV6GIgfI/1RcvS3WhYdI64UjyP5Qwo0IA4RI4I2n5OdhoyazLAB+yq6WdyKxJ6/Q3+RSWV2ORdzSj+w+Z/qZuRhN7WFw7tgdYEvuFZK5vetmxPHWtCFLJgQTMQmivmUYSt8Zee+bO/J9MeebwBy3QO9Pl8zVQjTl8DkkbTzMZH3IbEZ/QCdwXp69+IraLLgLpHUgfLpVePPJb1Y9YdzqFMC7ZgiiOPZTOgnBYoe7dFcX4OYQqmvOPcDBx8IU0Wltc6qtF1rOO8w/IJ5/CS+4mELDictRKh9rRt/fxffIM42BCF8qimpQyUOVIG+/NO993pKUFFry8p4pACiXDVlCh1Z0WorfjTJCIMmVfYEkEYbfQ01RoZQMiCI7SE0jdLTcOukLLQAEEVt4gEHaQj2G00wfsatMW/gLOdUKYtSe9IeqdX0tPTVcG/KpKVlY/x5CYRX7GqSJbZS2flQqk7i/CCDlefZWeAuN1vaCFjqdE50EVbXnMtE1so+6wnkBARwr2UcHMR5GgqXLV7mTqqYILrg1S9H3lq3ZqJHcZo7/ZmNEmzB+6lOCjp1b4cinZ7JT2bqwL1QUR8hNlctWoa2Fj7ejjXuRsuPepltZCdDdUJOYeD2SdS+n3pcxstSQloY9QWQslfQM0mS2dRbeZXYnvryp845CFGaBZAmvWuqEXP42SHzu+qyobDXRTL0Wiss2QTFZS8mMn8mHe6A90IVwTxfyR5L9HkqKhzkJ3DDBuYpThZfkEGMJYaSaMpvgEG7RaRKG0hBY7i5NldZwFIUbKJazQ6uqpYDmn4BpSQUmCS4JQQ6VcM64DJu4ZrXq0A3ziu2hIjlGzgSU8KHBzO75K1iVoTYvGGYHKeJphggovj0nlRYvb5uebp3izcNEnWxTAm9fu8E3/FKOXZdQgdXjU0j41zJphgggnGxckGrBt4M6qSPUPXeC6CzHGB+Vl2lCD7W7lsBjBw9/wWt4FzzgJBBNOCT3YNXo8CWEDs9uIpjrLNKyeGCirXT+MbWRAagWX8Z74ydaM/qTr2YdSrXUULtVLh0xawie+eMlV/WhdFjYyFdlL46wtiBDoDvd5LCd63TqRX0numMkKbNTLrF7C92e0gqifEvi9G+ysaAYdKQTFlHLyqb3nPHEIcbsEzwKMQGKNV8YXJf6uQhS/hzmUtoK/C/jKR/04wwQvFVdjfsw2Wj7Sh4wT66se++LHVctBY+CoQpsGnvYRnO5EItev9yGyoXdfCsdWyoLhqZowl+i0ZcO9Tvjnz64FUghmIYv1MX/6NZ8Vasj8rWwd65UHqL6K/vfaF9A/v974rP/331tLftHc3fnIrf9/c6xjGUkSsFuAAbO+WYR9xJPqq6zwM8A3wDH57O7pzmOEjyMBvMPdm4fCLaacpswEGbh6w4R7VsNEp9pIxivvNKUnvOF8kKHmNd0IxXPWcmadcJ2o8La8Q/jqe0hdwj6RnqnwUyhCD6WPqYZeu1gIMbtprMNr3npxBXew6PfkoYtm/Vw4h5l1FXhqxg4zuGkS7dbW/gNixaY/P9phpxVJ2NzO7pYB0WAMt0IYo9mrZk4BnPJNqPxPD++CC0hay1BoxoK4f3S/r/Ui9P5ML19SQ+0nWpUXXL5qILxc2iYgnU9QiWLvkfa5XuAf7K4wGciDSVFtEElQAQSr/UvKjD/W3ATc0N+duV/gwCyGyiAEGIlhR2vd56HbphbeAeblYJSnqtFvKPvCMGWAmdkiMYi+ZCLUTbxaycqGj6CmpKsyc5ymaRFblPwSiYaeF2iYaxYmF+yUME0UapBLSehRHU9s10kkqyMyyVyFpo//y3sOIXVEGjqVrstRX1p1OXq3nlSrTqdmDQu2umjYdDX3ZWefrUKqRfpP1FvISjyKbsUilomgO27DOoMWsuSc9A9itEq3gpJ0nB19MTVHbBfj6PIMqxR65MPISlJGa8HvulaY8cerfinY0qcq0CED5Y359hU3hvL3Oom+N4P7iiTTrLYhDB5Zx7c3xrt4t2HdXblpxk/iuPjv3ZgG5IAKbkRDu0ECF1KxX7EarvXpeSeyh4NAGi5DOwQ1hf4EVeD0KQgEcgzmP6B2aDRg1VMlumD4HLNq1dbojlpvg1cOEAJ5gggkujq1A9MIl8JWZxfsKYaT8dyTGShUah/p93ryvD3+qbmiLXzFN8CQbeIIJJrgKBPO3cOVIyFNgpa9/vRoW1+09dZQBnL64+XMQQxzwKPnvkJb3NIRnCcUJxek3CMUBBo5rHy2lkHNnvvnaIbysF6/pOhlZEJ588itWN4qPMzaVfq4F9GnsL3BaPHaScmlU4WceJxIVyar3jwLtQtHuaL3vSJRPV9DOwe5xyW8KGE8BfCGyc3U8kevJE6EFqOJbnvBIsMJ+FOJJbltA1GAXSj+5HET07yVKvuXr+52fcMATTPBCMMT+XoP/cwDqQl3cJ3yR8ZiJvGJhSe/4uyc4yMvtxvAbA3Jk8VK4pIezluhJaqT7En1BBot9OPI9tgIa3GXvT9/K2EJ/y7JLsU5HdwpyXjXfkItLTrT0H//8P63UPmzfTX8pl528/k2rbEbUWtf8s7jLv27AjLcSkenrNTlZlk5RFyys4PbqOgNLCWW6FVsRb1GdbuUg5TRlPWlZMzoOzIIJDlQgirWm66vWL9a+4ZpLNyGGutoVtHfc7utGY785ZW7GKaEudB1Tjq2W3QNuyr71cTBV98haOViFBo6qanQdZKLHuH+XGH5wzNfaachEUX+161o3i+eD9ja+FPiBR/fqqFOev7TYriB9fd635Xl0B7au56zOgY7lMuvWlu7ebWNe0nCwDJmGpudbu+ER6vueQ7j3ccJGp2cqIrM2rHcEPe//W9ST9I6sUHLhmj3QsnKhM9Cdvka4glcdVQeLTl9bDG8JbnieYhtdkME+9Zs/0dTWJhrxWkoLyowiWQZtP4FYkIuA7zu9wkbwXUncL9cec/PsV0i1iNRJH0oAt5R94V1s9tLL6qYgqquDlAj0jeFkqKlYtykJ2+oKqQitKM4++X1wiGZ5FpYO9pgT4ccz7G6wFKEVteWORg/jYX4uj6TR6NXi4XzDzmcy+9Xofq+XtG0tnk4W9phtoxeQf8b8Ck4X/VMWmkzvkV9c+jKdTxu1SnE5edd4JFWRyvCfwb8m/xmtFLEq7/0D1f4LZ7kGIH8AMP8IcvDto1Ra67taKxWuMt2z1Ycs5tl9wOst1FUerLMsmheFPHrDWWquZcQ1HPtGeWAp0ged5lpmTvDiA134YDfXMnN/r1DvR5o/zBwr6u5TM2N1fUkc2M+4+xaPkpRFLV0mXia+zlIm1UhRekv7sv5NI5sxeUSoyPcMPnmFXaDfhTmNXBhzQOoeLHoCX/uY0tdKa6LlVPg8A9QgGTBkuntkAQ2ssBmnf2JrE7yKmBDAE9wYjGMGOKZh4ATXgIdX44AneBnYY278hUeaP59kf0eMD8+d83ph1O/ZWPvKccA+JvNxE0wwweUgprSM4zZQ1wG9YusV6rPRccxsBVbY5Mzkgiqp0/itcRDFqZJNUfKfke2+6+jFCe+7S2AxEP17wgJ60B43AaRXQIowaMMJ3XA45a3tuhTAAluni4AFnk+XwOUhyNQdL5QuCkWI4c5pvmp7C0yNtVfjfF/Ozt867dtRO0GKntT+On2tGJgSFfLf4RWdJEFtT+Izkv1tQwp3mrQaeL46YtlhzF3c9Pi6Kj1xtMSHvfXK1LGpy77xer8Rk5pzggmeJ65u+3wam3tyzf4z49DAYhmfghUIcskn36IaXd+l9pydHuVxZRX1WNYSLPXAUpJGU2x6YCnAsTuUuAHN4JKp00eSWVEVVO3IfKJkLcXK/chP+vnKXOF3dyN62mnUbGW5XN5LfGvm4Z//s3eJwwZ8U3xgiKIaXWXaMsWd94F3i9EBhAg4eDTMtXhiuQqo2S4yPVMxVhudpzrArLdQxVXNCvdjdarrHMqYMANbUKFn6EBhS9cXjXDWEo7KYaMTW6hzkjX3cOx5iyPlbiA9KrZaFjGovvBaz1mhaNdci3PgHXnRYFZyja+dDdktaGPuqlym+VBW3+tSwWnILjd8ANM42zLNwEn0m8amR9UPM5DFZfEFlV/U1amuo8uq0XX+XHaPtnWcA77vrlb9WtdpyujuPovj6ZhyNletmoa6UFcsRdI7QL8lh/WOcITGcIlb/8SpIdvuaYDT1+yBZoAfECuck/003ypJYAbr4eCNWWnfF++mqQqSVdRgkYCh0O9If7jOcjvwAXx+N0k5SBULx2nx5x5zQmfcYqaNLuhn4dEifKET4SoQGYRMKSL2VqedZ0/HttDSVHOUDlxhcQvQcZbY7tMVqtYa6TS9Bhk/8LjJ9Cda5G0+djCSdnddu52mlovUE3udVkr7y5lfucfnGo2OfdvW7D3yCQ41Oq/ztEW0QnweS8NOcPg5r88Qis88y9UqH37w5lvVL9uNpNGrKG87od8k9gws6DvKb8LvAl49/ytHrXuiEl5nKYP5M96Mas5n3FtnSRyQYO2alQt1Zwnvp0mxlF5JF5HYsdVyvR+hO5uVXT+luTcL9X5kYClCvu9sJ5wNWX2t61iyuEjEGZ+n2CLSJioOnQgPaqGWidtESDFHqbRM9icmd1Hg3Ud88ko2rb4bCP1N4Sl9fdWvBlCfjSYKLdnuZzdsV+P7COvbmi5+PjIeAfzI7RcP0sAT3AhMCOAJJngOaJ+/SBDKXdd8f4Kr4tysvr/epPXIeUCRZTL05BD76/fK+RgxbNs+M+DwFZksG0Jwr8TA5ytjEy2GzZMpuQkmmOASMJ9XQkGicDQJcvURo0Zbw2odDysdHymqmjfPkvZcuxTLcafJrjiAP0/+eyEIve/I56UU4dS1sr/jnHdBtaavb6NXRMmbjmwFguiao458zmOIXy4xfIZwVrzuNeCPRJXp8hgdCiPb/k46vgyVfEOtfse8PcWs62n1nm8FeUZ0bsT79z5UPao4CyXPzVjscouLtCyejlUvAcTXW1/Uw1ws/wymYDlgC798ugL4gsPAy0NUepfgxa8dL0DuPMEEfw0x14xpI9jfs+W/Q7/h47C/iVA7KxdGunwJVesZ68nKBZci7M4GVyLIRU4hsINPntNNLh4L799phLYP6JflGjE/97RzoB+l4epHpKCrMdUDlOE25EhF27YW3vs3hv6+ZX2urzkLnWrm3uKtf/vJndlfqtoLhxvrb93/3YONfoxfIZy1BpbCKp0D3TmUHdO7J864XKOx1BB5q/V+RAiRxU7qi1bhP6QowzIrbz4W4b7Fu0m0hKuLbXqe0uBMy5iynrM4lN3kCw3nocwPZIAprA1dnVecL2VWsbZ0I9o1N+O+fffJB25PJ9CEqdE3bpf9taCFutAVMll0V3ncK7lHrWcoCFJ52zsdXhCVgyyMMRxTRoUs6kIXG2dDpolLbvon1L8FLwc6I/0H+4H6YcaVQbsBxjMyi9A6EU397MgW29mQmUGNdZ2em3msGl3V6NYHLkGYkNoNQnhUfc2MAf6Jw+tmcGe9uj5Fj9PXnvVmltXNzkA3va48YQ0NrLO4JLn9nhGsFpEWM3gpwnl2fR5XUMLz7O8wE6WtBxy0MzTKxMVibaLLbNroNk6FlL8SwEbDUwy3Je1N+dEOuXn550A7rFlEFil2pX4fY5pdDRuYZQ/o0f0Wn5cxQqh9nLxtRrUDxR7oe929/Nztn/0ZYTpplQHdTEge9LFRqs7TN5am7F/oBfu1KkDx63ls8nu7U+w18qkKKxtaeoZBmtoTlmy0Esm3eHjI3T/i7pPC6muza1PUG5HUTv3WW4kH7/BJIfVWNdXfYeb3lr6v4/BN1lnQsEzyNfptog0ydZSdwXKaeoVEk8jAUpympnbsejdV2k85PdmYbfRNOWR0zfW4sdoA+i3ZKurG0lFEh/DEtg2t3o/M5vaMcEWkPtedJXEe672UkICnjWa9H3EOZZZxVFnPWaKhpKPos9K+OI/rLA2Zfs+zD9hEK3TuVKvcgiLd4qvI/t6FvDdIVO56ozzh5DQFmWPjcfFYsWWgo6vNd1bSX34xIrJn++hhFjNyxTyRCV4UJgTwBBM8B1wwh+l62d/OI5QXk+T3SomAz6V+Ry72Kuz/NU2yn6t9H7nARecBL6b3fTVJ39MgKsaz9/nGKYYnsowJJpjgKrhi4OuZGLNx+AxzlxQl2/XtugxsdC3QlQ90tZBi4UqCTwabXQhnyn/hKNP3cpBS7oPQOIl9F0IVOI9Sal6/RvxKyJ5C956EsPUOLhl74Z/lzKrvDNWvTdxGG4f6tYnaY2U5nlX1iamxY4mMp7G/Yoa3NUY+bhC6F9D4JaTACnCZqYus5yTiMAUbHvtL4Hfsoj9oYvmh7sCzq8ELDQMLXeISQCxMu08kdJE3A7B7Eb78xtWxE0wwwRVwhtDWd3sOYlndDN4F/AX89fi07kkIcmVo08FNDPHWZi8dNJk4wmlzC57e1DFlZy0j3I/DRkewNYLyPBIBC5NhcbdsecThNiyAQc9UqmGSjxU0LEcnA1s8sJNlXdFfs9Kf8+DT13ibjXAsfMcaWIrbCCVaoFqeF3QWdaEbNjrp6Wq9H/FJxHDWoqg7pszHMCWr73WdNdlpyrXirJS1EuEqvaQybTmHcQ6gDC24BU0wYAPL0o/yeoGal/gL2DjIrKIvWuKTGkuNzoGu5yw39hgIan+DKlufag2guZahBPuuc7KzLfv0qnMoO+bxE20F1iPgJyzsH1uzMy2jo36t6/xCJhN4acjtOXBCR5/xLKrRdQ5lYRytTrlOzoLZFY95F2dNJgaHMAX7OFnXcdp1n/Y2Gc5aDS3UK+mhIHFugU4TBHdYw5UIA0IeKumdQX1BinYI6IAB4eG8T144OQtFb9AOWsdOstEmKsS7WWpJysJ+OUJrhQ2fFc7QEF5E4t8MZhkjg6lhV0jc5/Eu06u2LWmNMoZOx0JZJRpGMZELzLzBWptoG32XXJe+id5Gi2CXSLbJG7QFLzvP/jZ3cjxrE01SKWoLedIRzbSWlBihjz74eorKuicPN+iWMD7jXh8jpVW1VLuJEiJRGST0pZKG8nnvTj68sUNuicOnvUU71Bdq6Yjd+/7Bb+eN+l4v0TOV3tbtn6a0mdghsCfFvuz93sBSIkYjJVX/v/wXIVoi59hB9xOX8ZTNX/Q9rbo4h2oUUJOujDuc6gBz3yrU+5FldZMkG9mlZXXT7KW/Fv5kncWgybbAiry1zuIKW+sspqTqrLx/mEy457Q7e+vba+LklrqzmmQb4co8RSCClaWmYUdoiYlTYfEdRu65XwullQrrJoCc490qvEou0O8CsKJ4Wb+/AbhGX/5w4+Qoe04raTSA9M4XZMCCj8/czDeve78neD6YEMATvGgo9mB8978JXhrGZCVfBQ54TOr3jPe+3I8wxnE+d378Wtjf4CTgWbzvV4bxvQRuYoqwf76GInMmmGCCCc7FK+idexxjslwjUSYe9H8GZLsPMA+PztdonoMI5Dy6MXoNIuAhCPtoKUXfIhy5PgvoGOjjMViVV0YBXLna26/71JyP01NprbR22ihpHNWv6IeokhrT8WWffPDPUkDvghepeGwVpVGHyz5T8nsGShD1Gg6q3oPd65C0NiD6fIq04E+iv/6R1e/9Mwvm3oBiz33cGAA0ugD0mb3g/MzcpLqbYIIJXIxU8Z4GnwOu9yNZj6D1OeCB55frzwPoOesk+3v2FoM084azFOSAjXBFBK8eI5XPZH/93RAKTmXaEnvobLuWvD6lhw4GZI/Xck3vvgNo1OZDetZKDvrlXGQ+V3pbs+xa+1Mtu6EZ+SUzE25ZsfbeTrbXU7K5KkuYa3EeeHTmPoCDHFuoJ8LVRJiUVBV6ULOX7uV0q6gzBZ/yO9999i/XltigoKeWjWbNnG1upAB0L7+25d0v9mHmhLdExt1bQH/f6vUUoXgWRwBQFzq9kj6aQ9WPq2xFBVGAlBeX69/QxQPjzNvWyV7G4LUgXFimAwHDPXmoDV2wtqFo1ydZ3ee9BwNL6bdk4dkrVNf9ljy3dFAzYwm7J2WtSCG0ZyRiC3XB+qcMs7Ax5Yb+at7uPXAnPYYYcef4peu+2gIdPWfhiciFmFtc/GI/daMBJJSqyO51zzKRffIpanXSEWzAV4hmqZVIAkmI0MrTEtWXeBKYZ99GE4bP6eMBGBnMabuY8urAnGHLGkAAAQAASURBVE1Ya5QxbK0D6lvVL1up8DpLPTJzPAQ0rF1ybdQIjSRKiWQES7CquDVerUtSJrTGG8tsfsS7VZKZQS8vPXnA6136Yv9bg0xFkkvd2UFLqQ8i/ZYsTtMtZd/pv77+2bJwPhdCW3EqPzHv6TnrCQjpbUJqS9FOsZfs9RSrGev1FeBBdVrtdAutfM9UhA6735J7e/lsvFnXwp0DXZm2bieeCWW8GrKfdWaW1U1FsrKy5ds1C472iH33LpcVtkQRK4jeFXULyIY/TVIWdDsnMM9+hJb4131KIorTktUMjQ0WSyR/S/5Bm+gqD9ZZCiMv8kQsuM7S7/F9HUfD3iV7z9440HIpDhJ7HYC7UIHqcf+nVwAJlRUFecmbczZgno6u1vOKTXx3WFAPsMJmlWmNhmI5HMLPA22yVW9gO4QXP5Ka4FKYEMATvGgkCq3nahN/lRhgkTb3FYCQFL84HTAvm0O9Cm44+3v21X5R3pfAKM7ZThwbS4wcD1yU8W3Dl54mNbjCO2AF5vtObuvVlLH6H/9GMMEbr+phnGCCCZ4fxF3mNCHvGcPUF1NCQHbDvFAw8BA0LM7MQD0DIsnJW08D4f8M7ABX5lTaAbfY50bPCBq4WwUIzxK65JEIIHthG5uXj/SlOOAXL/wVmDqL/T1p+2wT12icXeyJKOsqWaB8ytdhJPsbDP09OYkvfCyP/j6p/R2zCKwi5DvuN6J6/KWT2IVdAFJXMIIWTpKr1xcDfAaW4fCC6mef/T2JQhcgLhE71QP8GMSxGrPAe56mDhNMMMGNg0/QiluAYH995/8hifCxO4JHDAtmaIjSG6kSPulcLZ7ZcJbOEBafhEgCFhyheEYEDwtSEK97KbTkEo2ik8kq6up7XSEeBULRLmGk+c7eWgYLKxR7ctdudYyp7Pa9jPqDD99qrPZvJw5T3VgjbZW2U86hTCtAgorOJ489USSr2Msb4UpnoIuV6znLKum8zZOoJNhrXbeKvaS5EVenulNLB5WDFGDpOjOoTteJySygGl0nKrNx/LdabHQGq66L8GPxuW4p+4pklbqztagCuPbXQ1gYfgKg5K1T5BqIFq6mR+JyCp3TPP6u6KnFrQgSDl4SiVC7ZsaSRjMRrjp9zQgfFW1+0FguXLMNDYN6P5KergJ1HWHPOxM7VBO209eKM8kwlrOdmFo6SISr9V7KWGp0DN3Jyu70kThuQRI95iVAR3Gl1UEm+z5YWD/TwRUoH3M9AdXo7k8rkt65FbLNXtoUjQsAmEQgmaIWOd4xKojeFhERw5FnN0JL+AaLBfxiTJCUIk02z65OR8M+0HLTdlGvdq2UnKiW63nF5xo/S90JodrIYcq7TO+Sm6MorKG3uaNitT3578PBG4Ku3hwsAjHabam/1fuu0NwnQu2sHA1K8AeWMhM7zMoF00i/E/7ZavIB0CZqoy2Eq//h7e8860/bA62uR2JZKxFq734+i/eDYCw1kkazZsYwI0mj2S+Fmrfazpoh/LedQxlPhx02Or2SEs52CnspPWeFDS+G2YP4QRAsu2B/V9hCoo0ekY4d5BU21ln+Zf6yRuYk1/t1T68qBNNnEwQ20Twt4G/xaJ3bVVKCFIji3OdzsUyKA/FkdsPEZn57D0iweRSt/RNIQZHwLB5l/ApBUj19lwnuaFfRaKycMr6YtouK3ecRPIQqNKENW0fGpSLC0qUbrt4zPcGLwoQAnuAm4Srk7l9DXNUL+hHExp55eRWkwBfCS9/bhfOnYC5N/V6F92Ucve84U35nNJCefOlLwJvDuujagrjzkmau12Dm1Cndl4yJ8HeCCf7awq8BTrvd+AsMMcEjJ4yeG85tDZyjNLJJGdBoidysS2w3qJKsMp3ioKOrLgd8dUS8mcFmwAs6gPDsVV2gh9Ar0CtcORJ4/Huo6AF/uSJg26MVz+09F3OaImv2BfO+QoR0Zsk3kvoNSH5HXPx7zAUv4NO+AjUyvuhEoI1eJRmkfkUumv/ncP9f6YTA99kosrMKu5A6hdO9HKpQvVpp9wLYX0CDWxfpiRQU79loDGh0yYQuYwp9BjZuSM/iBBNMcDqGpLQ+zN54t2Tr1LHhhrMUnAcYTcqKUbnXdHIsExeA2Gp5iDkWGcNT1BNUAN84F3g4eMOlgVdPN6wOzGGI29PAUqyibkS7kt6J2/2qaYSzVtJo+vey9HRVk+xiL5kItetZgFjWEkJhwQGvJB8/Kaxaa3E9Zy1X2g8+Tv3G0i/+Pz96H+OWGu9ygHkQr765UK5FnM9lcPNo3VrChpZ7Wx9YyjNmltVNwQsKLePtxLNqacECvuDx6jzANta0zkNYwNmWD103ZCjBGs4HsprvEsbZlt31Tx2X53rD+bjdb+idXLhmDzQRTHtL2a/rETfKdwjC4ybA1BpLjX5LttCJemrdA/+0wcLxa8NCneo6hzIcPSmck/XFI7JZyGeDzsxuZHIA9X5kJfkY6Ax0nyYH3LRd6JX0Z2ZCpDiHs9bhF9NOUxYGvwNLKepJZ+3/z96fh7mR5+ed4CdwBCJwBo5EIg/mRTKTSbJIFquqq7oOtfru1m3JtnyNNZZsj48dHzt+bD2Pdz07s7te+Xh2vGtbHtvjU7ZkW17JfajV6mpVq7uqq+tkkSySSSbJvA8AmbiPCCAAxP7xA0DkfTDJYlbj/SOfyEAgEAgAEd/f9/2973tiYCRejfur7zoYZd3oWSn2AVRhGRIQgHVww3pHasMUjMPt1j16tFWMtTj1pqS72PS/EZMDqgsOYe4tzKKdPQD5utZ0G+6ASmXzOe+AcH7OEdJRMmgeKm3FbafwV1hAq5h9LBv4wlm9KfjO1px61T8H0KvodqV+vnIfFwVF8xnZZeX0mPI+4KaWxlcgJAqLYVIpfF4qFTDqIWyNfF1bargExStOdT6MUg8aljLoTOTrWtixGnTWysg+8iP29QmmVRoNahXcAdJ36L9bGEslNXkoL6jfzopbjeon/Ev5uuY36gAezgzPZnFyfq1uLz3H9VunJhLFE4CYEBCK5abyJ89cmIpSSOLTpKwfc9X+wEB8jHlAKJiDZJfpDZMLMAu0TbMFF9v6+wciHkVjDXBn62XN7s7WATXTzPIxVfk5/SbLUGqOfNvDPT2oqBmjPd8iqqbF+tSo53z2vpoxUqMef9x0LlcJwdugggLfwUojyVhVpNPNUV7tBlb2QUDPE4J4lXyV03fw92PjgXdmmJIYeovTOMOIWMjSAziNhnqjlYSUxHyjuThjkq8CXJqGTrrhiIbOXTxqdAngLrr4+Ah/t+JhpcDFgyTUflQc8MRBXKA/ct6Xh1X97sT77rx+vym/mwddWznX3Xtb2V0Z3MeD+1vWPDbNa6IV4fNEoSv57aKLLvaDx6X33RapUc/u+SAKhW05MAGNlIFrW8/b3dFWAKs0ZQq65nDqVcJQOorpzKndHpQebrqSpDXlv3TkAds1rN2aUXvBC9l9u+96P2r2F1BAARXcO5iStSH6VeGHY389zbnz+0IE9L2pX8BUZTVjlLXNhd+2hs+7TP4TKhAXZSE6KW/HmuYJruPf8OqWuhv7u6kU3LYOrHRUX9mdju4hcP9Qxczjn5Y3CemNZPnZHWrjPgfFetP5eSfs0whaA/dBHLO77G8XXXx8sSlSd/PQXt9iLzwK6m45wbDzJOzO9dtdottCVVoW0+v4xQ1IcDwzDHeGqu6GtQ0TE0X6r+CY6yk1764Fe7Lrcz3r0GYfczjzqku8dJv3pcOF2LRUzVPSUetx59DT3511fOpZKbX02as6stlQzHDoVlJLN1z4WhYjRR7oDJXWnf0WJd2nDuv3mLS5a0Cvd301GdOTarOGPNOyGu7pcIUJIw/lhYLZ2eMs4QOqsoMiuGEWIuJ9As03LmhREXF6xj4PCCodOwliTlmfZUSckAc3cb016a3cpG8Fuzkcm5tn5IFW2LPRXntYr5ecnZJuu0cVIcftbSK2Sq7k7dQiA/aw3lmFt9dH7TnZZmz9lEVkLCDk5u0kYMHrV+uO/mdX1+d6IiNreCouo957/k7W0iIj9oxds4VqpTlfM+K3DMGW8Ff0YU6C2co/fg6cYAcvTLZ8sEsw1PoCCzWzB9ygg4dqySFHa9WSQ/WYjXLTEbr9XjrfgofKOv4IVFDW8UfIuzBc6Alio8xn0FzoK8QAYctcA8H+CsWwguGm+vt8CuglPkACLhoo57X7dUwlVjZwjxhrbmWtbvjsSsFAC2WzQAUlqQ0FjAKwyJkCjVV6n+HO7/P8uhWdwyVmISSKEWFq3SnI7qmWR/qux4if5Y6Hmp/1QK2q1+TR+FwyNjawejsfcqt6zblcXX4mBhgosnHT6zc/9J/KWpqXinLeSBQjzdkGKepV50zpNFBdd8iRWlitvV06KX6DfqnyTXcf0O9JpBuBpdkTdo+5Sqxect7+3uTMZC0ysrbU6I3ac2LyhLgsjLUuUm6qMVYmOy40IUqC5XUaltNoOIxGc87udAngTjN5R6U1rXkY5rGqSNmqyBcWcTm1JM5oewBo0AEnVcZhhXB/CYTBTolpGgVWl4jaebVEDxsjfm8CDEABxmCmRJ4nBZcA8Mt4JGy+jtneEzBNuDWkEWw3UNbs/dm4+rbR1ArfgekHqt+rHdnGV0vNnV96wiyvu9gdXQL4hwD7J/A+LjhiofDw9tKNR/Ks446PkAN+krHvH+AuvO8qAzrOrev3z/uyc8rvhoHfVqmHsWW42Eb2CSB9d8fjdJMutHqjT0h/zbv3Jl100cXHFsen9hMu0A+zh0OwvwIGboWyjlOlCjhFBrDSSkpTH6KWE0TszsrUh4zs7WR/28uWiuSiNndYO+hiKxtvnxs/IWjTwCKhdhiU1tRAb4uCXd5lCsF26OR6d/dRfwgICUJNseVjm8s/Y4uid9t6r4I7g7Yt19uxjepC30r9skspyMbyaVMdmAVtI+/bRr1FarYtjn0SBav5dxOidoCatbfIVRzMgbKBH/+0vK3seGXnItlrb4X+bod9sr+HKGgf/mv8eETVXXTRxa7YSQS863N2XtO78RYpLrlbM193wkZJcedEos4AYFquxe0AgjYv+EB5vG16eocyVbC/zp7mE4ULdK7ktXvMeskpWNhNftTOHt1cUwE1qtdLTrFZdnYop9o8IwW/UX/tm5/7yc+//5v2UF+m+PPq7/21uV9Od/CdzbrIC6OtM9YLCZrM0Sz6mkoPsr1WlR0zuo8yhEGFKViHoaY5szqs6/MqKeRorXgl1DealWwVS2o0Jh31lNN5Qm/yoOrm67+gWv22ChC1x9fxm5aKRIx4GZc4k0LKmSt5tzHwOAst9tdcUxWvzR7WEQSw3opLEBhFT6qyp2auqaDKQ/moPZdwNxtQQnYMCE9mw1LyKn5bJd9wiYeEJ7PfnhWJrdWGspX6FUnJS2ZvO7a59Z1xiPMs22ulKZ9nsgBERtYAvz074p0HsmjP229d7ZkIe9bT5wOryZjqMfGj51VWwQkmBKGI+oxeLzkZoLrsYBT1oq4nVVIQps00cxbVr+t5VR3W+6Jxw1LW53raEuTqVEh/T6UHOVKzexz1kjM/lM/p3hN+o51HK868qKw8VPxkEsRKRMeYjxMDlgXrCECEPODC6CfuwVzH9471QkH3yu48MMOwiLPtofBvuHCe6SBZN9UryqUx5jxKw0dcxVxVxjQKftYCOW4Hhi4a0yPGrMtQP4g5fsv6ol0qD0izeYLfufGjtOY6sO7QPKWCYvPbKh575q96/2WQvEJhlYHzU9NmUqYX51q16O0dWLptRmRcLAT6ieHLKgXNuMtT95X+eWukhpyvazNzPSImuTmbxEs14WAUsng+WfBLlVRSGxxd9OnWmkcVptAn/EsF3Zt0BDCoG067x6yuO/BQnXKsLPThpj6sah5fwLc2Iw2PdQy6tI0zClXMNB4dWcepKmafsuw0bLrmMBUpbLTq9WWoYN0FqM1RusPdMsBV8C83N8m3tvSzgakV/16CtQ+gaQfOafeDPbSxzDYQK6+ypc5+AjDmBKglcYjv40rL6fN6018nTIkJMFDF6a/A12CcxvvkV0jUmqrfqxt3exXGBRPsodF4XG+mi4dDlwDu4iNAeLb0SGOAjxiHGyc/SQlqjzUS+OOKR3n2jpD3jdO/tQ+4X7dn9q363XYucKVB+hHc/PfZBds/2gf/6JjgJ6Q79uR057voootDw7mzUfMuOD5FlsChM4APDWG51louaaw5DctUbCqwDClQDsv+VlpM5K4wj2jSdJv9hQee0ocPAxZKkf0UsdG9N9kDBxLU7gllo7p309TA/Qt/Pa2NN1FlR8T+bkm8flDjGfi2tTrf5PbMDsUe29k7b0Vn6hu714GdFWB2I5c5u0vJJ1ROrakEgvfdVu36gCS2AFQb9l2vA+1s4H0ywYWNb+FRF2ZbFcCPbjB4IC68E4ezgDZgvSVK66KLLh4XHI6UCOU9xHP3HuN3IrFl/jf7HkhuCRvq5CDbhyGYYGH520ld7+vd3XrgAi1Ci6sLfhEpKghgSTXtqmkP6z6jIdnMfMMliEnxFEk1hUl1W/YajlZeGbv+ev4CuiNV8NonzDdLJwHDn/ql1N88NZSwKaqQDjd1w94adqpxxwO5YC9MtaYZFaFIFUezbPC26OHWJCR7WBdu1ao4BgXqFIINGq56SdWTqmekQMtLWfWYgsZulB02t+BikVQz33BR6ytJhseeqTaUO9bEHSb89iwtd+J6Su3MABZaXoaolhzoVHUHZdRRPY8MeEYK5ppaXXDQ21HrfgOeozrU1Iz6MjYjrLSNtTvzYpNqgBbrfMa5UJEasqUCQ445oISrT0rE7E2X7zIuN5Ux5srIcfpVjOfk65xihdgyUdNS+6TEqtW7mB+M2nMVxQ5YZSfgt2eVhs1BZbo+YVjKM44PF61oRbEvFwYLik2zVbJlF8AqTMIaFGAVIuj/VZV/tFZdduCFWfSyCk3219mjf+7ktW/ff6G67tBRBS+eKEbE91ZMEbCHdTWq952PG5biq5YKsicfdp1xLhSdLk3KqlSekd6oU5vi7Jw1rElZIGtpJckFjDHfnt8Qs4p+KTPNiTHmgzQqmKJy+3rxyyPeexEpjho7zTQQobBAJExOR3mBhJOSjFzBPkAyg1amHMVcJeZRaiYUUHpIPm8k1XgNBWem+gozz2pvrSlRHfkKF2MX47MML9RGVr7Xh5tUUrN7zHDf3ADJ37P+qE+aB0aZt0+67k5OxnGlzgQc2NasvqVGDyBbhpdKQfaadbtsMxbzg73e9eftb19wXNVPKXFiTmwXXrgaeCFTJxBmZsKYpQIuFpTBk40Fx2IjP+Jc5Hw9UI0amZwk4yZPpHTekcZ3lct3TkyICGES0EuwJ5tZ0wK+tQGSgB0HECCjUAayhBV0MVXXwJ1F7SOu45xhxFDcCuUx5pYnY0A0mHZSZQApDEWkLKUUTw9gZfGbfK+KH8Y6WMxNOl3xb/vRJqFb5qB4cuS/bVwtAZy2IINHApHge4WSRWCU2hwAX9/yrA/wy83zttObmm6JjLs4LugSwF0cM+xT3dtNC96KQ0YCD++9yQM8fDfwycGj58sP6vNs4F7Z4RRvq/flyKnfrdvsJ8bsIbHtSxwJK/wYmOCPEB/LN9VFFz/kEDem/RCHjznl9CGwhQ/bHrvXdWPMZQkfLglYxRSWYqYi+edMU5KdahUFjIdwc9lH2/TIM4DFPg9P/Qr073vLh/R/7mR/NzHBwjy51OJiDVgAD6iw/nAvSmuf060duls7Z8uv5kj1vkLjrmZr237bDXwKha3s77bf+f1M9dsJ+60DNxGZKxu9nXes/WbBSzvakLWO5Z2x/5hb7SDfz048Njvo0BYSZVs1m0DbCNon4d2cAL0jDlfdPST5rYAblIPkHHfRRRdHhGZE7qFo4CZ2Z3+34kBTZ3aIE96ETia4871syB3Y5TjF7DT9gWe17KnpSVVuRc+Kv1mPabNqTboURBJt265WUICXT72bKffMWKGnfbenbCOf8939xveeqw86L5y7cTM+gUHSCq3e1+RqLTxUXF3Q5GitmnQQblkl34ZhcEMIhqDY4nrdrQxaWhPIIuBB9tSEVfIZ58Lt8JCnxemKNy57ai3FLXaPqc+rqE2Vs7CJFsGrYccqMNDKuli2E6UicpQrqBVHQsdV6nPRR6kelG2GJmWzlqZJ2WQ9BhiWYpWdktvstyfSjYDPaEie4vqayhAs0Kx4gR8FQEdPqjFfSRoqZtY0wUaL0yhoY7vH1KQKkLdpfnu2iEsji1QRgbjCDNlPprNQUTFuMFFBFdm3s4ysWr1OSfdQQaq8k3vGXFPlobxkN62SkrdcpTnfmQtTTklPlIbylqvXu65Ixnv5y3nL1Sg7gHrSuTrleHDyQ9DberF1eIZq3NH0Qmur23W+ePG1r/3XL3yDl3GhPtuMLgYCnuIFx7XXR14qTfnUYd1nNHCUZt4dlSM1l99GqFFd8N/0TAR7sthByr5a/ImQdyFGHIk7xfOSanrsGQ+VEq538q9E/AseKiqVRXqxerxU7nAGEJLoCel2RbG/W70ARO25G/bx2eqIz2ionsKwfV7HlazHDEu54Lgm3KQBjVwFtx37XU5McmuMuRXlHMAIYywSY4Xwt/m0m0oGbc4avv32JGtQBCdjn5odt98RptNRSpekq2l8Bq5bTKySdVMxCc4wHLRqdqk8br8zZw3nCj1Lig0H1KnOnqguOGbwzSRGOcvF0NJULmb3mHd7xrze5B/la7/Fz44qc4piAKdY/h1t0qWVM2jCnKaiqP3EUwQEKS4mIPptlTMXpkakeRf6s1wfJOPvW1MoAOHZ5mCgndH7wM10GjLwfPNRwLlc1U8qasaAEmlYhzuwAkXe/aD5XfhGh1w3v0XD+rHH1Q5l890OMrv8QWuS5s1tnvIAVdiL0r4KlLq8y7FBlwDu4mOLLgd8eHgP1WF5dD7MkY2Nm6NKGnjskug9he8Pn+/bWr8j78vWlh97Zbw9LO9bPoin5P6w6QAekg++BSePWqjxCLwi90aX9O2iix8GjO91Hzwm8l9TlfOxbSwuDoeDJgG35b9qKwnYna07rSpACNY5PAf8ESWASNrD5goDrOy7AjzcbU6ICAXt2rZWZuesXPEq7YIt1FpZbpHEkf2xwm1P73VIb6wAt6sG9aBiKjbEtIC4KVzm9nwREUsGmIrNaTTK2mZWz9zyhTfwZfAD+kb2N0vY2K4u2bbwq+zVgN+k+t2gE9qUCtlZAW4yed4xtnYWCq1lIcAR6NubA95P/XYSUhsZ6Aq4Wn/3g8TjIoCNljVoJ3bhgL32vdM6xO9R298B+B6Z63UIpthh2NFFF108cuxHCtwpez0wWobGG9C7zYaHQeqBxfT2kcP7pKjb9zqdKg5oyVtbbW0h3+x8huBWAdwIoW2m3BN0rw2QdFMJ20IBtXzx5NK1xcGye6RedlZnHdqnSqPuROVcY+XNvrPh7KzH5XlGb7LI4obWvsh7oIcm2Ubr8uttJcu6AcLRbLbkidlzK7beQWdiPjUiD+WZ9gOekYKgM+1hXUQCM6XKkVo9pdo9pqU334tIUB4gGSQrWNVxFoXG1N2MG8GFoaIAHntFI7duxTQpO8q8aq+sWr0eKWP6VKekFy0VkNzmys0+dChDT8ckvPb78hLHQ9LTJt0FSu/7cMO6Q1eb2tlcjzfgKWIHKLW8kVet3lWa/G4v8QrquwwLVjiDFkdZx98Wzs7cPz128m7eoymSkShGzEV17Nzd6nml2lAWi4O93vVh+91kPZYoRhplR180niAC2Ow1+7OOeslZXXBQgmLr4NMw2arDnRCABLhBhTW+9i++wAVYggr6vNr/7OpE3511/EGrZrO8Xwq8Fnghs0b/K8Z0zl+8FXkuK9lVy2HSKJyay1paLtl3uvdDG6W8twJ4MeMoI957t/PnE1ZEUk2f0ZDcZqrWN9twjcpzTkkv1YMVu47VzDw26sq7vKBIhmI39JIvYUUa5Vi95Mx6zNVbkc4Cb4U+dVgHNE/pSlK7409VQg3gOheBc/a7dqnsQn+LTxYI+sgkiM4nJlff0US56/mRQq93PWqPD5CU0WOsVHDPMLLI5yooOq6spYmoYEt3rsnueynVZq/hIuAp5i0XWXDRKDuqCw7mkc/X7JOmPq/e8weooIVLFcVeqfX9avkvD/iWdEnRUYJkU3jKyHc5IahooIIyy4irJZz32DPn7fHLXBtl7iy33Nm6+l2DH9BYRXIhhbDSWBUkFw6qnMa6iyS39EgumIY34PM4U1XCMI+6ZOCBk7AEKRhG5PWednPt4OLdjyU2KZsfBbpn+hihSwB3cfxwtMzuGHN7bzTRyhL7YcAhaNHoQwtBDnQAmzbYPx/8EIzvPiVKh8BBSV/2zfuyJ/W7aay11NHB2/rojqRvGe7t8NBBceGQz2sf26GZ4KNlf3sfL/vb5X276OKHDbtzwKWOGdNPKg7B/u5Z/sVY2X99KNJ/AY01ob8sa3aH0Zpy3qmK2H8MnsCeDD3ANmStpAHYVCQXVoWGvsHeeU9YWSz14aKFD/SdSe0lNPfsukH7tXa/XW77qNKx3mj1LttMsJD2hnagqPciAgX121nytb+o4nuydbkNU5FMxd5a3kPTuW3Gx1a3ZyBHKEWgc00FtYKyNdPXtFRABNQJp81ObCgCd+F92+Vf3UJvdJC+B633BBNchN7tJwJG9yd7bZPQ2Y008FZoHTGWm35fnW9w9JEVaVsjNgW0vY58J+x/euKjNrg2noxwky66+GGCSnaM+X0aPADbs7/6Nuu2gbgqHvRnvsukkE13mc6ZMWc3UMJ7IA0hKD2IswVQYaGVDUxzWSiJ7ammqbKkmk3WtvUUEbI7y0g+qsk+e1n3utXib1z/lGyvydFaxsPZ4fu5EY+/Us0rnh5Dj750526x367qzcBaHXVYZxj9D9RmgZF4cBLk0Vq16ICmWFmONEXJqqeQSmprYXd9TbVsaj3l1KdUOfrA5LnNAQPqpK4nVU+PTktxG/AUw47VCHkh4hwgYaDoKO2shwESy/TmCbowXOiiLBiQZsWWLgxNyvYST0gxjdyyFMWGUVea563zQ+ltSZzbH00K1pBjtWrK0ckNQ/Nsi5Ocb7j8dpySHiHvl+YBJAZICLFvGdlN9bNkDVQFPYsGhCm50OPEGlKNUwAzjpEBkpWA6g+kVonZ7Y4iDlH43LXODNrWdMn3Yt9rBYI+u1VQpUH/Wq2hvjc7qY7q9boTg1ClgooUNrHVBl5IBjCH1eTvFp4e8C1Fs+bU+pB+zpt71qa/p8r9tarhAFbe61tR+9SornlKecsF+I36QHTuAyXtIDoszf+IsV5RuMbQn2R5WvLpvcUKbjfVS8Z3f0950UnpZd570/oUuqMU9wFVT83ZozfKDj2p3rH7nSd0STWpBwBLdwY8RUCRDI89A0T98WQ9llQD9rDut1X6onGPPVOqB4Fh+7wLI0tAI+fE1uNfAXQUB7aTrJgYBv0XjR9o5LP4/0B5XkfpJa715lKTn6suOHBQuuZLXMQTyOStYE0KvFV8ude7qJGbLZ/yqcWp/ElJNZv6+CmfHKk5/Xqj5NBn1BI+MXPPM1l4oe9tW8yTlOwDJE+zONq3cJ1LlYCpYgywriO/4X9ugOQAyTJykOwMIyoGICKQdVw9FEwaLvSzxPuIA88bHwbjaeedKm9DGfMKksb9NcacdHblneMwjySK1vaFYhy8MI4ZlJ3LVfOijMJbsYtAcDQ3EZ9LxkL8NNF4WrtX/dSH4OJTV7hzk6vNib5Poj/zxwBHLfTp4hGiSwB38dHg8cQAf8Qi4EM7Bz4aOEb22uIQzeKHV/0+fId60x6EVaDA0MFaPEerRtoFu3wtt+39tZ61X943VevbNALcg/rdXfK7DfV7fdsjeWh07vZQZPBq7TAc8JETqI9HaNLlfbvo4ocZe9pBdz705JHBh7vb7lnX7dML2k1VaU1ZnmGkv6UGfoD2CgXCMAAZKEMYUuBuTXjeiRgWJ3yltcE4ZGlZ9zUhyUjaA4pXsL92rcng1rPYNeocjAOur1JfPYgRtBfCB5wCFW2dh53qq0jLLfaA0IOKw2gA+ZjTaVh0JEM7Dcu/WjZVuabY1Iyx4WmplnKlUwe8QzdCkLtOo9GmeIVmd6tUV6CT5c3SI6zCxXrx10ABdOS+Zl4YWXoUDIXCJoZY5PuGKOnI++R92w9tXbm8JRCkVA+KlmKb990kBaunNqqEd2J/O4N+2xm9O1K/+7F4KUBhm6LO9whmVWZbPOvZXfXB4r0/CjpzcgeT5P6WkHc/Kjdtfyr8x+ZrLVD+KIxtuuiiC2hzwJ0i4Kg9t9/n7zQx5UggjP+Hdnh0J/8DsTK8cctNj7YhLnTF1kPi5W5t3NLbYoLVZtBvacoHrVKt9KBaq3odeFgtRaRz5ifd79won1Ojev2es5pw0IOmS4ancK8++BdO/PY0MW/Z7847Hf71u9GxvOVqE3j8UfxU88iGpYQdqzGraJfKWQJACde91UnNVlmNawAGHntGHgoMOhPOPn0mebr6roNJqnWHp6cgNKNqdN1jz5iWWm0ous1GFL9USSU1UpTwBc4VARfGGLNAP8lZRgKkVYwA6RyhDFqY5pdhhuEx5t1UyriW6U0Q08gBYmHOGrZ055LZa66psr1WLTiaoQ1l5KEaEPYV84rdP1oRbzbfcBWnQk11tai8xEy7VuepUXbYYCxwVyh9B0gAQbJl5BAFBT2C4WdthhNBci6cDopAFs2FvsxpnVKOWB/xEtHLXF0hWqcO2LH7aSxbgyelqe+lPpsve1JBE90xXz99MzGq31A5zZL9xEA0FRlZE59IohixeYpW2Zm6qlVzjtViRD5Ve91xLtxfrHnUuerAshT84thrGtm7P3lifvX0Z/reuU7sw29eflled1jpNxfGUHgxNjPvcC8vjKQHA0/br6/h/07+C2bONRG9/hV6FvW+oioBT0vX/sD1IxPW6rLkDJItSbXh3qlU4bnB0cVQRb+ZGI31pAyPonoKyXqgnlJt7pqkmpJq5kpeSTXzuAZtRrWhYM947Jnn7PPr+EUQso7LY48D6/jB76EiMpLnpEiq1tdvTxRxrTCalOyXuPdvlJ93WLaa1Hi3esFvq6y819f8KbycPi/f0cj1Ex9lvip5VCpr3iteGjrymHvOQBkOzAMBOZMikHl+IIkvX9dyLq+Krr+tosF1TJf6fd/Lg6OLf9R8P6UY6/hWeFZGD5INkrWjLBMdZV7FWCYqvm9hciHyOgpkgEvM9pMMkjuZWMCF+p7BOixivspVE4eNBYNlGCixDF4b9wxKLY39pWmulrjkwd6HlG7NbX0ZgrCAc6HKOHrMYSrSWW4BM4xcjZ0Wyq5kLKTECuFwiWswzsQ4EyuYV7hawi+Tr+5LDnsJ1mAZxluJNO6WjvYSzUvLdMe/+9nnxxLjcMmDbX9BLl185OgSwD8ceOKFIAfF/pndPUPj9vuShxAB77uh5hzHPCpb451fYjc8Zur34C/XOV2gHQ6xPZS99//oJh8YW5rOCoX9fFd3aVjvn/dlu3ygw1O/j4/33RbitQ5OA9ct7I9Eq70vPGp5RJf07aKLLjqxP7Fpc5sDTop6MjHGnODSdtpAI7UnARzroLnapaDTsJry304YLR1wEqKgd5C+ghjepTgULM5087BgMwfsiGJmN/y7aflA7K+ApO2D/fW2Gq8Hon6HW9+fnUxfIgdjpETE10bFbVs+u+E+bipSu3JzGBtpYBES3CbshV1heaNKuMUuC6LXVOztas1QWFHC/aTaVO4WdvbBNy29g7u6irmx0vO0nrX5W7ppDwZuQKGskS1v5HQruEWC2saVapZAaePKdqaasXHyX77hsnRno+yol5wPEhYFNsX9diLbseyTWvJfN/jYonhusb+708C+HTViyfrDRnhshdZ6C3t+t6cejRR4dFe65eGruMfM+7bxkbxoF110sRGj8pwweEjWA4M2wyltkPc+lAX0VvRuFN3uNH9l40w4EccLe2UD7+J+v+k62fm64maib7FmKbYomjZUNotchZK4CD14RgoXHNcCZH5KvXmPeO2V8mu5T6bWvG+nXaNWZWSw8OvzP0Wwlrdck+HF2eR4tuGyh3VxV8VDvz3hkPRJ5oEM2pg056IM5AitEKOPVK3PG2wUp0KhSmWEdZfTdtscAqLu3Cw+ysiRGuC3VSycQIT8fCNoWEoh2CBFQbFVRcBty/9ZqGndVA3UPhJpfAHSwCjzCvoUZ8RbPM+0inHPOjsgzWTQVCoVFKCXeJjcHMNLxV5c2D2mvqC2T5dgf+WhfAHMBa80UpQaNsH+AixAD/JzNecJHTjtXfHbchnJMUBSiJLD5AKk3VQ1sgbqWaZ9Rt7Ab1cKYo6dT8mbiuQ0lkxF6qdHoVzG3WPkKgoLBEPk69SzaD0Ys0QzMMqCgvFdLiwXP3s2/M6869IqXuuuc/lE/2iweO+Ss1pyDE4uDtvnY8TLuKIUiwHnB9ZFr68SeGVtyeytLvjD0WzAt5ava2bJ14iuR8K1VL3PtDd+wprV+77bz7qNT/zSl36gGWvVeOinTxknuSavWX/g/9mQJ37O/i17wjsfeCrhn/cqSwuM+kinVN9p9A+Ijln2uJRNIfWTu87ln+H1d5k4e+o/3LPOTpJzjOZky56RHFGrPmhbW4r2DNvnV63ePimx6uzVpKwo4Zx2HfBQqVtuJDQpO18fNiwlV/IK6j1qz5XAsBRFMrBxyX4/LdmVupqzOQeIf7/+nKU7XZ5Cpeiz6s6VuZAcqYVsFdtQ/oLjmoeKj1qF6HsE3FTGmKvh83P/EosNQ1lU+l3oDao25Hv026XFMnLQkV0IjHkDNX3UZaMwy8hc8dTsO4Mzvz36K8+OcE3iMmKaohyp2T0mBvW6s/X9yQ86ExPS7RSBFIEwuctcVSirmOenplmAFNzDypO7xu+VYaMSV0yf/IYB4G9xrq+W6IE3S1xcYa7GxdMA0jSoMN4cl/lny0Bq0gNMGM3Cq6D4Q9msumoABGEaVKw0jhEuzXG1BFso20sdXG/7EuKXubRlovKl1kLJwiNxqk7UzoxJvsqljzsHPAA9kJU3z/YZcwI0Gh/FMXVxcHQJ4C4+MjweETCt1t5hpMD77Ks+2diR+h0+rO3t4ajfA5K+u3w32g/twQTve4e7YPcu8/6wx9MfhdUzBw36PTLJ79zG/uBT+3vWLrh+YA74EP3EW0fRknuk1G+X9+2iiy4eHgtPBAf88GYbCoUxCux8A+0nubJFItlG5+Q/FbO97F/tiDFSW7rSTaerU/jrZqtyeBu06x8N2MwBO8dpFKivYutoldaSOKJYW8jo/cCmYlW3M4L27k9WKHqy7oNHe3h2o4jakbps1PXu8k1o62vbulsBoQ82+9wPPi9lR69pU5U74nub7HKWnjbRK74A7UqvbQxOS3qrkQUMVANXjJX2BgZuA1UkSWcIKHtlYLW53s5XUSiL77CbzR+2gbKJ/d122l/b8WVr09/SnRvcLwUWNjbNN2V/VDaaFas2aFtAi4q0sxpsU8K7sL+ndny0YDWrtboFHNnUvSzQUdZqu851aDO14s0ph022FhDR1Im9N4SDBBh3wvcY84y76KKLjxp+CmPMzjA6xjytu4BT0kflObHBbHVELO8ZD3wwjLbo260i3a00cEfagHcyDchQXfA3aWDh9rz1wljcuKtdhrodDwl22TNSKKm+BwczBb1gQKSlRRaxtSoPygfjQTCEHKlJqpkl8Bw3gtL9e3yxh5UvBV4r2U++Fj8z20Cjlkp7w+7sSDB9c21UTzbzbuslp91jZsqaEjUGbdWiFDJpTHKrghvoJxmiECDtodLreOc161P5cxVtQe1hZU6KRO052WbMp0aYRI7UIiNrQ465rKXl3VqiGIkG4oliBCFcnoXRZkWoTuqDzsRAq3BUqIQorBBxU82gTXIrS3iFqIIBgQQxlcrt/Pm84cq5Iw1v1mXZALlhz9uDYXJLZm9veH1p9kTzk219LtWyAwj7bKs3NcKsz/U4e3RxGuVILfLlNb8966USkeKT3HJTzRN2ofeyIki+VWJ9xPtYdhpWnEEU/Ktlv1E2g3JJCaqZhLqMOSgvjPQD/dk4WqyKbVWpBUh/hiSwzGmFyrPGe8PK/RV6IhgzDP6tyj/+Xe8rs4xonvUz0luzL4/M14f7bOsZVQKS9cBL9mQeVwX1LYaf47omZWvlwHxiRHNXlFgcsBcCFk7dUzDqmlV2XlcGiu8+FZx463ww93bmhXnN/eXK4j+p/bH/fuQ/nTTu+uayBc/Ii67f9hnZ+lsBezjXF7jRZ2jLyulnmJWMwqKyaLfsnyJ/VvrmMj92jndnuOAlvYA/QAb4Gelr99wDA/Rdkq6u0puQ+kq4Rm3SGEsxKR4npklZjRwEgNMs3uVECRdS5Tlu5QhmbZpHygwFKiqVO7UJ2WYsmb31lIpB8ET2e41ev62yfrvHeUKHk+Kroob0vpNxivSfW7XKTsltAlkCLuI+0uv4zjMXYyVmlOaUbD9rQCy7EpHiuHBkG85U9XLig9pZ2blaJQkpija8J0GCAQqpXuvFAi+ypkSrn/bd4PJNomPMZdAusvB7fMJPJk/wD/P1ai66LjdirPSTil5L14ZtDqOBgfOtKovgxbqPZHDtJrkqwJl+3lkB8EO+9VdgDIAynJQ52QPgiHKhgHS+af4s1Liplz3ubD2taW2lzZRydrJ1QVnRwqpmBskzycB4nGWkCwDOGZ6bb1184NI0kkYxg7c13nGM0VjFdhLKMAwmeFsB0josggu8mJ+RHUqwAgFYioVkiIAna/UrBKYyrmTGOV+tLTOTZP42c4cayu0HfrgkM+ZE0jomEG8d64m5MtkNw89G4cFcYTHYFMNGq4pVob7KjAkgRVhdaWoJmwLo6mb9XL7aPJjBI3xvXTwySIBlWXtu18VHjtu/+fdyN3758M9/IhXAD0kAH5TT7dx+W+1ve+UDZnETAXyIJOB9U8hHKwLekfd9zBG/B3m5w30fdqeBd9/nR+kQ/tio363jtAOofnfifT/cYf228PFQ5/ngIuCDEsBPMvt73Kjfb/3Eic+fOP1RH0UXe0CSuuXfsUExNX/zmyN7bHSg+uEJKAiPcP7f7vdxwdJtEgS7qW6rAH5QTnRmSdASmB4oBngXrGyzK3N6gx30ISBpD+yjN2CfJs/evQJ9t8LTUt9uIcyEV/NOvsq07FL2aZGyCSpm229ZoP3BCUkxkO9zt2lmQSRvFe92Urlb6dssYQOXRlahvJM/cz/JTU8Uu9q6w841Bm6RgbftPrct/zbVfpsKv+2J3ja2dt53KQIrDdKbpvGXodRK8z0ofNDLA0u/bTeRcEq4Hpl53J5FlO+hu1ZL20mjHxEmH46ifsSw/vFnPupD6GJvdCvAY4T/cOufXsn+Lq1bw376AG08aAjsx3m+E7tfNrfurWN7QQBvPoDdj2EHB2nvZNrSnZJqbvMYWLrTXFPtHlNPqs2JTZ3Xxk59sJjW1ll3GTBJ/7Or/fbEpDTdS9xCmyOQJjA3MxlX/YLotYf16rTfGdXNpFqddTQjfsuok7oIbb3guOZCb4fyCm7MRfkHfFKlouNK1mPKfPDi2Dffrj+fKEZ6vevJegAYdCb6pMQktwuEmiwgpGp9Kzf7BNM8EIuvyW6RGqtIhkiEFa/lorzASeHEa6CkCJznTgbtHesFoCI1PFSylrZk9vpWbUq4OOG9cZL6Otbn+Z3/D39utjpSnfZXi44mp7XWPPlqVK+XnM4eXVLNQWcCyNc1RTJkm+GU9DHmw+RGmQfOMm2g9GfjZc3uNCxTkcKzJZabjLIZlZ3/qcoIZMGL9RaANAhJLIWZDxg7SaJCTNgVh6EHIpBsCtOsN2AUSYMTMAUnYJHsn+51uXIOo1GTbG/0fiJJ/zq+DBowXZ8AnJW66bLrJV8qqVUXHKPnE1K4vLoQ+5uj//keUSf6gNX4PU4XcfXoFR/mp+NLln/ZFvZr0g9+z/qjMkZDymXQxq3imlQLkv107t3bgaGKMXqGK4ZSyRAw8b3N2aeZV6gu4P+w/MJ7uQnNUwpRTSNb19XCU40T/iVNyurF3oqnInKX264tHiqAWCNk08Kmu70g1ucJruN3WbYiLqEVFs7P4mMt4SrVg6fsiyqpCuqcNXxOmi7hEpkgqVpfruQ119RqyaFGdSDYkw07VsUTAdNS83XNb89WSr412f1p+b0i8rvVC6Py3DiLvay6qd7lRJjcAOtRFqZ5KsrKIJmbjFVQ73Kin3gfcTEVUtSxecJ16s9xeyR7V101SGGekp33qqjCBBpuwQpWmtoc9j5ml1gyqMqkq1yS+V4HOXoJrsKPyE1FaWUE1xzOn4CJpvG4OSELz2fAaVh3lFE65mJmCc8yHGh52oQovJx4R71qNH/v8xtGyrt33Z3jMN4heRrfMJc0S897nOvcPk6/GFSOMSdGlOHZEl+j9gaJVFPZ/EhxSfztGOV0EgHmNI4RanMP1gj5MrT00K0nCs/tNoSymf0pm//Mt7710uc/f9Aj7+IxQ+rWf8cIXQJ4Wxwtgbc3Acwx4IC3oX738+k/fJrvgV4OOGorZn/cbJs37rLnLD07mfg9NhyU+t1zvLe33peDBv1uS/0eiPfdisMJgg9IAEftB9aRPCTJ+kPP/naO9n/r5MUuAfzko1v+HSN8LAlgQA8quxCEB8JDenWI2s+drTuMRrOKSIPeUgC3XoP5hztKAcE7byGA23OuD41tcn+jLc2xwBGWeUN7ayV3+XwNfBn8W3Nw949+UspGzk3YDKoZIzXqcWfrnS8tCGAh+d1ExAqyti3k7YRQBrdVwluPduuudniz2zxk4F4hGqKw1au8gnuZ3nZnsI0EsU7n584i8IHQqo0F8GxIPWy/8AZ34uzGxN8NdaBwdV6D1R3Mnw8EH82kwV02kVBtD6sD1oD9Kd3bOEIX6KmWY+rR4siLTPFRiG/CTgmdh0KXAD4W6FaAxwjfuPUPZ7L/VvQN2i2CzrbA3gTwQ7O/YpS3G5vbkgt7RgqCry1OhR4YQW9CK6m3ubzFJlr21OShBwaxflsl7FgVb1Mstx8q1YOGpeQbrnpK1ZMqeiuQuJP6LW4hgFv/ys/VJl3xwODKz0rvfqv8iafc737A+WS9905yyB7WRYDCudidm3MT9ZITheqCo1lp6ngvp/22iuBH+6REjPgNxgdIOrHdKJ51eQrAktlbnArJ1drl5969cu+56rqDteZt0Hs5fca5EJHiFZRVq7faUAxLWbnZN3Z+dtg+r+Mq4RKa7wrqMtGxVgF6msUy8iJjq1Y03QjINmPm5umwr6jUK8u58OmTyZu3+p964YppqYv5wdPelVuzJwdHF/+u+a9Tin7CWP3flD+3jn+2OjLoTDgl3WXZltcHA5G1akMRIcROSR8g6aHSy6qBomC4KJ9ipYxjkGKJ+pCx5F8tL4/GAJ+RB9RsrabY0prmM/INw+fJZjLBkBkgejuNglOv6iFFXTK4iTkuOxerRLDSSIvwCtyEBAw0s/bMAbloHwuqt5PnYsF4OhMLFRRFwYjT9xaf0Mi5MasY161XlvEumb3FKyF1WBcJu8WpkOrX9YTa/+xqsGz1LFWSHu+9QjByZu2n1+fysYyBaStHJtVElapUGql5EsCI9Drwa/zclRvPNTn+JUff09mnpbvL9N1zBfzZetSbNlO+W3d61NM6NsYdqWu/P9j3iezqlKY+r+t5lVXUCV1PqOqwXk85m+Q6eEYKfqOebbgAm7sW8BSHHHMlXBHyMeJjzJWRhXb8MteSDN5jIEXATUXF0FHixHRcZ7mTIpAgdqM64TMais1I1gONtKNeclaLDqogP/g1eSYLjbLD5q49Fbgh2Po4sRhxFUPBqBKTiYuPFVhkrIh8hpUijkY54nIvAJPcnjAWf0P5ySrqM9wYZgb9pRV1up+1FXrGWLzFeJL+F3grnNBf770wyvyQsaTGazXVVghowWxa1xxqtlaSglo6wTrcgBjmiGz/R1VA6qHghtu8scwyfOoCQO46fpnejVcLb7A5q7VRwHa+48crCrxxKEMG85S8ObunAnVYgkFYAh2Szam3VnUDD9oJSQMRviPGyBNgwIXmvNJ2Y7lzRqm4LFdwuzqK/DHmJoxZ/2yZVwGWv8KK8TisoQdgGS51sLltlrcNsUaQvv0KHhseiZIFPNgyWcdjo9RgpcVb7/PguwTwsYAkSV0L6B8aPJExwI/NBfojRttKuv0R7NCrbXO3teSBhSDb8L77N3ne0SjxIHhkyburDHS23nZKbs7HnOyjn/hRsb+7T1Z4fLxvBe5vXLMvw+e5oxA4fHhwDvgg7O/hkuQOTbI+Us/n0b2Skz5qdJK+XXTRxXHCpoLkI4KaMcra0dyOFQqh7SSe+8QqA30slzW7O4tTtCbFrbWzVFNg+Cg44H7IbqblREtCdB8OAXsfkot6tkUAi8JhfuNNZBvz4A5EYH3nR4U0ZytJtnFNe4K8UIRs3U2n8bLAtsTqnlAxM/j7NtYkpiKZit1U3MAm4lmwv61DftCmaS8L6YCC3l4j2F+Vqkp1p+/VVo2vWCkWhAN5iEJ7eSs2sb85QnrL9rnN/m7ifQVSNaGdoZ5SN+f76i1ibxP1u1WluqlcLNY3/u/mwRt8mPJPvMddwh47oDdwSqQbhw8GzrYW9s8BHxX7K2qiI2d/98zz3g+EwG5ho8yuXf22C86jY4K76KKLI8QYczOMCC9oYIz5dotgVJ47gPnz1jHvrT022Ndwr1VsCC8K2VNTo7qe3GEcq25Z7qCBO9lfv60CCMZX2Fy3733NjW0GDfLtW6TYSbE1EccLHvBsuSy3bo7VBcc196Cci5Wj3glvvMjw3dRYPBXGRz2lBnuyLnt9pd4b60kZYSVvueweh6CZ5UituuC3olnZl602FN3uAsZZrNGYYXjEe08wuM1XkR2A3WNScnROgnJIugtdBPSGbDk3a8p5Y9g+f4krwAyjYXI6CuChIuShAySWiZZxFZEBfT242nChI53RkyUvZW5+qz8WKgGL+cFe7/q9eg8GMzdGr1w88+XsrxcUTcd1gfiIvP5J3l1DDUmF5xp3qsng13pfuMC7cam/n/UYq6FE1gzY/LPlJpfmBjBVmQzO5SoGUSnNPE5vlSUIk3w5FspmHUajkmUt+KweWPEZRiY2BiDhc80tnhsJ2nTddlJ75b2aZMNFbv4zgeHX0udGPEbGTJ7Ue0a1c7+9oAxGjeysMikb1m+MvJJB02jMW0NRKfFh/bxiT2eSPeGwXkaO2nJL9PadXV+9FVF79XpdVaO6Pq+qw/rKzT6tlr0TcAez5p8/deM75ejNXrsbWz+Z7+jPfGXl+errjr/8Z35nNtn/ndJ5LfzSecf9TDEcKlbSo0xG44xUhxe8n4gsSc7cjcC6GjBuMZF3yT/aP/322olRq6KOLf6d4PV/kP20/GytbygesuVWzvWuz/V4L6erC/6wr0hvTQqaimSU56OplFqVHWpUL835SvhW9D51WJ8N64PO3rgU85MB3FRmGQb6iAsdeZhSCcfLvGdHHjM+LFSGMoHGkjxRl2uaUZG9ienAUzc4HSIn5L+mpQJD0ppKaiYwXKoHgSrqS7wn525mAo7hXNzmcuWUe2l8w0YcSCgBO/YwpTHjTk5xutyhIvUTxkpoNe4xjL9U/XcLF/t75/RGrNCofDuUCwb1OX1ULhl+TZGr5LIEV3p7ThEfMJacuUbNbsOFqZBRQz4jq2sO7b0EYfgAK4VUxHmvio9GgRL4JHIZXnSTqBFL4R8j6wawBfF/CYqwAv2tSasGNkCBCISa47L8p9xtHbCpSFn6dWThQN7Uo9PK6FFgpSlBlmRqc02+EzZznw9idyqt7qMCoGsOXXtQkSoUMvjF8hhz8VatKbrTY8y5s3U1Y/Bq03L58bC/tHKU1+DNEm447cYjYe/j7gKA10bUTqmBx84lDyULb7DJNXSSxCWLqJ1kvcn+Pp4j7+IxozsB8NjgYRXAfPTNvm3xRImA96UA5lAi4K3Yh15nWxrYMfLA4s+qUm9t4Ni2v3SID917wOy3nV/iEB/u/j/NnWjgI9n5w2Mnw8Ct2Er97u71dxjSd6fN9kX9PqTqdxMeGQH8ONnfH9a43/10AboK4GOBrv7jGOHoFcACT0ZNeFSzAB/m/i7KCadhqdmaU682ha3bntLlozCCrmwmkhsFGvpD+T/vmPrBxg9aTGlnB/2uZ6PoOdJqy4Z29HluL3dG+Rr4FApimryCsUJYxexkUo2OUFgDVUFvWyL3k8yibS2f3FQVKkaro9rp4B2ipGAoO5CUW1nn1vpwm39tK3HdVDWyIu63/SpCItxJD+8TbW1xZ0HoplpGzhFKEdi0vbD+23ZXpXpQ+EYKNNVObSzs8LXcWgpunQJYaWBarZRfWuzx1pM5B96WEXRbECwWfB3B0buEAdOx5a5S4G3xqGu8w9V14gw/vEB6Jxyh4fPup6JTh7fT12kf6CqAjwW6FeAxwnu3/l4y+39vKYBHxMqtdtDbcsDbdAy2Qu+whdiL/d3RU3rTE8X1JNWRH7yd0nd3tF9aUL+C0xL3Qb+tIjLvxcIGD4xtw4ZpVT60LqcG+JAv1YDB0cWX7O+mrP4/Iv3XK5z7gfXcVP6kpJpnnAtuqkl8lZJPvK4w14XWZJocBBCOzeJCbfeYNnetNOeT7TV0qrIDHdlbGzt3d+b+aTIILlCfUuWhGvDSqTfETX+M+TFmRawv4KIsPuIKagVlHb/IAO4nDgRIzzCaJ1goR6+vjJ+OJp2egsOmX79/PhzNrr4Z+cUv/dsK0SKVBLEeCt+pPjuQTft6ii9LV6at8WQ2lParg7a1oLQcpHGSGy8n3olbl9+P9Zwirhn2urLSYyQbOZ8nk0EBnZrXVpA1n55tWKyGTquG6almAvHE3dHxqtdjK8qj9Wtp+0gDY4pPXOJ7wWL6Pe+ne9azXk88XFqdD04M6HeVNSN9E/+L8lv+iafu36nfrYazxD8vO+eb6bPhc9y4FIt8Pe5dg+d61z4X8hgZUyE6l66ptnSvFn0r7TSrxXKv/VKungm4bJnkeOibfHnOGlrI9i+sDQD6tIoFEn1nsgRqX+75eoqAgjPM0md5PU/PTcZmGf5p45snjAUqfNB7ec66+LT0xruc+Wb9i3rap9gM2TTyUmT1NU0erlWnHQyDRd+z66vvReRgTT6X/6L8eqM4vurNtoXay+WJ246YpTsBX8oWv+lhAZ4C0WlLI5+p2T2mFi4pNuOEZyWhhwBJNc9J03krmMHvk7KAn4ybihAHhyj0ER/M5j7UhgzUHMHnjWtzSo+BeouJs9yp4knhNVACpEMUZhkW0l4DxY79A+updCNgWMpfcfxaCZuO7KJcRrbjqOFdJCw8n4M45/DGdH9C9aYsz5X7o72nZrKW5pR0W0pL1kKR3pk+iqe4dY+zq3hPFdWsNxEjDlRRR5lPEAO81IIUC0jPGzcNpVLB46IUNfIYmqIsqHM1Z6lq+mTnQpU62GEdBjE9sjNZbU5Qa41WxMBQDCVmGLEhN2hqfGcYadOuoq5uF/A5QgHSYq5MiIJGasKYBdRszZmp8ipMP7AHED12EZ37IA23LZ2agPEN49MZRoTz0CZVkkCIksYagkGYhmW4glVl+u4Gj+vHhkstM+1869UFH9xWAAsO+G4ZwN+xWSeuHvBFuwrgY4GuBfRxwseVAOZJ4oA7OcUjdoHeiv21aw9hCr3fPuCe2N0tcIdd7f/TfBSf2k7Y9m59OOyf2d0dW9t/u/C++yJ9eRjeV2Bb2+e5I+psHSIJ+FGyv0+g8PdJon4PLfDtEsDHAt3y7xjhURHAPBFl4REaQR+6qGhnynYGSQCkd9DFHtQOepMHTxaA5IZNHt4CWmAbI2g2vrqIBN6TUhrf0jDdDp1Ru22IfN9tY3dbG2wggLcSsXsiREFBZ4sMV2CMuV3CPrat4vpJiiMRBG0/SSCLJlhnkQS8nwNrQ5g8A26qbBf329b7tiPi2g+ZllptKJ2ML1tJX9FP37YUXAL3xib4brxvGe5t2YV4s25YhyIUoG+vGOC+ltVmZNfNfC1x1ja2N9vjUed6jEK5JfrfPzbxstuW34fDw2cSd0IQ7tvla+4LB9IEn8X6i10C+BigWwEeIwgCmFaFs5MXdGfToM2Pbu0eiMHdBsZUpMAKbLxQtP2c23hwGzqEW8Cel2W9lTxAky3ecygqEoKbb1PYt8xut1173lsbwq5/CCowyLmzK5f8V53odeq/Xf1cPaX2RePj9jvnmbbQZspjs66eu8X+RtkBNF03dFDxjBSarHCb7dY77p9r0EPf2fXV2QgL0AMqbaGqzV0bC9xN1fpESutlrrkoV3CLWN98S4oRI17GNcasYPhWiIn11/LPZksee1g/L98BfGVbBt/NxKjNU5vsuV/Ela9rwjp7VJ57htnn+IF4ohATa2SAobmVZGwsrzgn3/gw/6zbP1tuzg68D33og4oZsPlXy6lRz23j+TeV8/0k/aROEX+TZ/NoLxi3rigjIyy8aX0qKt1/ybjZR/p3lOeAzxhXg8rMrPG5t5XY88aHQ8wUFCXNqRL25+9+987pi1rO/juBp36GrxgoZu7E9wOnBlif4F7dcLgqhZLrxLDxblrTAtPWQmjkdO2DmmIrKFr0vXj2fK92P6GfVNT3jMWnRkQv7U3lXAV3gVAW7Wb99B+3/+cbTOQJXmRmgGkD9aIxPaO8mEKPET9t3LmrTKwQOcvd20zGiP8aPzfKfBW1RuN68blkPVCa88meGgbVWYegDOWnauiEJ7IDviWflB1lbo1+wEvaga2Mq4Rr3Yo5JH0I/Y3apK9aunP91Jde+KZphd+Mn8cgeCL7c9XbP1CDw9L8NCd+0fj2XcU/xRkv1enymOzOAwMkhdQ7SBYoIz/LzSo+mUKWYBqfgm60JlN4qPlZn2X4LHeBHiN5V5kYyKZLmpSgP4XnTV54mfd05Pe58Bm+W8UjaOD/lv/T84pSvem3a6YWLgV8a7lkX6rgFYnaql+v153OHr1RdmiekmIzXJ5Cbr1HDRWAT1ZmDXfhzfxLA44kavW2OXTGuZBa6huQG7boYlBazhMsWNpp6TYwxtxgzlwPNNL4+km+EL/2VuziRLZgatnoXJocyYuhgffjqWc8whZUhLmkNa1NtaqYq8RESssYc6sMqFSBzpmdnUkrgot9IAU24BoAV5vUb6NAffWBBNY5jlVFklvm0qL73cH+iktuOzKm/TdLuDM7pp+Uz8j7V8vN8fgdrBvU5vitEg/s7B8LfqRlpt0p7RXGztv6PPtbocudEPTw1YO8bpcAPhboEsDHCR9jApgnhgN+rATwtnvegodMBT4kGbwT9Xso0vdhwvnaHTQBN9XYhryyx4Sj4qrj9G8KdXtUYt9tt+Sg1K/AwyuAH2X07yGo35P7tkbfikfH/j4xns8P6e3cJYCPBbrl3zHCx5sAbuPh68CHmenVjgFWM8aDtcug7uyNfFA1cKXj1rNFBNzGtlVfp2XZntix8OuMBfHCwJYNhALYAwM76g7bPs9bH9p6/oUEtrUsnI2PYP6cgGBqBRO8OzvbDtwV/+7OMYvdir900Lftp4uXa/eb2gv7LBQ31YFbHZ5TtT7RuxetbUt3ClPNB9B36HED6R3ET+2CsNIg3eh4YFu9b7nl/yz+igUxYcHbWi60GN9OePchAhYQNPB+ttx3macB+3N+9oH74IzvTjA6JGWzD6EG7j3sIT2G6nFPsqdF7XQJ4GOBbgV4jLCJAGZnDrgTop+w6fbROb7bXsu7kaPdOh48ZKjwgz0eeDLKLmNSobZ8oMoV6Dy29Eb/knY2sAFlGGr96yV2ruQeTn7SdnuRcL4RSFr+fMN12XnPLpVPWi5JuveB9dTN+ESwJyvuztUFv7NHl1SznlJt7ppIXRUHI8jmat3RN7qeWtBQkIfyUXsuUYz4pUpBsQ3U0gu5gXbGsGaroNYi/gXB/OUJujCyBDRywAVSvcZcoJIJkKm4fLeUs6/x1EJtZOW9PgCV/nOrFxzXxphzUY7THyc2Xx+O2uMLtRG/PeuUdNNS83XNKjv/R/+/OJ27/3bg4jD3ho1U9GZcH1QqrgCgfTeBTvLLsei9eHGg16snsu7eu72RJEOnjftVPEvKYAqPn9QCY4CXmof1MrKFX0aOcHOW4R/n1cz8izlP9nL9zZuBy+dyV+72Pjf5wduVvmBNotQrDXw7nr/kxotzrVHxBbS3EstfipUZKxIeWH83T89rkUtnLVusciWL7+vKSydY66Hcn1tKBIb93PMangFuJ+lTqK0r4QCpVaJpvMtEHdgucOVVfsxTtv3OzAuTPQsTvXNr2ByWLSbFfzz34c1AYJnILCPPcH2UBRelK1xM0NdPMkvASXmWkYKlLa8PqqGCXvJJbjOzpulTqhytVa86MIldKOX6bfWSMzKydsq+GJCyReR1/BHy6/gb+VgaOZXU7B6TLHpOpcyzT99Cq968PVF3Oj938i1VSqUJ3L5z6cWJ7/4k90qsvs/5SW4/O/ehg8Z8bGJKiWlknXgCpF7n0nnunzeuFhR/np53ORMke4r4OoqBOsZCj5E08CvkTUVyxb0YlGOSL5d16A3nUrV4qrehFQz8dqVwhWcMXBXcafzfKX4uXggD+pSKG1T6RtdTSa1acsieWmRkTcRp10vO6oIDdzNG99nnr8oNe9VWH8aoSnk3VQe2O8mR21rMl7GFwrn5hm+gll4ye8VPwNmjT/rvo8uyq96wF2uWOilNOymPMp8jKOJyY6xMGLNtG2fxe72jjAoaVfy7aaAxw4iKOWQstR9aZWDIWFpTojqyWO80GqZi86+WScNS65nzWDeaU2wFryk0r+LvJQ/2PuqrOEaQToMLLjfHR6lRjxjXZAmLIYxCRcw9FWOKNgEsDkzN1pzXqgB3YAXzCjPm41MAj8iYVTzw0kDTUGrDhGDvA/7b5qOWbOYrZ+8QGEWSqSWxqUiuZiCRkEd/sAzdDOCPF7oE8HHCERDAPFnNvk4ciQfgw7N0+yWAeUwi4Idkf9vYsRu4df221O92T9/9I9vnZ9FpwXc4HNQI+rG5QG+b6ct2zb49SN9tvYwORPoK7Ej9siv7K3BQDvgQYt9OPBrq9yH1tT8Ewt8jifXtEsDHAt3y7xhhXwTw8kbz3v3jCSsLH7IaFLbDBw0DbnsIbyCADUjt46weqBrMtnwR2Z48PiodsHO8w9OsDe9Geiy6MexDsL87xP1u5X3b5mybtjRwC+FsGt9+FL0V3IAQvnQuAMLXrnMzscGmPbhb1nAaWTo0wZ3U7Bhzgo3e6XjajO+2y22yWbR7soQBjZR4s4B4v5v2KYxe3FRUjLbjS6fe10NFLAjSty3bEg04YENfexdjXtHn2so7ZqHcEYu7gf3dVvi7Ce1TnWxZPUehDBEotzTF7XctKGH2zQGL5452aL52QNROzcJl29cutf1xwI+uojNg/YAc8EGp34+qaNxpiNFxPF0C+FigWwEeI7QJYDaKgNkfB9zZVTgQAbztkHADAdw5S6zN7O6HGz4Ltw5zHROHtInVlj21DTdKoQNegx5Yg2LrOHuBVkdF+CKI+2kvuJEjtQv90yfdM4Okp6yx242BZD1g6c7nvPN2mz5VHwT89iwgKFWgUvIZDSVvuYRIWkzYqi745aG8lXWaedXp13ER8BT99my+rk047owyd7f2iua4/+3VV8Y8mdR6QIro2ZRnvGc15F3wUgVUHMDN2kmbIzVelr5ZPHXKvZZMhQb8K7K/3mefKeFaLw+lXaphKWHHaoT8j/HNILkg+VuMG7gyaMv0JojdqE4MOhOA0rDlkYcojdnf01EmiGfxv5i9rkhr7oRld+XWe2ORXDzdq0Xn0ndjTweU+d/h8yfRy+S+X/xZxTtjK4dPuq/W8PRz9yqXLhrxVcUXoTzM+6vELuam7wXGzmbvU6Hgesaeyy4Mx56Z+/r7Iy9ezL61qAx5KraQMbdsP12OaEouEbVyd7WIql9W1deH4it3YiOr+mfCSkmSskXi05zzYPpIA2/wrBr3ZCXf6ej8guTqt3SyrrgrfNb9jp/U71lfekZfm1FdDT2gNRq3G70zd4PPX7pvt+nra333XIFPBN7350e+Veq1uWuT/vvD0nwJlxPbqhWdlKZ9pA2USnnoDrFUw3/Jc2eRcPnmQCYsUXLke+zmolotOuRIDbB7zOHYnJdKSMov14Zj9qRNb7jdKw6rLy0ZgyRrNFbr5w17waiHJux3f6f64rBhzJSC/0D9yn8JjFn1cNS+NpFfeToz+5+GXwpLJYvVT8bvZTRnRQGIrmdOlqZ+MPyiy6Cs1J65dWdm4OR6IBwxUv7KugfbnUD/oLGkkffFs4WgprjygP922XTJ6DgrVWqYfrkUCBqaYlMqScVvGJF1JbxMxIEticdR7/968Yy5pgIhWyVddjlP6Oaa6vTrUXcubMvfdsSqC/7qggNgCp5BjtQwsIfNmC/1ovf6+9aoU9LtdU+iFB7wLV2/fz5McfWqFrtcyqk2BK+cRh6uVesOuVpDJRwtpkwvICZMfN75QR5/Sap5LMcJaWGExbc440IPk2tX+wKi3m4bPo8yf5PTwiN9kludDeQx5tpi3M65vKYqd9KxFLfpqwszZETA4vmOqbHjAKlRj4FvhhMK5U3WQcJouv2KwkcqPFsiDd8Hmk3+b3xAgcekAPbDGADPfRHzDQDnH4MUuFrsr2jyi7nLbWOq9jkZb4YWt9E+XVdL++KAuwTwsUCXAD5O+HgTwDwBHPAmKvFxEMA77Rw4OvZXYBsOeNOaiV0ffWjG93Hm7z5+tLIoNjC7W7letgvpeSiZ77Ybb8Ju1C/7YH8F9sMBH07pK/DI3J4fvlP2iBqFTwzvyxFRvwJdAvhYoFv+HSM8QgUwT2hZ+JAF4UHrjXZ6E521H2DAwq7PPHQpuLINmSdcyHav/drzsptPye64DdsWftGWUJItIuCN1K/I9+1M9mUH0lfwrPs85xXcLso5QsDWENyDwk1F1F1jO0piD7S3JpG8SabcyQd3brlppejFh8l1cr0udFrdeUH3brV3tnTnZg3TthrfndS9W7HSQfoKFOtAR9AvsLbF0rkt+WWLCNjdfN+UIdmigXmgn2o+SgeVuxet28SFvbfcf9V3EtifxYvgTY4kW3cntE1Htxbw4tUP6jt9cPHcI0F70LFdEdslgI8FuhXgMcLVD/7eir6BAGZ/HPDWJKn2WG+fUb6bxoYPjKNvtS5rk9s862Ah4vvxhZ7d35abnqVuiTfWW8tqy/i62LKiWIMe/tDzvx+RVt/i7L3ZSVuoBgQ8xWccH9ooeXHdJ1jCVaoHDUvx27PVhmJYSr7hEtQvMOhMLJm9UXtOthlKw2Zaylwp5Jcqq9+NMIR3Ml1d8A+OLvqp3kkOjftWgYqnYktG7hWCQNhXVCOZkC3nk7K38+eH/XdfsZJLkl5FjVI6Z0z1KTNx+m5yykXZSyNLwIXuoqyRPcut8FQJhXyf+44yauCeZUTQwEAJ10zu9GnvimJPF2r956XkbT38ee/XC9ZoQ8oVcTiw9RJPEHNSPgHRGX9pLPsBbi+1Se5U8V1jKEg2aIVt0vwtTrupnGMuzcCXs79eNC5/N9bz49nfqxQC+NBWE4v9I9UAvbf162dOqoasGI66tnCbyX7WbMj3iZVwjjJfJ/ABw6dZTBDzkR4kfY/+V7gazq35rCyQ1jRP1vIYGefVKhOwjDkoO+9VyaJ/TqkbgZwWHHjv9nvPvnhLGSjT4yO9jm++eGk1EzQC9lRSq77uaDpaD0MJelCHdWA4Nic+vpWbfWpU1++q3Ed9Qddzqmyv2SOmfUAXou2oPZcsB0q3fbKjVr3joA95sPYneuJzVfNtR+x53+LT3q978f/W2o85rEalp/Dj0rvfvveZpEf5TOD6p23fkIulO5HhCu5RFuLEguR+vfzHf9T97RKRWUJZAj7LBEak+SLOWYZly/4J6S2V6lvWJ4CItBIke4rlG5wEnuGGRmaFniA5lWp/Nq6uGmZQdupV0pAh+XLsXeXpIDk/60kGf6v8hwbcd3TCYVbixOaKp5bjTf/wsZN3vVSWkyO9kUTMPltBOcPKCuq7tRcu2e8vSu61WxPxa572b0f162PhzEwpKPzPBblb/dDxINCj3PodicwQs1XeeGEI2VOTh/K08rw7ESHvJ9P+VxTzwinHTSVINoMm3LDF7M8avjp1WrNF21NC+1vU5Vlu0fZzEuMpkdqTbbKbjQJAfgWP1MwAbuJyKx9nHFOV8zGn0BzrODsHOOKFOv0pxUrxuk7D8t8ssw5fA1ie4hsGjwHj8NIAjjGsNJLcei+0qtwhWEB/XlFvGIShDOutmTHtYew0RJunSOiDa3PNR66WYB864C4BfCzQJYCPE46GAOYJbfZxRAQwD0E07kgA81hdoI+W9xXY3ATs/Hdih/U7fyJ7nuGHScndSTi7CQ/f7KvgXqZ3jNkcoU2TzvaPzqPd3cyZTYk7bWxt8y3tLB3Yp+fSHoxvJ7Z2AB8z9sf7+iS8BwyJfDJ53yegf9euwh8RugTwsUC3/DtGeLQEME9oWfg4k0HaGcACG8o/dqWB9+8C7d24ZQVSUGxFT4FVfTDY3hbCBXo/FtBt2PuQXM39P5D/CrauMwbYA2qLg1OajY/2TrL0AGk8og/Suf8ZRraSowI5QgqGgSJibg9w0DtDTKoTNolbH3VTAWKsxOnfNitEtIoqHVzjVjEx29G92+5K9KSW6QXyBNfxl+pBjz2TqvX57dlcoUf1FGSbAQi6N2rPAYL3bXs7A6UpnxxpCZg29c2FTWUn6bunvfDWrF92qgl3Kv/aBHAE1lt/O8ndbfnafdK9bQitcM+Oj++n6tMgvD+699HpfR81nqSZgvtBlwA+FuhWgMcIWxXAbEcAb4JoSpiWKnxZN2GfBLCA7Kk5e/QHotut/G7bzl/dwbdsF+xnXHwLvM0AXYAUuGF289FukALrW+6edJRh3pYyWATWF1treun/kdWVm32kUCf14dicJmWfZrafux5qNzjpwVwgkiA2nz+dLXnqJWd13cHahrdw1pm9V/GGKpV42fPKC1dThQHdUzEayup3I/JojTz4Ic+pUuFWLkga3JADE/mlWrXukCO1kK0yOHr3OenWu+VLJ1y1y/bfXrIuT0jvfIdXTrIk47pOeM4a7pMSk9y28AcpPMWHI+9OGUPKHC9/YD1VjK0DOYK6FW5IxXUrVsR1TjeuNHpcnoIr7ZpaGAq5KiHMW1OaTIKeMA6IYveYo7bivULQbpr6vPri5z+s2uoOSZ9fO/3jPW+k6mN5W2W2EXnaft1NxbB6YtK9OYY/zete7Gv0lsmFKT2duJLu1ewJ14e9wxopk8C7nAMyaKYVNrF6pFURl3uORFXKnDSq15TYGr6z3EkRCNKwUQBC5J/mWokTJsWz2Sn1vqEPKrhQ3zMwIIn5GTk5Egplsw6j8buxn8lijbP2n/jcW299kik4CR9CAOwPvml9o+vAsP/ufP503nKZa+rnTr61XOq/dmNMVGJ9o+si4vd2/vyz/ismjSyBmqWm1voqoUZj2i95zFFbelYPKeGiek/OhOVotTjViP31xo1/t9D3ifGVD4v9nz5zx5n1akv2H03/i7diExNnzOh/+0bmpR87cfsb1y/8dPT675qngr4yHlvmjjVx/taHxRquETkbe1bl/lywf8h150NeiCqGjZUZhkTo78X5pPs7t73B1jyGKpRa9Y8b1NZQTuHG6HgFj4vSKrFpJp2UFQwZeZbgt3OfK8352mHbaq8+MbQQtK8CWtnhda+UcdVoLFujy/HBLJ56yglgUrU5ZE+tes8h/hU/H/lkjQpoVO855IFatexoPiTOthMyYMBkMy27M1B80JlwSg8q+bGNoTgDJDJoYuzgphJjJUcoRL5OzU1VIyv4VzuOT/L2GzwjevjCPtppNJrUr+jVZyG5obt+dYuxU9sF2nkZLsEgDJAa9biz9RUtlsbTT2qFsPgrBoxZetJ4soQVdHEw/SQVymPMNYeQ03CH7Nd4s9wxvDxq+AHwwec9lCxsQfxfoplH7oYgeDAHZedStTnf18AMys4b1eYAMEVzhgStWKIsJB8QwMk6K0ZzVLBnm6FLAB8LdAng44SPPQHM0XHAHJwG3uokfMQE8CZ9bed0G+DR8L4brP/bn/twR9dvV8Z393N4UPfmrfG3R4sBElt7efukkwX2SSq399kp8G3TvduktbHzcGgXXe9WAce2OADXuwseJw3s4yAfysdE7/uRtvCOUN27H3QJ4GOBbvl3jNAlgA+HA0UCb58A0sZODtt7EsBCZXtnYxHYftZKkwOuZ5u63rait3NZoGRRauCxbc8Bi+0F4wtIMla1uQCt9F8RQtx5JJENGkTR7yhrdsDAt9KcQA6tgjBEwcC1Cz96VNLetnZWLIuFLAFApeLCAJaJ0tE2aj9lazW4Syk4QMJAUTBo8cFbnajF8jK9QnBcQZ2zhoUPpOit11OqnlQBUpAGs6WHqMJqs/nVtBPv6YhrFR6Yoj7szJHdxUC4AkBkuw0qoLMN8V230BsULHzSRgUwMLsrn7yJ0z0oxbsT+lpu4zuzv+xV+53dmKi9C44X9Xvc6N6t6BLAxwLdCvAYYWsG8Jbl3Thgge3Tpra1mtjzKrR1MvrZFuXwiLKB99yt4LhFwSKOZFsCuBNthwYxJc7TIpV7UUd1/YbKEGPnZ8/Z78ZIFnFc5mqeyIx1riHlGnjvW4PLCyOr1zWhbqyuO1CRSzWg+o5D/Xk9Jqf0XDCn2mz2Wul7PvUZPebJx0v+WD7/S6ffXNRHv7o+AlgzzvgVz8t/ePWNuzFWyqN/IjN7Y1B166OOSnZcf36hXDd8p0Zv9SsLZ+e+eXv05NXMZ3uCC9PEZHRAt8ID0lwRR4wVLw2VStiwlRoT6zb+4dJP/fjJ795ZuDDZs4R7IVWcvOi9XihH3/pw0pOpDD4/F616a764gXw53pgbmy3NfPp9j+2nel9bJHijPHl9ZTxUraSzLvxMjiz8gvff/P2ZX45f8fR9YT3iXwA+fO2yPFSzm+Z4ODU1H6umHPJg7cdH7k43Amnkpx2zI+73FxkbZD1u9WYlu5fqQupUPu1JRt3mokqeUK2SDrmcJ/ThVD1jkpVd9boz7CtmG65xe6oWqkcdsyFyPmo3rJO/JP2BkzvRrO2WcrpXmTt1+44grt46c3GKM39s/f83W39xqdf5evLn3rx9wn7aHI7N9UkJHdfczAWpYa5OaRRRX9CDJ7Lrcz3VkqMvsj7qzM9KfsU0jKI3FCyPR6YSjajXnlleO3UmsiCv8ZZ7UFLNkC33ucq92drpHPZ8xdEXyn469TuNlfOz0sQH/e6/MPt/m+7/s3rebu+bfzbzB47/Mr/+P7zy7nf+ov0TS3mz9511r3Mk/XOLc3PDjqeluROv/UZj8I/49G/fPfPSwMI3Zv++EZUI/VXZ8Vb1xls8+yM4zpAcPxPUZxxvVSUdTgBwD0bBTjHSm/x/J0IZbEGK0JMF4V08CH6wwRjmKRmFpZGT7zMeoriGMsXZE6z7acwRuF8/c2v2ZHXdAajD+rnYnXQjkKwHzst3htCdGDfrJ62scy2gNqb9uaCtvuQMT2RD7lK67Fm9FSELclPpK0/WgIFYPF4I1+PO6pJDjjW///SCSexiKW56uAUGnp8tBDzFzJoW0BtNs+h51Xs5PSrPlerBZD3QqQz2UOklLsKwt073PM+dTVbMnctOw/KvlrkOClyncaMZqTNjkt+YxbvGBl72EgD9ClE7znF4uRWRMwQKTEOmZX+TbA6jzIubk3FWGQiSVyiEZ0tMw9cwp/m3h4tnOgh+TAHofxrJDxpMwzAdAzgYgHGYBgPGN85pzoDakgJvMcoWZHl5fz2GLgF8LNAlgI8TfhgIYD4iDnjbHNkjI4C3TdVt72EaDsL+tm9glzaepw0WFm20P+vtZL570r37PIEHIlmfHAhXnF7i7TV7EsDinYoB1eZkna2DE2O7YdVOG++JoyF694l9mkJ3QnDeYg7wpqfv29t58y4PqPp9mMbZx9Hk+THzvm10CeBjgW75d4zwyAlgnrjKcJMI9RAw8GXw75MAVjHFoF3864+bTr2jVbCffGWjqejdjOHtbGY7yr9GgfoqJWsbWldwuu2H2pPWRYeiczMBK4tjBATp6wU3DEKqpf0dbJoimmHZaVV1t4ILU7EBBcUPiNPVlvluneTXTrRlS+DFVnQm3T427CQO7jwkwR93biayx4Q+WEzhbz8ksn7f44KbSgZt3YrphejdZNTuMZvSn1lwQgHWgVZObptU9YEbFlpUpfDKc7U6aIkWa6s1jw8gtb/Jf9rOm9Ut7BLF+ha6dxPWwLOPGOAjh691mnp3Y5R3J4C1/QX9sr/qzgseCLeYAwG9g6pvL4vq/eyW5YWWhmz/KZht9G7s0x1zdAngY4FuBXiMsCcBvPsM+zYNvNURGna4WO0yet19+4P2N5ZgcB+vvudut90PrZuMuKWXtlRoZwG8k2m/raJIRqIYaZQdNnfthH8JELO7zst3LnOtj4SBq5/1Eabrhs9UyNMzz8gdYt+tfcpl1OOFsD6vyt4aCkITHDtXii95KOP5ZKH0Pd/ZUCbam03e1vRPliZDc+WGlpnXCsPmEKX5lZ5wOBVO2a87wk8HZj8onLauOOM5d9+ncxg8PXTj07mFN+tPPxf+bS0XyAdKg8aKgeOkMfWm9hN2Ct/nkp9aiGXFin5HPx0tlkZuBn2f/tY3U1/6FBlHpfIj6/9Hejh2Zf7HvhPy/sQVPqf9/bcvfqY0d85x8fuDhnl78cv0V4fsf/COMpSi//qtZwoDDb9RR60FfGtKw5YohXs9Kb8tF6YQkLI3rJPehiNus0/qqTy+lEvxVXRdxSHptXJArXl1Xzoixc+wksD9FPcDZFwGE9mbGKS0vkAlsRw4LSkFn2FcU8anmZyyxn9U+t4y0SpqjcZpFs8xs8ipDNUK7gss1KlWUE8ZM3al4DSsguIPxK2yJvlyWTVtmJLsLFULd3ANy3/n9K8GolcTxD7MnW+UHfqUyjxEYAFBSTY13z14L6efThWXfLawLe9zJ9PFoee9s9PFEwOe+/Y1dcHdH/ZOpVbO/MH3ev7Gz/xgUJn7b1d+bODy1PLM5Avh96XVnshw8RPv/ZPvDP7cyNe9N09Onhy/fbL672/W/rR/eOp/9f7CnzO/60L926s/FTyRzdzVbP01/1r9p06+/qeu/+r7w790JvdbjhUqbybCAVau4P8feq2bCZ+fG0/9eVMp+ZTrDVcloQS+x2dGmNfIDXM/TwToZUVE1RTjPQP/65zdjeSAYfSfVcq99hlGDNxTnJllJERumhOmpebrmsuoJ+sBv1QB8parrVF56uKVUl0wnAzb5+frw0CiGDHXVLvH1L+vYj6Ix66uOzyThUbaYR/Q6ylVn1flSC0yspZZ09oTKS72Xb1y7zm7xwTOxe7o9aBqz/RJSRslN5UBki7KN5gYYt1AXSQyXZ+I2uNZSwM6pcBtDJBcbtksR8i7mtM09TxBPxkxdzNFYIBEkKxGFlAo95MamI037XOWYLopbG132sXAqlMELJjgS9CvsGJw2o1HanlnCtJU2AOI5JNwy4Hg+eaRiUm0elApa/YH3MEy/ADz1f2m5x4a7dzfZ8+15vuOg/eBl3WzOeCBgRaNLT72Nu97HbzQ1mZnoYx5hZLF3XLznOzzLXQJ4GOBLgF8nHBkBDBPXKdvE46QAxbYncjclv1lTwKYfXDAW6nf9pmfbu1hGnO6eR/yywBjW9qV6dYPdKFD0vAgsl5uaTsEoq0ru8DQg+Zj+8RuksXscn5Ev2+nR3dHBXWZraT0UWKTYcihj0TsRxDAnd8HA3cW7QYTMwzPVkceeDi3Fb2bJLw7DVEqDdKN/Rzqk4e1PVQaR4VDmDy38eRQvx+1euOjIn070SWAjwW65d8xwt4E8MM7iDyRZeFDVoMGPmBlL3ZFxVSpKhhtArg5hRxIg74P9pedC8JWHWgOy05XFVp08jIUabxPfZWrpeaNtl/BFwbQM7ggMPrAgMsvE682VZN+mV4HQGD0gX201HnRFYxv+32roGOekmuKDXAYjXzMWTd8BUVZoUehLEJ8RcFTRu6U+QqvYxVjaykoFLcV1ArKOn4PFeBRM76mpW7bKqIlGth6kLuUfx4qGjkXuhANi2QvhbJgwaX1wJ3I0KvWy/PxEf09VT2l66+pZFvtliVYhCBct5pVd76Dw680cNl2fBv9LamuBlko1lFtAPaOPXTyuCEbLtuGSrLPsdtLHGbK4O4C33KHZneXLTsrRjFiOaBoWLzTnbDPEmv/dZ13oyb7EWEBerYxLP1YoksAHwt0K8BjhG/c+ocz2X/b/ndT02bbWfhjzG6bUdXmgB8QwPtHp5mZsXFm26EJ4G2x6Tq5+z6NloXp1pl2ozDbcuMIt0w4ih3PGm060/qlSsS/IMJ98w1XT7Xc610ESrg8VFQqL/KWDceEseRnraAoOnLUyC8qfTlCOvLfe/OvEaW67pDttZCnog5lAmpJsaeT9ViiGHnFPv9a/Ew4mv0H8v/jB2t/9VpZWXbbwoOr1796HrCPmz8Vmv7wmueLvVXp4m0/C9++8/Pj4zfWJOuUVTr9gXPBb42+86v9Z5794Db+z1AidjlxKx4aLJ9Ixec+39coPJX8Vwt/u/pbv/EPf27ht3SHFrQHlwoXzVjmRP3rVF7IzH214gkOrs6k/M+aYxdvFl94vudvrnz40/mfyp64uvRXHf/zbxr/i8d5v+amGpVuuZ57PvfqOp+pBBb/lfIzd2oT5+x3761N+N2l6UJfsCebS3sDoWIu7R1RCnVfLlfoGfbf9WPeq59Q7gQnhlZlqu9Uwq5QwVVySaqZS2m4Tdlm5AuRQrAhgjBkm9FTl5NVK+heG0JPWb6ZUp+kmvp6MNtw6fPq2dG1bLgWdqyesMoVXF5paYj1M9ypoN5joI9EjmAFtYTzFMs6rjr+13hq7tqF8Qs3M5IDaNTC8w1fcSrELZohby+DTjNuI8Xo+YSh2H/c/8abX//RT31mCvju3UBG9a9+e+Av/ux/HNJqv/bmyz9/5o2B7Lv1mzOqiXF67Dfu/KHRiXcnB85Mvv9P3nj5r5y998HKkHYxfvfO+KfNSvWmY/z3GiMnl7jfAy5G9XXZYSuGyhPSymmmP3vl9zJnQz4jW7nWsEvB/Nmgx5FxFXL2hYb8G9Xlf3gmZMxV3m14Klz73JczSqaPuEo1k+vPBBwaqaBhCyozqwzMMlxGdlMdNdbP/4cPGME8L+djzluczaLNMPKBdVFkjtDyWBaFsZvKUvnM63cv9Y2uS25zyDGXtbQ+KbGOP0J+vj4s24wls9fSnaUpH1WotWZOhAFkTw0QLVBhtC7ba3aPqYVL6I4gZkaVgroVVvU1X0lp2JKW/5OOd2R0B7Z+1g2iTlYAF+Xf51MDJGX0Kuo0J8ZZLBDM4xTVe6f3TxvCekf8BcaYFa1aA3eQnI5T/Nvs26db6bbT4GqOjkXUjuCABQEsaRQzeKQNTPBpN0CpwYrRbLMn6xQbeG1A035JmGs2CtiegQm4DidaHj+CcxWy4qWm7/RjkP8Cz8moLQbBKQjgTwIwDxMdl8dIa64qkAe5ZSeehOkHbLE53Xzj+erB2OsuAXws0CWAjxO6BPCRQNTNO5G+ndjsAXhQEfAm9nej7ra58+vwKu9+gPtXfmXi8mUg5MAZkmqSDRdvrSyODT1dw95wSaqUdtIYXK3k378y849/OSQUHuMw0dzznnm92wl891budrocf8wgvPvaliNjzIuSYow5xfy/Rk49u25F6ncUGoBrNnXXr9vCgtYSNKUFcOW9zC9/Jcmb4hJaPqCW4tR22586IpO9JxJR+4b+5qHx5Pg8/1BqfHdHlwA+FuiWf8cIXQL4oBDUr4GiIwO76IBDlIT9r9LhhfuAAGZ/53ZbI+i29tcDA5iqXFNsZc3uNCzAf7PMXcx/zaslvvCtbwGuqGRa2oCUMSVpZep978uvmNQcljtgZnOyPTZXm5+5klrl/rd+Ofg6kdO4Ynj7INgKsSvBIGZYBpxW1VRlIKOF7EqhbPQAOUUQuh4XpQwBA1Vk2vURn2FExQB0FMH1hsltyu4VbmwVFBfGo57bdzhsK//dTxE7xrwggD9tvB+djUe/9K1ZK1q/HcQqgwm+26n3z4SfQbIB2JrU7JUPMtStX/57C5hFCuLLI7osDlhuNc/cLX8UITLl8AWeTwI2ODlH7dSsbejSukWyvvOORKW6KUzY1zrCR+H5vG/sovo9cur3h4CL/UjQJYCPBboV4DFCJwG8T/Z360NtDjhV68s3XByUA+5kYbeyrWd32PJwEHvbxVN6W7q3FeJLGlytnAFPayZcZ2kgIntTyEM1eSgftec89swASTGVDSjVgx57xrTUEWke8FCp0Rgg4aaqUo0RT+PrI67jNAn8uvVz6UYgZMtdv3++uuBQJ/VnPWumf1kr+lYr7rOhxa/mL/vLlbOx2fVkeLrRV7/tDMmpPzF432k5X3u776/8aa03PeTEcrLuc/d4IuvWgvW9K1dEV9C09eAhpK5l8BTef/3r731/Xn3+E/o/rtc/V/4X/3Hlz//Jl567XvvLHwL6GZRXXiyeLjwzb5Z7X6n9xr/Uf/HF+FffjH76xRL3S7+aOPG/9JbKJ1cHT/7Y937tu1V5vPwL8TUW/4zvy9f/yb/54v/5pcrNRYUevcdVMc9/8GuloDJ35vNh4/pV7cIJg5yr56trL2bcVlL3q6FCohjxG/W8YgfMNTXsLK6uarK9Vi04qCKfqlWvOhiEtRbbVIZnIAurEIGh5schR2qAcM9u4grQrGn7PpFdvakRbjL0Z32zS3ow3KilPdaEdFvUqHFiBUtzSPp8/nTUlu/zTgfJvl1/PlGMSKpp6U5zUa3WHSxAALm/FspVcv22cXvqj8rv3AiEjYTdFkulrlx4tyfiu2b71VO/sjb6F14IPlVYWwNMnCDfzKwMF+8PvfjZrNfmXG94cDiLawlv8O0lfe7XFm/8jPXU8HeeMjIfus69i+YtuWVvfMHqWcwP+quVp3puJYqDlzx381nPT6rfyCsDDqP++65zT+fvOQLrdWoDRsWGnFbqgN/QKwq+9ZI9UtWRB7M5j5FxLlfNsOzUq3qfUlA0hfyaEtWRV4mtlZ/5XPV3fZW5+71DwBUuLtPbTv72UolIcTG7MYPmpRYg/TTXeoyksdqwTxniJyLJ6BPKrP/iG4FxF/J3rKe9umU2lD7KUXP939/5UdSmRF5cNAadiWpD8dgzHiq2ureM3GNL3Fi4kJVdF/uuDpDMWv23CqNn/DdC5Ia4H6dfxQiQjtN/iqUUXjfVftZW6CkjL9MbJgcoGC7KOUJtMx7xdRDZK2JZTGoZY9ZNNcYKILJ4AXe2rmYMDFjYGGcLTDfTcKwKkova3IZrRskCKDWapkpC81pule/twd94c0+Mgxv6FaD5FOdloOU5nwQv5hUcI9TmKFn85uZkwkeLS3DajTe40RN0GC7AndYVchimW2vYMMK1quRmuVvmkodXS80RQv4gB9AlgI8FugTwccJREsA8oc2+Nh4pB7xPPBQBvCv72yahw++XGpVf+eTf+FsHPbbXPrz5ZuEvaaRmGJlhtDPlZSdRbOc2+0R7kNCGoEtFWsNB97YJflvFb8/upOR4dNiUgiMwKs8NkJzk1v84/FdHBw5863r11Te+8IV/uY8Nn9rf/vqOQHR70OjcTlQaOCT0xuEluUeFo+rQPTzdO/roNSK74snkerdFlwA+FuiWf8cIP5wEsDDUOuizhPMzu5K+WxGiJKzV2mg2FAT2f3qXW06wAx0rxwHyfe41JZrGAzQ9yr5D+Oe/deqAo+X5xfxvJd98euEPVQjkXdFrWsxGRKccougkXydkJ71C1E0VKCPnCCkYBkqOoIwuiN4UAQe2NAEXRpZAsh7z2DObXshDZXfq1LRUoNpQhGjGb6sAimQk6wFR4Ik+UarWp0iG2H+pHpRthlh/oHe9T+x5zFsrwEFnQpOyl7n2WV5/evjfH7QCnJ9f+k//6Zu//Muvd1CqgvHttIGOdpCp7o713o2a2l0J184E35CtWaQVLKJ2wtI2dtAi+tcpYVqoNuzSgzDgI4RPamqX9R1Mbra+XOcbabPa7KX95RFwwPvfZxf7RpcAPhboVoDHCL956//7g+y3Nq3caRL/1jQrsWWnGUb7PvjA2Gx3pHYN092PY3MaEq2L87b07cNgFMB7OW3pTkk1AUt3BjzFzhZWPaUC9rAulm3uGhDwFMOOVaBUDw7b50U5pFIRgkhR24Qdqx4qo8wPkCwj27Gf4U4ZRz9r/dn4v9Z+IY8zSKOCmUGbrk8szZ4AquuOH3n+naVGT6IY+ZyUuoc8Uwr+ROCKZ8V3uvj7Du/If1t46u89l3nliwduAE5959UP7/677Nxvfj/77Kdzb14P9Ob/WeKzP8/932auih/43//6K18rrp3+6sLpz1n2oQHju473z53kX04905v/14mZ6/zSj/K1X/w7T1dXC6PxpauXMkM0tKKmVscr33tXednLvRlG/tLtf7cam1jSfICak6WA/h1eXEyceqH31rzVP5U8eW8tiE6oXsn12vRfV7lA33h2dVlr9iAjxPpLaZcLk2rGIftqVdnBXXDBOvL5WlV2kEPuf2BVIg/lewplSTPLP4imXS7nuD5sGPfWgqFiJZ7w9F3Krt7X1FE9eCKbWdMAzVMa8C0trw9G1fyAZ+Ub33k5FivlgjZ9XoVmx0b21OweU0+qQva6crOPFIDnmcJz3nlnpWR3Z2Yzp37MSr8mR/71iWcvT1468Mdx7bU/npj5895fdRnMKpH3618I2+NAMX+26FsD3sqfefpeRbqYu3szpp7MTHrmYkux6+FqVE2rUuqvTf/vqUDfB70/MoM7ZfW7qNik4gv8wI4zahSi3DcVaZWBCp4KqpNcnP4y8svG/YzSuMnpEIVziVr/B2/mf9T9nvLCGmoP+oec7Jy7WbQG38qfEd7mz/inhqU5sT6P048Zpnib/h7y89aIC8MtpYZYN1CKOGOsnONuhkCC/rucEFEpGYZvWCf1QtTnn89aWnZ2KD7tkan9mS/+riml7pYvJfE+z9Itol5X40X7m36j7KyUXC4jo/TWqfWwtMagTEkc6hqqm6pGdozFW4wDaXxtjZZocbeFv6KPLXyhx5gXMwBirPSTDJLrYzlLj8ZaM3y37X8phm/jNOnhO61w3FLThvOBSXILbbNokb+TrPOGwc96mvMqo3ZeLXFRYWCSdz8A6FcYmMQKU/sBzvEHT3882t9O+CEPl+CZU0iuliO0QJsS1iALSbgMKZgHL43VZhSRUD9fbe3qoOgSwMcCXQL4OOGHigDmCeCAj5wATo16BPXbnr85Fr/5f/rZPXJnd8Krr374hT+V5KekbcroVqjJ7nsoToXUqK4n1f1s/JHAb6uI0vyosH3+DXgn06Py3F/Qon/p7J8+3J73zQF3wseO5tuHzc09Kont48RJjl5kfiQC3666d2e0pfNb8b8Nf+6zA5ce36F0cSh0y79jhD0I4Idnf3lCa8LD1YGbQi62ou35zEbhbyc2iID3kwHciVYmFjQDovJ9blORZhgR7Rs7jgCZT679oc/+9F87yH4fQDr9PyOd7/u/2FNJb/h0dqJQuE30XOxeDidq1SHpPRSANXxZS3NKept51Us+1VMA2v1NYNNUvyOEiNbbD+krNDeP6DDaaBeBlu4019RqyeEZKQQ8xX574isX/ki/78ThditJv3CQzZ/q4HofQh/crvfq1vYcMFCs45Tote0rV3gnCDPqrcv7gbbD+j2P5yTcby3s8+vZjns8kLmpCO5daMX3tqEf3bS/I9zVE48uAXws0K0AjxH+w61/eiX7u2J592QuN5VYM11gA9ps8bZ5wG3elF1kwTvpencaKYuL3pE4Qm96ofbldKHlvNJychbvYtCZAJySLiafyTYjV+jJWw/uIkIVqkZ1QDDBfqkS8K0J71wxlS3fcLV54tKUTx3Wgz1Zq+x80f994AxTFdy9rNQJRZi9xmU/qRtMzBUvThf6+qJxP9U8smEpmbvaj01+ayrx4imjyqrzq78dPfsLWWfpe3/cpf+tv/DPD3cmpt559Rf+1e/8d3J5vf7VfjKpdHU91Jv/Zw8o+hGZwS/zxld49i/8yfBPcOL/+R9f/Rt//MX/+BvpL/65D8IhpZR52v7vZ9TnRgzfTWnS43pnJXb6rhXLSvY/uf713428Esm4X89fOjn8QRE5XRyiwkvhb5l456yhvmzjlqYNSLNuKi6cEZZ/j5df5vZt+oGTZO/VTxVtNVGCrpYHJ9xzIZZvcDpkKYqUjrGeoZEjeNMaH5LWBpl5lwsxq3i/cSJG0V6p36sNDviWiri8VByS7iyrHlejYs/ruK7cew6asweqU47mVMsh0FGHdX1ebd9k237FnpFCac6nRvVgT3bSvnQreTq15q3edqgv6ZqjtLoSUaO6Vq0E19K/GLX9T3/sFw/3cbz6vYX/l+P/+Dnj9z1K4Z/z84nkWLhnVa8Hw/ZELh1J1kLZskurVlZnNbm3FnlqTVt2yIPpXEo70TN/QlrP4wBkdNnSZiW5ZqkRKd5PfIWYh0ovq4BQwVZQg2TLuMLkRPZtBk2jcc5Yf3b2K3qfcl8bepPn1+j3ks6gudD9mCmrvyEV56zhfF0TP4d8XcuVvOLghU66NOeTPTV5KG/pTuClwAd5nIBGLkj2FEv3GBQJzeKlhbPOen4IyJY8gnRXh/V6ylmtO9CJRUvxgkdV9XrJWV12EEEN6fWSs5p1vNy7Tpp7qOcu36u77GH7fWEBPbpRvxShMM6H7WAaoIJb9M/7iYtZrUI3HKKgoCuUgTHm3Nm6w2i0826cetMPKRkLAQoFYRDlNAhm0zXFpt43NgT9ZKAMxebIupbEyjYVvTNmM3wHuFbmpYGmlbSVBZgxOdlDInWAxNxHCiFWFuGSJ3uaBylpOKI0CjR0oLkSNvhgX32IF+0SwMcCXQL4OOGHjQDmI+WAN7O/PAQB3GHRPMOImNAkHvmC9tyPez4zPDx4iCN89dV3vvCFfwrAU03fYCH6FCVy78YOiLe1UGxtIJS35ZZtxdH1Jtol+MNjF5LpEOgULm8d4Xgn0/9zj++PeM8d9uM4BAEMnNzS79sf9Xtoge9J0NlueLhlsyfZ+ftog3s34XGJfUW1/The6eFw0J9hlwA+FuiWf8cIuxHAR8L+8uTWhIerA7P06MibaGAR7MoOts9b4Y+bTr3a/EdMHj8EIhBqzv9rl38VVCe2vz78hz898Owhdjk/vzTy1FfpTXDPju8UMS9FL88jv1SrGg6q0Ee7BUMrsgtaxV4rCK3dJnuwAWzYvoV2XdfZKX4YiOl9pqXm6xog/GDEv/mGa1SeK9WDQCclvEv6755o71m8hdKcr/lACsKIRvBXTk5+/tLYIXY+P780MvK3D/gkIQvuVAMX4EJHhu5e9ss+6cAeLVorBzF7wIN9GGjQD1lQH0tV+SiKw4esCXVY284iXozLhrasP+boEsDHAt0K8BihTQDvGd21Vf7rptqmTwSNtC0H3IntOeCtVO5+Jkk/CgJ4O3QOqNviAdNSqw1FthlOSZ+tjggb26g9t5qM2cN6PaXaw3rUngvZckVcmpTtJT7DsHB+dqHnCSZrozZHymXZlqZOpxsueTwvHOOyBM5yZ5R5A5dCRSNl4J5lJIP2G1/7Y/JkzW43baHapP++WfIHqdk9ucr10dGLr2mWP57w/uYvD33r7ybHn/sTh+s4/eDqe//Tr7//37/1/q/+2dN/2LFy/r3S9PBXZ+4EB//VbcBQ/lw8/y8/o6D/4zMDBqkgYZWUEchbl/+5+/KXfR/22VK/Uv2p6D39009PPSO9/gMGVjPnKy7XaVelYL/3pvX8zNXTn7lw/ZvvXj7z/FSu0AOc9s0I5ehcefwL7q/0Ec8xmaXqZ91m+BpKYYozOYJAFTVV75uzIvmGa2i2jsa8opiL6uTIAlBQpWQ9YOnOSeeiw52bz58uKLbiVGj05NLsjcFmkGqbyE4DyJ+qVYsOgFlwwixo4EV+qla97WAQ7+V0dcFfnXLgQY7WqvUHtaso7fSkig5TUIaXYQHWoY/YUCnkKX/CqHhupi98YuCLz3/ycB/H9+5f/0Hib5ro3+f51957IWQ4cwO2esmJQshWiS95qEEaauCFNZjklQtXU3dHMqO11e9Gzn5izYgUo/Z41tIE7T1AskZD/JbbfsiVVkHYJoOXiUZaKs0YcZHeMsOw0MgCHipZS9Ok7ADJNIFVq3fJ7O088qg912no2OncEyHvJyMSecPkRplX0FdaMlI7jmWiZVyCCTYtdTHfPHWNsgMDW6g5ryJvuRppBwr1lBOF6lWH+pKuz6vC/dvZowc8xU4/yE4by5d520BN46t0FMNuqnVqIQqA+N0J+a941GlYpiI1e/gdTgNiICkmB6uYGQJjLGbwDxlLpiKF3y896Jm35/u2HZKHW4bSUdBgBYaxPkAKQRFLwVqjvspNk3NOZkzuV+l5OBr1EeESXIVLHY5DpdZ4Y+ZQet+t6BLAxwKSJD2ETWgXxxrTT26/r43wbOkj1wEfIUT7L0eIVv3tpXK4agOAVl+SD+FDgNWn4AI4AG5tqZXblPCmcvxArj77QGnO9zDixSNX/baxvW1162wUCfV6qsPnDvlxXLly+1DP62zw7Ur97p/x3fNTc+2syXhC8Ej53a14XErfJ1zUuwlHO/2iiy66ODzMFtE7tNE676jY348dNNa01rKBT6GwykCQfHAv0rcNp2HpmgNocsCHdizc0koVHZMI+dp0dINT9L4xPDz4K3/b/8u//DZA4T4FH4zw305Vf18CeMWHBmXiAY9gwKtlB+VWVLACBlXFAVRxiFCoaq+j2Q8IQ4qq7tjEeD1gTFvqCrvHFG6KQDsebNPyLsg3XEo9KNsMoUgQ7K9T0v12wg7dtNTOlcJr+mESQ5ySnm/0bn0vAAvoKVUeqo0H5W2euQ8MDw9uidTdD4S+XBSB4rmzMNpqzmxhf30t3a1T2myVfBLYlV6tAK15n/3Qf8AjFXvQW98QED6KhMEFK+DeyO9WNi7zuGrOPciRw0JQKt5DkbW78B/F7TboWlJ30UUXG5HHtyf1uxVtJ1VghhEXZRGi2eaHdoIYq26mgc8ehVpgqjXATx/Qp4Ht+lq0rp9nqa47MPzVusMzUnDZy/cKk8GebL7hGqilqy4lb2lCFuy3s5gfFA9pnlJed+Flpd7bb0+oVPIEx5hHIkwuRF7lvup4c4pTSJhD/su21G17pFELlwo2LRgXkwg1sm5qGqVb9F/m2ps83/epdWtdTdddsXz++uL5U77M2w2vpoeCuuOdN38WJwP21D/6a79TSDxz6Aagtp45Xfz3y+fv/933EokkX//PjP087v+c8PXz4QoDv/TVzxss/9mffum9ryyFX7SFtLcLL/22L9oXnv4F/s2idfmfL/ykVVLXLpj/+eYn/tHSF85Orrl71nzu5FeLp2bfeRm4OLL0zXcveyYL8/GRcXtqKHpLLvdMqeM+Kfu8+3tJ+t2GI6UoXgoBlKXcMBVGKJ0IfOeD7M/PVYae9hQjC/Z3PTaWbem6s5T3UOZaccwzWfDN24pzHua5fnJ84HJcMQ1F58zp+HIt2jeaXZ3VqMIoXAcTJqEEOp6xgl+pSM+aVtkJKDZDthk+tVg853JK+gDJ1dj4tNmn31Sr8w4AHyjgQddV1JZS/DLcQo3qekrYQ+OeSLLkfafs+ttjv5aM/61Dfxy3f/v27RNfun2iNH9j8sdP3mgE14Yzzt8Mv7B6KxKfdchP1arvOvr/u9UJxx0d10JtJLOmvf7WJTlaC1eyL/zkD3qJ+zGXiGrS/AnW7zG4TPQsdyq4TTyjLCaJrVl9AWkeMFBosb8eKuv4PVSAIqEyJeA5rqcIeKioVByWTaaQtDQkfJYZpWDVnIID7vWuyzZDk7LD9nkdV9bSAJF47bLrQmocJAsI+llB72dNkKZ2XAq1T/L2CuEsYTvOtOS9GzjhpuLFdAZKduxzDJs0esiXccl+7bv18Ym+O/2W7jqVuMVEqc81xnzb2/lZrmukaGX6urP1dvqPWG7TAZtn5SqthTKsb+Q1DFKTHsDAt0JYSId/wCf/JL8xy3CMFXEm15Soxlr+nNtpNO5rQ0PGkr8VP9wMERe4DAOQgavgBhPpdJMVttaw+civ4LORrJOvskyLjn7CcLXjbxtP5qF28ajRJYC7eKLxMeOAgU7rniKu+fmlw3PAvj/VStK63lp1ndULAH2ODW2Fs5DYgevdhWzbtIddoUb1djdwnxh0Jh5nBvCoPLd5uuutDcsr/X2H3vnly2fg9X1v3hkG7Nu+a7WnmXP/UfTUJmFq478fbzwWde/xIno34dHNwOiiiy4eFocTof5wQzC+fQcc55qKBPjbvYaHQL7PLTzHxDR2MV8eKe8e32o7u1/8o/m34DLk4D4UmhMBxRv9xmjTDDdk4PJCy7ZXGHv4WtziOrjBYINkaKcZgR0VYFMcXHKQVGVPzdmj5zvuqruzv8JcUUCoe512nQ51b+ffrf/uBPF00QhrZwBHyK/jb2+zuQLUO954hSoOnA82PhDm55cOyP6e7Fguw0SLL3UTsuHwUbNwdJR/egPVRs06GO/rgwisQwEi4N744fZCGQpbAot3gmvj3MFOCnkrnezaYfnIIb7MByUS2hDG2+GdN1ho6SMEDsp/HEL9Jq7wQw/E+j883tFddNHFUaGT/RX/ivgJwNWcCrRdV2R37Hot2mwcsu21a7Sj55A4eM9BXFG9Gw0VDHgDElR9DiKUbvlm8eFF71GB2Ugz5DgT1eolp91jAniI2nNJJWAuqDNTo3KklvFoQg0Mw8P2+TC5ZaIqRoj8EDOrxJ7xfm+NfuoRXWqYQX29PDTtmvDYM+eZrqKeYcpD/b/y0xWUgmLriWac9YCe8laXHPdOBe0eU7Hqr0x84GZgKTP8Y3ESI97ehcM3ABOWs7J6bvm/vflbrTUz/xngwxWA5X+W0GTO1L4Sr6OF37R+F+/feu3SVOP+57/sxH3OyP145a/+q7M/9QfzXzp9brp3MnC32B9dDi+Zw+mSCwfUmCrHWMNXs6Vtzmsrg9dcgziRwzWCfN942R42NU+JKnnDFXXn4rmwPaifrGal+o86AnpIuv89vK5JXW0E7pkngL7RrHXXGZ/1lGZ9pQFII5+sVe87ZnODcqAWqlXeCw+qUV3zlGRvrao6ZHuNz2D3mPo3VSKg0qg7VM/6uP3OCf/6IhEX+jNGMi9VQhTv0b9CbIzKtZwau1zKqTZ9SgViIyU1mvFTvfbOmOdHCiOe9HzDZ404AcLIDrO64OxfV9643vOHnnnr646fe+XEbqkxu+NLnw/+6rX5qdefo49P5bMn1G87bdmIf+6tsed9Ee/3nH0X/8xbP8tbv17/wwP2+72O+Ls9LwRW6589/7241RO1qlXJVsY1SDLCUtKaLKAFyeuSEiDtpuo39IBy61Rl9opyskSkjLyGr4QrVeubKXmFgXOvd12z5yoEaU0wFSL1kuRK1fryDdeiPtgoO+olZ/sXuuRR5aH8oj4ICFMfv60yZ28W22PMzzIsTKQHSABTnHmD58eYdVNVqBi4NLxOPB6qi/hCZMaoneOuSlVHBkRO9grRCu6ExM87fiNHyCOZDgo/x1cApcWzjjEnxkcDs3GAZVBR1x+cXnEVCU9vNObcmiPuaWnbDFAwg7I/buZjzY+1n+QsI6PM/5fyLyaF+0oRwYVHHTlZNoAha6XiUoZH53z/f/b+NMqR9U7vA3+BNQI7EplALlW51a0l627FS16uzSZ7l0hRarVbaslqty23LC8aja2xZ8zjo/Gc8bHPUevMHNvjI3vsHlm21LLdp1vuluimmrxkd3Ntkpe8rLtV3sqqm1tVZgLIBBJAAohAYJsPLyIysCZyT9SN50MVEggEArEAf7y/93n+ZBdYX1x4MrOWbM261mAFrsFNMLdNhTKOOIB/hUCMr27xTBELW8+ubAD8AdYomIAx0pgvEgP3yH/uqdsDU6B7SVTkqyyIjIvArnvu7gnp761b83x0BeAB7LwEa21DODs1sNhGzWGIbmq4DEEYsBWJvuMjw0Pfc3ITioiS7vvNgKPuzWj7tXO3bYDmlevRU2+RGen8dq9HrdzX4vcdc3SO65mbN6ROA26vMvS92maIkQa92B5fW7Zs2RooNSor+1rrj1unslzniImp9DOkkfBTuVabP/Ha/oO5X//i7Sz7kL4BRUhB2pIq7IM1sgmCdRQH6TpTrlZH1VCTCamF5UTNGGxF7Q0CaZsWKGWRXnI5/S5n/1Bo0xAs0K8saVpTDjlzHYudci6geLrSDoDVdvYobMSiwxnALmCMIlWhxNz0CQHw3Ny1v/f3Pv3FL35rCB/wFIwbHl+zCCwb6FcCcEqdk/9E1LO4U1D8ShdY7VfIWWv7juN7Ym7are7BuBM8RTNw9WAtnMKO36Gicen0KzVPk898AvprNRkrln9t2bL1wVa/YY3F9saZAzTJ9irzwgS8yIZYYT8GfPhFOZzEVDC9FAQCS9ni8liPzy7rZ/5pPsY74vRlY1Up8Bm3xTIT6CWXqFvqJbdocSop1f3diMNXC0mVwoQKintCXQw/ytSmhCdykmSY7BQ6sMCGE+9tbUOXSfGSw1kCCkSXfN/cJ6I0Y0vSfUBDecwcsFGfK74xpruC+r9wl16Cm8TiucybkX/lw/cfMJZJxpIP/Dsvr3s8t5KNaye2fyzcei7w+4dNx+Y9rOutG+5f/9yLxS97A/hTvPPXvlDh7TnvevC+ho7/h9/bT8Q9dc/XA/92nN3/W+J/+LH60dzBTCi+/OHwt5e5C5SbYzOoirS2+8J0rnbjcTkc/sRuoR7ZfxIBpmaTqeJ4IrCnNeVZ17qYX1dZdIacuTFPaqeZeFqdrW8p0eu5Ztmt+A/cITXuy0tKNZ5I5nfuOZy1RGxPLQVlh7YWvuafPwCyu4riVz8ZXln2xIG7k7v7ipRJRxy+2r1fe/i4PjGhlxOBJxHyStP9TfXjC77HMfINWa3g/xFzYfZ/hT/cDwT/cO4/SW74PeM1/4cPSuvB5IGfAz+Aj9KPgu+Wgszy/ML2L4b+8Y9evrdSvz33kxsHzYgyHfm9Nz7mman/7Y+cvB7aw7381svO56ofWnisVvc25LE/lv/Cn+Grvx7/0z+Jf/anm9eW9rZ+K/Y53Xnw3Y3PzM0t/1ImFX7lf56kWZKczmS67rq2Mh7xU60RvFHI3An/YIfEc2xpKGGyTZS4tj+uLUuyWkHZZmIe5R1uZpiSlOo1d0p3ymIypRfNi+pFSaDOsvcIInDP9TjTnE67A9lAOFUc98xWxaXdKLuKy2NmcHrcmfc4NEBYh4EIeRH+LH62RMmFyQIVfDKVBTZ2mIyyraC/oN2vypJba+aJ++Td2WQ1PTkGhX1Cd3mwyvwUKQ3vNO9FyQNOvHFWfam6UtaMn1QlNFDhCebnWVMHkDxU2392OadwiGmLYiKIFWTED3+judGB2Js6s6UZOQl8mtczC358rDKvUFV9rSxombIDTwNdw6ehTJGcYksERx2ueQVeghWLJxiIQARyNAtIEd7fbS1oy9bVlw2AbY2GLswKPCz9Pb4WWRcZFGaPFmXi5saTwtz1k4w6rayUWiM+FcDFTi8XqbB9WLXdBYC76W9/6jZM19Lz8PX6qSRIDr98xy8i6w8n8Wun9fsECwMO8EZ6/+dO24+rI7XvxfY/nztcoDvSeQDsPA2jPVlsnS2Lrj7otVGuLVu2TqURmRF4warKjuqUT8nVWrFj45bZ3wPUDrdCO+XqgiSjQnCRtU1ueFBf4X7G9Whu0OS7Qfqvtt/jegIHqHHcCbhBtQllDpIQgHKrxehBiQMfQYmdWitWJCCR6XJt9hv7CsCEhUJZhnTF4F2/fsAm95UlTXZqHocmWvEBHlrZzqcPdrbKT0XFGyHvxysYcMnCSEUvwLYN9kPRmNo/Bz42tgsnY8AbG0+/+EURABPoBTDFYNU8BMEFMkEjflNxAMRCAD6IQAXGu9axYJDR7kFzMQTWcZiOlBXkD7jd2kjgKJx5gqH87qfIA6einqu6390p02JO1vmyeLzgJVu2bH0QpBKBdM+HVpkbzIAVqiotmmsOQx2pRvno8WExIlRcHjN/I4uhlcPs6I6PQfGZf06TzssQ7M2APYFWX1gr1W6UXQUfcWd+C6VRdlVDyqxrXaGi4g1QA6bZyxONa4WglsvK8wvrqzvzk1/gre/zqRlSTpwK2qelP3CnGPem1yPzeQoy2p5jsvnC049Xn/7+r74cdjViwfyjzbi+5ap+0u96Zy5ZCS7cTe1kpZs74H+6tbExM9eb6w/Wdm7lJ//O5775X3558SUa77Xo7+I0zj//1zx1X+mVf6vyesM/8w89P6zXf7Bek2j+VY/rm7reTKWcnw+GHmVDBR+bj+TQK+peNvz0B427iaojIb/l4WBPuq6jAZ/Mv6N5laInVpB8i7WV/QXXCi+EyZbDnilSa8xtMxmjMc9bsuurORYKOD4pfQ8PY1P1A5dfD/kksuMhR7RSzhTd74djvxr9cpFYsTi2H44lqf3sy3+iNMffJZb0x92Slnc0P8f6/edTLkn9FO/PhPZkjS2n/9ecm8+pqzktlJLDtwtPgloun0nsKWPxwI5MIcm1Xa59S743if7pqde9U1q5GfVJ+7yMB1VHcVBq4Peh16i/qq0FyEiok/z416u/s1ecD4T15aWfKi85n0v98Jv3v/iJk5Yf41Q//xMbH4v/s3u5+9+JvPKt5i/8hPSNCoo/1Xxla++684/fvn3vs4UVf8nhlO/XkvVZpSI/Ke17YtVE5vXJj0RobDP2UW1vwzvxNOSScU00I7/PK4Au1XPeSFN1B8NFyp6tWlx2aLse38RBuel2/5zrAe6Kz6FnkKZJjlGoU6sRrFPPMTZBYZynqyy4pd1iMxZ3JuPhZK4ZEa7f6EROBKEDIUdFIGTrWGuU3DgHLg4q+PaJzJCuobg4qKD4tAON8Sk5Oas9decbyo6GChFC8jpPAWaSSbzElaw7p4d8j9zud6uKpyY5qk6Hu95QyhpJ2ABhujGdVCsAtTTNHKtVCl3pS7sikOVxawYlcNOH30Jc3bdo6kgdvVwCEIMNCBDzlYgQ493DR1eMZTpYMiVy7R+6r/U49NUV3LeorbBa5b5+Nm10bdm6ANkA+IOtkRryuwAGfGb09yHcNm6vwK3WxlszeRZZX1z79NznT+g5uHVrshXdL8a4hukR2zGaMDT6HQDATkaejgt0Tynx68jEwD0YMACvPN/VdO2EWgfgxcN4Z+vR6Temc+SPInswyCLRhlDIzLQRdx45R2EUZSNeW7Zsna9OUxCeoKPbcLrgPiC+XP3Q8tstK/3dom8H344+zbeIrZXuLjwQ899FTtqElr7VXDzxdv4HL9/94hsZYoCTGNwR7tMge0EDH4aoWJ4gmrYK7hsBjIhgDAjaTRb7MEUlrjp8tZ7oN+7MY2Q7dwTHiKhnPxXBZSNSTtwv2iGH2E8xqVDZI9QR3TxYwq8gkmBWmSsZrQ3NF2q9uqS6nWqoKRcaXv/8QWk9SAxiBuZUmAyWqBYY+nWtMnw8QbB2TxAVYAD8jAUOU14ixq4xt070JrZePkdGExsSqZtKXFXTbYdKiav1UmuwWy+5RGkkKiWPv4b/sGpqO8T9bnOc+tPYpW33DPjzZKz0vLV7TKZuKtM/R/246rdnzOm+YvPsmGhbtmz1kWh+scMMYJJgwDQBm7O1rOr4QumWFfqadwrCevh9dHeIj/dAl6O33/2Dl/yw8cFrXawIRXRcBNCLLl0LMkUrNk7D8+na2reCBOHA9V51iTG4hjKnvhO7fce9+aeSCrwmf2pBXpfRtPlXq/h+t/ivNQK5TG1q2pmaTk/8SXzq4/IPHoav3WFZYaxOLSjllus3/teH1xZeeLq1Pumbq/zCxB9lJmb/f8WXPzP73kJm/l8Z+14hMP8l5v9B4YT0F5heuPcP/8sv/8RH2L7ButECbnUb/r//FA7p1k/LX/b/00nHHyW3io7rH+bg67zz7z3vCZQTWv5d+cZuc+qJkvPg/oX813zUC3I+we60hlteLWkhv9cxs/bjwrTPnW8kEwtOXNP8cVDTqkQeyXNLvPdJvu/HqeHycLCLI0Fqj5kdYmV5x0/yJrsb3HgkR5BDPnQN+evezxZxT8rJXZxu3A+bUz4pG+Dpc66nFRQvaoP8S60DmPWg3mD1U8ntzcnpqJqNP07e2if9E5Pb4clYTp3PbdfqPEws5IjJHITJZsov3vY9vMXbSJS4tkloi8RLvLFPxINnozl3Q3r3fTm2ROZDueUt+eY35Y+/711KNwMo/PAr9yaDd350u3/Zf5RyB+6H6dDdwFIp8uYM2a83wknH7SfqZNqZPHhF+RfNj/7e93/mZz72esTp2lFcMefOv6hPfSL84/vll9fqIY9Dm5JSO81ETg4vsBFFk9F2JV5g3Ut5lYUFaaPui7/VnLrpe/IJvgOUcD8fK+TQFSoy2hOiU7iyhOrU7vPKJMkGAQdF4GHzo6pUAyJSLteMyA2Hjmcp9P52PSFLWggdR0iWNDE1EyjhTTEpvL/7REQEdAUF2CdCK8ay/r4cA5bIP5JvX2en4m3qshTNZclRuR71avvupzozuFUdL+41nXHc6Mx4XMWG+6FeHfO4N3QU2IcnoEK6jfuW+/toOxr53C8f3r4FKz8GuAdAyENB5z7ca79tSrzKjNHlI+Sh8ONDtIxlJUL3+vwMff3HrRvP4MijrWdXNgC2NUo61zjo8/P+mus3GXBsrcQKq6998c7n/+OTrW1lZZ9l2IaYMTQTNBp6DY5Q6wcaj0N/j8RRM6S9nLEP+JSyYmCTAVsXeOug+HPcPNnK33jjPcBIfn4RpmCCKVfbXu2550/h0L36ztSLkefoRa60rrlTnJ0RypYtW7YuWudDfy9eg+iv9ft2fyg+d/jEMXy5+lRky6013VpD2dGKJN7Y+PZP/vWTjABubDz94v+4y6Mc18e4DS9CBGVOrZfcaOhpVyumrAhZWp22vIYTddy4Z8zgvib9FSOngkf2f3dqWhlQe3gcmtnQt0MdUFYMM4mxPyBBsoKyyH4FZQYtR1gsJvqZbREXTx83hlmE31c8VwxRCZBcQakYNFsYeswXjbl2YrBGq/bTN0M6Lv/8gaRUHY7K//aN9//jXzuJB+S1174NWAK1A/Bxgv5WdPMNGDe84+N9zLIBACag3HfPe/w1p7/q8NVoN16L+ifQ1Y3F7M/iaV+y+/YA1TOKWI++GWrr7zhA3eBAGfhn908PFXb7jPVfmE5Gf4EYlM5z4wOw1r7TbPpry9YHWGJYwxjiWFg8bG5vLjBv/XOG1Fbrax6gm/4eWnj7qF8BICnV1kMmPx7AgK2TzIZMPui5KvFhm7JUO/T5qpUP6S8y+qaLcSMI5AYepabXXeqGQlp5b4mQozLtTO0Q2WjM+Z371abiPAg/Ssed/qnFibRLUj/m/X5Cek3zLoH+dT49Qfk+dzfqc6Xl4NSNveRB7NrSk485v59u3vB78v85/6ISZXHll3yR/RLhf6X67T+Utc/yd/q/1UEq/+BH0T/z9/9QfhT47d/st8zzHvy/QF1b3JxpzvtT31v6pcVrDzyB1xe1/R96r93jB81KMiu759l6K3xdRvPi3WN2Ux57jnBc3tzVEg+W4mMczIdXGqgxTYvvJPkWJJIz5ffYgAJM0AwirXJDWW9OsDC+XPxIwst+TXa4tAaThMkmmZzj/QLjbumNHLE72mZajmYJZqWgE1eC7WXuqFSi5AAf+od4cza5nYxMX393vfCc78afrnO/ZRWN/y/JiQ2kT0OJ6sueV7+Syt1N6BPSXflBbOMP2KQ65yFCTX7/U1qjJEdLEWmV6ymmf7Xyv2/IY+83n/+SdP1xZOZh+RPfSy++MpYOO7Or2anP3lj7a5U3fu/xi3/rIz93ssOxMvZ439PYf3QjrDZ3Fpw/k/jKP/ujvzF7ZzMwtfkvUp+JSVsvzzytPx0vRUs7mQnH3tSHl374g/RL1yZyoZLnmn8j22wipTbqczlHpFIK+uR6tuw/kB3WtinAIyePuC6idB5IuRJev9H0ZIY08Dov+akIUlthEliQHo9ReMR1iCpSatKZXGOuhDfm2gHqEKOE6EpjtAaPkc8QNv8UpXWU3CTbYxxkCS6yrlCdYsuXq1dlR+iHZVaMyZRl3JkUqmEu+i5EaFlvVNxv6mxBDHdAJwrfhwh4IUPjgGYOaNHWk6Uom8+6L/7TLX/q7Q9ZtGVC5S7Dcced90sAt7qSHrvXacvW1ZcNgD/wGikTsNCZY+Cj0e8pQ/2Nndx6oSzswZdY/THfee21T/3csWuO11779s///G/CDaZ+gmmDLJpF8yYwxADEUejxWOj3rEy9MfJhzoxrrtKZjL3IRorJEt6ON7LIxlZu7cdb4x+a+eRxX+W11779xS9+C27Ax4k7uS4RseDeXj9mRL+cfvGJ56SQoxJy5sTIbKY2hfHDz0ztLtWj6Xp4yFV139n9M/Iqy/bU2rJly9YJJYoif38X7BlJjZ5Vw88ecmtNoCpLoWS1FfLcT1vtzZ9u9s+CFgTZClb3YA/llqaIwce3YI3Ul1I/1H71uV/+yeng9eNu9srKOt/5fT78S56/WHPeqdZLblFOCFbnvu6u7iqt1ncB4zlmQWj1x8wZb6p/NSi4ozOm6pshcaPnYiFHRQwqWWWOTwk0C5i417xhnSlovSfRPoOwO+XS6tU1n1ggCnjRABXvHiE/nbXK4Vf/cwDVpjIlpRZYf/UjL25s3DhBW7433liHIDd/kQwsONiGu+AFYfBOgK81zC0Mu+YTzcwSwVlFe8IjX67DsNXTv2Uq7syL0UMg5MwV6hGg0PAOfpapQ4o8W+hmxmaiZmk9aA1lAYQvuV5yi3NGLFndVTyzBX0z5J5QS+vBvq+qnEPjEmGTtfIDcWmcR4eU2ZM2ADYvTHHOmBS8w9Nmy5YtW+3qyIJOMj3JNl30FxBNPQU27mgAfCT6HUatlfTLQujJd4dMmBCL9ft0LRsTra4homjBYFEdH6SiPNuw1Gnvo8su8cHr8df0zdC+v7pXmli88Qh4vLOkphUlpMbiucxm5Oemvl7AMSff/xN+4UB2Pcc7wA+4C6iloMdZ2/m9ceULagj9UfPOTem9eff8b77/N4lUxyZrnt1bP3j/5l9me/G1f/jWZ1976eVjDwB+57XXvvI//U60+pv7f3h4ZwgK8LyHd3XmPXziz1F8C/cLxNTvPpr+wp/G9ff2bz1MRD+ST3/X/dKvPP1SZUIr0ZSQ3mIO2GnefELsVemBgpYkscW4LGv3SEJgi1diPIqvJXkf9aOy8gOtNX0sDRNIqzAPKfKvJCLulP9hqnbbU5UdW/LNPK5JduZZWedWCZdPS8jygYrnR7zwEk90GkVqKaZV5AA1L2UfegXfP+EvTU8mP839tz78aYdW5EPMR7cDj1PcoKp4SOL+sY4fd06v3vZEvpeiTnXJQxJWcX9JJ4D7LnjwT+1HcvrMrWRuLOFSCcxP/HnpS9/iXoop1euIZ8qPViazuTl92/WRz31HT0o/1LXt11+bfvX447H377//P73+cwvOPzn42bcK/zXK9l8K/+hvfeIPSsnd7z39uf+L5zf+m8K/T1RP7welhveaXPHM7//u9kf+bCP33luhj9xKfW3545HZpF5tBN3N6ubU2l5k6nYu1/A6ZTVfCkhKtYBXVHFPqwkx8qbV5QIRM7055Mx9u/pR8VBJ0jab8zHXTqkejTuT321+TGykW1L9VH7cfHlKOrwmBUI2R259VCbZruBbNMZgfeiTbCeZlqnIqNPsipJ2Nrnt3tIBRYcM+GAbojANr8AbcAsy8NOQoRXKk4SaEc/+FXBY2hFO4yhS3wG4529x1isru8WvrWdDEtBsNi97M2wdrfd+5zfy73zxvNY+agzY1Okx8FDG334f+Q/73C90u/9DD2GF13/Mffjk3/t7kYDnp3/tl/JZJwBB0FZWHgG3bs0b90jQBFZWdkD++V99nxelM+ik0qfm7tfrtx8zG9yBpp+6p6men5JMl+kc7RJT28TQ4SJri6wvsr5Y/b/rzo8tLi1kGlHKKsgrK5tQBxnkW7fGcIAT3KxsrUrjnn/yvf9xZTqark8KdKpvhuiVQtxzeNSWrWdP/+Xcz/7MzL3L3gpbR0iS7PJvZFRMbrz7381fxCsNUwr2K4fOs4w8wwl/bq1ZlaWWE3eA37dDHQC4nwTiLVsGHIWhUwa/sYZleEzuIb9TBvjk3/t7v/Tv/ZV8VoYqgDtEVTUqwBcB0MRDKyv7oP78r256/kYI0NddfBjKUDLMoxiz0wU6WjDMgg+MjcHw+IrFlPZWr5YsWZHi6J8/aJRdHdDXyg5FTdjT8ism2ylUBI4dJhLGRwWwlmo+Kt2V2/CqGO+ngtwvVtpPJUJ+muQUyc9o35ly/afzi3+lEHZUs5JYYGVlD2mMZh6kW7dm8bUO1KO9HSmm/tr7725/c2r6J3f21if0PRfgGW9RXrEPlbgKOGNqUGsUmt6wvyhLGqA15ZAzZ90SsQ/7+acHq+NZJ16J9U/RNTldD5sUudDwxp35IacJnkxnQiCuigZgYIEijtXYRaX1m8m8ou9exfzn5r/705e9CbaOll0BjpB+463v/OfZzgGEngMyYjRGjK5Ym46ZWmU+z1iG8CpzVvo7/AdvvwEiq1prMz+mHkDiOKEpgMWE0Llt5ufq3ZbvsJM0m3Pveva2oNfEGqGE4WVUWl/fYrJ+dVdxT6hAdVf5j577h1HWckSCeXkv7LjD8rZ2b1Uef6d5I04xtzu91hzbWYsoc+pzU8t+KrVyOJOMbTVif3/m9//r8sc/GXvru5mXXvwTT2GpNDP29b/p+vwby+6/+Fc+6aJewwk4xhzvf28FuHlrAahFg679Vqzf+srKmMTK6//Vf73/iQ8nvbVffOeVJ2ulyOzM9a+ndyYSWvBhczK/cO128qt/tPDqvFb56O439FJUyo1F+OH+wuJO+Np45akW1mJP1Nev377G/gbzm0TCZJNMz5I9wPEGL4eoBpuel7XsQ4UQORclB9EoT2X0cS29x7VrrFMkxK5nu+m4daAka0BJiereaJD1PXkyXEkr+7X0/NiBdnNX1ha1A+SchrzKrJ+aA08GxcF4iUKNQAGHuxlrSLkkiRdZWyPswuHE6WNXvOspUkEcTnYdeLwat3PvqhGXW2uUI85Qsure15GpFjzuRzprNB8hjYHPqIc/Am64RTXqAfYjY4BTPihrE38gf66KssgDHd8bzRd+vfLa/5J7+VO1P/ehxOwnbgTzdeNcW3nC/LS3fIBDemU6TJV8hRrOlfVcIJL+z/6fP/ro5P/7lev7r+395ptO+QfxyP/r9u+5XOnSk8j3xu78a/t/963EL350/ff/8MN/5/P/62/U7yT+5Su/5Gm6fyv9mV/0LH9jc2nfiRyufC7Fk4latu59vK3gIpvx6orLE6/pRZf4clfiqpiCKSYLiu4e4oQUvbqdMVUA4GbZLfmqr7q+l2Ky1lSCUk5MgizhrTYVs/EKECG/15xE9RQVCXheWhFdkz2oM6QVfE6KgIyO5vFWdLf3AJhldatyx+/dVtC9aBQJ1nMHzoin3vRn9905Pb04iROP2nwamr5RWM6G5vEyll/fkye9aMFCzlVsVJzRwHKKmNGYZgWg+Qigts79ku2pHWH99a9+9QSuNlsXLMmu/0ZINgAerOOODx4v8PlkAJheDFjMOlwBqK60vupCsAhuD9MTFP/bz/1O7N5Xdz9aWg2yBgfwvqVb2JLF0oFRvC4DUIH3jxpQSPSKalzoHEE4VwA8Q8p7GNNz0er2BGP5sXQY0A2FKR9QlSXRO2eHSQ0vUO4Tnmdds9lv2PISJ2Hkw6tA1IvWPcZq+mwGbJstW2coGwCPhOzyb4R0hQDwkfOfz6GYrCqewqT76OWOo1CyWpMdyr5WVTxHeH/por/+XjBY5Ij18+fdNmo/YJvGDoVtHpVbbaie9+CDyEvM/xr/xYf/43o6+uX3XmUb/PAUsuCHMIxDHvIwi6da0++7Wi0/cqBAxmjxGwRfe6WXgJRROvot9NcwoWJ0irUWfmbOR8y1cyRQFBi1gtyzDumpGHkZTQTWRcl5KecZk1u0+LBErOAbpmKs4NsnMiQtFnWRWR2J4BlhdzAHzTWCGjKg4tHy4+vhCSDMvhNXlqCXskxFw1vGU8EHbJEoEB0jv0VchE6LETdr6rU1BNtMtDazrAtEQ60ZBEeoQFQ8a8voedxz5cOs6iqow4U2QM8UHn5GZQPgkZBdAY6QegLgDpnjM6IDV08GLNzAYrDCCoB7JPyb7LZLHeFw1nGhTj/xG2OHuQvHmezSnT/X9slvTmgTs9xEddCv83qgF+7NGgWSdVYcEDvkvqLhggnYmqp7XjqoB/O/Lv3PVYIO3CvEZ9jzUMoTzRKaJafi0dGSTO8TSdYXJp1rFWQV7w32E2gFHHGtEfY27ku1MYK/+cNf0Ndd/6c/e7/wcP9Tj1I7Nx35v/3fFyD2//m77+XHrqcf+1+QN/7tP3lp+g0g+G/+W9ddfMfBLzd/s5llI0N+PPFehb8Y3v8HX/hbNerPvR4Z+8hudHO7lNCWdu8/5VPXtO80H0bzBca19xo/pDwJn0s4AtF48z39TU8lo0svsD7/S+OOH33r+mc/pL3xW/KfEeVQgehBOT7ne+RpRr7fmN1JT85Nrj8pXAv7i+bMv6bqTgT2PA6tUI/IkpYqjl9zpxRvPerc2ajPAc87H+0SBEwMOUN6vTkXoBKUcgfNSFDKPazdfs75pIxnXEqu1G9rTfl55yPgCRN6Q77j2GpIxUXWRWU1STJKzolzm3iCnTiajO4k+xavzLI6ybZCNUohqBVCa2XeBAw7eAJ0Cp/xKblaenJM5mCHGRW3onni7DTy3q3E2PWU6k4SUFbXClNTZS11N776+M855r/3T7x/Zb5Y/+OHN5bmd35t5q1v79zx5/LRyZ13pv2ex5PJSc8vl3/0P1Z+0ueqU0+vlm+Hs43sQ/2556vZZPOXJw8yN/b+2dt3/uKnHr24/6bsrpabc67I/Umt+C8bn88qnu/t3vn3J771j+5/cud1N2W/5+Wa/hUXt2EJlsENTsji+UxNf92FG6owDhU4ABmy4IM5iAnPugtQltToRG7vvQm96JpayN2MrzwpTSe3YwR5dVK0qOPJ0zlpuux37nubjiLep9WEOY8TwytsVrBm2S9uiywfH5V3uPUqb60xH0Av4vGi5QjPkK7RKBBdYD1PNMx+FR8QJOulLCr559jOEoyQ0fDJlBdZb8UvrRwOjzf1FgkGVu3euiMlGwCPhGwAPEo6XwDMs8CATRWmfFVZ6vnQSRr9DhjxPBIAAwFLRqK5vAGAMfoKmO3lS02A8ALSC3Abxsl82P8/8Ne/rn+muDx2OPNRMyaDC+670xa/BjDVXs1PWwI3hDoMxF3VeXcJfnoAfJGu32NJ/EzqN2F2yJV0L5lkml6OllP6Wi5SFcvsAHO00drjuZsomw1FbNj87OnIsPe/M/evfmbmExe2PbZOJrv8GyFdHAA+MtL5MgCwKTHPz6ziMgv+2FrpuJP/hPfXpTVafx5JfzGbREHMaLHRIRPuHqkKbNA4aCWerVZbXa+AaZm4EymCaxHmqP6Ux72nP/7Ii2/Lt/6P2udVqbH6zgIPIACbsGlMn39aR3G0vURMwgciWPqasXlm1GGgxYA94zXA6a+aw51YIn+tDSP6aYa0GN88sv2H6CvmoyJA73B76lTKMwZkONqrKqYkCvoLjFESEFpDznJ4aonhKuu/ClUVt4ZvmzhQMVqDWTl0B2m23mn+eQHVoKjizINlzhcU/ZL7UWczI6eCInowK1QGGKlFouDpIfSaPj9kQvVgiWBtmxlfsGwAPBKyK8AR0jAAWCjuzPud+4ts9BtsMekvFl47yGL7oDOuwDo0ZB0UqjaVp9WEubZWvwPrqoaQMDt2rFxs5+FGDmgqDOwZrl/R2EIkUZuNGFTjX+Ev9bfuN9sxxJ35EHqqFBsPbWZqU/lSoJF1Ra/nnnM+0aX6z/ANmQrgp5bBb8z9is+QFnPCdki8xOYTQmU8+8w5OBijkCWkIu8T+fXcuz+OBPcJpPF/d+UzC+HkzkF0LpqPUfqE/3uFShB17Gfe+2/ejH9kl9sf3kxt3n4SX04nv/9vRr/wez7f+u+P/Z3vZl/8K+NvxgL5B77SX1x//0/nr7/NvKcZemMn/JlQ6WYh/fz3l1cixKL65G997Y0fMfMXYY+dXV5+AUnj0feITxB6kdrEJ9/97e+O/+XE9Is3lP33a4sUvHFPrhCWUvU3wt7F/f3JxU3n2HP1919P3F5jbqP5fKYRephutUyoZ9yiYlTXFHwoc2rEXwJyGb9jrEXN0ajXW4sJCaOqSDY2G140VXf1iaLXXaIm8gRqet0FrXAaJaGKlagbwgyLiFRR00pgKRt35uecGxv1uTnnxiRJMfQEzOd3pbB6I7X9KDE+TqOBWtc8KTm8xrxoNgwUiM3yvo/aNuPBvFzzBlYbt152PG5U9t4Iz7+f/ojHV9ttBMpq46MHBW8s/XL0rTd4rlafeKc0o2i5605JrdWvJR5/KFfK1KfeHpPubOjvVJ/7ucCP35hylZrRKSnjbDo/+6Ov/kno33AFyg8Cc3+h+T/kvZE7P974/Tu/mPYqk0rtj1IzryS231BnNlMz9ZL7oxO72+Pa07XrbKPXXFPP53b+KNI6qyfElWBcjGYDiwd4Xq3pJdfU9F6u5nfGVH0l5I6r1bTykaVll0PVpXqI6uP69TFHPijlxoxZg+6ykvM1isV4RXbmSy1fUb8WJPWMEp3I5UuB6q4SC+cKHu/10NNCPRJy5pqq+8DT9hMs5trJ1KZEvM2UlFLxeppOn5SNkouSSzE1y/uA2UsY8zedmGgrxslziINpjpNPyzzW8MEqTIKPQ/cUtKbS3jNu2Los2QB4JGQD4FHSuQNgnikGfJYa/GUyDAPus87qEF9TzikkL9Lneecv3PoSP3+IgYXrV9TBLfr7luV5zx02qh9z4LWMD1qr8IEMeHgAzIgzYJP7HhcAi+U1gtt9so2Gh8cnlkhzOu9XuQBdKVpsJdy2jiUbAI+E7PJvhHRxANhUv2rwyKJlvFfKyPlLwGDRMLgccYqcZ4zAZ7GM6fo93qrNxcv9+/7uYww9HSVjB4pJ7mLCX6oGEHAQdx4uWGoSXqBZQbpD7c94Hn5ofkX9C7/lfnlvfUJ/7KIIb8HTJg/rBCWAA7EFfoJ+KKMEuC6hGDYIwGuUfIIELxyOh4qcQ3P0R3h/rQC4wwHccxqQOblNkFSGNu8eS9OkBXD1oZse3GGemGcsTFb8271t06RlysAYJcF9p8nsE1JxAzligDUDRgBjDR+gocioPmqPmR7jIEuru614iTxjKrLYM0DH/D+BxjkOBjafcikykTZGrLdZuQk3s3liVFByhIXLGehHjo+UyRVOrw8KDN6EiUuLhrYB8EjIrgBHSMMDYGDBs24OyHSPtxwPAJuyjA71cwBb7b+HlmLrqoZgwGYMidk2q3dOdfcWWp2+0Jq7lTIswrFWWLRntiYmvcEhDPaMH9Y/5tS3vcKs5KsWGl59MzQ+v/uS601gmqSMFiUXIVcl6OZApjyplZKyX8OnoVxjf0Z7/5F8O8lkhsAUqQieL/GpdD3hqtR/wfcvmoztE3i9efd5UrPSEz/ZP+LjH+MHPyp/5p6y++7B3V1V+0W+NrY30Sy43n15vOnbXfv+9dB7a/Iv+Ga/9brz5pj2MBz8mfJXajf+HeV3d0Of/sHO896pZFBa2+E5LTn1q19/+MNtfvYX/smX/u5/FvjLf/fTB/qXf4vP/XWe+njrH1CAf/1ve9zv68WPJrKp/b3bH5kp/3ByRy9OJZzTeQ5QVjTGQaZ6zbMfHUOLVMLa/yb/+Q/zjksLf0u+FSHnpjxF0oEr35yXJKI8DWv6t+Xbd3mUIXCPpJvcH/HJINkmoQr64+ZdXaqn65MehwYU6pF8KdAou6biSa0p57OBRt0V8ZdQXSg1ydeqQvOlQEiq5Er+2/HNx/UJEX3smS2EHJXtd6dQUeZUh68GVHcVc/7iNXcqQMUlqblm5KelbwAHjEXI7xGMktsikWKyhPcFVvaa09elVRV5Gr2Aw4XjKeOJ8njKtzfB9s9r7/9e7Ze3knG/t/CFia8U5GKd0IupN2L7O6539d2fuIOcy201F6T9R9GPpSaX6trB7Xx+8v7Xvv3xX3F71fFi6sbW93/35V//2TffbN5elSkoudqfKp+/U1h+69qnylLxej61412IeKUtqR7Oq9Fw9j1eSjd9/oZ3y1Ebp/koN1HWJ/5c4qu/VfvpaLm5IctBrZHwZ/S9sX1FypX89ZJb33OhMP38TrPsLjS9rV3hrE7NJnc2J+sl9xde+INp0lIzuiIF9sqz477NKLl9Ig+LLwBhpfS4PhFyVPZ3I2IaaOuaFZW+WT9swgT+pYPqrgLEgkUppgrb95ym5WK1aWcq2wj7nft6ORRUiuJ2qR6dc24ssC6mPvipJNiJkpOpZAmaEy4xxlHdWjO0UwZYsfyeqsAG1RWkCM0cwP1Sq09wyENBx+2h2j6DdxWClim7ti5YNgAeCdkAeJRkA+BL05kDYMsKTSPIkXLfgr/Ft5Ze/cf85TV9vvjGGMuw1sGAgfIh+jVldQPbALhdVujbEwALdaPc7oVFsMzgl8sRG37I8lzVMw3blq1TygbAIyG7/BshXSEAzCWbgAerqnhqcmu6WzniBNxaEzBhMKDkaqKF2ElIcD8GPCQArhwahYeZ/5eus60R8jA1QWiR6r/jeTg//3U+86h55633X9Bfd/EUIvAY3qzjlqg2OSiBjzEH1SaKA6dEqMmEhHBuiOHTRJvrRfQSk5Sqabs0x15NmdaK7nlRoqI7Q9brQy/j8dEa14mQM023PZcXdFZgV/HcY72ciX4BhSoQpbBNTNweXNEpVBX0LH7hBjbhsbmqfcI5I3vH3LAxDk5TBArXkUDLAmYLJZlW0MJkK/g0ZBlNLMNRTmjBnmPkARVZOZzy0PpTAOyTbe2QSjEJDOMYrjaVQj1yYn9wU20d0NJ68GRruFoyewCrxo+/NSPynctpEmwD4JGQXQGOkI4LgLGMyViHXMxf/ccGwAxiwBjcVMzUaaruw0/XgSy5p8yVizcyLADGYo50Q+TQS+px1jy3CvWMgoZjrCZAr8iHMF+o2lT0hux37ptBF+Kea47dGWk1yeQkyX0i19l1U3qex5PsNDQ5KfunycgcpFl0a0jyQVMLxnm/oQUBv7Zfkxx74cm0HN1mvIxHIeCh/JT5rxc/8lOBr6nIM6QTbC9qB9m1n/69udnnle25wg9vpLLvjc1lx8d+8ht/lHp5PiRvKP8o+bvBvxniaeSff7mxBzD5H37Slfxu/kdMluHTFD95R465Xe+l36r9q3MfeTuwlq3/92+E/p3PeZXVapjy6yTZn9xIJetkxzzef66Pj+Ne4ju/TQh+5jnGoOxFduGcQPLR3KW5i+OXoEQzgpSjqOH9kMdd0psFan5PxRut3cGf3Hc/1ZurSDJAc5fmJo5P0MwgRaEGi1Sf87j3dTIQ41tLr0bJb/O8h/IK8TXmvWjTJIFdph2UKijl5liaICDCpdP1sNmLBIi5drxNR0VqiHyRNX0+uO/INbyAQJhoxCaKUkz1Zh2VsUbImZuSUgfNyNbBNdmh7Xp8L3gevp1/AWiUXdGJ3ES2/ubqtdsvPZ5qNFbk4ISm1hpyLPh0BjUgbf5B7WdDzpz3afDj3kI9oE2pu78zNruoFsPUn/O9E9S0P83/zKrf+5w/PbfZ2PdM/aM/nFf+jDYrbwXRY0XnHz+58dFI7sW7X/1u5uP+DdZiY0Rr8by2nJ8cq1SyTU9MLu28HmEOsnhu1ESxqRdd0x/ZyWcDpXeClJj6zJ7iP3i6fF0vuu58bFlvyB6HtvrDm7HZouwvev0HG8l5Na3ceWkZyG9O7exElDlVhJZX31WufeyJx6HFOXh0sFjQvJ+c+PE7B3clX3VvfUJfdvk/dlB6JyiSz1s55/6s4twPSjkVr8h0EXt+szZv9oJZ0+fF5SP+FXngWlMGZl3rohfMfn3qhvO9LGEvmnBdK2gymoYsqtZuBtw6xGZOp/gYeNQGg4lT/TaueSQP1RVKTQJRUhn8DkoNHmut5jw2AL4s2QB4JGQD4FHSRQBgbAbcpSMH6U4HgK06ckBQiuD6CfgC31p49dt87GHzzur7N/VlF68DXQVxvYnTkoN9g7ahlbNDvwxNf63WkDPRGAcRMkCOmOm6OGobdIwhxe5HRSnf84km3DVDSwZogCf4SF0WIa7g2+JsbBa2PsiyAfBIyC7/RkiXAIDpXw1u9eqAe+SzLklVxYMl51mNysfmvqa0PvnPQkOWgtuHjeiECXgYpev4HQSiuF6Fj5P5tP+P+OV1wj+uv1TeiGcbXv2+iwOoQQ58Bv6pwKzRIW8cxsGPtTCxVnpilK2D+wqJ/r4d6LdfnvMYB4CMCsiUc8QirazqloRRRsNrlmHCR2ve1lA6nnIymcw4ybRMRUbNEZlku3uT2re/lMU/Rkm11GAqboF4ez6l4yHzT3MDxJuSUcWfYgPMzTDfvkmyhblZRhXPEgj5KswatEo0bB6+7/KJdbJsmOH7CvfUB8UrfA6yAfBIyK4AR0jHAsC0M+BuAGx+opofkm3Ilv4Zy/TFwKJ+KDS8Itq39fnZbz0DGfDRAHjA5pl9LkQQtPFCgm8F9x1STC00vCFHRZY0YUi1RpuINqh6Q26qbq0hz7jSL/l+IHjVMndFuwQfFQXNicttVHLiS3CGlIb8gNvBZjUpBcYpiG646XpY3wwB+p5LGVPDrka24XWH1LgvH1ZKQLoZuu16ONaUv7v9XDxcoOxZqcc+PfX6VuG5W8Hl+9nbStH70blvfj61vvXaahne/+/em/l3Pe5/qFd/3TPmiL79D1LA4kvcu8YbX2NdB/iZnyG0gT7H/a/z0efYfZ54mv/9Tw/30wz4aYV1FOB5D4svUXqHxRswBSHYoXiDQAMpBuPwYxo5Sk8JforVr7G4SNOLNEP1d9msI0OxgQoTDr6s8dMys07yU1SekrhNeY/QL0MN/DBHbj5RjxQxZmQ+km+XcRUYD7FXYHyd2RoNQEHLEF5l7hZPxD0iUyRCPkpuhvSPeCnE/sPmHfGOnlYTE3pZUqpqKVhoeiWlqq+EnP6quqag07t6EgbxHJ5YzXmt+qH44zeL8/FaPhwpNVV3OjN2O5bexxWt1HxK/se7Nz87++Z16WmqOb4peetN3y9KX9pkYUaV/1C+6aXykcp6BPfff/jnDuYbfzX89VAu8r5j/gcEfiH0jXz9ZryS+mpjKXkQA+olt77pwmfM2XoLfJC3mHfKUEZYsVuLiV8t7dnmnnhrZsOt4M7KwZR4aj3jRkbfc/EtuAYByEMEDiwzw0S5egBzeOK12EQx1/BOBjOaKs+MP42qlZpPrTd9XjSnVM4RVqgE0B2UCkT3CImE50I9sr8bQUMtKIdXZcBIqwYUphb2FP8B4Hfui/7B5r7v9gItsr7KvDmuCySZXmDDWl0vsp5jIsIuRjcfZV8jC6olPhpYoZbGofCjx+zaPPgCZQPgkZANgEdJFwSAuXLjd5esCwTA3eqJhN234BZ8gcyC/2t8dpWFVebW9PmjK2NTCbrppH/+oLv9wzV3qmcTuOH9vsbyZ+n6HeDTvZprzjFhbSZ3JjrvcOlnJlna1gXLBsAjIbv8GyFdLQBM/xrmyBbCI60BldvWIdYdpFynUbip06wMFQOzWuXGBM0c7lsQgJ+i+rJnPzL2uvyhLca3mdwinqlNiRFYkdV2mOemAJ05z0LdZt9xCiKtt18fBDGTr5/lV8DLCJkBnt0rKHNin0bQnAUobueYEMYF0+nbQXytxl+6ePBZyYqTBS2+IlkygIi5FrfPlQebsdInaDAsro4TvKgNg48lGwCPhOwKcIR0JAC2+llpB8Dm9zUgJnlbp9Qcw19rqr8VuKm6G2VXK2AZ2ISiAWX7r6Rb3SaEYTev+7UWWvWPtbuwkGktFZnPpXo0XQ/HnXkBhoEpKSWIL1DGK9ofuHCE2QfWmBcJt8A+kfXmXIBKRWp4m44iXtEhVcQdV3cVfc/VqoZ8eAItbhedyMVcO+IQiEqs2lQiUi7XjESkHJAg6cKRJewqKxVF+kvS16+nHj1pSrf/z+u/bYFaN/+DO4/+q/eAxZfwL/H2bxOCV1/m9Tf5zH+YmJRSKDTrrL2PvMf6t/jETyItkftwwre9/7X/RL8tI32KcQiWwY80CS+S/l+IT8JdmlnyH0+EH6UkL1s/e2fmvfdy8wkg8sMUX4M5qECIrX/K9A0Ocqzu8rybdJ3QNQITSGK80WcEUdwCqEY9wObktCB5GsEH3Fpkc5XZCJkk06vMT7IthqFEv4lpkuK2wPDAw+adAJUiXrekCltwpjY161oX+0QtJsYCm6aNVauPPa5PvOB5KHzDcWdea8oiibot/bj1ZAB2YQLKeOI1fc019VJuZycytZDDUTsIOyb0clgp3VDLP1YiP+188/X0R9wTe9VSaCqwUijOv+h/tCaFm03eSb8U92cr/kr+YCJX8qsbime8pm+6SBkNtoEFphZyaBxMNYrLY88vbG/IcnF5bGoyt5OMKHG1nnTr912sGXnmJjmWIQo7IMMqvGQEFJUtYT8xlLiqfkfBa8lI90OMSXfJcauwvxt5bmrZ23S4JDVEtYBboSL87ous7xMRGTBrxoeGp+mMSlsb9bt1ZylTmwrqJb0YXFtNTMZL2bIXGb3kajWhoJUz5PRXRTr3h6Q3k0xaMXBPdfRD6dceZZE1QYs1fIs8kTkIJatuVW/x4DdouZc3qK604pSA+4Nf29bpZAPgkZANgEdJNgC+BA1Das8TAAv1xcBz8BLcIrPgX2V+lfl+szuPVNyZ9zsHfSVfLvHtWvn6yK3Z1NmC2wvoMdznde34aFu9ZQPgkZBd/o2QrhwANtVRnDyTAFgMBmYG+p4ZohS05D/31GBDcKnZahW86AZwTuG4AREIwAzV257CvFtEj6wybx042yIuaJlo0WpiM7Otr4h37jb4YsA8M7vlyJxna6rbyMkaAGPFwD0lqHD3MjkmABkNkDkQyTHDpEmfQIIBD26EfIJM7BPIDJ3eb5mVvcO3ND7pKyrmGVtB2SJ+4lUd1yt82N7SVi/ZAHgkZFeAI6QjAXBxecwKTc3MNuvQzSJrHRHQQj2CoI80EnTh28BSttNGrNIaBxK9M1JdyXO91mNdoXm79xScYdwOpgQGzoAP/1LL7VBcHutwPojJc/qyy7NUc0+oYX8RIxZFREObQDHYrNakhhctRxgQDW71hpwqjpsr1DdDntmCNSy3uDw2tbB3IDvizjzgcWi3pfe+lv9ZIOwvhpw5YF7aMK3Gk2w/zyNZ8+7I8jJ3Ys1oRtpfYGNho+KSnRM7b3z372jrOj21+Cus/ja/MkN2CufHPNVv64VlMuPc+6t3qqzGbvC9a3/T79/elx3j/+nvbn2d2zLxz+Cv4LhG7q8mwv97qvZJDxV4wuPv6nfm0X5KJoTruw1XXi99PAEEnqaabyJNwPNQgAKFf4oPXIs0r8Ma+JFugwcmQWHfcSf3IWkyt/Yw8vKEltdkzYHi0tRKJSiFD3JE80SfEvNS3uRGjYbAvWPks4RVvAmS+80ZXarnmhHgaTXRVN3VJ4rnVkH4zkUOucDw1abypHBtMfxIVL8zpCvIXjSxbx8278xLG+vNOVED6+XQgcdvJk7vrU+Io9/MKAXZGdLqBdlZ+nLQaiiP+Es7a+NkUJbUesk9pleyHi/73PzQlio1UpnxakGZmUxKSnUnPRnxl5p7Srbh1fdcAGUoQdGw5CZa3l9lThV9jovLYx5nzRmrqmnF/OkxeaOU/L6fIFQtHndTZrDRuAF6F1rNrRtZ16S/sPbNhGeupiddrSe68UzXdIdLmVMBZ0wNao1wcHdG1fNKc07aGKOQJOFBnSf/VnPeJ2UKRIEQ+y4c7zZv6Q3ZXakXDsZRarmMfzKU2dqZ1IutiQ6eYE33tALYnbGqM6aKFjOCwfupJEhaf2WYMss5kT5t/mDxNh3ZRtjj0CJSLkRVIbPKnPjxEiUnLPheyhOot3mwTWyaTDyZpYC7rrf24VuwQeOAhkozx2qVpE4SCkN8ftgaUjYAHgnZAHiUdHEAGJsBGxqS1B6XAR8TAAv1xMCiEQK34DbMgkxmwY/BBU06eN647sLa+vrQ+6U3n4kuAABbdeb4Nsm0nR1t63JlA+CRkF3+jZAuBwAzRCnYUZY8M6Vj1phfL96g/yj6qx0Bd1s6qvZrHAzlBu6Q+xXwGTv/JoyhRuVyxGm2ohAhw9sWSGbm94ruXCbWNfvLdr+K2ZRXNOi1drQ1w41HRVYj7xilCLtHEt9jSazN/FdDVvFEKWjIQLeT+DSmYTNB2szWnmQ7yXSEnPgTmGT7IicLCh5svefi42QE4ZghfQI2PDwSFuPO4vaxjMKm5SiwlDU5xLNhNbYB8EjIrgBHSMMAYI+/5pk9ZBk9OwFbx4K6GfAxALBQuxV4UCPhZcvtbgzM0Y2Be0u8hPCYGmbKzuEo4bMUqbntXFn4lR2+Wmk96PHXxJ0tq67hWA0sZYNaQ/JVhZ0XiLl2nHW/7MyaaEqkPfud+2v6fMhR2d+NIGJ+u0ylnvGa018Vj15beJIqji+GH2VqU/lSQABgt6TOs1el4aeywIaotbKEVMv36T6RvfLsdbL3SP38f/rf7vh491tc+7O89c8pwEeneW+buV+htMe9RdxfpvxLCd5Kfekb/Ot/2yPdpkK0Ns03iv926sba57XXw+v7/j9I1b+P+hn8JSp/Xq5Mhv0/3Hd+V9+vEXuB5hNSPqJhj/PbuuNFJC9Shcb3cPwNmstILoDmA6SPwArVP+dxP9YpQBXcEACd6i2Pe10nSjOPdINqzOPO6WpUVp5ouRcS3kr+wDsf1VbZIf2hMbcGIFN4JN82O7vlidapVYyI5GXueNH2mpMz0trb9XtN1e31HxTqkVnXegmvyPEWHN0kvmLGWAVlrzn5knRftBwuEN1pJgC3pDrr/rqzVG0qT6uJuDPfVN1byUmns+oYq4W0euYgIM4Nz3gtFiweRBvm4ahnFNPy7nHW9LddBPBM1pzXquoPFWaZWtgrNL2lHwXJwk08zppedLFrNGRRjekRGCczkDJ8uqZzmtbZ2Jqj0HFqCbOyAhk8szW95PLPH4i+vLddDwPo64yX6tHV1xY812rAmK+SXPdPPZ87iDZCjkoUvaw5K7LznvP9rOSMkBe9rjXk/eb1cWm7Th0QQTh+qiXcDiLvkFCoHDQjcYrbjYmHm7OL19Oru3E01HcUz0JNX3O1tgowYib1zZAwBIs5EMJ6ZMYOCRe+WF58nlhLI//8gTXcyHotmy6mUj0KvOi8L+a7xMgv8d5dHmCU5bFvl8DIizZm5VZXWK1S0G1/8GllA+CRkA2AR0kXCoB5hgbyTqzhMe35m4CF+jUJdpsH61bLDtK6TYsH08UaL8s52k8XTF77aZj+vmcus7vwmehSGHC3bCr8gZUNgEdCdvk3Qro0AMwxGfCzVDeuDMF9TQ1fBA5R+zV1JA+1NM4I9RzN3BHLu29RS+OKwxx44bYRhHgLaMFgt9asypJGcJ+QiR41fFHyZsNac4XWqOF+LzpMwvMwfXMVqlEKYqumyQgjqSCmgAC0Z9tBY4yS1bx7tui3n8xXETxYJB+uMj9NZp+Qgg6ceaOQbpnth2XUbeLTpDUUQHDiMh6B88VcgTEOziplWnjHr0hjETNHepiFT9xL2MqGOwJah9HowmAbAI+E7ApwhDQAAHd/UAguIgCwtY+D1QEs1BEX12bhPT4D7tRZAOAeXHl4ZQx+prZRtCOeErMAY7WVYSuMvA5fLewvNstuxX+gNeWQMxeRcoI46g1ZRAoDbTZoUyafVlDiKjAVTwrW1VRbNZJgwM66L6yp3kDyoBkxLY9Auj4pFrvm2H3amFBLQcV/cM2xmy+Nfz7wtUxzKkHVLb29r37mlcpX0xHnq2/+8B++/Dde0mrhyk4u7Hhl/Su1/4f25t//5edX3tlaUuZ/+8flv3an8ebipOfLxVpiz8HOh+/dKL4Z/2qSBXJLCf/Gvutreu3zHvcf6pnrVCf+Wmz2d9Bxr+mPP/5i3Pt+/eFsQFnFQ01xKE811mAGSlQnPO51nTWqH/K4A3rV73GX9OqMx72m46E643Fv6UDuRiKyk6LMk+fnr6+tV6OeihZ1RvIurfHW5POAj9pjJmUqOyS8lCv4xIRFDTlDOE7pCeOiPS1Qb/rKeNR6NO5a86IBe81JH3pYyjkoxchj4Zde1EdcFyElKSbFjh1z5LfriZAzF+dgX3Jdb5aXS/O7Hp8ZCf6kcK1RdgH1p2697vI4a87xqppS/EsHrYhv80zLGF32Ng2IK7QM48bPCpHVPA7vwbWuFr9GfjIqU3f3DmSHmFjg8LWmKQgOGovnciW/w1drlF0RR0WKqV6tnq6HXwy/M8HBLkGxc8bIJ9hJMVWj8YPUTyXGU9VSsB7MAyJv/EPSm0XGHJSAAtEF1pPN56alPYXdEm4ZTex/UZyL0eO95mSUwhMmbkpP1hmPNmtPGxOAuArmpYNH6biwMlsvAgGwu7sNYvnwOYzjfgB3abUDNvdhnws5sJTtqLKswZbmx+Aia6LTsBhzFv/GvlJiHN6EIlRovMOjTb7Zx1hva7BsADwSsgHwKOmiATDP1ljeCXT1ALCpbhLcsgILxSECwG3jntnDqBATCZu6ajD4EmXNALxInS0DNqXh2z5FMt7FyE6TfsZkA+CRkF3+jZCuNADGqGdu9Wn2Nloyi6vh6e+Q9l+hXGcb4GFUM57SDwZLEZwRowiMG9HQRaMIFO/FDzGqUY9b1YGq4qnJjmwkMpbLlSNO4620OKWIOAb2CZmAVkQZAyZCNk20wtUKRCmY5lfrFloJ6ImrLOFpFpw4wu4OM+IVt4kpVBXDoyyjmURZvNYOMxc/t69D/d64RhAQPYYXWc8xcQEkWAyB5YgBMiogU04yjeHwFjeAMQ5MI84pJcYQw2SvTtWXYtIM9hyw2In7B59encGq56bOPohH3d9PNgAeCdkV4AjpN/7wzS+uZYZc2DTGdZuAO2Zm9+wX1mI2wwcs92PAHWswGfBCnyqxV6x0xz3HiN/PQMpIfhatiANY/YiDZLaAxcjpjQGtbqYOX81MGxYBwoBgwOl6WN8MYTgUxVNaf1rn/5QhRmApK9KG0dDrLrFy4RsWhBhQNxQzsLqeUSKOSq7hRaNedzud1XrJfW3piZoNFpre6xNP5YYj5Mg/rl/35uuVsHPJ+XROVTcUZbl+7Rdd//w5tq7nIsGDHx00Phws/kjbJvGDVO6X7vizq559ffPVFz3ewmb2z7654P43Uv99TXa8FvkLwMfz36gSaVS10vjdGo9dWmKMR9+TfzJAJq5lnVrs3Yj/Bo0Z7XWP1typvNgMHyxobyo72trS0uSjNZe74X6qV695SpHonhSfUx+qEZdbayg7muqTqwmHRiio5ZR9TXXJBxMRmYJGqCpT0kJ78rggju9wQ8TVzJBOMVmj0SDwHO/8iHsVZEAYfMVtL1qU3D6RaZK7TE+wPUsujSx62QJVfLuEFlnfIiH6K+80E3pDHnPkRedmWdI8Di3OQRmPS1IVKgF0B6W36/e0pixLmrtS38zPRPyl5lMl62mlOk/GS8m03xOoiUOpb1rO0lLr9GuZYsUZVTKCmrMwBm488Zpecx0uv9CafOCeUAERQ22usrqrOP1VNa1MBkvZhtc9oZbWgwKCPj/5MNsIA3FnEksISoJkjPwa8xv1OSCELjuzSqtNLm4cm80JMdUg2KzuE6pIDUD0Bi7h1cuhqtf5YrP6uBxw+w8e1yeAj7nfX2uMp+vh4htjn331nd3S2KN0XJz8/vmD7rrFDIo3o9fFvApAwHXRj1nMt1DTihJX1bQiZoGYPbxNhCwypTGaeZu3raWayLU2r9NFNkQPb0G1BQDW8N3lQWythAYZyMAGzXeorXO/xKqdEX0c2QB4JGQD4FHSJQBgPsAM+FiA9gQA+LgvcTKZJFjcjloeaj+y3VR4SD1L8FgkAV7uNpzr/nyWDpapqzOS+Gxr+JZ+NgAeCdnl3wjpMgEwQzPgcSM2eXR1sqrsuBXgtjH0cxw1daB3k2ApQjNnSYIRChj2F6/lHvG6ggpbaf04GDP1q4rHrepqVHZpDTXiqsqSL1cXDwknsVtrAFXZIe50aw3Bj325urgh3MbHfocfeO0wI1i7gi4Y8GmioU8g4epOMi1TiZA5j4oxz1iY1rD+Kgvn3Sr4WLJmovbTiQ3BZyvTWGMykpP55JS4Wi+53RNqT1POkRsgXt2agYkNgEdEdgU4QjoWAMa4MK+5U25J7UiBppUT5qUPAEZc3cfqsDskA8aCgbt9wEZKc+fdXRi4nwbhYcGA7xo3LPI8V/O8XBBPx8JuWwjZ1N2Wv7ODrwtVm4pbUkXfWSv38s8fmGTrcFUGYPYs1PS6q/VCAcuGBQAoQhSqLT+olQQ7Y6rAYCJHVyT9htgvEFWarjQBoIg3vzI1NlVebwb/3fA/KuNZYD2cm15a/pfqh1zyazVpHHdJb36f0l9KrMmfv9b47rfmX35OW0/LkQZVubx09+B3v5n4zISWv1Z52NSCsrdalfAX9jWPHGvuJJXp6LIa2EsRQb0pK69rpCkC8URAS4mxx+qEhwT7kbHoenafRc/k/vuR6bu5ZVeuwUP2P7aYibivv5H2Tu8nlWk9TFDTPFrz/ci0l9Iac3d5pOJ5l5uiZYlIw67ii5DPEXZTzhA2s51Np/tNnmwRj5ITrUzM9BFgjEIZj4a8xjyg4k3XJ9P1sHU37hEapyC6OytUvGiLrO8TWWNONK9NFa/venwveB6Kxs8RKZcqXhcvLRzDgFoK5jJ+x1ittBxsJTyLejtgHOKiccQ1CMI1+BGEYRz2Ww95XmzhZMA9oTbKLtFGWkzJMidmHU4ysMxRuOZOeXyH4FJw3BnSK1xfZOPt+r24Mxmi6qBcbo5NSeka9S3i6fqku1L3+ArepuO96qzpfhbtliWlKtzqZiZzYClb/MpYyyu/C7MW9/xm2+UsNnJ8frfQ8Jo0t6m6haX+wPg1Ybp4hX93TZ8XH2KZ2tS0M/VedVY8BIhuzYL1XnOnnlYTdNFf6z0LnvVSPWp6gmmfGeNDn2RbDAKHklX3m7oZEF1d4X7JjoYeVjYAHgnZAHiUZAPgi5MGm8dZ/soCYKs6DuXtoxbo0okh8QXoZA7aK5I7PUDn5Azu1rPHhm0wfBr5qABlvCdo7/0rc//Xj8185hw2ytZZyi7/RkgjAICfGR23MDtZ+XeCFzLUOKChguEGFvS3LQOmQwGI0YOyBYycOrV9dqDfcMmcfwatgM3d91vx87lvxNVThw9b2J2FUXifEKCgmxHZ4inniopFd2HhAxYBeuf0Qt0SrMJHRYznCmZsju2KHm9iyQzh0+PkCsqRPYOtbepGSMJ5I+hFcXlseKxypDrYsw2AR0J2BThCOhkA7tkGWETpmp+T3Z2AOYEJmF4MWO1qx8tAANxrJaf/mBqUoKDCLoFfyHaS4+43noAU3G2jvwL6ZmpTMdcOxvdCN4TuTlBQ4qrDV6vuKvqyq8dcwGVYas8ETuCJ1/R6q++pwGb7u5GIv5TwZ6LOnYNmZLueaJbd46HNUj0qYngbZZe6rABsA3hma3rZRQT/4sH1iacBKvek9Y/nv531zofk7du5N6lQDTv+RP5CGVcK3yTbL7EpQpizBJ/nkTM3uxNpTpGeyb8fer1MBHVCponyWMMPnlZzXx4ZBWQdVLgGTtRpWf6WJn2YopIIPExVJz3uXb0643H/qU6GZhHpBpSMMrUIX6AqeQB10nUghzTkHNEGeo7YDoktEiGq18m9wawXdZE10V0iRn6KZIFYnO0swTEO7rIi4mEq+Mu4ckSATRZrNHQU4N3mLdEAuIBH0MEZ0uvNuSkptUdohjQg0HKKyQj5LeJ+KrlmRKR/j1Mw40P8VMwFRKyIQmWnmQhQKeJ9Urgmevd6/DW2GRuvZEteAAVnrKpuKCIbXORLs4tnsnXETc+6CXqnJnMF2Rn2F7ffnRIkNbjvGFPKgCOULNWjzzmfbDYnbkpPRKrzVnPBh65LdbFVwWa1JjUmSY5z8Ijr4s0u8d4+kXe4dYsnNRoFouJ9ie68ZgKKgK9P16632XzF9XK3/ZIPGFMuHlhumxHrdy3B7BZULC4Wa/C7aRo+DNbu+cHSobttwe/CSWyu0Fwq5KiIi3eRjUXWxjiYIjnFVmyt1PqB9iWA3ENSNbtD8FCyAfBIyAbAo6TLAcB8wMb+hE4wMDcSDFio5wHt5sGDlx9ChSkf8MEcvzs/XdjQm+gIePUTpIfUFWk+d/V1AuLbIRsAj4Ts8m+EdMkAmOHKgGcp/3lIHSv8+fQvdxr1w8AdC/Q7gn6YoWXdFO24ZFqBaSYtzlqY8bmdDKKl8bmsur98uboJpIW/2fwXo8oVy9Rkh9i8ULJ63gBbOIZNVCxSuBV0GW27NVR2Llpl3od+Jr2BL0AnmAs4DAa+IlbgKyKzjSVQ/LN/4RK3xNaQsivAEdJv/OM3v1jqA4AFDlloCzfuB4CFG5L2T8VuBnw4pWN4BrzQK12559OtaLNbFgYswmD7vaDIfRX8xirBZTHeS7cp0MpoO+nsgPe7AApmdO01d0pvyB6Hpjdkv3PfOiuoO4zB+iom4tI3Q/rrXX5lzais5PYdtQBrls7ET/HM1XRPKzg64qgAB9FGPaPUS62PYhElLUKkQ1odpYbqKsjO6hMF0IsuQEmoEV8lHN+5KT35YeqjO2sR4O7C7v6BO0PA6a9G/KVcxn97djO1l4i7sovKtsPr9jjXfE35eelHz2mrVZmgVmhoQW8lf6DNe8L7OflGRHvfv7Gv3nYpydpO5HaItFaRY/s7hTvu0HrV3WwFzKDhVvWilgg8THETfPCn4IIEjFOd8aDhzujp51vNj0tyVNVCP5RnnThdHOSNcjNM1ocuugVPkRrjwEupgt9LSUE3e4VU8EfYTzENPGZGQwa2mdxrThbxApVSUFKqcWcyQj5KTkHbZjJKTkw4+1M+kSBZIAoIl/AeIT+VEt5SPTrmyAelnIoX8DSdTxsTY468S1KBXDMiAqWzjXBTdQcC6VI9miqOiwOx+PyjjeR8PeNuHWsNPe0iBW489w5Pm6mFPSCTjogje+el5afVhOkFFwHR1V3FM1uIO/PNnHs36AtqjQPZIVy84lyVGw7Zmc01I25JFeg6QVJM908yGWI/xaRwOeeaEdHWWpY0s1O1OPMHZY1kDvOuASZouZ/vcgiJRWfutRajFaHZqAReaeU8i8xnsb6m6m6UXQ5frbQePLyIhNN7yJGquwZvbpdIWRevZZqDBQaeJt1KhF5pje3Xvk0zx3dLAEk7EXqgbAA8EpIk6RhtXWx9QLXyAWPAFzkkd+syGLD5itbD2gGwrXnRPbdwiFMitFM+1nZdyrjeyKmna/k8qLBMud/LnfdLn4fCZM3gQSHbIizkozIpZgjbsmXLVk8dyfM66oQRLRqPW5LJ7cF9w6ty/KecUsX27RR4Kw1zoEKkfQExfCMYojjupf57RnRK3jP+NXsnm07iPRgH1UDIPiif3F6s7GuKkeLWGkYEgV3NDGpARFUr+5oalZV9MZLaMhxXFQ+gRlxKrjaA0Yo1uLSG6VGuyQ63hnghl9ZQQDxkfQm3irl54tHMgj+UrIottMLj4d+ytXmw8AGLLstTbGkEc0xE2DXpbxY/59xCuKMsXGV+mvSVnSxozmkbvuTztjVs7K1urvBB1nETpG3ZsnU8PTAoYE+tHbaq7dAqc4IBm/QXWGTN/DwUEOtU2xbo01tXcJeOLGVrJakNKgb0ksttYKduFRpeawizKbPfp/lox15Zm50Xc5cGmYPNEObDp8Hd1gedWLPbqfqplJxewO/cDzXlQsMrcqQx6LWgZdZYaR0XJkULgN/YOcJkPG4gXqX1r7ALl5aDymfV6ESuue+em3iUa0YwgnlL60E1oxwWXUAKEuBD3VAoU1oPllQooyyp9Sdu4StV5lRgbnJdb8gRKfeoeX08sdos3c4mvWuNgFpSJoOlPI5cyV/fc6disTuJ++naguoq+qm4cNyUVkokXpNn7/AwKU+55UBBS0QSb6FNvynPflze/lbkpQU2biibRdn3hOe9svZe4s4N7eHOvKdBwkEKCGt6UFMd8kHmQ/66Fgxqueq/6gAaWrAUkYCxXG5zft6BkkW+m1velufqsh4lt8Okn2AV3yyr73B7jfkx8h5UQEPO4VApT5GCYJ5ogm2ZsgOPj5qKx4uaJeilrBkn4riU9DXH9iUXfuIcPK7N41rPEU6QrCAraFFy73B7hvRuc0aWNK0pe3HsSaFqUylJ+KmMOzcE+hUWW12q+537Fajg9TYdekMuOyoVqaE1ZWSkelRpOgTRjMVzbkmtl9yo6AcudKjCHshQRd90UWo1rt55MN46hRRQee//WMIPPvgThQSlVOtc0pfHisUxcWXX51R1TdH9ITHhgHq4qbolZQJh5HWyps+/w+0Fz7pIWn69+hIQclTWLIVNAW9TdQvvctsFYvHXtsolBWLga++0rRiXn4lgYwDKZ1U1rXjGa+4J1Vl21UtuceE4Y2rAiDcHJKXqNOKmY/FcruRX04pojawrrr5WYOuPMgGeNzvj5UXQdCDW+qww6e9hcasZi1Yo7vOojA/bAWzr2ZE9AXBkdGkOYKERHc47rk6MY08cA3jK1z0TDXNw547j5zi3s6Uw5bMtxUPqstDsqCDhAfqAsOHTm317ynYAj4Rs/8cI6fIdwB3q/oq31jACAY5u0XiseuzEJuDLrfpMCWwXOc5TRHfh6NELnlCzlkHhZ8BW3i4z9VpUswIzn/ncR9MQLCDxAEPwGKXsOQNjUxq+HJEynks3EA9Z4x3ZFXhNn7dJcIcOPvvLl70Jto6WXQGOkH7jH7/5xdcznW1ixe0+sclmuildKdBCPU3AnSnQ9DLF9pz01q8NcM81DJZlVf0ioEWL0GOtdZGNVeaqTaVQj3R8Yvc1NWaMvBOx2wzELrbqBc/Dzdp8oeEVXuTtd6d6eg0HSe0DzgGLb7h14wEE8CwZ/V/3XIfPNfewSGExfJbKXKvTaqPsUtOKtVlsj9faawUOi5dT4mrEX0J15Rpeh68mKdWQoyJLmsehLZIJS0/3iVSQP8n3gDKeGsEQmb3m8y9IrznwZAmuMXcDdQtJQ/ZTvUGygQ5Mse9hv6SF/HIBCGoFt9aoyo5Nbt/QliuEH0XGF1n/Y342wXaUfFwrpOWQqGQ05Cz+p9y5xnurzFfw+dDzRN2U9onoKDUaM6Q0ZBV5hvQCmyL5WaYioy7y5AG3sgQF50syvcr8O9y6x+NdQsAWcW/TEZRygIq31lTGpWSUHJBk0gyFFjnPEaP9RI6waEt8l4eiLfE0SfHQLtO7BF/k8RMmcoT3yxNBpTgjrb1dv6eWgoDiPxCR3cGMI6840FDXFEpGf+he50Mr0Fiwy7IxpVLIYP/sGqnLXaeZmJpgQlYBgLHMaWuq7sXwozV9Xt8M9bTgi+eKR82uwFhcwt039M2Q01+tl9ydp5+4xHpdBa33232NPICEYTXuUM+rr9dVZlr5O1KgRTPgRdZjXykBPKT5Dj98F6B8ZX60XWXZDuCRkO0AtjW0Pmg+4AvWpViBTa0Y2zBA3UOcA1KjB7+XU5xIx3UVd+usELKI/gOEy6R7ATUqV2XHJeLqbvPuxaDZbnfIBbzo2aofGa3g2yJxwRtzHjon9GvLlq0PqKxf66P+K/kWbBk21iN1Mjx58Q7gbh0ZCt1PwiucNlYiYPDgXMdjaRMwzMQx49fHlmEgPv/OxOcq008sqtmq4qnJDl+uXpUdnF3PlAi7MpowCgOLrAsb8SrzgviaPYOz+BdZzzGh4plia5V5hSqW7sJnKJny5OFQ5SEPPvMXOlKiBDqTqX6ypBVO6Z+zZcuWrcHqwK6zvZfyz7eyIrrpL7DKwgwpL6cbSOkZeWIySJPBqEZq8QDjck9ZgE0/BKU15cGzlvyWGith0LhqU3labfsJLwBVd5velmLGuxD2XGMalQDGb8+/IP7cXh9rbXaid95sXykocTU6kcuXAoBpRxY9UN0Tql4KAi00excMfKuXXCgoIVUtKG3W8J8w8O1SC/2a4fxmG1TrmzXPFkmpQkj0JNYDLoqobyiqOAzjLZSoO2t62qUsqe9tLAHCvvm68tJMKVsNOScpJgkAP3JczzbCc86NAPp3youSUnVLqghJ3q4npp2pKIWa1Lgmp2s0ouRS8lRC3gE05O/KL0yRlKl8jfkx9r/JT7/EG2/IkSi5N3hZQ15gY405Ge1P+JlZ3t8iIePO44KJHGGFihdtmTsh9mPkH3H9EdcLRK+R3iU0TfLbfOwmT5LEk0yL0GNxqrzefBGISLlSPTru3NgjNNksInFTeq+MV0ELk9VR6k2fX6psEY82a/uSF8KCEOeakYiUnyG9zeQW8RnSa8wDS7xXpbHARpoAUMIbVIrjUnKruaA15Zng0+164unydXdcxUtecgBqSiGGZ7YGUGodLBH07YypbuNoevw1Z7wq4r5bR3MXZo1TdBOKKB9X1XcUMHzh5jVVcumWDrtvv/mKf/4gEdjTmq3yPRHYK9WjgGe2IKiw+DfuzGtNudDwhv3FmGtnbXY+5KgUFK65U61m2OF1IFObwl+Zda2X8FbdRp/s595a0+en4slUcby6qxxutjk1UTUwtq+1ta3THtoCWcR5biYKiAtz8CeM0nemxTV3a0WLbMyQOqS/ayWA/bYfs8n+r2DL1sjJngA4MrpkB7DQs82ATzNqeUoHsFDldP3kTq/4MY0gHRqAhIeU6DP3wdClu5kH+0IuTKNIiPvpyhLiGPmOBOzzk+0AHgnZ/o8R0lV3APesnUb62/xY1eCJy7/tE8VHn6ECMH0+qx3QTvhMJPKlx0beImymUhcm3dYI6/OTiIwWt83qS8zb22Fmii1xv0mIz1VJpi/RCpxkujyQ3R7pAO4mCqeRSBZd0+cXPOuZ2tS5GoutBkFTZ/J2bAfwSMiuAEdIv/GP3/zicqazWmgHjR1mWUFlaAfAQqIZsPW3arcDuJ5RBC881JFG3mHAp2qQqsFrs3QUVuKqM6b2XsoIeTb7/vaTiLnubtw+qKGpdZvXjHc3YLNFanRPMP/A8qhYQ7uZWLgwRQ9U65sV4M2EuJ2B1aIrtGBYxs431yCe1TfjmhYV8/hr7glVdFoVRuHWQ4BibLl54mkgoognWlzNM17TSy7//EGj7Ir4S5KvCuRLgequ4vRXI3ol5/HWM+7YbA7IlfwRf6nQ9C6GH4muydfcqSkpddCMFPFGpJxC5Tmefqv54Velt58Sv8cbecZU5Foz0ZAKs7yfZFpBA1RkIMkk4EXba04CRbx6Q55zbhw0I+NS8q3ayy+53gQEG94jNM/eo+b1KSm1R2gm4/mGfiPiqBxEG0Az564WlPH53f0nkXrdPTOZ1BrygewQzZ4nKYacW+vNOaBSCu56fIC+GRKLSb6qLGm3nA/Xm3MRKVfCO89evhnZl1ylejTuTNaailqP7uPJlwKJwN5OejLiqOTKXrVgQfKGaVt4td0TKiCM16ISCDkqIWfuaTVhhbJAPaNEHJWdtYiSUNWCooTUFhh+28WC0YXXepbOGs16Zw9PAzHnQPhirZZxcW50XICieFjT54v/bGz6L+2IQmJNnxfTKcSGFZfHpp/fEVsorjL//EHrbLSeXWZKvK+1GWL5ntZ/a/duc0KDWFI81P2s4vJYCy1bLjdgwbM+TkHMABDTARdZP0S/ovvvCtUV7pdYtfv+Di3bATwSkuz6b4R0JQAwzy4DPr1n5UwYMJfnnjmPI3us4Ohh9Kyefu0qTPk4Oy/I8LoiSHiAniVafAFB05fi9LUB8EjILv9GSFcOAHfA3QFFy4h+ZR+3DDtZ+Xfpc/4u4OiYnWHNweTz5rXC7hCzxOL5LK97hT3EVcWjRlyhnXJm4YIymU1ZqXA/mQGM57cZl1LgDa7EjmTAprrpwpEyIU1PDYljBTywtsPsB48XPOvVpgK4JXUwMjnBezFlA+CRkF0BjpB+4w/f/OJaBnobTLuxh/XToCcA7vjQ6wbAQj346GafWWvHij6Gvv07e62wXxB0zx7AVpntjc0b3Z+NxeWxng5ggcQ61zgAAFugtbnBbUnaHR7uuwD++YPF8CPaP+3F+xKI1NpioPjGmACE0LUDjd1lZttiAjOrOXuzK0XceGJfG3S/I07nQTezo9W0Iji0/mbIea3q8NUED66X3E5ntV5yu6+rjbILqD91u+fURt3l8NWAsL8oS1qqOJ4I7IXQNUdDwNpFNhr4HZR8VAAFTQBgBS1D2IXDR2WXkIhiriCreIUbuIIMCEOwirzXnPZJmQpKjnCIar2pZBqhAp7nnY+e1OfqzpL4frzOrlMqV5uxhlRSmq4b0vIm4yGqj7gO1JpKthFO18NxZz6EXsAjDpMsaXPOjZ1mQm/IcWcy14wI86sZEi583kBIq+8kI6a7FzBvuCdUcfhmatmq1wl4HJrekM1/AZFhLoKaS/Xo0+XrIiy6leOdw1Ov6U4Xnnb021OWCZSeF2vIWNOhe8p8NOSobL87RfvZ3nHmiz97zCbBuJTEKWT06BUnj7j0rHMjem5JPaOIcHIs+dUDNticYCfqLvHB2El/s/Cd1g9AQX/v99sRtnrJBsAjIRsAj5KuCgBmZEf0BuhMmOtZAWChi8fAF3NYzxwJmxq/uuN6J5AalS/ACzKkRgu7jtbW9lOesTBZ8S/HocWXHu9sA+CRkF3+jZCuHAAWEjXDkbXKKFaMw0dAC524/LvEuOxzsv8OIzHafOnOXbPZsJlfbd4Qo1gmIBMnwwWa2oUn+IJe7Cx0TtMHz7ucq+DzUh5QXw0PgE0dCW6P5BZWdY9+Huvpp9HJMLANgEdCdgU4QjoEwF2ywtF+nwwdDLgbAKeYLBlZCMdmwMdFvwNWZdVAf7NQz/crwp9F7HOKSWH9tC7Q8w2a67fSL2GFbGtc2hMAB2C21VrVvE8wP2GF7P3cBTzjtfH5XTOJQUzKsc7mEQw47swDT9eud7ZEFSs0GV6gRdHaUO4D0Iw6p1vGTu5BrE11bHYC6/e8CJEWJlHxlvfWJzyzheIbY8IcfLioqKbWjCa1a8Y2F41/RWPXidbinvGa09+Cx+Pzu3vrE7efeytTmwo5cyJTWjSB9lNRjLhvL9oWcWCcwh6hcQpeNC9qBcWLCvioFBlzUBIMGBCnvbfpcElqrhkJUAFE398yXpM3h8muslBBceMo4tkjlKlNCVYd9heBfCkQ9hcLDa/1nBQHNFObapbd4eBuoR5pZhQppjbL7lzJb8LLAeow+woJrq/EVXXDOBXMUZ+sMfFxAcYgZnGfFy0zABIWNhwAP6TgLoGlrHD39pxDJjajnlHqJbc41c2E9u7Tpu98AqGO6QsBw1MOgaXsNXfqaTUh+K6YHiF2JiBmrYkSywS6YgHrn+K2+ZRxCireCHlxGqwyN0N6kXUv5WnSMuUW/RW/+75ELU0zZ9Pfk8gGwCMhGwCPkq4QAGY0R/T66awG4M4WAHOxI4MXeUADl5QM6YcSjIPvCow8Hl+XHhl9pEaFvIrt9KFfYvbgMywbAI+E7PJvhHRFAfBpdGUDojWjB+1xtXWiPOfLNQFfFgM2rTCn7xtyJvJbeL//KPZ/sXMNC1M+JVdTIy631qjKjitYBIr+wVEKosfwma9/lfnpVsdptg8d5WcgQX/F7TxjGcJ9NuDYDPhZ0nExsA2AR0J2BThC6gmAO5jogHkh3QC4u12R9VPuaAbcp6fmCdXPVtsLLfdLurZqhvRW+zeFQHGCp4p7uhOSDyNqLT5aa+ps29Za3bQWE213x+LDvSeg18Jh+K2I2JWUar8oCNO4DDzeWWpD0bRTtMDhxrTY26YB9rAsI+J/U+3PWmhlQfcgdv2M2r28v+ZOE47PQ0IpQr+Fy1NkgNMVA24iSRMJm2Ha4sSPtQ6QudNCjoqAzSZrD2qNA9kR1BqFpjfsL+7vRuYm1wv1SL4UeM65uysrMdfO452l6ESu0PBO6OXkQUx0SqZCeKwoS5paCkq+qkDypXrU49CmpNQC6w+4nSCZYhJI1yf9zn3B6Uv1qNaUZUnzO/etB8t67EQKNPDmzj1rlrIwuTZ33QToiFke4MHtG1pupCi3kr2dNc+tgglr9c3WHAhx2zzQ1oNuhod3GscXjGbexV7ddheO8VEgXsL6FoTv2ZwGkalNxVw7gtea3v0EyVXmxA2fpbc3YLbwSDFp8l3ARP6Aj4pYzEdF9DoBFtiYYiu2Vnpn4ZaCHmE3tlbqSH6+P+zbsnUoGwCPhGwAPEq6WgCYZ4UBnyFkPXMALHTeGPjqHMdLHAq8soPRQ+ji0wJPryuOiq/45l1x2QB4JGSXfyOkZxAAm7o6FYjQaSquExSBl+gAFppjYBfUi9IVIcEnkFk9apA590rSdAaHktVRsQhrBGUOuMDK6gQvdGTIygccAw9Qh+PZBsAjIbsCHCH9xlvf+eJ32shHP/prsl7z86pnBLS4YW2C3vH51nPax1AdcweqN2Uc3A/4qMjrbvK9yIbV02xVz3gGk7eZb9DsRdp7kzIMSLvo2Lym6u7XiFcYLjtsoxgG1hnSAmIViArLqQj+La0Hray6JQFWrUC6Y6920/QHPR469gHq5//OtEylPAKBMs3uG8KyvAwJy1w6rd2YYfqMBdcsQwrls6o1Sdi6nwVBFyet6ZqVJU20GQb0hgzMOTc26nPpelh0Q3haTQS1huI/8Dv3e/ZEEAfCPJ0W2aigmHMLuq8RYV0VbYOFQVkQTbFkZ9i42Xz322Mdp5N//qC6q4glO451ZzJ5x1SMBwY7f3CIZs12uW3LFNuPXXe2/INeuHeAhksCMPeAuCEOonkVdDSz6NYiG+YnldnEt7s2MxfrnvuCEfgMhJJV0XWFt2CjhX6B+yXABsAnkQ2AR0I2AB4lXTkALHTVBu+G13mMu50TA+bcRgmv/uG7ssOCs1faRjxy+YEDdKVwrIbvbD0oz55sADwSssu/EdKzDIC5YnXIccOfrTpZBfjBTIE+UrdhH6KXvRlnqAuZaFiY8gGX0kj4uBKlnUJV5dyL1SPLyG5L3MC12TC4r9765H902Ztg62jZFeAIyQqAB+DPbtbbU9Y+QdaJL2fLgE1wZSU9vdcwGAALdVlOrV5b607wGx7BngC4Q91Rt8L+2AGAe/cD7qNO3mYcMjMvt55RhNWyJ7o2bwvXY4S8II7icAgAbL79oXam1U1rqtva283/6Opb3K2eBlDVCCIe3INWM/pudA+mma5TjA0wuebdNuopDMGAaZkVIcZr+ry40WHMFbHAQETKlfAK7tvRK9pPZYGNd7gl9r+J4QVuFDzenElgNqkVyNnv3C/Vo4DAzCIpWmvKGHnggaVsU3WLvOgOh67VqttqmeyvqmllUJxyxpKnnWjf4VrXwt17u2MP94x2H+byNNfWfab1W9a4xEznMcalYeJ5LNeI2MkC53c8ZCZCi2Xizryg70LWa2qRtUXWxyhNaOnQTrkV+PQWbNDUya/xqJUIQ8jDN/Wh3/hl6J5xI+Rhsb2CXq2S1PHBKhQgBJ25BOcmGwCPhCRJ6p/PbsvWMFq5YoN3Q+rSXRfHlbmTz2TLR+iQdY+oXhEkPDgl8rL3sFvVY2tHFy9Xf4gQMDNbBmiHmQsYQwREs5DBy1wpYm3Lli1bV1pbVymB48T0dxR1ZekvRu2Xbh+iwqgAew4aXnGVLNMLzq1EDO20RrBia61TWY3K5YjTl6u7tMaVmhfYr5Q6jwrK+lo91++lLLiI1RXXf209QEsFBTCbzJ18W4fTYNhzgg3oGCi3ZcvW1ZFgkP3or8hHHWY9M/2hnNVdRzueNGHwkCi0Y2vNZku9n3t3CMhkBi8DoJdcetfeECrhPdJHaEqWtILlQ6+ptjKWO7azLca53MU129FpB/3FCPv1zBbEDWvYr1XdtsUS3hJxLNiyutsDLWONqjb5qLlLh6G/0IqMtlpRj6S/DO0Q7Wa9AeNGRyEnJiQo7XtV+InFO2o3Lusll15qM0Pr/lp+IlDdVQqzhYJlEoOZFC38pgVHRPzZsaUhRwXXzjvcAnLNSEk6XEDkEvupWOcNCNwrVlXAi5ExjnHViBPMagQvrQerTkVfczGBMqcWvz2GT7yXw7NO8Vf1kouSS7zHNqevefooEAPVsq9iveLZzeHKIiwZTxR3zhoLFMHfnsttrueuZZkOc7nJ6YfgvtaPDtE6uvU6aSUQU8VBcfhq4s5uN7a4okWVYrJec2HxaMdEEGHaBhZZy1sat0TYNetkIWH8LTXANP5eYfp7D+75Ady3qK7gmkcyP2HjcI3bbxBYxu8w5mEM4Wb+nMyXu2cM2Hp2ZQNgW6fWyDHgkaO/Vll39XHfyGgdpn66skjYqgGH5iodBXOIcIBGAhJPsXXkMhfGZQcTYhsP27Jly9ahSsY35qV/OV5KcTh3SW2AL31vD6mOIcgty5234SHc7jWH4Gq6h82Ca8XoxqfAXvuxyJ5xj2FlX1MMO0TPeYFVxeNWdYxiL7ZW6qj6OgrFwpTvXFsRn3cFJdbfbz2TbBsvdEQodIe8h+Oyw1rxzk/dG3AkEhY9Dm0GbMvWFZRgJx0M2CSdw1+2ZtfzY0m8ioAug/yIAPjnD4SxT0QrD2hoeqhhGDCd8M/kSWtLnQuKTR2AgXv6mzvwag8pvZDng76dUK1uYNF12JSwOVpBl/kRvcrcIhsmD7bGVusGEfTQJs9sQTcptbklguBa033Fq/U8BToY3jD0d4C0dluqOEBW1lu0PHpkenBsUOZ2S8a5oQdceinIA/TXx6xrLhqnbtHy+kKiEy0aet1VhMLSoVVXUqrCyBtz7Yi5BeLOkKOSLwUkpbqTnhREXxzf7lNdrMqkv+Kk1d92iY1QFYXY4cabMddqWhHEtGWgf8ViSe840wb/iXFkxUvsGn/Otk8ReICA0H2PhfX0MK/WtWGTnwHT3EyvvWQl8dZrc02fF45e806T/pbq0Y77rSvsmBATJoultlSjMqDsawBe3LcA4isUPVcX/d4T//oN4nsLwH2rawz8FhSZCUCRVw22fQ++W8LXnwQ/1rgH92GGIYZTbY2+7ASYkdEVjYC26uoPJ5336N75RUDbOoGuIBg+UqPZjXgkIPFgXREue0U24zSyI6BHQnYA4AhpBCKgbxn1lf8UJtrLrSFPXx+euAK8rFmJV79oP7Gsw5dzhpth5EzDPTULm1fo2F2p8u9E3X+PeMpxSfAzqeHZsB0BPRKyK8ARUkcPYCsDPm4E9GIXwByQAm2VlcqcoBmw2OYBDXGPkTRrqg986tg/HcGwHejXRH19N6xbm33gaK/tMTkixn4zNy/uzKfrYfqAanE0V5kTftN++9zjr4mg4B6PCaevFU4/6M/ae0ZAX4y68r0HzzDorZ625g6ZZWEA/BYnd8LoNEwP0uyfP5CUqhnc3TGtYfD8BisVNl3aHmdNf9vV8mpjbLbYCRk8s623b84eME8YYZO13ra+unmGdIeQH0pMVhAYWNjZjyTr/dRuyhcb3722fkfTmglvXiMdTbgBfTPknlCt79H6RHFozHV2XEfd4c9YGLBba7q1hvKORgkewjYUz6sH8L1ed970kaqRcOGXWK0CFPqwZ9PvC2AlvrfAUoGbMT+hZNWt6qxYfpMa7Y0x3h1Q7vrpeQ/KkIRF455V48aQIdJ2BPRIyO4BPEoaAQDMFRqS6NSFja/ZDPjqK34lrSFD6mr3Hu6p8/aLnLcuLF96sEaFDdsAeCRkl38jpNEAwKZOQ4Ivq4Y8TetfU6cp/ypn7QM292QOlPaVX+XWvxcjUQReqezxU8p64VxqPvaV4sFCw9ROw9dXw/BgK2VJMm26ijuUZ0xG2ydyZOh0zzV36FhtjM9QFZQt4jOkgS3iNgAeCdkV4AhpAADGgj1OBoCxfKaJa7nnE08JgI/QaXDjEBh4sEQrViumMqmV2cn4UP3ob/+tMsNvzdVaqTBGOnHMtdNzTWv6fHF5rAULj7IoH8rcpQFL0i/tG9+RyWx1eQ75Ns9EIg7a8urHY8AC/SaOajl8XAlQOjFon4vTw2zJfIh4+22/iUitR8fczwLVG9nLguP65w+qu4qAoAJ5ltaDSlwFBJAWJFh4hQHrlph9ds3tEcuchK9bZaZD94Tud7sWO+5qscRcY7zEguXP9nPVvNKt15ToFiwc9mYQNLDI+jQZmQPMbJt2UNrUqa1zv9SDjx5L9wBYhXsebkzQzB0+JEVwxVtE1jVPbb3thlWH0BeD+1qg7w4zA+IPY2slVkCDjcMfmK2w6yZ+iVKTN9vDAJK9QK94cdHB5p5xZ7+dYwPgkZANgEdJowGATV0dEnzB1gobAI+6Rs43fHWutRPpCg4XDq8rAmWvyGYI2QB4JGSXfyOkqw6A+30HaZbuU6dc1fnpDEvEfUif3dpsnbduX9Wk6BPLvHzMgd2r0SxZlHm+XL0ccV7iZgxZKV2pguo0uhQk/Puf/G8u+BVtnUB2BThC+o0/fPOLaxnrPT1NwEcC4H7TR8zG5/0cwFb6e9hu9vQ6K5uptTWphbyaZK5DPQF2B51tlF0CsPVYeMjN7iDTFr5lkjnr4/24fgsAW1+0T+L0STay56aaGsZWeyZaOF6e8FkalANGd1trwPiR/NsEt+aWZ9oJ9FkB6e4Ou8OBVXE+D3IDn2BbzD6+gxlwh/pcni09MPbhkTKf+6DHesyMAeu8ikLDu+BZN1ukmwxY+IAPAbApMYa/0mYFXh3a/9qhe3DfgKa3Z1ByuG9ZqvQKR0z8sw5Hz4JMZsFvVqcdZap4Rxq+aXaz+B14bmoP3VqjFXO90o4nxFsO0My2qLMUoZk7NAcLz/H9UutkzmoVAABePElEQVSGVYJJd/iJ7xvv9ye/+tWXbAB85SVJkt0D2Nb5yPw8vUQ6NdK9fm1dlvoh/LkrMY7WQyPSb7ifBvQhvvpsuF+zugseQLS7DtuyZetyNOBbRoZbRkOhYfy142ezRUPpPOrDqA2AR0oPIQDRq0JJTy7Tbb/SdT3KV+LdmWWe2Y24Q5kFv7UUPKfaz6yUBhdF1oJqpMsnL+UOL/LwVmNbtmxdWXU0Az6lBnwsdPfKPQP6e1x6twwYfWRNCdtosWuFDyDR8lnqJZc+tFnZapE06a+1cemhrEBrgDpAnZH06/HXDhOnLfys+GBMkF1xZEv1qNaUD3ujWiFZT1qWMNq4noD39WOKSh8ouwmzhp/1TLzC4h2193ju3LzzSKUOwISx/dYXNT3T5o2O92j+2bHlpgT9TVhuC2acMHbakTtQVG57XY92hHh3dGs2wLOecJFCfdDruA4P2rs0/fxOWyC5MgS+fcDhPDRzy62AvN/T+9Wu1vRs47105Ks3VTf+Ckaj9BSTCZKrLCyytsxdDZ9MmYX12Fqps2Z+CLdw34JtXi1SXeFmk0dlOH4o9H3j31sQegHKUDTyhzTDC16yFO1bRg/eGQA1Kru0hlvVoYV+zXJU1HIVFK/hlV5lYYaUl7JMeZm7Xsrz8kpVdpYj/hwTs8q2e1ZHNsKuDLQsgXvr8Px51cputnm12Csv6jZYnvXqNhT5UJpmDinCWUdD2Dov2QDY1jnLOsp2MTjqcrnvbdsE/Iyq38zaq+wY7rgWRoEHWzWADXO18XA/IqsR3D55x5UTysbDtmzZujSZKbvmj/kR/2IaJLsCHC0VLccrDmljql/3wJN5zxU0DVsLpRULD56FMr2HZK4AFbaqo9gbXPt16ARNRqxF0Q4zQL82H93l01UomRZZP8Fm9EuiposN+6jYqNiWrZGQIBxHLjbA/ttvtafYqBNJA0CGbC/3ZMd31gDomILUCSmXYMamm/BUtNva27Vdh41LO/DcGtxtNTdNMQ40yq6Wg7Mf1u33Nod3oJ5gRwksGjNum4A2cEbB0adhvcPUNmI7xRyCMn27OpjW2xO8KbEZHYdAGIUfGAgzBipMwIQlxdrch7KxhWJte8Yc2QftL1G0/IllDQOOvliDeNfHmS5QXB5r9VBeyh7OjfDjeaVg/qmXXG1h4wnjeuzQMCenOI7G+Wk19NczippWWg9tHrqx21z18wCNsmvVdzPsL+acEaAgRZd4b5s4kCMmL6iATHmR9VCy6r6lcwuy8B34BGzgDhAp8irU0nwImjlWq7yvM9HFg+/1fx/TMrlvUYFaA7/xi8O1QOAa1b/pEYgXqD7nKUy6tYWgSKgGwJljOovV+LsggvoX2fCiiswGkf0g4l7yjIXJAl/js2bb45rsKCz5gZj4bWCNCLplUGFx/twGP8TgtV7vxDriLU6zaVjB9ROt9GxboyIbANu6QJ2rLdj2+9q6FHWP9l5ZJNx9jYzyyPso4mGZg8XDwq5NlzWqOBgPczWGO23ZsnUVdaxvEHNQ5sy/d45Fs+xa0VZPCff2BgRgxmidhfHnhmVgTrla9LRTJRiHPSOAfQ8wLrqrEM501grtlDlFyWdtojZMtWMtmYSPJEcsS7D/MwbJh17GY71nmrRMud/yPTfDqpMVbAPY8JBaZcHGxrZsXZg6HG/AKnPDtAG2ytrRvF/+c/eLnlwDAmOXj3rucb9ze+XEDpbHX3P6qw5fjY532nNV1kDaAerj7xQIrR9gNgmxU6kGYmprY+72ztE1z4G2bY6Bb6A18xQ20E4pxgoH7woN1iAIPkjBggH7fWdaUA1YVcKYGaB25SoPlgkagdn21OKezDtguVFsvxEzwLli+ReLq9X6RHPNMlzrehdYmgpj2fmz7X92K2B5FydSx+dAm8++44Q/ZQ62cZZ2BB44YypmtHXR2CftMLu0HgwsZZ1KFSg0vDFXa/9ukYiRD5PNEoSgKKV2mGFyC9xALFtqDeSKfwNQxAVNHSnO4gqLbu6XehDfe/5WbHLP8OTQFPWdVgNgoKnDx3C/qeNvzZN2qzq4ZQ5WmVeoqrjNci7PWIYwxqf0mj4vykYx+0d88ot/M4QzhH1UJtleZX6R9R1m1IhboTrFVs862T3VDK2VkeGl1j1qVFZuaz0Ohzm71HqIxa+Jn0BK43D0eJKtKygbANu6DPUcgDvueIQ9imframp0kfAzNCY4GA9XFY9b1a8UJL4iadLdsgmxLVu2rrTMTsMDvsKyBgk7b922sENbo6hiexVn/mkOeZgwOA5AFB5esTKv+1R/1n8xxdZKJ7ACd+i44c+C1EbIRMgcuXC3WjmEZ6rLyq/u5y+0ZcvWecs0AZ+AAfdb4bGW988fSEr1CDw8mA4u9bm/5/Q+E14ObkG6abR37bcB7RDU9Bcej3MPAJ/dYdEZdFwe4+Va5ExrMzsqcSsJBGvP4D7ot+PP1vZbA5w3LSTyKPYprJYngf2DGbDcfpTXYMkCCAXDE3h4zOIC73diDKlEmx3cM1vrCd2H6pVr7rcFI/pYPMNMilZh1zBQCmU697YSV50m0bd0ngZQ8H/uQGSDi8XqGUVMRwAkpVrPKFPxZLoeFt1txf31jCLiypvzrfa3+mbI6a/W/W695Godjg5KbUZeH3+SRJv6HWvNkj2jGZk6DIH5xUE3j3gf+ttDAx3/1oCEEPuA8MgK5YhFyAjaquG7y4PMh/10jyIGkDKw0WqC+6rRB7ejJ+6rH6KWPmTDJu4VP06teFSqwFvghdtGfX6r9aKrC7zLzQq+LSM7u0B0j1C1qQBPqwksH9GZ2lTMtSPAsEmCgTLeRdaSTL/D7UXWzNgYQYWBMUqrzEbIIHN7Ya0qSxrBmbVka2j0Y3Krf7BVYjt7xlyl4TaEez1k6+rJBsC2royemeEJOwPQVoc6zgcxy/YKOkieXR7cIZG40g2JrxQSFhrAX68Iee3YQoXaJW2ILVu2PpAa/FV18bXlFfxyt3W2EiNoacu/IzTzj14XhTm132+JbR8pCSuw0OlruY7C5jzKrTOnvx3qrh41fNutaQu2bNkaeZnNgK0MWDx0JiTYqu6GuD0B5Gldwh0aXE0pA6Hj4Oap3Yv33PIHxr/HQmULvcJ1n0IAaxuowFK2yNjhVj2Au6hpJRDrZMD1TA882a8VtH++xRFbUi2uUAuPNEFvx4ETILx7zcMe2WHSp8cMQLjUfmf3AsNLM2YDLFmYutiSTZiAMroAH137chD9FQHXAu4CRViwpCibPYDNWOlUe9PfUuulAXyoaYW0Ym26LNizx19zT6jVXUUcFIF4nTHVZL3/uudPf2/ilXQ93FTdBQXzfrHCwFK2UXYJt6tecmFC5Q4nsakHhk25l6d8WHXPbxAvJ1suW9ny75EaAx8sd1J/64kXWMr2PQ8t70WQdbHk2hIhRyXm2inVo6tOZkiL5IMY+QzhVQAWWavgmyJ1WOktrEPLJH3YKnjlsNR3m/1xV1oYuOUVjtNZZN2Cla4fqoLg37YsA0BmwS8qt1XmLfPq1lZZQAK47XnvYfOOwMDX3Cm3pGLwYOEGXtPnr7lTq9KctUnw1/ipjll6P+T56davFzbla4CKW15ohRSWI85yxB9KVguT7rbB0l7G4Ja22l3stq6wbABsy5YtWxcr80fZVR4f5DIaeF+2BviGR4gNXxEwbMuWrWdZV+4T0dAzM5vQ1kirAwnHr3Z2dMly47hzAa9YR2EgtlbKLPhFRadG5XLEecoVmuXWDjP9GgZffYledx132hWjLVujq24GLGR1A5+JQX/Ihri9Cc1purp2qGca84D1P7Bk3nY/dCT66olyj9SupTmuqfFOFNfaUV0xwt1QFmjFTMQ4UpJSbbOWYrzT9shfveTyDGOvHF5iD6cs7tIHlvxkEdUbO9OTwSqRLy0AVkdB4ocypIy913FczD8f9Drca3C33Wi+1nUD4wh2G2332u+0HusFdFqwVi+5PLNVz2y1uDzmgULDu+BZrzYVt6QW9Hngf9Y/seBZj0HGMVVoeGOunYI+v+BZX1uiqbqtUwHaLsCi8YrdVN66VeYROQEJtl59p+8AbbWJm1jdslWHb+2B0VPZ3KV3MaNYPLMFj2XhQsMbaip+5z4gGuiuMmeg3w2MMHwv5e52HousiwHAQwxsSji+bkGFw/YXK2CyYVOBXk8Ug8CWOZfWkUZRqpmT9n6WPxal2hgHSNz2vNe2kR7xLuYAceYAgv76qNznlRD7SaZFsw+z5BNrXmS9X03rVvXYmtHaN2sp9cU+z0HEwraLYLcBHhHZANiWLVu2LklifPCKY2ChDx4M7tAANnwmw4tnqKtvGrZly9bIa1DK/hCyfqeMH3+y/zCrvSzZGTC2uiWm2o9EvdehI3mwfEUZcGHKF9opK/uasg9nNI3P2jMYMLu1nX7Nl6UL8DrbsmXrAtTBgE+sBc96dwp0B9lV4uqwltBzAn5WdZsRrSr2f+iBQSX76Uj623PNgkh1YHfjK1LgurgzXxSFb084DXR4eQXAazcQ99uow3xpLHRzIN7rZMbDLzCArJuvaE1C7t4MEeXds5/u8BoQFi12WsrY1I4cbPMw3bVsjFXDn8DmAbGmWFs3r+sNKnFVXVaIoW+GPLMFcUzFVTwlpfYIXXOnpqRUiP0UkyW8MdeOeDfiIl3wrJec0fRStam6zdbRh5eqeV2YvYczp+7La+oMV9VPYncNuHjvWt5XjMBPtKZNFJfHpp/fwXKBPK0mXvW8tcdhjIHg66vM+akkSGLpib7ImiiERHKyaKDbepppBbb2Cd43KnwMvy/tgaA/BRjtgaxPpDNxR0Q0myZgMWnPDHAe40BG/QJfNWvOMUoRdsXCSaZ/xL0t4m5JFWDbR+Udbi2y4aNivsGO+UBmKHSHOoc9nxqfYOJ9WT3NOYgAiM7Etq6+bABsy9Y5yE6BtjW8xKkSGJ3YvQ88DO6QObzYravmG7bZsC1bts5M3alWRy7fU3vtXUtP/LVyFeivLVsDZP40uH0VoelQEleZGRktrtZh+nBfuKyh0BjjWR0z9txa8zSdgwfUVMNIIyi3LEst7TAzxdYl+ox7viO7PrRl68pKwB7RiFfkf4pc0NPomjv1tJpY8KxnalNmw1Grju6ZKrR59CLH0GlalvZTaihbbV/1Y8+DqRWk6+HOrGZablTRBriNrz/ofPrxnLt9fMz6Zqib6fZMlu7t6u6JbE9wjMS2nd4/2i3hT81AzNgJIsx5AA630rF+3vF+GjLFWuy3NQAVxTz9xE4OLGUF2RW08mk1gZs9KQT4qZTwWidnmLd7NOE232Ni4Fs4MXePWQDwUfnqLQ0TDz6MzJcz3dsxME5dcfbmSwGTiAs9rN0OOXNuSS0QBdySKhhwCa+ZnA8ssmGSYJOYvsvNRREKLZax3O4EpSvwBaO8N+rhquIpTLrpVXB2F4Hd5dYq8+LOCBngXW6aSBjWs0YeVxnP5/my9YkK1U/yXVHU/QGfg0iUnPWtiXdnBkSb2BtYXTisBmPLpcO3Y3Qsbukq1fy2hpQNgG3ZsmXrCqhoyQMZIZkD7nYF0Es9fcNXjQoL2WzYli1bx9bWUVOXTgBlTzbHaEBrIlu2rpoeQtziBhg5maWNuFrHjTkc5sU7e0Xx9oAZe4Up32lg8AnUMfCH4TDu8BkPkBg9PO8izVof2gWhLVtXTVZiJ+jvKfOf3ZJ6zZ3q8AEPasDZUyfz/mZ7kbMjseJpfMbHbfFrlXqijGgAOumvOIZ3jU6xp1Pbweqzsn6W36O7roq93RMcnmZnnonu9vlTMf6dgImuZyW6nvXAki3MmRrZrfutz/ljvfREw1d6WfMF8uzXDbovpO+3McPrwcA/+2kw/bWGOfc8i6x3HnWOddBfoNDwQiTmUvcICcQu5rh0LNYBg7GwUssyLdesQpWF9mptoZPpWksmTfbJlAENn0zZfMiHXsZjGn/7vSnzIfNGjliWoODBi6yLQJqeA3peypMczok0vxd86C/wsGy4d83nHtLftZIVZg+SHQE9IrIBsC1b5yPbBGzrBBqhUOgO2bbgodUvTfpqgmGO8rjYo4G2bH1wVeo1DeisnLjH+io5W6OJLVvnrfTRi4yM9rru2Ry9UtDqGL6y9ViHxDjjRXb2NV9LjDye06vYsmXrSHVQn7gzL/pcDpAgDeL2ImsWH9iGlXwAbkm10hFBnoZlwKehZWfVEORYsm7wsfilcsziM9H/oTFjS4bYgL7Mz5C+GRrw6PA6SUdnsYDJTc8V3psaHOVtlRW4brbnJGMJiO7Y/iFNrseVsTE9YXzIUYm5doA1fT7kqPRcQT2jDLoqj+VgHlJnu0KT+w7OCe9353EkS5pw/YYclTV9Pu7MD15eeIVD7NNumQVWmfehy1REWIsAukCSaZmKsOommS7jqeDzGuS1gyVbO7WLRxdZyzOWIdyxJTHyYbJiVWIBcc8OCS/lZe56KYuX64itXmVexET/LH+Sa5/4IKOJAlL4ks0lfbk6IJJyQkkLR8/CHm2zE3PP1g+ZD4xsAGzLli1bV0yjaAW2yrYFn0gjZBe2qmPkMUjjkjbEli1bl6ozT2BeGcJHeAVjn8XXtz0F0NYwOtJDb+sy1F2PXf1izKoLmLcXISNGOW3ZsnXe6ujA2gH/rKTWShRmujx3k2yf7PI33YeBpWzP6GA4t3a//cDPOb3ccT2ss73uNJ2dm1C0UK4UxLrw7UJX39khmvIOZsCDn9t7g8U2DHjvx9rh1thqqw/1lL1++ykFqeMcuH6ti4uWE0A17hfbfLYMuP2gi0PZVFutH0KOSqHhLTS8JtQWJNh8tAWGlw6fbiZItzUAxrLZd42zsafE8pnTJaILDd/fpE84+fESzodWuh6mHl7wrItYe/HngOXN42P9gBUTZQxz8BwwTsHaXdg6mUbw5g7fdlN1C/e/ElffTr8ibjhjKvB1PmO+oniWOBPizny6HrZuyaqxcuvrAqssVFC8qICPShmvNQQiz9iHeMP4yw+xRdY35Wvib9FO+J3IjcNtncSMm+4dfG39DT5oX9q6QrIBsC1btmxdPY06AxaySfCpNaJU2JYtW7bOQOYgQveAwhVEv1aJJq8bRy9o6wOtEW3/MYxEj/CeeZ4jqH7ZLeckUeb5cnVrx+Kzkh3pbMvWCEngEE/X/d35pVb62x3+LC58S//II0zA3S8nmMTF0d+7kOkTk3tO9LffyttJ1ZHuW90cYzfxcPs66xnL6pQecHEYgtuvX6/HXzvyuYfqjvO1hh6fCfUs9rk9WMuwdPRSbRKbamXMJspV2pexPiVg3N603HnX8pSi5c6zOvF2jbVZTi1JqTZVt6RUzcbbIqzYegMwwXB3KHSHWtMI7howe7AhWOBh66QRc+/tHufAycMdu3Za3z2pZfBbO7GOu9ri8tjaUvc981bXtRJXRVv0wFJ2jXksHZ17T5dRQTnspN6R+u7x194uvdJ6LfGvOiZOkrfb62mx08TnwFp7LWdBxXN+KgmSwnAsPvx96JNsAzvMCBOzsfA87d8RD7grW7Kj22S3XhpN2QDYlq1zk50CbcuWkBiptzHwGWnkQqRt2bJl6yTKgmo0HL115aFvh2S7DrT1wdYKjBskGMsNW0fJLPP6dSw2lVnw+3J1ZV/jREWgFQbnmMhil5G2bF0lTRfpoo3HRb+0X+nW8f2T61wRbMdL3O1z/xlq2bjRE1ytHcNd6vRX6UA+5gYbEbD1krvT43sXvg1jR7kw2w3KJoUSKEgkPx8BjzMG5OuHMx/0uX1hWj56kUGyosphtt9c3spHO55oMlSGxsCDjbBFYyVrcLftOHYc0zV9vkX4LOTSyoN7y3oWZYawRx95JhxLS0cx4P701/Q393uDwxDcjn1o/umfP+huD9z9rCPuz1D8Z8Y9ASiiflsRZLb4YAwOA8l7r9CYjtDP5a+XXK3Dl7HweHOPmcErKYqZMbFY8Stj+Ns+Nx7HFeEqFnqH2wue9QqKmPGzyFqS6Um2TfprTg+y/rnKvExZ3BZFaVXxuFW9949xtdedtq6ebABsy5YtW7YuRDYGPmfZdmFbtmw9UzJ7i/pHjf4KbR29iK0PqJ5J16+QiXuBMfC132/rTHWG1uQIuxF2BRlSqFp9IbZs2bp0Dea+xj2H9Ne0eQ2jFJOn2LTTqV8scIAOqtGSYCED8mxPoCMNi9bWtgPl8NXa0I6VollAsrnAIfMTtCjV/paHg3BH9GbusMYmIHXM7sUXIA324ODyNmDArj4WFBfsc3AM8t3e1nbrcTQd3i++/MZq/qaVXApbqvhAMM2mbSuKWfhirP2M6hU5fpYSgPz49PcIpG2oY7GePNhcp3BUn3GmtNUkLS4o68V39wgUGngl21Td1V1FL7k8/ppntmA96C3TML0OnJB5ZA/zwdsXU1Hm1HrJLQDwNXfKLanCBIzxBSGYrmj6az7PrTVDOy2zb2HKF9opu6fWgKosxdZKIs7H/abeWlpMbl6BOSPs6icHvWtbV0c2ALZl6zxlmz9s2eqQjYEvVgMGB202bMvWB0tmQTJy8OlC41fPTufR6szWM6DAM5r8PA6q4fQVlv3hu8HZOrWs9d6JCzzTAiIMwaYR5JTbZsuWrZOpJxcZjH7p0wh8wMSOEt7BmyFAS4tVnMwaGOjTMbcfiZzo3SK0pVnjuRdZaD049Gv2o0qiweehehlGPbMFvZ36tBHcwbv3uF2KTZk7KtX+5+VK+H0XQG6nvwu9F7/SEgXPguV2T4nQaaXL7W1BwgLymd5fSalazzpxwqz1gqziod7uUnFe7XY955TqPhutKeL9F7NeQUPS326JJ/bstgt0+30FEu5ez6ApFGZS95Cfe2tHtDeWlKpeCgKe2QLGITP7QOslV89od/P2kSH8qqIo8RaFFn2CS3hFBDSWrwZBf3uOEwoSHNopVxVPSFh+xTxsK9cQI7obANUVGo2e+8LWlZMNgG3ZsmXL1oXLxsBXQIONI1XFU5i0/R+2bD0rsqejXaTsvW3rSD17DHjP4L4mA87aAPhyFFsrnXKSnzAEn9X22LJl66x0MvoLTLHV0eXR2gZ4gM6mH2dP+jvg/mF0eRRTIJmOUNneGKm7xe9mqIPxBJayRcbOJXL5UmKch9dSn9ujKHEVLhm3B7wd86B0xHEvtGziwh5qMmDzeSYG9vhr3SfbERSztdBw72WwhpmCYC7Ta8pCx/kvCO6JN8ctqdXmgNkih2qUXc72C7ZfFDOAahzT43qmxfLt79o8ZIGlbPflb27esf3KHRf4AqioaSUQU61YfZGNRdamSRt/rrc9K2sU8KZW4Bbufb01QSdD29fOCkB1FKO5PvCyAbAtW+cs2wRsy1Y/2Rj4Csut6rE1vd+jIhzG9hDbsnXVpdE1VHhV5YcZ2BpZv69Vdu1na4CKcBu0y96Ms9Wtrhu0J+PZuijZtZktW8+qTkZ/+yHhbnWTGJP+nsr+ezLT6pCrvTzG2en37Sfj7YvQ176LJdoDZoW6I7KPZQIesmetrdPI2rf4KVwb6GA2j6b1IFqjg1VQDo2eJgMOLGVNA6vVRC7I4hHg0LxSTnM+9D/rBEa1mllNFbsKQf98Z9L302rixCZgIbekmm7gp9VEv8WcMVVsoUC/gLnZPZZWjLcs2vGKK9FMMjhWC+0AetElnMH9OL1//sBs3DuI5atGmHyXx9r68bLgWTe/LBZZ6+jy22YC2YI34HZXryXx58O2P5s6zQr1HYBSE+BRGSAE1/pusa0rJBsA27Jly5atS5WNgUdQIhym20PsmrqMrbFly1Y/9aS/V9N6KJLQZrru165erzJbtk6pq3kNnkZ22vPV0HnQ32ky2z2a0dmyZet85XJlBi9wJP3tuMf0/nYttrHKXMedVuPvUPTX6vm7SA2PgUX67uX5hgfRXyBmAcAm4Jltf2snQOl3+6zBBsNnJWH2FRj4AJYhCAddJuBlyz2BrpUIC2uv42viQEmpNlV3aT0oXKTifmEXLlp4cEfG+KHutr9Ex1nxoH2xofteA57ZggfosvYueNbXltqMtv1A9Zo+f0oGLCRI8IDQAut+E/c0VbcJg3s/x+yz+wCKvbr8HonVxVW8a7mcu3ZsdVcR0dDQmgSgxFU1bTibM+2zQ6wvl2htnme2IPbhDGkvKhAj/yHewPJF4MvVlX0NQIOMMeX6NmxZfoCPw5gl87mP39cvAUzLbD9j81mfadkA2Jat85dtBLFl60it2Az4WZCzctlbYMuWLVOjVXv0o0ey5dvh2cubskvED6y2ek13GFHZ9duVkVtrVmXpbNcpc4ANgG3Zumx12H+PS387Hhrc27sH/R2guwP/PKaUuCqccEe/bveLDsYwp0G/wtn5oH8zY1PqwO7FFi141jO1qULD0oC5wxXafefJdNcASAvtdwodSYLP22nd7XJ+liTAcAcMNt9vFw4UiHRwwrOwsbZYpopuYJ0OvNpGELt110CAs5Zt6MDAvWS1zApfcr8lBdZdW0LfDFm3vN/C4sbJSLCZXtCP/gp23n2/pFQ9s9Xi8tig3WU9+TtQvWr5s+c10nMyR9dxb8PPimUnZ3oFA1gVg/ZDv0VcfFmEyQIK1VXmxddBi/4CMsxYflZbf4+MGfdbfqK6b0EvDBx3EvfbcT8jIxsA27Jly9bV0zPmCxlSthXYli1bti5GV82A+Ox98g9Jdh/aDPiDp7lnwiz77F2zIy47+dmWrWdYZ0h/O9bT0QbYiiQ7KVQ/L+kpNCC6trv7aYc6+3eevIvoEDITTnuiyq441mG0ps/HnfkC3qMXtchEbsMGUAul+m/VYP/iBYRIF40XyljmGo2uQXmpz5/W+xNGIHP7QTFznk3+J+4Rx9q8Iqz8tZ5RxIQJK4gVSzZVt/UiMhOkTQpbXB4L/MThC2EkNh/Zida6gFhn3Jn3O/d7dvNd8KzzHKv5m8c7Y48pt6QOMP6K99iz46+5M4+A5f1OSKV9MVNi+UQX9D1KorP4sJH7dwECS1mTmo9TCLFvPm6iX6syC/5WjN+trqnV3bV99zLtkiJHbaStKyMbANuydSGyR/ds2RpSthXYli1btk6vwYlMV4r+8oH85A+AD6Kg2VXiB0wjTX8/aNfpiEiNnu9ZdaRf0JYtW+cnP20JSx3014du/VOhOvyaO+jvgPaZLd09Zg9aQ92+xiMhU8fTuw18nSxH6c9pejYpyJ60UX2HD/j4qNJEcel6mHZ0N1hip4nnWndpT7LVtsH0OHCHLsMBfOsCQKy5VSOaNCGynZfamwH3k3izm4dubCvrxTi4WEj/YvjR6vxN834snHjBs74Wm0eELWPcMOKU14xv7ZCjUmh4rWu23jBfKO7Mm5RaPGXwW7nmTpm4t9pUAPNPkSpvTltJMRn2F5kfatZCB8dd8Kz35Mr9lh+gQdfIkVqAtc7uzmIH6puhvmsebN5tV2f4wXHobwf37SdzpmBhyicaurXxXVHhazAOez2e7rZ/Aoy4bABsy5YtW7aumGwrsC1btmydUvKoYcWO35/PgAaH2plxW2JQcrQOlq0Tq7PN4kjJtpheVSn7mrJ/viZg00RikmDzHo2g3STYlq0zV63WuqxKeEWPXgFUBLU1MXAZj7gqxSWp4gZyTETY7bfmjvkcKSaPsVkLRy/SoQ7gdDJZbZGD1DOLtecMGeuazGcNGYxcPAkZNUGRx1/zzB7yPCvbGyCx9zpanJq71DNMaHZ78Gxn7O3wodCJ45Gt3hJF8lFTDljoitQ+MhT3XJWFlMXOa4W+ovWvUMecg47kcON26/C1x4bHnXkxM0A0/V1jfjH8SBx0QXZL9WhaCWNhn9ZHxb+CyArFXDuxdnO/eAnTCmydjiDUTX9Nj2nPfr1WQCs+pqyhBQmSCVfy7cC9VYId7Yo5Smv6/DV3ynrOi0vgSERt9Ukf+SpHSzm8QMwPouPF1Her/XpU0woDXMhWWWZyiDMEz/owANhUi/4KdfzulmGz7xMbB9R3OkmwwzH8K9u6TNkA2Jati5I9tGfL1rH0ATSE2bJly9YF6KrZfzv0zHz4x/oD4Ct+CGyduczZACNt/y1d9gbYGiiR6XfeWdDdcYIyB4uHw962bNk6M1m5DmA11XVgYAysK67QLP4sftovWLGASX9N+2/pmCnEx9LZ0BfL2oaCLscKLu7Zbfc00cd9TNKmTVAvuXRLM1R9MzR852BTJiY8zeapaaXlHj6Wq/tM+GsRApACX6/3bu7/7odihlf4UmKix2AMliEBY+3Bztcst0Wx12evdl4UCoGlrHlA0/XwIW1lHrOZrj4vbvid+wvOfbo+H6xyS6rVDYyBgcWzPA5twbm/tgRGerN1VR20tWPNPV/OmJsyaJLjZ53fuPXywy3iwNtvvqKXXJ4BS7e/F3PnmK8+JP3l9JgWMEz21rksg1Z7rDOz58KB/su3n1Tm9BHrtwPGV4P4/O/bEaBnNIJQH3LhCFLfMf4Qv9YHBkTbulKyAbAtWxcomwHbGkb2uLCpZwYD2LJly9alqLvwGAkD4rPx4T/SqM/WGUrUdeJKHDDacsX1DFySHwzF1kp2P2Bbtp4lWdmM1QpMHwzcDX27ZdJfk9l0J6n2hhz9OWX38mdLf48nKybZhAnak7PPWZmjA41PxqXMw+R37nMyAHx1VOwfKj4MQut41gNjsl2Hlfk82hgvHb1IP/nn2+ZLmcHLJta1mndNw7fJgFsroVLCa6XCPV+r5/3mnQOelalNxVw7PR+lqyV5xfhQMO/3USnjFTcm2TaXDJNd4sG/bP5ZkTtdtEyDGCxz51i3vF/485kQ3w55Zgsmrhbm494fj2d1pg1IkOolkZKdYjJBEvC1dw3oDm5ptQE2fo8UpnxVWRK3W+2BrWoHvS377632R22NgmwAbMvWxWqO9q9LW7Zs2bJly5atc9OITip6hicU95vWfRu2jv2b39ZVV8fh3hjZS/KcNLpE/ArrYqzAtmzZOl9tB4prh81BrZinw+zVjYH7yTABH01/hYZx3FoRjtmY9ljo91jLd7cTPloicbeDFx7fdHsMlSDVK1m6XYe9e3tuiRUm3aW4POafPzANf1ZMeGxZNqmzfenF22p77hzRcvVYvuTuhe+CCv//9u48MM66zh/4d5qjSdOmR3q3pA2IXLuKsot4/Dgqh1ROOUWL4IIocsilsioUBFc5FkWFcgiLFqGIVhbYFbEcCyjociltgYW2FAptLT3Sprna5PfH0wxPZiaTSXP26ev1V+aZb575Ps/MPPNJ3vP9fheEMK4tj1+aVWbvnnme852BLlUsuTpfWrEpWgc3/YKPj/eN7JJ6eUUYXxcGR2/59JTOza3l0YK4IYSXmqf+Q+kr6Qw4tC27m7MjO4Y3oh121NN3N02IhtjGrwMZX2BI73/HrH9nDw6ZC/RGoW9jGDI4bAxZDk3996LSmj/UHxja5kXP//aPX/qiHlYOasyTT5dWbAptL+xOlscuWPzdlzn4uKuTDWS/CLvXsaG7rY6Wb68pXbIoTJkUVoas9d0j7b4eVLMku0GIsuHsCX9eyQp6d4ndVeBQbvqbABj6lv9xQJckYxwYANubZW0/ZCwGPClH2y3yzBrNtmhKrPLfdnPf6t78+6UshNXt12Kkh4iBIRnS+WhGBhzahzGLQk2UAWcMAo41mNr+ZkFzwuRPUHImN11Nf0NWoNvpHrYmBs6QJz/tTggaX+11QduWDuQ4sXkfum7JsPSZiSKfdo/cdlfmadm94LT73QLa9KyO0vHytntDF2Pg7P3Ef31D+7ORc4hwnhWOt7YWiga81i0Zln7Go+co/b5u17o0jA61Ubib3pZeZDf6oaZ0STrQjVLhRakO3861YWRG+lu3eWRF22jjklR9bcvg2tje4i3juXJ29NuR5WFiNAK4o2+lNIbyfxz+0otj96wvbNXbjFNU2zK4Ntd3VlrrS1LlzT2S+GaLkvsuy371dvMPvay3TMYHRDTJdtjy3HV5zfYdw5LMDPjVvH/CbLt/3Wx/BMDQ50wETR4+QbO9GkJF3v+YA8CAEq/0NmRlwB3J/A4926yxIYwMIWz7I1wrer//o2TAvSh7Nj+RMGxzmpZWllbXhlyrAodcwUxHMz+HXCPD0uFKFEqVVmyKHqtT3Z/hubW+JOf2eISZ51EKXRK4cJ1Gv6tDGBJC6PiTMZ3+1rfVfgWWf/nHb3c8jDgKveJborixZWPxe+lagaOFe2Rx367q+NAKbRATvXqjcaU57q5pn4Wn3wrpsdrRCyBPBlyI9r2NXsDNf8/xHGxYOKq0YlNRRfOgIZvST+Lipqmhg8mZs0UJbjoebm4tH5FaG497m1vLV6VCc2v5W83j0teNlZuHh83Dcy4jHZ/8uSI0hrYBxPHphTd2tmp4ukHOQcCLQs3IsHa3sKBmwhu/DUcWmAHn19EznrkxOk+9N/Q/Lv5dkB6Rfl1lfZ8jfRFY3DR1csmK6PUQfTpEA4Ijg0N9dP2fFFbk/IyYGFZu2d42PrhqcV3maJxlYUs6XNH2Q9+cT7pNAAz9QQZMTtLfjtQZCgwwUOW8OCd4DudC7JKVARcix0xp9IIC8/juWNkWAG/T6W9oK8BCL9dgo7b9pHzbYWQwbHOa6orjs2x2tOpnehBwp/IM/22qK25qS1WH7rY6vvhltuxudDSbdE6p8uZuhrg54+Gt2WEho37fCmFyCCGEhSGM6+B7SwvahvxuaL9x9/Y3a9qnJumfG0IIHXwaLtgyEXRof9QZ6W+I1igtD0XlzUOrCvpe4XunqzvVUeG/m90ye4Dygo5v5g2Do1fv0N1Wl7YdV/nY+nTEOPTD7V9sGQ+aHgpcFUJVAal8Rxa0C//yvxqjt3ZR+2cqz7K+GeKxbvTWeytkDg2P7zZ+Mzv9DW2Da6N91oXBi8KUaL3hEEJjKB8c6vOkvzuGxRnfL8k5BfSOYfG6MCpqefCE3/0+fKqjHUaidLO1vqT57+XdGt37bghDuj61eAih/asoU0fThm/INYR9q2cXiO+k/L0uZV8A098GiKQHBFeExnFtXzReFsaFXOOzy8LGEFsqOIeG9sNytvM/9rc1AmCAgUH62ykZMMCAkueaHN3lL8Nsr3T8iT/JFwT7RPo/j9n/pe1BeZ5oskl/+1Z8ZHDthCHNZal+7AzQHRlLAudt2eGMoPljqvRSoyFX6JvRMs+9OePhiqnr80yv2tUVhbdGganM+rYfdsvbbEPepWTbp79RilNasalp9+LOe1LAWNjKQY2Za5QWrrobAVXhpVRFVuN0DlVIMlfwgOBo9vKiqvqhVfXpmcbTJzwa5p5juuy08raliLdCVwrLdGgdQhhbtK6iaM3ipqlRBpyeqzkuGvWbvhm1LOSLF5vfLc9Imjt6c9VtHhnFw9Fc01EGvCyMzX+dWR4mxm+m88XsccDDw+rhYcvjHjzhd/Oa9suz21R5c5e/0pEe6Zvz9RzfWFPQANboVZS9vaCOdXNd7dhrsnxsffwZTHcpuurm/95A9iLQ6Y+DjCS4o0UEQmhfq/sbf1sjAIZ+YhAwbIU+GIYCQH6FX4S32xg4f423zLoGA8DQ2LPwStsWazDnZLBoolW+szEYEwyJEP1PP+c44PS/++PDf9vlRu+GUPXerXQyFIUK2YuDdtCBfEsLp4OK+MZUeXP+DLirurZCcOHxTKcdjE/6GsU277aNJY1kRUiDhmyKelv0bnn9yvKwewhLY6VIlE7FtywNofq9KcGzVRatrW0ZVzmoMZrINwoLuzQsuz91ZbbnjpSPrY+mFi8ZU19avWV4dHqgeTSOM332ou0dLnTd1blt4wO4l2bOAJzxKKUVm0JsjuIonV25eXhNW+Lb3FpeOqghO9UrSdU3t5bXbh6Rnqs54/mtHNRYWbQ243lvrS+pX1le3n60ccXU9dkZcOWgxnT6G21JZ4f5v2uyMQyeFFZEo0snxSbRzjkOOG3HsHhIaePzrR9sainLOSg53tuQZy3edOjbpaHbUctcMXD6+Sof2+FI+i2vqzxf+OiOWK8yrsaRjK8C5P/yTUVojMZwF/LI74RJE8KyEMK7NRWVy5tL6pvi9zaXl5bUN215eS8tZH8MCAJg6D8yYNKMEekSQ4EB+sXWXXu32xi4IxsMD+0n8Yg3nsHvEsKynk5/o+HFyZjZ2Eoc24HK5c2143Ovxwn0u4ycJr5UZ8gKZjJG+nY6L/SWrHRF29KnsRAuHiqMDrWVod14xPSeY4PJshckzoyEMyatDR3MBd3Ngb+FxsD5w5t4pDe5s4fMriKiQD2dLZVnjudLT+C8ua7t8ruhrVdp8YG5G0JoGzOac7Xmt5rfS39D1nywBYkeemmsJz0bbuVfXrfTx+osIS7qYOLrjl5OOSYhTyeIu3fx8OP13obMqa2b6orjMwlHz116fvXKQY0hhOiJi7/pco4DLknV17aMq22fDb+7aUJty+BoZd/alnE1pUviw8Gjt1hGf3NujH5lbNG6jO3ROODsDDjj8hK/uWNYsjxM3BhKh4Sm8eHtkCVacXZ8ePvQ1Nt/KNo/dBAAxzsZ/zka+hwtAFyxW9uXSKJ3WXTy3w2hLm95n/VySue+6Wy+o1dUuj9bMuDQ9XdK/K+SXC/sjIPNuDf97h4daleFyjyPkzO2z7Occ30oSY8Drh1fEkJJiM0cE20paWhtLkuF3cLYCXkemQFEAAz9SgZM8C/grWIoMEAf6+Yl9/0hNPimcJucH/3L+roX2534/4Be6eUpoDe0FfnKPLYFJfVNVYu3DPLo6mjgkobWXugR0KHalsFV7bfkGZyXZ+bnkI5I20cXUfAQjxyinXeUJUfbcz5Qule1YWRGSpExILg7iW92qJzeZ0EZcCQ7vyn8K1y75xjgm1/9yvIQzf8cH34adSZ7Wdws6dWaM85bfBnXLo/9TZ+K+OjVcbHUtpcGOxYob/rbY1OFl7cdZnoN1+xDLuQ8ZC03+17MnyWKXaM3dXrZ3ejnULQmY2MIIQp6o5+jkaBVxe/UNk2ND6Ld+snAQ2hoLVvZPmDOnkO4Kqx7N+QbsxtCGB/eXhSmZqe/5aG5PpREEWOUNR4SnlxW+uozmz8STYIdNasc1FiWaogOKp1nx7/iEEIora4tqS9JlTen8/X3gvb4cs7j2n/5IApfswdqV9c2dXHG6ffewnmW+y14aHv+0De6nMa/VRNtqQxr0hvzjNLOvoDHL9pRHp+e/zljLuh3ayrSGfCiMDW6Ni4KU6tCab4PGAYMATAA26AK41EA+kpPXWnLYrtK6oDgAr/Ylz0LdIP5h3tZ9gzPTnjhqjtvwjZkywx+HahaXFc/smzjiKJO9xNfSxjoJTmTrey5YQtfDDi9hxxbs4KKjOg3ezBfFBuE9qt+RjPBxlWGNdEA4owxwQWuYNqRjJNQU7okGgqZ3hI/e631Jfnmms6TBHfSiRBCZmRb4BDkprriHHFv/GZ9+7gxa57kDQtHRXNox4+08FM6dLfVm6MJqDtS1W5u8H7TwWDNjIVRM0RhYXpR2wyt9SXNf+/gwNPnvL6D9K6jtG91CKNi22OJflFFc6grjrodzVOdKm+Ocs3JJVvCyUlhZTRPb2MoXxbG1oXB8Tf1ijC+PDSuCpWlgxrGtm2MZnueXLJicsmK+ETK0Turtb4kPdA8tL1fmlvL32rOfIem7814B4W2i0DUpRB7p6eX8k0bEpo2vjewOYQQJoaV7Xe1JP3zojB1Yng3+vlD4cXNYY/SQQ3NreXxzoQQJg9qiEa7Ri/DdzdNSI953dLhQROqit9ZXDV1aFX9lpw49nouHb2paWNxqGobDRx9+zNai3rDe8tyV0xdH52ojG+NpGdf6GiB7dLq2rC0MnMW8fRI+jGZ7+5oTun0twHSv5g9z3N0mBWhMYQwIqwbGdZGY3Z3DG9Ez0X8tRFtzDnJc1VYl36m0k9Q9MOOYXHGIs3xGHhRmBpF9SGEUaFubc2Y+lAaQlgbqlaHYVF4vKGzLwEwQAiAob8ZBLydMy5k60T/bpL+Amyjcl7At/VUuPCKLmMWaKVgHxD3bp0KS1YnUJ70N1K+pqG83ZST7w0LFvpCX4pWvsyWf8XHjuRfo7dT8fAm58YoPBgcNuYZatZ+tuopoYO1gQuR8yRUFb8TBUXZO8ye8zZ3QJtnJF+OTuQYrZt+lE6GIBcyTDA7oMwKjKNUO3s91wIVVdUPzcpQO+x2/w4CjmmqK+70eKPx0B3dmypvLq1uLi3kZVC4USEsDGG3tpvpc7X7lsyvtLp2l7bXbfReWBHGZwS90dsnHfiljQvLQwiVYU1IhUVF772XKwc1TkitqAxrFhVNqYnNF11TuqR9GrtFSao+/d7JDoPT76CK0Bg9YmRwqG9LgttFhjkvC2llYWNHDXYMS0oaWncsWx9CaAjDPtYwf3jZoN+Fj5SU1kcjnqMwOGMm86ridzLmEqgqfqe5tXxyyYq3msdFGW30wohmh25aWhxCW9Cb/jpj7HkJWV+yybgZrdDc0YjqDt8p0UNsyHwhdfRli/T7N7pwVQ5qjCK7aOz1S017Vw5qrC5eEh+K3en1fFJYuSyMXRS76uaZrSFtSGjMXkdgUeZw4S0P3dLStTlj6C8CYBgAZMBQCHEvQLLFr/PbehhcCFMEA9sIuS/0sTzhVs7gs0vDfzsVPUS0z0lhRciKebJDnYwpQ9Mjg9vaZy4YHNrPaNpRpN1RMNxpBJ7dIHtX2Sc5M85Jhzfv5lq/tv3MzznX5Y0y4MypnuMZ6tD3cqmcT3q7RUZzpZKlFZtKxtTHB3p2VXYGn+5Jjnyr8DVWe1m6b1Fvo9Gu0ZDoTs9G53ODb523YulvaMvLdw8hhNLq2ngam37DRiFr/DsTi0LNjmFxNNR+UaiJj+zMmfnVtgzOvwpsaJspOv1D+mY6DF7cNDVjXoF4+htyTSAcQhgSOvw+WTR4tDw0hxC2LBnbXtXiuvR3y8rC+rVlZf8cHvld+EhoSz2jHi5umpox7XO2qGU06Dk9P/aWsblR8pUexR69dGvaXsAFTB4eDTHPnqsg94wC6bd2euah9mFzZrPw3r3tXpO7rc747kJty+CXmnbpaCByWvwZjEZsh9jLpjaMzDkTQ4boSNO7SjeOtrcbVr52cNghz54YKFIhhNZWS7ZsA97+34drFz/X372gN/XQihVsS3qn5kyUstBZKcsAMvkfThxa1a3vldMHUinl3zZjw/I33nri7v7uRQhjO2/SW1Z23mQAUcsRNzSEpra55rZdw0PWum+Qadf9vtHfXaBzKsBtyMNv/t9za3LXQMXF72ZsKQ9rC9xtfRiR/nnTpqoQQng716fUxA3Ro0R7rgzrR4X6EMLYsLmQR1kZMieQX51rSdva0C47ifctw5auxmSfhK7K3mcOGScn/YQ0h5BezjVdJE/cENaUhZEN0a3UoKaiQevfe5SG4rC6LHM/kehzduKWHDXj0PI9TWkTu5XBRl0tCs2b245qc8uw1pZYWJn/0dMyjmts1sbhIazL9XPUuDFWbzSGEEIYHPv1fvljpLGwEqg2hIa2nzNeG425n9zSUFcUmoeE+uZQUp5r2t7aMGxIqN8YyivD+vg7pSlUbGodmko1pre0tg7evHloCKGoaEMq1djaOjiEEDXYtKkq/YjRz0WheVPr0Hj7+INu2lSV3lga6kIIRWFLzlcZ3puKYFSsw82haFjYVBlaGkMqhDA4tK4MRcNDy7owaGzYvDIURReNxpAaHFpDCGW1IYRQ3BhCY2gYE8r+/t4Z3jQ4bBocXhg84t0wIaNX8ZsZb5D067Y01DWFivgJaWdNWagvDiH20kpru9xlPFD0vkhvz2wTf0dHevCP1q17tRdwHUgNamr31s6W/82e9Y448QM1Uyb6j+1Al1L/AQBsV5R/AADbGxUgAMB2JZVKDervPgAAAAAAAADQMwTAAAAAAAAAAAkhAAYAAAAAAABICAEwAAAAAAAAQEIIgAEAAAAAAAASQgAMAAAAAAAAkBACYAAAAAAAAICEEAADAAAAAAAAJIQAGAAAAAAAACAhBMAAAAAAAAAACSEABgAAAAAAAEgIATAAAAAAAABAQgiAAQAAAAAAABJCAAwAAAAAAACQEAJgAAAAAAAAgIQQAAMAAAAAAAAkhAAYAAAAAAAAICEEwAAAAAAAAAAJIQAGAAAAAAAASAgBMAAAAAAAAEBCCIABAAAAAAAAEkIADAAAAAAAAJAQAmAAAAAAAACAhBAAAwAAAAAAACSEABgAAAAAAAAgIQTAAAAAAAAAAAkhAAYAAAAAAABICAEwAAAAAAAAQEIIgAEAAAAAAAASQgAMAAAAAAAAkBACYAAAAAAAAICEEAADAAAAAAAAJIQAGAAAAAAAACAhBMAAAAAAAAAACSEABgAAAAAAAEgIATAAAAAAAABAQgiAAQAAAAAAABJCAAwAAAAAAACQEAJgAAAAAAAAgIQQAAMAAAAAAAAkhAAYAAAAAAAAICEEwAAAAAAAAAAJIQAGAAAAAAAASAgBMAAAAAAAAEBCCIABAAAAAAAAEkIADAAAAAAAAJAQAmAAAAAAAACAhBAAAwAAAAAAACSEABgAAAAAAAAgIQTAAAAAAAAAAAkhAAYAAAA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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotter = plot_field_comparisons(\n", + " z_neg_0_2376_slice,\n", + " true_fields=[\"pMeanTrim\", \"UMeanTrim\"],\n", + " pred_fields=[\"pMeanTrimPred\", \"UMeanTrimPred\"],\n", + " plot_vector_components=False,\n", + " view=\"xy\",\n", + " dtype=\"point\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[2560, 1280],\n", + ")\n", + "\n", + "plotter.screenshot(\"./sample_202_volume_z_slice_comparison.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_volume_z_slice_comparison.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "4cd6da51", + "metadata": {}, + "source": [ + "Slice visualizations show that the AI prediction captures the larger structures in the flow field.\n", + "\n", + "Such visualizations do not reveal how predictions look across the entire validation dataset. For that, one can project errors from different samples onto fixed points and build aggregate visualizations. The [`../generate_volume_benchmarks.py`](../generate_volume_benchmarks.py) script provides this capability.\n", + "\n", + "Below we show the error distribution across the entire DrivAerML validation set (visualized using sample ID 439, the largest STL by geometric size).\n", + "\n", + "![Aggregate Errors](img/resampled_volume_errors.png)\n", + "\n", + "Overall, errors are typically higher in the wake for velocity, closer to the car surface for pressure, and in the farfield for turbulent viscosity.\n", + "\n", + "Such analysis is especially useful when geometric differences between samples are modest (e.g., DrivAerML), where resampling techniques enable meaningful aggregate visualizations." + ] + }, + { + "cell_type": "markdown", + "id": "378a4143-6dd2-47cd-914c-992c334b7d5d", + "metadata": {}, + "source": [ + "Let's also create line plots for more detailed visualization. Here, we visualize the wake behind the wheels and the flow along the centerline under the car, similar to the results presented in the [DrivAerML paper](https://arxiv.org/abs/2408.11969). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "31e4f602-fd0c-411a-8680-bf03b67ad067", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:20.978895Z", + "iopub.status.busy": "2026-04-10T18:20:20.978802Z", + "iopub.status.idle": "2026-04-10T18:20:21.087655Z", + "shell.execute_reply": "2026-04-10T18:20:21.087015Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_line\n", + "\n", + "centerline_bottom = y_slice.slice(normal=\"z\", origin=(0, 0, -0.2376))\n", + "\n", + "fig = plot_line(\n", + " centerline_bottom,\n", + " plot_coord=\"x\",\n", + " field_true=\"UMeanTrim\",\n", + " field_pred=\"UMeanTrimPred\",\n", + " normalize_factor=38.889,\n", + " coord_trim=(-1.0, 6.0),\n", + " field_trim=(0, 2.0),\n", + " flip=False,\n", + " true_line_kwargs={\"color\": \"red\", \"label\": \"True\"},\n", + " pred_line_kwargs={\"color\": \"green\", \"label\": \"Pred\"},\n", + " xlabel=\"X Coordinate\",\n", + " ylabel=\"U / U_ref\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "53d8f87e-aaeb-4303-a7b1-420f663bdba0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:21.088746Z", + "iopub.status.busy": "2026-04-10T18:20:21.088643Z", + "iopub.status.idle": "2026-04-10T18:20:21.212980Z", + "shell.execute_reply": "2026-04-10T18:20:21.212609Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "front_wheel_wake = z_neg_0_2376_slice.slice(normal=\"x\", origin=(0.35, 0, 0))\n", + "\n", + "fig = plot_line(\n", + " front_wheel_wake,\n", + " plot_coord=\"y\",\n", + " field_true=\"UMeanTrim\",\n", + " field_pred=\"UMeanTrimPred\",\n", + " normalize_factor=38.889,\n", + " coord_trim=(-1.0, 1.0),\n", + " field_trim=(0, 2.0),\n", + " flip=False,\n", + " true_line_kwargs={\"color\": \"red\", \"label\": \"True\"},\n", + " pred_line_kwargs={\"color\": \"green\", \"label\": \"Pred\"},\n", + " xlabel=\"Y Coordinate\",\n", + " ylabel=\"U / U_ref\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7a0a55e4-2870-44f5-9ba3-98f2655b41b2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:21.214213Z", + "iopub.status.busy": "2026-04-10T18:20:21.214133Z", + "iopub.status.idle": "2026-04-10T18:20:21.337134Z", + "shell.execute_reply": "2026-04-10T18:20:21.336722Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rear_wheel_wake = z_neg_0_2376_slice.slice(normal=\"x\", origin=(3.15, 0, 0))\n", + "\n", + "fig = plot_line(\n", + " rear_wheel_wake,\n", + " plot_coord=\"y\",\n", + " field_true=\"UMeanTrim\",\n", + " field_pred=\"UMeanTrimPred\",\n", + " normalize_factor=38.889,\n", + " coord_trim=(-1.0, 1.0),\n", + " field_trim=(0, 2.0),\n", + " flip=False,\n", + " true_line_kwargs={\"color\": \"red\", \"label\": \"True\"},\n", + " pred_line_kwargs={\"color\": \"green\", \"label\": \"Pred\"},\n", + " xlabel=\"Y Coordinate\",\n", + " ylabel=\"U / U_ref\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8782f6e1", + "metadata": {}, + "source": [ + "Generally, the line plots show good correlation between the AI model results and the ground truth. However, the model struggles to capture wake behavior." + ] + }, + { + "cell_type": "markdown", + "id": "6bc8d9c7-3927-460e-ad68-8907cb3df99c", + "metadata": {}, + "source": [ + "That completes the volume benchmarking notebook. For surface benchmarking, see [`surface_benchmarking.ipynb`](./surface_benchmarking.ipynb). To run these metrics across multiple geometries or cases, refer to [`../generate_volume_benchmarks.py`](../generate_volume_benchmarks.py)." + ] + }, + { + "cell_type": "markdown", + "id": "b501b75a-a8be-4b67-af2c-01e97471fd2b", + "metadata": {}, + "source": [ + "## Bonus: Computing the Equation Residuals\n", + "\n", + "Continuity and the Momentum Equations (Navier-Stokes) are the fundamental equations that govern the fluid dynamics of the external aero. We can use the library to measure how well the model's results capture the mass and energy balances. We can use the `compute_continuity_residuals` and `compute_momentum_residuals` functions for these.\n", + "\n", + "---\n", + "**NOTE**\n", + "\n", + "Due the the large size of the mesh, this computation can take a few minutes. If you are not interested in the this metric, you can skip the below code blocks. \n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "52fb2f2f-48ca-4dd2-8347-2abe94e14313", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:20:21.338383Z", + "iopub.status.busy": "2026-04-10T18:20:21.338306Z", + "iopub.status.idle": "2026-04-10T18:28:17.709981Z", + "shell.execute_reply": "2026-04-10T18:28:17.709314Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 0%| [00:00" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_slice = clipped_mesh.slice(normal=\"y\", origin=(0, 0, 0))\n", + "y_slice = y_slice.clip_box(bounds, invert=False)\n", + "\n", + "plotter = plot_field_comparisons(\n", + " y_slice,\n", + " true_fields=[\"Continuity\", \"Momentum\"],\n", + " pred_fields=[\"ContinuityPred\", \"MomentumPred\"],\n", + " plot_vector_components=True,\n", + " view=\"xz\",\n", + " dtype=\"point\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[2560, 2560],\n", + ")\n", + "\n", + "plotter.screenshot(\"./sample_202_volume_y_slice_residuals_comparison.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./sample_202_volume_y_slice_residuals_comparison.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "5399c41c", + "metadata": {}, + "source": [ + "The predicted residuals show good adherence of the model predictions to the governing laws. The residuals are generally seen to be higher in the wake area or the areas of high shear." + ] + }, + { + "cell_type": "markdown", + "id": "ffed9737", + "metadata": {}, + "source": [ + "We can also compute the residuals in an integral sense. For example, below code demonstrates integral continuity computed on a box surrounding the car." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "576dbcb0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T18:28:20.988712Z", + "iopub.status.busy": "2026-04-10T18:28:20.988619Z", + "iopub.status.idle": "2026-04-10T18:29:18.128840Z", + "shell.execute_reply": "2026-04-10T18:29:18.128253Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'integral_UMeanTrim': array([-1.41115921]),\n", + " 'integral_UMeanTrimPred': array([-3.78166219]),\n", + " 'integral_Normals': array([75.99999984])}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.metrics.cae.integral import surface_integral\n", + "from physicsnemo.cfd.postprocessing_tools.interpolation.interpolate_mesh_to_pc import interpolate_mesh_to_pc\n", + "\n", + "# Define a box smaller than the overall bounding box\n", + "integral_box_bounds = [-2, 5, -1.5, 1.5, -0.2, 1.5]\n", + "integral_box = pv.Box(integral_box_bounds, level=40)\n", + "integral_box = interpolate_mesh_to_pc(integral_box, mesh, [\"UMeanTrim\", \"UMeanTrimPred\"], mesh_dtype=\"point\")\n", + "\n", + "integrals = surface_integral(integral_box)\n", + "integrals" + ] + }, + { + "cell_type": "markdown", + "id": "4dc1d0dc", + "metadata": {}, + "source": [ + " We can observe that the integral continuity (integral U) for the predicted result is further away from 0 compared to the true result. It is quite interesting however to note that even the true solution does not respect the continuity perfectly. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From be2d3f629a9c6715488e43ae40d7e97167d41e14 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Fri, 10 Apr 2026 13:16:55 -0700 Subject: [PATCH 19/25] add last notebook --- .../benchmarking_in_absence_of_gt.ipynb | 1230 +++++++++++++++++ .../img/chamfer-distances-drivaerml.png | Bin 0 -> 104456 bytes .../notebooks/img/design_scatter_plot.png | Bin 0 -> 64785 bytes .../notebooks/img/design_trend_plot.png | Bin 0 -> 119036 bytes .../notebooks/img/error_vs_sdf.png | Bin 0 -> 111387 bytes .../notebooks/img/hexbin_sample.png | Bin 0 -> 678980 bytes .../notebooks/img/resampled_volume_errors.png | Bin 0 -> 9906769 bytes .../img/variations_due_to_checkpoint.png | Bin 0 -> 14832580 bytes 8 files changed, 1230 insertions(+) create mode 100644 workflows/benchmarking_workflow/notebooks/benchmarking_in_absence_of_gt.ipynb create mode 100644 workflows/benchmarking_workflow/notebooks/img/chamfer-distances-drivaerml.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/design_scatter_plot.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/design_trend_plot.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/error_vs_sdf.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/hexbin_sample.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/resampled_volume_errors.png create mode 100644 workflows/benchmarking_workflow/notebooks/img/variations_due_to_checkpoint.png diff --git a/workflows/benchmarking_workflow/notebooks/benchmarking_in_absence_of_gt.ipynb b/workflows/benchmarking_workflow/notebooks/benchmarking_in_absence_of_gt.ipynb new file mode 100644 index 0000000..f2311f6 --- /dev/null +++ b/workflows/benchmarking_workflow/notebooks/benchmarking_in_absence_of_gt.ipynb @@ -0,0 +1,1230 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "31129b03", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T19:58:26.746960Z", + "iopub.status.busy": "2026-04-10T19:58:26.746883Z", + "iopub.status.idle": "2026-04-10T19:58:26.749048Z", + "shell.execute_reply": "2026-04-10T19:58:26.748704Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"WORLD_SIZE\"] = \"1\"\n", + "os.environ[\"RANK\"] = \"0\"\n", + "os.environ[\"LOCAL_RANK\"] = \"0\"\n", + "os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", + "os.environ[\"MASTER_PORT\"] = \"12355\"" + ] + }, + { + "cell_type": "markdown", + "id": "8b06aa91", + "metadata": {}, + "source": [ + "# Benchmarking in the Absence of Ground Truth Data\n", + "\n", + "Comparing the model prediction with the simulation/experimental data is an excellent way to build confidence in the model predictions. However, in a test scenario where the model is used in the absence of any ground truth data (true inference setting), it is difficult to estimate whether the model predictions are reasonable and trustworthy. \n", + "\n", + "In this notebook, we provide a few approaches one can take to understand the model behavior in different inference settings and build error estimates. More specifically, we will demonstrate case studies to analyze the sensitivity of the model toward mesh resolution, model checkpoints, and explore ways to quantify out-of-sample scenarios. " + ] + }, + { + "cell_type": "markdown", + "id": "db37bc9b", + "metadata": {}, + "source": [ + "## Analyze the Model's Sensitivity to Input (STL) Resolution\n", + "\n", + "The DoMINO model used in this study takes in the STL file and predicts the solution on desired points. We can study how sensitive the model is to the STL's resolution by creating realizations of the same STL at various mesh densities and distributions.\n", + "\n", + "Let's start by creating a few variations of the 202 sample ID by decimating the mesh, subdividing the mesh, etc. We will use `trimesh` to perform these operations. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9a0e5a47", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T19:58:26.750182Z", + "iopub.status.busy": "2026-04-10T19:58:26.750103Z", + "iopub.status.idle": "2026-04-10T20:07:37.586730Z", + "shell.execute_reply": "2026-04-10T20:07:37.586095Z" + } + }, + "outputs": [], + "source": [ + "import pyvista as pv\n", + "import trimesh\n", + "import glob\n", + "import os\n", + "\n", + "mesh = trimesh.load_mesh(\"drivaer_202.stl\")\n", + "\n", + "# Clean-up already existing files\n", + "files_to_delete = glob.glob(\"remeshed_variant_*.stl\")\n", + "for file_path in files_to_delete:\n", + " os.remove(file_path)\n", + " \n", + "# Create a few remeshed variants\n", + "for variant in range(8):\n", + " remeshed_mesh = mesh.copy()\n", + " \n", + " if variant == 0:\n", + " target_faces = int(len(remeshed_mesh.faces) * 0.5)\n", + " remeshed_mesh = remeshed_mesh.simplify_quadric_decimation(face_count=target_faces)\n", + " \n", + " elif variant == 1:\n", + " target_faces = int(len(remeshed_mesh.faces) * 0.25)\n", + " remeshed_mesh = remeshed_mesh.simplify_quadric_decimation(face_count=target_faces)\n", + " \n", + " elif variant == 2:\n", + " remeshed_mesh = remeshed_mesh.subdivide()\n", + " \n", + " elif variant == 3:\n", + " remeshed_mesh.update_faces(remeshed_mesh.nondegenerate_faces())\n", + " remeshed_mesh.update_faces(remeshed_mesh.unique_faces())\n", + " remeshed_mesh.remove_infinite_values()\n", + " remeshed_mesh.remove_unreferenced_vertices()\n", + " \n", + " elif variant == 4:\n", + " remeshed_mesh.fix_normals()\n", + " \n", + " elif variant == 5:\n", + " target_faces = int(len(remeshed_mesh.faces) * 0.7)\n", + " remeshed_mesh = remeshed_mesh.simplify_quadric_decimation(face_count=target_faces)\n", + " remeshed_mesh = remeshed_mesh.subdivide()\n", + " \n", + " elif variant == 6:\n", + " remeshed_mesh = remeshed_mesh.subdivide()\n", + " target_faces = int(len(remeshed_mesh.faces) * 0.6)\n", + " remeshed_mesh = remeshed_mesh.simplify_quadric_decimation(face_count=target_faces)\n", + " \n", + " elif variant == 7:\n", + " remeshed_mesh.update_faces(remeshed_mesh.nondegenerate_faces())\n", + " remeshed_mesh.update_faces(remeshed_mesh.unique_faces())\n", + " remeshed_mesh.remove_infinite_values()\n", + " target_faces = int(len(remeshed_mesh.faces) * 0.8)\n", + " remeshed_mesh = remeshed_mesh.simplify_quadric_decimation(face_count=target_faces)\n", + " \n", + " # Ensure watertight mesh for all variants\n", + " remeshed_mesh.fill_holes()\n", + " remeshed_mesh.update_faces(remeshed_mesh.nondegenerate_faces())\n", + " remeshed_mesh.update_faces(remeshed_mesh.unique_faces())\n", + " remeshed_mesh.remove_infinite_values()\n", + " remeshed_mesh.remove_unreferenced_vertices()\n", + " remeshed_mesh.fix_normals()\n", + " \n", + " # Save the remeshed mesh\n", + " remeshed_mesh.export(f\"remeshed_variant_{variant}.stl\")" + ] + }, + { + "cell_type": "markdown", + "id": "7f4e635d", + "metadata": {}, + "source": [ + "Now that we have these different variations generated, we can run the DoMINO model on each of them and visualize the standard deviation of the fields on a fixed set of points. These can be any set of points; for this study, we pick them from the \"meshed\" version of the original STL, i.e., the `.vtp` file. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d840cec6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:07:37.588861Z", + "iopub.status.busy": "2026-04-10T20:07:37.588638Z", + "iopub.status.idle": "2026-04-10T20:07:45.062729Z", + "shell.execute_reply": "2026-04-10T20:07:45.062261Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_473319/2477101432.py:2: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "PolyData (0x7fff9c062920)\n", + " N Cells: 8883343\n", + " N Points: 8902069\n", + " N Strips: 0\n", + " X Bounds: -9.240e-01, 3.886e+00\n", + " Y Bounds: -1.061e+00, 1.061e+00\n", + " Z Bounds: -3.176e-01, 1.074e+00\n", + " N Arrays: 5" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pyvista as pv\n", + "pv.start_xvfb()\n", + "\n", + "mesh = pv.read(\"./boundary_202.vtp\")\n", + "mesh" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "66a5c8e5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:07:45.064341Z", + "iopub.status.busy": "2026-04-10T20:07:45.064091Z", + "iopub.status.idle": "2026-04-10T20:11:24.956440Z", + "shell.execute_reply": "2026-04-10T20:11:24.955789Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warp DeprecationWarning: The namespace `warp.context` will soon be removed from the public API. It can still be accessed from `warp._src.context` but might be changed or removed without notice.\n", + "Warp DeprecationWarning: The symbol `warp.context.Device` will soon be removed from the public API. Use `warp.Device` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/library.py:357: UserWarning: Warning only once for all operators, other operators may also be overridden.\n", + " Overriding a previously registered kernel for the same operator and the same dispatch key\n", + " operator: flash_attn::_flash_attn_backward(Tensor dout, Tensor q, Tensor k, Tensor v, Tensor out, Tensor softmax_lse, Tensor(a6!)? dq, Tensor(a7!)? dk, Tensor(a8!)? dv, float dropout_p, float softmax_scale, bool causal, SymInt window_size_left, SymInt window_size_right, float softcap, Tensor? alibi_slopes, bool deterministic, Tensor? rng_state=None) -> Tensor\n", + " registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926\n", + " dispatch key: ADInplaceOrView\n", + " previous kernel: no debug info\n", + " new kernel: registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926 (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/core/dispatch/OperatorEntry.cpp:208.)\n", + " self.m.impl(\n", + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py:57: ExperimentalFeatureWarning: You are importing from 'physicsnemo.experimental'. The APIs in this namespace are experimental, under active development, and may change without notice. Expect possible back-compatibility breaking changes and only partial test coverage.\n", + " from physicsnemo.experimental.models.geotransolver import GeoTransolver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading DoMINO config from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/config_surface.yaml; checkpoint from /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/surface/DoMINO.0.535.mdlus\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Loading checkpoint from directory /lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint/surface\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[93mCould not find valid checkpoint file, skipping load\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Checkpoint loaded; model ready for inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 0: remeshed_variant_2.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 29.780 sec\n", + "Processing 1: remeshed_variant_7.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 25.502 sec\n", + "Processing 2: remeshed_variant_3.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 25.574 sec\n", + "Processing 3: remeshed_variant_5.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 27.029 sec\n", + "Processing 4: remeshed_variant_1.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 25.216 sec\n", + "Processing 5: remeshed_variant_0.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 25.408 sec\n", + "Processing 6: remeshed_variant_6.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 26.521 sec\n", + "Processing 7: remeshed_variant_4.stl\n", + "[inference:domino] Reading case inputs (case run_202): mesh ./boundary_202.vtp, run dir .\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/cuml/__init__.py:95: FutureWarning: cuml.Handle was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/cuml/internals/base.py:43: FutureWarning: The `handle` argument to `NearestNeighbors` was deprecated in 26.02 and will be removed in 26.04. There is no need to manually specify a `handle`, cuml now manages this resource for you automatically.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Running forward pass (predicting surface fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:domino] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inference took 25.647 sec\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "PolyData (0x7fff9c062920)\n", + " N Cells: 8883343\n", + " N Points: 8902069\n", + " N Strips: 0\n", + " X Bounds: -9.240e-01, 3.886e+00\n", + " Y Bounds: -1.061e+00, 1.061e+00\n", + " Z Bounds: -3.176e-01, 1.074e+00\n", + " N Arrays: 5" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import time\n", + "from pathlib import Path\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.domino.wrapper import DominoWrapper\n", + "from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase\n", + "\n", + "CHECKPOINT_DIR = \"/lustre/fsw/coreai_modulus_cae/ktangsali/aero-benchmarking/physicsnemo-external-aero-checkpoints/domino_drivaerml_checkpoint\"\n", + "\n", + "wrapper = DominoWrapper()\n", + "wrapper.load(\n", + " checkpoint_path=f\"{CHECKPOINT_DIR}/surface/DoMINO.0.535.mdlus\",\n", + " stats_path=\"\",\n", + " device=\"cuda:0\",\n", + " domino_config=f\"{CHECKPOINT_DIR}/config_surface.yaml\",\n", + ")\n", + "\n", + "stl_files = glob.glob(\"remeshed_variant_*.stl\")\n", + "\n", + "pMeanTrimPred = []\n", + "wallShearStressMeanTrimPred = []\n", + "stl_link = Path(\"drivaer_202.stl\")\n", + "for i, stl_file in enumerate(stl_files):\n", + " print(f\"Processing {i}: {stl_file}\")\n", + " start_time = time.time()\n", + "\n", + " # The wrapper finds the STL by convention (drivaer_{tag}.stl).\n", + " # Symlink each variant so the wrapper picks it up.\n", + " if stl_link.exists() or stl_link.is_symlink():\n", + " stl_link.unlink()\n", + " stl_link.symlink_to(Path(stl_file).resolve())\n", + "\n", + " case = CanonicalCase(\n", + " case_id=\"run_202\",\n", + " mesh_path=\"./boundary_202.vtp\",\n", + " mesh_type=\"cell\",\n", + " inference_domain=\"surface\",\n", + " )\n", + " model_input = wrapper.prepare_inputs(case)\n", + " raw_output = wrapper.predict(model_input)\n", + " predictions = wrapper.decode_outputs(raw_output, case)\n", + "\n", + " pMeanTrimPred.append(predictions[\"pressure\"])\n", + " wallShearStressMeanTrimPred.append(predictions[\"shear_stress\"])\n", + " print(f\" Inference took {time.time() - start_time:.3f} sec\")\n", + "\n", + "mesh" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6f0eb529", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:11:24.958428Z", + "iopub.status.busy": "2026-04-10T20:11:24.957767Z", + "iopub.status.idle": "2026-04-10T20:11:26.688363Z", + "shell.execute_reply": "2026-04-10T20:11:26.687786Z" + } + }, + "outputs": [], + "source": [ + "pMeanTrimPred_mean = np.mean(np.stack(pMeanTrimPred, axis=0), axis=0)\n", + "pMeanTrimPred_std = np.std(np.stack(pMeanTrimPred, axis=0), axis=0)\n", + "wallShearStressMeanTrimPred_mean = np.mean(np.stack(wallShearStressMeanTrimPred, axis=0), axis=0)\n", + "wallShearStressMeanTrimPred_std = np.std(np.stack(wallShearStressMeanTrimPred, axis=0), axis=0)\n", + "\n", + "mesh.cell_data[f\"pMeanTrimPred_mean\"] = pMeanTrimPred_mean\n", + "mesh.cell_data[f\"pMeanTrimPred_std\"] = pMeanTrimPred_std\n", + "mesh.cell_data[f\"wallShearStressMeanTrimPred_mean\"] = wallShearStressMeanTrimPred_mean\n", + "mesh.cell_data[f\"wallShearStressMeanTrimPred_std\"] = wallShearStressMeanTrimPred_std" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d052448f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:11:26.689937Z", + "iopub.status.busy": "2026-04-10T20:11:26.689827Z", + "iopub.status.idle": "2026-04-10T20:11:37.933037Z", + "shell.execute_reply": "2026-04-10T20:11:37.932444Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/postprocessing_tools/visualization/utils.py:26: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-10 13:11:29.706 ( 782.904s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-10 13:11:29.781 ( 782.979s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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fxuFAAtIPi0Iz0lLKGFKOr4dloeCHg0TteuG81KUvS14oBCeTawyHc2EPN3SeoEpH873TOnsXtSwP3jokCp3Ni3cPj1cPRNmaHIGqBseIooXm4nNDI4CoHKOifm54r4j6sqRBDYKHCkVVIn5pV1xrr+S6WuaMF3wrkgkLp0lJdmGgsG7Po0/qRpPuqdZkqMMvRPySHlfLLS2Zhsjw2WuwvGGrpRT6ZQcgB0RPHkYB5iQaJ/2mvnJnaWdDEBcjKzLHNNYqimXWaw4LZ/vgcj+nKzG3WSkCsNomworT4rslxW8QE9OCoy3M2meQqyZbADEUsnUPa9qQGkVjfYAsSWvrlmOCmG12VOZtcoasKS+rf8/a1E4jdRu0AxaqDeL3RwKAHWx58OZ1M7CfbeSBNgDC0XIxp4WjbjFbydJmh621OgDKY6YScf2cpkTKiuWWx0w/pwFoDGcgsahcKI57iRXlv04+37rrB2Onrxx2zdbMYGdq6wuJU0txYUvVEaECwrZcIR1jp9UYUSbPfPb2145v3vEoTjkHqded8D/j71SG9Ts6YkbRH8w37YvBUEq2XllWlMoFXInHZTXqKZEyhBDBP4G4UoSAqZQABKk4JJzad3/fvwRKvgmBkHCyXpPjG6bihEQhOMkh4QS/EUhflnwj7cbTXgi+lHnFzymfae6ErZzS3bSqswmQi9ua5vG5EBHRvDAAExFN99jO+10tvDG57ffpx/N6s6G5QlOEoiRFYxJNJZgnaVtGRDwpG1NeEwQSanJ3uRNAXBRMpRBWCiHhiGrjSQBQRBSFnDCqobcSfbNe85DfBKAajEVCKViKE1ac4L65vpJ4ZhgGUPTNZLkx5YYAwPOkH6RfH2UvWJC+LzOKn1XgeV5ehVeG75eyImSW4HuAbJQOTN9ssBvLUol4KIX3xpqPVfvzwnjJ6+nSUyIiQ1IKVSmKhgG/FQLHNm3L+C2wQwPlBADf1v2cXszqZqftZ3UAxZwWjpWDVrVKtKxEXD+vKZGyn99Xhai12eVRK4i+ALrUZEydbJPJhJhsQ9KE8+zPRndtzsf1TF5J2KGOjB7N25FWLVOIhFY42RM/uHj1+APoaMkLw4E1IZvywijA3IOukm/uSC/Ob28AYHY6Le2T7fpA8KYZr8nJNYwONRWzmtnpnNi5q1ntB2DAMeC0IVU5t0AeZn5muBUowCrAhIQNI6j1jaKQg4laPTBqNagVI243bH10sKmY1VCtPUReIg9EgRwQtN1uADJAFxABRoAsgsbUiAIQiACW3HdF1S4DzJE3D8EBw+0hvXz2JyuPw7Fy7aHWNmNWZwk/rwUXSX5HQzjqAmiJ5UTEPT+yt3fcT2fCCTGS0HIdsUweir5XqstemByHKgonbT318fP1B7afdsqSITHZF/YWrSsNjvlNfgzjflyWQkUn4pX8tB/yi2EIpeSEIASEWldFrNSH4VJeNzsKSqSsBI3kISAQVwqmUgSEI8PByriaDAW/Z039zpZ8MzjodDneqg1lvUQIhZIfDlpwpKutOWRe9fOql1UrlerSh+//Y0cXIE/paVnV1QIpF7czEhMRHSIGYCIi7J4c6svsVBX/q1v/ZzTc3aSnS2rEUIpCEUIRRWFNisa8YrSKdANKrvD7RWddA2YAKPlm0Tdj6mR1ZW2UHSEEoqJYayJb6R0KUYKV9BpT0pxSJwx0aSlbmpZSgEBMndhXpTQlDO+zubgSQCUSSwnAz2vBnbSfUSF9SF+JuFJKP6sqZrFR2MlK/aBySXy71LLBfwgcmCnZmFCSKdkkgEEZT0mzURRS0gCQUAopaQJIBGsEyiMRL6cDKGZ1q8sOqu+8nK5GXXuwUv8YjrnFrG512krUVaySb+tBAHBHLDXqKhFXRFwAq0PbAbQh1YiJxRhsxqSFwjf/1Yt6bnN5c8pvHi53F/xQVoa6xZgvcMXyvcdcvmxZ+8A4onvRk0d4C1YCGER3FkbWa94z1O0MWmaX3ds50KkOBCcsCzPrNW15biWAyIp0T8PocnVH8I4OTBOOA8uE7cCEwCgaK+dXAgIpNAOwYc5a5TuHUbcbwMj2jmJWr4uAdR/hGACgFdhZW1XdLAN0i8qKNsz+8rkczH/ZbSAH5IBoELnryojMUdLs7aXn3CkJIYIfR+Ysc4og905pAQ6JoNV61PWzWjGnQwbXm9Ydz4toaSwT7Y2PO74hAWGVrGgmLJwYilmEG8WkJQs2DEDmpAEpi25Y97Lj+Z6kZ6BU9gsqyr6fV4t5E5DwPKgahICyLxuHtTIg4EDp9QAoETehFms7Jyw37Vculbgo1H6xSvvh4CQ0iELaD9fGtYOU+77CstKk3BszAOkMV4IxpITvQcpQxC3lVEh8YVUX4J+yqBXSX9XZEpS1uJ0tqImI5sIATEQL1O7k0NZk8vnUSzcm++LhvKKqplqaUBJRpZQXxgr0FRULQhSF6SiGKYorxS4HxnaxLCHcvWgAkPWaY+pE1ms2FEeHPT0FTO3lK+rSLwRGyz0p30ioBVMUhvzGfYFZ1G0tREIpNGpBqK40ja5VAFaKl9hcWln3rvVVgvsiMaTsUpO2b5jCGSonppyIaoF+PgSBoKqtukYHoERdPx8C9j3l5/VgjZfVq5lEzhFpwjEXgBrdV7I9ZAKwOh2tLQ9gVWh7TBSakVyF7S2Y7MWoGFU+e2tTSYabc7vzOfhFpVgM2yXZIFNGdqJNHf9/n9aLy7v70bEbHQD2YtEEmgBMoHHQ67FzDROZmJfTw8vTS7XBHmXAhLMbXVtKK4o74sWsHl2ZXh7f3aMMNGJi1n2ul0SzDbO+8fO0yviStCqfDgCg5FshxYbESLkn5Rt+XndH9rVILmb0KYWMAlkAwHIgD+SrD1GtAQ4691p17aIxo4Xz/hLx/P/bbk9965pgH1oPtln1/muSRf3zB3nvMec1pkRdAEGX6foXKFblwqv26zaq3wsJoDxqtbRPSlsXliulTPtG3PGSfrjyKilLu2LFjA7fgxAolyAU+B68MiDge1AUFFQYHnwJTYVQgEprjsqOVaK7Fo6VpfRLQdtvCUCGoi4k9DbHz2uK5UJK39a9vAoJ1XIB6eW1Uk4LRdxSVgNkXU6Wlcph34ciICU0HUKBolQrjSWk/4UTW+FoMMuQ8pTWzuBwIP1VXa37Tn5wSn1vcUfLwX0WRESvWkIIToNERAtFf3LvltTE+om9/zA+7pctJebHVRdmp6sWY1pq1F8UU5OWUnotdp6X2hQqQhSgFJCxtCeWJ5KieZM8QRfB0ENwpSUEXBmJBQFV1gbLDXJpdeYiIeBLBDVeEJBSCEAiISZSsgu+HJJxQEJISFF5oRCyUksmx4cbU+3hIKMmlAKEsBTHlmZQGVsxJfyI8oiltecrAyxVKoR1SOxBpFkU+v0WL6cVM3o45gKimNWDl1uddpB+izsTdfVsU4oON7gI8lv96loeD6Zvmi2fFLN6OObaQ1Y45hazodppUqLlIOQLRYmgaMC2YEdQXIWX1vedlY11rHI3J9Ey6be9fuyeATM2qatXLB65vDeNTkNfoaYQBhBFKY+wgDDgAMih21CcAdnu5UN6p71GG2lRBw0UANGO1IhSHG8oIxeCEEVYtrBsWAAiwjGkY8AuwAIQZGnUevzOaHRcklbRN8OKk/SaUr6RUArwmy3hALClCSBV7q5sr0CJlcOxTHmkOkpVNjSlxDagrXrSIgKR4OFsiTYyY80oEK3PpTNOvlKtQBazPl8/NBMQAdpmHOoc7z6X/SRyMevzcsZG+2nave9V058r5sPIh8Mx1xtXlIjr50PBbysAADV4SRoWACEBIKEUZPCrTXc65UcQBRCClFCQVA0ogC7Lo6ZiuUqjCGtQoj6kdIai4ZgLqRezWjBEGQDFKimW69uaYjjwfT/n+3lDiZb9DPysBikgZdhwlJCE74fCKnxZSgOKUioCUpYmgobxIUgJAUgfkEgqlcOUpVIKgFv7Jau6HpU/L0EYLhUrVetCQA9B16Hpn9hRBCqV0qGR7WqkrLY6EMAOAUDaWnA+/ckShAJFVazyvhNd/RDioggh0k44LgopL4yw/H9tPapI+4UwQlLxbcUtrl3a43vqykS3kGUf6uJY++yfPhHRUYMBmIj+l3tg56Oeanxy8z3jeoPQ1BG7zZ1oV6O+lxZSK7UIV1fF6S2Tmrq128uFMoY16o7m1Z7NQ4VUqpSIR07o2Kqs2IZlELIozDwsV5oh1QnJQsZr0mGXfDMkatVzErJ6n15rXFPXyiZY1EU+DiflGYCs3Gvuy8Aoj1nOkAUhzE7HHTHVqCssNyXDvq2nImEAELXJggEJ39YBKBE3qPvKrGsJ1tftEgDYtagkUMyFgoWAPRQJtpi9lk8AtQaos0SbWu7dbxvdYi5U6edcV4Ht27oSdVPStLzmdjUZRTGC4mrsatxe2jbeqZihcEgriSagBcvDY7IjO+pcnhnpedrHcTYAf22q3+zOIzyJlhyMFJrLMpr1W/Zm2vy8vnb1lha134CzGAMA8jC3YEUKZnCkvh0ajDaFVSf44HIwIBqn99iUVtG3woodHFbSa25UJ5Jec8o3AHSpySKslDQTagEQjerELre7S00CIuUbwSeCSi26QLXSe7ZzI2b8cDDzFE9dEzRXDlZH6rsczxE7Zd3z02Lo9E7L+9mN/dXWzvjVI/gqBHsY5PPRumbVcmaWrnujyg9HQc/5aQXPfYoAoJgLIxeuPBiOAAjHXDXqau02UP3uCQBIBRe6QEI4Qij1ZauxYI4oVW3wFctXGoroKHqjlhIpmWoJgJ/ToGpFR4NjIqjjnZC1phCVQcXTgKwsh6MupPQBJeaWHBOKRHO16lVKeGWUXaDaFiOonRb1h1x/fmr9wKf+SlJtYYL6ZzwPigJFg0CpFIKvI1strXYlKPsqq7FvpPNqUbUJtoB88HhSfiINoBU2IIHhBjQAzwAArL2wgmHA98AtwSt/YUUXIi5yCiLl1/eOndR+0ayfGhHRHx8DMBH97/T4zvs3Jbd8P7Vrr7I0rpUvauh7RFkdGm+Mte3cNHG8l1VObt+0t9ic9goiXHxuUnQbljm25Nn27LWRzYvGRjp2bcVSjCw9aaQ7MSS6g/vBEiIprzERzH8T9NFF3Z15tUefBMSUVsHVTo/7blmrVTeoe2315lVrzcda8+XRiNZql0cjuS0NEAjHXK3Dkb4val0oK3lSKlbJz+uQ0h2y6upv56pBA6Y2qZ3brCXVB+xqxq6+yZQX1Cp+p7X+tQctDFrRlWk0oF92t4oUABPOyKPH3RhtazTzE+VFDuLnaDsScBLOZFs69dhNybMXYxGw46L3ueZ9GXQUoIyiMYWWftmd9JpaUATQ0jnZou59J+7tQ6MHF0AeixyvtTwa1ELrXlb3svHkyoJpJwzFKfgmACOWru1z0d9Xxx4SDoB2bS+ARnXSEmZMmyj5QY9oczIbUyJuyu8GsGeoUvFb6wW9X/laDpSV5fzUZwG0Ba3HBQA16no5vbhDq2wZ1BXP+GDCMRei7jzX1jeUZ64EEG5wg/r5qd1rZzVHx+OpTwV7GATgwbr13UCkmvfnKKk+2Ylpz01V/3uNXR0kbKpiLoRcKJwP6R12bls8yMNKtTW+EimlYFUqQnOVX5GqL5VKzPNz4WCgcs8Oe/lwMHh1OOaG437Q6xhA0VaAajUzUHQ0KAgOo+hoUFF0ghstiYIJfdoXB0AYswk6DtQoUbdu3ixUD0HUVqr7thdBV/zgnNV+FwNk7VUA/LyuREuK5fr5kJ/XvZxWaRIyhYCo/DGrnP549Zk4sBMIVe8icxIhwADygAUYEQCfGHcxDsDDhPh5c/Z/0o+sjZ8368ESEf2RsQ8wEf2v8tSOP3ia8tVtPxoKtV2hjcbVdVm0lb3SA+WThkq9WwY74fmK4S5p2AXACbc5fljo2nFGahNaDK38Z97mNz78feEh0TWMPJ5bc90Xul4bzOdZklZRmjF1ouRbIWFX/nLW9bOt/iMwS8VafQfeUa/LFMVhP1FZI2r/BIQoj1l+TleiZSXqVoacrT01ailRt9pTWLjDVm0W2aCQujyz38av2AEAiFVHG45UI1l9cKvuezjmVuJT7WimtdINuh3Wtqg+ZXU6wXRHue1xzEKEY64ac1s6Jk/UX2xF6iw8efnAgyO3nP+xlT09Sp/ppYdl9/ETI8Xy6IaxyBUP7H59PL/9e2f9S/j9oyM9Ecs7Lf7YJn+ppuSbMan6cUvqS0TfSUpfGolFky91yB3jTc3rxco+2daHVbZvPjRwGurTqZDBEMRBKFIibgJ2uzYAoFQOV6qsPb8BBSkEpMwgXMzGwtEsHB0CxXQEUc/2zYlMBIAS890hq5hS9v06EJwHRYHnQ4hQpFxpswrZ0pCHFNIJw/dEpAxA2qEmMw8gqYYQKZl5v6t9SFdsV/VaRFJ6/rDodX0rVPByfvNEzpKZysw9E34IqDSer51ZLeFWrigIJeZC+k1KcVIakMLPhypXWlDPGnGDf5ZHIl5OQ62qfz8NsaewBYarFxKqXYgzQDcwUB3XukEgJpEV6DjYdtRzGgVQV2Dt14SDIoOG/RIQQWJUIqXghAS5t7gjrsbcSl/3Wr+GA5S5vw3kfmdsrq20p9SQT/v5o/bP4FqdUvZ+finwbb3WBRqiclBTNqisAaYOa7efQxDIV6/sSHX/qz/WVI5xhsqfI4kP9zz95sXHXth26n4KJyL6I2EfYCL632Pdrnt/ueX/DYSXjBrddmTl1eLuB0rHHWOfuLcUf6i8ZCzXKIzy4thwWgkZlpjwuhdZk75wi4WmZDbRp4feH9uxpH/4ZO872nIMd5+1XZyy2TzlN2YPgKJvFaUVUyeKZTNbbnZ8o00PGlXuS7+1dou5ciMARxpxJVk3QWiwuSj5ZkmaEEh7BhRMv6uudiHWWvJoqSZmv65dqJDwZX5rA4C6tOnvaz8pRDGtAj7sYPxeOSW1AhgD2oBlAKrVNUEeNKeE5WBUodrIVXrbvqPwcpV5j/Ztv682WwIwOx0EU9oIISWEVTI7Kt2SK32ABQBYXXmtzU4ohXZtr+6rYSVfRKwE5bbIsUZ4cLGcjIvBtev29P10ov/Dp2cKTjST3/DJS/5Ve6/m23Y5bKSKT4VPsTCuKmVd+gU5cY7cKv1RHX0n2IO2iA/oSiqnZz0vLxrj5b5d2qouc+vAUBcyJQDBWMTFnBIK2ZCQZTeescPRfEqYxXIIqgBEu58UmihKNS2MOIpqGpbIYTyclRqs0mDWQk5ASs/ximk9nEBxslCZOAcCfhlCraboMoQCrdTelB+ZjECoKBdkShGNjkzrsqw2hVJJEXJcDw1eWxZxbTStxLP9iY72oiUKi3wZyWWbixs7cxszTwx2FhOLT1qhLMUt+tImv9hhh55q6NjuRwHZbTsDpqmjWEAEhrtS3aP6Ia9kHq/tTmWax+WS56KhpGX5ea08EgZQTFfDMzSIElCqfJS1eYBqca7WyBaAosAWlZXBlRgVEECDgKLABiICQV/vNlTqfE0BU+zrHTBLv9/9PRLIA8NAR/WKBTAwY+OG4JI+YGSf/rbVTulB32wE35lwzNU7bN8OhVdk/LyuRMtqQ7n6u0n1izZF3WOx/32IzVijTM3D0SlPFvMqgHDMtUei4Zjr2WG1wdXaC+VRC6oyPQPPduxK1AOU2pNKrPoQQNA+XxG+EwbgDltq1IUQaswNC1Ht6j+jzOiMT6p92uN9KqPfxbxol+PnQk/GE/mJ3zy52X3baccutmb9RYyI6I+ENcBE9Kq3bte9j239qwmjK2vEJ7TFvx963Ynhvq7Q1sec4wu+W/BDTcqIIpq65d5wxG3yB9PKIlUrDOgde7D8c7ktI+3jF6rfj6Y9227MN5vbrYt+LVaOykSlZa9AybcMXxmWcUik/EqTxQ4lGVMm9lX8SlmSZrrcmK5uEEwLHBLOnnJnXCmYogAhhr1EZadrN8r1db/TqoIx9X5aiEr34NqrgqUxiaio5NgxoE3ABrYCDUAWWFG9yQ7+0jt11bwzulWGY24wc68SKdW/b7Cxb1fqi8ojFgScweqeeF4Qvs1OW2tzpu+8EAC8sYiXq40aXZ0hqSMfd/PRzomwr52gbX39+AvGU3s/Z5xypfHUMSNPAsj8h1u4UP/NyReZqcHzL1/1DfvqP2t6/Knw8SE5quiKDaNc1CFgymJEup2wV6rDttsRhz+ZaRz3lTTUASW6VUuE89LxoygXJ5xYkygCQlhlKzJpS9OyMkKg0x0ChCbsKEo5hKKiZEkXgC30lJ9wEHKrA4+N5psgAFtPyUp2ckbrxiSbdkKrzHbHGTFDUVdvKfiOpljluCim/XA8kjOUYkgUQsIJTlQERQB5hJdgvAjD8uI9I3JI2XlR//eLNxaScQw/gt5f/umv2s/aluuMTShpU63uhjFr9aTZ7ihW+Z6Bd2x7CosvUNYtWfu0PHYnFo+qPQ3CHfWicjwi89pkMI2tUimtlK826tWCkOzD92GriEpkq5+sGoxpLGH5yFebL0ckxgQ6NeRkZUinqAbpwxEw6noLBD/91E+XHUTu4NSJandWG7CVffPx5qqbxQSEqCRGq1pbPXeT6bp1M1p9T28oEWTgKSOf53Qvp89oSS6nvlzMeOqgVXNj5a3twUh0VcrPhYLB7dyRCAC9Iw8IJVLy8yE/pyt1Q6wrEbc8YgUtL5SIG8y27ed0COFlNUCoMffATfRfOeGGfWfyPaE7fvbcKZkl8Vvaey9pXvVH2wcionqcBomIXt3W7bz32Rc+1Jbol6I5kZgcEj3P4bSX5DETXsO41+BkrLHB6Ps6HolqfSu8RzZpJ+tqblTrHtI6B/TOv9k11Nl+d7KpaVSBkGvySnYdlm8XbfvuYqUEMOp2pXxjuZLZ4Qe1XYgLp00dACqVnxIo+UbJN4ZrMwxVh3Gu29OpN+hTYm39s/uScIeWCpaGvcZKN0Vbz++I7ytFVLtBjol9rZcFYItKJ8ygYfPSgzif4VhZa7fr76enHwKQfb5l6grA8yIr0opVaXa7RB+CQFhxIFDyzWS5CZAyp8usJrPquKuGl6S6R7a1huxG1d4YXnaqWB+S9goMXNi3bsOuq/s63Yuf+Z++F5Afw/rNWHkW8u99S2///S0dyn8t+/wnv/meX/zNV58U4dUirYnJjIyrwunLLXWEP5RrAoQzOn2qXrOtkFCKwnKTuSgAz9bUSNkZMWoHEIq6pZweirp6a0GYbv2xyUoHTrhjJoBSvi7/zGwPO0ftHwAgsjTr5zUASqQszH2NURuUgiGKM7cvyHDGD68spa+KPrT2/p/f/snSKZfjuTsRB+wnPnw/VuzO9AQ7BqCUm6VJl9nmeLYeWpz5hLvhrKH/lgJPLj19M9rH/O5t+WWwdUeGgt90Zonxs98aHOiGIZhYKl/duD5qWYAegglIWQnPQXnSh6pWvi+KAgloOgCUy1AUKMF01hKKCt+vRnQJ34fnQVWBam6XPiSgaUBdnFbV+h9igKBwiWrKbVIK/ki0p2G85BubZYOX1YNqYavLbu2cgERSGgDKIxEvW99CeP/nYeZwXbNtGyRDP6fbg1a4wVVjrhKZ3ra5USnIvJ6q/sZRWxNMoJ2sTjXcqBSCnZymMpNZtVG3nwvVugcr0RIAdzhSzOj7Bng/nKKr0ic09F83uvnTu9dqq/RbO9svbjz+sL4jEdGs2ASaiF6tntr+h9s2f/LMUt+JzqjlQ1XymUikx9irAKendqYT5jbZ1TlRTDTvbi694OgNWaNB6uW82uzq0XdkR9SxnduO1Xd4Z6/Hkj3usgmE0sHdpBCLtdrQPaLoG8FYzTu8WOU2VkpTdYIFKYMGz7ISffcN+wzIYCzZuvvf+rw0pcdmfZ/RoM5ZANLxwunKTW3lZX5Oq0wEagMQsCQMwBdoqfZGhsBOCQhkJSBgAYsBf+qJE7MsVVeIYkYFzJBVqnu+snt+XguqmyDrWlwDAMyOvGKWgocJpRASeQQnQMou2d+F/haZjJsTMGSyuTHtNU7IBrc1MphvHfLT3alxGYZRiEYmEk/0LuqI5FcPP2+P4KWHIYBGYPIJLDv3hRVLV6Al+cH4PyVfc8wxv/zGRed17zDX7G41IAbTMrEs3qcIe3lD41CuW+/cOzLWDqs8Ppoo5bRQ1FXbipaaBpCxdAAqSgCibSVpa+6YWcppJTsMBSXHKO0xAJhtTtDvWpiuXwhXzoOqlnI69iWRA5olDOd3JypL41PWl2b5xUGU8pX/QEdWTKzYM+xry07q3rzuTix9LXbfh64fb7/g3XJPvLi+oTO4dEOAN14JsV71tVBVNeZDUcPqxGTSbLKMTjfrhXqGil17x7qmZGYlqG6t+1xrzeP3Nzz4rBlQAfJ15SjVAbEqG3uIBr3NS/smNzYBb+oZKDlT3qW/1oqhLlGXNDQr8F0oKnwXvgc9DL+MUgnBwM6aBq8MRYXn1/3MpACAIuD7xZRU/UJnYrA9lvbMcIsq2zRnPLyox3KbGjLhhkyT2g/AhrFTLu/rjiW9JuRDk77hDlv72k7PNPOTr9U8N7hqtFIBW8yGitmQ1WVb3Q5q41oJNCoFAEnfaFQKpnAQc1JeI4CuYOw9ADFn0GsMNj4mtD3jNQNIekaXmqysDyKxb9TH6SBda+1ANQwr0VJ4eUrP6+5QZcRsAHpH3h2OBD2fUZ3D7BXJxn5OH4zG+9pTzmPCVO13Y9d3vdAlLawHJqIjgDXARPTqs2HnvZu2valtlxvzXaMTQgAOhAW3EEIReqi0U3Zmk3p7/x5tMZxVlhNp2K33RjQnsbtrceH+nx/7Dzf5x7RpgxEU1pfWTCm62pATQFwU0tIARFxx0r4RjCgTF0VTOFFlEpA5r9HxzbRXdys8ZcajfS0za6WWx6zq4ENl39a1FruuLfTURtF1y+Uxyxk26zr6znpWBF4EOoFcdTiiFbXcMq3CefYHqDaB1trsuucr25RHLT+nF7PatM6BZkdea8rWdn6JNqQrNiSicACswM5uudeSRVMWT072TSQapMQYjAkZ345lg7JzY+aElTn3ZGEvb/jdYDn7+qfvffjHJXMAyXObtMcmoydCnHhMdzvM5TpyaNBGo5nkRnfZcd/d/N3PvnNTx5pyPp63hKlONvueC78AIy3jReHvKbc3FP2Moc56uqStAXDHzFJuSo1uZGlWmO6sp0g6ejC50b5sWWdKOS/LzI9WAuJvm3e8fvf7Nny/tH09fOC4t2PTT5F67m+fl6tKvhkqeCVDBTDsxYMMHIxmFBQWpNz3rnj6z8a/tCd5TnyJlWt84Xl54Q9HLh4cSkx/t/HqGM5B6+LgQooBrdUN5m48a0tIwAI2zngqWi02B7SjMpxSa9B0XwKADZhAkHzN6p6g+pWMVF8yTW00pt7a6ZrtFFb/ZbY7SkyJK6WT1IlhaY5ojYBv5PxrksNLGly/MCp7M9tCbnMhcUzfjp+tPmlYSYzLuIBsQGlUbzaUkuPE0xHNL5lBkW5lhud93RaKWb3+cww37AuQ9QEYgNW1r7WFn9O1dnuZPhBMzZXxmg3FCQm7JC0A9QvB9tMe1tYA2Ol27ecMVNTXDJdHIkq0NKNH8SzKIxaAqdXgVTswn0HOrC77r3vu+c22pVuf0XouU0yl8I3GUy/tXH7AtyYiegWxCTQRvco8vO3B2zZ95kyxadHO0YgFtQVyAsIGChCjkFvghFESKGyDfjK0YxGKYe/Sk+zuQqRkTOy5Zktn9CuZkwHZFM1ljFmq88rjltbq7JtvU6Kh6I0PNyrRsp/TW5eNtmqDJWmWfCOorC344bQ0pF/3V3TWv6gC0tZ9W3eGzaDkyLJMEKj8vK61OYrlBhkySMhKpJzf0VDZh1lKmzUBC/Shko9nafY8s5Pk9Me1SXFCSzPTtvPtEID8jngtf9d6UYatYmhJEkBcFCDRpu0NSo6gYKHQhcGV2LlC7mpCRk2FE3I4hQ6vsbgXLQPoGkTXbrlCh5IX7slyS4fce+bQZu/B4WfvghxF/+knNDbbu+PWsX2Z1MDuRD8A+GMQa/CaC0JP/1ep+/PtxUhjeGnTw83nbkesCKMf0aIMA2LEq4yyE1eKaRmuP1Jpa8Iq5zY2zXYOYbYX1Fa7dnLiogCB4HeQaVsGKRqAsMpB7HRGZu0MfLDq3qhuqqqPnnzfGyZ/Nv7DTet/iQtvwDNnXNWy87Hvve3T67YfXxknfD87EI6WAfmWjo0fHPin9R0rBxLHPo5uQxb6hk9/caht+ta1dxyrNSuoG5wpaPweVF9b+68WtiUAjFUb4bdVB7IKgrQATOyn+hiVFwbP1qZTClQbYaC1up/76qjl7D3b63YrFCmXclooVl7UMQzTBTCcbV4dHWwRmaVP3bPiifvwILq6UDortLgXmeY125adkiotu7l58ajfEHR619oL5Yk4oCim4zuqMxwNN5S1zqJiFqCpEAoERNAkWwgI4eer019j33fWHYnURnIuj1petSZZ77QBKBG3S51sCOZXA0rSKvhmkIT3e7rqlHwLAsG0XoPlxlm38fN6bmscgNVl13o1B62gKztmVZpMoxKS953QcpD2BeyBqZ+9LTEsEJWVmZ/rVHpTR92E7VmxzAp125vswnt/dZK53NNWhuIy33/y1fM5NCKiVwoDMBG9mjy2/f47Nn/i5OLQ0qF+owzRChFUEw0DjwPAzmG4E2g6HeEzEIog+5qOYrNQVbEh8+F/Dy9LZiO+rautTmUe3RlKfQ0A1Ei5UklruUG2cUYsAJHlWcUqx5VC2g83KMWEkixKY7hcHc60+oc0CLpeTg8tyVQfagCCQmYQEAhHy0Ho9fN6sdIqVcxaDzn1kQCAccACRqsTGkEgWldfN+U1ckZyrrbQFgKA2eE4w6bZ4SgRFwKVCVSqL/Hzen5HQzA1S2hpGoBv6+URSzGLWnMOUsbhtKoDPRjeK9uDANwjR3owfAqeKYjYgGyeFI2tyC0SE6MI94rUOrE4IooWiotEMlRAmzNipFLWBkdOAHmgCL8R4+OQOp7dAysOdRueWgcPOHc5TunA4314YQAAlp+MNeeHSssbc03t2Z6uZKLdgTbmdwCYhNCgFBR3FA0AcjBK0kx7iSASS1vL72qofnwIx1wJEVqSEUC7mnb8MCBMpVCQRnDyTKU47MVn9v31bT2/swGHiQNYIhx1Aazo6nsjXgo/P7Jy0NthhRPfuDH3nb/7r9j548MJAMXZaqFjx08CCMZg+/TI7VY+9g/LzwQQUpzRdO/4SGPlVfO8DXCC7PqyyWqngGns6j+DHuy16ZFb9r1yttKmPArHXCVSdoaNULQMQK2r2zxWHYu3DrrC31VecoWyc1BJL9+7K/Jc/Ezv9qHvj+QkdgzguBC0y5D/67/NtA4+L5e+6K8s+EbaD3vjppfXQosz9e8oQiHpeQml1OGPn6XviajKA+WOjf5iAHHNzQS/mygKBCCqY4YpAooCQDohKEKxgknORC0nJxQnJc1TQxtywiz5VoMojvmNK9TtAEZFIopiDgYEoihEULBhZmEAiKFgwRlBY8m3QooNoOSbSa+51k8YdXM7FTOzTP5cm68IgNWVtwcj4YaS3m67I1bQVxnzmeA6+NNanas83FAGpN5hK5abUAqW4sTUidf4O27ecOrASzCXlLVlkV91HXNp+8EMVEBE9PIwABPRq8Z3Hvm6P/KNLmWyLZvSI9BRggPsBV4AtiFTxrYS/B1YeRaME6Edi/Fjlz7Y9NrHcic+Mbm6MZIzI1lntFG02GlpTOmCK6U3YanNjnS0ILiW8looUoaAGi1XK2wBwOwoAEKJuolIrlUbzHlNw15C2lowbJJ05p2C9ld/K+Z8OPXf0x8EjZ9bZ3tqrjedfb3Z4QStoMtjFiCUSAkQfl7381pQci0A57fHI0smFbPUIO1WZaDFT3ZiyPQLJUQaZaoVBauQMlBMhtUR0T2I9knR0IxMhzLYinxcjJuK3Yz86l07012RcNG3tjtiFAiq9IMOoosAQLYBecgxjE3i2cfQdhoe+TYu/5YxudPvSZXu/jaaQ9hVQhxYFELjcTj+MpRXhZBA/7IVPpwBtO3Cii1YVIDpILwufYJilUt9DZXsNw60IBx1lUhZa903hHWHmhr2G2vLIVEoSQPAvnG86wSV+QDKo+a+CaL2Z7+N2A8sHC1f273+rPTDVrEU+8BtQxJNt77l58ZJzw2tHc9GlUi5SSnUj5mkWO4ykdEVpwv569xHntIbluuZb5SuhIDjGxOjjX6u9pvLwavdO8ysnqx1fg+m4zKrjZwPefjhfRMyATL4NSeo3EYxq4Wj5aAtsdbiTN0zdKgpACFRWIzRiBgHcAJGz9y5J7t+0krtKF92+lAklv+fu7oewEN70fzRzs5zlowviTUj8hhOWCc7AexxOyplypm/fuyrJ28QxT/Rnmvc03/2ugfWLb/mhRMb+rwlunCyCANIe4lqRwiR9sO17510QuVxE4AS9ZxhM7I82xTNLXInM5bSpKRywlysjDQrKShCCOiKDkWERd4RBoTICzOoYS5WT6sjrBFZX/ErM+WWSsdgCQDlUcvL6pUhpiW8rIbqiNNeXR4OL0/tmyIYKI9YWpvt53V32JptluDqecnP8vkGrb5rvSoSwlmj7XJ3iPue7zIXl7VeHcCvuo5hQ2gi+qNhACaiV4G943se2fIL74X/6hJ7LEtozdCdEgDsAp4EYtj8KJ7ZhlMN9K6FcQmKJxt2V+MDocsfLq98Or2mXU2HrUxIcSBRkma/W525UgCAN255ea2U10PRcimn1W5MQ9Fyqa4jZe1fZofT1J4CMLazLbQkI209vzO2r8D6WVHmk3IOqoVzrcha+8/ayjGgZT/ve8CdmfF24ViQK3Sz01Yibn5HvP5Zs9OBhNZmB3MyxY4dhi9XiuHT8PBaf7B143BD42hkdzI9WhodAoooDmPF1cbTJ50/hGUb5JKQMgGBqFI6CbsWiS3HDvcJFTmzqfmFYdEPZRSiCGFACIh2iGYgCgFgHLAhqxMXy2MxmAvlFukP9Z3W+bkHzzkJhShEL3aXQspkqXfRmpbsS1iN9OnWOuOESSSKiDwvVuf85pLi70qvmsxFK62FbYTby8GNvtbqBDNXASKkOEKIom8AcKRpCmfa/FXlMVOx3KBbeKUpezDR8Sxh8lDD7mzMDsfPa1eseOzv7v/BzsJFK0v333bhX3w9emx5rNJDOxwrK1HXGdpXUWt2Os3RXNpUPjlx3zG//A9nRWio7a/lKZn12VN+svW0+b6xPVvFLID81IdZABKxaudhVNst56p9RKcFpAPffcz3/iSyLFNps1AnLgpxNdWoJDsxGJFFCGhSPX1w55auhgtSm9rd7eoLsV3/MfxgPwSw5J/e03757h53w1PGGc9gSVo2jSGe9RpHKh99pd2IABpEISjfFAVHBrNPyWDIuivlM5k7R97y3f+xv/v67a1NBRi70ZpEwkRhDImwL7IIp73Em9W+sO/mbCtjlXcpRtk3N+e6AeHbWnd84jglmYs6muoklFSbkhJCcRTTUoqWcArCshVziRjOi7AjLFuYORgQIoUWW5jd6E+iBYAtTQBZGCXfnPSag5H8gn32c7o7bOkdeXfY2jfVU/U0V3Nv3Xh+U5VHLDuYia0W/qf/KDBFOFZSo2UvpynRstaWB5BQCmvUXQ9tXOy85HQtzWUWN6FQzJ5/zTw/aCKil4kBmIiOdnvH9/znvVefYe/pTo+FloeyXToAq88zHy1gD5wYNo5h6E4cvwI9XcBbQ8WTtYfD195cunhnOdogHAOFqJKs1b240ugvt9cyiTdhTZ39pb7etb7T7IzhqQ404U1dkfPbMpg2aV6Vw1PXT6lOnPp4zvGu6g5tvzWSkRUZxSpXhuCqE46VtTYbAuVhM7RocpE6dIn/9EV9T2T/7wv3bEcZMAELyNWVe86paD0fwyevdpvaQ2jOKTmEsay4NWKOWelyaFtJ2QY8AbRBS0MAymkQvVAEBCAKEOMQeYhgHNxFwAmwTzBfbDzuDzhzQHaekvLa41tO3Pl4696BieXN97Sc3+ZF3Pxiu2k0p2RGEQMwiM5Jr3tYxpO5aK3CFkBTLCcsFxBxNVl3IivpNziAsCgACAbjDXKOb2vB3LCzNjyebgxorfaJDdhTB1i2qwNEBSv3X03a3ZU6s+P5CzbsER/9uv3j9w9c8F9r342vfORnO2VDh5oEhOMbaWn4tha0ra0tAPiw9sQS5f6uB59adV/hx/94bTp33DcHLp2tQu9lqK/jnbXXahCVZybh2kV48DckQfvwmR3XO5RUSHG6xVACk70YlkAHCnvRssYbi9z1hPGz4Qeew4WvxwN3QQDH/svxiRXtLR1Du0pde9qsAXRPorFftgMoSbPkmyHhlKQRVZPBcrDPo+WuhJIEkPISa8RE6Mldi6Wz4l++fsG1oXuvf8NLsrMgWyOiBGDpmOO22sfY5SeM5jvliXGUdKUQhVbOJ6Rix/RUAYatmHosbYmirZgJuGklVJYRV0ZMbVwoIiKKeRgAWkTSgWkLE0BEFEbQGEMBEJZwAJiwHeyrBx5xe6REyjcqNdhAecREtTFzOFbJukElbTCBcH219rRPSOY1KQUgyyPW1KGh9/+pSVTfCFpHHhDlYbOtK2kO92/d2NgVnUycVi564f/qOufS7hUH+ckTER0KBmAiOqrtHd/zH/dcdfrknkX2OE40hALXUPSCb20vYA+Sz+DpByGAtYvRdAFKrzdeXHLeYHnZz70Twr5oxUhZeONoqAVIV5p7yx21TDY1/c47+lYeVmPwflPrDK9YZt5fHsZskbi6fpZgPHvFjdnu+HlNa3MqrXlrL9xXv115GI6VteZ8Z2TkNGX92U/fk/xh6l5lmdqbDi+KmX6uPNYYsZ8vOVFzQ1+msdV4ZkwDNEAFet4b6v1tSXkt2gZg2CieCmxASxiKDRGBMCEcKL0QgHIKlHGIJJRRKEWICSg9ECGItRARpE6KrDdOfQprn8GqNdh9cWrjysm+9ic2Zp/ASA8GmzoaelfsXRxpbrEHsQqA31hw0GY5EGZ5r2xK2PGCZRfg+n4khdh2YQz5cTi6KYqIuE4uBkDa+qQfrp0cvxp355V7D4ENjAhA7m/2ZrPTObNjy//d9NPGW+7/yU3IXP+W0+/9ef4f/+Jrp1+Z9o0ONeVII5gVqfazRTjqKtGyEnG/aty62Hm4ddsWOR7/2eWv2+2v+PXgBdN+2pjNHFfjYbt5kDMv4mlrprx10O8XgNa6L3bHlUJCSXYrQwYKAK7GMy7sxZvGN/zNS85nzu/aPGF/6aVNgACWfubCMy4c394msqkVfQnDkHYQgCeRyCE87T1L0jR9pUvdsXQo1NDv6E1Z/bH8mT2/75TDfY0nFTut7erSDa3xEqzzUgMT6OjMNj3dFUrZzY9Yjauc8lNoUiIehIirpTSMTi1tqE5IKURFQQiRhSlE3Z8XIOs1B9W57aGBrN8SVhwBFHzLkWajOlGQVm3ErJpgYiTHNwEkfcPPaX5O97I6ICupdcpdXyXuBhlVibp+TtPaK6exViFcGxnLz+t+Vq/+ZZjxEc34aKZuICMrMuURC5CLV+3Z/XSkuCe1bPl4bJk71t898Obr9vtCIqJXDucBJqKj2pfuevM5w/2d/jhONnTHtxs1NyRiOwr+c9iyA+vvx6kGek9A+GJ4J4T2lE98sLR4p+xp8dKNYiCDREEalWoTiQmvo5LhpAAgHd0ZMUJRt5QPkoys3rYJiOCOsNqLMYi+tXGzxIx6qmm/Ie4vvtZvNnfE3bflrFlXzrjDrG1WfUrWPQVZDQ/VGYF9abY5QdNHYbq5DY2AMNtsxXIhICxXjQOA3mlrcV1YLoDSngZIqNFysOdKxIVQRMQ9TWw+Vz7de/ttDz216P7Vp2kn9iqLe1qsVEbGM14c3rUNSL55Q39s+y9eXIemP48V7aY9k4uzzz/3QleH+aM9OtAIKE+g+UJ0bEBvAtHlEBbQAkVCERAuFBfChdIMpR9KI5SXoABKGuIN8G2Ew44twhBivX9srjF8bqP2hqadjY3OxLdh7xzeUxqWQBJoCj28+u1oiyG+Gu7SEMaQya4UETS7G+2V5kRX4ws4ZgUayqJhU3hxAYYNMxNVxkYbkddhKQD8nDZ7ZelBN3Ce8wWjgACigCOAWaqClWg5L4y7jjv9/KZjTt3yX7etWur9BIv3iuPPnHhU9lQ6LQv4tu7ndUCEY24xG0IuBOCr8StvWDm6eu/IipEhIUSzuscZiby8BtozM+orpP4LUvti7vetZTFXOUaMWABiJ0zElUJCTYVEYUB2AVj7+MOZz97lX4zf/AA3/H8ofO3hh59HP6AAp3+6a+mFY2PxNi3dfnvj8SYKjjDGkAiKjskCgHZkjsVg847+sDUcbWrRJksnFJ4ciq10TtJ8FJQ3GVsTS57HMRHp7UVbXviTaCzA+p/mJVnZIprFkGz09ZASLj9jCAWAULv0dEyd6BQiGM7KQgFBjYRQAJGDUfvRKqwW4ItBr9EqNw/6jRB1o5fLpqQ0Hc9IShOAn9ebo5mJXEMl7kpZmcU3q9dNUV79A1ELq9VnihkNkMiokCimG+pOvVH3KdQ+Yr+utHrT206bnbaf1QAUc3rshInymKVYRUA65VBrU37vXg1Chyy1du+9Z8+WS3tXzyiQiOiVxxpgIjpK/eCB/1Sf+eeW5lxomeIXYoqR1Qu++UJBTuClP+Clp3Gqgd6zEToH9vnGs8WTn2xck0c4IxMFhAHkEa4mQ5H3E6N+MH4vvAkTQjij9RPbzKf6d9rDeTSEPmCV7/zrhA9teykhFEhZ+X9dPglFXb3FEdU+k964pbbsd54Vb9wMesyGomW91QEgrLJ0tLhSvNa/N37Tlp+h3e80Gk/tsGPVxCbhSgPAQLn1PG3LiaODYyg823L64nJqJZ4YLRwTTo5O7IIyutWbFE7/3pZ1Y/CQAK69EkmgeQ2EBjTC7wqVTSVU9D1FCaX88M6Ssg5iDMoKKF1Q2zF89tLnmk5+SpyQVcJrxdYu0d+L5IkPrRN5YAQyhQe/i/WlYI8AoOsaRPoQ/7Pexi7XXGS+KM7Jo+XUcF/KVMdEEkAJsQyUAfQAGECnicIkEkNetyONyVwUEH7lFxPU97MF9h9sgyalkbk3AlA3b1BdC+FwzFWirp/TlagbVG8mlELKN84NPfd/Hv3xXddvPPb2t5ev+enT/31DQ9tVt0aNsUy0tTOZ9I2maFbaOoCxocbWhtyEb1zdsOOqJ37t7Xly6+5rL/nUr/qMnodzV39z86Vz7dKshxMIGjMH0xTVpgsOpvmdloSDWZSmhHkBG7AkItWBslBtCl5rGR4IzttB3qTEThxfog0B0BUnisJfvvBo729/lb7iovD2Tc73h1d9MHTP35W2l6AClx6L/Pfe8HjDSQ+NnpaOCMc3zEgmgaKn5q/cnNoxuf2iY+KjSn5DIrGknB3U1JVyJ4A4FAehLNSEnABQRNQXZQBFEbWVUL/ogkBKaXFQGaSqHByGEx43LUAM+o1LtcGQ4gBYLAYLMA04RvWYU2jOw8yhOnpzUO3sW0mvMSXN6gDLlbrZoEoWgFepla2p9s3dd+pkOOpWph2u3vUpkcp3ozxiKlHXz+pBDfC+oqbfH04pcNq/p3xOlfevrAnH3OBJrd2uVCbbend8wt4l9j7jnPT6MQDvDK386Ml/csAPl4joZWITaCI6Sn3vvv/0tnyt2xjylyVGjYaeVDqSTOqTJbkHj98FexNO0tC8FuqJyJ9pbFh+zAa5bDNWF2QYQF6Eq+0lK8k27zeOevFgTW5TXRWK2H8GxtTcOy0D17/2jxyDZ76qVjsdLNQeYr89KkPRcmVCl3mQtu6OmQBKwQhPMijBDfVmVmx4YPduLVdqOWWtGW4fnEBDMzKNyJThNyEzjo4dfvdAuS1W9A0rNzzWpphlYbqxgn8K8j3WwABKsbQ34dl96aby1uGe7KR4cft54c1LOpDQ0bwW249dPmIsiRTD44bbIUZ7sk6sPyUEtOZMZGtBkVB0jJ/VeWfTBRuVNQVhHq8MnKBsO6P4QOMLebEJ/kP4w2N4qf5Y6pZXhJD80HGnNjU/fe7ZyfZlG/2GcWgtKEeVVBp6Qt3jwPRkpCS8II1kvWZHGqnqvDL7Rn4eMUPLMvWj5tZCcnUDq5itzG5ldtp+Ti+O6lPyHg4wQnK4OnNsfkc8siJ9WuL5SdnYJJJfHP3Xz5l/s/SUj7cA7/h26MWLztuCjlu9y3XFdn2rt5B6we8Z8BKK5Z6r7r1MfUx++wehr5Qa2tHyxa7c6cvuyL3zh5vPmOdlsG8/c0CubpirWUwZC24e5nETEqTrWToPTxeOuVqb3RTNmkrhuvRgPoErtj7kP/7YktXJLSvOa9rxyPhXS/c/i9edC38RZO9xnSfj0chro72x8XTxCT+8PNGXiDRsRWzNwFCy24eEnuttjz7ZjSETxfhIITLhK4l0ZJ0zUTxOJLel8iUzGkoc3zS+crUT156zVm0TTY97p0EIQ3EKvjnkNwFIKIV2fW/JtxpQKCh+bW+jKORgRFEITkOuWt1akmZI2CXfKvjmkNeYEI4pHMc3J/MxAH4uZA+ZQaPlGbMZlYPzqURcrd3eN51vfRWuxIw6YUx76Oe18rBVrPvKT/mk5NSsO0tzaDmtj0XshAk/r6H6xVEsV4mWO/KpbQ9i2erhaLcHX/5m1Z8vbp5lDjciolcQAzARHY2eeunePzz9kRO8bdEO9BuLVMPt3D1srisoFv7wGJL34AIT8WYoHw+VOjAZb3whvOIlrOrD4ny1w161jlbk/URBGhlZCS25TU1TouOU8Hmw3YDrn3olUu78k/CUm1cxJf3Og9nuKFZZzBgyd/9vB+no+V2xac0gO8wt6Ykx0y+09S5qi2+fQGwJ+puQcRB2ZKgJ2WG0bvNXjuaaASnMsjdhFsaMUMRtjNhdrXs0UViGAV9GFJHvl4smvbZUWUHejmWTjX0TyuTmNQ85x1y93V516vLwJpS71msnli1zt1WKwuhS+ztFskd5qXk0vadrRUEJCVU8pZxjK839ivk25cFz0/dHnneUX0MO4OGdeKFUORSx75gqru9Fw1VwTw3tOXvVVnfNgHbsY/mm7cbSlG/4WaHEZEIpAEj5++p7E4pjigIARxrBQlhxgoGRStIs+mZYcYL6PQCl6guL0nKq4VnaOvL6WCaoG4USrYxTVetjHNTUNSsFADKni6iLIF3ndQBW+2RMnQBwNV5sGR3Zsm6v/4En3vKvodLFi0zD2WMs60fLRhzXI8ObBRZjLArXQ7m1ENt+/H8GJ+Giv0D4I2vvDF/zyxcuGChF53slBOorgetrgKec16nnu1Y5XF9XPAR0SqBubLCgzNapA2hZ9cOeH5hZHdn4Peru16XWbeofs48zLx16ouPnGyfPCenrSs7DuPsTa49d3u3/Rt17cshy8dg5yw3oEYwtw1YHRivyAJpkRhasWF++vbxlNJvomZwYLUI8Xtp2F/pKSAONQAOQBVJAI3DZYvSeitLHw0/3rO3Dmrv9Mx8rH+vnQsE5UCKlLjVl+0a7vnf6eZKofKUlAGTLTQBsf99PLZDSt3VI+Hndz2rVebYE8hIQyANtElJAwOy0tTYbEp1qMF4cwtXrsFi7gKV0fNMUTjB+dSqYFg6ARDDJed3uSQDlUQuQwRjjU8/0tL4Ysw+dFbTBDsfcYF5039bLI2ZoSca3dSXiLtaHm3dnn9iqrT7LBvAfvZe8tvvkeX3MRESHigGYiI46Q+O7v/m71x8vk92RZDLR6BoiKWNL1qWtPaP3PwfvD1jTjY5OKMfBPxnOMmNTw+rnsGZQtA/KttpAqZX7w0rFr5COJswygNKehpJdX2FyMKF3f5XADmDWPZzVtPA8l6n9fmetqHkZf7QPIv3Wv6Gt53fF6p8womkxuRGeRGPXeV3rBmWLJYttGDFRchACoEGdRKzfW1Tww4ZSHPMaALh7o6W81nHMSFgUm0SmWQyfhA0qdAf6DnSVYACYkB05hEquhmKp0O9Zg3uWbx9bke6LjK07dRnKJ+iPjpy87pRrRxJNluUvioy1aDs9RVqiqCuar/pptXmrWLxGlC4sP3jy5ucanh6RT2D9BjxRgkDl/wAE0AssNyGApW2ILUPuzZ3G4omXmi+ZbFRtw71TvtaHtdVvc6QBwMwVbGmmI2FUGyED6FRTAGLqZEnWRgaeYerHXvKnNJwuyspDp269qTjBuNMhxQ5qCOtfUltT8q3lyvDr8YehDcbkm36tAWduvHCL0aSj8xH0pmVTe06bjGUGve7lyGUcJRLJrHn0TvHnd771g3jgLRdu67guoxQf37V27+bGsOYWw3qQMIN6xUClxWyVn9Ond4Se1nA+aL8dXDr5uvW1pt2BQ54NuGY/34CgxfiHvV3Hv9g3cnKud9OzjhJZ2bt9h3/ZhOht3Bjas8pPN4Ru64wDWKyMdIqBMSSWYHgF+lphn1h4FBKJ5/PYCjmKF1/A3r3wRzBRQnpKl4l9GqsLZ4axqAOJv0HqjZEhs+senPs8TtiWXtOfbgWgRF3FcpfoA0XfjKkTkIhJJwsTkLVLouCbTi33SgBICAeQKd/081p+ewNQ/c1LzH4GYidNAOhUU2HFnv2ClABkRNYuqroQK1HbmZTXGKxPV0eAq1XeTm/5P4e6P1ORZZla+wgE7bctF8BJ4U3O7sYXNopFXenoMjdnWzsu+cB8yyciOiQMwER0dBkc3f2vt19+TDQT7VDbcmPZeIOBbGyzq28p/X4dzIdw7iqoTXB0hDS47wj1L+7chOXP4cRBtO0rRVTi5oTXmZHh0p4GNVpWLNcds0p5bWoWPdTev/UPHWAUaANsoKXu2VownrpjBzA96E7tyPeyhiwCgMjS7H7T7xyjt1YC8L5ef5bxvMzkoWsruqKGOWDL8BLsaUJWApOIQaIEI414WRqacPrLvVkZAiAdTRjlLm0MkCvFNhMlE0UHYQchDYqNUBFGGIVB7GsG2YWxlB/ryu1wRkNjOydXj2wc39LS9exzZeC0T12X2vyL3JvPyCgrX2hfNi47Vulbw6KYVZsmlAQUcbn66OKSc0r/g5HfFZRdyMcxNoihDdhTggAUQABnhCCA3hUAED8Tshlud0iegKdXrB2XsXXyOABFWGNIpP2mMb8RgCONRCGXMioVp6Yo2ONRqyVX2eO8hkgZQIM6UZSVyZoLvhlWnJTXGMSbWivQunMcfLQyoRRMpdCgTkbhALBQAGBgX5gpwLSrF1bZt85Vnl+0fmdyJNX04UcHvv3BH664VkRcWKXxkUpTfz+ntzYE+yb/Mf34mXu+tqFvYudxb9p6zmmOUtrlLUvmY440Roca68NtZMWUxFJTHrX2Ox7Ywaq/3vaT6IAZjcOnPuzpTp6gTLxoxq6N9l/2/FM/bT3u2P5du0eXbFje9WyoRWlTE2PKkhWbc9uXAQh3TGjCWRHd3IiJFqS6sVegJ4wt7fC7k9sLohGbG1s2bMRT8J7CY03YsQcAUnN+7YK9TgBnhnBMCNrr4H4wvGPFohdx4nNyzXPyuL5SJ4CEcCRgCSemTgbf6KCxwGC5cWoLDlH7CxAXTqOahJRJrynth/28Dkh30CjltGDLUMQN/oKV8joUJRjJObQsExeFhJoMCbvSshowUQBgwgnDDt6tABMSk2hEcI1JacOoNsCW0z6Pkm+mgznAfMO39fKoWczp8/wZzmy3tVYnyM/BSN0A/LzWGRsPKU7JN87THrtn+zkhbzTa6ULi39suv3TV8fMpmYjo0DAAE9HR5Qe///fU2Lc6e3KWmi0gZshs7EVXz5TuuhfWwzjpbHjnrcq0qol/2xS7Bs7Fxub46pfEss1YPYGGfaUIIYDxckcWBgDp6O64Wap1y5xXAMb+8/DUiFsbrXcUWFyNuBNADli8n4OsxeD9dsOb4wxVb00PKQmb7Y7avJ+KygPJbazVdaE9viszNgzfj3d0LErsshEC0InRhMzYCJkoCqAEswy/BKMowzmZmJSxoh/OynBMFGNqukOMtWG0XQ4bKLmwJhFLIjaK9qCoGlcaWb+hSR21RMlEcSWGG8r9yLu7Rnon+4aV/3xQP+Xk2HPrDUDecHqbtwQnLfnFMSuXTuxdH11zRmK9oZSKivkG5ekT3Bda+iaLK7XozrISQujpkprEznWIjgFjyLUisQiLVkEYwHGwTzP7u3tCjhw1u5KyyUBhGHoB0VEZT6EZEhKyCGtYxl3fcqWZQnhKg1UAgJ/Xl5tju5QEqpPHNDaOpkuNfl73hwWiGoBqLkXSEE0ibzU4tjTatYEm6UbFeDAqUhtSJhwADswo3Bx0AFG4L2FxAWZGNi/1xZ9nfvIj7YwbbvnBotPHv3LsZzLQUjIxgNigH+9S0gB0YQOI5SI7I6E3qw+ufHRd29LeBzvOvAdNW7Ycg6Af6dRgGVSlAqIyThKgRNygIW5+e7wytvDLUX27yhsl4Zd1pdVVIq4/oSvNrp/Xmye8s5smt+SiW0RDMauHY6ViRoNQAIRjJUhc7k3c3W0FE9sC0NqdJlFApGSJgrR1QxQK0oDlAmhVkovFNggRQSEk1ByMLuRWK8+1iGQERUs4iUIqsTcvXgTSEP3ABpQHMTCCtI+dQKiIDXX7Xv8F7QUkcIaJhuPR2AN5NoYu7f5x4k+e8U93pZWS4UEvAaBLSUogqOBNCCcljepYVlrtd5AONQnA8Yw4JmTZF56r+Y4sun6xnMzGJpPCH5N+puhmFHgefA+aBi0U7vSFZSot0ss2ipAesyAaZF4zuxonwmoRQnSrwwBsGE1IAWhCMo/wrCNXSaAorTDsIiwABewbDTv4m1PyjT3lTkjUxeApg13NFI66Wlvlb07lpzcJIGhubUDgdaEnSzsj9w6EV55UgpT/ufR1r+1ZO+/LiIjooDEAE9FR5NEX7vvt+k8f17E7ZMBAFoD5kmduL2wcwuQvcPY7Mfmm9ugfku4jpegZwIkYOqZ9K47ZiKXD6JhAQy0T5v3GMT8OVKJmblPT/vvrTg3ABzUOlgPsqj6MVuNu8EYTQPOMw6vNGDyvxswHE3Dnt20oWg717nfgq7hSMESxIMNxNZX2EukpcU4CKO1uCAbBam+azCR3oVAUEbOnVdPDSQvFONIhWaluakImjEJKxp26NJtFYgIxAE0yCyCMQgIZA8V2jDgIFWEMoW03Fs3cMVcauijUHp6OLZ7wFqO/HaNOZsmIb+wYDjs7JtJbZTQue3/x8JoOjP/J607q6HzwxNWKUnipsbgKuYg60aAmO5XRpIh6asxSi01q6uxNzyovQYlACUFphbPYTFldpnBMJEupxgZz3G7W02ZiQiSqn2qjDcuCDSAp2zNQABRgARjAoqwMj/jdAEb9hOOblf+y5vWkNITllkctrW1fc+GgX3FQH5j1mmLqJICgvq4Zk2FpS19CSq9YTkQqAyZl0ApgGHE332iXG8dSBjS1S830tA6vgBseHJWjUE5KlBS3RdjJXOs9uUVRFPciGjHKJyvbYTkZR98W7Yzl5eXRzcW82hlJI28+IZdvQuNiMTYh41uz3WOZ6OqenZOZtgk/3KwURaQEwFQKjm9MBtMLj1jBcFzB6EpBp+VZw/A7En3DYS3he09p0QmYqHZpBiAibspUG8r5jGIuDQ0nvaZGdTLpNQFIeWEoon4UsdowTn5eh4QSLSkzmjAklIKlFMLCKUozpk6WpAU7FI6mgm9cm0iZwjGEg8oXVLSLpAnHRH4xhiKibCLbglTIQcvEmL67iJ3AHiANOQavH6k+7C3jrmL1m4ApC73Apccithw4H+7qUKa75emm0x7BcRvlkq2l5QlRgETQ4bbWttm3q6HX8/2MohiFmJuPuEk/7/gpuzCpSjs3OR4SihtRi3lhClP3XVNp1kRcl0VLepZUNaVBKUzGw3FPhF1IqVilqFJu0PK6cF1LRSEMswRFiaMQVzNp2RhX0iVVLhIjIdhCIC8MABEU8/UTHWNKuC/CBDCJRDCTXPBMSZr9bmewnW/r7mgwPN4BknAoWukDDCAuCukg/wvEReFM/YVV8vn/fPANvb2jRnO45OqbX/uRWQshInpFMAAT0dFieGT3/731hp7F6pnmowC0tDQ3l/VNpR0TmLwLJ/5d6JkLrlj64kbry1tjF0CsQer4tp3x5mF09qG3EoCBvJ+AEKNevNIIWgBzB+CDHgSr7uFLdQ+X1bV2Dt7LltPbP897kKqpDqWq12x3IKBY5SkFmXP1+40rhbia0oUDYLzcWQ3AlR2uzYRktjlx7EoPZuD58fb25sQAgE6MFqUBIIxCCE79UZZghFAwZclBKINEDCkAJoomimEUiwg3yBQEitLYipWTiGG6KT2iLZTiSHeKscXob0ByZWZ31uiJj6RSCVPkChNevLC+NDgwMTyU8/qwfBLGapx8LDa1X5Y6tqvY3bLJVtEZTahDCTU9pHRcX757kbs1nPK9JiWjdOabhSUK8WI6Z8ULwkwpzXnoBcUEUFCMYE806GGREwI2jDzCBREB4MDMwxxHsxDoQxcqHX1FSNglaUIipFSib8k3G1DMyDCABhQisqAqeQCQ0oZpwenBQAmREPIWCkGT1AgK+eoQbgVYBZjbveWDXmN8V3hvOgYg3OBq7ZXya1PjOIMzetkKQCLc4BYzevAq7Ce47teh3imEY6XashItK9FSrSlsfaP6mXtb+9pWHgkA8PMhJeLWfxm7tKQtTUupjEYWVpy6Edor/1qCAQAG7AKsGIouvKArdRuSeZiQchH6W5FaJAeWOX2RibK2sSg3AFnI3XC3IDOOLS42liCBVHV/E8CaEFbqiF8EsRS4EBM9Xfcl1t6P8xKYHET3pN9Y9I2wcEKKDR9FN6wVxVjJSCeVBmciOVqGnfeTUpZ8RS9JX1daQwiFmtqEMCwzUQ6ZMuM3G7oLTQUwIhsB6ef18qhZzGqRpZl9PwQI9GrDwbhrEdR+h0o1IdmFgZmfSGFGJ2wHpg0DQBOSk7LS1iPIxkVYgIQUSSRyCAMoSbPkm45vpKURfHLemOWMTP17N+PPXSha1ttsYdV/9CIunLia+lPlN7/afsl4IRVfFJbAHSfcsLiJY0ET0eEihNAOvBUR0eF3+7O/mWjvvdR4LDbsAtA2lrAHuydQuAdr/xp7T3vz0icf8P97NHYO0AG3J2THRQFWCnG7VoMhMFqp+EXtn96EOdtAVjMfzFieGTzr+/RO1KY8BVC90wvqePMStoSsPttysGdimoNu7my2OwCCCYfm+ZK4UmjRhirvBsSVVNpvr98gyNJmu9PQMJ7ekoeUwjIazEJQPVuUxiRinRgLSSeLhINwAukQCpDQUZCAjVAJRgwpQJqyBCCMQtDdt7pGBtl4RgauVjxVI00RxqSMNYhoGIW9DV0Gin57rnvzmDehd20aRS9SKZQMeFfqz9pdYnL3Tx+E3/Z7/+s44UKc34KeM27In+CO5OPPt7Tt1aNtRYxbnQNmc7cymHHaImIkVWooC+xUWiOirCpZF2FHMTSh7BHtlnAcYUG0OMLMCwNCFGFBoCAsWwQTtwoICWBXqWupPliCFRJ2CVZt50NqoQCEUFiCQQAG7CgKFpygkbM5Zdgo1OcHR0YgMC6b8iis1HYZfim5qimaE35O93J6eSymxlxICBXSUXxbDye8aR9xMaNDoJgLQwGAYi4MINxQ35K5/kqbLexOvwxndtsVM8oBgGK+rjWBjbBdDp5vbchO+kZQjJfTAaF32n5Oh4CXDQFS73TcYauY1QFhddleVoeA3mFDr040JQDACZdTvikU3RROEbGijDmeYSqOoTiQKPhmWDhbsCIsnOkDLkvYGDCkDWC37DbgjMhEWV/S1J5q0SetHkduAFohLUR2o20CLWN42kFPCBFgVQdEN6wBqBfAPwGyDXanmWmA8LVOOTApE6eX+/pLQ4N2yMkq7am+jalmL18spKNZPSodmey0EI0pnQktEUZIh64JXWsQBaEogIyryZBwAZiejKoZACXfLA8Gf32kEnHDUvp5TZglCBEXBQjowgnqMhqRKsCIYxKAgULtE7JQrP2pbETSnlrxG4adACBRgNWEpANzHHFUKoFlAWbwqpzsABCCE1KcEEz4CVMUhr2E2mJHrFJlRuK8VsrN8sNKKaeVcg1mux1cOcFQfGlppHKLXmy44IJl+R8/p5q+oQtne66fAZiIDisGYCI68gZHd9+R3HRMt+fJghtWtJQvy9i2E+IptH5E33nuKvXmn4d/hfgqIAI0wWnXUjKWEvECjCLMAsIA8l4CQDUqVW7Bvfy867jmiJlBa+dlU1f2AnuArEQHYKIytaaU2AmgGo+XvuxBq4C6mDGv0oKwOv9ZjuKi0KIO1WcZXTjL9T4AO9wllZ6KthaKuGqzkx3IQ/qQsqHV1MNjeS+hK84kYhZKkHIU7TbCgISMt+27+ZYlGGnZADT0oj8Y9QpAGIUwHAApxCFhorQ7SL+zHqUEBIIewouRBZBGAwADTlMhBtPRpIsy/M3Y+yjWuTj2MXelsnuyiL98FwYXt45d27N594bITnd4z48WT4b927N/9RosjmO8ffG2xScMNkRjSbccl1kRa8okY2P+ct3f2rpiQjQPoUMoYkB07UVHq0iNKU29YkgoIqgQNkQWAn2iDUIA5ZK0oIiwsFeJLfAQ9DiV8Itlc5EyBMAUjg7VEHYY+SIijSIZDGflwDTFlPGUW5GyYeZFuPD/s/evwbKsaV4f9nvznlm16r6u+77P6T59m+lpBpiGYQDBDBYIIpBACIUkh2XJlozCWA6FBf4gWw7kUMgKhxzBJSwLWR+EoDEgI2tgCAOD8ABirt0zfTt9OWff97rXvfJSWZmPP7yVtbJq1Vp7n+4+M1/yHyv2ycp8M/PNN7PqvP/8P8//oeYzi1VwSjtWQUTwIr+TKF8ZBoaJYZoNHSNtrvY1m1voa9CcA+G6MpyEVmm/HxxW1Fht+ZxEy6/ky3DTTzj5YC2PmjMTA530Gx7XAHcnnX6nddVAAKa03Mb8hIZ9ECKia94OcKFZtGmsHu9DY+lB5RoRwlN1oAnxrgzOaXXk8oGctLns7kweWi879oX9lbnkZBEHHSYP+UM18g9RP4URwyny4ywekOckO87FpPWL8aPnEx6+/LWn88P/78wZS3uQuFEe2J/8wv4jtQiaUdZxdsA0MIzrwzbGJ2dxHlzSaYfZ63HtqDGbsQ+8HmkHbXELX+7o1PfB7IW+DiwXAeokLqFbeHNf0NTvVjzCEHfFgVfPR0ywWvaYxQSxeID+Fgs4hECE16R/wlE5wtlR4a4ZzsVvKhcY+Z7pp4DZw7wIopP1WwlOfQFEJ75TX4DoBZ1V8Yt3Hv9b+3/W3f8j4/N5d5dfOX/5e+//puvjU6FChQo/KFQEuEKFCr/x+Omv/Mywc/+T+V/qJufuIOYZ58fIL9D7d0jTtPUPvn3817j3GOrgs7jrpK4a0oxwItwET4fqOSppqGRcqhmTXfqF7TOw1cO5WHoj+92nqBsiAB0Q6MGkYL+rxquiqvvFph8IC4a3ZMKzJzu1R5NbGmxgJC6Lw1X889rJihNmU9uspTKLZDhBpFnD8/I092rGcHlScbxSYZUmo5WzLYJD3IQE74w9YEeGEbvPuPcpPvBIAJf4KmF4jTstkYpnE6NoMuqzE4vbUqMIp8203wgd5ao2/r3E+DKfe5c7CYOA/rcAXv5VHv2284e/6/wLP+Hkn3Wyi/lXTV79iz/S/KtfGbf5BzXvvfe+mf43Hzz7404EM9V+/B5fsJi1hzvJS+uwlqv8ODtqqstLdqbKyUmfqV1U8SwpVVdxrtK5+HPxUco1olhZjhGj2FWjFv2DZWN2GQVG5BPWSHwVKwiVt4O4ahgRAIGKQvygSFWd4uZKXKYzaie0BnTOVBsYLFoXJ91soisS3fro3oSPvMsNMRGUMtuXH1k+8ap4JURZKn7rKGoBJBlqdp6vVidDA7Irt2RVtBwohKQfuDupvb9QwRyQmZ1N7XxqWfuhEcz1YV9LHWipyDEXjgp1f2sSCVkojq2cJ7JnMbUkfGk1u2evUxv5LPwk8tcIfit5m/kXnMVj3n/nh7NXMlQ7w/HsPMp/8Vuf6GfucfZo7rXMu9bhXn7qHMrCa7eTfTOpm4Np1r7IW0ZpEPPQ1iHNRi1dnAUIyfRqShbp3ha8dzV0ycQGcesL/yDKJpbZw1FXRaenuCcctRnoV0t3Oda2zwKBSjxJPBVfSiPEAQlIBNHh0Do4HNCB0CuXrATfJWrST/DbDPxCGb66TQLCSK5KN2UXfnS6yX6BeXF11xdeLxoLHv0b9i/9lfhTIP/Zxfn/7m0flAoVKlT4XlAR4AoVKvwG4x9/5Wf/ozD8n9e/eX/0gRXl0oev8eKn2f+/uCdR852fPpv8cvrJLoYDn4O7pE3jlEOXcEQ7wl36PytsFY2z/bX455m9Tnpvyv69FSKred6WCbysHyeEKVyFD/8g6W/5rMWCApx6uhFzOHuyU//s5dsfbiRuc33NXPyk8MGS0LZ3Q+WntVfnY4RcmoeG5LNXi91dc1wzhprmXNJIxbWJDznf7CY0GUfEqzVzvA6T93lHfwxImoxneTPJXddIgERcVyWOkUyyxo45BpnlzZo5Oma3wwSGEbs+8zbfjbFPGvstLvwIHqAWdF/TBRpMnlI/AAtGWN+Yc0D2w86u+sS/9Euv/9o3uf9vOX84/tbZcfcAfm30xd80+v/N3omyv33nF6KzwVcnj7/w9NEf2P9k52JwdDLza2fes69wP1VBQjDFjpQHaqbcCQ4qizP7NK8DqDrQNJKWNTxVtVNVq5MAHTWcKMthMVFuSrajZj6zukIpBOpqqu/lvrrUKrdS7KvJt+WdWAWwyNSiIadTZQ9oe/nYbFlm68phLA8dIJ/Z1n6Yz5yrp04x+05xY7cEMG9df63BR0RwFOrXJRSJu0aQGrX54qwWHvvBYRi+Dq5FTy8NlLZYE8v1jwKFifSVILncN+lL0vfdumXUUqOWGk5q3U1BmF81a6rYU5EtE1mukAn2RGzAYZqTZ+SHcuoTjz0/8KI8JTGd+efmX/nip+ZP/e96i186fnh53FE1M/Bt1d1Juu2WY2XOAUanlwhwkruA0V6M8EeZf9+IgMXZUn7XNYFWwefujpVMNiZj294UlN5IJVPLr83Nepqd+897B0DTiHd1FgOyUoD7tI+K5VDc1YG7jIELaayOOKDb4nK4xbvvCi/l4HrPbBV6yluVCwbMXljvhdlFAESn/tt4H0Sn/s80fvtvDgbz56dOt4Pi737naz/1iaoYUoUKFT4uVAS4QoUKv8H4z7/yn5pHf/CAX/YGMUDI8yG7/6E7urh/tBh+w2QPdoBdlA8dFp4RYZ+qw0sa85LT1CxvUZq2z583rsm/b4kr8fKK9M4gAH/9KBs5mxub7hdHk5sFtO8DOqRQMw0HnAdjILsIstlH+GFvqmQk7n3rVMu/+lpT8UdZa3w1qRUVpEE4GY9TBBzLMpPZYmdHJbaKVxxkljUdFQdq7uiio5KUXaAjWatvpNsEOOi0UNyXi89OxJXIUv7yvBNcLf5NFrugdlTiKM82Yp0n3JChr5IYJ8L2JBVF1HJ9EkDmqCYo6nWMGtyHO3DJouMQ8+0Hd/r7dZOz/X/lM+58kQx++OJf3v+xs6mZpj/x3db8Dw/Du9nDC+up/d6H8fxkYVmv7VluD732ue1+znt1XjtURte2Z0fm6VPzUag8wJWIrLF62zLK7dFiVwuhTSPZtV5P2QtxpsoJiBWNjLymGjPce+rMk5nOHAb2GI1U1yeNsGNVS8lMJkN6GYuh6o4xotw6yWqLc7Md5oPAQNtKBVPAaEKMYSaw7EYe2rV7Z4UeK3log1r6Nk+vcmi3YfsGXf3IP4w2XKBBjPoCsPbCfGZbLQFmHzT9g1AgS0hf+snYhHn43EKS5fG1erxmw1kiuquE4jJ9WjW2IYWZbNo5iQCGG1ntIqT8KhVADszhSdZCZag8EdfRIQ9F2VuB5+zvMvo6n40J7sq5cl/GWfhrj+pf/u/dX/o9P5K86v22e8+/3vnc7/O+NvfGXWvwgXr0Cgc1e8GBysyGJGPXyyO7FeWHe8eWiia4KJ7PD/LQRmTdKUpqj8f6Nrkiyba82c3LL9H+6MT396NsaiGeuRv7Kp5m7brZn+IO6egc4BluTKDraYkwww1IOoxD3A4jaKy2eoTH3PGJZ7g1EmSp/Q5oQ3u64RSNpOInuV83+6n4rhGRbbxAwwhSFaRcC4TegP4Rs/eieRBlxi/b6ifT8NR1YsOZ375jhQoVKnw/qAhwhQoVfiPxP37t788P3murUWeUyktUwMX7dD7LcHfPTazB14bhP6GjZ4Y9uMui7UzdnYRgSFPBiGaXsRaBY22oA9c8YN8IQXTQIfiF2dUldBUrGXWXTVfnCE5ZJgCX12j5t/Y9DMZHgFNfaMYroQ2YvWUMpNkLzbe23WqopGcdN8S3VHSLRqPTieViACDS7AVpJnHuTsR1c882YiAVN8ldx4zRzs+iXZ3mEY7PvCWjY3YPOE/wYtwIR8Am7hHN8UJ2AdeIx9Ng0fesTgwofwFlBV0m4k4Wuz1zXDNHmgO3mQxpeswVeOyoxsSKbfUYi5QQzlE21KGOtFEKS+ax77UYfRX/E3+M3WTw33p/+Od6P+oZ8YODZxF/gN8MqBD3ve6LH05OPnTvxyqd4gSSvL/4RBTbz0c/fH5mN0bR2JHa5NAMJh1j7HQXxvzysX86tW0nYG43I4LAntecCUp5ubWTDQGXuKOGEV6PSa4kgwj/OXt3ONU6sE/4nC5wQctTsxmeRxzi5uQ5eUNduMxiBg8V3l7UV+0fZRAvU4iXNGOmPIS5FE+kD5CIf5q3AMOJAF3Y1QrtVWjyksrOSsS4BLeeJlPbP4iMWmoEqRXa2nzYaIKCVilkXcgHjlGb6TU774WFNG0xx/VSltWMrLd0n3aLekvLXq2FWwsCHsyufTEhOvb8PDdqqREsyqT6JG+CiCGRuCeLZsOIm+bAUZGZBZaKbCNCJMJ9wf4FTfjKh1nty/+f0fyP3P9X/qW7f7T7t1o7zz5sffL38L6+3O/ywCNyCW0xf4iXC+NZSoZF4gRuM9KhyBbtk+xw3xieBi1dxmkDy8H0U+fheOnwPL1hYiaylm2BRCeevx9Gp0F9NzrRFDRr3rdPLhQRXlsGLtELDmokfmENbTJ6zZFH1KcNxPirR2iGOxM3wU+KlS84KJ8/LUWFAKdZ8zRrAs2iutVVz0I7PfPn0+um7lCQ3vnU0gvOg7GE9sts/9+cz4+O/sH/Y/ZebLv/5lf/4ZMHVRpwhQoVPi5UBLhChQq/YXh1+uxvfOvP/cPg93+Bb9gnwpx4iq2Y390dnu5n93rR//ubbQiF5l0WX3TsnfnUax3TGbK0v/JIdAXgy8XBWLzV9FgiG3Bqi/ktcuhGBbiZIoDnMC05PJ8CoCAs5tllL+g6BKUgZx8+A8Bm9LFOWSw+fX9S8GrWCEho56G1Yr8fDQKKSdZ2r81fk9zzVDxeyT5CLZ6OZxmAiKoF8zxbrl/mAMtk0QRsFXvEmv0C+5