From c0cd281e3a2ad0e2549b87af2a362712989552bf Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 13:35:02 +0200 Subject: [PATCH 1/9] Setup new installation and commit hook --- .coveragerc | 9 ++++ .github/workflows/test_suite.yml | 46 +++++++++++++++++ .pre-commit-config.yaml | 67 +++++++++++++++++++++++++ CONTRIBUTING.md | 24 +++++++++ dnn_reco/__about__.py | 13 ++++- dnn_reco/__init__.py | 20 +++++++- pyproject.toml | 86 ++++++++++++++++++++++++++++++++ setup.py | 27 +--------- test/__init__.py | 0 test/test_import.py | 3 ++ 10 files changed, 268 insertions(+), 27 deletions(-) create mode 100644 .coveragerc create mode 100644 .github/workflows/test_suite.yml create mode 100644 .pre-commit-config.yaml create mode 100644 CONTRIBUTING.md create mode 100644 pyproject.toml create mode 100644 test/__init__.py create mode 100644 test/test_import.py diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..d9a2ea7 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,9 @@ +# .coveragerc + +[run] +branch = True +source = dnn_reco + +[report] + +omit = dnn_reco/__about__.py diff --git a/.github/workflows/test_suite.yml b/.github/workflows/test_suite.yml new file mode 100644 index 0000000..14385c7 --- /dev/null +++ b/.github/workflows/test_suite.yml @@ -0,0 +1,46 @@ +name: Unit Tests + +on: + workflow_dispatch: + push: + branches: + - main + pull_request: + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [ + "3.8", + "3.9", + "3.10", + "3.11", + "3.12", + ] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - uses: pre-commit/action@v3.0.0 + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install .[test] + - name: Test with pytest + run: | + coverage run -m pytest -v -s + - name: Generate Coverage Report + run: | + coverage report -m + continue-on-error: false + + - name: Upload coverage reports to Codecov + uses: codecov/codecov-action@v4.0.1 + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: icecube/dnn_reco diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..f2d4143 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,67 @@ +# Adapted from simweights +# SPDX-FileCopyrightText: © 2022 the SimWeights contributors +# +# SPDX-License-Identifier: BSD-2-Clause + +ci: + autoupdate_commit_msg: autoupdate pre-commit hooks + autoupdate_schedule: quarterly +repos: + - repo: https://github.com/psf/black + rev: 24.3.0 + hooks: + - id: black + - id: black-jupyter + - repo: https://github.com/asottile/blacken-docs + rev: "1.16.0" + hooks: + - id: blacken-docs + args: [-l 100] + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.3.5 + hooks: + - id: ruff + args: [--fix, --show-fixes] + - repo: https://github.com/codespell-project/codespell + rev: v2.2.6 + hooks: + - id: codespell + args: [-L, livetime] + - repo: https://github.com/pre-commit/pygrep-hooks + rev: v1.10.0 + hooks: + - id: python-no-log-warn + - id: python-no-eval + exclude: ^tests/.* + - id: python-use-type-annotations + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: forbid-crlf + - id: forbid-tabs + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-added-large-files + - id: check-ast + - id: check-builtin-literals + - id: check-case-conflict + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-json + - id: check-merge-conflict + - id: check-shebang-scripts-are-executable + - id: check-toml + - id: check-vcs-permalinks + - id: debug-statements + - id: detect-private-key + - id: end-of-file-fixer + - id: fix-byte-order-marker + - id: mixed-line-ending + - id: name-tests-test + args: [--pytest-test-first] + - id: trailing-whitespace + exclude: \.svg$ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7fb32db --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,24 @@ +# Contributing + +Welcome and thanks for considering to contribute to this repository! + +## Pre-commit Hooks + +When contributing to this project, please utilize the pre-commit hooks. If not installed yet, you will need to add the python package `pre-commit`: + + pip install pre-commit + +Once the package is installed, simply install the pre-commit hooks defined in the repository by executing: + + pre-commit install + +from within the repository directory. + +The pre-commit hooks will now automatically run when invoking `git commit`. Note, however, that this requires an active shell that has `pre-commit` installed. +You can also manually run the pre-commit on single files or on all files via: + + pre-commit run --all-files + +If you need to commit something even though there are errors (this should not have to be done!), then you can add the flag `--no-verify` to the `git commit` command. This will bypass the pre-commit hooks. + +Additional information is provided here: https://pre-commit.com/ diff --git a/dnn_reco/__about__.py b/dnn_reco/__about__.py index 00580c3..fd5a07e 100644 --- a/dnn_reco/__about__.py +++ b/dnn_reco/__about__.py @@ -1,5 +1,16 @@ -__version__ = "1.0.1-dev" __author__ = "Mirco Huennefeld" __author_email__ = "mirco.huennefeld@tu-dortmund.de" __description__ = "IceCube DNN-reco" __url__ = "https://github.com/icecube/dnn_reco" + +__version_major__ = 1 +__version_minor__ = 0 +__version_patch__ = 1 +__version_info__ = "-dev" + +__version__ = "{}.{}.{}{}".format( + __version_major__, + __version_minor__, + __version_patch__, + __version_info__, +) diff --git a/dnn_reco/__init__.py b/dnn_reco/__init__.py index 53a9576..c594171 100644 --- a/dnn_reco/__init__.py +++ b/dnn_reco/__init__.py @@ -1 +1,19 @@ -from dnn_reco.__about__ import __version__, __description__, __url__ +from .__about__ import ( + __version_major__, + __version_minor__, + __version_patch__, + __version_info__, + __version__, + __description__, + __url__, +) + +__all__ = [ + "__version_major__", + "__version_minor__", + "__version_patch__", + "__version_info__", + "__version__", + "__description__", + "__url__", +] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..0be614c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,86 @@ +[build-system] +requires = ["setuptools>=44.0"] # Note: unfortunately need python >=2.7 +build-backend = "setuptools.build_meta" + +[project] +name = "dnn_reco" +description = "DNN reconstruction for IceCube" +readme = "README.md" +dynamic = ["version"] +authors = [ + { name = "Mirco Huennefeld", email = "mirco.huennefeld@tu-dortmund.de" }, +] +maintainers = [ + { name = "Mirco Huennefeld", email = "mirco.huennefeld@tu-dortmund.de" }, +] +requires-python = ">=2.7" # Note: unfortunately need python >=2.7 + +dependencies = [ + 'numpy', 'pandas', 'click', 'ruamel.yaml', + 'gitpython', 'tfscripts', 'h5py', 'tqdm', + 'uncertainties', 'tables', +] + +classifiers = [ + "Development Status :: 4 - Beta", + "License :: OSI Approved :: BSD License", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Physics", + "Topic :: Scientific/Engineering :: Statistics", +] + +[project.optional-dependencies] +dev = ["pre-commit","black","ruff"] +test = ["pytest", "coverage", "codecov"] +icecube = [ + "ic3-labels @ git+ssh://git@github.com/icecube/ic3-labels", + "ic3-data @ git+ssh://git@github.com/icecube/ic3-data", +] + +[project.urls] +Homepage = "https://github.com/icecube/dnn_reco" +Documentation = "https://icecube.wisc.edu/~mhuennefeld/docs/dnn_reco/html/index.html" +"Bug Tracker" = "https://github.com/icecube/dnn_reco/issues" +Discussions = "https://github.com/icecube/dnn_reco/discussions" + +[tool.setuptools] +packages = ["dnn_reco"] + +[tool.setuptools.dynamic] +version = {attr = "dnn_reco.__version__"} + +[tool.black] +line-length = 79 +# technically, black does not support python2. +# This is all python versions black knows of and gets +# rid of the trailing comma bug +target-version = ["py33", "py34", "py35", "py36", "py37", "py38", "py39", "py310", "py311"] + +[tool.ruff] +# select = ["ALL"] +lint.fixable = ["I"] +lint.ignore = [ + "D213", # multi-line-summary-second-line incompatible with multi-line-summary-first-line + "D203", # one-blank-line-before-class" incompatible with no-blank-line-before-class + "D401", # non-imperative-mood + "D417", # undocumented-param + "ANN101", # missing-type-self + "ANN401", # any-type + "FBT", # flake8-boolean-trap + "INP", # flake8-no-pep420 + "T20", # flake8-print + "TCH", # flake8-type-checking + "S101", # assert-used + "COM812", # trailing comma to not insert after **kwargs, which is a syntax error prior to py3.6 + #"F401", # imported but unused. NOTE: sooner or later, we should not ignore this + ] +line-length = 79 +target-version = "py38" + +[tool.ruff.lint.pydocstyle] +convention = "numpy" diff --git a/setup.py b/setup.py index e953c89..6068493 100644 --- a/setup.py +++ b/setup.py @@ -1,26 +1,3 @@ -#!/usr/bin/env python +from setuptools import setup -from setuptools import setup, find_packages - -# get version number -exec(compile(open('dnn_reco/__init__.py', "rb").read(), - 'dnn_reco/__init__.py', - 'exec')) - -setup( - name='dnn_reco', - version=__version__, - description='DNN reconstruction for IceCube', - long_description='', - author='Mirco Huennefeld', - author_email='mirco.huennefeld@tu-dortmund.de', - url='https://icecube.wisc.edu/~mhuennefeld/docs/dnn_reco/html/index.html', - packages=find_packages(), - install_requires=['numpy', 'pandas', 'click', 'ruamel.yaml', - 'gitpython', 'tfscripts', 'h5py', 'tqdm', - 'uncertainties', 'tables', - ], - dependency_links=[ - 'git+https://github.com/IceCubeOpenSource/TFScripts.git', - ], - ) +setup() diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/test_import.py b/test/test_import.py new file mode 100644 index 0000000..7fc2c82 --- /dev/null +++ b/test/test_import.py @@ -0,0 +1,3 @@ +# ruff: noqa: F401 +def test_import(): + import dnn_reco From 96215be11c50fee07003b437a66e59082e277634 Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 13:37:29 +0200 Subject: [PATCH 2/9] ruff/black: automatic changes --- .gitignore | 1 - README.md | 6 +- dnn_reco/count_number_of_events.py | 44 +- dnn_reco/create_trafo_model.py | 85 +- dnn_reco/data_handler.py | 647 +- dnn_reco/data_trafo.py | 293 +- dnn_reco/detector.py | 18201 +++++++++++++++- dnn_reco/export_model.py | 285 +- dnn_reco/ic3/llh.py | 313 +- dnn_reco/ic3/llh_base.py | 179 +- dnn_reco/ic3/modules.py | 377 +- dnn_reco/ic3/segments.py | 47 +- dnn_reco/misc.py | 2 +- dnn_reco/model.py | 970 +- .../data/event_weights/event_weights.py | 48 +- .../event_weights/nersc_gnn_event_weights.py | 19 +- .../modules/data/filter/default_filter.py | 56 +- .../data/labels/biased_selection_labels.py | 56 +- .../modules/data/labels/default_labels.py | 71 +- .../data/labels/event_selection_labels.py | 164 +- .../data/labels/muon_scattering_labels.py | 15 +- dnn_reco/modules/data/misc/default_misc.py | 38 +- .../modules/evaluation/default_evaluation.py | 235 +- dnn_reco/modules/loss/default_loss.py | 376 +- dnn_reco/modules/loss/track_loss.py | 182 +- dnn_reco/modules/loss/utils/loss_utils.py | 82 +- .../modules/models/general_IC86_models.py | 453 +- dnn_reco/modules/models/utils/model_utils.py | 57 +- dnn_reco/setup_manager.py | 47 +- dnn_reco/train_model.py | 105 +- dnn_reco/utils/angles.py | 28 +- dnn_reco/utils/learning_rate.py | 46 +- dnn_reco/version_control.py | 8 +- docs/Makefile | 2 +- docs/source/conf.py | 85 +- docs/source/index.rst | 1 - docs/source/pages/bootcamp.rst | 2 +- .../pages/bootcamp/00_bootcamp_setup.rst | 2 +- .../pages/bootcamp/01_bootcamp_train.rst | 1 - .../pages/bootcamp/03_bootcamp_apply.rst | 115 +- .../pages/bootcamp/04_bootcamp_visualize.rst | 2 +- docs/source/pages/code.rst | 2 +- docs/source/pages/code/03_classes.rst | 2 +- docs/source/pages/documentation.rst | 1 - .../getting_started/00_general_remarks.rst | 2 +- .../01_create_training_data.rst | 48 +- .../pages/getting_started/04_apply_model.rst | 120 +- docs/source/pages/installation.rst | 2 +- docs/source/pages/tutorial.rst | 2 +- tests/README.txt | 3 - tests/test.py | 156 +- .../l5/jobs/1/job_DNN_l5_00000002.sh | 1 - .../l5/jobs/1/job_DNN_l5_00000001.sh | 1 - .../l5/jobs/1/job_DNN_l5_00000002.sh | 1 - .../l5/jobs/1/job_DNN_l5_00000001.sh | 1 - .../l5/jobs/1/job_DNN_l5_00000002.sh | 1 - .../l5/jobs/1/job_DNN_l5_00000001.sh | 1 - tests/test_trafo_model.py | 154 +- 58 files changed, 21688 insertions(+), 2556 deletions(-) diff --git a/.gitignore b/.gitignore index 1479caa..cce9dba 100644 --- a/.gitignore +++ b/.gitignore @@ -15,4 +15,3 @@ tmp/* !logs/.gitkeep !checkpoints/.gitkeep !configs/user/.gitkeep - diff --git a/README.md b/README.md index 491a0d3..36a0b77 100644 --- a/README.md +++ b/README.md @@ -17,17 +17,15 @@ IceCube DNN reconstruction # Create data transformation model: # We must first create a transformation model that will take care of data normalization and transformation python create_trafo_model.py /PATH/TO/MY/YAML/CONFIG/FILE - + # Train model: # This step can be run with as many config files and settings as you wish. # The settings and number of training iterations is automatically logged and will be exported together # with the final model. python train_model.py /PATH/TO/MY/YAML/CONFIG/FILE - + # Export model: # Once the model is trained, we can export it, so that it can be used to reconstruct IceCube events with the provided I3Module python export_model.py /PATH/TO/MY/YAML/CONFIG/FILE -s /PATH/TO/CONFIG/FILE/USED/TO/CREATE/TRAINING/DATA -o OUTPUT/Directory # More documentation can be found here: https://icecube.wisc.edu/~mhuennefeld/docs/dnn_reco/html/ - - diff --git a/dnn_reco/count_number_of_events.py b/dnn_reco/count_number_of_events.py index 81ef3ad..6684da4 100644 --- a/dnn_reco/count_number_of_events.py +++ b/dnn_reco/count_number_of_events.py @@ -18,17 +18,18 @@ def count_num_events(data): input_data, config = data try: - with pd.HDFStore(input_data, mode='r') as f: - time_offset = f[config['data_handler_time_offset_name']]['value'] + with pd.HDFStore(input_data, mode="r") as f: + time_offset = f[config["data_handler_time_offset_name"]]["value"] return len(time_offset) except KeyError: return 0 @click.command() -@click.argument('config_files', type=click.Path(exists=True), nargs=-1) -@click.option('--n_jobs', '-j', - default=1, help='Number of jobs to run in parallel.') +@click.argument("config_files", type=click.Path(exists=True), nargs=-1) +@click.option( + "--n_jobs", "-j", default=1, help="Number of jobs to run in parallel." +) def main(config_files, n_jobs): """Script to count number of events. @@ -44,12 +45,16 @@ def main(config_files, n_jobs): setup_manager = SetupManager(config_files) config = setup_manager.get_config() - names = ['test_data_file', 'validation_data_file', - 'training_data_file', 'trafo_data_file'] + names = [ + "test_data_file", + "validation_data_file", + "training_data_file", + "trafo_data_file", + ] num_events_list = [] for name in names: # get files - print('Creating file list for {!r}'.format(name)) + print("Creating file list for {!r}".format(name)) input_data = config[name] if isinstance(input_data, list): input_data = set(input_data) @@ -60,25 +65,30 @@ def main(config_files, n_jobs): file_list = glob.glob(input_data) file_list = set(file_list) - print('Starting counting') + print("Starting counting") pool = Pool(processes=n_jobs) num_files = len(file_list) num_events = 0 with tqdm(total=num_files) as pbar: - for i, n in tqdm(enumerate(pool.imap_unordered( - count_num_events, [(f, config) for f in file_list]))): + for i, n in tqdm( + enumerate( + pool.imap_unordered( + count_num_events, [(f, config) for f in file_list] + ) + ) + ): pbar.update() num_events += n num_events_list.append(num_events) - print('Found {!r} events for {!r}\n'.format(num_events, name)) + print("Found {!r} events for {!r}\n".format(num_events, name)) - print('\n===============================') - print('= Completed Counting Events: =') - print('===============================') + print("\n===============================") + print("= Completed Counting Events: =") + print("===============================") for num_events, name in zip(num_events_list, names): - print('Found {!r} events for {!r}'.format(num_events, name)) + print("Found {!r} events for {!r}".format(num_events, name)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/dnn_reco/create_trafo_model.py b/dnn_reco/create_trafo_model.py index 9be5fb6..47a2de5 100644 --- a/dnn_reco/create_trafo_model.py +++ b/dnn_reco/create_trafo_model.py @@ -12,7 +12,7 @@ @click.command() -@click.argument('config_files', type=click.Path(exists=True), nargs=-1) +@click.argument("config_files", type=click.Path(exists=True), nargs=-1) def main(config_files): """Script to generate trafo model. @@ -31,66 +31,69 @@ def main(config_files): # Create Data Handler object data_handler = DataHandler(config) - data_handler.setup_with_test_data(config['trafo_data_file']) + data_handler.setup_with_test_data(config["trafo_data_file"]) settings_trafo = { - 'input_data': config['trafo_data_file'], - 'batch_size': config['batch_size'], - 'sample_randomly': True, - 'pick_random_files_forever': False, - 'file_capacity': 1, - 'batch_capacity': 2, - 'num_jobs': config['trafo_num_jobs'], - 'num_add_files': 1, - 'num_repetitions': 1, - 'num_splits': config['data_handler_num_splits'], - 'init_values': config['DOM_init_values'], - 'nan_fill_value': config['data_handler_nan_fill_value'], - } + "input_data": config["trafo_data_file"], + "batch_size": config["batch_size"], + "sample_randomly": True, + "pick_random_files_forever": False, + "file_capacity": 1, + "batch_capacity": 2, + "num_jobs": config["trafo_num_jobs"], + "num_add_files": 1, + "num_repetitions": 1, + "num_splits": config["data_handler_num_splits"], + "init_values": config["DOM_init_values"], + "nan_fill_value": config["data_handler_nan_fill_value"], + } trafo_data_generator = data_handler.get_batch_generator(**settings_trafo) # create TrafoModel data_transformer = DataTransformer( - data_handler=data_handler, - treat_doms_equally=config['trafo_treat_doms_equally'], - normalize_dom_data=config['trafo_normalize_dom_data'], - normalize_label_data=config['trafo_normalize_label_data'], - normalize_misc_data=config['trafo_normalize_misc_data'], - log_dom_bins=config['trafo_log_dom_bins'], - log_label_bins=config['trafo_log_label_bins'], - log_misc_bins=config['trafo_log_misc_bins'], - norm_constant=config['trafo_norm_constant']) + data_handler=data_handler, + treat_doms_equally=config["trafo_treat_doms_equally"], + normalize_dom_data=config["trafo_normalize_dom_data"], + normalize_label_data=config["trafo_normalize_label_data"], + normalize_misc_data=config["trafo_normalize_misc_data"], + log_dom_bins=config["trafo_log_dom_bins"], + log_label_bins=config["trafo_log_label_bins"], + log_misc_bins=config["trafo_log_misc_bins"], + norm_constant=config["trafo_norm_constant"], + ) data_transformer.create_trafo_model_iteratively( - data_iterator=trafo_data_generator, - num_batches=config['trafo_num_batches']) + data_iterator=trafo_data_generator, + num_batches=config["trafo_num_batches"], + ) # save trafo model to file - base_name = os.path.basename(config['trafo_model_path']) - if '.' in base_name: - file_ending = base_name.split('.')[-1] - base_name = base_name.replace('.' + file_ending, '') + base_name = os.path.basename(config["trafo_model_path"]) + if "." in base_name: + file_ending = base_name.split(".")[-1] + base_name = base_name.replace("." + file_ending, "") - directory = os.path.dirname(config['trafo_model_path']) + directory = os.path.dirname(config["trafo_model_path"]) if not os.path.isdir(directory): os.makedirs(directory) - misc.print_warning('Creating directory: {}'.format(directory)) + misc.print_warning("Creating directory: {}".format(directory)) - trafo_config_file = os.path.join(directory, - 'config_trafo__{}.yaml'.format(base_name)) + trafo_config_file = os.path.join( + directory, "config_trafo__{}.yaml".format(base_name) + ) - with open(trafo_config_file, 'w') as yaml_file: + with open(trafo_config_file, "w") as yaml_file: yaml.dump(config, yaml_file, default_flow_style=False) - data_transformer.save_trafo_model(config['trafo_model_path']) + data_transformer.save_trafo_model(config["trafo_model_path"]) # kill multiprocessing queues and workers data_handler.kill() - print('\n=======================================') - print('= Successfully saved trafo model to: =') - print('=======================================') - print('{!r}\n'.format(config['trafo_model_path'])) + print("\n=======================================") + print("= Successfully saved trafo model to: =") + print("=======================================") + print("{!r}\n".format(config["trafo_model_path"])) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/dnn_reco/data_handler.py b/dnn_reco/data_handler.py index 35fee00..b96264c 100644 --- a/dnn_reco/data_handler.py +++ b/dnn_reco/data_handler.py @@ -67,7 +67,7 @@ def __init__(self, config): # read input data self._config = dict(deepcopy(config)) - self.num_bins = config['data_handler_num_bins'] + self.num_bins = config["data_handler_num_bins"] # keep track of multiprocessing processes and managers self._mp_processes = [] @@ -76,17 +76,19 @@ def __init__(self, config): self.is_setup = False def _setup_time_keys(self): - """Add relative time keys - """ - self.relative_time_keys = \ - self._config['data_handler_relative_time_keys'] + """Add relative time keys""" + self.relative_time_keys = self._config[ + "data_handler_relative_time_keys" + ] - pattern = self._config['data_handler_relative_time_key_pattern'] + pattern = self._config["data_handler_relative_time_key_pattern"] if pattern is not None: - self.relative_time_keys.extend([n for n in self.label_names - if pattern in n.lower()]) - self.relative_time_keys.extend([n for n in self.misc_names - if pattern in n.lower()]) + self.relative_time_keys.extend( + [n for n in self.label_names if pattern in n.lower()] + ) + self.relative_time_keys.extend( + [n for n in self.misc_names if pattern in n.lower()] + ) def setup_with_test_data(self, test_input_data): """Setup the datahandler with a test input file. @@ -126,32 +128,32 @@ def setup_with_config(self, config_file): NotImplementedError Description """ - with open(config_file, 'r') as stream: + with open(config_file, "r") as stream: config_meta = yaml.safe_load(stream) - self.label_names = config_meta['label_names'] - self.label_name_dict = config_meta['label_name_dict'] - self.label_shape = config_meta['label_shape'] - self.num_labels = config_meta['num_labels'] - self.misc_names = config_meta['misc_names'] - self.misc_name_dict = config_meta['misc_name_dict'] - self.misc_data_exists = config_meta['misc_data_exists'] - self.misc_shape = config_meta['misc_shape'] - self.num_misc = config_meta['num_misc'] + self.label_names = config_meta["label_names"] + self.label_name_dict = config_meta["label_name_dict"] + self.label_shape = config_meta["label_shape"] + self.num_labels = config_meta["num_labels"] + self.misc_names = config_meta["misc_names"] + self.misc_name_dict = config_meta["misc_name_dict"] + self.misc_data_exists = config_meta["misc_data_exists"] + self.misc_shape = config_meta["misc_shape"] + self.num_misc = config_meta["num_misc"] self._setup_time_keys() self.is_setup = True def _get_label_meta_data(self): - """Loads labels from a sample file to obtain label meta data. - """ - class_string = 'dnn_reco.modules.data.labels.{}.{}'.format( - self._config['data_handler_label_file'], - self._config['data_handler_label_name'], - ) + """Loads labels from a sample file to obtain label meta data.""" + class_string = "dnn_reco.modules.data.labels.{}.{}".format( + self._config["data_handler_label_file"], + self._config["data_handler_label_name"], + ) label_reader = misc.load_class(class_string) - labels, label_names = label_reader(self.test_input_data[0], - self._config) + labels, label_names = label_reader( + self.test_input_data[0], self._config + ) self.label_names = label_names self.label_name_dict = {n: i for i, n in enumerate(label_names)} @@ -159,15 +161,15 @@ def _get_label_meta_data(self): self.num_labels = int(np.prod(self.label_shape)) def _get_misc_meta_data(self): - """Loads misc data from a sample file to obtain misc meta data. - """ - class_string = 'dnn_reco.modules.data.misc.{}.{}'.format( - self._config['data_handler_misc_file'], - self._config['data_handler_misc_name'], - ) + """Loads misc data from a sample file to obtain misc meta data.""" + class_string = "dnn_reco.modules.data.misc.{}.{}".format( + self._config["data_handler_misc_file"], + self._config["data_handler_misc_name"], + ) misc_reader = misc.load_class(class_string) - misc_data, misc_names = misc_reader(self.test_input_data[0], - self._config) + misc_data, misc_names = misc_reader( + self.test_input_data[0], self._config + ) self.misc_names = misc_names self.misc_name_dict = {n: i for i, n in enumerate(misc_names)} @@ -227,8 +229,9 @@ def get_misc_index(self, misc_name): """ return self.misc_name_dict[misc_name] - def read_icecube_data(self, input_data, nan_fill_value=None, - init_values=0., verbose=False): + def read_icecube_data( + self, input_data, nan_fill_value=None, init_values=0.0, verbose=False + ): """Read IceCube hdf5 data files Parameters @@ -267,107 +270,125 @@ def read_icecube_data(self, input_data, nan_fill_value=None, Description """ if not self.is_setup: - raise ValueError('DataHandler needs to be set up first!') + raise ValueError("DataHandler needs to be set up first!") start_time = timeit.default_timer() try: - with pd.HDFStore(input_data, mode='r') as f: - bin_values = f[self._config['data_handler_bin_values_name']] - bin_indices = f[self._config['data_handler_bin_indices_name']] - _time_range = f[self._config['data_handler_time_offset_name']] + with pd.HDFStore(input_data, mode="r") as f: + bin_values = f[self._config["data_handler_bin_values_name"]] + bin_indices = f[self._config["data_handler_bin_indices_name"]] + _time_range = f[self._config["data_handler_time_offset_name"]] except Exception as e: print(e) - print('Skipping file: {}'.format(input_data)) + print("Skipping file: {}".format(input_data)) return None - time_range_start = _time_range['value'] + time_range_start = _time_range["value"] # create Dictionary with eventIDs - size = len(_time_range['Event']) + size = len(_time_range["Event"]) eventIDDict = {} for row in _time_range.iterrows(): eventIDDict[(row[1][0], row[1][1], row[1][2], row[1][3])] = row[0] # Create arrays for input data - x_ic78 = np.ones([size, 10, 10, 60, self.num_bins], - dtype=self._config['np_float_precision'], - ) * np.array(init_values) - x_deepcore = np.ones([size, 8, 60, self.num_bins], - dtype=self._config['np_float_precision'], - ) * np.array(init_values) + x_ic78 = np.ones( + [size, 10, 10, 60, self.num_bins], + dtype=self._config["np_float_precision"], + ) * np.array(init_values) + x_deepcore = np.ones( + [size, 8, 60, self.num_bins], + dtype=self._config["np_float_precision"], + ) * np.array(init_values) # ------------------ # get DOM input data # ------------------ - for value_row, index_row in zip(bin_values.itertuples(), - bin_indices.itertuples()): + for value_row, index_row in zip( + bin_values.itertuples(), bin_indices.itertuples() + ): if value_row[1:5] != index_row[1:5]: raise ValueError( - 'Event headers do not match! HDF5 version error?') + "Event headers do not match! HDF5 version error?" + ) string = index_row[6] dom = index_row[7] - 1 index = eventIDDict[(index_row[1:5])] if string > 78: # deep core - x_deepcore[index, string - 78 - 1, dom, index_row[10]] = \ + x_deepcore[index, string - 78 - 1, dom, index_row[10]] = ( value_row[10] + ) else: # IC78 a, b = self._get_indices_from_string(string) # Center of Detector is a,b = 0,0 # a goes from -4 to 5 # b goes from -5 to 4 - x_ic78[index, a+4, b+5, dom, index_row[10]] = value_row[10] + x_ic78[index, a + 4, b + 5, dom, index_row[10]] = value_row[10] # -------------- # read in labels # -------------- - class_string = 'dnn_reco.modules.data.labels.{}.{}'.format( - self._config['data_handler_label_file'], - self._config['data_handler_label_name'], - ) + class_string = "dnn_reco.modules.data.labels.{}.{}".format( + self._config["data_handler_label_file"], + self._config["data_handler_label_name"], + ) label_reader = misc.load_class(class_string) - labels, _ = label_reader(input_data, self._config, - label_names=self.label_names) + labels, _ = label_reader( + input_data, self._config, label_names=self.label_names + ) assert list(labels.shape) == [size] + self.label_shape # perform label smoothing if provided in config - if 'label_pid_smooth_labels' in self._config: - smoothing = self._config['label_pid_smooth_labels'] + if "label_pid_smooth_labels" in self._config: + smoothing = self._config["label_pid_smooth_labels"] if smoothing is not None: for key, i in self.label_name_dict.items(): - if key in self._config['label_pid_keys']: - assert ((labels[:, i] >= 0.).all() - and (labels[:, i] <= 1.).all()), \ - 'Values outside of [0, 1] for {!r}'.format(key) - labels[:, i] = \ - labels[:, i] * (1 - smoothing) + smoothing / 2. + if key in self._config["label_pid_keys"]: + assert (labels[:, i] >= 0.0).all() and ( + labels[:, i] <= 1.0 + ).all(), "Values outside of [0, 1] for {!r}".format( + key + ) + labels[:, i] = ( + labels[:, i] * (1 - smoothing) + smoothing / 2.0 + ) # ------------------- # read in misc values # ------------------- - class_string = 'dnn_reco.modules.data.misc.{}.{}'.format( - self._config['data_handler_misc_file'], - self._config['data_handler_misc_name'], - ) + class_string = "dnn_reco.modules.data.misc.{}.{}".format( + self._config["data_handler_misc_file"], + self._config["data_handler_misc_name"], + ) misc_reader = misc.load_class(class_string) - misc_data, _ = misc_reader(input_data, self._config, - misc_names=self.misc_names) + misc_data, _ = misc_reader( + input_data, self._config, misc_names=self.misc_names + ) if self.misc_data_exists: assert list(misc_data.shape) == [size, self.num_misc] # ------------- # filter events # ------------- - class_string = 'dnn_reco.modules.data.filter.{}.{}'.format( - self._config['data_handler_filter_file'], - self._config['data_handler_filter_name'], - ) + class_string = "dnn_reco.modules.data.filter.{}.{}".format( + self._config["data_handler_filter_file"], + self._config["data_handler_filter_name"], + ) filter_func = misc.load_class(class_string) - mask = filter_func(self, input_data, self._config, x_ic78, x_deepcore, - labels, misc_data, time_range_start) + mask = filter_func( + self, + input_data, + self._config, + x_ic78, + x_deepcore, + labels, + misc_data, + time_range_start, + ) # mask out events not passing filter: x_ic78 = x_ic78[mask] @@ -398,21 +419,26 @@ def read_icecube_data(self, input_data, nan_fill_value=None, if nan_fill_value is None: mask = np.isfinite(np.sum(x_ic78, axis=(1, 2, 3, 4))) mask = np.logical_and( - mask, np.isfinite(np.sum(x_deepcore, axis=(1, 2, 3)))) + mask, np.isfinite(np.sum(x_deepcore, axis=(1, 2, 3))) + ) mask = np.logical_and( - mask, np.isfinite(np.sum(labels, - axis=tuple(range(1, labels.ndim))))) + mask, + np.isfinite(np.sum(labels, axis=tuple(range(1, labels.ndim)))), + ) if not mask.all(): misc.print_warning( - 'Found NaNs. ' + - 'Removing {} events from batch of {}.'