From 31f117ffbfa1901156fe88ecc5152897e70eb2e9 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 22:54:19 -0400 Subject: [PATCH 1/5] osf repo linked in readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ff335d7..c2279cc 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ This repository contains benchmarks for comparing models of object learning agai Alt text +If you just want to download the raw data and images without using the `hobj` library, check out the [OSF repository](https://osf.io/pj6wm/files/osfstorage) for this project. ## Quickstart From 0582ea9ebac2cfd95ca333f4775b2537856fe8d2 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 23:29:03 -0400 Subject: [PATCH 2/5] download --- README.md | 24 +- examples/loading_raw_data_and_metadata.ipynb | 58 ++--- hobj/data/download.py | 63 +++++- tests/test_data_loader_cache.py | 58 +++-- tests/test_statistics_builders.py | 102 +++++++++ tests/test_statistics_dataset_usage.py | 220 +++++++++++++++++++ 6 files changed, 466 insertions(+), 59 deletions(-) create mode 100644 tests/test_statistics_builders.py create mode 100644 tests/test_statistics_dataset_usage.py diff --git a/README.md b/README.md index c2279cc..0e899c3 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,8 @@ If you just want to download the raw data and images without using the `hobj` li The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -# todo +Note that on first use, the packaged dataset is downloaded automatically into `./data` from the OSF repository. In total, `hobj` takes up around ~600 MB of space on your computer. + ### Using `hobj` to comparing a linear learner against human learning data @@ -56,19 +57,22 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` -Note that on first use, the packaged dataset is downloaded automatically into `./data`. -To use a different location, pass `cachedir=...` to a data loader or benchmark -constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. -For more details (e.g., how to load the raw behavioral data or images), check out the Jupyter notebooks in `examples/`. +For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. -### Need help or have questions? -Please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! -## Citation +## Contact +If you have any questions, need help, or experience a bug, please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! + + + +## Changes to codebase since publication +This codebase was overhauled in 2026 to improve its accessibility, performance, and quality. Along the way, minor changes to the statistical analysis procedure were introduced, along with changes to the names of the original filenames (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). +## Citation + ``` @article{lee2023well, title={How well do rudimentary plasticity rules predict adult visual object learning?}, @@ -81,7 +85,3 @@ Please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or o publisher={Public Library of Science San Francisco, CA USA} } ``` - - -## Changes to codebase since publication -This codebase was refactored in 2026 to improve the accessibility, performance, and quality of the code. Along the way, minor changes to the statistical analysis of the original codebase were introduced (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1). diff --git a/examples/loading_raw_data_and_metadata.ipynb b/examples/loading_raw_data_and_metadata.ipynb index 81a37b5..a144c4a 100644 --- a/examples/loading_raw_data_and_metadata.ipynb +++ b/examples/loading_raw_data_and_metadata.ipynb @@ -6,8 +6,8 @@ "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2026-03-27T02:01:44.301642Z", - "start_time": "2026-03-27T02:01:43.898649Z" + "end_time": "2026-03-27T03:25:10.359485Z", + "start_time": "2026-03-27T03:25:09.984598Z" } }, "source": "import hobj", @@ -27,8 +27,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:06:16.259921Z", - "start_time": "2026-03-27T02:06:16.120069Z" + "end_time": "2026-03-27T03:25:10.486574Z", + "start_time": "2026-03-27T03:25:10.360160Z" } }, "cell_type": "code", @@ -326,18 +326,18 @@ "" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 8 + "execution_count": 2 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:06:48.946791Z", - "start_time": "2026-03-27T02:06:48.861054Z" + "end_time": "2026-03-27T03:25:10.596806Z", + "start_time": "2026-03-27T03:25:10.507768Z" } }, "cell_type": "code", @@ -612,18 +612,18 @@ "" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 10 + "execution_count": 3 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:07:47.382485Z", - "start_time": "2026-03-27T02:07:47.352896Z" + "end_time": "2026-03-27T03:25:10.975037Z", + "start_time": "2026-03-27T03:25:10.964839Z" } }, "cell_type": "code", @@ -633,7 +633,7 @@ ], "id": "353a0c85e5abca6a", "outputs": [], - "execution_count": 12 + "execution_count": 4 }, { "metadata": {}, @@ -648,8 +648,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:08:37.557729Z", - "start_time": "2026-03-27T02:08:37.536141Z" + "end_time": "2026-03-27T03:25:11.263172Z", + "start_time": "2026-03-27T03:25:10.995997Z" } }, "cell_type": "code", @@ -668,18 +668,18 @@ "image/png": 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", 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" }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 16 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:09:12.544394Z", - "start_time": "2026-03-27T02:09:12.521145Z" + "end_time": "2026-03-27T03:25:11.289879Z", + "start_time": "2026-03-27T03:25:11.264772Z" } }, "cell_type": "code", @@ -692,12 +692,12 @@ "PosixPath('/Users/mjl/PycharmProjects/hobj/data/images/MutatorHighVarImageset/MutatorObject000/MutatorObject000-Image03.png')" ] }, - "execution_count": 18, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 18 + "execution_count": 6 }, { "metadata": {}, @@ -712,8 +712,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:04:06.781468Z", - "start_time": "2026-03-27T02:04:06.398253Z" + "end_time": "2026-03-27T03:25:11.682894Z", + "start_time": "2026-03-27T03:25:11.291361Z" } }, "cell_type": "code", @@ -983,18 +983,18 @@ "" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 5 + "execution_count": 7 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T02:04:29.333688Z", - "start_time": "2026-03-27T02:04:29.257067Z" + "end_time": "2026-03-27T03:25:11.784589Z", + "start_time": "2026-03-27T03:25:11.702409Z" } }, "cell_type": "code", @@ -1289,12 +1289,12 @@ "" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 6 + "execution_count": 8 } ], "metadata": { diff --git a/hobj/data/download.py b/hobj/data/download.py index 78aa9cb..f58205a 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -5,6 +5,7 @@ import argparse import shutil import tarfile +import zipfile from pathlib import Path from tempfile import TemporaryDirectory from urllib.parse import urlparse @@ -13,9 +14,10 @@ from tqdm import tqdm +# Public OSF node that hosts the packaged HOBJ dataset files. +OSF_NODE_ID = "pj6wm" DATA_ARCHIVE_URL = ( - "https://hlbdatasets.s3.us-east-1.amazonaws.com/" - "lee-dicarlo-2023-learning-data.tar.gz" + f"https://files.osf.io/v1/resources/{OSF_NODE_ID}/providers/osfstorage/?zip=" ) EXPECTED_DATA_RELATIVE_PATHS = ( Path("meta-MutatorHighVarImageset.csv"), @@ -52,7 +54,16 @@ def _get_missing_expected_paths(data_root: Path) -> list[Path]: def _derive_archive_path(repo_root: Path, url: str) -> Path: """Return