zoMR7T9lmaPzcYjGmt3J4d4oB5iOOoRPkLw1/kkWV2Sl0qBnhHJa6ROCpZbenLTlcR4zQgFjeYyMIb+U8SQgRUA0LkHsoBkBYqZtpuzmRv9u3Tz3zK+Yb7m78y+9TFSc+5P6mZTUBTBZ/4JXuXbuNCk0VI8ceq1awP2clrHcZuU523JqEDDEXs3UjSxZ5/YeTpizCohTM/P5uF9uvIl7k0zMRXFgg152XWrQdJZnq2kzXyeMd6HlnBN607TWvwyBrPlTUy67ZhTVWgVDdRTqYWSolL2EEuaTrMHBVqHnhACDiE2qfXXfpI++eqaRPOc88pbqtNWDcuv5s+Kt9i5V4No+kmoMwOgBVaKlisHtH500Yytt16QpaR5WS54SZsOkyTh7a2XjPchMVVDrfhJiCGp+iUHvsuDuShpdNfgXxmRye+s7OYTyzNtBGuavws29t5aGdX6aMFhfa2BNi69TQ68f2D5SYjSBvEe2pkESql5pk/zq29MNzJ48SJpqnVzo6zRSbEjXwuEk3mrXPVsuzd3/zVL//YDx/9s//Hv9p8l/B/7Z27h1PJYpqxfuOg8CR8lw+BmGCg2ikuwh2OL2ge8CrBn+LWzf406yBi9WY+Ep2U+LoIIoY/1/qwEcyNulXbC3WfF+dBdOKD1D/Xl9ACVJBm50E2s+alkGl/P5w/3TFrmVFLlZ+OFi1oTVUyMFpAWw19Yo9IPyEXNGska3m8BRLxY3zdAFipvqn4wChrAeVQ5xX6Zx19s/LQAtYucIUIfFauV86D8a4R96zjVHxbRVOzA3zoqXfzdi06z5sdx15sOUiFChUq/IBQEeAKFSr8huHnf/H/xQ5ennSZ4Y9o8eJv0PgJ+5e/wd4nrPP/6d9RkMKBhbKxfmXOD7M4XIYTe4WHMKqxflQlkTV7uvITXnFitsVSljyENJXVvLfMfjewmt3pdN8H29psyaRbTwb+PlKDzVpqdiOE7NLPZrZzf/z21kJl7Jkj7c1zfVMs3lX8swhKyWAIkMteJ3O5nIszyd1VmHiae0DHOg1IdmS4Os7qDUODQQPGtBsMgIYMNQGe4yV4IU4q3iRvAHlkGn6W9b01DgzARJxJ5h5Z5wiH6hzoMDmUvqPmCT6E/YbXSyZ+m0XHtiQlgQ74pIG98A1cNVF7/YajuDSz5sXn2k84+NZ333HqC4ks1zISI5/h+iQRXijuTBU5kwpAm4T11DiqeTsybR4MzSzoK8dXyWnePDBHdWOKQtVrUD/N7uxbo46KbBWjDKgh0mPkZUZLkjT3pplbyyO1ECczVTa+nHvWwoqzmZIsWywusHYW8zOpK0NlmRnm7NgXp/O8bs4xVISjlBIRy8yVoSJqmTE9N3xTMsNMDRVGyp0bPTEjUGL6tpGcsHckLxLLiUoWcaHpX7l5Lf9Ryk5JryL2nTsDRyEzCxQLZLH0o5LIRpUqS2e5ZBnQNOJR7jWNZJS7y3FTySh3m0YyylyUIhcMBeQTCySfLG2u/L1ZNrUcP8vGiszIppYRqFZtBvSPm52HZ5JYElmYxjSQfGh4SDa3lr7rWSaLzKzPFblyFvnUMsykoxL3IrdVKm6aJwFT81lmkXTwkpnpdHbmaSOPUhvDlTozp7HvjeY2iTnvmZO50epJvFBHsxPnt/yFP9v8Y8iMVw8Pv6uzGoRL1QhIfImBPi1PRTH+TFygRhLhJfju0lJO5rkf5a4IEtqGnyIe4NQXZj3NZ7YeQ+Wn+vfJ7IYSWipY7BvDk57UgvmBOVJmyg4IkbijHkZg2bvkMzubWdnUdh6MopMuUxs8fz8+nzXs3agfdnS49VFjdpE7u4/OOsyBSEnXGFoqAnyJV3S3zWBQhDoPpGWr6GJx2DQHmvd6KilT3+wiWEY4w/xZA9AvJuY3adeX6Eh13cDejfQxAZ15UTf7NZI7yI/+j3/+5Lf8+Rq5reK/950v/+QnvrD9gBUqVKjw/aEiwBUqVPiNwfPTF5MX/8X/+Phf3mHeZmaLPPkKB+/x3fnnOz82a/25f3oOU6hBrQ0+qgMBhjsdqXcjnCFN4DV7oOVf94rqCiD+Xhyd+VvkoZvsr3x4sbGqYD/1FacVUERwDjV4xHZclnKArxTj4miy9uktUc74zS59sxsBzv3xRzjEOrTwWzcHmxtEytm/QC2djcc6IFm8lmkRJjggPXOsm9gq3jEJSJoy0rt1mETijmnHuHuc6FhoV2KdWLg6VYK3ZMKiGYJldeJl8DMKyPpeHllWJ1bBUhGai2urOMHbYegS28wAn8wjQZi6DbuVL1wDZdlJ7ntJ1HIXnjHzGkCMirBTabZe/l2v9amfnf4oMJ9a9i6v8737xjOESLm6VEwROKoupLnyx47EPVDHKBJ814oS5QPvmbhEAcmJHD4yz+fkd42OpUKXeEhrJl6NpKnmr2n1LKUYLzAtsPBScmBfjfdJI9xPsLyb76FNvC+POQJCclAHJH1ph7g9lcxyR0SUUojUF7nkGcxE8mPjrlqIn+d+PlgoP8vmyFxyb3fxZJ4HQXIWCCCS5UkWdK0oy00EQy15rCzyXKxUXMnFlijOgzh3QytgsdixFqv6YiJMU3PXGotIuPBtR6ULQnF61hjUjtimke7mc9uMcxyDeU1AxMvNULV3jMtJ3nVUcr5o1khmuB6p5RrT1GqbKbalskzGogzsBdE4iLDt2mB8kiOilKq7eTuTVBxrRwXNSWTUB2EDpTDN8NkBSnnNxOhg1NL+B01ywz8IgQwHl/nUIlh++y7tlBCzvjB7y1ThCS2gacSpMXRUOFOeEkX6ay/PeOAdfP1f3QVpczmQrkfcFS5ptBm11eUMR4QWFzrXv6X6gE98IUuV1SHy8Ue42ptaW9bNp5aDOA9L39/ih0oTS0dF79r9ecOf535dl24CJ/d9/BO/CTCzEJlPLfPC9/ej6FQnQ8vuw7OReGaQ+kh06r8e14DhdGcUpPoCJ+I6hbG2FErvlAMgFd9W4ShrjvL9fXP0PF2GQI/wKPwF5lPb3w9nH+7AlaL7BgRX7w39/UiPQywuWbtuDHqMgAjvKXd+pM+f+Yd/8z/9sd+adprKym85ZIUKFSp8P6gIcIUKFX5j8N2f+cvffvzJCOex8Z0hOdGwu4Ps2KdjPvmN9P2vU4MZPHYxm9CDT9B/rzaip6v+hrgJPpDmxdxq6V0liAT3J3lkOUE6D/Wv3FtwzagQfjdQh3sAPIddAJ4AUNuSfLhEF7oFDZ4WcdHLLm7UjHmrrvn7kWa8GtmlD5TXlDdtXV/GPXurtH0tJnxZw0bJcKJ7q2xbLJFFsiTGxki3meXNmjHaldM53hyvyTiSJYVusqTEifgJboMhMKblM9eFfHX8M+AZydR3JbKKQZLZt5fq9HxWr31yqFcmuVsziMXdUQxpukQP5cU99VyBnQgKb5gsPDtuWVacAwvPuHB3nZimOrfhxO35p7Ojf7hw/uTEV4a+I3loj333LNuvmUOEmkpC3H05LkcQ6KjRGe4JB/ucaBrvShSoZW3VEPczfDMW70K1OurDGe5dToqhVJ6E76ql4VBMEOPpse4UY+WrJKYG6PBar0jaDItIVI+wowg5CHGVgVJFvrtlrHp5nzPd+ISjKbaFTvTFpGWDKnmSeQBB+dmbZp1IXF8lFkTi6vcNrk6tL9u9KQADbHMU5V6rCLRugWO4OhV4ngdAYPZXO83FtyDOWrk6rCmAIwCc3PWNBLD1OxeFrxLdjUg8F1oqBnSZH6colTw/MhwV1UmmiN+yT7IWUPMTID3zzXqKIU4jn0+N6GTpkeXUU38/MoI0D+3oxDdri2xqrdivRlPFTWPgECHsMmrS7717595PMm5j4FxS94i1VXKLS32DRNDFdQVa6hKIJYjwI1xBHMIa8Uy5jgop+LC9Gy0jvYPF/GlD5zJcxyhr7VqhQ+gY4YobOyrCgKwJYnZDw7fNmtWKchUsjEfpoyTpi9M0h6PFAWD2olqw0Ex19uGOfxBFJz6f64/yg/vWCTDJ27F4TXOQ5D4Qi9s0h9OsM8q97CKIDLMBg9wFNO9FX3Apivut2C/bfydHueuZ8TRvmwSWii0V/hPufWLvDz3dfWoZaZJ7v3L+8vc++tG3On6FChUqfERUBLhChQq/Abh89uzl5G/83OHvOlRPbeZjWmd/Od37cX75rNX89Kea//VfqsGH8C7UDVQHfojJw+CUwxO1H+GccACMaALz3B3nDoCSXGcwQvis/haFXtbhw6cB+GZpZb0QcgV2CxdWSoWOBNQNYu6KBq/hWuMNifrakTbYLzdQX2D+vKHDC2/HWr3fa0VKUvEpF6RZZGo8BRORxp4/n88TmhdZY0cliKQ6npOkI2MKb+dIHJ/53hX9QwSXyCXSCrBWfR1iHf8M2CqZZI26SkaRZ/oLCa1F/5p/rE5aFrcDunqw9nwe0R4RtrgA/OFSKN45iaKWu/Dc1FUeiedOrHF+7vWGNFq/8vX8D/OlnT8+zHSWuB2devXSkGrOmRTT9gaDRPyeKjiwuInyrghqEXoQkLiELqEnMYoZBzG+R0RRZDUWD/BU6BEujXkhwo00fxZXBxlEygUiXJ/EJ2kzesUeOsuUq3j1miS6/DWKER0d27x6drSsd7E4HOUe0DQST8WxeJ6K11K+S3e+XE6WnJNrvr4bu5wsmihGWSkpNGsBTRX7RuyoqHR0pf2Wd63Nh7NubC4AdXDFmCi3U1SZ1lmsgDZn8tQJhUVT3ezfN6J57p8ErfnThlZWs6lV5mZOPdVib3YRmL3QByNIy+y3qTbj7SO8JjQm8T/82/zbL0/er3l8sa4vqMUlV+8pZj5JiBuQ6DpDLmGEr+PnE3yfeKbtpou3S+nZ6sEWM0gltFRwZVS9Kgg1ytym4TubX1WSzFsdKg+tbGYfT6139l6O3aC98/ppdk9lTX05I/FUkNYeT/LQyqZ2NrX9/UhCG+EZd1cBzP1aW49MNrP6tQ66dhFE+vkvF5r6HhBtst/o1HdmlllbmL1Qe2i51omtMgtm4tQfJO/+ia+6X/rjCfJ/OrmsqgFXqFDhY0JFgCtUqPAbgF/5G1/68jufo5iy/9avf6v3owwHtJPzTz77S2GfM9iBxy71I3gHdcDU24mwkeUkWGCGM8uaZ9mO1gZBLS70bOujxBaXCeklnMEerObGZTNnv/CI3t+a5XsDtucD39zJtQIw+Aeb7PcW2LvRVVrmDdgzR8XBt89oXywOylKwTGaThQWCCK5zPi9qFOWuq1o1Y4gQqGSH4arPQLkAEkJc+jiipSOiN847FbeuEoqw542ty6xgYcdIgBh3Z5Wdy8AjEaifRqsxnez7qacUnMvDU9Xb5ZXfSL+jjp5ylI1nv7fFfTX4wDrhgRqLO3/ekMg685t7UDOHgcSh8l6xX5PEV/GQdouhS/yYwZj2Bc1EfJTyVrHcRU+GdFr0XcKhdAIVX9CEpbdWm8GSMwtAoBKf5JLGSuDtMhbo01iNYVuNYrwV+33JgT6U3jordkzE1xa+1x+onnXcg2nWAWJxR7k7wiVrrqW+FzgwRyeLJksGmxyYI8eIVkGwugEQiTvKvWWur4qb5nCe+3WzPxf/+eLgXftJ+b4DxXezjDd8Pe9x4mqxV5aN9VX7KvKIBnT0tdeLoZgqHDN61+xP3+lo3p5dBBQuWf5BdGf/+HjSY5mqGmwIv8BIvKaKR+LtFoRziivwDx7f/4LDxS7dd7ofittavc0SPOJYebpwrhaB9ZYhnQuauowQEOHWiAfSXr4qCy2zljKxEFF+aupvqzB/1rB3w2WcfzFao0XLV17d7MOVs7eroqbh6vcaZi8ygoVZs6Lcbar4y9nd32J/+9vZ/Z71+oP5o+Xw+XNDxPBTYPbkyi7hqnj41GZVnGlqa433plvj1BdmLV0Vc/L3o2xm36YDb9N+51ObieWLaPe+5+nBvjmagmtEf+/z7/5P/uT/8L/68j/5cz/+W2PDeXZx+qC3v+UQFSpUqPD9oSLAFSpU+PXGy2fPTof/12fev+YST2i9N/jag6f/ePru7oe/ym/tnPOL1Hw+YaOgZmA+gvfoP6hF4sUq6Et9QCMU54w9USJs5vTOZ6sCp2+TZbvepiucqSX7vb+tAeDLZjXg79HR6ubdivnnde33dtzOfu9ZJ0vt91YtZ88Ynq3UP0HGy+jXVh3JM7DKhVj0stZjy4f1mI9pNWSol4EIJ8Ef0Yhw2kwinIm0+uiXF9gqrqtkKm4eWfOZ5fVigKtbSXzheWB2YlclHcbAhJbPfJ9lPqGrJnHL9YaJAitO63E6uB/EaqfBZSL+UDVP8frUPJKH3/6Fp3/it82Iga517OXt+EFy8WzXeTABZlk7NeJ57rlGLCpxiS9o+ngIrooaDLSP1ylHPl6TZQZ1V0a+it3ixUmLfksY0o2Vp9maR6ivJCYA7shZrLw7nA1oAiGuLuWlL1cTqleyBxyrO/oIOlXyun+vSwTtHiM9/lcNiodLM6g6bJTH0qVcKbLBbRW9Y/S15e9KeFwS2pUptPgOka98wDcTHbztGJHm2AfGiOvYQn5l4/MKNWKfWCAmcAlXGfM+UYvLWAKPsA3QnuF+jm9E+H3VapfU4AM4yZpa5o1OfKee3tk/rpv9B43o+eJgLed2vYcj8Q7MYXm1jlZY/N9+x/v/7j/6zh+xuz0bCEhD7Jhan6aWfBV01ZhSsDpIgj+g5RNrcylHhU0Vj3JPk15fyKaWEVjayMrei+YTC/HNZd0m0nPfeTBGSSSuI752otJdHclSQNb1z9IzH3h9svvOuy+a9RNEmsZQ346rIdZlzMK10lNXNtpy688BUMr1nU+t8our9bLGa/D3l4/Q0iB6ZmezNVk+OvWd2bIPJ7u2ClKZ9JId/1/a/9XvTO9l4Rybb/fPKwJcoUKFjwMVAa5QocKvN37hb3zpmz/6z32b5gNeXMrBJ3/tg/l7D37hzPqRu5f8I4wEo85uDwWN95DfTvwpL5L6gOC19E45KE00idfLchjBgtmbSoyuVRO9hofgw1X24g/GuvmmrpSWtxw6m9lvU+Jo/qxh70ZrgZTb8GJxcMWBb0bdHMTiDc5bRrBQTJnPAURoNSaLq/9l9MxRTS0JT5uxLVeirkCEoz1yKanBIxpNGUf0Ttkd0dTBz3oMwrzpqWQqLuD1Yk2DN0Y8jywTasYwXj4ACmGomk3GHgMgdQ0PUs+OWxZwqg4jbAWn7HgkCjHF3B2rF+f07B/6gN4sa8XijcJ6szZz7k8aKjnT6mjebKikbg6BV7KPIsLd56TcnZYMUMT4HYYdhhGeKyGKhKBEgy8RRnR1dqi+VB38/Io9hAD3UL0e0i0/0lKouwq+y+O7cgLtQ17FaikCX8cBrxP8AW2fWDtLrQZWj9NW2Cqyzc2HQT8em6R1lYNKBDhmBOhk0V0rmmYdx4gcFZ0vDh1Zj9q9jhsOrS3HVtV6ym17jEI8odvmEvAIDwljdEJ1tyMyw73HyYBWX9qOiprKHYln9sJakOahfZI1m7kLaI13ezeufa5JonsypXYyhz/xTx/+7I+qZgr4kvoMXWaATttGcImFZojbog9cwJSDKW4dD5iLP8qviKsRpNnMmn244+9H86k1n+6AzKcWE2sV/WGH9hA7D61Rz1txVxWkuqfZha/zcldC7gffvfv5L/xKH/ufla/+fePdutGf5p04d/UlN1U8Cqh/9jK7CKITzz+IQOlA8VVV3ltuWnnr7S11f+qfvdwYVNNPzR4OZBc+kM3s8qHm0x2tLb+4uDd+mP2+r/0//8vZn6KJNgyvUKFChR84KgJcoUKFX1e8fPbs2enf+5uf+YkuE0N2Pn0R++HgO1bvk5eL+965UqgeROxNUT34DHKP1zt7I2kPqQGhcoEz9kPcWdaaiAtLQiuhFZ9725NsV2m6enYd3Nw/LWlcKSg/eNZ7A97aEesazFqahxahFZ36tUeTrUz4baivRir+OHezmW34qUwKWzDTHBsBi1VxTrnIGhjUjFGH8YJ8hyTC0dcwl6varPo/Y1qIZsKNOZ5mvyv7KyAwRv1sTztgGdoFelnAWZBlvO58ZlmRlVoear7LKagIZ58IGFLfl0nqqoVnW3HqDbnY7zRlGtFOVKC567fUeyPqZ3H985/j6133W+knx1FND/hY3IYRN8yRJ95Z1tozRo4RByRAjcSTeOOFSZdRVOR8eirWqb+6sy7hxj1scrn+ykVAFfyNYzkKcVG8w5OYmscMOOYoxH3NUY+h5ntDuktX4RseEJeoDSuGfEWDuU7ubn/KtryU2VwlAI6K7lsRslSYgV3r+Jbj3n62Ge5MuXfZ/oge8Spe/9J6hAO6PlGIpw/TZuirOMRzrMjPOidZU/mpIctv8Cj3JLKVn0por39Bri4uErde6k+Cf4eTyYO9xw5P5jx7//LTP6aAFmcD9kJswCtocFzUlxII8QTe4ckHPJqy9oYOXcv3wp9PLKe+2FIyd9WBD5eFsim1uYpbXjaV+dRa7TOd7D2sf+epMu9y8kQexbnrGUnPKG7K4lC/F6j3wvnTBlrOrb+53O7bMGTdN8B5MHbWY6ibG2XG9+JR7un3enZo56G1kpGzmW3W0n/S+Bce/8w//cn/vf9XxPmVs1c/9e5n39jDChUqVPioqAhwhQoVfl3xs1/6knHQSsVz1fl3Z5/7t3/170c/5r16v/HOe3V++rsI0oImqg0e6Tt2tG8NCE5px7inHABzPK2YOUonAW4zu7qJt4YQwm45Oe2KYhFBKHRv5gcfOx2+ku3qn+2XN+yZo7MbfIn0bFIX5Jw92dnY8XvogkS2vRspd569yJQJ0KozllUNWFP5ixXD3ZGhz1x3fUJzR0YJrkvcZ0c3aZQqAz/nHqB577L8b9bwjASY5k4eWUawyCNrMwdYQOHUFsBc3JVN9CHnCInyKAoIRU3L9gzAZTKilxCcceAS6iBtjzjLjaTtdHh13+r+2uxHtM+zGSw8FdsqslXcEK9mDoAZbo2kydAlUrKMtO/KqKhzE/h4QKuIgr6OgPnqYkVIChdoEI8ZqLDEUYd0PRUDQ7ra3OtzvA+84mBAa20oVigexQRfJ8aveO/sGu/aHM9rB3lTOzbCl20VTfOOoyK9ar7Sfj/KF0Qn9+qh3pop3CtcxD02E3e1IHxUqMGvueMTv+JgxqFjRJ83X//q/NNlrpvPLGZWNrXtPVHB4vqLAX/5ewJwl5Olh9khY+EufOH+2UDuCQh7EU6X2aXUlvH5eKsI9phAD/4HpSJpSe6tvi/ZRaAp39taKC8hxS43Riy/ON7//Cd+/kQO31HPUE8GVrv8GPSs46b4o6w1yj1tlec8SKWgoDqbV5/C34+ubJ/fqp+CoCn9Ru+aRuypRPtL6xdwqfie8nXkziigU5sMg4UmyaPcy0PrdVjP/kXnCz/zK3/ln/v9f+b46Z/6KGNUoUKFCm+JigBXqFDh1w8vnz27+Pk//X//N/+Tpho9k3s/dfpdN/2bcmrsHR3tTn5WDeGzECNHEKNs0nvGhbuLEKlNzyQ0F4Ls0ssjG539uzn5XiesEYQwhSk8KnHgy2Xm5bK67+auv24icBllGUWBbAR7byC7uDEZj48i/wKJeDqXWKaxshKJLMlz7yAfZ3nRscKQNnd3jbnPvMlIl2WOxe1z95DzBM8hHrCjWw5YyllNRsf09P5h3tQi1erUeWgaQRZfbPg/i452NvyF8hdJ7oaGM5HWA/XCJfaZNxh4pIA/yqwkj5sWkLBzzkGM6UqoH55j2ctVrf3Vb0xp/SN+Sxo2D/ZOzvMmKAntKO7EPa9hjrrmySxrx+J1reOZclvrXWnR191xCSO8Fn1EybI+bqm/gCJcN/pyJRT4QD3WrO+QVxvP1SvZC0hQ+CSHvB7SjXC1AkyRPPyKgwQ/xrug+Y48HdHuqdEIv0kf8PDbEONrU+gZbo+RRxTjXxR2XKzT4ys3aVi/jOvYZKiuimR9860ZvpvQ3mAD2neLslIbZ7vLiTbQjgh8tUmAy3jNnRlun9ZLDmrEd9VxjP+u8+Fc/OfpoXbtGgZWeubvHgxGnrH1IOXg7RivyeVL9t/lSf3//OPP/v1/zLO9Bwdo/7YQe3mZUkRB33yx6dK/SoAV+9281OtY+SdfM1K+adf51H45+9RO7fTX5L27nM6Um+a+bUTTrO0acZL7sSzds7QKbQapClIzSPXhzVpqBKm9S3rul3Tmm8775sxhYJR7GHgqWX0pbCLbiOoGqfhNHXJfwzMSV0UtGBq7//30nf9Z78iaRACm+ez0+MH+4ducq0KFChXeHhUBrlChwq8f/vaXvrT3Iz8J9Jh+7fSHfuQr//HrH/0hi9HneCJfR1rgo9oo7ZzcIWqap/SO6QFj2iHOXNxELQmSritjdqP4W6sp5sYktPiot/hwD17AFC6K6r4RnEEdZlB7S3vn61HWt+YVf3TUP9O/iokWQTHWwd5LbJ7L7EVmLzIv/Gy2+au+Zww32O91J+ErCFc683gGKG8hsXNm7JAtlq5XUngysz51L5aPdblkQVtDx6WzJMVymDcvskZdJctoagAMf5GHthMsiurNW9CxzgLmOwx1ySWPBJGBCppgxfnCM6wkn7nNodR31KVLTeuzEc4cD7LFgH+me/bJ57/6paOf+CD3sksflNmNJgHkLrRYRkQnPcauxInyXCIBJXJHnV1d6ZXt86Bg6Mu7csSZRxhL8FrtXe//O/KhglgFx9wBfGKdNaolxBC3uO9d/TEg8UkiXO2YpYXQId1DPKDHU0+iJgPNb++okwhvRtSEBL+9tMjCJfKKBYoKTzpnWPdhRZjfnr6C2CoEUgnYljz8NmTYJTq4IexZYxX5HEmgib1PrN2Yu4wvaShwCaENuCr6Mb6ihyLG7zGKVKKnOc8XB4C9FxHMyb3N02gRO/edZVK0RHgePsKQTr8em/Dy33n6xV+qDdlrMw6lq0OggRi3xeVlYd+tY6FHRcGqVPxEvNOr2A3ZTnc1LqFbvKej4L2zdQJ8K/F8Ob7zMBj1GA5Uqybxs+yQvDXKXZksHdDy0F4pvc7MAnRFotUR5s8aZm3BGgHWz/ebGe98as2/3vH3o7JzwSj3ULjmpv3e6kepZx1TJIG3doYPG3zH/dd+/G/9Ge8zvy/2at9+fV4R4AoVKvzAURHgChUq/Drh5bNnT/6DP938c/9eW9Jvzj71b5x99UlsfXDs//6D58HZSPnwDrRB6dksk30/kZ2hagCncqDrow5pheKmOuNu5c1TW8xnVmmKvSIjsmVlDwJZWsyWcRUXXbTcKv3epAdLQVh/sGqxXP3bKMRSXdN1fE0TNruh2dWNlf5Pw0jc9Ry8HqMGgzFt/e+ygk5xrmnebqhkLK6MyYdZHnlmI2rvyHiRLdlvqWeuioEQZ4/IFzfCbjLy8IBY3Bg3Zt8jmRM7xBOa/UIHpnAvm4o7LQrJSmQZ/iK+8PQYrtPggmIC4JL4zF2JRCntyXzAQG/0holSLPYn3jInc9ZkvqDxIc2A+fP8/sFf/LN//a/8kQf7zXvGdyb547Ffnz3bcWaWWV+Y3WicXxUTmkgrUtEMT59CwCW8StQWdCw0EDDX1YDLj4ZH+FienqijEOcOZ7qa0epiXAln6uBdPizWrT00Ia5OMNb2wrpQcPkILS5bpfYeob6PLblswXd5DLjrbz3KEqteLjPPJozotBmUWybiDyiF0d5oprWhzd6WOVzGTLk6eFvT7wNebzQo1z3WvDfiToQH3RaXQzomuMxWkeR35US/ILigecTxDLcJvoovaL5rP5nudE6y5tKM6lpnmkZclEFexjgMaH+Ob3pE8f3l78IHP5996vOnx9wL3DTE9klDbIEBHZAhXY9I0+8Ir0Y8w7VVOCplLmiTZ0q+ylcIQeACgqLi2kVRS/yt1FZAXp80/d2DujnoMb6g2TQHz8b3blJ0tYeWLsyr15i1FIhOPNTml/0tewBEp75f5GVojMQbLQ500aybQlH0Y/aYJ31pjTqh2iMMfcOD2na5vkKFChW+H1QEuEKFCr9O+Ftf+lL3X9n9i613HGY7zB//3F+w/sDvvPsLP7O4f8/mAhcM8JEOC9eOW5aC5zzQHsJRqcQIYKs4FW8izuakfBvh3YSeXIaljw8F/3biun4sKblqvant9wZt2FNec88+XZs7Zu3xbXmeN05YXWJAk7oGAxcfWjNZHqpuDM6yhwgwAwxvkcfmeK9Onq8OqfyFgUhoUcclAU5lX9+mBK/PDsLK4CrEDnDQGZJCKh6QiuupZJpvKV277Py27msX6FQ81CTESfBbjJeuyyIorHhtxGKxPZWesjeUnaFqLsh9kuiHPv34gP9q8UVyPCNRfurvRdGZ7yjMLg0j1jevYYwSlSe45bt5LIeH6phyrK/iiDMgZE9nkpc7fsJRS4ae8ga0HvNULd2tCt8vSYrMYrY+NF3GnsRAi8uh6modONx20wd0Z7h3OIkIXq2coj+ClgvQpD+i45VY8Rr7/QgH3NJuW3rvkmQCM7U8ywlHbQYrBl4++wxXy7/ABU3NM+9ykrFgGU++RItLj2hFnj0iXY05yq/07WtWWIxyr2myevZ09HiE1+KSw8CCGdR/IebzgS/JHFeg8MGKB8vS1lFcvEJbieobQ6L81Kkt5lNrPtk2+wpujXYu4NRTs7YoQqk3vyrReZtdpqpzMtmdPVm9chLYctfmU8vfC3UtX6e+MHRu/8S6nfBuYe8bfTj1V7WFtYeWWUtHPRkt9oGmSka5C+ybI9eIR1lrlLvvOE9nuMccTqT9H5986ve9+5//R+63/g/yadJqmlqhQoUfPKpflgoVKvw64X/403/6R/6rP/jkondoyO/71quDO7Nv5b+S/NQf+OKv/neYoOAd4rabeYad5AtXjVSvT5Dg6/zSCa2CAAiibHUlbM5XjsG3TMw3NvZA16ZZmcs+3yYCf2Ro+WibL9dHQR5aZokA75mjDeWkbg5u8sQqY5y7nvLK1W42ki1dopYArRXZ2DNGZ1mTaai8BSIKhaHISjNiEeUtJLKS3NUi8Bl7Ma5H0pflhFvTvFRcW8WhLCnfKuk3zl1d8agc0638hZS8r+Yza330lrrc6s4keJ6co/BJXabAwrOBqGkVjmZ2jG1gS1Gj9WF66X8xO8X9RDL5hck9FPnMNuuL2sOJ8heAp2LHSFpqtBqNZX3da29VVuuGtEKc1do76sxjNqRLIWl6EuoLFNaygjVn03EDak0hXZ7skgY0uoyBSNwQNyi5NJXhEQW4r9i/7aXM2rG3o1kq/zWic0vL74cM7zKKWPqEtxm6RKtWSxH4hgPOcDUv1cnDSxtt7qyWV1iVSnrJgUv0Dk9GdKaqMyqR0unXOyXLdP10XfVWL/VpaeV58r/9Q/Kf/ffcwx7mwf75MY8CUoQQWyvzMUGs/EuaQIJfDqzoWcej+dIQK7vwb6SOYenfbunfayM5n1pMLZBNf2YBeH3cqndyQAXpJlMtjqGL9GYzW7thGcHCrFl6l1VKsG6wtbcfyb5LN3YQI1i+1BsVr9tOs+Y75gBa++Zo1d4n+cmDX/pq/L9043E28H5l+Oyn+NTbn65ChQoV3gYVAa5QocKvB14+e/a7XL7Se2RcLOKw9t63Xnz7p36o/7fz3j9TU22USWjuZk2kMZurHc+bPOP+GQdDmseyh2KOdymN8sw4zb0dlUzw0hc7t2eNLhHBs6LML0WE4QzqRTLwvY8S8PyGbUWo7FXI7EeAvx+Z3Td4VhXOOrehYSQbMYcrT90NhWcjGXiPy5O5JgYKyyRfY7/6v3lkScCIBjSWYq84wPP07n375fP0bs8cl0dokjU06V3FPK9B80BvUUSzo19zrHYP7k2VvwBlFS8+tJTtELqkunnqGXacL1yVqJ1Iq3OSfku1hzQRmSv/W8/v/dB/8e2H//zv/KuTe0A+s+Yzy1EYweKudTrHO8uaZKrlDJddUrjECf4WiyYAjuTMU2FM8Jq9gHmIK8Ul92l4xNe9i7dChDucFTnDa8+VpsEBiRaEQx0Ivf5EDWgVyvCbmO41QqrzgTcucERnLTb+Db3fPOYGdM55hwHQpx3h+kQesUt4fQdtB73hy7XKWtdct1VqX9Z+A5KVQq7Zr15O8C9o1s3+qozySdCsPZrkoWUG6eoCRlnLU/6qqpNGiAddefe1glc/zWd+OJ7HQdebXUpNu0/rVGSP8JXs60O5hDqZXCcAuypenWKbA9ZV7wl1dgZcFi/mbm5eezRJz3yzlnJNTJbQ0jbXzoMxz5YVj9A/LKXI5LXlkh6+Wm/2Iju0QdtifV/TRV3u2KktzFpaPu80azfNIVAj0Ybqj9WTxzzlaO/hV76Vq9/7H37t5E/9+Pdz5goVKlTYgooAV6hQ4dcD//Svf6n1v3n312jkofXbX032Hnyp8bT1sz/2B/+F9t9lCOB6w2nTnlN/zlFLxokKIpwhzTk6UXA5r53lrZoxTMV7lS1TIpfZvzeRzBdCoAihBwhPFWgaLNxXfFOvhxdCTy2NsvT0bHPqWaIlbywmvLHLR6TB19lvLB5Z21WxbbytmXPDSHqruqxX2bPx1sblsrF1s38+bi5tvfJcNWpkWXEc2VGJayTn047hL6a5E5rO6vhh3rzIdoBUXOAia9RJbDNZUd8Sbnx3MN8w8SoaLvqefWfaM8dALO7OsvyM0xBclQBWsgxetRLBnaDqbULAJfLxdIR2x+r/5v/Q+aFf/ot/53fcfb54MBbXiu08tGdPd15+en/PHDVUMhbva/P37tqnOsRA63uP5VmgkkOOy10SGNLyxG+py8c8HdJtMfQIRWipy80LE9DZwtf8zHW1pAEtX+ahcmrMNxp4JB6xx2y00gRLPDfe/iC+heZ79VRcPVSJusr71THAb0uDN05bnHzD4fmAKNHlhbbvwOGy5O9V9HVPRi2uxlOr6y0u3/4L5RLd5eQlB46KtBsWoIK5sbKkEoCReJ55pbGf07wrkU/sEtWd8SV88BU+cebttXNcYoIBrUPCqFQBeNUj3Xn97mmUtW4eoBJWPzhl4Tfa4oClSxYpP7V3AZz6wqyl0VXIcZrPbNNfNFU8wnMejCS0zZptBIutFcKB+9ZJIj7gquj54mDfHLkqGmWtkXit2mQ42zFr6ep7/maD6K0XK1Bo17XgqvjwadD8BPPEyIbSbKlBnfkhxy0uf877sX9+f2pMFvEtrwwqVKhQ4XtFRYArVKjwsePls2fTb/4HX/nnf39DjQ1/8eOX/2j2W1rB14aff/dpouysZYtB0rLGdBOcEQ3gOXcT8SPlLNNKUUCae+dZY1kQqJhvertxfO5tGj6vbJO0uhvqXF94WurWKubwAqYlQvsEHrFGdFYT2wieAFCHeslM68bkvRIPebsw1NqjyfWVnopdFdvqKljUJrqlMvDyzNcm2zcpmSWSI4ATDlB+nbhpni5c8ywOdlTiqjjJXaBmjKK4NXUNTUc1+ou9aRHqfLzYlchS3sIzk+PF7uqwQF3Ni2ZbhiN9Vb92DVe3VSIrDpyekexyBgxptrSgLYjCTvKoYTLBHy0u9jqx2LGy9VZXxbCTiOudpMYn28f+oybDhtkaL/aawdSrJ6/Ce9mlz96oa514efssa75c7D+ynz2Wpzo6eUx7LHTU0CU65vBQjlEEzEUzMaC0AJQ48NXVxQQthqHs6X0B7Y+1UoljAl+8Fv1b5NuyyKlXLeXf2+IUVtjSaPVIjGhf0CrfFp1n+/2gJqWY7eLk2lV7a5+CZbnmsA0z9oEe4/LAaiz5s1ztpcckxNV6rD5Ij9HqElzCGjGKA3N4UnxrlJ/eSEiL9ToNeHawfDJnxzHveRRuz4NSoPjqdKccrVbaKvKUB+is1/pn+tNv3Bxbrk8aFq/h/M1v8aparw5dVkEqoe08GEto1R6lWqc1aylCQ8UC+8YIiGveKNDPzNVgN1Wsiw9plr6KE3nHfgKk4ves4x5M87YKXDOYmz2A7CLgNgK8yXi3QvdTh2c79cX7Nb+zO5DIfrUTPebpaw59lYzFGj34sPlliQ317OLsQW+LoXqFChUqfM+oCHCFChU+dvzjv/6l9MfffU7jNO/9q7Of2+196VXS+sX7vyNV/l1F3LRyX82Neox9xm6EE9GL8U7ZHdIs2V/xarEHTPSaYi6XrwU/3yAt3is21mFaalxTFKVtCIVpsW+4LgXriMRQOC0aTLVLVOlcj4qFNyoW2x2Bik/+pkrTUEndGFw/jKti7e6jfZvfdNYbe+MS3uHkFQe6Z5O0fR7HdRW1jPPMrEeZ2zPGNWMI1AxS8USYugrE1impsmS/mr5anRhYXHpGsDj3rNXlSGQpf7EiyRpX5ZRQElvb4tgFUU5tKRntmOMl/RN8lWhdVycARw3TSmThGHacx+uiZYIHWGK6Z1/p1QYn7/zukbRsFX3O/vZMeUBb7Z2e1U6D7r1GXDMGj4yB5r2nHM5wH/MswvVJPuChS6STcjWD9UhiAk/C6w9dURjr6oEc0tI7ehK3VD8mgJZbipH2CJdk+IZ3JS0uY2oha2SgzXDGvnqrKIM14rmqJ7ytgXoTcQZIxR9lLU8lG2HDK8xw/VUA+XVGVAqP0OzxkNcRgVe4bQck3rVXNmU+rAmzT0KR5n1JY8WBI1xtxQwk+FNcENn2YmiFOHfr5lWDlxx0GAJHh+kZTGHfJHWNnTjyvFggFs8jDpdGfZ6+KL84qUbdHMSLq0I+2gSLrVZS+qdmBvdLmRrr0EbNSy1XRPlzBOWnDSOhNr14uhud+gjt3WHPPFaGAkXWZsnAZd8coVi+TbsVOn47zr2VZ5VEttkN2Wq+tW1InfrC3o20EZe/H2mNWuvVThGVrTXh85l97/GLi8XhY+vpJc1L+TyKy9Hn/GbM0Pv26auKAFeoUOEHi4oAV6hQ4WPHyd/80/Lv/qaRNGY4P/S1n178NjP5e8P0J5uP1NcT7L7bVagxwZj2kJ0J7Qg3xk3wQlwFae6h1FzWq3cKKLK+twyavSmoVkqOzX4h8z4tUn8DzWOvYQbdQhneK/Y6hTrUijZBSU+uF3qyPtru1gjqFdSNW2D+rOE8GJfXeMb2uOVEPKBxgzHSWFwvbwOaPN/hdJv8uzy3S3SHk1fsI9TSswMztiQmZ+wdmIvMKXXAIga0ZZRNvOr86kbMZ4WKG1qIFLm7yxjmDWpn+AtNm+07U+UtgrvT8OWGCFyctxvvBLOazOtqXPQ5BozCVkrAHy0me3a6Z7sk+ySntBP8BG8sLVQ2ZOf+qGZdzj98d2+Gw1L3FlB+fca0Nnu6k7yzY9djoCbxTHkz3JokKPY50ecIVOITBcwP5LUOP5bSQMYqKCf96nTpkeroZOAWwxhPV/1F8AgPVEjpyfVJgYiSwnbtqfaYbYjAHuFVBLushnZtnFdIiucyxr9V4F2/Tze8srFVBK1YXFf8JN/MntWI8KDjEW15/AR0AVgVR3g1kiGdC5o6U12T2whv9Y7g+ve7zQgY0NQFkylosMYhr2OCGfu62JJeWTf7sbij3Ns3R3ph45hp7pfIoWgfrJhgVkyYrFHuNsdHnA9pz4hjPJ0MvGTasqbbpuKvgq41VqR3e1btrPQLsw6nnhpByg3C9Th3JbTnE0szTE/FQCBxqLy62a+jmuJTUnpvh62iVPy6MXCVD4zyfYp3c/5eGJ35t7xEKHq70Fx9mWvdi+rr6ccOZBe+ZsXzqSUXgXSyl/J4Vx0D7/DBpXdgdGPnfIFVzVQrVKjwA0b1s1KhQoWPFy+ePWv8Pvu/dH7Xmez90V/8hx15keAZrR+LLZkrzyc5pesRT2iPqCd4fdlBMRfvUu0gzPFeL7Txz0aQM+mLnXm4LR6vHAW9gY4QKKaFDqxrb2rr4mlplylEEAhBwZwjzX6F7jXVN4QZnK6f6P4tQ3JjBux17JmjrfIvUDcGdWOQiv9isb+1wVnW3CvsVV+xzxoN3jaBFQBjZtgqERARZVukc1vFbSZALG6T0XO5C/SMJRFNxZuKk0cbubvL44cv6l4v1uVVdFx0+VzKW5QGQzRVvt4tqxMrbzHNXYxSGrOQKH+1bM9ltYwiwT2QwbdVGwixHbKuOgkHLzu77KSvHbkzE9c2Yt1/tzaBPeDJB3f8/e7h3smBetlUoxEtfz1rOiwkzSFdT6IyPUxuSApvyqVaZixvcq1Vcrj+b4TdYbZGgNmkom+DtXctxSk0CfzoUc3buG9pnc4zT3Pf3casaiT+DWnnFDnGbQax+D7xBU2fWLPflloVOl5e9orfBiSrilARXpvLNvh4Hroq9d4ljdU7Aj3mLlGMr520ZrieSkZ4p9fSB3Sh2iTXrunLixzQArnDSf3PfzH+d/7pyXf57Gfji/3a0eCYFhm1GE8XiO4yuqTpEiXXb3QJ9c/2V8RvC2qsXqpsYKn9XosQ0ZDQzsNlXHFtN4rFq8NM3LucDmnNlFuUa37bx2gVGj3N2uWz3ObjVcJ8atUfjHW5Ke2w1VDxuHiJuWeO4twd99CseP6s8eHrfT+LXp8e1R6926xNZmbnj/IPvoj33wYtjO/VTL9ChQoVbkBVYbxChQofL/7W//DXjj/5R05l9/P9+Se//HcWn3KjSf07P9q2VKyUUoqxapyxP6R+ysFz7s/xztk/VruzvPUsvbdkvwBSLpOTvqzPZ1apcsmbJIkrFC0vYSoAvcIKaw8ewh5oRtkt/jR2NwqTgC/40IVdeASPoKxfPuda9mK5D1tiMZ36wrm/Jv+eZc3bDZ8TuW22fZY1P0wfro6Q4I1p3zRBv8NpILG5KDqdiWnIjjkCYtwdGTZXJtKwzMReXsu6eZWsXVd84S0uvZtsuu07U/toqQynr2ob+wJObbEaqBnOqKBwMZ4rUYSpP6auGu/ZViJWIjHugOCYtt6tyQjBkehT/i9Ev9Wb2+6OGrbUCEhzf5w1557h1Ja8wvDTOPfOZN8l8nQ111KXujLyiTpMjtl7zd5r2VtJix6hJ+HGLY2pDemKMBRt3bQtVLjE3C/X5T+tCV9tFoAWw40np82wRrKySt44tsBIOi/l4EKaH+Fbsh0yzdtrj64wzdq2TiXddvAm/Sb9Dfm3RqLZr18YZUd4PUYtLvXf1fdDBJFA4rYMD+VVl5GuS9RmGJBc0ogIBGlx6TGL8HwSn2SGIyBIiKcP06B/wOs2gxpJ3eiX6+6UMcparhGVh3cmbix+JJ4rAD/3l5mcY8eSekb5ZiUEl1f5xmucv6lifRX6T0Kr9PXf9nfGVkQnvma/cu2tX3axfAjnU6v2aKL8dJy7iIC4RC2Gpd+brSct7uW1vzRfe1OggnSrScF16BJNG7ZbjcLC/crHQZDQNmupU1/o9OY8tJrmsKb6wO98MgZ+5ZsbLxcrVKhQ4ftFpQBXqFDh48Xn3OzP+u5k1vni6d+16iQdFcX1qZIH6tIhSggicSaq7UkCjGiEygWV5l5yRbGW0Jmlhr9Q/mLTLpibhd9yFDRFrHKd5USzXoQrPywWtKK7oef5t2onK4qqhd9vwHOudObu9p2KzmmoLeugcUP8s0Yq/hurAe8ZyxrCNRKXaEj7gubjpZfXFVyJEnw/jwbpMgnS8C1ZZLaKfZlr6ttkFOHoaWvHPgPS3D3OdhGWlaiu0dcVap8Ybr+EV3UjWJjt+GoqXoiWK+gCSHWVAE0ZocRn3mDgMW8y1rfWigWFleQL9+rF7kDqKM7Yd4n9NDTh7+79sVH+TpIFM3MB2CrqWhGotto9Rc+/ba8e58YMoaEGjVJXAxKPWKDPTlRObS2smHWXVynBJxwBLUYxtQjX20ZQAZ/Ul3mEEymb9UBon7nPPMK5koUFj9BnHipHlYbpulfzuOTPtCb8rgnEW3t0hWnW2QhsjnMX0StvDCLQ6DHy3hRwe0EzwjvkVQuAId3rllcByaGuDwwtWW71mLWhDR6zY3XkMQNaXOjjfMAj3ZVDeXXMnVUaMCwTdOtGP87dkXhFhwUYids0SsmxpWuZ4d4lHAKQXGILqaeAmEA/8i5hV41WHDgogtItFTXMZQixxsqzYEsOMOAX3lfXsGKeRQLw1SbtG2/6aa1c1hgQRrSaarj6StUkmeGWXd83L3UdtoqaKh6V3rLlbyw4dw0NFXsqicX1VDLOvXv2iR5kHZWdmP6Z3zS7kUS2Lkc8muzOdsZDb/+OzA1/QWdrYEiFChUqfO+oFOAKFSp8jHj+7NlfGP+9r8r9L2Yv/ObfSn7CnS3q/dDdVRdKoRQJ7hxvwM6J2h2pJd1IxZtkzYlo5xVLIit9uZO+2JnPrPjcW1z6Wb+UhFYmXX1uifBdQ1CotYEglAQqoQv3Vpy2dCDZtuY65bssGWIBp7fowGUIyHxqllXuPXPUs05uStvTFkS3i0kbk/hXHOjp75aTA2Amoa/L8OQMvEfkub4+hxhoMp6LN8kaK/9nW8V1EuB29gssBt5NqpPZ3iD5Uu7SfGZJpEUzbBUneAgR7hkHQCR2Qh1YuGrhLPlcInaLMMFvMxVoMQbSQXjZ2tuj/7PsDpQ1y5upeKn4CKme3wtANrOi2U4q3phl5GeXsb40HVXrSkSh5QbMPaKYIMYf0hrQGtAq1yXSpDfG80hacqktmDrMgA4zn9QnXXFdPS5txoe81Js8Yh3Zqz9uDJIIgsT4A1o3DfsFzdvCnm99YKZZp7x8sTjcGoxQN/pb949wY/FH0tEldsobNfua4fYY6Qxbvf46+wVC3CHdId14XR73ivR9X5JYaqvzD6Vb7ssqBvuGJOT1ay4Py9WJIkDuHwHvfJ5un8ZxaMXSYerK7MpxrThpLN5M3DT39cdkPc3Y7EZmN/L3ou2D72930XPqi2Xqb+nSGka8sb/y01WMdDl0+S6ndzkNSHS7tyltNc07F4vDad5ZsV8JbQltrdNuhVZ9AX8/cu6PEfaM0Z4xAlwj6lnH++bxDznvr37QdOCAa0T6QpQ/b+8OAIL0jAbCXX4+j+w/8/KDN/a2QoUKFT4SKgW4QoUKHyMc8HqHKhY3fAokDcN+nQ86QVvFLrNz9hLckWrO8VyJE+WFuLO8lYg7EUdiS/mL8LnmqYVWpZjPTJby7zWx19+onFlqcFkosdp46KxYH5T2LePasW+EJn6qdEAf7sPzUptLOIX94nQ3BDX7+5Hy09KJr8/O1apz2oLojb3T2YDl7rYYlD6tbXOixMOIsBHZ9c6fzg7uGPNdTtFeuyXaGpCE4gCekYzOm7ez33lo+t1NKVvvYASL2Xda1/RhWabGKrzecsepuDvixWq+AyAesXaBRhCF0hWAIXXVHscj6aEYUNcHSfBquex9gj2++lD92FPp6h3nuHNxz/JWWoQbmLXFxDP83Ls0Gw0ZoPCIunApTRQDWm1AaKuRv3qPUDwq2g1YN4gJdMnfFpdDmge8BmJqnsw8ok5xh33mEXa/RO1eSberPH/pMj2OafjMW5zFNHw83bLNEFqhVuM1n9nyrEp0azLqBpbvU8AzkrrRR4u9Cu1grF2jbBU1zTcaKUlxaXFz9aQJmwtwlxMUZduwm7Cygl+R3hUZjqlt0OZond2t+P+IzrWyxpsP7Sj3miUTrB6jJn297/Gh37T54Ff5whfpfpdALVoPziOcLiEQ4kaFp5oe9tVB6sbgephGNrPNWspWH6xtmE+twj/9qs/jazEyG1d2h1NEx7+IjtXXl//G14Op+KdZs6Hi06y5KremlWezZmkPZ23s7O8vL9MIFspP7cimlKhcN/va33uVDe4RtyHG0+nfLzk4VCcta/hs8bB8OXWSgLz9lfc/8ZNPP5TeW45ShQoVKrwlKgJcoUKFjxE//Q/+6gfN3ufDDxIlKbXGxXQeT1LDydVsopoxjlKMaCS4rko0kbjIdlBKIiuPLSLrViK6viESLhVKp+aubwrhrHC0mrIFa0xh45QCqv6ZvkT27MnOjfZaIqDPXqzR4dDPS/5YekGHQ27jwNGpX++uUhBVLJ4r/jrfuGISqdwQK7mOce42xSsfZEhrf1tlGpfoWVoXMkDy/FIOD62zi3y3aQwdYk8Slygg9VRiqxihzeRMdi+yxs3dkNWW6HnNLxyhy6Nn+As7WGwdUL0uD61lgLQSCk0VwVUJkOCfwAF4TBeuWrhm+XJ85mPaoThtNTYis/l8uLP7qPbgpUPdNiJQ46w1FlcHFwBObZHNLLPLWda8a8S6TxE+0C3yn1+x32UUSdRWQyDG94i0c9uq3wIe4ZGcxcqPCTaCn2M8kD71DrP+FZG7cpC+lBrQVTPAY+wxZkmGvdIRCJiHOBF+QBKW6mCdcohaqXyyMeC3oGkOKTE3/XG5bLBRMPYW3O59pRssWevqwSl1sLu83hh4xZ52vWpxOSzlEnjMYmreNQ/34bV8g7ucvOQAaNL38KGtR8ZT8WibEJoU37geI48oWca6ywmH7r/1af7cN0mQDpbMwfaY95mHtEDCqwFf+zqUldjVJWtHq21R0FJ6ybW+IbJucsDa1pyzrLlvtIC2jD5qVWdbRfeskyT3PZWcLdZ21Aq2ZuP17sbDIMqfoz2uxNMGaTq7WzucUTy6mhIPaFO8oWgaQ6SF8Ln993eMQZP+NzF+0zPercuHE/Xs7NWDvTtv3/8KFSpUuB1VCHSFChU+RhxYTmf8cjzyn6lg5riXZnfAkalMHfwMzPEd4gGNZ3K3z04qS3OUxaUfn3ulfDPZ+O/1D0QwhW7BLa+TsqfwdH39nm78BhpZezgGlJ/6+5FTW9QeTvy9rTRgGZZ6hcttfLvLLaJXdrlixjLO3VHWSvPterGtoqY53LvBzucN2Bb4aWXIIkeBYLi2l8+OFz1bxQ4xQoNhgj/DDoyRreIQZ8BOKm76qhZfXFcarw1F+bzaEAi9gNWNs4EnOo76enuQ2CouOS5vOuDCJXrAC3fpZoY/yfROCXYifoQT4TrEPU6M8cj8bW0xhwFJUw0DEj3CDZVYywItYvdC5/4Wj59LGh5RU/paD2zSl6U/MC0uPcJABzBDh4m3jDHAI0TwCFcSpSZsl9QupSYiZfa75RbpQSrGMKZxSU2vaNH3S+Vny7V/dO3imbjrIcnX4pu3QQfrTvN2MdpR+W/tMLcebIYb4UV4ybbXPAHJAa82jyF0ZNyRsR7SSFyP2YDmHc7ajPQArlcATq+zX8AjDrfSWvwRncGS/QrIVtvqpoq1+g1c0HzJgUuE4Em8L9Hn7vSB0xM4g5CYHR2D0JGJfthLQ3I1THWzf33QzG5kBOm2GGhuHNwbv0033ouhNFsMXYlc2W5RdgtsFdXNvmtsUtyb/rRbQcNIGkbiGnHTHOqA+RmupriraHz9JfIl9mWZ+d+TUUsNP2W9/ynr/UTlCCccpQT1ffb0mxTzxrjrChUqVPgeUBHgChUqfIz4S6OfCcYXfz/7JJCIVQsTUYmjIkfFTTXRYcMJ3moGZ6sEkMgqisreFKUiq3+IoQ99OCs5Wm20fFZaLq8/gxc3975orvzFUrfpRs79ifIX5qb0sb6bnhOG24oM75eCsbdhI8VunLu3+DzbKlpNPffM4bZIU9kzh2u+PjdPl5N00VDjCEdElGNFC/u+/ULn/eqI3AR3LI0wv3KGWt2pNWylvnBDqSTJQ+sGZx2hKBG86rzORj7gAhjRAEZ09enaz8PV1bmkK7PrCc1EPPdb3x7E937pzu+Y0vFI9jgJSPTrA+WnwYOJtxdL0as9c7Qk20IkLvCS/QS/I5P7BQ0LxRnQ0tfQYthhglCkBAfAkK5HpGfzWwdcRPQYxHge4ZBOmQlHOMfcHbC3osFBkQYshdXwkE6Z751wOL1G/9Lcv1gcFn8HpeW1v+fp0qjJVfGmaHkTbiVgbQYbmwNiXaCorNOuWkS4Ee6RnA2kq1+StGTkysyV2cpEeXUin3lnOwGeBSW9PSbQMuNV9a+Cqmp7p41rKYvees1I2iPaMV5EN6kr4Of+DowB2sMhEONHuAmBbi+lMdGvrtLcL76ba3/KT83arYruTdT4rd5mCIh+o6G/CD1Gb7fjEqn406w9ylpbTymRXV7OLv3RrC6RrcOYR1nrRbq/1aHgQpofyCP9TWnSf4cndzlp0j/g9WOe6DVAWwYh7oc/8Ts7MgO+8/rZW3W6QoUKFd4OVQh0hQoVPi48f/bsC1/7+b/z6T9e98K2SO9iNjsZW+80EuUqJoAh5gk9V+KrBFoECF9sZK2u2ztLKWAygku4KyhFB15KOUv26gifAuD99QPuUUKRSLp+yuWHyMpD26gtlJ+uNukUuBsuXc8UFRRnKZc2Wc3bgxuTgVdoqMRV8U1z1pILtNSNQZx746tQ2HU56i1gzvPlNecSmrvThdWxCHF2i3DWSNwQJzBG+pjplYnu6kQfQWbSuq7yFkSWfTRNX9XsYJFuMuHNAzoSozih12YSidNWuVsQnv79YHXPYuwGA+0C3WbSp1G3Wt/w7n0t/1yi5BPGt5ZHUzEwFjcPLRSrQsTL9UsfrIGOwgUi3Pt88Jrd5WMgJCpwCb3lX+RJGKsgxvcII1ztn9SS4c0xyCJCS/WHdC5pQCNgztK6aSlFRtzVj3+ovaAVgJadE+JgqXkqrmV46nPaKmquv+tOxL9eBRc4zZoU60+zZtNIRqu0TEVTJU1zmIi3vTB1+UYp2DSdWm72iX1i77ofFfgkHrHWdb1lvugmxRVAUHBJTbuIhYU/9uonRCerh2oZSzK7yh+OyunBa4ZeAqCTnNf7KxHePsenHH6X+D2Geu2lQTfCinOfuX7k+zdk41+kh0DpW7kGw184tcV8Zr3xG1p7NMlDy7wlBLrU6dWnOPeGZhNR++rklobLsupFEjjgqfgWe/llMkgBfz/KpnY2tZ0HYwntETagAl2K6bBpDm0VpeupHDr++YCIZUI7PnGEp8tc3eW0TyvETequP3IxwTY3O1GhQoUK3wcqBbhChQofF1yT79R/77Eyf3c6+O3DL//Tf/8y/UYauk5DTVGM2clVBkxUM9hSIebtqFQIAcTwPvShu0XlXVu1zqwJ1ssdXT+n4O9FeWhnM6s05xOK0iO3QgiKSsIPYa9wh57CKczeiv32rONbsi5X4vA963Sat7fOs+N8U0Ce4a4sjssYz00dl+5J4jnJoXmeiqf5mEuSiBfhtJnoKwMmWeMiL8ZEPhr7RVNfAPLQktjUx7SDzZInK1vsupoDz7mX4PnMGwx95gMMbcM7kq4/ynbOlgzB1V7Wmg7BhGb64pOPDM7ynbm4ZxzMxQ9xtcwrkRWf+/GZlz7f0RRrnLWQq4KuIa5H5BJFOPGVIC8eyWvZSwj0lXuEMYFWt4AWwwhfwCUcSjeWLXHOwJDuQDqX0ijO5YQ4Ed4xR0M6EXaEHWKHpUpIQ+mccBTiuFfB9FukvZXwthHJrNnvvjm6b53ct0+aN5TaGpVMiTT7tVW0nf2uoyZJSwZXWigAAUmboS7ze934qstY1/IFPGZbw5s3ruuSmh6TVbFl/deUy1Wj8onKhFyTvVHurXq4b47ccpg3gOhk5gR/n+Mhrfu1JSeUMXKOHc134tgjGtDaGndtq8hT28d242dq5Z/81timCF/Th8fizsSd4ZxyeMFmrn4qvv6bZu1p1n6R7o9zV/9tZb+62rCEdq4Ze/GXTe351DJraXbhz57spOd+er78aRvLlgCWHqM2AyDB7zJ6h6ef5f0W/bucPebpI54+4ulneT+Q5PXD9t3gDN7aj7BChQoV3g6VAlyhQoWPC9/95rdfNXumMX/vg6//8lTZkPesS9o1YsBlPqEJOCRg6BnkPPe4KioLiuD+JHxeZp7rInAo9BQhfGolmN2ACOrQE6YKoAZseGWti8YIKH8/MoKF8hfg+7XFqkl26d9SDmQJH3wpLS+VO3x4ClO4fLMIPM3bt1AOV8UN5XlGDNyk2Hjb6E2EB22XuMwKjrNekyGCMo2J6jgyDIyRSyLgSiygC1atZtGjfmOZ/fsRqe8KVxw4sgrtV90iluqb4kgcKmdMC4YUblhNLofNZmu8VKdRjDTJFxYqA2p/57/57r/3J5naka9GRlMrhKl4ce4pf+HU0vnVDRVPxbps8qrY70C1DomAPjs+c82r+zQC5m6R9LusA0yoPxxxbuDnRBFOQrRavwGP+LWOEyht1fm9QMRRhHPE2YrLxQR9Gps6LzcGWrOuCdsqesd+AqTiP18c3LTLZidVDHwwfwjct09vt8Ka4fr4LtHGC6gYz1NbqK9HHOMdc9RmdDv1vbF7xKvdY2rXrJ6V7kSbwYwDIMn90fqLoVjcutEvB/nWSPY51h8S/Jm4yvgtwk+/83m6OUAaO56M24yeFEWGXaJxYbK1wjh3G0ayxbFZAJz7YwcksufTnS2bgWV5IclmVjbbMWtp8epte6LBBua556z9AiyDU3Sm9xuriF/tFtpmLwJUkBqgzZ/1b+B8amkCbwQLXax4WakYgDj3XHUl/9ZIPCKH6DFDn1iXE6uRwfhT8XPDS+xYnBgrlr2W+2X/YVM/36piwBUqVPhBoiLAFSpU+Ljw9NmvXPj9aN75s5OH4cT9X9hfNtTCMFBKNM9xiH3mSpRSwA6gYMdIhoFPaAFOsFgmjupJrJ7IFWUp6StmMIMHINAp1pejoFeffLhbRD7rgOTONc7L5ppsZpvdCFQ2s8r2SNHZm9Rbja3pvn7RhxkIhFvaSGQrPx2LS06cL8MIrx/eVlHPioBU/IZKrivADZXcxJ8bDMa0VwQ4X+StbBTjkkvDClN5JUaReQsn7LYZO0SBzDU36y/2CnrzPbJfQGJrcemloeVeCb+C6Ckvbi9OLrzlRL90Uya0dhi6JPtcrM6dUI/FjRomoCAR21Ux1F1iS1qxcs3f05gzQgeRCihmWQsdnqowgoUXLPQ8W2DJGdbPe8yhT9Kij9BXOx0mIB7aoRqdkroa647MUDR4DnjsABH2dQfxTdZaip2noMEKBrRUEWS74sboHGB909VbFXfVh9fRv46KGioe35xhXoaOjt43R26JTaWbFuVL6HcH19frodtApFxd7ngV//xRoUmvZr9DuuU45+Pl8AqQ4A+KwAfXiJrijvIr94FR7jYNfTnLNTPcpQW04BLWSEy/3+jxwa/yI5+je5/FgTH31BMe9KWlrzfBv6BZvr+uiqGp2e/1b2jJ1Vnqn+lPv7E99dqsLSsA27tRHloS2rfZQW8MjphtRpd5az3gTxBi8bb+aGxFmdOqIDV1SaReJKENvllLjWBRbqOxzaJPHCLgkNf6hZZH/IAP7Vjawz5gf2fOXVLfaXgvEw7Perl8mHz51Xd/8pNfeMtLrlChQoU3ogqBrlChwseFWWClyqnPx/cPp/c+nb9IMQ17h+keI4VqyBBd1UaxmvJbKkYwiundPLTMduztlhSMlYCkrYi0jW7EWnTfFkYmV//tCnvQu7FpGUWREjFrBUMT5s8ba4Y81xHBN4D1+OpybGIX9koB2KWDzafW9BudVYTjOHfH4r5Y7N/eT1tFuujIBsbifpg+/DB9WM541NJWgr8K8RXI03wprotMnfY8XaZQli9zIi3NvlJxJ+K8Kex5FZm5XMhD83qUJuD24nUTLFn9E3xiqLyF/tCxzgAdLW9hNou6RJogn9D2SEbS1bu6pCMaAi7Rgiye9hqn4zY7nko8tRZvf9c6RTCCNA8tsxOtxtMjHtM6k4MzDsa0Q3HDIqcXOJLzFv0jzrWEhRDhRDir4YiKIr3FRxtoM/NJT+TohKMh3SHdEHdL9Oz6wyUQ4sxwdHT0kM4HPEoIjjm6LGrbnMjRxl7XUQS7djRrncttL3GaKi57RO2bo/v2Sd3s2yp8m0pI1wnwTYWRQnFj8TyZefK9sF+NVdS0R9wuHowhnTbDO0UVMpfo6h2BaE17OV77S562OYIJ3uorMMM95aj2xxyAR6S+M2uZMTueLGs+nXL4Spc4Kz3hiXgNldyzTu9Zpz3ruKFuzPWYP9+5tmm5PZsuhdbZk53o1H8j+80u/aWZvPDdrIf+1hRXNs06F4vDs6ypo503vowfFSpInQdjsxct2W/p2hsqjnOvbvRtFepVPYYthgl+QKL93gLmOt+7Pezb0dzuzwkgwo7mDsNz7gxkB98X6yNUtK5QoUKFN6JSgCtUqPBxYWGpkAauFTkHh4PL/d+N+lRDKRw1Q7U13Rqwo8AhDphrJjApFAmnlmpibPipU7PmMwsPvEJB68CgMJQKb5BzNa6HNndv2roUjf39OJtpf2M1f94ArggwxWTxptMBT24r9ns12QzUWi5kaQ6q/PTqo/DYeXrDmTbx2H4KTPN2nHueis/yJnDP2hKzuqIoeoYvWR7jhtiSpYm3MwlrHXNW6pMMZC0FWiIrD29xplmPzxSAdGZtRo0XbbZ5X6mNI4RZs2eeNxg7xAthpJrAXhGi7Kl0RN0lHdH1SGLs1THmeHkwbvf4+9KNnu+qe+M483zxaubwMjvQgrPyF1YvXt1UgUsa5RvcAIRV7RyXUFhLMY3ELofnR9iHvAJidl7TBTrM9Fy/xRAhxu+rndUZE4I+rUNeJwQvCx4VkPhqGSYKxKtN8IJ93bkTjta039WgFX2/WByuNmopMhbXU7FrxGMdBnzDYzwqicOuipLcR3GlkSrsq3jmN0eo6hI4rWuJCl3GEe4lj+5wdhsHVvikEW/IO9AiMMu6ynGMF+mIEWGjJpM2jRuJq69u31grcdxj5EpcSrHmDie9J/Hr/24OnA3phvNgaAxbnk5BT/BmOFtGHxmL00SKCl7buKaI8lIzsBzBrKXRmb/RxKylq6xaILv0VwYE2aVvBIsVJZbQAqJTz6ktsumOc39MuVCWwC1hz997JMcW7Jmjurl2rx/z9JRDnXYR4h4Sdhm36PukHuPxgR0MDZ+Y53Cf1HfO489NnDuKoSTVTLVChQo/YFQKcIUKFT4u/Ndf/ltuK3gZPLys3f2MOj9QBMkY1JzaHoPVfHQizUkhZKWynK0G96b2nanVjUDC5/VlrZ0i6HZ5Ar/4GKyvv97yNmxpk80swKktQOZTq9CBl7gqXrLSgbXkq//01PTmQkdLaKny9K1mnR/OH76xzcXicHUpdWOgBeE9Y3Sd/c5wTzk85TDROcpCQDKa1yMcreguxPNUoi9twM56WSNBsIijFzXNWu1g4fbi0tYbKgDrzbFZ5gA6vj25VkbY7cVuLzbbMVet5SJvhOKMaczxKLRnn9RlCjSZuqSAR+KWii8n+A6xfzZXl/yLky+fToJxWJuIe5Y1UvE8FZ9lDYksCS1VREfvGaNV/484PeJsj6WJ7qiIsz/h8EQOh8XH1dWv/ttmJhDLzmtZPgoRzmu6fWouoUvoER3J2RFnCf4HPHzJXojzAY9WFBcIcS+leSxHxxx9wKMIr2wXtxqZrUM9zdoX6eEyiEBrfUVIqnY5GmWte/bJ1YHWlcDRemj088XBpnH02i5vlhF7jK6zXyDC9UiOOHtD/LPQlvFmV0unjaV2LEfHcnQpDQRPQk/CSLyZuCIk+C+5SnjW34imObxvne4bI6Bc8LYno4YMVuw3IOkyarH41c/UjnYB8vcRn7BlIngkEa5LVFtz8lv2zFXxnjEq3hrIUgTeNuBGsHDuj7OZfX0Uo1N/FYoCon+dskt/+o12NrXymSWhpb932oDK34vMWmrvLq9omDd1ua1U/IvF4fcs+JZLH21FUQo4bhjxtQLCjGmvcqqBD3h0SSMmOOSlXtNvtenDLvgsPMPynrsk31FHYP4nH7xPhQoVKvzgUL1Xq1ChwseFzHGail49fTg/99QouyRuNBJlzXGzkmTkkJzKLupK8VP+QsdFr8rSbNdbfaGmttkpbxVnyyvXG5Q+ObXUrC2iM1+LwPVPDySyZ093splldkFLLrWFU18sp6RL/ZTlIT4Dz99+hIrTR/oSrro0f75TTjkGplm7bg5uP1A54+6qQlLWvHfNtaiol9PyxWswCHGdLAJfD4WyTHsV/irM8XT13QRHX+vz9F75aGY7tkMrnb25VMni0rPvTAGJzeiq2JVsmNzoiGhzPSOyZ4wBl8RZmqhFnswjZWvJN8GNsT3SkdRd5VIEISfKm+P58PRrHMbjT3/6xXOpS2QrfzHOmqOwjiIPrTy0AWs3Un4Wi0feqplDhbhEiApUEuLe4TQW/1gtM4F95k36PilFhHMZWuxdyY+ozTYDWgpCcVzCu+q04L0CSnt0N4qE4hBXfzUu1RoFPeWw0H6X35xUAiAR76ogViH/3rNOXyz2Ma6iCbQT8p45LEmCqnSwqxXAPetEP0Lb4x7K70cU2na4zHZugUfS4hI45qh1zQSrfK4Yr8Osv0x72OxAvNI5CwzprLRxh6jH6IK1AVwq2IZcq360XApIOgw1Ew7pJYavHsFXOIbPRATD7LjpAD7JVhdotP+2ueY+vcUNS1+pn0pkO/fH82+U36psZ6rzZzur+tvRmefULLNmrSypmFqAU1+YNQuY73r665zI1jJpb+sv9cbQ63HhKzbOPU/55QtfYRV48oCLDmOXMKbhMQbufON4WTv9HOtTedKqpSo3hEfN0fmomqxWqFDhB4nqN6VChQofC569fO5ZnjQEsbrxeb0/qf92K6nRJIpxaixWMyGHeOnSqiQVt66SacmXZVUFZwld+LcnXCgU3IWXFARyK8ktAmFv8XteX2N2I6fk8Jyee4Ddi7JLX2svWxywQngo6DK094Eb7K+2QqcofoaNya5EVon/c5Y1XRXb13SVFTZygJNiMtowrqcd6hOgE1AjvD15/Trrav02sLJokS0JcKlHE5qhOAKDxZ7E5sahrE6EeNcimTeRhpYVWyvz59IR1jiwEVxlXG+SsmJNgudykeAnpE0uE3E9lbokQ4JIbFMtnaICUmCuwnf+xCf/k+4fffqNe05tYe0uy//qAxrBIg9te/nGQWnvsZoBijMOgSPOIRvQAkLcoiawM5IOqt9hdsCrmJ0YbyC1t4rDLxlc6a2BSjoMX7I/pq15mv63x2jFhBFOOZypLQzqYnG4oh/lE60Ij04jH+fuuIgmaBhJ0xy+SJeC8545qpuDadYu+LAC7lkno6w1zr0X6YFet2LC105VutKVg9St2cIdxpr6nnDUYnSd/bL+lYhwQuygXPu32DSkqw91SaPU/krEXtpTbTuwvap+dA06UjdgjrDH8Vd7vcd1gA//Hp/8172mZwBNLj32kIZHfJMP2cotzCZ6bD+9WByOxW2oRDu060JlY3ELhvkGebYckKKXV6R3s9nUBKRu6/Jv1yuirZ9u82ZqK77bO3Md+jksB1q7KkJxURzeJW4ztJjE4g3pxATv8GLv9HJp6yAA9ov5Jw7e/83e+786/ZEBDnzkblSoUKHCLagIcIUKFT4eKLUI7KHTcPNFPT7z8oVj0DdqjlI1NXdJL0rho4HMdVkahI552sCfi3uRNzcoYSmvscj+7W9sur5wwxHWGNdyvVNbaN1V586ZXSWRNZ9Z9U/3ATNIs1ljJbysQVczErmq4vOW7PcGzKeWWbNNf40o2kY0zdq302CNK/lX71jmIddm1yHuvFClJMsT17dSR0otEzygf5UDzOJyOZO2g4V9NKUoaOTfm5Z03e2IXtQLe+d16NMpAGN54dvlxjJcibQInGDvcTIuxj1kSdFDsR0Vk+307/5InLv6NMpP9WuR5YLC6l29MWmoedc60afXlOa17KLQgdCBJE1VCuUVIuXA7tqFKIAhux6xj7PUfrddjUBC8Ip9hJB9FA36Zap2QXNDtywPFBvUd1uDmzDO3XF+FW59ljXXs0MFWNHjtSPfvEJBjaTFEP2GQqJyS1FrLfs0+gVfbcFr9h7z5JYOh9hdZi3OPlgW1L46e5PL63G9/nZGKrd8Kk7kudr/GY456sjQJZxiA97PXjWbuA1NxV2JfBJhcLlxpwpsxF80zSFZq2kO9SZX+WWXO38vBtnuM/+2kctr7eJZvV4/A2maw3G6f2uZsSvMnzfM2sJAlP+RahTLqujUWdbQ+RfTvI1gG1GE5xMneCHukE7ACTCklSVngPhwDjWoQYex2zzDB4bjQKnso/ShQoUKFd6AKge4QoUKHw8UrsprZpoagUPrVaMzanbnWIDLfKzqCe6kqOyymnkF5giwVZyI2zPHbGwGPOjCXagJwJkQUBQ3udb4puUbYPci1jRnUf6i/ulS4PHWg0Qlb2puy4Ddgje4O69hmrXj3Hux2E9zf2sa5NpCgXHurrW/hoAkypzV7sq1LnNnvaUcSy9gfn1fo6RZGauaVW+C4S3cXuzfm9pBeW4tRTA5a8cpehLnbipegjsXz2cOnNADvCIIeSTdYZEO7srSq8klnov3I+F3v2ME47Dm7cZWL5LI0n+rk5SVrnHuXi4OUvEpXgPMcGfinnHQZXzAse6m7tcRl20ZxzgDaoNVdK6g031f0w3Lkc/FtayqHy3Z73qDxzx5zJM7ReLxdggIF+nhuGD1Wxt8hEfxjSdDQF6kB+nN9tG6ka6ffCVcQ6DjnFf54Vf/Xf69Yg/4kEcnHMVbg5wBuKQ2ZC+4pgfqvbx1o+kW/V7hCO0S3eFkbTiuDY5AQBKQtBlcuVgLOgS6RX+PWW0A8PiP68uc+KQIHSbAKYe8HWwVNc3hKGstZWEVNVRcDI6Y3XAtAkLe5la+RTtZb3sztIm0vRsVycaeRBaI/vdNf1dnaqgkEU8XHHZVDMxw9QudMe1LmiGOR3zAa8NNTve70TveMnzGI/WcGC/Oe2OsTwYjFM/OXr15ZCtUqFDh7VAR4AoVKnxMMPyGtWeeO9a8e/bz985ftPJLrT2c0YrFStZMU7fgItthgwtp6Ei5lZBxDt/iTfV4boRTWzi1hVNLEdILP7v0dTrolv0Es5Y6tW16SMhm07ehwT500dGJ28qmlo4GwFnW7FnHe+ZITytXf3rrNGuvmifrDkajrHXL0VsMnTwBPYaywJ+mVnp1hOWFuCRtJoCrEsNf2H6KiOEvVt0zvEUeWgWnvW2KHL2sJxde9KK+LWRaEAxvUYRJS2kAlkhwI3EAjyTBR9C8d7hedWqfC73gEH/dOXivaeWR3eoN9+qX+oBX0tbmnZJVoqZAraBVM9wnPPiQhyccIlrD3Pkmn3jCvVDW8ns1LypdEhsfyrV8yzjlECER/0MeviqZNm0gFV8bGq2ndMr8+c70G53N1teYkYRvcDO6BQ0jvmXrOzw92Jb6G+IOr4Iiit6IIHJHTt+RJ4EkR3L2WJ4c8PqN1YBXtklvxKZ4zuZQlNcBM9wV+w0kCSQ55DUQME/ZOaX29X/9twvIBfeI7VgO5ZVfsPEWw4Ab0g3WMc1agniFLCyFO3cZTm3x/TLeAsfHTbj2C7ltv+zCnz9rRKe+ZuBFIEyk85PzNyU4AA2V7Jmje9bpY/uplrhdFS9f24mvfcJqJBc0TzlMCDxCn3RAY0AjbJmpcuiSdpyoZb1mNyP/cHY4NvWX5c0WAxUqVKjwlqhCoCtUqPDxIFdHRnJsKMdIDIIkhMNjVy2UUh6LFNtlrgW2ufIinKBkJCPIjjGeZLuAoYmQlBJ674DAXQFFHy6gBrG6MoW+KQpaQF2tdIJUmx6htOqrjCAt/l3bZ/XB7EZGbaFdsq6ahDAt5SGX9xUFa9mtTm2xrHSywi7UINzcPZtZ4BU1TpZz1WneAimSBpcc1VXxKGs1zeE0b9eNwTRvb1Q6GYvL4nBroWCQ1+wv0giSVFyTVFkmSCqurWJEBPosg58TcYEkd812bLaxYvMqm1dQ3sI+mkpspbMbFbxlU9hwvdreSpWWRNfdiTV1dPFGNF2iEY1yydlyYLsrka/mC2pjWnv9Z7989NlWMJvk7tJcTan1s1ydqqGSUqg8nkr2ODnjcE9O9OoQ90MeAl3GR5zHeAOaBxyvjuQz95n3S/nA+jwxgUd4IkfXw1ATfC0F6yNff3iBadbWda02MH/eKOeFSmTNnjRqj8bKX1x37s1DK5vZzoOUggmrIF0tXD/4Bjx1IwG+W1Tc3YoIzydwr70r0tA8E66xstIgBKQr6nuHy1cFoy7KKe+t7y0f8Ph2brh140sO3pEnQIj7Dk90odoWlzsY5+zsGf30CAT9VsRORNvJ+cShRKjW2yjujoptFdtmLEUn7lqnL0tR0MpP7V3m03Jl4I8o5a83ny1adXu05Rh6TWmQ9YM0+3DHqS/An88sfz/SVZeM4M2x0GNxx5kLzYZK9NuZPXOZiP5isW/bEUVaQYthhBcT6NeHHaZ2LLY3BxaekXrKxI7U/O7ek8lkDjsYlWBToUKFHxiqH5QKFSp8LPj2sw88I1ZKOZjYk6cpZ7v3J9QTrLh49RbjzvF0ICuQ5t71KVr4on7b5K8DNSHQ9YELlJvL9VVLzGdW+Kyh42CVv7B2IyNYZDMrD7fErK6g/IXZjZxaiSoEt3SwEDBLesuqhucSWgfuwuVK3AaYT63o1N9QXdYTNZfHHWXNsTijrHmWNS4WB2dZ47ruOhbnw/TBxaIsKopWhQKVkOXAdF5biDtJdvW8XGRNM0rEDXFAJqKle1GrdxOldm8ZBX27Yr/1IJPc0ftoI2hPtBOVrUXgFeJSgG6EneDtMDDmgW6za44b5uih/fyh/eyOdbZBMu9aZw2VAN2S1ZM2nd7jGBjTfsKD8i6v2Y1wdVy0dvQB+tSWZsXrT6W39BN29KCV45+v1dHhqshWIfnG4r2F4CezJzsgsycNiaz03Js92Sn/Rac+IKGdXfizJzt5aM2fNfTCTUdsqEQPCxDn3mjRSvMtUdAv2U/YarMEEOJG+Mm6Sr+B4yIyvHQ1IPiSHsnlgbyMpPGBPBrInluURBpK51J2pAAi5UDr7QMEAl1GqwDp8raejE6KYOYPeNSn5RE/5ukPyc+F4p69u/vhaz78+xDiDxeAp83AJQZ8iWsSb/ZBiq9a8WeraJa10twrbrHoQS53RPmpU0/fUuNdu7BtzWPx5pm3ERhS3lFCW0Jb+0g79QWwLP9WHC29Vp34dqxiE8ov4zaC533iJzwc0gGJcPzhIvWXkRGNk7TFZJfXl9IU15fYfmuz6goVKlR4MyoFuEKFCh8PlPpy8G5NWbsvGgFn99Vxnj6aePWAREGCN6Ie4yS4AfMIN8QpSu8ohHnuFoLtBsrSmIDCV4TQ2SqaycrdyggW8bm/EoGd2sLajZZcV0Ch/DS7LETksgOzbBbpAezdaD6zlufz4dF1+XejG9QeTdNzz969OYJ0BtNN96xsZhv+QvnpVmvf8sfyvzdhLG6Plex01TvJBXBILGPuSTTC2AzgFhqMXNwBO1d7b5sQm+0oaC9TqaOXtxpiyY1ScB6aZvuaCAxp7jpGkuC6uDFJo9g2pq0zTpP12zCWNioD9mFhfns3aE5wCmarbBVdBQUIKHm52GsYSdc6KT9Jr2VPKfSamiRH6uzq8hUCEc6SFQuonSPOgRhf+xLHEqBI8CPcaCPyWZa+UAoS/C2OTUIq3ihrjWWbzRVkl74mtBtPvuYw6blv70bz6aYIPJ9aK3Vxufut8Iy4bgy8vB3nXtMcbrFNLs4+oH2w0nKvdeuSZhc2ROC44Mwe4eHavldoM47xPMYe4w4eEF+5Z/VZN39eIbhWnajc5e3R0cgFDS1l68YR3oc8HEizS6jX6Uu6nNKAuas8YqTmEAIu8cVaT26kjDVjcNOm1U7za8bOtzS+ffNoVqOuxnLj2wflp6s4go3zRqe+D6uqwh8VEtqryIIXi/171inQUkMduOES9mncYbjP04VnAHYE8K2Dh2fsjunEuecwjV7WSCvBpkKFCj8wVD8oFSpU+HiglGfxTB4k/dqcIH6y+K5zlGCn+Jr3rOQIXVxnA/F1IifrS6uPHeHuRot1+iasWTfLkg9rLXcZYFyedN4mTi7PovyFNmtdrnsTifD3Y81+lzR7q1CjjYQv19ZdTUY3Nd31K35rCFwUtWE1Zjj6ki+zunKsOHMAe6VHlfqZ4C5zg4uVO2q++vdqbfFf+y1iJpejvX4hV7nB6+unWSPNvRAnwYtwnsl9YEQjxkEY09YR0SNpf5tPPuNen50T2QWe56MpHUtFNWNYHro71tnq4HvG+JH9rGGMnqQPUlkLRlgteiSv2XvCgzOdoLul7wxpvma3z86QbkwQ47+WvagonrSBQBL99CV4mwowTPP2i8X+JvstcMV+WbtNTn3h3B8798f2bqTV4LdUESW0r6uIDZWcZc1U/Lox6FnH9tbKRsUubRlsvlwp/roMuwwvaW6IwJc0XrH3ir0h17KXC7ym2+LqTml3sXKDrQ5t/ronlkt0d6uv2NX1bvzE6O41+9IKcV/Q7jIy6Dz6SQCZYkVzIBYP6DBZJQBfv49vhK0iT8V7xlqU8pr93tYOb7+lWzbnoT3Ot6edAxLZG1H0G4hO/fTcT899LRRv+S3a+tN0fVmwVWSraIY7pq1rXz/myYzsQx6+bh2ELfPyUS1smQn1jDSSLvCu9coOFt9++baJ3xUqVKjwRlQEuEKFCh8X5socPb+HIsXZv88hpygSnAnNMfUxjTneoMgv3aQ64sK1IsA3zuFv0yTL672lACtGkG5t7O1FZjfaLFRbpsQr8W1mXRdTb0I2s+xdTbbXU+nKs0bNZU43OXB67l/P5LzqyRsno+t/Wyx8hVXEZqa84cIuv0nQaDN2iDfm1Ttq7hrJoXVeN0Y7KtnYqlOCPwIHLsEOFhJvcODlfywVA0lJ2YuxI5wz9gdS95gn+KfsnrD7jPs+8wRvh8ybneUS7KhtNkVXN1EAW0WP7GebNE+W70guaei2egbvESEk+DF+ouN7hf8/e3/yLLuW73lCn6Ve3u/tuzvtvTfiRfcyi8qkwKyqjDIKqwHNCDMmlDFiwID/hH+BGSNGTGqAUWAUVkwgDaiExDLjvYj3Iu69555uN769V+/6MViSXJK773POjTiJGehrbmfLpaW1lqTlOuu7vr8mkIJpPDJ8z5XeDsRxjwU6K0iaMGR+3QwflYpfJm5t3J/dzNeM5VC81ZGTdOwiCezt98Pm8ado06G9vS5bELPPw9tqXaD20aGVfaIJjz2iEO/QELpHEuKF9Fo0yifVH10sYhTiVDv1/gmPz3jf4sAxPX9/w/dV/pLvf8n3feKXfLyQZW106aM/UIrSPeIpy1/K9/oZ6ZEzhz//HwHufwSht9hNuK9acQlecNsi3hVS8be7M2BbxasrjdsplvwOfq5SROnrf7vW27rDtVB8R9nnMQiHBY6/WI5BW0Sn9/4TZ+331xrRL73qUBWr74HxiEzf1Yh+RL9uHxHjPzLqk/zGmHliyzpCfn7ktg4dOnRooTOB7tChw9dBbkws1x9s1UINk/ldYg3ZLrlYMjBRCjVi9SBXrXhVwW5sG7FtxMUuqQ5J05yy+VXatrK1TXH6O6OfoQNQTcNeLw1+HCbbYf+388aJooxeqvwsD20FqsXc6g3WRUs9rSxNZE8h2VjO66JC5af971Y1clLr8LfwA0UsoVLiSrZWsh0COh0xIKG9/WEI9L9d1/P3fA7epteAtzvrm6W+JIJIsBuh+CA3u5ljXQZp7tr7eL/F9Woz9df2W4onFdlEQT5+2A2HKtE68FoaQpPhZ3xG8Fg916/ModPAsqZHZLS1uG4+7hnLQDlrJkMWteGBzo3kEv6KP36Uy+q+Zuwu07Sv3twzCrkBvuXNmrMZI1uF3zlvZtn1Sry73Zh8/J39ptYlVVXSJwYV4E5ZaYPbGaO/47cj5gg9FQMB3ktuSxPfomdVCLFI/Fp9LDhvpI0VIrUntJvdWVQTfiW0tJEqcGjSXMG+DJPNUEe02g+wIwOzzoI+4VsZibs3e/6M8i30iJ/J3rD5Ge8XnIfKaxlCT1h4B/GxfNJnvI0YeRQZ0TxWz1gBC6500qOAKXCoHj8yCWgniIrwI3xPwjPmmqv3m7mC/8S3v+AHvR2IG+DOGINMmAU4A1LvtXsGK2ANEcHYBM7ZRvT0A9cpoDgwsU5zf5VPPBXNsmer3P3OmAPbfKLXOEamjIwFMFJe3Yuh/906DyztBOFfhVXwvGRr9r9d6ZfAZ6GxxtR4eUpop/e+2U/5DItrbTzvX4dGM3OYRh5YBFYZt6+oHFB+mgeW6aeA24wi3gwPTsTQY73gckcKbILBjzK58v+s+u6XDrwOHTp0eAIdAe7QocPXgSkjLVZ4qXWf7xj+g/qlT0Y5/4obLpH7OVkqHqLahLcq0yCijajORwqX2+Z5iNpLr71v1iiV/jS0LsI60VVelr4ZJlvb6WdJYDn9zL4MS2fgdkMts+rTPSmw+f3Z4G81g20U86/D8NYr9vs1Dlyf0iuAzd+dO/3MeV1MfP2rEB0BeGsB/W/X6b2vt/2rsLrYTzJk2eWpeA/pYKCCrTVQVszeBPqktN4zFtX2UCUDc6VduNfpy8MWGpfxVFf2RZSXHq5lgNKtSFlyxWQkCz0urrldcfaGV8AZ6znDEGcj41/xcbmLFQkwYQnjO7n2VaKrfUiv1y3n24Mn2Scey3KpJlQisEJvj5j3iM9YRPhBFQLq2KrNI8OKU8f0Zk2CFOO/l+vqJg3M+QA2u7OVuMmbkdlPd/d+aaR6cqRpga4t/H7i9kv9WMP7HQBPxU09vDUU9pV+punvhMeFNPnqiXERYP+J73qkz0oCXKuksoieUnoCf+R5lV/KJwoOHqtHOOcsxNOkt0+k1Vr99QW3mrvG4rsN9ZgPPH/JXQa+pON/l3/9r3j/Pb+F0W36YfL80e27EvjEgfbD5+yIgzGyyp0VzpW5nNofgFl2o7nuS+vWVlEqXnLo6S0UIQAEcxoOpiFQhSr4LAjAbmubFxG5YOxXHCW09aqK2U8/xxW8wm5rh7d+/7v26ps5DXWdQJU2abe1tXGLOQ1Hxn4sXbDM2AERLnDDe72AFTF8pO+xDblZ9yTaeAN2EzPt+G+HDh3+iuhMClCUTwABAABJREFUoDt06PB1kCvJp8DF/M7cZMoZ2MQpLoiCNeMFR+SLnrk8Qrbk6NcTxn4Hu5OtDewe/Gq6pvxM+an9aq38drJN6zIESbYWQrItTP4Oaz5iBChHTHnrGPzuUdeQvNlH3O1/t97tiXRpoHgYOqo0b0w21m7ma6vI8M7f/jCseHjFfgF9SH92M18rh6fqTHNvnY/J8404IycDqVueB7txKQAXEuvhlZ5Zd2UAM56ZD/VDhr7DUp31pJUmteC9T91LAYYsgDkjHfjKI47w5zLQ57lEviT3XFsqcogn2/DfG787ZyXIBZsJ+2E2tT7u2xK2+eSwvS3ue64Og1T1iWP8ADfCD3FBtH2vtoU+fhlS2rY2D70vw0FXu3XY592Dj7Db2E0Xzf3pziDTH8B5vaq+6j2UAbH2Jx2/sQIS3np5YLX8DrQD8NFzmpXKNe+veX9ojBvRi5o2z5qv1uvYB7hunnzO9lTW3wVXi9LC/BATHnvErQpLWit9Cgf+EM8nesFtFRE6Fj/Gi/Fj/KofAW6AM5fJklH2dwBcQkwwsQy3GPleyZndEybQ9TjP2pf+ylxemUW7toocFa108L+yw8pPDxewjF5qTkPlZ8dzkmucetb7PaJr1gbzXwQ9FLVrRv3dUmfmyk81TwbMfqpbqefQGjEPcBecB7gTlkCI856p9u6O6L/l4m1+9cttaqvt1N49+ULo0KFDhy9DpwB36NDh68CQ/8I+X4Wjjf0u21qr8fCRaY/cI6Vc9Q9xFQS7cc8qnQwFW8VFbNw9E2qGfa6j2nEkVnNlwirbvz8DnNAqSO/x2kqF+TPQIMaNNvW89YTWFtp5YCUbi1poK7OfNYwPfeEVAD/BhToMr3VKqzkVw6aYg878dvqlErYEMNI9XwaDPBDDy6pIv1oKdogBjzg88MTraQfR8r7ZKnpmPnzYFamtlJfZvewgqNUTd6kUTyOdnmrXOFHJZjcamNgq/lFePVP3DtFHLm/kfqFAL6mUlqgwBgKcR/rGbuzG0XPvcS6jRPkxXlhjs0MVK6WAlbhOK89t01y0JUqPWS4ZXxHOZFRpwjBWMGU5lsdj1vvFjjJGcXFIO0MWoqWwyc+0caw5Dc1puJv5bA+frwD2ZaCHtB3aNPmMOSuUN3Pm77Z2I6BahWbXwlvfvw7NJula7iae8gaVzfwxHEkpBAJb3C3XfeJn7eDPPcq8UMAjI1rEWPBJPaKwZgKtaoL1hDsBl9H7E1bQR0RguOF9GSp8olc0trh6qPTF8/EeGPeJQzxgghScWXhksmH89ubyPzUAnt2w+raXuspjLTKN8cPyt+oSTlnODkRgrffe7cYvVZTknqeiu914pGJUIUYkuXdlLiPxVvlTsdyVn+nbYPbTYlR8arWouXrXdhgxp6H/efHA60g2Fvgt/pze+/Ylyk/1EqEzyMx+Wr15IvFai3tagX/P1XPuYFsW67/n6jV/ujdHj71v1pydk3V5gDt06PBXRPdC6dChw9fCP0sCwE4xrewqWz9Tt66KE9wV4wgnKef6vVIDCXYjIM3ddT7Sh/aQ5tanJ3ytbwLYL9fHS9W2yyTARSvJ1tr83dnm786SNyMJLQksCa3kzTA5wkbqFR7vX3rvG03X4jywdkerCmEDwaGc9jMR3jYiaVXJOVPxv49fb3Y2IhLbKAw/G/lbKNq0a4QwkmOBZGX/TMqPDIirrKfW+YnMT3LqugQhDy3lZYcFSrV5v/8b3iybZMMjAbZMApxUvDGr58Z4xOIb3p2zGrOkopoATK2Proo8IwJWu3EtBHR7LFXhwnUZnfa2UAtrVyNQhDsu6ykVxQKuhGVQ6KLENR/qwZOq/mgYvZNyX3pfCJWHUqEmz8V2/4Ql/OEQO3gmq9y9243T/Hj08guWFyx1GqpTODwvOpYxuCUUh9jzZn6jeh/1tseqCoXlNaXXQgRunr7k7C3Xcyat/l3I8poPIV6feMIcZMK8Yr8al7wP8TQJv/yG4TzoLXaRDCmyXj1Wl3l4dUleSNwjFSe5d7cbR+KNVLzK3Ui8JPfeptd9Yx7lT7Df9qMyp+GJd8Ox98axSH4SWhJau5lfemF8MZI3Ix2VrdqjFweVn9qXofN6VV93qxTgPjFIjwjknNUv+H4h44VMdZd16Li3XGfb8ZTMLhNQdejQocNfCx0B7tChw9eBUspTQIJafSCOsHCABHfZtPHVvBeIxE3FfchH3t5W8EC+oH7kxNEjxVXBH6R1WJp/j1fl9LNka1VGxZ9gv8WJQtCobfN357qSao82Yz6ZfWQAd/BTvSt/ER+uE6SRirf5WSp+Ih6S68mxjn0dP7irsJfm+wmxT6Kp7IjVGWufRNtC+yR16lsPw7sRZ1DkRkJ5mTs9xYFPdVbQInDreQnzrLJ6FREmrFdMxiyrkmdsPEkAg+Bc1raKZlzPM3GzyGHjEvnEQxZTWdVrHpgLSiPVJD9wxaxtC2zzyWo32eaTj3KlraNppCBudlqI6UV470oj5xh/yflMioDS2uB2xVmVQjYVf7mb3O3GqyIWup1qB+BjTz7ZWBLaR0fFbubrT3jrf5a+JyDstrYEbZ2/stStl9S8ccT8afarQ0A3z2PMo1su8GiEeIdxsE7ZP0eMIkbaBPqs1IePkurWb0YLvHq5oV/ZSINLFOP7RFvcd9xscd9xfcuzlRRRi32iMY/f8cOzvwH4V/8rZj8CeKxBPEKfpOLbYxallbWApLlXLTnp1QS9oblutQeoVj1GBVc8+MnrFaYyF5FTrGt8+cuhVsropYAzSL/0xaJDQ4e3/vb74eb35+Y0NPtptdTSWpG5MpcDY47wQj5OZL6Sc19iV8JI3Ej6QIQb0V8Uev5ZjO8TP+9//JafDHv9L//w/Rf1rUOHDh2eQEeAO3To8HWwKyLeJhZ4rM5Z4d3LNCqF37iYrUrPWKa5J8K5eWer+Jl53zMWz8x7QHl74av/6/lxEWyvCh3O3oo9Tj+1Lg4MgI9N9sxpeEJqO6TKn5osPlClF/pi+HABA9jUOXCrP0c/J7Gb+ZV750rcKPcSHfp4l4uIxLZEVvGfguyF31S8UByHCHCIBiw9Yk9iXxJP4vrE3CWu+jA192K7RFY8O60v6Vt0APMsKpJRNQ+emUX0ox5JjLdg6Jas45oZMGcwZ+8JDIxZB9HM3a1D7BvuQ3E1B+6R9GSvEPaNxdT66KnoLh+1ROBmeCfpG4uRuXBUtMom1VHNY6sy+s+C85jeXCbjmhoZ4c8qYVNYyOQdV5r9SlMWl9Dezfz0vrlKUnvU/lWoY6G12tXQvPdnmLa28yGBq6LDDMCF8Ht6AOpMQhyk5G1BnxfgfOB569CCq7oIvOBKU19KuhsxKjd6lQKszaE/8PzQbVvjlmcPjOu9XTDRKxSVezDCVtwHxvqxzhh/5PlHbuL/rgP8+T1ssaI8YhjRe8dVIE51E1wJP+9H2cA2P3NUOFIRyEpqIawPqioDTVnJ1vqcBoo1u9YPTQofY+Wn/e/W9mXoX4dfur5WOJ+XfuZ1o4MWXBUBL8pUzKPS3F1gztgj9ogFmTHUb8yd9P+r7G8G4eCc8G+sHWaXBqlDhw5/NXQEuEOHDl8HOzUJBHDBX5ExyckFImzgQV0uGFQTMrvpeKkjxAxUDBh+5l1ETj/bPVZBnj45P2twTqeXoVN0lAfbol51UminPw0PW2lLvvsp4pM92ZtzHufAn1CSNQcGNk+VOsApYizhrVd3XV6Je7cbo5R+CnlkYag8NNyL/bNIxdvshiBJTVtzJNYuwQI94jPWZ6zP2EQUBMAm6qnlwCj9Np9QgPe9bt+g4B/Gu3nVaHvirjFnuJJJjOsQTtggeDWmKkiEm+beJelI3norHfBMPKIJS5/4io++1gBr7TtKB8FudNir8eT2wxS2uHsb7NrYEJgx1t6kSzkHlpwvOZ81zXpbJC0VL8kL8pMHVnjrnbIR0LG+zWmovLrUVqdKAvjX4T4O1mfAvw73trVlNcvd5MlQWMfaFwJxfYme8T48ps0eIsBpWUE/0m+VifA8VhFeRXfrZXySTxLPrbhbcfWD2+/EBR4Yb8sVugrvuK6WNgLxnB8S4Bf/CdMRgMu6Kl+atbPizGtaX9cjYJWQ2oaMVHS3G73Nros0SJ/Bn09GIjiGw0WNfStltK2ag8YX0GD/OjT7af+79RNlrsylXkDRvgAuYY94wgwkxL3h/ZjZuJYAPcT7ics8sP7k5z+p/tRumwZ06NChw1+CjgB36NDh68DK/lXfBT5gxhYCSX2if2xylZZZQNa7ka0ibQhtnoVAsjWje692ljQqqejLCZ550sy4yWN1upF2JafmgZ/kwNP6FzkldZ5ECD70QeC4pvLFsC/bFY2IEGGX56Gp+kIuCDqjLwIi63zv97uR8YNcxbgO0QV3cwYuyZyBK9FAFhdyd0ZzEiwAJY/9xHT+KZJcnnRRE5YDnDNWQxYRboK/YHBbv+PlKZrKztV/YK1Tj9Ql3jOTFpEVXT4cEW/zyTafpLkmGNKirLpau+amu8WdMXrP9Yqzwh8Y5oyBGWOdTjYQd8aoXpW2fN7fG0jF1w6iq9ytj+96084g869D/zqsJ1w9dmvFvw7968Cchs7rlTP43HzR4a3f8OoUgKOOqceS/bTxlutYehN5/FI5dN8f7Ki8aRPudAKkCXcR3iP9Sv6dM5kx0mmQdDCttuz8WR1ovlgA6BO7hDG+CCA//Pf+B8WywAtEp66V4Dl3QBVZ7cAmXFbiHKxJlX0qv41UjDBSccWWj9HmPcz+k4GgmzD89In3T/W4m8sln7hl/e/WzuuV0cuqoahtFlrFrszlwHgsluFw69qvvv4F04jegumCCfBnvpszicUHLtTGJ4kwP/MyO3To0OFz0BHgDh06fB0Yqp9lgE+OiU2+KrWdjYxqGYAriK2i+hRNB8fSVrtOPzuYmwrN782vlU5X/DHPT5DIphhs9NMjx05x3WJy+CXz+jI01M9gAn85lFdPSgRSWlqKgJLEVm5q+FlRDLFV9Mwq0xpJEbt7yBJYM0YYyPJaHuySaTgltwzysa2igUoQDC+LH+rJXY5cud3LDD/zX27sUoPyX27NszqBkaGK44qNC4A2fh7JyiGsHtE1DzoIVqVafxTjjP8rZwzjCMEldGsZayrL5KpbI3MZ5W70lCdwYajsqGibT2r+0jJjXNr27+NsyYkRFONVHLJP3JcY2ccn0w+o1rw4g9S/Du3LUBuaHsnw3CQspT2qSGiZ/UzX0D7lGMJb/7hg+NnoEVc39i3XcV3XlROf0wfnBwsQ+67iPGcGBM1XSkwvxDus/0kcZqfiBbcTFoAj4ZxJgOs/zAXkgvDMCyeWx/qcrXvgvRwdtb4+feGr3B0Zi5ERF5mQ4Mpc1gOhHeKUsfHRFvPQHhkn6LSgvDR5M9IjSjvx+tfhkVqaQ0gnzaoL0cpPD3t1txv/Of1OWxDomwkEuEXCMHhk+J6rR4YBzpJz4B3XIOY0/JXEV5L6HImH16FDhw4/Gx0B7tChw9eB8JPhARYGCS7rEVuQDaNFla6GxqxGb9oqGhorkDT3BipWfpYHlnUefb7WcYgyAlad0B5nzoafelfhAc1+cvZ1ih5/AnI4CW5AzyovAQ5m1z8Tu5l/RIMqiZZE1m5mhz/1V0EvlWL6vtmNEFYyeuDKJR6xorh/8oq3IE5NZ6tvp+JtxAGUl/X+ZulOa7prM/6zZr95YCkvM/zM7mV2L2ua9YLgqvjMuqvvi3H9WuQtwCNxCcesJmwcKcIIWQTz5TdEOKxdHVG2bNwhvCqdEvf9UeHUuvWMutdr4wml4m93k2A30U7UiXjb3Vl1tAjFdDAqnh4mW1xHjET2rLtIJNMc9jpe0aHI1sbBuKqF+f0yUvgEjqY+0ghwfaKKBj+2oi4fxSliLCC0Uv5GjEIcgQB7zig8SCPsSvAl1yf1UVFHjOdIKLWHd/UagAf8D5Ed5UjBvZ9zd1ayO2hY49sq+pz7vdpNdBltBR3VQmcdInkzBOyjoQ1OPOGp+aFdNrQrtd95tQLMaWj0MmeQteLV1zH421LOvfXywAZRfqoDSusCIxWN6il/VfTK+liaQO9H+JxJtXAQ0/vI8488nzH+M98+ZM9uNxfJm+GrD6vJ7frssWO/HTp0+GuiywPcoUOHr4Od+ucq/D9DALbDFtkyQqklg4PcuwA9SQJc/UWbrdoq8pS7Edfwszy0rPMo2VpFqt7DDLJVHmCpZbks8wBr2tAsD6pdkZ7APTHz43jb5UTzoMJPoZrVlfmHD8/WTGfbMqj+mQjvPGdrNVN3KkQkNNPQsl9k+Vzci7hOPl0Vb/OxGDp1kAODa5k5p22yfZIQRycHHqhYc2CJrHjWFMRq99/wM/Msqm6CUSVqrt3OoUpice3cs0sVqyfJUC3HrFxChInaKK0JC25JXIcsgF8y+6P68/ZHfD+7eT1fcT5nOGHp44W4Ie5UVqFyA1wRVSYnlr6xaPWh9fA1ORkZsaMiUGnu20YIssXVXo5l+YNRcWyYpOK/lbM64ckDa7dtBP7RmVftywiQ0D6iADdxyJMPfAFqI7CGFv8pQkAfdPvp4M8hnk804fEwQ+8TWDDx8BoJgSHAFmFMgwYH2NWGR0SpEj/RXIzfJ25HxnrSMeFBxlJcqeykv8nPfrp5llPcOf8xQjyt4s9l7BL0iIBAuSMeI1zdVpp/lgv0Stwrc7naPZUEuOqt2UslsPLA+hSvLg6PVHzoxa38tJHzWUCh/NR53RCB9ThMNpYzyMx+JqGlPX51AmpzSrVdXoinSe9yN1mJNzYX1VrSiLn+dWhnYG0loSOlzZksmGxxN/nZSlzlp3/77fvN5vtn8hHA6KygO3To8FdDR4A7dOjwdWAKmkwKAQwYWUSp+GM2q5rrYDWjDo4ZRRc1nYfpu2H0cGwSeYTHHqEXeWCb0+jU0YqoBD8O94JbnUhTp9anWa4WlhVfSIOp6cy1E+t1XEII9/D6Cys+QLK1eDPac2DtABxZwNhOZ+LlgWlOCs1qvrtyVNzQigEkwZszHLB84OqCUpUVgAvufuJlq1HlZXYvS4ODWGJKQJWmzsW2hNbBEgbs4z8r4Iz1kGUiXqxcbc/sSuyqMClD7MSVDKv4gautinvb/yr2jAUD3VFtCB3KDQqXKGyToqeeoa1CPaG/3409FbUiZoEsmVzRsiBtDh0BxYh5iFfxsUO57zBMWrKxwHNePxVwqEI7/vNTo7LOopTZT+vseh8CulymudB5p4qfXmPx5iW3IV7Ffik9cj8TAU6AE+Hd8L5xLdgLLg9jYrWgLZ+DE8Gf49rdLvC0W77QIw5xdcaqnnrom4MPppvD9/8n+A+RX2NGuT9Ovue1dusIcF9wG8g14BHp5la7yUjFK2k0LaFVJ40cePxW5XcP3qFdcXjnc2Ag8LTKXLcveOKSq3GyTyI9DeHwBa1O/05EG2+PzcUYYvEqAvxnvqW0HRgxX3HmEr3Hj/F0pLRNfna3GyOMjPjcfP8ieuAe/88k7QDhHTp06PDz0ZlAd+jQ4SshXygjD+0AtZtYK1yXBHCJPZKGQag0p6F6u76jyPaxP+OEOalwuFsAkq156lB927uMkm2trQPz1U+Yse4P/mz7Umm0pT+/Aw+2XxoO+iSSrbUPcQQgmgCvPDtZm8BQJWnubfOxiMx2w6I3Bxe0YXzB3aGx5TUPh5evbZvbVRT3qvZdoMxIXN/jVu6LIiAx7gNXcUlmtNNvobsCgkukI3LFuDaO/X/4r4DUM3QcrCqDcYVTlGmbTxoPvXZW35jrb6l4OmjWdnemRTYtAhflm2OmtaNyibRV2Mq1a/Qyv2GNXyDZWMmbYfJmWFmcatT13uTNKHmz95stIvQeM4ttwRlk/e9W5jSol1vuxmmdO5WVrOR8JWdltUUKp0fGIW51P2N6X6QAawQ4H3lW666uuV9rfv9ZcF5lAH6S/fqNYNRPB6UrW24tzG13ZxMJcn28D5B5BnAjH85ZA1NWEW2hdWQu9hWHTwkPe8/zfbAAabFf3TXvqogO2LwbT+FgpeYEqjdQ+HTmof2jASnNnuXKXIxUHOVeLN5P2XUsnnvQ7gPjB8a3PAvxdAIqHX/7dndzu76QwB4Z8YX14d/j/339938crqM85stXFTt06NDhJDoFuEOHDl8JxoRcb8VpMXlJcTcyjLGBNHdtlez1PdZzhqUdcwN5aDn9LAnqHPiYIfShZouAcnpZQ6uQeiO1Whpk7EkFsFUghB/gW9pZYL/YIrpeeR0KYAq9llD3c6p2+lnTBBoEw99R2lJa59FaHE01NuKA9NSy3u4tF2iWqxcX8JxmuN0VBWc+M+/IrrQVtHkWmWfwftDSgW0/a93sIgTXfo8gyq4nlREC5QAF/VaUSyoqwveojC0XIxZDdhHhP/nvo26wJV/QcyXwlBcW4YKXIVdAT+JAuc3xcHQw7jHLboBQvLtsr9yOxPOU1zfnlQisg0K7KmwN0AQfnQ27bMRREXXjCD9F36uGwgqQbCz/Ksy3tlEzPa0bLddNnf3rcPv9sNH1Y0TJGWT2ZSuwloxUPDYXQCsJ8APjygF4v+4ASLGU0CPyiWL8RzXWe75IBwYC3I9lWmCvSJlzvOtRjW/7RL0jHFgvXsgW54tWprQ98z6atAD8G/mdyX8G8ILUd1LPcCWOuAxxBUY86gdVhvsumqvk3PTeN/vacrgp6gorcUZGfEjLD3ssoWX0UqdvfSKVWhNPeC4cNFDYQn9+5dUFFq4BpZp9txtfmXAwhGgmAEvF/ym9prg/6cp3L8Bg6qbzeHhmTD6/Ix06dOjwaXQKcIcOHb4ijF7qYJAy5PsRWyA6YuqsLVoXvQNdTguA5nlkv1h7x9PkyIGKWOwGvMsIMHqZ/XLdKjPU87PmeXljNtlW/Pa7WzrwqQhVRzv2cyAghMKDtO1qT32erq4UdirNMA9NUKOdkOd5aCGkUqThGagEaMWaQljJUOe1arHfFmrKrWbaDRHY7mXWefEgpsZ6aqz3FyBF0xfm+sJctcSrM1b1lEsLBoArgU821s9DDwCSF/LDf5P/F1cQYC3zG+aAW/ZZ5yNtXFnz7hWewPtrIM397W6yzSd6xl/F7NWo0gVtce54pnXgwh+46la55RK6RC0JvI6GO3rzyRq9zOilytvntqnoSl375dAQ+hicQea8XrWSKo1UfGF9qOy963jBxxGP+uMeo1UB7iPjRyaBuAgzGS3kvLqEGP9zZOEAV3+OR1QGIKIf4J6z0tU+Mg5oJhwSWclZLP6iFogrFf8he/aQPTtSY+0HPmExYVG4OgvouN/VUJwTTqzUVQuGHsGEpf6ZSJkPqeq2rcIrYwlIYNkXYXjnhXeeBNbuwZPAqsRzauOn/ms+9OXOAzv4YWh8UVzAY2G9JbSTN6PjSu9f/OpyVaSNGrQa/HRhW4U6SLXZT81psf0T5ib7BSv4AibeoUOHDp9GR4A7dOjwlVBMoLYo6RNDhKV1PIEsd6s4w9fcD1hSZrWpVxC1so/Kka3GtyZ9Mfy0Np/dK2hDFY3M5aW5HKq93iKhlWzt09O+oy3WSMlRGiy0Ih7/fATwCh6E8HM47ie4sYS2hLbRy/TkW6uyK9dC8jwwgdluUBo/A1xwV3DgspIQZ84g4fi8tiLMPWM5NfZM1fD26UzsXmadR5W1s63ieuGpuR4Yq6mx1vuLpgE4Y+1KPGQBoqNeTdgAHllO6hJPCK6ZTVh7JCHORi5/+nvkBenYADzSRtIaeUIZk9ZfIBEXcFSsJ/cjFV+adYVcInFLQ+hirWdvDl374xIKOIQvuG0Mp9pjOpKLqDxazz2jH+Luwdem0cnGbDzsz4DZP84wNrXo1nUcJb0tBDVhtkfs1VRihEZm4E91VqvB+gNU2x95/r4Mi2ViveU6oEi4dSvPa2PV09GVqgqXu8kqd1e5+5A9219joxsi8I7rd1xXiZ2BiVoEOIVDxVl5Laxb3b/mA3DFxymFtUUkLiLKT9OH4sFtfxzuArv+HE/djFYMPwmtfGv1vl1Hd58VW6s4K7AO77Hy08bCR/ucz68e4MpcVmb8K3E16dVGBANjflg+Fb/6PGTPVrm7m/k6DTXQI/7n0cPZ6u9xoX/7ZV3p0KFDhyfRmUB36NDhK0EhKD+zb5b5T0qCYYJnYlwy28j4kcmheWksbmWl/Li7PrfuhuZqk7lVMOdm8UaEpAOIDijl9Kt4tvvy69wdGdI3Fo7y2Y3XexYnR+JpyQlj49b+4HSgZi0F/5zgWDW0Kz86Of1E/drMFUTbzUpo5YEtoQnK7mWjMN8W/ymIVl834mxyZ2B4KC7k7oGrsGlEumJ40TQQBS64T3BDLvTXnrGc5QWXLs2bZf8VQE3NdVVYX4etEltFtorU3mq42BqwrEWUEoEh8zOCEY93chMrd8wMmDDOSbU4rP5I5tvAB850LTfcx3hzhntz2bKFve1zuScVf7UbT62PgKNiYFUErBLPiKLc09c0MuKRuQBll2RvyXjMsm4hfPiIHMIpy5mMazS4CIi9awdtLiFFROia2f2RiFmHSz/tpovxcMTYdWTE9eC9Ghcs3ScFf40X7OlKhDdjHOD6eNW6g0NQjaH9b/hTHFhvfOT5oZfvIyNUNpXljDGw4mzC/JZC4G0FvtJcC7gyl5EUqYaaDK22kCYNtj9hcc9kWt5Tf5Gl17ZWeiPxqvTmf+bbnooDcbVddCq+qyJw0Wx2s48yYPTTp2mmhFZ27xv9rG4ybfSz4IfhE2cdQvWyNPf+kjfQJ9GK4lZ9XWXXV8bSNaI499xaZmNbhfpZSGjngRXe+s4gA4xe9tr64SV3z/lH3pP8erI7mQe6Q4cOHX4OOgLcoUOHrwIlCSCh9ZP074NJ4pkO8RyrmoDFudNDEGJcR8X3BblCIMyLmdP6cOJzio4eHHD6qfK18XP7hKGKbSPaJwVpeQUf8uqSa+kra+wHesKA9qGjHfzLafCrpw9/UhmW9M7TeXQA5WWyNSUwABQSF/8jDFTiat01u9rgNIX3RhOeNEyj9aqDgN2kSTfm/cedzmiss/7u4plbM4eWgvcCmgPXVkfqD8eXpBGQu9ZwpJz2WBGu1fyO65xU/Y7w2lIwkWCpegiuCmI5ygfaiyCz7GYl3pW5nGU3mgO/za6uzCWK1c5t3ZC9qbaA0tRrDJMxiypAV8s5/I6b6twrc3m3G1d9sC/DktYe6Wjh6Pvp0VQjmzXSW+w6of55Kjq0fHaJtFp+8ldYIsIb751+x4CmpnpnTK8qcDz9bg2thgKcCD/Cm1Akh11yHuIhe5Ksw1BPWMR4D4xbFVZmxpF4ejvKvSh/dmF9aNuQlF+0n/OUZUh8yd6F3goTK7ZcN4b+mMcQV7/EpqxmMorxyyjQY8+IjtmQfJbGWrHfInC0gOBdRtH9FyjAj5v++OrTxdr4xBLKZ9UwMuKBOQdivJ/S6184P2jj81V+XZWqZ1oaq/gld/+M/+fN4o4JhDjW57svd+jQocOn0ZlAd+jQ4WtBZr08sEcq0STJwLyX6T3TEAfRAZb2GMuqR5LmbjUjTsXba2J189H6pFEOdop4F2Eh/Eq92L6etbgItgoTcdfSVpOaTRzOUAurSmbwCI/wA2yg96nbUZ39dPjZrwn7MjT7mfLTiskWRrYKQPk7dxoBG3FsFafigQxUDHLE1Lkw15X6R+9O8DZN1mGr+Ma8101a57F5Fvkvt4ZXTOUHKknbtu5H74+EOGFt2Gg3YI9kxVmIteJ8zGYkM31yTgqMWQ1ZyP8NYlJXAVHzUZ3X+MzRp+0Z8UjFfWNREie5MpeOirUUHOWedgYWWOXuLLvZ5pN6BVvcLe6SiTaEvitkyX1LXpGZVkCcvauzHA704/gME+KqqDNIdaSr6lM/PFLxK+tWfwbG/NAkt9WhUya7cyYzxtpEeV563vaIvdqyiEe0/Aw34I88/1B+FpzrtlwCj0ggpveR51phrsvC2nf3k3baFRNeibsS5yG7aZh8C5QJe3RVHlEgbpZfm/+z/wmweSA88zJXxbiI5uGNd1qM9x0/AiNzWYV3zreWrtrpZ9ZlQWtPdlHIAzvfWrsHX3SYOgEweqk5Db3LT6vxFc6aOZY+HxLaEjwdDrp1QvEZqVj31lNRKv6fk28j8V7Zt+j0SGVkbJ3RuorMZ1+GF6M3M0b/ht8EEyUbVr87X/u7n9f5Dh06dDiKTgHu0KHDV4JhXj4a0agX5QbOlN47rAtmMy4Bx4jPWae5a5vJnOFQVraKAi5tI0rFc1Q8243OrWgjl5X4UAqGdbPkthClVRHD12bPx0I9lwLvLLv2jDiqQrOUjKvUVU7lCi7hwwNsQMEAetpE+Uvkkk8FGW5jBlMIOcix8gXY/jDsf7v3yJXQqnEXNY8G8SyxexlCKm7PWG5kemM+ADre1Yg1DOsU1Cndtn2SMaslI80BFrTtM20VT431bDdUXkoj0RFn5j2A4KtkTyFq90cEBb5KRqyqJNICrsQjtbziTntpDtWjHYsd5+HYBDzSW85cQo84yohl+MAwwo5xPYJbbhTKJ4mJErkKVDNHa+3haFN5HfNZ/6sFYU1pWsldV+KS4yhPJ1LWY6FPBGrJBCZb3Fj8pZocPp1U/NWusb/pA/yp0XX6eP2ht3LPVtAhrz5ReR3qqeNTCmvkOrQJdExPs+IzFjG91s4W6l7ElaSsC/t4HtEZizOYM6kTYJfwlmc0/X41Tng1C5oPG2WiYQHoExcRsKBHQec+Krk0Hwew8R3fMwSqX4RPEuLE+CFujxjmupKtwjPc1c6tc12jn2lym28tu57YuXYrJbQKR9+t5V1i9FJdQx7YRuEbfCI7+gHmuTs5LBjaeWAdJhmuQ/np5yvAIxWvcLXq66nIM6O73fhuN74qDc7j3MMgzgvLc90BAksrwMBYxan4M3CJ/nP3P/pPv/3PdmJm2N1stUOHDn9FdK+UDh06fCUUOZCCrQf8CdcrCUGYj8DoWSulVDWPLjRGYbMbuUYyUHGauwMVbwrH4Jb4VHPN3VNTMfzU6VvsZ/knJ25rcclBMVRxXQQ+EnaoaqDWECh6sIE+TMGv049P2odWFdcv6gl/ZuCzFebTcPoN21cgD+w83LNww4kRpXsV506cF0bLV+pBRylDCGu+yD5JFQLaldgmcnFDHIdowlo739ZJvq1iGLYWLwaqyGB0xnogy40azyvyXOfAMGLlEI3KR3/G2iHUGYCvmS3KGxSOTCsWf5XFl5tIzlBYMRHcuzcxSUwvwvXohTg9klBcV4VTNosGg2o8wTT33u6urszlaueu8sJNvRJ+69eirZtXuHtj7LKmLU5V55LJdn/uHrYKRyZe6ZVKKwr0vsaDMw+PN4s0lF6pHWtWs8nPjgYr0pjWnZk5YFlNzwCPSJoEOMBdcu4RzZn4RCHeO657xJS891T+3upolRWJMsJWr6R/rXNXnB1S3zT3l/lklR+2srfCHhmL1nWtOKuiQOtsw4/5s7Pdub5MbVNQhnx7DHmmt28Il5zrXumeOCoeqXjlY79e82aYbK0qhJV3GUlgHV2YUH5WmTpH9x54/d8+SmjrsFjKT51+tk959YnVMVnlY8xCcQV2M9/oFSES9M7qX+pj5jPZbzUwhZER69WE6m7v/YFxaboKm9NQB+RLNkPg4W7iGRE9FmryX1vO7X9MBv+Nh776dScBd+jQ4a+IjgB36NDha+Ex7yk/C/u7Jb0r7u/lxYY+gm3EtpFk4o/YlFLJftbpGvFDPhqqxFbx0Fxtsst6nU4/M/wsemhN9ARU79UmDyxrGip/d4Qet+hraQg9NAov1uzeYx9J6ESErTrL7cG3iofmpLMo8DM856pZ+Akm7Nf+/blombya04DczUs7SuXtwDT8HZQG6jUbcm1oPmFdqbshTiJFEuAhy+rfBcMBS4f4jume5Ctlq/jGeviYXdTuknILy8yimCPxmUJzYJ9kxLq6GY5EqH3WpQh3BK7EKEY8uiSAFeeZa/rLzIpTl801/APfxdtAwZpxzjbGTaS3UCCEpeprExQq8zHoHDaVCaumuXvhV45w4LfZ9Xf2j1WBbdFQUWbPzZpLJan4b7O9V+Th86rh0wPM6Wc6tvNpFs1+yIFnRH1jfkrofc7tJyyKm2e6BL/keyBWvUcmz3hfHbohWHC+rZHDekNP4JAhH+5ZctZSnje7s7vdeGTE470Re7PTAnBlLp0Dt+ct7oSFLqMp95B0pBaG+Qg4QeIvjWBkjnkIeS4wYbkoWx/z6BH+mW8PG2yFKzN66QlZXmguyXmXoYR2du8b/bSQbeu3vRFP+ghWuatMNMVN733tcKvP1NvJ1nL6me6e3igLFHHCd1vb7KdGLzs1MvUdrv/7BA7lX91QlHsua59owC3PsT+K74abI+nzOnTo0OFnoiPAHTp0+CpQ5HpK67MDRCzAIdXWqpm4KLQzsKsSSiNbqE/exFbRM+v+w+4SUF7ae7XJQ8vwM+8iLDnwnkNkMy/ZWv3fLo4ZP58SjVmXmp7Ry9jah0Gg2ydU33yYCRtVGCdzeOLPoMGUXLOu331hBSW05Jve+8fiA9ea020oRira0s9D0zyTqbGO9Z0ReiTXPMS4FM7AQyjk6zXDaTPe1ZBlvNeBN3MGezvmQgSutazaDqSOzourCo6tSe+Bsa06k/VcDYElw1/JLFKD0X2eXBKOTAXhyBrepwIuG4T0HO8/siQmdt1IuXOGI/Z8I8Z/ZHTFbYL/nqvq2YkopYr4z54RrY76ipeGnasibfKeA3+fflPnwCcfYm2M2Cp8ad22OPCTODnAdF7fo+foUNWr3WRaGDx/YnjpNt5x/eKTHLhEne66EjxrpQhTTHikDIvVauivg4O6VrlbD7lUV301Dtlvqyop7LpHl+rjr/kvPmqhex4Fo37E0COZy3jBeG/DrwBcwj7xFtdWoafc1QFjd9q5fI/dhtq+PLCSrdUvH67RT9maR045hpERr7EltLffDwFnUFOPS1pevSuKr1WB2oYzyCq2bfbTKppdxYorkflpKD89629WuXt2uVhuB3a51LkUl91kbC3+MH5588/uBj9+7xj/nN985lV26NChw6fREeAOHTp8XYxJRywiBmOCW4qV/o+7ixvjEbQha4L2Ji15wrDMIouw3o2emfepuCgevDGhlVd+dA1CK8nW8i6jklYdobuX5kpvOCpe5eNW+CtzGvZ6GRC8qfmvPs2Be/Ct4B+jED/DIvrI+XrzuMHqJ5FsLfCTreVfheGd37J/1tht7TInEQ1Vs4StIpvYIQSJcR2ifeZkcGlEgW5BG07P6zGyW9GtZP+vU3JjzYGv1YNDpJ/jXowXULzmpze80ttj1kt6r3hMLgH81Q7wlnF1r664B8wgc6L4w2gCaIvWGP+DTiErSttZv6dhawA8pDfrIoqvCzW34GqMVbadhxxd+D79ZqRiHTi6JRTXkYr/Nq1xs+bx/rfr7ScS3hwzen6yuE04NcP99waOV/L57Fcjpi381jsATHjUNPgDzwPcl9y+pcn8P+dqmn2P8Q8djwfGfG+vrs9pnjVS8fSk87MsGF8TaqV4xgj40+43Bv/JiP+d5vX+cifjtYBLIIz3D0OYMwa54sMdN1vcu90IREK7UlmBZGsZPcs4ofbvZn6No4rRS7N7v/ftfmnjpBvwMWgFuMo8XGe/X/RuaZy4sSi5tH8VanE4DxpXdJQP64zBrhFdWGEq/nhUGJ//lF0DK3HfZN/eW9/Dv4z7Z6nqJqsdOnT4a6KLAt2hQ4evBKNnvR/GeQ9xSXuRv8RHO86JMyiJU4TjSITgSFTNZ10jPjPvUvHepC834uiATAgg5llknkXmedR7tfYu2jPyQotoTHD3Vrz3u9H9bqSlnqn5cV+gstL1U+WnvW/WvW/Wx6SYA/JW5GE9rV0VAmdb5/xCSPERGp9PYfC7R+f1avC7R3MaDn73CCRvjuTTzEMLRRpYOg2Svoe2ijfigPgkF9wCDtFAlghXzKS4+kRT3Anrw2rruGbmk+izpkZRuMr9u7/KEq9460h09NAND8BrfnrNT1K/wQCkruEt48yz5y97VizAktEgjtWGgcyAFWc6GrNL+C0/+sTAmskPvK6NlOLv1P7IKTTvf/pmJKG9/buz3czfHxdW4n6ffvN9+k0qfnnKkXE1UtFLq8yd23y42++Hnz129meekn+Blbiz7Nm+eLvyxt4Llr/gh1/wwxexX8AlOGfxgecxvQ81311gwfniIP5zm/1Ce7QLCC/l9pfy/ef/BIBNflay3ypU+WdD8Iil6E2BkVqO5M8CbEHw55EdSSwucMbSJ/FJzll75SsuxveI90nXNIGsXVR052f3fjVyjnSidrVGqRhLaOk83vuCX5gpqDJv/tk2JhU0JQ7v/O0Pw83fnYd3vqbZu5m/+f25FpzreGXfDsz5wJxr1d1WYfUZlQthK3H/S/nnSXbm9uYtG4IOHTp0+AvREeAOHTp8FRjISHZ64X8QbUI/dFU6YKMUtpE4RmIZsU8yUmul8FWSqL2O8bBrTJgedqM36auHIidwSQUhevCQGjnUE1x9rMrLU+zXpfYz4Fl2rbdfWHetmbHyKpe81tT3AA0GdnpW3mi99vliVtxkA4cV1q+6rD55M9JRbZ6qs9aRwuewtscjeynvxrIFJqwdIr8l/Db7NZLVtTxMZA0MWJ6xASrT0HrK34FKekZJ1VqzcHVk2ydZMqp26hBqE4J9gVUG2FF69jbwV5kVy5jVxnXVn0BhYo2YV1xOoW64VQetnAgRdbp7YL9eKT/t1YMtN8qot9l1nQLV8SU2z59E0eTm9+eb358f5VRtwbN9kar8cMGyioH8RfgT3+mNAPcIs4UZ4zYHVp/+TNXSVcEH9fzo0Rj//bG2Gm0o/Sm+v7Rvv3N+mNpH5N8X3JZdHa04q25TjP83xt8VZvmbYnfqKZcYcAlu+HDDB+Bj2RmXcMTcVuFL6+7KWCovdfpp6zeTbM3ozpPAqn7FEpjb30/yjVn/aWd3Xr4xyYVclJvmG4u8+dsPahvBwetFqL8K9kLukbWGJ14vX/Dm0c69ziBrDcU499LcT3P/8MV5YX3QHHik4tfqbnP+yzg4+8tDAHbo0KFDHZ1VSYcOHb4SZEfPU5FpBGaURbiPnBvgkUYqVsrIchdzbxfXk6Q+F59nVwNzJcUMU+rz9N2jB+qoIXTw06D3apM9+klgexeROY3KAvsa3mWX9UBTSTNSi4RW9uB/lsvmyd0nyhyvTFpU6XOhSdxxSiXasTB5MwTsy0B5GYLyUudV2iDdevoe2Pr+5KEJWRqYZzoyc2WuLABDHQ0IFgxHrEOmCAme0/QBhiJOlUO0kCHaEFoVGWJ0Za+st6l4QM9q5uA97oC9327kWS13Lum5JAom62U4smzXiEbm8CHNXGNwFz68enbFo/fvsI6cFeMN9g2Frrtk8shIX1qIGxTxxmstPL32URtUCBLZwQ9D7yo6drwxkFPxktxD1ULjirvKrg7qB+h/t96LwJ8eJkWPB3/72cS1XXOl/a5+HvsFfsn32gRax8FqoTJ+1ijMpIWY3lG2DGgD6RnjQwvnCi7hc25bHHiTn93VYw4fPtFjj7hivxozRi6hXjdxCd/L9a9lvoG7R84f4YzeYsd4fY77WCZRAsbM4QyYsPx7fgPYKkxwT2X99S5DvVwooa271f/dXAIr2Z40gNdxsJzAKgyqW28DnasceFXuUejYV6cqrKHyHvk09koyJBu73LD86xBgY5lVyK4Sd7sxjH9h/3C0wrG5YDdZibuTnmH+iR7Z7EsSEXfo0KHDp9AR4A4dOnwVGHC/m0bKs8kBRU8JDrEha4WxwQdCnJEgOhpWmTapjQMiZJ5H6bthsrXqfKCIWdrL0Ea8WysPTHN6lEHXq5X73XjPUkTlgZ1srRpDloY7sUZ42u9XHf/yFAoBmS9LC6yzEH8LtCe+/lV0Oq7sYdN7GH6mGYLMPfvibmqubRXXywxYLOj7JAhnbBwihxhEB39+uiGPJFQFfd3mo8fdCMUND7ZRWDzGuDrk1UEXG76zZ7KuyqyYABM2V7IYPuThpQlkrmnHEg4tVOEcmeAFBi/Cf/yBf//3PFdwzUdgxDzEDXGPufAWm9/ZP6biv9XstJW6ufmElZf2f/fYHgE1DnxE6T1xsftv2gS6uedzhlXy49B5vT5aciXuKv22rQMfMJ0HNaL0l34aRbTnptOvy0Hsq0+hChz9gefnLNza6X/iux6xrlz7DJ+oIaznH95f04mVjLfp9UvrthX76oKlSxiL3yfa4j7nDqSyGpjKaswPk/ndBlhDAGeknmHH4rox9PWtCHGBMfNHvpnzzYxRYbSSe0pnKT+IS6czGwHZvWe/WusLqKdB8i6jPLCsi7D6ae9mvtFLPxHirlcMgmRrGbPPZL8VPk2DnUFm9rOy/8p5vdZ+FvZlqPxUC78t9jtSsTZKT8U/HngMVuK+sm4t9fDT8Jur+z+TfKIbHTp06PBF6Ahwhw4dvgo0nV2Je5Z6/AOra1fgkQuFEeEAloonbByiIsiT4oz1nIaTqk7Sq+pMQkBh+Dr26V5/M/yUrWVNozy0KWOrSmCpXpU/8oDK1qRjvbF79KpMwsk+InSTloTwAK9O8NufJwUXxaUs/uQp9RnjDwB82+DA4Z0HXv/btl/uCUqs7H6Wbi1gHO8CDGAlttLZpxQYKyDBL6afgksc4gxY1o2/j8IjBiIcUA6RL47mwD1jleSuYyS2iqoIVzHOoKmqNshvGQprznDIUivPQ1lcqdk1M+D+0vMwxutlNDK9VRaNLCDzbCfmvTvwMlKMiEkg7o/qlS8x6hNrFKl4q914Le4XLEy0ZOEjjPYk0z6969hZfN6wOlD7+UTMpwIXLEdSo74n7kBd4P0ZjPcUWqGz/sR39YZ8Ik4kDY7xH5oScd+YX4ncNRPPHsUFywfGv+AHfdNcQg9vi7tkfMUHKG5miBvyLDu7gjt9or7HqasWDH1JgSU6JLgsOT9n9QhTVsCMkWdE87tJI9m47P/sHnxzGhbs9wBGr62jVpy5jRB+LLenMIMe+ER3/s+a9D1Fg5ONxcbqf6f7LBLadb3XnIbO1m4FwaoCki13k7GJ5sCp+LF4roriMg/2cje5sYIX0R/iS+1F0aFDhw5/NXQEuEOHDl8J6hdq+685X1rWpcIlDnBAIhxfkshQZ2ydmiupTwLLSFw9iV6Ls84umjPvPQfeB7sqCVL04Dn9DME8DwETtZt7UFPtKnLSZil7Ymyeh6DsV2sJrWRrNVIBC4QQQkBp53iaiLRp8JdIu0ftokPwy9bvmuUHxw2hdejg/rfrKg2SpsSHNDgNCpNLjH1UCJ0HeCOOreK36vmODMEjBRYMn7YOrnDB/ZpxiKMQh2gEyCjEQTGx7mlR2/LqT96s8lgi3lKNPFydcmlBbwJL6SlF5hhWJOtLW1eyvrRjiHAW/4reLB7EseZNj7SDgWXipzj7noh6t7tqtQsHIvBhH4u5flvIPTkY6mbUoZ3eF8G603vf7Gclwzl2V06PvsO1j315WOGSPRuZi1PiG/DAWDPJPvGEhSutksVdqnx96+gR+0R1I+e/EC076gmPk1JwrnZqPuwSXrAM8cr0wgJEcjxIckv7fWDcZ5+jqxre4zIPcK886hPP+YXD3VWvKKbXgEQIsZecRbh1W30Bl+g9lYm71AM77y/hh6F32fYmAIxe6vStozKv8rPjBtU+fFPy3h8FFL8DcAZpEu1tiSvT5c82ij7yo9cKcB7Y4a0P4l9HrbzTZj/NA8s8FuPaMyKg8o2Pci9iH697Je69XCcMl7/x1nNlnbAQ6tChQ4efgY4Ad+jQ4WvBNmdXxsgmt97jkcQ4CY5PGuIpcIgd4jq3SPC0Zlib8BdsldAClF/JudrmueYYJoDkoWUUJ6o8sIwW2WtIvm1BWE8l88A+ZuZalgtgU/Opa/a1jT1F+UIpuLwcHuEcHuFOMeC4DLKBn6B3kIsYgHoSYM2pzOY92d/Do/NbASTAScRziARivHrIrIlsqnL7OvHQea0AxTWzFUOdGdjT3KB2PwRAVME8y4dC7UmxX7DQfz5yobdesUGIlR+RuWQLegPi8Wqx9vYDI8Aw9P90W8y9et54aiuZzPNRJC6okblMPjuvTNvqfR9hqDmCj+1o1AES2TrF697nc2vVMtycGELHdldrHxyV/UVLcJORCfAEDT6d+ugwlnJBiXvEN7ynOSC+ZNx/LirB+QPP65rwiDmcbUv+ud2drfIjWvGVuWxdeJ/4Wj7Uez7iccZoyeSqxv8DXL9kwtKHFwB2lK+dSYgNaParo8QtQdtCO4Q94gA3yl1zGnJ3ZIA5+/WOBvLAPmoyrd9X2UPd9qO5FvYSQhjARnhU+CTKbk36vtAi+giSjeX3s/DWcwYpYPTSPLDNWgIkHQdLp0dqnRvlno7EWuWp0uGvKsW4r5KIb7fePMtH3Wy1Q4cOf0V0UaA7dOjwtWAROSruZaLn4BsZACH2FQ9XRXiWPULcFaM5w9KuFmp/lZfloSWhuZt76btBrhXa5kQ82VrRg5fNvGzmZY9esrWyWSkCPwEBZPfoIWQPfh5Y0Z2nne6a03gpuMDVIVF8wgq4fvDJYi3otgLNfgFhUzt3QJFb94qCGN8dqYMiFXBjuy4ZSWj1v917ikpxo8SclMHDEISVDLVyGmHHlempgBDjVMGxKjhEDjqplbgSORK6Uvjw2TWRrXk/qphbT0bHrh3wiP+BX95xGeNGWMAVC8PdPlye7ceF0CP/yIUAD/Ti+TmrRlVlhX1zMbVup9bHRFwgarCmJx9u7ahOo3VYXNoFDwZQaKd3fnjblvIPtNwT90aOHNn+MEzvfQmto2etcvdter3aTU7Fpr74pF83e1G0R4RIT6Ibeb/vTO1HfPSjMwa1Pq0mDvfUoUNJzxjXLaI150RIcz/Kj1DNkYr7xrxxsbJssd/q6jyJgBg/wNWt7ONsXZaXVyKm55X3JMQZ86hPWTEJCrtoEI4qvcnWCn48Eu/K6KVGL3X6mV7LqMIpKz9rr274x7b1CtXtkSRJX8h+j/8K/OvInIaDv53bl6Hzeq38rG6nrfzUvgztMr5XCytx73bju914bxQdDHYzXxf+rfXmN/z51+//95G4H7vJaocOHf6q6NbUOnTo8FVg5WIlHj7soHAEJdgNn1t3DnGK7xA1fXDlCTE1jyw94csf/WRrEbTeXfszC8qnlN42Hj3Dz1CqltmoaRENgOGnyt8ZvSy695oaXc1A91xAEcB5bTb4mfJWWw3+jDMfQVrMVgAGcFHIbcVHz3Fnx0XgOir3YD3FLKLUqAy1A0VgQYYgoaW8rOqqToUqkODF4jQqxDnSTIk1kxjngsglQgaAo6KjmvmeIp7SgZsiMDBneMbaIVzIYKQWYzaubJbqXFGQoRXnIx5/0iIdSAIwZBEdhBoesojEDZQL9M3lNp94RrzeeccfUav7Rwdtc3Qd29c4qvzUvgT8Vmg3reUeq53j4+dgcKX3vtm3KqvUlnn2Ki+k4EMd+IHxBTyd/veGDx955hN7hDDR6X+O96cORY9kwsI78BleHIzgG95/5HmA0yPxiCfM/lxEfgMY8ziGJechXiAOsOKstH9u5JdquaHW0ZfYJWpRX59ozOO/kd+4RHHTxyAWz/pf/gvgfAXv4DnByFwwjPEXpWm9/l1E+D3iBeMIt0e0zCdajs7br68iht9RYqz8DMToWSCt5Yz0p9HJCFj6ue2XGaUItfDXgH8d7bYW4LwuFmgktCo2rrMTVzS4fttPPYXdrLH642ytX33zfqjmU95zx/l3d5+fC7tDhw4dPgcdAe7QocNXQWqqwO4B/YDtiguCd6Rv8kGPYMjGIRJRBxxBo9BJWlRB5/KxzsNkOzg5+9/XWGzkgQXKqGZdx5gJomeZyvDT3us0eDM8zmoeIdhHVa1VWOs2PMVsG0VO05iwDPJMvXwNgXAOXhmd6BuYwR0EJ22h60jvfVuHuPJTo5dWJt+POweplgmKvibiOiqOcYawkuHTjLc4BS/Gc4kWDIA1EyjTIBUXXbv243xS2m7Q1YOrViSEuRq6xFc8ADEODGJxImW5JECEMwKERHk2yFvcNAU8iUWhkKRYheGe66CmbPeNxawVsVmaXak7hxedPWac/NfgwKfx5HgT0BKfom49W619VOeuctdTHoaAOqTBMf4nOXC58VSxOp7L3SH11ZgcGIYAN7xfMK0O9UiAoDYONQ3WxDgsjcZbyvb2++Hgbx/1bVmJW/xEhD7xNR+a7Dc6Y+ESLuW8X2T3rV2aEOe1DMY+qedEDENslwDGQfMHosdVIC5PItlavW/WTUVX6n/N8xBBeZnpZdWRdizAOvziYuidNA/5Eoh2GE42ljPIzGloNl8y9Z4rf+9nUY8CrV3cYe/lbl+Gh2mZnEFqX4Q7M5iyerX8yJosHoeYf/k1dOjQoUOFjgB36NDhq0ApslmQXN1kDgrMyBhqSidsGA6b/qwCqXiHZKH6q7xsN/ess0h5mXcRRw8elDSyxUZqZzv9zOhloLKZb0SZeR7VK27QEQEltWlcvdpy24c7mLZZzQGTPS1kHylyQIN/Ag7cfXWpb8uv1dxemw37MIUphHCn4CQHdvqZfRGq3t5Dr2xdAeagcLFWXnkfBCDGRXjg0i1z+TZ61dT59JUsGPg4+oumwRM2+3P3NHjvB1wlSWrrwAf3MhHPVftHuWDgSrxQeDgeyYjNLZNY/IqT7rQRwpaoX3hpugU/H/t4CIFy0COw6IDyjHi9c9tR0J54qieDY30pB5ZkO3J62WfTYJ5aSQH/KjR6WXpfjJiDcEQC3O1G7MYvrdtWHZ8UgbU0GuEDXpkm9+m+Pv9ZbKxOjG94H9EL9jGl9qjLv6vdpH7I6Vem6TLaJ/eSMQup8fwpS4+wPqD3V1T8FnzXeCzu0wXiE06sGDfGn8tYj/Aq5bU2lnaIzlk9MrJVOFLxMtjnCq51L9ubqHyR2Cllz44Ov3P4exjAFHz5vNB1R9rQ7NfsZ/3L8FR+tboIrLGb+eGtB3DradFYJwfWArjZT3WKL2eQVRzYGaTO6/WVsQQ2OB/c37xI//79yHn7QV6eTIfcoUOHDl+MjgB36NDhq8DYGSOPc7UE3mb8U/mwlv4ztQVi3KFsDqmFnjXGNffL3dwzzyIJTVDxgxc/eP6rTfTgHqMjB3xJBNCBhUB5vWPztmMyXf83c71j+4fzRsmwCrl8jAy1REL4BA0+qgaHB9T3Cnrl9nFvzdohv6xqP9FtrA4kW8voFRRIs1+JbFQZWkwEZSD5buGZk6hQEXMnVg4Q4rQZLHgkrTm1gE0Estd7Aa3QHnlitbsmbNR4oF1PhYlax7ghbv2mJspN8CIcGHnEOgq0Q3TL9LW8ReGSuLJBTWK8WJylGm2xruXhyub8NWwl8bwAB8ZXRFfcJngfuAJS8d5lV3vV+YiNdlOnPlx2OeTAJziz5izqGAcG/KsQYGt9ziBq1XpYOrz1B3/76Lw+kS+nxJUOCn0gKrscMcqt8K5hTD7qET+tA/dIFkw84lMK8OejRxI8aYwwMhertOhe6fdeXN7U+lj/uuKsZLkykxGMgBfchrhbnBVnI+YV+33PVZ9YPzN5TnjjRQxDnPdySVMwv+Wm/jvUQbC0s+uh3XKytYyZpyPYfyZ2M78MVaD7foIGf1N7b/wcI+jSEmRjsbHA63+3PuTAmuv6V5HRT/VJqpeZ09DZWprc7raW2c8oia7Ry/LA8q9DoDR+Ft3K9NHgApAHefZfeskvf/HbHrshOzp06NDhr4eOAHfo0OGrQBmCwwbXzPDAUwOED3n/WjIka2tgQqzcYDdCKdeI13kRxsY8iySygGxWsOLwp0HZQoM7HRVg6xPNPLRMQSIrD23zPKzPFCWsdgqo9O2wPLGiKQKKc7ijiMx8lG8ckYIPyhxizzqE4KB870neewjN2fVEWvPhGgt1BlnTHZQ8tCrKt9takIIYezNL2Ygzyy+mxn27t8WXI5pSGUW5QadawZ/3FZQc2CdxdHpeAGJxR2rl4UXiRjgoEjxHolt1ASAqVI5LPBdQDFnGRZ6owRgAl+iOS81GPnIRprz5EwN75ZQRg1dMPIkdFfnEIe5qnyq2yUjrI7VBek+YHrRwbIXlSM3lfdHmo7uZ7/RTdIzu8qBObc0x+tSslKo2Kjp9AjroLoVvdruaRhToz+BOZwfh0FrQlNUjjuh9Jgf2SXV05To8ggkcFYErJPvwV6IF8MoBdZtPqiBYmtCOmIPU6WvF7SPckVTMVqYs57tnOSjIfCccmyabkPNK9a0chicsqx56RFXmrbads4AOAX3CRfkUDh2JgUPXgS97exxUp//UrZTTO995vdaOvgWtLYILsNtau22RscnpZ2Z/L+3qYNHOQMeLzrQaXGq/jXfIh49jP3fNafCd8+MvWA/cPwc8/wf72T/5S66jQ4cOHZroAut16NDhq0BlkCiAiBS2nrmSfl/FQ7UesgYSXMq5T7X9kA8fdpWtmwDKy7KZlwbWMarVQiV+HhaVZGNt/zgJ3gxA0rdDCc3qJOWn5nm4e/Tr9fS+OQjAK/DbkmHuSx601d53nCWe6n6j/Lc/a/76eJAQpUSysYIfBumbvTWh0ctKC2iVbGydCjh73KdR1dgwbl5H8SWqa8ICwlrGtzKtbC0TvKSIfysbRgke0gztW3teDoXsPGEd4qxkFOGEOFIefSgZhU98JusiNrW4wBmxJxmwZDAmGLN2CD1JLMyigRmkJNrOWdBxj9ZMyqw2TeVcGn+avT2NAxPTkYpHKr4yFlfm8rAmaRil7rfMaei8Xpv9rP/tavC7R/8q9C9D5/XKvgzNfur0s4oMP9EVp586/dSchtWjOezb1PowtT5MrY+2aiu9z7l1CKU1KuufEjpaco94XkvM+wQe+bQxq0+qPze8q/acsT1VvhWnarubVB3VbqjJ1qrswFuhobe4KyYIsXjx4c9G+J5vgBgvwntgHBl5napGDJZyXm57mkLrUzTuuHnPVRExWwQRp5cWz768jUbvIKTzp3ByHUSOjMMC6oumfMcrSbbWbuan935455n9zJyGVfamZLvPV5xsrbCZ7Sm89TQfTjbW0wGod1trZMQgObP5s/O3nP8s7bpDhw4dTqJTgDt06PBVIHaO4lKtI2frwlLcqeQfYS0DhTFk4xIjaqNGMa5HPUhVo5703SAttA6xe7u0rnsURqcnFLZ9gWNV12d3AqoIM4MS++XaRhV8uGXXKvAI6pAGP20RfaLMIc7LVMNXUvrxfvnU7/wTx40mfXL6ebIEcAZpEoBgncV1Qvi4Gz5XABtGA1YgCZ5DdHSaPWSpnX4T8daa6iimRBu0xCogoThtHRgJlbNhPGCpb5UmyQ2j65oYr/eHuEOWOvFMiDVis6QHeJKgSPCBey4Mwuk5+Rx7tx2zfCOvQC3VWGu/Aa4SPBWvpTFfb3TvuLnypw2hPaMgln1j/lJFiXh3e6n5aP1UX6o4ukUMocAGzPNQf7VDu4oRrWMIt2qtIvQ2GgJgZMRVxxoHoE/iEblEdW/e1nOud3bKaszjR54V0bA+a4yvXYInVhLO2XpEHkX/z9jqRZAJ948UDrQuQbWMUg0Sl6iPt8V1VFSFgN5t7WTbWD5bievtzhwjqqy+Z4wjvC3u87rJtwBc8fGOG5dwqem9MGFhwHf/MVaYWLGFQ6izW8MjozHzW26A9+ViTREHCzc9lpNJIw8s81Ox6+qoJzM7UeKUU/rntvDEsd3Wsi+LNbbdzA+PpTX+HBylwU4/01GyXDFQOCLAdmd/7jJihw4dOnwGOgLcoUOHrwUrDk1lGMamZzJcmokRbuV8ohYhDgxdZoBDvGAU4rbYqUSW8jNEjF6mLSAL293jwYHkIF7RSVIa3XveZaT8DOEIPS7/muehE1hoM9QW/9kekswTzXFoEc0nWMJLeAt+dfrnnfUlyLf72bbRy4xhzhL3IlKewjChTIBUtvzSerdgEGMDDq7WaU/NRteMgQTvvorEJcy4DHF8lQATNgsGRVKWBp2UvlroXQuGwCve/iQv9aMUSPHqIbhCHF+SqoZbJgogAe6YxuIu1eiSMCU/I3l8ZHaDKYQ4F2zey0Wg8x4dX5eo7z0oIc1n+iQHrujuSLyp9dFWoWZFWoTUHqFaAq8Zrh7vk6pHMgflp/1v19oO1uhl9mXLJvYTo6WVCLeCRzTi+KH99dW2HxgJ4hEvOJ/w+BRJUTrxr86Z1IZPWhHaOvsFJtxPIGIInLOtOPAN75dMw3pi6hK2ikYq1rfXvgyTzRAwe2lV7G43HonnKbdvLPQp++hZNadf4I4blyjCGzFfyRmIR2xULUoxVoGlnAHf802VHlkbTn/Lj9/zTRViTULrcLXCujhhqX5KyvUz7zIqAxycwBEO/Jkc8mQxbdhcSb4/g/r6V5E5DSW00ns/2VpH126Un65yd5W/fmb/9p95fw7Fm3AkvXaHDh06/Gx0BLhDhw5fB0oyAehZfNxxdRbYuTqzN+Fu6KgMI51x4ZIuS9e4an4joZVHFkoZCsDwMj2zjx8qz9KjLponUM4CnX4GKtlaOqJpEbO0miMeozz2y/Xu0XcAVFKxC51qKAD/UOPlM2jwZ3T65cHp0EgdBN5lmAf2k+6gJ5FsLWPmG71U+VkeWOb52o3EPItlp1C+fSRamGjWOmFjSySQ4NqlVvbA5QX32u/XKXfeNeNQF4KtziGsqjC5jaBlEzYIonSKIjRbLu6AElC2RNfq4ZaLumS6ZuyRRHI1VivgRhZDtbjHXzAAecOrWNxMLYF8xhkbn+SDtIMnaWPZ3aOnk2bZr9Zlu7USx9hpGUYb1bpptVIjVcitqfi2CjXz7Bts87Op8TEVT2uVTRpcLn6EFm2v0X0TyktNL913J7Aq0ViffuQs3aWS9bXQJ3466tVRzBgDU5anpOKqwzq57ql6JpR+5uoIBdOUeMJ9iFPzCpZqTUQ/RI9IU9mp9YHs2Upc5afaEdroZfVqV7mzwvmueSuWjAFXCmfgPvGYRYQX4y1lvJAJsCM1QC5YfdfbOEMpTjyrvHy1ScK8MHlgxaRPvFUyy26APGi7NO9DQB/heKUFei2DroR2Hlogn/75f7EO/BTLrG6j7onzOrVDOw8scxpKaY+gy1R5jyojBf8q1FL8bmuFd2d6Z5v9zmBKsrXMmb/b2vZl+A/WKxVNbpz4/yHtm9ahQ4cOfwk6AtyhQ4evAmPHT4TQDzNicBa9nQqTfLrd9QbGziJVYoTKU3rij0IRl9ky4wcPhXsBYPhZ2gr3IkV5/aUZCqst5BbllariWiVbS0+ZzRavKCeLu0ffPA81FzHPQ/CNXpb8OCyYyUu1d7I9wmRPkNvjanCzZEVejvr9ljNhfVJ072tf0GRreZdhdP9lvsLRnef0Lfv12vCz7K1v+GtAeZkzlOTBss7qM2GhTGUUi6PVtz6FR+uYjUssgk2U4FU+rRPWCwbl9e25exEKq25E25KCaxwYxTUPwK1coEhw1zJsGUJ7koTKUTAWIuUAS+lFygUuub3n5oaH/WN+l3pX8VTdbg8CCDsqMvzU8KvFkV3VyhEIKCS0StFVGQrlZ7VMMHtleCUuOZ4R2U3ls2/MQdW1Stq2CwLkgd0eqIedARRGu9hx24GRihsBn2tFPOJP5jE6iinLyRFm22jDJ/FOsGst/0YMC+H3Sa3vGe8+8KLiwOesHhm6BL0yZ69LpM2PR+ZilV3XR/Ju5pdrBMXebT5xVFS6QMtWXMDDcwlj/C3uhuticQIIbfx0cqszlbGUixXOR65DnEcZVc1EuNrsubqZerx5RrTenUgIXDVxAvXszcpPDSQP7eOvkXbNdQ78861InH7WXF4pYPQy7WWt30XhnQ9wV+ypihX7gWZgtsHrFZC8GZn91Pg204Q5vPMRks1w+YvLZLxQcgNgflmQsA4dOnR4Al0QrA4dOnwV7FJRlyMFkgEsR5KL66jkzNxMzIeyVMONdJM7INljYVZnVNloj0wN5WBTDg4dlim2k62VB9ZON1QLRaO7Y+yVFkt/bRqXCueyN4GWw94d2XXiSPP7j9TSnZ5GcZIYvdS6CHrfrM1peBCy69PQnsCilTHDQIGIMcxo2DcLQiaublTvnnFZHVzKYCXDKsxVghcXsXAdXSDLXaTscf1ZNQl2/Tb4koxZhzhrGQMO0YQ1iEPskrTuoEMMTFg7RB6JXYstvJLJmCVgiXH5P7QBQ6Hj/TZiPomgjWZ7W+Vnys+UlzVbafWw2NJc1+jt71hT3NvfxpW4d7txKnsOsN1NtvlZeszrWKhuGMpPjV76aYdPQBoc6bDKqtsrcY+0K61Sn4spyynLVgVHq6mCmbXgk05Yjbl3WR9Woj9WLNV2yLBpLB3pr2csNAeOiyDkJLlXhbkG8sA2emmrd3e78aqKmFXuHjFfcrZgrEdtn6hPDNi9la1CTxzNI3vuzCcd86iNn6ubsMUVcIik1pnqcC3as4A4/dSaBk/c98OnrwlndOc5hab9qcdWvWWfUoM/UUmytTZ/d64/u5m/m/mbvzvf/jDc/jAM7/zwzj+a26m1p0GSa17uzuuV9nDxr8JGjLfAspY9NwYEN6FDhw4d/kroCHCHDh2+Ciyb/H49ILW5VWCZxkBt49yZ7wZBrtUSqf1Lqv0hw31WDM2Es5l3fGJ2NATTAW05+sW7iJKtFd17Elr1FnXJyhwxD6za7FOeqPDn0GBpfn+E/kES4CdQMq7s3tNSpE6c8/nItxaIBJbq74yeFjxzTIWQR2UWKAFIyszMIc6MixA7EXcmFzp2d4Sz0uGd4Y5phE1pmJqJe7u7yMQ9SYNrF7OQAeCTjFgtGAiyYHgrF2vGLrE+tQoT7ZOcyRq4ZYrgEp8R/Vo+bjhbMXELVrzxSAOcb/j4yPM/pEgjXHDjgabirXP3yCM7RQoEwJyGyk8L73ThQCJrnLzajRG2u8l2N7nbjaPcTcQD0QbSdapGRYNB+elpZnusV6fH3UhFV+biylwkp8J9VTzwM/iwpr5jHk9ZNVcV+CRnrM84skZzzvaGd17zUG+5qz7VTjsuuuKx1qPLJ52wdAkmLAVcAp2HqbDiFqLcW+UuYE5Dcxqa06BxJ8v+rXI3zf36ld5yM2O0xdWjbotbeQgDoj5qHukvdwsmt/IswhVYHQuCvWA8k5FuKMpdkOzB24eAFqxp+EXxn/WPPQ/svf3wwQ/r2GlPL2x82bKHZrxfdIpGlSFpt7UBCe29E4GfKj81p6HzemWWr7KFuCvnn5zL22EvUO6XRcnu0KFDhyfQmUB36NDh6yDnn3jbGds5ANP1u0z+ed9YK2U97gbKUD1jg5Dh2ioBNrsRkIdWYfAspIFJJb0+5cz2REZWaRpIF1t5YHkXUfTgBW+G6LijrzaHZ+h0wRJa5nnIvef005opNeWs8WmH5CO7Ggep2bte8KU2iuY0NHqW8jMCO9lYXxQxK9latpYNMwPDYLdDaEQSKyuzVVzV60oS4pQuvsMpERBhR02nX+3la6n43Fjf7i7OjbWlChKrbcul4esKiPYBDpUTcjGRzUIN9HNdyBA11D1KlE4QLSiJcCldhWNxH5U3wLBkjZroEbFg4EqMkhD7cv3jI1j5csIyxvsgVwdhqBmqeF2PqNSIoyZNj+VjXzn2tGt7VuJ6+aQKi7XK3ZG5BPrG/Ar6xoLspipZnC3l7fnSWGh1RlM7o3/C+/c5t+xNdg8Wd+pVlJshnl+aND/h2dsj8YhbBtLa5jnEGVd+v1X55c6KcjtMwrOCpdux2FEejM2qzA3vlly6RDFePUtwZQg9ZRnhpdoEunVFn+J6WsI9XKSZsprJqE88IAR++N/w4r8zykfJgnFh8CwRkODFeEMWayZhaQsNbPPJWtx6BCynnxXe5gcYqnhdjoEWPVZ+ZvTKMFR1ibX4KT35wjl+uX89fF7jxR3YWkD/28YdkNBO7/0qOvSkvx2t/k3oxGfBjrO/Zk87dOjw/+foFOAOHTp8FeQ7pxf+6QUPN1agQNZcsqC0WX3Mh4vsYrG7/JhdzLPLVNyNOED+lLXnJ9U5Kf+RYwX2X5KtqQ0RvYuw93oN7GZ1S8UjgnDv9dq6CI/l7TgmBbfbLXcdpQn64Bn40Bf8Txk0AuCUsVh198xp6PSzmnD3Kf2u6aGHKryqlbfzX2zNSVQ/dVtlZhZxiOvpZ3Re39ZnI+NQHL3tG6s+8eNu0Lze+kNqdlNAuxyL9hpt2D06EldNaxnwQu6ACBfkjitgyGLJMBE/wt1yFouXk72/+q8PDtuqtyqsxT1+9KB7R5H+NJTQksDSFqq1mvdLCa0cSG/Tq+1uAvSN+TafjMzFStxmgqKizdrz/HLG8umxwHuuG8a6T1QhIExl6Uv0yZr9E+z3hndj7iesDk9JPSPzjKrJ0W3qLzIryv3lTn/sSIAx9x5rzTk9nRKpGvkiI5l7RLYKX1ofX1ofG9dxDKvdWKviTSW1+NMnnrLSbW1xl5wB3/47ROItOdfZktcyifFAHMJrbg9binIXyB72Y6Md+bm8mUNihEtjeWksD39cEljmeajK0IBt7MfK18RBr/Y3rbUnaG4HZRkA/KuwZd2g/FTnQCraefAHfNRuC2r3xeHZOnTo0OEUOgLcoUOHrwMnecP5D7ze5NcK3NViJ/mQja2Sc2ONsClJ70acj9nF1FiDpIG1n+W3pvtHZv+Hwo4cHNqXcXpNU1LB8DPlZ/bLteGncmpCiQDZzA9+HB4pUBT6JDkHNLmFEN7CIzyWG9UpZ/UL+QS9yAO7ftC6PDqflsbnkMyJSOig1FAlF+YapfLIBDk31wMdh1nYiLPILrLcAdYM6xUktehNtRshfpkJBkG7fGfSDP9TcmB94pXMdJwtfYqOfTWSdYhdf+xFCGj2LFp/0bG1NFu+YnHNIsb1iAWZM/TLLNN9EHCJnnFX5arRSPbdk9bf9v7a1/TNMP1pmD34wY8DrWsp/2isoJNpo3Q+pFl245RuyUcSBVdXXDzPw0f5bw1FL3Tyob3we4oRCWN5rO/wJb2Rd7rvnrT1T3+xs8JchNRz/MfIf4ysx8R6l1hh4s8jvcdf7Bd6PNbPZCbCmSzPWVPzBI7wAFtFtopeWrdXmkyeuKKVuKvd5CG7KVP1Nm+vMJNRldfX/mkh8OJ/epX8tv/IMMZPxJsxCnHXnFmYIMlBIDvPiNOfhnW3WOVljXtVw1rc+934/sRI2D16u8cjPreNazp6tT8zK1JZ9lPrHUfgN7er7G4CQnh7xI5a+engbx+dQeYMsunF0vMiYxT9qdf7Z9/80y9puEOHDh2eQmcC3aFDh68COxNX9UJ8O98AC8VQwkDWkfJtlc5lhDRMl2NxdnMP5CmjYjmw8ZPKlPbEWbXtYspYhRHWf4T03cB+udk9+vmjZ7/cAA2LawElycYqGjpMYLsvR7sbNPf5gDBTbKAHPrWYTU/UdFAzJFvLK11Pi0b8rPftOg/s6InknEdnxoZCqQ3eUK1fjT788HY4IOkZKwdX8tFWbImsrYcrLhA2UtTIgn6Mfc5DVW+KGxfyrGTiWipe7C77KklzN8X1jVUzOjeVde9ENvrheiQ67POK4UQ2sXK0tfNGjc9Yx7hV5hvNkxFu1P2Ejb5FKxnkKgGGsvDV3Tt5uWCwyb2/sf/l3yPX3Cr4yOWlxCgS8VDEeKnyn1t36914LW7zzh/La1Xuq0Q86wJQyk9buZ0apx0z1F+J6+0mq9ylzJrTwkjFI3OhtxPxHCMCkty7240VUsXZPnpuqx5P/dU0tKBm3HvK/lln/YXGiNtL+kL9kL/c2VFuhQnAnMLe9d2+rkqftsLEXxp1i2iNR4YUVtwTYMwCJtp311aRbYaj3Fu1VmHKhTaBlbgjFdsqqvqk01Yh+xTBfWKPyMYCVukq5hUQiecQ9Yh9ohBvyC5gAuISzaiFhs5d++U6+cPekDd9O7CmUd3C+dJcRrm7lmO+6JD+NLQuQuWnBgRvBkdfC40ra70q6+x3JkxhBrfwXXmHD27Mv2VoN3j9jJzXq83vzx+vnIBf2NGfT1L6Dh06dPhZ6BTgDh06fBXkNinBGeuhcQsY7+nLFgrh7txoh3vaiGtOInd6OEeX9rdPBHSpTqqkvKbsKQDBTwPvssh9Yr/YSGhF916ysdK3gyNnCf3fzvu/mTv9rJo0nxBDPkMNngoIgTCDDQRPmkaf/l7G6Norlnlg55+XGbjuf2j0UpTCUCmuKNPws404CKaK9U3I5i7wuBveZtNFdtGqKsR5lEKYTXHXMiyy/uajzW4Y5qNt7rgq9o2lreKsZWZcPs0Vw3JTXJKx9gfGWTAIxZHynkc4AYXu/ZJ3jkQT1q946xCdkf6t/OsRATCWwCW+53ok62/UhxB33P/9Tym/7U1uuUa44f6GO8AhcogiXLvMhTM04hfW3bAZkmq/XNLcW0SN3gfjbZVv4bgOrCXfVe6uilBhbax2k1VhKb2wiWwVOSp6ad++sG9fWLcvrNthQds+Ic/1jfmBRUB5iHjE/Ilzj8Ij9J5Mm3SUG9elRCuW0W06uk39eVSwXyCAd/AOtuWnuaRjRXm9Nkq9tzy7kYJoH3HqsAsHR2alGzZwkCxKtjgDSW5erIDV/z0arTJ9E3QvruT2Sj6uZRKL+16uHAl7Eu1v9T7eXvFJtpby0+b9wDPi9thrYjfzgx8HtUd9+qEXv6jDo0IPZtCDvy2F2da9+beFze+rePqsxK1WKHYz3+lnf+MvrXJYqrxLg9ShQ4e/GjoFuEOHDl8Fsdj/Ln/aEKyMLfDjP/A3D6vli0edRMeuQiKJksgM3w/9l1vl7wof4DmcHdfRDiB7SfdkKKzjiO5d8Hqv19mjb/hZ7/U6D+1GiZoOLIEZvBmdSD78RK9qO2ju25TFNrA5mIa2Ttyfu/+ebC3rorEz31pPGUaWcPqZ/XpvfZqHtjKSgZEi2CpQnlk1MjHv2V1uzqzd3DUn1dqEtP6GOCkuQqLlWWGxu9iKA5BTI35i6cl9O/ev7G+nECs7auXpFUQhSCiOVljPWCe4DvFbXv6S2JFI1zCSx5/UywjLIYRRjJviudBPFjH8cWLpmjU7esZdjP8ow7FaBrighubSVpFSFHpsXUY7blYgheS7D1ZUK8+xsfHJcd1Ic70PiOWJV2RvEvZpnPRVm4tSalTvsivqx0qsxCV7BnhG1DdKrlszI79Dcz/lqegzyfCXJg32SVtRr/aqb4U5bJ+qJPWdcNIa5OIRPiecMwlxXnAb4c9kVCdz2/xsVVdW989tj5W4V+ZyX6DZBEKPeI1z+66cOTkgsmB8x00gToh7x3WAO2UFrJlUInkq3ip39mtnANRTlxWkV4jEHRlL8jHaKb0GbW4Q3TfzKmk8ESNN8K5C8zza/sO0HM/H3jb/X5JXB3/bXiLRcaG1dbQZ7YiQAY9ZW/Dv0KFDh78EnQLcoUOHrwKLLJNwrBbuC9ODb0yc3a2ARxLuhknuALu5F/xpnM09IHv0dnMvDSyQhuBz1PitKc8eL/yECFzbzkPL8LPowctmHmCehw1n4LJsHtrNPEPSaOjzpeCf4MeDYlcn2O9hfWWVTj/1LsNWE/arI1GFWuj/bl6xX50FyujvAM+I9arE0Eops1IBroqVm+VhMQHd5k7hzVtrecJmzfCOaeXHu8mdqkxfJY/5AMHau91Kq4ahrBwpSF0oTr1Uud34spDhHRcbxr+TfwMkeO+l/xMvVpy/krdjAkNMj3jBQMlWsRH1DTBYrAsTXAFwJBzxeM66aEiwVfSYXevAVCfQJv8ays9qe54gE+WFf4JvHIqWRLk3y27qEmVVla2iJPdWu8lqN66Ju21NuDAuPWYIrTP9bHFBRjJvqJInevuC2488e/oy6ifr2FfVATsWLfw2is85CAsN1GyhwQ6TugKs4RIAvsS+JK6EoWgX5WVVoG/MizdClYHo4KJeWrd9Y354vakUmdgC3AjX/OM7YDa5WrvFe2oii295w158jp5ztz8XEvGUn7Wc9vXtGRK/MO8QPBX3jQXCajde5y1TbSh9hhtpcmv11AThBrzLyDwPJbRAEZ22l/4MaL9c/zrUPrqfdQ4UiyRh+antdwaZhPZu5idvRsmbkY4el9772+91oAGZBIbtzsu1pC4NUocOHf5q6BTgDh06fBXkgkTX91wtYPian3x+7TqJ2Ai+uVKGMc9H5iRCk15FGpiEpY9uy4m1LsTtdz6ZGOmIElJ3692LbNG93//N3AkstLpy7wG91xvVKwU9USjM8zAPhgcVf7kU3AMONK474Q5QfPN5TBiSjcXWAq/3zbruRrjPDtqE3t//7Xw/2VUoP9Mz40tz1VMrSTLJ7SsnGMnaJwnFBnxjdQ73vgukH/ppYH58bg78wFWJbxR8u+C9zaUKTTAKJgzaH7ipne6lYId4xoUr7kCt9LmpcgFH4kR5WuwdsFwwLLU7UaIWajBm48vqoxpdlpTDW+8yCEf2XAauij8yfcXbsVrcQzpxjj6mG+6BGO8f01+vcdc7V/eq6GZbBG5VcfTBN3Tgl/bd270wW0rDn5SCC+W8uEV7/9XsxjOiKK/9SBSrA8pUI3hSDny1EpfdBCYNHbiJFWdHFOADjuQSnsFH9eyGD09fRnmyLLkIcW54T6n9tmsN4PIYB97CO+gVvsF2lAPh2AQ8WZ/jPtIHxsxCngtMWGhP4OfcLRh7xDNGL6xb7Tt9rIPtFZnv02+AkYq047RthNt8cmncAhliw7f/sfcDkxjfk7hHMmdS8V6HKMHTldkqQnCIJDivx212+pnyMmAtLrUujcxlIt56567b7spIaOXhkyYexcgq/jj9zLqoJxmW4uVzeMqn4Awys1/mu4bkzehYPPwT0C80HeygB+H+FZdsrGSzD6rnQNVEhcexP8vG7HI6C+gOHTr89dAR4A4dOnwVmJkEcwbq1rWzJUQfMQwGsllIHONVAqPt79JIHbFebpHeoxy4dqy5caqSo5xZdo8eYPiZ52d5aCdbKw8tQxG8GfZer0FpMpxsrWNsp9lJDvvY3HsO5/CWMmZTs+QMXn62GbdWpX4cUjOntF+tjJkf3bdptNHL+q9WR2J06X8NQ4kSQ/Xz0HLtTeRCPGFTMVtzHCHYN1s+9sL3ffWLbJM7LzUBPpDSyoDPjXu12Q0nVjMkUkUmhZkqjLkTcR0VA7ZEWzV2aubNM7msP2ePZMQqxjkrlxNcyfppbMW5N5rdsHCJgBtmkbh/zr95zp+es7k7uIcjNQceGcZ459btuWKbTx52o3qZozHVag+2QXebxuoKWB0hXbK3wf0kDYa60fhK3NXOPSzyVBWlPKgUK3GA1c69hMEBBy4DPh3jwE185Nk1HyZyksiHOEs1HTPTX32SMQ/VjTjS5fmTxs9b9qmmBQR/sdMc2CU+g0cQ8IgDnOpCHMIx6ADONnogHTyL2gDWvLfsn6zEXeECo9zzjCgT/0bd3dz8YsYfz3iIefggFxFegCPgSBTjOUQCNlGEd87qkRFgq0jn7y0z90qyNa3Q0uxUc931znFUmBR22kduTx5a0b3XPHTsxutfx2VonkdPFfw86qth9tPdtnAP2W3tL2C/FTT3bgWFPuhSpf0CTj8zLx8WnP1evt0Y3hd1uEOHDh2eRmcC3aFDh6+CDGOsbn+1vDvP5r0JQC+ZD2QtAiJp7iLsFm4pc52a3XzerEcOt+XIodY55aHo3ku2enKJzg+cB1b24IFkMz94M9jNvN3MKywPj1RYsxPV0/CWsd++DEWZXntfgQ2EQiif41y5748Q/DAMfhzqLKMHs2QAc1ql4Gx9QMQxYgyllNrsbNtcIbgkA1YTNgj2PiqPGN4O2M094G36/G36vJXfSHRKIZFWcxtxslKlrBtR+8Q+SSiOtnx2iKu+OxIXU2GJSu/i/WO94eGX8o8uyUf6N7JK8KfJphetMtfIHHXJo0viSXLBPZC8vlnDn2UKTGTtk5yxueGhppIypDBk7RuLb+w3Q+rWudLclIMjh3v2WOXuS+tu71/aKnec77QgxYj9y2hALQCW3GWjzW5yOCA2uCHuHTd33MSnDRIC3FueuYS3PPvIs8N6BEId80xAeJT+R3keybA+9PaYl8LvExy4TCqrsyJZUV5VoxVgyljQcybNywYhFe9tdn3kdpTYq/TNR3xlLj0VIViEgP+/+M8F/HnmSOCRuEQOkSuRIE4REEtAXKLHMgq0NoSmeeV5MyYWwrv06j4b3WcjymzA+4OBFd15CN5FPUf30VuJdxGZ52GjCEqv/RUW1F84ijTpDW/98Nb/OeyX0zEOgH1OcvGvGm4dz2VuYs934xvz4Vcvv/s57Xbo0KHDMXQKcIcOHb4KrDiXTTRWy97EeDsi+G9ZvM3tm9SXdC2FnZ7hZvGj35TXtMnxMVVJDnMgHSq6pbB20lC5fkph3lyaDSvA8DP91buMHJ1t6DIC8sBKCgvGJ6NtBRCUk/grOK+R4XMI9dxOURciy30Ffiz3Vo7B/pHGnvQDrNerS54UGZWfiihlGOxAKURcKx2qVYwzQAay3KfnRQBjHDFz4kfH8zLlpUAirnk0bm1bqZKPu/MbZpZKWmVCZfvEIe7+QQkobKJEPEdFG8YLGZSHiidoYgCRONdqPmQVi92LV1acby9tYCCPY+XGuK70/7W6fGn9yyF89yH9F9c2cMWtHko+SYR7W8R/4o6r+mAZqnhq3W7zyf1udDAmD4fofs9IxVPrY3XlGrYRvlSxrcK9zFh/9q1hcBxS/tOMIvblGBmxp6K73fgK+mWapQpV4h9hPAGnth5Tb1Nz4AD3ObenutuiWi5rvSf1DCaeP4+YQ3iC97ZOvocAXrRLRbSthW/48FGeBZxUU5+KJN9a4IC7bKz3/Mr4w43Mvk/5m/8RQRmIa8FYhAnLoBa5LcEDOWdZcmABMfwU2RuuG15ab+fCXMW56xpxnLtraZtAV5bMRRCs6sQDcwPvMiozUe93ZjPfu4zyRP08+ppsrOO38YtxfMhq45pka9E08H6g945zyfPNbgS7v0YHOnTo0AE6BbhDhw5fDwo+Tm52nrJeWev/bcafMiff+SyDfPi4G4DkkdmcVzXnWMe1taPzsKMJZmoK2xMCnYBolijRvZeHVrI1nX5qnof2y3X/1wvzPDTPQ2sa1kPmnpgNChdCTwD6WgKDAO7gDt7Cj3rP0Rn5wVw9gBnM4PHIJD7ZWsnWatHgZGtJaPW+WderMnrZYXCskYr1BxgRiVIoeioB1S8JqieJgE9slclR9Q2weztgt3AKZW83CHejd+nzLHf1xuNu0GqueXVi1cRVHTV6yHrCOi403ormYRNtZByzD6lVbcykH3AORGLF2HYimWME7thdFRPlGHfNGOa/5odb8/WvXZyoaHHF2ZjAI9Wq8jUffRLgktteGanr3Lqdmh9n2XWUt4N+HRlCJ9EceFLc/GN355NVHZxygtx9Dla5G4n3yWJb3AXjug4szc8W9zm3p8JBhzhLpvrjk17zXu+3Y+ktsiICln9Q6XFds4HUd1bXdmtnrC0rBERueK+rWBZqcHWH5ST7FY7kDSrfHvqp/UF+pQ8PJ4Xzqidxj/YCUJFeqyaAI+RBo8N1133gYTdyjbgeokyKzEkA6dvBkTUv2r8s/eJq3r1iK7r7fPH2Cx/GF6BRp9NPB7977H+7asQXrJnP2IRnrIxt/lmp7zp06NDhs9ER4A4dOnwVvP7Vrz9mv/gX6t//Sf1TU2XPz5EXJGKvGfhqdWT63t7esyB3WssPXBw6wSFpnHgC0podJlvLqAssuoLQqhdWftZ7vW5z4MNWPDiHS6EH59VXuNR5jyi139NzyurIpvwEekZ4/BSnn3mXkZapnX6W3fs6uI6uyruMjF5aTaM1rszl1PqoPyMVe0bkYj7n7tq4O2dtqWTKw5R7bTw8ZI1wbc76JFNjAxheBqSBlS9ciSzgMR8Am3yoN05d2rmxqc3VGzxkLcNyL4l4G0bATC50MKFQ9ilkfBK9HeH8KM8mhCPWLunasTfOAMFbxlYsLptInB27GCeX7OXu9/dnEBCL7xMjLOhFYn/DTxM2HsmEJUXqGqfORpta3JPLKLU/q9zV1sWtorYK9dFjt6fG/D+XcdRo8M8gKaLjHi9OkZ0+8XPuxiyP8tsX3Op/n06G9MgwxK08gTUa2Y8Oafichvl51a8+8qLYtsJkdFuwprE8fCc/+pIifCc/+DU6Gu/pta6ofY92M19C6yT1LV9EV+byV9Yfb7i9vPuD/i2tXS+mhzBmHuAEUqwHJTpktLCWyRZX1xFL4V5RMb1mSPkCfWOxb7z0ENbbZQCC009aAKx6HvXajy3ZWk/aC3wNrvsJOP3MvgwB5Wf2ZehfRf1v1/5V5Fxk1XpLij9nNHN/CZB3HLhDhw5/NXQm0B06dPgqUODZbx+MX22R7T/9W/7x9yvX2slukK9nXJ6rzZwqzpAOpHM00BAIhp8pL3MVQPzgNY5L3QjwyWC86vhpersM0CpOf4emxD3L9DOqmahCaevovYyj6YoiYj+975Vhrop5pCp21rGpd499sVbH67u1KnLePqBDwlbRU41eFt17VmjVY0FX02jNdWnOs0fmMhH33Pzg7GKUGhmLK7V+VN5lPhO4YraS4ZphqJiYMcg5MvNMcEHiR9c9j5WX6Se4Fbt2RQJIbCk3KzurHvMB+eCF9X5vQl6G4w5xhrJeMNiqUZ+VDnwV4iCDULkUT694aj6JKwmKCx5icacqXYo/JkThsGZJPw43r3xcQSRUzojVD3L540eehQBjVqB80vI+zseCX0aojnB1TmDdwRfm3btdzTW0YQTdtMAXULywbu0jeYZqZ8qx53uspH5ex6MWt08p7nCDvBwjPNo2OxVvtZuURtrHq5yyOhIHq1bnO65/wQ+f6lsBn/SmlH+BYGwK3j4H0gt4V1pBRxAU0Z7b2MIfAfg1gBUm/sLQcbA+8jzEdotYw1Wtt++4viIEvuebo8wu31oIpndAR2srGiMV943FjNGIUSK7EfB/ofc/FncUjCHk4lxWIW5YhN0qLsolek4U44W4+spqmaIB0rcDw8/2zrrwY/IKkNDKZr7+/XqXUfTjAEDRjP/cftJaH1bFhTSGpcQ2cDT+35E78jXR/3at/EynOzJ6afVqUn6mX7amn4Y/njsXxf6QdC3n/7g76xuzf+ud7dChw/8vo1OAO3To8FUg8Ic/REpxoaLffvf97m+s6E3W3+x0ECxbxSIYXlaXKap/yo3iSx5agDmJDC+ze61IVAeizRMdOv21mlwavdTwy16VHUvfDtK3g92jD4cikuCBX6q12tS5+mjrZa1x9U/37QlVR8pq71pXve9iFcFL/5sHtnVRzKr1/F5/VuI6OtdoDbYK+8YiwkWBQhlqi/3MCVcMEzxHogvuXZ07FwF8Y1XIZfsndcognOh9P19WVqAC0ldJmI9oyOeFFHzH1Cfpy0q3lIjnkwxZT2Q90WsGekgIgEM8kOU1jzbbpRSCUT/eyAoB5lhxPmI9YZPghTjetTeEX/z0j0CEd83DFR+u+HAlH3xSAR0yGsFrmrNaKrw0WsGrjj+4S3P1rf2mwX5bl1niO/vHIiDW8ftWnLTK3btsrL9dGcuDGFqnTizbPCbprcSdZTe2ip5iv+3ajux7Ibdt1fDYtfgk3/FDnfoW+5e7Pft9B3+s+QCHtWjPR9H8HUnJfvX2gvMQB/jIs3dcx/h3cvO9FOy3YXwuINivVmV8OIBSDa4ulZfW3chYijBLb85lpedMP3wg+XDnkYL0Ja/YL+AfmEMHuH1jMVQRItW7JdlYhp/pkHuNywmtPNxbShcev2V3nF527FYLSLK17Jfr4lv95RVaOjbegQf7v1VC6fSzwe8elZ+CmNPQnIbj3mZU/FgaY6j/u7XzemX2U7Of9khWqKlxZxN9c33g/N2hQ4cOPxedAtyhQ4evgtfffBP+r++v/+diWCpPvIU7nbx7q8SNsUeynYsPKDfzX2zyyI7nvt3LDH8XP3qgWlpbHprmGYDyMqOXub0sfvD2ZeRQ7JJajpm6+CYndOayvNIzzoZ+ovzMfrmW0A5+2qesPGiO/Vmb5sEApoKCreISOJbm9Gg9rd3A21Jhbof4apwV3XtOv3i3J1uLn4ZFOlBhtRtPrSMGq56KUboSle8Ej2G0nhDaBEv6DjHSr1oa+MGu56ZzE5c8NME1xseCYIH3bAtIZBVW2cLAXBVBsDRHq2I5NbI0FxGwphJt1HjB4KW8j5UT4ihwiRHlqMgnsSVcquEl4YjQIRbworhKNOrJLlEgrNU4EW9iozyASJyF6rkSe2ojMGYDgzu51jGxYBHhBuJVd1QvGdznY1r9pDGI7nejKHc9I27ElCovszVS+8aiyP564pm3Dmgd+Mpc6spn2c3qIE9s6/SRiqf2J1nuvuEpyxHzFWdAU/stxl/L2vkXfN+upEmpfJJD6otmv4sa+22Fv6prv38sxN4GSu4WnnnB2FxyMWZ9zVrT4AhX9+KaD9/Lt8AVH++42eLaKvQMd7VzGytuh5U3D8W5OyK5Vnf/gfUHRfTrfLXQBzwu4rU4smB0zcdbbpYyHrPUTDjB09mAZwxBUvHWuQvUUwHngWX4aWXqrDeUn0VvTvkRqL0IXD+oAJz+vqrq1SehrVcPCWGkDs78mXD6mdlPw7tPpizHvwp3WzvZWmY/BUYqXomrB6dnRI6KVtl1uRNAi8Ph90OdeVj3t78JV7tOrenQocNfEx0B7tChw9fCXcZv4lnsWucXfZk6u58wBMllJb3bbIopIMrNTG9nR3Ya2PYRQltQ093cBVXoJ7VAwc2tPY/9LBzy4eLcI1UoP/UuwkhrKYfHz+FceKvas/lNfUO4V/ShDzrr7cPB7H8ACBuOX8OmrPC86tb+Ylq7vMuwyga8D7cjhwWL/UopEekZSZ7JyMre40aS9YmhP5RVjLNg4EsCZMZ67Q3BBNLQJDQ158gjy5xEElnGOJbYVO4ORLk7ic2qnTR3LaMi201+KBLiaBNohFS5a0ahOChmXIRi64WRWDnAoHxSkThjFSK4aguIixIkBHAIHMw+ORKb5u1PKTJiwdgljPFjAq98PC6pRwLLGC8UZ6yWAa7ulk5g0yANRziwgBoWc/rjAbEPObBWI1e1jFBPW0QDd7vxSyO2VTi1PtZV0ooPv7TuKNMOT62PhzTpWP0Na+dPpv89VUkLPskNH47sr7PfFqKmP/Afjx3qwQtS3wknlh3ldiwX3KduYRgfYl/LB2DJuc6HpBn7mCWMt7h9YzHaeasiOnT7CiS0VCnJDlU8tT66uTFVH2M8hzAmsej/+N4Zwzf/baa+BM4aBmNWoVwADlGIq+OZj1kGuAtGjUb2rrz7VrNZcc2Gn5lepnOSH4XTT2tW0I0R2Hu9Vn5WDUvt85+HdnTvOYMsCW0CYcTRJ3UEn1EqvP0M9nsdarG3io49tT6S3XhGFOUeYKvoylw6KmI30QNYx692trZ9GeZb657+C+HO7RN/Rl64Dh06dPhsdAS4Q4cOXws7uP277Hf/oXqeZEt54f17f57m75FvXEnOzPWccVXS8DNbqTSwWqxMf00Dy4Y0tOyeCSoNDl5cT5Dekp8cL3qMbBfM6t7j3uv/ZlGVMc+j/nmEYvuHs0Ylej7vwcsaj/kDANp79A6+gR9BhA0oxRYu4SUAjzVNuO0efOyqghoBPlJK75K61lTO7Ju1CcA5qwjXzUNlKsnlxrh9r65eGbP30h+yHLKOxY1xXZJYvgWGrENlX5w9vHt8Xk2U89A0z+L00TU8M350/XEElLxXlJtpqffc3PjGqrzTta4LopiwWTLQ3r8pLkKobH00VEUUaE8lQ1nrk0Jx3qoXv+KnIbMHbu7leuRsuGS4DHnEjPPVcBiL21MzE7cnH97BH6y/0W2JIAqEpbqIxVaKEXNXepFygB9lUq2ovK9yw9bv83EOjKNiW4XIMX9LqeTu4sILI+TsZmQuiwy0B0+zbrK7Eu/KXNrHIk61+PBRkb/FaqasRuoLuO4ng11VOKS+Ov6zbj8Ym1Tev1GtV/Na/rDX8Kbc/0cAuWxwYytMhvME8DyA8MzLXAOX/w97fxYry7Led2K/yCkys+ZhDXs+8zm8V7wkL0kNpCSQoi7ZVrdkQ5JFCbbl8cU20A8GjG4bBtptGzZheICHfmjALbcst4Ru2VA3ZMktStRAypIoiU3ycrj3TPecPa+p5qrMjJw+P2Rlrao17LMvyUPqof4orF2VGRkRmRFVO/7xfd//C8gqX+gO4xh9l9OEoEqS1AGhE6Hb9tr2vrP1sMV+q42MhjVtiOmomUe8yQIVkl98PK32DPzYRIRW7fnsYwRiPAEfE+GB+JgIDSyKDlKZf9ft+cMkufDTVdML8zRyEMJ+nQL3Cv+s58MtMcAKKGPXqv6GuQryilSnK5cqg1EKg1t+H39L9uBtNWmvmW8fCY7ibW5cjIJi5XoP1yr0652akkqMgNq9oprAmfhN0iUeb50+y4+6yhzIyie+b06em8sUU3vssccev33sCfAee+zxZeGnf+ZnPnv+N9q6cN5c/PR09tf/9xz8iDR7y1O5F6h5ih/JWtHK7ia2Mtl3uusQ34oMb9l1rTB3Fdeo73XiuyGxV05t+UtzQyrgmyCgirFv95Mdy/CaYEql3rTGBbAx5woH8Eitj29svIe1BLTsEp1+TWg/vLEP3EyDr5diZ63sHyThMC4jt04KCshcPL/s7ohgMUk4RlAopUA4VqdNa2roGbw2qwTH4M6FFosFLZcEYVw23aDIorVpN4sdwL+7ksQGyU8alp+jKGPH7qF0XnVxXDT6SGBtIhW3hql+tGcMEA7V2MV0ZTlVzV3LvASkuZSp8gOylszvqqciDNXJM+63IfKatt0IzYVzkg1b5lPdQgiUWQ5+uOSz9+NPPuYHqohigyvQkYvH6mEoWaAyagmlQ86uSGGx6eLlp6sceIHXvu3s1jBt0WAFtO0pcN85BTUvO68IzfXLrqfMJZPmi+fFFTQwwAq9NvneQrQ2SX26TCsC2WX6+uz3iJdXKt7+GG5H/47Wj1wqJr6Rwnqy7i4raEAIPXgOwN0ty3AVUOATTJK463f0RRtO63jgABMTa+IOfEfe8Ih9dITnqnjgxKuyu+ULTbVhZAdZS5mHzmOElWgBjzgl8IgRfJIT7llMgN93h8x3AVeWmrLLQkATQCfA9GQxofUh70ToEBOhF2Vtc95+FgIbHbtGXsZOOfIr1rrBtprdJdR2gSxdOcm5rnYI/IOkvPAvM5ajUHjDPI13CeRvzw86OIwr/2evmV9JrXTFMlxZgKv3bWXa9nSwKxHXwFRJpxsYVPIGjyO0g/WO+zRzy7fltChXoXX03373R39bPd5jjz322MWeAO+xxx5fFgQ+/V/9Yvin/6u/0X3/4Y+d9vvf9uMpJe1yuZAOUMa2si0VSLWkC+4tUSp+3qzWbXqQoDAjH2q5rJXjNvJLbrztBX0Dj5VrxW7p5pYq8RUn6jJy7P7lQYltULUvdE1FfLgP460qz6EhrLZafQwfQLRl4z2rLLBbZd4HIIHH12+kRlMxgGe19fi2gutgZh/g3A8fLTZe0Bv2e7fOyNRl5mCFyixxlEIptOMEVkqJEc8nH3CuSVO0i0G4x4vYap9JY7vVLLazKAR035ixJtq4PWvLt62uARoqXftgb9vjqUZfTaUJiOKQkSsGxZTm5V6GrGPDT+m31BLBVbFHkuBqGp6sDtXUMXQWU6fIJEFBnycX6APGMwKsz05APN4zny603VJzLVnVjWMmM5rrRkTaagK9Azl9rB6CDK35RVkT26uk95odeHukrmyR7I6RqPVWSuWu7FvGU8nArp2Wb5quW9sWdaFrGx83YsA8QfuYyr15Tu9mP+faI8Df4rodxp1LX/VX4c1rs5Zr4s/BrPAnybqqDQ+a1B4QV2Lj+7Cq94bG9ZFtN+lzeHil+/jUWaw5jdGxeEBIEqF9EjY+ybK5pfU/dj9uKTOwT1aiQ8xdzgw+4EkM9Jkfc+oT6vzvn4L4OHEm4ia4mzoM/hs8AWK8ygvax6/sz0N7dlG07X5MLWqVnO+4Yacrm5W93Z/L4ztHrg7GFj0WtkWzqPeMYmUd5X5LkrH7W+C9FQO/wsM30b+vSCwcHMb2IJbIVeHaq9y3knnRbdvTKhlYRX1XeNt5pCv2m1NmlDM6x7MXq8Z8XNj7PMB77LHH7yz2ugJ77LHHl4Xv//rXV/D47/DzxR/+z/K/EI8pLHFKIyJZ6a1KV/lFGTtQ6QCL8nOE4N6qyvprRtrurmVCyzqTbbbaGDduw02nrpMT2T0tO0U3i2NnnaREAImcfBREa4maTeH62j7cr19VlG9Ddhp9dltvdzvsw/twwDpaeIOq2VBAbma/12sVwocLIHrc2koFvG5xWrmFgkccYlai+2oBKFsB7zSWmlSTDjhDaDLzMJtlaEct7F6t4Co7N1HGjhsUm46srcQCwqr0di7Z6m2AqQ5VYcbrSy7frAclxovxFrQ8kli8Q8aJODOaC/qeLC1vaScZsuZIjilF0JhUAg9j/8jR5JA5IXDXvDyanzlGBDQLIMad0wtIkbUidCgGaFiTFrvSwTvPeefzvOhUMcOXpW9buNfPoMrG7Kkk3b7w2qS4ghATktQvE4p5xSVtJoecvGaU75j2C45ecDSlmxAYgsvxfeVrrbR85QWzdbw7gJuUAAk8hyewghgitooAYMDAJ2DgKTyFX4MVPN3dZjqov39bX98OF0e8OOIFgi/rgTvmJMRU8zzEICSlf+UpV+x38/EFh1pihD7zPvMe8w6T59z7/D/MAJbwgtbT7IiXXVk7Dhxx2mEOGIIYz+An6MoX2pQaqUJz5YtH91WQqzVs3iZXPtf/BsoKsuQ8fL02rw5hxcC3Q5e9RhYcxjfmMb5yUCK3jByJXIlchLO8My/1s+xoVfSAVe09/oJDU800CDE55aYvhV58IsdSCnnJHnvsscfvHPYW4D322OPLgkAJB//JX+/43zr4/KNfAutEvAdZq1yel/ewBJEytplqu5du1mf5WFcutSiyF5UNEDPSwf2VG+ZZ5LBjQrzJ15SNmVF2rWNXjI+7p26yA+dj3w2Wm4JWkHuVmeVK2O2lnDFQm2crs3AkrAB1VR0aCFhLFo+FAIKtSvsQcpnWVMF9eAZn0IT7Qrxb/hZEj2vl6qvLX4nwfPwqbWlCPlQLUWtxqiTL7rmjj8QZc+eITw6YzWkMZTRfs3BCFk0/Sgg5gaOdarPYutbeZlGuxkXzSBmnCm0VLm2k6/MS48UyuKdeKAjEVEmAkSt24LXLgCcxikScjloWaaNhFswQUBoBx5RWy6367Esh/+S0+0N4mBn2XIb3Zk8L4y4OXRFi5aYEiXix8kA08R3OU/yIQ6DvnJIfLdbr9Vf4zLMota98LK4kQ7q5vFwacV0V35A9+NIkm0xVZ3O4y8y75pA8p5fgIxyyZnFnHFftnnJ8xAmvjRDjYyo78Ot4PgekwHbcb5Vj2SftygXgGgGCae4k6U6sL1vm3AaMQYPZCgBu1hbgrUzaO1tVIxisvaDBz7VVCWJV6DCuKWgVl6sP5eSM40yCuXhXalyg2xJUxklgIDOPJMT4xAKeRD7Z1xajT+DRV+lPkK+Qd6xqh6XNZELzIU9n0hFIamq3gVZmgb72HfwuifANHi6bt2ot7LX9CwYIR+1ogXiNa17Qr92218jtRl6sHPcgplbUK1atDd3dGIftRs6WobiMnPjMBxUcxvZWwqezolMF1jfs9V7MjE7lmR9yUjHhUXlsVDn2bSuxzmnsLcB77LHH7yz2BHiPPfb4ElHA+WNakf4X/6fUApnl3WL2mbwjpaCQxM4im8R1ExcUlspid9txdB30K6CIn22ZXm8gFdeYiaAPEnPhb9aC1+KFbyHPWzWkK4fnTffeEiUqyCywwgyCdOXsXruxG+/2oV+v3VcCah3QeF6bdoMt387q+iu0NoRwiyRsHK3jijmvWeOrbmH3doC0XuU3dhOWhmVWWjmFQokhaLozQ+iycIyItxqw8sjGHBRSesQCueVfyBAfDGjcIM9i+8Zm1/x3vR+Bs9Pueu9AqxSIxauIbibaxTTVIha9FQkuoO7Lc5RC6Kp1cuBDNW3PF5Fu2aYkhi4iqDHi0xyOlXieikswP/390/BXPInbZG0u2HUCEKjyxxoCI75SymMT+03Lni3yw5v97a/PoqszTZCbZLG45CCi6j/XRnNMuw+HcnJ9nMNaYwloM9lRHEYdcqkCLXXFN86V7XpCzFFNZWf0X0GAb/N59kk7XGwacoz409xNasN+rz4x4rLuCzAw3t30AZbIEppwZ+vgRnPuYt3jddOTBIi7fuZbmVZ+JQrNyxn9WLwZAClBhHZV3FJmITuM9MCegYSYQJIW0+qgQILflkmMZ2j8raj3ATz+DcZv0fOx49L1LId5x9Nf40ODW7maV5e2mCT15tBF0aJyY9nhcd8lqZPd0bsqfbflHa0u380CqxsuTl4OAa+RVVT2hrjiXQSHSXzmV2/sQSyxY4WXDLZyJ7nJNXpHwrr+KNUll1r0kIg/L/V9K6l+iyprcIL+nEcIuQSZBJr4TfPJC/N2LvoH7l1Ph7XHHnvs8VvHngDvscceXxbefe89H3q/n4PxaWXUKWcMzkbhg3mg5lPprPf1hSyy9SA1kwA2wkgVzXilzXab2X4MQ+jtGH7NxW5OkY0h8WZcMwIDghXkxdi3B2sD3Tql7a24xsP9LQ68gqECGEIEsqXnXGFDa2O19g7d6ACFdb/i+qp1wlup39zKhP2DS+vi8+zwDfdxA9NhVj3mSvRoWdihWygQVNeaaDvTaCO2nYpA7ipN2uN8TkOTadKobA7vTS9Ml6lwxJr93rSkN2Ptxo5zvJXxaVOsXqkn4g3UxXPuVmOUolO0wdthj0JXVQJaglJTaT5V937k4mmoZgqyFi+Hh63hOFiU/shQkB9YC3oJthajVdzrrUYLLnTHk5Wh1SJ2ksyfW7m2jvTYKB9IxT9RQYKORS9Ud91VtcUjb+PArDu5jtS9cbflxpDgrQdyGw32K86389Cq4waIrtkbdx8x29ToyvgM1Nwnvo3ldnYcjrnDub9DT68iIOuyqFzKNw1lWl3KIsWQQATP1vS1/qrDeCvL1yvupipTy19BTYMfrM/708QXFnfCjr4AJjS0JGOalTXYUHk+46ukSsy7acAjcVUSiNmwXyAWrwrofcC0JB0wWXekBxM4rrgdx/PTx+37MW6CB7SZVEmYqp5n4t+1z1/kB4jspjL6brE7eldnytb82Ew/RXcwGz+tnpekK+d1xJSDw8QeRI0wAypl7Do71LoxFeTuQeweoILcjZ0ycm8j1V4jr+zGZeRaXOZjm5e6XUmmb/V947yAkjN1B/h1/W6UdxAk28fr7bHHHr+T2P+m7LHHHl8WHj56NIRP/nM+79/74M8i4L/v+Om0Wa5a5TLEKJ1X0b8IZqTX77my0ts+IlcOMwWECQw3FtEvvGr74xVGcC0YGMrIWecfFsrYyZ4100qu5lavvDoqMa5flbpPZfW9EB7JZSoj2b3ZDR+OBYRQLsOAH8NjOIclPK1r3iCuL7mJg5aRU0Y76rIr9IyOlhgkwovwErRPjKKvFkrRsNPUWjOExkWil2WQLgcyqgKDm8zuW581g4nbzfHrfDZXR227P5ens00k8OUzJsFdSmsdACxMpWHwLkejjvKcSnMzRXxSX7InQyv3LNtk7cXi+OKseZHrucFDPBxTtmTSIepiOhKvOo/mjYM2sx7GYyFTmOBPTTDLNUstpsu8zaTL7JCTYDfu11XxtQcr1w4IMMqP1mHANwyFXAs+v3oe2foDQJ8ZkBJcKYYwpn0L+72p3p3hqKKITVxZQW84fxW9mta+omyMe8X71zXSPs2cOJUYGSMj5CnyERLvzAyE7fu73vZVRLUBWeqHOkImiNRfCgGhIxeb76ghqPYLQoysY4AvH86BNQMGMmttAqTlMn47RrsST3kne7YAHrp0BIlwJlk1nkY6WjIEX9JYvLn0qg5oMYI4Kk6rJLf9xL23DB/cxPKvP9lXDMd2uO8NxWRzElTbnvYP55tz6ReZf71GXqycYhRwyXtv6KUKMhVkIGXkxmd+ZQ2+Ukl1JDsPKoa8bQGmSkNV19fADJjV2xNiCHySeX6nkKKTLxyVvHfvC2UP9thjjz2+C+wJ8B577PEl4o/8zM8I/MpHb8RH/5U//pN4f8C1X6TdctySlYehFL3RUtpBvTa+RmmvluzWb4KdTKHbVwUPlsGDxa1dvM5jdzlwunLykV+MfcDuxelqy4/xNg48hmfChVwux5d13GP1NxISYVxffr2afk0JBkIohPAIDmpxrBWcwYodmr3hw+PbecztENClAXwSpVDQa2QJduQ1q8ocI21WmhTwJWswD0gtP0fDS9aE0Qiz663LWsQbaag0E09EROrRFRB80hnNmEuh2ljW+rpyWQ5gJW3qBz9n3bekpfXcjAedlDYGKSDGPs366bKU1GU15p7lxBXXEnBNyQhG8BLnJHOMaBaaTCo3bABaMt2+h6E9bynzBc9VZCHaVUkm/oYGD5hfKfRFlVzSYEHGdF5w+IqSv