.format( - len(mask) - np.sum(mask), len(mask))) + "Found NaNs. " + + "Removing {} events from batch of {}.".format( + len(mask) - np.sum(mask), len(mask) + ) + ) misc.print_warning( - 'NaN-free x_ic78: {}, x_deepcore: {}, labels: {}'.format( + "NaN-free x_ic78: {}, x_deepcore: {}, labels: {}".format( np.isfinite(x_ic78).all(), np.isfinite(x_deepcore).all(), np.isfinite(labels).all(), - )) + ) + ) x_ic78 = x_ic78[mask] x_deepcore = x_deepcore[mask] @@ -425,20 +451,29 @@ def read_icecube_data(self, input_data, nan_fill_value=None, # This should never be the case! mask = np.isfinite(np.sum(x_ic78, axis=(1, 2, 3, 4))) mask = np.logical_and( - mask, np.isfinite(np.sum(x_deepcore, axis=(1, 2, 3)))) + mask, np.isfinite(np.sum(x_deepcore, axis=(1, 2, 3))) + ) if not mask.all(): - raise ValueError('Found NaN values in input data!') + raise ValueError("Found NaN values in input data!") # Fixing NaNs in labels and misc data is ok, but warn about this - mask = np.isfinite(np.sum(labels, - axis=tuple(range(1, labels.ndim)))) + mask = np.isfinite( + np.sum(labels, axis=tuple(range(1, labels.ndim))) + ) if self.misc_data_exists: - mask = np.logical_and(mask, np.isfinite( - np.sum(misc_data, axis=tuple(range(1, misc_data.ndim))))) + mask = np.logical_and( + mask, + np.isfinite( + np.sum(misc_data, axis=tuple(range(1, misc_data.ndim))) + ), + ) if not mask.all(): - misc.print_warning('Found NaNs in labels and/or misc data. ' + - 'Replacing NaNs in {} events'.format( - len(mask) - np.sum(mask))) + misc.print_warning( + "Found NaNs in labels and/or misc data. " + + "Replacing NaNs in {} events".format( + len(mask) - np.sum(mask) + ) + ) labels[~np.isfinite(labels)] = nan_fill_value if self.misc_data_exists: misc_data[~np.isfinite(misc_data)] = nan_fill_value @@ -446,27 +481,32 @@ def read_icecube_data(self, input_data, nan_fill_value=None, if verbose: final_time = timeit.default_timer() - start_time - print("=== Time needed to process Data: {:5.3f} seconds ==".format( - final_time)) + print( + "=== Time needed to process Data: {:5.3f} seconds ==".format( + final_time + ) + ) return x_ic78, x_deepcore, labels, misc_data - def get_batch_generator(self, - input_data, - batch_size, - sample_randomly=True, - pick_random_files_forever=True, - file_capacity=1, - batch_capacity=5, - num_jobs=1, - num_add_files=0, - num_repetitions=1, - init_values=0., - num_splits=None, - nan_fill_value=None, - verbose=False, - *args, **kwargs - ): + def get_batch_generator( + self, + input_data, + batch_size, + sample_randomly=True, + pick_random_files_forever=True, + file_capacity=1, + batch_capacity=5, + num_jobs=1, + num_add_files=0, + num_repetitions=1, + init_values=0.0, + num_splits=None, + nan_fill_value=None, + verbose=False, + *args, + **kwargs + ): """Get an IceCube data batch generator. This is a multiprocessing data iterator. @@ -560,7 +600,7 @@ def get_batch_generator(self, Description """ if not self.is_setup: - raise ValueError('DataHandler needs to be set up first!') + raise ValueError("DataHandler needs to be set up first!") if isinstance(input_data, list): file_list = [] @@ -570,9 +610,9 @@ def get_batch_generator(self, file_list = glob.glob(input_data) # define shared memory variables - num_files_processed = multiprocessing.Value('i') - processed_all_files = multiprocessing.Value('b') - data_left_in_queue = multiprocessing.Value('b') + num_files_processed = multiprocessing.Value("i") + processed_all_files = multiprocessing.Value("b") + data_left_in_queue = multiprocessing.Value("b") # initialize shared variables num_files_processed.value = 0 @@ -596,11 +636,13 @@ def get_batch_generator(self, # create data_batch_queue data_batch_queue = multiprocessing.Manager().Queue( - maxsize=file_capacity) + maxsize=file_capacity + ) # create final_batch_queue final_batch_queue = multiprocessing.Manager().Queue( - maxsize=batch_capacity) + maxsize=batch_capacity + ) # keep references to managers alive, such that these do not shut # down until the DataHandler object gets garbage collected @@ -629,14 +671,19 @@ def file_loader(seed): # ---------------------------------------------- # Create NN model instance for biased selection # ---------------------------------------------- - if 'nn_biased_selection' in self._config and \ - self._config['nn_biased_selection'] is not None: - - cfg_sel = self._config['nn_biased_selection'] - if cfg_sel['apply_biased_selection'] and \ - cfg_sel['biased_fraction'] > 0: - biased_selection_func = \ + if ( + "nn_biased_selection" in self._config + and self._config["nn_biased_selection"] is not None + ): + + cfg_sel = self._config["nn_biased_selection"] + if ( + cfg_sel["apply_biased_selection"] + and cfg_sel["biased_fraction"] > 0 + ): + biased_selection_func = ( self._create_biased_selection_func() + ) nn_biased_selection = True else: nn_biased_selection = False @@ -662,16 +709,23 @@ def file_loader(seed): if verbose: usage = resource.getrusage(resource.RUSAGE_SELF) - msg = "{} {:02.1f} GB. file_list_queue:" \ - " {}. data_batch_queue: {}" - print(msg.format(file, - usage.ru_maxrss / 1024.0 / 1024.0, - file_list_queue.qsize(), - data_batch_queue.qsize())) + msg = ( + "{} {:02.1f} GB. file_list_queue:" + " {}. data_batch_queue: {}" + ) + print( + msg.format( + file, + usage.ru_maxrss / 1024.0 / 1024.0, + file_list_queue.qsize(), + data_batch_queue.qsize(), + ) + ) icecube_data = self.read_icecube_data( - input_data=file, - init_values=init_values, - nan_fill_value=nan_fill_value) + input_data=file, + init_values=init_values, + nan_fill_value=nan_fill_value, + ) # biased selection if nn_biased_selection: @@ -689,18 +743,21 @@ def file_loader(seed): # (Multiprocessing queue can only handle # a certain size) split_indices_list = np.array_split( - np.arange(icecube_data[0].shape[0]), - num_splits) + np.arange(icecube_data[0].shape[0]), + num_splits, + ) for split_indices in split_indices_list: - batch = [icecube_data[0][split_indices], - icecube_data[1][split_indices], - icecube_data[2][split_indices], - ] + batch = [ + icecube_data[0][split_indices], + icecube_data[1][split_indices], + icecube_data[2][split_indices], + ] if self.misc_data_exists: batch.append( - icecube_data[3][split_indices]) + icecube_data[3][split_indices] + ) else: batch.append(None) @@ -708,8 +765,10 @@ def file_loader(seed): data_batch_queue.put(batch) else: misc.print_warning( - 'WARNING: File {} does not exist.\033[0m'.format( - file)) + "WARNING: File {} does not exist.\033[0m".format( + file + ) + ) if not pick_random_files_forever: with file_counter_lock: @@ -756,16 +815,19 @@ def data_queue_iterator(sample_randomly): if self.misc_data_exists: misc_list = [data_batch[3]] - while (current_queue_size < - np.sqrt(num_repetitions) * batch_size and - data_left_in_queue.value): + while ( + current_queue_size < np.sqrt(num_repetitions) * batch_size + and data_left_in_queue.value + ): # avoid dead lock and delay for a bit time.sleep(0.1) for i in range(num_add_files): - if (data_batch_queue.qsize() > 1 or - not data_batch_queue.empty()): + if ( + data_batch_queue.qsize() > 1 + or not data_batch_queue.empty() + ): data_batch = data_batch_queue.get() current_queue_size += len(data_batch[0]) x_ic78_list.append(data_batch[0]) @@ -784,7 +846,7 @@ def data_queue_iterator(sample_randomly): queue_size = x_ic78.shape[0] if verbose: - print('queue_size', queue_size, x_ic78.shape) + print("queue_size", queue_size, x_ic78.shape) # num_repetitions: # potentially dangerous for batch_size approx file_size @@ -834,11 +896,13 @@ def data_queue_iterator(sample_randomly): misc_batch.append(misc_data[index]) size += 1 - # --------- + # --------- if size == batch_size: - batch = [np.array(ic78_batch), - np.array(deepcore_batch), - np.array(labels_batch)] + batch = [ + np.array(ic78_batch), + np.array(deepcore_batch), + np.array(labels_batch), + ] if self.misc_data_exists: batch.append(np.array(misc_batch)) else: @@ -846,14 +910,21 @@ def data_queue_iterator(sample_randomly): if verbose: usage = resource.getrusage( - resource.RUSAGE_SELF).ru_maxrss - msg = "{:02.1f} GB. file_list_queue: {}." \ - " data_batch_queue: {}. " \ - "final_batch_queue: {}" - print(msg.format(usage / 1024.0 / 1024.0, - file_list_queue.qsize(), - data_batch_queue.qsize(), - final_batch_queue.qsize())) + resource.RUSAGE_SELF + ).ru_maxrss + msg = ( + "{:02.1f} GB. file_list_queue: {}." + " data_batch_queue: {}. " + "final_batch_queue: {}" + ) + print( + msg.format( + usage / 1024.0 / 1024.0, + file_list_queue.qsize(), + data_batch_queue.qsize(), + final_batch_queue.qsize(), + ) + ) final_batch_queue.put(batch) # reset event batch @@ -866,15 +937,19 @@ def data_queue_iterator(sample_randomly): if not pick_random_files_forever: with file_counter_lock: - if (processed_all_files.value and - data_batch_queue.empty()): + if ( + processed_all_files.value + and data_batch_queue.empty() + ): data_left_in_queue.value = False # collect leftovers and put them in an (incomplete) batch if ic78_batch: - batch = [np.array(ic78_batch), - np.array(deepcore_batch), - np.array(labels_batch)] + batch = [ + np.array(ic78_batch), + np.array(deepcore_batch), + np.array(labels_batch), + ] if self.misc_data_exists: batch.append(np.array(misc_batch)) else: @@ -902,8 +977,9 @@ def batch_iterator(): process.start() self._mp_processes.append(process) - process = multiprocessing.Process(target=data_queue_iterator, - args=(sample_randomly,)) + process = multiprocessing.Process( + target=data_queue_iterator, args=(sample_randomly,) + ) process.daemon = True process.start() self._mp_processes.append(process) @@ -911,12 +987,11 @@ def batch_iterator(): return batch_iterator() def kill(self): - """Kill Multiprocessing queues and workers - """ + """Kill Multiprocessing queues and workers""" for process in self._mp_processes: process.terminate() - time.sleep(1.) + time.sleep(1.0) for process in self._mp_processes: process.join(timeout=1.0) @@ -943,45 +1018,54 @@ def _create_model(self, cfg, cfg_sel): # Create a new tf graph and session for this model instance g = tf.Graph() - if 'tf_parallelism_threads' in cfg_sel: - n_cpus = cfg_sel['tf_parallelism_threads'] - sess = tf.compat.v1.Session(graph=g, config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': cfg_sel['GPU_device_count']}, - intra_op_parallelism_threads=n_cpus, - inter_op_parallelism_threads=n_cpus, - )) + if "tf_parallelism_threads" in cfg_sel: + n_cpus = cfg_sel["tf_parallelism_threads"] + sess = tf.compat.v1.Session( + graph=g, + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), + device_count={"GPU": cfg_sel["GPU_device_count"]}, + intra_op_parallelism_threads=n_cpus, + inter_op_parallelism_threads=n_cpus, + ), + ) else: - sess = tf.compat.v1.Session(graph=g, config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': cfg_sel['GPU_device_count']}, - )) + sess = tf.compat.v1.Session( + graph=g, + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), + device_count={"GPU": cfg_sel["GPU_device_count"]}, + ), + ) with g.as_default(): # Create Data Handler object data_handler = DataHandler(cfg) - data_handler.setup_with_test_data(cfg['training_data_file']) + data_handler.setup_with_test_data(cfg["training_data_file"]) # create data transformer data_transformer = DataTransformer( data_handler=data_handler, - treat_doms_equally=cfg['trafo_treat_doms_equally'], - normalize_dom_data=cfg['trafo_normalize_dom_data'], - normalize_label_data=cfg['trafo_normalize_label_data'], - normalize_misc_data=cfg['trafo_normalize_misc_data'], - log_dom_bins=cfg['trafo_log_dom_bins'], - log_label_bins=cfg['trafo_log_label_bins'], - log_misc_bins=cfg['trafo_log_misc_bins'], - norm_constant=cfg['trafo_norm_constant']) + treat_doms_equally=cfg["trafo_treat_doms_equally"], + normalize_dom_data=cfg["trafo_normalize_dom_data"], + normalize_label_data=cfg["trafo_normalize_label_data"], + normalize_misc_data=cfg["trafo_normalize_misc_data"], + log_dom_bins=cfg["trafo_log_dom_bins"], + log_label_bins=cfg["trafo_log_label_bins"], + log_misc_bins=cfg["trafo_log_misc_bins"], + norm_constant=cfg["trafo_norm_constant"], + ) # load trafo model from file - data_transformer.load_trafo_model(cfg['trafo_model_path']) + data_transformer.load_trafo_model(cfg["trafo_model_path"]) # create NN model - model = NNModel(is_training=False, - config=cfg, - data_handler=data_handler, - data_transformer=data_transformer, - sess=sess) + model = NNModel( + is_training=False, + config=cfg, + data_handler=data_handler, + data_transformer=data_transformer, + sess=sess, + ) # compile model: initalize and finalize graph model.compile() @@ -991,27 +1075,34 @@ def _create_model(self, cfg, cfg_sel): return model, data_transformer, data_handler - def _get_nn_biased_selection_mask(self, icecube_data, cfg_sel, - data_handler, data_transformer, - model, counter): - """Helper method to obtain NN model event mask for biased selection - """ + def _get_nn_biased_selection_mask( + self, + icecube_data, + cfg_sel, + data_handler, + data_transformer, + model, + counter, + ): + """Helper method to obtain NN model event mask for biased selection""" # reload model weights if necessary - if cfg_sel['reload_frequency'] is not None: - if counter % cfg_sel['reload_frequency'] == 0: + if cfg_sel["reload_frequency"] is not None: + if counter % cfg_sel["reload_frequency"] == 0: model.restore() # apply model on loaded data - y_pred, y_unc = model.predict_batched(icecube_data[0], icecube_data[1], - max_size=cfg_sel['max_size']) + y_pred, y_unc = model.predict_batched( + icecube_data[0], icecube_data[1], max_size=cfg_sel["max_size"] + ) y_true = icecube_data[2] # transform data - y_true_trafo = data_transformer.transform(y_true, data_type='label') - y_pred_trafo = data_transformer.transform(y_pred, data_type='label') - y_unc_trafo = data_transformer.transform(y_unc, data_type='label', - bias_correction=False) + y_true_trafo = data_transformer.transform(y_true, data_type="label") + y_pred_trafo = data_transformer.transform(y_pred, data_type="label") + y_unc_trafo = data_transformer.transform( + y_unc, data_type="label", bias_correction=False + ) diff = y_true - y_pred diff_trafo = y_true_trafo - y_pred_trafo @@ -1022,43 +1113,41 @@ def _get_nn_biased_selection_mask(self, icecube_data, cfg_sel, mask = np.zeros(len(y_true)) # select biased events based on difference greater than value - for key, value in cfg_sel['true_minus_pred_greater'].items(): + for key, value in cfg_sel["true_minus_pred_greater"].items(): index = data_handler.get_label_index(key) mask = np.logical_or(mask, diff[:, index] > value) # select biased events based on difference less than value - for key, value in cfg_sel['true_minus_pred_less'].items(): + for key, value in cfg_sel["true_minus_pred_less"].items(): index = data_handler.get_label_index(key) mask = np.logical_or(mask, diff[:, index] < value) # select biased events based on transformed difference # greater than the specified value - for key, value in cfg_sel['true_minus_pred_trafo_greater'].items(): + for key, value in cfg_sel["true_minus_pred_trafo_greater"].items(): index = data_handler.get_label_index(key) mask = np.logical_or(mask, diff_trafo[:, index] > value) # select biased events based on transformed difference # greater than the specified value - for key, value in cfg_sel['true_minus_pred_trafo_less'].items(): + for key, value in cfg_sel["true_minus_pred_trafo_less"].items(): index = data_handler.get_label_index(key) mask = np.logical_or(mask, diff_trafo[:, index] < value) # select biased events based on absolute difference - for key, value in cfg_sel['cut_abs_diff'].items(): + for key, value in cfg_sel["cut_abs_diff"].items(): index = data_handler.get_label_index(key) mask = np.logical_or(mask, abs_diff[:, index] >= value) # select biased events based on transformed absolute difference - for key, value in cfg_sel['cut_abs_diff_trafo'].items(): + for key, value in cfg_sel["cut_abs_diff_trafo"].items(): index = data_handler.get_label_index(key) - mask = np.logical_or(mask, - abs_diff_trafo[:, index] >= value) + mask = np.logical_or(mask, abs_diff_trafo[:, index] >= value) # select biased events based on uncertainty weighted difference - for key, value in cfg_sel['cut_unc_weighted_diff_trafo'].items(): + for key, value in cfg_sel["cut_unc_weighted_diff_trafo"].items(): index = data_handler.get_label_index(key) - unc_diff_trafo = \ - abs_diff_trafo[:, index] / y_unc_trafo[:, index] + unc_diff_trafo = abs_diff_trafo[:, index] / y_unc_trafo[:, index] mask = np.logical_or(mask, unc_diff_trafo >= value) return mask @@ -1073,76 +1162,86 @@ def _get_label_biased_selection_mask(self, icecube_data, cfg_sel): mask = np.zeros(len(y_true)) # select biased events based on difference greater than value - for key, value in cfg_sel['label_greater'].items(): + for key, value in cfg_sel["label_greater"].items(): index = self.get_label_index(key) mask = np.logical_or(mask, y_true[:, index] > value) # select biased events based on difference less than value - for key, value in cfg_sel['label_less'].items(): + for key, value in cfg_sel["label_less"].items(): index = self.get_label_index(key) mask = np.logical_or(mask, y_true[:, index] < value) # select biased events based on transformed difference # greater than the specified value - for key, value in cfg_sel['label_equal'].items(): + for key, value in cfg_sel["label_equal"].items(): index = self.get_label_index(key) mask = np.logical_or(mask, y_true[:, index] == value) # select biased events based on transformed difference # greater than the specified value - for key, value in cfg_sel['label_unequal'].items(): + for key, value in cfg_sel["label_unequal"].items(): index = self.get_label_index(key) mask = np.logical_or(mask, y_true[:, index] != value) return mask def _create_biased_selection_func(self): - """Helper Method to create NN model instance for biased selection - """ + """Helper Method to create NN model instance for biased selection""" local_random_state = np.random.RandomState() cfg = dict(deepcopy(self._config)) - cfg_sel = cfg['nn_biased_selection'] + cfg_sel = cfg["nn_biased_selection"] default_settings = { - 'max_size': 32, - 'max_imbalance': 100, - 'reload_frequency': 100, - 'biased_fraction': 0.1, - 'label_greater': {}, - 'label_less': {}, - 'label_equal': {}, - 'label_unequal': {}, - 'true_minus_pred_greater': {}, - 'true_minus_pred_less': {}, - 'true_minus_pred_trafo_greater': {}, - 'true_minus_pred_trafo_less': {}, - 'cut_abs_diff': {}, - 'cut_abs_diff_trafo': {}, - 'cut_unc_weighted_diff_trafo': {}, - 'tf_parallelism_threads': 10, - 'GPU_device_count': 1, + "max_size": 32, + "max_imbalance": 100, + "reload_frequency": 100, + "biased_fraction": 0.1, + "label_greater": {}, + "label_less": {}, + "label_equal": {}, + "label_unequal": {}, + "true_minus_pred_greater": {}, + "true_minus_pred_less": {}, + "true_minus_pred_trafo_greater": {}, + "true_minus_pred_trafo_less": {}, + "cut_abs_diff": {}, + "cut_abs_diff_trafo": {}, + "cut_unc_weighted_diff_trafo": {}, + "tf_parallelism_threads": 10, + "GPU_device_count": 1, } for key, value in default_settings.items(): if key not in cfg_sel: cfg_sel[key] = value - if cfg_sel['biased_fraction'] <= 0. or \ - cfg_sel['biased_fraction'] > 1.: - raise ValueError('Biased fraction {!r} not in (0, 1]'.format( - cfg_sel['biased_fraction'])) + if ( + cfg_sel["biased_fraction"] <= 0.0 + or cfg_sel["biased_fraction"] > 1.0 + ): + raise ValueError( + "Biased fraction {!r} not in (0, 1]".format( + cfg_sel["biased_fraction"] + ) + ) # figure out if we need the current nn model prediction nn_is_required = False - for key in ['true_minus_pred_greater', 'true_minus_pred_less', - 'true_minus_pred_trafo_greater', 'cut_abs_diff', - 'true_minus_pred_trafo_less', 'cut_abs_diff_trafo', - 'cut_unc_weighted_diff_trafo']: + for key in [ + "true_minus_pred_greater", + "true_minus_pred_less", + "true_minus_pred_trafo_greater", + "cut_abs_diff", + "true_minus_pred_trafo_less", + "cut_abs_diff_trafo", + "cut_unc_weighted_diff_trafo", + ]: if cfg_sel[key] != {}: nn_is_required = True if nn_is_required: - model, data_transformer, data_handler = \ - self._create_model(cfg, cfg_sel) + model, data_transformer, data_handler = self._create_model( + cfg, cfg_sel + ) # create nn biased selection masking function def biased_selection_func(icecube_data): @@ -1168,7 +1267,8 @@ def biased_selection_func(icecube_data): data_handler=data_handler, data_transformer=data_transformer, model=model, - counter=biased_selection_func.counter) + counter=biased_selection_func.counter, + ) mask = np.logical_or(mask, mask_nn) @@ -1176,8 +1276,9 @@ def biased_selection_func(icecube_data): num_biased_events = np.sum(mask) # calculate how many unbiased events should be chosen - num_to_choose = \ - int(num_biased_events * (1./cfg_sel['biased_fraction']-1)) + num_to_choose = int( + num_biased_events * (1.0 / cfg_sel["biased_fraction"] - 1) + ) # add random events to obtain approximate correct fraction y_true = icecube_data[2] @@ -1187,7 +1288,7 @@ def biased_selection_func(icecube_data): # big to make sure to still take events from all files. # Keep track of imbalance. num_chosen = num_to_choose - biased_selection_func.balance - if biased_selection_func.balance > cfg_sel['max_imbalance']: + if biased_selection_func.balance > cfg_sel["max_imbalance"]: num_chosen = max(num_chosen, 0) else: num_chosen = max(num_chosen, 1) @@ -1195,9 +1296,9 @@ def biased_selection_func(icecube_data): biased_selection_func.balance += num_chosen - num_to_choose if num_chosen > 0: - indices = local_random_state.choice(indices_unbiased, - size=num_chosen, - replace=False) + indices = local_random_state.choice( + indices_unbiased, size=num_chosen, replace=False + ) mask_random = np.zeros(len(y_true)) mask_random[indices] = True mask = np.logical_or(mask, mask_random) @@ -1208,9 +1309,11 @@ def biased_selection_func(icecube_data): return None # apply mask - icecube_data_masked = [np.array(icecube_data[0][mask]), - np.array(icecube_data[1][mask]), - np.array(icecube_data[2][mask])] + icecube_data_masked = [ + np.array(icecube_data[0][mask]), + np.array(icecube_data[1][mask]), + np.array(icecube_data[2][mask]), + ] if self.misc_data_exists: icecube_data_masked.append(np.array(icecube_data[3][mask])) else: diff --git a/dnn_reco/data_trafo.py b/dnn_reco/data_trafo.py index 3ac585a..896bece 100644 --- a/dnn_reco/data_trafo.py +++ b/dnn_reco/data_trafo.py @@ -9,7 +9,6 @@ class DataTransformer: - """Transforms data Attributes @@ -18,11 +17,19 @@ class DataTransformer: A dictionary containing the transformation settings and parameters. """ - def __init__(self, data_handler, - treat_doms_equally=True, normalize_dom_data=True, - normalize_label_data=True, normalize_misc_data=True, - log_dom_bins=False, log_label_bins=False, log_misc_bins=False, - norm_constant=1e-6, float_precision='float64'): + def __init__( + self, + data_handler, + treat_doms_equally=True, + normalize_dom_data=True, + normalize_label_data=True, + normalize_misc_data=True, + log_dom_bins=False, + log_label_bins=False, + log_misc_bins=False, + norm_constant=1e-6, + float_precision="float64", + ): """Initializes a DataTransformer object and saves the trafo settings. Parameters @@ -88,8 +95,9 @@ def __init__(self, data_handler, log_dom_bins = [log_dom_bins for i in range(data_handler.num_bins)] if isinstance(log_label_bins, bool): - log_label_bins = [log_label_bins - for i in range(data_handler.label_shape[-1])] + log_label_bins = [ + log_label_bins for i in range(data_handler.label_shape[-1]) + ] elif isinstance(log_label_bins, dict): log_dict = dict(log_label_bins) log_label_bins = np.zeros(data_handler.label_shape[-1], dtype=bool) @@ -97,8 +105,9 @@ def __init__(self, data_handler, log_label_bins[data_handler.get_label_index(key)] = bool(value) if isinstance(log_misc_bins, bool) and data_handler.misc_shape: - log_misc_bins = [log_misc_bins - for i in range(data_handler.misc_shape[-1])] + log_misc_bins = [ + log_misc_bins for i in range(data_handler.misc_shape[-1]) + ] elif isinstance(log_misc_bins, dict) and data_handler.misc_shape: log_dict = dict(log_misc_bins) log_misc_bins = np.zeros(data_handler.misc_shape[-1], dtype=bool) @@ -107,41 +116,48 @@ def __init__(self, data_handler, # Some sanity checks if len(log_dom_bins) != data_handler.num_bins: - raise ValueError('{!r} != {!r}. Wrong log_bins: {!r}'.format( - len(log_dom_bins), - data_handler.num_bins, - log_dom_bins)) + raise ValueError( + "{!r} != {!r}. Wrong log_bins: {!r}".format( + len(log_dom_bins), data_handler.num_bins, log_dom_bins + ) + ) if len(log_label_bins) != data_handler.label_shape[-1]: - raise ValueError('{!r} != {!r}. Wrong log_bins: {!r}'.format( - len(log_label_bins), - data_handler.label_shape[-1], - log_label_bins)) + raise ValueError( + "{!r} != {!r}. Wrong log_bins: {!r}".format( + len(log_label_bins), + data_handler.label_shape[-1], + log_label_bins, + ) + ) if data_handler.misc_shape is not None: if len(log_misc_bins) != data_handler.misc_shape[-1]: - raise ValueError('{!r} != {!r}. Wrong log_bins: {!r}'.format( - len(log_misc_bins), - data_handler.misc_shape[-1], - log_misc_bins)) + raise ValueError( + "{!r} != {!r}. Wrong log_bins: {!r}".format( + len(log_misc_bins), + data_handler.misc_shape[-1], + log_misc_bins, + ) + ) # create trafo_model_dict self.trafo_model = { - 'num_bins': data_handler.num_bins, - 'label_shape': data_handler.label_shape, - 'misc_shape': data_handler.misc_shape, - 'misc_names': data_handler.misc_names, - 'label_names': data_handler.label_names, - 'treat_doms_equally': treat_doms_equally, - 'normalize_dom_data': normalize_dom_data, - 'normalize_label_data': normalize_label_data, - 'normalize_misc_data': normalize_misc_data, - 'log_dom_bins': log_dom_bins, - 'log_label_bins': log_label_bins, - 'log_misc_bins': log_misc_bins, - 'norm_constant': norm_constant, + "num_bins": data_handler.num_bins, + "label_shape": data_handler.label_shape, + "misc_shape": data_handler.misc_shape, + "misc_names": data_handler.misc_names, + "label_names": data_handler.label_names, + "treat_doms_equally": treat_doms_equally, + "normalize_dom_data": normalize_dom_data, + "normalize_label_data": normalize_label_data, + "normalize_misc_data": normalize_misc_data, + "log_dom_bins": log_dom_bins, + "log_label_bins": log_label_bins, + "log_misc_bins": log_misc_bins, + "norm_constant": norm_constant, } - self._ic78_shape = [10, 10, 60, self.trafo_model['num_bins']] - self._deepcore_shape = [8, 60, self.trafo_model['num_bins']] + self._ic78_shape = [10, 10, 60, self.trafo_model["num_bins"]] + self._deepcore_shape = [8, 60, self.trafo_model["num_bins"]] def _update_online_variance_vars(self, data_batch, n, mean, M2): """Update online variance variables. @@ -170,9 +186,9 @@ def _update_online_variance_vars(self, data_batch, n, mean, M2): for x in data_batch: n += 1 delta = x - mean - mean += delta/n + mean += delta / n delta2 = x - mean - M2 += delta*delta2 + M2 += delta * delta2 return n, mean, M2 def _perform_update_step(self, log_bins, data_batch, n, mean, M2): @@ -210,8 +226,9 @@ def _perform_update_step(self, log_bins, data_batch, n, mean, M2): data_batch[..., bin_i] = np.log(1.0 + data_batch[..., bin_i]) # calculate onlince variance and mean for DOM responses - return self._update_online_variance_vars(data_batch=data_batch, n=n, - mean=mean, M2=M2) + return self._update_online_variance_vars( + data_batch=data_batch, n=n, mean=mean, M2=M2 + ) def create_trafo_model_iteratively(self, data_iterator, num_batches): """Iteratively create a transformation model. @@ -226,104 +243,110 @@ def create_trafo_model_iteratively(self, data_iterator, num_batches): """ # create empty onlince variance variables - ic78_n = 0. + ic78_n = 0.0 ic78_mean = np.zeros(self._ic78_shape) ic78_M2 = np.zeros(self._ic78_shape) - deepcore_n = 0. + deepcore_n = 0.0 deepcore_mean = np.zeros(self._deepcore_shape) deepcore_M2 = np.zeros(self._deepcore_shape) - label_n = 0. - label_mean = np.zeros(self.trafo_model['label_shape']) - label_M2 = np.zeros(self.trafo_model['label_shape']) + label_n = 0.0 + label_mean = np.zeros(self.trafo_model["label_shape"]) + label_M2 = np.zeros(self.trafo_model["label_shape"]) - if self.trafo_model['misc_shape'] is not None: - misc_n = 0. - misc_mean = np.zeros(self.trafo_model['misc_shape']) - misc_M2 = np.zeros(self.trafo_model['misc_shape']) + if self.trafo_model["misc_shape"] is not None: + misc_n = 0.0 + misc_mean = np.zeros(self.trafo_model["misc_shape"]) + misc_M2 = np.zeros(self.trafo_model["misc_shape"]) for i in tqdm(range(num_batches), total=num_batches): x_ic78, x_deepcore, label, misc_data = next(data_iterator) ic78_n, ic78_mean, ic78_M2 = self._perform_update_step( - log_bins=self.trafo_model['log_dom_bins'], - data_batch=x_ic78, - n=ic78_n, - mean=ic78_mean, - M2=ic78_M2) + log_bins=self.trafo_model["log_dom_bins"], + data_batch=x_ic78, + n=ic78_n, + mean=ic78_mean, + M2=ic78_M2, + ) deepcore_n, deepcore_mean, deepcore_M2 = self._perform_update_step( - log_bins=self.trafo_model['log_dom_bins'], - data_batch=x_deepcore, - n=deepcore_n, - mean=deepcore_mean, - M2=deepcore_M2) + log_bins=self.trafo_model["log_dom_bins"], + data_batch=x_deepcore, + n=deepcore_n, + mean=deepcore_mean, + M2=deepcore_M2, + ) label_n, label_mean, label_M2 = self._perform_update_step( - log_bins=self.trafo_model['log_label_bins'], - data_batch=label, - n=label_n, - mean=label_mean, - M2=label_M2) - - if self.trafo_model['misc_shape'] is not None: + log_bins=self.trafo_model["log_label_bins"], + data_batch=label, + n=label_n, + mean=label_mean, + M2=label_M2, + ) + + if self.trafo_model["misc_shape"] is not None: misc_n, misc_mean, misc_M2 = self._perform_update_step( - log_bins=self.trafo_model['log_misc_bins'], - data_batch=misc_data, - n=misc_n, - mean=misc_mean, - M2=misc_M2) + log_bins=self.trafo_model["log_misc_bins"], + data_batch=misc_data, + n=misc_n, + mean=misc_mean, + M2=misc_M2, + ) # Calculate standard deviation ic78_std = np.sqrt(ic78_M2 / ic78_n) deepcore_std = np.sqrt(deepcore_M2 / deepcore_n) label_std = np.sqrt(label_M2 / label_n) - if self.trafo_model['misc_shape'] is not None: + if self.trafo_model["misc_shape"] is not None: misc_std = np.sqrt(misc_M2 / misc_n) # combine DOM data over all DOMs if desired - if self.trafo_model['treat_doms_equally']: + if self.trafo_model["treat_doms_equally"]: # initalize with zeros - self.trafo_model['ic78_mean'] = np.zeros(self._ic78_shape) - self.trafo_model['ic78_std'] = np.zeros(self._ic78_shape) + self.trafo_model["ic78_mean"] = np.zeros(self._ic78_shape) + self.trafo_model["ic78_std"] = np.zeros(self._ic78_shape) # now calculate normalization for real DOMs - self.trafo_model['ic78_mean'][detector.ic78_real_DOMs_mask] = \ + self.trafo_model["ic78_mean"][detector.ic78_real_DOMs_mask] = ( np.mean(ic78_mean[detector.ic78_real_DOMs_mask], axis=0) - self.trafo_model['ic78_std'][detector.ic78_real_DOMs_mask] = \ + ) + self.trafo_model["ic78_std"][detector.ic78_real_DOMs_mask] = ( np.mean(ic78_std[detector.ic78_real_DOMs_mask], axis=0) + ) # DeepCore - self.trafo_model['deepcore_mean'] = np.mean(deepcore_mean, - axis=(0, 1), - keepdims=True) - self.trafo_model['deepcore_std'] = np.mean(deepcore_std, - axis=(0, 1), - keepdims=True) + self.trafo_model["deepcore_mean"] = np.mean( + deepcore_mean, axis=(0, 1), keepdims=True + ) + self.trafo_model["deepcore_std"] = np.mean( + deepcore_std, axis=(0, 1), keepdims=True + ) else: - self.trafo_model['ic78_mean'] = ic78_mean - self.trafo_model['ic78_std'] = ic78_std - self.trafo_model['deepcore_mean'] = deepcore_mean - self.trafo_model['deepcore_std'] = deepcore_std + self.trafo_model["ic78_mean"] = ic78_mean + self.trafo_model["ic78_std"] = ic78_std + self.trafo_model["deepcore_mean"] = deepcore_mean + self.trafo_model["deepcore_std"] = deepcore_std - self.trafo_model['label_mean'] = label_mean - self.trafo_model['label_std'] = label_std + self.trafo_model["label_mean"] = label_mean + self.trafo_model["label_std"] = label_std - if self.trafo_model['misc_shape'] is not None: - self.trafo_model['misc_mean'] = misc_mean - self.trafo_model['misc_std'] = misc_std + if self.trafo_model["misc_shape"] is not None: + self.trafo_model["misc_mean"] = misc_mean + self.trafo_model["misc_std"] = misc_std # set constant parameters to have a std dev of 1 instead of zero - std_names = ['ic78_std', 'deepcore_std', 'label_std'] - if self.trafo_model['misc_shape'] is not None: - std_names.append('misc_std') + std_names = ["ic78_std", "deepcore_std", "label_std"] + if self.trafo_model["misc_shape"] is not None: + std_names.append("misc_std") for key in std_names: mask = self.trafo_model[key] == 0 - self.trafo_model[key][mask] = 1. + self.trafo_model[key][mask] = 1.0 self._setup_complete = True @@ -344,20 +367,21 @@ def load_trafo_model(self, model_path): transformation model. """ # load trafo model from file - with open(model_path, 'rb') as handle: + with open(model_path, "rb") as handle: if sys.version_info.major >= 3: - trafo_model = pickle.load(handle, encoding='latin1') + trafo_model = pickle.load(handle, encoding="latin1") else: trafo_model = pickle.load(handle) # make sure that settings match for key in self.trafo_model: if key not in trafo_model: - raise ValueError('Key {!r} does not exist in {!r}'.format( - key, model_path)) + raise ValueError( + "Key {!r} does not exist in {!r}".format(key, model_path) + ) mismatch = self.trafo_model[key] != trafo_model[key] - error_msg = 'Setting {!r} does not match!'.format(key) + error_msg = "Setting {!r} does not match!".format(key) if isinstance(mismatch, bool): if mismatch: raise ValueError(error_msg) @@ -377,7 +401,7 @@ def save_trafo_model(self, model_path): model_path : str Path to trafo model file. """ - with open(model_path, 'wb') as handle: + with open(model_path, "wb") as handle: pickle.dump(self.trafo_model, handle, protocol=2) def _check_settings(self, data, data_type): @@ -407,31 +431,35 @@ def _check_settings(self, data, data_type): data_type = data_type.lower() if not self._setup_complete: - raise ValueError('DataTransformer needs to create or load a trafo' - 'model prior to transform call.') + raise ValueError( + "DataTransformer needs to create or load a trafo" + "model prior to transform call." + ) - if data_type not in ['ic78', 'deepcore', 'label', 'misc']: - raise ValueError('data_type {!r} is unknown!'