the on-disk path for the downloaded archive.""" - archive_name = Path(urlparse(url).path).name + parsed_url = urlparse(url) + path_segments = [segment for segment in parsed_url.path.split("/") if segment] + if ( + parsed_url.query == "zip=" + and len(path_segments) >= 4 + and path_segments[:3] == ["v1", "resources", path_segments[2]] + ): + archive_name = f"{path_segments[2]}.zip" + else: + archive_name = Path(parsed_url.path).name if not archive_name: raise ValueError(f"Could not derive archive filename from URL: {url}") return repo_root / archive_name @@ -95,6 +106,25 @@ def _safe_extract_tarball(archive_path: Path, destination: Path) -> None: tar.extractall(destination, filter="data") +def _safe_extract_zipfile(archive_path: Path, destination: Path) -> None: + """Extract a zipfile while preventing path traversal. + + Args: + archive_path: Zipfile to extract. + destination: Destination directory for extracted contents. + + Raises: + ValueError: If a zip entry would escape the extraction directory. + """ + destination = destination.resolve() + + with zipfile.ZipFile(archive_path) as archive: + for member in archive.infolist(): + member_path = (destination / member.filename).resolve() + member_path.relative_to(destination) + archive.extractall(destination) + + def _find_extracted_data_root(extraction_root: Path) -> Path: """Return the packaged data directory from an extracted archive tree. @@ -120,6 +150,26 @@ def _find_extracted_data_root(extraction_root: Path) -> Path: ) +def _extract_nested_images_archive(data_root: Path) -> None: + """Extract ``images.tar.gz`` into ``data/images`` when present. + + Args: + data_root: Extracted packaged data directory. + """ + images_archive_path = data_root / "images.tar.gz" + images_root = data_root / "images" + if images_root.exists(): + if images_archive_path.exists(): + images_archive_path.unlink() + return + + if not images_archive_path.exists(): + return + + _safe_extract_tarball(archive_path=images_archive_path, destination=data_root) + images_archive_path.unlink() + + def resolve_data_root( *, cachedir: Path | None = None, @@ -141,6 +191,7 @@ def resolve_data_root( ).resolve() if _get_missing_expected_paths(data_root): return download_data(cachedir=data_root, url=DATA_ARCHIVE_URL) + _extract_nested_images_archive(data_root) return data_root @@ -171,7 +222,8 @@ def download_data( Raises: requests.HTTPError: If the archive download fails. - tarfile.TarError: If the archive cannot be unpacked. + tarfile.TarError: If a tar archive cannot be unpacked. + zipfile.BadZipFile: If the zip archive cannot be unpacked. ValueError: If the archive path is invalid or extraction would escape the extraction directory. FileNotFoundError: If extraction completes but the expected dataset @@ -192,8 +244,9 @@ def download_data( with TemporaryDirectory(dir=archive_root) as extraction_root_str: extraction_root = Path(extraction_root_str) - _safe_extract_tarball(archive_path=archive_path, destination=extraction_root) + _safe_extract_zipfile(archive_path=archive_path, destination=extraction_root) extracted_data_root = _find_extracted_data_root(extraction_root) + _extract_nested_images_archive(extracted_data_root) shutil.copytree(extracted_data_root, data_root, dirs_exist_ok=True) missing_paths = _get_missing_expected_paths(data_root) diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index b610678..1228772 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -1,4 +1,5 @@ import tarfile +import zipfile from pathlib import Path import pandas as pd @@ -125,23 +126,46 @@ def _write_minimal_packaged_dataset(data_root: Path) -> None: ).to_csv(behavior_root / "human-behavior-oneshot-subtasks.csv", index=False) +def _write_packaged_images_archive(data_root: Path) -> None: + """Write the nested ``images.tar.gz`` archive expected by OSF downloads. + + Args: + data_root: Packaged data directory containing metadata and behavior CSVs. + """ + staging_root = data_root.parent / "images-staging" + images_root = staging_root / "images" + images_root.mkdir(parents=True, exist_ok=True) + + Image.new("RGB", (2, 3), color=(255, 0, 0)).save(images_root / "highvar.png") + Image.new("RGB", (2, 3), color=(0, 255, 0)).save(images_root / "oneshot.png") + Image.new("RGB", (2, 3), color=(0, 0, 255)).save(images_root / "warmup.png") + Image.new("RGB", (2, 3), color=(255, 255, 0)).save(images_root / "catch.png") + + with tarfile.open(data_root / "images.tar.gz", mode="w:gz") as archive: + archive.add(images_root, arcname="images") + + def test_download_data_extracts_into_custom_cachedir(tmp_path: Path) -> None: staging_root = tmp_path / "staging" / "data" _write_minimal_packaged_dataset(staging_root) + _write_packaged_images_archive(staging_root) - archive_path = tmp_path / "fixture.tar.gz" - with tarfile.open(archive_path, mode="w:gz") as archive: - archive.add(staging_root, arcname="data") + archive_path = tmp_path / "download" + with zipfile.ZipFile(archive_path, mode="w") as archive: + for path in sorted(staging_root.rglob("*")): + archive.write(path, arcname=Path("data") / path.relative_to(staging_root)) custom_cache = tmp_path / "custom-cache" resolved_cache = download_module.download_data( - url="https://example.com/fixture.tar.gz", + url="https://example.com/download?zip=", cachedir=custom_cache, ) assert resolved_cache == custom_cache.resolve() assert (custom_cache / "meta-MutatorHighVarImageset.csv").exists() assert (custom_cache / "behavior" / "human-behavior-highvar-subtasks.csv").exists() + assert (custom_cache / "images" / "highvar.png").exists() + assert not (custom_cache / "images.tar.gz").exists() assert not (tmp_path / "data").exists() @@ -192,10 +216,7 @@ def test_load_highvar_behavior_uses_custom_cachedir(tmp_path: Path) -> None: def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None: custom_cache = tmp_path / "image-cache" _write_minimal_packaged_dataset(custom_cache) - - image_path = custom_cache / "images" / "highvar.png" - image_path.parent.mkdir(parents=True, exist_ok=True) - Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + _write_packaged_images_archive(custom_cache) image = load_image("img-highvar", cachedir=custom_cache) @@ -206,11 +227,22 @@ def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None: def test_get_image_path_uses_custom_cachedir(tmp_path: Path) -> None: custom_cache = tmp_path / "image-path-cache" _write_minimal_packaged_dataset(custom_cache) - - image_path = custom_cache / "images" / "highvar.png" - image_path.parent.mkdir(parents=True, exist_ok=True) - Image.new("RGB", (2, 3), color=(255, 0, 0)).save(image_path) + _write_packaged_images_archive(custom_cache) resolved_path = get_image_path("img-highvar", cachedir=custom_cache) - assert resolved_path == image_path + assert resolved_path == custom_cache / "images" / "highvar.png" + + +def test_resolve_data_root_extracts_nested_images_archive_without_redownload( + tmp_path: Path, +) -> None: + custom_cache = tmp_path / "resolved-cache" + _write_minimal_packaged_dataset(custom_cache) + _write_packaged_images_archive(custom_cache) + + resolved_cache = download_module.resolve_data_root(cachedir=custom_cache) + + assert resolved_cache == custom_cache.resolve() + assert (custom_cache / "images" / "highvar.png").exists() + assert not (custom_cache / "images.tar.gz").exists() diff --git a/tests/test_statistics_builders.py b/tests/test_statistics_builders.py new