4WhDkl6TKvXDebfm2jYRFov5cDIVS2l7VI9Vp21QRbXSDArWi8jJ66dnyOIwId7EFzjeH4t83YFzS296AqNul1/6+5XcA6T9begOpjQBDQrIMEf064uyMS/bFs4sGaeMsfqdOuu1rUGmB7zQEyLEYL3yRQ4fg9VSzIFsxx42W4luAKaxGxR+Wo7ICv9pPSq3T2J7Hzke2G1oSaXr1cw4Mti9bGdH7rLG9n94RIU/kH8bH53fNF9nSGuXnaYucO4WLnlzg/dTUMDy2/1r7g9V7AbGdfI9pYO3xqfZW+syrUg1ojOZiq+4OhEjqbizei+jOysfJ1dnj322GOP7wJ7ArzHHnt8ifjer3+9CT/2t/9jL5uf/OTRPyj/uH3HGSZRUM56jHbWdtsLPthde91Ij7fQ3SwKbzDh5iMf0MNk6/C1VfyVD9c4cBk5bK/hNtR3y1J0iRCGsIKVEMnaY3OzQq1kb9eu0VscePOKYQxBzYED8AVf1hmD79fG5FXtSnpWZximLl/VGEtlFk5XOyF81xsUiNBKoSylQCns3DzwzZzWytVJWwO2KYN0OWBsxBPwSEPmqiLAPSGpby+G2U7Vrr8Whe6ppbdrWa2IsE/al4v6SVyOYCzuZjikfuZTmploQOrHnjStpGXN7gb9i1mQjIhZR7/6hGauyZ/JnR7PfZZ2MC/JJuiT1uH60UdIjP8kb51lvXlShbAaCWI00N9wV8FTibZ2XZGvso71fMjKHUoQy/Xl+00X3jg8IEifmSDm5g2ercKvTYYj9HMOn3OY4FcCvBsZ3lcjwTeENxKnQLJONYgCghOXbryr3Bvu1tXY/Ti5xf/5GDoA0kCGSKO+ywCSa9yYS0uyQIIPMqNvJBhLBxGDX4WbLmrzY0uZhjV1VRyLHkm7+jqn+AZfhEg0go/RRB/zRjWQw4pjR4hPpq3MU511ZulgSvslB2NagaSp+JF4CK5KluVay62MHSvIvouh2sE2Jb5ewZUfLhApY7eM3TTWwWH8mpOkWLnZeXCZ32hTfne4l7/ZTx+3t4+kS2dTOD4LNtQ3XTmViXj1eSt90pLYKUaXEtxnRWeUH1e7RWfcmdMzBFosoKvSqPQ+L97JSo/itZUO9thjjz1eA3sCvMcee3yJKCtCd86HpfNp8WZcup92/li4mDTLSEQapFYncf3MDbJrizPZ+svl8k5uP3sLssiJnzYBPUzcML+p+LWsSNz8UQV5eKMx+Ypra5WC96C294Zb5x5t6VptSDvCGJ7Vr4v6wmDr76YkwgCoY4M3obVntV/oCLjiIy2qXnYf2LMb+g+AVyRKMVUdo0KlCJ3pAdMl7aRp2aa0TeYY0ZI1WWrSFgugH8zWNvxxzXuP2HKDFTfIneM1swms+aRsTormzkALsbgrWj5pR5brQ5f7C2w2RCoO3KUKA6bNoiOxgKHhL0q9KNdzorMWphZBz0xl151wr9F13YvSZ70RsPiDAYN1N1ydAcpbaMk6zI847zEHWjLdcOBF3rko2jfuj9y1z1pqR9lrW9U5ulH+d81kXosGj6RdGYFfxYF3L3lNPjyiXTHh5xzG+NeJ7RVMaL1cb+fs4Eq+XyDzrcy3NpfxtA7WrXDdq/n2O1j3ZDPPV3UN1T7R5IYLM60MzQkhwlhaNWskRmelPy86m3vz602NCF0Fwwu4JJ6sh29MOyTrEgH+f/CzAPeRAEIy343btmNEyyIW50SGsXiP5EkgJsYzN3mnr5OHXf+BEXZsvJtp/+qHtPuz4w9jr7H1Q6pUuqx27lR8GgB1+qIbH/D6tck8XKwciddxExK7y2/1JXYldotRsPxWn50UxF80fjXSlZOdB/FZsPxWP31SfZtkLvpZfjTKj1foEzn6uHj/29kDs2q/p2Z35MLFN5n96PjOzW3tsccee/yWsCfAe+yxx5eIH//GN4pKrenz88fzO8/L4W8cvR3E46Ni3CmWHeucUiw/t/x8Z6F0hd7uHH3VgWvHL+mKudDmwrfCfM2Bry9C5RUfSVdO9qzJlo7LTTXsHqlMXpXn54FcduZGFhMKQ6nlsurq5ZbYyEB4JJdNbHAOj2FZt9LfMohFDrW9i3W/dLpbu5FGosJjTjSRshh440Rsl9hJ109DL4yAR9yUmZb0Di/baqn8gk7dkAYEfbnyrZyfrXbSsxbAXedFz1rklZvx1uK4IQstaRX3W4/+1hCI5KLjoi3CVC7lhU7p+pJ7rPTM6LnJPUuAz5FqA2Ja9cW0mHbl+Vi3rP9b9oxHM2kluCvdQpABdBGIO85KtxLcDvMET0tyzBmQ1PbbxaUh9yoHXhQdoKXMJQ1eE5jdKX1tuq7KzqrsZKV/49ltNMTMpDOTzquL3YAvJLU1/BvMqTdc7ZPedG16vZFgmqxr8JFgx+FXxshqt+bglg7OYFZnBv4UXtTO9hOIYYVcIAkCEq43nuKuz7ZnSY1Ugkg0cCl/JZKUurLY3+XskBMRFtI9l63UXiJG9FTC45PPbHjg0kuhQ+VGXWlcJzQtvCod1BlHMV6KTtCbGx5aM0QksiW2LT8D8cLsmv8zVx/hziPbFL6E18i9RuaFmT9I/GFiBbkV5FsUVwHpOvh/XafdeLWGH6yZrXgPF1UMcDEKyshpvDEvI2drfL6bibh1WxtDcbp0KjpdXFz+CrkqadjT++6p8a25Mr8mb1xk9jJ5jX2fPfbYY4/vBnsCvMcee3y5+Kmf+Zm2y9d+9ZeHn708jBeP6a8ax7qYtMuLVrnsWUurk5SxjQDbK0Iu/95oMLn8sPPvNX6yffYKx359DixAunJWH3ZXH3VvbmIMz+FD4UNZf6xMXlV0Ygjvw/uCv9X/7ejLirOdC6vdem8jLT4MhIubHLCpsyXdRCk2DrohZiedrPBCBrqMjAqVUkph2/TC0uBEXjPXlmnppKWbTBA0uYtpMr/Di6YfIRAIsWCudsbqbHNCEAK1cJTZWuVzwChFC9KXi8qb9HK86vocEldVIcrGlQTkjEEoeUpgpAHk2s09tRz62R0XkBh8Tu8dnNH1JQcClV28RVsWPeKOLD2W66fkA/izXLM85CVCh7km1hIHmAgPqR7aNe/6+uNCvA2nOrC3In7l2vhtjciq6J4XnfOiMy86mfirsrsququie8WDusIKXb0M/o3D+gpc2ea4TmpDMXfl7HVS/gI9bvAgqNSPN3CNBNP88qvsX9upuQ0Prh2J1g2QwBkCopGK964QYMg6BW6f6ssVt22BKS3glDtGgql0EKZ0QFwVH1izDZn0lXFVAjKlLUKKn6A7zBZ0U/Er8apDzhOCU1zgScao+q3q4iRZ8yTOtZWgJ9IQEUR8MT1ZiKBlJ83Y0JqrIK9eTj+xgi8mojdjiwlbQe70k3TlWEFm+Vk+0sm5f2mYrbyGLXszV+Iz/8aQ3SuoxKsqd+UqAZI9iLPzoFg5leTVTXT99XZZdltRfmYFuT2It69oYNLSn5d6UFJm5Utp/DsfvPu6D2ePPfbY4/WwJ8B77LHHl4vf9/Wvl3DwFm+fTLLTz1vJbKbfKiL9oHxBKVKWDVJL51f55w2RwOwura6w0Nu0hXYYsrnw17mFeb112quY9u6Hfi3tMwQR+jCEg4r3bvt/1gvEGB5zlXS8X5e5/xo0OIAQDtmJCWzAo2vZlTZUrY5FDMV0ZXZlyZqgA0mVpQyhUX5R0A4NYMQDkqaVewrQ5AmORzKUcYN5w1roXq3+O6nDMutqJanoAq5Kd/ojIHRYHDByxbiSNFgITKVRDWRctOKidYUDA1rSild2WEQ4z+QONdNsXCT+vHC8TD6HOTImXC605JpMiwmH7c++RbronTJI0I4RAV4gL2GCk2ROIoBPqiXusgQ6MjvmvM/cVfFd5/yuc7Y9EBI72dNmMa7opWye7cb5eWvgrg2ecF6sE/cuRKeiz4t29UrFv5ED3zCct5COM47OODrjuPp4q4bWlTqvKC3d8tLXMiH1WHVlcbnBcUX7ijr10TYCGO4eOQOBKlvaAUgt+RZCB+kgPvIVuLt1SQj3axm8IUDc9efHocApd2LcGf2pdKZ0Kq3sCC8Tf5QfbZ484KlkPceq3wd8H+NJ0pKJR+yRALbYj8yy8bzqHKoBU3iBCIujIGrbibhtJjFeVU2M9khaTENJq5qTem5s6KsVZF6YbT32V75u/KkSSc41Io13J/nYj542K8vtuqxSXpjDF6U6vwkVha7clcvIzc6D9EmrUrR6Hf5MfauvJsN2I0ufXA7EvNSZ+Lq0zoqjs6IDnFnLx8t7IJSvvdmzxx577PF62BPgPfbY48vFW++99zyj3eAP6g9/6jvnD6NvTYJG9ti/t3ghIqGae5JYnWSLJ2xbNeXy+A0c+Da8oswVYilXz17ntzdzYFkXToRxbXGtVvnn8CHEQiSXRPRqFg/BFw7gAp7WaY2ewYf1+Q+5Yfl4fT0ZQB8O63PvC/fluot1+Gix8dyuMvoccuJds/hF6Of54Ux1gATPqFZHZ1pyIGnW/1kIugqjFSp64GCaKkPDIXQrc9zl8lfpHKSBcUhuEAwT3I2hTGRVuTcLglSse5oPQeJi7fYciDF4mWiBmTQNXouZx2o9FC0dt2yAIyqHZcfIkBOBOc1YumGHgXdRSH7KYOW1CJABRAiQ4J/nmznWlgmgiT1ZsyCX5JLZCsXYz0d+unLKlb3Fgb1E9KroXnmw23bdVbk+e8852zjpntdO1Af2DMRVyY0MYhOkustWLx/meltBZh1mHWYVE77LmSGYvzru9notu68e8+rvdXYWi1fl/r2sYBsJjHYJcFLv+zTqsjPkEDmEQzhAOsgh0qlfVRkNbr2zU6XF7q+t9xIS3/XjYz/q2JlWCLG4M+lPpEWdJ3lRebrLeg/onnN2YM0PrJnL2vzYYQa0mLZkAlLFft+X8Q/Ir/+Bp79UJE7x8RPg0TG9l4iPRAAizBnMaBuCSj5NSxKLh7CQzubp9e1TVyXNev6oIFd+roL86rN61ZjcTIZVkGfPm+nS3iktAqQrx2sUWNZuPbe8bsJGv8pqXJXQ+25wc2NWmIGsPm9VKZeqgkYVh9bJvWxcxo6HdcedIRCVt9a9xx577PFbwp4A77HHHl8uHj56lMB0CX2+551PigvnKY2Tt7syWz0snkkpgZo3MP6dxQ1xe5eQq+8vObLcVOY24nq92G+ZAwNViK+sSW8Vu9uABlxAjy/I+9qv434rB9EQBBq17euypZt5DiN4DM/gDJrw6EqJS0SPW9dzkFxBlRi2x7wjU19FMa5S0vQz38oM7lJ6QGUBFlhKp00MDBi9wbnqJP6dCDbRv7hBUf2VxAFW4sXlmsS2ZeGLQaTNslErWlX9bTD361TADbUQaFqLvNTjojnNB9N8ALRkAVQc+FhmB8xFSNraMZltSseIGCjWMcn6xKQ0euYiwRF48q//qU++FS/ozqSVoLM7Lh701+JhTpk1z7O2jCqhrOoJBqSe1MmshGHt4bzxNV1z4HoRvyg9Qa4Mu6eMq+INgVmV3Uz8mk5Li+TAmlXxww1r2rCmVwf9dpayIcPzy4RgVG7SWuKOzDoy8yTWEmtJbri+xpTuKXdOuTO76jywRmXi99lx6w3I7jDusth0zbnmA098zfwLXKx9mDc3h19bejvAWvl5raae1O+BCELkLtKv0yCF5Pe96NCO2nZVl6EJTGhdaxXgwJ7fc86A86LtbdnqPYkRCSQJMAGmLZMfkF+/z2cCnp4m2lQbGPcnSAPGEBL7eqVbEwk9iU9kiKCJDbraLDDoaBPTLizy9qLUtwTzSv33NYhpTYa9MPOHSTH2rSD3wrw6sl3Qa+RWWGxVJiDXRLC2q721C8nZFyuEfzeQivqmK8erEiY9abWVebq4N8rvPMnfSFetjjKz0n267BVT/fU7b/2Otr7HHnvssSfAe+yxx5eP/+nP/ux4Qdkne+j+8L3PPoi/eVEMloT9Yn5YTvPSW5VuMdFusCWFdblI3BZDuqzTDfMbFohXmOrrGlh+exx4AAiNOt3REO5vm3xfweorqSqhL/jQgwYs4ewmznClijGcQwMG8AHcvy6XtV7Aho8W2xbgCostvtRnXpkWfUyE97w8OOFOqsITNVjYrazZ3NRVdaEp0yazOUE1RA0WnkqVl1Wkt0IW2f5xZPm50vmGpQE+aYOFR9pm6YlJ5TJ2dCWtFa0Wa2daRxmEZdFclk2QVemtxBuXwwVNhBTtS2qzLCVNaZw3ByI4SabnhpfIgCrnKxNa8wXCPXk2pAjKWdsdZgQZYYKb+xYjZAQDpAsQtx3gkBOftJoFR5wAx+s0U2LKdYfDBwtvS0/ICrJitLbxmlKP86Os9DfTylUxMMqPRvlRQ009lTzPDzPx7zln95yzheik1NdZ0dagr49HoqfSWdxky43RFQ2e0x3RfsHhnG6lGm3wDb5mNxHOFr2J0NUL6KydGa7X7/VYVP7PAVn1AjSLyvwr4BhxktKfJjstBMiVxEk+ciUHUocdyeRN0TE8gQkIVKJZVbasjeD0A/K3vfmDy/TRhuZL+tXVSR39m4p/mYxKZF60n2cH21+ovsyBAHPESZfZ+/LxI560mXhE95Mz5zuZ5WWj//u5QPoBHCGQ9dx4aFcjZgiOuEjEQ9BiEtGp+FrM5pcjFX8h3h37bGjNL29PBJFaAP825/PrQllr2P2k05/avdgKMmeQHD0cu/eW/iBGBJTXKJx+3PFKr5FVr/CNxfGbI6uRe418a+reznqvD8cXcfNNzd5rSG1VqIzM7kE8F62CfBY1fCvxhuNB6+lQZi9eDsrIfu/h3S+uaI899tjju8GeAO+xxx5fOt58772PvkWZEEn74MEqP1N+M/t2+Uea+ahTnKWFi4ilsyyyL9d8FXYowTYRlSxyaj3n6wz26kXXWOsXMuPbOfBzeL7LyP06WBHh4LaaX0mD11Vtnb5ZNGiHtXAA99lR1boJ+YW/WUbfc06rgyMuo+/GtDf8R0v0sHic2PoFB4BXpJ5vGRyDmxIu6Qos6WryFrEmH3BRUARq3suSLLZhM3wiiWW1kzUJV8ZVpnKXXUmrIYsGC7dy6BXZZPSdSXMhzZoeALISD+HQHtd3Lwhn9Nuy1KQIHpkIjgEQAwnig0flfyoJzqJ0jPiL8ved/5y80enPfrEvL1tMjelnLUveggEyIuno8YMw95RtpAoGfsQThFOOAU+SHvNxfrQoK8ElUUEOssmJFT1prnsIC9EL8RZFe5wfjfKjjfNz254tRI/yo3nePrBmz7PDedEB7jlnvmUqlems9K95UG84sISYLjMtyfaIL+gt6AWYalg3I1u9f8Fh9ar48A2JlIS+zN+Uz9+Uz4/k5RWS05PFHc7vcP4GjzuMq6MxToxzxIujrdRHbiJOUlbKz5fY5OLarnZSC5UPrnKqS8fuE+QEmSAzpFff7iYTUuX8fB80mbY21Z9w5wX9mi0KSIQHMqUT4SHiEiel3jxREQIxj+RxiwnQZYbgSTyhdcrxqRyfyXHzLJXnNP7Gk0pM+SRDgLeQDrYRwJcMYSrtqlmPOMJzSZpMuzKX+qt3xz6v1LZamBYGwQry8N2pdSlKfzuzvDwviLhhFr47aQeryqSs/Fz52VKrrUvFvb9oKTNDA85BDOTnwclng+TMr6J5b27lWnuv6tjWGf8gRtbBw/5B7Axjr5H7B5dBFq+mxNl5sPxWL33SasdldN4DaYj5vuKFZAUIsneB3mOPPX6H8YU/gnvssccev108evToBF78Et4fVEq8cth7tHx6fvjQP10t7zcDZzaJepu1XZa4awFTEZRCcBuZFRRmXFE95QZ5FjvB/aXyc2saAGbk60FSxk4WOYhay58itRDqLm48LDepxeyUFFDE0IcEJpWHc53RtHflMm5uuuJ1r5KlqZs8gxUMr9l1Kz2hvnD/WhObBert1T/PDt/w1lo+C7otptuXj2lr4knR9SWNlQtMaBz6q7E4BrslJLgiXahCH4O7vHzBHYQ2y5k38I+j5OW6u2trcHU3QsNeOmsN59TDZOgq9Net8waJiCgOGKd4iXgoQNLSQ8SzTEVm+tYqsOZNFk2hUEVblk21sER7atJczERQIAbVQQxqum7c/ixz7pC33c/8PwTJyQlzOo7od7x/GZznfAdm4OPPjD8DSLo6aduHcnKmjo+5QDhTxzHeWNota9a3ksfZQ0ASJ1066apJLRpURg74dj+phmCdNkkU0umrRKm1ONb6eMmBPTsvOgs5rJyfD+x5Uuq1X/Q211Dr0N8KBn05agKgSQBPfYGG84h2iA6uSH8DqBFtQTrbGXVrjGkFoo95eeV4b9dFwTU3sV8g3k3/WyGqZ3kMjbUj9A5OYAlNWMKDLeNwA/rQR7YsgplvVU/rc3m4OXhaC4ABBj8SD1iV3bX8lQDcdc5cFXeZAm/IY2o2F5D6mK6sfPKBueg8mSDYb66/VV8dQIgEZNrKPZWIjnE9YmhX23CGoMeicsDWmGOSEw420eOhNaudvFloQbC7sRYxF9sbE/XwX/0irz+7d5cIC7yqpIhqWWYpGsTuxV7kYFsIy9Apz1wgPw82jHet8LxyqvevwYS3cZ2cK6+RpSs3OQvCN9Zpk4AqgXByFjS+Z1yMgmSrAzdiE00wonHvjaddNfue8sMg97KVRSmP7r6Oitsee+yxx3eBvQV4jz32+N3Av/OzPxvPCcwyKsvFu/f873w69g6mQUihpJCePyljG5GNEdj1M0D3Ez1IqkTBbpDrgQFx+okb5AiSOHYvsYLcDfM1+4XbjaFfZPW97gi9uWhSByLGMAIgkDpt6k3V1gaoWxu68eRlhfW5aLfYGB5fbeZWy0yNdOWUsXvlbCjm0pC4Li99Zpp4QssrDPAZj2aqbzW06ynggg4wJ5jXpHxBV4Q7vGyzalsjpXPdW1M1y8+VrjkwZOU6GLJyE3UxGddTAc8dSTzWMcB5qVfiAZOimYnuqaVvzRFSvBaLAxl9L7/ekmVEaRuxTcYSWbCWNEqQGAwypdLeckz2wZO/mR76J//P7HjhfMIbC/qZbxEgPmjkE/gUEvypAWYMYnG9iuYJwCOeHKqzcb7OEKv8LHy48IfJ5h7SlVNGzjrWessWtxBvnB8ti+6ovhZYuz0jCItSL0rdsCZtewa7ItL1IwowgZhDTnf2LADwiL3avfmunN2Vs+rN9hwIMQPmAaZ1A8sVkARtbk45jY9J1u4Nl1V2dnmtwFXP5xtfL5DJxv6KrK6x3yoN0jG8A8fwAN6BIdJAHiDvIO8gAZubyH0v81RV+xs8qbyyTzmO8QzBCzmcS3cmnUz8z7OH19lv9fFYznzSLosP+PRtRl+Tb32v/OoDPnkw+7Q9ndjNTB7yS/8C4OExXRs5XideEqj8nI0EHVkHh2viBC/AgHgSI/S2JLKzUofWNLSmzTrq+wouvVq4/vhq34q1qwUIrSqjmNDE1C4m0hvOQLAt98EcSJdOZaFFSJeOFWbbNPh1MaqN+fG2dr1U0tP+YXyF/SKs2e9Z4NV+1xsH6W037MpWvPl4VnTyMnhgTPD8M4D8t6O/tccee+xxM/YW4D322ON3Az/+jW/8nQTrc3n+3h0R+ejuXzh+9uzZnR+5m382cjpzXMvLSCr7rbhB7vRSRxmUUkGBApR7dymJowfrCtdBraKUn7t3l5K4WdR0w/wyyxGsTZDXTb4C6hV22t3LgV7FToWeAlhVJt9N5a8wNd9yar1SVTtduEJuVxCqTVkmtdvzDSpFN92LQCWE08iTcz8MsuqJrcpuw5pGaOh0YdseqEnm9NpMpCgtV0kpc/pBmbR6i5MTr8UywakqbrGCxlwCTZ6SNmQBR11rMWY9PGVsO+3a6qUul7BDxgIiUvk/VyO7bbteSAMEpRxlNmb5SdE4dMaIdNTKE5PgaqXn0vBV5tWZZqSJihAflSALFIiHipEANUW6qBN+5OD8I3hf/slv8icfy0FDlnfNMxUiRWWoRkHuuwhtNUpwE3FRHHJyJsex8jSJVsmijiZVfk7kbj/7dOU49fzcPr4Qb1FsB7lumYjrm/88ffSG9/j6OFZuz1Sa2180Z6vRvMuZR3yXHQ5MJY6124fXQYL2STY1ydbfL7zykiYn4EMA8TXXhjEEENRy6EADAqQq4IOGIdxZaz5Xf3Pfy3wr15eb+DOGMe7nPET4rFaEG9MGXJUMrfnFOvvR+g5CMQecApr4EU87stQs4CXgGPGnuXOS4cPPw4/hgsBX7kETCdcuA3ErTNACHvEZh5sNtA7zkzrLUyUhHuKJEOO5m0EUtDKL0gPsbuxGdvXDlUX2ztOd1D8+PrVfDPHTZvBgqfz8jnORoav537TnTaWWRXs+cBelVqrAtoF0afuHazf08I2FCvLVt3q7Y7g1D26bEhsls+rvYOek18itMKu2fsrIBexB7BwAWGHuH8b24PJHZmsrDheyp+2KpQMsHWcYf+A8fagef+/5r/1T548j8jNff+eWPu2xxx57/NaxJ8B77LHH7xL+tb/0s7/6H/2kvCOZ0h++//af+ht/heFffJmHobPwrER5mavdLLUQcY9XWAqlxDiXi35B+TmJo3Tu9JOKHRUTjVJ2LymTrV+z21jn9okYgs3KcnP+NRyhKw78ERxAb4sDJ3Ch1tlZtq9F1jTv5v5w6bP9fs0ZNmaxcyGCIUSK8LrM1U21rXHZnBVk/pAyci1QQV7ZwTYcGDpdZhVxMvhVFVHhScmMga/iiXSNbi2I5rQqBWYRLmp7uSYzkniYAdEzaVptowUz8YCQrGOPcvEy8VzStixTPGq3Z4EMnYnXYLF5PpnoRHmbgWiodCVez15OylZWeo5tPDGuMj7pUC5eqOMDmb07/nQ1cDLtuiaTDoXvOp9nKqjZbw/1GNGoU6TBy4NWAT+XfH/WKpeq5/IZj2p/6Qh1F9HEbcefF7lvHeqTM44R5qoXV3K+CneLlgN2P/YhqaKslQLK2LGD7PL5X1LWXfJ6A5WVz9NHQEuZgXuyOXeD07LszqVrM6sqfz3N1VVszRcfo28q32Phk2zn/r3D+HK34NYvGiQ1m53AxZrT0qsTd2196aT6+DEAD0Ag4VKjTda3tLkk972442T6Ms7hlDtALO7ZluczcM4RkJb+i3zjQystZY6t8yZTTXKHs0c81RJ1WAKGViOZCzRP4yrWQJ4D0OY7/yk2XOR0H1Bx2Mn9EGjLaEoAdJhDO8EDPIl9lcbiBaRVcuAWc494SXetTS0si7besgC7d5fZx92rD3QK3R1zK6CHxu6tt5Yy0aE1C21QZOJnpTcdt51BKsZDqbaV+io5feSoIJfY8Q8oV255fv1H5HIeeGHlFH2NEu/mbfbsPC0uf2/TleMMUWEOlOdBFXJcGYRVkNnBjgm3rcxcLjeD3Adzid0ycion6nebLz5QHx1yoptPf/PbGvYW4D322ONLwZ4A77HHHr9LePO99/7zn+PO11a/2Lozijsf3f0f3j3/e58Ofuo982uR05q6LeeotBYhliUiSpQkds0c1CYeuFqtKV2ZfzEjH9BgRhrk0gt6OxI4USS7YbrV6vwhxFxVBbq6pq8p7qbYRDivDbPPYSU0FPcq+62wUpzDoyu8+hWmYNbOlCgU+BBuEeAKPiBE0N8yCH+B+W5dKF05FW+2wryMXDvIqRPAuCqphK8C/IosjWkL+CRGgkaR4pAQTBX95sxYnpSi8VzSFC/FazAHDK7AgPFSmnPVmNKwOknQTsQ4kbgdxFHmgXo8YTCn0WZ12TVwMWvrr6DW2wSVIJZSirz0PNIV3qRogqTiBYIo2rJsqbnAQC6O1Yvcsxovc0eyxNf+mbH9DJApqos0UAbpwRzaqCV/6Nd/6W/CV19Mf/ngIBJnpDsHPEODTx64+LhkTlLGHRtIaQIPeVLFTCfox/Kg3rOoZuEVk7sAybkf+pkK850RegUNvjrZNvOhaqKSd+61mFyrrq7n5n2PG3AjJV4La93Clq+wX0Cz3LECC/3Hu/M1gWdbH3us/cwrxLUReCMEvX11DN36u/MS7qy7yAAmiE/e9RaHbus08yHuOkvdSvAr6ltRTeBzHgEL6a7Qq7J7UbTrUas8n5MBZx1mR5wEkm7urhrSilcvjgL/NHfIOIE7vIxcu3KubvGORo5A8OdF3LINTYFUAoPvk/qkU5oWti+JTzKh1WOeYGLxAI8kwIvxgKY1E2jiLcUDmirlnUn0SXdnOLt1WESlfoDSwwRBYlv5OeBSeeCrUMxM/PNl3+4l2fOOFeaPWtO5tM7KdidcTi66yQ28dwe7TtFbfVi/VZc/aIpt9luhjFwi1x7EzjCu+nYj2sr4VjKvvSEkdjvhktAQ4h+exOe9j7lzFra/an/8fReffMd6iMjX37n36p7vsccee/wWsI8B3mOPPX6X8OjRo3/tL/1s/3yRF24D8/P3v1qOXv7RaRIuszSyyiUUpdWOrU5SRbs1/ahOonPJOiw/3yxngeD+0g1zM/Jf0e5aiecFTOpo3hga8ASC676c17Wa6+aqawN4CA04hz40YAUfbRb0wrXr68Ov9hsVRBjXdVKnAq7koP3KsLxV+S3tXG81XdnpyrbCzNpKE1ql4anej2ibNckG4YUcJvhFXo5VZ6Taz9XxmK4eVllV1UKa1VWa9R6EJvMwTRaBmnet1WaHomstq5IT+j25uCMvEXEl2e63u5Uqpgqa9kn9KppRanOrAKxK1xXlSmLEq8OrBUFS7M8zAf2RkRhOkFOYIUCMTKEFn8NHyARlLds/PfgYaTL7gBe+N4s7mhPiY02HvG3lvuuY0p8VjpFK3wg4kos2kw6zRzzdeeabNF1ATSHCBwsVrCdtMfazZ83Vh93KO3T17d7qw14xDq6MY6UJvMFiyz5W1aMlZjdLzuZ0QBKsJbKEKp3PdrkvniFE6BccnnI8ozfbFXPb5D2qEJC9wZPtAo4Rx8iVBsVHBsg76zEVQfTW6S7cgXfgDtyBHgxhCO/AO1uGX+Dt+qIAGSE++DhJ2nu8cpJ0fuhmnkrE78hFQFaVnNObV7cgnMjh4+zhRdGifnZDe95RszucdZh1ZRZI2mHukx3JxJPFZiCnMlx67bjrxB/o7Ifd7IfcT6P1Y/mRH1qXybQbt2wEIx6CR9xi0mLikWhMIXlLpi2ZPpSnm2RIVa+0GF9SERwSBK1ME9Mk1cpsPa+NFjYIjDbjKOZCl5Fdxg4wtBaZ6Kz0o6I9k/ayaJWxLbGLotuK3cbqXecjiZ3R42G5crwwuzHPVhWL6x8k6cqxwjx8tOBmxeatEZYb56JYYQayjgS+ZRLORZ8Vnc3Ho+aFbyVz0ZVNeNEvj5qjtj39MX4x5msfn4DIPgfSHnvs8WVgbwHeY489fvdQwOzDx+8/+uzn3L6ls9PefyO0//nfS35qyMmw/fz84qE9yFEKZTUwWmXACn1pAUZq80JlO5MytrPIcRtZFlXKKxv31C0jcFCbVVeA0FCXRqcRINy7FiJ8aUCuj1Q2kCdbH6krr2rbJC5awWMBxfvArpf1jZbDbURCAPfq1m8I92XbABg+WERPWoDXyJ1hnI9uVVvNL3wrzO0t48y86Ayctd1vRgfpVCGRVJGTBX5pLItI6al0Tdg0Qop2SQwuQp8xQoID4kniYg6ZzlXDtc150Q9VGqgFwrDOK6vJNOPq2o15s7qLjVl0QbMnIwVjNUiUzIohAmo9HPOycWADMpSxp5K2xHYmOjHSRZ0hgkqRj1EtBNRj5AyVIA+hQfkh9o+gPzIP3sblX34uP1morC3jDMuP8H/DMMCB3HeVwjFladrtwxHClFCryJd0rrqAq5KHzpOo7F6UlaKS2P3EixwUVlCEg1htJUO2e7EVuFbo5CPfvb/0D2K7n6w+7Plg9+PWWhTa15bpW6fj/GhDfTPxKymsENNhdm3YL99G6C0rsIxp3WBkvoJrs6/PvF3rSm3bpPVW6C83EepMq2BWcB1V2Op9GG1ltB5CBNHWNyWsP9qIWyvMDbZq2GCXBGX+Ohtzh4uEpoCPicWL0THegs6JHL7ID7b53tCeH1inB3JKndu5w9yTqMNSs5gxREhwZzQBHzNqHmiS4/ws6ejFL06AH/zDkCCa3HfjtlPRao/UF0+ovgh4xD7aJxWhqkpAY6LK+Z9EkEoiKxbPJQZBiRF9sezm4+u+KPVPxwu4C4nCJ1s5Lli+c+G3ULSUBi6KNgq7l5Sz0L27+Fr46RS9jA6rX8ttCWgrzJNzn0t1gKCK4A0fVR7LefhoUUYuX6CPtb0Jo7xGVilduQ+zzdliFNiD+KqTwu7cOys6h/asrQxwuhyoID8rOm20m/FUpvgNZJ8DaY899vhSsLcA77HHHr97+IlvfOOn/vs/+9XRZz0WZWT9woP3B5/9i3/DOYlpS55b/krSDJOS5VKUATNNbRipUNv9Nh/srgnfmX6BpcsX+nC/DmPbdrmsWPG17C038QaB2irbAIRGHUhc5f6N4B4MtkzBzwTYtZlVZ27v7r3a4fn1DLxl7DTen1QGnPwiSJe3LlsrjeLVh91inU2KRalXRbdqKBIdoc85WtBN8VP8MW2/iCOlARS65S7c9oLmpsLRmp1fGswLKYdMHGUO7HFUunkStlm6mCbLqkifizu81GSarM2qNhDVhimhJ6MJgxTdkqUvqVcr3FZ2J1elK2l1ZC3e20knelTIY9BIpdacQIK0YIGkAGUBAgGqhTyBl3gXZvFzJsJ5LMcCzWm8liUSBJwkyzxrMXTPD/yagGQidJiJUOUrzmSHqEhku/eX7r2l3Y+Vn1+xsyk/s3uxe28BUmVIarw/sXsxlfizaJCLorUqO33n9JH75K5z1lJmXnTS0gc6zAy+d2WC7prXbjH33m4Ivmqx3RqFjYVPuGbYxZfssgZAcBMBxg8bec1IL+GDD/fg3frVgwFyH7lbv7rIXaI3/OiBn3c9esQ/5Od3vLUd2oe7cJfaprvu5vhhY37oVu+nMjyR/qkcJqKBGG9B96Q8fJEdbt/00Jo11MRfex/IjJ4vaTWaM5pTGSbiTgg7LKvbT0QnOAk6O3aXXufsL2cCHCCa/J67anczT4nwRO6fSnfISdU/X/K5dEVoyhTQpAIecUumgZjNE/Zrm3A1kUJrppUpYyeL7F0LcD0ivU3w//oZZCs7ftqQxJbYno5b42d9EIltSRxQTWVy1+rK7Pnpg+xpyxnG/sHl/KnYbwUryPxh7D6Y5+e+CjIVZNXQltE6SdLVMb0ZayHodGWvvtVffatfjILsafvm/MbXJtVZ3plcdEePh8rPJXKyJ63uyOlI9Mvl9wHk2aN7x69oe4899tjjt4a9BXiPPfb4XYUBPvuVd/tvGr81jxtP7v0PHpj/6Hvkxz9JD9v2Yl50sIqutXRVkeE4doxqXhoOVC3pXP8tptqM/Vojq7Y2XLewVqu+ah1Z5fLdZChtwEgI1a775031+HUwXpUK2K9LrGN01drYew8mcF5Ra2Gorkpt8Rqm4PV6cVcm+hqSc9+rw57Xdp5bjC3UhqDk3K8skMB50U5K7VumYU2BCB2hoR1iDiQ5N41ShwZ9it9jpvvZ/MQBmrJ0SYy4QhtoMkvxWxKlLBLctizn0LWWPhkwkLGHyQmT+r+bPhcvuNPnQkAp5tWGQcUtlZ+I6+EBifICtZhKs7IAN1TqkBi8JktXEhRL6TXkJV3kCeoEeYSaIEPUAjGoEwSiMcmYYYNiTrKg2WfgMh+TiK6cTpOO9idGVdJlgigcUzrGauvRTA1mEs5VL8HzJdUqCdA9mU8UmeiNnXXt8Myuy8B1NbWKZCtaKr10chZayizQF7UymavivpNs6njB4V3OvliqfPvtTYXVdqFrJ6v8z7oySNbosbhSrsPF5fmt+lwjdpJ+gYN/hS3n7krG2U3K3LecpATibu2HD5uPgD/Z4f+bhmYMK7HlCS1DsKR3JkcROt3sUIigaLGe3pueT2jF6IDkSE51lXYLjuTsm3zQYS6gJTKER1zEbefz2Vpu6/SMt/8g2aGlvOVcBgKHsq72gBNDM8H7Cr9+LscJToKnSStNrEvuB4CH2eRJAqKiMyqa5mK903T5VMPcCnIz8vHR95IydjLZSXIeP20cHCzmghVkxcQvI8c5SFWQHeTpmWpaxWHw8EyV/nzVsMLUa9jpyk1XjtfI3QcLiZ0yclWQ20GO4D5YFBdBGTlWI7fCLF06/mFshbkzJHrceo1x3Xq4UK6cdGWny1bje8aVwBVghXnlHV2MwjJynOHa96SM3IqTp9/uAf5B/OjgozC6uPjcgPzMD7z13bS+xx577PG62BPgPfbY43cVP/GNbwzV3wp//n/0/PiN0eetX733ppP8hH3WdungTTq2NSs7lOJIokoFVpfpRLWVZWFbUHFguWTCXGeqWyeueDJX5pQLAUUfhrXk8oMbacMtjtAb89R2Wz1AiNUl0z6Ac3gM7wtjdlRkLuv7HaDB1aI2fLjMR/6OgmvN0K4jOfc59xvvT4CF6EWhK2noe85Z5XkboT/nYShJmMrU61ZXWQ17wXRB8w4vPbwUM2Q8l9DgemQprkfSluWEvk+KtRj4046MAI/smO+MGTRZjekK3OEl0GSeipaKACsUuJIAc9Xwyar+hyqNxEPJSlyvbLWtly5RW8VG3MwjaWr/1FSZcdUZcgwn8BiJQGHfI/DwFxQGIJoiM7o/zuffRmPuynKu+s3klB6EZIF7fngENFOjzXylO1PCOT2ERHkJnhE/Vl6LqRGNNRtCUvrLS0onyO5Q3iyUxUK8K4poQ2tOnS9na+TWg/eCw1DMYeWdftNoBphqRyiuOnMTEZVXUWgJuSEh7YRWD64oYF35zlVmWH+av4a3wg4qi7GTlJlvIThJKeBPk+p45ntxx6kK5Npy6oxHmVaOKR1T5lqdcjfG1UQzukb8Ce0n3JtJZ1GFmG75jCzQrdLfZFeuTsTigUR4Apa4L+k/5kGbSVvWt/yQ541kITB8fgY8+FG+708O0Rd6aubt+1MvFCFlABzIqJkaz1tuyK4hiHFjcTeDoUliPBFxiYEB8YhDICCdRI3oaecKSdaDtdRz2F132/IdK3CsYC2uFj9ropiugiyqv/IKK3HL2Pns7eFBsTivQ20rBusfJCwdr5lbYS6xU0ZOpQhQmYjLyF0LBAjR5y2QcuUkZ/7WA1NfsAuzhY2SVvakVR9xvUZmNdYbcEC6uplXW2H+pvp2mjUeTzS9vQT0Hnvs8WVhT4D32GOP321cCItPfv3Hjt5+2j5elE4a94fxz7U7f9Rarn65a3dEIU5eeo6VKZHAmrkYJSor/IlqoywUWFYVEgzihnkWV9klbzT+cvXUgzpBy8YXegzhNSPtK+rZaac+UnFgWHNdHyJYQQw9eC6E6uZWRC6jYW9tdIvE3GTaTVfNncKbcrvMJHx0GTN8vZnn+SFwYM1DawoSoa2irKpZ4R+00zRolnE6ot9gAY0U7w4nc0IjLpDizWk0WCJ4eIrEwzRZNlkhPOL5Av+YkyXtBWFlq3TFtNVaGnpOA8UdXq6kBSTKBTxMhFvduyk9o7wL1W9J7Km4anRz0xKjZnCCQGmo7JcKXsywDzm6wFcEiuhXSOCnv/ULf/t7fuyB+c5y6HY/yDjFKbPc80UlhV6dcggYCaaqWVWuFJrEkc5EVaG/hNbMVWaZH+xwA5EbTMHcQIPXA6PUQjQlLXu2KDotWPM0BTBgFqMjdIT3OQ8HzFu1yXF7GsRrKW9TvarMt1fxSoLqq5tzIF3BlKFfs3TNUiDTKtN23LHBdRMJZrmTrA2qcdd3k3LzkTpwN/fXgVdx23aMtE6jxVFY0d3scD2gbiIX3gFUuwIuh/hkmsTgt/U5cMrdUzlI0DbuiHBJ/wn3zovDOtPvlRuXF/nBI/dJjBbpapJKDXtGu3osMV41SicMMxrv8OmBnCZoTy+B73xaCsx+4oPy4ePY6FTa45anJU/EETDoBG/qhR20Ebdyc5htIgUuaTiCBKRVc4AWEyl3XraWnroSE6GHxu4mA7UAXMuc5MOBvTSBbjbnwLJsz8ZtN8yyyMlWNoY6eZky5/qD3iLxjIzCbNGywtwexFXcb1VzunQ8wQoyK8gQipFf5eDdxMLZg9iLnHTlXJMS2JYGfF0yvFWJpCvni+KKATrh8pE8/ZXFwzP/GJE//40/+Fot7bHHHnt8l9gT4D322ON3Gz/xjW8E/Oxf+aX/2YPuy08+f/SPw6/9kTdGrdE//sT/Y1YW+05hYSM2YiEKwbFMxXalbEKpLItSUApL2Z3E7hpr5ptRsGWf2nJAvUKKBfza9rvBClZb1t3t0leNwLyKA6utj8AAVjCCe5VftBBxiym4/ucLrcF8sV/01fJbRaPHrfDRQgW3RPcJwHnRpmi3VNJ3TpfGcnywlFKcS6fbS8bxVt3CvFY0EpEUDbhiIlohCyCts7945B6mj8nwQJrMlnQWEgINmWfoC9WvKhQI1SKiteNtqwRUVLqFpRc0X3DU5+JN9ZkIsiFuBvTlrcZCcA6Ql8yfkrqkcNemkfKV9/h/pd8fQuah5wVT8BE4Pn9c+FbStluMUmm21dyIntKqJNi0ioecTqQdKa+aGFmp1/3bGZGtpFYwtOemKlZx3asPXIAF3iI/fOQ+uYwuFgBN0mKaKv8FhyAjWiNatQ7zVafmWFVBsNeaeA2Mpd2pRbC2USWt3ehjTQgrAauArFvlQ9pCplV26AZzq/5oZdpya/fmzLeStg34s6LKMlVdMn7YuKxCMDQ1y0yrDhcgCS1NolkAp3IXZMJDwBDO6b1gGKNT/BmdWdm+yn5rVtlSRitTfRrTCvF80S2mHkmV3rkasB6LNhNf0hN6JemhTPx5Ebftz/8fWRfeY4QwbXYW9LUsgRjHk9gn16QpwRn96mOC02aRMKiaT8WvpKG7YNAV+13SSfAQlkUre9HY7nVwb6n8oqmMq4yjEuC+8wwILVXxZxTLrldGNcfW9W+UUm6YJ8fZ2aKfLwL3/hxYfbvnHyR2P5bYBb+SylNBXoyCyg85e9JCka5cwGvk9gBnGMOtWnrrcdomw7zOb9Hu5bdjtmrk4dsfnx4TNCjLvQLWHnvs8SVhT4D32GOP3wPEvhM8+fj+97zxtHOEpX41/OE/8tE/szuysA4P3JeepaM1y10L82Ti53h9tRhLS0pBiVIKUZSSnTSxLDdIs9i96oZ6m024J6AIYFzbgSuJ2nN4+Jqm4JtwhQP7darhdaOQ1EbCW2v4QqdoXscv+obym9LyBfbA6pKFaF10G9Y0TFdx0Kw6ZXVbPL8MyJzREGjKvFoRN5g3ZL6knaGHMl7QbBF5ZB6ZR+5WQlUKwUfwlNlo+1zeOOswb5fUV2mCF6gFFtOyCRJaWe2Q7KTiLehqxuKjNDJDzZE2QDlBFvgKWjybMLSZw0cZQJ5x1KLR4v3PP/v8a18rUhdDfuQ6JgNybSUt200k91WCdyYHU9VUCKLO1FEiHqBVgrRQgLgqaaKX6MsR2d4TEVpW6pF4dvXEVAtS0aDWCtIAtJSppLAepw+GznzjqctaNJhA0rd5lhEAqdILui2mVYsBl/bVq3x4ezMAgCpbUnejKX1lvG88CmNa10l1n2hKp8usCvPeIMH121mHixlDTaJZZtqmbQMzhh0ugLhtG2luyPOMYYILLBiMCY8Zj7jjEq0YJERv8nIu3RWWj/ExcwYlWUoY4Rr8WDRg0JXGcksZYFHqK3ezKLW2k6joYM+qadZiuqDTlFmKrjzPj+W8vgVPSzqjlxDQQj2ZObCE1XHnV7w3A4qBnLYYP5c3DXYX48rsKXc0ps0iwakswHXqLgFVWZs9Yk/ihEMfM6M9oRGQTvODRRxmK7vqsB4kdtc0VYqiqeYOBqGnFgm6zdwTk+KnaFcliFhBRmQDa0cYUaCUY537TUVuBTmQPW1VX/ZiFKDwGhkQPW55jbzmtwGQLp16Akv2tLkTRvHF2Pbc/i7cpK+j6tWPmpcd/5c/Of8pLMizR3ePfus17rHHHnvcjj0B3mOPPX4P8BN/5MfD5D8u/sG/O7rz8bdPPkBZy/t/4uHs56zkT7zjPv2WfY/CFaWwRNVG4Fy0kU2cnRIREid+0VjTRUtBgaWwqgWldY1G7jLPHkzgLkxqa/BAiK6v4L4bIzDbkbcC4O/aey8jh/kCGvwFHJhL0sUXVHblkjJ27NsswGs+tlb0vSjaptSrZdINsmXZR5Hp0OlFo3Fl1zXVGj1Fp+I1mFdXeZgqxHfItCcXTZZNIpdIYEW3IdMWXoqTCk2iBSHgkrTValsNC0UVQln7uVdR35KpUkRSPFEY3KRh6TmSgIEIBRJQPl5HiFuag2M+fIrjAqxgDMk/4MGf4dsl9+VZ7HXwXtjnmSSoLiI4Rpa6pWWZKK/NBGGmmgb/chZU/upCVHYuyvaWPBsV6ZXYVbXH6aL0FuVBy0qBlj1zVeKqOJNgaImpjb0te9aCOnKVrNSuMijucFYVyCn7zNt8VhVICRL8BB0o066inwEwBJq4+rsZbbO9xXALtPoC5+cYr2Lafh0qHGH75LJ2p0jmDEuMT6bJNElCsy0XhmZlzjVbyuGwtvHOGE4IzzjuEznMH/MwIBX4Nm9oiQU/YOLDt3gT0MRTOgY/ptYe29xhlbILOsxcOwG06lwU7aYyCMvNTM7bQLPUIJEyy/IIuEDu2h+l+CHpip4IS3oOUZvpkUw0q/bF7OmoJZiHP8LHB+8j0mbuSmdOZyohEBGn9BM8ETIaDiuDntHosJLdHNGb/p5z0GQWkI7K4SIO4ufhmv32Tac3B5rWHGixQNAqdcW4GHedj0omqhmI4TId+tp92m0WWWLhuMVoHX8rkWOF2RuHp+c6LEZB5fZcxq4HVph7CKhKPmDTu4oV+wfJWila8d3ZdmNZT73qtyvYEh185TT0D5IqAtkK3R9r/BfBd958fJFxKD/zg+9+F63vsccee3w32BPgPfbY4/cGf+gb3/jb/8uffO/PtM67B1kafvyo99bJH3o4+f8a3jYpnp2kVkCp1opBQmDNXfFX5aVpaycncFmiFKWiFBQoQan1C7WrTlTTlV6tXxVDlbEyhBjGV/OOfte4YgqesONcvea/r6TBr2UKvqzrJn/nm5Gc+2GQX/eCbinTsmcgFJ1FrXK8EL0QL4imifZnpQ+0G+KMvwOcS/8OL+c0qqDYFO2JEWjK0uACh/KyRdJfje20LDwLsN1J4aqGmroSimJcpzkWcMVoPINb9d+V5A4vP5P3pmVzfVMikbgdkQxtVGbEfcHRYXaWP3CdJONtJIePUBALzbuUSyKDzLFdvlPbKQPQMBTSv/Zp57/sDy+mOjESoxLo4s9Mbtz8YGlUMxEH0CoRaRrlV70UhcHvyXyi2qE1G9b5Vze7IUWVyjVey94CKsgXpQcsysOWZYCWPWtY0wYqxERoqmS/zixCb8SofDGAg9VmWj0QQ+BbCaBJNImyBl01S1TTl5Wxmk0ry5VbKLct2ZiDzA2qiTGyhi3SpdIohvY0oenLMrFaXTU260jUNLdpkAesEqsJ9Dmpdl8CZdb+rQq/NIEq1omRFKHkie3GPOgx9zJcCRe+c2TmUoKIspUUKC06V1LiK/JASWmn6tgzpLqnmPioO8IdYqN6Ir2iDIRwVXo+KMuXUkSYqeNOOVvi+uSZHPlCl5VVLqqvhlU4uVo9T4+HkgMn2ZHHKhffL3OMvYi6WCVZWcaO3Y8lslSQL8Urxv6ylzRV0pXsmTSR94BYFabUPefsWM4fMvYxJamWDCEb3W9y8c5zXooY0S7ur8m7Bg1oMW9j5rSAMxmk6CYzwCdL8DZK1AbdYpbin/BAEYEspb2Q5mjVzSe6uhfdN3Y3WZZu31o6JF2WBt1i7okBUvwRB5vgjlE5LCbajGqFKqXcMLdC8TRWU6wga4er6pvbDZcJFiJ2P2pZ6UK0HeTdgVmItvsALhTjwN/KDJyunMs8SZtI9ctflfrH5RncB2pyO4YIltCsMq7LWuxAaiZ8Cyrqq4K8rcxc9E82PvveJ3/775gfzVtvsBPesMcee+zxO4w9Ad5jjz1+z/Cv/y9+1vobP/3hWw8+jt5dXhxnR973P/774i1oPnTSpbG0spRYSq3NbspRpqHMqvbJFHDDPIu3fsfqklAiNe9dm4hrm7CyrnpG34WkPnLDoutGp2W5pJ3qpmLbHLh3EyutabDXyKsYvJvKfFc0mNdkwvnIt8K8yoS0W4kAu6GqgjBdNUI/QXwUwUEYn7ZnqQF5yZ07vKzEqGY02mtv8soULCvamhywTW4bAGl6ubvuoSe5S0odQoyiySKhryVLaxrcVSPHTnPxpuXazp+Xemk1U/H66iLD/zn3x75yPDrW32makTqFryIv8RUXC5gRWDxLdpL5xOBDPCaG8bm8HV3IEjVBgJeoOzhJpueWaa+7dCpDFC2ZJOoISMUfq1ZIGoiZS+3GLGTPGyjl3luWkZNGDqiNYc0CFeR3nPNF0W45i1SFTavIPNcVx3LVe9ZoKs2+G42tbqZarhVgqVjc0iliRVbmiZU17ETKMlPJohQUHnYuSrDmapCUoa3GSakhkVKAT8p7mljLYiw9KQnVqihlUC4Eyhy3HBeCK6OlAkxQRI4YESJQGI8LhEU9mxbQoGjJDJjgTCAk91gZaUwhQvosFmQus4xOjnvOspLIEtCySGiZegZuRKE6LOHlt8v7h5yVVnghzQTjYxTzOd2WijMVLLDuyLlSzFVxpjouy4lSsTr3lWtUPFbdnszGVm+sWoU0I3TuFAuGrlc+Lh+VkYtjF7FtHxXlwrUO87fUchL3At/EygvDmXpg/rD7m50yHcgzLx/KIpr7vspiJ+Je/s1GeBYv+nmUhiYtW6actcK7Xu+Iwffz43//w5fvvvmL9w5jqYJ420M5v+BwSmNJxyXRYp5y/4iLBR1gSmsTK/uMe4DBi+lFZXtctBozK75oUOc6svyiKhmqmS+pKPpyluEvaVcXamWm0orK9rhsFVPfjDSI2yiyyNH92ArLVjOxHPzGKinX+avfsi6MKudl5y37wqgSOBALlLFKig7QtmfzosPAAAxYiM6etat5a4V5GV3+qKYrp/5VEYAE7kN8KcJGUCeWC+okc2yVv91B2gqr5MPMxWurpG995sr0n5/b+A3Xz/78j//o1Qv22GOPPX6HsCfAe+yxx+8Z/uCPfeM/+KtHPxJ/9rJ9ND/rma4bPfwD7vNfwf0gS7RnJ5kVKFW5QatMea6daZWCqjiwAvd4xUmjVoHmquCV1NlagKKoDcKVidhaW4mrS6pcwv6N9gp1MwW+Adc4MFvu0DdWIZXn4WsEBvPdGISv2Jx3r6sUWRu7BHghWovfsKYtZRblTtjnonAbUUwASp2WncOhnz/3+ow8UsDgDhk3hQv6iHgYl0RLJuBJnrtKmp4epYDtlVowDcslMnjbvXLFrGiBaFKj3GqIElxHJRdFL7SySFxQmbiToh/Yi1+SH3ifT5vMn3m+ddSQCa3ZiAjGqCHqnBclZ+llDp+qqTMQODrkq3+xH7+cPbv7g+98/E/EoBI4RqYs3w2SdPA5bzUY/zrvLegixMpDmNOJlIegMSs6bTUflXpozXvOsvNmNrVauU6dTvvDyZvkFnaA5yKoXobIS+5azcwSIxCrAynLpv0kz1tjKQ5ktUj7pcqsrCiIRKRVxB6GUhTiSNyQeVxZF4XKyTmm4yvjEPclAbY1lIZczGjHKMgVa8nmrWHemQdTaWyuDjCKfoQHqtbZYoZr6HpX9oTqKfYUFBzJRYKPoiMFKgjIBHzxUKrDxYwBYJFpjGZxwRsR5T15eqqGmniF55MUyClHWsWfSfdEHXokn9MTWNIRUPTGtENJI7xM/I6a/5rca6v5RTlASMQHmY3cMnKscEWVGTvMrUEssZN9x/m8n5RJPFEcHZw/zEbOIp6XXmpFv6zedu38q9ZpNztt2qmbt9qZvndSuumnp1nvbjn9/KM0GRH97YuTf+snfvlnfu693//pe81Ph/L1pLWMdfsTr/s5x5/LMQBljieIL+aUwYiDAecV901Fp2iDt5DWsmwhrMSTxJ6uNIgb5AiWzpXOa6ooGuOJGTGM0bnollpolU6lGZWtUdSNn1cOEeuQXTfILD9vhUngSbPxHHhgzQFdi3Wv48/Vxp1ZAantv1CHwMCJMwlS8T2VDFSyetQ9r4XE7MF6qhSjIBzGKsgldoD8IkhxEPCvadcHW39vgCBrI7PXzNOl6zXzR+H4cdRXQXZozxrWdFiej8vv+eWzh/TFbsewz4G0xx57fFnYE+A99tjj9xI//Rf/vb/1c3/6J9z+3zz4apQ7vzb8/q8o3j75RxetP9CJJrEVnKjjk/JOz41S5a9Kv2Fl1KQ1exk6vXTHAryDGwmn1MR4Y1xda6giCgv8W4y9r8CNwcBfVG7nMFzJDXtLBa8TGwzIOsI5qKKObzAL+8PkBlYuO0GLxdgvIwfBvb84icLjxuxUuiiVDO/L08ncat6Rl1VJg24yb7OaEy5o9iVJcI/k1CW1U/FGqRgA28sRxwPTsABXkj7TJY3K87nBwlRe0CKsw39py8q1T0dlr6tWKKZF48CZIPgqqySme1x4ZIHMRZDfQEWI4XnBeb7zxDdvzsE8ePft049fyve5g+zRseu+zLL77ur7XaOaPsvHenAuzkfq+8bSTJWfoGPxHlrPu/ai71q23S+8QUc7rpO3xWpbI7fsvSgDQVtT5yQPe3qJWxKayXnLbkfMpCmxnxTJS91tPbeKwLGMQ5wpfDErpc+lEaqoLbNDnqUEM2mvsBxSFJ4kJVxIA0VJ0xeTUyiKAS8Q5nRHtHxMgvZlk53IuEQeSQM0yVx6srY9elfn2y6WVMrMBfCJ3AlIfUyLqWKVXZs9PqlPCqJJUpTPyCer/N5TZYuQ4qLkTO7Xm0sZNBI6Y45ctfqUewHpkgclxUve8HESvKdq0GOuUC+4E5WdauPKs8yiaKPUaG2U1nMa87gh8V0Ulb3dC/PK6s7CA9wwVX4upVCWlp8VkW0FqfLzc9M4pyGxU8VN3FdLRyWJaixwptIaWueR+voHw7Mms3vmYuA9uzu5KAaz05fZ15++/J5/+6u/+Vd+w1a0/9l/0fhJNzYPXrxzMPIODhm5Euv1NpA3o31R3C+t2Kstt7F4nlgzaQKr0pPELhPHjDbi4WIFhdKZxE4ziLrOBcKUZoC3kFYmOrDmsbjV+/PxsIydysPF9Qunb5SfVz9cTWdp+74IA+atrbBw1DpHNEKkdIiJ0VU49105e6EOQ0wlelCFplcXHdjzpNQL0Qf2/Lzs2IPL7Q8V5FaYe3Xu8U2+X3idvTkqDep+s1SDGBg4J8Bb7fFdzoCuTI+TZ/8w/0G/XObHq//5++88unvvC+vcY4899vitYU+A99hjj99L/PE//Mf+13/5h9pvv+i4Dy9Oj1a97PHB2+/C4dn/7yjlN9zf37SXXXs+yTvYFpasxF1bdZXKYieL3Rtoo9p+c4tMVK2rVPNhBVBWYcPcHjm8U8XloVdw4Ktu0te7sj7jNbMvlmB9tTU4gQsYgg89GAObNKHV5eurysip4gC36ZApNXS1MovaydwK8jKys2dNgPCMsJc+bs0OXY7fSl8+a1tLT1KXZJ3uSAyEnqQp2sUovJTcNCxvAR48QzwsyU3X37aNG3EqrW+BHqMTjrXKmiyX0rzH6UI1bJJD6yRRbi4aaayKZscegSQ4qdILaT5Sn4sBH45YfZMMnPwyx/M2B67afPR//dj5rw3M/+ZX+as//OLhWw/9D18+/NGx6fTVt7+tv/KpenPs3jlxjrUuOoWnLLvfykflsKmm06ztZ7Gdzu8mT6XwnqTOuTQXgqcmi7IzG7XLOAfSlasHRRlnslB2zyywFsrCKr2kgdCwUoMtIksVUgoqKyWLsUsckbTH802ncwGkMtDNS+ulHHmqciWXkCyj9MVkomKl5uLX91k9W73ZUKnVmNJ6wm8/C5FrZNgnTfAywSIdEXqY6tuWor0qRFmxEN9FZfgeia/SyurnSaaQOmLZqx+2JHgZeq7aCLnl51Km+JnSQIqOlKdQgsSlXqkDERlLu6nS5boGXcy08gtJbCso4ud+tNaRq76bBZRpptwgtoIcpex+shnvKoVz00oBXZpR2W4qo7WpslIV9nKatxeW7al0oE6jUgckj2m3QXtnhfSHB6P2b2QL+Of/x988gZ8MyL6F/3W8n82KH//OvW99548e/b0X7o8s3PSiWQo85+453lA9PZCLc4YB6RmDfllclJ1R3Mkn2uklZWybsbf5yll+bneSMnYsP9eYvPQcZYAYd1m0tEoRicuOEW8l2tI5gvZzu2c2g9Uk67kXsT72rQXQYlLdfp95te0VK91h5hPPpDemfVedAV14ydEb8iTFbxEulAEihatiTyWuShoWAwAa9jQrfZR6nh+WkUvk2oPYHuDETvS4VfWhZsJfvH+XrhxnyKJXUupDe5aVgaviEJPg+yQ+ZlhO/v1nadx639MZ2a1CfXvssccev30oLv+D3GOPPfb4PcDf/YWf/9/9v//Nyb33P+Xt1bJjddM77unXfvGvjjv37ebxS/22215+Yr2PbWHb2JakjgpKUPlp7fx8Getbe/tt/2WXCXMLe9yhyltXWdblVTdcuD7iNSpL1LXabj5w+2JRfVGBK6WvFBzDOTTgPiTwGB7dmtip8f70ypE6Kw/b8XvF2E/OfSzr7R9cPj27A1jWQj3/5oE9djBtlqEs2qw8MRf0RBgyRmiy7DHqy7y5WnqjFAMtop7OPQWs6C7wgVMuM51oMl+xIHBJlqqZ4c1pRLRS5SV4uegcD3Cs7FiduMoMGd9Xo4P0vJtN9HeM+hj5iE9P+LBePO/shABw74e5vyT8ffzDjxqP/sT35ffs8t4wPT38ijf4m2n/RzoXftjvvHh5P/hr6eTev3x78Gk++PX8K2uPg1IDieileC2VzldhPllrEWWR4/p5FtsIbpA5vWSt0CYC9NXCJXFVIgIil7sY1f++5eZRC0KgUq8061w6ChfjSipIJtpUMsgiRlylVCJOZdATEUpB8KysIrpuaTato3CkTphUB3NuKxztyB1tjt2yLqhCCnLcplp5ks5pupApAkmpNpSUqqzBoAIri/EUZHhKka4jF2RW0VOlcvGisuXZaSZ6Vd2d2v46g1LFTItxssTZ/UZb1S5Y9d6/t7J03rOWqfIBT5nQmuf4KOWoRNVX+SpNlBeQAbn4DWuuMWcMuizvMn2gzo8Wyzee/3P70+zlz3P2go9rJ1wLHAjgez1+9PcT/Chn/8Zbhe7GZjp31HIRfDvpd5KTD9O3yqI0+AecP+PuiKER7YhV7R1MyoE/c371/D7w1mDyNG45x1E51a0gUn6uVYpCEjf1rEUSDsNpRX0BMY7y82LqX7JfpfrWYmBfmKBphTrEHHC6GaM+8w4To0KgUgWfq54m0cQKDMGETrzegwg08Zxegq5k2CJ1Gf5QmYVdlazK7tlyoIJs8wMosbMR0quyCqcrZ60gfYs12Gvk7oMFcGjPqjHqqOkhJ1WHj+Tk7ph/9z8dJD/4+5WXfevP/XceHe5zIO2xxx5fCpRSewK8xx57/N7j4Z/+k/oDP+kenZsjLNVvLD6IPmp88s8+Cn5oEb45GJxfuPdndqciwFgWlkIpMa4Yx4z9KyvmmwjwK9jvLSbinSJqXWLThFKV/moWrcOP/YMkubhCNG+q8XeWBj8H4F5Ngyv2e4XxjlmLst6E6xz4kgvV/xZjPznXCG+9Mzu711v+xqD5lQv9rdNO9M2hjKXScJa0xdKIN6fRlmWDRZPIl7LLRTudhGMD5J4DmIZduCojHNHuZUnkORrLZekSnXAc0UpxK6foDH9ED8WUQYJbPfxEWo5KB2ocquVQjQdM3uDZ8NdHgPpl5AX/6Blhndlq8wS/78/ihSTGfdPKPv0E87Yb/OD3fTt/63tPf3nQ++At9+/8+t9Me2/+uY/ntC2yrx9+x34n6WX/H+kBiFh+DuTjtWxvtrLdIMsipxY6EjfILZ0iSukcSqULSWzlZZJYyl3zhC7zTLxQZpG0EFyJU9Fe7b2cFk4lYB6wBGxSFJKXhbi2yhDxSRGkFBBXZWnpepe0VlK8kNVKQiOOlKKUEhGPPMVR4EpWDahHmuIJoiUtsRBxSRFyHIVkuA55jpPgVmrGm/rXj/LKYkFtLH9qrT+H8lVucNbWZkWBa1t5WjYQwVKZuNX3MsKTzAGULhUKS0lqo1BeWX+/oDqe2cXcz40Dl189UI5fWH7ZbKSeH1Xle9bYs/IUT6s0tbRWmYdJlZ8qr68ufJVplQEL1XzO3a5aeZg7an6g5l9d/IqnFv53TPwLfPornGRrDwK55aYt+IrHG38e53t0763Bu/7omx/86K9YD7PSmi3tZeani/SFabVWF3YeRbmjS7PzMBUraV8kx8Ay6s2iwO4lxcS3grxMHFB2N1F+sWlPgSSOCtZH+vbSVcaxTOiUWbvTVzuezwFpgImVPuJk02GjwsrQqolPOY7xqmGr9gtecrjRJF8/5C2syq6nklT8RPxdhbw1inFQRq4VZsn5LRHAClBeI3MGCYpK+Oqec+appM+sEjY/zk/OnzT+/je9/P13w9L80p/57z66++Dm2vbYY489fnvYE+A99tjjXwn83V/4+f/WX/7fOg8enHKEZfUbi649fXT+7fLFyfPwYdS7VzY84/VnVgfHLhcBllUaxwpKFRTJy2ZNd7fYKdxAg7myttuYeW/DlQDSbfdj5YZ5FruXzSnlNYpdI/DttasvKnB58os4cBXr+2S3wkadp6TCNQ7ceH+y+rDHDgHe9pS+fLs2/wIiXqs4+HoxHg2KldNrPG989k8QjnmJoCXtcyHCirYRV5MeySnQYH4vfVkL4qKfFXnDKhpqwjAk7y1PC8/JPcs0LAUThmN6CxVW95yhL1RfQab0aCPqpSpTXvYun6C4w+l7q0/1Z6n6CDnh+Sd8mrOCu28zfIvWD7j2S9p+lv6pt9799vNv/rKJ/zqfZ2QwcLn73/uj8wdfpXT/D42vtJLS782TpEFZVpmQzUgj4gaVXVfqF1CytrgKUrp+nkUKcNwsTyyva5SbK52TF5Lays0RIS8lVcorKUrJLOx8bXQtShQUJUBZ7hiHi9IXE4urlPLFaBUXpb2+fZFK8zkvnbUPv1KOynNxHMlyqWegyBXLrmPlNwYAA7k4WsWm9G21puveTn7ga44GN7rii6BUIU4hjqPyAkeEqAouVUhuK6eQ3JbMrmilZFaR2rYvVphJaluNvEwcpZRyS8ks1SjIbcntbKFRoCwsZXtlkdpOKM1OjFKBtywsz7YLEMcqUEpbuaey0IpQKEspS4HyVGbZ1kOeZipQSmWW22QeWOVBftGLxvrcWAU8g4Rv/RKfnVf9XX8P1Nb3/0Y0oQP37/DwLxxZXx1EB93fPP7aqHF45oZdRomETjIvTH6xbPXjZ3YUnaY9EXHNckW7Cs9+evGmmVx+Od0wB+Ucrzb9kMRRflENwpEzCsgySwJStxfGlh+Stpl5JEBA2mV2mRFaAaS1MlWVQTquiK4COOcI8DFj2ld/lOoxXxXdBN9XCXBednZmRTWysYOQj4J05ew+r6uPrfKXbnwwaSnjW0nDmgKZBEec3E8//xf/UJ8O7ntN7996643/8X/pv3n7I99jjz32+G1BKbWPAd5jjz1+7/GNP/JH/0Mlf+6v/aUyECmciRdotbgY3muijl4+eyH2Sh7q9qTjqFnRVl6anLawLGIHVYUQVlaS2iXythXr5WL9Rup72yr3miOtAJKtbChB3EYBKoscKQrXy7PEA6okJV4zvzms97KHr9Z/Bq6prSb12WrNXKUeeVgfeS6sINyts791FQAV+wVWH3b9g+QyJdI1glTnBRUgXVj6+cJ+EBSrTuuojKaP/PHjTPSAcYN5Kr5LMpdQk7mSLghbrJqSlKuw8XxCChaUWGNICbzn+EgLS3JbHNtVidcw2A3mqawtwAuaFYtzMC1WCxrVKPsq8yQTxYHM+2pqLUoBecyLNwm/zg/YxG997avjb350MnjDvHf68p/6M/6p5/3av2eiP91/49/+/uUiT0nsO+//k8ajb47vIdgq6g0uJlFbZLUqnaaeAqrj5xNPFalbWEAWWZQFZQkgQp6jcNwim4rbSilyZReeh1yAl5tT19FFHitEHJ3lldaQCGWBsmwnR8RqZsreuEnXhsat5x8D5LJOU+0hSG6VkVsVKlIbsL3aTijaCjMqH2O33JkwdZ1mWwrrGgyhAHWZVf2mymXlqJyKJ1tJIY6t8kIcYEOYgXnZBLTKU3GqeioPejI7m1WWw/WS474XPUvDypm6WFKsLBCmtu0VeVZFHJSMam9rLpsoAEW+ZHqmbK9cNRQqU27pWzlKBfYyUwpLjZU3sMZYSlnKsizfmitbxbLU9rJpLZWl22bhFpE/TqPnJI9RMJozO+Nx3ZTFJa470m+/rwYgOmf1n5xaw9PiG+7bw196z2bRezRvHNvt9EXz7dxz7w7S58Fbd3nRzb2R6ktsPGM/NUfxWUiiiH3yjKKgLLOV7YaFxLbycwQxDogktvLzhsqWRQtr4YgxYSNTOhSjxWyUug95CcxUr81EwEigiV0itVa4E00ccQikyjf4Q04UGPwQD1UzYdbzUWBVdkE0cWhNV2WXUqp01gvR698uQekMcO9nLkjslJGbXPg32c5Jl7bXyCSyF4EHcp4/vOecuSoeS6sxCy/Op/nDZpOFrPb6z3vssceXi70FeI899vhXBc2f/nP5Ud8kD7x+cb9/0XdOsWiev7BPPv8N/aNFt2O3gpnXx3Xy81ZmXCwLZdVBg2rbGPtFjtBXLFqv4QVd4dqPpR4kgBn51Tk9MFaQxc9bbpijlNUoy8jZMhTvNvCa7tA7RdQ6spfa1fkjAA7qMgGMYAUPawnoL6rbHyZWkG0i+i7vNXaAMnaSc7+2+AnC3T+aTqZDUJZ1MvjoXzZYPuJJQ+bVfyWZ6BTPiPeQpw/kmV6JbfCXE5lDCmVduQ8J3KPwnKjnZIQp7hIfmDBIlVu9MbiZ0i4mojVXDYXSpEZ5AyYDNfkBvjmYjvVFyinue24R86DIctzpr2S/+tUP2s+//Zv/Gfybf7g9anzz0fvvaP7xoDka98vYVjpNJ/rO26ctWbYTe+4X3XKVSJYXelWqvHCjsuGWRlHO8rCYOhKVubEpCqSkrF6F7RRFqihL28spS4TCrImu7ZXVE1Nuqdxi+6kq5/KjZJZySwRP5Y7KEdnmkxZZuQ6mxZS+qU272SiohmIbtlcqt8QtyOxmY1HZgZ2t2qojSeYpZz0GklsSuUCRWteqKvKt3NS2V3IzRIU5mbW5l+2/+eq3u8O+aVc5peRWNY+LzAJst3xTxZ9JoLwSsIIM8KwisBbVZA8wVfirq/Imi0AlrspbatWUlTsuPcnVEmcFnxJlzBZMc2JQWy9231z/u02MG/BDmrCN/f0oF9WBB6zu+qWnUCzpTu3DsXuQefpeY3Yevj33Q8dvxcHdlSsodVLeIXYePz40F+JacbYoPG3sYFoulLmwPN+kC0t3YxTKL1DqndazohdUXXgoT9adEB7xxCdLlFtND19lVaB0LOusYz7pjLbBBzbW4AVdrRJNsqCbKA1EaAUZgasSIBPfteottC2bfyb+ouwsZMu3pPoGxE4Zu8l5cMMGnwLwwhxwHyxaygA6X8lvfjR/Nrfff6ujLv7+n/2fPDraS0DvscceXxb2LtB77LHHv0L4u7/wj/7r/+f/y7j50Bq0VCjHrZNH1nNL4Z2dzM+TJ+77K33HHPUOguV5dic5a2FZWNZ2WODl++vxwGwt3a6YguG1uejlr6Ub5tnK4RoHdsPc6SVxpZx8pbmqt9WbHX7+he3WSCCGSF3KHG+ua7AWb/U3scFXLr6VDHuN3NlKC1yl/VxT3/V9XxLgO28lizeay98Yvv3Wk/jFs+bZ5w952uFM1h7CiHDC8V0573LRl1lvNbbT0jY5IEvwYAEDMOBReI5p2nO365GdMPSUmdA3uBEtg5cqb0rfIwU1Vw3AV5lHpmCopj/of/iueTxITfQd1+qrJ/9/9u47XpKqQB/+c07FTrdvmpwYhhlyDiIKygBrWBUVwYiuAuqrqGtC96eumAOyuqACgrooBhDBgBFJYsBAUjIzMDne2LHSOef9o7r79g2TYJhgP98Pn6G7urrqVN26fevpk6oLk82PrvoLdBH9/35MyZHDmP3H7oXr182RhcDUrVyg953+eAIVJDnHRLPE2ll6c6iRmNyoyYeqapQ2iRUau6b8inIqcQ4GlcSNNngAYNJWyrAcpeJmaBy7Ikx7KLUcJRwlsq1Wx83/JVZ7Bp7AE43pm1xR19qCMZZtFOy0m27jhxdLE0kIIewkKyMAsNW8et0Is853fBFmRNlHICABZIzIJCGAimXbUCpxlWONrlf2rK6Npr9e84WjXREo2AAKqI2YLgAmlmFaW+uoZMRrpGsAzhQlnzJFN06CjNMx5FRiTfGqrSYvTxdajk7PsJ1N0PqmwFHC1p7SgRmL1qZmq8hKa8Iz2QACsDWAjKwUUJFCZFC2kfSgVFDlbFJ2RhJsBgaAOoaGUFUAMAxgfPpt/0+OD71ThmQBZIGZQJ+LvkWQ04EixHToomO7cVRwh/J9G8T0MnKx8MqiMIy+IfQJ14qsvLCtEa+vWMrIfntTrV94niwaHWbCkgspoA20gkqQJFAJEpVdWCogmZtZX0n0ofbjgVaetBbJlYHJ9Ol1VdNlGb0Z+Yw2vWbwcTMni7oxpqb9rImrxoY2nqmHxvcQjKArzcAR/BK6fBGmqbgVfcc1P2l9VjU/0JoBuNUxWLQCcG1VYZtNbHL7DwOAMaiH3p/+YM3oK3RFqlxf8eFvTr5aiIh2FjaBJqI9yGknPu97Qp952ddrana3o5PY2mj1z5CbnWl5pxLOqi9bE7q10N40Ld/TMxAVdVX64WjGyek4cIC0LXT79tKbtmYD6a0YH0GdbBJP7Mo7YVUDCJlRmd4wGfLSuhevL0gzcFyzAT8zt5IM+Y3tmGYFmtLNuZcAIQGMBXgIyPFxfbKgWbs7YTqlbHOFVoadA6wFggljX01oZCta9TNRxY4q+VarRX9akA7rOmUp1j/huYGfW1TegJ7CjCje/MRyM2cfqB4MAHARu4irqAygmEWpDD+bk55B4rhuJUIeyrEtJzEGUa/rVqJqjx0jm8VoOhz0kOl1EVeQD+GGwomNE8AJ4PjpQFAidkVi8n0q33fUygdU7qAfbHzBwgcuS+b1ORvcvnh+ddpzv/uOBd1xECq75ggTSLXClW6tX6y3MoEv4+nlZfsML7PLnlvfqG1oDWklgWP3WGJYTFsveksmFyp/FCaDkjDahRLGQpdVUrl41EsbpasQbXXZBoDlahXJVlA0aTy2kgUzzYpV0neidAio0NjCVjCAVrAsACaRwtYwgNGQMjR22uRYw5E6/eko34SxsMZ+cA6aFcNojoJl+VaMjdHi+euHawe5jt2F2AqhPNjlGMKVVlRUodFevj4kwtX9FQzNOuQxM8fANlUZOK1mz9m08K1aVlW1AahIWiatet2xewalHWhYjk631v6ScBQcbcfaxJbINpq8mpoNwM7GAKzGhE4xgAAuYBbJoaqQFds5OBxUnrMCudDYs7Ll0HWqxk1LnnPrSiQSiWMSAwMTh/AFggCZvC4F2oepy6LSeUs9rmyBEKi0/TJM0P492YQ8PPm/CBgF9tXAJmQ15GaIDZAiTqZBzImKfZtrPRlf1IZE16jM1iGlqNajLoHysCno0ui6kQBCAAOQlj8/1KN5v0/Bc1S1EKscfB+2DWnlDk1mWKbHMtnabK+/rjIoG13WBtHMXLhyOOpZoDZmkpKIEycuaW3NVGu6dTwM3a/rAFzRFcAVgBJODU5N2D7qgfAMtId6XpSGMDcrgkZn9LHY2+iIYZCmXxMbv6y7Ko26Xz0xJ3sRjB7/UYPG1OtNbjZRAx4EhBvNLD28aajWN8OB8d65/wk7dI0RET0FrAEmoj1Lz9kvT0zOnt+by0Q5q9YtR/vksCuiTSvDoXquWp+zpj59xmFSdPmjsssoV5WyzebQU9YDt9cGt+ouxIQ7NgBOVgGQ2URmkmTQ33IGbnCyiTO7AkAN++GA5/UH0k/qa/KtnOn1BbpuN8YKbjfFjbaA0bDsRjfmRkVx80Grb/NYAAYATAM2AwByAtVmK+g0DG9hzOenrq0GGDAz59X22798PxZ0ibp+bKW/adlcbJprVsCgxwxG8AZN9xD68qYyDyt7TdmPq4kjChuC9OhrPZ4dmw3ZaU7s9sZr12b3NUhCyBhuDuVh0TuM3hoKAewYbhk54Tl1r392d2n2sKkOukf+ZdOKn189Z1+sff2xo72zuhHmsN427v16341mRk1lSxu6Zkxfk9XlgbjfVMVcd21BjfYFg716hV8etR6Jo2UwGqYXZjrQh3q+EPsomV4JuworMH4FuVHTHRgfQGIsG0ljDKfYAqBgw5ggdn03NlWZ9pa1TaRcG7aCbkTZ1snz2poity/f4slOxtWmthotb2nNg701s58IeuO/bu46/L6Z8yViVwQALMQA+lDNopLFMAC5MVzz28qMV+1jTO8Tme5Npjc0dlZFypbpMFppCXXJE07jYBtlcNpbbluASZs6T1kqMT7rovHUtI6lEfu3g4ckrYv2mj2BQ2ObRCK2PJH4TqycJDH2glq4OYc+DM/ZuKk6La9jEyd9SQ5OFC93e7rF6CxT88QGaeyi2mQC3w+G5XoVrUIMBGWgB+s2t5V/W6Vqr/ttz8Pp4x6gx8bcLgjAysA6GGImkj7HFOSg279ZTKuKfFXkIOSo6CmLXN0UojBXgwOBcGMOQjgZBSHiwHYyKv1qLK7bEICQTpfpsWO3p9pTGPBFnJgMDBxTWWCeEEBGJA7qXaISCs9HCMATAYA1mOOLuIyCK4IyugGMikLzSIQQiOBHwhtGofHZ2Jo5aqz5DNBoCx0CqOkiIELjleGN//5QAFDDfjCQ8fubA+NPNTGSm2t0gBeyln/oz5YZyB0wA8bc9uqPsP0zET2j2ASaiPY4N9/5+9dedVEoemfMDRzH8a2aY8W+iPOi+sjyLkcNb4rnj4z2uvvme+eaUVmEJXUlG45mvN4IQoQjmXEBeMoY3AzATlbJbAJAZpQObKtnbKgoE9i62Qk2rtlONpGZRNftuGqnVcRp4gWgA1vXbBhj9wYAkiE/rlnAFjLwuI/bLXz2povT+Ydls62llAglVgNCICeQBXpFowNwSw7Iiq1MevQUjf2NMDDIzisnA/6pBz96Z8/RAHKVkZ77bus2g3OwqccM5kylYvIGZtB0A8ij1mOGejAIA7eqE0cA2Ogs1EjSeYD3qa41cF1nZMiZPShyGrkN6NtkZgzK3nxWhoX8zPX26ebJB2ea4pu/OQQkwMLXzNtUOO6X+y0+QSw/Ej9XUWadPatsvEh7g2rmatVVHuqvaltm6vvqNYVo85xwQ9+qZXoDVAXGBZpD1aIb6IL2rdiXNat7I/qrJr/ZzAxjpxpl/WxgI3FkHYA2thSJgyQ2tgolpIM0BscWgMRYrQbD6WnaznSnS96ErGhi2WpLnPaAbbU9nrDNNATCUUcNr0Q2Lq8rz3z4kdFXHAqgCFWFPwK3x1haDM4JNgRhn2UnLgarq+vr/6Z6Tuof6l34iLufqTm+UoFl+VABJrZGFo5OZ6zJicARQd3kbSQALJHIRjW1HRrfxsTe4wnsyQurxs+JAEBi7NDYnkha3wWkXxBs5asBk0jEshGkY5k05h4b9+uT1jD72cCH6kniXlHXMraGB2dsfGB0+gJ3WI92T9uQz/damwsIuuzEwbCDwKoYWVcmAzwBAKNPYCgBgBgwwISxmLYSjNvritOf32IbeYHs/rD6IadD5lCeXtwoZsbSWSfnBCLvyLiC3lgaTyR1kR8I+lXJT0LLzhjpKx3ZSWi3faOH5iACAhKZRXUI0WNVMrKSwJst188W69LZhn0R+4jyYrSC7gJGI+EPiF4AZdFdwGgFxbwYHRTTPUQDoh+Ai3BQTANQF14GUV14zWYo7d1DGh+YifFtGdZUMWePAliVzBs77rYcbILGLMG1x3smnaPxZ9FoTz6p7nlw9uHSyvm6Fqz4r29t+TQTEe0EDMBEtCe6+Q+3v/6qL8DPd/WJolMWUvpW0iOHdTBr7caBgShfRVel1tU13cK0aV5BDUc9JnYhhciaYH0+nSUYYqruwZiYgb3+sJl7xyo62ggA8bpcXLMzcyvCVyawdN1Ow3A44I/F4LqV1gB7fUE4mHYZBcYaVDeaZ2+tWnhci+WpPpZHgEHZuB/NSdQFpIV5QCBQb92tClSbSRjN8Z+fVh5ulNzvDwCja7YzuwyYPMKlC1b+3T0ysHLx7wd7wj/ON6u6EPSbVZ5JAtix8SrIhcbpxXCPGc1iFIAVGWuziYu5JM5nnI3lbL6GLhPP2GxPqwmzSWaPKmewpFRaN++oocvX9B7p3fjX+lxbAU5mpskWhnpnh93efbrfMVHZZAumenL45+7yukqmsEnOfDBeGJncuqhYrFW7k7DHWlaMNnfVNsm6wlrAAUYBF6YG2DAuYCFYkLfsYNBbMiR7lyfTa5bjxdrzaqHyPBlKkWhjm8RSsQcgMRaAIHLVFmo+U2NDNzXD7YSa0im7y25zg8LRrepQE8u+3Oh8DIiBZcEtG6MXHjn9D/dOOxTlRXMNlIJjQ0cw3Sg5KrACIxMjA1WvYvMyjG5C8UCYGqwTDnzUWrB5eHqrwXNa4DT0tngisUXS6BE6iTY2ACkSNL8m0O0ddAENu6r9dDtbjrjbdROSVnc36pBjiba20+2bSQf3munWXHf0wOjxjSsGw/sqAiguwOhKGGC/g1B/FOpEu/5kghqQRd9My5LKLVqypLAC8GASQGGoDsQoVSAtBKrR4HzCJ8TkVNxvoSsD14HnAllYRYj5EFlrY3G2ljK0MiNyeiIdW8aJ5QQiXxXZKrrr8LqsOoSwZVKPu+rGhZCQMJHTFoYBIZy8gLTsGbWsjIv2ULpwllhfRDUvSmnd7Wysi5vRVwhRRldBlAYxrUuUN6MPAjH8SKTzIQkIEcINhJvuIiOioDVV0qQYnBZiWE2rGK9ZFd724TN2XkS8NhfXnMy8ig5sXXMabWomnK84ttfejaHV807sg8Hb5xx1wZnnbce1QET01LEPMBHtiU577vO/B7z5m58tDXbZRSfrRXUtjezOOwP9s+x4vQjCKJOpVcoOyhuxbzGXK9e8DKQ0sePPLAcb8mN1Jo0kjGZ/17b7OQMIEQ54Ts1OWz4LX8EgXpdPK3itnqAxrtXsihz266vzXn9g9YSo28JPULcBhAM+GuNgmXTgq/b0C2As6wrANMbKcrIxIOKaBQOsB3oA3zSSavpv28w4Y9vyAKMaz5RAYpAFAiArkQUCAUvCclDUEBKBwKAARGOWnQExbmbg7dKIvm4usXsDkYlN3S72jla0mxehh7C0uf763MNXWadu6FkgavXcaLmEcBVmH4B1NjY6CEPT7SIOjeuPuNlKYm1KTAXoQ7XYs7x4qIgOWG1mD7td6HIWVdY/O36sq2TuKYrS2XdLYEUv3AV/LSyxNx6xxA7Nw85MY5AYJxuWZ5marUM/KVa0f79auK9xhuvJJjVtQ9SNJJxl1hb1iulmva2VhdAYmBjwYQZhYpQS+HXYAcRsYBY2F/ar16Y/lBSD2M25tYKowIVtEiMR1AoAgsiRXZGxpKnZKp5iJKfJxvLthNGVm3m4NcLThKftyy1HN8ZYBoStTd2GrUMIxCZtmVxwBvcbvOvBW5Q5doH363tNHm7OcpJ40M670ApuMRmo273SDFZyud7BjaYEsx4jmwBg5GHMOQplWTPGqLh1nRkTC5FNJsVRU9WeJ6x0qY3EEUFs/KTZHzjadqNuAyA01laaPKSNok3dAZCOZT3u9VgCULG0HJ3Ecqw7cc2ePHq5sLVXrG6s+aYys5QtzDvo8TnJA6VRjKwEABdY9hAAzKsl3f145G4AyBXUKnvxvvG6DTF6Duip/3VNMYAPZPIA4KfBbQZMFRqI6giAikIEdFkoKeSBLhuiAMTIB3AzEDZENwyAApJp0La11lkoavVQuDYCX6wLZEZKlOV0I+t5WYMIe60kTLpC6Q5EBeEmwo8Lsp4Yt2650rVdV+qKCyFlFhC28FQmDKpwjSxkraoto4rIhLAi4UAgEk4JuVC4opGZEQp3BFkIaJHV0BAoIVuXbjeqOVEeEv2BsACVJlgDbYRq5m10ozKCfFpjYhqNn1ExdmMGqHHp15jASX8iatiPKwJQ9ZWZ5goTR1BzMrEUq43enOuSRhmj9Kuf+6JtXUtERDsBa4CJaA/1xau++sU/3Ow43rTioO3bkMKXSbccEVKsWDdtXS1TQ7YRdPM9+V6nXixCSlhWsikfh04j+sqpqoIxrkuwk1N2b5AMZezeQAe2zCTJUEZmEplJRCZpTeRh6nYy7MtMotOhenqD+po8jGkNgoXWWNCT2zmb8U8BJ9McaiswGAaqwFzAB4aB7nFrNgTAMFA1Y8NoAcg25wGeNen0SQsBMCzQJ7DOQl6iFxiR6JeAbhxSRja27I/f4/g9+/3NiYIN+uVoVowAiBJ3sVl++j0bBg943p1Ljtxcckc3PTJj8DdDqicwTqLdxDg2ogPEw8eYe72q8TeVZZAMzJtR6p1rhiqbFx6xT2V2btWt9/btf+Dynyy7MoqAGNBAv42ZB6N/BtzIge4AAQAASURBVNRMO5ktypl8qJ2ayQdaOFrVlazqTE1nTKIriT+YdCeJZyKplDVq2VkRzBIb55jHe4KahQCAtT4p1RDdj4rBqML0w3PZrqDuzBmY1b/OzHKNdAITQWadUuJEABScyPgxLCmSelREmtwSK6naE4ZxSo0b9app61XEEzSGzkrPuaMaVWxTNaLWJQ+AiuRh0x7tqUWZ5festua5TpD8bbO9dMGMXlS8fAg3QRzC70JoYGJIT1mzgsc1fDOYGfjT6rJCwcKMl+YrOW9NfMij1fntNdJ2Npnc3HoCVySRsbOy0Z556wG4kWmbz9A8V+l5S5o97dNz25pCqdltuP08GLR1Hm6sNvbKxBLaIkFsIbH8TLnv4Yd6RTB870Ca1VyM1eVaQAIU5+OJw/8tyvpSJN2iXinbc2uDXWHs4ol1+kDP5HO1wVjW/QeWZX0ID1EGfgWxg0wdYa6xRasKCLg5xAHcfggLKELY0H1WJT9bJkEGwxUvLwRK6C/Dq6LgoV41fUJgQ21GEHvCM8IxvkFduDKXQDtG2TpyhKetQgIpIC24DgSQOKripdXCbneckwl85fohAN+qQog5YrOWpiYKroh8kQyK3j4xEgoHQghgQPY2qnPHGleP/auMF8MD4Mhoo+rrtSqAiOBWTKPGeHzfYAFAjXhWT5h2G5EZpUM7HPTbG0WP/WyEaH295/cPq388itJg3/5dfhfesehZH3z5W7ZyLRER7RRsAk1Ee7S+s1+qLde3TG+xorxcYrmQoleUinJkzaoZa2Inkm7BqkYmE1ouij1yZrewJCxLlzNpr+BGl+CJsyWhMQizEBDIzKkmw77dG+p0pBkIqydohN7AFn7b8D+BlQxl4qqVht7M3EpjuiMzPpe2V+BO2el3QtUugGGgDvSYic2VTWvAZ4Pl7dtpbn/fSY2ct/SRHgp4GpaDkkBdAsCcNCcLZCXkxLacucUjyaBvQseZXU4PsA8jWQzDwFdBQY04JuirDxz+z792/3lov3cuvfvQN35v2Mms+MtQOU60U1O5mvSf7a44zL3nkE2rHktmzl5SfOSJ+Flfu/tnF7xk8YdueiRGBPQcgo0PNA5p8b7Qw+jvh9cHXYDuQ9xnJ7YIPKkShNrXClqjErujOr9JT4uNVUWhqgs17ZlIeCKZ66zuwsicYFN244AZQlSFGUB5oaWLCLyZ1vT8gJm2HoXQ+N2iFMHPIarCRSIRWxGsBDYcFUZuGsC8pHkmM3HaqbVmfDRjWEFWQuPbIqnpxtl3RYK2GtHWWFbJqIdtcfrqWx8aytTttO9rsbs0Q4z0lAbtFf+wh5HLY0VhXs/+Mx62pitjQ4wLpVkZJMbuE2UjogXY3PXbh+z9c+HMGaNe7u/hwYhlK7qnsbN9lmBsV2fmcbm0WVQHbaNJt0uDbvskUo0G3uN31N4MO+3tPKFh9pZkRdDeWlvDVsY+Qj+kNm/Qf6uWKo3lLuDngTrkIVac7Z+mB6LpB9fyZoXpjuELJJHxQuNHaDTfmIdaLMJ9S6vc0vBj6lBv84iz/L4ckLPgFeAkiG14FSQ+sl2AgMgBXRA2kEB0AYDOWImTiTw7tr0BFEdFt4ITwq+ZfNX4afvwLpUoR/kiiGLXtg0Su+CWysjDiiKTq8GHZcG2kNgmsXVkq8R2CkorR0UWpHS6E+GbrB9lrZplxUJKX8SQAlKGwvVlLISEQCi99HMvgZ/AHdH5bquaLomMV0tnDG7vJ9LeCnrSIIImtNWIF9dtJ6NkJtGBHdfssbegLSqPf+x4MSobsf5x4Si5aJ9+OfSHN/2/BTM5/BURPeMYgIloj7ZyzepjP3Kelh6kmJaPS15BSCks2S/LBTFSGrWXD/XalnatUMGJkBGFXNTXD98XltSVjMhqEzkQVjjqN+uB5VjTaAgnq5zZVTXqN0NvWpuRkZlE+AkETN2Z0MZSDXvhYKatmrctoG7xo9S0rWWcrGo0fk5vBVufwCNAHfANMgLepG0Zgw1ArbVPAwB9Ar5pNJnedmHGB3LR/njcHarXG1g9IQBYFpTqsWvDOg+DPIKcLNkijJXbJcp5XZoVr58bPbnkocfi62GfgBlvOvMf3fvbXY/V1s8erm3qleue8+hDA09uus56XeYb34+ABEgA79+6Z6FSvzWxuuAVoEsYLmNeH3IFqDJUiKAbbhaqH6aI2EV1mqsUEBljMGoVE1hlFGBQMr0lkyvpYlX7s+TQLKzrHq0XqhvthyqmB8ksW8EL3G54qGVlAjGK7irydeS1sWtxJoxdAGmqTGs+c07NF6E7qb9rAjuGpY0jRSOGpckKgIVEwU6MHW15iiBTd9L81qjCjWXa4ba9GlNkYhNb6WDL7bWmE2Jkzo33K94/XQ3F68rRn6rOSdPLt23SJx+xfuaMAF7NeK5oNJKP2ga1yorQF8Hhmx7vuu/JymxkMv0bFvUOofcJ1RrECGgl9rbmx+mStIq41bcZ47sx29lkyqy7ZVu75Wh1RYajsfVtjs0LNa7K2h0bc1vYIgGQGMuH7k7iPjGE9YNRaX38z+q+cyEVlq2HAAonFHpMzen2V3cdtDbpzTmDBm5kvBrcqDHTT+N7jcWo2graHkqqoufxv4w+BgCiOS+Vb6FPQE6DL4Es4CHOQsOy8kJ1eRWrULJ7N4uuSPiACOEr4+REKETiiroDBSEKqBmYjEgimARWAL+KQhX5ks43ql4Bk1gQMLElswmEMImt665SDqQFS9oZiAxymdjJRp5VF5aElF1WvSzyXbIWSy8ULmQjAA/oXgA5GdfgNmPt2L961JPdkQktQAhfmdAWvtKjnvCT9KkJbB3YEIjrNiCcTBIH6W+BAMz4GNz+/ZqA0W6uoh97XGJQzJkj8v4nDl50wSvO2dqPm4hoJ2EAJqI9XZqBhbQikbV82/OVsCWkmC835cVwOcot35wHhJACQig4MTJhsUd0F5JSTxK5kNLri0XGqFKm1S7ayel02GeZVVZPBMCEjvATCGFCW/iJGs1IPxEZhbQSOKOadbxQI144MLFCz+sLdM2O6/aECXrHkmp73Bxb2Ey/7bXHgUEg0I2JITZdIQTWNpdkgB6DiWVpptzJXYjHrTJVDm4rvOMnVk8YrMsCmLPfulC7VePkRNxtDcTGFYAtwqKpzDLruoPBA4ceC+8c+uctSAAXkEAMJID9wgPrpjxz7RrThWwe+QRmCCaDQh/MWugyqjHcOjAb8SDKGxAYlAwsQAFFCWcJzCIYG7VuNzGejdAOtLJEbPySlR8xxSHTP2yKZZXrEuFMsa6vWukWqyu12U/0znKNbYl6PcnUbanhSMQaDupOZKMaZQFjkrY5fjKx50RdctRD6KGRfhNYNlTSjJESwsBNoJGO/wwA0HDqcAGEZqwKPkrn6Un77o6f0MjU7GbibVwibW2nDYBtdjPeJzd4YO6PavlI9Qm4+1juZlXqmbv6gEOGTSad8CYtW2JsWyStcA7AE8GRm9cU1v+zHiFacJSYtq6M2ffpReOKF0s0C7yDmfZp297bkInrtdpOY9LwXenqrkjCesbEUmRiJNZz5bKKEd7K4b7RZXCgpufKA9XBRzH9tBnKzvjh5qGuA7VXGjIzXVGO4ZdMVsMGIJFo2BJJHGcBOE5VGxuJNbdsa+/Jkinmf/nHVuveLgtZAQvILoCzEHVja+TiwHZNoIrZUmZGbDmxEBaUFSHwhCNqQggFe5oagR04QsUq40AP2nkDL0RxWHhV5MM4DxjbidKWCFkZ1ozviiSMPV1zVGLDsiAtWJY7LRY+etyKbStYUkjhSZWzakKImsznRC0WblnkmqNaiQQeBGo6X0sn9RLN0a5aebhtYVsNcNtjIUxgmdDWgZVO4BTX7dYqrW/7mv1JjFXaiMG1ViGU+87OIL73re9j9S8R7RoMwES0F1i5ZtW1v/7JxX/8dSRy0pZeJkmrNfplJS9HHR1uGCkOhQ6kyIoQQoyaHmV5UXcvMt3hYMHtiUVGy6xR5YzM6GBTweuLRUYJX0EKE7lIPw19BQET2oAQfmJCB0DaB9iETn1tHgJeXxgOuGO3cRNMNd1lw+QsaiY8nTAo9FQ1wK17yBAIDAYBALPRzMBmqpjdvmSLQxBNyBVORkk/EX4CwASWLIY5EY2tLJCTZRjTJ4dypjStvjqvq9Mqg7l/bFx+a2JK2JTAANNtHHA8pI+4aKs8gj65yjqsaDZ2V9aLaqJDmDXQZSRlKI0Va7FRoQAEQA7oLSAbQB4MzEZctOtexhik/8nYwIpq6IkD11hZmRgTCeO4WffJaKMRouiGWaewvtzjj6JrGP1Dpg9AaPyw7qc9Ti1Hy67IRQLAFsoRdQA2EhvKReBCS8QKFoRI06+CnSbJEH7cyMMCgIOksWXj220z/SaxGxprilmLUrHV+DnaU3S1HbdaqwK2GURzbvw8/9ZcdXDtH1WpAguYZmP0uUc/NGO+JwMHKoaF2AIMGi2NXQtJ8ysN9ZyhZU59vVhVqR9WrOS6BjDjcbXPFJfDtho/txUynZFot91C2GniHd9hOCPC9GfaLjYZF4ktgjDOLrCHjQgzj2ycV1u7fv/pCl5+XW3tPYM9L5yfgaXr0Ub0ZGZUBcQAelzoOtww7WNgkF4S6TcjVmwP6UK3CrucqAKzv/O4Go3E0EZhLxy6a9kMGzaQ8aEXQGVyGKomuT63ALswGiWm7s1cV8l0mayQkLnBrDSbM13FqO5agbS1VXOEQCXnQ+gRdEG4kbAcoQHUhadga+Fq6Fg4ADQcJFbZyqqSpxIHlm1lhD0thrQKTug5dV8qSCGkzMpaTeRckWRFRQhRR94IVJAbS7ZCJMZNjGfLqG7yNeNmZZwurxmnfbyrblkd0blGPm4OKDh2ugWSTdlGAB7r9DvG6xpUj68Wui7nTBN57xOHHHjBmW/eiRcGEdFWMAAT0V7ji1d99eI//jJCTluOZ0e2a2BJTyQz5casrA1X+gYrGgKeDCORgRAJ7JLo1zoberMS7dt5Y3UnImOEFJDSRA6kbE6YJCCE8FWyMW/1RCZq9vsVQgBpGE6GvLYKjVaqbKsSab42ZTQex2wp97ZV+TbH1prUbRjjYm0AeMCoQVFga3G60fp66n2lRg18jNUnt0ae7gnDIdefXRNeko10zZEAemTFFqEjwi5Upodr7aTuqNhS9SVrNvvr15r7kNYsKhtqBlbM2KdqPEclwtRlkkilCuVRayRRK2DqMCF0FZUSqgbrFWYAPTZEFxwA/TBzobqtMGuVTaFq8hbi6oakp+TmZN3qGbVGkmAQdQ/rHkQeKOTRk4OcjdEl3aPo0nCG0JtO6DKs+8LYRSJNIlXUaIHsObEtksSWngjSZs9p9a/VnMNWwQZEq743GF/bHhu/lg4LlJ7c2EIi04pTO5u0t5xPx3BOK1dbAxrbXY3vFMRUY2vFg/6EwaIBWI6e1712bn1D/k+P1hdMG/j75uJ8VE44caUppmMyt8++mzS7AaeTGHWjdNjmBzcOZnqDgZEj5m7EtI2mUeFmIQmN74lAiiQ2PoDEWNsxtjNM3W5G9O0YH3vn3Wmktb6eEwlHR5YA4ArVarjuIYhh51EFYCEGYEPZSCw0qsQtJA5MAKe3XpGlst3t1T1HVXNzNjy2rn+/vGWEKFW9/IDV043BGELBqSKbwPJNmMDWsB2YGMIyVh2OEHHd5LtF5ejy3Wu6Dpcra5v/+ghO3GdRdnNgFTPL16EOAFbR0ko5NnSXFXvdxvY9a0PN645sU0JXXXTVak53Vnki0kmxbNzNKACAoy2ZSJn4QARLCDiiroQDgSryllAKtiWSyGRqJqNDL6n7sCy3Pxa+gWX1OGVLJqLxKQdPJK6IHcSxcLOiWkceAhGcCG7bVMPN6t5JHYDTYKyMa4morHsdEY+aXGtttL0TBiayhKdMaAMwoSV8ZQJb+IkJbdkVqCdH41UVuyu0Fk6bIUZ//84LF8za4UHqiYieGgZgItqb3PyHO1575cVKuEo6nh1m7EhImRH1rAyysh5rq1p1wigRQijh2CIJRbaCghZ2rIuQfYGfkTkjiwmkhJSwpJASQpjIMbGtAwdSxPVmRYcQTjaJ646TTazuUA37cd2aOONlOqQW2m8c255uRftwWe3Jtr2iOH08YhAA3YA3ZQUvACBsBteJH+ZmYlvoiR/2bQ2hNxj4gN9sUx1OCMONNR0/sbojwAgvyYq4KAfzScnEiYlVNqn0xxtmBWu8oG6rmgk0IqWHkRTtUle2pvKeKZdMNjBZBxpANggLyYCsGDmiTAnRCIZHUbRhEoR15HqAaah1AUDZtiqmcNxR1uj3BtcO4YQ5+Mvx0zNhNfhbNemD7rYy/sxEGuSTwJoWWGLQ9NcTy9iymvhJ7EbGMomcHNIsR/tdtTTuOjJwkAAiTUpodu5FW4PnNGSmdYAGCEx+Qkocqxo1U8fasdUMkMjJU/i0r2NqNprVv5ajfaECY/UUR/apbQ6C4ZkjNlY+OnjM8//pTwOAWKYVoZNrbrMilCLuw9C8hx8a/Ud11vHWugVHbtTTq0LYW5jdN43BaSfnyEwdbk0ikVitYZx33FZvPLbjrsTOJhNq0TMitEQikdhQgPEQou3rjPTbgSwqAAJ02aarsGntepObUdhk5fwIxoI7e9VyXVdh93xZdIa8xEUAAxs6gi+RDhJupxsM4UXwu6VKCu7m7mML5Q1qIDTLHxjpnpdM7wngRZDdIszIqidiI7QLA1FPhOVAuDA1YYdwIFBDUUOH6FIwCezRzb12Lk4vni6vFBo/KyuRyVgiTo/FEUkMWxnHEvGI7oWUnlShdIxy4pEspPRm1dOBryBEwap7stbWszf9ZBKeiEO4ABoP2l6aOOpVq+cxBAQCnYvh1o0LgYyI68Ztrdb2lqYJH4NCmNAyoSXsMHxwxIpHrH26RM755GEHX/D6t277501EtJMwABPRXubmO+94/ZWfq4tiJNysDPqtzbZUQqZ9gJGXgUiiuJ6UkhzSST6k0HBGRI8ydpeX1HPTS15eSAnHhhCNO0UpTeyY2I1G3MZ40RirGYYxENKfVTWhDSHCYX98pzjRus9zsjqu2xPD8JTGKnibT8eNUGXGMmfr87l9OOjmIkCMC70hAANvcn5obWqbY1M3expjS6EagHF8ZRVDADm/Ng3rTKyCxNJR4oS1QjBUjDYV4yEvrOVLw6gDNUBCFa04mxV2ULKLAfzYOAE8bdwctDGmOym59WF3c8UMYmgDTIwAWPxcrDnwKPPoo/vv5w+VShtuidELjGBjVx8OmLvP8vvjWdOq9pySzNVgbTJdtTiD5qBWW5HWH8pCiGbNoRRpZGqkIwBpc+JWH+AJP0gFB80K4TQVp91ElbEVrMjYrkgsqLqZevDnyW2MTSwb+0izcfpw3ARCyGaDedkn5tVWrrtpaNoLFtp3P1k/ePFf/GPSCud0HeFP7ATrCmUhcWSwqDbg3Xa/3n+xO23TUO7gVVYBGPvJS5FoY6cPYuMBom48F8kUI3uZNPpKtE1i9FRt4d4jQWO36WHZzSUJMD6Mt8aUtrvC1vl0hfJFpbVO2rjdan6v4SHswaADKYyswC1sCmrDQTYacvpyQWHmUCE3tzKQXb1ueEbecjMlIXVXIXYynkx6ZtmOixF3hpvJqbydRw1CJsZdu8p99IncYjU4IOE6ISAsq24hEYAlEhtJAtuBSoSdwE6vnETYAGLYkc4o2KGx3UAGxgIgHJ33anmERkaAiIWNtLG9gI+wLAquSCLhaDiWSGqmGAjHlyowtklsIUQ0lHEKsXCV8DTQiKB5GXiyOtZcubl8fDAeX5HbtpoQSIxb17k6nFbFsGlLxo01J/cLmfIzUGu1oa43lIUXW3O7TaAf+38fWTCP1b9EtOswABPR3mflmjXX/uonn7vz1prIZq2wKIZ9Uc8gSKSTSCcnAkcmKjZBgLrxIEQ61+WI6IlNNiMCx5Eqm63Y3YH0ICVsSwihahkhZVT2m5k2nRaoOW2SFE5GWd1xsDHXvHeUY7ePzcdeX6QDO64742tRmg8mh9KJ9b1tuRfN5Jlm49aaG4AZbVtorb4RgEFdAAazAK8VdydtEGg2UgSEgdlWJ+FmVrf9JAls20+SwALgdke6Lq3uaJq30VJ1aG1ibaLYqldFvVasDxR0dUY46AdDcljBaszVFE+z616mhJ66cerwq6YAYwbD2TPjyPLXzouXHRvX169WA8uw79F4oIZ4EKYXfs4Sfb4dQlhhMlTEfYNqDkYW7b/JneMb2E7NThJlpyNvWQHcqsnoxC3FmVaDZNgqKU2Mo5ajRSYWtsnIwIKSImn1BEZbXV+LakuDrY7BwaRRyNqlwya1Hqd1qq1gPDarbSLTEY8nhN72oua6SvuK1dMeeaI0bZ+e5Q8OF+esmH74RtWdNuduRd/G4F7jB98SfnxgMrTwsTtqixdv8tw1zWsoNr4jgrSQgc5NPZB1epUFdlpxrdItT7q4xmnl1S0yY7tqrWYDAWADJSALoDnmeSoDOFMEYJGJkUjYjQmcXCjAWCKtw1cKloSSaQ9tAx9hFtX50XohhHRzFZmr5Xu6lLe+uzvMzNzvrjtXzJg+fX59kbX6YGv9xuz8BUv8Yrh5MD9zZZRPlDUwYm3UvTpyKjW1aXWkgyg9Bx60FLGXtnWelPgUnAB+moTTytswyqXDfYfG1nUnqTpA44NiRs+Q7VV6MBQLW8G1EacbVMJJ4CSwIUQEBwKxySSNL2IauVTXXAAyl7THV18mjkgskVgibs1h5EJFotVBt63uFxM/uBScus61dtE8QAHANJtUmMgSrkpXMKElXCW8dOo4YSIpGoOHN64YE8no8aoVDcuenOjGY//vwgVzOPYVEe1SDMBEtLdauWb14R9+V01kPUv3ytGCGPJNPYEdyIySbtYKEsstJJU4iGqJFwpfCScRdiwcCBRQCeHDskI7L1y7ZPXqwIeQcbU5aXD6H9KWhBJSuD0JhLC6I1Xyo1GvOalSez3w+KmGG8sxvo/cuA68W6vpbeXexnIz/iWMWxmA0YDApuaW090V01rc9qQtGu9KCza2ceHNqKpRz55ebXTbC6xoxG3tGYDbHaT3tSZIe/dZOkjvgM2M/FrLq0EbKK2jRAeRVas5QdnX1TxUwYwUkopE3Rgkxi5bhcBkRk1/WVtzzHp1yGnW0NpkqGfJvg/EK4oj4fJcdc7m6sp5eq1tw9yHNTOtGN3ufjkJ11IoP7zM7LdIZ+EhdlRoKe1CKSVhUMvmashYUCPoGUEuNF5aJwwIJFL4sQnspO5MqKqybC0LEYC8rKZJyW60oW20nlXNls/NfrZjQ0O308aGaDQbdkQQG99qGxlLGdsSiTJ2urVWu+I0A5vAxliF6sQIZWdjAIXMaK8YtjeV5tTF4N8eLZ945IaufWpVH0DaWzjN+W3DOKc1ikr4SVrR/az4r+u9RYOqUEJWGSuC7Y4PqY0AbJptudtSdFLfamXvhJuIwADNGGw3J79qFwMZoN6o6bU8DRuqKhsvOYA9vq2DY6asAU5/SYvTkmpsdfU65bo27jSRdxEl1nQ4bo+VC+NKXnXLghN1u8PI56fbI8LS0EbZjrZ6NgVupSqribPIrFLlcmgwPRpaEj5eXL0isLGhmgOgbAwjP4KeRNgSwkO9hq4aPB+Bj3qabANk0v7G7VRjdiQE8JFW+ZoMAGXsNPoKR8Uj7V+giL7eQctptEtPR1mLG+3wHUvEyjiWSGKTqZrMlLW1E6p28yJwrXr7q6Lt1bE10azZbfvmLjRZABWTnZB4hat11QUQl91mqcVYaxQhbE8JT4192ISWzEcmsmQ+AqCHRbxmFAZ2j/3Ld/3Hac95LoiIdi0GYCLau918553/ftnXYMleu94jN2sYY7tlt8e3zcHOct+LdcZXxtu0Xo8Mq0BkqiIPAQ2nZAo9suqLcgI7sRxl3NDtCmu5OMhASkgLUjbmFJEWLOnNCCGlyGgT2eHmPIRwMkpmTTjitTWZbo6q1WpBjbZ+wuO0x920PtZMau3cCsDNnNy+wsTlAsYgMjBAaBAIBEAP4AGNUaubN6nNemNvelWVfRhjFaM0zVrFsFF104zl6RSgatRNAsv2E3t6HQYmlDAQvjKhJdxEl9xoyJ4+Z32s7bRItcTyk7oOIgRRPhmG1sYYrW3P6MCIyMj9ektyqK6WzBxcj+mjy1GD5VbccsaqVZMZ4bHP7lnSt9nq2nfAW7BvZeO0h35/T/+BD978+IrgoLv9ec/e9Osni8fVsiIrqunJiCABRPADeGXdZQK7NbeQyCTC1jlRg0CkvRhW+qpKZHoq7IyCrYWtcrImkfgIW21l0V7l20y2gZhY3yvGesxarRpUF4klVGvJuDxp0D430janGrIcLWztOnHOKZ+w+ff1viVd/3y4smDfv+QPKtUyKrYsR6nEssYaVBvh6KTmpNPzCkfB1gAWOZvnD254sG9BIXaWm/R7AWScOhq1ptCwQ0gAcTqNUyLTKt+0P2rbeNSTm9A3ThbQDKhJszoXbY2Z0zAsm6fCtxqPhYSxkMtDCOQyGNbosyEkCkVIaevRBAXbFznLCNepetlsBvUEWQ/VOGf1WaYWyUyoK5Y1K0aSIIGpBTAJlILWUNqvl7Ko2FEgopqlk2BTxVZRhEzdeImxkVgAet0h0ZzheQbW2VAOkjq8DEILcYBMAitEJs26ANIH6eO4vdrcAIASDkyrx7it4USwlHEAKNhB5KZJcsqJprrzFWQb3X0BWCKOoryyGx8glkhq2s+quNo27VZ6tQtHC0eZxBK2Sj95PJEAwhaxJRIIYYlkijbPaM/MgBAaTmKctrrl5gqxBSAa8m1PJaHdit+Wm7QidHoGZPo1mYF0lXC1J2IYpMOke6Ja2dhdHgjsbuuX7znntOeeOOkyIiJ6xjEAE9Feb+WaNT/8xU0fu+M2E3p+JrGl1oXM/j31A3P/LHmFnKtqdhekqNeckaFocJNVr0BBhSKj4SRwfKGMiKVI0rtXLdwwKZrAq5ocbCedVBNS2llhzwhbHYYhhImdaNSzsyZtBZ2ENoT0eqNwJNNqPu1k1VivYDR7yk1ID63o2557G+lXjy3XupFex6KygdZj7zUaJaBgUAZGDAzgGxiDokin9mwbk0bYGQUhkrq0PWUVozT36pInu8JGoSLLBBYEdN1KQhswPZnA7R+xZZQYb1RnAZjQEp6CMcHKTNGrV40t8yGMEY5qxHJt9IhErEySmMAIK4HRMhMBxsSW74ReUrNE4qK2WG8yAl2PPDR4LxZ95kX+kuxQPr86v/gwf+O//+DSDa869aH7Nqy73flJfd7x+5hVRx0FbZy4tAlzeitPJnbBRSUTV2ao4RHlmdF6txXGsEe0H4dWUldCeMZgsBpXSwIuTOy6DlANbCtwoKUdSSSWjhxoHxU0ZwAGEMBtr++tIw+YZHw74dh4gfFMIoXdNppZYE+YwQgY39a3Kc23ADDVtEOuULCVBeXIYEFYKtRrtswUR+57aOYLHi0VW29vT9StpyqWVnNYrP5MZZbelPWqK+JZQZCrxx5s3ZsZVZaVkaFlorSuclh3QzmhnQVMpH2oqlFZCIGuHlGuIp+D7RollTaWjpW24PmwHWRsWDaEgOvBdlCrwvcbDSIA+FkIAceF0RDNckqr0V89VsgYDGh0K0QKovlvemFXDHICSeR4oYmTgqqX60aIOpQ2SiNKTKxUWzNpy9Vp02LhaC8xUabxE+mTI1lRbvuRZUJkJOJ0YQLbRmIhdpD4CCzE6U8/MWOdwENkQmT0+N9cBbuMgoM4xsSG6+nXJQp2WtkLwCRWM6mqeMTHltm5pHUUuuaYWApHp2l58sDg6c+8dfgqknYulpl48ndunkgsJDXjZ0XQasDvyqCsC42NNDOtJ5OwNbpbq9l0q8bYtC9HW2v48U8NANMly0JAmsaXC0Vdfmzl3HxfeMM733nac0/aykkgInrmMAAT0b+IlWtWP/bkE6+65CtK+aF0D88PHjPw62JG2Pt01XqmDfUXN8g5ZVEoi4IM4ifL/daGDe76gZpl20IlcCBECNsTiScCKWJtHKvuD3s5HeeUdu2MFDnIooK00imIg/VFCGFndBI6EMLOaqs7VmVfCBmHjtcbQchwxE9vE52ssnqiYH1uXInHknCzpndcvm3FYD0uD7fH3cbcuIBWjRW0wqBAYOBouBqFZoNnoDGaV6rRTFGbyJZdofA0mm2iIYSPKICrQ6nLnvC19JQquUlgQYhuv+YWqyV74u17stlLagLG2G4MCOEmMhcBxoRSl50ktJpHZGBguQmMsbJh3qu4pmpr9GDANnGXHl1QX4a/R39/3kvckxY7vdnZ2eFSduFH7//ecju7aVrt99+PHpT7jsYF4UsptN3lu8mozLowEJ4Ng+5sMktvEHnfcaEtR8Wwu/I61tqyI9sLjJPJuOtUQRmnYvVpjdGBHlMJrJmOcOqIYkdF+TyQJBkEcaSFI31dhhBWrSKEiOyZ9UrZNto25UE5u09WqpHQRgRO3gtKwpIjKuv7UtWjnJ3UEk96dm2jhGNDG1VxLD/QcUZ6QVTJIgrgZaASKGWZCKGHbhtaQwooBcvyLCsKSy5cOBqW5QkfUSnqxfS1G+dtXl4rTCtVopKwapmC0ggt32hbeDqtUDVVLXxhYsuTRhTKMiwgjlxfI4M5C3s29RSEgNHoypQjk9lkeoUQENLAmEDIbleXlAkdGKPLIqq60LpxpamkcQXWFaCgFYwGDCSgNZRCZGA0qglUBEsj0jAaSkFpVBXsBErBjlFWyLSuG8BGIzxOGO+q9erkxhNTsVwts3FrHCxPJK1ZkSwRO83vHiwjlDCtOttIZwC4og4gg8BGAhgLjZGrbCQ2kjp8BTuA34p+MaZoDR7rTNpEOX1aM+N+R0wsdc3ZZm3/eFMdeYKpdg7L1emgdFuXlY1z0orlnmjLus110sJnRZDG+HDKXbbxkEDAQpKub4mk2Qd7XJtwB4kO7Pm6/Ol3fuLUE0/eZmmJiJ4hDMBE9K9m5ZrV1/7ixi/cfHu/UVE+WFCpJFb1aPUA5sy/d84h5ey0Ibt34+hcSCmLganWzWDVHR3B4Gg9dERWeTLpkiOxsDMmsIwom+7RJCeyUnguHBuODdvS1Uxc9SAsyGbXX2mlMVhmNKS0uuPGENNifFtoAT3ihcP+pH687bW+razYqhnWY09b8VgbGA2tAUArGAOlYDTKgKNha0QGTrPqGM0aG2MgpDez2iibMY1yolVzIzIirmu70TwVMJGlq14SylbVTnHOaDB20ywgoEtWPOp4MyuNXRjjiQRa+7KuQt8AFeWZSBrARFKlm4IBRMELwq44L+v9GAaiuVg/p7J6NN73oaXPT2YsOL74z+4u8/INj6/a9OTjBxy16ubanx/tXxflM5maJRILcbcYTme7rcN3kNhIMggUnAiWjSSAr41TMXkA4VZntfVyjhUHlqVdX/smACB91zJJImzHl1DayVhl0x8nAIyVdSMtskhqxo6cnKfrxpi63+OpwCS66ndBaz1qwbFU2bWyIZRWYdYkQsXGsrWUYRz5je7lcQTHg2q0PkAUwfOhFIyBTuB4jW86lIJWcFxo3agaTSL4OagEcdTIqPDhaGgFraEN4jCtgYdWyGQRRzCm+XYNrRGFiAxsY7lKOLZJlLBV4yeezlQcbWtq39ZIVzEm9X4FgHTgpLFO6XZz4dOxhbsVO5cARmYndDWGhyQjy5HxlXFC2I2vnWKrdbBZGTiinj7OIPBMfcrtV1Fo5d7GyGHGbtWjJsYOjT0hTJqaI7Jj56XRLD+WcLSpOdhm6/ek2VrE2cZ3AJarZDbZyrRbAFyRAIiM7QkVpv3PBTyodHCssU21Wim07bPReLt5aK1+ARaScW8f/6Nx2rp9ewgU7F5T+sJbLj7tJFb8EtFuxgBMRP+aVq5ZBeC6X9x46e9+VRV55SYzlIKsu26ofa9b5kooWt6cusrobqztd6D14qT6WEUvGV25MdELyisH4CbGtpFUTM+IzkGINAAL19VRLq75jX7CafqVwpsRqEoGQtoz6xBSNKfibOsV3CibHvWFlwAI1mfbelS2J17TSD5oxtc0vbSScJqCdNpfV0NpGI0kRqhhDBxAJVBJs2+wHtt7mngBp6iFF47FYCmhDSyrKEpKuhXlAY0/EQbCxLaJZFJzLUep2O4rjsa2hKtDOGnxdcU2kYSVyHxsQpE24OyVg409ApHOJNoO4JhIxMNeq1R2PpaZyEOSlWUYPV0M5E25Px5Ztf/xZslBL+3/i+nu2lg4cOlrP+3834GP/MW69s4ZgfFiKCWM3VZTV0a+9aOPTMYV9fT+u2oKaXWWrjkyEwMoyIpqTllkiaR9mKvWxL9o64HpIEmTT2tEotYSAMo4yti18R0yhaN1zTaxJRwFCOEoXXNUJC1XA5DZOG3aOvUcQmZ76zy3qBVnnOaAUu3/PtPith3ZQH18edK63y1UY7bYuRhAY2xkAM3GvRPWabYNboz1JTOxl1Y8iokxuKb9VgKc8FJBViRiABkErf69rVfr8LWxQ+1jQkVo211T2tU23MJsye2xsDH/s0A64reJZeOg2n/isQDQ+Oqq/dZMjPtfO8vREBCZuPl6c1VbNQavSqfdAjApJHvNUd8skcjx581p/n41yjX+ygegjd0+e1b6dNKBGwcKQDfq5zz7zPe9/d2Ty09EtIsxABPRv7hVa1YCuP4XP/rGbTfUZD5SbmK7I6UukY19ofZ1a1Ik3apWy4mMla95DiwhfWSsWATVKqyN1UylgrLOA427XqMdAy9JCrAdWA5ko/rX7UlkVwyrOYK0FJAyJ5Oa8Jpjz7QNCg1jAkuNeHHdamy5vRtwo06v2dRZ67GFOq3HU41a37RVaqihE1gaSiEGLN18dfJnu7FcLRwtM7GuO8LRwlYmSaMaANElywpO1fhjSaw5w6eJLcDouqtCy3JVrqdqEstxwor202olE4lWxZqHJCNLkcm6oqa0Uzb5Rh3nQHZCgdz+GoCsDNLUaiPJo2zluo8/Jjlg9mD3tPz93c+dtvCLx/32OZvC1T+7deavhha2Wm9aIknf1bo7T2urIuOnL0Umk7b2TAcHat9LmnPSoYxaj9EadNc4rRatkx+k0rqv1pKyzqdxYlw1YCIbIx5FUsVycpB7WlqVq5O7YbYkzcmEIgBAN4C2mtvW0TzNCX3bpSEobv6bZu90727zQSrXVi08MZ9v721JOgdy8wLG2LRSaJ9peXLPewDIicASiUSsjSPbKkLT2t20cUFo7IkhttXR2gCJbIy83ZyHaXzkbXtXo+7XbrsA2hpopD38Aaixka6l1WrR3XgQxhJCwBr/JULrm7VEQkDaxoOuJ3bjmy+gMU+bHLvw0qmzTGw15ihyNADfiQB4Ih3CG54IGr8mbZdWkvZqhp9OlN1suWJa5w0wEokylqWjC055iQAADWOOPeiQk088ddJPgIhoN2AAJqIOsmrNyut/fq2U+N7t1wyL6SMoJBIKNhIrgOVDWUJBICPCLqecQVhCFkDZdJdM+9i/BgaQwoiMDrNWL4z2RA7CN0JKWDJjJZBSSKsm3G675sg4hi8EbBnXTB5AzTSnDzFGlzxVt5K6HFcJ3Bjaqu2pbmbaVj2wbrV3bb6kkka76Pbuvq0yt/3f6Q7TBpNpo820w2QrzqnmdLXtQU6VvAxU4IhWvVya5fL5ILSFiWVavzrBhECoa05Stdvvpy1nrO9iQVYsEXdj2DGJtORLDl25zyK7NOvAn/S89rXf/Oi+XetsiVuqR/zmofz+Ofl7FOpxrhJmfKFsN7KcoBsjEEiMnQg7NvaI7hl3/LH0nSgjywVULMRpdd+ECq7GkcJBMxhP2dtTwWnv3FjXhVaPx8hk2uoJm5V4zeGC0sN/ihm41UV2J2olVWd7trzj9wlTvmPyFMFpRXGrY3CrDfKW6qtjAzTXr49LzlZX2xhRBpar00bIre9lUjmRXnKm1fi5sWHtA6ga3w1EqFsDNTe/opry2MyEp8ayYhVbjS+wAAgx9n1W+ovZ6pJg0iCqALh2bEE1vsuwNWAsKMBIKKQjARhbwwZQ125jgG6YNI3rWEpb60RIW0NaOmk2S2mWoPEFiYBlawgIW0MAtvGcyBIKcWzLKM3YmcbI6sJG4iOOkbGMVkLa0ICwjLZj6zUvfAOETrcLZQ4/5EgplQGEwaJ9lwCYP2/BFn5yRER7BAZgIupQq9esXPbE4/c/fN81t32nZPWURFYhEyRuAFs6BgJpBSkm3T23FGRZwUksP3J94brwPVgSQhadwLGTGJ5jJ7YVi7QPsBQAhJSAGVV9de0YMVYvpUfcRl2uHuvlK5wYSpu0c2+r86fWJpBxSUKptuV66hv0Cb3yikHrcavHYDatBBMx2lppN2pTmw2DI5NpdXcM4kZ6F7ZWJQ+AimQ64w6a7XvHlSNOq0BNe4vWdnYu9rN1ALZIHFF3kWRRcWN1cnb9fifqof0P/mHhnM/fdd2Q+Of0cChZPXrbZ2L7hXjMHHfXzJmh8eujOeFo34k8typFXEDFQhLCr8PXbbW1DhLLiC454qDmQOcwimaVb2uQ5wDZOvz26lQFu71x9TbFOmOJpNFeoHUGEmliy0STBoV++qZoIrsdb6k3a1xT25V+n5rtvrWIgaR54v1tt5Fu38UWBkZusBwlHC2a3854ULaIAVS174lmE19jQtim7gAmragHYNlaxdJyFNKfWvr7FamxKcec5uxljUMwcCWUaJZ8qhp5A8BknRiOTgfcgq0dp2YAxyQxbB+hghXDcowK4cGYKM4ikZGRjY7ZjWpnk86u1f71SnO6tbEPkM+/6rTmS+aoww+BaP04zJJ9F7X9dMwCRlYi6iQMwEREWL1mpQFu+MV1Sshv3H7TsCzUhQ8hItgmtiCmyMDpeKoW4lD7gLBtrZyMyPih42cc41jKsRLbimFJ0ewGrODVdC5n16oq54oIQozoXFviBXQ6ulUjBhuVjr6rkSSNeiSljVZQGokyIaB0PNysTB6XeJtbSwzs9H4daUPEdEJby9WQgBDFnmEAlmyrj2s2eNZmrKpWGRsQrZ6u6VcD8YhnuSqdoEVmE12zVWRZjlKxlRZg6mQSj2UbpzsE0OWV0k6Y7X0ICygfGCn5/GOnHZBcG/zbjOymg5xNJ+PugULv6x/9/g/+s377MSfen5sFIKj5SdVJU7QngpyoeKi3BuAJ4Ke9edMxfvOoeKj5iG0kFkIBJPDSAJzAqqAAIEQmgdWa5TUVIVuD1zyCxvIJXSIbJ6dZ3R0av1H1nUgTW1vK/+NN+bd4LEqlca5ZLWkA2Lkkqe54fk0rPu22iuXJpXuK9wU78rap5oWatI2xJ7lcACA0tp+tt2raTc0GIDIJAJPIZNSzHJXOGJS2SfZEAgM7WwdgiSTUPmCQyMZPJ5JpaMyYEFYCIaBUYDuuUGHddUxSr3mAaYx1N9aZvzHzreXoRhVx84K3ml8ACUenGdVzYtjKEokjAhi0UjeAtvmWkrrJJcZCIqPYgYFJROPXJz0BibF8jdAoLayCUnVLWuqzL38BYI464mBoAFiy374wWLBg3g78CIiIOg8DMBHROKtWr4TAtTf92Ej55dt+FllZCKGFk8ReCDsNe+marZbD6QeoNraCo5xM6OV6szXhupaIlPRsmSi4tpVAQMEDMNhqnWsAozMigTEZVGoqm0EF2hitoU2ibaN1HEujNJQKlYBK63vbOt3VbBiTVO22yZMaE/A2H2vEaM5YYwCDWAOA1NLWSNKFwvdiX6jQuBAigJv36pCiajJwASGQ2CaxACE8JFUHQlquat337yir2d+4NUausJUnxr5iCI3dJcvTMpUVh71s5nyz2ZlWfWLmQcc9Mdff/KS1/4XZP8748/Wfu3P2LcsPtVyV1j/7uXpGVvKo+AjSqVwzCC3EaXNlZZwR0e2hnkfFRmwbBYE8yq0utAm8tOatGYadEH4MO42eATIJ7ASWj7AVjFVbQk7HoE4fjx8lyEmn1ApqhdBYJpZbGFp5i7m3ca7StJUO7o2xRGzahtHamV2Lt1jAnXerEDc7IUfN6ugskDFo9umdGIJjKRyNZqZ1Ew0AsfSdeFFFCSQiMsd4gRfEvTpZLxLHTlae2P37PxWU4+RR7gFGTH2G8sr1aFm+G0ZCaZMIaO3aMYSAttJe+lHiqMRqG7ZdAGmNtEjjrnBMYzpfV5vYam/v0CgkgGYgd5G4MpCtoG+gYQNGmsYxpo2ua3EGsTSxGPvCKE6/umpUGtt+7EB9/N9fdNShh0CYJYv2XTCfKZeI6CliACYi2pqVq1dB4PEnlt/98D+FEBff/otQ+JBSwY6EA8AViS0SGDiyDgNt7NBkIIV0LN+LbRfCtZX0mzfSnicrEELBtUzYaPPZDNDGGBMn0Ma0aoC1NtqkDaEj5SfGhjFR2juxVV1sYGKBWELrxj10a+DopK19pg1YZuxxmpYBy09gjGtFSKSFBJaebqK3vuxMCX3EYUcAENCLFy0GAJgF8xesXLW6NWESgMeWP9EKZPf848HGWF8pIT98423tw9JCSsuFitDWQRF2MUQsAYFm9SYAYeuDnc3BosNX9C0QGTep9PTlIGepffpXnZq7+98qf/7bT+YtF91XrOjOyiCDEEBGlB2oLMo+Ag+BjRhA0qzZrCKXPk1fBeAjSGDZUK0m0OnKIfz2JWnb7zQPo3UoU/3NTNdsPBZjIyelUXlUd6cNCnIyaH9XVfvNLYrWSE5eYtKJplpLppQONG0a30Q0Q2qtccg72Nx6fKdWseXj3PLiqV+e0DfcgS2bizzA1uNfhnAUTPprpRyj6pELIGeV+qKao6Oeapxk/Gef7uzXPbzw4eV6TVQ8zHUqERIIB3HO3TTjmL8Vn3dnsXsO6qPrnpzpDC40G52VcvWG1bc9fOjDg75OpHQ0IBp9ZccOtlm1O3bFjqvsHbuM2x+3Fby1zM4maI0G50SRsU0iPTuKmiNFu0gsoZSxImO5yoTGgjGmbquoMVeZtJLPvvzUow49GMIs2Zdxl4hoZ2IAJiLaMStXr0rvmB97cvk9Dz0AIaCdL9xyc8W1PGUybqn93tgRSgiR2C4sCSGkYwnbAgyEgDYZE9RlBgKOiowyRhvf1OJI1BpjbhlomLQiVwPGKGNHyg1No77X1C0ASdWaOMbt2Ed6+8TCsGxlYIStYUzWDoyl5qjqef9+5mGHHilgBLB40X7P9AA2K1etGhceBAA89sRyaAnZymASRtz78N+vfuTmx7yTEllEJgs/63bLQw9Y/pLczS9ef+f9jx7yDz/zzVunZdxKF5RnDTtQtlAWYgNtQ/kIEtgKdgk9gSlYSCCgYVzUA/gF1DzU0waobtogWACAMhYgAriJcGwkofHr8MdeTTtCGxsCCraLsSibQQAgjdbpmNIh/Nb3GzUUKsihWT+sjV2KujwkthOl1chhs2d1OmyYDz1SzQIQjm5N3JpWKgJwkQDIy2oG5XRJmrFD+LJtdKm6zkdTdKU1AEzNNrG15YRsthb0p9he81H6hUsMmGZuVwah+vzZp0ICGpAGloFljjr0EADQbZsQgMGSfRc1l5jWFy7LVi6/5qL/8J+f6z5gju8iVlElk7OFfbB4sjR6aKm7nsfGGqyZ2GgJby26f6JfntS6jsn+I0A4WO7+50rL/fP9TyxzJ0bZUEAIeGkbAAEHCAT8tMlDW99aezsDcNtbmqc5bQ6tYmlnYxikg05lnFJ64QVwHZPEWn7wlDOPOvhgANBmyaJ9F8ybv+3TTkRETxUDMBHRTrNyzVi6e/zJZQDuffA+CBghDaQQuPiWG7XlCNG8V5aiMSwzEiEQwcmYOmDq8Bu5whhj0uhrKWPXlQcYE8uk5E4YWSddGc1pSdKELC0tLPWZ018AYSD0UYceCgMIvWTfxQJ6zx+sdd83Pm+1/9zE6kdPAX4msyg+sH/dK3K/OmHjstyvNvzuLSd95IQ7WiuvXLVyfCoRYw+EeGzZ8mbQMjBSCANhDGQ6nu09//inlGMtbwWMgTBCSmgAWsh0MF8DmQ7kK6EEcPXN37YQV5CLYbsIARRQiYUtoWwkLiJbGDQHlI7gSYEKsjEya8LZC9TmJZXwRU6EsLIgMcmb8Ie5+4Vwn8Q+VeGUknnWyuHio8uVLdzbflmefeA/3V5ddV0nQTa2kIjGOMbp4F6mYnJjh50AMIH2L3zRi9rOhoGWjctDGmgB1R7nAGOOOvyQ5sq6rWVCawMGwJL9Fo3bZptnupby9ltu3vzoPese+8r64/aJp89ZI7tqwouQ6UflILFielifMfJY5GUe7J73uDngn5X9qqur9vCKt/cf8ckf/GY49KFE2tgdIh2nqvlveh7sSdW/U9b9tlXzAhgfg9s64RsDGDsTffplzYl/jAbMUYccIpqrLV60HwyYdYmIdj0GYCKi3Wnl6lVoTlLS0OqZ2s6IqR+Pew/wzOeQXenmO29/8VU/SjJzkO+CyGUOio+e9siB/orTSo9P+/wDv/z0Ke/q/dC8Y07b9ob2AKvXrrz5Vz/8+3c+PDIAdwjYCAtY6GLpYRBFDL7ZX7nvontx9DoxY4XZJ48orhYPyA4cG27q/+N3zJByKmU1t/h7te9ZL7jotBNO3N1Hs9usXrNy2ZPLHnnk7oJjblh5TdnpU1a2nO1HrArBppWl6W876MQjFx0EgyUL9pkyXq5ctbrxyEyux208f2zZ8rFltrtkny39Tv1r/t4REf1rE0I8U7MfEBHRNrEKaCukUajVIIPMflF9wCnYZRWqbqs01BvaBez7aKX+qu2c/Gf3mzdnwVvO/dB++x918bfeHT8UZDeuUMDmCLesw8IIdrWY2xjtmx9a7s8fKfceWPjzy3Hbgvsfdf4YmSdQno8n/f7bBma99czPnNLB6RfAvLkL5s1dcPKJpwB4Az70FLawPUl1wfy5T2HLRES0t2ANMBER7aHyrz69Khd7+2dhdRfysm/2xtMy/zw+e3f+hg33zZ0576hD3/LC3+7uMu6wNWtX3nrbD2sl3HfrVYvWLqtnikfmRs2xduXwvu+4b3xw/f5HuY+fUP9N4YGh4QenLzjzzLkHH5lo69STlu7ughMREe312ASaiIj2XDffdue//e8P3EU9yBdFzp89a2B+duN/ZH6y782bbvwKan/7jy8suLB72p7emXkr1qxaCWDFE48ZCWUJJS2tLEBDY8lCjodERES0k7EJNBER7cGsBGEdcdbKVXXiVOJskDibkjnT9hkdRfji9fesmPfEEdiLA/Dc+Qta/xIREdEusEPzBBIREe06p510ci4/qIdDE8XCCiuxvSnufSCZPdw97bh9ccfq3Mbh+3Z3GYmIiGhvwgBMRER7rhvP/0AyEnbFQ8IKc6pmJ7V67IxkvZlFzHxk8PHgut1dQCIiItqbMAATEdGe67TnnWTparjZ8pMaEmUSpRO9QvZn5uCxSx9bN1B8YMMtu7uMREREtNdgACYioj3ary54ez2oI4xMouLYXh3PDGJ/8LnTjp6Fvicek9buLh8RERHtPRiAiYhoz6ahS5FVLVVDEcX2cJwdTnKjfu+s/bB+05xvbvjm7i4fERER7TUYgImIaI922sknfvqkk0ypOj1e7asRHSdPRtPWTCu6i3DIZ//gVcP1ldW7u4xERES0d2AAJiKiPd1Rhx5YGrVqg6ped02sdJSMRKo63ykchZmP15+IN+7uAhIREdHegQGYiIj2dKedfOIv3vaOUgl6aFjXIzcKBkN/o9tfz2LdXebJygO7u4BERES0d2AAJiKivcBpz3/OJ49/LkaGKmvDtZszQTkpe+Ho4Zi98tffG12/u0tHREREewd7dxeAiIhou3zobW+D0Jf95cejta4Hhoqe1zW3HFth+dD7bnug/7hDZp6yuwtIREREezrWABMR0V7jQ2/9/65680f8sBKVouUDhT/FS5b3Fu69cfNA1dvdRSMiIqK9gABgjNndxSAiItoBq9as/NFN1ynj/+a3n7dK4VVX3z1/7oLdXSgiIiLaowkhGICJiIiIiIjoX58Qgk2giYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERER/Qs6++yzhRBvfOMbt7TCqlWrLMsSQjz44IO7smALFy4Uk/zXf/3XlGtefPHFT3lH6RlIZbPZBQsWvOxlL7v55pufRtm3aPny5UKI+++//6m9/cwzzzzzzDOf6b0QEQGwd3cBiIiIiJ4RuVzuuuuu+/KXv9zX1zf51SuvvDKTyVSr1V1cqhtvvDEMQwCvfvWrTzvttHPPPRfA7Nmzn4l9nXTSSV/84he11tVqdfPmzT/96U9f/OIX33TTTS94wQueid0REe35GICJiIjoX9Pzn//8u+++++qrr37f+9434aUkSb71rW+99rWvveqqq3ZxqY444oj0QSaTmTt37rOe9awtrXn11VfPnz//6eyrt7e3ffuvfe1rX/e6173vfe/bxZXeRER7DjaBJiIion9NjuO8+c1vvvzyy40xE176+c9/Pjg4+KY3vWm3FGw7PfroowMDAzt3m2ecccYjjzyy6+u9iYj2EAzAREREtJc588wzf/jDH65Zs+btb3/7EUccUSwWTzjhhE9+8pNBEExY87zzzlu2bNltt902YfkVV1xxxhlnTJs2bcJyY8w3vvGNpUuX9vX1zZgx46Uvfektt9wyuQDXXHPNC17wgunTpy9YsOCMM874zW9+8+CDDwohHn30UTT77t5www3Pf/7zi8XikUceecEFF0wu22TpG//3f//36KOPvvPOOz/72c/ecccdrZcuuuiiCy+88IADDujv73/5y19+5513ArjzzjvPPPPMmTNnLlq06C1vecvQ0NDWd9HT06O1HhkZmby77T/8a6+99kUvetHMmTPnz59/5plnTrnOlP785z8/61nPKhQKz3nOc+69914Axx57rBDi+uuvv/7664UQCxcufPp7ISLaCgZgIiIi2sv8/e9//+Uvf3n88cevX7/+Pe95zw9+8IOXvexll19++RFHHLFmzZr2NRcuXPhv//Zvl19+efvCJ5988re//e3b3va2CZsNw/Dkk0/+2Mc+tnTp0u9+97tf+9rX5s2b95KXvOSjH/1oax2l1Omnn37OOecccMABl1122UUXXZQGxc997nPtm/rmN7/5nve856UvfekPf/jD17/+9VdfffWzn/3s7al3vfrqqy+66KJXvOIVBxxwwISXvvjFL/7617/++Mc/fuWVV7que9ppp331q1895ZRTurq6rrjiio997GN/+9vfjj766DTcbskdd9zR09MzZ86cybvbnsPXWp9xxhlvfOMb999//69//esXXXRRX1/fi1/84gmHP6WRkZGXvOQl06ZN+/a3v53JZF72spfFcfztb3/7rrvuOvnkk08++eS77rrrxhtvfJp7ISLaNkNERES099hnn30AXHrppe0Lh4eHjzzyyNNOO01rbYx5wxve8PKXv9wY8+Mf/9hxnPXr17fW/PCHP3zAAQcYYx555BEADzzwQLr83e9+9+LFizdu3Ni+2TvvvDOTyVx33XXp08985jO5XO7ee+9tX+ePf/yj67oAHnnkkbR406ZNW7duXWuFVatWZbPZiy++uP1dBxxwwIUXXjjhuKZPnz4wMNB6+qUvfan1eP78+fV6vbXya17zGgDve9/7WktKpVJPT89nPvOZ9GnrDBhjoihau3btpZde6jjOf//3f0+5u+05/M9+9rO5XO6+++5rX+fPf/6z7/sAJiyf4Le//S2AJ554whjz2GOPnXvuua1T9KpXvepVr3pVa82nsxcioq0Aa4CJiIhob3TKKaecf/757Uu6u7uvvPLKm2+++e67725f/rKXvay/v/9b3/pW+jStdZxc/RvH8Te+8Y0vfOEL06dPb1/+3Oc+97zzzvvyl78MwBjzxS9+8cILL2wNZJU64YQT3vve97YvOeuss2bNmtV6Om/evFe84hW33nrrNo/rNa95zZRjVgN4xStekSbAVDpv0Bve8IbWkkKhsHTp0vZZgn7yk5+k0yC5rjtnzpyPfexjH/jABz7+8Y9P3t12Hv4XvvCFj3/844cffnj7Oscff/zHPvaxbR7aggULpJRXXnmlUmrx4sVXXnll+ylqeZp7ISLaOgZgIiIi2vuceuqpkxceffTRPT09EwKwbdtvectbrrzySq01gJ/85Cejo6OT5wd+6KGHgiB45StfOXmS3ksuuSTt3Lts2bLR0dHTTjttm+WZO3fuhBXmz5+/fv36bR7X5De2tNotp9IOzP39/e0LZ8yYsWnTptbTk0466a677rrrrrv++te/Pvzwwxs3bvzsZz8r5djtX2t3T/PwX/jCF27z0JYsWXLJJZd8+ctf3meffT71qU/V6/UpV3uaeyEi2jpOg0RERER7HzNpYOeUZVlKqQkLzz333M997nO/+c1vXvSiF11xxRVnnnlmb2/vhHXSPHnNNdfst99+W9ppGqGTJNnSSy2O40xYIZfL1Wq1LW25pT2dbv2l9OmUC1smTIO0lW1uz+Gn53zCkba/tE3vfOc7X/va137nO9/5n//5n+uuu+4Pf/hDsVjc6XshItoKBmAiIiLa+9xyyy3/9V//NWHh/fffPzAwcOyxx05Yvs8++6RDYe2333633nprOuLxBLNnz545c+bGjRtf//rXT3jpsssuSxvrLlmypKur6+abbz766KMnl+dpHc/utj2Hv3jx4u7u7t/+9rdHHXXUhHV+85vfbHMXQRAIIXp7e//zP//z1a9+9T777PPb3/42bcjd7mnuhYho69gEmoiIiPY+t9xyyze+8Y32JeVy+bzzzlu6dOkxxxwzef23ve1tv/jFLz72sY8ddNBBz3nOc6bc5ic/+cmPf/zj//znP9sXXnfddeeff35aQSqEeN/73vepT30qncKn5S9/+ctXvvKVp3lEu932HP6HP/zhT33qU/fcc0/7Ovfee+9nPvOZbW7//e9/f6vvdD6ft217dHR08mpPcy9ERFvHGmAiIiLa+7z+9a+/8MILf/vb355++ukzZ8785z//+ZWvfCWTydxwww1CiMnrv+QlL5k+ffq11157ySWXbGmb55133p/+9Kfjjjvu/PPPf97znmeMufHGG6+55povf/nLrcz8kY985O677z7++OPf8Y53nHTSSZZl3X777V/96lfPPvvs1jhbe6ntOfwPfvCD9913X+vwHce54447Lr300vPOO+9rX/va1rd/1llnXXbZZeedd96///u/f/e735VSbqlP79PZCxHR1jEAExER0d7nyCOP/NznPvfJT37yi1/84sqVKw855JDzzjvvggsu8DxvyvVt2z7nnHMuvvjis88+eyub/fa3v/3Sl770yiuv/M53vmNZ1lFHHfXHP/6xvU21bds/+9nPvv/971999dU//OEPARx33HHXXnvt4Ycf/tBDD7WP0rw32ubhSyl/8IMf/PSnP/3GN77x4x//OIqi448//gc/+MFxxx139913Z7PZrWz8ec973g9+8INPf/rT3//+9w866KCbbrppSyN+PZ29EBFtnQBHFCAiIqK9ysKFC88///z3v//9u7sgRES0NxFCsA8wEdEU/vKXv7SmAPne97434dWbb7755JNP7u7unjdv3llnnbV8+fKtbOp3v/vdv//7v8+aNWv+/PmvfvWr//SnPz2TBac9ztavpSmNjIzMmDGD6a5jbdq06U1vetOsWbNmzZp19tlnb9y4cUtrLl68ePKsRUceeeSuLO0Ek8vTshtL9a9q6x8vf/7zn1/84hdPnz59/vz5Z5xxxgMPPLCVTfFPVafZ+p3MDl087c4++2whxB133PEMFHmnYQAmIprCwQcfnE6emclkJrx0/fXXv/CFL5w9e/Y3v/nN//mf/xkaGjrssMMeeuihKbdz+eWXv+AFL+jt7f3KV77yP//zP7ZtL1269Prrr3/mj4D2FFu5lrbkox/9qG3bF1544TNZLtpDRVG0dOnSRx999LLLLvva17724IMPnnrqqXEcT7nyj370o7vGe/azn71w4cJdXOZ2lS3bjaX6V7WVj5drrrnmxBNPnDlz5lVXXfWlL31Ja3300Uf/9Kc/nXI7/FPVabZ+J7NDF0+7X/3qV9v5Pe/uZ4iIaAvy+fw111zTehpF0Zw5c9761re2lmitly5desopp0x+7/DwcLFY/OhHP9q+8EMf+lB3d3e5XH7mykx7pgnX0pbcfffdUsprr712FxRp73X99dc/8MADu7sUz4gf/vCHuVxuaGgofbpu3Trbtm+88cbtee/q1att2/7d7373DJaP9kgTPl42bdrU09PzyU9+sn2dd7/73TNmzBgeHp7wXv6p6jRbv5PZoYunXalUmjdv3rnnngvg9ttvf2bKvhOANcBERDvk7rvvXrt27QUXXNBaIoQ477zz/vjHPyqlJqz8hz/8oVqtfvCDH2xfeMEFF4yMjNx+++27oLS01zHGvOMd71i6dOlZZ521u8uyRzvjjDMOPvjg3V2KZ8TDDz+8cOHCnp6e9OmsWbPmzp372GOPbc97v/71ry9evPiUU055JgtIe4Hf/e53QRB86EMfal/46U9/emBgYPIk2PxT1Wm2fiezQxdPuw9/+MOO43ziE594psq98zAAExHtAMdxXv7yl8+bN699oe/7QRCUy+UJK69evXr69OldXV3tC3t7e4vF4lY69VEn++Y3v3nvvfdedNFFy5cvNxyisiMtWLBg2bJlGzZsSJ+uXbt2zZo125P2gyD4xje+cf755z/DBaS9wEMPPbRo0SLXddsXFgqFBQsWTO6wwz9VnWbrdzI7dPG0/OEPf7j88suvuOKKXC73TJV752EAJiLaAUcfffSNN9444Q/Dz3/+84ULF3Z3d09YuVQqFQqFyRspFovsDkeTDQwMfPjDHxZCHHfccfvtt1/aH48xuNO8+tWvnj9//gte8IJrr732+uuvf/GLX3zMMcc873nP2+Ybv//978dx/MY3vnEXFJL2cLNnz167du2EhUqpDRs2zJ8/f8Jy/qnqNFu/k9mhiycVBME555zzpje96dRTT32mCr1TMQATET0tt95667e//e3//u//nvLVKQc+lZKfvTSF97///fV6/Qtf+MKKFSvWrVv3yU9+8iMf+cjHP/7x3V0u2qWy2ex//ud//uMf/3jNa15z5pln/vOf/3znO9+Zz+e3+cZLLrnkzW9+8/asSf/yTjrppNHR0R/96EftC7/1rW+FYfic5zxn8vr8U9Xh2u9kdvTiAfCJT3xidHT0S1/60q4o685g7+4CEBHtxe6+++4zzjjjP/7jP/7jP/5jd5eF9m6PPfbYd7/73e985ztveMMb0iXvete7CoXCW9/61re85S377LPPbi0d7TqXXnrpBRdccMkll6T9wK+55ppzzjmnVCq94x3v2Mq7fv/73//jH/+YcM9KHevggw9+xzvecc455wwNDb385S9XSv3gBz/46Ec/es4552ypEo861oQ7mR29eO69994vfelL3//+93t7e3d10Z8qBmAi6milUml4eLj1tLe3d8qWYFO6++67Tz311KVLl1555ZVbWkdrvZ0LaW/3dK4lAHfccUexWGyl39Qb3/jGd77znX/84x8ZgDtErVb76Ec/+slPfvJd73pXuuT9739/qVRK7z49z9vSGy+99NIXvvCFixcv3lUlpT3dV77ylf7+/ve///1vf/vbHccB0NPT8/nPf37KlfmnqmNNeSez/RdPkiTnnHPOi170ojPPPHPXFfppY9sGIupoV1555T5tvvWtb23nG++5557TTjtt6dKlP/zhDy3LmnKdrq6ukZGRyctLpdLkDsO0t3vK11Jq1apVc+bMmbBQSjlr1qzJ3bHoX9XDDz9cKpVOP/309oWnn3768PDwsmXLtvSu1atX33jjja3MTATAsqyPf/zjo6OjDz/88O9//3tjzJe+9KXW6OLt+KeqY23pTmb7L56LLrpo2bJlX//613dhqXcCBmAi6mjvf//722eHe8973rM977r33ntPPfXUk08++dprr02/HJ3S3LlzBwYG2msFAQwNDY2Ojs6ePfvpFp32ME/tWmo54IADli9fHoZh+8J6vb5y5coDDzxwp5aU9lxpG8Jarda+sFqtAujv79/Su77+9a8vXLjwhS984TNdPNrrWJa1//77f/CDH3z2s5999tlnT7kO/1R1pm3eyWzPxXPDDTeUy+V58+aJpvRLk+c///mf+cxnntHyPx0MwEREO+a+++5L2wtde+21tj2xI0mlUrnlllvq9TqApUuXZrPZiy++uH2FSy65pFAonHDCCbuuxLSnar9aTj31VN/3P/WpT7Wv8JnPfKa3t/fEE0/cTQWkXW3hwoULFiy46qqr2hd+85vfXLx48YwZMzD+mknV6/Urr7zy/PPPn3IcI6Krr776rrvumlBHxz9VHW7rdzIt27x4vv3tb9813s033wzg61//+p48Ngr7ABMR7YCHHnro1FNPnTdv3gc+8IH777+//aWjjjpKCLF8+fJTTz112bJlixYtyuVy3/nOd17zmtesXbs2bdP461//+hvf+MZXvvKVvWKiPHqmtV8tM2bM+NrXvvamN71p5cqVZ555pmVZ119//TXXXPODH/yArRA7yre+9a0XvvCFpVLpNa95jTHm+9///nXXXfeb3/wmfbX9mkmXfP/73w/DcE++16TdaHh4+IILLnjPe95zyCGHtC/nn6pOts07mfTx9lw8E14CMDo6CuCggw6a3Klnz8EATES0A+68887BwcHBwcFnP/vZE14KgmDyEDWveMUrfvWrX11xxRXvfe97K5XKUUcd9eMf//gVr3jFriov7U1e97rXLVmy5BOf+MTb3/52rfUxxxzz5z//+Zhjjtnd5aJdaunSpX//+9/TUa+UUsccc8xf/vKXo446akvrX3LJJW984xuLxeKuLCTtLT7ykY+4rnvhhRdufTX+qeoo23kns50Xz95IADDG7O5iEBERERERET2DhBDsA0xEREREREQdgQGYiIiIiIiIOgIDMBEREREREXUEBmAiIiIiIiLqCBwFmojoKRLiwl2+z17g4F2+04XA3F29z5kWMtYu2tehQGYX7WrMsbt8j93Akl29z9y8ij2rsmv2NdceKFobd82+WvbFygx20QG2HIIHdvEe86gvwBO7Zl8zMNqPNbtmXy35gSS/qbqLd4plQGlX7xP37Kod1YBHd9W+mtQw1JO7eqdrEjxZ29U7fRAY2tX7xIX/QqMmswaYiIiIiIiIOgIDMBEREREREXUEBmAiIiIiIiLqCAzARERERERE1BEYgImIiIiIiKgjMAATERERERFRR2AAJiIiIiIioo7AAExEREREREQdgQGYiIiIiIiIOgIDMBEREREREXUEBmAiIiIiIiLqCAzARERERERE1BEYgImIiIiIiKgjMAATERERERFRR2AAJiIiIiIioo7AAExEREREREQdgQGYiIiIiIiIOgIDMBEREREREXUEAcAYs7uLQURERERERPQMEkKwBpiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiKijsAATERERERERB2BAZiIiIiIiIg6AgMwERERERERdQQGYCIiIiIiIuoIDMBERERERETUERiAiYiIiIiIqCMwABMREREREVFHYAAmIiIiIiIiIiIiIiIiIiIiIiIiIiKivYgAYIzZ3cUgIiIiIiIiegYJIdgHmIiIiIiIiDoCAzARERERERF1BAZg3Hbbbeedd94BBxzQ1dWVy+UWLVr0hje84YYbbmDL8B3C00hERERERHu4f5EA/JnPfEYIIYT4whe+sD3LUxs3bvy3f/u3pUuXXnXVVY8++mi5XK7Vak888cT3vve9M84448gjj3z00Ue3f18d66mdxpYjjzwyPZ9bX+1fTCaTEU2HHHLI09nU6tWrbdtube2lL33pzirk3uK0004TO+Lkk0/e/o3vUb/v7ZdNO9u2C4XC9OnTjzzyyDPOOOPiiy9+5JFHdndhd4KNGzemB7hw4cLdXZZn8JNqjzpMIiKif3n/IgH4KVi7du2xxx578803p08tyzrkkEMOOOAAKRvn5P777z/uuOOWL1+++8q4F+BpfPoefPDB22+//Sm//fLLL1dK7bzi0N5HKVWpVDZv3nzffffdcMMNH/jABw488MCXvOQly5Yt291FIyIiItqz2Lu7ALuHMeZVr3rV6tWrAWQymS984QvnnntuJpMBUKvVLr300v/+7/+OoqhUKp177rm33nqrEGJ3F3lPxNO4s3z1q199/vOf/xTeGEXRVVddtbOLs5c54YQT8vn8hIU/+9nPtNZCiNNPP33CS0+zyn23m3BQSqk4juv1+sDAwNq1a0dGRtLlv/jFL/70pz/dcMMNT+3SIiIiIvqX1KEB+LrrrrvrrrsAOI7zhz/84aijjmq9lM1mP/ShD51wwgnPf/7ztda33377lVde+da3vnX3FXbPxdP4NEkpp0+fvmHDhp/85CerV6+eN2/ejm7huuuu27RpE4A5c+asXbv2GSjjXuATn/jE5IWZTCYIAsuybrzxxl1fpGfU1g9qxYoVP/vZzz796U9v3rx5eHj4Va961YMPPjhjxoxdWUIiIiKiPVaHNoH+5je/mT744Ac/2B7bWk488cR3vOMd6eNLL71015Vsr8LT+DRJKc877zwASqkrrrjiKWzha1/7WvrgbW97284sGe219tlnn3e/+93333//4sWLAQwODl5wwQW7u1BEREREe4pODMBJkvz+979PH2+lTvL8889PHzzwwAMbNmzYFSXbq/A07hRvfetbLcsC8I1vfCMMwx167z333JPWwB9yyCEnnnjiM1K+jnfBBRdUKpVKpfK+971vd5dlB8yaNes73/lO+vj666+v1Wq7tzxEREREe4hODMCbNm1Kk0ZXV9eCBQu2tNqSJUt6enrSx08++eQuKtzeg6dxp5g7d+7LXvYyAJs3b/7Rj360Q+/96le/mj545zvfyf7VzxDHcXK5XC6Xcxxnd5dlxxx//PFHHHEEgFqt9rvf/W53F4eIiIhoj7CnBOCRkZFrrrnmrLPOOuSQQ3p7e7PZ7H777fe85z3vc5/73Jo1a3buvqrVavqgUChsZTUhxM9+9rObbrrppptuWrRo0VbWfPTRRz/+8Y8/61nPmj17tud58+bNO/744z/2sY/df//9O1Sw4eHhq6+++pWvfOVBBx1ULBZ7enqOOeaYs8466/LLL2+Vefvt3FNqjLnlllte//rXH3rooWeffTaegdP4lEsypad/MtetW/ee97znuc99brFYXLJkyate9aof//jHO3H9llYr8Vag3R5DQ0M//OEPAXR1db3hDW/Y/jdiz77SAKxdu/aiiy468cQT586d63nenDlz0l+oBx544ClsbftNeWmtWLHilltuueWWW1auXLmV94ZheNllly1dunTOnDme582ePfu44477xCc+MeUZeOSRRy644IJjjz129uzZvu8vWLDg5JNP/vKXv7x+/fqde0StCZ+2Mgz7LvuFaletVq+//vqzzz77sMMOmzZtmu/7CxcuPPHEEz/xiU/smgmcnvJv6w7Z7YdJREREUzNNH/jAB9IlX/3qV83Os3z58rR66qSTTpr8aq1W++xnP9vV1bWl4kkpzz///DAMjTGtO+Djjz++fSOf/vSn0+Wf//znt7l8eHi4tfHNmzfv6OG0b7Ner3/wgx9Mm7BO6ayzzlqzZs02t1mr1T796U9PHsm2pVgsvve97y2Xy9tTwp1+Skul0otf/OLW21/1qlc9ndP44Q9/eEsFazcyMrKdJXkmTub3v//97u7uye89/fTTWwV7Ouv7vg/Atm1jjNZ6yZIl6fp/+9vftvM0fvGLX0zfcv755xtjWhMpveQlL9nKu/bwK00p9aUvfSmbzW5pg2efffbGjRu3p2ztZ3hLtufS2p7PlgcffPDggw+essCO41x11VWtd4Vh2OoUMFk+n//Sl76ktX46B9WudZF88IMf3KGjbrdzr5kwDP/3f/+3v79/S1sD8JKXvGTFihXGmFaniX322Wc7D3l7bOdv645+Uu1ph0lERESTYUIN8Jvf/Ob0wbe//e3t+cO/nb71rW+lO3vLW94y4aVVq1Y961nP+n//7/+VSqV0SVdX12GHHfac5zxn3333TeeS1Vp/9atfPfHEE9PRbp++7u7u/fbbL318ww03POXtxHH8ile84qKLLkpnYe3q6jriiCOe/exnz549u7XOddddd9RRRz388MNb2c6qVauOO+64j370o5VKJV2SVnk961nPmjNnTrpkdHT0y1/+8rOe9azHHnts66Xa6afUGPPyl7/8l7/85YTlO+s0br8tlaTdTjmZd99999lnn51OJ5PJZI499thWG++f/vSnk/uC7uj6EwghWpXArUGttk5rfdlll6WPW+/dpj38SlNKvelNb/rABz6QdlgtFovpL9TcuXNb63z3u9895phjdvq00ttzaW3JE088ceqppz744IPp01mzZqVnIH0ax/G55577+c9/HkAYhqecckqrnj+bzaanqzX6d6VS+cAHPvD2t789/cDcibTWkxfusl+olg0bNpx44onvec97BgYG0iXFYvHwww9/znOes3Dhwtbk4TfddNPBBx+8oz0CttPT/G3dHnvCYRIREdHWtGfi4447Ll34j3/8Y+vp+a9//etNN9102223bX21JEnS+9dCoVCtVttfeuyxx9on53jZy152yy23JEnSWmHDhg1f/epXW3ny5JNPvu+++9LHT6cG2BjzsY99LF3e3d29atWqrR/CBK1ttrq2HnnkkT//+c/jOG6t849//OMtb3lLq1vmzJkzt1Rt9fjjj8+cObN1Es4+++yHH364fYVly5a9/e1vb90zFYvFu+66a0tleyZOaatqa+bMmf/1X//1m9/8plKpPJ3T+J3vfOflbYrFYrqRU045pX1562rZnpLsxJOplDr88MNbJ3B4eDhdfuONN7qumy6//fbbn/L6qQlVecPDw2m1p+/721Od/vOf/zzd8tKlS9Ml26wB3vOvtL6+vvTBiSee+Jvf/KZ9aw8//PA73vGOVtnmzp07ODi49VO0QzXAW7m0tvnZkpJS/vd///f69etbKzz++OMveMEL0lcty/rTn/6UjvgNYPr06f/3f//X/nn4j3/84/jjj29t7Utf+tJTPqh2rdrmiy66aIeOunUIO/GaWbFiRftEX2ecccbtt9+ulGqtsH79+ksuuWTWrFnpCrZt/+///m/6eGdVje7Qb+uOflLtOYdJREREW9L4C92+6PLLL08Xvu9979vKO5VS6e2vbdtbvxNtVS+89a1vbV9eqVRat19dXV033XTTlrZQqVRaXRzT4YLwtANwrVZbuHBh+lJPT88VV1wRBMFWjmLKbabOO++8KIqmXPOnP/1p66bqvPPOm/LQDjnkkHSFhQsX/u53v9vSTv/2t78ddthhrTVHR0en3NozcUpTp59++uRg9nROY0s6Tg+ARx55ZMoVtqckZuedzFYt1pw5cya06nz/+9+fvnTBBRc85fVTk5PMueeeO+W1OqUXvvCF6crXX399umTrAXgvutI+/OEPt6eFdrfeemsul0tX+//+v/9vSztN7VAA3sqltT0B2PO8m2++efL2lVKtOYpbbW7322+/DRs2TF45DMNWG5xcLjcwMPDUDqpd69fzl7/85Q4dtdnZ10ytVmvNlFYsFn/9619vaWuVSqV1Hlp2VjJ8ar+tqW1+Upk95jCJiIhoSxp/dNsXjYyMZDIZANOnT2+v0pzgjjvuaP3NTls4b8kZZ5yRrjahZuDd7353ujyfz997773bLOuEdp5PMwAbY37961+3D5zb29t73nnn/frXv97KUU/YJoBTTjllSzfrqYsvvjhd03XdVm1Dy3ve857WrdKyZcu2vt8NGza02hy+7nWvm7zCM3RKsYXO26mnfBpbdigAb6UkO+tkXn/99enyN7/5zRPedeutt6YvnXzyyU95/dTkJHPPPfekKy9YsKC98nOyxx9/PD3nc+bMaZ3nrQfgveVKm7IPartvfetbW/mFarejAXhLl9b2BOB3v/vdW9lLaySq1D333LOlNeM4bp35Sy+99KkdVEtrGqRcLtf+zdQu/oVKffCDH0xf7erq2mbzImPMhz70ofaTtrOS4VP7bU1tTwDeQw6TiIiItqTxR3fC0te97nXp8p/+9Kdbeue73vWu1t/sF7/4xVtabdOmTencIQcffHD78jVr1niel7792muv3Z6yRlH0rGc9q7XTpx+AjTE//vGPe3t7MV5fX9/b3va2u+++e0slab99vOOOO7Ze7Gq1Om3atHTlq6++uv2l1atXt07Cj3/8422fAmP+9Kc/teZimdAW8Zk7pUKIrY/M9NROY8v2B+CtlGQnnswvf/nL6cI3vvGNE94Sx/GKFStWrFjRPrDZjq6fmjLJtBrB/uQnP9lKyd/73vemq33iE59oLdxKAN5brjRsNRymwjBsNav+9re/vZU1dygAb+XS2uZnSyaTmbJGt6X1owHw3Oc+dytrGmM+9alPpWu+8pWvfGoHlfrVr37VGpzsAx/4wJQl3zW/UMaYdevWpd+rAvjRj360PVvTWr/oRS9qnbedlQyf2m9rapufVHvOYRIREdGWYMppkFrtsv7v//5v8qsAjDHtM0b87ne/SwcUmeyaa66J4xjAOeec0778sssuS6eQfd7znnfWWWdN+d4JHMdp1abuLK985Ssfe+yxT3/60+3T2A4ODl5xxRVHH330y172sieeeGIrbz/00ENPOumkre8im822qr/+/ve/t7/09a9/PT0Jr3jFK175ylduT4Gf/ez/n707D5Dkqu8E/30v7sg7s+6qruru6ktqqXUjCRAChDAWtmBlzOELsM3hwesxnrV3xvZgG3tZe7A9zHhsbFiM8RiDAXNZF0hCAnTfZ0t9V3V33ZV3ZtwRb/+IzKyso6u7pZbU7fp9jEtZWZGRkZGR2fGN997vXd1Z25e//OXuP718u/SSSy65/PLL11ngJe7GU7fOlpzBnblr1674xh133LGidJAsy2NjY2NjY53Grhex/DpOZT4ky7LiGnWKonTGlK7vXDnSrrjiiksuuWT9ZVRV/bVf+7X49mOPPXYqz34qTnqQr+Oiiy7qHg692rXXXtsZPtrpEXMi1113XXzjmWeeOa3N8DyvWCw++eSTn/nMZ1772tfecMMNcXGyLVu2fOITn1jzIa/MBwrAZz/7Wdu2AbzxjW9817vedSprY4z9r//1v8743Mtn8NO62tnzMgkhhBCyjjUC8HXXXRcnmZtvvnlhYWH1Ag888MD09DSAuNSH53mdkjwrfOELXwCgKMqKeUpvueWW+Eanw9ipeN3rXtep0XWmFAqF3/u935uYmHjwwQc//vGPdxdw/rd/+7f1S3R2N2qto9Ost3fv3u77Ozuh0+R+Kn7pl34pvrHiFPPl26WdUs/reCm78dStsyVncGdefPHF8bxWMzMzH/7wh+MYsI7TXX4d7373u+N5U+68884TTRP65S9/Ob7edNNNN3Xq6KzvXDnSOoMn13fFFVfEN9Yvrn5aTuUgP5FOwed1dGbAPulU2J30VSqVTrRMEARsFU3Tenp6Lrnkko9//OMPPPBAfIFz+/bt99xzz4lm6n5lPlDda+vMtHcqtm7d+p73vOfUlz8VZ/DTutrZ8zIJIYQQso41AjBj7P3vfz8A3/f/+Z//efUCnWFUn/3sZ+OxiJ17uj300EPx1CA33nhjpxswgGKx+NRTTwEwTbPT3HGK3v72t5/W8qfuyiuv/Mu//Mtjx47dcsstnXZdx3F+7ud+7o477ljzId31UdfRaRddMXHu008/DSCRSHTPw3lSl1566fnnnw/gyJEjBw8ejO98WXfpSU/Zu72I3XjqTrQlZ3ZnDgwMdM5fv/CFL1x44YX/+I//GHdkWNPpLr8OTdM6fSVONB9S5/5TnP3oHDrS1m9H7RgdHY1vdH+gXqLTOshX6BSaWkcngp4oi3YMDAzEX6r1ev1FbxKAvr6+T3ziE0899VRnd632ynygisXiE088AcAwjNM9Zt75znee1vIndQY/rSucVS+TEEIIIeuQ17z3Ax/4wB//8R8LIb74xS92SqF0xP2ft2zZ8va3v/3qq6++//77v/e979Xr9RXndnHzL1b1fz527FjcOnHeeefFQ9pO3Ul7SL5EnPMbbrjhhhtu+MY3vvErv/IrtVotCIKPfvSjzz333OpNjeetOanObuk+oz127Fh8Q9f1+HLDqXMcJ75x8ODBuA3nZd2lnd6bp+60duOpO9GWnNmdCeAP//APf/jDHz744IMADhw48P73v/93f/d3P/axj334wx/uTNXT7XSXX8dHPvKRT3/601EUfelLX/rUpz614jN13333xflz9+7dJ+1+HzuHjrRTXGGnEHSz2TytDVjHizjIO06aaQF0+rietLOrqqqapjmOs+bMvTHG2Dve8Y4Vd2qaZhhGNpvdvn37nj17rr766ripcx2vzAeqc8ycf/75p3vMnGIvm9NyBj+t3c62l0kIIYSQE1k7AG/ZsuXaa6+95557nnrqqSeffLJT/APAww8/fPToUbQHs73rXe+6//77Xde9+eab3/e+93UWsyzrX/7lXwAMDw+/9a1v7V753NxcfOMUW1C7nWIb0Uv3rne9S1XVd77znUKIw4cPf/WrX/3ABz6wYpnO7Jfr65zydrczzM7OxjeKxeKa7eenYnFxMb5x1u7SU9mNL92Z3ZkAdF2/5557fv/3f/+v//qv40F9U1NTv/u7v/upT33qD/7gD37zN39Tlpd9cE53+XVs2bLlJ3/yJ2+55ZZ6vf6///f/XtHM2xkb/LGPfewUV3gOHWmn+IHqTC12ZvuvnkMkSfrWt7718q3/zB4znbW9iK+auD1cdAo2ngln8NPa7Wx7mYQQQgg5kROecf7yL/9yfCOuuNPROR+KA3CnpsuK86Svf/3rcQmWD37wgysaIsIwjG+8iI5nnce+Am688cbf/M3fjG9/85vfPLMrf3HnWCtYlhXfOJt36cu6G2NndmfGNE379Kc/PTk5+Ud/9EedM9pGo/Hbv/3b73jHO4IgWPHw011+HScqhTU7Oxt3vkilUr/4i794imv793ekdU+7RV4OZ/aY6eS6F3EARFH0csTCM/hp7TgLXyYhhBBC1nTCAPwzP/Mz8SwaX/7ylz3P69wfn4IPDw/HvbZGR0fjmjS33XZbd4/EuP8zY2x1i1/nhKNzyfzUdRqgXhmd7n/x4K4zqNOAduONN77oKt4f/vCH45Wc5bv05duNsTO7M7v19vZ+4hOfmJiY+Lu/+7uRkZH4zltvvfW//tf/uuaWnO7ya3rb294WDyt9/vnnO3OTAvjc5z4Xx873v//9yWTyFNe2oY40ckac2WOms7YXccy8iIecujPyae04a18mIYQQQlY4YQA2TTOe46RYLN58883xnU888UQ8pc1NN93UaYeJ53uwbfu2226L7zlw4MCPf/xjAG984xtX11npnGrs3bs3biU+dQ8//PBpLb9aGIaTbeJkF907c2bE83y+xKfuNjo6Gnf4XFEa+sV55XfpWbIbY2d2Z66m6/qHP/zhffv2vfnNb47v+exnP9toNM7U8itwzj/60Y/GtzuNwEEQfO5zn4tvd+YBOhXn+pFGXnln9piJJwuI13a6lb3uv//+l74B63uJn9aOs/xlEkIIIaRjvUF3q3tBd/o5d88MuboX9N///d/HN1aUv4r19/fHlUKDILj99ttPa3M7GftFcxxnc9vx48fXX9j3/Tjnc87PbMfLbDYbt5wfPHjwtGZziaKo2Ww2m83uLruv/C49S3Zj7MzuzBMxTfNrX/taXDeoWq2eNM6d7vLdfvmXf1nTNADf/e534yH33/rWt6ampgC86U1vit/rU3SuH2nklXdmj5l4ciYAvu+f7jHz3e9+97SWf9Feyqc1dk68TEIIIYRg/QB89dVX79y5E8Dtt98e99GKI25vb+8111zTWWx8fDyuknXzzTfbth2G4Ze+9CUAmUymOyd360yt8ed//uenvq133333k08+eerLrymRSHTmZIoL6q7j2WefjVssX0TJn5P66Z/+6fjGpz/96VN/1O/+7u8mk8lkMvnrv/7r3fe/wrv07NmNsTO1M2u12kMPPfTQQw91JnHpVigUOvVaJycnX8Typ6inpyeeGjQMw7/927/F6c9+1O2cPtLIq+LMHjMvbm1Hjhw5I/OHd7xMn9aOs+RlEkIIIWR9Jym7+sEPfhBAEAT/9E//9Mwzz+zfvx/AO9/5zhV1reJe0M1m83vf+96tt946MzMD4Od+7ucMw1hztb/2a78W10Z+5JFHVhTZOhHf93/nd37nVJY8qU56/+pXv7r+kg888EB846qrrjojT93tox/9aDyByhe/+MVTrA515MiRz3zmMwAYY7/927/d/adXfpeeJbsxdqZ25vT09FVXXXXVVVd1uj+sMDw8HN+IJ8E63eVPXSfofv7zn3/sscd++MMfAhgaGnoRU4ae60caeeWd8WMmPv4feeSRTv+g9QkhfuM3fuPF1aM6kZfv0xo7S14mIYQQQtZ3kgD8S7/0S3HW/eIXv9i5St3p89zR3Qt6/f7Psa1bt3ZOQT72sY899NBDJ93Q3/qt33r00UdPutip6GztV77ylXVapUql0qc+9an49o033nhGnrpboVD4+Mc/Ht/+0Ic+dNKOxOVy+b3vfW889ctNN9103nnndf/1ld+lZ8lujJ2pnblt27a47/ETTzyx5iw7nR6hF1xwwYtY/tRdeeWVl156KYDFxcXOrv7IRz7yIir0nutHGnnlndljZmBgoFMK/td//dfjOXjX98lPfrJTe+JMefk+rbGz5GUSQgghZH0nCcCDg4Nve9vbAOzduzeux5PNZjvFQjp27dq1e/duAN/5znfif8737Nlz2WWXrbPmP/uzP4v7V9u2fd11160zraXjOB/+8IfjZ+/r6zuVV7W+d7/73du3bwcQRdGNN9742GOPnehJ46ksx8fH4/6oZ9zv//7vv/a1rwVQKpVe97rXPf744ydacmpq6tprr41Hpm3ZsqVTD6nbK7xLz57dGDsjO1OW5XiMa6PR+Iu/+IsVD3zggQfivKeq6o4dO17E8qel0wgcd8iUZflDH/rQ6a4kdk4faeRVcWaPmU984hNXX3012sfM1772tROtzfO8//Sf/tMf/uEfAlhdQ/GleFk/rbGz4WUSQgghZH0nCcBo94IGUC6XAfz0T/903AFyhbiRqtFoxL251mn+jWUyme985zvxONJms3nTTTe9/e1vv/POO7s7g9Xr9X/8x3/cvXv35z//eQDDw8Px6OKXSJblv/mbv4lbto8dO3bNNdd87nOfs207/msYhvfdd99ll10WT/gkSdJnP/vZMzIx5mqKonzjG9+Iz36OHj36mte85kMf+tCKuYKOHj36W7/1W9u3b3/mmWcAJBKJb3/72/l8fvXaXuFdevbsxtiZ2pm/93u/F9/45Cc/+T/+x//olK2+//773/ve98a/fvzjH+98Ck53+VP3vve9L5vNdn696aabBgcHT3clsXP6SCOvijN7zGia9s1vfnPbtm0ALMt6z3vec/31199+++3dc+w1m82vfOUre/bs+cu//EsA/f39X/nKV87si3r5Pq2xs+RlEkIIIeQk1p/O0XXdQqHQWfjb3/72mos9/fTTnWU0TVtcXDyVuSInJib27NnTvTHpdPriiy9+05vetHv37ri7Wqyvr++JJ5549tln41+vuuqq7vX8yZ/8SXz/n/7pn57K/UKIv/u7v+seyZxMJi+//PLXvOY13eOWJUn627/92xUPXGeda+rM8bh58+Y1F1hYWHj961/fvRP6+/uvuuqqq6++esWp5ODg4L333vvq7tIVXvRu7IgrqAF44YUX1lzgtHb4GdmZnWI2ADKZzNVXX71169bOPWNjY81m86UsL4TQdR2ALMvrv5xOj0oA99xzzzpL3nPPPfFiP/VTP/Wy7pyOV/hI6zjpByp2Knv4VJ76RXy3rPaWt7wlXvike3X9LT/Fw2Z9r/wHqqNYLHZ2RSyZTO7Zs+dNb3rThRde2P2l0dPT8+CDD57ie31aXsSnNXbSb6qz6mUSQgghZE2tf4NPutx//I//MV4ykUjYtn2ixTrdxt7znvec+kZYlvWnf/qnuVwOJ/a2t70tnkL2DAZgIcQdd9zRfeqzwpYtW26//fbVj3o5ztd93//rv/7rTl3l1WRZ/uAHPxjvhJN6WXfpai9uN3ac2QAszsTOLJfLJyqTc9VVV+3bt+8lLi9OOcns27cvnjhq9+7d6y95KgFYnONHWowC8CsZgMWZPmbCMPyHf/iHsbGxdY6Za6655tixY+KU3+vT8iI+rbFTD8Bnw8skhBBCyJoAMACiE4VfPbVa7bbbbrv55puffPLJubm5arXa09MzMDBwzTXX/PzP/3w8KSWAhYWFeIDZ8PDwBz7wgZf+vEEQfP3rX/+3f/u3hx9+eG5uLgzDvr6+yy+//MYbb3zf+973ojvCvTjNZvPWW2/99re//dRTT83MzDSbzb6+vm3btv3ET/zEz/7sz64TMtf0Su7Ss2o3xl76zrzrrrv++Z//+dlnn33++ef7+vouuuii66677iMf+ciKEugvevlX0bl7pJFXy5k9ZjzP+8EPfnDzzTc/8MADs7Ozi4uLuVxucHDwiiuueP/73/+6172u86RxZelMJrNiaqWX6JX5tL7qL5MQQgghK7C4celsCMCEEEIIIYQQQsjLhzH2MhYlIoQQQs6g73znOy/ugYyxl28aNkIIIYScQ6gFmBBCyLkh7rX0Isiy7Pv+md0YQgghhJxzGGMnnwaJEEIIIYQQQgj5d4C6QBNCCDk3UH8lQgghhLxE1AJMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDoABMCCGEEEIIIWRDkF/tDSCEEEJO2x2H93LN/di+u3eZ1RvzW3c1N732kje/2htFCCGEkLMdBWBCCCHnkjuO7L3p8JOCZ3mP9KHRwZS+94tHlU94m1/t7SKEEELIOYC6QBNCCDln/Nn937/p2HMsn+X9KjOMXOLpI9Kg9YSzY9v4q71phBBCCDkHUAAmhBBybvizH976J81FKAoigTAaD4vPu1h00pN2ODYy8mpvHSGEEELOARSACSGEnAP+7JZ/+cODJeGpCALh+e4+XQ7qJTdzdNH47fOueLW3jhBCCCHnBgrAhBBCznaTc1OfnJ9yKikRCYQRosir8XRYjIKo9HAZ1earvYGEEEIIOTdQACaEEHK2++rD9wkk1VTIdR9ChPO60W9zWFrYrPT2v/f6t77aG0gIIYSQcwMFYEIIIWe1ydnpPz++N6gl5YIthICA1Ovke8sZUZyfYaLmjPX2v9rbSAghhJBzA02DRAgh5Kz21fvusbSUpETMCIWtszQA9LPajBg83lT/YOu2V3sDCSGEEHLOoBZgQgghZ6/J6ak/OrwXmgrGwqIR2QrAAPSzGUQIVfO9F7/m1d5GQgghhJwzKAATQgg5e3317u/D0MF5ZMvOosHNAECGuR6LSlVWD2Q07Vd7GwkhhBByzqAATAgh5Oz1B889wzQVjIFxNREwPQBjYLCFbtrSJ0aGxzZterW3kRBCCCHnDArAhBBCzlKTx6d5zoQkh1UTHHKPA8YADPH5p92dTS8jji682ttICCGEkHMJFcEihBBylvrqnXdB1yBxOe1CA2RpRC4xWYYQ24IjBxKD773kza/2Nr4k9tFJq7if5dFgMtNR01kiDOY9fSg7LgXBYIIatwkhhJAzjAIwIYSQs9R/O/BwbixTkbNgHAAEPKG5YZLx2phYOFTUxnr6Xu1tPG1Tz95RncH87W81dHgcqZRqXSgvKn1FY9h19IgljvekBo9ZhVCe7/VHUz+hNdyLBt89og++2htOCCGE/HtAAZgQQsjZaHJmKpELbGaA8ciRuQIIMR9mNslFNzSexvjvj+Zf7W08PQ/ccYd261uLOrLbYV4GAFauv6qzaX3sKXGBD3POMBtMtZr6eZhOorR1fnrh2NejjH+/u39r7tr3Zq5M6GOv9osghBBCzm0UgAkh5FUzW5xkEsCgS/CAADyIOBgAMMbAEEGGEEKogGg9xpHAGIQAAC1o3S18AGBAJMZy/06aCr96z52Kjkjym7Zkzyf0gUDq8RFGrq/08Km9jbH3XHPpq72Np6o6OfnIJy4dHCu5b1GVYXWfv9V1RNnpsfTAELkZP3NbeKUTaXWhAfiryv/ebN/BHIBB60HZf/2lyhed5rfutoZ25D69I/umV/vVvGqKlclaeEDnYqLx1Ez9yyV1+EiQj6Qoyb2BBXs6JcYK74t8/ub+tyKMRo3e1Ws4VjoeQYo/YgDiIeXt2wBwYGEajAmhxPft6B0Ea3/44mVdaekhcvy5Y2PZnjP7SgkhhLx8GAAhxEmXI4QQsr6p0tFIlgRXFqvPcon5TAaPZkpPzci9iuy5LH104cGmqTm8EUjZgLsOzAwiMMwj6TCjCV1EAoDCmxCiITREAkIIIRBFfqiX/Ww10hAJQLTjsIgsmek+RJSGg0ggEmlWUmGJMDIiV4TRe3ouhhAiFBfndzDhb8vFTYjB2Z+TR/7wA/JAj6XkK8VB3zeUZKSOOik1TCv1Htk+GI3OveUXOwsfLR5vRRmGiEkAFCkAkA2iqeMHwgwsxgH4XtJThSE5o5bnpDDLVMaQRNg89iiABqSGwSymSgg9KB7XPLC0ojSh2QxMcBmezU1VBM/N/gsYS7HwOYyVWD4NB8AA5hg8G0aclFLc1WHpCE3hMqBHrioedNgV2cwVxXw9zKfUdDFEGbWkdKSnv+YPAfBhZJWZrZhRnZrCUUsZs9H5U4keBbYABDApS4tIyiJQ4TsiwZnnQwZEn/AhRAPcguRF5vv6LuPMjSJNRAqEQOcfeiE4HAZHCA0CYIGIVAgoUmlP7w7hMkAIoXHeRARACCEDkCJ7W89o14GHKOCtdQoJLBwrDL2sB0PxiTv2zt7o2v2zyYEjmZ1HgWkMxH/SYINhG+ozUMGYyZ2Mqg0Eudu03DtT4599/rnIckzuM8bAmcE8h+s681yuA8ziOhhjDODchwEGTxgO9Bp0MNb6HxgYwFhYNMFY2FRaqVlA7rcBcDOAABhEJBggRCQszlT/93s3tfZ8JBjcS/ODDK4SuptzW5nweRgOZ0df1p1GCCFktfhbnwIwIYSchslq8UB1BjIXUJ+oTSWU2a+US1xmsywFzlNyaZg1pliacZZkHuOswXRwDtZq1G2dT7faeeEJsxIWysLorD/LbAAVoUMAQmRhGbCsUKsEGqIQkYCIEEaIIgiBKGp9h7d+AhBxSE7D0YWNKOpjs81IgRAiEn6gikh4oYowvCKsX9Z3dS8W92S39Q6MJ2D15F/2HrZTxaNgiMDBEDGZ83CiNMEYwAHGAi5n5SaA+/cd+tSPHpGyiQXeL1hCeDo4y/Y1U7rVi0U0pJGtclZb2FHxRuu1bdb9WrOsqZ6VMwCYkzaaQBLBqAoAJupGIWUX4w3wDSm0Mx4KnoFFNjqbHQTQBK+DH0K2wTQAWWgqbABDmAcgQ5LRaEIJEKYRyai5SMwhYcD2kVRQB2Az04LehB4/iwPDwdJ7qqM1WbEKqQa1e4dY0E04FnQATegJ4QBoMr0XFR12/EAHRknkOisHBBjbJGZKyDWW7mzxhAGAAW609Ccram1MNc69JyQAjMmzAFRmxWsbZjMLyCbhGHC6llp6UQDsSG8K3RIqhIj8AKIVlX+p73zuOxCAQAhZi2rx7Uvym0MhQUBEAgKSEOM9o1IYAYgChIJF4DJCzzODQ08eOfb+8DzxfLBZnjGr/QOHfMmAnUfzG9ENg3yusw2XsnIaRwf/w78Zi9h+NarvGqht2nl3/urzJidunc0sDuQMeOBQmQTG6iyeWwsW030YcdY9HvWDsQz3qt0BeOljy5Z/hNsNyJ2P87p7NQPH4E6SlwDEH0Yzcm5K7daiRhDIF2bGt2eGmQg2pYfXXRUhhJCXhAIwIYSsZ7I0c6Ayz5QwYsnHy/N/UpnOKqGueEziNswqDICNqbOTwVCGOwAM7mjcUbnNOOtDdZ7lVube9hkzWzqHxj5vO9DVG3PpRuu/OebkpCKEEBCIRMXPiSgqB+oginakGcJShYUwcgIVQqjCik+va0FGxA1QArVI6WNVJ1Q12CpsCCELGwKIIhG1Gqm8UE2L6oW22D322kRkI8Kegc2benbG7X4qcwYLY634yjg4m6gcZhLzuQ4glJNM8vcW98pwQ67LcCNJvWvx3oLUNGH7kso4Y4wJWU5wG2B2HDCAJPwFpAw4DFhA1oGxCdMCOP5E48mHApZLQ5aFnlLUsM5TW+XjQkEvKyrpn//NI7eNvPAP0IBewAZMwABkeKOq2vSsPsPO5QAs6P2LxqAPi9upWSOXsTXhgNkwRGQcE4KzNK8kogXNAgIk5AO6UlQqHgrwe1XZ8qweo57L2TBqRqaEdBFZACXkfCTrkEvIAShFOQBxgq1Bc4UBwI70itABRE0FjA2n5u3IMLhtR8vyqsGd+B6DO7bQ47fcYLbddUEkjk+leponvLilEUCWOfH6T2Ltf95P4R/9VYsMSuWZMNu+XYlvdDYeAhq3ATiRbkeGyW0AGrNVZnvCUJkNIAEnjvomHAFAiO4IDYEUPBNOGout6zhA1nchUA1ZJqqbjpnuLW+574G9vTsLkjiSHLTQtx+FfeH2OlQAKnd+/YkHc3N39ThgD80/c4k6/ppBOT1w+7a3vOXot+41b9g1fUuYSSusLht+2cvJesQYuJfSXDAPKmuYZs1zesHgM802Mj4zGGesPR5BiIwFDgNVmD4z9xpKFiFjqEDyeaTDmQi39PIagAbzA2EuigzAZts7rdN+PsArdqQBwoAjhEAkRBQVWLUeyUpkmZHzruyFiMTFmbEd2QEIbMpQJCaEkDOGAjAhhKx0z+F7mcyfLh/6fPUYU+QG651nOcY5GM/IXq8y7QuzEuWr0AekCsBmo2zXoxmAMWVGkxygHWX5itC71HxUDwu2MCpCb/W0BJYt0F5Djjv9ynEvMuNhKyqz0Gr0FRAQYQgAUSRCgSgSYSTCyAt1N1BFGKlRE1EkgghCiCBUhCXCMB/VlNCGEEbk2SKfkm1LVspS0uFapCYB0c9qjIHxYEC2ILuepEqyVpZyDjRT9hhjVVGoQ3MiQ5dsNzKtSDe53WlmNLitM1vlFgSScAA0oMc3TDiAsLpaR03YFgwTtiUMkzmAyKJ44F/2Tr3gZBVb426/XmWKXhTZLezovDKUqZcb/+fH//iLfzEyvN8bVes9PS7yc7ncnJFLIDwuemw2sA/oRXkz5ndPLY5MH6odnikeRmkaxh1oAhMe9oxioB+sgME6+HZgHNalhq3lqvqAZlQPGLuFXt+PLQ0oDfQ1oB0XAzXoAMphPs5mlUgP5hOddypqKHK/xRN+69em0n59ImdH1Z4oy5x4F50ouEZNJWoorceu9c8yT/pyv3Wi4zbLHQiY3NaYfaJlymHOZI4ldJPFbbkCgCUMs/0QjdtuZMQ3VGZ393nudqonDQIAPGHEjcnrrMiLDJXbXmTkeanTzhxGCYk1AdjQAeRRBiCE2IQZC/qomBuJKkeDYUk0VeZYogCg4B6fYoayWDUX+1J+NFDIP2ukB/lsOOX0nz9ZW+SOMz+TGreRs5mWYRYLbIhUVmrWOTb507IR6rxucrfubkvzxaRbrqPPN2GyQK2gnlO4m60bOoAyM0adeQA+S3s6PAN9OGZU7BJ2JDFrwJ/O9R3G8ATyFjQXxlQ0ICDqQquG2bizRufiVDXSWj2lhYAQaTiIIh0ORKQKG1EkC+v9+fMgcElu87b8CCKMZQdO8R0ghBCyAgVgQgjBZGlmonZY5tEf7/8XXQu5wqtST4lnGyiAsTmR6/SEzHCn3cYb51sc8La21tL5HmUsw5ysXFa5HTcddfWTBBjzhKkxGwxgmAtGTO5Mh7ll8Ridh6xMwu3byLUCDIxWdBFWpMfbUAk1CJGxQtOsiEhoaAJAJNxQTbJiIrKbkoEIJverPOcikZZLnjAFmCuMztPZwjB56yk07qjcAuALU2EWGPMj40gwDCDL7IowAAxKZTvS443R4ui7mgAYvMh0I90WRiXS430WNRWe8HPcyfJSgZeGMNWH8q3/7QUrNN2A90QLhXA+KexAyJIPT9FZ7/WvfXvhrYXbFnLJ/Rg7gkIBpUkMlVEAcDwaKvDSxXj23bh59GtT9a/gkefwhAcAY0ASuC4JaROkTRCvAcZgXW7MGUPz+nAJuUkUSsjNskzce9mCUYcOwIsMV5gzYQ7tDQ4bStRQ3HqrkKSW8t26AjBz0FqdUTfL092/ustad+O2YmP5I1r5Of4Z1hW3rgBCSwUApKQPgCf9TthuP8uUyk8YfU/oRTcRn/b642GyAmzF/SdUC/MA7MgAkJVKbmTYQgeQ5aX4lcaXVCDQgA6IJFwAW2BFqNahOcKoRrlFkQWgwXaFAYEtaAzCyrNpAIzVUmK+IOoLYaLqmwAqQi8HeUM0E5HvRcaIVEtg3gitvA/BgwE+53DV9AJNL9cMI+M6AByme0g7unsE2yyoYKggr8MpYgRAv5gRgARJFg0Nlop6UwwLiBJSx6KeOhQB2EKDgB/pDdFKwtUw7ruBWqS2UrFwhBBplBEJAaHAZsAH+i6IQuWS/GZEYnu+HwDEv58aeIQQ8jKhAEwI2aAmSwsHa0efLe5/sPajhFKFJM1IgwZ3qjzvRr2HRW8N8cDdVvQdkCtZeKHcTDJ3+ahL1ojyVqhVhbFUZwgMDANSJe7IanCHdbXr2sLIymUArUy1vE1YWErUVMOGEt+pDNhg4Al/5bDD5TqNh2FDcWuylvTduqylfG1rBWgND84yO8tLKrcgBMA8YbitJmW0ot1aax6Sy3G7bk4qLctXDAC8yHSFoTEbQpywla9NAF5kVMJCJdJam20p3PSD+YTc1xyUKympuB2zF+OFwfkXPvntMSG41QiHG4cS7qJRrktB0xVa1TE0TX3rn13EehKdNR/HpuMYnkeuHhY0Zo3w6SHUPlr61tDnntl3C56exCQAIAsAuFLFBa8HuxbWW439w+c/gItnMHI0GpwQAzq3WwNf49fFbQDlII94SHaXYN4EENYVMLi1TmMvtLQf/2oOWTzRyqhZ7pjMTknF7jV4wowvE3iR6UZGSi7GTxr/tdOW3rE4WwgbyyZuUPotADzhdy5bZNuXRQAY8ZvVzpvxaznhhYmXwI1aO6r7nviGtvyQcIWpMcsVZvynztN6wlx98NTDfEoqQcATRmeFM2Gu8ykTwFI59C5xS3t7A+wVa/YiwxXGtJ+NVzAoVTJwFWYBUJgFIfbguQWRbW+wUUS2FOayosQY6p5kcj8fhHJYUZlrBCwjVVTJyYalUJiyZDPOABayoH0lS0xjaBEZR+gAa4R5QOhwHKEJgTS8OSRN4bYqicXdOoSII3ET8VBt4Uc6BKpBGkAtVCFEZEkZoyGaUl1lI8oChICqMMbqyGlw7EAVItLh/Ie+LQA4cyHEpflNAOPCl6JwLD8uIxSALCIAikAY4hUY/E8IIWcJCsCEkA1ksrRwsDb5dGniofKdm9S6LTkFXrO5VuWF59iuATY7y4bKyHnCuEo6MsyKSRYkpEVNDH2X92ZZCYxVkLehN9rjV2ONMD8TZAEsnYvHAzXjZbpvo3Ujy51Wo193TSwG73DGbcQVkpaVy9LSvjJg8UQQWQoAfzbh1hVzyI7DzfImRwEgDsP+jCml/LAuQ0BKeXHOkfuaUVPlCW9ZR+t1ZbnTrxzvumONhyWj1jasrMm0vLmvHhYsYVSieLCryHInbgAfkkpZuK+V7tmJQxdj71PPjv/V04O6X9/f2HJ+8PhFM8/O1bb+KBz9ySvnf+Kti4elcW9w0G430sZPWkEBQBatkPkePHLFv95x4HP44V5Ulm/qNSpe8z4E79B+fMXrbsVVk2LHQpTr/DUlFT1hol0CCsA+b9uKFxs3WXd+oqvDcyf0dnZdHMZWpF90Wi9X7zEAgBeZKre8yIx/VblVDwtWZHT3M4+tOxK4s/dbM/msCIedlZ94Dcs2KQ6i1vL26kqkZ7nT/Wt8I8udSrRi2071ZGNIKluRYazqy62374mvFMQvQUBUwqUZoXNSyY2M6TDX2YZBqbz01O2xyiqz4isO7a4TovNCDGZn4Zl8sQENK86R4sHJAp4wykEuJ5XLQQ4QBrPTkeuIoOokRpTFiDH4YRCqPVKzImTfkxI8iEvWuQgbAU9y32GAEGCsEeU02G6kg0FjjhtprP2eOZGqMwcCSnxpQwgv0tH6phHz9XzabNSaJoRIweVmoPuRlnUhK76cZLxrqwWSUjnBvPhLJScqOVYx4Y5jQjR6wSVD4gaEISLVY75RVR0AzNWZAFwuNQV3hAqBOaFd2vMmhBEY3IiLIBS+2DO40w/51uzmuL/8UGrTKb7LhBDyqqAATAj59++uw49CYn+071tDavlC5Xgfn1V5RWZakellnnuG7/KYCSFeI56digZEJN5