.format(data_type)) + if data_type not in ["ic78", "deepcore", "label", "misc"]: + raise ValueError("data_type {!r} is unknown!".format(data_type)) # check if shape of data matches expected shape - if data_type == 'ic78': - shape = [10, 10, 60, self.trafo_model['num_bins']] - elif data_type == 'deepcore': - shape = [8, 60, self.trafo_model['num_bins']] + if data_type == "ic78": + shape = [10, 10, 60, self.trafo_model["num_bins"]] + elif data_type == "deepcore": + shape = [8, 60, self.trafo_model["num_bins"]] else: - shape = self.trafo_model['{}_shape'.format(data_type)] + shape = self.trafo_model["{}_shape".format(data_type)] if list(data.shape[1:]) != shape: - raise ValueError('Shape of data {!r} does'.format(data.shape[1:]) + - ' not match expected shape {!r}'.format(shape)) + raise ValueError( + "Shape of data {!r} does".format(data.shape[1:]) + + " not match expected shape {!r}".format(shape) + ) - if data_type in ['ic78', 'deepcore']: - log_name = 'log_dom_bins' - normalize_name = 'normalize_dom_data' + if data_type in ["ic78", "deepcore"]: + log_name = "log_dom_bins" + normalize_name = "normalize_dom_data" else: - log_name = 'log_{}_bins'.format(data_type) - normalize_name = 'normalize_{}_data'.format(data_type) + log_name = "log_{}_bins".format(data_type) + normalize_name = "normalize_{}_data".format(data_type) is_tf = tf.is_tensor(data) @@ -480,8 +508,9 @@ def transform(self, data, data_type, bias_correction=True): If DataTransformer object has not created or loaded a trafo model. If provided data_type is unkown. """ - data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = \ + data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = ( self._check_settings(data, data_type) + ) # perform logarithm on bins if bias_correction: @@ -505,9 +534,11 @@ def transform(self, data, data_type, bias_correction=True): # normalize data if self.trafo_model[normalize_name]: if bias_correction: - data -= self.trafo_model['{}_mean'.format(data_type.lower())] - data /= (self.trafo_model['norm_constant'] + - self.trafo_model['{}_std'.format(data_type.lower())]) + data -= self.trafo_model["{}_mean".format(data_type.lower())] + data /= ( + self.trafo_model["norm_constant"] + + self.trafo_model["{}_std".format(data_type.lower())] + ) # cast back to original dtype if is_tf: @@ -548,15 +579,18 @@ def inverse_transform(self, data, data_type, bias_correction=True): If DataTransformer object has not created or loaded a trafo model. If provided data_type is unkown. """ - data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = \ + data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = ( self._check_settings(data, data_type) + ) # de-normalize data if self.trafo_model[normalize_name]: - data *= (self.trafo_model['norm_constant'] + - self.trafo_model['{}_std'.format(data_type.lower())]) + data *= ( + self.trafo_model["norm_constant"] + + self.trafo_model["{}_std".format(data_type.lower())] + ) if bias_correction: - data += self.trafo_model['{}_mean'.format(data_type.lower())] + data += self.trafo_model["{}_mean".format(data_type.lower())] # undo logarithm on bins if bias_correction: @@ -570,8 +604,9 @@ def inverse_transform(self, data, data_type, bias_correction=True): data_list = tf.unstack(data, axis=-1) for bin_i, do_log in enumerate(self.trafo_model[log_name]): if do_log: - data_list[bin_i] = \ - tf.clip_by_value(data_list[bin_i], -60., 60.) + data_list[bin_i] = tf.clip_by_value( + data_list[bin_i], -60.0, 60.0 + ) data_list[bin_i] = exp_func(data_list[bin_i]) - 1.0 data = tf.stack(data_list, axis=-1) else: diff --git a/dnn_reco/detector.py b/dnn_reco/detector.py index c587556..9a823b2 100644 --- a/dnn_reco/detector.py +++ b/dnn_reco/detector.py @@ -1,12 +1,18012 @@ -''' +""" IceCube detector constants such as the DOM positions are hardcoded here. -''' +""" + import numpy as np # flattened IC78 coordinates: shape [10*10*60*3] x_ic78_coords_flat = [ - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, +] # IC78 coordinates: shape [10, 10, 60, 3] x_ic78_coords = np.reshape(x_ic78_coords_flat, [10, 10, 60, 3]) @@ -15,38 +18015,163 @@ # Assumes the detector is centered around (0,0), which means # negative indices are possible. string_hex_coord_dict = { - 1: (-4, -1), 2: (-4, 0), 3: (-4, 1), 4: (-4, 2), 5: (-4, 3), - 6: (-4, 4), 7: (-3, -2), 8: (-3, -1), 9: (-3, 0), 10: (-3, 1), - 11: (-3, 2), 12: (-3, 3), 13: (-3, 4), 14: (-2, -3), 15: (-2, -2), - 16: (-2, -1), 17: (-2, 0), 18: (-2, 1), 19: (-2, 2), 20: (-2, 3), - 21: (-2, 4), 22: (-1, -4), 23: (-1, -3), 24: (-1, -2), - 25: (-1, -1), 26: (-1, 0), 27: (-1, 1), 28: (-1, 2), 29: (-1, 3), - 30: (-1, 4), 31: (0, -5), 32: (0, -4), 33: (0, -3), 34: (0, -2), - 35: (0, -1), 36: (0, 0), 37: (0, 1), 38: (0, 2), 39: (0, 3), - 40: (0, 4), 41: (1, -5), 42: (1, -4), 43: (1, -3), 44: (1, -2), - 45: (1, -1), 46: (1, 0), 47: (1, 1), 48: (1, 2), 49: (1, 3), - 50: (1, 4), 51: (2, -5), 52: (2, -4), 53: (2, -3), 54: (2, -2), - 55: (2, -1), 56: (2, 0), 57: (2, 1), 58: (2, 2), 59: (2, 3), - 60: (3, -5), 61: (3, -4), 62: (3, -3), 63: (3, -2), 64: (3, -1), - 65: (3, 0), 66: (3, 1), 67: (3, 2), 68: (4, -5), 69: (4, -4), - 70: (4, -3), 71: (4, -2), 72: (4, -1), 73: (4, 0), 74: (4, 1), - 75: (5, -5), 76: (5, -4), 77: (5, -3), 78: (5, -2), - } + 1: (-4, -1), + 2: (-4, 0), + 3: (-4, 1), + 4: (-4, 2), + 5: (-4, 3), + 6: (-4, 4), + 7: (-3, -2), + 8: (-3, -1), + 9: (-3, 0), + 10: (-3, 1), + 11: (-3, 2), + 12: (-3, 3), + 13: (-3, 4), + 14: (-2, -3), + 15: (-2, -2), + 16: (-2, -1), + 17: (-2, 0), + 18: (-2, 1), + 19: (-2, 2), + 20: (-2, 3), + 21: (-2, 4), + 22: (-1, -4), + 23: (-1, -3), + 24: (-1, -2), + 25: (-1, -1), + 26: (-1, 0), + 27: (-1, 1), + 28: (-1, 2), + 29: (-1, 3), + 30: (-1, 4), + 31: (0, -5), + 32: (0, -4), + 33: (0, -3), + 34: (0, -2), + 35: (0, -1), + 36: (0, 0), + 37: (0, 1), + 38: (0, 2), + 39: (0, 3), + 40: (0, 4), + 41: (1, -5), + 42: (1, -4), + 43: (1, -3), + 44: (1, -2), + 45: (1, -1), + 46: (1, 0), + 47: (1, 1), + 48: (1, 2), + 49: (1, 3), + 50: (1, 4), + 51: (2, -5), + 52: (2, -4), + 53: (2, -3), + 54: (2, -2), + 55: (2, -1), + 56: (2, 0), + 57: (2, 1), + 58: (2, 2), + 59: (2, 3), + 60: (3, -5), + 61: (3, -4), + 62: (3, -3), + 63: (3, -2), + 64: (3, -1), + 65: (3, 0), + 66: (3, 1), + 67: (3, 2), + 68: (4, -5), + 69: (4, -4), + 70: (4, -3), + 71: (4, -2), + 72: (4, -1), + 73: (4, 0), + 74: (4, 1), + 75: (5, -5), + 76: (5, -4), + 77: (5, -3), + 78: (5, -2), +} hex_coord_string_dict = { - (-4, -1): 1, (-4, 0): 2, (-4, 1): 3, (-4, 2): 4, (-4, 3): 5, (-4, 4): 6, - (-3, -2): 7, (-3, -1): 8, (-3, 0): 9, (-3, 1): 10, (-3, 2): 11, - (-3, 3): 12, (-3, 4): 13, (-2, -3): 14, (-2, -2): 15, (-2, -1): 16, - (-2, 0): 17, (-2, 1): 18, (-2, 2): 19, (-2, 3): 20, (-2, 4): 21, - (-1, -4): 22, (-1, -3): 23, (-1, -2): 24, (-1, -1): 25, (-1, 0): 26, - (-1, 1): 27, (-1, 2): 28, (-1, 3): 29, (-1, 4): 30, (0, -5): 31, - (0, -4): 32, (0, -3): 33, (0, -2): 34, (0, -1): 35, (0, 0): 36, (0, 1): 37, - (0, 2): 38, (0, 3): 39, (0, 4): 40, (1, -5): 41, (1, -4): 42, (1, -3): 43, - (1, -2): 44, (1, -1): 45, (1, 0): 46, (1, 1): 47, (1, 2): 48, (1, 3): 49, - (1, 4): 50, (2, -5): 51, (2, -4): 52, (2, -3): 53, (2, -2): 54, - (2, -1): 55, (2, 0): 56, (2, 1): 57, (2, 2): 58, (2, 3): 59, (3, -5): 60, - (3, -4): 61, (3, -3): 62, (3, -2): 63, (3, -1): 64, (3, 0): 65, (3, 1): 66, - (3, 2): 67, (4, -5): 68, (4, -4): 69, (4, -3): 70, (4, -2): 71, - (4, -1): 72, (4, 0): 73, (4, 1): 74, (5, -5): 75, (5, -4): 76, (5, -3): 77, + (-4, -1): 1, + (-4, 0): 2, + (-4, 1): 3, + (-4, 2): 4, + (-4, 3): 5, + (-4, 4): 6, + (-3, -2): 7, + (-3, -1): 8, + (-3, 0): 9, + (-3, 1): 10, + (-3, 2): 11, + (-3, 3): 12, + (-3, 4): 13, + (-2, -3): 14, + (-2, -2): 15, + (-2, -1): 16, + (-2, 0): 17, + (-2, 1): 18, + (-2, 2): 19, + (-2, 3): 20, + (-2, 4): 21, + (-1, -4): 22, + (-1, -3): 23, + (-1, -2): 24, + (-1, -1): 25, + (-1, 0): 26, + (-1, 1): 27, + (-1, 2): 28, + (-1, 3): 29, + (-1, 4): 30, + (0, -5): 31, + (0, -4): 32, + (0, -3): 33, + (0, -2): 34, + (0, -1): 35, + (0, 0): 36, + (0, 1): 37, + (0, 2): 38, + (0, 3): 39, + (0, 4): 40, + (1, -5): 41, + (1, -4): 42, + (1, -3): 43, + (1, -2): 44, + (1, -1): 45, + (1, 0): 46, + (1, 1): 47, + (1, 2): 48, + (1, 3): 49, + (1, 4): 50, + (2, -5): 51, + (2, -4): 52, + (2, -3): 53, + (2, -2): 54, + (2, -1): 55, + (2, 0): 56, + (2, 1): 57, + (2, 2): 58, + (2, 3): 59, + (3, -5): 60, + (3, -4): 61, + (3, -3): 62, + (3, -2): 63, + (3, -1): 64, + (3, 0): 65, + (3, 1): 66, + (3, 2): 67, + (4, -5): 68, + (4, -4): 69, + (4, -3): 70, + (4, -2): 71, + (4, -1): 72, + (4, 0): 73, + (4, 1): 74, + (5, -5): 75, + (5, -4): 76, + (5, -3): 77, (5, -2): 78, } @@ -74,8 +18199,8 @@ def get_dom_coords(string, dom): tuple(float, float, float) The x, y, z coordinates of the DOM. """ - assert string > 0 and string <= 86, 'String must be within [1, 86]' - assert dom > 0 and dom <= 60, 'DOM must be within [1, 86]' + assert string > 0 and string <= 86, "String must be within [1, 86]" + assert dom > 0 and dom <= 60, "DOM must be within [1, 86]" a, b = get_matrix_indices(string) dom_id = dom - 1 return x_ic78_coords[a, b, dom_id] @@ -87,4 +18212,4 @@ def get_dom_coords(string, dom): # Get masks for real DOMs ic78_real_DOMs_mask = np.zeros((10, 10, 60), dtype=bool) for string, (a, b) in string_hex_coord_dict.items(): - ic78_real_DOMs_mask[a+4, b+5, :] = True + ic78_real_DOMs_mask[a + 4, b + 5, :] = True diff --git a/dnn_reco/export_model.py b/dnn_reco/export_model.py index f365fe5..8f5e1f0 100644 --- a/dnn_reco/export_model.py +++ b/dnn_reco/export_model.py @@ -16,13 +16,22 @@ @click.command() -@click.argument('config_files', type=click.Path(exists=True), nargs=-1) -@click.option('--output_folder', '-o', default=None, - help='folder to which the model will be exported') -@click.option('--data_settings', '-s', default=None, - help='Config file used to create training data') -@click.option('--logs/--no-logs', default=True, - help='Export tensorflow log files.') +@click.argument("config_files", type=click.Path(exists=True), nargs=-1) +@click.option( + "--output_folder", + "-o", + default=None, + help="folder to which the model will be exported", +) +@click.option( + "--data_settings", + "-s", + default=None, + help="Config file used to create training data", +) +@click.option( + "--logs/--no-logs", default=True, help="Export tensorflow log files." +) def main(config_files, output_folder, data_settings, logs): """Script to export dnn reco model. @@ -34,17 +43,19 @@ def main(config_files, output_folder, data_settings, logs): # Check paths and define output names if not os.path.isdir(output_folder): - print('Creating directory: {!r}'.format(output_folder)) + print("Creating directory: {!r}".format(output_folder)) os.makedirs(output_folder) else: if len(os.listdir(output_folder)) > 0: - if click.confirm("Directory already exists and contains files! " - "Delete {!r}?".format(output_folder), - default=False): + if click.confirm( + "Directory already exists and contains files! " + "Delete {!r}?".format(output_folder), + default=False, + ): shutil.rmtree(output_folder) os.makedirs(output_folder) else: - raise ValueError('Aborting!') + raise ValueError("Aborting!") # read in and combine config files and set up setup_manager = SetupManager(config_files) @@ -52,29 +63,32 @@ def main(config_files, output_folder, data_settings, logs): # Create Data Handler object data_handler = DataHandler(config) - data_handler.setup_with_test_data(config['training_data_file']) + data_handler.setup_with_test_data(config["training_data_file"]) # create data transformer data_transformer = DataTransformer( - data_handler=data_handler, - treat_doms_equally=config['trafo_treat_doms_equally'], - normalize_dom_data=config['trafo_normalize_dom_data'], - normalize_label_data=config['trafo_normalize_label_data'], - normalize_misc_data=config['trafo_normalize_misc_data'], - log_dom_bins=config['trafo_log_dom_bins'], - log_label_bins=config['trafo_log_label_bins'], - log_misc_bins=config['trafo_log_misc_bins'], - norm_constant=config['trafo_norm_constant']) + data_handler=data_handler, + treat_doms_equally=config["trafo_treat_doms_equally"], + normalize_dom_data=config["trafo_normalize_dom_data"], + normalize_label_data=config["trafo_normalize_label_data"], + normalize_misc_data=config["trafo_normalize_misc_data"], + log_dom_bins=config["trafo_log_dom_bins"], + log_label_bins=config["trafo_log_label_bins"], + log_misc_bins=config["trafo_log_misc_bins"], + norm_constant=config["trafo_norm_constant"], + ) # load trafo model from file - data_transformer.load_trafo_model(config['trafo_model_path']) + data_transformer.load_trafo_model(config["trafo_model_path"]) with tf.Graph().as_default(): # create NN model - model = NNModel(is_training=True, - config=config, - data_handler=data_handler, - data_transformer=data_transformer) + model = NNModel( + is_training=True, + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + ) # compile model: define loss function and optimizer model.compile() @@ -82,49 +96,60 @@ def main(config_files, output_folder, data_settings, logs): # ------------------------- # Export latest checkpoints # ------------------------- - checkpoint_dir = os.path.dirname(config['model_checkpoint_path']) + checkpoint_dir = os.path.dirname(config["model_checkpoint_path"]) latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir) if latest_checkpoint is None: - raise ValueError('Could not find a checkpoint. Aborting export!') + raise ValueError("Could not find a checkpoint. Aborting export!") else: - for ending in ['.index', '.meta', '.data-00000-of-00001']: - shutil.copy2(src=latest_checkpoint + ending, - dst=output_folder) - shutil.copy2(src=os.path.join(checkpoint_dir, 'checkpoint'), - dst=output_folder) + for ending in [".index", ".meta", ".data-00000-of-00001"]: + shutil.copy2(src=latest_checkpoint + ending, dst=output_folder) + shutil.copy2( + src=os.path.join(checkpoint_dir, "checkpoint"), dst=output_folder + ) # ----------------------------- # read and export data settings # ----------------------------- - export_data_settings(data_settings=data_settings, - output_folder=output_folder) + export_data_settings( + data_settings=data_settings, output_folder=output_folder + ) # ----------------------------- # Export trafo model and config # ----------------------------- - base_name = os.path.basename(config['trafo_model_path']) - if '.' in base_name: - file_ending = base_name.split('.')[-1] - base_name = base_name.replace('.' + file_ending, '') - - shutil.copy2(src=config['trafo_model_path'], - dst=os.path.join(output_folder, 'trafo_model.npy')) - shutil.copy2(src=os.path.join(os.path.dirname(config['trafo_model_path']), - 'config_trafo__{}.yaml'.format(base_name)), - dst=os.path.join(output_folder, 'config_trafo.yaml')) + base_name = os.path.basename(config["trafo_model_path"]) + if "." in base_name: + file_ending = base_name.split(".")