file mode 100644 index 0000000..b5ac802 --- /dev/null +++ b/tests/test_statistics_builders.py @@ -0,0 +1,102 @@ +import numpy as np +import xarray as xr + +from hobj.benchmarks.binary_classification.estimator import ( + build_learning_curve_statistics, +) +from hobj.benchmarks.binary_classification.simulation import ( + BinaryClassificationSubtaskResult, +) +from hobj.benchmarks.generalization.estimator import build_generalization_statistics +from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult + + +def test_build_learning_curve_statistics_returns_expected_dataset_shape(): + stats = build_learning_curve_statistics( + subtask_name_to_results={ + "task-b": [ + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, False, True]), + worker_id="worker-1", + ), + BinaryClassificationSubtaskResult( + perf_seq=np.array([False, False, True]), + worker_id="worker-2", + ), + ], + "task-a": [ + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, True, False]), + worker_id="worker-3", + ), + BinaryClassificationSubtaskResult( + perf_seq=np.array([True, False, False]), + worker_id="worker-4", + ), + ], + }, + nbootstrap_samples=4, + bootstrap_by_worker=False, + ) + + assert isinstance(stats, xr.Dataset) + assert set(stats.data_vars) == { + "phat", + "varhat_phat", + "boot_phat", + "boot_varhat_phat", + } + assert dict(stats.sizes) == {"subtask": 2, "trial": 3, "boot_iter": 4} + assert list(stats.subtask.values) == ["task-a", "task-b"] + np.testing.assert_allclose( + stats.phat.values, + np.array( + [ + [1.0, 0.5, 0.0], + [0.5, 0.0, 1.0], + ] + ), + ) + assert stats.boot_phat.shape == (4, 2, 3) + assert stats.boot_varhat_phat.shape == (4, 2, 3) + + +def test_build_generalization_statistics_returns_expected_dataset_shape(): + stats = build_generalization_statistics( + results=[ + GeneralizationSessionResult( + transformation_to_kn={ + "blur | 0.1": [1, 2], + "noise | 0.2": [0, 1], + }, + kcatch=1, + ncatch=2, + worker_id="worker-1", + ), + GeneralizationSessionResult( + transformation_to_kn={ + "blur | 0.1": [1, 1], + "noise | 0.2": [1, 2], + }, + kcatch=2, + ncatch=2, + worker_id="worker-2", + ), + ], + perform_lapse_rate_correction=False, + n_bootstrap_iterations=5, + bootstrap_by_worker=False, + ) + + assert isinstance(stats, xr.Dataset) + assert set(stats.data_vars) == { + "phat", + "varhat_phat", + "boot_phat", + "boot_varhat_phat", + } + assert dict(stats.sizes) == {"transformation": 2, "boot_iter": 5} + assert list(stats.transformation.values) == ["blur | 0.1", "noise | 0.2"] + np.testing.assert_allclose(stats.phat.values, np.array([2 / 3, 1 / 3])) + assert stats.boot_phat.shape == (5, 2) + assert stats.boot_varhat_phat.shape == (5, 2) diff --git a/tests/test_statistics_dataset_usage.py b/tests/test_statistics_dataset_usage.py new file mode 100644 index 0000000..e532a75 --- /dev/null +++ b/tests/test_statistics_dataset_usage.py @@ -0,0 +1,220 @@ +import pandas as pd +import xarray as xr + +import hobj.benchmarks.mut_highvar_benchmark as highvar_module +import hobj.benchmarks.mut_oneshot_benchmark as oneshot_module +from hobj.learning_models.random_guesser import RandomGuesser + + +def _make_highvar_images_df() -> pd.DataFrame: + rows = [] + for category in ["CatA", "CatB", "CatC", "CatD"]: + for index in range(50): + rows.append( + { + "image_id": f"{category}_{index:03d}", + "category": category, + } + ) + return pd.DataFrame(rows) + + +def _make_highvar_behavior_df() -> pd.DataFrame: + rows = [] + for assignment_id, worker_id, class_a, class_b in [ + ("assignment-1", "worker-1", "CatA", "CatB"), + ("assignment-2", "worker-2", "CatC", "CatD"), + ]: + for trial in range(100): + category = class_a if trial % 2 == 0 else class_b + rows.append( + { + "assignment_id": assignment_id, + "worker_id": worker_id, + "trial": trial, + "image_id": f"{category}_{trial // 2:03d}", + "perf": trial % 3 == 0, + } + ) + return pd.DataFrame(rows) + + +def _make_oneshot_images_df() -> pd.DataFrame: + rows = [] + for category in ["CatA", "CatB"]: + rows.append( + { + "image_id": f"{category}_original", + "category": category, + "transformation": "original", + "transformation_level": 0.0, + } + ) + for index in range(30): + rows.append( + { + "image_id": f"{category}_blur_{index:02d}", + "category": category, + "transformation": "blur", + "transformation_level": 0.1, + } + ) + for index in range(30): + rows.append( + { + "image_id": f"{category}_noise_{index:02d}", + "category": category, + "transformation": "noise", + "transformation_level": 0.2, + } + ) + return pd.DataFrame(rows) + + +def _make_oneshot_behavior_df() -> pd.DataFrame: + transformed_a = [ + "CatA_blur_00", + "CatB_noise_00", + "CatA_noise_01", + "CatB_blur_01", + "CatA_blur_02", + "CatB_noise_02", + "CatA_noise_03", + "CatB_blur_03", + ] + transformed_b = [ + "CatB_blur_04", + "CatA_noise_04", + "CatB_noise_05", + "CatA_blur_05", + "CatB_blur_06", + "CatA_noise_06", + "CatB_noise_07", + "CatA_blur_07", + ] + rows = [] + for assignment_id, slot, worker_id, transformed_images in [ + ("assignment-1", 0, "worker-1", transformed_a), + ("assignment-2", 0, "worker-2", transformed_b), + ]: + transformed_iter = iter(transformed_images) + for trial in range(20): + if trial in {9, 14, 19} or trial < 9: + image_id = "CatA_original" if trial % 2 == 0 else "CatB_original" + else: + image_id = next(transformed_iter) + + rows.append( + { + "assignment_id": assignment_id, + "slot": slot, + "worker_id": worker_id, + "trial": trial, + "image_id": image_id, + "perf": trial % 2 == 0, + "subtask": "CatA,CatB", + } + ) + return pd.DataFrame(rows) + + +def test_highvar_target_and_model_statistics_behave_like_datasets(monkeypatch): + monkeypatch.setattr( + highvar_module, + "load_imageset_meta_highvar", + lambda cachedir=None: _make_highvar_images_df(), + ) + monkeypatch.setattr( + highvar_module, + "load_highvar_behavior", + lambda remove_probe_trials=True, cachedir=None: _make_highvar_behavior_df(), + ) + monkeypatch.setattr( + highvar_module.MutatorHighVarBenchmark, + "num_bootstrap_samples", + 4, + ) + monkeypatch.setattr( + highvar_module.MutatorHighVarBenchmark, + "num_simulations_per_subtask", + 3, + ) + + benchmark = highvar_module.MutatorHighVarBenchmark() + target_stats = benchmark.target_statistics + + assert isinstance(target_stats, xr.Dataset) + assert target_stats.phat.mean("subtask").sizes == {"trial": 100} + assert target_stats.boot_phat.mean("subtask").sizes == { + "boot_iter": 4, + "trial": 100, + } + assert target_stats.sel(subtask=benchmark.subtask_names[0]).phat.sizes == { + "trial": 100 + } + + result = benchmark.score_model(RandomGuesser(seed=0)) + assert isinstance(result.model_statistics, xr.Dataset) + assert result.model_statistics.boot_phat.sizes == { + "boot_iter": 4, + "subtask": 2, + "trial": 100, + } + + +def test_oneshot_target_and_model_statistics_behave_like_datasets(monkeypatch): + monkeypatch.setattr( + oneshot_module, + "load_imageset_meta_oneshot", + lambda cachedir=None: _make_oneshot_images_df(), + ) + monkeypatch.setattr( + oneshot_module, + "load_oneshot_behavior", + lambda cachedir=None: _make_oneshot_behavior_df(), + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "subtask_names", + ["CatA,CatB"], + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "transformation_ids", + ["blur | 0.1", "noise | 0.2"], + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "num_bootstrap_samples", + 4, + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "num_simulations_per_subtask", + 3, + ) + monkeypatch.setattr( + oneshot_module.MutatorOneshotBenchmark, + "bootstrap_target_by_worker", + False, + ) + + benchmark = oneshot_module.MutatorOneshotBenchmark() + target_stats = benchmark.target_statistics + + assert isinstance(target_stats, xr.Dataset) + assert [name for name, _ in target_stats.groupby("transformation_type")] == [ + "blur", + "noise", + ] + assert target_stats.sel(transformation="blur | 0.1").phat.sizes == {} + assert target_stats.boot_phat.sizes == { + "boot_iter": 4, + "transformation": 2, + } + + result = benchmark.score_model(RandomGuesser(seed=0)) + assert isinstance(result.model_statistics, xr.Dataset) + assert result.model_statistics.sel( + transformation=target_stats.transformation + ).phat.sizes == {"transformation": 2} From 1e7bddcc8782c7e0bdbd4e1bdd64d2916051b002 Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 23:39:32 -0400 Subject: [PATCH 3/5] platformdirs --- README.md | 7 +++++++ hobj/data/download.py | 20 +++++++++++--------- pyproject.toml | 4 ++++ tests/test_data_loader_cache.py | 12 ++++++++++++ uv.lock | 2 ++ 5 files changed, 36 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 0e899c3..0662f85 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,13 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` +On first use, the packaged dataset is downloaded automatically into a +user-scoped data directory managed by `platformdirs` rather than into the +repository root. On macOS this is typically +`~/Library/Application Support/hobj/data`. +To use a different location, pass `cachedir=...` to a data loader or benchmark +constructor, or prefetch manually with +`hobj-download-data --cachedir /path/to/data`. For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. diff --git a/hobj/data/download.py b/hobj/data/download.py index f58205a..e1eaad2 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -11,6 +11,7 @@ from urllib.parse import urlparse import requests +from platformdirs import user_data_dir from tqdm import tqdm @@ -30,17 +31,18 @@ _DOWNLOAD_CHUNK_SIZE_BYTES = 1024 * 1024 -def _get_repo_root() -> Path: - """Return the repository root for this package.""" - return Path(__file__).resolve().parents[2] +def _get_default_data_root(repo_root: Path | None = None) -> Path: + """Return the default packaged data directory. + Args: + repo_root: Unused legacy parameter kept for compatibility with the + public function signature. -def _get_default_data_root(repo_root: Path | None = None) -> Path: - """Return the default packaged data directory.""" - resolved_repo_root = ( - repo_root if repo_root is not None else _get_repo_root() - ).resolve() - return resolved_repo_root / "data" + Returns: + A user-scoped persistent data directory outside the installed package. + """ + del repo_root + return Path(user_data_dir("hobj", appauthor=False)).resolve() / "data" def _get_missing_expected_paths(data_root: Path) -> list[Path]: diff --git a/pyproject.toml b/pyproject.toml index 4569a12..d75a317 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,7 @@ dependencies = [ "numpy>=2.2", "pandas>=2.2", "matplotlib>=3.9", + "platformdirs>=4.3", "requests>=2.32", "tqdm>=4.67", "pydantic>=2.10", @@ -25,6 +26,9 @@ dependencies = [ "scipy>=1.17.1", ] +[project.scripts] +hobj-download-data = "hobj.data.download:main" + [dependency-groups] dev = [ "enczoo==0.1.3.dev1", diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index 1228772..d275d50 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -246,3 +246,15 @@ def test_resolve_data_root_extracts_nested_images_archive_without_redownload( assert resolved_cache == custom_cache.resolve() assert (custom_cache / "images" / "highvar.png").exists() assert not (custom_cache / "images.tar.gz").exists() + + +def test_get_default_data_root_uses_user_scoped_directory(monkeypatch) -> None: + monkeypatch.setattr( + download_module, + "user_data_dir", + lambda *args, **kwargs: "/tmp/hobj-platformdirs-root", + ) + + resolved_path = download_module._get_default_data_root() + + assert resolved_path == Path("/tmp/hobj-platformdirs-root/data").resolve() diff --git a/uv.lock b/uv.lock index 84c8fc8..4a3d1e8 100644 --- a/uv.lock +++ b/uv.lock @@ -700,6 +700,7 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, { name = "pandas" }, + { name = "platformdirs" }, { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, @@ -721,6 +722,7 @@ requires-dist = [ { name = "matplotlib", specifier = ">=3.9" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, + { name = "platformdirs", specifier = ">=4.3" }, { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, From 6d38f83f65ac6e4c8383f8077fdb3106f1aa7c8a Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 23:52:29 -0400 Subject: [PATCH 4/5] download to home --- README.md | 15 ++++++--------- hobj/data/download.py | 12 +++++++++--- pyproject.toml | 1 - tests/test_data_loader_cache.py | 16 +++++++++------- uv.lock | 2 -- 5 files changed, 24 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 0662f85..9825640 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,10 @@ If you just want to download the raw data and images without using the `hobj` li The `hobj` package works for Python >=3.12. After cloning this repository on your machine, navigate to this directory in your shell and run: -Note that on first use, the packaged dataset is downloaded automatically into `./data` from the OSF repository. In total, `hobj` takes up around ~600 MB of space on your computer. +On first use, the packaged dataset is downloaded automatically from the OSF +repository into a versioned cache directory under `~/.hobj_cache`. The default +path is `~/.hobj_cache/pj6wm-v1/data`. In total, `hobj` takes up around ~1 GB +of space on your computer. ### Using `hobj` to comparing a linear learner against human learning data @@ -57,17 +60,11 @@ print(result.msen, result.msen_CI95) # print(result.model_statistics) ``` -On first use, the packaged dataset is downloaded automatically into a -user-scoped data directory managed by `platformdirs` rather than into the -repository root. On macOS this is typically -`~/Library/Application Support/hobj/data`. -To use a different location, pass `cachedir=...` to a data loader or benchmark -constructor, or prefetch manually with -`hobj-download-data --cachedir /path/to/data`. For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`. - +To use a different location, pass `cachedir=...