cfzDZKOrzLJmoNM1sZdfAzez1FnQH5lE2uFS8SqAW5mY7TVJLRZgBtBPHmh2YwbKSs9Trtd1BOrLU5sFM1z3tGlkMALR04NaVuBOs21DiVOzWu+oJLz3F8i3pmjNJS/kA3HqriVLu6yQfFhcWXr33stzJSaXVDXRJOAWUdFitp4gEAAcmgEkMdW9PO2YYYEvpaEgqa3ypdS4JZxjH+1DeiUOvxQNfuOcX7yuXqkH+aCOzm+1/3exDT3rXPFAOvvY7LzSGMpMYPI5Nq3NjnIHT8H7u8afVH3zlkc+gvOrljAHX/gLkdw8f3vyGz2RuOI6Mxq1NbKr1YlF0YBaRb0Cvhz3trN5q7wUg91lx7l29o9YUv0yccsiM1cNCnN+WTT/78siyFVc31uZFZjnMm9yeDnNr/PmMnUQsZfXTeMSpLrnGollmG7zVj1rjFroufKz5KC8yIKAyK75K4kS6HbUGVFuRXon0ZRvUNfUUgKipBHMGT/idEl+IBIJQylcBACzJfAAQUfyp75eOL5WXFgiEBgiZeWAs3k9BpEaQq1EmI1UdYSxEaYBFrsS1MMH8RsCTUsRkDoAx1CVzZbVqxlrlCdq/A6sPtpWXyTLMMbljRXpOKkOI1oEtACGSwh4UU6pgKW9eCfw3912tITQjI+dU+4JLes+/fvX+J4SQVx5jTD75UoQQcg666/CjT5cP/KDyYE5uXCVP/I51KFeZ03gkLXjukPpU3/lhKNuBXgswEB2+yn9B1odF2RaFYPh40d7MUyM1X5cnw/HH2XnTbDhu+GW83aiL5XOfiM7P5eGzdaPdUbPVqCsqobbUObWzZCTWOkcXEAwMblUChFuT4jUAwq3KYNHSaevKhy79rqV9KenzhAeAJ/xW/2OwqKm2shwDBFsZt0Scfovq8oa4fpQB6LDa6Rdob7gGq4KCiLdICACeMI/4g92n1UNyeXVzKAAH5jyQR2Z6dst3FpPclIUU5RBsZVYPm306Yf7fPUe3DU08gKtL6EnDR7t6FgAPCQfmB6dngoN35259bOLr2O+uXP+lGkZ/FsaN22b6zntg/KqnWXKzmD+PPaOiDsCC0YA+hU2AKInCICpzjTSALEIAPaniYoKZzLaEkctOuZGptQOtyuy4gBMQd9Y14zJXcdvjdJhDmMsyOye3klUKjgkbwBxyya4eywB6ULJhBFHiKuneZ6UtdtgT399ZfwUalrPa9ZbjVtkhVtkbDgLYzqo53or/D4ebO8t3N9WazNa4tSLvpdrt0nW27PqCKlmmMKe7GlqXOWMRnXX9FF03uhcQazxiHV2D81f/rQKzIszWYl2vLp6KLCeVOut3IgOALfSsVJoPR+PW6VnkwEQVCQDg6DS6tnL88o8zU2R1px01ZQBRq6OHiBoKPDNqyHK/VW2oPOkhnlgYwpYkAGmpCMAVZlwlL2z3ZncjkwESBA/Tx0Rfa18wxJvQBDjan0/W9VW1IgOfcM90L8W6v/JqUGtIQ0IN6RX7alYMVdhgCu5ocjolFm4RRy7H/hwrDpafat4bzQ3ffkHmjWs8ESGEvOKoBZgQ8u/KDw49yGT2Xw782xZ16kK59Ib6981pJ2gimEGQw1R6RKT0Y8g104mMG2Wet91LmTzdf1d+dHJoXOVKTg4/qP5g6747joxf+gP9vDm2aY5lGtDrYV7jDoCJYHCHcgiAFxlOpM8G2U6vyOUlc9dqvGPtduHO2SgDgMhSg3nTrSvLekSj62x16Qx1xWNXLLbiGYWWCpSB5totlmKt8LA8a2SZk5NK3a2XcVPtygdFQog49mIaI3Xo8ZbM+8Odhu7BuMmXW6l2UWiAJeEkWGuq2yLywzh+E36gHZI/duB1YKwQLaREY6c7nT3O/ntjx33V+xO/UX1heNMk+jgGVDswDWXz1OFjTwYNYOvTd0/8fytzbxYYvwL4qaFNOwbmzn/jHbmRfUzbjoUCKufjUQF4LGsJdT/GD4rxJnQZzdYeiCITtsVMRFFclFiHo4nm0XAbGDOE24DeDCVwHi8MAJIEAGFYDfOhaL1BNaENKLVZL90Zvz0klWDJmlkHw1Zpzg41QOhwN3GrLpw6tPi9TgtHAHWmO8y0mFkPFXBuMs+CxkTa5AtNoSaYNxcNpeSSG5leqOmSnYYLIAFHkiwDNhjTYCe52whVMNbHKgBSzE/Cy7DIY7YjkjoaTWiMYUIMxC+9Ad2FuYj8WjMMm25kWsJol7Pumrx6dYfnl7Phem1rNpif5ARHrPMbgCy3K60+56v+tuaaT3RCJVbl9hULstZIfp70uekvW3LVrZXYCX851fWc9K/ttvlgvjX2Pr6g1o0n/Xh2rovkwwpv1qH3i/JOHNyE41c5e5uRxtNfemPfZSd8FYQQ8oqgLtCEkH8/7jly32/vuyWpBVm9uZmXRsWhoca8jEXzkGerHHXI1Uh2/eZuI+JbNGXRSxs/VN9/WErdy7cySRrQGu9WH+Hh1JDqPonxMssdCbfOimxWKscJsB4WUnyp6VII4UWGG+qzYTbuAdj5Q2eRtQIwIkuJLIUn/GDe5InAnjM7f27/YMui7BrzA7Plyy//k4CW8nnSZwxdnZxfjCxzDL5UvSkFJ4+SDivu/2zDmEcOQCKyG0JzYALsKBsEUA97psNsZ6uGpHJKLsbbOoZpnVkOzLgBmcUdpxnTYY1j/t2l27752Ou/K+cgRCKs7mFHdyzse7B45bcWs//ceHDzlYe9nFY9hmCfe/THiGaxz2vt6PhnChhRkf8PsLdddHz8Wj3NHspkfKaMBHM5uZwKZ3Zioh6pzMlPG1ovanul3dypT2g7hBCHwy2QOINw4cfVjJJhU+GaLpyapJjCCb2gwfJgzAsNU1qsiBwYKkHO5E2AWUgi8E3JaYokGIzI0mXHEzo4n3WTOckph3pOccuBlpNcMFb2lUG15gjdkFwpsBgwINX7pboQlhnyEldVJjncM4WfDzIejKRl+888dbEvgoT/zKVXzXNz1s0NsNl+WFVFno3MfliaaM5BVyNdkpx+sShzpypYP+QL2d4ImWfZRUmpCs6cSPPCXCTMInOqkWoKjzE0obrQFOFMRH3xRNYM8CMdwCI0MKa3W49tlmAcAIOjwPDLwmhN4sWQk10GVm5PaNw6jDkDY1nugLFWZu4+61gjcLET/OmMR+qVJz9DUrlTw9yLDAAqt/a520/6wO77gnkzrCvKQLtE2eltz6m9xnV22qkufwqPWmuRYC5hTZtrLpjcUQEYT3i59riJfpT6UBnC0V04eKX9+N9Y/6UvuOa92y8aMzMnf15CCHl5UAAmhPx7cM+R+35n/62DejHUk7rsb4+OvW3itvreaCYQY/f4GIfwoeRQW0Dyp/XD5audwa1/33/JIdafUQMmSbu041fKRw0+/3y0aZ4Nm5J7nKW9yJjwB+NuwJ0n6pOnRFcVHDc0Unxx3h+qxmP/WjnsxN+ojAULhj1rJsZrzcPtU8ClrLuyZTh+SGeh5am461Fdw3eNQRtr597THFoJDEmVVvRlAJCEM4TjSTgAG8ExAMexKT5lF2HUEJrDEkcx6AmzEuZblZkY26JMgzGVWSnm5lGKo28j6vFYqArJY2HNk5VIziAYZHPXPfFIVHr8u+prek13PlSSqF3sHZB46Zbj75+uFS+tHd2i5w67LP3Z2yJAAgAEAANGVaRfj+EBKFt36BflXGXLs6O5W8OLK5A5jzKhl0RRV5o1u9eX3TGxACh9zONNnethVJUSRs0SSsJ1qoYtw1qAobCmI3QAVZYrIQ0gaNejmhK9AByh10SnL/nKXZdmbk1oadZqj45vLy2/Sp9UU9vdoT2hA5iPMtfz4xVIWYTjjeMJp1I8ulf5zUfOex/UBUz+2rtuH7ywCW0xGKh1FcESlhwX1m5tlxmkmaszp1+a/c+P/n35kLbNnFjYeen8tnARalkdnmL9I5E3y/RqNFiBakPtk3wAQmBWtFqtVS0AMBVkRSi4pwhbZllbr3Nbk8BlZgbCkkqBlpdcxL0AhADnSAoIUfY1ADnFRSTKwsyhGa+zHBpx/af2/+LNZZGjcCMAY2HZ6OxWngzBORgiS0PEwADGeCpo3Uj4Uas4NltKm+u2Zw5J5Sxz9wZdU+my1v1paTG+Y2WJMoG5YARAOTLW+RxFTaWxP6ullppG4/H2AOLh9wCklL/Gx7P707ni9sm9hOsCp/HQpUXjkm+N/WuH2OSOavxGDEnlYen4ICpb8YKB5k4c7Ckm/6T63h+WLr/56sxbCjte/GYTQshLQGOACSHntrsOP/rJfV/WddGXlVVdZywCuCu0me07B6S5PQul6n/pqz1fSeXZwcULrDdcdN9A/+NbtnKZQ5YGJOdX1YeTVctN8WfRu0+88ZgYrET6ZjbTmo2mVdxKAMhKZTcyhBD1MJ/iJQihwlK5BQGDOdU42wgRLMTjCQUY4hzSTiMsWDTsWRMAmIiacndOjk+OeTJoLbDUENQZPNw9HpK1/ysAaKnArSvGgMUTfjBncNPlpo+oax+tGEG5Brai6SnLnaxU0rjdNba59Z8G0wG8gGWFkYWI9otxxLMcRbmKiEc4syxzVFgQIikcEQlbyItRbz1Q8z7Pzz/WqzT3SOVFPdd/OKGIuezCHff/hjP6+8ob9BeywWJ1tD8ZhYmid2zLzh0D9fHdQ9F8/dCTLIXDmc+8T9+U7JvdawEpP/V9kc2gEsyVG3Y5sziRvLuygz10xdX6RbnMtD7q6L5RVaqHU77vhpMLeX/CesFr/BjjvUgPIb8LfAzzu3fN6tvrfZhgo3MYCCPThTbFE0nhpoRswLPtZMpoONA8FmUQ1KAZ8A34KnM8oavc8SJd5Y4ndDvSa0KrwQQQ/4x13179fswjH89NtfRucdyBUQCbeSPINUdZY9xhLwBTFswFvHHixz8a2WXBZEKCWKr2y1KRlHLjdWR4HL+5Cfk6z5phzqKjNe7x8pnywEA+zOqTYd+0GHhI9DqRrnNnPswAeH71Nrav/wgrznIBigqAqBKfP8Q/vUXEY1QRNpefVwgcbbQ6zdaR0goMgFuVtXzUy20AC5GBSIBJ8WZLGQYBSEFoKW6RgXNwFSKCEFo2cCtcy0WIEM4LbnpRhfGMAPN5MuRGIEoRgKzqM6AMA4wDIqf6ABhjZWGk430iCRvhTnV/vT3K2hIGA+xI15hZ4KUelFJwE3AKKD+HzTaMOZa7XNl7DOl+YM7fVBb6GhlVgCeC9CWL3a9e6SoPvvp9X4ad5O8n8bJ3OF/6HomLn5tDVthQgGXTgAGIGq0qcdNhTudWnelVaDtx4Bjc3eVHL+yZ+v7e3T/1IG597bNvzl3wcm80IYSsiVqACSHnpDsPPPEHL/xL3izJhiKbyiifK/E8ABPOW194odc9kn9yZm8I+apNj+Qv2idvfoxdMM/zA2o9pTf65Nrr+XOH2ZZaqCzIuZlwaDbMxqsdkCsANGbnWaUksl31n1rfk/UgHy8A0Zo4FMCCP1iNdAjhTaTdhtIJt1rSVzfXg0UDgD1rLGva7erAbAw4AKKm4jYUoHvEL5Y1CwPLHy60VMCTfjs2A2CJbVVu+t3NgEupas1SN1g9chgANivT6vLJXftQyaK4lJUZACTgNKGXgtwCsgDqUaE9szEGpbIGa8If3CxP6yHLs+O6sOxIC0Mzi/I14nFE4snG2GvcxfuV7O7JibG7nviBl33z/sP3PdHaqPi5eoBFYIeKnRdBBx6aRvpn+0p/Mz/RNQLxml+Gt62vP6j0z3pHd/a7c+ViLdv88XwCwCIsYPBaAJgf3JkfY2kuG3IQjCBKOosYqyNnuiHXZYBpjgAg6QtCqxrMj+BUkAMwhQKHcpz1TGMAQFPkARyL+sAwF6YhkOFuNdI6ezjDHJ07EABjTnvS4+p68xWt51rpyJj0nILm1bc/c/d/fWHzEHaUUPmfv/Cpi9401z5uYwNSBYDaVVvLFfpmWNdJ3x+//UD5xy+4/+ldAXdvy1x8JNzsRHr1xI3SbWsNfO28/adw8rCiUbpb1JTjYzVsyl7jxJfjuy/OdD+jLANcS3uQ49HXkTwSwQ8AgPMCLKQhgtCQPRFGKcEanNmR3sfdAVYDYwtCkWAvoHCeMqkz501S6RAbSzqJuWTp9Y2jeWtqKPG4XoadN6bzW3+MXVNIgbEKCnPI1cKetathvwxyXT1QypHeuafcNeK6s0znzjWXKa+clartJZwDBvMJa2rlxZ3kjkqcgbcoU3HJrsvwzG5MXISnyotvfs++nxSh0rOVf3Hz1rf0UDswIeSVRl2gCSHnpLsOP/pnB75o6OGAYSU0X2eWyxI6rPMdNzlZ2/nQ/S+UUdlz6f3nb6rJuaekiz0pyRXpIu2gL+G8xequdOPvtQsBlEXOjQyN2yqzOyf2CeGYzNHhHMNAHPA6zzvvD1dbeUZkmJPhZQBeqHXyc9SUm4dbxVHVpK+O1cK44XdlI+tSmk2M1+N44E1k3IayLO62bi4f+nvCcYIMDMaADSBqyOqWmjeR5omAJ1sz1vJE0Op3usZDl905KJVTUql7K0YxU0LObJWDsh2YWdYaGOwIYzLoB4MvzAl/EMCAVE7xEiC8yBxAZRd7Rhf2iJgdd0quhuHynCYqAihmc7bQHsUljjAutl/Y+fAzzzVS7kHMB8XwYdibULwTMrBZxeiHVfmGnenUQmq+Uv1r5+4foLp887eoKHkYAHpHwA0Y25AcBnPQsw1+vxoUuKNnoCPplBt6TqkAOposB6Dq7HB1RUK1ou2o6aajsYZIz0IVtlo1QgfavEgq3JmL0vEuqgpdWDIzgzXfAAAZ7mSkirq8vHPMawfgapitro4iK9/3Zf8uX85Kb5G/tfm5w7P/9Lw3g8sHoV2t/s6N/+25YKz1vGzZ8ybhGHBs6ADGYA+hWHjoB+OTuPO1V1SH80VkFpBpRFk70teO5S/+pEAsaxo9zfUIS46actiU4wd7DWXF30/0nN2/qMlAiptbBaSe1kWcTnd0QPQrQZrXXifvg0BSUepYuKToNxp+8OzB/P3PFfaolZ96jVjUZ00lKrD7+QVzYlDhTQBQkk1mWDJnnDFZqkcFCExHuRNd2Fru9Jpoc9w2mK23pwrzhLl6KrJT1J6xydCZfdgfenErWUfUUPxZE139vc0hK56cPMvsuIrehTg8jGOjmNqM0q/P/8bkE6qjbEvtKf5rz/j1g+NnfJMIIWQdFIAJIeeYO/c//nt7v9ObmJYNZYtZMiQbaJ1bbnfKVz764yltoLpZeyjc7Cv6hLJ1Xhnuk0VGqfbyqQPYcsG8eltvAUAjzMWpdUCqJKVyZ/09qGRRim+7zOykTQfGAst6kXHU7xo6KESGOVWhi6j1LRouGPZsO1EwqMnAq8vxDQCdE/rEeL15OG0M2ACTe1vntZGtBvNGqxF4dQWsbmu35bb7SK/119Se0rLovGwR1p3Ps8zpU6ZO9IxxO3D3Q8thbj7K1MN8XAc7xYsJuE3ogEjAvRKP5VEeElMjWExURJSty47ITjUB+Ibqm5KLrAB8Ha7GbKHnMF+aTtW/X8yGmNSFNH2hNVhLW/7TC43x3nrfIW8qwIE7sO3nkdja//Qfz61+pW/R0T+K9LXwr1T9AV7PZmvoq+ryBLZ6cCbZuAejBjWMzDmkq2EWDEacGxnsqFW9yegUfIp0sLjxdtkui2OwsGQAcQsnMwOwVhAFoDI7iWU1qRvtSYw8YXhCn13eeNu1R9d2jfrMVkzsfOy57LHHK6XcJa9jU2Ob/7v2c8UoAyANz5CK8RWKDMoAKsgDkESyH83X1h646IePW5+fe+Lb794r9cxicEok4/fUb0dxVxgas+PO/06kVSL9RSTYeN+85KXW/ps3kZYSfthUACEllq5BxA3IajJQem1m+idYQadGHTLc1ZnjRFpV6FfyKVuqf+i5h61//u4zdyAN5FXwXmT//OLtW+e+oX1gHzOLIuVECcbgCtOQNTDmRN5ckAbjOckeVOvlUNVUH4xVwnyVx8Mc2NJs3t3Teq+4zHGCsco5bq85+faLJVo/2m/obLCJLW8fXr34mr+vUeu7LR4Y7M+Ybl0xhywAPOnzhB9PNJ2CXUBpJw5ehhdusW/88iPb7UoWyUzq/PnapTe85BdICCGngcYAE0LOJXceeOKP9v9jX8oaMO2CNpfgniU0AKZwN7HiSLlYS2O2RywyMzTMptITKqk3y4cHvPLzLKsWdxzN5A/mjKPOQHvC3hCAFakJhJ1zvAwWBWMFVGzoAkuz7KpoziOtMquflefCpeovlUhFPOJWCABhU1raXAEIJC9oxel4CKXS1zpHT2ypxpEJ8ZQ5QNSQ3boMCC0ZLDUFi3Wmdll1Z9yEveY1TRGtcWfngWKp726Gl8XyJ2VsqeupDmtZG58AIuGF+qyXHpWmhzCTjUpCiCryC8hEItiHsYtg72Obm8LQkpYZZIaV42EEVoIET56HyWwArB/+iOobUjMnJUeY+8tbF6G4ojA3pdgYtW9fDPaofZnn5mfmsreg+YGfPfQPX7/6z8oXvRVPf3/ZK7lQxWQBk00M3oaZR7zdDAODs4M7ZjGOS4aeLQ/mhXOoZihNPQuOeQzIcq0BCcAikgB8JOuQwVBCTkM80etQk2l9QCPMz7bedwYIlgwBJjiPLMWeb80GpCWD2uZaLTQz3OmVppvQ14w4GlwNriZ5KrO9rpmEusWDigGozAaDFxkBj+aQyl0xdCzQLgyT91u733z4r35y2xV36JsA5FFxoOvwNFjxUw1jdhHDlhBvde+74Im7qw97/3oMEwu7Lx16ah/GGDgEEsxptqe2VuECrZ8poBfobJsX6QDsrrbiE7Qbr3lYnr7Wx7OjtUJta1NAyFg5xbMkXLXrt+61LF8nAAxIFZU5KrPPi4TErCsWp8STh568IOXdAQbUgVFg6E93N8/L3xsM+f6Ujv4CPBvNBjREbjqo2GFeAQSaGnNcJV33LIk5Y83jNxZr5Vzzm/IFz0VjABjnAANjcQUvMECSAYCzePYs1qkBhlU5mQcKqwu26uDpdD5nSMJJwDG6puN2YNowDViLKLRG7LffJrHsP+iXj9XDHsFEJTKy3I4zbSfcDnVdENSYBUDllheZcXHsXGQCUJkFBi8yAZTDfCXSuekB0MY9pakALIpHgghREZoZ5CEV88AichPoPV/dn8/sKjoLQjS8yZ47ho9c379l1UFACCEvI2oBJoScG/783q/cXv2RaUTbktWt8kSJZex2k9rrKpMTj81lmiXtsvxsqnBc3e7KqqTwGu/zRVAPc0dEbz3MAojHTC7rxSrEuDrReZYRzMXJpz3G1TXg2Ey3oFdZHsAiy3Zaj+PHd59pC0tpHunqAr25Jixl7R6zq/pMtluIRLBo2rNmpzJW1GiPDcaKcLE8aZz4T8aALffZS428y/pjd90DDMiVVufn5ZJwGtBH2YwDQ4fT/QQH/LFJbwBRdKX0WCYqAaIa5Y6Em1Vmq8w24VpC60F1kM0YcC9gLwxjfrg6m5h2cBQ8rumbBBLgPfBN1c4rvsEAlNEH4CDGjmOgKHJNaKZw0tMzQ7fdWkW++pnZ8V8A5jGSQr+P6e+D9WAGCOfhe6gBDBgELt+EBCD3A5uBy4Ek/GHVGpQBzOv9FZYvIhm/lIPY7CFhQfNglEUOgAWtKLLdO3LNDsPBggGAtxskO4NdR5VZxPEVAJCAEzfPxoOl19SpPGzAMeBosF0YAFoHJJCAM4TpPljmgmIFWurufbsG8ak3/8IUUt0Pj6mRNIu0J/TXs4lffPZhFjyz4M464SUzey7am0o9jREAmzDrQF+xVaszeTwhUByAl+2B0z95yDBn5UpewtqAE7UWr33vqDwbD3a4Eoe2YXrPQ3sfec2belyRuP0Lc5/yDns474PY/tO6aWTv7n/TQzjPxOIMBoPIeC4Ya33SWdyG7MSd2DPMqQqtn1dTUlkIvHv+UPL4gc3a00d73vmFwZ2A8CLD63qxs2FmQKrZQgNYVegDUtWGYXBHZc4bEJTA8nyuyR2TeWDwmCczxeYa4wyc6azp8WSOlxhjNtPBGGMMDA1mLK8MsPT6HZhFka93z+fc3jH1sKBxy23XNk+1y1+v1zK/1Ia8cqFaWJgJc913x8MuOr/uVA8mYe/AoQKKu9ihLy38zF0/3AZFRSJtDFjfuWiMOkITQl4x1AWaEHIOmFyc/td9d91avr8nYe9KTDOJmczV4cxiqAl1B6a2PHzANyv13v4jqdGamtktFwX3Izv7nLTrO82r5lQzA6cqtE49HmYEwFKGy3C3R56NbyeYO4CZ1h8YA1BA1YHeZBoAD+Yx1u8Lsxq1hnF6E2kA6uYa4vR7OBWv2eh34izkLxhoNfyeYODo6mae9j3Bghk1Fbc9y8sJBxa2+z6vMX4YAEPqwuIppWWGAamS4qVVT8R6UbZhACLPKiXk4rlhRjFjw3jO2lENtfP5kR3iWRGJksi8EOzU4Ly9dmgqVRvGgiWUOTYYIBpFUWH1Aupb2bHh2qxSiVR4jMEt6L7BVSeayQ/m2UI8p2yF9TlMPci2WNDAEM8b7AhTR9OE64jE89PDEHjDXbWB8GFXlIa+PmsaSORwxAd60bsXtQUM9kLeBiSAnWDnwdpuAJjKDZaRLyJ5HAMl5JrQXGaWkW1Al8LEEBpb+fH4bZhEvrMLmswPRcKCVoPGAGGpADyd28Jox7nuSt3L9t6AVLEj3eiqZiQslZlLVbw0Zi9/CHK81QqnRpLHwzg8ByKRY5VImCGrv7f0QrgoTe009kU7vx/uykplldtJODmUASThJeAAaAp9X3R+xJs/hafOP3Tn7LuOzH/3hp7e3Pf08yzoTSwVwfIiI+7/PBtmOwHvRVn58tcLVS/l7OOUT10yzMlIZQAqdy4V06FIRqyWDybFgjsAz/iVWw8cBwO2/7/Xbr1EGsW99w/+1K3YcVwMeMKohNla1Hl/W+9xmjnVSB+QKrbQe+WZRpiLR1/nWbk3KO/6xl1veeqxH/yX374l0xpwG19B6H7Nl7CZHcKpg/W7tSH9BwBMeDZT86zmMJ0zmMxxvT6T+2VTyEzjnEXcdVkyYp7Dk3GDscPM+FuhyQwXSTA0oCXhAWgIRQCLKABYFHlA2MK0oNfbszqttT8BoB/l7p1rwAagC6sTfh2YDgwAOiwIODAsGHXoXmRWwny8h+JZ0ETX/2e5s00+FM+jZsJOwf/0sz9j77W1PvDeDHzHesfbT/HdJISQl4gCMCHkbDe5OP1/3PnfBpLV8WS9kKiZzC0iPYz5MrIWVGPaHdn75MB4bbLvoiNqT5/cuHjhaP9jE8/veMvnN/30A3Pj3PAiSw6biteQjX4nbMrqWH31CNs+qQoGjblJ5qZYOR5FCYYRzAFwYBSRAYML4zgG4oJPnQwcnxiGi4Y9ZwBo1eBh4GYQP/XS0ER0dWJcU9dfwkXTnjVa95646tWq2lprDPTVkgEAuc/iXSMnu9Jy6yEZ7vTK08vSNANa7YSGDluHBTCn1eLEbOgLInvAGk3DPo89tyU8fNVz06MTdx67C/fei7E9OPI07I+8bU/28ESFbd6eTm6WXKalkxVTrwxV5xQ3Uh3PyevlbHaGFRgHY5CYZvAmY7CZWuWFEstUWB6c2TCG2RRj6EENgAH7ALbMYPih8JLZILNTmt+EucHZvTdNPKr8xV7tDcgYQBpMBbLwe1UFnp9XrbwCwEHa15niCA85B0oJ6aqhy0521F7oMx6JKmnfgKPrZRSqumnAsmGWWLYONo2RInIA5qMhAHVoACpd1YC7GzYjS0G7QTiyVhRzAtpjJtGeIKf71+VT5rRkmAvA4PZWfuQduD//i7f84LMfu/arn++77vwPbvoDAAZ3Vrfex83CpSiX5+W3Vo5eefTOTcXn7tjxpspwf1Gkn8dYXCirAa37GPMisxLm1qgRvdbJQmQpnY1furMh8+SyKz484bf3xgnHXnEzOKNpufWADHN75WkAr8eRMdGIovnxHxc9/bj6/JHvh69XPnMvA1JfvO6XH76rtHvgzove/mQ25cA8hoEFf7Bd9C5ejwMwg9txJ+p4/b1xRTYhCqhKD0zsUlP86a9eVbjir9/xxjmRLovsJjQqULbAtkSQR60J1Wb+MJs1mFNAWWbKMJ8d5tOMgTHmcN1kTtatOCzLGHwDJqs6LGPyWvxdZSNTYhkAYLCgdn+fJJhnodUZXAjYSExiEAIl9M4hG++MsijMIddaYtWuiuf67h4v3ZGA04OSLYwmjHZdwFYotmGWRL7R1c48FwwvDRUWAJDltsmcQWnKhJNFMcmcg9N9P3jwfEVx/cRwcnfxmyPnXz+8bHI1Qgh5mVAAJoSc1SYXp996x3/fmli4Ij3FDGZyF4AOp4aeEjIDD92dnCnqbxwq6b26ql5QPxQ9OMey/fW+rX84+PN7D2/xmsqyuseMAXE9Kqb0LjXJprlbi/Q+uaoxxxV6Uion4A1gBgwmXBPOIjJLXZQZqsgtIguGxWCwk3nCRbPT/TVuZ24eSRsDdqcO7RpONC8R4E2mV5XAXXrYiRP06kgMxF2ge+yVj2LLRwsztk05DKD1+PafelAFkMWyZmHGAMZmMFQOMhNu/6g884G5e419P5y6J/FcMgw2ZRKBJFdm7Llebu8Ln65hUzK5rxHWsU3Btl3I/6Sadjy+FUG/yhjsrFzUex2mVnnGZXqBNRlDxD2PJfbyXTYzEtw1uMMYy/OKzdQE8yymMRYPejQqKADwEfaish1HLhQvjBycXvgWemo4lEbv61W5V5reunWocjiayqSas3CBp4EM/IwqH/a4DehAAt4mFVvQyBaYgQr6j+bSC0hMYciCUULWhmFDb0D3IsMVuh0ZYFjdTBpn3Rx3ivXU0p1NOb700Mq6XdkYqzqLdtJyXB0tmDdXROLXqk9cgOM7HnjS/Zf7s1e9eyD5o0cv+ej/23tVhjtZqdw1cRc8YcRFzgEYEf/1yalGdEB+8uBU+UD0oRsWkJpA/wKWBrQDqId5AGsW6ArmTbSDerve78t28iCWDuJ4LMCaVl8pkHutE8269Fr18evw8Gxj8Jr77z2W31TzanO/ej8ABlz3/0BtXru1+sN//eCvPGmkXWE60OeRXQgGAVQjvd1zWxuVZ89jkwBU2NF0+cKJSnWzvnlyio9lynC94raFC7SSGHyeqQAuwYwnDI7GLEvpsFUmpVjJ4joYSzLX5K7N9D7WYAwNpqaYH7JgFDOLLNPLqiZsgzUZYDLPhGfCZgwmq3ZeTufLw0bG6CqLXsTAAlIJeMcwPC8y8b58AdvjGxXk50T7ko1o/V9HP8pZFJeN6YDoQyUvFpfdBzgioaNpI1FEvgkNAg7MIvJxpbf4AgqAijA63yRZ7vTJUwD6UM6i6LLEbc9eau9tQtGMnSY8x7rpxhO90YQQcgZRACaEnNUu+qff2JEsXpCZsM2+kPmm8CyoBdRNBM7j9e3+/rkLNoWallH41vkDM1936oOIrn/7X8lvOxj2hItGaHU6DzOvKQNQkyEArymryVAdrXW6EKe5C6BHnl0MBnvkmbhdJQE3h4rGuoZiMjCwWQzGPaLBWCPMz4WZ1bFTWMpScWCcrDDQqiTsTaS9hrxu1l32+BMtZgw43PS56Xd1RBVCdLX/xLcZMszR4XQXxI4fMC6OgAGMMQHBOg1OzGPGMTbY8DO72f7sMw+6Py7+SBlzdl2Y2pJbVApx01AictKopIpzu4LDm7/3Hc/Nlvq1mcmsEUyNW8bw5EJmHMb1amEETk5aTPdVmNkqBsSYx8wqzxxmm22uJ5g7xGcZB+MswTzGUeBVxlmCBYzBYI0DbIsJpwf1BIJCuTpcOiyeA3yIKoJn8fidyF4C73Vq2swPXFjy05KyN3SdfKQic2QW9XiPA5sR7FH9rVIzJwE4gK1l9FjQSmgFhhmMxEdCDZoBu9i+P9aADgYvMlRu+5EBoBzm0H7nK+0LJYNSZcW/uHq79nLM7oRqhtUVd8d5TZIXtYiP8NlLP/+Dh5Rr/58L/mwme8N/3nLTC2F/hjsmcwBYQu+E82DBBMAT/q9Lx7ekvhY95Ax85M7n7/n44az/Pf8taG/imn2e4zQeNZSoqXQmuTmxl+dc4uRrXWMJLRXwhC/3WQDG5Jkbo8W3zH+JP3ZAE94T19/05h/f/L3/yzvkgQHnfwhX7od91e6979j8b9lLNFgV5OOLHfP+0CCvepF+vrQ/zyZ7UKtMh/bQpm3TxfJQj+IYE7qpCGcOySoUAI24zZwBYL4wVG6Dcx+mDrnKNEsYceErgzu65KjcigfxJuEWULKZacAGQw9Km3AsfhW9qOaxEN9OwOth1TgD28hYMBjgIOMgMQvNgoGuj3gTegk98b7pbvKdwkhD6Mtyb9cZYFLYeZRbVbVE/KTOCI4tX6p7lwuAlVBYENleVBaQdWDa0BfQGg9cD/MzXRdTNsszCrMY0ItKlpXmSr13/3ALVNXYqkKI72zZdf2ezSd9swkh5CWiKtCEkLPXn9/z5RGzti09c8A8f1DMGMKzoOZR3yyOWhPp0cT+udHRUFW3SxP+PX6xvll6vfvo9l/8Ung+AgBCKljcjAtQsbBoqAKeJXut8bTwGpK0aEg9NsAgUAu1NHch0CPPAIBAAg4YysgaQl9kmQTcAdgQEKyryAyQ5KV4PpWlOxkARJYi9XRmMWFrl23uiM8uu0bwSkkfDXlFha0TtxiLtUOCEHKhKYQQoRBNmZn+ssW6Tmn7pWoyHnQq0PmZ6JTbFeiuIi0Aj+llpAULeS3U9t5774GEO3whv+hi1+xxOysBa3KDM8YH+dMsd/zG8VJ/dktwHKGvu05YnjhyZGTu0NHa833+Hzx/5Vuxc8d0/iKedzMsi8nsYF3PHGO907wHYA5XItZncNfkrsqdAqumeHWYH+cS4wyMY7w6wRg0J2ztIRs8BSSAPKQLcfVliH6E6je8bx2d7b8AQxr0n1fre0xlZHDSfQuAS8QkwAJWa+SYBXkRqSTCBiQTdjwe2YIG4AI8Gw+adWAC2A4AKCEPQGbSHNIASlIOACQXgCotDfrtO8E7F1O76leluu43wrwt9O4ivYdE5nVRg0lzz0eb3/bui6/7mT9+8JMX7Hzm1q3zo9WrXmcLYyZqx3LWjq9NRd1SCxZMlmbetLbr5ptnelDYe1TevWU6+/y93qUrDs64S3PUWJV4T34tprPESTNr12DpNZdly2+fZH0rt0xL+eqWWvynzfLMb/LbpqUezOdHM97Ea7b2l9V9f+0d8iABOeCyt6rH3/VTj2Z2/SDb04DmRcZwXLANUzut/U9nwwb0CEgH0jG5v2+4Pok0hqFDKRlhQRyvMcWEk4ivpDE2zYebzABjOg8BtR4VbJiT8QUOxiJLBVBN+hAsGzkmtzVmNzgHCgDmkUvCKSLfgL4LBwDMIyME4n74x6A7ImGwpoNkA6oFo7NbLBgm7Pjhi8h36kLHu9eBoaEZN9ICkYBoffaXZ2AB1KELkR1Cs/NYA1YRhZxY7F5h579MQDCRFYs6LBuGgNBEU2NNS2hxj2hVNBGl44dkuKOIRvxdJRA50Ppz88b559mPl+zZTWrSf7x+/HpsXv+dJoSQM4JagAkhZ6O/uOsf7irdvSd/1DEHJllhXEwYcAuw+iq10tGFwf5aMTdSCL0cX5j6puUHcH7yvQ8nd/xruGtpFZ3RcXGL5dG015RXDMFNbGkw04/v2aTMKcz2haF0FSvqLJ+AO8BmqshlUHaZ4cKw4xpCjPnCcIXurC4RvKqU1AnuX0nYSvNwet1Fumpfoasua3yju0xr19d7YkudrdU1dFyZWPM5elDJoLz6n4cacovIAPBD3XpiKpw/Hhmpyq7LUopfb40aFQBSzK0LLd6QXqmW4JXulfSj0Yf5bNSseeHOxtP3LZxvvLDfEtLI85P5Lcae8yphPlcfG2+w3EF2HsCeY0aW1Yb4/CCfM5m3mx80eD1RDyOTF43+rO8YrMo5S1Z9zfECU1Vtj9lgi2AuUAezAAW1EIkm6rM49B2YwMAl8N6w2xsr3HvpeC0XzSPtwtRgATBhWzAKKNkwARiwEFf9WecNEXBYovPy4wda0JvQpzHcngSY9aFswegeMLmOFQ1oHa9XH7egN6D/ufPpLwc36Jf8Vc9vD1of/pkDKJSQmwmHZrpGJseu4sVfnXh46gf/KwnkR1HYvvuF8YEHcNG3vDevXn8nOUcNpXMouSfsk7+KECs+aCdf/szRUr7cZ8W9o/+z90wleWRPc3+6OH9e4/knzF84ullcfPdtN39klgGX3oBdo6i/9+IvDPxMnlUOonAVJnoWSg/1bl1AdnRqwR5WVGF5MFVYW1AEcJ54Mt7WAqtG5b7e8ixjCEy2WBh43hyvM/kAG59kg8Uo78K0hVGJ9Pi61VJH986eab/qndpBAEk4CWHHk2w7wrRhGLAKKC2KfA9KABZFPq4+tWy/ti9e6MKOB+pPYigFuw5jDNMl5OKqV2OYLolcDuVWdwaButBZuxN09+qSwhnEFGtvXRI2gLwo63EqFiijkEU817fW/dgpMRzP/g2ggsJC3PsaaIR5W+gQqAp9QKokeWeYuhjlc5Xyph/+MAVZRTpn9FrPX/uasULvSz0CCCFkXdQFmhByNrpz74P/9al/2NMzoyTUMjIWtD04/NrKE3JUP1wW2V7mGL250Ck2+ZM3zvz0H6mPX/ULX9K2TfhjAmBgS52Ru6bWbAXgzp0AWgWrglY7cDwYWGjdebizfA+qYCgik4Dbj5k5NrRUQZexRpibi+KxlMtP+teIACeJweuO/m0T3fmWLUu/J2D0rz0aOcPcVqP38icA0INqGuUVf3BhTKE/fq6Dx9LRsTmEEUa39qdLC2G669FIcbfeLiDUK1UTvNrZSBMegE7Bnm3CrzM3iepoMMMa827dn52WFo6ndj9xaOz84rjeyM2guPutD120jQeONVBkjG3lxwbZoskdxpnHdZO7Gb/KOXQvCgzOJcY5NDdKlm1WBJ8EWwBjYB4wAlgQDMG4Ki16zEFtbDC6tFbGwD36RbP+kKq4R6ze882jAEQQMlmawUgPKgBTULdh7MDhnla3aYRwJWg6GkVkDTRtJOz2lRGHmXFgPo7hBJx23SAkluY6sqvoBdCHOgAZtQS8fdjjwVDRNY4XBoASJAChSMxHmTz8EpRQagDYhvkRzBjfu6/xsaev+uEFTw5fvIh8EfkScqrgUpR0ERV49TxMvcE9bD1r3P3efwZwiQp+x7X3D1/qwfx+cNXqjtYnEjVb9a6i9iHaaiVeuhaz4gg8aQY+w6cf8YDhuOfzO9MHn2bq++fvvdR62BxaeF7f/gzO/9nyj5hfXviV6cL/qT50+Mqhy3c8cHmhKFgCrgdzBqkeVFp9HwRUNM7HrIp63gkivTTkVMExdGAaR8AY4hmUWQqsATYOv1etDGt3sjc9hgsqKBwMt02Huc4eiBoKT/oQIssdACazrcgAhMGctFRcaoQFACSF3YDe1c24/aO9e+thQeO2GxkAdG7H3wIKt9FpW+8aRL1yP7d6eay4R3SeJwnbFA4AHbYGKyGc+EsmL8o2jHZd6Oay9QocwHj36hyYJWSb0L3IqIa5zsXBberhzrZsw2HG2eNHLjz4UIChLRDi+z+16fpNO07xvSaEkBeHAjAh5KwzuTj97tv+8MrCZJRJlZC1oBVE9V37Hs0+s+/5nVpqqwQj3VOvPHcA7v/tbP2fO//Hzv+jiLQvjOPBUj9T1hV945/CVpjhe0fT3rKSvExNBupYbVUuZXFd6CQvL2vLYkjGTR9dw259YRwNBrofu+z1rB0BVsbgpSmUVgzoXZZ1V915ak6UfgGMK0dO9Kg46nff48KYEv3x7YV6rvz8DELBR/pgqoiWNqlXqi2E6c6Je4q5BXmu89c8ahocF3oJ6fis2xc6AIU5JtwxHBvCIoB04Ihj0VNSQjpeCaarY4cbfXsnZYbye69X65XJa4cTAyLHqj5PaMzayo8Nshn4yTgGV3I5T9I5R4EvaJ7Q3Cg5bTMbEgezwJJgvQiqqpr1KiMJAIodKVY4PdRrQy+KwiJSJdHThLKI/BSGRmHPA0U/PchqmuzVof5EZRo6u18v7Mb0ZXh8AldKqF3m3GIjE9k6N5y6XtAcEcG2oErQYdRgt64OaKgB8AyoNgzYXnsOVwNVG5m0XfENZi8vTFVExkGSQ51AxkVyEv1xnI57Yl+I43jUKb3nn95iwH32Nc9jBMBh7JzEQHuHly9A3cf86/Bc+faZxz82c/VXtiZGtnxh6JpQJGeiwefD/rh/9fpH0ZpaeXh5FeioIWOpUNbLY61PgJbye9P1a3pnn4e+TZq4ij+35eC+rHxwYXS07Fz6XXPLRWg0jjcT0tidAzqAPlQA9KJSh7YJ072oDuG4jiYENrFF1REZu6xOharlsSYYA2vGw+GBuKLcPLAZLAX0wevRfIMfLYw8IK64F5c1YcyEw9PBUjv8oFSeCXJZZgMwuJ3iRQh4wlCZ1fVaBASScAzYltBb3QTaH3YvMtzImAmyWe5UOoPGhQCQ4a7JbI3bKl/2Me9MDd2AHk/lnRROA1pXu2/3YGB0h+34T/H1mjzKGmxXGBrsBJz42p8JF4AFzWx9JS57U9x2AI63vNouKp5hTkaqqKzVmeI8dvR4rf9Hd/RC05HJw/PEf7zhJG89IYS8NBSACSFnl6Pzx973b/9le75k5wclqXXne567L3fkyKE+Fl4wkITI8cWD/2ipT8L9pbd/4/ILi0gDKAYDAGqtLrit/Lui/rOwleZEannnTAaGxJZ6a2ZgYEXc3apMrB+Al9Jv91pPKwMDYBCW3Or2vDLosmUnqafcq7RjnfTbL1W7eiSuYSsmujfGhTGFAQCH3M3R4WOi1oQis/5RpnY6jYtheQECU0GrH+OK9CsENuPoBEa7n2XS3xQvqblRWm/28IUUqwSIZPAy0kNYHImOS43FxgJvHJ7xZnnjQC33SCMAxn71ioMim7g2x3ltU39tCz/ucXWTNx2ZrMZ783wh2Qgjk4cm5xzwUrJZz896YUIKEnwhMZBgDuPI2JXElK/u9aozSElgHPWtQ2KbeGR417xIvCDGi8gzCBtGNSrUoMXNbnakfaB4+Ehvck4kd7GZDKtscUqOgdlmBkYjMfvE1WbVsP3pnCk7PQBcgyXtap9daRoFADl7v4SKYocz470Zp6KUIt/kcd6J02/VMGxoxzBoQy8iD8CG0YReRL7Unp04Dkg/bz+VWnig/Iix64X73Gu3Trz+/M5A5WlsUtFsQs/Cz6OcgUhMTZiTUvJX79r6n/DAr7xFgjKJHgCLyPMoewS9AJ4Pt8QluzrhNu67u+bkTFnmoKvEVyyYN+wZc53jqn00dN0++UDfJXEzL0/6AAqpei49D4GLcLQHQuY8RC2LqB6M3mOZiXT9sD9ucNuMeEIq+ZFRkI73ojqEYwl4OppJRBE8A3aBVY5jaIdTTJjzjDGDVSInY7BKshSolsts4BAwCdTBADEAJIA+IAFvTPMM3szLxzB6TAwfFYNPYs88sl5kOpEBQGNWa84kAbTnBHYjQ+OWyuylE7B2l+O8aJWhios2G7BtGL2i4sDIiiARmnOinBb6Ln6vb+/hvApkPAMus0MENRTmIBQmJeAYwqlBYUADWtxjWYHsI3CFacOwujowN5dF4tbXjtd5TwVaubpdFmvF22HCsYTeugG9tcJV7/V8MFQTOoD+dkfoJJwcKjqzbt7/OvuQQDJj9NrfOW/79Recd6qHAiGEnD4qgkUIObv86xO3j6cXkSskuFMUmWEx94an9/f87b6n390j7zaFquLHk5N/6134Ttz1iZ95otDjiKX5S2tL05aKNZNiZMlqMgDgLW+w8hcMdbTedcfaD491n9v5wnAjvV+qzoWZ5Y9bvoa119fVCtOUo6aEKFpaftmDV23aehu4klSwTxQtVqffuPPnGuevQA35eOhvPcwlqwvVugUhkB1iqpNiLoC6UHt5rRPg4zHAKV7tnOHHTUYT2NRdSGfSb/1aF2otkOcWcgcwAoh+qQHDh+FX0P8M34I0BtLNTePHBoOFLc1nGtMj1gsHnvjOI/7IaPiFozpw9Fe2F2Fj+5bjl/YZruNx41K+EFaihiFplahupqs8PezW5gqqJGGGjziuyqXkdm8h9VST7UXIkOQI+1V/XKrnjMPD5y+C2dAkIZlwHJgmPCYqWYj4XL8O/Wv9I9NhDsCDUQ+ArOYgQkXTESGbuuGLyZplKh9YnAgFnGFsmt3ftGVFDcYdRwoqLGJKuOiNsCYr1CDJRikjz7owXWQiOBx6GYoDk4EBrIDyPown4CiQddhDmKqgkIabxMIw7NBNvulHz37pHe9hx+67cHBmL7ukjJ55ZCso1KKCzJt5lCuwZrFZh4WRfmVEeut/f/7+a6+/nw04MAdQrUDpQ+UIS8fpBQxZ5lSEkU/VgWVVuUzuANCY7QpDY7bKrHpYiN/HTgNy1FR4MtBSgVtXRjKNHngAFtvd3bstRFovd4smbz2wocj9Fk/4UVPJp+poKgZ30qE7K9Km7GbhVRoJmN6xeh9P+PlkDUA2cA87veXqWD5Z/6HVD8BM1iAwxmchYThVqof58+SJiWhgSDmYRRESS8BhDDMY1WBt4scl5lpIeMz0kexnCzUdAbIM8JkxGEznjjVF3FRpAQzYDFgQTQgABsQYJrePKJbwDBwNe0voPYrBRZG3oAkh7FBLSwsQSAqnLjTW/rgroulFhhCRE2o5qRh3dY6H2jagCyEGRUOKGmONhpArSdsd3j+drj1XcdS6BruJBS+bD6HKZdtHPfdsVq15RnYoqCRnI36+ZARCLmYVDUgze8gIckApUwlTTEcpK9vQLahTGBlHyRaG3e6W313PvCSy7a8CO26/bUR5CKjM9lqlvFZ+m3Tyc2PV90ac9gF4kV6NtNbw+FDLRIm8NKWAO9DzKF8uTf64mkIiY8/ruGiNQ4UQQs4sagEmhJwt7nz6/r968i+UgZSiS1lURlAaerSZ/5tHKr83UhvUNjv1u27D7tp8/9U7Pn/BG+NG3nkMxGmtGWWdSK9hzRZgBtZu/l26E50/AUieX+7c0QmXae52pkRavnjrli+Mo0H/irZcnOCXpRmJhRCWHNcZkgoWgMZz+VU743Saek+8rJoM1NHamn8aVWYVtrJlOAHXgBOPYh3GnAbbhaEJ6zC2AK3uysesnvDgMTguSxjpkTSCIC1Vp4LeTqrdrBz1Ir0epgHk5XkAJtwsqgAqyEAsjf71hT4dLKt5E5Z0bgSRLUeWLBecuGU+Dtjx2gbQLLCmifIb3IP9jzxSzGYflHuLh5Tyk8e3zNcW90MHcr/Syxjrf2N6hE1Jg6M2DxljCckzuWuwuh6IY+am56SdF/vTF84+re33+Hmwe4xSPn8Luy5gySorgLFSNDrFUhVoYCwLV+GWzC0ABZQAOMLQYavgdmTYMDSwuSgDAa+RaSadmTA7yMt2ZOySjljQ+/ixlHDr0AD0oJRGECCQIQOwoAcIGtC3I5yAyoAsipHIpKKICWnOZbt5o4GCJLhhNFOIgjJfzMl97oH75Z1VSXmXezxAshoMY6q+ENW8HYOHmGI2kwCeFzmW8CyEe3CsV9Qd5kaWm0tKOpzLv7Y/fI+M6QIs3L7tPADHYI7CSmAhQvpxDAXCVCNpCmmN23lWCkSrObcGLc9anxQTtg67hLwqeDVaOoCnRWY7m/N4ZFjVXs2xIv15Phof+fGw1finJYzIC2vcOF9ZmI4yOakUzwV1NTs4wY0sfAYca4fvZFerowlnHtmkcBrQ4z7MFvQ8yoxBg+XCBKAzqxn1JfhCkjkNGIwhyZwGM5JwGNBgBhjiLsHn4UDn49OHqgHLQDMBr4CKgapqRYoVmYu2ANBEPO5VzEAkIQS881UAi/neGT1TRO6oGDguRg5gvN7uutyHCgSyKDoinkfNcmDGty3oeZSkkKeC+cUwm/UWcm4t26zsr5qmVTbrE1lrVpoPuS9YhoVGTlWgZpiUyHq6IhlsIKhkVDvIKFoooghJuyIsMANaNQKHk5XDZsZAhdUwPzooAVU9n/NKHkszHb4OwdwpNmBDA2BBc2Am4C8ibXdVlo6/V+Nqf3Z7QHLc6SAJJ/6WiO/pFRWb6RDLArAXGUeDQQArLuGNyrMqszdhVoO1DYcdmDqz9os93/zWVmgG0jlVd9xffQcIIeRlQ12gCSFni6Nzxz747d/aPOYvGINbMXGeM7P5yZna/5x/9leGpav7tjnF8vdmt47z6raLvpXZZUFTmDSN3riNwhe6whxf6Ap3jvhj7QDcTquMAQiLhj1vdH5tYQytlFgHlg/iZQCwSZ5TuL1mAPZhHPX716h226pC03WPQLhocNNnhh9vSdhUAHiNdot0Y53OOKff6bnzOIHE1nr8pCv0S9WktEbn507hqzkx2LkzPhWORwcu+oPVOSuaXUQYoXc0k24CSEvVWpDulIBOIW4Q1np4TeVOhtX6xJwLvYpMJ/o2w4wrNAD1SOvq/trVhm7LrX7pXTugR6oleNVkngFXg2PC15niQ7sez7BaNXH3C0cerc0M9y7uX/ANiT8WmoAE5D+0Y9t2NrkpN67YvNeqsown6TN84M3yc0O1I5zjIPtJnw++UFAeiM5vMn0WWUNyZ6I84lmJW+M+GePY7BeZ6Y1I0wZsHVaSOT0oGZ1pYxhsJPZhuy0MAMfEUBpxWTW4wrBC3eAOAJ1ZTmQCIgsXQEVoWbg2C+N1xI2rrjA0ZgFwI8MWRrlrgG7cJzn+OSSVAUyHuSx3SvVUnjsARENhSd9gDkt4Wbg2jzpDLjtScHyRiHt0W6I1ELl8CsOA49HC8bMH8yaAqK7wlB/fkAeszhau2WV6aT1dF19y7dmnVW55kany9jy0ywbVi/hj7TYyWrIaf+6SLA7GzIRtMMeBkWPFpUcsXQlrReh4KGzneRvQE3AWkYeADqsPJVNYOqwEnIIo6aJpwusRFSGQtitCwCz6zbxiHPcV4YkmYKIynKjq2QMYO4qBKYwURa4ocnFP4DxKI5iBgAEbEEbkVYLMgp9c9JPcqit154VyxqyXFbcqBfZAWNVky0wKoamXh1V9gG2uH3K25xymy2GyIucCTW3wMCeKAEx4/XY1YVV8gztI13TDgAMG1RE6ambZrxsFSa+ZZd/KKZGdhgtNq7gsq6HiIhtm66GTBqDYwjfg60wAJaRdJItI6bDjozeOx3HVaBuGIewScgYcQHRKYdkwmtASIq7xJg5ia2ffelHX2BAAQHcJ6B5UMyjFl9vit+z7B9/8zP6EnxrRe+0X3nTtWP/qa4KEEHJmUAAmhJwt/vLWz73g/YuX7c+inEP99Xc9svhp78Bv9TRfu23QCMbue9o2Bh697JJ9opU551m/BW1F62szys6HmXb+XTYAuBWAV+TV9jKtYcCrAjCANPdadZK7q1Mx5kdGNcpW21WjWyVkGFtVohlh0bDnDABGvw0gvt2hJoN1AzBedAY+0bxHAMbVNWpf9aCaFq0oMofBTkfoTpGbRpidtxPh/qMIA6ZrbFNfKmjqzOmq/7z0r8mYcrRzFWAQC1WkAWZBjXfU6rbf1doZeOntGJIXlDjzdL2PjCGP+jXuwbw3vf2ZI1qlIub8kicdL4UzZrJ8qFELEB2GCSjA9Z/sK/WONbb2Lhr5G+vfqOSy97L3H+TMhXmU91WjXBU6OGeMpblryJ4jDHCWlcqMszgGgyPPywnmgiGLYoo5cULejNkGNDDE87j2s0oTBoAia53K67AcmEXk69BTcOrQAeZF8TEJNzI7lwGsqPsIEQAqrXsEwLLMrgg9DpCt0kfM9oShtiKlAOLmTSMJuwE9KdxO05wrzPiJZsJca5YjAABP+DzpRQ2VJ72upwXaUyLF/FkTgDJgLQ0Mbh9g2a7Jwwzu6Nw64g9vkacEYxP+UPzXSqQPyWWN2UeCoSx3TOZo3I4HytrCyEklRxjdfSysVqknVhF6tpV1kZNL8QtJS4vLPqoCYEjBKaCoC6tVhburwpMN04ERf0zjzt51obfrTtkFUQaEJqwkHCFQEEUdVq+o5rBgCi8vqgLQRVWxBADZjqycNCO2C+BJXHEIOVcYKSzW0VODeqF4brRiD87MwrQOmpnZRtKrSAcq8lQz2Qh0qJoZ2Pm8M5KxolRmMBn6arRFqWWVyk73YNPUS6KgozGFQQuaAceAXUI2LyomAgemiSBlN7eUn5ePeUvTh5toTdG1ACSABFACbKAOGIAByIAGJLoOKwNoD9P2DbWZVQD4OnOQjpt2i0gAcJAAUEI2vnRVEDUANjQXCQ3NuPU4rnwuQzoq+nTYZeSa0DxhHPWXBeBtXd85Cbg5lDXYCeEOsinGUIpe+7mvZzC0GUJ8/ye2Xb9zOwgh5OVBY4AJIWeFyfmpx8u3yMP9unB0eGNOpXqrd/Q3ehpXjV+Kw+If5hevvuyxHZuPRXmw1sQwVmv077LhsPNhplP6uXv9wlZazb/LrFmqauWj9faZty8MldmNMD8XZUblWQGhw+4MbAOQZg5E91BkoN3eqyYDpdcG4C8YcQwOm0qce0+WftFe/+nF4HXS7xrrbnNhVJDF8mHA8W0/0ufDbLQwDxFBCGH0Mj8Aw5rpN8Xc7jVXRbrd9it8odfDtMbdlZux6vW1K5MJgK0z7HkQCxlU5zXBdeng6wbH3UZqzh8ohYNFYLrhKai/US3t9e4IexN7F77wiflNmHeB68dRH8HE7+ypZlJC1C5gh7zIH0KNMYSiyYBU5E/4vaowVMnxg4SAULkDzntZJcfL8eWQiLEqU+IAPIP0AssmmWOimWOLYMiweRNuHnkwWDAZYLLjALOZaUEXcTZmLK5x1ZDiBMMApNrl3zovOdduF62Hhekwn+U2IIExMLkS9ZbbgTnLHZPZgKgDGrObnNfDQp0BwHSYb73ZDAAiV/EXEqsLNWtpXxmw4mAcNhS3pmhpP34T3Hrrtr+YkZJ+5ChyvwXIALYo0wC0djtzijmmsHeqE7YwwNCrVtVIakKvQYvLKW2V5x1h2FHSRtKQbTsyGIPLUsumLxbIcdtgTlpejIOUF5mdpmwNXf0yInTKF9eg1UQ8EW6r1+7KKYWW5skWQAgBCFGHWhd9AkgJOydKOuwaBkbFdIhIEc2sqAcQNvSj4rxQKXgiMZ/S0w17UtdnMLzD8UfKfMfCs3sbqS2VY5uq06XnU8c2Bff3aYvokxW/ohhyT+a8rfULE42illNUPyM1DW6bcC1EAByYIXwONAxdsbETewGk4T6jXWSgDGAEiwex+XI8rpT8Ws41IPm6JJtACXgWIgE0gUVAhYjbv4tACjCB7u+8FFrV0zptq5sBA7AhCy9jeUKgOpxIiqqm1wBkAQdpG00AuVZX89ak0FnAhC+AErIOdBuaBa2CQgYlAAOwZzHkiZXft91NLUZroi/RhDothky4OXb/1ovfcvhAEenc4/uLFIAJIS8ragEmhLz6/v77f70X/2rJhYvx/Gj1UP0gjj2SmHnv+GXShPNb8/7HLvvxnm0lpBljNWTjhFtCGgwJeF3z8aIYDNSgtyJsV//nteo/d/eFZl2lklc0/7o98qwf6dUoqzNX+//Z+/NgWbL8vg/7nFxP1l519/v21z3dPT0LBjMAZgCQAElgiIWASdqUzc2iZJomTdHhRbaIsEOWIhh2hOlgWJLpkBV0BG2SkoYiGeYCgAQIQAJAAASHGMza09vb73t3r71yrczjP05WVVbduve97umecUj5jdfvVWWezDx5Kqv6fM/39/v+jPBJsjPbFYIYZgW6e4GhpWeVdGLHE6t6ZwRkvhUce/OwZ28nWFGDXxjPIcNXs9+b1prsX6A6L2cCXHDfSjLv8aCZPXxGmgrXobXfkH0gj3xeNq9eSLUsyqskSsbKPdOE+bn/21mXTX3LebK8TWwwrDNAsM/pLZ60xGCXQ2+Yev0IEH3oI/bAY7RbOQpePzVrb7wx/MTPfek3fpNX9njnkO/5LM3vdmrfjKc/6LzUbp902m+8/l1upM6k+VhsHou9CTJRXoRXM3uAEOIl8WAeZa9DqapECK6JZ1p4rIr4uniaP4OiEItgEFLTnQ/xzkQzoOqJSUgVRCA8Hw/BvGhwKKqCXFLWR8WqEmaVQEmti64ZtfewTpI3zSY2Ild3AbM2TQuLMtHQvrgqNIdbj0HYexMUG/VR2zwHOkZ3g65Ufh7bLlY/bq2Ej/EUSu88Tq6TC92Lpi0R9pVsEbStbk59F6HyS/8yn8noh20h/Kr53wri1AMVZXmma90814foY2sq3BNPUXyEexUVVbQUzGiiTIm/1x+/0bzl4/7UvV85jK79/GDv93T7X7nXe6N/41ND++Rj/kn72jXj7Ol27ZON89dEkFwXxrTedE+rRjSQ8mu84s++XCEVBHOBuiKiT6i3t8Pj5rsTfMQJNFEpnJLsOUnbTIfNWniU3HRsFQ/2qyqk2Z3wBspFPYO34BhAxWQzK4Osi3mdowlbPayXEA4qRmzABgC3wIMapFCFClRYZayKxHMSzwQSaSRS2KECEpmvO4Y0eqrhY4dUn7JdIfoqrwEDOqc0gbPp3iDTts8DQOdc6ODn+UW0616H3ifFva+9+8P/0dufptZCZep/9VNrnrYSJUqU+CBQhkCXKFHiO49f+fKv/u6T/+VJpfMa928cH1pfiUd/h7f/g1drO9Pw5x6Hn/iBd19tHYptIUQsZJ8WgnwquWxPBUyy9knWvBj/DKRdLzgplGZZtsKqvd69TAGGfJ7d0KLlXOO9LPtXH+Hbmu5qTrI2+PmDwHq6U70zvIwAN0WUR3QvY5OB9r7Sb5eNqBVwNt3tfjMQaZckNW7vIS7QDO38nLl1EdbNoS3COfXVmGTNs7TxItVuVGABojItbBPAvr3g1YmSthHd5rFH7IkoQH6Ud3bEyQ6HjZPEDhNA9EAQ3HFTaSIYu/Vjdg9F51MP/V/4s7/9/X/+cz3Zbnz8S7/7F447f8quO4k/rT+6V/2RH1S+3Tdefvl06+7U2PJb8Ze5i+BQ7TqzQN+qiCpECCShKwK9BejQl8LfYFgROt03Tw/eFJNINFyGQtClVuTGCvpiq81ZSPVAaE81JYTois1T1Zr7uKEIReWJ2NdaeKQqwMNkH4pP+9Jwzd+mJ5V0ZEezgGe3niw//7h3B8sjXXy1EN+L9X7163SmIWsxWQvIQKc61CHN++bTaVaxDH+fg5W6R2+pl7fpncwtiBWg4qwCOIY/nG4cpi0U+1bPT2V7lrIeZTParACCTHoiCDIJeCIMlJw/dS2jB6BUqDzdum52R9NO3TwvhkZf51mPtqfCJl0Bm/Rf4sEn1FtAPRzYvcz7ncA3nPvtjeGz0H/ndq+b+Xd7I/vWZuWsU1Niu/Lx6Gvd2ie3vYOW3Zua5unebhQ27rU6N+gJSEXYpaVLmuuAYeAaz/xCNd3P8tXr957xGB5DDx6g9hAxKshjmwfv0Pw86gfAgzOooP4heKjPwc/BCOpM3wVQXY5TgJrB4ZS6wY6J6KC6iA7mTkEclrA9E4SrswDpDixqHi1pyIl0Es+0g3Sw5wAhDeApG8Az9n3cPp2n7B6wiHy+F99pinD+gzNnv5r37vG0QuQRBbjfa3x92PvYn/hn/z2u3UHxiz/0kc9/+hVKlChR4kNAGQJdokSJ7zx+4df/D53X5R11shGdWPfi7Et85S9/t7qu9n71G8d3P3nyqnsotvUMPEQikEQ+7jrqJxwjEtnSlvm/5kboTJx4suZHz6klhbMpgOyCbCVYEnvXYsYUlG9PHtS9nUALv5BTlKIO/JxTvSiW2KQOtL468lkWcjWL0CWO1pFTpas99U9tkT1DgWNhGGTpbH9Rhsv/u8h+UbM/l3ReBTazsGfhTdOuNBdZ2TnsgoORLUIQMRLYUUeu8ALcQNmRqCeyD7YdJvoD8frRVNpCiFGrOXY33+aVrepXXv4f8oU/9Qf6au9T2R/q/bK4ZySfHfTr4aOfHsf/qPbpj55+yY/HyTtDW502G+Pb2f1jee0V+0FfNnersTLtbWsSmo0TsypFGFNFMKQKPGVXU0uX8FXx5Iz6lhi5BCOCOiepqCuiBuOHvOSIkWQSiSoKS0wesQckZHNmWhfHU6bAOS09DjajlPxZVSrup21UvBidC5p5nr4rSLtZNMwQkd4enc8GX3e1nkRvShC6sq5RTYyLj5Am1Fa0Z/RdI3QMP5YVgE2iTAIKwcQOlEekekoeP+oci7ZRTQ6qrWxib+50z8x6R3THwttSPY/AJbiuHiOoKGuCrBJOkApUligYZHVBfNt43Ju2nsVVoK+2Mt+ePzPZ2AZlVBMURsXU39Y+NZTKfFv3X4k6oEXIphGCCuKdgZJH1Jsi9ESoNcknagfEGEczUo/wkGtbhLbqbT09M78Yf9Fx/smvNeWP7730xnj/J88+J46t1PTk6bizawjkYPDk9idNOT2krULjSDYDbMO1PYIntANcX7nAO9wFtEm1i39G8wZHEl9f9B1u2vtqWx0SQgzXoIc6zr8k2ZDaPoNv0AC+Cx7P1FoJU+I/4Fh/LxYjVBdAdFCnjDJGWX74UcpOF9FBwfQYjrF0FMstOIZjVA024QwkTGBc+NxrsJHTYDuI7QCgcUjQtm0GiRTXoEvDQ2c0nN9Td4pPzUt2nve7yQB4iQfANv4uz1rh1MVvcAI4fdr/6mhbnv1PP/2J/9fjXRyJWc5OS5Qo8SGi/IkpUaLEdxK/8Ru/svnKeaIqG9Gp92TKv+T+972S3TY+9eTrB87tk09vHLMLxHiuCEOkj+svwnRXSXCca7NrBLH03FvLfoF4bJmn0twIChZWLxhIenlmKhiVaYGLLpqZXlKtaJqXAOM3Opf5YNVe76rATk695+YJv2jGLxynTVeEKyHQl4uyKsm8J9MdgMkRgFLGRrtQsniJ4o7m5V4vsl9wZtWM1l4wCyzA9BIV2MKbmp0wbyjmh1wcahUhmwzJk0JzhA1TwFS6HpE+KJFm2DQjEcVUxrRa43cP2p87UjvDrPGLMMwkii/W9qm9zhYgfm1n5w8bT56IJDHSrmq+wrNX0u446vyRs3/+bnCn+faJtO4/PfBeey1MhLNZOyZFRRsM65udh3F6Z+/4m1/OPvqSe895Mz7d+OjN+9/8tds/2DS+9uCV19yJePbRu9eEmGAFB3sH39sO8RCc0WKWg+0RIZbuN0COhQ54ToFEebYxbmaZVPI4beWN8pCExYGGndtoO9cC51oe6pyP9sTOJnY0soBoHpHa1Y+Z5dYtozpdy4QPqe6aiWUmFnl5LYuhvmjV1W7ANJX3+NpuI8i64/q0Z0cj66TX7m7Ldyc3O0Z4vLWFomX2IuU5InCMIM7sEBuIM69mdquEIKbKmyZeU5yR5RKxSm18m2rSz1xSw6gm2dC2tvxsnDNeLUpnE3N6JK1tv0d1btalqhn6g0Z9j9EdMj3KGraQd8RJqiqAKfxtNa4QCeJP/a7fzHzjY1H3q/Ff/8rNm8bhH7952vwvTnd/gGnoJK+Z77Zu9WgBL4vHk13RxTZVRRIM3NjH3mASkJyp3XPqFeKnXNON2/QjvBAvRPrIAZ2b4mSbxz210RMbXbf10t3m7v6gfXhuvxFzDm0IIEREiI9QD1ExfBF24CHqBoxR1xAb8AuoIXRyDrxjsmPid2YpwRv5x27sIwJoQIiqQxcq0ILxjPRug8Xim+TNdm3DCdxCBTDBvhHbQRx0PDBsBkh87ID9kEqTrvbBOs72jtOmzrbYZLDPYZveNY72ObrWH0j6XjekCxOYQBWmBH8v/vSPZVQCbPkHf/HL6rsWntIlSpQo8cGiJMAlSpT4juHx8ZPf+Oq/a77MjfCsejIwfy0537j9jc/sff/xG8Oj7Bs/eDtSsi+aQtCnKWgWqO8yNMnBO02bl+01KslSPN86hsa3ng+iQJD5lrdzlRJbLE1Ue70LOXHUjllmNdEScfy44dwc2lvAKgcucuarr7WCG9axLYKr73OctoGTtNkQ4VBJlMq6mfJ1ZKQSVU9Nc2OkxV3P/qmLqGOe6NcVYgpVf20RbprDs7R+yWVVFlgm+E9q1Vf6hc0gqIuobg7nb4u7ApwIz1VBSwz0RiUIGqYdqam0EUylMZUCCJX9DfHaZ9Txs9/u/+7nPxkriWKYV21d5teKv52+DKJhRFKEvyWaNav3+2Lzr9768zUibvEKg5pwDkXvuwejSVr9zN/+a2ftp2Hb+eZfjc9G3xSKrU98829+mZccTqvfDHq41m+8/RLq73/VaTJ445uTG0y3McD6vyPfIp7yaZua4P6f/8Hvgp7s+rx82+j2MtK72w2FYdB4eDq8vdXLxLWHyW/80G4g5K7g5kF6s3vw6KWPvlXfwCZzK6Yc3Vc7XcwOaRfzHBvFA1Vviggv6E9q+lEx0ix45mr/qIuI+gZ9B+E6tcTeXjxdeoSO0saOmemMaF1VyCNs0VVQJbIwPcLErJ7W6m4tSFXtMVtNkrOs2RfVMJMqVf1JbVBxgaZwPeE6RrgvDgPkNSZGNgZ2earXBRpkQ8OIqdpMknq1Xe+P2MhU/WHD2VfGvYZzQxnUISNRnUEt3mRMjYZq+2JQIabGNTEYMdGJvqb50BLmJHMaab+XbU4iM02dzemz02m9MZ18Q939SOpfP3r0zc3o5cF4/G716//i1T/18ft2PdkcUQ1QAWG/HXc7Y/d7vypb50IcqO0KUZf2Tc5PRWWChUJyri2UK0Q+bkd0QU2QExyHIMDTKbJj3BPV3GK7SbeqwgnOV3nFlw9evjOqtMzK0yCPGR/Bp1CAn4dDK5WHLivgjPiawb8tK/9paN5CVRAuKRgVGi6z+s0IF+FABVKUZr868lkvqtRgDDU4AQouWXOckOw4KsQ+iZPbTuR1AALlBb3BntNnC5Xu8TSiEuH1aPdV6zhtNmcLbdojukOvxXmboSmH3rOQAB5BAB4coM4ZK3aD/wjjP6PWKhXgEiVKfKgoc4BLlCjxHcPf/8W/lkZ/VTLy3p3aP59kD/hb/+GP/PDZ0+wfv9n7C9/71cadrtE4FbsIfLFibcXin9nrSdY6zZoX7K8sZoG18ZPGBSk1b1z7aG9VX1xOkrw84/cSBXhdhPb6li8AHR6c+ZYuIGxWE3MjGL/RAbydwNxY42i1Fg0japr9tQ5Yc4zTdqjkIt5bKYTInp6o4Zg0U2bLvOaSZUsrCIXXdSLXiKpGH9hTpw7hKTtzC2hgkjbPslUfLBVa/uMagsqNMTDtelYnnLlAA2xaw6qh81SXslv3OHVF2GHUoNcW411OWoxdMdJN7FgBU1dEoh4iv8IP/rJ4/ft52/vP/6n4yf/Jz27ePcmaF9PFG0YEYpjbjItZLWgS5RWfikHakkYUKvcj5pNPit/Z50iK8PbxyaOsU2F6/SA52eCtU9uhfigqyZu+Rzf569+cR4IrSKF5CzUi+qSVZTTYqtqH4a4VBXe2Kj6JZGg8s71g03jZfnTMq5s937BJG4Yh1HTqX3ea3dRFURcT6VppxUpuCQHCJdg23FANPHlmN3w6p0YDlZ6bmwM6I3LfcttOI7wgd9ISQjBSbuZrlVgUHOKE8BIEO+agbnaBWFWAttEDPMImPUDHMN9AZ3sKE3OME+De4GTMVk+Ie+puR/SrKnzA3XPV3BCDEW6dKMm8PSZtMbwpHrd54KqGF/V3x0fmSHXO3o2NTuX+0fn261alW58ccQojprcZbO8KMZj6zSwhNRg5tbgexVXhRsnIqstHrakp2Uh+17kW4iRiMyQIMrNhIrNxzegeO9ddI75tHNpm8q796tRsKJVuWqM/+Rv/yLA4jOnWX968Mer+F70bG0e/+p/EGw7f839yjn7ko09oHavrvyy+V6uaukxRIDyPEOjQ6zDo0O/Ootafsq1HMaSia+pOcCMqTwqJsi9zP3/IGVzjWZtBhalkvBMOpBpWeokdxCqAMwAqcIIiN69Krjv9XTuicf0XD9W/QPwIRODCBHrQgSEMwEZVYAIOWDD/YnUKOkh1VhgpBfCve3Y3TTqmFaRTz/R3zZCGZBhS79L0sfuqo/2fQyoSv8/GPAF4nHaO0yZw0z6yRVAl+jjf3OfZSzxqM9w+PLeDmCfwVRiBhD743H+Xf37AJ//3f+Evbv4kSv3i51/+/Cdep0SJEiU+aJQ5wCVKlPiO4enxI/eL/1vnpi1Osd5N8Pn5v/AHv7s/kY/ePPlffKLvWUPREmQ+boVZouNF9ltAbn81h1JANrYQpKeevRk414fxN9urzBac6pTn4mIE7lXhz2v3Xn3AVdCKseklRa77nqjvHFew30R5g7RVSHWeMdQ4UaMJgFLKbJFNLmO/+harRr/DqKvqDuGIVpMBNH1s3aBqDM7mlZNmmJ7nMqz/uOZUp9ZGkAV21pX2tTwf8SxtOCJaOEsDUCF2CAGXPF46ELbEDXGbnAGJIxBE1I/oRFSe0gb21Lv3/+bp7/yZ/RMdL6BUvlbiW9p2a1io7XzDPppf1BZB8eHZtAIENehh/xqf22TwEfXkcEf9C165y8N4zzunWb8b1+iGaiv4bOISbPyZ75tSdZgAAfIasUvY/1cPXr4JcExN8JL9eNgB8AIq18GFm28+qONEDE53ujU6j6EFr/zNd3qKT10n/Thv/OAPNuxh54sPTCWi/f1plXFWf9RqqmmwkXYnhmkE3kAJskGTgY2XJlZfyZGouAxF6um46YGSKlD5E5plKlOgVGCJGumZZW4mz4QAieC6ezCeNHw3sohVpnacLJg6UtiI7JnaBhyRjDJ3zzhPhThQO4ZSUyX87MCxpk+Vs8XvJKL5PergTJmOYE8d9Z1KZTqdGOm1QTDunIunXndcVRvmedqx48TZdNPuOP1G52xww65Vzc6ROqD+jTfTzzjN0yNxH/EptmzH/VL87Lt/j2HRvyb/02s/9Tnx5a8brx9ne5AvbTSJH+BUMuMc+3OMfkc4e8aDMfLX09s6Txj49T/wPTsMLOFLgruqm27++ugfxBsOP/Zvwp34GaGjKg1ON1X/jKYkcEUAzUh5HfoxFVCeiCbYXZqeigLcYpCEJskINhm4+Efs6/rM76o7NREBA9UJkG16En+DYddtVZi6e+N9DoDKYVrxAwXJhgPYfqwUlh9Pwv0jWTNecfdOHiqL5FXHDuLEc+yDmFNowMvwCCTUZ+HN+vunX78862IAHgqCTh4pM3hdr1uZPbbanHap+6oDVEhCKuf5WZD4p9x8zLb2EQCOZ5E4Uea1zP4uz+aDUAsHdjD7PXdhRPYuWYI5JTsD+Lm3wDqn2SGd5VOUKFGixAeNkgCXKFHiO4O3/uUXxI5LlJlfSc6/RvrRT755d+9HH/5/Ett+17XuqVd6otFgoD2fLxDHxYYk82JcEJqFqaKwiDA3Al0E2N4kPV9vPRVPLPPcm5VBQgW28JIXpquXt/tAOfBFvA/2O8xcpnuA9mXV7lZAqGS+t4g8G1qo/ki/FbZtti7VfosD31X1PU6BGn1gk+BM7fg4usmmOVzhwMX07HhigUSIOfsF6iyz3+VAaO3DLAklCSBVFIm6JHcg61NDEQq3QR+19+D4mvHnPzdMmyufhvCSOe+db48yaZvF66qlBRRFVUQTXIVwCY6pvqM6LdGf4ELWoAecqmtTkaVZo2dk97gDuMrYF8fP1M4z0QuQte/9zJk4eiA2txk6aev79n7xmP0JdqbqpwhDDB999mPbPFFsnFIBapz04Z1/69Nn5MHkE9wqUe/3toq64jjtfJdKgC4m8K9V544YAuqs2g3z0NgQBJyls8/94vejBsANLQpibwfNIMvEVnt/BFCJY+XpsFlBvGMcAU3RBec1uh5TmErOgApRm0GVKeCRaC+oFoM+zVPc24yBM1E5xwaMl6MhnTYO7Df66h3RTsN9paqmdzxy9o1Z4TNf0sV4hNem93ro/ML3doAa0RhXwK+rHwQcI3w83dOf1FHhAf0FJPA3Rt/bqo3m7Bd4nOwOjNYgk5+yv9nkvnqtWX2d0Qn3/hlNxWs/8KD1mvkb8tVt1Bn0aLUVPVoTZI9WW/S7tAPltamHuZZKl/a+eDZ/onxclNJEcZdn75JbRo2VC4xxA9y7KA+pZVUUHlFItU032A0nGPMHT2FX+mkiDVNGITu/fXv/Y3vNa8Hb3Va7Hg4SKeyWbYdZpRcknsM17G6MBJHHUc8/62KtI6+XBG07kSKkEeC2OX3GNR87VNV73JSEIRI4xJ25XhFSCfFOqM/Z75kecNgxBzWz26an37bUAMiCBvhoQVtnkV/HgPHX2bnJSxM+93P/z5/7xOecSvKlo8PPL9h5iRIlSnyQKAlwiRIlvgM4f/QoevjvWxWiX0+e/hJvH/Ib/+vv+8PmV8ZfpfvjP/x3xe+piLglhidip3DQUuxrMiuCOkwbuWQnlFJ5EZlZSwUi8y1g8vCy1FOA4ERyIouXqH20V2ygAjs58+Kx5e2E74F5rufAhdv5tiNnudM9LjLeBRbLCGos1DCXf1OxbznBIlNaLR2h/6mbgwuMWOnzzZuHheuqwJp2FwxEboaGN80CCyHSrsytsOanE4BKlKfJsCQq9iJE7nDuErmMdCZwRD3CDbD16sgR2xWivfC+zZY0QhRDtS6CfL/BAAEAAElEQVQBmAXRPUmboZKb1lGBbyuE0HVcJrieCifC3aQPDOhIAlcFCBHhaV8uU0ymyI44iJTXV3ekMiJz8oAtRHae3qmZ3YngmGso3lY3EXw9+2O2EYBI8LRcnygPvq8o3Z9N9wZKftZ82sOefxG6LBQzTep+cd5l30Zwnw6ITFlptqhmFI8tnU/u1KbxqDAluBAoAcRjOy/rVGDaTm1q1pKjTf/tdBu4aR0iqBN3GJhi7GBKZV8X98fs3eXJJhFIj26fZp8m8JE88ZQ76p6uqdNjIyV+m+pU1Glxn9u3m+dTRgneMfHQq3ZVy5mNRo0oxPsNKZmxXyBW3iBtD/LHbCnJa9ccHM30SVFJiux3PnS7Rt8jfIO9zkb8sVt8Jeb+AT/4n9M5j9OO1brZ/WF+6xF/YII7YSdWnu5MqGSXlksQICcif8KrRD3VCZATVfyuqVnN7dUEtDHuV3lNlwvSz5hH6OO8TKRUtUKjPfMekwz9lqnvz8Se4H7RvdV27/VoPJUbG0yQSBl2W23JELBbdqWX6CMBK4iDjpfInFHr6r4ne9c8wkBJncOs/+6zMRN7F4tWz9gHdOE07exVJTpO95g9ey85D6pEbdVz0SZYzySTNsO0NUpCx+7GPIIjGDE65vSML4boDId34Q+nx/9o0vo/3j/6y5QoUaLEh4KSAJcoUeI7gLd+7QtMsYfJ4dd5cMazf+8P/Yg9vj7++sn1H/m/Vn+yyrAlhnHu0LLI+51kLUeEsZLASdpEqYYRDTNnznUvXkgFVupbTnWqNcbq7dHk0XL87brYZm8nUIGVnHoI7K0QSE5zE2ktKecuVi8i6Kq8gLCuHrxtDoAwk3kx4e8sDb4MxezcoU+aAiowzNtcYL9FxV3VRTSvVFTYvHr68YwMaPZblH9zL+hOmHblythMsqYjIgTPplu37CcXB79Br68qLcFc+5WMIuUiOGI3VO6ZqHlE529PRr/vcydpU9tNLz08KwIvAEPlDpPbd52HxfHRDKdKdEYTxZloQrNKJAkivEh5DdEDIrwI2aCnYKqqHZVsG+9EykPQo101D+eFlxPlLSiuApQtfN2rIvUdpx3XCKSImmb/TMTjrHacrcaTK99eMUUrvjW92NwQ86HTpDn+emc1PX7VHERc9qjGY8tBpeNGPLaBd2sN53ZO0ppGOMjkTfvwDVoAfLcetCbJLocNzrq0PcI25xWiGtda9FzGcN6GfZ5tqEk9DDQ3A3qiMeJagPtA3PwdXgK6qtXFm5Ph8WwwHRFsWYGXdo4uuOJd3FLEvDzSKU0F3/eRp7/5Sz/2yT/9C1/5Owwyxlvc+ldf3f9dx3bjb/zY7V9Sn7aNIMo82wwmuGNcQRO4zvGB2rnOsT7nZJ11n35+rnPUo60bxJkHOEYAnKlmk67ePsGVBO9y5yUe+Ng+G6CfkI0NJj6ORzwl6dNpqe7viM/sccSMu1ZwPGJAMkykSDwz58xQ6eevFYTUQ+S50oWAqyFVIESe06iRBTh6qSJAeoSa9A5mTllz4RcIlTvI5I7Zrxk9FBNcD+8aR03O9VqVHapmP7GCWAG3oE/2DOA85C6cwFMAkq/fk3/kpiGTR2fHtzaLa6AlSpQo8cGgJMAlSpT4duPg0aPR//ln7J8g/Sr3v87DP3TznU/d/GzlZ4Nfnf7OZz/vGJEmIeeiWZxzT9JmqNxQuUPlohRkCDHM3LVSFeSkRnhTszINTvOAv8nD+vI8/oL5LwIIThbBoPF4Jh1rihTkP5uFSOmreLC2EQ6V3DYGCHQJopqBzNrkTHhVg/oOQylmd6d8Sw0mwgBIw7bpRKTL1Hb5tWvkgZF52naBROkZsI+TKDkPgc4Ca6U2ldUJAa39qsCaM9KRckepu2+djtImMMmaVWMQ4urPZqDaTdHTomufimCzyVmk6kei7ZEMVDsQzgk7Pq5HVDk5/2r6qvItxIsZQCoQapy2te/xfBNFbqNA6LdtYJdnKCIqkZCNWQhoRZxVxJkCl2CgOrviGTCgc6h2bRGs5GYnyrMJQM3d17Q1NyhbBLYZjNNOhAc0RTiYPUKa+j7XElz5Vubb6cQC8dwKW4tbzkdrKRBDIx7bTi2p3hmil4oe1rV3tNZgHyd7up9b1jM9ShPcZ3xkk+19Dj2CZ1zb56kPJ3jbBJ8Mv34smz62gnrYrzwLzu7W7FBJOdzjIKTR4OQOj0/xxmwdi1qMd5+tiz12jKCp3Isa79qbA3bNfqDcmtmtEnmEZzRHtD35682HfMKhF0MX9QbWtVht8VOn/1ht8TvqbiKCdpb0DHt2Gp6wA/RozXNfCxDFKwIewQQ3Vt4gbQJNlObzR+y16bv4QKikxA+pSnIBvkKyx0GPbY84wOnQf4mHOuW4q1qSySzi2vaxoepR7zCiNQRCGgqClkIRIgMcH3tOeoNCnbmQSh+PAsudP/CaBq8MYNPoSyFrRk9L3B7hHkdNzjcYSsIKSSJF/pMwgQH4GHXqLq90edunBk0YwPeMTv4b2ROefLt7WhLgEiVKfBgoCXCJEiW+3fj5L3zh9kfgm3zj13ic8OT3/vCrXjf4stO7/pf+K2dzn1NgIGZTrrRZNQdn052RcudBsPmJXkCAVTpX9rSQ3bhO5Vtz2EWrLYVTn2a+PQ+BXkjBl2OYuZvOIUv0CaBm9ICaQVN5QKQ8t0CBIuWdXClVfSi4UBFAiAnTCAcVmqLZIB0WW1+UdueVfl1CRy0xujr9LjdQ2ISjrJFblPnLtZ1mbmQ68nnmAr34MEZpY6QcBJo/V4zTWEiHUJtgDWhsEyhFIOwmhMJVEGD3qAM+rpOJT/Dl8X99qv5MkB3bCExvihIqtIU3VaElvOniasXnRBFmbs0oJh4vPUVVoolyN8UgQLbpM9d+VVdrp/OI6Jkm3I2Ud8DuphhEmbTNC+y3oAYnSkbKA/QjpJlweJHaCS5SX+Vr/3BbF9b6IKDmfxXJcDy2tQLs1BJ7K8jXhnRTATBQ7iC5s2sOakZed/iMJoIurZd5MD/7Cd7b8uU73INGgNNttf2W2TyMB3t5gHc9HEg5hDf7bD9l+yOKALnNnafs6RToWHmx8oAgcweXrS7lhC2PVhgoV7/esg43Geg49ibdLtl//9PiQDrXbsWP/wZf/Zd84hqck73Ndv3oR3/0X45b1m/T6gk7ybyrzdWXLjyDrg+shBqkTf1pDrLdpgibZl8JFK09fAU+sgldmntMgAqJJA5oaO4KbDB5icf3uNlS5z21gciz+iWhZNJnQ3Hu09EFjiokHrGWiIE+G5r06qJN876FVObq9AoivHz78i+ALXxb+MB1jtv0dfaviSUJr3HS4gSYeob9FCbQB8hGZD6A7TCO8yjo41/6h+p/9AeEBOM7EyBTokSJ/9ajJMAlSpT4tuLg0aOzv/0ztzc5e5ujhOiPffJg/85nT/9xFt766598CYFtREII7RYD4jRtnE4bV/otr/oSLTkb5Zrt+3afWjrQrCTmRj5pfi71ZRbwzIy6rIWeN1+cPTeE+2GIwxfjY2HZy2pWfxiI31amCZAOKubNVb23eHydyDVnPlVKuYQx0pmFQ8fzWruQKDnOHCDtydg3515bTmVadL1a6tvsExjNYqfrIqoaAx9HIh3CPs0dFcybtRiH1JvqrMfNSFSAoWrHQsYic8bO4Mc+O8YVlUQU+JsKrMnDulNL7a1Ae0EXsW0OEGKJl84HTHCdY5cgwuuplkfoikBrsw26QIScR0QDfVraDTjU2ppq6qUQDX2JlZhnBDlpnA3FzGVXKd9e9FaRnnm6S+nYBpbVXbVmTedbRZEM52eeM2FvJzA3/eUGHE2biOZN60jrnGeqCU29NPCqeJwxBR7TPuMTL/HIIz5g4zrY7UHzMPbbtq4J5Hc8wJbHUobvclMS7s+01nn9oTjztqzDtVHQc3gzWzXPDB0jiDNvUw2ADn3tK3ZEbbDFb/3V+M/+h06d+KtPefSUW59i+02a/wbV1r1Ph9tfsT81oB0qKYUMldT2clWiFr3nBhiMZzRSimiA1J/vQEnS1qYVTHD7dHQs9CH7+rFpM4CJ1nVDqh3Vn59tnyMfWwiesp17aM2yds9pbDBscc5CFgbos4G25pr9rXmvFsCrM6crneUrCYCeWmHF+V1uMgCCPG+FNr1rHGnfrA79AGe/n1pBlv8kvAmgzhke5Ge5Y7FncafDoeLeUz6hgm9Q/dLJ08+//LHnjWKJEiVKvGeUBLhEiRLfVvz8F77Q6BKk3D9j1+a3vv8HfsB9y35z9F997g+OU7duRJOsJRS2ETsiPJ9uoxaSXG71vDKBv1LOfYEgV4Uq5jdeSpVnZYeewx8aImqafQBxVc2hKzDO2idp89uUHrxMfTU0+1W+oSax2J6Skai605nkXsCr7dWmMawag0nWBFBKpx3O2S+KAc1ZKWCSuRuQKpxJKGsjuHTsVwPV51wOV4Xz102GgEfiqhGCkDrQpwEEuBGyOW0MxomkBwgvyc4rZiVBIbxEO4THYyue1BdFoWdrKzWjN87athEkmadrAs+7VSXq0fKQzIriavarSZ22xWrQRYmufzepBjt5pVzOaBRyj7XTFYWTa49ur2Z2528HaQsoqpqFtQwRP2qwSnovDOP8nw+eDLPMh0U6sYzK+tWWx8kucNPOafA97lzn6Kvq1Q79PZ4CE9yviVe2CBocHbCB3KjsJdf7g8HeCJxKP9X5q5Lhyzz+Oq94RC3OW5z32HjKniv8yKz0aGGe3zT8OPMCJS/EQqujtNk0wi3rUPeqbfQlgUswwb3OUYgE9cbeZ2782QN7+GXHgZgBfPXLfP466stsTCcvfebdnd2jt+NPAAPcndmC1wR3l9Xv/iYDSXhAHtMbFUy3XRGgFsWxB8ptKs8WwRnNALnLswmuR+jj+mxvMGypcx0RPaeyklhBnw2pwr64A8jc7jnHOY0AF/CIZL4EsxTwPDuPD20d8zzB1f1xCdzZ7bShPdOu50y4SqTLQevFhg0GEl/z7WucAFvhsdcNCeAIjqAFv42KAI6m+PAs5gDErMubv/I1/sTv/yuHD0sfrBIlSnwYKAlwiRIlvn04ePTo137mZ/70Xd64xyBB/KXfU7vuWydPrlduHUxvIHSqpwdsWiNhMMrcq6boBV4MRSl4wSt0GnD11ig5q7Bcbuc5WJAuBWJRdFcphNAsd5C2Zl5WNEQojUin+L6nMVnB2XRvds4FOyz2plGoBqQl4sZydVy9fb5xRUZeEJJ11LeI5AmkUyALTXfPIM0WvSpIwfvmqRZ+q8ZgLgxHc/lXESP9hUGxCrP8dRYUPguFkFOg4OBdxMoiBSPldgq7dVGWUMliGHmfGhAoB0GAGyi3bj279eUH1qde/tnRZuZX1rl5KxDx4zoC5+ZotoGz6Z40QrRQv8TG1UTlj2gxH1gSIuiq61Vx7hIM6QTISSVDuffF7QsXhXkIwDLVn7NfjcsCepVvJ6feC2fzzm608M880/iDg4pHFsoza9a8wNgKHie7Lzt58PMBu5sMJgTvcvc6RyEeCsX5CXeucxIgQxE+be1uMG7T7bU4p1ohATxirXy2OO+zscHwGs+esg+DDr0JLoJ75p0arFeD1dKXIMQL8Zp0Q2SLLnQiKnH16JtnXP+fc/8/zo/47QPkb7F/jc3us5d3DgLLfSe9OVAyVFLXjZprp8zeeoQtusAmA00vXYKZF/QaDNLWphUAE9wjtbfL4RlNfYbzvEZRH/BIdJd0OLSPe0a9qpKUaV9saApaRJtBj2aA6xGds2qfpm+vTY9Lgp+ZrewU6f2AdpPFg1ohanFeIdIU3SXc6PesICOAR6Al36dkI9KnTDIOY47IK3rNH//m4zNAJe6j48NbO3tre1KiRIkS7xslAS5RosS3Dz//hS+8vssk4Cjhc3v8J5/+2Gen76Z4f+3lHx5nDuZsBqSTPLOLM/K1IvAF3XC+YWbwK7ypc3MYP1434bscTnVqbwU6h3Me8NwwoqY5mLHc1rY5mMc9rqEQCqAqIh2+qP2Qjtm7OLnU+t5Fc+bGzFZKirBm9ooT6wnureV59vy0TVWooJO2ljjwCvOB9NxbX9hpMrb3JyhULM3dGftVqMBaZOcWyIOapRBrultXAKPc/ndxxbkFtOFNmViAU51anWUOf2XEel1EHetEt4iQjgrdC0sAKEJhAx6x9rAdqKbMsscjNWpv7lTPh7hZYBmVKaB8KzjxvO0wOJEo4onl1KaLEykxxEVRW15YKV5rXvbmjKaOiEbREXl8Z4NuAyK8p+zOA6eXh2Uphj9Rnm2s2mLZIrhpHQ3S1iBb9ccWlcTewqxOgfeZ6ztbDfF2Q6OSaIssHcn8/uDtBkZlupLjfRHjtKNJvqaFZzSvc3TArmaGQV519ppHqMfp64UIWx1D/pTdGSUbI+gw0htf5UmNRFE7pK4ztGtGd/dKF2itskqCKiGwwfBQ7acM66/t/OpfPPrc/86BWH9adajB9O9jx/FP7P3i9dat2PrxR9Pbg0xuKgVi5Ques18B0KIbqNxBapdnAzpnNG38HXNwXOjbIHNJdqWIamZ3gjug3aSnA6FbnPs4ilYbVFHmVbTp+2xPSfqiM1HunABXROQRBbghss0AkGoiCRWEyO4yE5b4u/iHXGMWF30J8m/0nP1WiDxCSajLPksmHTVxQmUFmd2NF8e9A5D5nKQ8DYE89bcJA7gGT6H+m/9M+T8tPPfts+OSAJcoUeIDR0mAS5Qo8W3CwaNHv/ozP/PDLhP4zGsc/PQP/UT45pceTTde/vgobarYEpWsyFyXoJZFKrUii16CIl9WAE51alanwYlcDZyencqpTu2tPKpWeMnc5icPbM6jmvNjN61DBLWlsyx1/q5291GzqrBiif0myosyedHsaoX0UhCU2vTnsYjatKZoXRPh6QDLOXeyRSCFvKLokQouITlxooKYJiiyrGo5IWoN+62LyBZhm5GrwiKpdlQInLLj48xVtkTJw+mqW69TXUn9nY3hxWWOmQice00rhRB1+h6xHpMQp6lQAgVxLimhp/gKhmnzB83fbQwfvsMfHeI0K+OR0lmXCvC2gnSyVB3XXtwp28bANcJZ4xUOvLooc8CO/rzaqj9XpO9xhxXau7qIs9if68ysPt45+11tDiAqiam1/fdIgAtUH23dDOBbKJxqEo+t5zvGrYNOQg6OPKc2NSrTy4ypj9LmTSNwRDD3GT6YVRi+zpF+EeH1aDeJBziaoLoEE3ZtTO23PGEXuAco5vWZ5yx6ff9mj+pAyUF8Zx6MDcrFj6m0Vf+Ia3s8PeDap18/Adjkk/+Xna/85WOg5QAYFYjZ++rDwx/afZXHx2KPWRb3igIcIFv6AQagTW8yu80mXVBnNF0R5BHExb4hXSPQsdDztRVJ4OL7ONDykFKFEl8n/eqwZ4k/YbdC+Iz9PZ5ViFpqECIlUXNGiRW4M0/p/VlEdFET3uNpn40CAX5ONkmFqE1f4uuGkkmHSYsTJINduwl2N6aKAvZRjwBqr6G+TGUWOK1/SFsObfhazJ88Of0vb13HLqepJUqU+OBR/rKUKFHi24Sf+8IXbtm0DU6nND7Gg455vTX65Pn077qb/W7T2orBQMwzPC9LBi3uei4DXoJzc6jbexCcetXbo8nDOuBUE7M21WJgznkEQENEQ+WCuGEdF+W4qybWs15d44hZml9/Hk84S/UEBtmS3jsnvcCmdahjJvXbDYY2PjDfEiAFtOlP2KEwMw1ZCpHVBDu80klLeIl50c1LEd93mM2PE+qOOs9TR5fZ76551uE0Uq5TzHicdWiL40dcv7hdvza8KbDe+Gq91L/mAx/Rgn7RQ1vOgkJ1RHyA01V1BFvGqC4enf9yMv7znZNps2CAhfCmBiL1NQFW1dsjEHlINoByjdAWwVm6N5P6Vzqz2ttZBded+cKNfmbUCgfmKhpcHLFEeYOstZrFWhzP2UG1j53HjxovKN56O8GcnSp/XtYLczPQccvmqRcce5okvycyHI8tr5rUPtZNz7zkxHNuX+oYF2ees2yCraEZbJueS9CGEE87M2m6pC2XLn4Ni1su7q0Z3V21RgTWwdha+B2ojiRwaHmEkopH+KB+7ft/hpNfiqs/mrf/egwnfG4bvomocX3w0Gq+umH0x7j6h2KCO/f9LmA9h9TZs2eCHWNJBNYYpC1obVqH47R9YFJVkT5zJffE6mvSu8GwR2uea9Bg+oQ94BUORrjtmSP02g4Uti8IcEglwH0u7724P6QC9NnoMOmzHehaxLsndstqeD434E1EFfMWjZDva8KY740BhIOKYQ/h8fV/xNPeSG1bpRF0iRIlPgyUBLhEiRLfJvw3P/Mz32PjZxiv8PiT31W7NZq+k/7yK997rDbNjTBnv2hTq5Xs3tV3KrCKVr3bxkBPzR0jGqbNhjkYZs1i3G/xUBBGJanemgovqd4eFRkvs1fXrWPAFsGGEGIW2qcDmAViWEyQW8/B1VN25jvmpBdYThuOAGmEG2JgzdyPNMvd5cgjDJDz1ME5IioBUhLqANF5T4Yz6xpm1Bd4P7WUFEB8EnmbIZAc151OOisObAlvqnxzLo2aInRUuGC/hdnwiGa4XCtllDaKDYScVm6M10qdS525wp9MqZXgZwV9VZGiPt9i5v+bU/fTjR/1d9I/+tmZVTXFAs4rwboL8T8/rQI2zcPnLLms3asA5YlQf1LPjYC+uOlsujdQVyy4rB7k3BrG3+hcPO9FBMeetxOYlUT5BcJc+IzMzcCZ2Pa2/nyX04yfR4aNSgLK3PTNTbGWq+d9yFxHeE4x3ruwjjBXSlcwf9TfK2rmmkDopgjjTOpYgAnuS3T1h6VDskO8wwc3GqdPmv/geH7I12OeHPBDsP/ncJ3hZ9SzgWgfsDP/PobIgnFUL6TizsKVXfwNBgFyrq826S2JwDD/GPrjmqgkg/g2EGYyMft9WnviaABNuj47FSLgkH30r4ciQHqMm8LaYZySvHShHPEGE51E7WMXWfFKJaRLoQr9K6BouxXg3uPmPAZb0khRyR1R6afedkgVIngINRghQjgBUBE8xKjziWs8GQ6BLx0flEbQJUqU+MBREuASJUp8O3Dw6JFJLlDe/XH++cb+S/fNB28dnL6y6YpYCBNhjJVEaHKbLo5cJioamoBtmUMQjhEKsI1QJ+s2TGwRNEwaDGPlnqTN9YevBDnPcN06so0Cp1IogWa/EZ4OpT4Tl+YSj9O2Ozs8yjwEYSaHF9hLQ0TSCG8bD/Vbl9DVVXwKPfUIz2l2dLlM6LNwffJxj5aJQYA8TndDJeWMEL7PMsJ6ajsRxFqyE0nkVq8Ntf+z/7gmN0Ozk0/ra8YQpRYz4eUZsUukwFe5JOVnzfGFcViorPNjL9LdlU+vcJUKsUMAQpdBkiLPM+xTC7EBpfCFDfSzfeDeJPy97/z2cfaj8zNkgaV8W6udc4O0zLdNzfAVCK5bx/YSzV5ZTrmytzOcqeZFZfdKGpxjnLUL7Pfq1QLUPIq7GPd/5SE68NuoJBeLP2nYW3k89qrJVrFq9DIZdmoJFB2ql58MtdS4afVtESx1dIVavbAEGKtlIl3IMS42qJnd5lIVZTVQ7mC6+7L9QNclepc7L/PgGXtNunko9Q/9/gd/92+9dG31ig9O2PsynjXdvfmOI+9QCA85o6mlXWZx3RsMdCUhTRTb9NrwdGYKvcngTNAUYXGxQ9ubmVXL3AxQDJQ7yHYA2wlAE90wJHAJhrQbqtelpVn3RLnAEbU7ZMUB3GACBDjA3EFaYyV+ROJ7mqJfQndXoIs2BbkduoRuRFUvgbUY+NgVmXh4tE6sa44dxDyB6xDAEB5ADToYIfioCi2T5qOD6V35V8wHpRF0iRIlPnCUBLhEiRLfDvz8F76gVbn6T9O9fvMrrY1bT4Zf++ydgWhUzaFrJInwxkqi2am6Sl+6Zh3HSjoiso1QG1QV987K6oaTrLVKAovkREetBnZy6tlbweRh3dsJjcr0CddvNA6GaRtomH3NfM5pnl9QnMZpe5aSGmq5NVRymLmkzXn53zBzpRFKFvRpj9EEtyH6lvAbs/nxokuzv31cRCtS3hvitR3GwATnlAYzifiiAlYze25Bar4x69V7Z8IqOTBAIUh7jrMtSYegyylTnAnbItjieHlbjgh5pDbf43Vnp18roq4EGy/tUh6RR6zHU5LM20dUIiQwUo0bYnx7PBj95O8fCSsPj1YAwan0BMGJhPyo4FhWK9P54sjBdOeO/WjhQr2yIrPUN9ZHbuu9Cpaf1tWG6yKkw0w2RTRYskO/lAZnvrUwwVoSXa9gwsqoJBdydAsREZUESM88pzaNx5a3E6zx2VJLF7O3gsy/IgZ7RQ5Wl+1jLR9e08ccj5PdXXOgGe+c6669sieiQVHqVADvxndemrlSv8sdoKG6EV6D6deb5jZc+wlC6Tz96/k6ywA+BWc/z5YV7zWevbT7EDgsJPmv2COf07hGAPTpaGk0pLLBQP+2SAJoShEOCpkRwkvMqgWoyVJBqbPpXtPsT4TqZ815dsb8N+Eaxy6BHtlnbDfU0fzAQDh7HEgabejSCGYcOFRVLlhetegGyCvTPZYw78AEt0V3gpMyBSRSV2zysSWNiuzZPkth1y2QICGEClznoxt86e/8U37fn0EYL3j1EiVKlHhxlAS4RIkSHzoOHj26/+//zB/a4+uH3PqY/ZvOK21L1Cv/tLPxbzoiQrBtnPVEgyv4KgDXzBMEtghtESLEPifAM3aKrRLlDdPmULkqtLVQnDxuWFsB3pTAalYn5FWCIhXa6twQ2318u16fCDE9P60Bw0prqNyGEQ3TljRCR4S2ERYvMU7bmus2jHio3O3ZdinCl+3HaeaZRmAJH3AJo1xXyU/gEmwRADpw0c/3KhBD2kAw02GmmecRHWTbb+ZeTQCb5uHKfLRK1JqpxBoRMp+JCmwRuCKM1BqrrVUsaIaIT+LKy8O060TnlnMjnyIvVS2CGhEwolVfvjqgFNGFSXOYXZZPeIHHXJRVC9g0hxVjsEIVXREGOLp5k/ExbRSR8I7Y1AnASSZtI3y3t1m9g61MQAWW/6iuTxOceCuXmzyo117vzrc9SG5tm4Oa2Z9fcVWUfl6u8mKHWuXArKG9+btESSnCULmXN2XlSpcQ1OVXBXobj22zOjVXCfDqyY3KNJ3Y1Tuj5NS79CoAOLUk8y1z01+79wIKqz4LrLlJLrZY3jfKOk0RHqXNXagZ3cG01TQBHk93X7ZzWuuIQB9WNc6bmbOSU91cDv0ABnT2OdziaXz7u4GnPsGnXoOv6v1NqBl0QLXwW1YdQ38385LOsyq+gM5kVjODtDb9kEqI1Hv1feg8gprZm6cBK98WlcTcCNIzLzn1wHNuDfWuQeaimtKQYeY2TV0z6TCZeb/r6GulFEKcUR+w2+BIQIcJih7bXaoeiUccLMoIT1YUYB1ycjX7XZfnnOOQ/fbsl6FLo0tjnxMdbu23zNitNAOfAB5DANtwAs9gDDUYcuQyhn+7+fTvGI1HZye3NrfXXqVEiRIl3h9KAlyiRIkPHb/+d7/w8e8leEBjl6/t/ODb9c7Hw/RfX/sjvybubprdphjHeImSdRGNlidhm8ZQRxTHeI4IbRFuiKFL+Ixt4Bnb++qkKiIf9zzZHeIC1+1THXIsZHJLjAFujrSebNfCzPSBhvJaYkCdoNr2jB51pIhC3O2tTpU4MyYIPKIa8aHa6arGdjYyjdxp5oDtMdwRZ5i45vlUVTTXRXBdndpigpl3XifxFjP9itAGzhGyT1u3PM52w9mMXN+CThJuznjXbfEwQroFPblGvMMzFz+iorccsOMS6IDtSMgzmrYIIiVvWMfF9ONVFAiS8k2miQrNLLDspoVK9WazHTi+aep6RUpJMwRcwlhJhzCeTeV1+d8VApwoOVYO67FOz7wktLgm4ooxmL/1cfRuTR4iPB1JvqzPEiOfqFo9s197cti6s/ev1SYgvGnl1th/VNe3k0cczI6s3h6udKZq9JdZ7aUc93n7VznwmiGYvbdFEOEt8bQ1o6WA9Cx/AMzN4PlG0EU+LLTifVEEXmoqKrFzKwHicb16Z3R1faP1VbUuQZR59qoJ1vMXSi62qBndmsEguXOUNm+KoGn2H093myJsinCUtt1CWSlNUDetQ6Z7emybRjjI5CCTJHub9qE+e5VIEkxw+2xM5LT+517hi2+3f/WrRU35YUbdp/EIrze91n5QdW8eCxelIpXf1AS3SuSwiPHWERw9Wivc8j63QSVZ/tlp9qtf67WG6p1Rsf1AycHUBXRQtEzbjhGeTveA2PCkCLXtfHdWh0zBOVX9eoNJgBPgeCQ+9txxoQhJ2FvUMAOI8Pq0W/TmpFfXuHYJLtLgCa626NMGCp4KejT3mARIBaGU3D1ufsPHAw+6cGt2ZAhDNv6o83vfjH+zf0at8c7x45IAlyhR4oNFSYBLlCjxoeOf/5P/209WqVxDuPwzp/IN49Vq9jSNq1SQxCB61EdZY8wiznPTGCKomgO9wRa5SXJd9RFsMDzKdp6m2xOzpV2OheBV84klQgRNu98gSYRq0KsQjUXHYAIixgMiZCiiJgOXIDL6cz75OscY6OKiVZIKUwf/dXFvKmpAj2yDYZPeFh/pGecO/k1x/5BdBDWSlNRVPlAhBiTRITvBTOHR0CZVy/VRxMwu2NWTcs11a0RN3BucgDDFBNDzyw0GcyPouaHOPichcj4pf4kHuuKOSxAhE+U9meokw0sU4ItMIo5tL8kCC5SoSntvMHfAMnR0eiHp95CtCrFEdnURE8UuZ0MaczdajVHamB8yu/XZhsAiT+q+jAMvMFZOXbn6YdD7Y6SW2V1CLXwd0dZ7taLVYfg06wDSiG5G2X2mYSH5c9aZ53twJUrahMuB0MsNmeuS4sLGC6e9EA69prkCWDZGWu34vLUu2+vtBFwpz66BwtsJUKiJLaqXejXrpvGjBjB5UL+y2XIPuWqhoHmxgPMLnXGBuew5P+Egcx8nu6BAIdQgkwPcpnI3rUVtMH3gpnW4CffiO02jv2kGuVnd7DoTXF1nqEqYkLl/4+17cMdZNBnAIKaq+NQPQMiR2wQ8FSL6j9JWzZh3WA1pu4T6W3xGc3NGg+cc+JjVUrfFDOrqnSEgKtPlUPMlHKfNhsoLng2znZbdb4OrQlesWYyYM+HiyErCZRdoybLjfZT/fuaP1lN2dfXmKq6H1AnJi7SOebQ5oVegxwFOixPJsNKfqtkmgGfwDEaoGNHE9uPOH3O885DrYJuUKFGixAeKkgCXKFHiw8WTR49+3+i48hOIx/z6nZ8YOrtbKmim7/zr+mdsIz5RW01GiZIrSZVVoz9RLR25KiDJ3KfpDjAxW6dpAwGIm8ZYiqFlnnhEDfrzE1SI2gyesRPhRXhbajAUVoSMkALGqpUoKai5xrk+j0sQKa8rWnsc6klqRcW74hDwSHSU4B6VPU5DHDmbzEl8HeMn8ftsdKkDkar4OAm1h9wcCqdFvz9TUeZcd2V8qoQt+ilV05i4BBWiDv173G7T32NRdKfDsMuq+VaHITDfrqmvhubb0ZU1kPQktWGEwDCT+n3yVABRV5KmTstWvim8DM1RA6vIfvWA+zg+znxqfsRq6m+SyXF2Qf6dnWZ6LmPfkpuh2QkvoUpLIvDazZGSrghDnOZse4SHYiDaPRqe6NZFCNjnI7Xzqjb0Qin/YT0/4VwBnsmzs0T0i1ctitPqEua8zGSvloJZQ/yL2wZpa+3BxdbpuWdvBWY1r1AVHHtOLXnBSkiALnQ0N7vKsa7P+ipXs+t4bNn+UsLq5QHONM3+oujxJW2uxiBtSeEBNbObKK9p9gbZ7vySUkSD2SJUkSqP0w5gmwGKm9ZRNAtaLpx40SfthrX5//4jB//WP7z+Ke7/q6UOqFOyXyX5E0aFqc7276uWNKKzaV40a4I7wa0id2Y/HSsJ/BHeIm04bV1kuUIXKruE/Crf1oL8oGKjmbMiSDttsz9QrT2OD9luiKP1BxcgmRRdoFt0ZR5XkovVDbr6h2ZmOK/OVAOUvkF9p/rWqkQ7s9+uFucRlXk4dJthpZ9aYZZIwzqFNxejDAyPUT0mGUYD4yeNN4cZoMSLPsklSpQo8YIoCXCJEiU+XLz1s1+Q/8bW4BaTvZef4fSNepYE3+DjI8NOkHHmCWWMVGOMszTrFaAYZY1Rug3URbRlDBDiNG3URdSwBk0x9ETkEjoqFAIQMTJEokQswj6tSMlQyAnuM7Y3GJ7TmGRt4DRtItg2h46ygJYYSGSIGynpiSjII5ODiIqLv4F/rmeBioqYukyKpT5c/JDKIfs9WgK6tHzcjOpDtjYZTJSLWAp01Pm683DBClFC5Sbv5ve7uHNe55vFMdREtwidfdelAY0+nXOaM30mTwCea79qhY2wmElr6puTkOneMHNRKu5mTC1QZKlRy/TkO+1Ksx0Wp+BzTR5YL0wpgETJhfy7DkZlim+FZ7LiTVd14HWB0DNDZpFfVvuQsSokDmgEOO5M1GqppJc697fFM7bmdyG3w/BUXiRbTnXhh9wQkc4Yv9Cl2Y2L5Q6zLoH5Cin4EtMsfcT5dG9w9RIGoMcwLzuEFmlfnP06talZTebVgFd7wFLPRSUxK4k3C8q9DMmptLe4JKZ66cyDNM/UtUWwpOVelfu7BCkinTSrM6VX0nr1rh1zECpXX2vOt10RMKvLfZw2oflSniqcXzsP1lA6Lb8TNnxg+r/5KH986btZM+AM79fDzk8H+5r1CR5mtweZK9N2zcwV0QnukPaq7x0Mac/vdZy2iw5YL4ii85lTm4J0bo7eTbcyY3yXh75wQ20LN8cliwt9Nia483DoiMrBzKE63wQR3sJi4IXh4ut6xR0mkqHVz+zz2D6FKtyCRzPvq2NqFTJBFcQUywh/+MnBr373x7/07PHnP/Lx93TFEiVKlLgaJQEuUaLEh4teSvAKqRS/0v3soeiNRKOSnQ9NEyGuGU+FaQghbCOWqhniNczhs3S7LqJJ2oqUC9RF1DAHtgi1Wls1+omSKGzESLUCI/DIOUyXBogK0VwRrZL7tZ7TABrEQMd64ijDNbrA4+zWxAzn8kXIKxPkNY77tLbUqCbO789miz4uvC3YBHo0TayGOtdpqBGVp+zMxRODycfpdui7+PN5Z0TFFQvmXCHe5ZnWjTuMgC71fU4k/hH7On54ntY7I7pEePe4PT+JJtLH7Bc5tp7Qz9N940eNeGxV74xyEWmGhhFKEc09cpPM08GTyjdJZ2TPkdlImTUAQ06BeQGkpfn0lez3cPqiXtD+k9oaHXiZHNZEVNzSYTRLOZauCgOx0Jl1oRedEtxheD+0b07HNmqgmvPehwXn56IIbM8NnBQNqz+c7qI4SHbuOA/XiMIX6evzsoMvtGatFAy4Itw2wjCTwytpsPASXS9nVsnpvfXgBfs4h7npewoW5mELOLXErE4LDljP6cYgk4NsF7hpH61y4BfoCVAzusBx2lyhvnPoKOt8r2rpQOia0U2UdzbdGyip84RnzHPBfj0VBkhJECDPaH4tPt8F+zRo/qWdwV9fFAQ2NhAdaHL98F6wZ/doX+f0Abd1r6rmgvH6SEV7XhvpqOAXDSSZF7zAYsfKUKTnXmExQsVjEzDPpbkRJMobivbFhPOLY3jI/lz41Y9jRKVLa6Us8PFyh9d1J8cYV9KWhC5Bn40W5yFysXQooQMenMEjlsOxUT1OW4hvsvWTzg9U/gn8OGYZAl2iRIkPGCUBLlGixIeIo2ePzvgP+s5H3hpf+3oWh9amO41q00HdUg3xZFecJVSH1F3ioRkPVHOUNYGRcl2i69YTv5AVnGQyxgszd6RcYGg0G+YQ5SAGAuHj3uRsSuYQxnghsk68yVFCZUBzSx2awrSNCZBQs40xIOC6cXpKfT5Nj5CSyCFogUHg4+pd97lVJQ7U5qGoAR/nLeARGwIR4T1lZx7yd41jPc/TbHZpLFQua8/vqMX5PKB6H23fWomoRDgBslh4qZh3NycJVaK+aq0M+JNkIdqowAaK7LdhRMPMbRihDs68iKyfedfGwUEVpZxthyzTPRfedDY3Btg0h7kSW2C/ftasiMH8baLcIvu9LMrVbIec5fP+zLfMDsUxUqEl5LTIOnUuri5EVKevNwe4DUAxEC1JNFCNhfiqOOD2E9fdH4wrtNEGYwLAqU7jiXUxxDrzbbOSa9EHyQ5CNcw+81DkVdX2CrZ5IRaay9rOTrrcoGb2EuWt2PNeRHrmpRM7r9O70J+54npzxGOLseXUpuCZ1USXRH4OFDnFPZZ6Waq4c9n/udiNq+7/cbL7kvPgOewXEuVFmaejnZkFLdeMbpi5l0nlAyUHqZy/lmnHNQIgyjxdNFtHQAzi20kmbSPQvZ6H9Xqz+ORpRTb+4s7Bbz4c/JcXrtGGc/gITXWyR+uI/fmecdquGQsR2MvXa7zwQnmhaJbBux5rV5kCK51YxX26VBUK5dtRTTbMnkCAeMJHt3gk8ft0WnRDKvNfpz4dH6d4Eh38osuO+4V645JwfKUpdDIrw6YdsDfBJQiQLQiQTWhxYofKDmKAACZwC87ys7ODiBFtKl2OplgtY+M0rJvRXzm4V5YCLlGixAeLkgCXKFHiQ8Q0Ia7dOYkqvXEztBwhaMWHnzAe7QnfoZtgj2kfi03AVw5QNwZ1MbSNaI8TEB7yXDUQJEo+nW7XjWikXK0Jx0ruihMEbYYN+gEbe+rdSFSAPk0fd4NhU/UQ3TpdWXBhybN8iQMcl4O68nqiDfi4WjDUbSpEu+oQoWvJesDTWUDgN3h1n2N9HpfgLg+BDQa65ufejPquEKOQSj7JVPqt1xX1ucwLBMghnfGyfZSuonSo5tNQMUhbUkjXCL+W3L568IWXODfn1DdsmgNbBDJtLxVHXZ5Yp72pULZdmSYjEYeystedNVDFxmHmVowF+/WzZpi5QCWXalR3uqNtny8LZS3ukptheCbRobzLozY9l/a18XzjWLlSuZp7V1iEJQfCmY93n4ZHHKhFRH2fOtAmmH6klVMsBQJrMwAvnlgrpCw4kbUCD2yICNRGvmSwoL9XJQNfFgu9umvlGDFvkChP52/nxaXno7Uu69ioTFfL/84jWVHL11rPPzV51k/eC3HgYq8XfSMe26sJwEvd4sIT8d6Uam2LfTbdA6SItNOyTuh9sa5ynDZ1ubWb9lGUSSlCW/ho96y0tXmBgc/DfaO9qrH9qU3zvw7/HZ7+P+J5g8k52S9j/A+wgzgIt0PpntNwRaity07SZpjJPMsAdGGkFeqbKC/K5Em2HFd8xTdHD3pgTx7Undp0abMClQeoB7tptOuh6IiBQik4ZB8I2G/T11WOzmlUiFfOHuAFeC3O73F3vjHCk4QX/djm63Fn071hQYS/YR+dCSTh/HtaIQESOfvE9QNbXLWQqIjjMwI4ipmO6Uh+PDn9e9aqQ1iJEiVKfIsoCXCJEiU+RHzxn33heHtH9oOJqCEEiLvp4Pve/G2nzdNX7p6zd0/cCXCHNDpi5HIWC4kQdfoecaDcWOTzIwVb5tARoSRqi1FN9IXBLZ50aTQYNlWvJXrMyK1Usi9aQ0wQIW6LQX6KCzQE6NHSYX4R3jO276hHCI7Z2+FQl9V5KnZcglyDVWyKwZLTqdCeW31mysmQzaQwp+xSC7hgAQVAoOQzdnWvIiUDIed1RPWccpy2ySnQYp6oE3ejTDaMcLgc9rltDtbW+902B4Ce6K9lv3OvrLgvvJ1pdFYhS5x6tpb9ghorp6MU0J1ud6wTFGPloEBtg5JGNFotenQV7dHlmtEKcFuPqgKhAiueWPb8BEKByHOPFb5wY6QOgfZmA95TdQThbMAjPFAWAbATHfVgXmYmeVyPfRvWUEqnOp1fDtiwVgyE1nLgdVTuPUYiJ0pGahFUvL50s150WO5z5lu59Fe89MrrVWWYtT1+QfarM40vHA2oyf169c7oeYbSFzsqkszLBdjnIY9nNiDtuEbgGsFx8t6yUpuzC4XKrQGKTevwXnJn84Kt9Bw7jEfe+cl/FvPnFs+2gm+EfLyDehtqyDsnPjebdB9mt+d3NsQt8uoV9jtO26Fyh5fEb1/Bg4WX5IWRlDf/6IvPwOFR6+7WtS3jXV+5PdHYghZ9HU0QIEOkR1gh9ghXPNtbnOsXGwzmcSguwYB20RRaY26pPXfR0xikrdesIxdfb6ySzr+kQVt6vTBfkwxhAiF0AcY+74S8BQKSfzcUP8zrwQBv/9Hx0a2dXUqUKFHiA0JJgEuUKPEhwpD0hqaIRN/SrkXq4w+/Yv5yMvzTd77Kd73JDUnyCvfPSBpqIEXkEj7iptb0RrTC2WTLFuGuOOnSsEVkEbmEArrUUYTCDdltMRDQVy1fuCfs6iq7XRoN1fdFsYakAIaq3RWNOzwCHsyyahMlh2nrXOw1zL4twrfUa2/x2jajWGQekUt4jTzxbx6QHOGdq+Y1cbxSMHMOrd9aygR0QSO9UW+ZqNYheUUZV4SofDY5mLY27WCctk/SZkNETbPfNInyKbIApZ11aiCXta+a2XWN4EmyyyzLNz95oQhqjuV5dc3ohZkcjDzbnWShBTgbhuFEwktUYOWWyLMjdSJud7pdN4dj5TLdnps858V+s+KF1s7gZyxOgUDI6Zr9QmWBVfSjutBmoW16RDOHoUK0tKIvmkCTIYrhaGhf35ofbFSn+BaIFQtopzbN7ZRn9DVRchbvfQWhXVPdt3DIhSOXdWAdOxpl3qoMeMW9s6DBi/jnqw4pvL48TDo9856bwasTy9fu0pZaWplcDah+zlqAQgcM66TZy4faNYKmcnXZ3gFyR28xwstygFcuATRF2DT6KFwRhJk7Tjs6JRg4m+7NheXl45SP4z5xjS22XjOeFk43gIfPuA18Hsmow/CUac3sLtYvVL6StbT2BECivJO02TDCqwXfyyBkAphVizUfR2HRSnFM9ZP4CHRwSkilRRcI2NcisH9hkS6i4rFUgqtJT+J5F0ywEuUN0tb8FhpGpBXvJt155baU5JxqwPU9DgDO4RQACbfgLRiRjfD6fETSzHgr5kHMy338IXhgGe9jfEqUKFHiMpQEuESJEh8iHj78Dzadj/x2526FZKj4uBo1Dp6e/MmXf+PaZ/qi3hCjWzzd5swlOGU3RHpI4BE3KsQ+bh6etzQbVgPV9IWLoiIiEFrgfcQtbWFaIRqoZpzJxIgGcC4a84NnEpM4n+6OlHtu7AJDJXOOl5MggWrN00QP2Nk2h45RMBlWgEAgZtV3HqpbzERU1whBzMgqoZJD5WoSa1MDxll7RdnbNgdhJplN8GwRSCNE4YrwhhXaIo/KXp2UA+um1LYIts0BKFeEthGsEK0rptlNs9+fWlYnmp47ZFkcVGQ2BbLAMhcEVQHjzN00h7aIbBHWRFQscTQ//aYxPEufVy0258ZCBZZdmSa+FfvWwrxYkfmmtbE88mLpBpy5+bPCFWGh2dIj81jdqIuo/tZR67rzrl/TiyrhiVznZKWY2SlrNESEYpK1EMIRoU2wNJgrycDP4cBcRoOfJDt3nYdP0pnG9YK68WJxQXk7gVGZvlCFXpYfg0XfiMeWVy0sdojVdvNml504HlvOvM3Ycib552lWE2CN13ThTgdpa6DcmtEthoKv7bwUIYbWgXXU71K5o0vus3AhJZsz6bJp6spDnVC5KAZKNu3+ytGb9M9oeljNVyv3nvJT4+zLy6ceH6C+Kx/Iuzw+Zr8qom2zP/+mn6QNIMxcaYQ1ozfO2kCYyaFyQV2V+jvvyeLxXEXRBGtl1/3Tm1u77+oq2U947YbKiw7JmSGfJJSEVySZX+dI1zPXYTIuvq5APufAep2ueMgwc6G1aQVH7KOoIDsQEjP7tGLX8DyowgR64KE20C6Hos12DyCBjqTawjo9Z4d3nj26tbFNiRIlSnxAKAlwiRIlPiwcPXp09LXNB7/vhlJqqjKl1HSa9T9+/cHOpk3wEsfnaveITaFENAt17lIfo1W7QRNASKT2QA40cVEM0wY0N6xjbVLlsz3JWsBprp7NSEgqEOQFbGZa2XC63TBioC6iYeYiSM+9dGJd2z2OpAlII9o2jh5Pb+uTSCMMMy+f4S0m6wJIu55RmeoKnEZlmhtNpU29t2FEUoRNsy8z6RphUYDVAclzuCKsWb3iFu2a8/ySMJdA++IuHTh7kSdPGuHcmGeOQdrKhqjASnwblWDZhjtRgWV4BfY7O48tIpsQxZz9Fvs4ypzRJVHfa6AUKMNLbDAq03U0sdgYu1Dx6FRtb4kToEt9L9++tFiiM4GrxFXz1LLI6MnTquqYojJdOe36NQKlENgiiNWCay0dcIEDV1TsiXDV4ujqUGkFebj70mlXoBdKtLl3Eem5F49tB5FO3le51GUZODiWHOdfRqc2LZZTWko3vRxzepzXFi5gTW5wYbQHyi2MEsu+WYsRiZQM8wUmBYRKhqkEmkb4IjWEclPoWfVgjeMFhVOPk52XnIfF4P8zmlWiN2i99HLHh/Cr8bV/xzkopAEPMsRri3TWlHTtd3ao3GHqnqTNyz7KBS4efvmPgFNLLl+SUEPargrV7HHK7a9mZ9M6cCgqPqv00p25ZLn40CnaQTfpBip/wvUvyWq4viLJPEQ0oFPh0MXv0ZKEHZ6ENJDDs9erlX7qPZt9l6uoFrQwd1BP2Onz9IDtDdzXnJo7AZT1nqtDlShRosQVKAlwiRIlPiwIweil7S7WS+LwGekpm1YQTNvRDXGeCHdE+1RsRchEeA6RgBopUFcDV0QOYYdRg94Ju5LwGdtAhUgS75mnh7OoZl3g96bx2CWqmtuuMg7TbWlGVaOvJ8wVIp9c65tkLTKd0ikS5TUBwXCzyabYsQ4mwgWqRE0GG9YhQuiEt6rRZ7o3VO4qjVFo9nsBatscAjqYc0W5vcg83x902GGRyo6ztiYG843aSKnYQM+5h6kbZvIiDc4GmQotAKWcBkJO0540cvlXFafgmoX6WbMm4rFyCmTh/UVyTgmsZGLakPk1QAu/sW8tKcAAPJ7e2LNOdUxyk8JSwjrS6BEHOBOcKrEI6tda7peOmJ551lZw2SGgiiHZ0giBqtm/9ELLHNgjbKieFF4L+hSqQC+Y/epVtR44oxCzdssrAXrRZC1lSic2ef2b92YotYoLcm88tor9fn6UdQGa/V5aCvjyPug1GqBgVK7mfyfKCzM5KIzD+yiciy6/dLnsmWQ6DXgxIvpzPHUsoHXXce8vGUdVt1Dn0CW4U1egE2vnPlgXcZI286fg/XxdNPLjVbAu+n122vNR9XB7u2UMdgrLRnML6PkLqfwOw7kOrDXhZWW46xG6+BEVbVDfpufhBUggEWFDyKFylZ8vlwwrkLYwmYhAGxB6hCGyS7UDcl7VfP7T2AYPAugjHMxtPuMxuYW7E7e6X4MfE8YLLb6UKFGixAuiJMAlSpT4sPDo3bcfbFYzoXb79x+9a3g/cMcUpwP2nrEzZKdLrU+9N905N5NN6xTEiDjE9YhADGjGyCO17YiwTxOFL1xgoJoASgyzpjQiTJFkEmP4jK0kk/vG44np2EaoKUQlT4LNKUfV6FcLkcaAEGxYgSa9TUUkPHcxU1SSvETwhnUo5yxlxjLMjeAyxqFjHT/YwTyb7m1ah3OWi6ZDGWG2pzPuXBG65uKiifKeTHeApfYzaDEqzOQwcxtGpMlGPDDc1pSJgWHo2puGnOYFkGZT6pqIOuZJ3qW0vjyBf//T+dkJVOLbdnU6Pc+9YrPAMmW6TDjVXASOkI6KLos6VuDpkkhQE/169KUj93NAPLEQHgsKclF0VXMZ38ldo5d8nqtEHtFKJmTxBI4KhNB54S2KvkdqDQdex5QKvYIb9lGkvPWeWKtH8a3S4OKZmJ9vfSz01XjP7BeYGVzdtI8u7tKLPkuM9+q+FMXt2evLqiUV7/HxdOcl+8HF/WJsZfCln43vfWVpT+0cjuEunUf9B7c+ck7DJbRF8JwvxHv7uqwThRXZxMqrea07Wzyxo0xORAh8k70bvFmszVZ8raXgtQiptADo06oQt8Ej1HHRgI/bFr3Q8g7VblSX4aSuP6Chcklbt6y+bjO32gpwJI2cA3fAhwlUoQoHsAWgngB4vwUfYz8coCM/SpQoUeKDQ0mAS5Qo8WHhi8++7BnTatz9B+9+7GS6+T+O7k9S/8vuKyPRHNAMsnasvJFwR5kkpWINdZTdUDUBm1CbVyVKxkqepQ0V2sIr6ABCjFJXhz3HpnSMcJg1fzP9noaIGggEsZKY/UnWqhr9C6rbjIgohND8pAVIomgueijOxYJyVI3eJGuv3uEljGNGTXdnewXQNPsUA5uvhFYFXREOZuVnh8rNheiVC0Fz4UOzRiS8NNJSLe2SfuS2dcZ15rSU1RgBK+x3BZvm8DRtvG/hd47kaS3xLSDxLbuSoETi56WJwjNZ8aZimQP7WbNiDoAQt15MsS3SSwWCLnUgztxdI7TPD4a4gFNNrI0wnqyky+YHxxPLCizDy69oi3ULGYoz0bhwqK7MNJPDFaGQE9zqvFzTUuPFEzn/iC/0B91mkLZyl+DlJ21R/nf12YaLrd83Vj/bYl2lKy7xLT0Sa78mtgik8JaU2xe8yFXN1u8bp52Fddysibnr9H/qR/fqv8Tr3FupBhzAAG8SNtXpvjgKkBFyfIkrexENEa37hl7S4wubzY0gndiXNFcAgUONYdZqGP2re3IZ5jx5l0XmcJdGCyIqHThgB7BFYIvArYWkrfkdTXChvTOTeg/Z3+NZGyr91OuFABWYwAS2wIM2JIgtxClAct3xun0ZTb709NHnX/3E++t/iRIlSlxESYBLlCjxYSG2nKlwp+G1o636vjXt/M5bX/zU7jN2DGyBsEXkmMkok3URVczBXPKKlXuWNjbNUaRc14jO0iYCFVj+k5rcCnVKqigmpiJO04aabAhvmna94QbD6bY+WSNzEQJa+ckFjojiXEFa+BUJxETgiHCF7SSZHGat2TuRz+oKXDp+3NDHr8CsJsONQgf14dlOw4igVYjtXINEeU+Sndm7wuxZXEpltdL73lCYMUsRAkFsZYFIJiZZgm2TpXm7lZzYwhubaC37rYtopCPGnwsBuvzvLJg8mUeVz8Y5LwVcGGV75m4tieYtBzRdglh5rgiACE+vZQxoRspFZVsvGUNoVrtG6k7PClVzhVr5CKennnNzBGKYuUx3dRmkSdqaB0JPcFFcE3NXcHmm8tCAABmgvayEr1zEatmbwq3l/+hc9EugQCxq5BQWXJasmNdz3jUJtB8MVOHV6okL7ws23elZhReuMLxW/tXQPufFLYO0pUXjphEyr5B0JZoibJr9x9Pd9TxTARynzR2dv1Bo0qPxyi/8kvjjsByQ+2bI3SFiSrAvezRaqt+hf8TuPMXgChosjYBMrf9qvxi9N6vJzAV6zQGHo85Haid6p8p/BZ+PDhMgwPGIJaFkGNJ4Rv6j1qLbonvEvn77Eg8O2Q/xdEyEFKG29dJRMB5hhAeDDsMW3Q4TyTCRhuU5dhAvRGAfNmACrwKwjwFx07Cn4SvVRNjfcnRJiRIlShRQEuASJUp8WFCGURP+r8jrY7v60eFvnnvdofqep+nedXGKwDYiIcRN+8kU7+KxUeaOcEdpPi+cnnsowlMJONWpfX28oBGBBUzPpVGdZr5lVKzMd/Qsr4c3J7pGZbqwegaESM89cyMQs7fQbBhFsU6g5VOxeDu7sRkJmVzyE1rwv9UsxalNnZtDPS/cfH98ZGUGKJZ68t6OXYYrwiTzGIvEt0CRZdiucHsqMJcKFCkF1M1hfjpFrNy1p3ZF5IroLC2Wir1EK1cAZju0fStZSqi+cGOFDYlybRVqgdcldAiL9stD2g36Ea6uCayPTZS0u7GhGoNxJ1OWfWMUv3VRz8/jnOOJNbfwGmaunvVXjV7xKlUid6Zr5VEDCp1Gvi7U4ALy8VDPabZoWjixIn58SSGiNUxYFf7+QGlw4fS66FE6sVe/ERfV4wUu7czjZHc1ArnQtigOJ8qbM14pItcIoHUFB9bU1xZBorylzhRf+rYO3j5Omws/OVDwY+E7X/6f7dywjuOqc29W1VbBSIfxbhG7BhDiBcgGvQCJ0RtnrbVfE6396jSEyzr8wrj0G56OLQU+bgNCVaFgAX3ZB9Bh0uIElmq7SYb7s/TdQ64H2C36EnlOA2jTn+CCOhPKNmfuWUZQJXQJ9jmShC26+5zrM9hhNpUGOHYQc3358t3FJfvt3Z2jhx8Tl4ZnlyhRosT7Q0mAS5Qo8WHh73/5733fy/tGVq0pOxmd1CR95dVEIqDNqD+TNy0RrRy4b57GSDJGuKCUP5tYK5za1KgUyrQqgCyw44nFxEKIeFKf673M/xE41eliyieIJzbgTCyjOrU2gpnU5rI4aME3VqeKAmD8RnumgK2ZSa7wk3hsmeeeuXHB2Hn50GX5d/mEM86T2wt5swTLD0gaUYMZUxKQGWnXNbypCq2cA8+uojMbFfhZ82Kho01jeJY2KsbAzx25Vzq3nv+oYIX9FpoLVDGmGYCztH7TGOgNEdIpGPz0aXpEEVJ7hsfIgWqMlFtNT+KWsy8e/3+zH9HFlpzKNPbXJxbK7XAuDF+3jte2meAeq13dowlyk0GAnChXd1S/9WfE+FKqoUCwrQNlnx+5PAvSvoz9rrSdY5UMX3WN94d59SP9DY3HVjyyeNRwbg1VYCennnNzuNqrVfX4yi5deMgT5Q2yJa4bKpfsiq/DYsfZdG8wj+a4gMmDurcTmJsBqHHWrhm9eat/Kj+18UoWPfnZlUOGkD6CLo1Df2tv2JXNJt1zmqBA1YwealUEnrvER5kcXowReAGk5x6K1LcufRjyRQ+RZHJiEAnvAbdd/IqKIR/vNn0Xf2XoJevC/gvmVR5xgC3xJX6A1Mm90UwBBmpmN1GeLYIWfWYJxgZOPRwAjcNZ7vGXYRMqAMwrmnsQkFxz7E5MRiIc15j+x/e/8u/93p9+L8NTokSJElehrC1eokSJDwv74+y31Ge67LxsTMc0M59jUa2ao21xXmPYZjRrqCoqbjPSM7aK0bdFGGVOfVYuKAtyN1q5FRqVqdlZmp8tqOACaya28cQq/plvDE9k/LieBzNfdoJCoK8WnEFV74zyXUotxwavhzErvZOo5STG+R+ILhGv0nNvPs2Nx1Zy6qngg3GFaYhIJ+91pzbMgpxNM/Mt/0ktt7lWef9qIprHREcXZSsFsGkOZzd49ZgsbntJZ145nVqM28XztdUoLJCHIW2gq+qHbAU4+kOLlQv0um3nb8Uv8w2jMnWq0+RJbaFVXjhtNrHSc0+Hf9siXIr4LTSe4Orw5mscN+jNt28yaNDb4XBx6stH4izZc1fyXa8aNhU/rr8nN+b8hGrltGrt1m8F8diKx5ZTmzq3hostjxrCS3L2e0XHFl+i/M+95PZlF0qUdzbds0UglxfOBpkMM3fZ40rtmP2b1tFN6+imdXzTOiYPmXavuHWnlpibvt4dZLKoDT9CfqLeTf8513YX8q+GqMAhBJhyVGEKVAi9GZOsmb0b9vEN+1i7ed+wj6UIXZGX+942B40Li4BXQaF8C6WCE7n6MKz7YONMonjKTkIV8HF8HF85zH631PIfbf4c0uizHbL0w7jyFmjTrxC7+G3611isFtki0Nnv+xxFVDoMb/OWHWaN+z4BdKELHkzgFE7hLTiALgTokKCgLXefPhxvdm52p4lTfQ/jU6JEiRLPQ6kAlyhR4kPBo4PHYmtaMyuiIjaHvexk2HVfBhpi7KgIjBoDl3hEE8Qmpygi4RarTT6bpfJqyK3Q7ATLWmAOw0ucqpVboc53qVVLpAWW38YTG8GSQnux0UwTnDxsVG+PklPvQrTnklHwFRikrabZX+OudEEmyjcHti7xqrU1tJ5ctYz15P+9oWn2x1k7nDrx0EBkKIVpWe3x9DCv82S2F+x/pNyakrYIEyVdIxqls0hhBVAXEaDl34viMLBpjICzbGmXCs3gSQ3Wjp6yK6muw1T8FGsimn86fl5wWOmSv4F+O/voZ11Xh+1q8Fc++2X2jEpiVBL/cZ1Cu8IFdXSA5YDaQEBuorbSovDOE5FLcMyepz3DFWei6RLOAqRn7S9c6mxmaTa8mMJ9iWg8WwfJpb01La7GegH4/Z5tHeKxxaOGjoh+wYzfJcx6eNNeL7wDtgg2rfnYLnjeTev48XRnhde6YqkEtxQyVHLHHBxfmpSr4rE9V7OHyt3UrxTABHekrInDbcHd76LoBT18Qh0IqPRTtzkBdjn0ceeiqE2QKK9m9FwRRpkMM1ms/u2aIZeY2AEX19eEl+Rp889ZwVAopQKbmu7DpLjPx/HZ7qihJFw4QgvOqcK2JNSB0Jr06jRg/Xfxsi7+PFbaJbjG8bz6l5Z/df2kEBnSoDX09E3fIyfLEflPfgUC2AQFE+yz2K7CG8Q/JFXMi5cVL1GiRIkXQakAlyhR4sOBwKtEv91oC8tsRYeblvR30qo5RSBFvMPZDmcOYYPhJnlZnSaDCnGSSaBuLGIms8ACjJzsFSWZXL9Y9sS6AheOnW9XpBMrflxf27aI2kcvT0i7UgqeFw0eKvfJdCdRcuUa44su08sHFpW04NjLfEsFdvy48a2owZGSJ2mzkYzynqep08qYZpreG5WEhfadv/Kz5jht5ArwgoOomjmsGAMUNlFdxIu9sz/2Oplrel5weFo3ePMPV6FHmPGMJPSot9XoCpl1OKMfm9nGGPOZ2p6eyezicC0+NQVUbo3smzo8QZ2kzUnWYvlhmWOTIYr73JoUqI5W247ZW+3YcgeXqc6Fm7+g46XnXnDsXXXIi2ONTqjWbn0f0FLw+z78JefhS87Dq83StQhcM3ov2Q9fsh/uGIOb1rEtgh1jsGMMXrIfNkWkJd+V87hGqIv07MyiS5ahgOqdYfyoMS9pO07bxfF4MuFeDJW8YM8c/Q3wwSORhkdcIY7wVG4MnkN3RlcsaxYqS+sQDP08bOvY/rkkfslPSv4wXPpBLT5H5+YokoZ++w6vX2zapfGM7SP2Qx2LrAhV5VxVe6pxqK731XZPNeYXOuR6VzVmQ1X4e/bHVYGnwvnbtuq7ykexoSa1cNA4SujCmyyifxIYo8YoUAHqDAWqitrM94+aO3IyFkY5WS1RosQHifI3pUSJEh8OBBOjiWlgmkztKPWO4h0UdUYNRghcQo94lsCpAIewyUBLo0sC6QtMyK2NoPpqr9BYFV6/0EniiWVWV2JxLx6j0nNv8rB+qf0VOUm7iODYS88u2n0tJo8naWMtAzE3AufmsHpnCHg7gVNLZieUkwf1eGwlp893vr0MJ2mzIcI4sRCaB6o4rGa+kd+sb63QskS5UeaOlDNSzrybdRHtWWc2ebSwLcK2eaL13jnqIrZFWJnP72ewNhaFl+dSm4ZdmRqV5OJg1kQ0Xz7QdZt1k0AVdCLN1XGqxgBQR2dHXO/5t+KJZSynTzvVqdxaetimZ1567uX7FSdp80Fya+2zpKBB75o6uUhftQh2GQc+m+6te0jWPTd6XeLMC448bztwahcf0W+Jr15ygm+VDKeTD7Fwqy2CopV6zexpblkzezWzB2xah5pVzprk96JTDELl1szuBQ48X01LKMRWhMuh/t914+37X+Ho1OF06eChQtmogFA1ulTljPd6hXzacdZOlMeMCY/TpQWvbaMPKlRuwwifO+zLSyEsf15Lx8aP6zmZVwwvF1J9HE2D+3S0Guxj+9jnVH3sp2z01LYm4x7xuarm14S+6ugoDH2nEV4wq1PVp+Xj9ul0GN6InjYOfesgVhMUKL/QXRu2wQOP/NAJnKHa8Dqnst0ye2OcRydPrx6TEiVKlHhxlCHQJUqU+JAgbsh9FTsonP4byYFpXktq5tgjcQgNJUBscXLGTjALe1ZzQ11IlNw0hmdZQwVWPLEQTM+lfX2cNyz48eotWies3Br5j1az1EC9QISnQgljJaJYXQicBnMj8CA4WeNcvXw2ipWWNIITr7YRAA0jipRckad0fuAsClrPYhdez/oGzY3A3MA894DgOCeB8djicd2sTnUId7oay30Vts2+I8LBuDO/VaeeGm6k/++Q+Ja9uHcF5LHNS8ohbfNk5bSJkitR0CPlMN1uWyd1ES/Is8jl/QIWH+6CG6tiHLEaZ27HzHdFSEeFK+McIwG9tpIoiWIiVPuac0YN8B/VC+3VLMd4/mGreGLJ5aWQO/ajpR7OwrHPaWgX3DWPWKHPFKPwFQiGc/2cS22jVWBPHiyNYU571jzLa0703nCRcK2xznrRSxQzgd8Tls2fv5Wo7PUEsmZ2a0Z3nHWW968ultlbl359jvg+xT82vxEX45+Bx8/47BGii8uwjeyzoXlgg975LDTAzXlv6yRtXrePnVk9JzUrshUmu5dW7S5217ecanLVGlwB8cTqT6rNmnREhOAedypEmqzqjl3n2J3FP+v04BCpfbBmf/tAl6qHE2AHLJY2+nTCQlnmY/aKRb+uc7LDcYW4w7jingYt6T0J8yjsCnjwCGowhhPYhvZiV9JxptKIOq3QdZq+qIn4RW62RIkSJV4QpQJcokSJDwthZPhPGtNe5dRrKAleilhMayUx0FRLE+WZoKdGaUMni/qPawAK+9q40HBVcNPIfNupJuvbsGBxs7erDZJTLz331BIlWzOTNjcCbztwVuXii1CXSWjDzEUxTttzFShWniPCcNUEa378krxjbvjmhu/tLPRAeysIjmX8uJ6ee+nzZ8aLs4WZFJB/CLkDlpUF5mrDooa5zH7rLzw3HSnncXJ9tKLTrqUqs41CTlf4if535jJNj3phey4g62WUeOaHpALrs+dZnyZeAhSeEJzK1NwIzI3lT1PN4+1RqIVB0fKNX9rpdRtW7uGu/fDy45QKrPEb7RX2u9R+Pb/7ltXgiydbPeULKcPa/upyVfIFr7tki/Ue/6zrrwLFOOscp81EecfzlaYL3Sum1q9U7W63jSm8uzb1fgJV7DCThAHOXPvd51hfwRLhIG2FSjZEhMIWob5LFFWjj1IbV1YIn9+O8KbOzdGLtNR/gmMvzmaG5Cgf95zmnJYfsHOPO306fToRFcDH6dLQodHP2L7Pbd1yTn1DKrp9l8bMArpyTlMn/WpUiRLSLi0f5xmbIY1EGsyro7fJc+THsA2vztgv4JN0nKBldVttg+F3h79tNCZj5WB+CEW8SpQo8d9VlApwiRIlPhS88+CeH+3Ja1F4Ksd4Nds4tdyaGFtEkrhCIoljkiF4xOHcu4icIbtG5BKdLarpiLQrzc6q1rdiPWV2AqNicyZiv+DxKxb/XAkVTyxHsKQDX3KcuREYlWk8uYSiLB0PalF+SQW2nl7P/a5CJYFh5jaM6EX0n/lsXffB3iI59ZJTDy0Fjy1vJ3gu4dA1SBenS1MwyVLAqKn0bOGvo0JLyIsllxavXBH1pts1c7hs67WW9KwZSsO7uI6gFuWPLmzWr87Sxp6IbCMEEc9EYL1/pFpdUd8jjJAKZYuwUZk8SLJXss1fe3CXefVmoUAYM95reFNWonZnl6sX0jWvgpqVTlrf56VH6f6Ky3HhyY8fN2YK5JUP7XxwVptcuuP9o/hBrFeGVy9nVi9wRHWx1RIaIryX3FmtAPzBMXqNmtENMvfxdOdFnNtR6n58+67zcN6T33VvCvjcLl9bbth2UAligBVmuqWLv8HgnKZLUCWcIEFJEVaNXqK8Ydpq0F+JATmf7rH83bz89pVTnT6v6vIS4lRiGZHyZq7jS5/EjA83NxgwK1w0x5wDr0VEpUsrwotYWb/Dx+0AqB51pPI6U9uLAQ4ghNpMCgaqs9ceU2l4/WmyO0TypP/9G3tvEIgP8nkuUaLEf+dRKsAlSpT4cGCIo5suQryanodYzsaoTewSS+IIxyWa2eTSZDAviZSnBCvCzI2Uq+d568+/yo7Uuh1rRbmiQLTUwKkmKLLA1imga9vMIbykenv0AjowCy1GcVHWG2auDogdzquzXKlprfRBeIm9FdhbgbeTj+eLxz9rxJnsTh1Q+WpCtlAr7UpyFftVoDhL6wVRV+XnUWuV4fV3YlfWcGB3MyyUR1pVIYs4Yms086E9ZbtLHRjQ7FJHkSgX+J7K+iHMJrmpsrb70nCqScFWjYNk50F863y6u3ztdZnea6+xpEdeyVEUKrDjsVV4SF6EpF3W6r1qry+GSx/LxSanlpgb/gtKsnM0zf5d+8HzHvkPoNdyqbTVFcfkjYo+WGOsWx/j/Al3//hS24cxvo0KGe7YIVISqRmrPGZ3gqtvqGr0rrhgw+w3RLT0O3DpTVy17eIhx+ONYdZMlOzTGqpOpLwIGeFFrKZyaHH4kP1D9qO8RO9zoIO9+7QWPnDLyc8+7iG7PRrDXXu4VwGowAZcK7Df69DJ345k62D3JcAOsqiF33qRXpQoUaLEe0CpAJcoUeJDgcKILIdEIBhTqT06OH/ldYfYIVKa6CokUYwjlHBF2KB/wm44s2lpGIMYOVLuFTLg+gIzinhiwsV6SM95K7dDIJvYwYkEnIl1SZzh4njhJfYWL6ADF49FBZZYI3u+yKEzLN+alpRfdDlTQcGCeKhcaYbx2ERNUQrLSUdGkitLKs8BXkt9C9izzmwRFDeOs4YrIldEq/WQRPHg/DYSf/X/RHZlmivDl392o7QhlVsxByAi3LpiJFr+LJTAF/mDZBG6huyGR7+TbV44obJv5B+x8KbV17rJk0Y8seKJbQWm8NJZ4/wfXd941Z6t0L3z6V7D7NtGCJxPd6UR5YRHgUDrflKEJ1lzrbqrAnvy8OKz9DzltNhqfcOr9n2rWPNYrqX5K5vExT1R5mFQFEU/aO4OME7ba4uNLWF5mSzMZG327fq+cDL+BCcbcL7aydOEag1vkB42HUkYkVe3mqfFagcswBaBFNIWwSRtO8aiUFOcyeFqee1LeG1gX261veaQzLeHFVeq6UQEE1xozh/ATQZugazqdUldjs5nRycMazY7TxXW0MLvvHBdXgZshgDZmL1w8SWhR2yHyg4zgA50ABLhTFtGIvPxTaQIqYfIZ9ThY7flieUenU82Qb399ODWxt4lt1yiRIkS7w0lAS5RosSHAyGOaRmVZCIMhRM0dzYYBMJyCOdkzSHYIjjgBoBCiijAqRD7OLGSZ1lDhdY8mDk8ldVOrg8XzKFY4RJritzkTdQlnDh/bXiJ/6ieb58Vg501KQRaX2Au3nbwPE+s5YF5H+x3BWrNayGT6u3R5GE9ftQoFkx6LsLMJcs0b3EaUyEF6Lop2JXpc9kvcFEkb5snSZ6Cu8zolrjYfAlDXTQMW8i/K5+y0vHRaqzcudeujzOiFSl3ha/OHcWnp8knb8kvL/Wj+IHmb+0bw/jNjtwOhDddCfYdZu5Q7aANsQSTrA06dZMkj/wUwMF0p2FEUoQzJtN2RAjEmdS8a6hJwvJzqwI7OfUWlOZbsbm6quGHyYQLp18T/3xpT3I0jChUbpi6TZOrayB9i9A20Vdx4GX2u20OCpn5qivFUee1G/U39+H+xWPHAJL4GVuaLl6UWONMDlVrmLnbECoZpnLDCiZpO8wX+16I9euUh2KPnw+F9pmvGb35twg4E00Kym2VyCN0c8Yb+Oja7M0KUQeALq1OIdd3Dpewipxz4L5qeSJs0AuQ0NnnpE8HiZRhXS5+nTTjlYwATX0DnIjaA25vML4nOx/hy/9Yfl75IVZZC7hEiRIfGEoCXKJEiQ8LLYsHZxWxPYieWY3m10/ZD7BAtHIn0JxY3uDJE3VDF0aCOkohqBj9mnJHyiryH6V1ucugVNr1Fq7C6+jxJQeCYFqsUaRYrTezRJkVoCmx8BJ864Wu8uFDeMlVZYovgfJNyO8g9qVbm86n1MbFAstrwjLXIFHycLp5lT9WztNUFph2ZZr4+oN+D2OowBbRfOAj3JXdC6lWcTRkg+HsuJxIV26NVg5BUH2tt/R++fW2OdC20sBJ2twGR4QH0x0QixTu2XENsz9MWydqHddaeqqt1cD4S5+m90iDuaz5c3Z/K3BqU3PzPTPYprmaEPsdwDr+eZI2Zy5oCjhlo06P34XvZrV1HwJQhDj7nD5jC5SLv8lAxwbbIngQ324Y0TBzGyIMM3eocwdezPx56Xq5PdiL31reuKBmz56l5Wdqgqsl4ioRtFr055qwP9O05y+KeHphY4B08SI8j6MH3G7Ty4ig6sklo/4eLcnEwH0288jSqvK/4uOv8KTKRohbr3znf11LlCjx3yaUBLhEiRIfElQUyXhijYa1FlQdusI1ETFuH+ERSUI97YrwdGJw8eC5zW/xhFlgm1qaW6cKpl2PucXR1Yz0oiCsxPMOvLB9RoPNjcCZWPHE+v8HDvxe0RDRIHVBaQcsTIs0gJzIZb5ltsnnymuprwJIlLvsgJVjyfB5LRSGnKLAn9luLcmuK6r70sdd4OS5Pr/J8YhWjzqoQrhpaIswaAwzQy+dCMCpTu0b4/nbq3s4u6YSiJO0Cc2GEUkRNUR0kjavWyGwbfa1mVlDRNIIw0xur5aZvezMavVmL9z9pX16QTxH931hUv2h4tuwgjQbh5qxTgS+Sn1VyaxoWU+J73u1nb11vGNe2lqqfN2nQnTE7rwu7iRrgRpmDqgXYryXM1yzOuXFyiDpkwQnXm0zJFNDw2W6J43QzYMjgnGmlV3yLbPiTFrLncxorY6Unhs3rGC4MHHOYYtggjthZ5PBOc0KMkBGVF0mQItBiAzzQGu3QuTjRlQC5P+PvT+LlWVZ08OwL3KMzKzKqlq1hj3vfe65fc693fdeqi+7OUikKEhsCtZEQILkF8OAYEEPfuGLYQh+8ItfaAM0YAMmYAgUYMGQAZuGDZCwoRZJm6NFNtlsNrvv1Peec/a811hzZuT4+yEys3KsqrX2XrShzg/rrJ2VGREZmRG1Tnzx/YN8XR5MGVKLC/NX9Ku/dZutsR49evTYi54A9+jR456grgapsYnd6wsTUaANXKw1qAYEg1Je2gXgvAgEneMqcR9qlys4wHZxLC65c1RdgRX8xNfSFhJVK9nkVK0ggOlPl/Vsw7vX6HIBfcBCLXw1NJ4t72O9L2N3JRvdeLYswk3vxpJMkkyeUgBQtbQQhAFtmgeU7ma/AK6S4UMWFBw4qidz2gXG45L3MsmI2YoVH8KI3sXHJ+rKVhYAmdIcumRWDGLSHRcAW6dXZ+Ioubp5fRxudAAy2RWzk0qt5kHtofOrjjZDMkGebOkiGZ2qC3k5M3VOTBSJdrpmhWyQaq2XruKTstZdTPhTasLk6czeP/d2deET9KKtzfzs1qqZdpXLrjAEKddVH4Q51JunT44f/kT+ZSojtUEeCPChzzBCrmQWVsGOMj8FRMpr7LfycZ+sS76Welmcgp3lWioyJwVhCXOZmMCo8YpHyLORD5RZ7eIVRlIWtiBcZFYSS0zksd+I/1yGtAOX6ZcATLF4h4eyivQu/gVGAayCXcuA0j7413j+IrhwPftI+2i3kR49evQooSfAPXr0uB+w9IjFrwETCQiX+AxgJsIQ3EKITPgVASwTIkRpPcrgJSMA75PT7an8cnJjKVZUClAEANGbQbjRGjSDcgZ7iLrUUWZvVaryw5J1dBeMp4UtrsSnYcLk6+rUD1+5qKTSOaDiRgWSzBEXRdcIQOprKo+ri+lWPoxGGTZVVtfp/vBgyYyrk1r+pKIbVddrlLYw8n9tZSE/ztgwywlMAIPORERW4QZspOkcKlcyn2HFjnNP7B00t36ivIUSpVyQiZKQmB20Dj3t4sDMjqxTP/GKsEYtNFiG+E42ejXzzV2n0B62+1FMNFxrgGU8KxHgfQ1IG+OIrIoV9G0sfG8DWqdHQDlSV8ed8tPL1ORqFjj6PYaPx78XABdv68W9nwFfZsc+jDmOkCU2H+X+8ECN7nbfvKU7vsasuIX97mkhu5x6muqE2Ylik6gxNnIaS8NvrogaE5YG0uVoz1cYOQg26BS0JXMGcIyFi5sljiQfNuEHsGZAAF5usPJEzF3j+Jj7EIDSi8A9evT4ZOgJcI8ePe4LXy1suV43EMLRH7EPPht6GHOEF5g+w1uABWSO2DwjwKX12LG6vEpKrmJbEdi0n9XFJcUuGwRWV3Vl69maCFxD6YrhRCgR6PaWS1CdqGKRuIPwFC0V+ETruujSMp5F+okfXVoHRCHaIslCQOedKfUt9TR1XC3dsdpep8OxmvFYjQURmQA1gmOx2r9oywOs23GJEjffeZ0DN+ElIzBcJaNjdamrwkb4FNCwuaRQm/rasUh9PY/F3dB8Kyfa77EkcxmfAThVly18pt0AYRcHVuxYceJwPaw1YQziYigVO1bsGLDq2V+zKrjjTNo/FT9amd3XgHQAjsiqc+BPiW0nBsoN6GgJvkf4BVxFAOAsMxiW+Ce/ezL5Wx/w3XqNZQp8CW+kznM6RwABDoINQ0T8TdTiOrun076WenqSeVi0Pk1376tINpp6EgIgGSawMQq14HxLMpeJKZnw1kO7bfBr7Dcia5GMi48jZS7Nqq8wyolule7mXZZuCzrz1+kEgEh5pM5DWOP5mx2P2qNHjx53QE+Ae/TocS9gLBlAWTmxpYpvvbqMHXKQ+oCBAABHeInTMVYAlhiPMJtjEOQ6sEjNdbaoovz3duWV+pq6DdKbGc1W0EJC2nhsk5PkV5QdyYebd/P1ZKMbThzWODBa9MCWvMEFS2T4GDYsJd/U01Qn8s8tYxCrjnZQTmBK895CtxJFiSGtoBvlOpfdhBUZjmLqGfVFRGaQmgMWrituwDlPK/E1xmMSlf8TRZ6qFLJwjYU23n+h8ZZH01YWYDgGk/qwB0O8CCzcrPClDBKevxZWpbvbXjUfUG6jEFi5O7nqW3tL9YjW+ZUWDtyd+ogBUJ1IdlWSFmZFqqNZTgSgLfD4R9DgcgPY3cxhhXZY4Ddm0SIZc8ZlfOZPih00kfayX8jU3ABXRZD7AAN4/my1/C7+yK8g/bv4phTlzQLoPezzZHy2mGEs4APuO2RmLOFt/AKArU9HRe/t7PJ+HTlca+o1B1P8c6t13IxBrDqxYkctTDg+y2hwhw8wgHXanl9qmW5pvwypLZ3k5e8lmVL/b3WK5sR/Mhp9+TKaj5Xf/uar3/jOD/Y+Zo8ePXocgkMzR/bo0aPHbeHLNY0TQsWT5U+X4Fd0vMQwAhdkCBjnmM4xmNMgIAsEAWOFsUdGsZ6LryuRmQtEb4bka5k1rq+JSykg7zDMpfqJ7CO1FS4IEqjJ+ho3YVbUmXaICNTNG5udleV3r847wKwoubb8c8s/twCEa80/t6g1KVQNMgdSTv/SEvutRIEmAEhmHMCWsso+gwCsE9dLXeTsd03Guj0IVv6A+SPGNzu5wU4WE5XWzQ9wWbssr9oI7XfZkl892rEjQPmv9llR/py/WGov0rlT0HLBOm3tUmUOMCuSfFKd+vKnZSelUvGjDYip+rO/0BaqE1Xz9OzBMjUvktFV/OAO3XYgHNRM6Pe/gYE6a49Stq23PbpIRhfJqAirZqrjr/4mjjwo36nUE8dgAjBhUQiCIKvcF6MtSlwHCCA53MlG736gg560jGSjyT8OrZXCteaf8+jSIr9FGlmSuUjG63Qi5dkmxAEM/yIZLcks/5Ytt7Jfl4mBMnMxCx+wiRpDb92V69GjR4+7oCfAPXr0uC/8a1gAMM8dYWHDXYIrYAgYAcyA5TbPAIAFXAAc4bDIMElAm30sgCzYVZPcblFlIFQtVmc4LRXJ6zI4JFcJmsvH5LrVMLWjP3tBJTJMTUPiFsjwV/45rxGX6JKHr3Y64jIWLvOVJVOijRoVz06lNyxfi9CCazOZ8fjaLHH1DGsygtT8EB9fJ/tdf8tPqljxvmek6mFpPPLDCVYg2MgkuZv4VKSmTFnEEbyaEQCeKgDUI1FplajyseO2lVOSv5c5cLNY9xQrY0egMmMQK7tDSe3hPp+CBtca20W4tpf9c+sOt16mPCKrcac9P3nanttQQQKojbDt3GoJ0uxPVuB4z//Cn3j5Cl/9bqXI4Ab0AAQEcD0YPkwATp6qOiR+qi46YoO3vVxCsta3qaFbSt4C7dsljTasM994tupKVC5pqoyh3cRInef5ovZDlnRZIH9O1YXb2CAYqXMAPszxRtX05BPO5R49evToTaB79OhxPyDl7xybeMPGSACCBq9keLekoQJm5tKND4MBAsZVZjEIgACW+pU8wPKXpJraNCuV+rphR6Gn55VaO9NtrdlqCF2qkpm05h8585fdEV+6UTCiO63jtvyQAQTG8jPSjBjJRks2LbQzW0C/GkrfYOPZql5CkliZA0lRsljQJYJZp6aE4MoE0KoslyXfqbK6ToY7DXIJyMTkLBVwPhAKj+oFO3yx9XzNPcMQGMoLXjoaKssIpkhNnXGwFVTFRFAf2h39qkTb2s69AvUox61Ntt+nbiOdiXKoF1aduJWKkK+n5Q2a0kToeJi21j8S5UnRaNgYRKoTNaZ6ZwdcRfCDudPdkXdHOqluVccWZbV+IiOuBIDeL75nGm+H3wN+s1JhmYLeAgLJSFioZMCOyBLSmloR7X8Bmnrsq2FYiWvwUQg3GgjhSutSPYxB3DBjqY/XqboYKF1pxpnOvGPN5+kE1RRTktlyJTCr3t0jMGlVLlNMmczi+VtC7hbuILAoiNXVGNGnnb89evT4Q46eAPfo0eNewFjyJ8/T/9tGE2AplEGwLi7NaTDBxoAIYbEsUGqZb2wbCTdls7cKD/FeDfhJoB6J1NPCjdaIa3qrENBlEMDiK0t/uqqcyo8vktG2JwD5WnS1W/79tJB3pepHgLCjDzIwb7jWjObaXmhbkZkpNaJeC4LFzNh6skar0XK1ZVMJbGUhDaErF5qhd3is8kThWuRpcjNCt2PG44YnbTsJjshECl0R+fiTzSIPWKXuUFlCgc6ESQJpmiDe1ivvqtQfo3u25LUILLmyVDtidry9QCChsbLRuCztaw0eW+HAzmer1NP8c6t282SjKZbG7KjZJf/css58rA+nwSiN0KdmEm3KeZvzeSuLY6fqPBMV79qvYyzqYYR33BBA2eN0N/sluEqwJNNkvs4y7pogUf/rf4hv1yu8CXED2ACAOUYeTBnuuCi1JBPprcjsR4ueexsggMFwYv24a7yyUTlVF+au+GTZ1pJkyAPlppSIe08YAllAZ35AllR9ixhaMrxWdIL5ql+s9ujR41OiN4Hu0aPH/YDw9xS5GoRNoZbaY2xFoTmGl3SywLDsIduxFu1cxIlLTr7Waibd2aftv6Vm2+xvCx/jbYlKqexDdGnlZooHrVbbFpr/IiA7Gb4aJtdVK9Ns4UoAA2MGD8sGlpGnSadfIHtpjMeMx/qj7XZGhz0mvY4et/gAtxUmocY3W1E98jQSWpOKNGsOWVA7aSMESGeBVBSvEjciMwA//q2/b5JfpAVu6VXXQcdnNKLmAkg3NV2cQEg3enLVFrAqfxUVK+j8PoYTq4XIXJ2fzIqcz1aKHRuDZkC1ruFoLfTR/KoD4Voj7wDn86wzWKeTqOox2/LTDR/8wCoRWetkkt22pUxzymVxsMpXXzxIowR01KwAHgBAgIHIqe8GZkQ8TPkyNV0WlFrbhX0uFTux6w20nDKc2Hi2ZFbU8ZLJZeKp9mGg3NwqQLfOfPmzu1jBkyOyLpLR6/jB6/gBgNfxmbx0iUc+9PH92wf06NHjDxX6TbUePXrcCxilE49dAgIsgTEbjhbQS8bG5MNgYALHE6xdzEHwYZgsWJC7Ts1coJNlURF4S0JZ6mupr1UKlQuXReB2BbH9o5RM9aernYog1Q935FgqXosV71EZd4TPbcP6x0f1ANTdCDcaNhouOIDBd28ApJ4OCjI7cFUJ1wxIylVST1XHaOcTbTsCErkbcPkkq5fNT/hvBqVLpbezY4xIvm2ZB5iBMGGrAKYHAwQZF3qVuAMWAJhgydYYBbZmdgUiah2S5ozKp5KvKXaUXFtgyKPmEsBaY26rU598rV0Hzt+Cf94S6Vex4y0tISoHCU+9fcO9XxCu3uxTy8Kbr4fOZ6u6oXgbpLnsqbpPKuzmwDuS0HbBVQK0kduO25GMAl2c++b38WuNgMRngBPDB0xac+bZMC7wAIDOBBhchR/Ifm+H/ZsY5T9T21E2BrHcFFOdaPs3p/E9OFUXXU6/nwQy/VUu+WZdlfr86/gMgKMHE3hz6IhbI+r16NGjx13QE+AePXrcCxhjcCL7bMV/TApizYM4UkG4wfExWhzJ5NpHwIzyZWKyOzgwACD1tFBGLa6R5FZQjaPuOg7XmnJtlShNPQVOe/uoEJUmwldD49mqlXeQr2++LvnxMgBwXqxkeOcmuQpfuZL3fpwBtnQAltmfVCQl9ksEIPI0fRc9aF+AD1hosuA6LbslNzhZx8vUt6ypmYm54rC9InOQpUFiBgkweMhWyRGZazIBcDLfs+MnQ2jmjZNq9ZaLPuzfu9gi9TTFjglIN5pS7m1znwUEsK6oQtkTgeVbGNua4VpTHU2ticMse4fJRgvX2v4e1wbnIAPpPeUOh0xMfUgHnmrn95b+twKd+brqR2QtU1NyYACtwm/t80CZFaf1uf7r/7F29CquFdwAZEKj1IcuyPJgcogNzE06RoVs74c69Y1m7t9dndx/Acjev3XmA0g2uswyXf/DUp3AMgI2gEo24E+Huj92uRsAGM7TB0uMluEAQR8FukePHp8MvQl0jx497gvs2E993QQNYuVmOJJLGhMhASG25FaQEeSGcD622/zNHDzZMQCC/XQNUHWNSLV/S8edAqb9fIWOIqlsvCb07pVcskI7ylVbpHInqfIUhM3Xw/WPjpKSbS35+vrHR+sfH32k4/E2iDEVOZCUXSGRy32sHjUxUDryQjWfsQHFShhvHfpGS6VrS4wEzCIKtM6Ch9olCDoLTASzl/Cy/9/VGqT6Qecb2J5X7BiAOvUV6a5clPDrE6ZWsQvGs5V12hEeqdYSAUT6se98tnI+WxqDZqypvdUPL0eH17ldB6o//2LYbwEZz3mZmi2P1jF25T8/5yPlUhzTy5aW6Q3WqGcJcpT5RTLqiP/ciZZvd/to3GaYCAAUO1bs2Hi2NJ4t2/OEtzWzJDMgGaYbV/HDwnT5I6EzX4aP3kaQLj8HAUDMDdGWJ6lHjx497oxeAe7Ro8e9IAWTfoACbLh8c2SMZlABGBREMMtirQ9zhFVxIvuXdiutLL5ui8PUDIW1FeWK44rG4b0cNuvL43CTi8ClFko63FaDbekJbhmRiKqr7ApYuNbCHx21NPMRWp0U6JKNDnjyjDGIKIijNatxSxIa43EHb2w/oTMRkblTVszfNkG3osiv6pmVIarqwCUReMjCdeIOVOgskNrvEVYAPDJ0JiLiz/TXXjpKFEv5j3+pvLdyMLadrISAtiIA0SsXQOgNAahODMC/sJwXqzYr9/Z2SlehTn0L8C/krGaQOWDRGk2q5Dac8ZD9E65xw2ajt6qzp7QxiPUTXwasbmdZOW7LDO8OAkDr9CiPUbyb+gKgU3UhUu6q83KZNJgqE3v9DPh5pfQ7YH0Kky8suPKMyPIz4VRdlAMj7+iePAxfux1d6uzrrcCszOxZpk9rGaC2iSpSfkEjyVQXyZgr/JNYR+vMH0lxNxk3dXKTid/C55wFvQLco0ePT4ieAPfo0eNeoICYHSlqBOACR88oTpFEME0EG4wVMBAzs11/WsBlcAHYFFbWYmVOW7WGzSM/y/ON9Vr5xE4iaj9fbzlwjXeBiQtu2xGz4tyHc0ugq3lKWtveYw6d3wUAI1/bfNOVPrebR96FyWwrJtdWuC60ZRZ6Jnc20apCFHU7Lsq33LKtAwMWRsR1FkxVbHMCt3A0IqFnCmr1zWeUuzoWzRuZLMjTIBHAbArB4GLp4Ri5J7Ag8xqD72Lc9haqbe84qBUlJDelMEUM2OjOi1XGftu7vIsDk69Hl1atcLjWnJNbSaONXZVb4Xa191NuydL3mpkJ4oN9ZT4KJRE3Iusicfdu3xSnHGXmKPUywsTrv/zVn/j36hVc4GqNBwuIkXMNN4DFEQB4Fb/o6FUnj1XsCC0ZgD8eBCC6tAALQLjOvpsWMqMGiS5/YGmoXJgrLxNTpPxYe//x3ZImACMVIxVBygUVEn3R6x49evT4lOgJcI8ePe4FRCBPhwr2YG19EFcaBTB0BNc4FeA2xWBKBG4iMBD4MGyEIbiHIlMPxde8vgpr0Xirn5pJfRvMWZ7kJyL19HCjNfPc1gp73wz5qRCXnJ8KGdAIQCLr7qUBcpm7jwaTr+0Pa1Rq8YCb7kfOr0omwYxR9W3odqw/Wrez3+67rMlYx8e7+raNYaaqPI7ks+djp05EG0csy/OVUdYhAEywNBHIzNI2Qg9GQTjfxyfKy/k/wp97k046Wtx1s1biWtjnG06snfgAK4Uu6wji1c2BmRWpjuZf1DgwDh3Llv7jjjS49Z6dLXV1j4VrTb221Km/N6LbMjVHqaUz/5PF4WozVYjICoh3F2trghCRpTO/WdL4T75I/quf1U46wLGKYDQC8AjnAtY/wfcBcOYL4q2h5rs6rk59Z+qTr3mdm2J3R1in1ttM1MYgDjeajDuQdWjnoCzJRPyQl4Krm0zc2aBdVlzQWCZD4oybitCZf0MuIu8uBhw9evTo0YGeAPfo0eNewFjKrAgh6MPAgcq9ECNEMIZYMVJ8cAYGMIAZCCC9f+V6K18GKnYMT+skve3MtuNqg9GIXHBLK/laGrcAAyAuOQBxYRkbLSynujlwyZ7R4EJVrKNb+93TaBtuQSMy1k0pwMCANFV4jFJM3Wij6iUBbc+d65f3UXQmuW75fPkt1QJKVT4MWbAiE8hkXhvhEItKAyUMWPD8d/7gd2sd2EbA6hR86+cKAutr8RUHYDix/mzVKHkAtW4UKUtwh0M/8YGulDkfJwi3tnRoewQg2Wjq9CPuclts/3S0NKQzPyCe2SEfMoEJAJbJGDR2G8GfztRvvo7rdcYGnG9h5q590gSsa+baFGxgEjBUFsv0tO02u5AemkrqkyFca2CILi39BAfGol+SuUxMAC4LuHJ39lugkJR1NWvKQnA0FEg/suEePXr02KIPgtWjR497QZoqAJgV8wer9ws11e0IPIQZwuQIgTxgbykRcIADbN46Lx2yfC7UZdjPVgAMJxaXNXGmLepSvrCuMw0qTBlpfweIulQg6/QTBgGifT/bYv45BxEoW1oag6TaEpnTwsC41Paem3b1pK1SA7rVIBZtx9J4PkjNSMp6VXruYjHByiu8LgnrFYbIRK0sVFXrXZoHbUVTT1fs2H6+0vaaKLe0Q92X6oguLeml2YXU05qCXtsdD5ifh2PfwCL3Ad5b9FRdPNU+6Mw7qNHdXaE9dQfK7FRZbIMtdba2bcNV5g32SyRU9Ufh58/aGrCgBySQzckRMvfmkMyu72FnV3xNXOwPg//pQQg32uab4frHR+RvA8DnMfM6sSTzIhl9Fb2QIbI+SZSsRzj/Fr65gmtRVwKzHj169LgLegLco0eP+8IYMYAFFGWkhYGWIiXCAoMZBhZClsV8LtmL5v9UV4ctIaBROVE9uSWZVG6z1mhLDK3qPZtHnWduxSw+HQ25K7bLb8ORbDOXyhlLfZlTKuPqilUKMtzO5UqcYTfjbiz7SWjJjNfoqGLFpVHrfNVBaoIwUBZSAfZgbOOKEwwIE0LGwVr5zprMWf4ch+TWajxty5MrdsSsiOXvJ7m24muLPD3nq7SzduUS+Xr4yq36AGcI15p/zpOrTi6RhQc/lDN+Uhpca7U24gTGo+aYl39cJgbKTYdmuGM+3YEnZxioM8525IKut6ozX2d+RHxTJKolxKb/+N/7pQ9epaYLPFMAE5pIp3ko8gVGABxlftt+AmBWbDhx/g39FLj9yG++GYav3OTKSq6sg300sCTzdXT2OjpbJxP5c+sb55jT+Cu8AHCGzZ0b6dGjR48mehPoHj163AsYaA4NgIBqQruxhybCDxjIEMM+DAuhgAkgBDcoIAYTAZAZAyc3vMP8lUrGsXQ7804qIlhJY7+SoluxnW1rdvettv3B7i6pTlwrlVxbVefPf3EIN1oh/wIs9Awz1+Bl7Kv4hld9gMto2VZoR40CZQ/OGI/ha4zHxevTW82AqcW0eEUGQDIDcLmYhZAjmGMIaR3NGOPRsbqSSZlIaOKS89MgveaKnahT0RjWfUOfTSF4L4fb7YM8aY0xiAEkG1c/8ZkVha+yaMCqE6vHfnuLBGZF+oksprXOBP+CW4BiR9GVpTqxOvWTa0ux47r8u3/2lcvhwNJ3Ae38mKPsO/oxOMbCB9+gHkC4hoisIsBSA52TOSKuM6GrothQu4bzL2vLzUPgXb2S/DKZeVj1ERYbnEZk7Q8B3eyLr+lPV8m1hTtkO+v6Vh7yba3OiHCjYaNZp36y0eVui5zbh9y9eOqLZOQqt08jTPBgTrH4/4ddwx49evx3DD0B7tGjx31h7LM3AwCwABf+azzaXsKKERNkCmYMsZDLrgAycQ6R0MQVB1rcL7cfijNNglR2Jc08gRsU+hYomt3p5LmlH4e1ny/qFCtud139Fw5+tKENcu03VqzYfzPQMo4qcTDp3YF8s4CEHlybuq9KVqnbif5oLa8NEA7UJQAwtk7cNRnyjUrX32N1dZUOj9VVudUl3GMIH0Y53ZG0gj7BBsD38Oad98W5NBveaAaYOqUafz6o+54ubVPDTZa6iZ/6BhBuNOlCCTDAKlxzDScuZdKqv4XskBBd8WxWo6UnyUaTGZIMINkMw7VuDOJ24+didG4RzPkWFT4VXCUYqLNP0tQh7BfAIhmjHFt4i/Zc0y4LNulEpCYAV9mSNwdxFM7MD3CBItv1ElAVsJ8Dv44UgYPoHSypAG9v42sA2A4j/xJkMcWODEfbFW3+Xwg3rO7LWKqjAVDsuMKEd/ZkmZqccSgH53wmTLEwIUz4NoIzpvQsuEePHp8QvQl0jx497gtjKwDAkbgLEa0NjcJirTmngYHgDFc5XSEAQUFdiAAYdjX37K4FUEHMbrNKqpWvCZWtLW0tJDssMKlotr0nyUYLXw3lUph8LXw1jC6lxyDtcBK+T1Drp9TX4hsOULo1USb5WMn8o10TCSBIc+s8BPT22oCFA3WpMyF/BurygXY1VVYP1auJdvFQu9RZ8FC9KnfYRuhuyQhAMCBzIGWziwGnwesJEsOJ2+hE2zSg9gJVW1DiJ7469fVnS/vFlpCHmy1pCTfa+scT8rXGjNh+ZlakH/vhWm1c2TaYHayl6kvbwl24taXwxxoYVzq81goBvBXL1PwqfPFJPEX3sN/8aTgTDfZLnd90AMBF7C5Tc5maYUk33kCLPjtKr7GsviNlDPZHsR6NfOgATPgyDRIAlwUgMB5n+bQPfcfErKhluu6p3hzHgwf0gD2QcK3551ayuXWALkF8kYwPGnECABNigrmNwIOZsD4EVo8ePT4legW4R48e9wQaMwIwh64AjwEPeb5PAoBzHD/A9RldFdGaPbmQJTAr1u1YnQp4WkeE3hb9t/M0lYTZXUJfI6x0l+S7Wwqu3BHNAqoTSYfPdmGHGrXuTZkznBhpEs5p6wYMANDtWJsI/40DmalogjJPCK5NSy7lW7pdw7ZZElrqa+Wwz2ktGJUMh8ZkJhVRnCyONSaQhX2uBwP3mOGirqoCOFIv1vHTSwx+5U9r0+Dd1F4oNLKnwns93PKBzimxTx8myMxYAKJXbjaahJas1NtA3wwMzouV/CgPBt+dVRotzA2wd+gPKrQteFjZj68GwBjExrNl66VTdTFQZ+tkcpGMXkdnT/Xzjw8dXEfbZDSZwFaS3cV78ybIVYI2xRjw55dppYFfNjD9AbwXXDHWnNyNXFwxAAhTs9mIywJXXSzTEeqidL1b/MQXu1wkPt2W2W0GOVxr4Xo4+OWbw6vszRgsuS4INoIx5ia2s2LTL1Z79OjxSdH/TenRo8e9gKDA1+EggDKxLaZvVzNjrBiUOdwl3GNcttAMQuRpqq3Vm+wiqC2so4Mh1wrXL7YWbrt0CAdGu0W0f2FZp/4+v9+yJplXvwcmHK61bQKZnLRHnqblkWsiT9N8tUJ3K7L87vV3nWYkM15wYG0iIm/QrJH93u5fEBiTNLhsKi7T/xb4gOMJVkNalIfdS0cATrCOYIexk9qqavoynm240ZRrS52WGmlOGbln4mveS9d+vmR2NUq2zI91ye3nFWPsLg6cv5DteUmD1z+eGI6MmdxSHDiQBu8vVyl7WPG2anepX0bhGnqqLgbKrKVXB/L6g6nfVfywxL72Ut98VjOAIKNGi5SL9CFnwlCyfMWLsFKDHQMh9Dj1oc4xIgAgQQZAjjJD0hTD6U18csiTqFMfF43Iap8cdxrM9Y+OAFhnvkyLzeyoq4+n6qJ5coqFD25ByN8mxJgAYIK53Aw9wnxC83d4cNZnAe7Ro8cnRW8C3aNHj3uBgvS1LYPBsBTmuTHWEQIgYI7BAAsLoSlNBLcGetnqKZlxUE6taisqajlqR5mbtbfVKLan/cNLlq7Q9h8A4UazTn116jsvVp212luhO5g07ockvdVdAH1nkBvrySa+MbsMtkloJFq2VpkZpb4aXJvJjMti/lun/AzaUWYvaitLoEaz6zepf2wdWMBWFiB8izaMwaANhE6+VgQAF5ecfLVUfHuw+ckkubbKJ72X7ubHE/I1cckr6jHgvRy25MfqHB6KLvnguzfl3Fcy8UypctdM241bzomPnUW75mKX/FtgoMwy9ttstbX5j5j8U+09ARrzT5X54RU5E646X5Ipf1x17qizMOUOxfp1+vRRpbBM8qt9CK1FYiEWMJfI9pDKRr97kwm19+TU/0TfdkIeqm0L9lE7a85nK3XqMzvawX4B1Py9H+H8Ec5dzM7wvvhtwjfhTzC3IKZYAHiDszHmPvhX+P9NmMAePXr8dxW9AtyjR4/7QkIcDDbSUHd0ZZOyIcAYAxhbY8RZMIdrstBkQW0Rpk4Erngg42BV1UAAJR2YSmIdk+F5c/koj4aF4jwDUDqmajGU4maVjutW0w0dWILtVNaq9Ns/5zjnea3bhuPac6K9A22npeoYLu2sneo7LmA9XjetnSNPw/uB/nBdPp3MeXDd7o1pPd4UjQTXpj0RjMe6HUe5P6397YZAxDqOi8+s9JE1SubnBkowZJSq3ihdKUwwnmhTEXp6XpgBSK4tccWdL+eyavRmWG6OWTE/EeLSAuC9HFZus2v0Oq+Faz388dGOmh/BSG5f8Y632lMtfOW2cmCpBEoRuE6AP9LAoZt9MeCr6EXpLgeIwIAg3ozebChiwzT9JkxLxr8uYNyAacAGMVcASO/fDcyIrJBKX4o87fmydNJlQeljxybdHaMDsP1vtb3hg29Hu17mqboQKeeKiFKrZuhuwg9gle2cJSwIC+IGYxsBgAiuB3OKuvFFjx49enwMegW4R48e94UxUgA6kgG8J1hYpTBFAQyDgjNchTAJsBBaVLEprGTEaQqAZQmoeVArvOtyx9rtcB24OEftH3aBJPf+JNpOszdtQmLlh5gVGYMoXyWzorhiFctN8t86W0U3X4jrdgRQWen1fj4KrsyuO/tvHe8XIyWPf+v9fARAm0jJl+zP53u4/Z6ho5aTpXMrsMXgjyY3s7EV1NryXg0AqFPf+XIGILm2Nj+ZlOTcrLA69asZWSsKsP181ZavdcewHuSAWnwwBvHguzfln+70sLeXR++oA99RkL1IRhfJSNLgdTqpXNsr+d5eEF6nk6+iF1v2m4WaO6irrd6/OhNr0qLAGB6hbNZ8mQDHiD83VqY7wwCAi5mDzHy66GQ9BLS8UcZ+2x6DAIJ69Cl9pPUT3/ls5Xy2ss62zcpjYxDnJw/VhTdfD3dcvUhGXBEDZVZjv+9wdo6HrVV88BuM5fER5kN8sPNYYj169OjxqdArwD169LgXMBA8Xca9erWg0UDzR26x9PdhuICOwKAAjPkwaustbSqiN6UIWHSAlIGSkln2Fq3Jm13t7MhFVK/V0coOrXh3n8ty9CfGzsV+7SIDSG490JaZU7WTQCo0bRLUvILNaVCXf7fyePZv6qu6HSlWElxz7+cj3Y7tzxcZi6ayHt58daUzlZIZHtAlGIzqQplyte8rOF/Mf+x4kSVQ8SVsGyJ+IlJfDzea9O91vjMHQ3JthRu1s2uA/nQZ/uSofn6Xwn/IlKbBd2etZYxnS/kcYRF8q9k+cDtRtTkZbgfa+XGLwh203QT606G0Q3IoPydPSz1NBjartbVMRlPVdxBESIYKalGJjRPAxno8kTmoBcwA1gYmZPTpnXGqXSbKgnC163n77dHL9z9QdZplRzJ9kWpFg6kPgHydWZGTpzUaTH3y9dTTko0uXdNTT/PPW2K/O5+tWim9hJR/L5KRyUQzzpkH0wIHUBOBpffvFIsxbo6wshDNMFpR+sWn3yjs0aPHH170CnCPHj3uBYwYrmwSegRVdVUyUCjAFsIneLst2iXgUOeHeh1qFkCnD3C5cGtFeaFVYOzsT2u/bqvF0f0IwrvBdCcp053I02R8Zt2KJR9OfbXWq9TXktlW1wKgjgW6kBeLPC3ytKJk5GnROyf1tS0HrtSpnqG2fwkWwglWC7gB+Cs8CcGbVUAIB4/C2sniYikYtWJHih0VM8dw4uj1cPOTiTxuF4EB76XMa9Um3+0aTdo9QTIv8bvOCMOJDSfKfzpZyo7e3V7fLd366apZy2XBZ/o3xcevok+TBqlAS5cPNh5OriwQdbFfVxGuMgewgRmCX0zG16Xrj3UcPQSGIIBT6JMBQkBcdqJpR13r7FQ7d1lwqixltqTWt63Ytx/BgyF5bzmpL7MiderrJz6zImZFih0bg7jmPGwM4h3sF4DBBIBTdbEjyneN/doIplh8jq/HuAlgc/gcYVfdHj169LgzegW4R48e9wJimDspsyKHkQIVQFTIIASg0Ou21MtC6NeUkIr011BoC42vPf7zjjOlkxV9uHyLqnjS0DN3iWyVKwdLwdu6VLin3isk94u8jLylgcEA3Y4VS2Y5MgFokzqzVXjsv3HMqQBAQgUQz0zFSvZriJK0Ck23o8jTABZ5GjxNt1X9UQxk7z8ivk2DVK6cD0d5lHwYHAFHMCMXDAFMg0Ttzmfwlf/nT/CngBDQ663G11yDYHZMvhZfW5nOxgBAsWN16utgyY0lj/HaBSvyV22nUHzVQeT2DD41nMy7C6KlWCEFN1AvqvsyztZdJ1WTlR0Sm5qq5RhtkomhZIP7mT5rbfjjkU8hgtQhiS9Ts+pqW+ssqVOPfL2FZxIAkuxXfl7DOHLx46utR8fbCN/3QSuY8wVczhEQIGACeBOfFi0l17zJrl0WADTVPgBAMu7SihU7Au6Wf3uPrYGUf1svFeejS0tmnwZgDOJwrUkynFxbLfsFOZbJmCtCpByYHKj2Swfg4qOABeARzoFGuPgePXr0+Aj0CnCPHj3uBwzIbfYW0H2VaRAuNg1pbwsfRrFuzixsDzWrrMh9beULRbf1/mWtmGpXDCduqM2H9amkQ916nU+5eHX3+Df7kXr61q4yiwtGkacpPEbuCcyqKX/J19I8cnIyN/23jv/WibztydozAGRORdmj23/jaJMgM7QGzGmgP9qUKlGUmlvttK6oVw4nWFkUzjAUHbRBVjhjCwB4WbJ/LrUUbjTv1TC5tpDRjAz8pELCU08jX9OfLncKdG3X9maKQssEcV6sWmjJLeZR9cX5RZTpu6q6XXfY31ilkKPOlsnoTXQWphmdi+huvK4FEXGANsn4TXQWEY/IkoGspDcvV9qMFEqdjy6t1NMalyp/UBwER1hihWFp9fQnTVhDQIfupwBCDD7gwQam7E/2ZS4ZGrgskNmVQOBMAIhSa5OMO7RiAGBWfBcZf/skHRd8PfW03bGpydf1ExnPH8Ygli7ExrOVfuJ3sd9TdXGqLrginDbeayN4hPPP8I2L+lUf3M95vgnPhxliCGBNzT8vPXr06HF39Apwjx497g1OnFwPNCS/jHQUhIut6osQ3GzYtk2wmkk7QhDAylGCczTE3oq4UVxtHjQ+EarZgMtSc0W8VezYABQ7lnGAW3qxQwquXNxZbDcqcao/AZwXq+jSUqe+sVEpSqKNAkBxQgoBojLjTeY8M1omkNDimSkV4+DaLNPaxkhtkfsGZ29Nt+P4xlSsBJ6m25HCo4732YaKbE8zDC2EZ7gCALhyKoTgWxGYAMAmK0s5lSnA5c0SZjixNhWpr8dXPPRK4b5kClYAIGka7b0c8hNRlogLiAvePFnqdrd7+fZ5t1JwKUNSmzrcMY+klFcIepLbKHbMrKiUY6nZ0I5+3wZ5Y8YgVp3ONFqbZAyCqwhD8aVOG6ZcV7vt5w++cURWmHIoMBThEn8TndXK1eklVf/x9UrY6urVkHjB5UaYvX764Cfioij7DwL8mxEGc8CCDyNF9AAfBMzfj58VZZgVKflmRyFEP9EuAESpFZIp0j0bAYod4y5uwPlztA0ysyJ1Z84zWYZ8ffDLN43zLYT8VF3USO90m4E5gwfTw9ln+KZZ3YPpwfTBpSfwE5yv8c4n8xQfM0N69OjRo46eAPfo0eNeoCMd24E6EMbbdL2CcaoDAMFCaCI2IEqBRinTH1tXaTXy0L763010a0cdpKJBpDPJhaAd+wD4SSb0eS+Ht+YOdRp8eM1yI3ndj2bCUUbmCUAkdLCkiAKd0dpsG4JST1XHWz4QeVt/4Iz07lD/WoYpr+Vl1tf6ww2AFitN2YEiPHWHPbmL5ZLcY3ZxDLGm8YwNJ4BRWi6T0Nc2A0BX0LZ7Ltsh1p+sAKZasWJruLbCTaaGiUvOM02YFabRqadVwxFt+5Od7wp8tYcD500RwOBfWKU00V0N5gf5xejS0k+w+XrofLYCUJDecsLh7lsX+NiptSsDMMFRZ44CMGySiUi5TK77MbfbehETADR5b2s3yv9IpJ6mFEa/bQUkRlhck6v+pd99+h/i9/8vWaGJjvQD8KtY8/Ec7gyjOUYejMzouvjW8rjcpBSBdeZHZImUF6z4VF00pGAiX1enHi7vrJYfHEewDV020mW4LGhy3dLtgerc+hovAEyx8MHP8B5AkTl5CZgQJvw3OLMRalC/gf3kjn3v0aNHjxb0BLhHjx73gjjNbARDKGKYnAYwKYoRKFiZiABG1EUWqPjVhm4dtSLk7qS7W7LRzpXlof50ld2LgfwsOo60la21VOpYR99ainy0IAxs8xjfkhLL4K4AtGPBojC4UAEoZkQ8Tj1Vkk91LHQvD31c6X3Lp45+lo4b+wtS4U9mXJ2IbIEOgCFITbvpnbMdz+xhicAYhTB5blYwxLxltH31G9s+AXAFFvvbXhHAoE39ogqzYm0qCgIMQFxygNvPV4oVSfFNf7qKXg8r/cmhnfjhZldKmMM5sHXmVylHBwcuLgJgUO0ourCMQZx7bGbn/YtbBZpqjuuhU6sWJKmJrREywVFmjtJxw8MQkbVMxq46XybjTufeGqjlCAD5umJH0aVlPIuas91lgcFEuV70J35jFv1tIJSFZhFohuRvw/hTSwsxAAEOYKp9QPSg1a0348bJaEmnxRlZJSKrEheaACDdaOklr5vTN8I77354w4nCYJe188dgSaZL9WS/9R7kB0V3rzEi4Gu8sBFsSi/KAd/g7DHOf4anLiIbIcxa4O0ePXr0uDt6AtyjR497QaKywSUwgAHVUJIL00yhA9ApICgtUaoadrDakYi8jtgnZUG1VdnoEoFbCm/Nao1BpE2LEEqF8EggVtj7dYTJaSF4u1CnzXcOTUT1A+zXeazTGr9iup1EnlaKCrFdZ0eepsxNaQWd+lpxRQrFkbfDN6/aj8qDbttPfVWdVEqsyRiQqedOki3bJMWIEcBIgA/yDY0h5lkg6KIij08REIBjkGoNWbDM/8fHT0TZjLMeyTnnq/GVpR0LfiKkG3DjMUnaUXtbM+PuCXAwByZPZ3aNA0uU5lmpM8yK1amv2LG8Hq6H3fVuiz36sLR5Ttb6Lu23zTj2YyDZL1fEQeyX2j9I39fU0/xzy3Ai1SkU2koFV53rzC/O2Qh+63/334z+01NgawX9aoLv/QD6MvVJA8AhAPc6fsAVsUxaurck84l2scyV3lN1YbBAZ35EPEzNZgpideqnXmN7pcznD9kCu7doAhLLZMwVbrQlPap3pPFxU90mkB8DcBkmOvR0DBP06NGjxydCT4B79OhxL2CgtaMD0JDoimHiZgXHRFjiJ7AQCrl4bVu8xTe5vV8LbWhyDCpR1lqtMu8tjuvlDSfWn6yL3pf/qbdwkBF1x1PViuCj1eBmuxXC0qaw53G1GBBfWYx50iyZZBBmT9PzlqSnLoBkxgEoPNZtNcqsl/eGpamtctn2HINsQbdjbRK07FY0dkPqgwsAsBFKN+DC75cAgwQYC8FlecZjm+IXv4LJEQBalciSuOS2JVO5EMC8l0N+2uJqGG40gGvHQobIUuwYG33bVQDSUnqjlU5+FAf2Lyzr1M+cM9v2a7Lw3ZdWYYxtnQrF7vL1zesV+DRkOG+ISLEiZaeVrMuCT8h+AejMd1XsYb91uldXfUtSOSFz+a7XOVUWBft1EAD0DZ7/+//jkx//5Yui6BIwL0FvgSUsxDGcdzgFkMdAbscyGS3JlHe8iF1XCZbpiasETfYrsScPMB0QOr5t4iXXlpJnAP4YuCyQUa826SRMuaHsp8GtcHIp2EHgg7sAgA/cgHGPiaB69Ojxhw19FOgePXrcF8Z5SKFYVexYMyj3wCSoUMZYEzBGnuw0v1RAO/K3YZZatAtqPTzIpJJqxwRIR9+GMlEvWYRjpZYb1c+1lWntDBXd+NQSTTmUNNUTzxKgP1uBQXfiwsBYt7dukAqPAEp9NfVVhcdFBKw79TOrZT3e6LnuGnkq42WvS2zfOdXqlU9ux8Wi0IcR1q1MtxVWZAyZ9zoB/Q5Y7A9ZkKu+9UfgJ0Jc8NZ5FW407+XQ+2ZYT3eU90eUnTPbVMRqrb1vr2tuAwD5WnRpbb4ZlhmRf8E33wzLk2lP84cVPKCfFG60zTfDzTfDSrOfpv39eKKdZxGV0XXrlq4k19bm62GF/TpRi7xPEHk6XwAbmCb8H+DrX/zly197VCl4A8AGbHjQNGwe4QKAo8y7+TktU6P8lZS8t4v91j0v2pvE7kTicsJEl1b4yi0iP6tT/3bst/h65j+nyuJUWUy193KPw1Fmjjq7G/tFSQreINsd4AiG/L4st3v06PGHEz0B7tGjx71A6hHkax4Q6uNrdTDASuRpWHUIAALGEIuuFhivRoHev5im/FcbW26pTpV/Ccm1lS2CG7y31HjhG9zRp7vR4MqN7o83EEBpiTiRrzFAsWKpHTE7zSRZACDGY3MaRBtNsRL/rVOOgFVqb+dPowPbBFcEvcxFS4xFY4LqQ9ay3eDB8GBYFA6wkCmRinJFHKxjdbkiG+8xB5jiHanngyAtWoivc3sEkHpU8hBuuWntmFpO16/v5MB7WWrBx6pvMvW0PWLgts5t9l8+bt51Jum5t+kcppwzkaVT6phs9eH09eTaSq4t/9wqFzCcWLHjthxIWKbmdfygfO4bTB78665zXb8Tm2D9aDzHCID8vUnHbpaOOP8pb0XdBml3lPW2h+5onSkAwrUWrjVJgw/i1ZWW8wMAgKsErhIYivjISGZd99vAPMfDF1j/nB3dQ/s9evT4w4veBLpHjx73hfmzJVOMJTRNNZ/BB+iEbmJYDIwjVMDOKACjMVZzuFsO1Lk0bHjwZtakdJi5aVGstTylvqYe+S0OwHXL6qZ1LtotoiunuzvZrAXpv5r35FNDceLk2pIxvZgVK06aLBgAxY7SC51xAcKABQYLbpIhCPbnCwAWj/y3Tr2fLV1H7lFcOl1/Y1nhLAR0dnL7nmMytULWK10vHWT/2AhNBAAJmByBL1P9Uv4CCUFqXqnJ2R/X8DIGEBHnecuGE3dMttJgVcctXGtGa41m0V0n80vdgXn9C244murE21Srpa5WI1Hvxi0nUvOFdNezTv0iwpbxbNVZ7lMjy3gECOINybT7z0fF5rless5+Gx9ImkAT/ofBP/h//K3lZ9Xb/toJ0re4ObX8bbJpGExcpO6BD7UbBw93jnZL++2DZe9hrRkbXXWi/VbQbe91R9jnW0GG8s4jeNOpugAgSfUGJkdIya23DHr06NFjB3oFuEePHvcCBno40wHYIKHOY9IAGAgCGEMsTQTIZbpCBDaoZMoIANDt2pqsaxnUPN+hzdbLb0+JS06lIE9tbewQ/TpWZy1S8GHruG3Zj9bmGhAXXMlfLGOAqqSZ7k1QZAhoDJRtQKNkbkrl1jzKA1O1dbSAwuPSeaqVSmZcmwRZDK33Tql2fftjnwicXQ1grjHyoQuYFnIRuFRWh1j+ZowzUGq/i09ETpnCjarYcVnzLxJfddyU0EpFuganMcdaSnRfDDeaf8HDV0PyNfI1KV0m11ay1sO1tqvVzt7caSJRfTAlnBeranxpavvJsEzNaF+e28OhM1+k5kUyKrHfPU9Hvh5d8pLNc6VkuNH8C0ux4sYVclkw1T7IcxsyLvDgd41f+fYjLKsxiX//ErDBSTykS0F8RAsQtI/PXksAHWb/3FL3oLEO15p/bm2+Hpbtost3b3uvBNAT/cNdelVCRNYmmVzHD95EZ2+is/KdHHUmPzkQS3Lzm/bo0aPHp0FPgHv06HE/CCh5rzPAA9NBD5KviBDAHNKyxowAjLHckl4WFGsdJQtQVAbt+NRykrrLN3hX2rR/rhYo/jHspnjYsf5uXz4ejPxdGU5kOBlrLZub2s9Xzndu+IlvOHHxs