[-1] + base_name = base_name.replace("." + file_ending, "") + + shutil.copy2( + src=config["trafo_model_path"], + dst=os.path.join(output_folder, "trafo_model.npy"), + ) + shutil.copy2( + src=os.path.join( + os.path.dirname(config["trafo_model_path"]), + "config_trafo__{}.yaml".format(base_name), + ), + dst=os.path.join(output_folder, "config_trafo.yaml"), + ) # ---------------------------- # Export training config files # ---------------------------- - checkpoint_directory = os.path.dirname(config['model_checkpoint_path']) - training_files = glob.glob(os.path.join(checkpoint_directory, - 'config_training_*.yaml')) + checkpoint_directory = os.path.dirname(config["model_checkpoint_path"]) + training_files = glob.glob( + os.path.join(checkpoint_directory, "config_training_*.yaml") + ) for training_file in training_files: - shutil.copy2(src=training_file, - dst=os.path.join(output_folder, - os.path.basename(training_file))) - shutil.copy2(src=os.path.join(checkpoint_directory, 'training_steps.yaml'), - dst=os.path.join(output_folder, 'training_steps.yaml')) + shutil.copy2( + src=training_file, + dst=os.path.join(output_folder, os.path.basename(training_file)), + ) + shutil.copy2( + src=os.path.join(checkpoint_directory, "training_steps.yaml"), + dst=os.path.join(output_folder, "training_steps.yaml"), + ) # ---------------------- # Export model meta data @@ -135,44 +160,45 @@ def main(config_files, output_folder, data_settings, logs): # (instead of DataHandler.setup_with_data_container) meta_data = { - 'label_names': data_handler.label_names, - 'label_name_dict': data_handler.label_name_dict, - 'label_shape': data_handler.label_shape, - 'num_labels': data_handler.num_labels, - 'misc_names': data_handler.misc_names, - 'misc_name_dict': data_handler.misc_name_dict, - 'misc_data_exists': data_handler.misc_data_exists, - 'misc_shape': data_handler.misc_shape, - 'num_misc': data_handler.num_misc, + "label_names": data_handler.label_names, + "label_name_dict": data_handler.label_name_dict, + "label_shape": data_handler.label_shape, + "num_labels": data_handler.num_labels, + "misc_names": data_handler.misc_names, + "misc_name_dict": data_handler.misc_name_dict, + "misc_data_exists": data_handler.misc_data_exists, + "misc_shape": data_handler.misc_shape, + "num_misc": data_handler.num_misc, } - with open(os.path.join(output_folder, 'config_meta_data.yaml'), 'w') as f: + with open(os.path.join(output_folder, "config_meta_data.yaml"), "w") as f: yaml.dump(meta_data, f, default_flow_style=False) # ------------------------------------ # Export package versions and git hash # ------------------------------------ version_control = { - 'git_short_sha': config['git_short_sha'], - 'git_sha': config['git_sha'], - 'git_origin': config['git_origin'], - 'git_uncommited_changes': config['git_uncommited_changes'], - 'pip_installed_packages': config['pip_installed_packages'], - } - with open(os.path.join(output_folder, 'version_control.yaml'), 'w') as f: + "git_short_sha": config["git_short_sha"], + "git_sha": config["git_sha"], + "git_origin": config["git_origin"], + "git_uncommited_changes": config["git_uncommited_changes"], + "pip_installed_packages": config["pip_installed_packages"], + } + with open(os.path.join(output_folder, "version_control.yaml"), "w") as f: yaml.dump(version_control, f, default_flow_style=False) # ------------------------------- # Export tensorflow training logs # ------------------------------- if logs: - log_directory = os.path.dirname(config['log_path']) - shutil.copytree(src=log_directory, - dst=os.path.join(output_folder, 'logs')) + log_directory = os.path.dirname(config["log_path"]) + shutil.copytree( + src=log_directory, dst=os.path.join(output_folder, "logs") + ) - print('\n====================================') - print('= Successfully exported model to: =') - print('====================================') - print('{!r}\n'.format(output_folder)) + print("\n====================================") + print("= Successfully exported model to: =") + print("====================================") + print("{!r}\n".format(output_folder)) def export_data_settings(data_settings, output_folder): @@ -187,81 +213,88 @@ def export_data_settings(data_settings, output_folder): written to. """ try: - with open(data_settings, 'r') as stream: + with open(data_settings, "r") as stream: data_config = yaml.safe_load(stream) except Exception as e: print(e) - print('Falling back to modified SafeLoader') - with open(data_settings, 'r') as stream: - yaml.SafeLoader.add_constructor('tag:yaml.org,2002:python/unicode', - lambda _, node: node.value) + print("Falling back to modified SafeLoader") + with open(data_settings, "r") as stream: + yaml.SafeLoader.add_constructor( + "tag:yaml.org,2002:python/unicode", lambda _, node: node.value + ) data_config = dict(yaml.safe_load(stream)) - for k in ['pulse_time_quantiles', 'pulse_time_binning', - 'autoencoder_settings', 'autoencoder_encoder_name']: + for k in [ + "pulse_time_quantiles", + "pulse_time_binning", + "autoencoder_settings", + "autoencoder_encoder_name", + ]: if k not in data_config or data_config[k] is None: data_config[k] = None - for k in ['pulse_time_quantiles', 'pulse_time_binning']: + for k in ["pulse_time_quantiles", "pulse_time_binning"]: if data_config[k] is not None: data_config[k] = list(data_config[k]) data_settings = {} - data_settings['num_bins'] = data_config['num_data_bins'] - data_settings['relative_time_method'] = data_config['relative_time_method'] - data_settings['data_format'] = data_config['pulse_data_format'] - data_settings['time_bins'] = data_config['pulse_time_binning'] - data_settings['time_quantiles'] = data_config['pulse_time_quantiles'] - data_settings['autoencoder_settings'] = data_config['autoencoder_settings'] - data_settings['autoencoder_name'] = data_config['autoencoder_encoder_name'] + data_settings["num_bins"] = data_config["num_data_bins"] + data_settings["relative_time_method"] = data_config["relative_time_method"] + data_settings["data_format"] = data_config["pulse_data_format"] + data_settings["time_bins"] = data_config["pulse_time_binning"] + data_settings["time_quantiles"] = data_config["pulse_time_quantiles"] + data_settings["autoencoder_settings"] = data_config["autoencoder_settings"] + data_settings["autoencoder_name"] = data_config["autoencoder_encoder_name"] # --------------------------------- # Find and save optional parameters # --------------------------------- - if 'DNN_excluded_doms' in data_config: - data_settings['dom_exclusions'] = data_config['DNN_excluded_doms'] - if 'DNN_partial_exclusion' in data_config: - data_settings['partial_exclusion'] = \ - data_config['DNN_partial_exclusion'] - if 'DNN_pulse_key' in data_config: - data_settings['pulse_key'] = data_config['DNN_pulse_key'] - elif 'pulse_map_string' in data_config: - data_settings['pulse_key'] = data_config['pulse_map_string'] - - if 'DNN_cascade_key' in data_config: - data_settings['cascade_key'] = data_config['DNN_cascade_key'] + if "DNN_excluded_doms" in data_config: + data_settings["dom_exclusions"] = data_config["DNN_excluded_doms"] + if "DNN_partial_exclusion" in data_config: + data_settings["partial_exclusion"] = data_config[ + "DNN_partial_exclusion" + ] + if "DNN_pulse_key" in data_config: + data_settings["pulse_key"] = data_config["DNN_pulse_key"] + elif "pulse_map_string" in data_config: + data_settings["pulse_key"] = data_config["pulse_map_string"] + + if "DNN_cascade_key" in data_config: + data_settings["cascade_key"] = data_config["DNN_cascade_key"] allowed_pulse_keys = [] allowed_cascade_keys = [] - if 'pulse_key' in data_settings: - allowed_pulse_keys.append(data_settings['pulse_key']) - if 'cascade_key' in data_settings: - allowed_cascade_keys.append(data_settings['cascade_key']) + if "pulse_key" in data_settings: + allowed_pulse_keys.append(data_settings["pulse_key"]) + if "cascade_key" in data_settings: + allowed_cascade_keys.append(data_settings["cascade_key"]) - if 'datasets' in data_config: - for dataset in data_config['datasets'].values(): - if 'DNN_pulse_key' in dataset: - allowed_pulse_keys.append(dataset['DNN_pulse_key']) - elif 'pulse_map_string' in dataset: - allowed_pulse_keys.append(dataset['pulse_map_string']) + if "datasets" in data_config: + for dataset in data_config["datasets"].values(): + if "DNN_pulse_key" in dataset: + allowed_pulse_keys.append(dataset["DNN_pulse_key"]) + elif "pulse_map_string" in dataset: + allowed_pulse_keys.append(dataset["pulse_map_string"]) - if 'DNN_cascade_key' in dataset: - allowed_cascade_keys.append(dataset['DNN_cascade_key']) + if "DNN_cascade_key" in dataset: + allowed_cascade_keys.append(dataset["DNN_cascade_key"]) if len(allowed_pulse_keys) > 0: - data_settings['allowed_pulse_keys'] = allowed_pulse_keys + data_settings["allowed_pulse_keys"] = allowed_pulse_keys if len(allowed_cascade_keys) > 0: - data_settings['allowed_cascade_keys'] = allowed_cascade_keys + data_settings["allowed_cascade_keys"] = allowed_cascade_keys - print('\n=========================') - print('= Found Data Settings: =') - print('=========================') + print("\n=========================") + print("= Found Data Settings: =") + print("=========================") for key, value in data_settings.items(): - print('{}: {}'.format(key, value)) + print("{}: {}".format(key, value)) - with open(os.path.join(output_folder, - 'config_data_settings.yaml'), 'w') as f: + with open( + os.path.join(output_folder, "config_data_settings.yaml"), "w" + ) as f: yaml.dump(data_settings, f, default_flow_style=False) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/dnn_reco/ic3/llh.py b/dnn_reco/ic3/llh.py index 13cf26b..521cc90 100644 --- a/dnn_reco/ic3/llh.py +++ b/dnn_reco/ic3/llh.py @@ -11,7 +11,6 @@ class DNN_LLH_Circular_Dir(DNN_LLH_Base_Elliptical): - """The DNN LLH class for calculating circular PDFs obtained from the DNN reco. @@ -36,9 +35,19 @@ class DNN_LLH_Circular_Dir(DNN_LLH_Base_Elliptical): This is the output of the DNN reco for the z-component. """ - def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - num_samples=1000000, random_seed=42, - weighted_normalization=True, fix_delta=True): + def __init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + num_samples=1000000, + random_seed=42, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -81,11 +90,13 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, u_dir_y = ufloat(dir_y, unc_y) u_dir_z = ufloat(dir_z, unc_z) u_dir_x, u_dir_y, u_dir_z = self.u_normalize_dir( - u_dir_x, u_dir_y, u_dir_z) + u_dir_x, u_dir_y, u_dir_z + ) # Assign values with propagated and normalized vector u_zenith, u_azimuth = self.u_get_zenith_azimuth( - u_dir_x, u_dir_y, u_dir_z) + u_dir_x, u_dir_y, u_dir_z + ) self.dir_x = u_dir_x.nominal_value self.dir_y = u_dir_y.nominal_value self.dir_z = u_dir_z.nominal_value @@ -97,16 +108,23 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, # calculate circular error radius # (Note we want to get 'average' circular error, therefore divide by 2) - circular_var = (unc_zenith**2 + - unc_azimuth**2 * np.sin(zenith)**2) / 2. + circular_var = ( + unc_zenith**2 + unc_azimuth**2 * np.sin(zenith) ** 2 + ) / 2.0 cov = np.diag([circular_var, circular_var]) - DNN_LLH_Base_Elliptical.__init__(self, zenith, azimuth, cov, - num_samples, random_seed, - weighted_normalization, fix_delta) + DNN_LLH_Base_Elliptical.__init__( + self, + zenith, + azimuth, + cov, + num_samples, + random_seed, + weighted_normalization, + fix_delta, + ) class DNN_LLH_Circular(DNN_LLH_Base_Elliptical): - """The DNN LLH class for calculating circular PDFs obtained from the DNN reco. @@ -131,9 +149,17 @@ class DNN_LLH_Circular(DNN_LLH_Base_Elliptical): This is the output of the DNN reco for the z-component. """ - def __init__(self, zenith, azimuth, unc_zenith, unc_azimuth, - num_samples=1000000, random_seed=42, - weighted_normalization=True, fix_delta=True): + def __init__( + self, + zenith, + azimuth, + unc_zenith, + unc_azimuth, + num_samples=1000000, + random_seed=42, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -165,16 +191,23 @@ def __init__(self, zenith, azimuth, unc_zenith, unc_azimuth, """ # calculate circular error radius # (Note we want to get 'average' circular error, therefore divide by 2) - circular_var = (unc_zenith**2 + - unc_azimuth**2 * np.sin(zenith)**2) / 2. + circular_var = ( + unc_zenith**2 + unc_azimuth**2 * np.sin(zenith) ** 2 + ) / 2.0 cov = np.diag([circular_var, circular_var]) - DNN_LLH_Base_Elliptical.__init__(self, zenith, azimuth, cov, - num_samples, random_seed, - weighted_normalization, fix_delta) + DNN_LLH_Base_Elliptical.__init__( + self, + zenith, + azimuth, + cov, + num_samples, + random_seed, + weighted_normalization, + fix_delta, + ) class DNN_LLH_Elliptical_Dir(DNN_LLH_Base_Elliptical): - """The DNN LLH class for calculating elliptical PDFs obtained from the DNN reco. @@ -205,9 +238,19 @@ class DNN_LLH_Elliptical_Dir(DNN_LLH_Base_Elliptical): This is the output of the DNN reco for the z-component. """ - def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - num_samples=1000000, random_seed=42, - weighted_normalization=True, fix_delta=True): + def __init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + num_samples=1000000, + random_seed=42, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -250,11 +293,13 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, u_dir_y = ufloat(dir_y, unc_y) u_dir_z = ufloat(dir_z, unc_z) u_dir_x, u_dir_y, u_dir_z = self.u_normalize_dir( - u_dir_x, u_dir_y, u_dir_z) + u_dir_x, u_dir_y, u_dir_z + ) # Assign values with propagated and normalized vector u_zenith, u_azimuth = self.u_get_zenith_azimuth( - u_dir_x, u_dir_y, u_dir_z) + u_dir_x, u_dir_y, u_dir_z + ) self.dir_x = u_dir_x.nominal_value self.dir_y = u_dir_y.nominal_value self.dir_z = u_dir_z.nominal_value @@ -263,13 +308,19 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, self.unc_zenith = unumpy.std_devs(u_zenith) self.unc_azimuth = unumpy.std_devs(u_azimuth) cov = np.array(covariance_matrix([u_zenith, u_azimuth])) - DNN_LLH_Base_Elliptical.__init__(self, self.zenith, self.azimuth, cov, - num_samples, random_seed, - weighted_normalization, fix_delta) + DNN_LLH_Base_Elliptical.__init__( + self, + self.zenith, + self.azimuth, + cov, + num_samples, + random_seed, + weighted_normalization, + fix_delta, + ) class DNN_LLH_Elliptical(DNN_LLH_Base_Elliptical): - """The DNN LLH class for calculating elliptical PDFs obtained from the DNN reco. @@ -300,9 +351,17 @@ class DNN_LLH_Elliptical(DNN_LLH_Base_Elliptical): This is the output of the DNN reco for the z-component. """ - def __init__(self, zenith, azimuth, unc_zenith, unc_azimuth, - num_samples=1000000, random_seed=42, - weighted_normalization=True, fix_delta=True): + def __init__( + self, + zenith, + azimuth, + unc_zenith, + unc_azimuth, + num_samples=1000000, + random_seed=42, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -333,13 +392,19 @@ def __init__(self, zenith, azimuth, unc_zenith, unc_azimuth, abs(zenith - sampled_zenith) follow the expected distribution. """ cov = np.diag([unc_zenith**2, unc_azimuth**2]) - DNN_LLH_Base_Elliptical.__init__(self, zenith, azimuth, cov, - num_samples, random_seed, - weighted_normalization, fix_delta) + DNN_LLH_Base_Elliptical.__init__( + self, + zenith, + azimuth, + cov, + num_samples, + random_seed, + weighted_normalization, + fix_delta, + ) class DNN_LLH_normalized(DNN_LLH_Base): - """A class for calculating the PDF obtained from the DNN reco for models that estimate the direction vector components and their uncertainty in independent 1D Gaussian Likelihoods, while the direction vector is @@ -388,9 +453,19 @@ class DNN_LLH_normalized(DNN_LLH_Base): This is the output of the DNN reco for the estimated uncertainty. """ - def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - nside=256, random_seed=42, scale_unc=True, - weighted_normalization=True): + def __init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + nside=256, + random_seed=42, + scale_unc=True, + weighted_normalization=True, + ): """Initialize DNN LLH object. Parameters @@ -434,8 +509,17 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, """ # call init from base class - DNN_LLH_Base.__init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - random_seed, weighted_normalization) + DNN_LLH_Base.