` to a data loader or benchmark +constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`. ## Contact If you have any questions, need help, or experience a bug, please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo! diff --git a/hobj/data/download.py b/hobj/data/download.py index e1eaad2..62a797e 100644 --- a/hobj/data/download.py +++ b/hobj/data/download.py @@ -11,12 +11,13 @@ from urllib.parse import urlparse import requests -from platformdirs import user_data_dir from tqdm import tqdm # Public OSF node that hosts the packaged HOBJ dataset files. OSF_NODE_ID = "pj6wm" +# Bump this to invalidate the local cache when the packaged OSF dataset changes. +DATASET_CACHE_VERSION = "v2" DATA_ARCHIVE_URL = ( f"https://files.osf.io/v1/resources/{OSF_NODE_ID}/providers/osfstorage/?zip=" ) @@ -39,10 +40,15 @@ def _get_default_data_root(repo_root: Path | None = None) -> Path: public function signature. Returns: - A user-scoped persistent data directory outside the installed package. + A persistent per-user cache directory outside the installed package. """ del repo_root - return Path(user_data_dir("hobj", appauthor=False)).resolve() / "data" + return ( + Path.home().resolve() + / ".hobj_cache" + / f"{OSF_NODE_ID}-{DATASET_CACHE_VERSION}" + / "data" + ) def _get_missing_expected_paths(data_root: Path) -> list[Path]: diff --git a/pyproject.toml b/pyproject.toml index d75a317..14eef81 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ dependencies = [ "numpy>=2.2", "pandas>=2.2", "matplotlib>=3.9", - "platformdirs>=4.3", "requests>=2.32", "tqdm>=4.67", "pydantic>=2.10", diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py index d275d50..4ef9035 100644 --- a/tests/test_data_loader_cache.py +++ b/tests/test_data_loader_cache.py @@ -248,13 +248,15 @@ def test_resolve_data_root_extracts_nested_images_archive_without_redownload( assert not (custom_cache / "images.tar.gz").exists() -def test_get_default_data_root_uses_user_scoped_directory(monkeypatch) -> None: - monkeypatch.setattr( - download_module, - "user_data_dir", - lambda *args, **kwargs: "/tmp/hobj-platformdirs-root", - ) +def test_get_default_data_root_uses_versioned_home_cache(monkeypatch) -> None: + monkeypatch.setattr(download_module.Path, "home", lambda: Path("/tmp/fake-home")) resolved_path = download_module._get_default_data_root() - assert resolved_path == Path("/tmp/hobj-platformdirs-root/data").resolve() + assert ( + resolved_path + == Path( + f"/tmp/fake-home/.hobj_cache/{download_module.OSF_NODE_ID}-" + f"{download_module.DATASET_CACHE_VERSION}/data" + ).resolve() + ) diff --git a/uv.lock b/uv.lock index 4a3d1e8..84c8fc8 100644 --- a/uv.lock +++ b/uv.lock @@ -700,7 +700,6 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, { name = "pandas" }, - { name = "platformdirs" }, { name = "pydantic" }, { name = "requests" }, { name = "scipy" }, @@ -722,7 +721,6 @@ requires-dist = [ { name = "matplotlib", specifier = ">=3.9" }, { name = "numpy", specifier = ">=2.2" }, { name = "pandas", specifier = ">=2.2" }, - { name = "platformdirs", specifier = ">=4.3" }, { name = "pydantic", specifier = ">=2.10" }, { name = "requests", specifier = ">=2.32" }, { name = "scipy", specifier = ">=1.17.1" }, From 7d3990561160e0c6ee9d6967a7bb59095986d02b Mon Sep 17 00:00:00 2001 From: himjl Date: Thu, 26 Mar 2026 23:53:46 -0400 Subject: [PATCH 5/5] regen --- examples/score_model.ipynb | 66 +++++++++++++++++++------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/examples/score_model.ipynb b/examples/score_model.ipynb index a3f3b84..deb69bf 100644 --- a/examples/score_model.ipynb +++ b/examples/score_model.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:18.817707Z", - "start_time": "2026-03-27T00:59:18.512737Z" + "end_time": "2026-03-27T03:53:14.780221Z", + "start_time": "2026-03-27T03:53:14.450467Z" } }, "cell_type": "code", @@ -21,8 +21,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.589878Z", - "start_time": "2026-03-27T00:59:18.823770Z" + "end_time": "2026-03-27T03:53:18.055705Z", + "start_time": "2026-03-27T03:53:14.783113Z" } }, "cell_type": "code", @@ -46,8 +46,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.636794Z", - "start_time": "2026-03-27T00:59:22.620737Z" + "end_time": "2026-03-27T03:53:18.102448Z", + "start_time": "2026-03-27T03:53:18.084732Z" } }, "cell_type": "code", @@ -65,8 +65,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:22.681025Z", - "start_time": "2026-03-27T00:59:22.638912Z" + "end_time": "2026-03-27T03:53:18.142566Z", + "start_time": "2026-03-27T03:53:18.104374Z" } }, "cell_type": "code", @@ -87,8 +87,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:35.267760Z", - "start_time": "2026-03-27T00:59:22.682833Z" + "end_time": "2026-03-27T03:53:30.344682Z", + "start_time": "2026-03-27T03:53:18.143795Z" } }, "cell_type": "code", @@ -103,23 +103,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Subtask simulations:: 100%|██████████| 64/64 [00:10<00:00, 6.28it/s]\n" + "Subtask simulations:: 100%|██████████| 64/64 [00:09<00:00, 6.49it/s]\n" ] }, { "data": { "text/plain": [ - "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.005581336095785859, msen_sigma=0.00028932598735009263, msen_CI95=(0.005015524275339106, 0.0061321115686366525), lapse_rate= Size: 8B\n", - "array(0.07933472), model_statistics= Size: 103MB\n", + "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.0055892710374308065, msen_sigma=0.0002920322833183841, msen_CI95=(0.005035330830028786, 0.006137235513358789), lapse_rate= Size: 8B\n", + "array(0.07971531), model_statistics= Size: 103MB\n", "Dimensions: (subtask: 64, trial: 100, boot_iter: 1000)\n", "Coordinates:\n", " * subtask (subtask) " ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -177,8 +177,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:35.452286Z", - "start_time": "2026-03-27T00:59:35.384724Z" + "end_time": "2026-03-27T03:53:30.519863Z", + "start_time": "2026-03-27T03:53:30.453561Z" } }, "cell_type": "code", @@ -206,7 +206,7 @@ "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -220,8 +220,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-27T00:59:38.804077Z", - "start_time": "2026-03-27T00:59:35.459144Z" + "end_time": "2026-03-27T03:53:33.098646Z", + "start_time": "2026-03-27T03:53:30.525642Z" } }, "cell_type": "code", @@ -234,7 +234,7 @@ { "data": { "text/plain": [ - "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018127572924906018, msen_sigma=0.002420319066024304, msen_CI95=(0.013090980755670358, 0.022419578374065857), model_statistics= Size: 583kB\n", + "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018940506085930897, msen_sigma=0.002367913012416273, msen_CI95=(0.013982943571075011, 0.02329907055826775), model_statistics= Size: 583kB\n", "Dimensions: (transformation: 36, boot_iter: 1000)\n", "Coordinates:\n", " * transformation (transformation) " ], - "image/png": 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s99prr2mhoaEO+45zlP578B3pmwuULFlSbXvq1CnbtVu7dq02bNgw2zZ6M4G+fftm+Ew0D0GzCFx7nBecn5YtW2boMMGT5iHY3hX7a/HKK6/YzheaI3Tv3l0dF9bZ79u9996rzg+aynjS4QDOIxr44zzgfLjrcCD9eXDWQUT6yf739re//U19B47hl19+Uc137LdF8xE0mbp8+bJqsvHll19qpUuXdvq7dPV7q1mzpurMANclfYcDOb3ueocDrv6/9eWHH35Ydfqhd2yA/0d0QBIZGZmr9yTxv8nwHeDEc+BXvwG0S0xISMjQro+Tf5+D3OhbmJOY4hywjJLIDWflNGj+gOwvb2f5EZFvYhklkRuoxIEanug6De0OUU6E8l2Ux2EdEZkfAyWRG2gSgDaQaDuISg+odIImL6hwoXeqQETmFvC/PFgiIiJygmWUREREbjBQEhERuWHJMkr0upHZqBpERGR+6OzEfrQYZ4KtGCTRkwYRERGUK1fObbC0XKDUU5I4MUxVEhFZOzUZGxubaSywXKDU4cQwUBIRUWZYmYeIiMgNBkoiIiI3GCiJiIjcYKAkIiJyg4GSiIjIDQZKIiI/ExpRQjo/97SaU+5joCQi8jNhJUtIl5HD1JxyHwMlERGRGwyUREREbjBQEhERucFASURE5AYDJRERkRsMlERERG4wUBIREbnBQElEROQGAyURkQWhV58Hx/yf9BzzQoYeftjzjyMGSiLyy5s55UyZGtWk/ZCBcv+QxzP08JOdnn9CTdytHgMlEfks3Khd3czNLC+CTuHwoh5tV6ZmdXlu/ofSsFN7Ncey0+1qVFPBFXOzYaAkIvIxvtSXa+nqVaR68yZSrVljNceyu8DraQD2JwyUREREbjBQEhERucFASURE5KuBsk2bNvLNN99IbGysaJomvXr1yvQ97dq1k507d8rNmzfl6NGjMnjw4DzZVyIisiZDA2XhwoVlz5498vzzz3u0feXKleW7776T9evXS6NGjeT999+XuXPnSufOnXN9X4ko7xUpHu70b7PTj9XVMWf2uidKlC+b4fOcfX6J/21XrExpNS9UNMzp5+nrXb3uzwJERBMfgBRl7969ZcWKFS63eeedd6RHjx7SoEED27olS5ZIsWLFpFu3bh59T2hoqCQnJ0tYWJikpKR4Zd+JyPta9Okp/Sa8IoGBfz7Pa2lpsnTCO7J92UrLHHdaWppEpzvmzF73xIA3XpXmvbpLQECA7f67dPxk9Tn2n4/1gO3wN+a5tU9G8DQeBIsfiYqKknXr1jmsW716tUpZupI/f34JCQlxODFE5HvQZhDNIQoXKyolK1eU3q+MtgVJCAgMVDfjfAXyy8WTZyTu6B+SknBJzHT8ZWtVd3g4wLzf+Jcl9uBhSU645Pb12ENHPPqeWq1aOgRJwN/43KQL8dJv/F+fn34b++88vGWrJF24KEUjS6rl9Pukv54ZbB8eHi6XLvnutfSrQFm6dGm5cOGCwzosFy1aVAoUKKDKLdMbO3asTJgwIQ/3koiyI6pfb9V2MLOb6sPj/p/6e/XMubJm1jzTH39gUJC0HthfEs/FuX39y9fe9Oh72j7xqEMAtH1OYKC0fWKABAZlXiIXGBQkERXKq0AYUbGCWnb1ema6du2qcgk/+eQTiYuLE19k+lqvkydPVslqfSpXrpzRu0RETsREL5dp/QfLR8NflP++/W+VhZce1uG1j4a/pLY3ExzPxyNGZTjutNRU2fT50kxf99SGT7+wZak6fE5ammz49EtJS8143p1tm3DmrPr71vXrGT4Py7du3BBPbN++XW7duuXTuX1+FSjPnz8vkZGRDuuwnJSU5DQ1Cbdv31Z5z/YTEfkeZKPGHjwiR2J2yOYlX6tyLvuggDJKrMNrR2K2myrbFXA8hzdv+/O4U1PVOsyjJ05R2aqZve5pF3iHt2yTHStWOQQ3/I3PxWvRE//6fKzXnARVeyGFCmVIoWI5pGBBj447ISFBZsyYIUeOeJZ1bAS/CpQxMTHSoUMHh3WdOnVS64nIXFAZZO7I0bblOSNH+0UFEa8c9/Nj1N+Ypz/mzF73pAs8ZNP+MOMj2/KyyVNtn2P/+XOe+5uc+f2Q02xaZK1CwukzGVKhCLR6itPZe3v27CmlSpWyrbt79674MsObh9xzzz1qgipVqqi/K1SooJbffvttWbRokW372bNnS9WqVWXKlClSq1Ytee6556R///7y3nvvGXYMRJR7rl5OdPq32enH6uqYM3vdE5fOnrP9fT0p2eXn71/3c4b32gdClEOqVOj/Uv+q1uvEKS7LJ9u3by9NmzaVgQMHSnCwf1STMTRQNmvWTHbv3q0mQMDD35MmTVLLZcqUkYoVK9q2P3nypGoeglQk2l+OGTNGhg0bJmvWrDHsGIiIzOzyuThb1rerQIhU6PJ3pqm/MXeX8t+8ebOcPn1aVq5c6fMpSZ2h4fyXX35xWvtK99RTTzl9T5MmTXJ5z4iIyN6OFaukRZ8HXQZCPVWaPnWaHuqTzJ8/369Orl+VURIRkTFu/68Wa2aBML2goCBVRIbiMn/FQElERLmmRYsWUrduXenTp49q7+6P/KMklYjIQpIvXlIdKmCeFxp0uF+Obd/ltsnNrevX1fxa4pUsffa2bdtUM769e/e6bMbn65iiJCKfFxO9LM+Chi9AwEKvQ7nZVvRaYpLt73s6P+CyKcn5Yyfk2I5dcu7w0T/fdyXJxeddsc3t656g8s/y5cvl+PHj4q8YKIm8yF1Db8p+ymr1zNwNGlZ07coVj1K0cUeOyayhz8vFU2cy+bw/A+jN5BR5/PHHpXXr1mIWDJREXpRZQ2/yvZSVVVVr3sSh15367dt65bxXKldBatSoIW3btlXdhpoByyiJiEwms/Eqy9WuKT3HvOCQRdpx+FOy9esVHnVk7i4VunvXLkm7dl3Onj2rhrAyA6YoiciUrJoNjrEhh838s/E/5lhOr83A/g5DmAFGDdG7pcuq4OBguXY50ZYKRacCp06dErNgoCQiU7JiNjhSis7Gq6x1X0uHB4aNny91MgrJXyOCZKUmbr58+VSZJJp/pA++ZmHOoyIisoDCxYo5pJqdpxSDZPjs99V4lzqMNmI/Cgms+3iB22zXFBfllmXLlpVKlSpJzZo1pUQJcz6UMFASEfmpwuFFHVLNzlOKqWocy/Tjd9qPEgIn9+zL1j6cOnVKli5dKp999plcvJi98k1fx0BJRGQS6VOK+niVGMfSWQ3Wyvc0sP3tqjzTGWS32veyc/jwYTlzxn3zEX/GQElEZCKejFcJRSNLSsdn/xp4Qi/PxHp38ufPr4bIeuKJJ/y2S7qsYqAkIsuwSk1YT8arjKhYwWl5ZmY1X8PCwtSgyyiPLFasmFgB21ES5WH7NfKNmrAHft5o+U4MEk6fUeWZ9sHSfkBmVxISEmTRokWqScj58+fFCpiiJMrD9mtE3qA31chqB+X2UMN13UcLbMt6eaazmq8hISFSvHhx2/KFCxckNjbWMql2piiJsgE3Afv2eUhBOmu/lhQfb8v+ws2NXbGRN+hNNcrVqZmjz9m/foMKanp5Jir9OAuSgwYNUtmsCxculEuXLlku1c5ASZQNaJOGm4E7evs1HVIAuLlR3mA2eNa4Ks8MCgpSFXiQ1Yq5FTFQEmUD2qThidn+pozs1vTlPXhKt09RUt5AtjdS+IDrgiYTrmp/Whl+kxjCLKpfH5fbXL9+XZVJhoaGqixXK2KgJMoGZCmlz1bCzRjZrUhJ2rdfo7zNDneXDV6oqDlGs/AW/Ibx0Jc+UKLZB2q2nj592hYsMVkVAyWRlyDFgpsxsltdlfeQMdnh6bPByTWUSaKNZGRkpCxevNivB1z2FgZKojxuv0a5mx3uLhscKcpBUybxErhx9+5dSUlJkfDwcLl27RrPFQMlEZkxO9xVNnhOa4n6C1cjfXgiFecrOlqKFi0qly9fzpX98zdMURKRZbLBzVYT1tXx6M1HPIFg+uOs+VI2vITE2gVLBsm/sMMBIrJENrjZOoTw1vHcTEqWCqmB0qNzF2natKmX99IcmKIkIlMrVaVSph1C+FtnEK4GaD68ZavbMSWduXPnjhw9elQ1/9BrueZEEZOl2oGBkohMzVXlHfuasP7WGYSrAZrRoXlWAyX89NNPsm3bthxX3mlh0varzHol8qLCxYo6zMl4n738uhq42NWAxtP6D84wqLGvczVAc2YdmusKFSok7dq1k4CAANu6nAbJopEl/6xAlS6Vm9mwXf6AgZLIiwqHF3OYk/HiT5xSlXlcDWgce/CIX2W7uhug2ZPUJAIY2km2b99eOnbs6LV9isCwXUFBWR62yx8wUBKRJXg6oLHZjwcp0U2bNklSUpLs2rXLu8N2paZmO5XryxgoibzoWmKSw5x8i9k6hMju8Rw4cECmT5+e6UggWZF04aJK1WYnlevrGCiJvOjalSsOcyJfULhwYenVq5fD6B/ogcfbtpss1a5jrVciMqWc9E5jpo7i0QdupbQgKVemrOTLl0+++uqrXP3OqyZLtQNTlERkSnrvNP5WUceb9EGUt+/bq4bIQjMQyjqmKImITC4h8bLMnj1bNE0zelf8ElOUREQmg1520AQktHBh2zoGyexjipKIyGQefPBBqVatmhQKLcIA6QVMURJ5kRn7uST/s3LlSjl27Jj8vG2rQ+87lD0MlEReYrbRKci/2AfEq1evymeffSZXr3PgZW9g1itRDqreo1YhZDY6hc7fRqkwG7M2GSlcqJCMGDFCVq1aJadOnTJ6d0yHgZIom9A+DVXvXbEfnULnb6NUmE1WBjT2dltGdLyeWw9JjevWk8jISOnWrZt89NFHtnJJFgV4BwMlUTbhxnfg5422GxKyW+2HPkIXXuidJH2KkqzZlhG/ldwKlDG7dqpuEzds2GALkmYd8soIDJRE2YSbnv2NDzciNcxQUJDD6BREuQG97OhZyYkXLsq3Bw6lG/LKOwM7Z5UZs7cZKIm8BE/rKJNEditSkgySlFuKFSsmgwcPVqOAOMtK/nPIK+8N7Ozr2du5jbVeibzIjP1c+gOUA3Z+7mk1t4IGDRpIeHi4REVFSXBwsIshr7I/sDM5YqAk07HaTZP+KgfUayGb3caNG2Xt2rWyaNEip6OA/DnkVfYGdqaMGCjJdKx20yTrDJVlb/PmzZKSkmK5Ia+MwEBJROTjihcvLs8++6x07949S+9jUYB3MFASEfm4smXLqo7Oq1SpIgUKFDB6dyzH8EA5cuRIOXHihNy4cUO2bt0qzZs3d7ktCq1fe+011Ychtt+9e7d06dIlT/eXiCgrvNHof//+/RIdHS0LFy6Umzdv8gJYKVD2799fpk2bJhMnTpQmTZrInj17ZPXq1VKyZEmn27/55psq++GFF16QunXrqvHVli1bJo0aNcrzfSciys3+f1GrFW0ldb///rtcu8a+Wy0XKEePHi1z5sxRT0kHDx5UfRVev35dhg4d6nR7jK/29ttvy/fff69SoQiU6NtwzJg/C6yJiHyFq0b/WJ+ZiIgIdR98/PHHHYIlWSxQ4uI3bdpU1q1bZ1uHrpewjLZBzoSEhGTIdkAWbOvWrV1+T/78+VXevv1ERJTb3DX6zwzKIXHvKlSoEAOllQMlnphQ5njhwgWH9VguXbq00/cgWxap0OrVq6shZTp27CgPP/ywlClTxuX3jB07VpKTk21TbGys14+FiMibjf7Pnj0rn3zyiWoniVw2snhlnqx46aWX5OjRo3Lo0CG5ffu2zJgxQxYsWCBpaY4/RnuTJ0+WsLAw21SuXLk83WeyFjP2c0nZk9VG/0g84B6lw0N9ToMkf49+HigTEhJUjxIYGsYels+fP+/yPX369FENbytVqiS1a9dWA5QeP37c5fcgoKJRrv1E5mbk0EJ6P5ccczJv+epwUp42+kcFxiFDhqjJPljmFH+Pfh4o79y5Izt37pQOHTrY1iE7FcsxMTFu33vr1i05d+6cyrrt27evrFixIg/2mPyhC7mc1DIk/+Tr19yTRv94oMc9Efc2zMm3GDp6CJqGIA/+119/le3bt8uoUaNUahHZqYDXkP0wbtw4tdyiRQuVdYr2k5hPmDBB1ST717/+ZeRhkAHj7wECsX03dUhNYPy99LUMMaKHfpNCVhRTe/5Pv/aZXXN/ud5JSUnqvocgiQqK5FsMDZRLly5VWQ6TJk1SFXgQALt27Srx8fHq9YoVKzqUP6ImGNpSVq1aVWW5omkImozgR0bWg1HjEZDdQS1DDHulQ/mh2YYAsiJ3197+mvvy9UYxE2r/o+IOoLIh+SbDx6P88MMP1eRM+/btHZYxene9evXyaM/I18VEL1epVh1SF8h601MXegUKlA3ZpyjJPNc+s2vuq9cbFXcwnmRQUJBqRx4XF2f0LpEvB0qi7EKWWvpstegJ76isN6Qq9FqGHEDZ3NfeH685csHQFA4pysREjl3q6xgoyVRQqxDlU8h6Q6rC12+YZM1rjrLIxYsXq5QwKvCQb/OrdpREnuDQQtbjD9e8RHi4NGjQwLas13Il38cUJRFRLkI56c+zF0i3tu2lQEiICo5HjhzhOfcjDJRERLkIZanfzpwjad27q5qup06d4vn2MwyURES5DAM+oDkbOklhhwL+h2WURES5AJ2i2DdxQ7BkkPRPTFESEXkZhsdCZyjoJAX9S6P3MfJfTFESEXkZRv3A2LoYYH7v3r08v36OKUoiolyAVCQGfkCWK/k3pijJdEMecQw+MkKFChWkX79+qsKOjkHSHBgoyXRDHnEMPspr6IpuwIABqi/qNm3a8AKYDLNeKds4zBXRn1Cb9auvvpJ7771XNm3axNNiMgyUlG0c5op8hVHZ7RhsXs9ePXnypJrIfAKQjS4WEhoaqsZ9CwsLU9W2ybsDJ3syzJU/DKRLlJnKlStL586dVefmGB+XzBsPmKKkbOMwV2RVSEn26NFDDTzftm1b1esOmZtmpSk0NFQDzI3eF7NOte5rqU3dF6PmRu8LJ56D3PoNhIeHa7169dKCg4P5OxNzxwPWeiVLDnlElB32TT8w4PKKFSvk7t27PJkmx0BJROSBqlWryosvvihlypTh+bIYBkoiIg+0bt1aVfpAExCyFgZKIrI81ODu/NzTau7Kl19+KRs3bpRvvvnG8ufLahgoicjy0Mypy8hhDs2d9FFAdLdu3ZIff/xRUlNTLX++rIaBkojIierVq8uoUaOkbt26PD8Wx0BJRORE7dq1JX/+/Kr/VrI2djhAROTEd999JxcuXFBDZZG1MUVJRPQ/RUPDbOcCfbju2LFD0tLSeH4sjoGSiEhE7p6Ll75dukn79u15PsgBAyURETrwv3ZDgoKCpESJEqovVyIdAyURkYjkr1FJVm/4Wf773//ahs4iYqAkIkurWLGiSkXqTsedY5kkZcBar0RkSXXq1JF+/frJ4cOHZcv+PUbvDvkwZr2SaUabJ8qKO3fuqNTj7du3mdVKbjFFSbkyoPOaWfN4ZsmnHTt2TObOnavaStZs1UKtK1I83OjdIh/EFCURWUbNmjUd+m89f/68NO/9oAybOU0tY96iT08D95B8EQMlEVmmTPLRRx+VwYMHS4ECBdS6opElpd/4VyQw8M9bIeb9xr8s5WrXNHhvyZcwUBKRJcTHx8u1a9ckNjZWjQQCERUrSGCQ420wMChIWg/sb9Beki9iGSURWcKlS5dkzpw5kpKSYqu8k3D6jKSlpjkEy7TUVNn0+VID95R8DVOURGRa9evXl1KlStmWk5OTHWq4Jl24KNET31HBETCPnjhFYg8dMWR/yTcxUBKRKdWqVUv69u2ryiTDwv7q7Dy97ctWytznx6i/MccykT0GSrK80IgS0vm5p9WczOPUqVMSFxcnv//+u8pudefq5USHOZE9llGS5YWVLCFdRg6TAz9vVG1AyRxu3rwpCxcuVB0LsO9WygmmKInINBo3bqyyXHXsdYe8gYGSiEyhSpUq0qtXL+nfv79ERkYavTtkIsx6JSLTlEnu379frl69qrqlI/IWBkoiMgV0cI6xJDEn8iZmvRKR32rWrJm0bt3atswgSbmBKUoi8kulS5eWBx98UP2NbulOnDhh9C6RSTFQEpFfwsgfP/74o+rgnEGSTJ31OnLkSPUjv3HjhmzdulWaN2/udvuXXnpJDh06JNevX5fTp0/LtGnTJCQkJM/2l8xHH4OQYxH6n40bN8ratWuN3g2yAM2oqX///trNmze1IUOGaHXq1NE++ugj7fLly1rJkiWdbv/YY49pN27cUPNKlSppnTp10mJjY7WpU6d6/J2hoaEaYG7ksXPyjXPQok9P7d09m7Wp+2LUHMtG7xMn1+egZcuWWt++fbXAwECvnqdydWqq3wDmPP/W+Q2GehgPDM16HT16tOrNH71nwIgRI6RHjx4ydOhQmTJlSobtW7VqJZs3b5YlS5bYqoPj75YtW+b5vpN/Qjd16IlHT0H2m5BxLMKk+HhbV2bJFy+xtx4fUaxYMencubMEBQWpXKUDBw547bNxnVfPnKvmROkZFijz5csnTZs2lcmTJ9vWoZupdevWSVRUlNP3bNmyRQYNGqSyZ3fs2KEaGHfv3l0+/fRTl9+TP39+h6zZ0NBQLx8J+ZOofr1Vd3WuYCzC4bPfty3j5rlm1rw82jty58qVK7J06VIpU6aMV4MkoOtCXmfyuUAZEREhwcHBGRoGY7l27dpO34PUI963adMmCQgIUMF21qxZDsE2vbFjx8qECRO8vv/kn2Kil6s+XfUU5bCZ02wpSn2YJYwgYZ+iJGMhBZn6v2GwDh8+rCYiS1XmyYp27drJuHHjVAWgJk2aSJ8+fVRW7auvvuryPQiiGGJHn8qVK5en+0y+BSmH2INH1HR48zaJnpBxLEKs17dhJ+nGQnELimJQs5XIcinKhIQEuXv3boY+GbGMat/OvPHGGyqbdd68P7PC0F1V4cKF5eOPP5a33nrL6QgB6BQZE5EzGHsQZZLIbkVKEkGSfEPBggXlvvvuU//jdevWlV27dhm9S2RRhqUoMfTNzp07pUOHDrZ1yE7FckxMjNP3FCpUKEPPG3qWDN5LlB0ci9A3ocnYokWLZM2aNQySZChDa72iDST+EX799VfZvn27jBo1Sj09LliwQL2O19DjBrJbYeXKlaqm7G+//Sbbtm2T6tWrq1Qm1rPrKiLzpCQRJCE+Pl5NRJYNlKjBVrJkSZk0aZLqjmr37t3StWtX2z9GxYoVHQLgm2++qbJXMUdZ48WLF1WQ/Oc//2ngURCRt7Rt21bVakeTsUuXWJGKfAPyKzMW7JkYmockJyerij0pKSlG7w75gHJ1asropYtkWv/BqgIPGQO14J955hlVT2HVqlUql4nIF+IB+3olIp+Ayn0obqlVq5YqXiHyFX7VPISIcq/Hos7PPa3mea148eK2v9GHM4Mk+RoGSiJS3fqhxyK9e7+8cv/996t20dWqVeNVIJ/FQEk+ndog80KTLlTiQ9kkKvUR+SqWUZLHqQ10/caeashbUIM9OjpaatSooTo5J/JVTFGS5XHkiLyFZl/2HYYwSJKvY6Aky9NHjmBqOfd17NhR9d167733Wv53R/6DgZKI8ozegQh70iJ/wjJKIsozP/30kxw5ckTOnj3Ls05+gylKIspV9erVcxi0gEGS/A0DJRHlmk6dOkm/fv2kZ8+ePMvktxgoiUiKFA93mHvLuXPnVM1WjAJE5K9YRklkcS369JR+E15Rfw+bOU2iJ7yjBrT2hgMHDqhgmZiY6JXPIzICU5REFlaudk0VJAMD/7wVYN5v/MtSNDL7PeWg6Uf+/PltywyS5O8YKIksrM3A/rYgqQsMCpKICuWz3U4SY8oOHDjQoQIPkT9joCSysI2fL83QpjEtNVUSzpzNdlarPgIIuqgjMgMGSiILiz10RJVJIjgC5tETp0jShYvZ+ry4uDj54IMPZPfu3V7eUyI/rMyDbJXq1atLqVKlMmTdbNy40Rv7RiavEUm+ARV3kuLjZfjs92Xu82Pk8OZtWboPtG/fXqUg9bLImzdv5uLeEvlJoGzZsqUsXrxYKlWqlKEcAtktGDaHzCE3a0SS77h6OdFh7qm2bduqqUGDBvLhhx/K3bt3c2kPiYyTrYg2e/Zs+fXXX6VHjx4qq4VlEeaBMSf1wXuRgnRWIxKpD/sbKkbfYIfi1rRz507V8w5ykRgkyayyFSgxftwjjzwif/zxh/f3iAwV1a+3GnvSFdSIRBadvdUz56rRN8h6rl69Kh999JHqVIDIrLIVKLdt26bKJxkozScmerkaoFlPUSK71b4MGpU9UI6VPkVJ1oCilm7dusnvv/8uJ0+eVOsYJMnsshUop0+fLlOnTpXSpUvLvn375M6dOw6vYx35J2Sh2mejokwS2a1ISeo1IrNS2YPMBfUTWrRoIQ0bNpT//Oc/cuPGDaN3icg3A+XXX3+t5vPnz7etQzklnjZZmcdcclIjkswHdROqVq0qe/bsYZAky8hWoKxSpYr394RMVyOSzAcVdlDjnchKshUoT58+7f09ISKfg/Lp3r17y6lTp1QNVyIrylGDxzp16kjFihUdOkCGlSvZzo7IDND0A+WRdevWlWPHjklSUpLRu0TkP1mvy5YtU42M9bJJ0NtTssMBIv+Cmsto5pO+BjMq5qHS3pkzZxgkybKy1dcrarudOHFCdV+HDpDx1IneOVDQf//993t/L4koV6GmM9rCYo7sVvset9auXSuHDh3iFSDLylagjIqKktdff10uXbqkRh7AtHnzZhk7dqzqEJmI/BOCZN++faV79+5G7wqRfwfKoKAgSUlJUX8nJCRI2bJl1d8o8K9Vq5Z395CI8gzqHKA8snHjxhIZGckzT5TdMsr9+/fLPffco3rmQC89//jHP+T27dsyfPhwOX78OE8skZ/C//Ty5ctVkcqFCxeM3h0i/w2Ub775phQuXFj9jSzYb7/9VnWKjKzYAQMGeHsfiSgXIYcIWa56D1voTICIchgo16xZY/sb/b2imUh4eLhtPDoi8p8g2a9fPwkJCVEdCaTvjpKIsllG6QyDJJH/KV68uGruVaFCBZZJEnkzRYmnzxdeeEGNbI4mIvajS0DTpk2z87FElMcuXrwon332meo05OzZszz/RN4KlPPmzZPOnTvLV199Jdu3b+fAzRZtjE7+m91asGBBNZYkoDMBInJPy+p05coVrVWrVll+ny9MoaGhGmBu9L4Ydg4iSmidn3tazY3eF055ew6Cg4O1xx9/XHvxxRe1sLAwnn/+Bi39Gwj1MB5kq4wyNjbW1o6S/E9YyRLSZeQwNSdrKVCggEREREhoaKiqgEdEmctWoBwzZoxMmTJFNU4mIv+B7NZFixapckl0EEJEuVRGiT5d8WSKzgXQMDl9lfISJZhSIfIVGKQA/5N6BwIYAYSjgBDlcqBcsmSJlCtXTsaNG6f++fRRQ4jI94LkY489JuXLl5dPP/2UNVuJ8ipQtmrVSnWMvnfv3uy8nYjyCEYB0UcDQW1XIsqjQIkhd1C9nIh8G4pF0OMOKvDExcUZvTtE1qnM88orr8jUqVOlXbt2qmcP1KCzn4jIOPny5ZPq1as7BEsGSaI8TlH+8MMPav7jjz86rEf2DsorUS5CRHkP/3uPP/64VK5cWZYtW8biESIvyFZEQ9d1ROR77t69axsj9vLly0bvDpF1A+WGDRu8vydE5BWrVq2SmJgYBkoiI8so27Rp43bKqpEjR8qJEyfkxo0bsnXrVmnevLnLbdevX6+yd9NPGBOTyIrQobn9/wz+H5iaJDI4Rfnzzz9nWGffljIrZZT9+/eXadOmyYgRI2Tbtm0yatQoWb16tdSqVUuNbJDeww8/rG4MOjSkxkCz0dHR2TkUIr+GegEDBw6USpUqSZEiRdSDJBH5QIoSfUTaTxhqq2vXrrJjxw41qkhWjB49WubMmSMLFy6UgwcPqoCJ3n6GDh3qctxLdHKgT506dVLbM1CSFeEBdd++fSo35vDhw0bvDpEpZStFmZycnGHdunXr5Pbt2yp12KxZM4+rsWPsysmTJzv84+Oz0KGBJ55++mn54osvVLB0BqlPjJ+pY/MVkSLFwx3m5N/QpeSBAwdUsCQiH0lRuoIUHrJMPYVG0Mim1fugtP+c0qVLZ/p+lMs0aNBA5s6d63KbsWPHqsCuTxj5xMpa9Okpw2ZOU39jjmXyL3jw69ixo0MRB4MkkY+lKBGc0peTlClTRnVEsHv3bskrSE2iGz1k+bqC1CpSufYpSisFy9CIErbhtJCC7DfhFdWlGWDeb/zLkhQfL1cvJ9regwGaUxI4SLOvGjBggFStWlXCwsLkv//9r9G7Q2R62QqUCIbIIkWAtIcaq67KFp1Bey+0+4qMjHRYj+Xz58+7fW+hQoXk0Ucflddff93tdsgOxmRVUf16q7EnXQkMCpLhs993WLd65lxZM2teHuwdZbd5FiqxbdmyhSeQyFcDZZUqVRyW09LSVA3VW7duZelz0LXWzp07pUOHDrJixQq1DsEXyzNmzHD73n79+qksKIyrR67FRC+XAz9vtKUokd2qpyjVtUtNlbnPj8mQoiTfdfLkSfnggw8kNTXV6F0hsoRsBcrTp0/LAw88oAIaarza33j1LFFPIVsUA8miQsL27dtV85DChQvLggUL1Ot4DVmlGNIr/XcsX76c7cUygSxU+2zU6AnvqOxWpCQRJKMnTpHDm7d5fL0o72Hs1+7du8uaNWvUwMvAIEnk44ES2Z2YENzQ2XJOxqNcunSplCxZUiZNmqQq8CBbF01N4uPj1esVK1ZUKVZ7NWvWVB0boGkIZc32ZStVmSSyW5GSZJD0fb169ZI6deqoMkk0oyKivKdldTp37pw2aNCgLL/PF6bQ0FANMDd6X4yaytWpqU3dF6PmRu8Lp8zPQXh4uDZ8+HCtVKlSPF/8zfA3IHkfD7KVokTbRFYkIMob6GTj448/5ukm8qd2lGi3iKF8iMj7UKN78ODBqskVERnP4xQlBmrWofLO8OHDVaNntGNE7VV7Y8aM8e5eElkIKsmhZjn6NZ45c2aO6gAQUR4GysaNGzss6x0L1K9f32E9/6mJcga1W5GqxMDo/H8i8qNAieYgRJQ7kEuj1+5Ge+Qvv/ySp5rIjH29ElHWod0wijLq1avH00fkgxgoiQyG0XbQhhjtgrMylisR5Q3+VxL5QN+taHKF7hzR9zER+RYGSiIDIDDqnfWjws7atWt5HYh8FLNeifJYkSJF5JlnnpH27dvz3BP5AQZKojxWvXp11b9xo0aNpGDBgjz/RD6OWa9EeQxtkIOCguT48eNy48YNnn8iH8dASZRHTUBu3rxpGx4LFXeIyD8w69WCMDDz6plzOUBzHsHwWEOHDlWDjSMlSUT+hSlKC8JAzmtmzTN6NyyjePHiUrRoUdX7DrqmS0lJMXqXiCgLGCiJctnJkydl8eLFcunSJQZJIj/EQEmUS9mt6Dzg+vXrahkVd4jIP7GMksjLkM361FNPqTElkdVKRP6NKUoib/9TBQerCT3usO9WIv/HQEnkZSiLXLhwoRrQPDk5meeXyM8xUBJ5QbFixVT/rfHx8bZgSUTmwDJKHxcaUUI6P/e0mpPvBkm9TBJd0xGRuTBQ+riwkiWky8hhak6+CT3uXLt2TXVHxy7piMyHWa9EXgiUn3zyiaq4c/XqVZ5PIpNhitJCmI3r3d52atSo4RAsGSSJzImB0kKYjeul8xgWJkOGDJFHH31UqlWr5qVPJSJfxUBJlEVIOZ46dUrVbD1//jzPH5HJsYySKIvS0tJk2bJlEhISwso7RBbAFCWRByIiIqRly5YOwZI1XImsgSlKokwULFhQlUkWKVJEbt++Lb/99hvPGZGFMEVJlAmkHLdt26bKIw8dOsTzRWQxTFESeWDjxo0SExOjhs4iImthipLIiVKlSkmPHj0kMPCvfxEGSSJrYorSxxUpHu4wp9yHHnYGDRqk2kti4OX169fztBNZGFOUPqxFn54ybOY09TfmWKbch5Tjd999J2fOnFHZrURkbUxR+lD3cvYdnyMF2W/CK7asP8z7jX9ZkuLj5erlRLUu+eIlSUngcE654fDhw3LkyBE1+DIRWRsDpY+I6tdbjRLiTmBQkAyf/b5tefXMubJm1rw82DvzK126tHTs2FG++uor1W8rMEgSETBQ+oiY6OVy4OeNDilKZLfaVyZJS02Vuc+PcUhRUs4FBARI37591ViSCJbffvstTysR2TBQ+ghkoabPRo2e8I7KbkVKEkEyeuIUObx5W7a/gxWDnEPKMTo6Wjp06CBr167N9vklIvPSrDSFhoZqgLnR++LJVOu+ltrUfTFqnpPPadGnp/buns3qszDHstHHZvQUEBBg+D5w4jngb8D34wFTlD5Oz2bV59mpHORJxSCrVQ4qW7as9OnTR7744gs1CggRkSsMlBatHJS+YpDVKgd17txZlUkiu3Xp0qVG7w4R+TAGSgtUDvKkYpDVKgchOCJIrl692uhdISIfx0BpkcpB3q4Y5I/y5csnd+7cUX+jx52VK1cavUtE5AfYM49FbF+2UqUgAXMsW0n58uXlpZdekmrVqhm9K0TkZxgoLSS7FYPMoFmzZmo8yXvvvdfoXSEiP8OsV7IEZLMmJibKli1bjN4VIvIzTFGSaSEFqUtNTZVffvnFVkZJROQ3gXLkyJFy4sQJNYr81q1bpXnz5m63L1q0qMyYMUPOnTun+uRE59XdunXLs/0l/1CxYkV54YUXmNVKRP4dKPv37y/Tpk2TiRMnSpMmTWTPnj2quj7at7mqtYguxipXriyPPPKI1KpVS5555hmJjY3N830n34bfSEhIiNSoUUP15UpElBOGdR+0detWbfr06Q5dip09e1Z7+eWXnW7/7LPPaseOHdOCg4NzvcsiX5lCI0ponZ97Ws1z+lnl6tRUXdhhbvRx5cXUqFGjHP1WOPEc8Ddg7t+Ap/HAsBQlUodNmzaVdevW/RWxNU0tR0VFOX3PQw89pAbS/fDDD+X8+fOyb98+GTt2rEND+vTy588voaGhDpM/QVtI9JZjla7lcgI5Efapx927d6tBmImIcsKwQBkRESHBwcFy4cIFh/VYxtiAzlStWlVluQYFBUn37t3ljTfekDFjxsirr77q8nsQSJOTk20Ts2nNm9U6fPhw9TDFrFYiMlVlnqxAyjE+Pl7dEHft2qW6IXvrrbdkxIgRLt8zefJkCQsLs03lypXL0332B+hAvfNzT6u5vypUqJB6gEJNV3c5DEREftOOMiEhQWWLRUZGOqzHMrJVnYmLi1PV+9PS0mzrDh48KGXKlHHonsze7du31USuYZQRdKCOvmH9NYv3999/l08++UTOnDmjmoIQEXmLYY/eCGo7d+5UHVPrkGWGZZRDOrN582apXr26Q9ZazZo1VVMRto+zngoVKqiarbqTJ08ySBKR1xmaR4WmIWje8eSTT0rt2rVl1qxZUrhwYVmwYIF6fdGiRfL222/btsfrxYsXl//85z+q2j/KKceNG6cq95C1oLx68ODBMnDgQFVhi4jIlF3YoYwRNRUnTZqkKvCglmLXrl1VOaTeaNw+m/Xs2bPSpUsXee+992Tv3r2qYg6C5pQpUww8CjICOqhALgJGAWFWKxHlNsPbsvhiuxkzTq7aZPpr+8qIiAgtKCjI8P3gxHPA34CYOh6wU3QL0dtk+isMkXX58mXVubleIYyIKLexHj35TZnkY489JkOGDFHNfIiI8gpTlOQXUG595coV1SHF1atXjd4dIrIQBkryCwiO8+fPVyPG2FfwIiLKbcx6JZ+F0WHQNZ0ONVwZJIkorzFQkk9C0yAMw/b4449LqVKljN4dIrIwZr2ST0JvS8ePH1dZrazdSkRGYqAkn4R+gL/88kuV1crsViIyErNeyWfUrVtXjVFqHywZJInIaExRkk9AF4YYaxRDZKFTgRMnThi9S0RECgMl+QQMrbZ161Y1riRGASEi8hUMlOQz1qxZo4ZQ0zR0r0hE5BtYRkmGadCggRoqzR6DJBH5GqYoyRDFihWT3r17S1BQkJw5c0b27dvHK0FEPomBkqRI8XCHeV5Av63ffPONVKhQQfbv38+rQEQ+K+B/421ZRmhoqCQnJ6sRKFJSUsTqWvTpKf0mvKJqm6IpRvSEd2T7spW59