7tJw4m30Y8JzEr0x2sA6ZpJ1WjAgpIASwrPiIHCy2Fyyf7WvLX94KYpym3HO7g2s6ZAkaclc14tWXljFQ5MqI4FgMwK+oaGIbgPg1MwwQpAFmsNADCBP36GiQOd3QxZsCKj3EIpEDTpT1Z7316pbj4ZOqIHl07s5sCdNFh2ZvPNMPV0/4LLny0DvyOl7aQ1B9Z2XqwG372JLq0DWtqWWCbjdo7cypr3lZlqH07VufxxmXBr9gItHaFwre/ua3RZm4rZhPsqfC5bkBcuMI3/nX9j/KxSVP6jL7MESAuMCIgoa3C4t3v1t1W+P6WZ68HtB+42YZ/Dtbb5erDlwC1Vs7u7SvBEP9fZx9L7MOUXyWiZ8tpDXSSj6+ghgIgsC+IlhkHabXjRo0ePHrdHT4B79OhxLyCu/DMC+doxyAKBTBAMEgaCFVwCGRD5Ag8uLQAE5VBG1DjYXqqy2CZBqtWicolqu9WWtmaQRSyZDsKcJUNqXyO2ob5qvR39MJxYf7rSn64MO+InvlISxr2XQ9kf/elS/uxtLdxomx9PtrISA1hui6spJFSDbZVedSyYmXFCxmNzKoqee1+NDn8EFC0CqdAUXnj/1khvfQQbHLh8UL80w3BGQ+RzKSJusmBFZ/QOBDy9XK3ILOYJPxGKFdUGIk9zVTrZMgPrR3s4MLWdrxXaed2/aOFm9RN34bN3oVWpp4Wv3JIt8UFYkhkRj4hv0vEmHe/p1AFwlLlMz8sVsc0w3Noaydh13Sa+BOvEVzv2Pk7VRfE3hCOg85fGX/+bzqOiag4PEJhg7SDiEACFZNaob50JV15/+2YA+Vom+DeI8UGjVvlruTcFV/673uodp1crIuKbZAxAkOkqorXlJZlfRy9k/O0LOkvBWBo2i/Xo0aPH3dD7APfo0eNeoCJ1HipgeMXUpyAfGMATGCCzea57hU6wusJpxzKLGplgdyRGah5UG9geU+53SgDjJx1r6LxA+ZRiRyjyOeEwr+CWK90lq1Dypbn+bAUgubbs58v4ygor4ax0ZkXJtXUrd0ECAMYUBWAgShaWNlgYJfm3NiDqSOieGvkHBydryeKD4NoETN2OI1/L7aXzJEkMkjloLKif3rbAqg0yG2Ee9YpJK+gQRSgvrMg++Xf02Y8jSvBIu3iLjLuoR6LSHgEMihUhT6FUegmsMmeqaLzwljJts6hRghhQdTneNZRtk+fQCbW7zSYqLeYRpCoovMp3YJmMAcjAyG7KuSIcZS5l0jvLiRd5Kt0KGs+RXFuqE6P1febbE3n/MwYo21iSCQaDWTKssQnxgPHkP/o19vU/Lt9HOQZzYY5WHp6soZsQEZ0ZLIACDnEpO0lY1YNC72eVcodr1wOyfaOe/7U0BrHx2bK2eWGdZaOWPX5zK6oEV8n+MiyT8Z0cgFlEXGciBL+OH3SFvAbgskC272I2YCYw/XQEvEePHj16AtyjR4/7QQq2SlbM4gACxgZhkgAGRTGZ1JrqFeAIfDKaGmAroWiP8tJOdBtNtHAoAihjRFvWRfXj/EQ9YMwdaPD24n7WUqMW8qN27Cu2Li647E9ybalWlHqH5gtJN5o6JRCgMDBmHgtQqk4ihAhTU1MCgwUDZqypZHxIBECbBABKAbo7V6a6lQCQbLnIaBX5mm5Fka9FnibDazVeNSpzQH4sKGjbRgcxcIRjWi6YCzAfhgExwfKC+FUy/JGKMR5h+BIcOgtO1NVLDPmxX8kUnbfFrNh+tvJeDTumXV64to1SP+ziwF2vqvQmuyNjdVZppcGN0x+B/Sqif25ZlE/UjvuWcwItyVwm5pNDeW/7HNOZ77JgmZrZH5Suyr5WldDLTWb/+Bfc2Gj6ic+sqLHtQ+WkPj8x0z+r/i5ttpdd4OgG6SliMZBWLAG4zvxNMjZY8DY+3fEIu9GSmamlEIB9NJgIjEmJW3Ui58RPPU0aO2z/lG072N5VVwkK0ivDVt0eFKZ8SeMd1FeikPQDWBNs0ijdXb5Hjx49boXeBLpHjx73Agb262lGxjjWV/qUCEOsAYRFas3qQstAAEBnwZCF9Wu7nNl2mI9S22F76cz+uXyxS43O0nI2zAJbyu80HaRar9pL8hO/5Va+BkCd+vaLLPqutIvWjr2ytzC689OEG40kW1aUNDDUiWBmjFgFEJIJQGOi+jayD4zH2iTQrVhv2A8D0K24SPOrTYT+cCPL6A83+sONrKhNAvMosB6vK6+iTHprH+UYbzdE6lskPhmXOPmAY58MH8aEVgBmGEpd8QQbALBBwXCTjK4Mix8LxSq9lupDpNINsnKybWhaZ9TuabYzTeu24q3T5FD7/KHOKx+Bzka3MbGo+6eEU2WhM1/+7KzT0nsZP2mTTLZ3vG33Gy2Hm8zSuNYYL1g6IU3tX6H1P/lL4dFTvHiYnV4CAoCL2GRzcgWZPpkgOMr8Iwfg8P2s+remGzLgs0z/28p+XSU4VReF2Cs/PtHP5XuQ7/xWaX43yeQ6fiiHrBq6rBMypTCACeYn5G2ifrHao0ePT4n+b0qPHj3uBUacJD4APKNkFNB0FqxhATApyqxVCRzSrYsAGAgMqstBuaeoLNW2vm9+bnCnttItUaDDjUZFMORqreTGil4Po9eDImKw2MbLaXDgluXuATSYWj5IKHa8fTT5r6/Fl1Z8aaEUw0keMCuuNgX96XKX92NxCwYGEDESRaTcPBY0kYPAYVsfPGbGBZtFlfRGdc0q3+ogAGA8Vqw4FZp0LU5mJgm1VLDGJGvjV6Ym1Cx2hVMAEyxnGMpzEZkAvoW1b32GZ4BDV4kLIPU1ZrXtCxDI18RlQy1s9mUHdnOQw1hK9sZuzZu663x6JlxulACyTg8gRXnxU2XxMcljJQETaTMNUkvHmBVZp+VdpM4XEV3x2vf5VJ0bytYlfsV0ZeC9+D5mFnC8LTecgpnQBHHU41091i4OeqQq5ZcRv9un4p5G9gxzM91RHkIsq+iqc0edcyZkml/Je3Xmy/Ey5L7YrX6AZWq+ic7a7dUbkGmQJD7DkmEJEOLkFu+hR48ePXaiN4Hu0aPHvSBS9H86WAJjK0neOtxNbIPCFQ1i6MXGm4BRmL6a8CdYvQaIWthpDqra+FHD+pRaDGQ7crqWPxp2rNhxfM31JyVZMitDihXlK9FWwz/Zz4YZapv7605XvfygbAPcvExgPNaOc4e9Rnvaie99U/gNUvRqGG60rDpr9pMYY8TyKFiMpUKFVWwlZL83ZJypV5vkuDw0jMfaBIqVyH0KxUpSP3MP1q048rVUaGq+hUFCZTyWxsOpr6pjAFDHotKlrgHKD6iwlZY0spTkWZB5wi4AmAgshAts86+u4DhYkockHz79cXWUW98zUM8jvcs4udr1+mxslu241OxJwzH4AFRmUufF7iJ3w14fYAAuC1x1DkBX9hUu95M1zgCOMjOY5QIh8YtktIv5Z/HkG03US1K41vBqqGTplwkEA0GYmkVvOQT/nd+Jr/C7P8LX/zyr90zHdARcQ+VrG8kcW5oXEhdNir6zF8kNTz0td/9uLbpvzDommNJmDHKqLAxFuOr2RnJzwVFnTrkcVa4ejk06ETszYJGvp55WnjwXyUhajR+xRcC4Q5prJL/64stb3bdHjx49dqAnwD169LgfRNFDWF8DDuJLQE01QUyQwRGB8tVZiWAQYca2tK2gELodb91Nmwu7VqZR/lS50hrNiCCtHzcaGPSiN2zLylJPk8XDjZo7ATcfuMFv2wnvAcwnr2g4sXbst2mVxKxoSwqxvZEUh8tFK4GUqvJj6mmqFUFRwBgYI1+FoqSeinFel0yHBRsyHBZqLDhSVjfJoNw4M2M1jxGtmrFiqtqY+e9sebPUVwHTerTx3znxzNQmYDxWx0Id195S+W1TxZ+zGIh8oPOYSUG5hTN2LYOKM2BBrs90A2JCqxkDCBzvPbiwoQo/byifCdtZkN2I8dh+tor3xxKjqicwOiZhNwdGmxN7R1FQ3tc7MmF0e4c2ztyVEh8SBwt5KCy3fKzO99CqDsYoa+nMf8JESGZdYCzVYlZkONGuMc3tPuSfAtuOGI8gvwLKXF6aYpGQtXj8pYPzBw/wdV71VYSXczy3ITB8i7EBfwx4OI2IG0xcpm7jZp2dSK4tcVWliy0bFocNa/VPJT8VzIoLu79TdWEwUX3tVG/hNojIConLuNwhcZHyZT3iVzuYFcGrj4scylP9Qwz7G3LG1HCK6dGjR4+PQE+Ae/TocV94RwAQJiyCEfibgAYpDE4RFRFbAOSURy7VLIQ+DIMFIBMA4408wIeqZyjpht06XkNyjN4MpAhMngrGCvLJT0S+MM2Zj8Rembel5E6BrlyKkHq62mqs22xHSrw8VuyOaLc77sQYFMasBL6qWMmaDCNxJcnckAFgrF4C0BE4zNgUYbGo1AIAMMYTgMyjIE8FTCAwMzYnAmBsa9DeUFOrOw7Z7yo1lWc0JrY1icDYBCuA1hh9m34RMDOAOaHVjLkDLAwKPHUUgZvWCgb+4OmjSsv5h2N1KU2js7dpVQ3vO0Xgj+PAqE79PchfCuWFb01TD2a6hxCNtqqKHYWvhsazrkRcDHkcrFN1AdCb+IG8cDhB7ILO/DA170iRqrtCAOznK8l+AYiUA+PcmxcTvHPfhD9/j+88NZB5cGAIGDeInxuXxrBkIkE6fABDFqzIlL9rd613pMv8vt7bHFsyvGOTBQAznFiGvJLWzo0Aznekvpt0Uhznuw+jrsI70Lpv4rJgTmOwN9lftnSPH0ePHj16HI7eB7hHjx73gljX8nUUs7A2abEmZ0mDME/2SxUnx8q/trKQxyS0qCEOVNdnW62wcv6gNRyVSub6z1ojX0turPjakqKQXJWKy/LymtB2uOf2bSve3f0LN5q44AdEgiWU3qU69WspnZzvzmQorCJilkS60ZCvkcEYCU2beqnYxt2x2EK6/vqpC4JW5AemgjZQZQhAANSxMI8CANo4SIUKkDoO1LGoD1zNqXtHnLNq1KtmuXM6HiCbMx6MGYZFqTFbzeEM09HVJRJvXG50yIJjdVl5gu3tujpTL8pP6s/FTwQ/8avFdzzazgffgdq7/6gmbt9WsyqBWZF+skPIrZReJuPyx006ltr+nWEooojb1Ar9xK97KVPLWNvPV2UvWVddGEzIPy9EGIM8NgOAwbaNJbBOoUUh8rakFbTMezzVPgyZWGXx7TtfNflae8ajHej8JlbLgAAwK5b250syr+MH+duu/g2+5SxwlJmRRwg7VRen6sJlgVuyzpAfmz9PtPNayQLhK3f9o6P1j44Wm8EyGb+B8VPmxnEfBbpHjx6fEr0C3KNHj3uBijQGALY6OcMFYQkLdAPjEtMJeS4tQ/AQnBFz2SIANxF0LuCakm9NMGRthSvHVR2vXQImKZXEV1w7Fqmnpb6uWgkAw4lzN9pyYVSMkA/x+G051y3gAAD4iWgP1wQk15ZiR9Wr1DiA/XwFgv50hddD75uh4cT6s1VybSEXXpjCCABjjMdEWiR0TWgBNyxtCcJAWW7i4yA1LBVVVHYBaiOnjIQJMDPWxiChbbXfmkcr5Y/PSh8Zy1biFa0+G8E45WDQEchLT+lNyLgFE0Qf2HHWr5Jt83lyPFHOLfdHo2/rOl8A06LlVWoCeK6/iogDbntiLaB+vjoZt8HDWXFGDTd6rbzhRADrNMHdPvhelN90iw38x6E2jLdrMXzlGk+XLV/iajMuC5r2sQYTd04FnIGwM7wwMasaSrhj36HCflkQpqajzoqHmoE9CuIXD6Gtt3R3CJx9ifjU4IgCWAAsBB5MnYmI+HV81kj/2/KnbkfAZ7l7tdN+W3Yd9dmZX1XsCAyutghTCyR1+PHW8vz22ygOgg1MAHkQ7/y8AgAuWctk3GXZLutONV+WlCfDfPvDP01lPvgVVwC8xSlRAgCsyxCmR48ePW6NngD36NHjXqCk9MS5ectOjRQMSOEsUjMkwyRPQHcBA+ISJwAYEMDkCC2EHIGPbeJZxuPMB5gRwKr+wDtcKFtXgVVHU7BWMitXmdoU+pPV5qcT58j3Xg7ayXbtc9s9u9ajxe12nKpfbFzv8rckXxeX9WBdSe7UGm40naBYEbOLBSUDAEUBS2itGBOKb8zNw8hMhpay1FhwpK4KJXOgLDfptHK7jtWzMhIEwIxRefe0NSDvshwGwtS0FdQ5MACZnEnaHROTBy4tQ2YW7TzAlQHxGk9CMnO7+ljnjnr+Ifq22zogOhNDFphKcJWMwJDcWOFaq2y7ULW3xKSHtv50BUDz9dTTxSUvT6EKCOFGs5+vFLtlaErFDvQKLrULVDqJyomPRuvQtrfuVI0Lupp5op8DCFO+TE1XyQNi5dR3k4wN5ZZMuDT/nmjny2TccD2lomBJ7u1U+KPXQzmskNloqympHsP48eyzX53+4p9/vT29AnwPw4tQf0q2GWlQfGz7sDog609u/Nzeq3CjGk68569E8UxdCYEJAAzFd8mU1DRKb5HL9xgLEyIA98EtCBezAFYAftVh81xmv04eGVtWBBAU0QQZZJtjNpcngiH3wQHEZK1hbsh8ivN3NEYvAvfo0ePToSfAPXr0uBdQwn7wPvyHLm4C9nm0tFbnGh6GdAyCgHGJE4tkFlk2pyEYOIVA2Tk4Y0rakYi8QS7nosKByyUrB+UrZRrTSjBaBOHU11Q7BpDcWN2FG2cOl4LbT7ecEpecF1x33wK4aMd+voyvbMnEpF0lPxHSD1CxI5JJgMqLbUVBkoAxxiNotkxrepMOHyvLt9Gjx9q7wttQQ3CkrG/SQRvvrYmTVda41TgLk8uSGlzekiAito2CtuXAVWfgonWOQNo/WxTm40syw9Ya4ylb/SI9/SNice7oggtUVtF0pJ7LZo6080067t7RaOHN4UZT8rBP4pJnMp2n2c9XqVcnuoYTMytWgI5A4i2v6WA0iG9DJP6k2C+cdkGmt1kmYynVyvw6uVQoAHSyX2o5qkHSrSWdVUrWbRN22/gSCNv46iCRmG4pJQ+AcyQ/ePk3Hhp4f1WtOgJCKGZgkCddf4smG96/LUh9ratjhhMrVpxu/SD2jW7xxSldVfLdrjDlGTWl/fGcJXEdYw7AhC9/m7CKYwCPUR+vOcZggQVhQhTFyrAR2AgsBAB8mBaC8n4BAAsBhz9j4w944BOPKDHU6A4ydY8ePXp0oSfAPXr0uBcQ8NtgYGzOoEdJYjwdU/yG9DUNTYoVUgKYrCCkhBkbljXCAQvXeRysgvS2+AOXqlSP81Ptwiyq6mKZURHAUk8DOEBZTJoyLWlpsMpA7kCDUeNalc+SAyt2BKBCXAu+U3USzj0J60QaADYawPmJr5boSuppTAuzWNBJHG50+KomNGbGcWo+1t5lvcrvaylLqsSCblAM+Q9Dc7Fe5XdVIlsakU1qDBQzt3Peno9TU8uTslosOKZLMMwwtCgUzHCxNCFCWCsacxZwBCBoTIkwfj34Uw9X/3cnFPn/9Ahgw236UxalPIuDRVCPfCPLQ1PZPeGngXyTznduyNe9V8PsQa3I+c6NLCnNWFUrdqY+APL1+MrSjn3v5VDm1zko8NVdaHDL266c+/RM+NYQaSU4sCB+EUn9cAzAVTJidjdcxw+XZAK0w6WaWTE/FeKiNc8zAXC+OwMy9gtgSeYyPn2iXQAYsTkn8a8GL//pX8XjfwPiMwP/721m7M2PgP8AiTBhwoMhswF7MAE02G+9f+Rr6UbrenDFihUrUuwoXOcewvW/FaiP7lYKLp1nLCQuiDfTrbfiGAup1tZQJrRNcgvgrO1kGR7MxziX1eXwy99HWAEY4TqALWCZ8C0EC4xZstaZ8UuPXxzS7R49evQ4BD0B7tGjx71AD1OdAIaLB6fDf+QnQcBIi8lYQmOknmJu5DFUy8s0gzLfTpMF69rCkaA7Vfm3RdGtcarGHTqD+lYqFlmR2iXfXflgS/fEwTS45UqFzIhLDnCZFQnIwxTnRdKNXgkey1paKI6MLMdp+dbEFIXSFEwBJdbj0P8xmBkDedQrEAgWQgBlA/VcJtOY2eaeV1vQlywzq2pwnQMfqauAzCg1NTUo8mXJd64xMY9PxtoVAKnxSibMEQoywECEgJlDzEG4ZGcAFHjAWNG+AfBcBBhkTR0rS6cq7pX63SICO9+ZAUxaBGx+MjGc2PlytrcisyL9aQQw+/nKeznkTiwp1uYnk/2UtGaofzvslIWx/+a3RfhqCMB41m0InYeA3n7MbYOXqXmqLnTmX8cPGwGKd6H8TEfa+yPg6/D54dVLDRVWCc2G8SY6dZlYwh1rv/tfmP/KD/DX/z9/Ey/+g7Bc5uqX8NkGkZm9VhNiiNklzi5p1LFDVDnT5d/LT4R65Cc3VuUL3vLNbvvDUrZ/YQygLFAzjbnCHaWF3EocYwGglf0CkOIth/8WZ60FduAxzls58yNcyAMTnvxtwhvRNfDNBo9/L5326m+PHj0+LXoC3KNHj3sBMbjs6lRdAkhX89iyPoRTXWdSALvA0RP6kBkkMoTgBkROLAnEbGURkCk5sGLFMlFktFE7ciC1ktIOEbjzY0HGShVR1pZLgnU7B+5gHV0d62qggzaHGy3cDAvXUxnICpk43LY+rtJpfiLEJdeOvcwEuijFw9TTmR5LZZLxBEAyN9WxiMnUcpPUjPoS5slxYV8KgHyVhMrMpJTlaMejVWjw1iJ6aw5NN8kQDANlieoaPjMNUJbyYEYDgwUGhE8GGDvDlQERgg9ovmRjnrsdpmR5LDYVkZyYm5GHJOvOVeJepe5z/bX8GFa5WSmVVHb76PVQf7pWj3znyM+72mos3f7wzIqd72SMIno1zB8M+2oWwvunY8K102i/fltICqcelgpYwmUBV8RFMjpVF5KP7WC/h1Cg6zyv0i5Qll47M5TIqa/9YhVfWtHrof50WSsP0JJMlwXvcPqfBP/tPwQAfPN/rRQ5uUb0SF9g7ME4wvIGuMSZR6Y4IDlTfG1hG2kPxXwynFixIgCpVw9AV+0egBazZwCGHStOrB75UNUi68eSTN6wW67BLBWQjHeEG+S+u5LETrG8hit/73lCYIolh19jvxbCCebFxzEWcpdtgcmIZiGGCYKf4Yh74o6R0nv06NGjA30apB49etwLPvv8iy9ev76gsYClXb0Mx6lFvkmRSB0iAuEaxyHMECaIzjGVS/1lvpaaxafrVCYOQVo28c2sE2uKTfmY6ueb5ah6npplW9oFSkoRKkmcGmhoPi396V7StV/MzoYbLbnm5GupJ/Mz8c7sKVT6ya2gU0+PrxpuqIyByThYKK+hIzIqT0MAyCiSIQEAUqGBIN7Z8QeHhEai05ux9BD5UG1zIZUHgkCIyJTXvNTdVslSMWXlljQcY2UhNBEE2DINF3MDvk8GAZzN1mSsU5546RkuSw8CEG7i0yjlAAXlSEXl1XblUbrOtymH9UHczli5f5Hz4QNX9lSLxnR7VGfD7us7C+7AIez3RF2cqAsApiIIeKyd28qsuOHX0Ytbd+SQcpTNNnXq6U+X0mrd+c6N9NyWBfSny7YR3J45wvKvz59rwK+blVIApt9FNFbkLUwSIfENmSHxQyJg6U9WhhMBxI99w4mKe2tTH0D0ZpBb4+98zu0MzArwE6HYsXqUjQjLLV9k8qFNOtkkk9b+mBA1pirZ7wJHJvwZxgGsANYIN1Msa+67BWSZ4uM13Lc4K5+R7NeEN8ZijMVDXE6wfDj/8HD+4fsffv/h4sPn5z/94uU3BjzGLQBIew7co0ePT4ZeAe7Ro8e9gAB+/Qen2nKgzpLv/9sj7++vxcm5TbPYgabZ5AnSBaYMbIzNmFZrNhpgmUeBrtOLavznVrWq9Wx+kmoecbc5rtctN456Xp/a1fKFxomafNPeQFsVcWnlC+WDUfDVjQaGQgR2WbAkE4xlGiNjip2Y01CWv0mGj9my1AIBsJQlch/gdG5GngpPBRB5auTZuhVrD6LKCLLGY1CXFLz1xA5S01IAIh1BRNIfuDQKeZ2AzGN2saIRgYHBIDFma0FGAAMMIBDDl1j9knP1/hs8A0AgIZ+dALYi0yQzi4xVMo9PN3XNTUbPrtoOtKZHap+dpTFgkOyXAKlGnkg18hAFVnLgril3OFq5xM5JuK+4850bYL9Qd6ouHGUOAEQX8ehUXeiKfx0/kObQnxkvX+jf7Gmi4xbX8YMlmafqQmRxnsTX0fO8fGe39KcrHQAoubYUWbQSIm5bcZma13CfG+cMiP8HBv7KNg3SiwdwnEwznWD1EpMAHKXUPh0PQuTrzIqSG0ux4tTT0szzPEN8xbWpn/HZdqsQia0lggx0Bwb1SGwLKoUVNCAdm5Nst+iJIpqhsN7irOwA7MH8gIcAPDJ9mB5MCxaAGcZemf0yBLCKfmUUmtU7H+QxtCQsRBbCMS5BsBaJNROxZWg/D7UN4AIf8OXZ699LP7/bdkyPHj16dKFXgHv06HFfWPy/fmaqgjEWnXx3+ntz49qJKQFRmOh+OsjWpYQAholghuE1TgQMudAxpeUtgXwVgGLFul2ysN0uaqvLIuo4vuNHarlaX+MT8gdpQ+NCe8md67sW4YfCjRZu1DusCsONpthxOenoE+1CLprBGBQGELSq8LvtR3buSFnLU8FNXf+JfDWd1523M/2tXV4jbC/mLxPYkB6TCUBjQmNBRGZWoGiDaIilBwOEIRZDzE2EI6wNCk5wYSKU5pQh+E8xPLd/ffq5krJ/ClTjFROuEndr/yzvXw0qJkc8lwqbo199qFqZnQPrfGemS6dZ2le0eVPabYNwB1Dj59Dikv0egotk9HX4/OvwufRHvUhGEfGp9uEz4+VnxstdfdnXqan24TP9GwBLMt/EZ1+Hz2uJj9r7n/+jTn1mxfrTFeNxfTiKQ8IXCwZgNg8f/o+Mot0jABa0WepiDsCHOcQcwNasQPak+kN55Gdp5xxutKYncOrr2cldL2F7Skq+6pHf8tQ1vw8AwDIZV5Ih5e1f0WhBk+KjR1vzChuBj4wJFzUWmARkBcTnGL/FmRR7l5ic08NzPAzIkuVmNBZkFR9N8nzSb8iZ04lkvxDQZiE2AIDfAv4RVDNWSQHR87PH7ePYo0ePHrdHrwD36NHjXvDk+fPZCiOavabP/yktnZ/D+YxWqQVgQ8YmUR9rG+QhcXUEE6wCmD4ZcqEWkAmAhBbf8MjT7EcbdQJlxoPrhqjSLo+0ibe1CFg1sZeqCl794+60w8hawJ3V4M6z+4ocUKsKxYoKjcvVFpkExJiMukRJqkyU+KWqjgBQTKbGAkkC6/yOyDwKcg5MupVEvppxSKG1uATLNmres8jGKLvISmNRDBFBY8F2wAhgsBAaJGoa7DDPJjrCStD5a/Y0JFNnImHL61+4w3/JLaqXx/9KBgfKPzIr1p+s8HYYboptgswetT7ZmnOvNm1aimUfNj+Z1Cvmr6L+3jpR7Bd8vCbc3X47Kjfb/PiIn/o4zAT6NI89lknBu+9zKEga9Drq7DNlht3RsJr7FDsu5YdfaK9D4Gfa+BjAT2F8GdbrcpiUeZ6H4ABWadOYZQtpgpHc8HQbcrzsCQyAxKVZaqHxB6R02nAibSoq4fHqUaCVlr4QLZMRkhFXBACR8qn2AYCDQIADk0IH3sCUv+1SgOsCMi2wX4pEIMCvMQIwxUKAAzDhezA9mI/hA/BhLHDEIUx4HCLiCiZcE6k+C+EAG2AAiuHSZbT5LiV9EuAePXp8SvQKcI8ePe4L33zAn3//ZqQvVs8eazMsxWKR2EhTKV75yTBOzTg1fdLX5A5o4VMWYThK+TrNj2X4q3cOWpyBa0dUPb8PFS23XJdQ/beu/hHslmi39VqdBconWgofLL61nLo1kwhTE8CQBa4SZm7ASaIMU20cAHBYqLGgeDcWhZZM14zMeVcZCf5oo/NY57F2tpElgxuThNrZI0KLGly8+uKYAGAeHwOYJ8fyYCvNlUh4UUNAL5Q6Az6AMTJ9lSsfPvyXl20D0DiQN/e1cq8qT1EzASDUH3I7i1p4VMulltZuO46UjchdpsDdQI2fg9gvgItkJH+i3RbCt+sDHHXmqLM93wMqj05bS+XKlcI0S91v483x+h8D+Pr3KkGwzhhwBQACJgEcwRyjTTra3fvkhkdvBuKSS8ZrOHGV/TZDQ1eet3yaHwv98aoc2S5/RMo2XOpKeNbOkkz5I1J+kdNg5HTXbIuV5eXar0QA6wIPl5gE4Kd4P8JcZg8WJTLsYmbAJ8BCUHu5JrwjbIqe6bMQHrABZqCXSF5hPJ8raTnpWo8ePXp8AvQKcI8ePe4LPvDXznH0+eLD80fnSzydvznD/IJOpQXgTTqYkneTuE/UdwEME2YhWHyIj8EqTCPaaHjnRF3RUKlb/qId3r9t0hxtFTzDifUnawBgMr9robMxMDhfzqI3Q8WOU09DtlrNlJdWzcZ+tmJWXOifybWVeppix1mCkzurwfVS7WfLiK8sGYcJwEUyesICpKmlhcvEgAIwUGgCK4DWqWHCtZSFZL9mlglJt9gSGAAgoZFQAUS+pizKolCVSDQlSrk6L/Tb7CMARpkOjHUyHKgrP3FNBDc0RHw81q6KwfTJuGYnT/B22wLDCuMTXAlosphJwmACGCruE+D3ffrOlg+Ub83qB1mO31KHvZcD5zvzvEpWfciCFZlDBCuY3fOqOjL5eX7ii8tGNLJtsQMmQFdlKut+t2zglpB7Q0ozt9ZObD2Bb40dGwcH1mvdrWh8qJYfssBgYobh09+zFsCf+I/w3/6ft2X/3nv8uw+w5pMlxh9w+gEnC3KDtOEIUP7ka+XMRoYdI0+GJKO1A3C+nG1+OukYwe3XXKZKAvI/mE2/dOndsJ3lLZDu0wA26VhG5N7AAEYcvBkTqwyZy9eEv8REfuTgHKKZRclG8BDvymdGuLEQmRAc+X6il20l4GdYv4Tx5w3rK/H2YUrBLUMe9OjRo8dO9Apwjx497gv//b/4F//dP/grj82Lz+3X+M//wub9u2/htUVBJkcQ3SSOg8BLXB+GtIIGEJUWjozH1pPM3TTa5ggp0FzMFeIhtZxHJj8admzYseHEhhMVx8hX88Ul/clqyMSQBUMEQxYUx8fqQranP1mpR77+ZKU/WTlfzviJkBXl7QwnNkp+y1vrRCKA1KlfRAN2vjNzvpy1LU1361m7S3XoRQBKKZQkOBM+WdkKmSnMTmQyJADXhbc2YCAwkAnCDsuk4ODGjHwVoODGKO6b+m3iVVOiLIWBzkrJ/wgANmREqQHgJh2AsCGjXMRCOKSViRC5HswRCRgKGSP4VEoUDNCJxp+cIWTvvldKu7KdJ5UJ09KfFhANWcAV8Vi74EowRHCiLJpP3FI9fwN7GGNTkLwFSkNPlU93wDZOchv0Zyv92epA9nuqLj7TX36mv+xmv7Tvp6PU3sa2n6sF0LjULE/gTADwyXwdAcCHv1O/if9rWLt8BieGLc+uUnOIoDTns66Qr25+Oo6vebl70jM/202wIgD8REC+/J0Py4/9Is5z3qHa62AASOgA1Z3bqyiU+SI69AbmNdwFRpLcdkHS44LxyoMlJkXMZ3ngwfwFPpvjSJ60EAawAQTgekC6SLV5ijfAV6B/gPQGf+/n+MlD0GvEivWf/tIf39GBHj169LgtegW4R48e9wUD+Gv/6/CH/73F8vPT9//at3/wX+BRevk1uzZBcxqBiAgbMkyKQFgzV4peOhO5cgEQMR6bU5G5/lKLm24WIJqYLiOglnRaxYqzNV+uu6a+plhxFiKVVYQy5cZSj4SWhQjO2h+qy5GykEllBctUPg8mVAQpB8OKTCkDAlCPfPUIADRfi68t/emafA1XFgDtuLZIBUBgFRbkfGdGvua9HHZ7EeNOmnDLBXXqFddCMg0mLjEBIjAGxijkYIxSIqExHs3j47F6ZSIMYRgUjLGeYzBQVpt4yswYIN1K1HFAQi1iYik8JqEBaHgCF4SzlNi2rsdSoQNbyhIMDhkbMkAsTg2NhcSyIgaEzP1rkCAGl1Zg0LExKQCzXFoJZpzi8hzHSyX0/009JsNWVkgn25vmt8vuXWSirs6xln5mHp4w1MBR5o6C6/hsyMSKOHJluPw4zZEqYiDtGtPselsLh6JMhxhayNEeKHZktJjjtjW/D0b21e6Eg2DTTKtD2fn2q23YJGMXwTIr3HZHahy17B9hiEB+wQE8Zy8BfOf/8F+/A64/VCp9rsOZwycBwhr6AiOdiWN1cZW4taaTGy5l/3IEO8OJFSsGQTvytSOAwI99dSKiN4NGoDtWtFcSfpvmFQSg/J1S7ARQK+Hf2t8GADjqLCILgM58BwFHYELUojcXCGDJkNfSDfgdTuX5R7gw4ctaRUUbgQ8+AmyEHIEJz4cOIDKZ+17gBuCAA3YDAD/g4AC5uEnUPg9wjx49Pi16AtyjR4/7wnd/+MOxCu1//59//r/8n76Dc7LE/Bc3T3756iWcrZSRayNzGkzYZrvSLwiIr9Zdf0sc2JwKyVozjpt5wW3JFeNxmeeqE1ZeKQ5YwJVApCZXAhyvwHDF3WNlKVswmNCZOMG5LDykbE25wniqLK8VNyKL536MKyoJ1zKKEoHxuDA2bkFOg4uHZlZsP18BSD1dXLV6SO6jTOVSLQUJQLhRw59M+ImQ9FukpqGKIcSSaSCCqiAmUoZIF7KGqYQA5hiMaaUj0BEEMPKQUOAPvWRuJnNTHQvzKIsLnfqayot4PCX+VjY4p/IavbRkL3FgAHFqjtXLAfHzZHoeHx+pK0tdESDjpQXEXTYvbNcN8onBwAYYG/DlSYvC1+yU/Zx/K2B/W5u0xzMrsWAwGHYcelrNNjq5sRQ7LntarlKTM1NXBQBXXYTEV8SlMoy0PCV2j9o+fkt5C6Wpcic0WAS1tZYFVYoVOxKXVruddq4My1Be3VGgs/afaOe60qESlzo1woKDixLLlbz3FB8ABLA2Ob/aC66IZWLsvF0Lp5JjZzDxNjqVvPexdqEz30ZgQFzg7Offf2L/9ptVtf7wGPQtOMGi1PFaZmkAICHNniv35cdCPRKbn43tp+tiaqnFzl1H72WtauuNr9j2O0VpoCtaKY7UTjp5HT901bmMjTfCYof9s0RhI23Cf4SLALxQg2t1pfOwFH49GDcY2ggf4L3A0J8kmpXqVkgE/EvAWzxYITERxfqDREPcB8Hq0aPHp0RPgHv06HFfIOAqweqvYvBX/1ff+wvG9RucPb/4mjyHljGz5hjlttCYxydj/WqGLNjJVFldp1lmVMZj/dFamXHFipnUFRkYT5IZB4M6CSTP3EEMorcDANqRaASJoTXMIVvaWm68yvCQCZ0FZaKxovGQzUPwAHxI85BZAEyIKXDNoDMfYJSOywS48g6I7eEsVRosO6kAhq2FG22nB+ABXKiDCRtVp02d+SNVWSbHkhwyzSdNRwwSKjOjgAwLNKa1gywnsEmhD/1IWd8kA2bG6piSmQlAGQmZLyW4MRVLVUZBvcO1jhO1S8E5B/ZTNyDDJNNSl0fKWmeBpgSSORPAKViwgUnCZGKEjU7+KE9qOiJ/wSwAQywAfB4ENx9Wa9NCAgADFqzJrIvAknT7eiq2OaLKnck9M+dVv/GshEz6+lg9X6bjSxlWuvroNaLLrJgfi9TTQu8AKXjb1CdhwrXObTsFwLAjxYkVO2ZWZNtx6mnpRm8qwOFaK6hy9NoFoD9dog4C4LJAZ/6BWrGLmZsfB7CAUX5w6KNExEVqckWcqouLZOSybBIuK0651Hq4IsPFHIQhC2Q85FIZ+u75u/S339QqDYHjJ4if6D9xP/NpG7TZZMGq6gacevV3yI+FYkXJDS/2AbeFfbV6ouQ9Ltlv/atdSNnVPzgMybWljFi5yA64LHCVeVFSKrpTLNyy70AJ23y/+ce9hPktzqZYjHADwIMxx5GF6P1oejTaHI8vhvCgAoACKEY4o2//nfTxryV9HKwePXp8SvQEuEePHveIBJhqEDF+638TKsCfocVRsrokQkpgAJGNACBDhhqWJIGYrSyu0+EAgamGQWqaSnCd+6AxHoMhejuIfB2A7mlgTDsSzEqSGc+14opNYJQFqRoYTgyw0NMMJ9YfrwGAaJW4QLbk5kqgK0FEXGeiMDu9wRAEwbLYp4JMj5kg12ZbdifDxgRkttHg3EW1M4tSVqqgwck1B5h27IebYZPBlkLFHkyDUV9JhxtNubakCeWSzCMgTchVwyXp5KnMSCg2GaCMBAltwzEGAhhO/jQ2lnM4RdPMjNUJAPhfu9aLJQUaf+g1b1pZo5dPErY0uMyBQdfJAICphvP4eKxd+elIowAMIIzZ2ocBkAEBQsCMYcni2EQAsjgLBRkmC7hx+fWv/6vfX0F6aJZxrC6vUrd8JvXUfOuhsEMAGBlOothxbVPjMnENRSBP6hMRd5U5J35ZCwJM5TeQVVbsaBsJqXF1FwomjH3z6jYwnEg78cv7RMyKVCtS7Fg7QacB7T64ed6jAyHprqRSpzmbWmJiQsgcPCin4Wkoujrzp5q/SceOMj8lOOp8k4xFytGoUCWEBOBEWUrlc6r55SsezBDWPxg++JONeitADJCeaIwMadDLITwYtYLJjVWMtfwKG06k8AgEcWnyY79Md0shsmq9ZYYTK1ZEvpYPRzP7GpWNosnXAaS+oTg+AJcFy9LfKHf7RwyuOpdvDw00I1qVwLZf4daLJUyxkKmSrjGSCZAAcAgAFqIbOANyh9zDY2ADuPAe8/CDtU6NHz550d2BHj169Lg1egLco0eP+8IvffEFB47+BI6nEH/ys0s+/OlX7pc//733v/L0MtWh0Jl6rakRU1gsFR6iCTaCmavUHbBwol2+jp8AWCcGGKJ3TuSVpDlGAIs8DWCRJ/WBMu9t+MVJP0aWH7zNJIVZiT/oj9dI2YAFgMuVAMBVOhqy4AZnphKA4SY9M5VAtrwBA4Pk5wCC1Niy31ZKUlM7W7ElNoRaUlCCPKM/WYU/ndTqdN+16y4AgXzp8IwotUIyLD1YxjqzE9roYj60HILccSD4iQtluYFr5yIwpzBGADgASGjMjAGYkwCAPG6/KRpMuNCvChpcTdf8RHv3Jn7MGDbR4zPtOmuK0ZwGA7Y4o2tJAQTpKzYCYcR8IhhsM4L5GnoIHpBxzs6+NfzNh6++q31XgAYyphGANbhIzYfqZUT8KnVBYFakHSHc6CXKmnFgxYpyu9MKB34bnQ6VwFUXANOZABASbzFsrkjBAFhans8dQvF+FO6Rd2XC9vOV7Ila5DpG0RkAd6e+yCmW9ClFB78qEICb8N/hdIplTUh0MSPgFD4IS4y9Hf69APL9CIMFUWoB4IpASlvuV68nrYhRj85FAGAjsCAA+mOr327ejwDm4Gp48gGTCzzwYQwxv4F7lQzLNyoy+hpOrB0J/XEcvR14rweGE1ftmUG+Ft+0J4iyn67yNouOtNpW5EbRLDtZjkHgShN9SBeP/dHLpti9f0GNg9rFyrR8gHcBbAtZ6HsLoQD3yANgIeLyz8scAPArACEJXmBIiJO9/ezRo0ePw9ET4B49etwXnj1//r3fwNt/DPN/8mQgvMg8oSM9/hvzX/ruj3yM3uGRBgFSQdAUAbAxNnoWbkpMFAGwB+pVROZ1OgRBsRId2HJgKq/2WO1UdlziMDLSjFxoRm+H+uN1csPFFbefrVM/d7h9OwDYDANtKiiwNuMUoBVMAKvEBACWHwBgjHwtvslW9oodK1aU+ro0dMzCcTFIlZV8LfWznitWnPpaeV2o2FFxVSJvJELV9DTcaJqv2c9WWZ6eCm4jCAPikgPcfr6Kr6xgGJnHszerp+maqUd+6pv8QYANQ5px5Zt08FhZzmkgfYABDLESzHBYuEmNgvEqo2CnnWXVDjMX/Eu9zollsVlAAHCkrG7SIWMEoiIi1CmuQDhn06f0Vg71kOYj5gdkgBERpFmBwfwzXF+Rpk9/+WJInMJjZWkrC6TMVheIT9fEa6+SWbHhRCUODAD8RCgVE/oKB16l5io9BWMn6tJR5gYTQ5grMtudjQGwrcrX3ObIcyDh8NEEUAkUdDAZltGG28M4d4xk4Z5a8SnouOGSTJ7TTqOkNzZhI/BhusARlpLu7ujDHnPe/GJI5kUyAuiJdgFWs4IuFSUAOFEXpa2ZoleCIzBICMbfnxuPG5W/+wBjA8pSPBjOznHmwQjBI+LlDkb5dpvkutkLJAAINxq3IhnpIPV0xY4gzcsLSDtzO9amVSeOlh2lBg1mCkDqcQDop+riQMZb4BHO0fDjbRHhARuB1xGfzIbwYNoILATXqJhagGAhABDANuFNsMyeaQlMAQ7ui+UJQPji8dPDu92jR48ee9ET4B49etwjfu3P/sWbP/K//Z2fT0++VKzUv5hOPiccr9enRx88xSBSGW0VtoAZAOY0yIRSIp0JnYmAzDUZ6thXLF0WjhredJ0gWM/WNSdh/fEKxNQj4RwJACqPnSMh5VDytVToqa9tSjFmjtWVkSt7AXFTCa4SVy46taOKC1zqadrUB5D6urg0AWZsNGly3NE/WZO38IcOO2fv5ZCfND0ASw/ccaEV8ZUVbjShDMzj2QPn6quvn9l2DIbU15g/VcyrNoGHQNAREGAg3JStPXc7GdZ4XZnsVaRgtuXGDF7i6koADKmQxhlAWLHhEHhC78Awos2QzQkQ0EfI8mYZtJkwMSOuM7HC9FvaL17efGv49L3HTiGDjROOtAuejleJFPyLTZPSnQqjaiD1NfiaOt2KdXUQXSauwUSYh0ZrSc2aPyXjkaTZLXOjoRXvfK3tPal87ObD4pLbz28n8HovhzICViXAW8fQ11P+NuXtvKKMtb7AeMt+G02HsEQ1SlYdjZqPtfO38WmQmpeJ21J0S31L8q/cdsHChABgkHiEizUdzd4uavWHwJcW2AvY8xkfHj/AjQ9zhTGVvy2+Gm40w44VO1Ynvhx6ko7fRMi2onJs1PJjyG07ccUVK2I8qk6H/D02Z4t8yYwlN1bq61hoxgtfumkAiKgiL+usPp8dBByimf5XkljpuwvgAx4+wPsAloA1ws0CR/JScaYoNsUSwAg3xckCNxhaCAGY8N5h+tAEHsIVXmQZ0VhRb9LZjYsxevTo0ePToifAPXr0uEd861d/+Mf+hjGYvvnZHzx9/F0y0kD88WdK4impZ6ZhQFxmgo1TDsa8dGSoARj7EJ881d9mAiCDyYI1GUAWEIuElts85yirwWUqBQYgvubaVMhjlodXZVZSFYxJJuxhVqwWoaTzZgyIEVuOsAgYv2FuRPyRJgCEA+sqcYcsGEovR4ZwwK8S91hdXlmuXcg1DNq06w1l+mfq6amnZVyoha1UVr6VFXOJVEpaErYkTO7kP+FGM5zYOpnpzGeKYgwTZsXwNMSJcqRigzJPACiEoVO2YrYozBbIByYp2Xa19DiFSr+VgsscGDfJwCEDRI4SrZPhQIWGIA9eBQOCCIIZoPGQzU2KDLbObajpCS59PPBJf8Pcp8iGkkCMWL7uZwDWZHIEx8qqcAbWjgTAw42e95C2Dpyepj9dZ8/TloEmTE1R8wZvjTsNaFM/H6y2Aaq/ro5ih6CbD/MTf5+Fc/2mhrMztnkVIjUNxissq2Oy3MAFcIRFawlJfQEImBWxcefUGyjzdTICoRKWrLFZI1Jzqp6XVN/AgzGk+bYA+OOlJpZh7bYrIAAcBs2PdKxTRD4MATNKze07Jzi/NKt1jFmxdiTCzf7YTjKugdKi/VYCyNdpMDEgm3X6aVjUkew3TLmjzmWwg9ab1tjvY5w3Q1s9wHuUol4V5NaEL2AVaZMe4P0CR7w7MpYPw0KwwNGDPN6+/5BHXIlMZl+n35inIELSR4Hu0aPHp0RPgHv06HGPiIH1T7754Z8x/9kVrZPBRL2Zfz4wUqGnIRGBSKogGhM+jQIYYWIaSvhAvaISPbKVBQCpA8vTWe7fduPnCnQ7BuC/Huh2XHYhdr6Yk9BZzai12sixurSVeURWSCYAHSIAl7p01jgLHKWyjtQVkTsfCgxRNKgzEaPFry8kfpm4AFN5rE6htec+yV/mtbWbLxWBeVuuZbVaXlS40S4NywEoTqZDf8mgTgXISFaWKsdAaMyM58l0rF4FZBTBr0wEOgtADlqQc4yaWlXpTs1usywFVzYyNqlxpK4tdRkT16QZLZHJAoOElGePccEAk+LtpskyDVzFofeCPTWhP8KN4hwjqPaBAQSdiWMFOhNR4bhL0uu15Meb68CGE2tTUdlw2VraZ7hM3DbJt50D197IrovlN/aRKPFhaXO7u3Ttc1u05/233FviCMsbuF3xlgz4AlwGTOIUFCJwq+WtFGCj1ArJrFPfZkeIpMN/3o2FgPmcXpWLuJj9gTu2/8pv1h5miEye9M/MOVkjLB/gcoXyHQFsne1B2X/JzNrmOSsIbXVk+YkPMHXiKzOrkU+7qEiVr1g+QYYsMNXg5cUUjOkIT5WF/DOlw4/IcpQZCDpaQnM7CGqpj2wEu2fcEVY+TAB+ZgySkWEp7fowah9lGQvhBAsAclhlTuAxsDJdmBgGS/m/hsVoiE38/MHDXT3o0aNHj1uiJ8A9evS4R/yZ3/iN/9O/jbPvpX8u+Gdfz76wT8IfD7/3+c3MTlZGIkLVJIWkFXSQGhtmAGxAAEFDUKYMtrLQia+TY4CKxEjBdYvlsHksUk9TnNwr1UqYFcssSto0ABBfc8akVx7TH6+r8l1JUPHUSxwxO5MErxJ3qDw50i5u4jNTCQwW6IoAKCL+Lj55rr+utaAzYSM4xXkIDrA5Rl6HIepjTWzvOgSAt3F7slNt6peUZFbiw9VH2BZpvdZCRYYihQ6diVlq4lpTp746DVLfRUKADGpFGzLMxOXK1QauQ0sAOkKNBQ4LN2Rsb1FrngDAYQGADRlti/Wq3WZV/kVuDR+QgcS1lGVBjAMyHJZTU4LJ4u0NCWKogMAYmRQN2OLn9IXufPbD4O//OP3XI2boSlCMs9y80CFW6ai8o6Id+eFmWJ4T9rNV6uvMiqqkt4UDtwc8q5pDJ9dWUbs6QN1soyL9ld7kXZHZ0n8iWv2R2BVqmLYFDPgAeJ4YycMp8kmznXwEAGFNhG9plgBIt215wiZRKMwrjIeYg0gyN+v8XG18eR7qwC8jeqz7rjqGdw47kLHipbMxASDGIwDRm4FixYqV5ZFWJ77No9TXxBUv7JwB4seikHzFJbefrmXh4qEylP+UNAThY2VpsGBFY/vZCqrCLAXY0t3dbsAcQY39jjE3yLcQ+jCOsAIg6e4EcxNewGxBloVgxK4/4FHBb4HMv9dCcIOhPBghK1OYPcvfFiIL4QiXAASGHKvIZHM6Nmfvf+vIPNTApEePHj0ORk+Ae/Tocb/43v/iL7L/8j97OMU37Obr4RfmIFyyqUhUPQ0oJUYUp+Y6dT1mQCEA69QYgNnqMqdJuSEfEw/Uqw95rlp1IiwrjmeW9Ac2pWcmg2Il6iQ7hvyHIM8MWQiAP7sBIMhckymvHqtLka2V2ZrMgRIAWFt59RwE3MRnAILUDGCukpOC5LyMnh6rS4MFq2S0IvORdgFgwbjHtkvwRzg3SNR4Ucj4e9Tp7gv9JSqBczN5OdJ4mRvrTyIdoFLSWu9lNZzTwZazN5cj17kCcDSdXb2cqlMBBcowTd9PFCunJQRLXQoYJgWybR0BpzBWVptkWixSHRYaLAQQkiGJsTyjs3CTHBWLdUeJDBbO0kGJBpek4KY5NFEAQydTYwEjgDEfBggB4yYJMAjSwHBM5+WHfUufgeEhvf4d9kWg2K9+4R79kfM5fRspjCyfMKKURzB1Jng5OTDAeGw/XXmv81dKSD0tDxZFeznwiboQZK6oIfsToZoUpzFMBwwbVY8qBW9BZMUl5ydQt+LkYdh9h1I7rhK4yuLQwEu0O91OBUZO0h7hYk6jDYwWbZcgqIM+5Sdz9psZLEjqe4TlCmOTRAjTgIjAgzbzDQLeRfg1D9pNZC2Vi6FuwAMgYK5TI1N6b7gMeqcdibTQgQEAjMcqj51J9u12JmKbsZzHAGyZ+dzXZO631v4DJTKcC8IiNW1tvkofMCuGrgMoHIB3Y4qlCXGEJS+l8z3CisOfYfwC38gzJmwABjwBW+5T3WA4omsLQVngBdu6+MpGPuCRlTkSXwOQvNdEwJFZ1C9wfBxcaiKdueN3OLEV7T05//NvfeuQzvfo0aPH4egJcI8ePe4X3/vhD//aX8EPAigf4ngcpM8sTx/G0cgwQqTkxQOmqaYSeDAdRBuYAxauyRyX6URJVn2qv4mIR2Repy7jsXbka0fSuTfLk5HMOXxNnQi0tWAqgTSotgGejqRmorPAVhaywJEszxhPR5BKDmOcBba6sBEwYMgiAJfp6Zii4gZzpktWLD++K2gqA4Bjdeko85f0LKOyxJCvWh/ioiWAakOosRF6MHUmXuiZZaY0aNyk40vuIm/Ofp6tI1NP7/ITbkW40UJv6DgXy9SEomSGxEmiPiW63Nb30qGlrOYY6Ah1BCAaYiWYXuMYlrICgDRLYmwgDMmwlNWperNJB5IVb1J9A72IL1Uy5iyFocqlYIdFJgt1FsirxMCITtkNgAua/pD9c9nKiHwwEGH8zg9cM3AV2fKcORYFX7g0+z/+wv73/4wOobMgk2gJANm5geixgqvU3XJgKy6MnyHpIqAeiea82nLg/CXnIZfapGBAyUysWy3Dy58PY7NU/XAbfTj1NMXWqr4At7rdLixTUw7C7rpHWErb5lshAAchgLkpqY4FopQ3ol5tLZALiNQ0wOV3zUbAEdzAHdJ8hVHBsSe0slnkvr26AJ7qUKZ4+WHbSroCW8L8EARD5wqnPsw8CTDI11JPS30NBCn/7hYz9cerjO4CAKR0LH9La4iB3KNpIcOsPCfXMFxYcvOIKfVtlVZI6jvBfII5SpGfj7Aa4TqA/QDvyuUL8TaALYvNMZVKLwAboYXAp61dNIcPBh+GD+MRLmQxH7oP3YLxICfAI1xJk4QQ45GIxfmThEy4lQj5PXr06PHx6Alwjx497hdffPnFuz/A9/8t/DFv/uqrxYejJ2dWevQHr3/6/T/JiGy29NOxrgQTtmJM2ZC5TnUwehM9fqBdgTEwZMxHgiBDQwPsOh0yXuSHZCS0+IZngjAAyVVAycySfHgFY5UYAAwW6Ewcq5f5UrXFY/NYuQQABR4MGyFHcIoLC4FLMzC8UKwAXLCMZEawZmwog/RcpqcnykVM1oxcK1V8JXWU+SYdY6vlEgAiBuA9a7d2zktt+ZUkCXOMJFuWTTnKXDYu1/qMR5K5qVbsTH3ytU5NuATDibVjX7oBP2dffTX8PnwdVgTG4quJKpNyZjalhp4aGgtWGByRtBwOQCgzW1AmT+kIwEiSYQvSrF2MFIHkuGAsE7YOydjAqLnaVqRghk2qm2oAQIOQVzkLlzRwGXQEAUyDhIFYMH1ImWevGqRqwMiEiRDAGa4f0Vfev6IptNBlZtqUr9PRkXahMwFiIOiKHJ0Sca0IrTUOXKLuFYrRMIdGPVZWcm1VlOTWcUcugJc+HApqHHVT4nCjKbau3ooAHww3c9juLCBntdEdIakdeYMG/HnD4bbzdg3yeaIsDBbUBGqLghVGMv6zRQGA7+H3vqFfid9eA3gdASX2+1AHxWAmrp+dBuDv6egCp0UOpNTXQk8FYNgxiMq7DA0mnoPHtW4OlWBN5rGy1JVgJYPPZwVYZa8km30MwEP1MkwtACQ0ZrHH2ofCSroOBgcBSvbnBfW1EBakV9LdAuWPAlbNARiAB8Or7krMMPZzRwmRm6+b8I6wMXPXfD2g4QfPH3MAKXxjnSSjb6eeip3Zs3r06NHjDugJcI8ePe4Xj549vwbmCY6e4zv/1c/Zt0fvrV9acTYJZnP9mFTF0pYMyjp1J+r1EUMIPlBXH9KTD8mxXOQNWLSG8UC7KqiLroggo1uZWJfMzOCKF9aAwRUHoOcxpVJvAEB/sgZwlQyHzATcifYLjxkAotwWWldERHyVuFwJIxboirCRxX01SfjMsBAEsEzyTeab8F1ihfnhE8ISEzBMlZUBoTP/gp2dKuczPFyRbivntdfiwQTYntBIyNasHkxpTyjP2QhO8QHAChMfpq2clzPNfBM9AwDGmBU73znUphTAJh07ynw8ma3IAEE79kN/hBjIIrDSJjUMxdRYILZOvxhhvWAwmLFJ9cL+GYDGAq1t5eooK2RsmQEYqddIphsyJuomlOJw5iqcS8HEANwkgzP1RtpCyxDQJgtBdMquQTBZfIzzNR0RA1+lADQRJaYyNBbEwogeeUxfGi/ePTj+s3+w+vkXgUemzsREFcVdMk9gJo6V5VXqFsw0s4LOuGg3B0anFLwdxSoNzpM514uiWim/eCcm3Gyq3WSaDlN1b3FrlwVcka+0ndweYSlgmjvYb3ePJNurV6w+4/a+Gdds2Wgwsq00AFk8PoCsPPWRhWCEBQADmxDh3COj0annGqAieqETwSNVwJhhANCAiaXnpJ4qKyhW3GS2ux9V+muYSgDAZJndypEqeDrKY5XnTvLYPtwAgithlJqCmWBgdkJQdYjOl0kYYS7Zvo3AKuvwjRdmIQLgY6vHBrCb7LeACi1Bxvk9GBZCCwGHL2NfWQj1ALZ5ZS2yTUxdpCBYMzGfnJEYPv/Jz/4h/mzkqT989FlH73v06NHjjugJcI8ePe4d/7Pf/M2/9Z/9uXGAX/9V/PTHKzbeHFuTzdc/j3+Zb9KRXHsOpFTIljpCjYVHbHlDLhgeqFc6CwbgOoLX0eOpurpOXcglU75E834+zo4KU+djX8mNopkVS0fZIQtkchr5+236xFYWN8mpdAYesACpC2BN5joxB8zkZHoMIjU9JfAU44hWIeM2IguBTj4YTATl9B4uZpkfIwOAM3yQv5eYCDJ9VGwXL3AGYIyFQWKFiSh5C0uNt2Ya7W1NH8mD8Q2eT7Ew4UsKcYEHReHCTBrAdXIm30glKw8g30breZMFKzIkyVTdhOZjllRZNAHABkOHVgAMhAB0BBOFwpwYl4k9VdO1aCwwWE50iQAYLNyQEaa6wQJdCQ0yZ+lgKwXnB+t0MGDQWDCmFRgzKNBYIHMgCVJDOAN2A4AvAmk6bS6DE1wthk8dNjNIGOzqS/3v/P3xn6d8okTE38cnJ+rKllmsCGCwlfkzdXETn67JlJsI5XllP11VHqZMX2vm0GjSrowGq0f+NgIWDuC3Zfby8SmRSg1K8T+z9O5CjcYfegeqpP8tnQcwxdLF3JSZcprklCr/spLoWcMKE4uKr0mjTM44iy9+qRTVCtoQpzgHsMJ4TMsYkMGQJ7Q6xXsPvxb93NOB538aL/9uVusHOo4egB1Do8jAyqIEgI1wQXyVmozHzIqx0XQ7ZlbU+fYIx+ryqmStPVQCkwVys69I1lVUFqlJ+XvZ7r5Q9nlNJlKswaEqIJyaG0Nr3wJzEGxgOnnIKxuBTGskcYQVpy0Zfoib9zgCYCH0oZfHa4L5O5zKQNA1GmzAk/Gu5HkZNAsAR+jDIMAyr6xFoolU98Oy97djzb4aPXr2Pvy5+Rk4ffHsUfur69GjR4+7oifAPXr0uHd8+ztf/KXfBv+jeEz4l//ez37r+eCzJydrLfztOCEthapAIQ0iSriuhRoTIGbLeL+AxgShyNqK62QI1FfM5lQE19w8DtSJIFHEjCnZffJoyMKJemFm+gkB7DJxh6m5hgmAhDa7HpRbnlWllTE72RwvmBVxUgC4ytJQZgnsB7hyWJEAUwAw4S9p4rIZgCUm8hIV5r15m6fYasJDzIYls8ZLnI2wMCBWGIuyezBhhMV7nErKdA0XcKdYGvDHWCC3ji5jqp4D2KRjELnaYpmM5XlXzUIT1fMTE57RV1fKryJNwRjiVD1i6TkaHIQCGMDQwUo6A0u9VydTRSAX6LQVvJi0+S7eJmcrS4GRDnUWgshiSzDoSigVY00JLGX5Ln5U48Cb1NjQ0ZGyhopTutFZAELIDEFaCCtgvh2stZAK2ZgRzGXAhzHIf8hm/5Qef//FSHu3cE8WI8aW5HoMz7TXeUrh4l51W1nni9nmZxNZhlmlqVWhrweaQwOg6M0wyzBclK2TzG5yW9Y5b+Pr24qD7J9vQXu765VeqWS/KFnblhhvy83qp0qfh5iFsOyS2225wIm6vEzcYd0MOzt6pF1oEJTbIp/ifImxSWKAuY7ARWCSr0J7gMXwD6I/7f6zv/13/7/s/XmwZNt13gf+1j5zzpl3vlV1q+qNGIiHiQQgiRBJkQDdskRJ3ZZaktVhy+52KxxSR7i7JXbYbYeH/oMKR9gRdneEZandYWuy2pLamkVwJgFxAAhheHgA3lhz3TnnPGefafUfJzNv3qr7Hh4IEKQj8vujbuYZ9j7DzlPn299a3/pGE24+NyfACjHMlHpAkgUP/b0hzWO2FR7lmwteql4ty6aut/9OUwynF+nibJrFdMw889+v/q127xfb46UOrfPHyZYzqiz99pyTcdleJgk3Jek5Yytlld8LDGkDITYhmBLUSSqJG+gyGNKr6hV1GS+L+lZ4vPBGmFNcBRjSA6kU4HO5lGphF0HOrIjDCVFC1OasWhJga8PCHZTe+aK68gymAC7pe/h6/g3u/ZgwAl0XAV5jjTW+y1gT4DXWWOO3Hddv3HR/zJ3+Sl5+Atp4d775Tw8++GJyr52fTYqWlCWlYMQVmxWh56SVglczwyWFqBTFhqTARP2lMzAiqNrTAMGeBpwGteeHVx7DWINxdmPTWZYwVZBKFxKpsvWeeB5eIsDHuExqflVdSYDdjmTTa/aOXOuQNdzDcBHu62HGeBs62eTwULZ9EiBdMZINNfHl7Sx/FJUtjiKxlV1q/BSn3ePYanhOC6GG1XnjwZUFUSvUzaBuADbcw7fbZvmCHhRx07Nj8VAQKSZtWXkbTksvckAJSIc06oyXRDcum6pExj7JWVQv08JqmYQyXqrE3jxe+oJGds2kXzZAlwS1Z6ZAVQkpFd/TZEvOKwtohQRPIRhZWJTESZGAJpOAoKfH1+UxjLs6jElEZVMSlFPZmRFcodjq8siFFQ48fbVTf2GwIJ+rO+lTrHix8ClKm07dJ4nuk7LuJc33qrv15OV8Ysbn3eNd1AH+ttGs4p+fCvmtmFhAoovL9Xbqbg3LssbvOzJwn3ietv3UZnUz8EkWpbZXVivAo2z7pne3WnBL7wFNBkCPEcqznDQ43eaoMxwQEnMuMIY7r1809GrGhyy4hCd2c39yL8i39PiYrU0zV3Qr4ytvP5/Pyq12f9VJ3XLOShN3dRyQ+CQpYUDYZDCm05dmP99ejddomqRhRoh4ktQq9z6haWgi4ohnrEu9YQaNRWQKsE1MxU6FELvM+61h/RXb54TgkL2dhSC8HFUD2svs3yG9ZUbGMhjbEgXEtiK6colCA23OhmxYahHDXR55VgFPU4CTecd6BlNICB8lX3/Iz417aHFz/+2NEtZYY401fktYE+A11ljje4H/01/6p//Fz3762UMCH/Pz02efOS5f+Hjja1/037efFQ5GURVV1yRguAjyW6G/SsOMMg0mcw3k4nXfq+VZ7AHB5iLpdHX9yue52LLCFIp+WCUMz7nWBRTEr+XuRiJRrot04uU2ExBlcN4+j73e9YuwwMCktgyGzvBMGqHaIW2EGX6moTcnya2aWiC6KkW2eguP1Y+wHYaRhIssu/m7r09SkeqqdFNCkEjFfq9QG5/EU6/dtYUDzTLu2ic+KE+/xj6izmZcDBqSeWpTANUpfl19FzukATzS3XnDSijji2jeJzp64utis6wMXEmBadmYFr199/H88ioeFhrzpqXSu7RmxkBI6qmdkydBFF9iFNfO5TyxaIIIOqAl49E1thl9jdsv+vWDB9/of+jZGL8KIG0xnOnWgmOzaJGee9RD7uU3lha782TgJQdeisascmDeSQoGIHu4qpU9RY4Xm/mN3NTy5CT81prwxY4rV1ku/bkSfj03tfzb839+F2iKXcYXPIEqXP9C7r2SBirATILluhq2wxBICM+5wti5CqY4Zmd1DijT8GG2vWjxSfYLWtUqW47WPY6BBtrQkxb9XfoK7eHAGZaZmONf69/w2PkEn/+Vi5YacKpsb5PcDqZ+K1EvxrcEnthmlUewiIWQIHsHGl89W7bq5205QmnKsBo/y1+6xc/KYFyVVpqru2nXOV7sL/PscsUjQRBxgB3/fqhpZW0NLC9dQBzo0uR5dHEYzMdLJacP6YXzkJYZYKkNaEO7aq3PJcl3SA8IiS1hnzZIWwEqSgxEkr7FraX/c4WwnyjMOXUCry1WuJx/nl8tPwqQf/cnaNZYY4011gR4jTXW+F7gRz71qb/Z481zbgkKwVufPdv52Pc9234r9RNTi5yEUuehq1UQq6wwCKGiQR5J9nTFICGbuRV9tadBACbKV/SWy5s+tcTpJt7MzZ6UfwHCzdjpzX2SLvOEC//VSuQZ68X7+mhWL2OvjJtDrkstl1481qBp7FiDeTQmcvfiMwhNZ8gi3y8miLDAmE6d3Nc4p6wE4VU1uMkgIHzMdiVYzV/933WCaA0bYZsriXerWcTbevg15zplCVCU0mnq0dlyy6wMXFOFlS7IxOIvl/++LZZsUH3AlbRlzmqVulW1JeKK7co4JZiqP2ebOv+b4DUFVcbSCNR/Ub6Bkoib+UIzCB9YtUiMRkhCcGhbTWjR0s1aM75vh7H6qYS+WhFsFS+qqiJLPouSEY4v0jIVRKJ8lQMD4aZ1evGSsS+u+ttLwQC4vdjtMbvXfIewZ7+ee9fHxXnk13J3MwbKuKprJe9qmuPiCi8u9FN7VF1cve47w1iDUIMnCHBFYt/etuop6MVeQKUrXmK/T4U0b3N4zO4yWhi9kvfOP+27x5U11B7HPsl1zq/r/Wp1wOy6PUdpH/ddzXid/ANB75qZ/Kmt4+HJpTMF2SZ/v5c1TW5L9QENsAuyypNPoYryizyhjZexs7N5ctu87mNZ/pqUCe0GwwntAOuRzDOZlU1nXDNDlBppS4aHbHV1HGB9SSZ0BBIiEWk4eQ4dBktpt7qAS0q8TK9oc14FLS+JMVSWe82ItJqDW2bzKiT4QKX0Vg+lGUENW5ULfiolW0Fi9W9zB4jIOow9q9EwBy5ct6qeE9iE+3z5hNaN26j+1EeeY4011ljju401AV5jjTW+R/gz/+NnfvoPfXq2DeD/3HDj9rnTqr3fefBLzgdwa5ETIyKiyEVkpFyIwFSWyTUz9MQeFpuL8FQF8Wr5ksHa0wBCr56bKDe1guo1tGojcauvleQiUQ5a+WPNseLdFG4mTm9ZteiJZM75lz33xGsm9/KD1dOUMHeifIpo7IKRynarDIDxBYPVy6487XEZIDTFrgYOBsaC9GTkVK+HQoRtMqgCqqv31yeShBcH8S2YcIQNLlderV5hZ/gKQXmEUU28KuVVixqcLVtP1Yu4iv1epfq2mIxogIZkSeUfuyKzRzLONaiO1pVEl67aqohEZkwJ6JQApV/UPbGuSaUqyCQ2Vn9DTifabsiwrXHuyzwHeIYKUvkrxQQjWwukHphg7/7jv5hd+2PluY5CsQNtIdQ0nYm/jDLICDyxWRlMqhu0PJ4lOV7IuWXsOMslsPLx8hIu6cMS5dNvducW0JctsmoHEyA/i9yNGDBR5vTmhMRAuMWitvO704SfuhdXL3nHyrTfLprGtszIqy79AhuMVqda3gHLSrz7HFcNVJxZwdf4Fnepyv9WmfZ0K0a3HIBbHN7l4MlGL52f7jvHniQ9RgFJm2GH4a4eV8HAlpqvsx36vo6j49yNM30AFkfLz/9H2e/54MnR4aUmn/eoeTj/PAt/gnzLD0lP2Z7h23KRkHzltV1ZqLErUd7pDTfk2NfLaRFCQwdAIsFF5q3qpjO+Jg8CrCVoMkQ54H5XxiCJBsigLtkxOy3JIklPaSZEFa1tc14V413y4fbl40oWau0qYvxl+aIB7YB4QDuRiBVJeYk+HRZBJZX2uwiZvnQdPKtuUrpxSgJn8ABOWCQjwytkr/HmlK984gasFeA11ljjtwVrArzGGmt8j/DMe1/4UkpwzC2XOzn5nQf6vus36ke9dP+hcw3j1MwMVS1FnqKaF9KX4kmy/Fz9a6Lcg2zmLsiVZlOXmcsZLOOipaqNtMK+bkwQie83Lh3lggMnp4tqN8vDWOXAqiCP86099+Sme2+qnVUf14quS5StdtcUGxg7z/4VUg1tGY5X6DHKWC85RY+LADhlYeQjAB2ypvt4qdZenfqrZBqlKxmDdWe4un5poLVah6YKfUwJUd7Ho1dq+4CzmWgS6blQzKNFp/h+2YxktNLeZfa7Ij+OqLeYjKgny+opq6xAxBW7yhJVAZGFE1VkxhH4ZbOvzbqk1cYqYvHrqm2ZtnTiSbocCbm/IKC6kieeYO2+609flw91bv3sjh69JbfRUSgWhgEW2jN8VZ2VHUsw0eDAe9DQYKIBK6EIs3uNS6m8E9ech1Wt6UspwaxER69ekAUNrr/Yn36zs3KhFqGn9xrhVgKan4VeNJlP3ACCRLmB2kGWn0bp1F1h1Fxq4l0jnbrmohzxdwFLr6lUA0/iKsTgjFbtqamWJZbh9woRdoejpJp/IfEXUuQmcXU/O0z71M6IAuIuYyCjqZVZ+so4v8m9igO7JDfde3erkmCLQOd9d85+mwwibEeHHYYV+w3JVGcRWcDYtaWrmSo4kPFoZm79PtIPb22fnNz5O4vzBaoC11s4g8zbnKTcDLBzCfpp6vuUZA1ImKHUzWiGP6bdXLDAlDDVoMHwlO1YvWqXhhl2xd6Q+9WzyCPZ4TSolHYlJLXio0ykd6D3Pc/p6KBJ1GG4tKGae25fdD//u/SsGtILiROiyty+KthbTbFV1llUAu9VQ2/1QbTBqBLtV6sKVx9CUhNMMowb+l6SsjE3vqq6pw0hoxD9NTb9XVQ/8ty1J6/kGmusscZ3jDUBXmONNb5HOLh+8yc/85m/9ac//dGQtx4Q/ezRazc+0t3tffj0Sw/YHEgoRiKZiaiWiJmHHs9ZhV4U/XgqqlRNlJfx2z7N5im+T+0W329czRquDjK9xIE1diUqgMf5FsDTxXwXRKgptuceP7kWPJPUDT0EyDRcLn+Ub68cLUBxHh6edMKtuSI9JmjmB8ydnK+gFlU5X9CTOScXINGwNQ+0XnIeOaNVI4gIFGok1attlXa4VX4T99qSyspGR4/PlyeWqh9V10GJy2ZkVsiwKkhI6pOm+AneiPqVr92LjedXLy6aQOSMq4jk+eKlDRUWXWbSaoBFaOgQwdckkLyh/Sp8vqoirCAJGiJdNKE3fPMLzR/q6njj93mD12uj51s7nACxBr4kgYZVReiaM6RoI2QahGInGqyOvbkV1oqXd3IachrOgwWeyB/nKWOqFRpcf7F/MSJjNz+L3M24jL1y5i4ik1f2rS5SmCO4mzFE6XRltF9KNn76El9C5eJmannlfbXMbP9OMoGbYjfco4vpKAAikiaDp4XfivRWjHdpwtRl3NZzSy0h3OVoh8eu1TyQKqnbS8pZ2wEabD4/z34P+9R2OJ7x/W36CkfsAjcYAD7tV7VdVbU9l6QKG26KDRzrSVLDNhmg2mEYEG9zGGoGBGQxXotJos1GfK5KdXTZTe/eeev+5052d05+4+9fnIhCljF5SGMXdumMbNhKq9jgrnPkl+2zJ5y3LqPoh05nbllfzej1aSb4ISmQEITYMe0Ld2uhJaMWI5QuoyaDanSFmrYZVbqu1UhEjErk5KWJItI9ToDOotPBosHlkFnotLEliiWYEchc131S3V3y23mw9KpWfPksezIKiA/ZmxE8w51qYYz/Fje7jBVCLMGYjutVIjDQhQi24DfRBDtly+N/8AN0XQNpjTXW+G3BmgCvscYa3ztIRPPDMKL2gBm4D74a914oOr0De/defKtPgGotjBGzYDsLD6IVN6xF5ZKlCRIS5ibKmbkrMcy6ot3NF1zBC/QpsW5l30oErrCQgkEp+mEZeybKL2jPCpGrsO8epxraMgiucrqad82891Uee3NRxfdudlA1bqKsdpDN7jU5CWsHE4nyeUB1OTdHbRobGgsk5bzK8UX1l0VP49Ifl9tNY1sGT+KlhDjDn62UNgFO2NniSIvyWe/kDbao2JGGy7fcchhMW9o2F8HPeRm4spoVrA3Gnto6AH02kquq1FyW0xmUdcCrZN554SRBKEofpW4mi51l66IGUmg125B8Q49VcK3WzxICtIMcoYdItBgoIe8Z9e+3elth9vXZtKdjFQbajMTGGgSS1DSoAqFrzrC+OLwGVZWsi7Hk1/MVAXZOyC+CBZ5mvKuVry4WXhoBEs3TcZ0wd7pwadNVCAqKd33sMY/nL2feIi56tZ8reJdfn9u5Pbniu5ECnGnoSbza7xmtys7tCcw0iLA7HFYbR6QdRoClFjDb5bTNafMoy0PjJYRvJQAR4QCgxzTuVCc7bLecIZsv8WVLbUDrFvcG2p7hRmQOxxsQExyW29UvpfK7qqtt6DAhiBapxaoM6e7w5inXVQeCnxA0bRK33UaS0YWYN2+9NPrp0Ud/4uRw5copjOG9HvV99AcgZBIEquxy+ib7s7J9VjSXsvNTqJYrqIQZMCtaNTPXfvs0qsvSYHAq29VwibChpk2GHcYtBgghaaX6BmJ9YpSAHIkzUwtJ607uks9LGa0cwI7MDtmr8nhZUNkhXSAmrIT0alVVv60l/ZRoSLuqGBw+Jebbq6KmEw3anHdgR5aGZ9RIOwwDZj2mCgnNWjIAuA+gD6GGfAW1APEZbzz/McI6qusaSGusscZvB9YEeI011vje4Yd+8FP6Fz/z0z/56Wfew8vfoP4zDwa77/M7rnFMqxyPMseKuBgvKHEQDM5q4uXCP0bkuvvwQX5tRSJTKi/oq7ys3k7SfUfoUuLza3k6c/3YdXtJpfoCbi/OzyNTCWiyENAWbB14lG/vu8d1d/gt+r+SIwErTJgW07JzcrAq012Ih8A4okohXsVqxZRlZ+PSH5dbFTfecI+eWF0J0TNhTKfJIMhTXAFaYkeNWsMvJ9aUw3kAZFw2QxmDRrJQLBeEeO7SvECXsz4bCV6LaaUJL7ZfOWKhI5NB2ZgWjbqDWwXHiqKiSr+sb4utcnRDbKWYtWQaataTk4qfhuMyHFtAx8ghOkRAY2SItpHHbMrDf9F8b2Nze0fvfYEP7un9SIJQbSyBp0kg4WyuHasiIngkPTch37ngwOBdG7uxm59HaeW7thr2DDpz5/doORWymCPYdEenRfvywtXcYFYG85MLWFkhUV6ch1ROb1HuhHktyvKziKq60lOUOdxKTJRVNuZXy7x6RU/fFlrOAK048CV2dMzOarHrJeK5YzldxpUbU8AsJOsw7gyH0SAB3AT6VaXdFSK5SbhIlJ216oEmls2E4Igd4C43gEGxO8K3ZbAc/5tm6BG3Ne/II+CAMVVABH1LmGB/XV8KSaFxkwcJ3oaeKOSB52qmMbGak//+tVs/yL3PrlwwAF7JOHiE1r1swzzwNvpaAzINz/KV+kDzaJF8uaDoh043cTrJsqGFOfxFyVwgJYzV3+G0qofkk3Rk3GSo0NFJi0Eq0ZL6hpIGpAITcpTQdayGIRkQc7ni9DycABbWYnqRzatASpQQgiaE53qzhq203ymBSpU6wQYjS9ii/wT73edo2WwVbr2MfN6Rx0CPaYBt2pGblGE/0YeQQAw1OEEfA+g5b54w+AMfAsizm9d2nx5Ca6yxxhrfIdYEeI011viewjPMvsZ7fxg942snuI9f2daDg+jlL+x8eJQ3p8abSbujceTNjOtQGnGNilQR0Qs5TxFpSDqZ+ycJ4HQSE7nZrPGt2OaVC69asVhW1QdOp246bdZujCUqknnZJNLpPH94GZ8MlXmXsAhm3nRGdTNcCX+96gB4m2NYbFCXga0FK5x2/u48p0OxW9Gh+cpvRWfmicfZQfOyOl0tb4qdms5zkrTyt/BuoNp2BqNid9bZrB8/nrY1fqsVoNqZPBX0CxBq2mACBJXrlQJ0OJvRrLZauRLL+sCCEskYw6Bs+KXnGru43eqK7crEXRhoJ/iZBp5YVAspUBoyQXFsyZC5/7NdOGAJ2kdaqBDFZ7d4I965MfnCIHzJnspWU0d9qZQ6CUhq+LN5DnblCk1WhvNTk4s/EuXetbE5j5LTcHnXkpPVjHFWIhHmsEXQJBkTvE1Q9MoVfIoJa7LKXbVyxirOo/IsrETdSkA251E5c6vo6CrU2d2M89MIxXyLIOe3jdR9N3iYb285IxY25hWqHOAnGq9KWwckVRJvQhBgQ2Yh2XX7KLybz6vCxjBDH0EMXVgq3KcQQsh4p4aSECYEh2yf07zDATAtLlLxm2LHpb/pjLbM0YygJucpIZBoeo8bN7g/4taWHh6xE5AkRLt6csT2NsenwWZLz7TletaEZzYK7ggcv/bkZVLYhKSF8cyD5u0jNl7nmT7NrCpWtLL1Ipdbs0eNbObWnhusNrThjL3LqbkRaYvxiGbFfoFtOQF8tSFpm7FPrMKWnln8vfSxZ0vXtx0ziwAAoUxJREFUlnlokqjdjiZTd2so9RA7oAXc4sGAZowfkYWkQ2y8NMpGAuJEQiAl8olTogHtlWxeneEvz2SZa12ldseyW4WRL498QCfEJk/FSHcZWq3tcdLmFHDjMhokJDCEr1ZNAzChnDB6yJ2M89hD9ac+/MzT422NNdZY4zvHmgCvscYa31P8vh/9lPnHn/nv/vSnb78HPaH+sw/e+vRtvv+Fphc0y9G46KhjBnk9cqZaFKhSluIYNSKOM+cbAmjDjCbFFlwwVQnzhQj8NlHQc7xLQfjp3XV2vxluJpe2WQjF1Vu6iXLmZGO+b/VG7kviXXJ7uuqgVgnhU+g5Rz24m89Z64IMq9OLi/OojF1nRWWCi8xS3p78XKUSM9ZgnAfqvHBg7t7OD8+8jmeSVpm4zfzkyAe8KAcGZSMyo5UX9/lLv0/qYhUajH0NLL7FC8igEoqblQK8UEYvMWGPFNUqFjqaxzwDGprR3CBaNSRbmQpBBau+LzPXZhpQRd1KgHaR/iJooBKPE3bt6WwzevS33mj80eFMw+ZFeIGqLgTpFVnXkySUYKL+YiQsNhJMlEHo13LA3Ujy8+hJFrmIiq/ib8dFG2jOC1xdRYMrXGjClzd4ih07vdjpobGbPWikU7d2MKmWeKCxW8ZuxcYvMop/C2EQ7wKLBOALVKVlq+zflQhYW5GlLuO+NisT420Ou8xCks79YXie0EUfwgyqYkNVUPDSIek5AD1n/OEoDzhi74jtuxyc0xxrB5iWc/a7aYaV1dyB5E3zyBKGWF+TKq7hmO1Njo61ii7WJsNEw4TAEm5zFKsbkFoaLmPHL6fPbpWnfP8f5Rv/88W5LD+cQBkihjf19hd5f4yfabhys1RjV8JcE7eMXXsWggYbyRPjpCbz2bGqyBDQZGQJNjhG6DAJsL7GIelNTmdoR2YBaaCTcFy4J6X3MGMfLHQ8tztxlJm7kap5huMt3qq62GECDNlMxGvTT1jYSq8QdX/hMx9eLgx+GReHXvkFzNieF7iSWC7vUkU7VB1FhCFJjB/QbNpRHpq5//MxNOENaMCE7DVQvp5TwFdaPVhbQK+xxhq/XVgT4DXWWON7jcIhchnD+5/la2+w9+qvfPaZn/jg/uxHj1/96b2X4sLBSFw0PMk8yVTRopRStFRxDEYQxBhXkgVHXZAhxO0l2axxVZ8rtOZqEfhdHrsmlyy1FjsrS1kYJNxMnN5Cgpxz4NamMwK+QzX4pnvvvNjpuUfkO0v66vRijd2nT6NSrf167t0YX9XY253i/M8ZrVzzkYZtok3v8M3GM7uNh4fjhglz2w/CII9Ncx7/vOJ5m+LXtPoQNBjV4ZwNtKqHVK8xArX4Cf6SwQJ5GQCnZbcm6Uz9QVFHiZwxKjmBi0XmzlhdOa0u7FAbDRmlGlZqb+Z73jAD9C5EzH1tU7CIizYR5bG/gZLAR5KjL4Y7fW0s74MvMdpCtEY60wDRrAwzQl+swpYZnZat1dRfiXK/ni/zwL1r48WleyLzXB9lW/veSdMZphrWzQBoajgu24HY07LNE7gYGxeU99L8xWX6VMm/HmQPmtUdr7RfoJw1nkz6/XaF3sszAk9ja9X8rCpipAlComHz8pYhdpfjADuk1WUcMOsw7DCeU98QQngIwAkcQzAnwBpDBG1I4Brj90RuUn49+NCb+sIQKWkc0xtqOy1D3yT7TtLVrGkePWYbKMWelbuuxCH2dX3+htw/LW5PTD4od1BtyOixBDP8x2wd6P1jts5pKKocobWOH+zr4ei6vPXr/eiE7h9m/I8uXcIGfP8OkXKotyZIi1Gsm6h6kqDzR1AZu8ROGbvzQuVgz4IozJb1gV80h76OgUT8QNMqG6LNqOKNHR1vciIQkG5x2pBJAwKduqk27sdY5BwF3kIS8o8ax2qaHzxqtJ/nZIu3BmxWiiswZDMk6WsNdEceW2oD2lUvIfHS8srX2CeG7ry4mgBkGj1R1XkxLABm+CGBr/N03ynBM3JndbuINCQJmMV4IUEDskDi3TCKE3qQztkv4N5k9BavZEze9zFaXVT/5Kc+cfXIW2ONNdb4zrAmwGusscb3Gj/4Bz7l/vXP/Nx/+ekwInqD+A7PvH6mXo3e+UH+6JvmGXXMIK91TOyZ7CIAVdGipEAco2UxKLaREmMW+u/Ky+lqAOrVr+9PLX0qZvXtd55ToKeds5axrAtXpHg1K7iSp2wZBMbOafA7hGrzlIfwAk0zPM92ms5wXGwvFy54ziXWVH+xX5yH5cx7SmN8h37nOC1aW+YoTEe537Qa+ibZkX7S3ulOj87DthcWEuSXTmGxb6LznMM6o4oX9zjLCIAA3+LVGNfgMbvzXRSQKsK5RuqTzvBrknmSxkUTYVA2Os40MuPqmlTxz1WMsq3yThUVkobjvpYJEKExEqOnzGvEBIiHHnFzfHjY7L74ZzfOv3mHD+4sup/PjLRkuKl2Iu2AtK/NqmUX28QmuuITVhl0xV46dXzUxM48M3wZyfwUDR7nrZ57nGpYGXR7kgQSni5dgp++0Zfu15M3r5jXXrq0pXdtnD1ohptJOXPTRSa8qblO9LSG9q6F4KdFzxVsOcOKzy/XVoZqNbXthf1VZTdVOU6d09zDbnMYkrVs2js+JiA8S+hCH87Qx5BCBj1QcKEGwC7xs8G03T7xd1Ofx37vNX3xMVuLwjzsyHFgEktVSXswplNTC5yU20Bdiq+nz+27x2+UzyVlEKpNNGia4bjc8MTOxN/lOCXY4rjNMFG/yl9N1HMH2Qb9x/9D9ok/whf+waVrsA3P7LD545x/6Nm3dlstHfbovFU8A3pWtgBUi0Foz4KlLRzg1XJAgqxqa9c5bZhDHws0VEEayg6nliDAAluctBhXscSBZDU7cW0ZjuxcGz9HjxAgQ5sU26bw5cvRR+ukDU4SbfSphTQCJlYabU6HbC7Hry7zjZ8aCyO6CcFcy1WAeYT2txg1F0+eofYq/y0rUUAc43cWP7MQmwVSGxZhP2EKd+EIJmiKpihEwk2PlpREdcpy7YC1xhpr/DZhTYDXWGON3wFMA1M84wWT7PY1vvaQzZ/93Dev/yvTZsd3Jz09S8pObGpp4Yk0PDJX0ov3L0HLUpCGDClb0zJABCM4RhM37z8Rtvcdx32+qwaelpf1EntYCaUeazAuAlsGPff4naRgQMkqjVdkUdFXfEmq1OJxfsF+34bhA+L0kguytLrmW58RJ8VO3QxqWXJHdpvOABGvE6WPkCA3oaPWHfgN4FJBYJ2/77eYrnbkkaD4+Fa8qvmWTIER9Xl3SlI2Z6U3Ew90pu5MuzXJ2uZsQCOSUVy0ImeMkopfOWwFpIimGnoM3VSDSaEdOIZ4cT0qJhIjI9RHYmrWNhuTeHNiue2qzFMzRZnHVzORtq92LK0a6Uz9Kqm16xzfy29U9FsWcx/lzEFJp66J3HmG7SI6enlGFQ1uih1rEJRtX5JUg2nR9o19smr02zHhxZq5kZIo0NkYjGYNoJmUQ53bJpUzD7jkCA0oxdm8xOtKyd9vVwimOI+cXqyxt1LaWpMySMqd0FhfkmX2bw0bYn2S65y/j0cuw5SoPoQRB+dfsZNOkAwUvH7KCJ5HZwvhF3iWvOll2+Y82Dlh75utvXayleL/WtD9/Tp8IJO+7hVlGEs+FK+GDTRZmmxlRENawKS81demLzbV4LRoNcWe5gegj/KththJubT1bjXNKNNgW45HtAO1MQm025Bo6BPvPBjrXc4e44IeXlyy57x5VaFrIfn7vMf7oa92RPsxPVv6E51nzGrsoBr0YrMQe8vYNWFeJnOjsoakTRmpomgV/NxhFGA9Eo84JNviOCXyJVbYZlC3k+Akd22mjxDQEYDM0BEkyIdoHscP9z/+sOE/o6cl6WN6N7k3ZHNAEyUk7Es1ncCAXkKwzEFIiCJsLBd5vx2GXMoEBsjKKNWgbgZzQXhlnFYeWouiVnpGMyQOiANd0Y2FLtOAMRD2E15DgXSe91ttOFXeyngr40svfgyFPLu5v/NtDdQ11lhjjXeJNQFeY401fgfwo5/80ab/T37+//7p6++nPOHrKXsPXiuCW91dnrvz5r94z4Gv3WHRmklQJ/fJPCfzJK0oZl4GnkldkgB/WnqIUEKp4pbeTuqmfhm7th95tSK7VBz4W3LZt9F7r0gkfloEXuHAgKyUxpk3wqoqWDEioG6G06JdN8One56WHcCWAbJq8tx8cjsuMxp5uxVPrnt7zPeyZVA3lPYcb29S9hpu/w29dW3jzf4xEubFIHC3c1RXtPc5+83wrab1iwXz9T52V0eP2QNqOs4ImBNg8vnb9kWSbVUnBrQjk2G+gRAtbcAXp+KpbTJBMFYdm+GjLWSAJjCGAGZQKaBvwLNoX9ONSPb3xr9RZh8oMwl9qnJKgVSFr9T3JVElXhD1jBDmJBZFq+2Wp6VSzlxYcZm6NFrmO7DQ/yvcNPc3zcjqvGbVpjOqXIub2Co6wFczFZ/YI8qAVEO/eTGLkWrYqhGIpc6knDfr9GKQ9JudlRutrbgcqO9uxGXs6eyi5O+TjtDytj+OuU3ahgXC+kiFHZ1Uu2zLSUT6nPb7EjYogRq6w6Pu8C3vCOskAUn98cA9S2U8l+VrHHMG11GBNqTQIn42iGIbh8F5aycNuMu1hGBA22rwIKinBLlG92Uwol2T9CEG6Olwm+MIGxPE+GM657q4vFJQMipbodgGSYAd4zfEokzKuft0A9s0QxSPxFMDdpc40qmP3aZ/TiPQRIGCu/88e+n/6h3/9Ww5jGMIYWcfngGPJsMjbn5BXxprc1Je+EVJmDtz76v5rkZVwtwJ5001zBDFJ1mw37ElaDIEQrIWYwVP4kDzgKw1HoUjyxEaIxZNIEEfITFMkGuYOprwauPanp5ucr/qdshmgleN5JiLMJC2nPe5uUw51mq2TgEsYbUklGRKwMqg8CT2JF7UGF9VfC8NmBHdFv2HbO9zHBIDVcQ7ShWP3fvmlDPYhH+BHlO8ynFBw6BwJ+e1jPwPH3zVC0B/6qPPXzEi11hjjTW+G1gT4DXWWON3Bh/7+Kf+m4x9j+YPoJ+j+bNvnHJQ+o8ft9u76clDE0KDUqbiYcTTdJBvNNyJK/O6GnHRWpFbBS1BKERcSykUhduZZtMmIiDvOrz5uwQl3EquWLoiElaMaBkXXS0MjPXFVgWELxyqdOXfK02SLnfy9ivf5qX1ig2oyFi1cDM9xfXqYnec4bR+res86gcNlHIYDFoamXmC8UqwJwEpi3rNTWYpnsXz1E5pBpJa9RDxsCsTB+qSQr0mqS/poGwAM7xZtluT7IJjq1r8mqgsVNORNHp6kvsy58UBGiP30RoCpQ8z9DFmCzPGbEx9zVN3S/725/kj7x/R2tSTSvVV0SPd6sr4SLcW3SkiHkm/2B6rv6hEjaIiON3EXwQbVxzYifJb5mw50E60CfTc4xp2qG2gLaNY/EzDadn2JfmkHP2Kbvecoxuche78/+IPVDa5MsupEc1zyH05rlF20hJhFBgv7jyqRaW505fOQFuhySeyNzZaaMv9gfGd8mbLKxQ911YqkV9q25H+NNooPUo5LaON1qQSMeeZqGZ+xJFruyRVzL4r9pRuRxJXUtcklIpwUx+5miBEmuCYGvlL9uEt84oWjJydG8Ov58edgLPQpsPWnm5QdwazmxsSiVf077ZebMWjcbf1mOsPgtqI7b42+7ohQl7U2oxPRZ4t74zL7dREz5eHZzR9eC+vK3QZNNT9qt5+htdfKLcHmk7YbhYbj8RLUxpq+7S38+mB3k9p/2j8S/dmN7/Wu9HW5GNvHX7u+kHi+O/Vo9qgoaPiuBZ6RXrC+97r/LNBfefm8cs24K4+f7qtO5xs6/E2h53XZhW91ITgg17/r2f3jubjegceZNyHDx3S+SAP93dOtDeiuanHMX7PjM6L5hO/I6BBCkpogYkGQEOsS7KtpxucWEJFGwwboNBm0mSIEJAHmm2fnWAJ7lkF7sIjtIAW8RlhZRU2wtzEfYTc9G8590oyn0wgIGtz2gYUSwPhMb3l8/IWd4f0EGL8Nv32ohowDM9pCwSatBgAVsIhnSUZnrNfmJYdX6wnc//qc1rntGpilx5aCWEVvB0TtOdLmk07ApjBDLbQt1CQDSZn3CsZZgzh+us7fOo6qmRPP0LXWGONNb47WBPgNdZY43cM//ZPfeYf/5lPt38f77vDKw/Z/NlfuHPtY43NckoUZaONwJyVWxidFt5UN+pOdpRv7LjnGcGkaDWd8WGxeWXAqNOOvZmTn/qUJcxZBMYsaLCBb4v8vo0yvFQCV3i4X8+rOjQozsZVHBielpQrdREYl8HFAb/dsfCtafBKQdp3aOUCc4Xz8iFViYgoXjoqaxtHxd4HnS9+efsjcpr0x3UT5lU7cdGcJ+guGs8W5UwVNhigpItipD6pp6mli2q2VH2hygHuyHRQ1n1ja5LO1EOlZjJgVrqI+FVHQl83unKW4D1iz6izKf2GN7DNIDy1egSbMEH6KEhKYQHKEzTBOdZe58FbjSCGF04nP795fSwtUUUYaasr4742kblomxEuFWCgiR1rUN0aVRB1ekn9Wg5o4j7fvHtdHszwOzK6PS7iZr+F2TopCEkDtm1xbtt7MtQagR0kTu86P39/832frhU2rh81u2NTj8QLzWzfPBAlHGzUk1Nt4r/B9KZTjKC0tPyp2R9tB6O8l0uzJbfjIqgTTZhFasJSFR4Vey3JJhqkZbDJ6FFRa5ukW1gVUxgIbVeH/Swop76pp5qozhxZaJKoSUxu1Q/EoiYwh32tTjztyWGDdIy6xB0dQtDgvEf8CM45aHLm8VCHpZecAjz2NDp1bXZqPaJTG3fKm5B88zeCFwdHdejvlm/e5/l75rbVkz7NbU7fpNlj+FDKm/Ib57SsHr0gw94gN4Bw1us4cvo+eWgl2pfDUrdi4yMbShBKXHe8rqN+5KT6omfG/1Pn39ncTmZ+8uLMO3+v+2wUvS+798iT9viLW3sb4775yod//EMPxmfdj/bMsOGErS/fPjmovdfNTNGanj8TPvy7eup7Tno6xMvIfiW7d3Qxso8Wo3xSkh547libjXGEWsKOjhH1nSTVwBc7KZoTDUAbkjbMaFK0JvNgda1LumseXteHmxynhAMaHSZVwzuMKvNnlO2z0/CB5XW0TzkgPieE2THS5vRlANdQlBz0MEPw8Td4z9FX4rabB1LYRsA4DwTBs0owQbnFxEojIexTEwhJ2no+d8MSv00fsETQWnk6yFDby4SOKoolK6OH+Xb1YGk94QG+MsOWEITMc4CH9LpMPKtuUs7tr05hAEOcfXbg9ROyjM4+9x/xeO82YR0t/+Snfu+VD6811lhjje8cawK8xhpr/I7h933iU3/jhW2vf3ztBzg94jhn8+Hw1PFq/vm2V3zTNNr+aFi0q8DXaelh9Kjo9ZwpMCmaF2nBFfSCOpqosOdB7dnB7M32/LWsKJBKClYExCA614e/Ba6yvLoKfj3zrk281b24cse3q8+0XP+2DljLBhadXC3qlrE3r4p0VXR05Ud96cglCTS0ZRCI5XK8LuDpbDZr1ZqllWhPx292bnUmj9NOOjrsmJYdlHVPrLNgy8CQeqR1D+upTfHrjCydaqWLzRZMOxN/9YxyDfpFDdFBWa9JZX6ms9Kdh46rVpJyol7FgRtMPGwizkPd2QfH6weAj9xfRHQOKRa1ihV0jPc37Og/2XzP5LXsx9k7PupsXAP60ow0jcX3ShMZG2uQaeAZm5VBzRlOilbDGVK2G2Y0LrZQ3XTGp2WriR2HvNc8PnGa/4f4y7e/8vKm/+Wvy8dvbw0e7e84GUW+cfRBc/vu2L81dbPh9vB0ltTsjkScB8Pzx94HjKWbnRRnEwY1s2udYrT1SmPYeu/UkyB7NWa3OG3N2JMJYrTt3H8YBxPd+9rd2zUzGeOOyiYzt9XoEznE3mjSBe3jAVLL+9P6RLyBesAJfjpx/XqWTlv1F/swE2Y69xRblGAF4JQAGM85fwhsmpEaM2K/JcOQFBqPdPOA++fSzVCfqa9JQbEj4+OwE0RjgbLWSAi8hMP9zoCG0h3RvE8rS6OMok3x93RPhC6jgOkNziKso9eeM18aaqcQ5xp3AZf89dq2x3TIzWFcorsjOi6myfBN9gPsfe2Mi9ZYw8ZK8WF0CwhFwf86wPdtDo9/letT/FutTl8a6R4bh1/7otdNJrWuW7TbHzz8kZ33FhONTl46Oj6/PX3j4F9zAk1qVicHxo2f46+8+XefKmcMAZjfyHZ3HiZlcNLc3CY8Zxsdw3ggjawM6jKqC5kGkRlxOcy4YYaBWh87oj2k0Wa8yWlAuimne/bQTZVjggdWDimO6H+VuMW0zyQDOAMSgBrUmNs3bw1xP4aXpHnkN+/MAMJpHvoIXpJmoZ+HJm47lsbyaTGQ3mCRTDGkWx3Y3Ptq8ZQ4ZneZBrzB6GyeUo8n8S3vbqaRZ56c4JsRzAhCEpSUaEbQk1FI8hbdGm89Cm5uMvSGFFG/rseAvEjxJd48npcMDk649TH+s71boJQl5TtUrl5jjTXW+I5QTWZ/25YYa6yxxhrfFfzCZ3/mr/1Xf27XvHnwkFe/zt0z+p/+kNyKso2N1HVO6tdjtxO7EY7BmLmKK1J3sqn6UtFdWcQVz6Odqf7NHjdMVJgojx8uCiOtKqsX+8rFvubt+PCVfPVikV/P05nr13O3t1p4Ri5v9S6bfaKHd+HBlbhPdgrTb3b8eu5dnzyxcVNs0xkuXYsuNa8A07KzEoCtN917gBgpe1vXzGFb+q9lByefPwUGRR1Adc89VNWQdG4BrexyuKePgV0OgYm2xnNLX6Y0K++rjOCMzvwqCLn6s7JRM9OToru4NtIxU4RU/Zn6CLvuoSzu7x6HwKacb9Lfl6NalnbPzuQNmCCH0Edn6CmArFzm0Z/fZ5uTR/Z+eP3opQ8c6cFM/ExDSxBI6hnrGQt8Mj1O4z2nNQ1SNvJvDNvm3AlSb+PMayV+I3Z3Mrd4keOP519pj7pOmbyy1bbZzrmefn/xxU7fyxoPnbPNMDj7hvfMWdns6KygnlM7KtuxsY2yp+YkLIMj2m9pu0P+unYnZXBdxg+00ZAUmKhf3Y4ydjV2WUSYl7Frorn2PjdYElCt6s1W51jOHJB5Seow02QxJwNzvXeh563c/ovXgA0z9lZ8rSLSkLTBQBZfW4x8kmqXDpOABMTB7VNDCNQOaJzIjiivyrPLuaOQNMEPSWWRLN9mdK47nsQB9mF5o2NGO3L6pl7bZHxKa5NRQdTHzQgr7/T5bsJp0ZLL80urM0YNY4FAbN0Mq1PoMvKxAXGoaYgNSIAusz2mqQ4sUUje0oFCQFq348arMZVNe+CfnPN60E3+66NjKEBhG25vc/0Fkj8WnHRvPvBbFv+ELYs/1OaYZow/z7BVgLhsAedlsy62x7RlTjY5AQ442mJY48xL6U5Og29YCvRl9BEJnB/xip1XQV4Wdqt+zM3FhwZ8fIedT9L4JEXX9x6ncypZlQELAfLIT9puFgpwzF6MZ6kFzI5kL1lEaliiR2wD6bx6GD5xKtGAdoQ949J02JOQiz+rz5LaIha6w9An7jEKSQKJ9znZ5STEBoxbR5nbT3kEBfQ5+ofc++jH/mLwCW6/56c+eOsn//X/1Tv1u8Yaa6zxW4WIrBXgNdZY43cSP/KDn/pP/2bvA3fe1BfYGnHnjM5nvnT+4z8Qmdektf+if/w1E8ZFgAiiC2mUael9S13W25vOrZJqeTZzn9z0wlFJL6TjUuZsaZE5HGwmtirwe0VnlxZVFWjK2HUuuOgi4Jm3D0V+p7XLTNS33z928/NoHnQNyxqwFdKp6z5hdwSX2C+Xha2qEUl8J2lCqoFPMi07viRpFjSn/dNWp03f+E7nhnd8TzpZ0i/qEuRx0QjNONA0WfHayQgaTABV6ox87IS6xRtprerVkyQkS9RTURRVPNJZUa9LWjPTXH0gVX9WVvVaFJVRvtFyzwQNyfpsNGSC4otVxdOpKjThkPIh2AtrJ13cKPODtOKTfq89fqVp/z9f3vtb1yfSPi6e2TmZdo8eftg5devx7i/8z80XNrdeOQ3/3ejw8NpgY4Na88bY9nv15w+PT+vJr3ZuvVWObhZno8z/HDc/Zr9u/DJ/OOmrfUUPXpVPFhoyAAdy+lmzIq6+JEBaBnUzzDRUrSFCqZDmknSYdhBUr3MMoFonzEr/rGiJJhLJXAAXcUJdnFWlii/u4vQiqMDxFYECgCmis3lfsmBOFwR4+Vd7zsTF5gSuWFXSRVx6KelUq7QBQFIh1SCmCRKRnkkN8LGpBlNagaQ54Yn2yqRxGgRW/UDSKnC9rqcdk4aaChpIChJgjYwntHIIOE6E18sN1xn2wWHYF0IGDQhIBRLxHxU3AkmBnpylGiD4kvrGZhoo+MZmGtbMcJa1a84QCAoLEor1sJZA8UstZ+J20IDkjKjHsaIeM49cIcFP8H1/Eh8EwWuWrzM9SX/jcxz8lb0bv/yv/GH5u3qv+MrLz4f/xVc7B/g7JNNG2cGxTfVti/E9dn1NNkgmtBsMz9hEifFR9cT2RLfl/Ia8CXpdH+1z2J7Y1v1TgAQO0QnlVzk9YlByN2MCDdiCGVRJ9nN6v4hIrm5mCdHHEHD7qeqc9FaqvkDe8VGyQFCGbFaGWAGzI3Zj9UCP2OvIsCqbDPjELGiwr3EHrIQ17IzgbaoBL5+CWoWjzMoO4IsdLrj0TIIadkS3x+iGvjli44i9W9yP2LTbp1t6ToSrqb5Jp80/rj3j7F4rVCmeLt+1xhprrPFdw5oAr7HGGr/D+Pf/9H/+937qj107GbRf4qUJX36T3k9//vjf+D1N5/G5RB80b35B3xNXNTxELjHGlXevla8LLD57+9PsjfaldU8n2eqCVayGHwvleEGSASTcsibKy9hdVFtVkHAzAUyYO93k6oDndw6H5oqs4Kc20eI8Sk7Dp9Zd2nHJhJfIz8JVEXjTGV1iv1dhuUHlcFN99Ux8Ot1r1IKhN9xk+HrnwL/3RhSNKPJBWU/VD6HGGBhSD0kz9QGLl2rgYYFUfUv1/p1VHzINErzlNchLz5XUNWm1xKoXSIqqqopU8whaTYKoSKJeS6apeqn4Y200ZFLxTB0hILsUL2MVwLSINjEbyC3kJnHYclP9/uM7PwcbL/c/eufrk/rWwZu/Th8dI99H8IP+/V89/dVf4GOvxnHvdf8PDSYHHz7Z2hgN9v5/jVa3DF7pN0KVb3Lgkwj8ut6qolLHxTzJc04vlbrYEBtVNZ8B1VApVI0uq/iqQlHq8i7nZZCpj6otzbQUZLJsbb5F4lIqqhIVS5PtOT0uFSPzmZ1lm3PvLgXKxFXroDjt+QGUiVNtI0ExI/UkgzSDTD2UurFAJikw0HnEbCTz2rYFwUy9mmQDrZM6al2EqWtqUszU7cgsCvueMR0SI7YmYyMzn6yq63okddC+HABQWPGEKUZEZ8V8+omWmY2pt5iKpIAjwTPypYwgFT8RP6yiBgiNJKW2PUkdY21B6aWhnlS9+GYK4pIUIhFTwJO0LWOFOqME/0yiEDOi2WIMtGVct2k4ytwHWfEGnDF8nYNt3vw/funa933pmz+880f2j8Y/0vX/zR8bSvnr+rFAytr08EODWTw9eavMvi/5gkxqj53ea+iQWkDqYwMNIkmrkl23eGtLz65xuDk+DR9Y+ugIjsDCOdNzZkNmJccZYxCI4Bi2AOZZwttQgzO4CXcBaPwQThtNIIYlP40QpTIWyENTPdtanPb1ALDUQmwsPhCSDOZZvqsJJTqmA8SEKBF2RvBO1YAXcygZkaqelO2VddISOzWJLzaRAMgpgBluQNyl7Xa0kQw18Z1HafoJ/vpRxI0mWv7JH/vEVT2tscYaa3x3sCbAa6yxxu8wPvXJH/7P/usPhflXwvR8+yUOhtw7o/vV184+eMvoWdvXvSh8U15AXIpKyDMrfHIhBD9BhpcQAaJr06sDoRetrP4FFrwUE6a1vbEmLkj8sKFFMbsT1V4Ymo6YQVTM3GzmcVHu6EqheGXp22rW30IKfhv2yzKx8J18s54Wed8l9PKHotTJ9LTb3mSYhQ3Z2eJkFJnxoKwCockIajpWSNXzsCl+XSfLE/dIoabgk1r1AA97k/tHslNnkhEsRcYKNeG06HbNFGGqPlQ0uDqeRSSAMNK6T9qgsa0nqkgLvY9kOAcE97DKdEh0g/jDPT8b21p7dGtj+tnTrTf4wJ97rv2lf/HqP6XPm9+EAvY+gT7GBmo/8uInPpU92PzAV4PN18qbE3XPZq2GWI315dLvmXGs1BilGqDqamrVn5WeFrP5OCzLhkknpV93Th21xZyao6WCalHOr0lZAlqUi+iDUktNVNKijNVDEyQhLxZzPaqpM2e5IH6hY9XMVKvEK+Z3acmEWVysC5IMUDl+lUcrywHVMjMTBEX8Yt5S6k7EiFdoGiJKKeIXmjrIPLJX/AIpz6wjwTiU3CETdMPM6mI9rCdZbRyL4EsaSO6S5uqn4voUqbgOk4nU2zJMcTMJfdFMggzPI8OQie+TTcUPZYxkpWS5+LlkKZKJIJlrph55FawhRlxGDZkgEnEamAwQITWhSOqRiEgqgUgqIo5MYWKEc2oILs5E6i0ZQhKSNzkSoZxkqUGeQ6HzmHbKzR3OXuP05aOf9hhnvxw/z/M/wMd7n53Vnrv/Bz98VOz+s+d/n1cakfZN+frN8bSXytZ08shyc/raUb5JXpQl13i0wWk3nfTeOCNB78IQDquxDjXCGeWI0CEyfOImcRd5hOyQNoiP8U+RgOAE4BY8bHHzmI0fpPUHA00sZ3AO1wDoQjKXgrNAMl9UdcRmjD//9aAxQXX/A5JYguUEzPynKnFCmBC0GVjCM1qLOJRqqycesABZGXomScsgKcO5XdyiuZH6o9JvGquOziToMWReLzqKSO8FG3sBHYbyHl7n407SKLRE1wnAa6yxxm8v1gR4jTXW+J3Hf/gX/pP/+L//S53ZbzbC/IWPEP4S3/ziaXfDGV3fvX/u7fbeLKPgjtyqwpJBl3xPUXkn7jeHhPkiEPopjfgdc3TL2HO6tioYE10bA5Redj/y9qfiJsb3ansjTbzsfs27PkXegeWucOCrO12+hV6hBr8N+13de/H+eoW4vdrjO5HhKm1vRnCJHS3QNMNHkx1p1vGk7Y2mW01OADpmMijqftnEnCssihvN2/BIKpLuYX0yX7Mms5QbgE+6QobTETUWBYFdSav43n7FrkUr7jZVt1b6rqRtmVj1u5wHZJ4kgRZOqmphCJbyDCyPrmPvcPNP4N/a2o8G/zh5qfbW/eF/8M0C7kH6jddf+rdo/gXvoLZ17/tub0ySs3r4SBpnup1q8E2NpuoW0yAnC8m2dexiQWtl4GoCSqGBKqWqql+WTSTLvWlZj0s3Ih2rF+XDoRCXtW1zpEUxv/9FqQplWajnkGmhiBaFk6ufFY6oxuXCk6oo0VIVEdHKzDyV+f11ci0rjmrEK1DV3FERTY2JUhTNDaimDqi4xfzGZ878t6MgVMvFLTWvWDShn9rS8SUHkiwQt0AoMwcQr9TMFDNPvHJ1VIlXilfoVErJhdyRbKZebjIwHo7QUDSjA1TNpurKajy/LILbAbGQVgNVJAOoyl+JiPHnXx0RzTCCSNMkrmQqxjdZZKQA3xRtycQIQmByXxIxIkZ8chEGzmZA7oq1IpkJUoxv0oYkTRnWJRGROv2Rqbcm49gighyDgdtwQnFG3fDRiLik/gzSQH6T9MMpz7928AuvGcuf3eFR9yNubfNL7Z2R7PzLzla4V5u5tQEfdWQWJJOW7R9PbvqxTWffTNMw0LfC5r3w61Z3iDsbtfgMkJza60iH+vvgLeovclbfb3DWdq2fbxSBNL9wKgH6MtRp/UvGL3Lzj3t+bBXi24G7V3pkQB54BIy3vepiWppWg0S9BBfUUksIWgxLaSX4ioaagMYr9leoNOk3hZQwwb94NMkFCV7cwfk3T2JUKHXDOZyWnXG58LdbPI7GZRBK4Jn4jFYVU61gqYE+ojdrhjvNR7/5+dtF9zrwUx9+5ub1/asfUmusscYa3w2sCfAaa6zxO49PffL3Iz/1//w7f+Hj45e9F7k+5vwLnPzMUfjpxpvXn+2eWa93+ky99iY7CAiUYKpqRqvB0Itg46c5oHVNVBAv3YCupL5XsNJs5npLm50wB4INa89CHtZNlDu9WXESmlrhtKea5fOSqsYAcpFLDFCcR04vvujibaVgnlaDNXbfeYfLe1d0YqWj1Q+63ODpU9YZ/p6ebJGkhJagcoWt3lYBT5JNBoeDnYPNCCFvdExvW86Oq/q9rthUfRfb0IldZAJb3IpwNUk8TUNkhO+RPMsbqfoTalxI2Muwdj0tuotZgOVKWWhQ1f3VodZDyQJNfckaOqszNrbkED1FIAxobrO9x9EHCR7z4JdOPvOQnN88hA/8foIW5vnAP2gMW40z2XikkVpuju8EJYFuiWutmrE20rIBfr0oUR3SyEulKCnGDrmq2tzUib3SZoVRZJL7IDU9qRWllhqpaqmUZVhqpnPVV7WMs4Zrirx0krJAnHmZLlWqUrFcIhfL+6aZ0cyIV2rmaCZLFqqZghGvhDL0Uldy8oLMUShwMVh1NJkL6+Jl4paAL7lLnuO+mPTfMA3HyY3kpbqUOOo65EZyxwlzdV3Nc8cFaqVNHWdhGwyQ4wIueV66Vl2VAgqL75LPihqQqutLHplJqcZqqGYClGoK5l1U7ZTqXhqu1ZErjuSgjjwlAy427AsgTcapiCO5CCKJI7EIGMmlCM1MjHgmM0bEsOc8crUoHS8wuZjAGOsWEjpZlE2MwZuWZHmQxamQ9ZExZgIPkYjyHEmRHtLGzUgjqEMdRpjXM9qcvXdDoExeT+3r2/32A27ckEbNKTf9yWu193iR22j4p/7m5oa5E23CNX1/cSZ/cDe+89zJyZm7UdoH18eHiZrN6Z3ec0fMsqLjzTZbh3v7NTmrj9FTZGsSpqX2oAs1pMSB3r8avPHs9Tbj2lBnreq6SGc0yAKTh+IlmgditRkwjgl8Mp9sQK2EFv0hXRUCUgWfuAnH7IZiQ5LHbFe/tVRDnzggUAjFJgSh2qrY70yDp7iwIFo3g6wMk8K/SLtfxGoAlEzLzpZz1GFQ2WKf0yy4uctJrNlXaz/yNw4j3ltHFbtiTb7GGmus8dsAgbUL9BprrPG7Ajt/5g/9b9xfuq4TJ2D6Ve58geOc2aefe3R9X6Nop8OwsX/oX4vdSDNPaooxlRx0YQe9pJ3zikfzd2u1noR59riRzapZvydU1iujoOfwarm3P1191yv6YTlzs5k7V5VRr1aAevvTRTuLrs38Q9GPnHoBSJS/c3dPHpWgsTu711gseHc0eIEnjKA3nVHdDK7csse4SbXq8v8Iygk7wEx9hXvp9b19E7uNyNjxSVZ/9QuqmpTNtPRa5myD8w09t/hWvSbTuo52OGpqsiSyY51L2VOaKZ7FywjO6A7oVYnBo2Jjxso8xSXBXoC6ZL7JXEl7pr/LYUPGz/eHO7NX66/mtRs0LPfvUdvkF/4GFgp48VlO4bk9CGl9FL2HRsS3/LNOb2qiepmcs3lGxytn+8n02PWSwj0rexNq/aJj1c1L1ymtTQOHVIuSUk2Zaln6ubXqoqqlZkXo6RTQymhqEaddHXWh84lmh/zyEi3UYxUr/xf7FCmOI7lHXqCVBdGg6AKB5K7kPkVAIpKpep7McryYoFQPCCkdpMyCukxRCcQ2sLlbFrglbkHRwPoww8RSphggw/EoAuIJDY8iwwlJKp6c4Ra4OS5ITOCRZ7gsuGuxmEbP1XUln5bh8ghXlzc1O9QmlxFIDtjF9Vl+DSR/8l9yV3JHLphzRAISEFcjxKNwyV0yVzJ3/nm+pScZUJPUldwV6xicgiArRQhsDjgxjsFkmBwjmHNMH2YY0BMMmDoi2DFW6bYwDUwN2cP0kG1okmwGsd84ZGvTDCc0U3F8Saa0x9QQOZdNwDpB10uMb8J6OK61emHeCSfj2pZ1W9flfoMsZHwtPUytniS+ptlBfn927OSCGZTGDFNH/JPC/VpGiOTohvf4R95TklpqqtphFIt3oPcBd6xFS1DcVPNAlmZ/VppVgviIRkA6pBaSH7FlCUKslSDGH9FtSf+Y3ZhgTCeQJCAZ0zmX1h4nljAhmEkA3OQecJeDJ6fShFnRSTRY/bFXK5srRYOrYJP38FqLc5SdvP/LX/7Iz37R4+AFtLzzf/nf3ry2wxprrLHGbw/mb41rArzGGmv8bsDP/Mov/+Rf+/c+Hj24ER6bHPNFXv08D3KGn37v4+t71m/6jVZj0zt092ZFU3PfdLN5YSSz4MBGlgbOFwLsvAyOaOLm/fByIPSVXPTiS0Vxo+uTSv4F5s5CiZufB9mTplMK1J4bznWP5ZNVBJi93qmOtnYwISrl0rG9LYrzCCE5ufQq+W3R4PqLg9WvN717V25Ww7YZ+jxlkbUQJsfaTghOi62zYHt3s2x4/TeSW803Xw1O3kyK5qCs7TqHG3ruYXf18JDdXX0M7OhRg9gnS9VLce2CL020OVkURjpkd0ZjTB1ItDEo6xfk3zoSFJq6EhTzExfqkl137nfl/KPha/tl6btp/MXHfcT7R+krj0hh+yNsTGlv0b6BeuiYMqYcoj7lBK1RbhK/3z+qbbXLM1uGpcpUW+dF7bjYLHJVVQqxBad5L8NJ8zDLxapbI3Y1NWQsSKxD7pEDGa6qRiQ5rkseYafa8CnAAlXyeoFbomjgySwmqLaMCYFlI0CprkeRqvE1y1U7blKqDxjJXHKfsp0X951GidYZR5J5FC5eI7G5k/s2qw+m/QEB545EVskdXGJb0DwvshqzXmMjjqfKyfs/1OXEpaZ2VgSU+DHeCRsLrnsJJR6QaFDRchYK8BJW3SDXRJ22pOIWSe6HbgrsZImb45nUuMxcMvIZDYsx5CWu0WyvkIE3mtAU9QpR4NpYTpuxUwYzEcBBAkq7SBAPKGdIgZOr50oWYl0KI1ldxz6lRyliXbIcr805IOI4pA3GBYGDdQoFqn9Nrk5eAJLADCkwOTLDTJACJ0VmGMEJkBQTc1YgSschEpwtTAOnh9mELbTllg2T1Y0YcqmdSyuXICVomhGCIH3Z6EtvJI0prZE0npUHp3Q3kd3gcObqB5LHs+1tPxxF2u5uPOyFmfFMI2fbDtvJ2df2npnmzefGL+u0nPQ7xgyzQMbta+FD53SD0AyadzVriMlh0qmnb6rFRh1PB840RRnejJKwLYPOrAneQFS9clylilsaR3QtEXDEJiDCiG6f5lg6TYbAqezM8GukwEyC6rlTk3RZIrj6US7/TMuOb5JUw3kt8YsHlTTFAj33qHradBjuynGLvpa48ewf/pNn7novEtWZTfS/+fevfEatscYaa3xXsCbAa6yxxu8u/Mxnf/HP/b//058ofnOjO3JnmAlf/QzH55x8+r3n1/enTqfZNPvd4n70/OOzHVPH9C44sFrXhMWcA1/U9eWyFOyWsVe9ltmz8KlY6CeJZbCZAOXM9a4tdNRV86CnaHDtucFi3RXP1dnr7UqOCTas07MAjqmOU5aq9cohaOzO7j+pm60c6bemweFm4mxccNqm2MDYtxeBRwsReI5I0zZDSxirH+OnhChHxc273WfaDWrGPj53ui//uotNyoZL2uNsl8Oajqc0UGqMmzqrMWpqUmM4pT0hXF6YPr0UD5jSPKd7RrdQf1Y2ZuotThCQmmQzvJpkIL5J951Rq5UctI/f9/jO67a5+xtfeOV/IoMNl61/FeDGLtpHfdRSjlCf8oRiTFkjrVFm5C3wKdokbW8WRI5ac15kyIwo8dyRNiZFA4gJYsICL9Ew0RCwZVgJmw7zGi2Furm6LtlUw7okjuRNpltFeuYEPWaJku1t6uFZgZsWDeMUDsVEa76apNDQlQFeSwYZrpSezywwaUgCuOSGFAhJXIocxyWPCQTHo6hE15LAYIESBd0fD+xhcfZrJ5uVs3LKvA5rpTFn6JhUyWBY4MN7/iAvb3y0TW6wGbElSggmNAFLmK3w2xK3OtNK3X0H1E1ClQ767uCRW40KdR3JuAh+LgAjGVCq22RWojlORBoUqesEY4rtWVxoeF4PJ1o7sJOCcBBWpYJoMnNIPdRHakwMSYHnkPlFGeZ9zcPSJUwmpes4wwIBB8kgA4ERF8c+QgvSPnlELaEAA6FgwG3iRpgWRjF1HA/TwWxgGmRNL687sWnNJBRDIEUm7shs3WUfYSYtX+xUmhgZlr3QmeQEG9L3JPXE1il6cn6QHUVxXt8eRqnjxNIOh42sdG+a5lSdtjS/mQRdipbvlPjDtNwNkq4pHUxSzqamHBb6cpp3mUW+PEzPmjtlqVH/WH00oGyTh950y2Px8BjTG0ptREdEBjSAVKIjthAqF2grQcV+L2UIL2cPVx6bs7Jdc4azsn1atFdWXXq6NsX23KPqe09GAckux9t6NChb6d3sb/6jHQ6eo9b4qZdu/OSf/V+/yyG0xhprrPFbwJoAr7HGGr/r0P3f/dEP2ce/Z+eLNcmNwbzGy7/G8Tn20++9e31/YlqpafzA9X5R33jZ3Jw5IY6pYqHLYYQRp2MvOPBqIHT10rfkjUL2qDmPiH5KVvVqOVCtjW5MgBUFeIHFg7Poh+XMWQRXM5eLr3yuKrM32iheLTdR7nQX1LQKUnSc+VfHIFL2wzLx09ifF7Z5O7wjE66/0J+3D0BTbNMZslLraIkrFGCdBzoClhBlQDtWX1U/a37YbHV2/fFh0W58+c10eOxL6mIbTG7ovTrjTP1Turt66JN19cwnBxoaT4gqETjT4IxO1fiUZp/OGd1x0Zuph1CTrGamdZm6aowkfuD5UX6zNSyHeP1J9IYf/9XP7P8Bys7B69vtLTeXZ8ttPWmdjdxJrkOKh5QehUdZIxtSKnlCXlD4ZEJZJ4MydGY1J9Z6jqNQz62TJ6oBEAduQm1CNNZmQljgqmIJjUpKWKBWQ1/itAwRccrsWZu+GtVv6LQ0GRBkPPQuccWKzlUo1avyYAGroSt5rm5kxqvbN5hUG0QLPgy4FEBJkGM8ypi5z1CBW2PSPRnYn7lvrnOwCxOYoSlaIgHTU05jRsVF+y99ktRrPNy91XJO/KI8dXrnNIAMNyWKV8S9Ut0Cd1pGT4wWzY24ZfUbCKQAAonNUym7nubZ21iNZBo6khsudqmYdiUOVwJ7rvOQgWBls5pYlyLVwBdbZxpic9yN/FHumSZWrSdBVkwmvtdLg/LBtN2t50PCrTxvcgjUkyk5Th6b84IJZFCvDghKJAcHqS7dFBzEBxe1EKMZ4pFlEONXZLiLs4lxcTYwgunhNCgaXlKrJU6UiZuYxh1zCxEx5CaYScOKj7Cc8CrwXZM1ZeZL2pSpxWuZ2QH3fLGh5D1OM6nVsmkutVo6DY+sK+Q7QfjAug8QD1roKWWEdpEAQqQJFgnBQjD/6ReBh2BbTh6YlPoxnWp5KpHAsWwCCCmhIFaCgbTGtAc0Vx+Yc4hkGgq4JhEk02BctieV7fPqY2qxY0NsaKwnVoSWDEMssM1xhO0wLjSfWv8rP7P5leMGB8+j5Z2/9KfX8c9rrLHGbyvWBHiNNdb4XYef+ZVf/hN/9b/8pN65vf/WxmDoGMwjfu4fIpB/+rm3rt+c0Cyc4KPbs/ZG+mXzfY+kt+DARlMXMSYq5vm3T9Pgi3+pIpnLxC1jN5u5T9DIYDOxZyGKV8/dnpUwW9o0rW6msSth/gQHnuvAi0erJq4E+SIoWjVx8/MQcHuJBFdW+1BArR8/bGBk3q8xiFnEda+cSAXBr+XpzAXx63k6defViaNMoktd7LsnnkmeNqOey7+Lk+syBlr058ezKKkzonuuzW8U788anfpmEBMNhkX3S59zsS2mqNZ0vMtjdH72OxyiNEh8nZf/nWgITJiHQGcEKf4R2yflTqaeJ9mBebjBKHEMteZWs/jAqH90fP6cGx//029mlt197j37yeMbrdeo1XVyQx+o6rae1HXojkvnTlrUKIZoSl6QFaQ5eUmeUqYUzsICLEJr5A03daPCwcFarSVEU23E+FU+bZXs2s2doVvOtGkk84lH5Xat6A+cjeV1cyS3GkYyKXEN+TJFFsiqHEgBeGf5tEp/dcmXtk+rzLBCWeXiqutKnqdBgiNuGUj+fcevTVq68/Co9+j48Q+9NyUypEFeqFsvmdaStDk5CuMJQ4YPOTzGwq0/4PqzzsPn2i75N/TDOvMSnNW+rCchRYITUiTqhF5KZWkGmpnQT5eJu9WSFXdoqnzdfGUDcsf17HLOJdNQM0e8YnUWplR37sW1yCvO1Q0ksZX2rpdYtOaGqgSUVy4/iFto7tz0xjUyR5KGjQ++8hvxtY3RYBDsXccU7mRga27ut0J3FNNy3Swgr0zCCpfW2WnqdsPhKT5UHkw5DJFs4U7tQIFUUwELXV081EUAF/ERD7OH8bAb/szpZE792Gxnjp8Zr5Bg6LQRESNV0IrMH1DI4mFV4G+bk54MenIeSNaUWKVoSozI5uzczUrX5pIigml4rs0kBUECJF3wzT4SLdhviITMtVthuhl6qeaBFIGMpReQnkinegA8kGvVNimRCFaCAU1EJqsceDlLuDKLCCAyK9pWgzFBU+xYg01nbDUYa7BpxlU4NFB3hkCXcUIQ4z/Do6YMEoKt4nBy3/vbP32N/ZtEdZKZ/pX/4KnfxxprrLHGdxNrArzGGmv8bsTPfPZX/tRf/c8/VHv1BeesUxtGcfb4NV79aRTMT9x8beO5c7YcKd7TyYPdSHz/q+bmzISIYEw5CjHG6aRzAnyRFbzQgS8IJBe5pombn0duLykTF7CnIUKwkdizMNhIQJxusmLF/KRNVIXsYX0uGl+foEg41/00cQEJstWdin5ownwhLF9h/ps9ri8Z9QUq72tkEfjtLM/Ir+emNi8ba6KcueHWk1hEQQ+fWP5E/PMuJwFVPKh2majSp1Ed2LHuHLH9RvZs83p9qxZbXx6/koV3X2nppMYYpalTD+upBRo687FdHVYh0Km653QTOqUWFifFT/En1D3Sc9394fKLSafTaOffb1576/Ezb+TuD/3zX3wtovtc9zx4YdTuPfajM820BNW6Tus67uiwVQzDclQUmmVEo6KM8/SUIiGzlA5liebMTyUH0BY0UCCkcJ1RrTmjZUhKwhhPp0FWdzwtR7RFspKyxJtQB6po5IrfeuQlXrFy48oFSTOSZxquqpf2bVTQJ1CXBJhquOSQFje4zISrptrY/rDV6Z5tPL4f/NxXtn5wy7Vxcv3a1+u3gExDn6JA28Q1mbSwveI8OIuLWf/0V4tRQfcHNjXCHNSm1N4cfGigfpHNk2wdr5RaBlSW0VeiMqa6cpXGrmYXXLpq1vGK1W2KxQZuLdPMFJnzxPh3vKu7LlLj+OWlY/MKMoeq/cWHau2e6T9jBxs///k7fZoNsg1kxvCEVoNGg9NDanWy5x3AdbvBrYZSTH1vy55HxSR3QqAkZIzHwE3z0nEApyhKx3GGBS4oEs+ZsFS5FB40yUPX0jgN2sBEGrl4VqLURLFTwxgxIo6pPmAEx1RSsBgp8DGmZmZtGbQFNXFHBrvmFKEnZ26uudSMH9cHeXScmxRJkQBSRJAAxkgC25gUfBAkhAhhQYNH5Nte2nRyX0R4yLVqAm0i7ZE0gDPZCiX1sWNpJQTVklgW4QALGjx/Yl5yUbiQiDONPJNcDqm5WFsR4DYjX5IIu6VHrTT+jV989rNvlLznI8BPvXT9J//3f+LKu7/GGmus8d3CmgCvscYav0vxM5/95X/jr/3lj0Zf3GiZ3eJEA8lPyuzv5SdDjn/i5qP2tUPZL/DwnD++941pt/kF89GBNGYSYkRTT1PPdFORBVGcs99lku0TBlSy+q626YwBEvfUjzRxFxx16Woll97XFx81doAydu1ZUC2/ZJ2lXLnX2yxEEyd+2FgsufIRfXEYXkPdblImrma+d5CUsSNROdeXqr3NhdRbEWBf7Kr+VsNucbT8usp+dzkNNK56OmYnVt9qOKH7SDcGfu+P3b779eDg8+lHt37ji/3Thxv0GzpRVYvX1Gldx4oGmik0sVbNmNpUm+d0PNIMP1O/GyZx1Hrvb/7SD70w/pfxlgYm+8uvNJ6ntkv57PWBd+1Rp/G6btV0nKmvSl0nvqZumZo80VKjInYK2ynOvWlsxlmWoBOK8qLqE/lc0NMmuJRDBPSEJCLsMDnYmjhNp+DccTPqIhKQeGiAdSgSPKUQHIEcx6GIl6bHODlujpvjFHgsdcsqwHuxWaGuI/nSCLq4zBt9k6wufKLqz+rGqYZLX2WfoiqPXJPkA5/7B27C+RH1Dzvu+w/O2IgJYm1WVlXjsgEEktfEHsiD66MHJ//kdFSweRP/9o23ru0PdPP4dHO1U8crpZYDywLC7wCNPYkyzQ2Zkz9pCPeu4Hjlknv/luHWFlNLXklVMqqW171Zjnub6X75lvtPXr03AWhv4cWcTuBCxAXYfx8P/OtB1+xE524jlCln7Rrgkos6DtmUhkvhUhpsjqkzc8mdApOrKxaYhg2Awh+5EZDIRRj5gO6MxkwbIFMNEVrOFBHHKSo1uCLD4hhPSt/kufFLCZrOcJ/DupmKkaZJTmWzLYOujHb658G9XMbINgJikQARJEVayCkEC004AEG2kRFsI5C2giKQwpdz2bJ4U9P2SQOJz2TLip8SjKUVkiYEVZz2hJaI+NgxzVgCmdeYW2W/cvFBqnLdRIt6ziABNhA7ohWQNhnOH1pCRNpi9H595c6D6//tL74PlIMXXC99/f/8b968vvcdjoc11lhjjXfGmgCvscYav3vxl//qX/l/ffaf3w4e3Wzd74XnhYmayVT/v9lX7rL9Q1tfun1wR27PNApM/snesFfT1zd7r+lzMxNipBxFGJGoxIiJyjn1NStSMCv/XhYxNpxxJZBOy85Z2byk+1bJuhdfWVS81Oxho0rrLfqBPZ3Hu17tifUErb0cUr38mz1uZLNLgamLduRpShztT6pYbhMV9jwAvKYCbs9KVFYh4k1jJxI1nbQq1tNyhp5jly30GDcYAD1GLfoRaUjap3FT7ytYooAY5Uh3Wjoa0uoXzUnZ+QTDT47GL3//C7/W2jv6Wv/N12ft4nRPH/uaKTR0eka7p4OAMlEzoT7SzVG4Vf/q44++OHDvu/e2fP9O7cbB3f2//+rXHtDokv3QttTA4dFWe0qtX3bHGmmpKFqqlqpFOf+Ql818IHlqiqxRjqIsFXUNsSphElfXOw+NN8kYoucQM71D1CH/vvqZWx/VmzRMw+a1WUZKWCR+1hcPkxUADlnddcziOrtkoQEyJ0zwcxxgSAeo2O9knkgKYC+n0b4dVhXj1YVPZ9I+gbhsEHsh5TPZg+CNX3X75CHHz77EXpRrbSp+okGeBUDqimYGqEKUX5DT1iu/1EmLPHTeeu/vzSn6ujmdNjUzRWYcv6wk1qXQugpxy0pu1dxwWR/On45T+Dbw3Xj9UBy/vCRZewWZI1EGHMjsIP5cSiu/89B9o9AYL2I8IaXywJor7P5PPHte35lQL9XdwHbIo9nDRjyWcuoGUb8dFbgJvk+camRwEnxDppTVPIhH7pAHYgscIyYhQCQmSIkQptpgOcEhlOqOykawLOnkTJtMEFzJMSJGfFP6JvecQkQ8k4qRwvgtGe+Ys56Mg7h0i1wDt3qSufk8LlqW4dATRDAsSLIgQhm4adMtApnRGZtIYCKtVPxUvOpJWD0OMwnHNBPxkQW7FZnSGkiDJfu9/MCMyGLxu4x9SQEP+6Qvwcr3qtGQdIvjbnL88mc/+DMPXPZvu93g//Hx537yT/zx78J4WGONNdZ4R6wJ8BprrPG7GsGf+ZM4zoE7eL79eieYdZ1JM5kWD3X0i/nh9zXOrj3/ptmPqRW4rlv+2ObRdLv+url9nz2MKcdRad0s8b16gRi3l0pUqPUkKi7eCrlMhgGRDTOqOaOKZs60fVbV85hjUVWzWr18dqpq4sUP6gv3LKfawItyb3+yuvelT09T35UlRT+w5+GCOT8hOy8P4xKi/WneD0CXsdNelHt7i+rERihLXHfXPxcjlXIlzjwdMTLpFkcgFQEGDrivSs1kgWZ97YrYZjkrCwYalQVxEQ61eRZHP5w/fuYffM3783/gl1/6Q+7ha0cvv/X5YTdUm6sfOUnN0Y/3X57cLbKd+AcGtwenv/arf4dP/il+/W/T2iVPcX60s1UO8h1XS073d8syBSIdTcqWlvpWeW2m9XEZaV5qqV6R2tIps3JcNrwy9TXu0EdJNMzUDYlDTRzyDc6DSeye5OmI8Qw9ZPIDt/sSxrvd3WLWOI+dYtL5/MPBDCcCmIEbMym4toXvgYEImhBCBB64lK4zDZqp6+Q4gjMhsoQJkUs+oV4sahdXMdKZXsr4zdVNF3TXX9CeStGdlWHNJA55ld/7xA2t9lrukqrrF5o6AjwzO+/cu1t+6bXnftyZFc98fvvZodZSdSs9dtmCRJkveapuS+yL5YnzxquNtjaaw7N671j37uvNmV6dnPwE0dXc6Mz9ztXap7r5Le63DJMWr8QrrgrYVhDNhcwJvOxF73E7OfUOz/SNUzdids7Z4nfZgAnsPUf+3o++Wm/H+FZDm/nilj55Tex+njdJPO9oNp25db82HWR1P8coYYybaOCSJ4QGMWRV8HBKiJDjAbk4ixgT8cgRSvWMyVW9qrSVI3m4MJ9zyRtMcryQuJDAkbQQ15MckUhmdSdrOBMxYgyFBJ6xlftBlQzhFWqEMCnLQLxcqyrpZWCMIW2YwtQy8SYSicGIZBJMpJGKzyIpJJMwJRjRYD5PKJkEKYEVP8GPSGPxF6RYEELSBL8t00BsSgA0GFUrV+nx/DatcOBQ0kDTfX2QPPb+u8+9BHD9Gb9tX/3z/87Na7u/xQGxxhprrPGusSbAa6yxxu9q3H3w4IX/218MnPJ59w236fX8cWDynjPbvfM4/9V89GLja71b9/3t2ESxtgIT77b01tbJo/D5OxzEJshPGojJrOfVyyzxEUFMsGmdjr3EhJdBfQsyvGHGCDUzBPrF9tzjtMLSk7lytIrdvB94+1MUTZz8PFymAZexW8buwulqwWMvnrdPsehl+8uPiVM1ksXuU3RXLxRp8KIcyGLHi4rqA0q4PzUr5tUVw94wk0hGmQYIntj5iipGWnVbzn1JULblzCfpOtN6MeiYWKAESnysJTgquwJJ6c+y3meS7/+3Z198/pc+U7/Jw3/rJ1qzeM+78/rn9s92Gsenzu99+R/Ki3788+mvvorfwplRB+/9He3MPJuWHbdUZJQXTdf2zMyt2dJrTga5kRKOZee4bJe5xmVtWvhaqqpajaoKuo5mubq5Oq7mQK6OQ97U6SaP3cdH7r08apLUHX+7EZeRMZFfZMGg751MCWAAlsxSVVzCw8uQJmxQ4phBIRtoCi3yhpu5nRxTusROFOONaFvCbFEON8OprJKBmYbLSrlPWEPNb0Hs5VPXbVvyp7T9t4cuaGeRGbeWd+onETY87G9/9at8qHO9Nn6j/n1vcq3yi3IXOcOV3hiQuORTGsCz00E3P0mSx/VOzw3OpzS+znuX0dqVCbMtQxbEe/WwnziMd3nkT2T/MuerJcBVjVQX7YmO3Fo+16i9svp33s5qkHbu4F6iwRWB15lXZMbxCvH0hdrjpjzIDs2141dNSdl7Lps+vv/lqcDeh5DICfvF7Obtl7s3Uw3HZX3hdD1H5WS+J0OL2ZBj1L82unsa0/IoXKzbNaYcR72jsgsUoi65R+xRKNSY1GXqUpRE9SIjcygoPHEVAusUOW4OeG7qYgsCp1BxU2BKE8hxQlLPFC6WxVMqoRbJbB7aYjAOjoPjiDG4LsZBnbAwvnHIjWcccnFzE/ZNR0TEMDffmhNzySSY0ESw+KGkFn+Z9JsRTKXRYJJKAIykGZIm4lfPn5C0KeOUoMG42tjDZgSe2PlzVckk8LkINkEkKO1Oev8bv/bc54/94IM9aYT/8a2P/OSf+dfe5bhaY4011vhOsCbAa6yxxu92VBxYS/fZ8GEzGteDNHSLrplsHKatu4/tJr9Zf/6MjXPTysRDKMQ/6Ixq3drd4PYZGxiTnzSy1MM4izBBU2nCWexG12YSFbAaDi2gjaqejbFWg4YZH5ZbC9EVKm32LAh6iT0Pgw0L2NMg2EycTlI5PGexC9SeHWriSJgvqe4celnL5em1K98VhKIfAPPs4hV4UVbx7WAjkSAv+qGJ8jJ2spmLEO5NV5KQ5832zCQyo0XTPEHC20zmMg4AW5yhtGTc05OU0CepLJ0s4Sk9FKvBv0xeKPPWj80+u/8bX7v7D/tTeNHnlZQcnv1RjGWjSdFz89DMWrXUeJpDnXa/L5oXp6hF6yQbXuaILSMvnWFV4zx3nUF942HZU6VPF8US5bh1HefqOppNtT4rG7m6tnRqJgk1uaGTzcmD4sG9XguEi1fuGWTgUVUOKn2nrIuOAwII8Ow0b7oFgWcSFTFTTcOwcNw8CGZOkBLNFsm9UxoFbkqY4awm6Oa4TzDGS5rtUvKtqGnuMC/w49TFXnZLnhO/kEvMOcEsl+Revucf7R3b8cm90O+HzYbfbn41eiZf2DhX1ZIAZ8GEK2PqkbavM22Y442Hr9XrxTS4rgWv1bZnNOb688Lfq1xJYL5KH77ihcHPlUVVpHdA+jbWWe+ApyOu327DuflWbpZTD0sivWTL296skHQ/HnVPzprdx7NZe+bVnW++dnaPZ5/DZCRDjn/8gw90KyGIywCIxDqST8r6ame+5L7YQh2HPFKnHp+0Anf293+t8wJ6zvT9+6lunfRujh3Hz7XjTUNJM5yIfouhg797/ro7zDVyCiPlQB1buAOKGl6dPEHqeB5So/QcIjSUwpVU6r47m5/O5ehiS90lL8QtjWMccRwxLqlTK13fOGIcyR0/N54xIg4L9isizIOclxEwIn02rHgrcc4AuQSn9Oai8BOmCZd03SzBAwlJA9JU/AS/2iHXAHDFeiojbSB0ONs6euOXfvOWW8vc922XI//Vf+/fXcu/a6yxxvcGawK8xhpr/C8Adx88+B//2T/9j37ll3rubNt73AizwvEbJrlWTLcnbyVp7UG+8YbbHppuIlGV0+sZ4/bqebsbm9ZMgnIcpePIq5VOLyuGURU1aGolIlIrJcgXZTmBJzKEF6m/lUSaePl5kM3Manhf0Eucri0GIYLTSarN4ofzN+Zgw5owL2PH6SaXCIKuUNwrbKUvSKlaB0XCvBiEZewuEoMVCPengMZOGbtONyn6obs316KLfuB07ZwArzTYM+PIGT8djB2SApWws8mZj1VoM0FpMARCMp/UJ/VJUsJU/ZHUVflK/OJb6Y33yIMX9cs1ho/Ou2+0XzLYl/SVZ8avO4opp0VB5srIaVt1W+W5pOofp6QUEfkhZUgpFJai4zAr1CULXWAQbJ7SizUY0M3VSdVBaWu/UDch2Na4lZDF8ZZ5QBJG6QMKSMHCEHKw0IRNyKFF3nHFIQskpT6kVblYFbjLAGalVqrblkmLaYKX5GHpygS3knkLdXOcd6BwVaRxzViHfLXwr8eFUOlQuOQ5rqorkleU2CEr8IKVCswuuXvZ/NkhAwSzxVHdTsZfz0+/Tv1FNm5vjkxN2iQ4Cb670pdShmTVjsdsn+puh+SGPDJ3k/bmY5MXeejeCw4soUuW4xULT68cbzkmLWHOtyFWf0ss2bVh7pXtSj4rA18KINXvqK+KLaMsCfBChFcQf3FJN8z5DfruWd/dSGz+XDLM/ON08JWXn30P6catbJLd7XTPt/eBSVmPxHpmfmuSopGuXI2lXl3zZ06um97j7jSLvvFKnlOccef3/+hJsRl4WRaqn2vqip+XmjtZoNeLBLfclnFO3irPW/mdogi8WWImRZ5CSqJEKX4NAfHQbccpCxRtOqUreWhEKAgS8UGshEAhXo7rOE7hGuMa40rFfsURY0SMGCMYZEF9WbU+kErp9TMJxtTny0WATIIqfKCyxWKxeYVcgzlVVhZc10/VR/AlG5T1pQi8QpW1Y6Ybyb2zL5ePz/3gfW1pBP/xCx/+yX/9T30nt36NNdZY491jTYDXWGON/8XgL/+3f+U//MXPdfy445+4Tmk8EzjFvpzu28f1eHqie3cK50T2JqaRUKvet1y3yL3m7k75yLlO7s8kxBETlZr6EpWaebL0x3rCKZrFA3Ll1a1M3ORRvVp1IZ8KtWeG1QdN3PhhI9hITFQASw5coUoPrmKSK558QX1XbLEqN+m8H3gVleXJjN9yENjzcEmAL1USfrJBFpHS8491SX2xnqTuCt1abrjF2fxQF+JpSycNGaGkBD4JSEA6ptnQsUCClxI+sNcfpJtalC/qNw6K12pl9nXed1LWr+mDqJwmhdMo7I3sjVPpuponueOUeVBO3LiUQa6KzlBFW5Q4JiviTR9ItK4w0PZj3bUa5upk6qgSkewe9s0w2Q8fTV3X3S38cy0eTbMS0rkbVTolTPAEAqQOORJCi7Ln4EBAFoa562ZOkDompQZMiaZSY+FlpeoBGU6xUr5oKdWmVQ0c8CmYp/IK/P/bu+9ASao6X+DfU6dSh5tn7p0cGGYUJM34REUd4giCgazo6iKmZVVUcBVFBWF1fSComFD2wYMFyeAQVGQBWQzIkyRBGGYGJjFMuqlThVPnvD+qu2/fMJlJ9Pfzh95bt7rqdFdN09/+nWDqO9gikSKuL+TrI5S1oBshI6HSxCurMxDDb+wdCnijLw0AIIEtodpRcBHoB/v7p+bLfyvO2gfRft2htBSs2sPD9JgKdnpehfYXMMtGZdo/Xgn7V2Z7kPW6vPyGUls+hJeG//rMXumk1kFD7k2bmsAJR83sVX/K6VOoHcEekd6rO0Oaxu+NakZ8/qjPDZbArs+hXY2dBmPmZLdh2jCJJH3IiCm1LShtZH2CsQ6Ek9TaSphpxapSbnwJra2lDdZfl8RzZrRMDN14cJW316Dp7oNM0nMJJMaWUFKoyPiZ2iUTUFoY12hXBH4U7f/SEy9lZ4x/5uU18w5c1dMVQ2YQCcTpdQnheTABXAnlQBsRatg2tIJUwnbLlQ5LjEvWSWMc0adaIBMU7RYv0RXHFRBKWA50IjwhoEUsgFBkg+o6v4AQQlpaOm12wZJCWMKyhUhXWaqttVQr4TbUcgUUvBhubTas6pFQe3rpz2vQM+zLwYadKqYFwIDJVR8ANCReDF3whgs/XvYGK4vrFll2JpZzumDMC5//8vSJk0bdGkREOwQDMBHtSe77nz8e9/NfCNe02MVWWfLtUFiiW/T36MHQlbmiWmXkmqijJFqUcJRwImQh0C6ixHNMhx867Ur6FcuDZcGyIC1RXSRJwKqtGCysho969SpJNYKqNS0AAANhWRmlA9uZWAYa1xkS8av5tAt0ZnIpzcBeV5gm3qGlfY1xsondHgCm2kcaMIGEger3qjXeWiquplwDPeAJX6UrM5mKrSvSnlAcSrcYESaGjy6uvc+nGThjFRt7Yrei6Jowgper9X/2EbmIBpH3TNSF9RE8F2H9Eb3oSn8omHxBZdcE48qxmKFXTFUvTUqWJAqrk4lrknZXRzk9kNGVnB4sJb4ytmvKxlSLoq5KpIKwlTSBtkVsPEuZkvRg0G/aK8YfMG2iGCVw5GBoIrS/Y+J+5T/PUMXklZn3JFGUy+/10jNiOZAgBkwGIoLqAhRgwQZ8CzIvpZXovAz8fAKv4rkargUZy0SJXAQvgQKgYSsjE6FrK/1W85WEGkDbiJtQAAnsyIwxVjblCpURRdOwf2Nhtk5B+ghdBGjYYXR0TNdVSrfbUBKxXyrrYmtQNNOef6F0+IRCNuPApMeRiL1ESSROEljKaFvE0l8je9ahp2XNy8kfXmmREAtmju9dbveIIOvb1eDqhfAUrAROAC+pPX0FJ4Rfb+3op7BV0gpz9Wkalf4cwEtL4qN238qPJVuweysGEtgJbBdBGbkWVKaWVwUFdyBv+9n8enT4ojRjzUui0IuWybJ9oN+Z3of2tZAAbCi39kVGei18xDaSotXe1TI4pSsOLlvivrtl3Su6t21Gb0+XjQQCEgpC+CJM/40qYWdEmM4aPdTBBAAQieqqSxDVar8jqrdE4xcKITKy1rkggQMB20qEACxhSQuWsKTIWUFWlIVVS7miceKqoQQrqk3yYrixcCI4Q+97Q6vCCQADoiOCM0a3ZwFAJMYtmxyAinEbn9HQOeppuDaFfjapDD5fUuvL7uy8yDnf3n/uV//p9M1fPyKi1wgDMBHtYe57+OFjf3il8IzvKQi0WKWcLGSssMca1MLkrCATDiyPJq1T2QHRASG0cNJPiomxZ2ZWFVq6Yy8biZbYzlTgwhLVMCyEkNIICMuqfsKzBCByMi5pB5aVlmGzVlyGCwgT2sJPGurGGBpRJ4QJbdXnxxU7nZsKgJVV4YaG4ZTpWkrDxv0OLwUP1XJNwz4YMVzY6wittsb6oRl+zOHSYcDGdMiiLwrV/SEAtJlCFoXGfX0TuYjyGCyiNYfB1ZiYHtlLF/kEAuPGcIvIF1U2iiwobeI4Ew644UBL1KdiVU58o41E3ImBdvTHsBSctLctYCrpjE1GAZAmBmA0YNCKAfdVpYy0Bp1wTktvz9T2RYPr95tir+nPLX1hg3Cm9A1EvSVvmlxvtxf/Z0O9wS4wMYdsK8w0mUgfWZRzuQR25FkJ3Mh4gXACuAE8Y5yiySaoVhfTEbBSqPqg2bEWIhpdudwUH2ECmRb9JOpJplogre9mQ9lI6h2hh84lRia6tFN0FiUHZuDml1s/sG/+keeSWbnC1O6Gk8Y2lKMiD5FEYCUIZVtiiyImFKE7X3xhzeNJy2Ft1t8GuifBbUFlRq4svTTzl5FvaKFMc2/aI7p+fA3bGqu0O1oyPNBWJ7gWqGZdM/RqbL2Rd3V14aiNfIpJS9PpH9PSuocwveKWEQG8yeV1uf5VOsbq6XMyKK7GxEl4tfO55aX28ch0ha5Z2Tred0zRarUmdtqOzHhxVwZ9+c4sygOiRWina/UzPU9tWNTiJSJRkBJKWFBwBKCEU29JvayqYEMghp1BkK62PdQdoJorBQTSfywQoogWB4ktYgVHCNhCVURWCDhQvlWpfY6rjuytpVTRJgYdRPWJrIaN9a3vJBDDGxBtLuIobWpDA+o/xcItIwuIqP50Gob+QiAxbsXkYVCpzYWeEXHFOBkRV9Jh9+mViyQAaI1Kn3q5IiwtZ7RCqRe+8s3pU6dswaUnInptMAAT0Z5n2cqVN975239/4A92PpAyAdAiijZUxgpgibwIbUu1xH0rgq5+054IO0QGQoTI2CKBwHgnltnYdq311riKyAfSF0JASqTLhqQBuFYZFpYw6afLxg7SAqhvAWrLCw/rRC0ETGAbIO0OnfT7uizTynCNGYq7aBymOzzxptl4WDCG4yvZEaYDg4eP5hXDHl6dtashWhuTteI2uaExHnsmakExLQbCGB8xgDyKABwTAGhBeTUmAIjgBsbxEfWiM4ILA1dEa+Lu/siH1pkkMGEkg2JU1iYIPZQtKBcqL0o5MzgOvTEswBRNHkBgMoCpGA8GxphOU2orVvLFFZPntRce6bMmW06udenSgXCpSqZ0lTtbg2z7xKSvbe26Ym/J8/d+6fHFjbeEC0xqQ8aG8IEOmFYYXyJBnPfTnBV5vqUAOxChbWQ6qZWl4SuRjYyT2IiNb2CniSERSQQLaaUXQ5MVj1WorA5qbUyGaaZNl8MZXdpV1Sm1ZJo8GwKwAiCFSmoRsZGH0EfgI55QeHV97LSt6A9zsjRpnM5lVa2rtoLtIUgPKKE8hBKJQmsRThHtnarsv/hifk2ypoRuAfdNWDt5ciitNIhG8AP46dNJWz46rm+pHfCJQjdU2hMjpUgsqOpwYmPSLyzSfUZ8eeEhdBFUA7mBgySDootQWq4tpCvtitfSm+9ozdpuznaffWlRdtLcrpVT49Wlrs7Ayr7hwInrEztx7FXlVp1gfb9QWvhRoa3cu2hFZrDiSFHr0F4/7xZ/VdKC4tC3HrVImQi7gBYXCkKkN0YaWcvCT3dwhUqrtS6UIxQAWygb8eisCyHaMJgO5LBFPJTBh/YRQkDB7RetjX+t75TupuACIhZODCeCU21Aw5MNdS5tpS3i3qQtI+JaEh7qDG0iCxAmktl8Eet7iyu1nODDl4u+fsH0yZO39CUjInotMAAT0Z5q2YpVs/7161Y+ac0UpVAOlG8qErGwhGslWRm2yUKuXFDrMuvczmXZTiXsGBkFB0J4IkhgZ+y41an0OuNCuyVM+0XbEkKIWgfpWu9oq5qNhchKVa1p1PtONwZjSwz9jHpBpvqzDmTS56XzM2++0lvPvdXtZvif4PiJbA9gUB0DPLQ4U+04w0rFSOeeAdCvcxPk6mE7GIxDr90wEtVH3GHWp+ViU9stXeAEQMm0bEAnYDagA8Cg6ixHAsbAGCTaRDGCMBf0xhVjtAbgiSCHgmsqjlEComLcvCm7yhaiHOpM5YC5U/se7+jNma7QeeDRvgFIF3IN8hLL10MDmanob891+X5rrn/tw0lvgokHdhYX9xZK9fZi5gRY++SMgKMC8WrSV0Z5NQoJXMDPAZOQ+NKTvgdftftZq1jwO+GiIj0plIAlFVxEiQ3UgqiFMIEV12JwCDcNUSXk6j+kp06X/K0Yz4WSIkmPMFYNeas1VlzrsXbWQH/S1zfwt3Xeu8aLCX4AF+mqs8M7S0soGzrNV/0YB2Cvl/6x6m+lnoNkNkncLllo7VznVhe4juAr2CG8pDoJ1jaE3o1u2mzdWBs77XRt1b4s0JAwsEQSG2/EcF8T2GhcmshLYJDJJkli2e3ZpBhFXd1exvL1YNkZF7R12kK70gjXMRBCCq/FzTsVJZ2cDmFLV7peOOAEvUrHfhxmC6tcXfKjkh3KDWUThqZvIO2n7ReQKyKfGEcKJUWcGCeqzY8dGtsTqv5DWMvq9Z9brOqKw/Xeyw6Uj0DBTivAjUJk0h/qV1PBTndWwraRpD2iq6EXtXxc/zJu+CDerKg4IgEMBNIliGuPaHggACAQOQiUq7MnoH6ohgHBgMAg2mPhNPwZDStgOzAomKwv4vStQwhRGfrmovo+0qZ6zepyX5yx2u27z/jUgne8E0REOxcDMBHt2e576OFjf/rzvB+02QOu0DGkbwfj3YHQznc6BcdzC5mOTKDl4vVy0cDqaRPW5vJ9aBO1gKqMnbeKvgwKplV4DkRm0M6ItBosBaQtpAVp1evDWakgRFl4aQVYjIjBjb9aVnqKobFzgAntagYeVuYVgKkGyKGNaAjAtZzcuIMxgHHbQ9kSph2qjR5+zPrnVmMACEukjx4ve6WI0iPXN8KgC721PpOAQSd6q4N+G4YYp/pMVwgbEGvQbQwCnUeSDKgMdDWfm0S3JgN2NGAqkYpEqFxjABgJJU1smSjX0do18JyZdQBeXdO9+lkVZ/2+Qvm5Db0D0MfO/l/vHD+jJ6h0zfFz7j59L8+66n+u/afTf/2X8qxn739lvScR+BPG9bXm28oFjTgXqErsygG0Wsv8BFInVpuUOtGehECsYakkKstYIwwSFQB9AGADLpDrgD1eJo4ftbQFGafiujH8EvwIIh6aAkoC0LAdY4WQjkjKcOsvR22ypaFKlzIyhp2G4U32qU6bkWCTnYHTxZbq1cXZWNuW9La8/Gqpa/Li9nExpIadiJEV46HzQsXwJdSMZK266+XMG7DuOXTtj2xnzs4GA163VQAkBluyRfgB3Ah+MGqmKz1inPNYWdcSiTYSgJXGreFzWQ3b17IACKv6byowriNNLF3RkoVlIeMjSXQ5B8tqH+cUW+1MsV/mPNs2gcjAdu2s7HBUpCPh2LGdlRm3s1KSphQrN/QRRJ7Wpr1cVArClKxEm0S3lPstDCRBzo43uHogXrsuTNJYm0EtZ7pCx7Ds6pxhdgw7zXVp2lSwK/C7EGyAnxhHijjSmbEWiBomK4L0wvmiUvsKyW4RDVXfHaEhrTpCCSDNq1kRAEgrxhAIkEnHGys4jlBlZBrKv9Uv7ZRwbUQB0mCcAdJ3M5MYN4ETGztMBwY3THg1qut+7U/piA8ABrl4YPBVWK3W3Z/+lwXvfNcOfCmIiDaCAZiI9njLVq688Z67v/nQg512ZS+5vlO+Ci8zoTzQ016Kcz1Rweo3feNXRE4gl3YYu5gUrHFrZb6cbV0tfA0HQiTGdtNOlcYWllC2FVq5yMkIx4aUaQAWtmy1g9jyhFUPwKLeZVqIaom4tmV4cbhOCBPYJpBhr9cwObNpLO02xN10XGxtu9bVz5dDUdnYbixbAuEn0GbYcUxDt+qGs6cfUbNW7IjYtmIY2CJMP/L6iFzEGRTrH2M9E0PANZEjoti4DiJj4Iowht+LdgOsNd0ACkmHnQSDSQbGmPTUBq1WyUMR2rSqPisObBVZiTaJqWi/aDylMyFsx6qYKAFMBnFGxD2inF3+Qvhcce25H99ren5pvjvITnxL9rl/e9dlv77p4iuXF6b/18+Tgptviw544+DM1VH8BDITsEwjO0WO28f726R9X0wmlZPMuv68tCAgFBIVwQ7KHVIbMegOFEw5cK2KmxjhSV0KoIwlpUoELERCGjgVSABJLZeq6q92Y3/gerQrmezwO3FTnV/T7CFrVeLRrFqBbnTCTPv6jsPAJNUbatGdLHsie3C5YXQlAG2kVTuyrlVN0+DtI9qrvCx65pV8h0yWJtnZnehEbA2q5cqaNC7j95dkdyXj9KMzhGusrHFtA0jbikrK9kxoeUnnBKvcn+Ta4DrCQkW5flKCJYyfjawcPGkiP9a2yDpWO0yhJLI+IAEBy0DmdcW12oVJzNBiY5ZMl8jOJgH8WJfspN/2kgB+GPW5JtEyV0myShqVQGZUMacCJ+nVsU4CJcOK0SabRAOR1EqHiQTgQKXDpD1UHChZL5ACNhIfFRtxEdWKt4IUsIrIxZuudTdciHRm7Pr+6RcT6bzcUsSAqM9ZDcCvTmym0qy76YnNRqg/ESDtflzt3J7m8Bi2U5uIK/01fUj62PSvPoIC8i0o2ptI2qNyqxIuACWc2hxd9aa6gcjE9S1CJNoOTSas94JuPFDjrw1vPh5iWygpVDap9A74ofTv+RzTLxHtMgzARPQ6sWzlikUvvXTCz36Yk/FeYXFm5ZXiskW5dXAUNmxIP4xDADkgmW9PqKhVk6eGGZRMbpXdUxBOLGwI4UB5JqnAi2EnwinLHFxXODZsCdvOyMSxtWOrCloqljfUQVqItL+0sOpdpgXqkRj1WbKG0f2+rsi4IocqvcPybT0G62F5eCjiahhj+0rmQ2HH1e1JUi0naw2tAZERsYPYwMRwYRAYGwJCCF/EjkinqC0O5S6BTjEgTVSdrtWqT4lTLSpLIzzECUwkHAMMmo5QOMagoNoqiYQx9SIwDDqsfomo3Qy0YcA3xawJ0v/aKO2W4AU6a5ko0nZkZGJsy8QZXWk3vT3x+kLX/CfnHpLptJNs26yWVy//n+/c5F1wyeQJR97w9dD2lJ3468LyQ+uSAhwJBygkOGA+kllOmJ1b6Ozsz00u+d1lPzZRkrT5tqpMkP2xo21t+b7q0IMuggG3LUyc0HLjcox8i4ijleH4XFLK5kwxas2LXhOpV+XkfFIeFFmUy7CEcN1gIJFCmURr24OwTKQqiZvxtakEg6LFGNOTLawp5YS0x1vrhOMHZXhOAq17RK8TZ1b6mWyc2DJuDwrLsxNa40FjkgHl5l3dK8f7KMTIZFVcsh3Llu7AYMavlIXrSMDNh8WydJz21avK63uDlgnuwNpB5IKWTqETOI6whFWy0apN0U0ibXXEFgBL2HGYZLItYWGaGhzQbd1vzDzTtnfFeELrVl/5ViVG1pWDwhIGJlJ+Jp/oQCl4FZ2txLYWltHGpN+tqCT9giNWBolGoqETGKOLLkyCJBGZGFrnk+Lg2qynI2kUtJE6RLaSKCQxkGhXFWRSXeQpjYuRyUgRp7NS1cvdclTB3MFQsGyUjr/1UQGQGz6Lm43EgYqHz+OFWpfddMBzbIYCZGOkrB1haNi2GVXKToOiDVWBnx4hhu0YlTay/tfG/dMf6qdoT3sjoGHYLQCYFhTVUL/ioSaJWiOxCbX3mWEDjDepXusecbr0T6q6DrYNIDEOgMj44cbaMHrsulAAMlbBgcqhgNh+KZl4z5mfXfCu+VvSNiKiHYEBmIheb5atXHHjPQv/90O/6ZbRdL3K7i85Vr9cVshlZHFDMuMNsGe6mVeM/Zu4bxo6O/Mr7Eq898xlBbky11kUOVskU7GqItySaSmblorJD4oMHFt4ru8i44a2k4SipYAWyMasa0EA0oKwRL0IPNaQ4GEzVRmoNdm4LBvibr3Pc2NFVw/9Wo/H2sBoaO22RcKOYDSsOA290MMKwr6JfFG2LCWAIMnaUsHAlmqo/mPVKjYCSMs1iIUlbMRpEUkIoeDEximKHGDGi/UAYuNWRDadDsdoDW1CnS3o7IiC9jRrhQfVYgYzJmjBgEYCY8rIGC0CkSkbPzKOi9iYtH4Vt5pinxq/dN7RM6aWda7db7fe8vgfj1m78GP/65Z3/f7O1hW39+XaMkqIOLFVYdAe31VaXRjsiNYuDzISf0siwJfoGIfsRMjxsjy+q89rL8NebSZEyFiIAbSgmEVBABX4CnYCO8p1RDFgCctz0pc39jqqr142RqK132JboVEagPZbbVSUcZV2IieHJIExyGSgVNYU3KwQWplEC9tK4CHRrXqw3dWeCQe1rJh8ezzgOfEAcutkux8GFS9Xifwuq7df5/2wEmayeVXRRhYSkc2E6XcKpSgvEmX50iQGiTYGOlJ2zpMq1qGCNpEWiZ/LmopJdJT2QQ5CGOOiZBJt5XLliuuJkqUjo43WUilLB0ppmRGFDIKsHZnE2EZZiGqrE/kJnAqGevlGJgNAGTuC7UIJIITtQblWJe0VXB8f+1r9Q84gqBdRN21oSK0ZOaQWtRRnQxmgiHy6sZ4k03+IWQQyvdvrwwiGHyRERsFu7DVtjFNALoeoBBcNSyKnB3esdKHvkR+uHKj6WH0fgYegGmgbOg87QuVQCGvnqifSNM9v9tUYNkXziFPX1EvKYx4ggZ3OX914AwDYbMdvVyiYdGVsuEg8UYFBThQsxA6UMP7FZ3xvwXxGXyLaxRiAiej1adnK5YC48Z47fvrQrTmr1G31tYZl24ny4aAa35pVsewPcOC0lmLien6nKfdqFKK2l42v15dzg8Wy6PPy6DdtMfKvmBkrdQeEgGNnXOP7KnFyBZGDLWHV4m51BmlZW094VAwGhvWFThkDoPJy69AQ38Y+zGn/Z13rBa31UBLWGsZAJ05rBG1MAGHHQoRCxqiWWROo2uddrSGELxRgpFGuDABAWun2dHFOYVnGGCEtGJOu+dSKAgSUcB3EEMJGJISIjZNYTqsZiGCn/wGpmFwJflryhTGJcYwxgc7AIDTSg/JFWYrYNSprBnOi4plyiynYIo61AyH60FHRfpyOYjUGQmRMZRzWvbjXkZPe0OJ05QfbJ1yyfuHvFjwQ/f0/bnpEj//Lr9PxmRLxZKy1EAbIBPAB34bdUSq0rh204lWDi5PCGkyx4U0FxgOdWDtxxvOYtU6PQ21IbTvKsUjqfUpj2JHJJMPHu0qhXDFGpkolxkmMnfaDLRvfE8oVAdISmbGzIkg3pnMjpUXOsOH46bBMAGmoSPdMN6YPBJA+CrVhvbU+typjEkvEBrBEjIaYNGZGSgt3SXWwa2XE9vRPaT221qF35A6R8WVDe1xRiXSmumWrEu/mPmtkEACowG9BMa09psl2dBJOX4oRx65fyo2ee3gD6v2NR8fsNDYDSNfrKiHfeFhtnAR2df7n2mMb5/pKj1xreXV7GypA4CNI+2DHkLZJVwUbWiQ5hp0RwegXKu2WnJaU6zVnDA0jH/aUbSglhvpOo3ZjDO026q0o7eOdFrTHevWgjR2aTDTita23Uwzr8+wKZUM5IgBMBmFanD/97f/8lX/53JgHJyLamRiAieh1btnK5QBuvuf2q/9wbZtV8ETFF0EiXEAEcMqizRExgBY/CNyudfYESGve8uXTV67qb885rrcuK593/cV678jY1XQqrbyrkPVLsgWOnc4X3WUPVpCvCK9WFq6uqCTG6Ahd63A8NP8zTCCD1RmgYbhvffSv0dUMnG7UBlpDJ2kH1LRftO3Ewo6gNUQEASRaWPGwWbUw7KN/u+zDcNXkIJxaJIgllEg/1FqicYrpRDgKbgzZIgqhyBgj6p+Y0zmfUUuGrijX5veqnsJHkPZWbUEph8G0vDyArn60x7D92oy4NlTcOn7K3C5nUn7V+Dd9KXhsxdv/S9513oXhm/b501WZ/rUVeGvQke7ZilIregUsAGW0DiADYK/Sq11rlvilxLwK0Qok0AfIdfmpK9CzzkzcUGoXTiJs7QmVLgUcmo0W1qpBtBYOq3MgmUwaBct6owUxoywTSeEkAISj60er5uF6njQ2BOrBexMtGbNhrgg2H0G3+L/waaqMTGZ4Tk5nhDKNITz90+i+ytvfjvpOTn0obL03sqkmPQ+VEJn6IrojjrCxonH9q4QMgsaCbQLHRlKBK6EAU4++qNV148bFkI2TfungGJV+e1JvcNpI34S1NpgubAjg+QgDeMKIuLZgFRrSotYSMGXtWNASyejCbG0ato3OlGbqfaS1lbUq9S/ahKmkmVlYIoDvWLqxG3YibAC2UA3TONsKthj1DYKu/RpqPxxjja7GorkB4EEBBkp/Y8ECGAvQAsmb993vyHcdubGnQES0MzEAE1ETWbZy+c133WZb6vo//GfOKtpCJcIJha8gS6I1DYEV4QOAQAInDTnp5De6Wg0zAELtQ4jIywrHgev4rmlxC0JaIVoGRT4jVd7S60S+Xg3OWbFrxRE814ocERmgrPNl4w4FVG2SATfqd4cVeOs14XRAb5p463VgraETOxtamQBam0AgSaA1YEwsrUxUa+wYWqyirC4cCgDpmi6hsVusogvlikptKZ0xymKFWifSerExzYTpxnoybEx09agGQIo4DTZ5FHwENpK0jDaA9hh2D/rHYUVivNVy6oT9W6bPyv994tvP6PpL/j/uVAdN+smEc3rueCj/xJ1JlxStmXJ716v2+A0mJxHnRMEzQbvoi0xOCbto8n1om5z09xRXW3Zgo6KQiXz7FTmxF+PXmXGlKIuGXDomDyqsxq20/ZWCaan90YzVy3T0bEDptlEbgYY9jYmliS0Ty3qTTGwJR9d3TjcKe/jsWQ2dZtNE7YoRQ2TN8L1Ht3ZIpDOojr+tXsr0W4xRj6pKo28aBeuZMFX7lwKrdv/UE1R6zvqyvfXJsePNzag8ouGyuuyQn/5qi4ZA3vB5ZszJt6URloi1Sfu6VwcD145sAJSMn15xWwwtsp2OFk5P5IhKxgQugvRONoBtqjVko43WRkAMfawSMBqBzgmoRNgwRmgDQEPIdNqzNAbDSWAnkFb1WwapjBz9D1hBBnpo6rWKcWGAxHKQ2HbkQHsyEkJJkQhpydqyUgZwhJJIPAQAPEQAWsWgDeWFYRRMQCiEiU3BN+kM0ErAhol9CGFsIyAgYdLnYuG9HzoD2pjqDWX2O2CubTQEDLDX3nMATJ02fUsvJRHRrsAATERNavnKZS8uXfz3fzx+y4M/DqWvhZtYthJOCL8+66k0IoA/ol9oWufQsNMY7Lux7RnLcyzfhSUS4dm2EtIKTItjKyX8AZMbZ/fbMka6+kstF1VMS3+Sq3/+Beodm40JrOqUV4k2xiBJ9ICtKiLdRzpKyEjIWMgIia7G48YJn0e8oxsDAbehniZFPDTnMABAGyc0fmSGekh6UHmrBCBJZ+URtm2UFHF9DqEYdj05ew1JI61hNi6IWm8DTDWqpf14HcRZFB0oH5UWFAFhI7MOre2q5KswmdG694Gt/zPzpAv9u2e+9MSLS5J2OSAsLJ8zoXxF8lt/Rs/yP6EHgd+y1p+wFu0+KuPRCyCHgoKdwCvAD+ClK+imhTUFux8d/WjXximaPOqr5goFIDHVPqLRFpdhdwSjLBNLVbLtXPVVNbElbG1lN9/ZuN6t2tl4z+3XRD2+ajM0+3GqHpsbK5kbrWxvTVXYKCnsxMQWlAQAJwGAuPqzsEd9nWEAICcqqtb53EMCGCgrMLbnRGHkekLZTuTGBkAUO8oWthMpY9fXH+60BmIjjTEOEleX0onQYUza99jTlfTfkWvCxKqO5hUQSW1krzRxraRsGsvOCrLhk5epTgBW6/GeGAkgNr42MkqnIm9YDzmtVNsIAeMjEkIYGCGEFEmoXWNsBe2Y5JxjTq6vQjRv/wPTx6brns3ea7aov64GU6czshJRE2EAJiLC8pXLANx8zy0Q1n8+dH1kZUIrk66fqeGExrdFdSCfFGro43jD/wnbEq4tM65xMkpmYFnKch1LQUrHim0Z1yeCFpaVFpoUvLLOlxN7qK+yNtUYrE26mi6MQaKhFIRI1rnQGnYs7MgEEFZsQghZy0iRVGXbzsbC0dVSodaZ9JO6sHIo2qacNWpQZLTRDpIKvLxQoRC2UBCwYMWwfGMqwothK+MDYtC4rkhsoZSxM7Kgje0gSatniUgLQkiMU9D5bXjNXaiMVdDG9kR1zZg8CnmUANiIDTKuLO37zvbHZr1/sleeliyb5azMOaF2OwecjL+6//ZfFDuTFywFZdvGTwK4G9DdhzYLQiK2oVwYDxWJSCL2EeRRUPACuAm8EvIxZAAvndaocXmbulj7gEgnELaRjFqqV0RGNv6HMw38I5dBbXzAVvRE3jqeUF4t91Zrno3fhuxa9S4OtU4BalQSHjal8PB/W1t4irRg7gqVfnMxR5QchWdltVLq1r73ySL0oHMoG5gIykOgIMvIAzDaxNqCMTZ0rKHhZkUpNm5WlyyhbKNcEVasFlcEFqxq3VvUmy2UkDYS2TCWuNqv2KT9rk0FngWhYSfGVAdjm0oZ+bQkq4wtocrINkbidLWtxEhHhDaUSgNwdZK89Aqbrx5zMoyZt9+B6UCDObP2BjB96rStePWIiJoPAzAR0TDLViyDEDfdfZux7O8/eKeQFiwRI6tgR8apzpMsVOPYy/qH9gAtcJzQy2V9VJxcxopdmQwilxEKlnAt5ckSgOoiQ0Io7cTaSZfYjRIbxhhd795soLXRJowFEm2SBDpJ670msjwo4SQmloCxjVJGShPDTmBMWXvVSrIxgHF1DABapyv0xlrWluo1MMZHmBjpWFEekRC2sFARHjS6ZCmStjIZ10LiBJGQsXAik00sFzAtspwIE5uMMnYaj+sZpt5teEytohCajCcqllDa2ICoDqeE8hDYUD6CdARyDkXj5/1D9iuOnz7gdbb4elKm0ukWOtz+eZl1B4V/POTS9x9S/m1osoFwFWQ/OhI4BiaEj9qkUBkEORRzKGRM2Cb6s6pkh9qKEw2pHRHZfiIzZdsZQFuITAVeDBnCb5wWK4OwAm/0E9G1aaUkVGj8hqK3rBXDRVoYr/8pvUXSX3MixKiJi+rCaq1vI4sC1+REWDJeeqhtqvduuoP0GDs7ULGxMwg3OzNzfaRrfaGgMfYZXjfG8I8ienhOdpDEkGnOj42dQTDe6DJ0Fsp+4149eLlLF1dbEzIqmJeJKr99zM6Lxc6URfAcP4rijNT2oIIx0jWlsuslQkbacYxKNBJIV0Ra2FIkELCFTtf9CoXvIAmEn/6qhJ2+Sh6CdCS/AJJaBk6b13hB68OGMwgVbA9BArs2HN7AIIuSgkzTbG0etZyCNIBjEgkFYxQkDGyowLgw5ivvPinNunP2mj196tTNXTIiIhobAzAR0aYsW7Ecwlq0dOnj/3gGQnzzgQdrSx8JTyS2UFLEHWJgADkAsc5UO09aFhwbji1cB64DKTMirsDNiDhjlaUVCwGjzbAlkXR1lVXUVl41iYbWKrGNrg0DBlyT2CYWiKWJpYlD7aVRtgIfWifaBlBMMg5UfV0ltzqprIm0J6ESIyNT/eQNJWAAmaC+ApMxHUl8zvEnCGPm7n8gAAE9e9ZsAICZPm36shXL6y+OEGbRkqWmOj+WePzZp2HVx8EKCHnBf/+uFhaEgmW0FA4cKMdKACSo5mcJ1SJKGo4vAhsVFxUbypVJsv9+g+OnVZxxfd64KS0Drqf29V8dlytMsdcMPDv3Dy+/nHvi9w6EghXDSlNEJi33KSeEX7TtAnIABOBAd6ggj14XJbcUWIExAjJKkIHK27FnJfD60RohG8AvIQegmF7TWqpJj18fzmpBxcZXcEKz0dmJ6jwkrVbf6DpzfQXa6itW7U4AbVwXSRludT8DANWusLABkUbQrZyDaoSGMvGojwBCiNEdHRyoejE8i2K6vdplF7aLAEAEvz5VVeNXCenOOYT1CbQiSJH25h36V2AAyMa5r0xi6SSBq3Xsa23ZaI2DOGMdcFi+u2eq6O5YhpZl6PJFOAmv+qLio7zPX5dP+N9/X380EGLFW3o8+I5ofTn5X39eX1i5tBfFohQJgEC4AASEBWHg2FaYQIrqZHXVKZdRzb2wRDI0jLk2oju9ENVtAIB0QjUM7yeSjultqOwCMF3orSdk2yRpvddFUDFeAgltQuOdveCUeW86AMCcvWaxqEtE9BpiACYi2jrLVqxIPwQvemnx48/9XQhIEV/z0PWwPA1ThhMKXxsnhgxNpl2oQMhBmYGUEEI4NmzZIsqurKSJV1gWBEyiTVLr+RwlQ+/J2ph0KiwN28Q+AqUljFGwYyONTutJRhuZ1DpMAnARiHpX2OpkWtBGJiadysZUtJtBaIzpMPHnjz3poP0OArQA5syaNW3qjh0NuGz58uGpAYBZtHQxYA3tJCCEeeKZx/7z+fvLM2bLfFbm/DX+lMnZPi+rp2V6D3WW9q4/8Malpcz9v3WEiYW0RBLAc0QCGA9BDzZ4KDtJqKUooLv69QSyWZQFKj5iicivlubKABK4ofDSilyIbAAXAgpOYKr15GSovyvSeZvr0rBXn/mpHmwcDM1Z1Sr6JWIPwQDaASSwsyimx6iHPR9hmi09BAlcg2EjWm2oMlqHIjFgQbSYSgwL1fl75YgGpNF0RJ25Hkobpdmz3t8bgBDGM0OV3jxK6SnSv0rEPkIBS0G6phIjJxGXkC2YbJpvw8QHIJCExnGT8KyjT6ueyMCCEsDc/Q4yELVFv+qX3czeazYaMvf0adOXLV+xeMkLPz95wfwPuy3v6XBdoVd6K/eaM068XBDjn06OXmZXG9mK/i4xUIE3J0g6sEp7xfUmu6Y47Q/L8q2PPPX0K/n0HLBNOkwX6fNUFgSEbaoztNf+1xWJFEntKwaR9i0PTB6iYYj46IXNhnOhfKtY/zVnIok4j37U1rWCMbG2Tz/qk3P3nZtehdl7zWbcJSLaoRiAiYheM+lY4tSLLy0G8PSzj2khErhaWELgR/ffpqUNAUckAIQF2+g0ZighbajQ+Ap2XJ10FTDGGMAYByo2EsYkxk60TGtNiZFSJBXtZkTomCSC5SJI389to4wxlhHCxF885rR05pu5+x2YfmCfvdfegJm+g7Pu9pv6scPFjMlx55R8WyxznuU7U/y+rmwl9LJT1MA9ybuWvPMT9Z2XLV/WmEtrZWik09UuWrLYghaolqhFtTMqBCCAvz/7eDVvChhAW9BpEVSgnpYMhAHSGX4BYUH/6r6fWVARvHQmYQHLQDtCAYjgArCFsmvVWgA5E5oEAOxEqCA59mRhCcgknNqFCjJLsj15qHXIW8LMe3HJcstasbrU/4z/ouf0Y5yGsEyUHieBNJCAtE0az1T6bDJax5ZjIUqMULA/8e7Tqy+FSacgqzFpedcM/Q4YYO5+c5GWn7UFUavkDxWA01Gms4cOM2xBL0zfwXP//uH++9Y/+/hA77eSN3T8v/GHDFrta8SEV01PZPyc7AeQReAj6kVrpxloKfe2rHzmr+tn/Wv3gef/+reRlo5IYsj0yxUoCwI6cISjIYTlK6MkEkv4SX2hMtdKImPXkrAChBQqMr4rwsh4AEauiAu4UJGRGREmWkaQvgnPO/pYwAhoYbQweu5+c6uvtsHsWbONwe7/b5CI6PWHAZiIaFdasWJZmkWq/YghhpLZCKaW58SYsz1X//x6+kj93w/f/7Fbf5r0THPas25eehk9KdO/r79UZ1simb1h/REL99/vyGnzdnUzt8jyZcvuvPHG8NfnjgeyEl0OvHdC5VwxFUpZbotWWQuACAEfq4LZ3e1LnH4t1+u7ywfHdu8+3sr1umPdc8kxJ//XUYcctqufzS6zctmyJUsWLf7747rFumXJH0Nf+k6yLj/ejYt2MFAsmH/a57iDZu0Hg9nTx+453NCHf6P120VLlgz94jtzJm+uJGvM9Gks2xIR7RkYgImIaDd13/88+PFrvlea+IZMp5trS7oyUatXmprtm5bZ0Orib+Gpp885dsHkvXd1M7fC/ffdd/G73z3XAWJYQIcL653oeQHT3o9wNpycKwScgWjJPvt3+0syfcrpiyoT/WKI3/XNWfz34LyTf3Lo/AW7+kkQERHtwRiAiYho9zX+tCOjibOicd2TOwY6s3GrX56S6ds/u2Sa2/fb+CO/GzjklRNP3NVt3GrLly27/cYbLeA3556bAxzAAgQwy8H48egZh/U2nP0RlRHZ6Dzqe3vNnhcZceT8o3Z1w4mIiPZ4DMBERLT7uu/hB9977TXWpPETu8oy50711433K3Mzz03IVBK79fP9l/7jXftPb5+wq5u57ZYtWyaAFxctAiAACRggARyBmXPmTN3BA2uJiIiajRBi7HUIiYiIdj1tZcKBMGxTsWUpXVT5nIpXq+7xpQ1T9YvzzcuLeudMb9/VjdwO06dPBzBtOoMuERHRTmJtfhciIqJdYcGhhyIITKkSh8LEiVZJSTl9qmWpbHXXB4fmX328smpXt5GIiIj2JAzARES0+7rl818xpYoOI6OSQNkm1pXYsVUQuK4XPfrvpd5d3UAiIiLakzAAExHR7mvBuw43lWCgIHWcVJTUSveqXClyQ0fslTxwYO+L961avKvbSERERHsMBmAiItqt3fOvnzfF0mA5F8f2BtURxM7aePwLmX1DG+9xy5ByVzeQiIiI9hgMwEREtHszBuXAFMtxICpKro3bX1LdRWUphajy5Akvvrir20dERER7DAZgIiLarS2Yf+iF75pfGYz6K46Jk3zcX4nsl4IJKsa8ZQuzondZoW9Xt5GIiIj2DAzARES0u5u3z35OpWj6BioV18SJidQL0ZRXwqmv9LQvGOhd1F/Z1Q0kIiKiPQMDMBER7e4WzJ9/+6c/bwrlpHegXBF9kV8JraeDmYvLPbnl9zyuXt3VDSQiIqI9AwMwERHtARa8c/5F73ir7uuXq19x169FodA3oBcPdkfxK78scCJoIiIi2iL2rm4AERHRFvnKJz8nEP/0L/e0llb1begKXafTD6HLExYv/+/xzx418U27uoFERES0u2MFmIiI9hj/9skvXXn6NzeEWTFYKG0IKquD/leNdcetKMW7umlERES0BxAAjDG7uhlERERbYdnKFTfedaclwnvv+V7oiOsu/3/Tp0zb1Y0iIiKi3ZoQggGYiIiIiIiIXv+EEOwCTURERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiICABmzpx56aWXjv4ZwNVXXz137tx8Pr///vv/9Kc/1VqPudvOtLEmATjllFNOOeWUndOMj370o0KIj33sYxvbYfny5VJKIcSzzz67c5qUmjlzphjla1/72ph7bs9FTF+BVDabnT59+vvf//777rtvO9q+UUuWLBFCPPXUU9v28C28MbbzLES0m7N3dQOIiIhot3bppZeed955X//61w8++OA///nPZ5999rp16y644AI2KZXL5W6++eYf/OAHXV1do/965ZVXZjKZUqm0k1t1xx13hGEI4IMf/OCCBQs++clPApg0adKOONf8+fMvvvhirXWpVFq3bt3ChQuPPfbYu+++++ijj94RpyMi2h4MwERERLRRxpgLL7zw29/+9le/+lUAxxxzjG3b3/3ud7/+9a+7rssmATjssMMee+yxa6655uyzzx7xJ6XUVVddddppp/3nf/7nTm7VQQcdlP6QyWSmTJny1re+dWN7XnPNNdOmTduec3V2djYe/7TTTvvwhz989tln7+SiNxHRlmAXaCIiItqo5cuXDw4OHnbYYfUtH/7wh0844YS1a9eySSnHcT7+8Y9fccUVxpgRf7rrrrs2bNjwz//8z7ukYVvohRdeWL9+/Wt7zJNOOun555/f+XVvIqLNYgAmIiLa8xx//PGbGHda98EPfrBxt+uuu+7oo4/u7u6ePn36SSeddO+99z777LNCiBdeeGFjR+jp6ZFSPvnkk/Ute++99w033DBlypTGw86fP7+trW3u3Llf/vKXK5VK4xGMMb/85S+POOKIrq6unp6e973vfffff//oE226bek41R/96EdvfvObFy9evLEmveUtbxFC3HrrrbfeeqsQYubMmSMe+/DDD29Jk/7yl7+89a1vbWlpecc73vHEE09sYmPqU5/61OLFix988MERT+oXv/jFSSedNH78+BHbX8PX5Pbbbz/ssMPSF/8rX/lKEASjjzPCiBfku9/97kMPPVT/0yWXXHLBBRe88Y1vHDdu3PHHH5++Yg8//PApp5wyYcKEWbNmnXHGGb29vZs+RUdHh9a6v79/9Om2/OnfdNNN73nPeyZMmDBt2rRTTjllzH3GNPpKjXljbOdZtvm12uw/Q2PMFVdcccQRR3R2du6zzz5nnHHG0qVLf//73x955JFb2DYi2gxDREREe5Q77rgjm80ODg7Wtzz99NNve9vbVq9eXd+yYcMGz/MefPBBY4xS6v3vf7/rumedddatt9560003nXnmmY7jfOQjHwHw/PPPG2NmzJjx/e9/P31s48+f/vSnW1tbf/SjH61bt25EM2bMmPGGN7yhp6fn4osvvueee773ve91dnYeeOCB9YYFQXDooYd2d3dfdNFF99xzzy233HLmmWf6vn/eeefVD7KFbdt///0nT5580UUXrV27dmNNevrppx955JHDDz/88MMPf+SRR5544onRj91sk/r6+jo7O4877rhbbrnlyCOPnDJlShRFY240xvzTP/3T8ccfb4w5+uijTznllMbGLF26VAjx0EMPPf/88wCeeeaZ1/w12WeffaZMmfL973//N7/5zSWXXNLd3X3QQQcVi8XGZrzxjW+84IILRly1xhdkxHUfN27cW9/61l/96le33377Kaec4nnej3/8Y8dxzjjjjF//+tdXX331fvvtN2PGjL6+vvQh9Veg0be+9a2Ojo4xT7clTz9JkhNPPNF13S984Qu33XbbjTfe+JnPfMZ13U984hMAnnzySbNxY16pMW+M7TnLtr1WW3JZoyg66qij8vn8eeedt3Dhwuuuu+60007r7u4+66yzfN/fdJOIaLMYgImIiPZIURSNGzfuqquuqm/55je/CeBnP/tZfcuPf/zjmTNnaq2NMd/5zndyuVz6ub/uT3/6UzpodtMBuFKpnH322blcTkp5zDHHLFy4sH6EGTNmdHR0vPzyy/UtS5YsyWQy9ceeddZZs2fPXrNmTeN5H3744Uwmc/PNN6e/bmHburu7169fv9kmGWNOPvnkk08+ubGRjY/dbJN+//vfA1i6dKkxZtGiRZ/85CdfeeWVMTeahvh32223OY7T+AXEueee+8Y3vtEYMyIAv4avyfjx49NmpJYvX57NZi+99NLGR40ZgBtfkBHXfdq0aZVKpb7zhz70IQBnn312fcvg4GBHR8d3vvOd9NfGABxF0apVq9IQ+K1vfWvbXn9jzHe/+91cLjcigv7lL3/xfX+z0XRjV8qMujG25yxmm16rLbms559/fldX10svvdS4z/XXXw+AAZho+zEAExER7am+8IUvzJ8/v/7rPvvsM3PmzCOOOKK+Ze7cuRdeeKExRmvd1tZ2ySWXjD5IOo/UpgNwqlwu33zzzem8vl/5ylfqu5155pkjjvnhD3/4ve99rzEmiiLf92+//fbR5z3rrLPe/va3b1XbzjrrrBE7jNkkM1YArj92S5r0wgsvWJb1ta99TSlV/+uYG01D/IvjeOLEifWoE0VRT0/PD37wAzM8AL+2r8lnP/vZETt85CMfOe644xq3jBmAG1/MEdf9C1/4QuPOt912G4DHH3+8ceNJJ5106qmn1l+BEV0L29vbv/a1ryVJMvp0W/70L7744tH7fOc739lsNN3YlTLDb4ztPIvZ+tdqSy6r1rqlpeUnP/nJ6H3e//73MwATbT9wDDAREdEe6vTTT3/44YdfeuklAP/4xz9eeOGFK6644qGHHkonNHrqqaeeeuqpdPqlxYsXDwwMLFiwYPRBjjrqqC08XSaTOeWUU373u9/97Gc/u/jiix999NF0+9SpU0fsOXPmzJUrVwJ47rnngiA48cQTRy9Ie/nll6cjHre8bY2jjjfdpNHqj92SJs2ZM+fyyy//wQ9+MGPGjIsuuigd0jzmxka2bZ9xxhlXXnlluiLxr3/964GBgdHjtHf0azJt2rTVq1dv7HXYxAPrJk+e3PhrOoB53LhxjRt7enoapxybP3/+I4888sgjjzz66KP/+Mc/1qxZ893vfteyhj5kbtXrv4mnf8wxx2z2qW32SqW28yyprXqttuSyLl68uFAozJ8/f/Q+jbO+EdH24DJIREREe6SDDjrogAMOuPbaa88///zbbrvtkEMOefe73z1z5syFCxd+4hOfuOqqq4488sh0eZs0kimlRh8k/dMmpD2opZT1LWeeeeaFF1748MMPH3zwwQBGrzyUyWTSqZjSPHDdddftvffemzj+FratHqg226TR6o/dkiYB+OxnP3vaaadde+21l1122c033/zHP/6xra1tzI2Nj/rkJz/5H//xH/fee+973vOeX/ziF6ecckpnZ+eII7+2r4njOCN2yOVy5XJ5E08t1ZhON/2n9NcxN9aNWAZpE8fckqeflmjGvDPNqHm2x7TZK/WanAVb+VptyWVNkmRj+2zJ9GZEtCVYASYiItpTnX766ddcc40x5rbbbjvxxBMBnHjiibfddlsURddff/0ZZ5yR7jZnzpzW1tb77rtv9BE2O+ftRRddNGnSpBEbc7lcHMebbd6kSZMmTJiwZs2at47y+OOPp4XKbWjbjm5SEARhGHZ2dn7xi1/861//umjRot///vdjbhxx8BkzZrz73e++4oorXnzxxQceeOAzn/nMTntN9hRb8vRnz57d3t4++uUFcO+99272FFtypbb/LNtgSy7rnDlzWlpaRk8nDuCBBx7YEa0iakIMwERERHuqj3zkIytXrrzmmmuefPLJE044AcBJJ510//33X3PNNUmSHH/88eluQoizzz77oosuGrF4z1//+tcf/vCHmz7FoYceunbt2jvvvLO+5dFHH126dOkhhxyyJS288MILzz///Keffrpx48033/y5z30uLQZuQ9t2dJPOOeecgw46KN2ez+dt2x4YGBhz4+iDf+Yzn7nnnnu++c1v7rvvvu94xzu2rQHbc712f1vy9M8999yLLrro8ccfb9zniSeeSEfnbtoWXqntPMs22JLLalnW2Wef/e1vf3vRokWN+/zf//t/x0zFRLQN2AWaiIhoTzV+/Phjjz32rLPOmjdv3owZMwC85S1v6e7uPueccz760Y+mk9mmzjvvvMcee+xtb3vbv/7rv86fP19K+Yc//OEnP/nJRz/60auuumoTpzjssMNOPfXUU0899dxzz33HO97x/PPPX3jhhSeffPKYwxRH+9SnPvXnP//54IMP/tznPnfooYcaY+64447rrrvuBz/4QT0fbm3bdnSTTj311J///Oef+tSnjjvuuP/6r/+yLOuYY46ZPXv26I2jD/7e9763u7v7pptuuvzyy3fma7IH2ZKn/2//9m9PPvlk/ek7jvPQQw/9+Mc//tSnPvXTn/5008cf8/KNuef2nGXbbMll/cY3vvHoo4/OmzfvS1/60tve9rZSqXT33Xffeuutn//856+99tod0SqiZsMATEREtAc7/fTTFy5cmJZ/AQghTjzxxMsvv/zjH/944262bd95552/+tWvrrnmmhtvvBHAwQcffNNNNx144IHPPfdcY1Qe7frrr7/sssuuu+6673//+zNnzjznnHO+/OUvb3kLr7766ve9731XXnnltddeK6WcN2/en/70p7e85S3b07Yd2qRDDz30hhtu+Pd///df/epX++6779133z1lypQpU6aM3jj6yLZtf+ITn7j00ks/+tGP7uTXZA+y2advWdYNN9ywcOHCX/7yl2mX/re97W033HDDwQcf/Nhjj2Wz2U0cfMzLN+ae23OWbbMll9W27d/85jdXX331ddddl65q9va3v/1Pf/rTgw8+2NPT85o3iagJCWzNWH8iIiIiItpBPvaxj7W0tIwuQb/vfe8DcNddd+2KRhG9fgghOAaYiGjz/vu///u4446bOHHitGnTPvjBD/75z3/exM5//etf6wt7XH/99TutkbR72qqbZ6t2ptcrvuFs1uhVlOr2uLOM8KEPfeiKK65YuHBh48arrrrq7rvvPv300zf92K26c+67777DDz+8vb196tSpp5566pIlS7a/8bTn2qqb5+WXX/7ABz7Q2dm51157ff3rXw/DcKe18zXBAExEtBlXXHHF0Ucf3dnZ+cMf/vCyyy6zbfuII4649dZbN7b/m970pnRJzEwmszPbSbuhrbp5tvZOo9clvuFsieLG7XFnGSEd1X/CCSeceeaZd9xxx7333nvuueeeeeaZH//4xz/wgQ9s4oFbdefceuutxxxzzKRJk/7P//k/l112WW9v7wEHHPDcc8/tmOdEu7utunnCMFywYEGlUrn++uu/+c1vXnXVVV/84hd3bntfC4aIiDair6+vra3tG9/4RuPGr371q+3t7YVCYdOPzefz11133Y5sHe3Wturm2Z47jV43+IZDxhit9c033/zOd76zq6uru7v72GOPvfLKKzf9kK26c6Iomjx58qc//enGMx5xxBFHHnnka/UUaA+ytW87t956ay6XGxgYSH+98cYbpZSVSmVntPW1wABMRLQZd911V7qKRuPGDRs2ALjrrrs2/Vh+Hm1yW3XzbM+dRq8bfMOhbbNVd85f/vIXAIsXL27ceMMNN/i+r5Ta4W2l3czWvu3cc889559/fv3XdFmvpUuX7uh2vlbAWaCJiDZtxYoV3d3dra2tjRs7Ozvb2trWrFmzq1pFe4Stunl4pxF4G9C22qo7x3Gc448/furUqY0bfd8PgqBQKLS3t+/o1tJuZWvfdo499thjjz22/uvChQs7OjrSdfj2FAzARESbMjg42NLSMnp7W1vbDh0GRq8DW3Xz8E4j8DagbbVVd86b3/zmO+64Y8TGu+66a+bMmUy/TWjb3naCILjooouefvrp3/72t7fccssOnRnuNcdJsIiINmPMt3XL4vsnbd5W3Ty80wi8DWhbbc+d88ADD1x99dXf+ta3XutG0Z5hG24erfUzzzzz0ksvAfj73/++o1q2Y7ACTERERETUpB577LGTTjrp9NNP3+wyS0R12Ww2Xazr9ttvP+200zo6Oj7/+c/v6kZtKQZgIqIhg4ODfX199V87OzsBaK1H7znmRmpm23/z8E4j8DagbbVtd85jjz121FFHHXHEEVdeeeWOaRftAbbnbefEE0/8l3/5l8suu2wPCsDsUUNENOTKK6+c0eCqq65qbW3t7+8fvefg4CDHSlGj7bx5eKcReBvQttq2O+fxxx9fsGDBEUccka5ks+OaR7uzrbp5nnnmmZ/85CcjNh522GEvv/xyFEU7poGvPQZgIqIh55xzTuNc+V/4whemTJmyfv36xsoegN7e3oGBgUmTJu2qdtJuaDtvHt5pBN4GtK224c554oknjjrqqMMPP/ymm25yHGenNJN2R1t18yxfvvyss84qFAqNG9esWdPT0+O67g5v62uEAZiIaFOOOOKIbDZ76aWXNm68/PLLW1paDjnkEADFYvH++++vVCq7qIG0+9qqm2ezO1Mz4BsObZutvXOefPLJtOfzTTfdZNscEdnUturmmT9/fi6Xu+SSS+p7xnF8xRVXHH300Tu52duDdzwR0abkcrlrr732Qx/60KpVqz7wgQ8A+N3vfvfLX/7yhz/8YS6XA7BkyZKjjjpq8eLFs2bN2tWNpd3LVt08m92ZmgHfcGjbbNWd89xzzx111FFTp0798pe//NRTTzUeZ968eXvWeja0/bbq5snn87feeuvxxx+/evXq448/vlwuX3bZZa+++uq99967q5/HVmAAJiLajBNOOOG3v/3tL37xiy996UvFYnHevHm33XbbCSecsKvbRXuArbp5eKcReBvQttryO+fhhx/esGHDhg0b3v72t4/4UxAEnuftlPbSbmSr3naOPvro3/3ud+eff/6HPvShzs7Oww8/fOHChd3d3Tu5zdtDADDG7OpmEBEREREREe1AQgiOASYiIiIiIqKmwABMRERERERETYEBmIiIiIiIiJoCAzARERERERE1Bc4CTUS0jYS4YKefsxN4004/6Uxgys4+5wSJjNxJ59ofyOykUw15y04/YzswZ2efMze1aE8s7pxzTbHXt8k1O+dcdXthWQY76QnW7YdndvIZ86hMx9Kdc64eDIzDyp1zrrr8epVfW9rJJ8ViYHBnnxOP76wTlYEXdta5apI+JC/t7JOuVHipvLNP+izQu7PPiQteR7MmswJMRERERERETYEBmIiIiIiIiJoCAzARERERERE1BQZgIiIiIiIiagoMwERERERERNQUGICJiIiIiIioKTAAExERERERUVNgACYiIiIiIqKmwABMRERERERETYEBmIiIiIiIiJoCAzARERERERE1BQZgIiIiIiIiagoMwERERERERNQUGICJiIiIiIioKTAAExERERERUVNgACYiIiIiIqKmwABMRERERERETYEBmIiIiIiIiJqCAGCM2dXNICIiIiIiItqBhBCsABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERETUFBiAiYiIiIiIqCkwABMREREREVFTYAAmIiIiIiKipsAATERERERERE2BAZiIiIiIiIiaAgMwERERERERNQUGYCIiIiIiImoKDMBERERERERERERERERERK8X/x/svlI/+hN07AAAAABJRU5ErkJggg==", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", + "\n", + "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", + "plotter = plot_fields(mesh, \n", + " fields_to_plot,\n", + " plot_vector_components=False,\n", + " view=\"xy\",\n", + " dtype=\"cell\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[1280, 1280],\n", + ")\n", + "plotter.screenshot(\"./variations_with_mesh_xy.