__init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + random_seed, + weighted_normalization, + ) def _setup(nside): @@ -445,11 +529,13 @@ def _setup(nside): self.nside = nside self._n_order = self._nside2norder() self.npix = hp.nside2npix(nside) - self.dir_x_s, self.dir_y_s, self.dir_z_s = \ - hp.pix2vec(nside, range(self.npix)) + self.dir_x_s, self.dir_y_s, self.dir_z_s = hp.pix2vec( + nside, range(self.npix) + ) self.neg_llh_values = -self.log_prob_dir( - self.dir_x_s, self.dir_y_s, self.dir_z_s) + self.dir_x_s, self.dir_y_s, self.dir_z_s + ) # sort directions according to neg llh sorted_indices = np.argsort(self.neg_llh_values) @@ -461,7 +547,8 @@ def _setup(nside): # get zenith and azimuth self.zenith_s, self.azimuth_s = self.get_zenith_azimuth( - self.dir_x_s, self.dir_y_s, self.dir_z_s) + self.dir_x_s, self.dir_y_s, self.dir_z_s + ) # get normalized probabilities and cdf prob = np.exp(-self.neg_llh_values) @@ -512,7 +599,8 @@ def log_prob_dir(self, dir_x, dir_y, dir_z): return multivariate_normal.logpdf( np.array([dir_x, dir_y, dir_z]).T, mean=np.array([self.dir_x, self.dir_y, self.dir_z]).T, - cov=self.cov) + cov=self.cov, + ) # log_p = self.log_gauss(dir_x, self.dir_x, self.unc_x) # log_p += self.log_gauss(dir_y, self.dir_y, self.unc_y) @@ -539,7 +627,7 @@ def _nside2norder(self): """ norder = np.log2(self.nside) if not (norder.is_integer()): - raise ValueError('Wrong nside number (it is not 2**norder)') + raise ValueError("Wrong nside number (it is not 2**norder)") return int(norder) def _sample_from_ipix(self, ipix, nest=False): @@ -570,9 +658,9 @@ def _sample_from_ipix(self, ipix, nest=False): ipix = hp.ring2nest(self.nside, ipix=ipix) n_up = 29 - self._n_order - i_up = ipix * 4 ** n_up - i_up += self._random_state.randint(0, 4 ** n_up, size=np.size(ipix)) - return hp.pix2vec(nside=2 ** 29, ipix=i_up, nest=True) + i_up = ipix * 4**n_up + i_up += self._random_state.randint(0, 4**n_up, size=np.size(ipix)) + return hp.pix2vec(nside=2**29, ipix=i_up, nest=True) def sample_dir(self, n): """Sample direction vectors from the distribution @@ -617,7 +705,7 @@ def cdf_dir(self, dir_x, dir_y, dir_z): The cumulative probabilty for the given direction vectors. """ if not self.is_normalized(dir_x, dir_y, dir_z): - print('cdf_dir is normalizing direction vectors') + print("cdf_dir is normalizing direction vectors") dir_x, dir_y, dir_z = self.normalize_dir(dir_x, dir_y, dir_z) neg_llh = -self.log_prob_dir(dir_x, dir_y, dir_z) @@ -654,21 +742,21 @@ def _get_level_indices(self, level=0.5, delta=0.01): ValueError If number of resulting samples is too low. """ - assert level >= 0., level - assert level <= 1., level + assert level >= 0.0, level + assert level <= 1.0, level index_min = np.searchsorted(self.cdf_values, level - delta) - index_max = min(self.npix, - np.searchsorted(self.cdf_values, level + delta)) + index_max = min( + self.npix, np.searchsorted(self.cdf_values, level + delta) + ) if index_max - index_min <= 10: - raise ValueError('Number of samples is too low!') + raise ValueError("Number of samples is too low!") return index_min, index_max class DNN_LLH(DNN_LLH_Base): - """A class for calculating the PDF obtained from the DNN reco for models that estimate the direction vector components and their uncertainty in independent 1D Gaussian Likelihoods, while the direction vector is @@ -707,10 +795,21 @@ class DNN_LLH(DNN_LLH_Base): This is the output of the DNN reco for the estimated uncertainty. """ - def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - propagate_errors=False, num_samples=1000000, random_seed=42, - scale_unc=True, weighted_normalization=True, - fix_delta=True): + def __init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + propagate_errors=False, + num_samples=1000000, + random_seed=42, + scale_unc=True, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -761,8 +860,17 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, """ # call init from base class - DNN_LLH_Base.__init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - random_seed, weighted_normalization) + DNN_LLH_Base.__init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + random_seed, + weighted_normalization, + ) self._num_samples = num_samples self.propagate_errors = propagate_errors @@ -773,7 +881,8 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, u_dir_y = unumpy.uarray(dir_y, unc_y) u_dir_z = unumpy.uarray(dir_z, unc_z) u_dir_x, u_dir_y, u_dir_z = self.u_normalize_dir( - u_dir_x, u_dir_y, u_dir_z) + u_dir_x, u_dir_y, u_dir_z + ) # Assign values with propagated and normalized vector self.unc_x = u_dir_x.std_dev @@ -783,16 +892,20 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, self.dir_y = u_dir_y.nominal_value self.dir_z = u_dir_z.nominal_value self.cov_matrix = np.array( - uncertainties.covariance_matrix([u_dir_x, u_dir_y, u_dir_z])) + uncertainties.covariance_matrix([u_dir_x, u_dir_y, u_dir_z]) + ) self.dist = multivariate_normal( mean=(self.dir_x, self.dir_y, self.dir_z), - cov=self.cov_matrix, allow_singular=True) + cov=self.cov_matrix, + allow_singular=True, + ) else: self.unc_x = unc_x self.unc_y = unc_y self.unc_z = unc_z - self.dir_x, self.dir_y, self.dir_z = \ - self.normalize_dir(dir_x, dir_y, dir_z) + self.dir_x, self.dir_y, self.dir_z = self.normalize_dir( + dir_x, dir_y, dir_z + ) # ------------------------- # scale up unc if necessary @@ -800,27 +913,33 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, self.scale_unc = scale_unc if self.scale_unc: + def _scale(): dir_x_s, dir_y_s, dir_z_s = self.sample_dir( - min(self._num_samples, 1000)) + min(self._num_samples, 1000) + ) # print('scaling x by:', self.unc_x / np.std(dir_x_s)) # print('scaling y by:', self.unc_y / np.std(dir_y_s)) # print('scaling z by:', self.unc_z / np.std(dir_z_s)) self.unc_x *= self.unc_x / np.std(dir_x_s) self.unc_y *= self.unc_y / np.std(dir_y_s) self.unc_z *= self.unc_z / np.std(dir_z_s) + _scale() # ------------------------- self.zenith, self.azimuth = self.get_zenith_azimuth( - self.dir_x, self.dir_y, self.dir_z) + self.dir_x, self.dir_y, self.dir_z + ) # sample contours - self.dir_x_s, self.dir_y_s, self.dir_z_s = \ - self.sample_dir(self._num_samples) + self.dir_x_s, self.dir_y_s, self.dir_z_s = self.sample_dir( + self._num_samples + ) self.neg_llh_values = -self.log_prob_dir( - self.dir_x_s, self.dir_y_s, self.dir_z_s) + self.dir_x_s, self.dir_y_s, self.dir_z_s + ) # sort sampled points according to neg llh sorted_indices = np.argsort(self.neg_llh_values) @@ -831,7 +950,8 @@ def _scale(): # get sampled zenith and azimuth self.zenith_s, self.azimuth_s = self.get_zenith_azimuth( - self.dir_x_s, self.dir_y_s, self.dir_z_s) + self.dir_x_s, self.dir_y_s, self.dir_z_s + ) def log_prob_dir(self, dir_x, dir_y, dir_z): """Calculate the log probability for given direction vectors. @@ -869,33 +989,36 @@ def sample_dir(self, n): The sampled direction vector components. """ if self._fix_delta: - delta_x = self._random_state.normal(0., self.unc_x, n) - delta_y = self._random_state.normal(0., self.unc_y, n) - delta_z = self._random_state.normal(0., self.unc_z, n) + delta_x = self._random_state.normal(0.0, self.unc_x, n) + delta_y = self._random_state.normal(0.0, self.unc_y, n) + delta_z = self._random_state.normal(0.0, self.unc_z, n) def fix_delta(delta, d): # return delta - mask_over_bound = np.abs(d + delta) > 1. + mask_over_bound = np.abs(d + delta) > 1.0 # see if these can be fixed by going in the other direction - mask_allowed = np.abs(d - delta) < 1. + mask_allowed = np.abs(d - delta) < 1.0 mask_fixable = np.logical_and(mask_over_bound, mask_allowed) - mask_on_boundary = np.logical_and(mask_over_bound, - ~mask_allowed) + mask_on_boundary = np.logical_and( + mask_over_bound, ~mask_allowed + ) # For those events that are over bounds in either direction, # choose the furthest boundary delta_max = 1 + np.abs(d) - mask_on_left_boundary = np.logical_and(mask_on_boundary, - d > 0.) - mask_on_right_boundary = np.logical_and(mask_on_boundary, - d < 0.) + mask_on_left_boundary = np.logical_and( + mask_on_boundary, d > 0.0 + ) + mask_on_right_boundary = np.logical_and( + mask_on_boundary, d < 0.0 + ) delta[mask_on_left_boundary] = -delta_max delta[mask_on_right_boundary] = +delta_max # fix directions which are fixable - delta[mask_fixable] *= -1. + delta[mask_fixable] *= -1.0 return delta dir_x_s = self.dir_x + fix_delta(delta_x, self.dir_x) @@ -907,7 +1030,8 @@ def fix_delta(delta, d): dir_y_s = self._random_state.normal(self.dir_y, self.unc_y, n) dir_z_s = self._random_state.normal(self.dir_z, self.unc_z, n) dir_x_s, dir_y_s, dir_z_s = self.normalize_dir( - dir_x_s, dir_y_s, dir_z_s) + dir_x_s, dir_y_s, dir_z_s + ) return dir_x_s, dir_y_s, dir_z_s def cdf_dir(self, dir_x, dir_y, dir_z): @@ -928,12 +1052,12 @@ def cdf_dir(self, dir_x, dir_y, dir_z): The cumulative probabilty for the given direction vectors. """ if not self.is_normalized(dir_x, dir_y, dir_z): - print('cdf_dir is normalizing direction vectors') + print("cdf_dir is normalizing direction vectors") dir_x, dir_y, dir_z = self.normalize_dir(dir_x, dir_y, dir_z) neg_llh = -self.log_prob_dir(dir_x, dir_y, dir_z) pos = np.searchsorted(self.neg_llh_values, neg_llh) - cdf = 1.0*pos / self._num_samples + cdf = 1.0 * pos / self._num_samples return cdf def _get_level_indices(self, level=0.5, delta=0.001): @@ -963,8 +1087,8 @@ def _get_level_indices(self, level=0.5, delta=0.001): ValueError If number of resulting samples is too low. """ - assert level >= 0., level - assert level <= 1., level + assert level >= 0.0, level + assert level <= 1.0, level index_at_level = int(self._num_samples * level) @@ -972,10 +1096,9 @@ def _get_level_indices(self, level=0.5, delta=0.001): delta_index = int(self._num_samples * delta) index_min = max(0, index_at_level - delta_index) - index_max = min(self._num_samples, - index_at_level + delta_index) + index_max = min(self._num_samples, index_at_level + delta_index) if index_max - index_min <= 10: - raise ValueError('Number of samples is too low!') + raise ValueError("Number of samples is too low!") return index_min, index_max diff --git a/dnn_reco/ic3/llh_base.py b/dnn_reco/ic3/llh_base.py index 71253d5..9a883d7 100644 --- a/dnn_reco/ic3/llh_base.py +++ b/dnn_reco/ic3/llh_base.py @@ -10,7 +10,6 @@ class DNN_LLH_Base(object): - """The DNN LLH base class for calculating the PDF obtained from the DNN reco. @@ -36,8 +35,17 @@ class DNN_LLH_Base(object): This is the output of the DNN reco for the estimated uncertainty. """ - def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, - random_seed=42, weighted_normalization=True): + def __init__( + self, + dir_x, + dir_y, + dir_z, + unc_x, + unc_y, + unc_z, + random_seed=42, + weighted_normalization=True, + ): """Initialize DNN LLH object. Parameters @@ -72,10 +80,12 @@ def __init__(self, dir_x, dir_y, dir_z, unc_x, unc_y, unc_z, self.unc_x = unc_x self.unc_y = unc_y self.unc_z = unc_z - self.dir_x, self.dir_y, self.dir_z = \ - self.normalize_dir(dir_x, dir_y, dir_z) + self.dir_x, self.dir_y, self.dir_z = self.normalize_dir( + dir_x, dir_y, dir_z + ) self.zenith, self.azimuth = self.get_zenith_azimuth( - self.dir_x, self.dir_y, self.dir_z) + self.dir_x, self.dir_y, self.dir_z + ) self._random_seed = random_seed self._random_state = np.random.RandomState(self._random_seed) @@ -134,27 +144,28 @@ def u_get_zenith_azimuth(self, u_dir_x, u_dir_y, u_dir_z, with_flip=True): The zenith and azimuth angles with uncertainties. """ # normalize - if not self.is_normalized(u_dir_x.nominal_value, - u_dir_y.nominal_value, - u_dir_z.nominal_value): - u_dir_x, u_dir_y, u_dir_z = \ - self.u_normalize_dir(u_dir_x, u_dir_y, u_dir_z) + if not self.is_normalized( + u_dir_x.nominal_value, u_dir_y.nominal_value, u_dir_z.nominal_value + ): + u_dir_x, u_dir_y, u_dir_z = self.u_normalize_dir( + u_dir_x, u_dir_y, u_dir_z + ) if with_flip: u_dir_x *= -1 u_dir_y *= -1 u_dir_z *= -1 - if np.abs(u_dir_z.nominal_value) > 1.: - raise ValueError('Z-component |{!r}| > 1!'.format( - u_dir_z.nominal_value)) + if np.abs(u_dir_z.nominal_value) > 1.0: + raise ValueError( + "Z-component |{!r}| > 1!".format(u_dir_z.nominal_value) + ) zenith = umath.acos(u_dir_z) azimuth = (umath.atan2(u_dir_y, u_dir_x) + 2 * np.pi) % (2 * np.pi) return zenith, azimuth - def get_zenith_azimuth(self, dir_x, dir_y, dir_z, - with_flip=True): + def get_zenith_azimuth(self, dir_x, dir_y, dir_z, with_flip=True): """Get zeniths and azimuths [in radians] from direction vector. Parameters @@ -209,7 +220,7 @@ def log_gauss(self, x, mu, sigma): np.array The log probability of Gaussian LLH. """ - return -2*np.log(sigma) - ((x - mu) / sigma)**2 + return -2 * np.log(sigma) - ((x - mu) / sigma) ** 2 def log_prob(self, zenith, azimuth): """Calculate the log probability for given zenith/azimuth pairs. @@ -296,7 +307,7 @@ def is_normalized(self, dir_x, dir_y, dir_z): True, if the direction vector is normalized """ norm = np.sqrt(dir_x**2 + dir_y**2 + dir_z**2) - return np.allclose(norm, 1.) + return np.allclose(norm, 1.0) def u_normalize_dir(self, u_dir_x, u_dir_y, u_dir_z): """Normalize direction vector @@ -316,15 +327,18 @@ def u_normalize_dir(self, u_dir_x, u_dir_y, u_dir_z): The normalized direction vector components with uncertainties. """ if self.weighted_normalization: - if not self.is_normalized(u_dir_x.nominal_value, - u_dir_y.nominal_value, - u_dir_z.nominal_value): + if not self.is_normalized( + u_dir_x.nominal_value, + u_dir_y.nominal_value, + u_dir_z.nominal_value, + ): raise NotImplementedError( - 'Direction vector must be normalized!') + "Direction vector must be normalized!" + ) return u_dir_x, u_dir_y, u_dir_z else: norm = unumpy.sqrt(u_dir_x**2 + u_dir_y**2 + u_dir_z**2) - return u_dir_x/norm, u_dir_y/norm, u_dir_z/norm + return u_dir_x / norm, u_dir_y / norm, u_dir_z / norm def normalize_dir(self, dir_x, dir_y, dir_z): """Normalize a direction vector to a unit vector. @@ -358,26 +372,32 @@ def normalize_dir(self, dir_x, dir_y, dir_z): norm_list = np.sqrt(dir_x**2 + dir_y**2 + dir_z**2) # define constraint - cons = ({'type': 'eq', 'fun': lambda x: np.linalg.norm(x) - 1}) + cons = {"type": "eq", "fun": lambda x: np.linalg.norm(x) - 1} # define cost function def cost(dir_normed, dx, dy, dz): x, y, z = dir_normed - c = ((x - dx) / self.unc_x)**2 - c += ((y - dy) / self.unc_y)**2 - c += ((z - dz) / self.unc_z)**2 + c = ((x - dx) / self.unc_x) ** 2 + c += ((y - dy) / self.unc_y) ** 2 + c += ((z - dz) / self.unc_z) ** 2 return c def minimize_vector(dx, dy, dz, norm): - x0 = [dx/norm, dy/norm, dz/norm] - result = minimize(cost, x0, args=(dx, dy, dz), - constraints=cons, options={'ftol': 1e-04}) + x0 = [dx / norm, dy / norm, dz / norm] + result = minimize( + cost, + x0, + args=(dx, dy, dz), + constraints=cons, + options={"ftol": 1e-04}, + ) return result.x size = len(dir_x) dir_n = np.empty((size, 3)) - for i, (dx, dy, dz, norm) in enumerate(zip( - dir_x, dir_y, dir_z, norm_list)): + for i, (dx, dy, dz, norm) in enumerate( + zip(dir_x, dir_y, dir_z, norm_list) + ): dir_n[i] = minimize_vector(dx, dy, dz, norm) if was_float: @@ -388,7 +408,7 @@ def minimize_vector(dx, dy, dz, norm): # Naive scaling: create unit vector by dividing by norm else: norm = np.sqrt(dir_x**2 + dir_y**2 + dir_z**2) - return dir_x/norm, dir_y/norm, dir_z/norm + return dir_x / norm, dir_y / norm, dir_z / norm def cdf(self, zenith, azimuth): """Calculate cumulative probability for given zenith/azimuth pairs. @@ -479,8 +499,10 @@ def contour(self, level=0.5, delta=0.01): [level - delta, level + delta]. """ index_min, index_max = self._get_level_indices(level, delta) - return (self.zenith_s[index_min:index_max], - self.azimuth_s[index_min:index_max]) + return ( + self.zenith_s[index_min:index_max], + self.azimuth_s[index_min:index_max], + ) def contour_dir(self, level=0.5, delta=0.01): """Get direction vectors of points that lie with the specified @@ -505,12 +527,15 @@ def contour_dir(self, level=0.5, delta=0.01): [level - delta, level + delta]. """ index_min, index_max = self._get_level_indices(level, delta) - return (self.dir_x_s[index_min:index_max], - self.dir_y_s[index_min:index_max], - self.dir_z_s[index_min:index_max]) - - def check_coverage(self, dir_x, dir_y, dir_z, - quantiles=np.linspace(0.001, 1., 1000)): + return ( + self.dir_x_s[index_min:index_max], + self.dir_y_s[index_min:index_max], + self.dir_z_s[index_min:index_max], + ) + + def check_coverage( + self, dir_x, dir_y, dir_z, quantiles=np.linspace(0.001, 1.0, 1000) + ): """Check coverage by calculating contaiment for given direction vectors. @@ -576,7 +601,6 @@ def contour_area(self, levels, nside): class DNN_LLH_Base_Elliptical(DNN_LLH_Base): - """The DNN LLH base class for calculating elliptical PDFs obtained from the DNN reco. @@ -601,8 +625,16 @@ class DNN_LLH_Base_Elliptical(DNN_LLH_Base): This is the output of the DNN reco for the z-component. """ - def __init__(self, zenith, azimuth, cov, num_samples=1000000, - random_seed=42, weighted_normalization=True, fix_delta=True): + def __init__( + self, + zenith, + azimuth, + cov, + num_samples=1000000, + random_seed=42, + weighted_normalization=True, + fix_delta=True, + ): """Initialize DNN LLH object. Parameters @@ -636,8 +668,9 @@ def __init__(self, zenith, azimuth, cov, num_samples=1000000, self.zenith = zenith self.azimuth = azimuth - self.dir_x, self.dir_y, self.dir_z = \ - self.get_dir_vec(self.zenith, self.azimuth) + self.dir_x, self.dir_y, self.dir_z = self.get_dir_vec( + self.zenith, self.azimuth + ) self._random_seed = random_seed self._random_state = np.random.RandomState(self._random_seed) @@ -657,7 +690,8 @@ def __init__(self, zenith, azimuth, cov, num_samples=1000000, # get sampled coordinate vectors self.dir_x_s, self.dir_y_s, self.dir_z_s = self.get_dir_vec( - self.zenith_s, self.azimuth_s) + self.zenith_s, self.azimuth_s + ) def log_prob(self, zenith, azimuth): """Calculate the log probability for given zenith/azimuth pairs. @@ -677,7 +711,8 @@ def log_prob(self, zenith, azimuth): return multivariate_normal.logpdf( np.array([zenith, azimuth]).T, mean=np.array([self.zenith, self.azimuth]).T, - cov=self.cov) + cov=self.cov, + ) def