n0sgyQif4sHTFFaSGhECQkrWcK2jBSkHiQB837jX5ak+Hi5evnPMR8h+eIlNZZlTjVq1EgaNmwoS5YskTt37uT484iI8gIDpYVE9estXUYOc7tNYFCQDJ/9vsO61TPn5njA54IFC0qXLl3UvHHjxrJ9+/YcfR4RUV5h1qvFU5TDZk6zpSghLTVV5j4/JldSlOXLl5c6derI2rVrc/xZRER5lfXKQGlxqoxy/MsqJYkgGT1xilfLKIODg1VXdERE/hoo2Y7S4hAUkYIEzL0ZJNFv68iRI9WPkIjIXzFQki2b1T671RspyVatWknx4sXlnnvu4VkmIr/FyjyUK5DdumjRIlXLddOmTTzLROS3mKIkrypcuLDtb+T9M0gSkb9joCSvad68ubzwwguqdisRkVkwUJLXetxB048CBQpI9erVeVaJyDRYRklegVE/0OMORgTZtWsXzyoRmQZTlJQjERERtr/RLR2DJBGZDQMlZVtUVJRqJ8nmH0RkZgyUlG3h4eGq+zvMiYjMimWUlG3ff/+9HDt2TI4cOcKzSESmxRQlZUmVKlUcKvAwSBKR2TFQksdat24tgwcPls6dO/OsEZFlMFCSx27cuKHmN2/e5FkjIstgGSV5bOfOnXLu3DmJi4vjWSMiy2CKktxCBwIYCUTHIElEVsNASW7LJPv27SsDBgxQXdQREVkRAyW5dObMGbl9+7acPHlS1XAlIrIillGSS6dOnZIZM2ao4bKIiKyKKUqS5IuXZPXMuWresmVLKVKkiO2sMEgSkdUxUJKkJFySNbPmSYOataRbt26qraR9BR4iIitjoCSbgwcPSlJSkmoGcvfuXZ4ZIiKMt4ueyKx0JkJDQ1V2YlhYmKSkpBi9Oz4nf/78qgIPEZHZhXoYD5iitLh27dpJmTJlbMsMkkREjhgoLaxZs2bSvn17eeKJJ6RgwYJG7w4RkU9ijQ0L27t3rzRs2FD27dtn68eViIgcMVBaGLJZFy5cKGlpaUbvChGRz2LWq8VgiKx69erZlhkkiYjcY4rSQurXry+tWrWS1NRUiY2NlStXrhi9S0REPs8nUpQjR46UEydOqHKyrVu3SvPmzV1ui8bw6HfUfmL5mmcOHDggu3btku+++45BkojIX1KU/fv3l2nTpsmIESNk27ZtMmrUKFm9erXUqlVLLl686PQ9aBSP13XssNszOE/ffPONl64cEZF1aEZOW7du1aZPn25bDggI0M6ePau9/PLLTrcfPHiwlpiY6PHn58+fXwsNDbVNZcuW1QB/G33suT3hXPbo0UN74IEHDN8XTjwH/A3wNyA+dg4QBzyJB4ZmvebLl0+aNm0q69at+ytqa5pajoqKcvk+dNqNoZ9Onz4ty5cvl7p167rcduzYsarnBX1C2ZxVVK5cWWVjt2nTRkqXLm307hAR+SVDA2VERITqfPvChQsO67Hs6sZ++PBhGTp0qPTq1UsGDRokgYGBsmXLFilXrpzT7SdPnqy6J9InV9uZEcp9f/jhB/Uwcf78eaN3h4jILxleRplVqOyDSYcgic68n332WXn99dedthW0UrdsAQEBatKbfdifKyIi8rMUZUJCghqlIjIy0mE9lj1NAeH9v/32m1SvXl2sDgGyZ8+eqoJUUFCQ0btDRGQKhgbKO3fuqCGdOnTo4HCzx3JMTIxHn4Gs1wYNGkhcXJxYXcmSJVWXdDVr1pQKFSoYvTtERKZhaK2j/v37azdu3NCefPJJrXbt2trs2bO1y5cva6VKlVKvL1q0SHv77bdt27/22mtap06dtCpVqmiNGzfWFi9erF2/fl2rU6eOV2s5+etUrVo1rX79+obvByeeA/4G+BsQHz8HnsYDw8soly5dqlJCkyZNUhV4du/eLV27dpX4+Hj1esWKFR26WQsPD5c5c+aobRMTE1WKFL3NoJzSipACDwkJkZs3b6rlP/74w+hdIiIyFQ7c7OdBsnfv3uqhYdGiRXL9+nWjd4mIyG9w4GaLXOSqVauqFHnZsmWN3h0iIlMyPOuVsg8dKGCYLLRHPXbsGE8lEVEuYKD0M6jlW7RoUVU+C5cuXVITERGZePQQ8jxI9unTR4YNGyalSpXiaSMiygMMlH4EfeMWL15cChQoIMWKFTN6d4iILIFZr37k1q1b8umnn0qZMmVUP65ERJT7mKL0g+zW8uXL25bRXpJBkogo7zBQ+niQfOSRR+Spp55yGKiaiIjyDgOlj0tNTVVjdNr3TkRERHmHPfP4QaoSo6mw03ciIu9izzx+CsNj1a1b17aMlCSDJBGRcZj16mN9t2IsSUz33Xef0btDREQMlL4FZZFnz55V43QyFUlE5BvYjtLHbNy4Ufbt2ydXrlwxeleIiIgpSuMFBwer8TRRaUfHIElE5DuYojQY2knWrl1bDZW1YsUKo3eHiIjSYWUeg/32229y48YN2bNnj9G7QkRETrAdpQ9AJ+fomo6IiPIO21H68AggXbt2VcFRxyBJROS7mPWaxx566CG59957ZcCAAXn91URElA0MlAY0/7h8+bL89NNPef3VRESUDSyjNACagrCTcyIiY7GM0ofKJNEEJCIiwraOQZKIyH8w6zWXde7cWerXry+PPvqo6suViIj8CzscyGXr16+XUqVKyZo1a1RfrkRE5F8YKHPZ9evXZcGCBbn9NURElEuY9eplISEh8uSTT0q1atW8/dFERGQABkovQwfnVatWld69e6sOz4mIyL/xTu5lGzZskLCwMNm+fbvcvXvX2x9PRER5jIHSC4KCgiQ1NVX9jTlHASEiMg9mveYQ+mx96qmnVLd0RERkPgyUOVSvXj0pX768tG3bVgoVKuSdq0JERD6DWa85tHPnTilYsKAcPXpUNQUhIiJzYaDMZhOQ27dv2zoQ2LRpk7evCxER+QhmvWYRUo9DhgyRXr16sUs6IiILYIoyi8qVKyeRkZGqCQimpKSk3LkyRETkExgos+jYsWMSHR0tCQkJDJJERBbAQOkB1GZF+8hbt26p5YMHD+b2dSEiIh/BMkoPguTgwYNl4MCBkj9//ry5KkRE5DMYKD0YARtlkeHh4VKkSJG8uSpEROQzmPWaiQsXLsgnn3yimoNcvnw5b64KERH5DAZKJwoXLqyyWRMTE9VyXFxcXl8XIiLyEcx6dRIkUSaJtpLIbiUiImtjoEwnICDAYSIiImtj1ms6V69elUWLFkm+fPlsWa9ERGRdTFGKqNqsFStWdAiWDJJERMRA+b8ySZRHPvHEE1KpUiX+KoiIyIHls17R2w5Sj8HBweySjoiIfDPrdeTIkXLixAm5ceOGbN26VZo3b+7R+wYMGKCGulq2bFm2v/vu3bvy5Zdfyvz58+XKlSvZ/hwiIjInwwNl//79Zdq0aTJx4kRp0qSJ7NmzR1avXi0lS5Z0+z5kk/773/+WDRs2ZOt769ev7xAsk5OTs/U5RERkboYHytGjR8ucOXNk4cKFqrPxESNGyPXr12Xo0KEu3xMYGCiff/65jB8/Xo4fP56t7+3Zs6c0bNgwB3tORERWYGgZJZpgNG3aVCZPnmxbh6zUdevWSVRUlMv3vf766xIfH6+yS9u0aeP2O9DDTkhIiEPfrYBgjC7p9GUiIrKWUA/v/4YGyoiICFWJBv2p2sNy7dq1nb7nvvvuk6effloaNWrk0XeMHTtWJkyYkGH9G2+8oSYiIrK20NBQSUlJMUetV7R3/PTTT+WZZ56RS5cuefQepFZRBmp/QmJjY6VcuXJuT4xZ8HjNi9fWvHht8/Zcnzt3zu02hgbKhIQEVZEmMjLSYT2Wz58/n2H7atWqSZUqVWTlypUO5ZVw584dqVWrVoYyS4z6gSk9BEkrBEodj9e8eG3Ni9c293kSBwytzIPgtnPnTunQoYNtHfpXxXJMTEyG7Q8dOqRqqyLbVZ+++eYbWb9+vfr7zJkzeXwERERkdoZnvSJbFH2r/vrrr7J9+3YZNWqU6i1nwYIF6nW8hqzScePGqc4BDhw44PB+ve1j+vVERESmCJRLly5VbSYnTZokpUuXlt27d0vXrl1VrVZAH6xpaWle+z4EW1TuwdwKeLzmxWtrXry2vgXjSGlG7wQREZGvMrzDASIiIl/GQElEROQGAyUREZEbDJRERERWC5RGDtvl68c7ePBgdYz2E95n1mtbtGhRmTFjhup54+bNm3L48GHp1q2bmPF40Z44/bXF9O2334pZr+9LL72k2lej7+bTp0+r5mb2fTub5VjR1edrr70mx44dU9ujdUCXLl3EX7Rp00a1eUdTP/wme/Xqlel72rVrp9rZ4//26NGj6t5lJM1MU//+/bWbN29qQ4YM0erUqaN99NFH2uXLl7WSJUu6fV+lSpW0M2fOaL/88ou2bNky0x7v4MGDtStXrmiRkZG2qVSpUqY81nz58mnbt2/Xvv32W61Vq1bqGrdt21Zr2LChKY83PDzc4brWrVtXu3PnjrrmZjzexx57TLtx44aa49p26tRJi42N1aZOnWq6Y33nnXe0s2fPat26ddOqVKmijRgxQrt+/brWqFEjw49FPJi6du2qvfHGG1rv3r016NWrl9vtK1eurF29elX797//rdWuXVt7/vnn1W+5c+fORh2D8SfRm9PWrVu16dOn25YDAgLUD+zll192+Z7AwEBt06ZN2tChQ7UFCxb4VaDM6vHippmYmGj4fufFsT777LPasWPHtODgYEscb/rppZde0pKSkrRChQqZ8nix7bp16xzW4ca6ceNG0x0rHgBGjhzpsO6rr77SPv30U8OPRbI4eRIo8WCwb98+h3VLlizRvv/+e0P22VRZr/qwXRimK7vDdlnheNG5/MmTJ1VW1fLly6Vu3bpixmN96KGHVFeIH374oeo7eN++fWo0Gb1/YDNeW3sYZeeLL75Q2ZJmPN4tW7ao9+hZlugHunv37rJq1Sox27EiOxlZkPaQBdu6dWsxo6ioKIfzA6tXr/b4t+9tvn/H8NKwXej1x92wXRiRxArHizI6DIqNMoJBgwapoIEbDkZTMduxVq1aVR555BEJCgpSN1AMqzZmzBh59dVXxddl53jtIXg0aNBA5s6dK/4gO8e7ZMkS9ZC7adMmNfABBkT4+eefHca3NcuxIkhgkPvq1aur/rA7duwoDz/8sJQpU0bMqHTp0k7PD+ocFChQIM/3x1SBMi+G7fJ3qDSAY96zZ49s2LBB/bNdvHhRnn32WTEbPAQgp2D48OGya9cu1V3iW2+9JSNGjBCzw8Pf3r17ZceOHWJWqOyBPqBRKaZJkybSp08f6dGjh188CGUVKi2hQgsqLuGhABXU0B+2N7v3JB/u69Xfhu3y5+N1Bu//7bff1JOqL8vOscbFxanraH8zOXjwoHoKR/YXXjPjtS1UqJA8+uijKrXlL7JzvMghwEPfvHnz1PL+/fvVgAoff/yxeiBCdqZZjhXvwYMAsmBLlCihanG/8847Pn1/ygmcB2fnJykpKUMWdF4wVYrSasN2ZfV4ncGDAbLoEFTMdqybN2+2ZVXpatasqW4yvhwkc3pt+/Xrp26on332mfiL7BwvHgjSp6hSU1Nt7zXjtUVn6fj9Iuu2b9++smLFCjGjmJgYh/MDnTp18vi+lhs0M02odo0q408++aSqVjx79mxV7VpvArFo0SLt7bffdvl+f6v1mtXjfe2111Q1elQxb9y4sbZ48WJVzRxV1M12rOXLl1e1Pj/44AOtRo0aWvfu3bXz589r48aNM/xYcuN49WnDhg2qhqDR+5/bxzt+/Hh1fQcMGKCaE3Ts2FE7evSo9sUXX5juWFu0aKH16dNH/d+2bt1a1fb9448/tKJFixp+LOLBVLhwYe2ee+5RE4waNUr9XaFCBfU6jhXHnL55yJQpU7RatWppzz33HJuHePuioM3NyZMnVTslVMPGj0x/bf369SoYmiVQZvV4p02bZts2Li5OtTH0l7ZY2bm29957rxYTE6NuSmgqMnbsWNUcyKzHW7NmTXUjQtAwet9z+3iDgoK0119/XQVHPOydOnVKmzFjht8Ej6wcK9r/HjhwQP2OL168qIJKmTJlDD8G8XBq166d5ox+jJjjmNO/Z9euXer84H/XyPbAHGaLiIjIKmWURERE3sZASURE5AYDJRERkRsMlERERG4wUBIREbnBQElEROQGAyUREZEbDJRERERuMFASkQP0dfzee+95fFYGDx4siYmJPItkWgyUREREbjBQEhERucFASeRHWaIffPCByha9fPmyGrNv2LBharip+fPnS3Jyshrct2vXrrb3tG3bVrZt26bG8MPwTJMnT5agoCDb63jvokWLJCUlRb0+evToDN+bP39+effdd+Xs2bNy9epVNfg3Bk0msgoGSiI/gvJADOLbokULmT59usyaNUuio6Nly5Yt0qRJE1mzZo0azLhgwYJStmxZWbVqlezYsUPuueceee655+Tpp5+WV1991fZ5CIAIer169ZLOnTvL/fffrz7H3owZMyQqKkoNBt2wYUP1fT/88IPPD/ZN5E2GD8HCieeAv4HMfwMYhghjTerLGC4sJSXFYRy/yMhINXxRy5YttTfffFM7ePCgw2dgXL/k5GQtICBAjRGIIYweeeQR2+vh4eHatWvXtPfee08tY7zAO3fuZBjSae3atdpbb72l/sbwR4mJibyG/D/WzHoOgr0acokoV+3du9f2d1pamly6dEn27dtnW3fhwgU1L1WqlNSpUyfDiPCbN2+W0NBQKV++vISHh0tISIjKmtWh9urhw4dtyw0aNJDg4GA5cuSIw+fgffhuIitgoCTyI3fu3HFY1jQtwzoIDPROqUqRIkXk7t270rRpU0lNTXV4DeWVRFbAQElkUgcPHpS+ffs6rLvvvvtUpR9UzEGFoNu3b0vLli3lzJkz6vVixYpJzZo15ZdfflHLv/32m0pRIoW6adMmQ46DyGiszENkUjNnzpQKFSqoSj+1atWShx56SCZOnCjTpk1TKdFr167JvHnzVIWe9u3bS7169WThwoUqS1eHWrSfffaZfPLJJ9KnTx+pXLmyNG/eXF555RXp3r27ocdHlFeYoiQyKTT3QDBDINyzZ49KQSIwvvnmm7Zt/v73v6vs1ZUrV6omIlOnTpWiRYs6fM5TTz2lasritXLlyqlat2gi8u233xpwVER5L+B/tXqIiIjICWa9EhERucFASURE5AYDJRERkRsMlERERG4wUBIREbnBQElEROQGAyUREZEbDJRERERuMFASERG5wUBJRETkBgMlERGRuPb/Aa9Xv9SV2MZ/AAAAAElFTkSuQmCC" 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" }, "metadata": {}, "output_type": "display_data",