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./variations_with_mesh_xy.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ae2185a8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:11:37.935930Z", + "iopub.status.busy": "2026-04-10T20:11:37.935821Z", + "iopub.status.idle": "2026-04-10T20:11:47.771216Z", + "shell.execute_reply": "2026-04-10T20:11:47.770672Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", + "\n", + "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", + "plotter = plot_fields(mesh, \n", + " fields_to_plot,\n", + " plot_vector_components=False,\n", + " view=\"yz\",\n", + " view_negative=True,\n", + " dtype=\"cell\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[1280, 1280],\n", + ")\n", + "plotter.screenshot(\"./variations_with_mesh_yz.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./variations_with_mesh_yz.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e2dc3914", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:11:47.772617Z", + "iopub.status.busy": "2026-04-10T20:11:47.772511Z", + "iopub.status.idle": "2026-04-10T20:11:56.742830Z", + "shell.execute_reply": "2026-04-10T20:11:56.742243Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", + "\n", + "fields_to_plot = [\"pMeanTrimPred_std\", \"wallShearStressMeanTrimPred_std\"]\n", + "plotter = plot_fields(mesh, \n", + " fields_to_plot,\n", + " plot_vector_components=False,\n", + " view=\"xz\",\n", + " view_negative=False,\n", + " dtype=\"cell\",\n", + " cmap=\"jet\",\n", + " lut=20,\n", + " window_size=[1280, 1280],\n", + ")\n", + "plotter.screenshot(\"./variations_with_mesh_xz.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./variations_with_mesh_xz.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "08ec8e3f", + "metadata": {}, + "source": [ + "We can observe that just by changing the input STL resolution/remeshing, the predictions of the model change. The fields show a high variance near the edges, which is expected, as that is where the mesh changes affect the topology. However, in addition to the edges, the model results also show high variance in areas where the topology does not change much, for example, some areas of the roof, wheels, and the sides of the car. " + ] + }, + { + "cell_type": "markdown", + "id": "1e834e9b", + "metadata": {}, + "source": [ + "## Analyze the Model's Sensitivity to Checkpoint\n", + "\n", + "AI models are trained using stochastic gradient descent, and that leads to randomness in the model outputs. The model outputs can change based on when the model is checkpointed. Additionally, for a different input seed, the model can produce a different checkpoint. Typically this variation is limited as the learning rate at the end of the training is typically very small; however, there are still some oscillations present. One can save out the last few checkpoints after the model training has reached \"statistical\" convergence, and then use them to produce an ensemble of results and study the variance as above. \n", + "\n", + "Since we don't ship various checkpoints with DoMINO, presently we directly present the results of such an analysis; however, the process to compute them would be similar as before, i.e., for the same input points, get the predictions of various model checkpoints and use them to build statistics. \n", + "\n", + "The figure below shows the results of such an analysis. The analysis shows the standard deviation for pressure and wall shear stress for two samples from the validation set (439 on the left and 419 on the right).\n", + "\n", + "![Variation due to model Checkpoint](img/variations_due_to_checkpoint.png)\n", + "\n", + "We observe that more variation is observed on the front, wheels, and mirrors for the pressure, while for wall shear stress, the mirrors and wheels show a higher degree of sensitivity toward the model checkpoints. " + ] + }, + { + "cell_type": "markdown", + "id": "154be515", + "metadata": {}, + "source": [ + "## Quantifying Geometric Similarity\n", + "\n", + "The DoMINO model is trained on a diverse set of vehicle geometries that include different types of passenger automobiles such as vans, pickup trucks, SUVs, sedans, etc. This means that the model is expected to perform well when the inference is computed on an unseen geometry from this class. On the contrary, a vehicle that is significantly out of distribution cannot be used for the inference. Quantifying whether a geometry is in-distribution or out-of-distribution can be hard for 3D shapes. To tackle this, we provide metrics to compute the relative distance between two geometries (Chamfer distance and Hausdorff distance). \n", + "\n", + "Below, we demonstrate how this can be used to build an understanding of the space of the training geometry data and get an estimate of how much the sample is \"out-of-distribution\". " + ] + }, + { + "cell_type": "markdown", + "id": "328a6c7b", + "metadata": {}, + "source": [ + "First, to demonstrate, we will take two samples from the DrivAerML dataset that are fairly close to one another, and then the AeroSUV sample. Our goal would be to quantitatively distinguish between these two samples. \n", + "\n", + "The code below will download the AeroSUV sample." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d824a377", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:11:56.744735Z", + "iopub.status.busy": "2026-04-10T20:11:56.744627Z", + "iopub.status.idle": "2026-04-10T20:12:20.724171Z", + "shell.execute_reply": "2026-04-10T20:12:20.723473Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading ZIP...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download complete.\n", + "Extracting selected STL files...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted: 3_STL_Datensatz/01_body.stl\n", + "Extracted: 3_STL_Datensatz/02_side-mirrors.stl\n", + "Extracted: 3_STL_Datensatz/03_rear-end_estate.stl\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted: 3_STL_Datensatz/08_underbody-flat.stl\n", + "Extracted: 3_STL_Datensatz/02_side-mirrors.stl\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted: 3_STL_Datensatz/17_wheels-front.stl\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted: 3_STL_Datensatz/18_wheels-rear.stl\n", + "Extracted: 3_STL_Datensatz/20_cooling-air-closing.stl\n", + "Extraction complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Merging 8 meshes...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Merged and scaled STL saved as 'merged_aerosuv.stl'\n" + ] + } + ], + "source": [ + "import os\n", + "import requests\n", + "import zipfile\n", + "import trimesh\n", + "\n", + "zip_url = \"https://www.ecara.org/fileadmin/media/dateien/AeroSUV/3_STL_Datensatz.zip\"\n", + "zip_filename = \"3_STL_Datensatz.zip\"\n", + "extract_folder = \"extracted_stl\"\n", + "output_file = \"merged_aerosuv.stl\"\n", + "\n", + "selected_stl_files = [\n", + " \"3_STL_Datensatz/01_body.stl\",\n", + " \"3_STL_Datensatz/02_side-mirrors.stl\",\n", + " \"3_STL_Datensatz/03_rear-end_estate.stl\",\n", + " \"3_STL_Datensatz/08_underbody-flat.stl\",\n", + " \"3_STL_Datensatz/02_side-mirrors.stl\",\n", + " \"3_STL_Datensatz/17_wheels-front.stl\",\n", + " \"3_STL_Datensatz/18_wheels-rear.stl\",\n", + " \"3_STL_Datensatz/20_cooling-air-closing.stl\",\n", + "]\n", + "\n", + "print(\"Downloading ZIP...\")\n", + "response = requests.get(zip_url)\n", + "with open(zip_filename, \"wb\") as f:\n", + " f.write(response.content)\n", + "print(\"Download complete.\")\n", + "\n", + "os.makedirs(extract_folder, exist_ok=True)\n", + "print(\"Extracting selected STL files...\")\n", + "with zipfile.ZipFile(zip_filename, \"r\") as zip_ref:\n", + " for stl in selected_stl_files:\n", + " try:\n", + " zip_ref.extract(stl, extract_folder)\n", + " print(f\"Extracted: {stl}\")\n", + " except KeyError:\n", + " print(f\"File not found in ZIP: {stl}\")\n", + "print(\"Extraction complete.\")\n", + "\n", + "mesh_list = []\n", + "for stl in selected_stl_files:\n", + " stl_path = os.path.join(extract_folder, stl)\n", + " if os.path.exists(stl_path):\n", + " mesh = trimesh.load(stl_path, force='mesh')\n", + " mesh_list.append(mesh)\n", + "\n", + "if not mesh_list:\n", + " raise ValueError(\"No valid STL files were loaded.\")\n", + "\n", + "print(f\"Merging {len(mesh_list)} meshes...\")\n", + "combined = trimesh.util.concatenate(mesh_list)\n", + "\n", + "# Scale to bring it closer to DrivAerML STls. \n", + "combined.apply_scale(1/254) \n", + "combined.apply_translation([0, 0, -0.296])\n", + "\n", + "combined.export(output_file)\n", + "print(f\"Merged and scaled STL saved as '{output_file}'\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cdf8ccf5", + "metadata": {}, + "source": [ + "Next, we will download sample IDs 418 and 420 as these are very similar to one another visually. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "72a8908a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:12:20.725453Z", + "iopub.status.busy": "2026-04-10T20:12:20.725342Z", + "iopub.status.idle": "2026-04-10T20:12:20.728899Z", + "shell.execute_reply": "2026-04-10T20:12:20.728463Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drivaer_418.stl already exists. Skipping download.\n", + "drivaer_420.stl already exists. Skipping download.\n" + ] + } + ], + "source": [ + "filenames = [\n", + " \"drivaer_418.stl\",\n", + " \"drivaer_420.stl\",\n", + "]\n", + "urls = [\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_418/drivaer_418.stl\",\n", + " \"https://huggingface.co/datasets/neashton/drivaerml/resolve/main/run_420/drivaer_420.stl\",\n", + "]\n", + "\n", + "for filename, url in zip(filenames, urls):\n", + " if not os.path.exists(filename):\n", + " !wget \"{url}\" -O \"{filename}\"\n", + " else:\n", + " print(f\"{filename} already exists. Skipping download.\")" + ] + }, + { + "cell_type": "markdown", + "id": "8f9253f4", + "metadata": {}, + "source": [ + "Let's visualize them together." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "507b58ab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:12:20.730030Z", + "iopub.status.busy": "2026-04-10T20:12:20.729942Z", + "iopub.status.idle": "2026-04-10T20:12:24.854599Z", + "shell.execute_reply": "2026-04-10T20:12:24.854034Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aero_suv = pv.read(\"merged_aerosuv.stl\")\n", + "sedan_1 = pv.read(\"drivaer_418.stl\")\n", + "sedan_2 = pv.read(\"drivaer_420.stl\")\n", + "\n", + "plotter = pv.Plotter(shape=(1, 3), off_screen=True, window_size=(1500, 500))\n", + "\n", + "def add_all_meshes(p, view_func):\n", + " p.add_mesh(aero_suv, opacity=0.5, show_edges=False, color='red')\n", + " p.add_mesh(sedan_1, opacity=0.5, show_edges=False, color='green')\n", + " p.add_mesh(sedan_2, opacity=0.5, show_edges=False, color='blue')\n", + " view_func()\n", + " p.reset_camera()\n", + " p.set_background(\"white\")\n", + "\n", + "plotter.subplot(0, 0)\n", + "add_all_meshes(plotter, plotter.view_xy)\n", + "\n", + "plotter.subplot(0, 1)\n", + "add_all_meshes(plotter, plotter.view_yz)\n", + "\n", + "plotter.subplot(0, 2)\n", + "add_all_meshes(plotter, plotter.view_xz)\n", + "\n", + "plotter.set_background(\"white\")\n", + "plotter.screenshot(\"./stl_comparisons.png\")\n", + "\n", + "# Display the image\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=\"./stl_comparisons.png\")" + ] + }, + { + "cell_type": "markdown", + "id": "e51b1187", + "metadata": {}, + "source": [ + "Now let's compute the relative distances." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6561b8eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:12:24.855995Z", + "iopub.status.busy": "2026-04-10T20:12:24.855885Z", + "iopub.status.idle": "2026-04-10T20:12:36.265149Z", + "shell.execute_reply": "2026-04-10T20:12:36.264527Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0576326009777469 0.05471310619482909 0.03239957394205294\n" + ] + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.geometry.distance import chamfer_distance, hausdorff_distance\n", + "\n", + "sedan_1_vs_aerosuv = chamfer_distance(sedan_1.points, aero_suv.points)\n", + "sedan_2_vs_aerosuv = chamfer_distance(sedan_2.points, aero_suv.points)\n", + "sedan_1_vs_sedan_2 = chamfer_distance(sedan_1.points, sedan_2.points)\n", + "print(sedan_1_vs_aerosuv, sedan_2_vs_aerosuv, sedan_1_vs_sedan_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e5850cd5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-10T20:12:36.266605Z", + "iopub.status.busy": "2026-04-10T20:12:36.266482Z", + "iopub.status.idle": "2026-04-10T20:12:47.633159Z", + "shell.execute_reply": "2026-04-10T20:12:47.632551Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.37145597389151735 0.35232097572016974 0.19132546164645767\n" + ] + } + ], + "source": [ + "sedan_1_vs_aerosuv = hausdorff_distance(sedan_1.points, aero_suv.points)\n", + "sedan_2_vs_aerosuv = hausdorff_distance(sedan_2.points, aero_suv.points)\n", + "sedan_1_vs_sedan_2 = hausdorff_distance(sedan_1.points, sedan_2.points)\n", + "print(sedan_1_vs_aerosuv, sedan_2_vs_aerosuv, sedan_1_vs_sedan_2)" + ] + }, + { + "cell_type": "markdown", + "id": "38cfde8a", + "metadata": {}, + "source": [ + "We observe that the distance (Chamfer and Hausdorff) between the two sedans is lower than the same between the SUV and either of the sedans. \n", + "\n", + "The distance measures can be used to make these relative distance queries and quantify the in-distribution vs. out-of-distribution nature of the sample. " + ] + }, + { + "cell_type": "markdown", + "id": "d71c9b3d", + "metadata": {}, + "source": [ + "For example, below, we take a few samples from each class (SUV, sedan, pickup, hatchback, van) from the dataset that was used for DoMINO training and compare it with the sedans from DrivAerML. We see that the validation samples from the DrivAerML are the closest to the training samples from DrivAerML. This is followed by the sedans from the DriveSim, sports cars, hatchback, van, and SUV from DriveSim. The most different are the pickup trucks from DriveSim, which makes sense as that represents a significant geometric variation when compared to a sedan. \n", + "\n", + "![Chamfer Distances](img/chamfer-distances-drivaerml.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflows/benchmarking_workflow/notebooks/img/chamfer-distances-drivaerml.png b/workflows/benchmarking_workflow/notebooks/img/chamfer-distances-drivaerml.png new file mode 100644 index 0000000000000000000000000000000000000000..5adc881c724f74a62facf5c0de4c0300c1268b21 GIT binary patch literal 104456 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zfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C7x)S*LE938O%?0S{ychuj1d1vk zA*ra`E=GU=0RjXF5FkK+009C72oNAZfB*pk1PBlyP@V!0e7bprT!8XiTeBiSfB*pk z1bQnVA*r`w>JuP9fB*pk1PBlyK!5-N0t5&UAV7dXX$pMek%e_~0ZMaijEVpO0t5&U zAV7csfr=84kW^92ZL$Oi5FkK+009C72oNAZfB=EM75L=b88_8F5hz`Ox`xL3 zzQ|B2-5DDh0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5;&vn z2oNAZAWuL-QXYmP0RjXF5FkK+009C72oNAZfB*pk1PBnw7r1B1;Wcsr@9w!%||NLE<009C7 z2oNAZfB*pk1PBly&}#t+Nxdf1fB*pk1PBlyK!5-N0+l9kWYZ6($OWjhRX1M(1PBly zK!5-N0t5&UAV7cs0RjXFBo?S^Xsnl&l$cLL0t5&UAV7dXaReUxq_9((=Yg{T!3|@sUHFa2oNAZfB*pk z1PBlyK!5-N0t5&UAV7csfi{77sg8XD_m zC8clOjF Date: Mon, 13 Apr 2026 23:34:06 -0700 Subject: [PATCH 20/25] use pnemo functionals for knn --- .../reports/builtin/aggregate_volume.py | 6 +- .../interpolation/interpolate_mesh_to_pc.py | 139 ++++++++++++------ 2 files changed, 96 insertions(+), 49 deletions(-) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py b/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py index 98e2e2e..15d23eb 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/aggregate_volume.py @@ -57,10 +57,8 @@ def _resolve_volume_field_pairs( def _interpolation_device(run_device: str) -> str: - """Map ``config.run.device`` (e.g. ``"cuda:0"``) to the ``"cpu"``/``"gpu"`` flag used by ``interpolate_mesh_to_pc``.""" - if run_device.startswith("cuda"): - return "gpu" - return "cpu" + """Map ``config.run.device`` to a device string for ``interpolate_mesh_to_pc``.""" + return run_device def aggregate_volume_errors( diff --git a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py index 0d1843b..9c2e0bd 100644 --- a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py +++ b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py @@ -14,62 +14,65 @@ # See the License for the specific language governing permissions and # limitations under the License. -import pyvista as pv +import warnings + import numpy as np +import torch from scipy.spatial import cKDTree +from physicsnemo.nn.functional.neighbors.knn import knn + + +# --------------------------------------------------------------------------- +# Deprecated legacy helpers — kept for backward compatibility with +# bench_example notebooks. Will be removed when bench_example is retired. +# --------------------------------------------------------------------------- def _create_nbrs_surface(coords_source, n_neighbors=5, device="cpu"): + """Deprecated: use ``interpolate_mesh_to_pc`` or ``physicsnemo.nn.functional.neighbors.knn``.""" + warnings.warn( + "_create_nbrs_surface is deprecated; use interpolate_mesh_to_pc or physicsnemo knn.", + DeprecationWarning, + stacklevel=2, + ) if device == "cpu": - nbrs_surface = cKDTree(coords_source) + return cKDTree(coords_source) elif device == "gpu": import cupy as cp from cuml.neighbors import NearestNeighbors as NearestNeighborsGPU if not isinstance(coords_source, cp.ndarray): coords_source = cp.asarray(coords_source) - - nbrs_surface = NearestNeighborsGPU( - n_neighbors=n_neighbors, algorithm="auto" - ).fit(coords_source) - return nbrs_surface + return NearestNeighborsGPU(n_neighbors=n_neighbors, algorithm="auto").fit(coords_source) + return cKDTree(coords_source) -def _interpolate( - nbrs_surface, - coords_target, - field, - device="cpu", - batch_size=1_000_000, - n_neighbors=5, -): - +def _interpolate(nbrs_surface, coords_target, field, device="cpu", batch_size=1_000_000, n_neighbors=5): + """Deprecated: use ``interpolate_mesh_to_pc`` or ``physicsnemo.nn.functional.neighbors.knn``.""" + warnings.warn( + "_interpolate is deprecated; use interpolate_mesh_to_pc or physicsnemo knn.", + DeprecationWarning, + stacklevel=2, + ) if device == "cpu": distances, indices = nbrs_surface.query(coords_target, k=n_neighbors, workers=8) - epsilon = 1e-8 weights = 1 / (distances + epsilon) weights_sum = np.sum(weights, axis=1, keepdims=True) normalized_weights = weights / weights_sum - field_neighbors = field[indices] if len(field.shape) == 1: - field_interp = np.sum(normalized_weights * field_neighbors, axis=1) - else: - field_interp = np.sum( - normalized_weights[:, :, np.newaxis] * field_neighbors, axis=1 - ) + return np.sum(normalized_weights * field_neighbors, axis=1) + return np.sum(normalized_weights[:, :, np.newaxis] * field_neighbors, axis=1) elif device == "gpu": import cupy as cp if not isinstance(field, cp.ndarray): field = cp.asarray(field) - if len(field.shape) == 1: field_interp = np.zeros((coords_target.shape[0],)) else: field_interp = np.zeros((coords_target.shape[0], field.shape[1])) - for i in range(0, coords_target.shape[0], batch_size): batch_pts = cp.asarray(coords_target[i : i + batch_size]) distances, indices = nbrs_surface.kneighbors(batch_pts) @@ -84,18 +87,68 @@ def _interpolate( ) else: field_interp[i : i + batch_size] = cp.asnumpy( - cp.sum( - normalized_weights[:, :, cp.newaxis] * field_neighbors, - axis=1, - ) + cp.sum(normalized_weights[:, :, cp.newaxis] * field_neighbors, axis=1) ) + return field_interp + - return field_interp +# --------------------------------------------------------------------------- +# Current implementation using physicsnemo kNN +# --------------------------------------------------------------------------- + +def _resolve_device(device: str) -> torch.device: + """Map legacy ``"gpu"`` flag and torch device strings to a ``torch.device``.""" + if device == "gpu": + return torch.device("cuda") + return torch.device(device) + + +def _idw_interpolate( + source_points: torch.Tensor, + target_points: torch.Tensor, + field: np.ndarray, + k: int = 5, +) -> np.ndarray: + """Inverse-distance-weighted interpolation using PhysicsNeMo kNN. + + Parameters + ---------- + source_points : torch.Tensor + Source coordinates (N, 3). + target_points : torch.Tensor + Target coordinates (M, 3). + field : np.ndarray + Field values at source points — (N,) for scalars or (N, C) for vectors. + k : int + Number of nearest neighbors. + + Returns + ------- + np.ndarray + Interpolated field values at target points. + """ + indices, distances = knn(source_points, target_points, k) + + indices_np = indices.cpu().numpy() + distances_np = distances.cpu().numpy().astype(np.float64) + + epsilon = 1e-8 + weights = 1.0 / (distances_np + epsilon) + weights_sum = np.sum(weights, axis=1, keepdims=True) + normalized_weights = weights / weights_sum + + field_neighbors = field[indices_np] + if field.ndim == 1: + return np.sum(normalized_weights * field_neighbors, axis=1) + return np.sum(normalized_weights[:, :, np.newaxis] * field_neighbors, axis=1) def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell", device="cpu"): """Interpolate mesh results on a point cloud using inverse weighted kNN. + Uses ``physicsnemo.nn.functional.neighbors.knn`` which auto-dispatches to + cuML on CUDA and scipy on CPU. + Parameters ---------- pc : @@ -107,7 +160,8 @@ def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell", d mesh_dtype : Whether the mesh fields are of point or cell type. Default ``"cell"``. device : - ``"cpu"`` for scipy cKDTree or ``"gpu"`` for cuML/CuPy accelerated kNN. + ``"cpu"``, ``"gpu"``, or a torch device string (e.g. ``"cuda:0"``). + ``"gpu"`` is mapped to ``"cuda"`` for backward compatibility. Returns ------- @@ -115,24 +169,19 @@ def interpolate_mesh_to_pc(pc, mesh, fields_to_interpolate, mesh_dtype="cell", d Point cloud with interpolated values. """ k = 5 + dev = _resolve_device(device) if mesh_dtype == "point": - source_points = mesh.points + source_points = np.asarray(mesh.points, dtype=np.float32) elif mesh_dtype == "cell": - cell_centers = mesh.cell_centers() - source_points = cell_centers.points + source_points = np.asarray(mesh.cell_centers().points, dtype=np.float32) - nbrs_surface = _create_nbrs_surface(source_points, n_neighbors=k, device=device) + source_t = torch.tensor(source_points, dtype=torch.float32, device=dev) + target_t = torch.tensor(np.asarray(pc.points, dtype=np.float32), dtype=torch.float32, device=dev) - if mesh_dtype == "point": - for field in fields_to_interpolate: - pc.point_data[field] = _interpolate( - nbrs_surface, pc.points, mesh.point_data[field], device=device, n_neighbors=k - ) - elif mesh_dtype == "cell": - for field in fields_to_interpolate: - pc.point_data[field] = _interpolate( - nbrs_surface, pc.points, mesh.cell_data[field], device=device, n_neighbors=k - ) + data = mesh.point_data if mesh_dtype == "point" else mesh.cell_data + for field_name in fields_to_interpolate: + field_arr = np.asarray(data[field_name]) + pc.point_data[field_name] = _idw_interpolate(source_t, target_t, field_arr, k=k) return pc From d9948fe26e04b994644fd2bbb75895759b733e65 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Mon, 13 Apr 2026 23:42:02 -0700 Subject: [PATCH 21/25] add deprecation notice --- workflows/bench_example/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/workflows/bench_example/README.md b/workflows/bench_example/README.md index 50a0f4d..eb8aab1 100644 --- a/workflows/bench_example/README.md +++ b/workflows/bench_example/README.md @@ -1,5 +1,7 @@ # Benchmarking AI surrogates for External Aerodynamics +> **DEPRECATED:** This workflow is superseded by [`benchmarking_workflow`](../benchmarking_workflow/README.md), which provides Hydra configuration, model wrapper integration, distributed multi-GPU support, and more. Please use `benchmarking_workflow` for new work. This directory will be removed in a future release. + The benchmarking workflow is designed for evaluating and validating AI models for external aerodynamics. Predicting accurate aerodynamic characteristics of a vehicle (e.g., a car or aircraft) requires analysis of both surface and volume fields. From f172a8acce8f3fbb735c8fd671c939b29fa4d643 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Mon, 13 Apr 2026 23:42:53 -0700 Subject: [PATCH 22/25] add files for DrivAerML --- workflows/benchmarking_workflow/README.md | 12 + .../drivaer_ml_files/README.md | 21 + .../design_trend_split_0.9_0.1.png | Bin 0 -> 2102704 bytes .../drivaer_ml_files/train.csv | 437 ++++++++++++++++++ .../drivaer_ml_files/validation.csv | 49 ++ 5 files changed, 519 insertions(+) create mode 100644 workflows/benchmarking_workflow/drivaer_ml_files/README.md create mode 100644 workflows/benchmarking_workflow/drivaer_ml_files/design_trend_split_0.9_0.1.png create mode 100644 workflows/benchmarking_workflow/drivaer_ml_files/train.csv create mode 100644 workflows/benchmarking_workflow/drivaer_ml_files/validation.csv diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index 933bd93..572874b 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -214,6 +214,18 @@ dataset: --- +## DrivAerML train/validation split + +The [`drivaer_ml_files/`](drivaer_ml_files/) directory contains a reproducible 90/10 train/validation split for the DrivAerML dataset (484 of 500 usable geometries). Validation samples are selected by sorting by drag coefficient, then taking 10% from low-drag, 10% from high-drag, and 80% randomly from the middle — mixing in-distribution and out-of-distribution cases. + +| File | Contents | +| ---- | -------- | +| [`train.csv`](drivaer_ml_files/train.csv) | `run_idx, drag` for training samples | +| [`validation.csv`](drivaer_ml_files/validation.csv) | `run_idx, drag` for validation samples | +| [`README.md`](drivaer_ml_files/README.md) | Split strategy details and reference to DrivAerML paper | + +--- + ## Package layout (repo root) | Path | Role | diff --git a/workflows/benchmarking_workflow/drivaer_ml_files/README.md b/workflows/benchmarking_workflow/drivaer_ml_files/README.md new file mode 100644 index 0000000..3cd309b --- /dev/null +++ b/workflows/benchmarking_workflow/drivaer_ml_files/README.md @@ -0,0 +1,21 @@ +# DrivAerML Dataset Files + +The DrivAerML dataset consists of 500 parametrically varied geometries. +However, only 484 of these geometries are usable for training and validation. +Since the [publication](https://arxiv.org/pdf/2408.11969) does not recommend +any training-validation split, we propose a split in this repository to enable +a fair and consistent benchmark. + +The dataset is split as 90% for training and 10% for validation. For selecting +the validation set, the entire dataset is sorted by drag force. 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b/workflows/benchmarking_workflow/drivaer_ml_files/validation.csv new file mode 100644 index 0000000..ef58895 --- /dev/null +++ b/workflows/benchmarking_workflow/drivaer_ml_files/validation.csv @@ -0,0 +1,49 @@ +run_idx,drag +419,338.7213419168361 +483,341.43947887266444 +62,341.9548854478518 +269,347.672112426193 +431,579.6546264799181 +495,586.4324166933917 +129,587.6439751778338 +115,588.986909635414 +439,590.8326976399285 +202,482.3444254302631 +18,400.10619317109933 +225,372.6748363107049 +382,501.05610911687705 +17,428.8011447414385 +424,424.62972691103585 +145,421.1749279466876 +271,406.48363238023967 +469,499.4502768066128 +409,396.1784525284034 +86,490.0235032405405 +417,544.1399512409321 +421,466.66959934555547 +489,393.7968829431706 +197,474.0753007655675 +345,447.1086651049102 +298,376.43211302987663 +159,374.13373137408865 +71,395.37643050080385 +171,420.44822729187985 +204,422.69000445603285 +490,460.8079038136613 +399,475.5339048169396 +27,373.99902987605344 +320,468.9652630914605 +468,417.5721447612151 +149,496.2946968761571 +112,483.59263566180226 +367,494.4457118176862 +305,446.84477431541836 +380,420.7721014210746 +4,449.86585819079625 +270,473.95789649091944 +478,428.8050044886181 +118,512.5674141409484 +460,532.0325059530775 +183,359.6917202574145 +429,504.1592032195152 +465,512.2382220462401 From b8a3e6e40d9df8bca71b3a31639a2bcf32ed8d56 Mon Sep 17 00:00:00 2001 From: Rishikesh Ranade Date: Tue, 14 Apr 2026 07:14:02 -0700 Subject: [PATCH 23/25] cleaning up readme, adding ci tests and contributing details --- CHANGELOG.md | 6 + CONTRIBUTING.md | 37 +++ README.md | 23 +- .../cfd/evaluation/benchmarks/__init__.py | 4 +- .../cfd/evaluation/benchmarks/engine.py | 37 +++ physicsnemo/cfd/evaluation/benchmarks/run.py | 9 +- physicsnemo/cfd/evaluation/config.py | 9 +- .../evaluation/reports/builtin/line_plot.py | 4 +- .../interpolation/interpolate_mesh_to_pc.py | 2 +- workflows/benchmarking_workflow/README.md | 257 +++++++++--------- workflows/benchmarking_workflow/main.py | 32 +-- .../{ => deprecated}/bench_example/README.md | 4 +- .../bench_example/drivaer_ml_files/README.md | 0 .../design_trend_split_0.9_0.1.png | Bin .../bench_example/drivaer_ml_files/train.csv | 0 .../drivaer_ml_files/validation.csv | 0 .../bench_example/generate_pcs_from_stl.py | 0 .../generate_surface_benchmarks.py | 0 .../generate_volume_benchmarks.py | 0 .../benchmarking_in_absence_of_gt.ipynb | 0 .../img/chamfer-distances-drivaerml.png | Bin .../notebooks/img/design_scatter_plot.png | Bin .../notebooks/img/design_trend_plot.png | Bin .../notebooks/img/error_vs_sdf.png | Bin .../notebooks/img/hexbin_sample.png | Bin .../notebooks/img/resampled_volume_errors.png | Bin .../img/variations_due_to_checkpoint.png | Bin .../notebooks/surface_benchmarking.ipynb | 0 .../notebooks/volume_benchmarking.ipynb | 0 .../sample_outputs/bottom_centerline.png | Bin .../sample_outputs/compare_surface_xy_419.png | Bin .../sample_outputs/compare_surface_xz_419.png | Bin .../sample_outputs/compare_surface_yz_419.png | Bin .../compare_volume_y_slice_419.png | Bin .../compare_volume_z_slice_419.png | Bin .../sample_outputs/design_scatter_plot.png | Bin .../sample_outputs/design_trend_plot.png | Bin .../sample_outputs/top_centerline.png | Bin .../sample_outputs/volume_centerline.png | Bin .../volume_front_wheel_wake.png | Bin .../sample_outputs/volume_rear_wheel_wake.png | Bin .../sample_outputs/volume_x_4_wake.png | Bin .../sample_outputs/volume_x_5_wake.png | Bin .../{ => deprecated}/bench_example/setup.sh | 0 .../{ => deprecated}/bench_example/utils.py | 0 45 files changed, 262 insertions(+), 162 deletions(-) rename workflows/{ => deprecated}/bench_example/README.md (97%) rename workflows/{ => deprecated}/bench_example/drivaer_ml_files/README.md (100%) rename workflows/{ => deprecated}/bench_example/drivaer_ml_files/design_trend_split_0.9_0.1.png (100%) rename workflows/{ => deprecated}/bench_example/drivaer_ml_files/train.csv (100%) rename workflows/{ => deprecated}/bench_example/drivaer_ml_files/validation.csv (100%) rename workflows/{ => deprecated}/bench_example/generate_pcs_from_stl.py (100%) rename workflows/{ => deprecated}/bench_example/generate_surface_benchmarks.py (100%) rename workflows/{ => deprecated}/bench_example/generate_volume_benchmarks.py (100%) rename workflows/{ => deprecated}/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/chamfer-distances-drivaerml.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/design_scatter_plot.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/design_trend_plot.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/error_vs_sdf.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/hexbin_sample.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/resampled_volume_errors.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/img/variations_due_to_checkpoint.png (100%) rename workflows/{ => deprecated}/bench_example/notebooks/surface_benchmarking.ipynb (100%) rename workflows/{ => deprecated}/bench_example/notebooks/volume_benchmarking.ipynb (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/bottom_centerline.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/compare_surface_xy_419.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/compare_surface_xz_419.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/compare_surface_yz_419.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/compare_volume_y_slice_419.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/compare_volume_z_slice_419.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/design_scatter_plot.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/design_trend_plot.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/top_centerline.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/volume_centerline.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/volume_front_wheel_wake.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/volume_rear_wheel_wake.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/volume_x_4_wake.png (100%) rename workflows/{ => deprecated}/bench_example/sample_outputs/volume_x_5_wake.png (100%) rename workflows/{ => deprecated}/bench_example/setup.sh (100%) rename workflows/{ => deprecated}/bench_example/utils.py (100%) diff --git a/CHANGELOG.md b/CHANGELOG.md index d81270c..bd8d332 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,8 +11,12 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Added +- **`run.fail_on_all_skipped` / `run.fail_on_any_metric_nan`:** optional benchmark exit policy; raises `BenchmarkPolicyError` (Hydra `main.py` and `python -m physicsnemo.cfd.evaluation.benchmarks.run` exit with code 1). +- **`physicsnemo.cfd.evaluation.benchmarks.hydra_utils`:** shared Hydra → `Config` dict conversion for tests and `workflows/benchmarking_workflow/main.py`. +- **CI:** `.github/workflows/ci-tests.yml` runs `pytest` on `test/ci_tests/` (including `test_benchmark_workflow.py`, which composes Hydra configs under `workflows/benchmarking_workflow/conf/`). - **Multi-GPU benchmarks:** `run_benchmark` / Hydra `main.py` can run under `torchrun` with PhysicsNeMo `DistributedManager` — per-rank case sharding, gather/merge on rank 0, and optional `run.distributed` (default true) to disable sharding for debugging. - **Tests:** `test/ci_tests/test_distributed_utils.py` covers merge/shard helpers and `_case_ids_for_run`. +- **Tests:** `test/ci_tests/test_benchmark_workflow.py` composes workflow YAML and loads `Config`. - **Metrics cache:** optional `run.metrics_cache` (`enabled`, `path`) stores per-case scalar metrics on disk so repeat benchmark runs can skip VTK I/O and inference for unchanged configs; visualization is unchanged. - `physicsnemo.cfd.evaluation`: config-driven inference and benchmarking with @@ -28,6 +32,8 @@ Versioning](https://semver.org/spec/v2.0.0.html). ### Changed +- **Benchmarking workflow layout:** legacy **`workflows/bench_example`** moved to **`workflows/deprecated/bench_example`** (superseded by **`workflows/benchmarking_workflow/`**). +- **`benchmark.reproducibility.log_env`:** default is now **`false`** (was **`true`**) to avoid writing full `os.environ` to `env.json` unless explicitly enabled in YAML. - **Breaking:** `physicsnemo.cfd.bench` was renamed to **`physicsnemo.cfd.postprocessing_tools`** (same submodules: `metrics`, `visualization`, `geometry`, `interpolation`, `metric_registry`). - **`reports.visual_case_ids`**: optional list limiting which cases get an in-memory comparison mesh for diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5084823..51f3f14 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -146,3 +146,40 @@ pre-commit install Once the above commands are executed, the pre-commit hooks will be activated and all commits will be checked for appropriate formatting. + +### Developing and testing the benchmarking workflow + +Install the package in editable mode with dev dependencies from the repository root +([`pyproject.toml`](pyproject.toml)): + +```bash +pip install -e ".[dev]" +``` + +Before opening a pull request that changes **`physicsnemo/cfd/evaluation/`** or +**`workflows/benchmarking_workflow/`**, run the CI test subset: + +```bash +pytest -q test/ci_tests/test_distributed_utils.py \ + test/ci_tests/test_metrics_cache.py \ + test/ci_tests/test_benchmark_workflow.py \ + test/ci_tests/test_evaluation.py +``` + +The **`test_evaluation.py`** module may skip entirely if `physicsnemo.utils.sdf` is +unavailable (install or upgrade `nvidia-physicsnemo`). + +Do **not** commit generated benchmark outputs under **`workflows/benchmarking_workflow/`** +(e.g. `benchmark_results*/`, `gpu_output.log`, `metrics_cache/`, Hydra outputs under the +run directory). That directory includes a **`.gitignore`** for common artifacts. + +**Exit codes:** the Hydra entrypoint **`workflows/benchmarking_workflow/main.py`** can exit +non-zero when **`run.fail_on_all_skipped`** or **`run.fail_on_any_metric_nan`** is enabled. +See **`workflows/benchmarking_workflow/README.md`** for automation-oriented runs. + +**Continuous integration:** [`.github/workflows/ci-tests.yml`](.github/workflows/ci-tests.yml) +runs the pytest command above on push and pull requests. If your team uses additional +infrastructure (for example Blossom CI), use the same tests there when possible. + +Update [`CHANGELOG.md`](CHANGELOG.md) for user-visible changes to evaluation or benchmark +behavior and configuration. diff --git a/README.md b/README.md index 9076e14..04403ac 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ from a Python interface, facilitating scalable deployment of trained models. metrics, PDE residuals). [Related publication](https://www.arxiv.org/abs/2507.10747) - The `physicsnemo.cfd.evaluation` package runs config-driven inference and uses the same `physicsnemo.cfd.postprocessing_tools` metric implementations as the - **`workflows/benchmarking_workflow/`** workflow (run **`python main.py`** there; see that README). + **[`workflows/benchmarking_workflow/`](workflows/benchmarking_workflow/)** Hydra workflow (run **`python main.py`** from that directory; see **[that README](workflows/benchmarking_workflow/README.md)**). Pretrained checkpoints and the reference evaluation dataset will be documented on **NVIDIA NGC** when published (**links TBD**; until then, configure local paths in YAML or via Hydra overrides). - Utilities to extend and build custom metrics, analyze, and visualize the results of trained ML models, both mesh-based and point-cloud based models @@ -71,6 +71,12 @@ cd physicsnemo-cfd pip install . ``` +For **local development** (editable install, tests, and benchmarking workflow checks), use optional **dev** dependencies from [`pyproject.toml`](pyproject.toml): + +```bash +pip install -e ".[dev]" +``` + To get access to GPU-accelerated functionalities from this repository when installing in a conda or custom Python environment, please run the commands below. @@ -120,10 +126,13 @@ output_dict = call_domino_nim( ``` -Refer to the [`workflows` directory](./workflows) for detailed instructions on -executing individual reference workflows and samples. These are primarily -packaged as Jupyter notebooks where possible, to provide inline -documentation and visualization of expected results. +Reference workflows live under the [`workflows`](./workflows) directory. The +**[benchmarking workflow](workflows/benchmarking_workflow/)** is the supported path for +config-driven model evaluation and metrics (`python main.py` with Hydra). Older +file-based benchmarking samples were moved to +**[workflows/deprecated/bench_example](workflows/deprecated/bench_example/)** and are +superseded by `benchmarking_workflow`. Other samples may be packaged as Jupyter +notebooks for inline documentation and visualization. ## Contributing to PhysicsNeMo @@ -132,7 +141,9 @@ community contributions to further the field of Physics-ML. Thank you for contributing to the project so others can build on top of your contributions. For guidance on contributing to PhysicsNeMo, refer to the [contributing -guidelines](CONTRIBUTING.md). +guidelines](CONTRIBUTING.md). Changes to **`physicsnemo.cfd.evaluation`** or the +**benchmarking workflow** should follow the tests and notes in **CONTRIBUTING.md** +(including the recommended `pytest` command for `test/ci_tests/`). ## Cite PhysicsNeMo diff --git a/physicsnemo/cfd/evaluation/benchmarks/__init__.py b/physicsnemo/cfd/evaluation/benchmarks/__init__.py index 6186e9e..af4c81f 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/__init__.py +++ b/physicsnemo/cfd/evaluation/benchmarks/__init__.py @@ -16,7 +16,7 @@ """Benchmark engine and reporting.""" -from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.benchmarks.engine import BenchmarkPolicyError, run_benchmark from physicsnemo.cfd.evaluation.benchmarks.report import write_report -__all__ = ["run_benchmark", "write_report"] +__all__ = ["BenchmarkPolicyError", "run_benchmark", "write_report"] diff --git a/physicsnemo/cfd/evaluation/benchmarks/engine.py b/physicsnemo/cfd/evaluation/benchmarks/engine.py index 0e74354..fffc94d 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/engine.py +++ b/physicsnemo/cfd/evaluation/benchmarks/engine.py @@ -43,10 +43,15 @@ from __future__ import annotations import json +import math import os from pathlib import Path from typing import Any + +class BenchmarkPolicyError(RuntimeError): + """Raised when :func:`run_benchmark` policy flags reject the result (e.g. all runs skipped).""" + from physicsnemo.cfd.evaluation.benchmarks.distributed_utils import ( effective_device_str, gather_merge_benchmark_outputs, @@ -631,6 +636,11 @@ def run_benchmark( ------- list of dict One result dict per model×dataset pair (or single pair in ``single`` mode). + + Raises + ------ + BenchmarkPolicyError + If ``run.fail_on_all_skipped`` or ``run.fail_on_any_metric_nan`` rejects the outcome. """ import physicsnemo.cfd.evaluation.datasets.adapters # noqa: F401 import physicsnemo.cfd.evaluation.inference.wrappers # noqa: F401 @@ -735,9 +745,34 @@ def run_benchmark( context={"comparison_meshes_by_run": meshes_by_run}, ) + _enforce_benchmark_policy(config, results) + return results +def _enforce_benchmark_policy(config: Config, results: list[dict[str, Any]]) -> None: + """Raise :class:`BenchmarkPolicyError` when ``run.fail_on_*`` flags apply.""" + if not results: + return + run = config.run + if run.fail_on_all_skipped and all(r.get("skipped") for r in results): + raise BenchmarkPolicyError( + "All benchmark runs were skipped; set run.fail_on_all_skipped=false to allow exit 0, " + "or fix model/dataset domain and paths." + ) + if run.fail_on_any_metric_nan: + for r in results: + if r.get("skipped"): + continue + metrics = r.get("metrics") or {} + for _k, v in metrics.items(): + if isinstance(v, float) and math.isnan(v): + raise BenchmarkPolicyError( + "Aggregate metric NaN encountered; set run.fail_on_any_metric_nan=false to allow exit 0, " + "or fix failing metrics." + ) + + def _config_to_dict(c: Config) -> dict: """ Convert ``Config`` to a JSON-serializable dict for ``benchmark_artifacts.json``. @@ -764,6 +799,8 @@ def _config_to_dict(c: Config) -> dict: "path": c.run.metrics_cache.path, }, "distributed": c.run.distributed, + "fail_on_all_skipped": c.run.fail_on_all_skipped, + "fail_on_any_metric_nan": c.run.fail_on_any_metric_nan, }, "model": { "name": c.model.name, diff --git a/physicsnemo/cfd/evaluation/benchmarks/run.py b/physicsnemo/cfd/evaluation/benchmarks/run.py index d2e3718..ea37c79 100644 --- a/physicsnemo/cfd/evaluation/benchmarks/run.py +++ b/physicsnemo/cfd/evaluation/benchmarks/run.py @@ -21,8 +21,9 @@ """ import argparse +import sys -from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.benchmarks.engine import BenchmarkPolicyError, run_benchmark from physicsnemo.cfd.evaluation.config import load_config @@ -57,7 +58,11 @@ def main() -> None: args = parser.parse_args() overrides = _parse_overrides(getattr(args, "overrides", [])) config = load_config(args.config, overrides, base=args.base_config) - results = run_benchmark(config, case_id=args.case_id) + try: + results = run_benchmark(config, case_id=args.case_id) + except BenchmarkPolicyError as exc: + print(str(exc), file=sys.stderr) + sys.exit(1) print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") diff --git a/physicsnemo/cfd/evaluation/config.py b/physicsnemo/cfd/evaluation/config.py index e7491d2..62bce98 100644 --- a/physicsnemo/cfd/evaluation/config.py +++ b/physicsnemo/cfd/evaluation/config.py @@ -71,6 +71,10 @@ class RunConfig: #: When True (default) and launched multi-process (e.g. ``torchrun``), shard cases across ranks via #: ``DistributedManager`` and merge before reports. When False, each rank runs the full case list (debug only). distributed: bool = True + #: If True, :func:`run_benchmark` raises :class:`BenchmarkPolicyError` when every matrix/single run was skipped. + fail_on_all_skipped: bool = False + #: If True, :func:`run_benchmark` raises when any aggregate metric value in ``results[*]["metrics"]`` is NaN. + fail_on_any_metric_nan: bool = False @dataclass @@ -101,7 +105,8 @@ class DatasetConfig: @dataclass class ReproducibilityConfig: - log_env: bool = True + #: When True, write full ``os.environ`` to ``env.json`` under ``run.output_dir`` (avoid in shared CI; may leak secrets). + log_env: bool = False save_artifacts: bool = True @@ -211,6 +216,8 @@ def from_dict(cls, data: dict[str, Any]) -> "Config": path=str(mc_raw.get("path") or ""), ), distributed=bool(run_raw.get("distributed", True)), + fail_on_all_skipped=bool(run_raw.get("fail_on_all_skipped", False)), + fail_on_any_metric_nan=bool(run_raw.get("fail_on_any_metric_nan", False)), ) model = ModelConfig(**(data.get("model") or {})) dataset = DatasetConfig(**(data.get("dataset") or {})) diff --git a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py index bc81074..8609746 100644 --- a/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py +++ b/physicsnemo/cfd/evaluation/reports/builtin/line_plot.py @@ -62,7 +62,7 @@ def bench_style_surface_centerlines( ) -> tuple[pv.PolyData, pv.PolyData]: """DrivAer-style top/bottom strips: ``y`` slice at ``y_slice_origin``, split at ``z = z_clip``. - Matches ``workflows/bench_example/utils.py`` (surface): top = ``z`` above the clip plane, + Matches ``workflows/deprecated/bench_example/utils.py`` (surface): top = ``z`` above the clip plane, bottom = below. """ slice_y = mesh.slice(normal="y", origin=y_slice_origin) @@ -104,7 +104,7 @@ def line_plot_centerlines( z_clip: float = 0.4, **kwargs: Any, ) -> None: - """GT vs pred line plots on **top** and **bottom** surface centerline strips (bench_example style). + """GT vs pred line plots on **top** and **bottom** surface centerline strips (legacy bench_example style). Geometry: slice with normal ``y`` through ``y_slice_origin`` (default origin), then clip at ``z = z_clip`` to separate upper (``top``) and lower (``bottom``) surface lines along the vehicle. diff --git a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py index 9c2e0bd..0627192 100644 --- a/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py +++ b/physicsnemo/cfd/postprocessing_tools/interpolation/interpolate_mesh_to_pc.py @@ -25,7 +25,7 @@ # --------------------------------------------------------------------------- # Deprecated legacy helpers — kept for backward compatibility with -# bench_example notebooks. Will be removed when bench_example is retired. +# workflows/deprecated/bench_example notebooks. Will be removed when that workflow is retired. # --------------------------------------------------------------------------- def _create_nbrs_surface(coords_source, n_neighbors=5, device="cpu"): diff --git a/workflows/benchmarking_workflow/README.md b/workflows/benchmarking_workflow/README.md index 572874b..cffe3f1 100644 --- a/workflows/benchmarking_workflow/README.md +++ b/workflows/benchmarking_workflow/README.md @@ -1,73 +1,117 @@ # Model evaluation and benchmarking -This is an opinionated workflow to help you get started with evaluating and benchmarking pretrained models. It stitches together the model inference, calcuating built-in metrics and generating artifacts for analysis and visualization. We use a configuration based approach to allow you to extend this workflow to add your datasets, models, custom metrics etc. +This workflow runs **metrics**, tabular outputs (JSON/CSV/HTML), optional **PNG visuals**, and optional VTK using **[Hydra](https://hydra.cc/)** and **OmegaConf** — the same pattern as **`workflows/domino_design_sensitivities/`**. It ties together model inference, built-in metrics, and artifacts for analysis. -## Running OOB -We use configuration files to setup the model evaluation pipeline. These configs are under **`conf/`** for surface, volume evaluations for a model and a matrix run for comparing multiple models: **[`config_surface.yaml`](conf/config_surface.yaml)** (default), **[`config_volume.yaml`](conf/config_volume.yaml)**, and matrix examples **[`config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** / **[`config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** (several models × several dataset entries). +**Contributing:** see the repository **[CONTRIBUTING.md](../../CONTRIBUTING.md)** for pull requests, tests, and sign-off requirements. -As a first step, you can this benchmarking example (link) to do an out of the box run. +--- + +## Installation + +Install **PhysicsNeMo-CFD** from the repository root (setuptools reads **[`pyproject.toml`](../../pyproject.toml)**; there is no separate `setup.py`): + +```bash +cd physicsnemo-cfd # repository root +pip install -e ".[dev]" # editable install + pytest for contributors +# optional GPU extras: +# pip install -e ".[gpu]" --extra-index-url=https://pypi.nvidia.com +``` + +Then use the commands below **from this directory** (`workflows/benchmarking_workflow/`). + +--- + +## Quick start + +1. **Prerequisites:** Python 3.10+, NVIDIA GPU for full inference runs, checkpoints and dataset on disk or mounted volume. +2. **Assets:** Download benchmark checkpoints and the evaluation dataset when they are published on **NGC** (see [Benchmark assets on NGC](#benchmark-assets-on-ngc-coming-soon)). Until links are available, point `model.checkpoint`, `stats_path`, and `dataset.root` in `conf/*.yaml` at your local paths, or use [Hydra overrides](#paths-without-ngc-hydra-overrides). +3. **Configure:** Edit `conf/config_surface.yaml` (default), `config_volume.yaml`, or matrix configs under `conf/`. +4. **Run:** ```bash -# Surface benchmark (VTP / wall metrics, default config) +# Surface benchmark (default config) python main.py -# Volume benchmark (VTU / volume metrics) +# Volume benchmark python main.py --config-name=config_volume -# Matrix: multiple models × multiple dataset blocks (see conf/config_matrix_*.yaml) +# Matrix: multiple models × dataset blocks python main.py --config-name=config_matrix_surface python main.py --config-name=config_matrix_volume -# Hydra overrides (examples) +# Overrides (examples) python main.py case_id=run_1 run.device=cuda:0 python main.py --config-name=config_volume run.output_dir=my_volume_run +python main.py run.fail_on_all_skipped=true ``` -**Multi-GPU (optional):** +5. **Outputs:** Under `run.output_dir` — `benchmark_results.json` / `.csv` / `.html`, optional `benchmark_artifacts.json`, `metrics_cache/` when enabled, Hydra metadata under `hydra/` if configured. +6. **Multi-GPU (optional):** install **physicsnemo** so `DistributedManager` is available, then: ```bash torchrun --standalone --nproc_per_node=4 main.py # or: torchrun --standalone --nproc_per_node=4 main.py --config-name=config_volume ``` -Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes `benchmark_results.*` and optional artifacts; all ranks return the merged result list. Set `run.distributed: false` only if debugging (each rank would run the full case list). +Cases are split across GPUs (`cases[rank::world_size]`). Rank 0 writes reports and optional artifacts. Set `run.distributed: false` only for debugging (each rank would run the full case list). -**Matrix mode:** use **[`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml)** or **[`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml)** as templates, or set `benchmark.mode: matrix` in any config and fill **`benchmark.models`** × **`benchmark.datasets`**. The product runs every model on every dataset entry; incompatible surface/volume pairs are skipped automatically. Edit checkpoint paths for your layout; comment out any **`- name:`** model block you do not want to run. +--- -## How to Extend (Add Your Own Data/Models) -To extend this workflow for your own custom evaluation or benchmarking, you will likely want to customize three things: +## Benchmark assets on NGC (coming soon) -### Custom models (adding a new wrapper) +Pretrained checkpoints and the ground-truth evaluation dataset are intended to be published on **NVIDIA NGC**. **Links are not yet public** — use local paths in config until then. -1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. -2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. -3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. +| Resource | Description | NGC link | +| -------- | ----------- | -------- | +| Benchmark model checkpoints | Surface/volume checkpoints + `global_stats.json` (or equivalent) per model | *TBD* | +| Evaluation dataset | DrivAerML (or successor) layout expected by the `drivaerml` adapter | *TBD* | -### Custom datasets (adding a new adapter) +After release, unpack assets to a stable path and set `benchmark.models[].checkpoint` / `stats_path` and `benchmark.datasets[].root` to match the documented layout (or use env-driven paths in your deployment). -1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. -2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. -3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. +--- + +## Paths without NGC (Hydra overrides) + +Until NGC artifacts are available, override paths from the CLI without editing YAML files: + +```bash +python main.py \ + benchmark.models.0.checkpoint=/path/to/checkpoint.pt \ + benchmark.models.0.stats_path=/path/to/global_stats.json \ + benchmark.datasets.0.root=/path/to/drivaer_data +``` -### Custom Metrics (adding a new metric) +You can also export environment variables and reference them in a local, gitignored config using OmegaConf `oc.env` patterns if you add them to your YAML. +--- + +## Notebooks (exploration, not batch production) + +The **`notebooks/`** directory (`surface_benchmarking.ipynb`, `volume_benchmarking.ipynb`, etc.) is for **interactive analysis**, plots, and exploring metrics. For **repeatable, automatable, config-driven** benchmarking (including CI-style runs), use **`main.py`** with **`conf/`** so every run is logged and reproducible. + +--- + +## Exit codes (automation) -### Canonical types +- By default, **`main.py`** exits **0** when `run_benchmark` completes without raising. +- Set **`run.fail_on_all_skipped: true`** to exit **1** if every model×dataset run was skipped (e.g. domain mismatch in matrix mode). +- Set **`run.fail_on_any_metric_nan: true`** to exit **1** if any aggregate metric in `metrics` is NaN. +- Override on the CLI: `run.fail_on_all_skipped=true`. -The schema is defined in `physicsnemo.cfd.evaluation.datasets.schema` +The flat CLI **`python -m physicsnemo.cfd.evaluation.benchmarks.run`** applies the same policy. -- **`CanonicalCase`** — `case_id`, `mesh_path`, `mesh_type`, `ground_truth`, `metadata`, `inference_domain`. -- **Predictions** — surface: `pressure`, `shear_stress`; volume: `pressure`, `turbulent_viscosity`, `velocity`. --- ## Config files | File | Role | | ---- | ----- | -| [`conf/config_surface.yaml`](conf/config_surface.yaml) | **Surface**, `benchmark.mode: matrix`, **GeoTransolver** × **drivaerml**. Top-level **`case_id: [run_1, run_11]`** (no per-dataset `case_ids`). **`run.device`**: `cuda:1`. **`run.output_dir`**: **`benchmark_results_surface`**. `reports.enabled: false` (visuals listed for copy/paste when enabling reports). | -| [`conf/config_volume.yaml`](conf/config_volume.yaml) | **Volume**, `benchmark.mode: matrix`, **GeoTransolver** × **drivaerml**. **`case_id: [run_1, run_11]`**. **`run.device`**: `cuda:0`. **`run.output_dir`**: **`benchmark_results_volume`**. | -| [`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml) | **Matrix surface:** **GeoTransolver, Transolver, XMGN, FigNet, DoMINO** × **one** drivaerml row; **`case_id: null`** (all cases unless overridden). Checkpoints under `.../benchmark_models/_drivaerml_surface_checkpoint/`. DoMINO **`kwargs.domino_config`** points at a YAML next to the checkpoint. **`run.output_dir`**: **`benchmark_results_matrix_surface`**. Optional commented baseline stub at bottom of **`benchmark.models`**. | -| [`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml) | **Matrix volume:** same five model names in **volume** mode × **one** drivaerml row; **`case_id: null`**. Paths use `.../_drivaerml_volume_checkpoint/`. **`run.output_dir`**: **`benchmark_results_matrix_volume`**. | +| [`conf/config_surface.yaml`](conf/config_surface.yaml) | **Surface**, `benchmark.mode: matrix`, **GeoTransolver** × **drivaerml**. Top-level **`case_id: [run_1, run_11]`**. **`run.device`**: `cuda:1`. **`run.output_dir`**: **`benchmark_results_surface`**. | +| [`conf/config_volume.yaml`](conf/config_volume.yaml) | **Volume**, same pattern; **`run.output_dir`**: **`benchmark_results_volume`**. | +| [`conf/config_matrix_surface.yaml`](conf/config_matrix_surface.yaml) | **Matrix surface:** multiple models × one drivaerml row; **`case_id: null`**. **`run.output_dir`**: **`benchmark_results_matrix_surface`**. | +| [`conf/config_matrix_volume.yaml`](conf/config_matrix_volume.yaml) | **Matrix volume:** same for volume checkpoints. **`run.output_dir`**: **`benchmark_results_matrix_volume`**. | + +**Matrix mode:** every **`benchmark.models`** entry runs against every **`benchmark.datasets`** entry; incompatible surface/volume pairs are skipped. Edit checkpoint paths; comment out any **`- name:`** block you do not need. --- @@ -75,27 +119,43 @@ The schema is defined in `physicsnemo.cfd.evaluation.datasets.schema` | Section | Contents | | ------- | -------- | -| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`**, **`distributed`** (default true: shard cases under `torchrun`), optional **`metrics_cache`** (see below) | +| **run** | `device`, `output_dir`, `seed`, `batch_size`, **`save_inference_mesh`**, **`distributed`**, **`fail_on_all_skipped`**, **`fail_on_any_metric_nan`**, optional **`metrics_cache`** | | **model** / **benchmark.models** | `name`, `checkpoint`, `stats_path`, `inference_domain` (`surface` \| `volume`), `kwargs` | -| **dataset** / **benchmark.datasets** | `name`, `root`, `split`, optional per-dataset `case_ids` (`null` = all), `kwargs` | -| **output** | `mesh_field_names`, `ground_truth_mesh_field_names`; `volume_mesh_field_names`, `ground_truth_volume_mesh_field_names`; **`streamlines_vector_canonical`** (default `velocity`) | -| **metrics** | Metric names or `{ name: ..., ...kwargs }` — see [Metrics](#metrics) | -| **reports** | Optional PNG pipeline — see [Reports and plots](#reports-and-plots) | -| **benchmark** | `mode` (`single` \| `matrix`), `models` / `datasets`, `reproducibility` | - -**`case_id`:** optional top-level Hydra key: `null` uses each dataset's `case_ids` (or all adapter cases); a **string** runs one case on every dataset; a **list** runs those cases on every dataset in matrix mode (preferred over repeating `case_ids` on each dataset). CLI examples: `case_id=run_1`, `'case_id=[run_1,run_11]'`. +| **dataset** / **benchmark.datasets** | `name`, `root`, `split`, optional `case_ids` (`null` = all), `kwargs` | +| **output** | VTK field name maps; **`streamlines_vector_canonical`** (volume) | +| **metrics** | Metric names or `{ name: ..., ...kwargs }` | +| **reports** | Optional PNG pipeline | +| **benchmark** | `mode`, `models` / `datasets`, **`reproducibility`** (`log_env`, `save_artifacts`) | -**Metrics cache (optional):** Under `run.metrics_cache`, `enabled: true` stores per-case scalars under `path` (resolved; default pattern `${run.output_dir}/metrics_cache`). Delete that directory for a full recompute. **Plots and meshes are not cached.** +**`case_id`:** optional top-level Hydra key: `null` uses each dataset's `case_ids` (or all adapter cases); a **string** runs one case on every dataset; a **list** runs those cases in matrix mode. -**Model notes:** GeoTransolver / Transolver **volume** needs matching stats (`global_stats.json` with `velocity`, `pressure`, `turbulent_viscosity`, or `volume_fields_normalization.npz`). **Surface** needs surface stats. DoMINO uses `domino_config` for volume. +**`benchmark.reproducibility.log_env`:** when `true`, writes **full `os.environ`** to `env.json` under `run.output_dir` — avoid in shared CI or when secrets may be present. Default in code is `false`; example configs may set `true` for local debugging. -**Custom code:** [Custom models](#custom-models-adding-a-new-wrapper) · [Custom datasets](#custom-datasets-adding-a-new-adapter) · [Baseline stubs](#baseline-model-stubs-surface_baseline-volume_baseline) +**Metrics cache:** `run.metrics_cache.enabled` stores per-case scalars; delete the cache directory for a full recompute. Plots and meshes are not cached. --- -## Advanced +## Custom models, datasets, and metrics -The Python API **`physicsnemo.cfd.evaluation.benchmarks.engine.run_benchmark`** and **`Config.from_dict`** remain for scripts and tests. The modules **`python -m physicsnemo.cfd.evaluation.benchmarks.run`** and **`evaluation.inference`** accept a YAML/JSON path **without** Hydra interpolation — use only if you supply a **flat** file (no `${...}`) or materialized values. +### Custom models + +1. Subclass **`CFDModel`** (`physicsnemo.cfd.evaluation.inference.model_registry`) under `physicsnemo/cfd/evaluation/inference/wrappers/`. +2. Implement `INFERENCE_DOMAIN`, `OUTPUT_LOCATION`, `load`, `prepare_inputs`, `predict`, `decode_outputs`. +3. **`register_model("my_model", MyWrapper)`** in `wrappers/__init__.py`. + +### Custom datasets + +1. Subclass **`DatasetAdapter`** under `physicsnemo/cfd/evaluation/datasets/adapters/`. +2. Implement `list_cases`, `load_case` → **`CanonicalCase`**. +3. **`register_adapter("my_dataset", MyAdapter)`** in `adapters/__init__.py`. + +### Custom metrics + +Register functions with **`physicsnemo.cfd.evaluation.metrics.register_metric`**, then list the name under **`metrics:`** in YAML (see **`physicsnemo.cfd.evaluation.metrics`**). + +### Canonical types + +Schema: **`physicsnemo.cfd.evaluation.datasets.schema`** — **`CanonicalCase`**, prediction keys for surface/volume. --- @@ -104,133 +164,80 @@ The Python API **`physicsnemo.cfd.evaluation.benchmarks.engine.run_benchmark`** Registered names (or dicts with `name` + kwargs). Examples match **`conf/config_surface.yaml`** / **`config_volume.yaml`**: ```yaml -# Surface-oriented metrics: - l2_pressure - l2_shear_stress - - l2_pressure_area_weighted - drag - lift - -# Volume-oriented (see config_volume.yaml) -metrics: - - l2_pressure - - l2_turbulent_viscosity - - l2_velocity - - continuity_residual_l2 - - momentum_residual_l2 ``` | Name | Meaning | | ---- | ------- | | `l2_pressure`, `l2_shear_stress` | Surface L2 | -| `l2_pressure_area_weighted` | Area-weighted L2 pressure (`area_wt_l2_pressure`) | -| `l2_pressure` (volume) | Volume pressure (domain-scoped, same name as surface) | -| `l2_velocity`, `l2_turbulent_viscosity` | Volume / surface depending on case | -| `drag`, `lift` | Drag / lift coefficient errors (expands to `drag_error`, `drag_true`, `drag_pred`, etc.) | -| `continuity_residual_l2`, `momentum_residual_l2` | Volume residuals | +| `l2_pressure_area_weighted` | Area-weighted L2 pressure | +| `drag`, `lift` | Coefficient errors (expands to `drag_error`, etc.) | +| `continuity_residual_l2`, `momentum_residual_l2` | Volume residuals (volume configs) | --- ## Reports and plots -### What runs where - -The benchmark engine writes **`benchmark_results.json` / `.csv` / `.html`**, then optional **`reports.visuals`** PNGs (when `reports.enabled` and `visuals` is non-empty). Comparison VTK on disk exists only if **`reports.save_comparison_meshes: true`**. **`run.save_inference_mesh: true`** adds prediction-only VTK per case. +When **`reports.enabled`** and **`reports.visuals`** are set, PNGs are written under `{run.output_dir}/visuals/`. Comparison VTK exists if **`reports.save_comparison_meshes: true`**. Headless servers may need **xvfb** (see **`setup.sh`** for an example `apt` line — optional). | Name | Role | | ---- | ---- | -| `field_comparison_surface` | Surface GT vs pred (`plot_field_comparisons`) | -| `plot_fields_volume` | Volume scalars (`plot_fields`) | +| `field_comparison_surface` | Surface GT vs pred | | `line_plot` | GT vs pred along `plot_coord` | -| `design_scatter` | Pred vs true scatter + R²; needs **`pairs`** matching `per_case[].metrics` | -| `design_trend` | Trend vs index; same **`pairs`**, optional **`idx_key`** | -| `projections_hexbin` | Hexbin; needs **`mesh_paths`**, **`field`**, **`direction`** | -| `streamlines_comparison` | Uses **`output.streamlines_vector_canonical`** or **`canonical_key`** | - -Register more: `physicsnemo.cfd.evaluation.reports.register_visual`. - -**Outputs:** PNGs under `{run.output_dir}/visuals/`; manifest `report_plugins_manifest.json`; optional comparison mesh `{run.output_dir}/{comparison_mesh_subdir}/`. - -### `reports` YAML shape +| `design_scatter` / `design_trend` | Design-of-experiments style plots | +| `streamlines_comparison` | Volume streamlines | -```yaml -reports: - enabled: true - save_comparison_meshes: false - comparison_mesh_subdir: comparison_meshes - visuals: - - field_comparison_surface - - name: field_comparison_surface - case_ids: ["run_1"] - canonical_keys: [pressure, shear_stress] - view: xy -``` +Register more: **`physicsnemo.cfd.evaluation.reports.register_visual`**. -| Key | Role | -| --- | --- | -| `enabled` | If true, run registered visuals after metrics/tables. | -| `save_comparison_meshes` | Save comparison VTK to disk. | -| `visual_case_ids` | Optional; limits in-memory meshes for PNGs; default `case_ids` for visuals that omit it. | -| `visuals` | Ordered list: string names or `{ name: ..., ...kwargs }`. | +### Legacy `bench_example` mapping -### Bench example ↔ evaluation mapping - -| `workflows/bench_example` / `postprocessing_tools.visualization.utils` | Evaluation visual name | -| --------------------------------------------------------- | ------------------------ | +| `workflows/deprecated/bench_example` / `postprocessing_tools.visualization.utils` | Evaluation visual name | +| --------------------------------------------------------------------------------- | ------------------------ | | `plot_field_comparisons` | `field_comparison_surface` | -| `plot_fields` | `plot_fields_volume` | | `plot_line` | `line_plot` | | `plot_design_scatter` | `design_scatter` | | `plot_design_trend` | `design_trend` | -| `plot_projections_hexbin` | `projections_hexbin` | -| `plot_streamlines` | `streamlines_comparison` | -### Line plots +**`line_plot`:** centerline-style strip plots — see [`workflows/deprecated/bench_example`](../deprecated/bench_example/README.md) or a custom `register_visual`. + +--- + +## Troubleshooting -**`line_plot`** uses **`canonical_key`** (surface: `pressure`, `shear_stress`; volume: `pressure`, `velocity`, …) and **`plot_coord`**. Centerline-style strip plots: see [`workflows/bench_example`](../bench_example/README.md) or a custom `register_visual`. +- **Missing checkpoints / bad paths:** inference fails or skips; verify overrides and `inference_domain`. +- **CUDA OOM:** reduce batch resolution in `model.kwargs` or use a smaller `case_id` set. +- **Matrix skips:** incompatible model vs dataset domain — check logs for `SKIP` lines. +- **Editable install:** use `pip install -e ".[dev]"` so local `physicsnemo.cfd` changes apply. + +--- -### Troubleshooting +## Advanced (Python API) -- **`case_ids` in a visual** must cover the cases you evaluate; set top-level **`case_id`** (string or list) or per-visual **`case_ids`** consistently. -- Headless PNG: **`report_plugins_manifest.json`** → `visual_errors`; often need **xvfb** (see `setup.sh`). -- Use an **editable install** so the installed package includes your local `evaluation` / `postprocessing_tools` changes. +**`physicsnemo.cfd.evaluation.benchmarks.engine.run_benchmark`** and **`Config.from_dict`** are for scripts and tests. **`python -m physicsnemo.cfd.evaluation.benchmarks.run`** and **`evaluation.inference`** accept flat YAML/JSON **without** Hydra `${...}` interpolation unless you materialize values first. --- ## Baseline model stubs (`surface_baseline`, `volume_baseline`) -Smoke-test wrappers: **`checkpoint: ""`**, **`stats_path: ""`**. Set **`model.name`** in **`benchmark.models`** (matrix) or top-level **`model`** (single). - -```yaml -model: - name: "surface_baseline" - checkpoint: "" - stats_path: "" -dataset: - name: drivaerml - root: /path/to/data -``` +Smoke-test wrappers: **`checkpoint: ""`**, **`stats_path: ""`**. Use in **`benchmark.models`** (matrix) or top-level **`model`** (single). --- ## DrivAerML train/validation split -The [`drivaer_ml_files/`](drivaer_ml_files/) directory contains a reproducible 90/10 train/validation split for the DrivAerML dataset (484 of 500 usable geometries). Validation samples are selected by sorting by drag coefficient, then taking 10% from low-drag, 10% from high-drag, and 80% randomly from the middle — mixing in-distribution and out-of-distribution cases. - -| File | Contents | -| ---- | -------- | -| [`train.csv`](drivaer_ml_files/train.csv) | `run_idx, drag` for training samples | -| [`validation.csv`](drivaer_ml_files/validation.csv) | `run_idx, drag` for validation samples | -| [`README.md`](drivaer_ml_files/README.md) | Split strategy details and reference to DrivAerML paper | +The [`drivaer_ml_files/`](drivaer_ml_files/) directory contains a reproducible 90/10 train/validation split. See [`drivaer_ml_files/README.md`](drivaer_ml_files/README.md). --- -## Package layout (repo root) +## Package layout (repository) | Path | Role | | ---- | ---- | | `physicsnemo/cfd/evaluation/config.py` | `Config`, `load_config` | -| `physicsnemo/cfd/evaluation/benchmarks/` | Engine, JSON/CSV/HTML reports | +| `physicsnemo/cfd/evaluation/benchmarks/` | Engine, reports, Hydra helpers | | `physicsnemo/cfd/evaluation/reports/` | `register_visual`, built-in plots | -| `physicsnemo/cfd/postprocessing_tools/` | Shared metrics / visualization algorithms | +| `physicsnemo/cfd/postprocessing_tools/` | Shared metrics / visualization | diff --git a/workflows/benchmarking_workflow/main.py b/workflows/benchmarking_workflow/main.py index b7649dd..0a6e203 100644 --- a/workflows/benchmarking_workflow/main.py +++ b/workflows/benchmarking_workflow/main.py @@ -31,21 +31,16 @@ from __future__ import annotations +import sys + import hydra -from omegaconf import DictConfig, OmegaConf +from omegaconf import DictConfig -from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark +from physicsnemo.cfd.evaluation.benchmarks.engine import BenchmarkPolicyError, run_benchmark +from physicsnemo.cfd.evaluation.benchmarks.hydra_utils import hydra_config_to_benchmark_dict from physicsnemo.cfd.evaluation.config import Config -def _config_dict_from_hydra(cfg: DictConfig) -> dict: - """Turn composed DictConfig into a plain dict for :class:`Config`.""" - raw = OmegaConf.to_container(cfg, resolve=True) - if not isinstance(raw, dict): - raise TypeError("Hydra config root must map to a dict") - return raw - - @hydra.main(version_base="1.3", config_path="conf", config_name="config_surface") def main(cfg: DictConfig) -> None: """ @@ -57,18 +52,13 @@ def main(cfg: DictConfig) -> None: Composed user config (``conf/config_surface.yaml`` by default, or ``config_volume`` via ``--config-name=config_volume``) plus CLI overrides. """ - raw = _config_dict_from_hydra(cfg) - case_id = raw.pop("case_id", None) - raw.pop("defaults", None) - raw.pop("hydra", None) - if case_id is None or case_id == "null": - case_id = None - elif isinstance(case_id, (list, tuple)): - case_id = [str(x) for x in case_id] - else: - case_id = str(case_id) + raw, case_id = hydra_config_to_benchmark_dict(cfg) config = Config.from_dict(raw) - results = run_benchmark(config, case_id=case_id) + try: + results = run_benchmark(config, case_id=case_id) + except BenchmarkPolicyError as exc: + print(str(exc), file=sys.stderr) + sys.exit(1) print(f"Completed {len(results)} run(s). Results in {config.run.output_dir}") diff --git a/workflows/bench_example/README.md b/workflows/deprecated/bench_example/README.md similarity index 97% rename from workflows/bench_example/README.md rename to workflows/deprecated/bench_example/README.md index eb8aab1..a615978 100644 --- a/workflows/bench_example/README.md +++ b/workflows/deprecated/bench_example/README.md @@ -1,6 +1,6 @@ # Benchmarking AI surrogates for External Aerodynamics -> **DEPRECATED:** This workflow is superseded by [`benchmarking_workflow`](../benchmarking_workflow/README.md), which provides Hydra configuration, model wrapper integration, distributed multi-GPU support, and more. Please use `benchmarking_workflow` for new work. This directory will be removed in a future release. +> **DEPRECATED:** This workflow is superseded by [`benchmarking_workflow`](../../benchmarking_workflow/README.md), which provides Hydra configuration, model wrapper integration, distributed multi-GPU support, and more. Please use `benchmarking_workflow` for new work. This directory will be removed in a future release. The benchmarking workflow is designed for evaluating and validating AI models for external aerodynamics. Predicting accurate aerodynamic characteristics of a @@ -24,7 +24,7 @@ flexibility and freedom. For **on-the-fly** evaluation (config-driven inference plus the same `physicsnemo.cfd.postprocessing_tools` metrics as the scripts above), use the `physicsnemo.cfd.evaluation` package and the example YAML under -[`workflows/benchmarking_workflow/`](../benchmarking_workflow/). The pre-baked +[`workflows/benchmarking_workflow/`](../../benchmarking_workflow/). The pre-baked `.vtp` / `.vtu` workflows in this directory remain the reference for post-processed file-based benchmarks. diff --git a/workflows/bench_example/drivaer_ml_files/README.md b/workflows/deprecated/bench_example/drivaer_ml_files/README.md similarity index 100% rename from workflows/bench_example/drivaer_ml_files/README.md rename to workflows/deprecated/bench_example/drivaer_ml_files/README.md diff --git a/workflows/bench_example/drivaer_ml_files/design_trend_split_0.9_0.1.png b/workflows/deprecated/bench_example/drivaer_ml_files/design_trend_split_0.9_0.1.png similarity index 100% rename from workflows/bench_example/drivaer_ml_files/design_trend_split_0.9_0.1.png rename to workflows/deprecated/bench_example/drivaer_ml_files/design_trend_split_0.9_0.1.png diff --git a/workflows/bench_example/drivaer_ml_files/train.csv b/workflows/deprecated/bench_example/drivaer_ml_files/train.csv similarity index 100% rename from workflows/bench_example/drivaer_ml_files/train.csv rename to workflows/deprecated/bench_example/drivaer_ml_files/train.csv diff --git a/workflows/bench_example/drivaer_ml_files/validation.csv b/workflows/deprecated/bench_example/drivaer_ml_files/validation.csv similarity index 100% rename from workflows/bench_example/drivaer_ml_files/validation.csv rename to workflows/deprecated/bench_example/drivaer_ml_files/validation.csv diff --git a/workflows/bench_example/generate_pcs_from_stl.py b/workflows/deprecated/bench_example/generate_pcs_from_stl.py similarity index 100% rename from workflows/bench_example/generate_pcs_from_stl.py rename to workflows/deprecated/bench_example/generate_pcs_from_stl.py diff --git a/workflows/bench_example/generate_surface_benchmarks.py b/workflows/deprecated/bench_example/generate_surface_benchmarks.py similarity index 100% rename from workflows/bench_example/generate_surface_benchmarks.py rename to workflows/deprecated/bench_example/generate_surface_benchmarks.py diff --git a/workflows/bench_example/generate_volume_benchmarks.py b/workflows/deprecated/bench_example/generate_volume_benchmarks.py similarity index 100% rename from workflows/bench_example/generate_volume_benchmarks.py rename to workflows/deprecated/bench_example/generate_volume_benchmarks.py diff --git a/workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb b/workflows/deprecated/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb similarity index 100% rename from workflows/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb rename to workflows/deprecated/bench_example/notebooks/benchmarking_in_absence_of_gt.ipynb diff --git a/workflows/bench_example/notebooks/img/chamfer-distances-drivaerml.png b/workflows/deprecated/bench_example/notebooks/img/chamfer-distances-drivaerml.png similarity index 100% rename from workflows/bench_example/notebooks/img/chamfer-distances-drivaerml.png rename to 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rename to workflows/deprecated/bench_example/utils.py From 2dd3806b883a294c29c11d8cd40e2ac4b735743e Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Thu, 16 Apr 2026 22:21:38 -0700 Subject: [PATCH 24/25] add tutorial notebook on adding a dataset adaptor --- .../skills/create-dataset-adapter/SKILL.md | 170 ++ .../common_wrapper_utils/vtk_datapipe_io.py | 23 +- .../inference/wrappers/domino/inference.py | 9 +- .../inference/wrappers/transolver/wrapper.py | 4 +- workflows/benchmarking_workflow/AGENTS.md | 22 + .../notebooks/adding_a_new_dataset.ipynb | 1414 +++++++++++++++++ 6 files changed, 1630 insertions(+), 12 deletions(-) create mode 100644 .cursor/skills/create-dataset-adapter/SKILL.md create mode 100644 workflows/benchmarking_workflow/AGENTS.md create mode 100644 workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb diff --git a/.cursor/skills/create-dataset-adapter/SKILL.md b/.cursor/skills/create-dataset-adapter/SKILL.md new file mode 100644 index 0000000..9be12d2 --- /dev/null +++ b/.cursor/skills/create-dataset-adapter/SKILL.md @@ -0,0 +1,170 @@ +--- +name: create-dataset-adapter +description: >- + Create a new dataset adapter for the PhysicsNeMo CFD benchmarking workflow. + Use when the user wants to add a new CFD dataset, write a DatasetAdapter, + integrate a new mesh format, or benchmark models on custom data. +--- + +# Create a Dataset Adapter + +Guide the user through adding a new CFD dataset to the benchmarking workflow by writing a `DatasetAdapter` subclass. + +## Reference files to read first + +Before starting, read these files for context: + +- `physicsnemo/cfd/evaluation/datasets/adapter_registry.py` — base class and registry +- `physicsnemo/cfd/evaluation/datasets/schema.py` — `CanonicalCase` and `build_predictions_dict` +- `physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py` — reference adapter implementation +- `workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb` — end-to-end tutorial + +## Step 1: Explore the new dataset + +Ask the user for the dataset path, then inspect one file: + +```python +import pyvista as pv +mesh = pv.read("") +print(f"Type: {type(mesh).__name__}, Points: {mesh.n_points}, Cells: {mesh.n_cells}") +print(f"Cell arrays: {list(mesh.cell_data.keys())}") +print(f"Point arrays: {list(mesh.point_data.keys())}") +``` + +Identify these differences from the canonical schema: + +| Question | What to look for | +|----------|-----------------| +| File format | `.vtp`, `.vtu`, `.vtk`, or other? Model wrappers expect `.vtp` (surface) or `.vtu` (volume) XML format. | +| Directory layout | Flat directory? Nested `run_/` dirs? How are case IDs derived from filenames? | +| Pressure field name | The canonical key is `pressure`. What is the VTK array name? | +| WSS field name | The canonical key is `shear_stress` (N, 3). Is it a single vector or separate scalar components? | +| Sign conventions | Compare field ranges with DrivAerML. Are normals, WSS, or pressure flipped? | +| Extra arrays | Are there explicit `Normals` or `Area` arrays? DrivAerML has none — remove them if present. | +| STL files | Are separate STL geometry files available? If not, the surface mesh itself is the geometry. | +| Inference domain | Surface (`.vtp`) or volume (`.vtu`)? | + +## Step 2: Write the adapter class + +Subclass `DatasetAdapter` with these methods: + +```python +from pathlib import Path +from physicsnemo.cfd.evaluation.datasets.adapter_registry import DatasetAdapter, register_adapter +from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase + +class MyDatasetAdapter(DatasetAdapter): + def __init__(self, root: str, **kwargs): + self._root = Path(root) + + @classmethod + def inference_domain_from_kwargs(cls, kwargs=None): + return "surface" # or "volume" + + def list_cases(self, split=None): + # Return list of case ID strings + ... + + def load_case(self, case_id: str) -> CanonicalCase: + # 1. Read the mesh file + # 2. Build ground_truth dict with canonical keys: + # - "pressure": np.float32 array + # - "shear_stress": np.float32 array of shape (N, 3) + # For volume: "pressure", "velocity" (N,3), "turbulent_viscosity" + # 3. Return CanonicalCase(case_id, mesh_path, mesh_type, ground_truth, inference_domain) + ... +``` + +### Common transformations in `load_case` + +**Format conversion** (legacy `.vtk` → `.vtp`): +```python +mesh = pv.read(vtk_path).extract_surface() +mesh.save(vtp_path) +``` + +**Combining separate WSS scalars into a vector:** +```python +wss = np.stack([mesh.cell_data["WSSx"], mesh.cell_data["WSSy"], mesh.cell_data["WSSz"]], axis=1) +``` + +**Removing explicit Normals/Area** (DrivAerML convention): +```python +for key in ["Normals", "Area"]: + if key in mesh.cell_data: + del mesh.cell_data[key] +``` + +**Creating STL from surface mesh** (when no STL is shipped): +```python +mesh.extract_surface().triangulate().save(stl_path) +``` + +The STL must be named `drivaer_{int(case_id)}.stl` in the same directory as the VTP for the model wrappers to find it. + +### Caching pattern + +Do expensive conversions lazily and cache: + +```python +def _prepare_case(self, case_id): + prepared_path = self._root / "_prepared" / f"{case_id}.vtp" + if not prepared_path.exists(): + # ... convert and save + return str(prepared_path) +``` + +## Step 3: Register and test + +```python +register_adapter("my_dataset", MyDatasetAdapter) + +adapter = MyDatasetAdapter(root="/path/to/data") +cases = adapter.list_cases() +case = adapter.load_case(cases[0]) +assert case.ground_truth is not None +assert "pressure" in case.ground_truth +``` + +## Step 4: Run inference and benchmark + +Build a config and run: + +```python +from physicsnemo.cfd.evaluation.config import Config +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark + +config = Config.from_dict({ + "run": {"device": "cuda:0", "output_dir": "results"}, + "model": {"name": "", "inference_domain": "", ...}, + "dataset": {"name": "my_dataset", "root": "/path/to/data", "case_ids": cases[:2]}, + "output": { + "ground_truth_mesh_field_names": {"pressure": "", "shear_stress": ""}, + "mesh_field_names": {"pressure": "", "shear_stress": ""}, + }, + "metrics": ["l2_pressure", "l2_shear_stress", "drag", "lift"], + "reports": {"enabled": False}, +}) +results = run_benchmark(config) +``` + +## Step 5: Make permanent (optional) + +Save the adapter to `physicsnemo/cfd/evaluation/datasets/adapters/.py` and register in `adapters/__init__.py`: + +```python +from physicsnemo.cfd.evaluation.datasets.adapters. import MyDatasetAdapter +register_adapter("my_dataset", MyDatasetAdapter) +``` + +## Why conventions must match the training data + +The field name mappings, sign conventions, and format conversions in the adapter exist because the model checkpoint was trained on a specific dataset (e.g., DrivAerML) with specific conventions. The adapter bridges the gap between the new dataset's conventions and the training data's conventions — not some abstract standard. If a model is retrained directly on the new dataset, the adapter would not need these transformations. When writing an adapter, always ask: "What conventions did the model's training data use?" and map to those. + +## Gotchas + +- **DistributedManager**: Model wrappers call `DistributedManager.initialize()`. In notebooks without `torchrun`, set env vars first: `WORLD_SIZE=1`, `RANK=0`, `LOCAL_RANK=0`, `MASTER_ADDR=localhost`, `MASTER_PORT=12355`. +- **STL naming**: DoMINO looks for `drivaer_{tag}.stl`, GeoTransolver looks for `drivaer_{tag}_single_solid.stl` then `*.stl`. Both now fall back to any `*.stl` in the directory. +- **VTP vs VTK**: Model wrappers use VTK XML readers internally. Legacy `.vtk` files must be converted to `.vtp`/`.vtu`. +- **Checkpoint loading**: Some wrappers need `trusted_torch_load_context()` for PyTorch 2.6+ checkpoint compatibility. +- **Domain-scoped metrics**: `l2_pressure` resolves to different implementations for surface vs volume based on `inference_domain`. Use the same metric name for both. diff --git a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py index a6799c4..30f10bf 100644 --- a/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py +++ b/physicsnemo/cfd/evaluation/inference/common_wrapper_utils/vtk_datapipe_io.py @@ -118,6 +118,22 @@ def build_volume_data_dict( return data_dict +def _find_stl_in_dir(run_dir: Path, run_idx: int) -> Path: + """Find an STL file in *run_dir* using progressively looser name patterns.""" + candidates = [ + run_dir / f"drivaer_{run_idx}.stl", + run_dir / f"drivaer_{run_idx}_single_solid.stl", + ] + for p in candidates: + if p.exists(): + return p + for pattern in ("*_single_solid.stl", "*.stl"): + globs = list(run_dir.glob(pattern)) + if globs: + return globs[0] + raise FileNotFoundError(f"No STL file found in {run_dir} for run_idx {run_idx}") + + def build_surface_data_dict( run_dir: Path, vtp_path: str, @@ -127,12 +143,7 @@ def build_surface_data_dict( run_idx: int = 1, ) -> dict[str, torch.Tensor]: """Build data dict for surface inference: STL + VTP + flow params. Finds STL in run_dir.""" - stl_path = run_dir / f"drivaer_{run_idx}_single_solid.stl" - if not stl_path.exists(): - stl_files = list(run_dir.glob("*_single_solid.stl")) - if not stl_files: - raise FileNotFoundError(f"No STL file found in {run_dir}") - stl_path = stl_files[0] + stl_path = _find_stl_in_dir(run_dir, run_idx) data_dict = read_stl_geometry(str(stl_path), device) data_dict.update(read_surface_from_vtp(vtp_path, device)) data_dict["air_density"] = torch.tensor([air_density], device=device, dtype=torch.float32) diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py index 8c35d6c..f9d0925 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/domino/inference.py @@ -74,7 +74,7 @@ def domino_count_output_features(cfg: DictConfig) -> tuple[int | None, int | Non def _find_stl(run_dir: Path, tag: int) -> Path: - """Resolve STL path (domino test uses ``drivaer_{tag}.stl``; DrivAerML may use ``*_single_solid.stl``).""" + """Resolve STL path using progressively looser name patterns.""" candidates = [ run_dir / f"drivaer_{tag}.stl", run_dir / f"drivaer_{tag}_single_solid.stl", @@ -82,9 +82,10 @@ def _find_stl(run_dir: Path, tag: int) -> Path: for p in candidates: if p.exists(): return p - globs = list(run_dir.glob("*_single_solid.stl")) - if globs: - return globs[0] + for pattern in ("*_single_solid.stl", "*.stl"): + globs = list(run_dir.glob(pattern)) + if globs: + return globs[0] raise FileNotFoundError(f"No STL found in {run_dir} for tag {tag}") diff --git a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py index aa77db4..4bb7ebb 100644 --- a/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py +++ b/physicsnemo/cfd/evaluation/inference/wrappers/transolver/wrapper.py @@ -224,8 +224,8 @@ def load( "path": str(checkpoint_dir), "models": self._model, } - loaded_epoch = load_checkpoint(device=dev, **ckpt_args) - # load_checkpoint(path=str(checkpoint_dir), models=self._model, device=dev) + with trusted_torch_load_context(): + loaded_epoch = load_checkpoint(device=dev, **ckpt_args) self._model = self._model.to(dev) self._model.eval() log_inference("transolver", "Checkpoint loaded; model ready for inference.") diff --git a/workflows/benchmarking_workflow/AGENTS.md b/workflows/benchmarking_workflow/AGENTS.md new file mode 100644 index 0000000..5eb4739 --- /dev/null +++ b/workflows/benchmarking_workflow/AGENTS.md @@ -0,0 +1,22 @@ +# Agent Instructions for Benchmarking Workflow + +This file provides guidance for AI coding agents working on the PhysicsNeMo CFD benchmarking workflow. + +## Available Skills + +### Creating a New Dataset Adapter + +When the user wants to add a new CFD dataset, benchmark models on custom data, or integrate a new mesh format, follow the instructions in [`.cursor/skills/create-dataset-adapter/SKILL.md`](../../.cursor/skills/create-dataset-adapter/SKILL.md). + +Key reference files: +- `physicsnemo/cfd/evaluation/datasets/adapter_registry.py` — `DatasetAdapter` base class +- `physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py` — reference implementation +- `workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb` — end-to-end tutorial + +## Codebase Conventions + +- **Canonical keys**: Surface fields use `pressure` and `shear_stress`. Volume fields use `pressure`, `velocity`, `turbulent_viscosity`. The same key `pressure` is used for both domains — the metric registry resolves the correct implementation via `domain` scoping. +- **Metric names**: Use `l2_pressure`, `l2_shear_stress`, `drag`, `lift` etc. in configs. Metrics are domain-scoped — the engine passes `inference_domain` automatically. +- **Config**: Hydra YAML configs under `conf/`. Programmatic configs via `Config.from_dict()`. +- **Model wrappers**: Registered in `physicsnemo/cfd/evaluation/inference/wrappers/`. Each implements `load`, `prepare_inputs`, `predict`, `decode_outputs`. +- **Visual plugins**: Registered in `physicsnemo/cfd/evaluation/reports/builtin/`. Each follows `(config, results, output_dir, *, context, **kwargs) -> None`. diff --git a/workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb b/workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb new file mode 100644 index 0000000..c04cd11 --- /dev/null +++ b/workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb @@ -0,0 +1,1414 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:00.205128Z", + "iopub.status.busy": "2026-04-17T00:08:00.205051Z", + "iopub.status.idle": "2026-04-17T00:08:00.207231Z", + "shell.execute_reply": "2026-04-17T00:08:00.206872Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"WORLD_SIZE\"] = \"1\"\n", + "os.environ[\"RANK\"] = \"0\"\n", + "os.environ[\"LOCAL_RANK\"] = \"0\"\n", + "os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", + "os.environ[\"MASTER_PORT\"] = \"12355\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding a New Dataset to the Benchmarking Workflow\n", + "\n", + "The PhysicsNeMo benchmarking workflow uses **dataset adapters** to decouple datasets from models and metrics. When you have a new CFD dataset with a different file layout or field naming convention, you write an adapter to map it into the canonical schema — and then every registered model and metric just works.\n", + "\n", + "In this notebook we walk through the process using the [DrivAerStar](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UXVXQV) dataset as an example. DrivAerStar is an industrial-grade automotive CFD dataset hosted on Harvard Dataverse, with a substantially different format from the built-in DrivAerML adapter. We will download a subset, write an adapter, and run GeoTransolver and Transolver inference and benchmarking end to end." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the Data\n", + "\n", + "DrivAerStar is hosted on Harvard Dataverse. The cell below downloads a single zip (`vtk_N_0.zip`) containing 50 surface mesh VTK files from the notchback (class N) vehicle category. If the data is already present it skips the download." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:00.208248Z", + "iopub.status.busy": "2026-04-17T00:08:00.208170Z", + "iopub.status.idle": "2026-04-17T00:08:01.662629Z", + "shell.execute_reply": "2026-04-17T00:08:01.662099Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting drivaerstar_data/vtk_N_0.zip...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted 50 VTK files to drivaerstar_data/\n" + ] + } + ], + "source": [ + "import urllib.request\n", + "import json\n", + "import zipfile\n", + "import glob\n", + "import os\n", + "\n", + "PERSISTENT_ID = \"doi:10.7910/DVN/UXVXQV\"\n", + "VERSION = \"5.2\"\n", + "BASE_URL = \"https://dataverse.harvard.edu/api\"\n", + "TARGET_FILE = \"vtk_N_0.zip\"\n", + "DATA_DIR = \"drivaerstar_data\"\n", + "\n", + "os.makedirs(DATA_DIR, exist_ok=True)\n", + "\n", + "# Check if VTK files already exist (extracted)\n", + "existing_vtks = glob.glob(os.path.join(DATA_DIR, \"*.vtk\"))\n", + "if existing_vtks:\n", + " print(f\"{DATA_DIR}/ already has {len(existing_vtks)} VTK files. Skipping download.\")\n", + "else:\n", + " # Look for zip in current dir or DATA_DIR\n", + " zip_path = TARGET_FILE if os.path.exists(TARGET_FILE) else os.path.join(DATA_DIR, TARGET_FILE)\n", + "\n", + " if not os.path.exists(zip_path):\n", + " # Download from Harvard Dataverse\n", + " print(f\"Fetching file list from Dataverse...\")\n", + " url = f\"{BASE_URL}/datasets/:persistentId/versions/{VERSION}/files?persistentId={PERSISTENT_ID}&limit=200\"\n", + " with urllib.request.urlopen(url) as resp:\n", + " files = json.loads(resp.read())[\"data\"]\n", + " file_id = next(f[\"dataFile\"][\"id\"] for f in files if f[\"dataFile\"][\"filename\"] == TARGET_FILE)\n", + " print(f\"Downloading {TARGET_FILE} (file_id={file_id})...\")\n", + " urllib.request.urlretrieve(f\"{BASE_URL}/access/datafile/{file_id}\", zip_path)\n", + " print(\"Download complete.\")\n", + "\n", + " print(f\"Extracting {zip_path}...\")\n", + " with zipfile.ZipFile(zip_path) as z:\n", + " z.extractall(DATA_DIR)\n", + " print(f\"Extracted {len(glob.glob(os.path.join(DATA_DIR, '*.vtk')))} VTK files to {DATA_DIR}/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the Data\n", + "\n", + "Let's read one of the VTK files and examine its structure to understand what we're working with." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:01.663900Z", + "iopub.status.busy": "2026-04-17T00:08:01.663813Z", + "iopub.status.idle": "2026-04-17T00:08:01.903383Z", + "shell.execute_reply": "2026-04-17T00:08:01.902950Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 50 VTK files\n", + "First few: ['00003.vtk', '00004.vtk', '00006.vtk', '00007.vtk', '00011.vtk']\n", + "\n", + "Mesh type: PolyData\n", + "Points: 376,991 Cells: 338,342\n", + "Cell arrays: ['Pressure', 'Normals', 'Area', 'WallShearStressi', 'WallShearStressj', 'WallShearStressk']\n", + "Point arrays: ['Normals']\n" + ] + } + ], + "source": [ + "import glob\n", + "import pyvista as pv\n", + "\n", + "vtk_files = sorted(glob.glob(f\"{DATA_DIR}/*.vtk\"))\n", + "print(f\"Found {len(vtk_files)} VTK files\")\n", + "print(f\"First few: {[os.path.basename(f) for f in vtk_files[:5]]}\")\n", + "\n", + "sample = pv.read(vtk_files[0])\n", + "print(f\"\\nMesh type: {type(sample).__name__}\")\n", + "print(f\"Points: {sample.n_points:,} Cells: {sample.n_cells:,}\")\n", + "print(f\"Cell arrays: {list(sample.cell_data.keys())}\")\n", + "print(f\"Point arrays: {list(sample.point_data.keys())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can already see several differences from DrivAerML. The table below summarizes them — our adapter will need to handle each one.\n", + "\n", + "| Aspect | DrivAerML | DrivAerStar |\n", + "| ------ | --------- | ----------- |\n", + "| File format | `.vtp` (VTK PolyData XML) | `.vtk` (legacy VTK PolyData) |\n", + "| Directory layout | `run_/boundary_.vtp` | Flat: `.vtk` |\n", + "| Pressure field | `pMeanTrim` (cell) | `Pressure` (cell) |\n", + "| Wall shear stress | `wallShearStressMeanTrim` (cell, 3-component vector) | Three separate scalars: `WallShearStressi`, `WallShearStressj`, `WallShearStressk` (opposite sign convention) |\n", + "| Normals / Area | Not present (computed from mesh topology) | Explicit cell arrays — must be removed to match DrivAerML |\n", + "| STL files | Separate `drivaer_.stl` per run | Not included — the surface mesh itself is the geometry |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Writing the Dataset Adapter\n", + "\n", + "A dataset adapter subclasses `DatasetAdapter` and implements two methods: `list_cases` (return available case IDs) and `load_case` (load a single case into the canonical `CanonicalCase` schema with ground-truth fields mapped to the standard keys `pressure` and `shear_stress`).\n", + "\n", + "For DrivAerStar, the adapter also needs to handle the format conversions we identified above. We do this transparently inside `load_case` by preparing a VTP and STL on first access and caching them for subsequent runs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:01.904711Z", + "iopub.status.busy": "2026-04-17T00:08:01.904590Z", + "iopub.status.idle": "2026-04-17T00:08:08.532705Z", + "shell.execute_reply": "2026-04-17T00:08:08.532093Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registered 'drivaerstar' adapter.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from pathlib import Path\n", + "\n", + "from physicsnemo.cfd.evaluation.datasets.adapter_registry import DatasetAdapter, register_adapter\n", + "from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase\n", + "\n", + "\n", + "class DrivAerStarAdapter(DatasetAdapter):\n", + " \"\"\"Adapter for DrivAerStar surface VTK files in a flat directory.\n", + " \n", + " DrivAerStar uses legacy .vtk format with field names that differ from\n", + " DrivAerML (e.g. ``Pressure`` instead of ``pMeanTrim``, and three separate\n", + " WSS scalars instead of a single vector). The adapter transparently:\n", + " \n", + " 1. Converts .vtk to .vtp (XML PolyData) for model wrapper compatibility\n", + " 2. Combines the 3 WSS scalars into a single vector\n", + " 3. Renames fields to DrivAerML conventions\n", + " 4. Generates a triangulated STL for geometry encoding\n", + " \"\"\"\n", + "\n", + " def __init__(self, root: str, **kwargs):\n", + " self._root = Path(root)\n", + " self._prepared_dir = self._root / \"_prepared\"\n", + " self._prepared_dir.mkdir(exist_ok=True)\n", + "\n", + " @classmethod\n", + " def inference_domain(cls):\n", + " return \"surface\"\n", + "\n", + " @classmethod\n", + " def inference_domain_from_kwargs(cls, kwargs=None):\n", + " return \"surface\"\n", + "\n", + " def list_cases(self, split=None):\n", + " vtk_files = sorted(self._root.glob(\"*.vtk\"))\n", + " return [f.stem for f in vtk_files]\n", + "\n", + " def _prepare_case(self, case_id: str) -> str:\n", + " \"\"\"Convert VTK to VTP with DrivAerML-compatible field names and create STL.\"\"\"\n", + " vtk_path = self._root / f\"{case_id}.vtk\"\n", + " vtp_path = self._prepared_dir / f\"{case_id}.vtp\"\n", + " tag = int(case_id)\n", + " stl_path = self._prepared_dir / f\"drivaer_{tag}.stl\"\n", + "\n", + " if not vtp_path.exists():\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n", + "\n", + " # Rename pressure\n", + " if \"Pressure\" in mesh.cell_data:\n", + " mesh.cell_data[\"pMeanTrim\"] = mesh.cell_data.pop(\"Pressure\")\n", + "\n", + " # Combine 3 WSS scalars into a single (N, 3) vector.\n", + " # DrivAerStar WSS sign convention is flipped relative to DrivAerML — negate to align.\n", + " wss_keys = [\"WallShearStressi\", \"WallShearStressj\", \"WallShearStressk\"]\n", + " if all(k in mesh.cell_data for k in wss_keys):\n", + " wss = -np.stack([mesh.cell_data.pop(k) for k in wss_keys], axis=1).astype(np.float32)\n", + " mesh.cell_data[\"wallShearStressMeanTrim\"] = wss\n", + "\n", + " # DrivAerML has no explicit Normals or Area arrays — the mesh topology\n", + " # encodes face orientation. Remove these to avoid interfering with\n", + " # rendering (ParaView) and force computation (compute_drag_and_lift).\n", + " for key in [\"Normals\", \"Area\"]:\n", + " if key in mesh.cell_data:\n", + " del mesh.cell_data[key]\n", + " if \"Normals\" in mesh.point_data:\n", + " del mesh.point_data[\"Normals\"]\n", + "\n", + " mesh.save(str(vtp_path))\n", + "\n", + " if not stl_path.exists():\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n", + " mesh.triangulate().save(str(stl_path))\n", + "\n", + " return str(vtp_path)\n", + "\n", + " def load_case(self, case_id: str) -> CanonicalCase:\n", + " vtk_path = self._root / f\"{case_id}.vtk\"\n", + " if not vtk_path.exists():\n", + " raise FileNotFoundError(f\"VTK not found: {vtk_path}\")\n", + "\n", + " vtp_path = self._prepare_case(case_id)\n", + " mesh = pv.read(vtp_path)\n", + "\n", + " gt = {}\n", + " if \"pMeanTrim\" in mesh.cell_data:\n", + " gt[\"pressure\"] = np.asarray(mesh.cell_data[\"pMeanTrim\"], dtype=np.float32)\n", + " if \"wallShearStressMeanTrim\" in mesh.cell_data:\n", + " gt[\"shear_stress\"] = np.asarray(mesh.cell_data[\"wallShearStressMeanTrim\"], dtype=np.float32)\n", + "\n", + " return CanonicalCase(\n", + " case_id=case_id,\n", + " mesh_path=vtp_path,\n", + " mesh_type=\"cell\",\n", + " ground_truth=gt if gt else None,\n", + " inference_domain=\"surface\",\n", + " )\n", + "\n", + "\n", + "# Register the adapter so the benchmarking engine can find it by name\n", + "register_adapter(\"drivaerstar\", DrivAerStarAdapter)\n", + "print(\"Registered 'drivaerstar' adapter.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can verify the adapter works by listing cases and loading one. The ground-truth dictionary should contain `pressure` (scalar) and `shear_stress` (3-component vector) in the canonical format." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:08.534490Z", + "iopub.status.busy": "2026-04-17T00:08:08.534255Z", + "iopub.status.idle": "2026-04-17T00:08:09.863825Z", + "shell.execute_reply": "2026-04-17T00:08:09.863255Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 50 cases: ['00003', '00004', '00006', '00007', '00011']...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3671180/550963088.py:46: PyVistaFutureWarning: The default value of `algorithm` for the filter\n", + "`PolyData.extract_surface` will change in the future. It currently defaults to\n", + "`'dataset_surface'`, but will change to `None`. Explicitly set the `algorithm` keyword to\n", + "silence this warning.\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3671180/550963088.py:71: PyVistaFutureWarning: The default value of `algorithm` for the filter\n", + "`PolyData.extract_surface` will change in the future. It currently defaults to\n", + "`'dataset_surface'`, but will change to `None`. Explicitly set the `algorithm` keyword to\n", + "silence this warning.\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Case: 00003\n", + "Mesh path: drivaerstar_data/_prepared/00003.vtp\n", + "Mesh type: cell\n", + "Inference domain: surface\n", + "GT keys: ['pressure', 'shear_stress']\n", + "Pressure shape: (338342,)\n", + "Shear stress shape: (338342, 3)\n" + ] + } + ], + "source": [ + "adapter = DrivAerStarAdapter(root=DATA_DIR)\n", + "cases = adapter.list_cases()\n", + "print(f\"Found {len(cases)} cases: {cases[:5]}...\")\n", + "\n", + "case = adapter.load_case(cases[0])\n", + "print(f\"\\nCase: {case.case_id}\")\n", + "print(f\"Mesh path: {case.mesh_path}\")\n", + "print(f\"Mesh type: {case.mesh_type}\")\n", + "print(f\"Inference domain: {case.inference_domain}\")\n", + "print(f\"GT keys: {list(case.ground_truth.keys())}\")\n", + "print(f\"Pressure shape: {case.ground_truth['pressure'].shape}\")\n", + "print(f\"Shear stress shape: {case.ground_truth['shear_stress'].shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing Cases for Inference\n", + "\n", + "When `load_case` is called, the adapter's `_prepare_case` method transparently handles all format conversion: converting `.vtk` to `.vtp`, renaming fields to DrivAerML conventions, combining the three WSS scalars into a single vector, removing explicit normals/area arrays, and creating a triangulated STL for geometry encoding. Prepared files are cached in a `_prepared/` subdirectory so subsequent runs skip the conversion.\n", + "\n", + "Let's prepare a couple of cases and verify they're ready." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:09.865177Z", + "iopub.status.busy": "2026-04-17T00:08:09.865071Z", + "iopub.status.idle": "2026-04-17T00:08:11.814089Z", + "shell.execute_reply": "2026-04-17T00:08:11.813501Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Case 00003:\n", + " VTP: drivaerstar_data/_prepared/00003.vtp\n", + " STL: drivaerstar_data/_prepared/drivaer_3.stl\n", + " GT keys: ['pressure', 'shear_stress']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3671180/550963088.py:46: PyVistaFutureWarning: The default value of `algorithm` for the filter\n", + "`PolyData.extract_surface` will change in the future. It currently defaults to\n", + "`'dataset_surface'`, but will change to `None`. Explicitly set the `algorithm` keyword to\n", + "silence this warning.\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3671180/550963088.py:71: PyVistaFutureWarning: The default value of `algorithm` for the filter\n", + "`PolyData.extract_surface` will change in the future. It currently defaults to\n", + "`'dataset_surface'`, but will change to `None`. Explicitly set the `algorithm` keyword to\n", + "silence this warning.\n", + " mesh = pv.read(str(vtk_path)).extract_surface()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Case 00004:\n", + " VTP: drivaerstar_data/_prepared/00004.vtp\n", + " STL: drivaerstar_data/_prepared/drivaer_4.stl\n", + " GT keys: ['pressure', 'shear_stress']\n" + ] + } + ], + "source": [ + "INFERENCE_CASES = cases[:2]\n", + "\n", + "for case_id in INFERENCE_CASES:\n", + " case = adapter.load_case(case_id)\n", + " prepared_dir = os.path.join(DATA_DIR, \"_prepared\")\n", + " print(f\"Case {case_id}:\")\n", + " print(f\" VTP: {case.mesh_path}\")\n", + " print(f\" STL: {os.path.join(prepared_dir, f'drivaer_{int(case_id)}.stl')}\")\n", + " print(f\" GT keys: {list(case.ground_truth.keys())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Model Inference\n", + "\n", + "Since the adapter produces standard `CanonicalCase` objects, any registered model wrapper works without modification. Below we load both GeoTransolver and Transolver and run inference on the same DrivAerStar cases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:11.815564Z", + "iopub.status.busy": "2026-04-17T00:08:11.815454Z", + "iopub.status.idle": "2026-04-17T00:08:19.893968Z", + "shell.execute_reply": "2026-04-17T00:08:19.893497Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warp DeprecationWarning: The namespace `warp.context` will soon be removed from the public API. It can still be accessed from `warp._src.context` but might be changed or removed without notice.\n", + "Warp DeprecationWarning: The symbol `warp.context.Device` will soon be removed from the public API. Use `warp.Device` instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/library.py:357: UserWarning: Warning only once for all operators, other operators may also be overridden.\n", + " Overriding a previously registered kernel for the same operator and the same dispatch key\n", + " operator: flash_attn::_flash_attn_backward(Tensor dout, Tensor q, Tensor k, Tensor v, Tensor out, Tensor softmax_lse, Tensor(a6!)? dq, Tensor(a7!)? dk, Tensor(a8!)? dv, float dropout_p, float softmax_scale, bool causal, SymInt window_size_left, SymInt window_size_right, float softcap, Tensor? alibi_slopes, bool deterministic, Tensor? rng_state=None) -> Tensor\n", + " registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926\n", + " dispatch key: ADInplaceOrView\n", + " previous kernel: no debug info\n", + " new kernel: registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926 (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/core/dispatch/OperatorEntry.cpp:208.)\n", + " self.m.impl(\n", + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py:57: ExperimentalFeatureWarning: You are importing from 'physicsnemo.experimental'. The APIs in this namespace are experimental, under active development, and may change without notice. Expect possible back-compatibility breaking changes and only partial test coverage.\n", + " from physicsnemo.experimental.models.geotransolver import GeoTransolver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Loading surface normalization from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GeoTransolver surface model loaded.\n", + "[inference:transolver] Loading surface normalization from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint/global_stats.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Loading checkpoint from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Checkpoint loaded; model ready for inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transolver surface model loaded.\n" + ] + } + ], + "source": [ + "import time\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.geotransolver.wrapper import GeoTransolverWrapper\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.transolver.wrapper import TransolverWrapper\n", + "\n", + "BENCHMARK_MODELS = \"/lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models\"\n", + "\n", + "# Load GeoTransolver\n", + "geotransolver = GeoTransolverWrapper()\n", + "geotransolver.load(\n", + " checkpoint_path=f\"{BENCHMARK_MODELS}/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt\",\n", + " stats_path=f\"{BENCHMARK_MODELS}/geotransolver_drivaerml_surface_checkpoint/global_stats.json\",\n", + " device=\"cuda:0\",\n", + " batch_resolution=60000,\n", + " geometry_sampling=300000,\n", + ")\n", + "print(\"GeoTransolver surface model loaded.\")\n", + "\n", + "# Load Transolver\n", + "transolver = TransolverWrapper()\n", + "transolver.load(\n", + " checkpoint_path=f\"{BENCHMARK_MODELS}/transolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt\",\n", + " stats_path=f\"{BENCHMARK_MODELS}/transolver_drivaerml_surface_checkpoint/global_stats.json\",\n", + " device=\"cuda:0\",\n", + " batch_resolution=60000,\n", + " geometry_sampling=300000,\n", + ")\n", + "print(\"Transolver surface model loaded.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:19.895850Z", + "iopub.status.busy": "2026-04-17T00:08:19.895307Z", + "iopub.status.idle": "2026-04-17T00:08:25.040991Z", + "shell.execute_reply": "2026-04-17T00:08:25.040506Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running inference on case 00003...\n", + "[inference:geotransolver] Reading case inputs (case 00003): mesh drivaerstar_data/_prepared/00003.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GeoTransolver: inference took 2.5s\n", + " pressure: shape=(338342,), range=[-1628.4429, 652.9105]\n", + " shear_stress: shape=(338342, 3), range=[-13.0504, 10.6804]\n", + "[inference:transolver] Reading case inputs (case 00003): mesh drivaerstar_data/_prepared/00003.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Transolver: inference took 1.3s\n", + " pressure: shape=(338342,), range=[-1468.3743, 666.6595]\n", + " shear_stress: shape=(338342, 3), range=[-10.7803, 8.0008]\n", + "\n", + "Mesh has GT + predictions from both models: ['vtkOriginalCellIds', 'pMeanTrim', 'wallShearStressMeanTrim', 'pMeanTrimPred_GeoTransolver', 'wallShearStressMeanTrimPred_GeoTransolver', 'pMeanTrimPred_Transolver', 'wallShearStressMeanTrimPred_Transolver']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved combined mesh to drivaerstar_benchmark_results/predictions_comparison.vtp\n" + ] + } + ], + "source": [ + "case = adapter.load_case(INFERENCE_CASES[0])\n", + "vis_mesh = pv.read(case.mesh_path)\n", + "print(f\"Running inference on case {INFERENCE_CASES[0]}...\")\n", + "\n", + "for name, wrapper in [(\"GeoTransolver\", geotransolver), (\"Transolver\", transolver)]:\n", + " start = time.time()\n", + " model_input = wrapper.prepare_inputs(case)\n", + " raw_output = wrapper.predict(model_input)\n", + " predictions = wrapper.decode_outputs(raw_output, case)\n", + " elapsed = time.time() - start\n", + " print(f\"\\n {name}: inference took {elapsed:.1f}s\")\n", + " for k, v in predictions.items():\n", + " print(f\" {k}: shape={v.shape}, range=[{v.min():.4f}, {v.max():.4f}]\")\n", + "\n", + " vis_mesh.cell_data[f\"pMeanTrimPred_{name}\"] = predictions[\"pressure\"]\n", + " vis_mesh.cell_data[f\"wallShearStressMeanTrimPred_{name}\"] = predictions[\"shear_stress\"]\n", + "\n", + "print(f\"\\nMesh has GT + predictions from both models: {list(vis_mesh.cell_data.keys())}\")\n", + "\n", + "os.makedirs(\"drivaerstar_benchmark_results\", exist_ok=True)\n", + "vis_mesh.save(\"drivaerstar_benchmark_results/predictions_comparison.vtp\")\n", + "print(f\"Saved combined mesh to drivaerstar_benchmark_results/predictions_comparison.vtp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Full Benchmark\n", + "\n", + "With the adapter registered, we can use the full benchmarking engine. The configuration below runs both models in matrix mode against our DrivAerStar cases, computing L2 errors, area-weighted L2, and drag/lift metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:25.042313Z", + "iopub.status.busy": "2026-04-17T00:08:25.042223Z", + "iopub.status.idle": "2026-04-17T00:08:43.056350Z", + "shell.execute_reply": "2026-04-17T00:08:43.055819Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config created. Running matrix benchmark (GeoTransolver + Transolver)...\n", + "[dataset:benchmark] Distributed: rank 0/1 (no case sharding).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Listing cases under root 'drivaerstar_data' (split=None)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Loading 2 case(s) from root 'drivaerstar_data' (model 'geotransolver')…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Loading surface normalization from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/geotransolver_drivaerml_surface_checkpoint/global_stats.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Reading case '00003'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Reading case inputs (case 00003): mesh drivaerstar_data/_prepared/00003.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Reading case '00004'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Reading case inputs (case 00004): mesh drivaerstar_data/_prepared/00004.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:geotransolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Listing cases under root 'drivaerstar_data' (split=None)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Loading 2 case(s) from root 'drivaerstar_data' (model 'transolver')…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Loading surface normalization from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint/global_stats.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Loading checkpoint from /lustre/fsw/coreai_modulus_cae/rranade/modulus_dev/modulus_forked/ktangsali/physicsnemo-external-aero-checkpoints/benchmark_models/transolver_drivaerml_surface_checkpoint\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Checkpoint loaded; model ready for inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Reading case '00003'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Reading case inputs (case 00003): mesh drivaerstar_data/_prepared/00003.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerstar] Reading case '00004'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Reading case inputs (case 00004): mesh drivaerstar_data/_prepared/00004.vtp, run dir drivaerstar_data/_prepared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Running forward pass (predicting fields)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:transolver] Decoding outputs (pressure + WSS to numpy)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing artifacts to drivaerstar_benchmark_results/benchmark_artifacts.json…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing benchmark reports to drivaerstar_benchmark_results (formats: json, csv, html)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing drivaerstar_benchmark_results/benchmark_results.json…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing drivaerstar_benchmark_results/benchmark_results.csv…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing drivaerstar_benchmark_results/benchmark_results.html…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Benchmark complete.\n" + ] + } + ], + "source": [ + "from physicsnemo.cfd.evaluation.config import Config\n", + "from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark\n", + "\n", + "config = Config.from_dict({\n", + " \"run\": {\n", + " \"device\": \"cuda:0\",\n", + " \"output_dir\": \"drivaerstar_benchmark_results\",\n", + " \"save_inference_mesh\": False,\n", + " \"metrics_cache\": {\"enabled\": False},\n", + " },\n", + " \"benchmark\": {\n", + " \"mode\": \"matrix\",\n", + " \"models\": [\n", + " {\n", + " \"name\": \"geotransolver\",\n", + " \"inference_domain\": \"surface\",\n", + " \"checkpoint\": f\"{BENCHMARK_MODELS}/geotransolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt\",\n", + " \"stats_path\": f\"{BENCHMARK_MODELS}/geotransolver_drivaerml_surface_checkpoint/global_stats.json\",\n", + " \"kwargs\": {\"batch_resolution\": 60000, \"geometry_sampling\": 300000},\n", + " },\n", + " {\n", + " \"name\": \"transolver\",\n", + " \"inference_domain\": \"surface\",\n", + " \"checkpoint\": f\"{BENCHMARK_MODELS}/transolver_drivaerml_surface_checkpoint/checkpoint.0.501.pt\",\n", + " \"stats_path\": f\"{BENCHMARK_MODELS}/transolver_drivaerml_surface_checkpoint/global_stats.json\",\n", + " \"kwargs\": {\"batch_resolution\": 60000, \"geometry_sampling\": 300000},\n", + " },\n", + " ],\n", + " \"datasets\": [\n", + " {\n", + " \"name\": \"drivaerstar\",\n", + " \"root\": DATA_DIR,\n", + " \"case_ids\": INFERENCE_CASES,\n", + " },\n", + " ],\n", + " \"reproducibility\": {\"log_env\": False, \"save_artifacts\": True},\n", + " },\n", + " \"output\": {\n", + " \"mesh_field_names\": {\n", + " \"pressure\": \"pMeanTrimPred\",\n", + " \"shear_stress\": \"wallShearStressMeanTrimPred\",\n", + " },\n", + " \"ground_truth_mesh_field_names\": {\n", + " \"pressure\": \"pMeanTrim\",\n", + " \"shear_stress\": \"wallShearStressMeanTrim\",\n", + " },\n", + " },\n", + " \"metrics\": [\n", + " \"l2_pressure\",\n", + " \"l2_shear_stress\",\n", + " \"l2_pressure_area_weighted\",\n", + " \"drag\",\n", + " \"lift\",\n", + " ],\n", + " \"reports\": {\"enabled\": False},\n", + "})\n", + "\n", + "print(\"Config created. Running matrix benchmark (GeoTransolver + Transolver)...\")\n", + "results = run_benchmark(config)\n", + "print(\"Benchmark complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting Results\n", + "\n", + "The benchmark engine writes JSON, CSV, and HTML reports. Let's look at the aggregate and per-case metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:43.057610Z", + "iopub.status.busy": "2026-04-17T00:08:43.057518Z", + "iopub.status.idle": "2026-04-17T00:08:43.060530Z", + "shell.execute_reply": "2026-04-17T00:08:43.060143Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: geotransolver Dataset: drivaerstar\n", + "Cases: ['00003', '00004']\n", + "\n", + "Aggregate metrics (mean over cases):\n", + " l2_pressure: 0.672576\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.774378\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.920500\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.955074\n", + " l2_pressure_area_weighted: 0.618759\n", + " drag_error: 0.694101\n", + " drag_true: 567.510562\n", + " drag_pred: 172.750172\n", + " lift_error: 0.954487\n", + " lift_true: 312.986585\n", + " lift_pred: 34.423260\n", + "\n", + "Per-case metrics:\n", + " 00003:\n", + " l2_pressure: 0.638328\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.778262\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.909745\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.960261\n", + " l2_pressure_area_weighted: 0.595028\n", + " drag_error: 0.678798\n", + " drag_true: 511.913775\n", + " drag_pred: 164.427591\n", + " lift_error: 0.592434\n", + " lift_true: 368.719453\n", + " lift_pred: 150.277648\n", + " 00004:\n", + " l2_pressure: 0.706825\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.770494\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.931255\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.949886\n", + " l2_pressure_area_weighted: 0.642491\n", + " drag_error: 0.709404\n", + " drag_true: 623.107349\n", + " drag_pred: 181.072754\n", + " lift_error: 1.316540\n", + " lift_true: 257.253716\n", + " lift_pred: -81.431129\n", + "Model: transolver Dataset: drivaerstar\n", + "Cases: ['00003', '00004']\n", + "\n", + "Aggregate metrics (mean over cases):\n", + " l2_pressure: 0.727610\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.843247\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.953114\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.950007\n", + " l2_pressure_area_weighted: 0.669790\n", + " drag_error: 1.156173\n", + " drag_true: 567.510562\n", + " drag_pred: -91.170686\n", + " lift_error: 0.345251\n", + " lift_true: 312.986585\n", + " lift_pred: 312.295009\n", + "\n", + "Per-case metrics:\n", + " 00003:\n", + " l2_pressure: 0.700833\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.816437\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.938634\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.944347\n", + " l2_pressure_area_weighted: 0.655173\n", + " drag_error: 1.110467\n", + " drag_true: 511.913775\n", + " drag_pred: -56.549348\n", + " lift_error: 0.285982\n", + " lift_true: 368.719453\n", + " lift_pred: 263.272172\n", + " 00004:\n", + " l2_pressure: 0.754387\n", + " l2_shear_stress_wallShearStressMeanTrim_x_l2_error: 0.870057\n", + " l2_shear_stress_wallShearStressMeanTrim_y_l2_error: 0.967594\n", + " l2_shear_stress_wallShearStressMeanTrim_z_l2_error: 0.955668\n", + " l2_pressure_area_weighted: 0.684407\n", + " drag_error: 1.201879\n", + " drag_true: 623.107349\n", + " drag_pred: -125.792025\n", + " lift_error: 0.404519\n", + " lift_true: 257.253716\n", + " lift_pred: 361.317846\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "for run in results:\n", + " print(f\"Model: {run['model']} Dataset: {run['dataset']}\")\n", + " print(f\"Cases: {run['cases']}\")\n", + " print(f\"\\nAggregate metrics (mean over cases):\")\n", + " for metric, value in run[\"metrics\"].items():\n", + " print(f\" {metric}: {value:.6f}\")\n", + " print(f\"\\nPer-case metrics:\")\n", + " for row in run[\"per_case\"]:\n", + " print(f\" {row['case_id']}:\")\n", + " for k, v in row[\"metrics\"].items():\n", + " print(f\" {k}: {v:.6f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:43.061512Z", + "iopub.status.busy": "2026-04-17T00:08:43.061431Z", + "iopub.status.idle": "2026-04-17T00:08:43.064291Z", + "shell.execute_reply": "2026-04-17T00:08:43.063898Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- benchmark_results.json ---\n", + "[\n", + " {\n", + " \"model\": \"geotransolver\",\n", + " \"dataset\": \"drivaerstar\",\n", + " \"cases\": [\n", + " \"00003\",\n", + " \"00004\"\n", + " ],\n", + " \"metrics\": {\n", + " \"l2_pressure\": 0.6725762111660435,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_x_l2_error\": 0.774377988125845,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_y_l2_error\": 0.9205000720628097,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_z_l2_error\": 0.9550736258290897,\n", + " \"l2_pressure_area_weighted\": 0.6187594984309936,\n", + " \"drag_error\": 0.6941009332805836,\n", + " \"drag_true\": 567.5105623429952,\n", + " \"drag_pred\": 172.75017237772892,\n", + " \"lift_error\": 0.9544868894412777,\n", + " \"lift_true\": 312.98658475660886,\n", + " \"lift_pred\": 34.423259525050895\n", + " },\n", + " \"per_case\": [\n", + " {\n", + " \"case_id\": \"00003\",\n", + " \"metrics\": {\n", + " \"l2_pressure\": 0.638327806516917,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_x_l2_error\": 0.7782619816383675,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_y_l2_error\": 0.9097453152615977,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_z_l2_error\": 0.9602609447359678,\n", + " \"l2_pressure_area_weighted\": 0.5950282340718677,\n", + " \"drag_error\": 0.6787982684292804,\n", + " \"drag_true\": 511.91377546492026,\n", + " \"drag_pred\": 164.427591094237,\n", + " \"lift_error\": 0.5924336324981554,\n", + " \"lift_true\": 368.7194530644236,\n", + " \"lift_pred\": 150.27764811273403\n", + " },\n", + " \"metric_dtype\": \"cell\"\n", + " },\n", + " {\n", + " \"case_id\": \"00004\",\n", + " \"metrics\": {\n", + " \"l2_pressure\": 0.70682461581517,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_x_l2_error\": 0.7704939946133225,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_y_l2_error\": 0.9312548288640217,\n", + " \"l2_shear_stress_wallShearStressMeanTrim_z_l2_error\": 0.9498863069222118,\n", + " \"l2_pressure_area_weighted\": 0.6424907627901197,\n", + " \"drag_error\": 0.7094035981318868,\n", + " \"drag_true\": 623.1073492210701,\n", + " \"drag_pred\": 181.07275366122082,\n", + " \"li\n", + "\n", + "--- benchmark_results.csv ---\n", + "model,dataset,case_id,metric,value\n", + "geotransolver,drivaerstar,00003,l2_pressure,0.638327806516917\n", + "geotransolver,drivaerstar,00003,l2_shear_stress_wallShearStressMeanTrim_x_l2_error,0.7782619816383675\n", + "geotransolver,drivaerstar,00003,l2_shear_stress_wallShearStressMeanTrim_y_l2_error,0.9097453152615977\n", + "geotransolver,drivaerstar,00003,l2_shear_stress_wallShearStressMeanTrim_z_l2_error,0.9602609447359678\n", + "geotransolver,drivaerstar,00003,l2_pressure_area_weighted,0.5950282340718677\n", + "geotransolver,drivaerstar,00003,drag_error,0.6787982684292804\n", + "geotransolver,drivaerstar,00003,drag_true,511.91377546492026\n", + "geotransolver,drivaerstar,00003,drag_pred,164.427591094237\n", + "geotransolver,drivaerstar,00003,lift_error,0.5924336324981554\n", + "geotransolver,drivaerstar,00003,lift_true,368.7194530644236\n", + "geotransolver,drivaerstar,00003,lift_pred,150.27764811273403\n", + "geotransolver,drivaerstar,00004,l2_pressure,0.70682461581517\n", + "geotransolver,drivaerstar,00004,l2_shear_stress_wallShearStressMeanTrim_x_l2_error,0.7704939946133225\n", + "geotransolver,drivaerstar,00004,l2_shear_stress_wallShearStressMeanTrim_y_l2_error,0.9312548288640217\n", + "geotransolver,drivaerstar,00004,l2_shear_stress_wallShearStressMeanTrim_z_l2_error,0.9498863069222118\n", + "geotransolver,drivaerstar,00004,l2_pressure_area_weighted,0.6424907627901197\n", + "geotransolver,drivaerstar,00004,drag_error,0.7094035981318868\n", + "geotransolver,drivaerstar,00004,drag_true,623.1073492210701\n", + "geotransolver,drivaerstar,00004,drag_pred,181.07275366122082\n", + "geotransolver,drivaerstar,00004,lift_error,1.3165401463844\n", + "geotransolver,drivaerstar,00004,lift_true,257.2537164487942\n", + "geotransolver,drivaerstar,00004,lift_pred,-81.43112906263224\n", + "geotransolver,drivaerstar,,l2_pressure,0.6725762111660435\n", + "geotransolver,drivaerstar,,l2_shear_stress_wallShearStressMeanTrim_x_l2_error,0.774377988125845\n", + "geotransolver,drivaerstar,,l2_shear_stress_wallShearStressMeanTrim_y_l2_error,0.9205000720628097\n", + "geotransolver,drivaerstar,,l2_shear_stress_wallShearStressMeanTrim_z_l2_error,0.9550736258290897\n", + "\n" + ] + } + ], + "source": [ + "# The benchmark also wrote JSON/CSV results\n", + "results_dir = \"drivaerstar_benchmark_results\"\n", + "for f in [\"benchmark_results.json\", \"benchmark_results.csv\"]:\n", + " path = os.path.join(results_dir, f)\n", + " if os.path.exists(path):\n", + " print(f\"\\n--- {f} ---\")\n", + " with open(path) as fh:\n", + " print(fh.read()[:2000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numerical metrics give an aggregate picture, but visualizing the fields reveals where the models agree and disagree with the ground truth. Below we plot the pressure field for both models alongside the reference solution.\n", + "\n", + "---\n", + "**NOTE**\n", + "\n", + "The L2 errors above are expected to be higher than typical in-distribution results. The models (GeoTransolver, Transolver) were trained on the DrivAerML dataset, which was generated using Hybrid RANS-LES (HRLES) simulations. DrivAerStar, on the other hand, is a steady-state RANS dataset. The differences in turbulence modeling between the two simulation methodologies lead to systematic discrepancies in the flow fields — particularly in turbulent quantities such as wall shear stress. This cross-methodology gap is inherent and does not necessarily reflect the model's predictive capability on in-distribution data.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-17T00:08:43.065261Z", + "iopub.status.busy": "2026-04-17T00:08:43.065179Z", + "iopub.status.idle": "2026-04-17T00:08:50.695237Z", + "shell.execute_reply": "2026-04-17T00:08:50.694709Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/postprocessing_tools/visualization/utils.py:26: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3671180/794569742.py:4: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-16 17:08:49.304 ( 47.592s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-16 17:08:49.372 ( 47.660s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved drivaerstar_benchmark_results/field_comparison_GeoTransolver.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-16 17:08:50.090 ( 48.378s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved drivaerstar_benchmark_results/field_comparison_Transolver.png\n", + "\n", + "--- GeoTransolver ---\n" + ] + }, + { + "data": { + 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_field_comparisons\n", + "from IPython.display import Image\n", + "\n", + "pv.start_xvfb()\n", + "\n", + "for name in [\"GeoTransolver\", \"Transolver\"]:\n", + " plotter = plot_field_comparisons(\n", + " vis_mesh,\n", + " true_fields=[\"pMeanTrim\"],\n", + " pred_fields=[f\"pMeanTrimPred_{name}\"],\n", + " view=\"xy\",\n", + " dtype=\"cell\",\n", + " window_size=[1800, 600],\n", + " )\n", + " out_path = f\"drivaerstar_benchmark_results/field_comparison_{name}.png\"\n", + " os.makedirs(\"drivaerstar_benchmark_results\", exist_ok=True)\n", + " plotter.screenshot(out_path)\n", + " plotter.close()\n", + " print(f\"Saved {out_path}\")\n", + "\n", + "print(\"\\n--- GeoTransolver ---\")\n", + "display(Image(filename=\"drivaerstar_benchmark_results/field_comparison_GeoTransolver.png\"))\n", + "print(\"\\n--- Transolver ---\")\n", + "display(Image(filename=\"drivaerstar_benchmark_results/field_comparison_Transolver.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps\n", + "\n", + "That completes the dataset adapter tutorial. We downloaded DrivAerStar data, wrote an adapter to handle its format differences (legacy VTK, separate WSS scalars, missing STLs, explicit normals), ran GeoTransolver and Transolver inference, and evaluated both models with the benchmarking engine.\n", + "\n", + "The key insight is that all the dataset-specific complexity lives inside the adapter. Once it produces standard `CanonicalCase` objects, every model wrapper and metric works without modification.\n", + "\n", + "Note that the field renames, sign flips, and format conversions we performed were necessary because the model checkpoints were trained on DrivAerML, which follows specific naming and sign conventions. The adapter bridges the gap between the new dataset's conventions and the training data's conventions. If a model were trained directly on DrivAerStar data, these transformations would not be needed — the adapter would simply expose the native fields as-is.\n", + "\n", + "To make the adapter permanent, save the class to `physicsnemo/cfd/evaluation/datasets/adapters/drivaerstar.py` and register it in `adapters/__init__.py`:\n", + "\n", + "```python\n", + "from physicsnemo.cfd.evaluation.datasets.adapters.drivaerstar import DrivAerStarAdapter\n", + "register_adapter(\"drivaerstar\", DrivAerStarAdapter)\n", + "```\n", + "\n", + "Then use `dataset.name: drivaerstar` in any YAML config. For other datasets, the same pattern applies — explore the data, identify the differences from the canonical schema, and write an adapter to bridge them." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 101f476f6edd1c3ef97f43fefaf6a15e406755c7 Mon Sep 17 00:00:00 2001 From: Kaustubh Tangsali Date: Tue, 21 Apr 2026 09:09:33 -0700 Subject: [PATCH 25/25] add notebook showing adding of a new model --- .cursor/skills/create-model-wrapper/SKILL.md | 228 ++++ workflows/benchmarking_workflow/AGENTS.md | 9 + .../notebooks/adding_a_new_model.ipynb | 1014 +++++++++++++++++ 3 files changed, 1251 insertions(+) create mode 100644 .cursor/skills/create-model-wrapper/SKILL.md create mode 100644 workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb diff --git a/.cursor/skills/create-model-wrapper/SKILL.md b/.cursor/skills/create-model-wrapper/SKILL.md new file mode 100644 index 0000000..4c84740 --- /dev/null +++ b/.cursor/skills/create-model-wrapper/SKILL.md @@ -0,0 +1,228 @@ +--- +name: create-model-wrapper +description: >- + Create a new model wrapper for the PhysicsNeMo CFD benchmarking workflow. + Use when the user wants to add a new CFD model, write a CFDModel wrapper, + integrate a new neural network architecture, or run a custom model through + the benchmarking pipeline. +--- + +# Create a Model Wrapper + +Guide the user through adding a new CFD model to the benchmarking workflow by writing a `CFDModel` subclass. + +## Reference files to read first + +Before starting, read these files for context: + +- `physicsnemo/cfd/evaluation/inference/model_registry.py` — base class and registry +- `physicsnemo/cfd/evaluation/datasets/schema.py` — `CanonicalCase`, `predictions_dict`, `build_predictions_dict` +- `physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py` — simplest concrete wrapper +- `physicsnemo/cfd/evaluation/inference/wrappers/__init__.py` — how wrappers are registered +- `physicsnemo/cfd/evaluation/common/io.py` — mesh loading and normalization stats helpers +- `workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb` — end-to-end tutorial + +## The `CFDModel` interface + +Every wrapper must set two class variables and implement four methods: + +| Member | Purpose | +|--------|---------| +| `INFERENCE_DOMAIN` | `"surface"` or `"volume"` — which mesh manifold | +| `OUTPUT_LOCATION` | `"point"` or `"cell"` — where predictions live on the mesh | +| `output_location` (property) | Instance-level access to `OUTPUT_LOCATION` | +| `load(checkpoint_path, stats_path, device, **kwargs)` | Load weights and stats; return `self` | +| `prepare_inputs(case: CanonicalCase)` | Convert canonical case into model-specific tensors/graphs | +| `predict(model_input)` | Run forward pass; return raw output | +| `decode_outputs(raw_output, case)` | Denormalize and map to canonical predictions dict | + +The engine calls `load` once, then `prepare_inputs → predict → decode_outputs` per case. + +## Step 1: Write the wrapper class + +```python +import numpy as np +import torch +import pyvista as pv +from typing import Any, ClassVar + +from physicsnemo.cfd.evaluation.inference.model_registry import ( + CFDModel, register_model, OutputLocation, +) +from physicsnemo.cfd.evaluation.datasets.schema import ( + CanonicalCase, InferenceDomain, predictions_dict, +) +from physicsnemo.cfd.evaluation.inference.progress import log_inference + + +class MyModelWrapper(CFDModel): + INFERENCE_DOMAIN: ClassVar[InferenceDomain] = "surface" # or "volume" + OUTPUT_LOCATION: ClassVar[OutputLocation] = "cell" # or "point" + + def __init__(self) -> None: + self._model = None + self._stats = None + self._device = "cpu" + + @property + def output_location(self) -> OutputLocation: + return self.OUTPUT_LOCATION + + def load(self, checkpoint_path, stats_path, device, **kwargs): + self._device = device + # Load your model architecture + weights + # self._model = ... + # Load normalization stats if needed + # self._stats = ... + log_inference("my_model", f"Loaded from {checkpoint_path}") + return self + + def prepare_inputs(self, case: CanonicalCase): + mesh = pv.read(case.mesh_path) + if not isinstance(mesh, pv.PolyData): + mesh = mesh.extract_surface() + # Extract coordinates and build model-specific input + # (tensors, graphs, point clouds, etc.) + coords = np.array(mesh.cell_centers().points, dtype=np.float32) + return torch.tensor(coords, device=self._device) + + def predict(self, model_input): + # Run forward pass through your model + with torch.no_grad(): + raw_output = self._model(model_input) + return raw_output + + def decode_outputs(self, raw_output, case): + # Denormalize if needed, then return canonical dict + # For surface models: + return predictions_dict( + pressure=raw_output["pressure"].cpu().numpy(), + shear_stress=raw_output["shear_stress"].cpu().numpy(), + ) + # For volume models, use build_predictions_dict: + # return build_predictions_dict( + # pressure=..., velocity=..., turbulent_viscosity=... + # ) +``` + +### Key implementation considerations + +**Normalization**: Most trained models normalize inputs/outputs. Load stats from `stats_path` in `load()` and denormalize in `decode_outputs()`. See `physicsnemo/cfd/evaluation/common/io.py` for `load_global_stats()` and related helpers. + +**Batching**: For large meshes, `prepare_inputs` may need to subsample or batch. Use `kwargs` passed through `load()` (e.g., `batch_resolution`, `geometry_sampling`) to control this. + +**Output shape**: `pressure` must be `(N,)` float32. `shear_stress` must be `(N, 3)` float32 for surface. Volume fields: `velocity` is `(N, 3)`, `turbulent_viscosity` is `(N,)`. + +**Output location**: If `OUTPUT_LOCATION = "cell"`, return N = `mesh.n_cells` values. If `"point"`, return N = `mesh.n_points` values. + +## Step 2: Create checkpoint and stats files + +Your model needs a checkpoint file and optionally a `global_stats.json`: + +```python +# Checkpoint: save your model's state dict +torch.save(model.state_dict(), "checkpoint.pt") + +# Stats: JSON with mean/std_dev for denormalization +# Surface format: +{ + "mean": {"pressure": [0.0], "shear_stress": [0.0, 0.0, 0.0]}, + "std_dev": {"pressure": [1.0], "shear_stress": [1.0, 1.0, 1.0]} +} +# Volume format: +{ + "mean": {"pressure": [0.0], "velocity": [0.0, 0.0, 0.0], "turbulent_viscosity": [0.0]}, + "std_dev": {"pressure": [1.0], "velocity": [1.0, 1.0, 1.0], "turbulent_viscosity": [1.0]} +} +``` + +## Step 3: Register and test + +```python +register_model("my_model", MyModelWrapper) + +# Load a case from any registered dataset adapter +from physicsnemo.cfd.evaluation.datasets.adapters.drivaerml import DrivAerMLAdapter +adapter = DrivAerMLAdapter(root="/path/to/data", inference_domain="surface") +case = adapter.load_case(adapter.list_cases()[0]) + +# Run the full inference pipeline +wrapper = MyModelWrapper() +wrapper.load(checkpoint_path="checkpoint.pt", stats_path="global_stats.json", device="cuda:0") +model_input = wrapper.prepare_inputs(case) +raw_output = wrapper.predict(model_input) +predictions = wrapper.decode_outputs(raw_output, case) + +assert "pressure" in predictions +assert predictions["pressure"].shape[0] > 0 +``` + +## Step 4: Run the full benchmark + +```python +from physicsnemo.cfd.evaluation.config import Config +from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark + +config = Config.from_dict({ + "run": {"device": "cuda:0", "output_dir": "results", "metrics_cache": {"enabled": False}}, + "benchmark": { + "mode": "matrix", + "models": [{ + "name": "my_model", + "inference_domain": "surface", + "checkpoint": "/path/to/checkpoint.pt", + "stats_path": "/path/to/global_stats.json", + "kwargs": {}, + }], + "datasets": [{ + "name": "drivaerml", + "root": "/path/to/drivaerml/data", + "case_ids": ["run_1", "run_11"], + "kwargs": {"align_ground_truth_to_model": True, "inference_domain": "surface"}, + }], + "reproducibility": {"log_env": False, "save_artifacts": True}, + }, + "output": {"mesh_field_names": {"pressure": "pMeanTrimPred", "shear_stress": "wallShearStressMeanTrimPred"}}, + "metrics": ["l2_pressure", "l2_shear_stress", "l2_pressure_area_weighted", "drag", "lift"], + "reports": {"enabled": False}, +}) +results = run_benchmark(config) +``` + +Results are written to `benchmark_results.json` (a JSON list of dicts, one per model×dataset combo). + +## Step 5: Visualize predictions + +```python +from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields, plot_field_comparisons + +# Just the predicted fields (no GT comparison): +plotter = plot_fields(mesh, fields=["pMeanTrimPred"], view="xy", dtype="cell", window_size=[1800, 600]) +plotter.screenshot("predicted_pressure.png") +plotter.close() + +# Side-by-side with GT (GT | Pred | Error): +plotter = plot_field_comparisons(mesh, true_fields=["pMeanTrim"], pred_fields=["pMeanTrimPred"], + view="xy", dtype="cell", window_size=[1800, 600]) +plotter.screenshot("comparison.png") +plotter.close() +``` + +## Step 6: Make permanent (optional) + +Save the wrapper to `physicsnemo/cfd/evaluation/inference/wrappers/my_model.py` and register in `wrappers/__init__.py`: + +```python +from physicsnemo.cfd.evaluation.inference.wrappers.my_model import MyModelWrapper +register_model("my_model", MyModelWrapper) +``` + +Then use `model.name: my_model` in any YAML config. + +## Gotchas + +- **DistributedManager**: Model wrappers may call `DistributedManager.initialize()`. In notebooks without `torchrun`, set env vars first: `WORLD_SIZE=1`, `RANK=0`, `LOCAL_RANK=0`, `MASTER_ADDR=localhost`, `MASTER_PORT=12355`. +- **`weights_only=True`**: Use this flag with `torch.load()` for safe deserialization (PyTorch 2.6+ default). +- **Domain matching**: The engine checks that `model.INFERENCE_DOMAIN` matches the dataset adapter's `inference_domain_from_kwargs()`. Mismatches are skipped in matrix mode or raise in single mode. +- **GT alignment**: When `align_ground_truth_to_model: true` in dataset kwargs, the engine converts GT data to match `OUTPUT_LOCATION` (point ↔ cell). This is automatic — the wrapper just needs correct class vars. +- **Results JSON format**: `benchmark_results.json` is a plain `list[dict]`, not `{"results": [...]}`. Iterate directly: `for combo in report:`. diff --git a/workflows/benchmarking_workflow/AGENTS.md b/workflows/benchmarking_workflow/AGENTS.md index 5eb4739..179d452 100644 --- a/workflows/benchmarking_workflow/AGENTS.md +++ b/workflows/benchmarking_workflow/AGENTS.md @@ -13,6 +13,15 @@ Key reference files: - `physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py` — reference implementation - `workflows/benchmarking_workflow/notebooks/adding_a_new_dataset.ipynb` — end-to-end tutorial +### Creating a New Model Wrapper + +When the user wants to add a new CFD model, integrate a new neural network architecture, or run a custom model through the benchmarking pipeline, follow the instructions in [`.cursor/skills/create-model-wrapper/SKILL.md`](../../.cursor/skills/create-model-wrapper/SKILL.md). + +Key reference files: +- `physicsnemo/cfd/evaluation/inference/model_registry.py` — `CFDModel` base class +- `physicsnemo/cfd/evaluation/inference/wrappers/surface_baseline.py` — simplest reference implementation +- `workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb` — end-to-end tutorial + ## Codebase Conventions - **Canonical keys**: Surface fields use `pressure` and `shear_stress`. Volume fields use `pressure`, `velocity`, `turbulent_viscosity`. The same key `pressure` is used for both domains — the metric registry resolves the correct implementation via `domain` scoping. diff --git a/workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb b/workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb new file mode 100644 index 0000000..51ad060 --- /dev/null +++ b/workflows/benchmarking_workflow/notebooks/adding_a_new_model.ipynb @@ -0,0 +1,1014 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:07.459739Z", + "iopub.status.busy": "2026-04-21T06:11:07.459660Z", + "iopub.status.idle": "2026-04-21T06:11:07.461807Z", + "shell.execute_reply": "2026-04-21T06:11:07.461476Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"WORLD_SIZE\"] = \"1\"\n", + "os.environ[\"RANK\"] = \"0\"\n", + "os.environ[\"LOCAL_RANK\"] = \"0\"\n", + "os.environ[\"MASTER_ADDR\"] = \"localhost\"\n", + "os.environ[\"MASTER_PORT\"] = \"12355\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding a New Model Wrapper to the Benchmarking Workflow\n", + "\n", + "The PhysicsNeMo benchmarking workflow uses **model wrappers** to decouple models from datasets and metrics. Each wrapper implements the `CFDModel` interface — four methods that handle loading weights, preparing inputs, running inference, and decoding outputs into the canonical schema.\n", + "\n", + "In this notebook we walk through the process by building a toy **SineCosine** model wrapper. The model applies simple trigonometric transformations to mesh coordinates to produce (obviously wrong) pressure and wall shear stress predictions. Despite being trivially simple, this exercises every part of the wrapper contract and runs end-to-end through the benchmarking engine against real DrivAerML data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `CFDModel` Interface\n", + "\n", + "Every model wrapper subclasses `CFDModel` (defined in `physicsnemo.cfd.evaluation.inference.model_registry`) and provides:\n", + "\n", + "| Member | Role |\n", + "|--------|------|\n", + "| `INFERENCE_DOMAIN` (class var) | `\"surface\"` or `\"volume\"` — which mesh manifold the model operates on |\n", + "| `OUTPUT_LOCATION` (class var) | `\"point\"` or `\"cell\"` — where predictions live on the mesh |\n", + "| `output_location` (property) | Instance-level access to output location |\n", + "| `load(checkpoint_path, stats_path, device, **kwargs)` | Load weights and normalization stats; return `self` |\n", + "| `prepare_inputs(case: CanonicalCase)` | Convert canonical case into model-specific tensors |\n", + "| `predict(model_input)` | Run forward pass; return raw model output |\n", + "| `decode_outputs(raw_output, case)` | Denormalize and map to canonical predictions dict (`pressure`, `shear_stress`) |\n", + "\n", + "The engine calls these in sequence: `load` once, then `prepare_inputs → predict → decode_outputs` per case. The wrapper never needs to know which dataset adapter produced the `CanonicalCase`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Writing the SineCosine Model Wrapper\n", + "\n", + "Our dummy model will:\n", + "\n", + "1. **Load** a set of scalar parameters (frequencies, scale, bias) from a `.pt` checkpoint\n", + "2. **Prepare inputs** by extracting mesh cell-center coordinates as a `(N, 3)` tensor\n", + "3. **Predict** pressure and wall shear stress using products of sin/cos at two frequency scales\n", + "4. **Decode** by moving tensors to numpy and wrapping them in the canonical `predictions_dict`\n", + "\n", + "This is intentionally minimal — a real wrapper would load a neural network, handle batching, and apply learned normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:07.462964Z", + "iopub.status.busy": "2026-04-21T06:11:07.462885Z", + "iopub.status.idle": "2026-04-21T06:11:21.038072Z", + "shell.execute_reply": "2026-04-21T06:11:21.037439Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warp DeprecationWarning: The namespace `warp.context` will soon be removed from the public API. It can still be accessed from `warp._src.context` but might be changed or removed without notice.\n", + "Warp DeprecationWarning: The symbol `warp.context.Device` will soon be removed from the public API. Use `warp.Device` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/library.py:357: UserWarning: Warning only once for all operators, other operators may also be overridden.\n", + " Overriding a previously registered kernel for the same operator and the same dispatch key\n", + " operator: flash_attn::_flash_attn_backward(Tensor dout, Tensor q, Tensor k, Tensor v, Tensor out, Tensor softmax_lse, Tensor(a6!)? dq, Tensor(a7!)? dk, Tensor(a8!)? dv, float dropout_p, float softmax_scale, bool causal, SymInt window_size_left, SymInt window_size_right, float softcap, Tensor? alibi_slopes, bool deterministic, Tensor? rng_state=None) -> Tensor\n", + " registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926\n", + " dispatch key: ADInplaceOrView\n", + " previous kernel: no debug info\n", + " new kernel: registered at /usr/local/lib/python3.12/dist-packages/torch/_library/custom_ops.py:926 (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/core/dispatch/OperatorEntry.cpp:208.)\n", + " self.m.impl(\n", + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/evaluation/inference/wrappers/geotransolver/wrapper.py:57: ExperimentalFeatureWarning: You are importing from 'physicsnemo.experimental'. The APIs in this namespace are experimental, under active development, and may change without notice. Expect possible back-compatibility breaking changes and only partial test coverage.\n", + " from physicsnemo.experimental.models.geotransolver import GeoTransolver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registered 'sine_cosine' model wrapper.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "import pyvista as pv\n", + "\n", + "from physicsnemo.cfd.evaluation.inference.model_registry import (\n", + " CFDModel, register_model, OutputLocation,\n", + ")\n", + "from physicsnemo.cfd.evaluation.datasets.schema import (\n", + " CanonicalCase, InferenceDomain, predictions_dict,\n", + ")\n", + "from physicsnemo.cfd.evaluation.inference.progress import log_inference\n", + "\n", + "from typing import Any, ClassVar\n", + "\n", + "\n", + "class SineCosineWrapper(CFDModel):\n", + " \"\"\"Toy model wrapper: trigonometric transforms on mesh coordinates.\n", + "\n", + " Loads scalar parameters from a .pt checkpoint and applies sin/cos\n", + " to cell-center coordinates to produce dummy pressure and WSS fields.\n", + " Useful for testing the wrapper contract and benchmarking pipeline.\n", + " \"\"\"\n", + "\n", + " INFERENCE_DOMAIN: ClassVar[InferenceDomain] = \"surface\"\n", + " OUTPUT_LOCATION: ClassVar[OutputLocation] = \"cell\"\n", + "\n", + " def __init__(self) -> None:\n", + " self._weights: dict[str, torch.Tensor] | None = None\n", + " self._device: str = \"cpu\"\n", + "\n", + " @property\n", + " def output_location(self) -> OutputLocation:\n", + " return self.OUTPUT_LOCATION\n", + "\n", + " def load(\n", + " self,\n", + " checkpoint_path: str,\n", + " stats_path: str,\n", + " device: str,\n", + " **kwargs: Any,\n", + " ) -> \"SineCosineWrapper\":\n", + " self._device = device\n", + " self._weights = torch.load(\n", + " checkpoint_path, map_location=device, weights_only=True,\n", + " )\n", + " log_inference(\n", + " \"sine_cosine\",\n", + " f\"Loaded {len(self._weights)} parameters from {checkpoint_path}\",\n", + " )\n", + " return self\n", + "\n", + " def prepare_inputs(self, case: CanonicalCase) -> torch.Tensor:\n", + " mesh = pv.read(case.mesh_path)\n", + " if not isinstance(mesh, pv.PolyData):\n", + " mesh = mesh.extract_surface()\n", + " coords = np.array(mesh.cell_centers().points, dtype=np.float32)\n", + " log_inference(\n", + " \"sine_cosine\",\n", + " f\"Prepared {coords.shape[0]} cell-center coordinates for case {case.case_id}\",\n", + " )\n", + " return torch.tensor(coords, device=self._device)\n", + "\n", + " def predict(self, model_input: torch.Tensor) -> dict[str, torch.Tensor]:\n", + " w = self._weights\n", + " x, y, z = model_input[:, 0], model_input[:, 1], model_input[:, 2]\n", + "\n", + " pressure = (\n", + " w[\"p_scale\"]\n", + " * torch.sin(w[\"p_freq_lo\"] * (x + y + z))\n", + " * torch.cos(w[\"p_freq_hi\"] * x)\n", + " * torch.sin(w[\"p_freq_hi\"] * z)\n", + " + w[\"p_bias\"]\n", + " )\n", + "\n", + " wss_x = (\n", + " w[\"wss_scale\"]\n", + " * torch.sin(w[\"wss_freq_lo\"] * x)\n", + " * torch.cos(w[\"wss_freq_hi\"] * (y - z))\n", + " )\n", + " wss_y = (\n", + " w[\"wss_scale\"]\n", + " * torch.cos(w[\"wss_freq_lo\"] * y)\n", + " * torch.sin(w[\"wss_freq_hi\"] * (x + z))\n", + " )\n", + " wss_z = (\n", + " w[\"wss_scale\"]\n", + " * torch.sin(w[\"wss_freq_lo\"] * (x + y))\n", + " * torch.cos(w[\"wss_freq_hi\"] * z)\n", + " )\n", + " wss = torch.stack([wss_x, wss_y, wss_z], dim=1)\n", + "\n", + " log_inference(\"sine_cosine\", \"Forward pass complete (sin/cos transforms).\")\n", + " return {\"pressure\": pressure, \"shear_stress\": wss}\n", + "\n", + " def decode_outputs(\n", + " self, raw_output: dict[str, torch.Tensor], case: CanonicalCase,\n", + " ) -> dict[str, np.ndarray]:\n", + " log_inference(\"sine_cosine\", \"Decoding outputs to numpy.\")\n", + " return predictions_dict(\n", + " pressure=raw_output[\"pressure\"].cpu().numpy(),\n", + " shear_stress=raw_output[\"shear_stress\"].cpu().numpy(),\n", + " )\n", + "\n", + "\n", + "register_model(\"sine_cosine\", SineCosineWrapper)\n", + "print(\"Registered 'sine_cosine' model wrapper.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Dummy Weights\n", + "\n", + "Real model wrappers load trained neural network checkpoints. For our toy model, we create a simple `.pt` file containing the scalar parameters that control the trigonometric transforms. We also create a minimal `global_stats.json` — the `load` method receives `stats_path` but our toy model ignores it. Real wrappers use it for denormalization." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:21.039742Z", + "iopub.status.busy": "2026-04-21T06:11:21.039367Z", + "iopub.status.idle": "2026-04-21T06:11:21.044768Z", + "shell.execute_reply": "2026-04-21T06:11:21.044319Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checkpoint: /tmp/sine_cosine_model_quw_qdm8/checkpoint.pt\n", + "Stats: /tmp/sine_cosine_model_quw_qdm8/global_stats.json\n", + "Parameters: ['p_scale', 'p_freq_lo', 'p_freq_hi', 'p_bias', 'wss_scale', 'wss_freq_lo', 'wss_freq_hi']\n" + ] + } + ], + "source": [ + "import tempfile\n", + "import json\n", + "\n", + "weights = {\n", + " \"p_scale\": torch.tensor(200.0),\n", + " \"p_freq_lo\": torch.tensor(0.8),\n", + " \"p_freq_hi\": torch.tensor(3.0),\n", + " \"p_bias\": torch.tensor(-50.0),\n", + " \"wss_scale\": torch.tensor(8.0),\n", + " \"wss_freq_lo\": torch.tensor(1.2),\n", + " \"wss_freq_hi\": torch.tensor(4.0),\n", + "}\n", + "\n", + "CKPT_DIR = tempfile.mkdtemp(prefix=\"sine_cosine_model_\")\n", + "CKPT_PATH = os.path.join(CKPT_DIR, \"checkpoint.pt\")\n", + "STATS_PATH = os.path.join(CKPT_DIR, \"global_stats.json\")\n", + "\n", + "torch.save(weights, CKPT_PATH)\n", + "\n", + "dummy_stats = {\n", + " \"mean\": {\"pressure\": [0.0], \"shear_stress\": [0.0, 0.0, 0.0]},\n", + " \"std_dev\": {\"pressure\": [1.0], \"shear_stress\": [1.0, 1.0, 1.0]},\n", + "}\n", + "with open(STATS_PATH, \"w\") as f:\n", + " json.dump(dummy_stats, f, indent=2)\n", + "\n", + "print(f\"Checkpoint: {CKPT_PATH}\")\n", + "print(f\"Stats: {STATS_PATH}\")\n", + "print(f\"Parameters: {list(weights.keys())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading a DrivAerML Case\n", + "\n", + "Since model wrappers consume `CanonicalCase` objects, we can use any registered dataset adapter. Below we use the built-in DrivAerML adapter to load a real case — the same data that production models like GeoTransolver and Transolver are evaluated against." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:21.045892Z", + "iopub.status.busy": "2026-04-21T06:11:21.045800Z", + "iopub.status.idle": "2026-04-21T06:11:25.143109Z", + "shell.execute_reply": "2026-04-21T06:11:25.142532Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DrivAerML has 24 cases. First few: ['run_1', 'run_10', 'run_11', 'run_12', 'run_13']\n", + "[dataset:drivaerml] load_case('run_1'): branch='surface', root=/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading surface mesh from /lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune/run_1/boundary_1.vtp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Case: run_1\n", + "Mesh path: /lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune/run_1/boundary_1.vtp\n", + "Mesh type: cell\n", + "Inference domain: surface\n", + "GT keys: ['pressure', 'shear_stress']\n", + " pressure: shape=(8828095,)\n", + " shear_stress: shape=(8828095, 3)\n" + ] + } + ], + "source": [ + "from physicsnemo.cfd.evaluation.datasets.adapters.drivaerml import DrivAerMLAdapter\n", + "\n", + "DRIVAERML_ROOT = \"/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune\"\n", + "\n", + "adapter = DrivAerMLAdapter(root=DRIVAERML_ROOT, inference_domain=\"surface\")\n", + "all_cases = adapter.list_cases()\n", + "print(f\"DrivAerML has {len(all_cases)} cases. First few: {all_cases[:5]}\")\n", + "\n", + "INFERENCE_CASES = [\"run_1\", \"run_11\"]\n", + "case = adapter.load_case(INFERENCE_CASES[0])\n", + "print(f\"\\nCase: {case.case_id}\")\n", + "print(f\"Mesh path: {case.mesh_path}\")\n", + "print(f\"Mesh type: {case.mesh_type}\")\n", + "print(f\"Inference domain: {case.inference_domain}\")\n", + "if case.ground_truth:\n", + " print(f\"GT keys: {list(case.ground_truth.keys())}\")\n", + " for k, v in case.ground_truth.items():\n", + " print(f\" {k}: shape={v.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Inference Step by Step\n", + "\n", + "The engine calls four methods in sequence. Let's trace each step to see exactly what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:25.144534Z", + "iopub.status.busy": "2026-04-21T06:11:25.144410Z", + "iopub.status.idle": "2026-04-21T06:11:34.247772Z", + "shell.execute_reply": "2026-04-21T06:11:34.247195Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Loaded 7 parameters from /tmp/sine_cosine_model_quw_qdm8/checkpoint.pt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] load_case('run_1'): branch='surface', root=/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading surface mesh from /lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune/run_1/boundary_1.vtp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Case run_1 ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Prepared 8828095 cell-center coordinates for case run_1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "prepare_inputs -> tensor shape: torch.Size([8828095, 3]), device: cuda:0\n", + " coordinate ranges: x=[-0.77, 3.94], y=[-1.01, 1.01], z=[-0.32, 1.20]\n", + "[inference:sine_cosine] Forward pass complete (sin/cos transforms).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "predict -> keys: ['pressure', 'shear_stress']\n", + " pressure: shape=torch.Size([8828095]), range=[-238.0141, 147.8146]\n", + " shear_stress: shape=torch.Size([8828095, 3]), range=[-8.0000, 8.0000]\n", + "[inference:sine_cosine] Decoding outputs to numpy.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "decode_outputs -> keys: ['pressure', 'shear_stress']\n", + " pressure: shape=(8828095,), dtype=float32\n", + " shear_stress: shape=(8828095, 3), dtype=float32\n", + "\n", + "Total inference time: 4.963s\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "wrapper = SineCosineWrapper()\n", + "wrapper.load(checkpoint_path=CKPT_PATH, stats_path=STATS_PATH, device=\"cuda:0\")\n", + "\n", + "case = adapter.load_case(INFERENCE_CASES[0])\n", + "print(f\"--- Case {case.case_id} ---\")\n", + "\n", + "start = time.time()\n", + "\n", + "# Step 1: prepare_inputs\n", + "model_input = wrapper.prepare_inputs(case)\n", + "print(f\"\\nprepare_inputs -> tensor shape: {model_input.shape}, device: {model_input.device}\")\n", + "print(f\" coordinate ranges: x=[{model_input[:,0].min():.2f}, {model_input[:,0].max():.2f}], \"\n", + " f\"y=[{model_input[:,1].min():.2f}, {model_input[:,1].max():.2f}], \"\n", + " f\"z=[{model_input[:,2].min():.2f}, {model_input[:,2].max():.2f}]\")\n", + "\n", + "# Step 2: predict\n", + "raw_output = wrapper.predict(model_input)\n", + "print(f\"\\npredict -> keys: {list(raw_output.keys())}\")\n", + "for k, v in raw_output.items():\n", + " print(f\" {k}: shape={v.shape}, range=[{v.min():.4f}, {v.max():.4f}]\")\n", + "\n", + "# Step 3: decode_outputs\n", + "predictions = wrapper.decode_outputs(raw_output, case)\n", + "elapsed = time.time() - start\n", + "print(f\"\\ndecode_outputs -> keys: {list(predictions.keys())}\")\n", + "for k, v in predictions.items():\n", + " print(f\" {k}: shape={v.shape}, dtype={v.dtype}\")\n", + "\n", + "print(f\"\\nTotal inference time: {elapsed:.3f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving a Comparison Mesh\n", + "\n", + "Let's overlay the dummy predictions onto the mesh alongside the ground truth. The sine/cosine model will obviously not match the real CFD solution, but this confirms the pipeline is working end-to-end." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:34.249101Z", + "iopub.status.busy": "2026-04-21T06:11:34.249001Z", + "iopub.status.idle": "2026-04-21T06:11:58.177722Z", + "shell.execute_reply": "2026-04-21T06:11:58.177159Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mesh arrays: ['CpMeanTrim', 'pMeanTrim', 'pPrime2MeanTrim', 'wallShearStressMeanTrim', 'pMeanTrimPred_SineCosine', 'wallShearStressMeanTrimPred_SineCosine']\n", + "Saved to sine_cosine_benchmark_results/predictions_comparison.vtp\n" + ] + } + ], + "source": [ + "vis_mesh = pv.read(case.mesh_path)\n", + "vis_mesh.cell_data[\"pMeanTrimPred_SineCosine\"] = predictions[\"pressure\"]\n", + "vis_mesh.cell_data[\"wallShearStressMeanTrimPred_SineCosine\"] = predictions[\"shear_stress\"]\n", + "\n", + "os.makedirs(\"sine_cosine_benchmark_results\", exist_ok=True)\n", + "vis_mesh.save(\"sine_cosine_benchmark_results/predictions_comparison.vtp\")\n", + "\n", + "print(f\"Mesh arrays: {list(vis_mesh.cell_data.keys())}\")\n", + "print(f\"Saved to sine_cosine_benchmark_results/predictions_comparison.vtp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the Predicted Fields\n", + "\n", + "Since this is a toy model producing trigonometric patterns, comparing against the CFD ground truth is not very informative. Instead, let's visualize just the predicted fields so we can see the sinusoidal patterns mapped onto the car geometry. We use `plot_fields` which renders each field in its own panel without any GT comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:11:58.179097Z", + "iopub.status.busy": "2026-04-21T06:11:58.178985Z", + "iopub.status.idle": "2026-04-21T06:12:12.564759Z", + "shell.execute_reply": "2026-04-21T06:12:12.564123Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/workspace/aero-benchmarking/physicsnemo-cfd/physicsnemo/cfd/postprocessing_tools/visualization/utils.py:26: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1795877/694182542.py:4: PyVistaDeprecationWarning: This function is deprecated and will be removed in future version of PyVista. Use vtk with osmesa instead.\n", + " pv.start_xvfb()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[33m2026-04-20 23:12:04.420 ( 55.921s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n", + "\u001b[0m\u001b[33m2026-04-20 23:12:04.499 ( 56.001s) [ 7FFFF7C73540] vtkX11Functions.cxx:194 WARN| Failed to load Xcursor library\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved sine_cosine_benchmark_results/sinecosine_predicted_fields.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from physicsnemo.cfd.postprocessing_tools.visualization.utils import plot_fields\n", + "from IPython.display import Image, display\n", + "\n", + "pv.start_xvfb()\n", + "\n", + "plotter = plot_fields(\n", + " vis_mesh,\n", + " fields=[\"pMeanTrimPred_SineCosine\", \"wallShearStressMeanTrimPred_SineCosine\"],\n", + " view=\"xy\",\n", + " dtype=\"cell\",\n", + " window_size=[1800, 1200],\n", + ")\n", + "fields_path = \"sine_cosine_benchmark_results/sinecosine_predicted_fields.png\"\n", + "plotter.screenshot(fields_path)\n", + "plotter.close()\n", + "print(f\"Saved {fields_path}\")\n", + "\n", + "display(Image(filename=fields_path))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Full Benchmark\n", + "\n", + "With the wrapper registered as `\"sine_cosine\"`, we can use the full benchmarking engine. The configuration below runs it in matrix mode against DrivAerML, computing L2 errors, area-weighted L2, and drag/lift metrics — the same pipeline used for production models." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:12:12.567956Z", + "iopub.status.busy": "2026-04-21T06:12:12.567400Z", + "iopub.status.idle": "2026-04-21T06:13:22.960492Z", + "shell.execute_reply": "2026-04-21T06:13:22.959998Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark with SineCosine model...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Distributed: rank 0/1 (no case sharding).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Listing cases under root '/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune' (split=None)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Loading 2 case(s) from root '/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune' (model 'sine_cosine')…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Loaded 7 parameters from /tmp/sine_cosine_model_quw_qdm8/checkpoint.pt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading case 'run_1'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] load_case('run_1'): branch='surface', root=/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading surface mesh from /lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune/run_1/boundary_1.vtp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Prepared 8828095 cell-center coordinates for case run_1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Forward pass complete (sin/cos transforms).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Decoding outputs to numpy.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading case 'run_11'…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] load_case('run_11'): branch='surface', root=/lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:drivaerml] Reading surface mesh from /lustre/fsw/coreai_modulus_cae/datasets/drivaer_aws/drivaer_data_finetune/run_11/boundary_11.vtp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Prepared 7792715 cell-center coordinates for case run_11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Forward pass complete (sin/cos transforms).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[inference:sine_cosine] Decoding outputs to numpy.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing artifacts to sine_cosine_benchmark_results/benchmark_artifacts.json…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing benchmark reports to sine_cosine_benchmark_results (formats: json, csv, html)…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing sine_cosine_benchmark_results/benchmark_results.json…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing sine_cosine_benchmark_results/benchmark_results.csv…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dataset:benchmark] Writing sine_cosine_benchmark_results/benchmark_results.html…\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Benchmark complete.\n" + ] + } + ], + "source": [ + "from physicsnemo.cfd.evaluation.config import Config\n", + "from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark\n", + "\n", + "config = Config.from_dict({\n", + " \"run\": {\n", + " \"device\": \"cuda:0\",\n", + " \"output_dir\": \"sine_cosine_benchmark_results\",\n", + " \"save_inference_mesh\": False,\n", + " \"metrics_cache\": {\"enabled\": False},\n", + " },\n", + " \"benchmark\": {\n", + " \"mode\": \"matrix\",\n", + " \"models\": [\n", + " {\n", + " \"name\": \"sine_cosine\",\n", + " \"inference_domain\": \"surface\",\n", + " \"checkpoint\": CKPT_PATH,\n", + " \"stats_path\": STATS_PATH,\n", + " \"kwargs\": {},\n", + " },\n", + " ],\n", + " \"datasets\": [\n", + " {\n", + " \"name\": \"drivaerml\",\n", + " \"root\": DRIVAERML_ROOT,\n", + " \"case_ids\": INFERENCE_CASES,\n", + " \"kwargs\": {\n", + " \"align_ground_truth_to_model\": True,\n", + " \"inference_domain\": \"surface\",\n", + " },\n", + " },\n", + " ],\n", + " \"reproducibility\": {\"log_env\": False, \"save_artifacts\": True},\n", + " },\n", + " \"output\": {\n", + " \"mesh_field_names\": {\n", + " \"pressure\": \"pMeanTrimPred\",\n", + " \"shear_stress\": \"wallShearStressMeanTrimPred\",\n", + " },\n", + " },\n", + " \"metrics\": [\n", + " \"l2_pressure\",\n", + " \"l2_shear_stress\",\n", + " \"l2_pressure_area_weighted\",\n", + " \"drag\",\n", + " \"lift\",\n", + " ],\n", + " \"reports\": {\"enabled\": False},\n", + "})\n", + "\n", + "print(\"Running benchmark with SineCosine model...\")\n", + "results = run_benchmark(config)\n", + "print(\"Benchmark complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting Results\n", + "\n", + "The benchmark engine writes JSON, CSV, and HTML reports. Let's load the JSON results and display the per-case metrics. Since our model outputs trigonometric noise rather than real CFD predictions, expect large L2 errors." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-21T06:13:22.961828Z", + "iopub.status.busy": "2026-04-21T06:13:22.961734Z", + "iopub.status.idle": "2026-04-21T06:13:22.966369Z", + "shell.execute_reply": "2026-04-21T06:13:22.966056Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "Model: sine_cosine | Dataset: drivaerml\n", + "============================================================\n", + " drag_error 1.032007\n", + " drag_pred -18.385527\n", + " drag_true 538.274199\n", + " l2_pressure 0.940443\n", + " l2_pressure_area_weighted 0.905984\n", + " l2_shear_stress_wallShearStressMean_x_l2_error 1.680940\n", + " l2_shear_stress_wallShearStressMean_y_l2_error 2.874242\n", + " l2_shear_stress_wallShearStressMean_z_l2_error 4.064959\n", + " lift_error 1.141213\n", + " lift_pred -18.771882\n", + " lift_true 164.201554\n", + "\n", + " Per-case breakdown (2 cases):\n", + " run_1:\n", + " drag_error 0.990790\n", + " drag_pred 4.699030\n", + " drag_true 510.200918\n", + " l2_pressure 0.950198\n", + " l2_pressure_area_weighted 0.903676\n", + " l2_shear_stress_wallShearStressMean_x_l2_error 1.751412\n", + " l2_shear_stress_wallShearStressMean_y_l2_error 3.157106\n", + " l2_shear_stress_wallShearStressMean_z_l2_error 3.887138\n", + " lift_error 1.228897\n", + " lift_pred -26.058474\n", + " lift_true 113.843685\n", + " run_11:\n", + " drag_error 1.073224\n", + " drag_pred -41.470084\n", + " drag_true 566.347480\n", + " l2_pressure 0.930689\n", + " l2_pressure_area_weighted 0.908293\n", + " l2_shear_stress_wallShearStressMean_x_l2_error 1.610469\n", + " l2_shear_stress_wallShearStressMean_y_l2_error 2.591378\n", + " l2_shear_stress_wallShearStressMean_z_l2_error 4.242781\n", + " lift_error 1.053530\n", + " lift_pred -11.485290\n", + " lift_true 214.559424\n", + "\n", + "Full results: sine_cosine_benchmark_results/benchmark_results.json\n" + ] + } + ], + "source": [ + "results_path = \"sine_cosine_benchmark_results/benchmark_results.json\"\n", + "with open(results_path) as f:\n", + " report = json.load(f)\n", + "\n", + "for combo in report:\n", + " model = combo[\"model\"]\n", + " dataset = combo[\"dataset\"]\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Model: {model} | Dataset: {dataset}\")\n", + " print(f\"{'='*60}\")\n", + "\n", + " if combo.get(\"skipped\"):\n", + " print(f\" SKIPPED: {combo['skip_reason']}\")\n", + " continue\n", + "\n", + " metrics = combo.get(\"metrics\", {})\n", + " for metric_name, metric_value in sorted(metrics.items()):\n", + " if isinstance(metric_value, float):\n", + " print(f\" {metric_name:35s} {metric_value:.6f}\")\n", + " else:\n", + " print(f\" {metric_name:35s} {metric_value}\")\n", + "\n", + " per_case = combo.get(\"per_case\", [])\n", + " if per_case:\n", + " print(f\"\\n Per-case breakdown ({len(per_case)} cases):\")\n", + " for pc in per_case:\n", + " print(f\" {pc['case_id']}:\")\n", + " for mk, mv in sorted(pc.get(\"metrics\", {}).items()):\n", + " if isinstance(mv, float):\n", + " print(f\" {mk:33s} {mv:.6f}\")\n", + "\n", + "print(f\"\\nFull results: {results_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps\n", + "\n", + "That completes the model wrapper tutorial. We defined a toy `SineCosineWrapper`, created dummy weights, ran it against real DrivAerML data, and evaluated it through the full benchmarking engine — all without modifying any existing code.\n", + "\n", + "The key insight is that all model-specific complexity lives inside the wrapper. Once it implements the four `CFDModel` methods and produces canonical `predictions_dict` outputs, every dataset adapter and metric works without modification.\n", + "\n", + "To summarize the steps for adding a real model:\n", + "\n", + "1. **Subclass `CFDModel`** and set `INFERENCE_DOMAIN` and `OUTPUT_LOCATION`\n", + "2. **Implement `load`** to load your network weights and normalization stats\n", + "3. **Implement `prepare_inputs`** to convert `CanonicalCase` mesh data into your model's input format (tensors, graphs, etc.)\n", + "4. **Implement `predict`** to run the forward pass\n", + "5. **Implement `decode_outputs`** to denormalize outputs and return a `predictions_dict`\n", + "6. **Register** with `register_model(\"my_model\", MyModelWrapper)`\n", + "\n", + "To make the wrapper permanent, save the class to `physicsnemo/cfd/evaluation/inference/wrappers/my_model.py` and register it in `wrappers/__init__.py`:\n", + "\n", + "```python\n", + "from physicsnemo.cfd.evaluation.inference.wrappers.my_model import MyModelWrapper\n", + "register_model(\"my_model\", MyModelWrapper)\n", + "```\n", + "\n", + "Then use `model.name: my_model` in any YAML config." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}
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