log_prob_dir(self, dir_x, dir_y, dir_z): """Calculate the log probability for given direction vectors. @@ -713,21 +748,25 @@ def sample(self, n): """ if self._fix_delta: deltas = self._random_state.multivariate_normal( - [0., self.azimuth], cov=self.cov, size=n) + [0.0, self.azimuth], cov=self.cov, size=n + ) deltas_zenith, azimuth = deltas[:, 0], deltas[:, 1] def fix_delta(delta, d, min_value, max_value): # check if over bound - mask_over_bound = np.logical_or(d + delta > max_value, - d + delta < min_value) + mask_over_bound = np.logical_or( + d + delta > max_value, d + delta < min_value + ) # check if going in other direction is in bounds - mask_allowed = np.logical_and(d - delta < max_value, - d - delta > min_value) + mask_allowed = np.logical_and( + d - delta < max_value, d - delta > min_value + ) mask_fixable = np.logical_and(mask_over_bound, mask_allowed) - mask_on_boundary = np.logical_and(mask_over_bound, - ~mask_allowed) + mask_on_boundary = np.logical_and( + mask_over_bound, ~mask_allowed + ) # For those events that are over bounds in either direction, # choose the furthest boundary @@ -735,24 +774,27 @@ def fix_delta(delta, d, min_value, max_value): dist_to_min = d - min_value mask_greater = dist_to_max > dist_to_min - mask_on_right_boundary = np.logical_and(mask_on_boundary, - mask_greater) - mask_on_left_boundary = np.logical_and(mask_on_boundary, - ~mask_greater) + mask_on_right_boundary = np.logical_and( + mask_on_boundary, mask_greater + ) + mask_on_left_boundary = np.logical_and( + mask_on_boundary, ~mask_greater + ) # Fix deltas delta[mask_on_left_boundary] = -dist_to_min delta[mask_on_right_boundary] = +dist_to_max - delta[mask_fixable] *= -1. + delta[mask_fixable] *= -1.0 return delta - deltas_zenith = fix_delta(deltas_zenith, self.zenith, 0., np.pi) + deltas_zenith = fix_delta(deltas_zenith, self.zenith, 0.0, np.pi) zenith = self.zenith + deltas_zenith - azimuth = azimuth % (2*np.pi) + azimuth = azimuth % (2 * np.pi) else: res = self._random_state.multivariate_normal( - [self.zenith, self.azimuth], cov=self.cov, size=n) + [self.zenith, self.azimuth], cov=self.cov, size=n + ) zenith, azimuth = res[:, 0], res[:, 1] return zenith, azimuth @@ -789,7 +831,7 @@ def cdf(self, zenith, azimuth): """ neg_llh = -self.log_prob(zenith, azimuth) pos = np.searchsorted(self.neg_llh_values, neg_llh) - cdf = 1.0*pos / self._num_samples + cdf = 1.0 * pos / self._num_samples return cdf def cdf_dir(self, dir_x, dir_y, dir_z): @@ -838,8 +880,8 @@ def _get_level_indices(self, level=0.5, delta=0.01): ValueError If number of resulting samples is too low. """ - assert level >= 0., level - assert level <= 1., level + assert level >= 0.0, level + assert level <= 1.0, level index_at_level = int(self._num_samples * level) @@ -847,10 +889,9 @@ def _get_level_indices(self, level=0.5, delta=0.01): delta_index = int(self._num_samples * delta) index_min = max(0, index_at_level - delta_index) - index_max = min(self._num_samples, - index_at_level + delta_index) + index_max = min(self._num_samples, index_at_level + delta_index) if index_max - index_min <= 10: - raise ValueError('Number of samples is too low!') + raise ValueError("Number of samples is too low!") return index_min, index_max diff --git a/dnn_reco/ic3/modules.py b/dnn_reco/ic3/modules.py index b0a28f7..3544edf 100644 --- a/dnn_reco/ic3/modules.py +++ b/dnn_reco/ic3/modules.py @@ -22,7 +22,6 @@ class DeepLearningReco(icetray.I3ConditionalModule): - """Module to apply dnn reco. Attributes @@ -43,36 +42,47 @@ class DeepLearningReco(icetray.I3ConditionalModule): """ def __init__(self, context): - """Initialize DeepLearningReco Module - """ + """Initialize DeepLearningReco Module""" icetray.I3ConditionalModule.__init__(self, context) - self.AddParameter('ModelPath', 'Path to DNN model', None) - self.AddParameter('DNNDataContainer', - 'Data container that will be used to feed model', - None) - self.AddParameter('IgnoreMisconfiguredSettingsList', - "The model automatically checks whether the " - "configured settings for the 'DNNDataContainer' " - "match those settings that were exported in this " - "model. If a mismatch is found, an error will be " - "raised. This helps to ensure the correct use of " - "the trained models. Sometimes it is necessary to " - "use the model with slightly different settings. In " - "this case a list of setting names can be passed " - "for which the mismatches will be ignored. Doing so " - "will relax the raised error to a warning that is " - "issued. This should be used with caution.", - None) - self.AddParameter('OutputBaseName', - 'Output key under which the result will be written', - 'DeepLearningReco') - self.AddParameter("MeasureTime", - "If True, time for preprocessing and prediction will" - " be measured and printed", False) - self.AddParameter("ParallelismThreads", - "Tensorflow config option for 'intra_op_parallelism_" - "threads' and 'inter_op_parallelism_threads'" - "[# CPUs]", None) + self.AddParameter("ModelPath", "Path to DNN model", None) + self.AddParameter( + "DNNDataContainer", + "Data container that will be used to feed model", + None, + ) + self.AddParameter( + "IgnoreMisconfiguredSettingsList", + "The model automatically checks whether the " + "configured settings for the 'DNNDataContainer' " + "match those settings that were exported in this " + "model. If a mismatch is found, an error will be " + "raised. This helps to ensure the correct use of " + "the trained models. Sometimes it is necessary to " + "use the model with slightly different settings. In " + "this case a list of setting names can be passed " + "for which the mismatches will be ignored. Doing so " + "will relax the raised error to a warning that is " + "issued. This should be used with caution.", + None, + ) + self.AddParameter( + "OutputBaseName", + "Output key under which the result will be written", + "DeepLearningReco", + ) + self.AddParameter( + "MeasureTime", + "If True, time for preprocessing and prediction will" + " be measured and printed", + False, + ) + self.AddParameter( + "ParallelismThreads", + "Tensorflow config option for 'intra_op_parallelism_" + "threads' and 'inter_op_parallelism_threads'" + "[# CPUs]", + None, + ) def Configure(self): """Configure DeepLearningReco module. @@ -84,98 +94,121 @@ def Configure(self): ValueError If settings do not match the expected settings by the nn model. """ - self._model_path = self.GetParameter('ModelPath') - self._container = self.GetParameter('DNNDataContainer') + self._model_path = self.GetParameter("ModelPath") + self._container = self.GetParameter("DNNDataContainer") self._output_key = self.GetParameter("OutputBaseName") self._measure_time = self.GetParameter("MeasureTime") self._parallelism_threads = self.GetParameter("ParallelismThreads") - self._ingore_list = \ - self.GetParameter('IgnoreMisconfiguredSettingsList') + self._ingore_list = self.GetParameter( + "IgnoreMisconfiguredSettingsList" + ) if self._ingore_list is None: self._ingore_list = [] # read in and combine config files and set up - training_files = glob.glob(os.path.join(self._model_path, - 'config_training_*.yaml')) + training_files = glob.glob( + os.path.join(self._model_path, "config_training_*.yaml") + ) last_training_file = np.sort(training_files)[-1] setup_manager = SetupManager([last_training_file]) self.config = setup_manager.get_config() # ToDo: Adjust necessary values in config - self.config['model_checkpoint_path'] = os.path.join(self._model_path, - 'model') - self.config['model_is_training'] = False - self.config['trafo_model_path'] = os.path.join(self._model_path, - 'trafo_model.npy') + self.config["model_checkpoint_path"] = os.path.join( + self._model_path, "model" + ) + self.config["model_is_training"] = False + self.config["trafo_model_path"] = os.path.join( + self._model_path, "trafo_model.npy" + ) if self._parallelism_threads is not None: - self.config['tf_parallelism_threads'] = self._parallelism_threads + self.config["tf_parallelism_threads"] = self._parallelism_threads # ---------------------------------------------------------------- # Check if settings of data container match settings in model path # ---------------------------------------------------------------- - cfg_file = os.path.join(self._model_path, 'config_data_settings.yaml') - with open(cfg_file, 'r') as stream: + cfg_file = os.path.join(self._model_path, "config_data_settings.yaml") + with open(cfg_file, "r") as stream: data_config = yaml.safe_load(stream) # Backwards compatibility for older exported models which did not # include this setting. In this case the separated format, e.g. # icecube array + deepcore array is used as opposed to the string-dom # format: [batch, 86, 60, num_bins] - if 'is_str_dom_format' not in data_config: - data_config['is_str_dom_format'] = False + if "is_str_dom_format" not in data_config: + data_config["is_str_dom_format"] = False for k in self._container.config: # backwards compatibility for older exported models which did not # export these settings - if k not in data_config and k in ['pulse_key', 'dom_exclusions', - 'partial_exclusion', - 'cascade_key', - 'allowed_pulse_keys', - 'allowed_cascade_keys']: - msg = 'Warning: not checking if parameter {!r} is correctly ' - msg += 'configured for model {!r} because the setting ' - msg += 'was not exported.' + if k not in data_config and k in [ + "pulse_key", + "dom_exclusions", + "partial_exclusion", + "cascade_key", + "allowed_pulse_keys", + "allowed_cascade_keys", + ]: + msg = "Warning: not checking if parameter {!r} is correctly " + msg += "configured for model {!r} because the setting " + msg += "was not exported." logging.warning(msg.format(k, self._model_path)) continue # check for allowed pulse keys - if (k == 'pulse_key' and 'allowed_pulse_keys' in data_config and - data_config['allowed_pulse_keys'] is not None and - self._container.config[k] - in data_config['allowed_pulse_keys']): + if ( + k == "pulse_key" + and "allowed_pulse_keys" in data_config + and data_config["allowed_pulse_keys"] is not None + and self._container.config[k] + in data_config["allowed_pulse_keys"] + ): # this is an allowed pulse, so everything is ok continue # check for allowed cascade keys - if (k == 'cascade_key' and 'allowed_cascade_keys' in data_config - and data_config['allowed_cascade_keys'] is not None and - self._container.config[k] - in data_config['allowed_cascade_keys']): + if ( + k == "cascade_key" + and "allowed_cascade_keys" in data_config + and data_config["allowed_cascade_keys"] is not None + and self._container.config[k] + in data_config["allowed_cascade_keys"] + ): # this is an allowed cascade key, so everything is ok continue if not self._container.config[k] == data_config[k]: if k in self._ingore_list: - msg = 'Warning: parameter {!r} is set to {!r} which ' - msg += 'differs from the model [{!r}] default value {!r}. ' - msg += 'This mismatch will be ingored since the parameter ' - msg += 'is in the IgnoreMisconfiguredSettingsList. ' - msg += 'Make sure this is what you intend to do!' - logging.warning(msg.format(k, self._container.config[k], - self._model_path, - data_config[k])) + msg = "Warning: parameter {!r} is set to {!r} which " + msg += "differs from the model [{!r}] default value {!r}. " + msg += "This mismatch will be ingored since the parameter " + msg += "is in the IgnoreMisconfiguredSettingsList. " + msg += "Make sure this is what you intend to do!" + logging.warning( + msg.format( + k, + self._container.config[k], + self._model_path, + data_config[k], + ) + ) else: - msg = 'Fatal: parameter {!r} is set to {!r} which ' - msg += 'differs from the model [{!r}] default value {!r}.' - msg += 'If you are sure you want to use this model ' - msg += 'with these settings, then you can add the ' - msg += 'parameter to the IgnoreMisconfiguredSettingsList.' - raise ValueError(msg.format(k, self._container.config[k], - self._model_path, - data_config[k])) + msg = "Fatal: parameter {!r} is set to {!r} which " + msg += "differs from the model [{!r}] default value {!r}." + msg += "If you are sure you want to use this model " + msg += "with these settings, then you can add the " + msg += "parameter to the IgnoreMisconfiguredSettingsList." + raise ValueError( + msg.format( + k, + self._container.config[k], + self._model_path, + data_config[k], + ) + ) # ---------------------------------------------------------------- # create variables and frame buffer for batching @@ -186,24 +219,31 @@ def Configure(self): # Create a new tensorflow graph and session for this instance of # dnn reco g = tf.Graph() - if 'tf_parallelism_threads' in self.config: - n_cpus = self.config['tf_parallelism_threads'] - sess = tf.compat.v1.Session(graph=g, config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': 1}, - intra_op_parallelism_threads=n_cpus, - inter_op_parallelism_threads=n_cpus, - )) + if "tf_parallelism_threads" in self.config: + n_cpus = self.config["tf_parallelism_threads"] + sess = tf.compat.v1.Session( + graph=g, + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), + device_count={"GPU": 1}, + intra_op_parallelism_threads=n_cpus, + inter_op_parallelism_threads=n_cpus, + ), + ) else: - sess = tf.compat.v1.Session(graph=g, config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': 1}, - )) + sess = tf.compat.v1.Session( + graph=g, + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), + device_count={"GPU": 1}, + ), + ) with g.as_default(): # Create Data Handler object self.data_handler = DataHandler(self.config) self.data_handler.setup_with_config( - os.path.join(self._model_path, 'config_meta_data.yaml')) + os.path.join(self._model_path, "config_meta_data.yaml") + ) # Get time vars that need to be corrected by global time offset self._time_indices = [] @@ -214,25 +254,29 @@ def Configure(self): # create data transformer self.data_transformer = DataTransformer( data_handler=self.data_handler, - treat_doms_equally=self.config['trafo_treat_doms_equally'], - normalize_dom_data=self.config['trafo_normalize_dom_data'], - normalize_label_data=self.config['trafo_normalize_label_data'], - normalize_misc_data=self.config['trafo_normalize_misc_data'], - log_dom_bins=self.config['trafo_log_dom_bins'], - log_label_bins=self.config['trafo_log_label_bins'], - log_misc_bins=self.config['trafo_log_misc_bins'], - norm_constant=self.config['trafo_norm_constant']) + treat_doms_equally=self.config["trafo_treat_doms_equally"], + normalize_dom_data=self.config["trafo_normalize_dom_data"], + normalize_label_data=self.config["trafo_normalize_label_data"], + normalize_misc_data=self.config["trafo_normalize_misc_data"], + log_dom_bins=self.config["trafo_log_dom_bins"], + log_label_bins=self.config["trafo_log_label_bins"], + log_misc_bins=self.config["trafo_log_misc_bins"], + norm_constant=self.config["trafo_norm_constant"], + ) # load trafo model from file self.data_transformer.load_trafo_model( - self.config['trafo_model_path']) + self.config["trafo_model_path"] + ) # create NN model - self.model = NNModel(is_training=False, - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - sess=sess) + self.model = NNModel( + is_training=False, + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + sess=sess, + ) # compile model: initalize and finalize graph self.model.compile() @@ -241,15 +285,24 @@ def Configure(self): self.model.restore() # Get trained labels, e.g. labels with weights greater than zero - self._mask_labels = \ - self.model.shared_objects['label_weight_config'] > 0 - self._non_zero_labels = [n for n, b in - zip(self.data_handler.label_names, - self._mask_labels) if b] - self._non_zero_log_bins = \ - [l for l, b in - zip(self.data_transformer.trafo_model['log_label_bins'], - self._mask_labels) if b] + self._mask_labels = ( + self.model.shared_objects["label_weight_config"] > 0 + ) + self._non_zero_labels = [ + n + for n, b in zip( + self.data_handler.label_names, self._mask_labels + ) + if b + ] + self._non_zero_log_bins = [ + l + for l, b in zip( + self.data_transformer.trafo_model["log_label_bins"], + self._mask_labels, + ) + if b + ] def Process(self): """Process incoming frames. @@ -333,19 +386,22 @@ def _perform_prediction(self, size): start_time = timeit.default_timer() self.y_pred_batch, self.y_unc_batch = self.model.predict( - x_ic78=self._container.x_ic78[:size], - x_deepcore=self._container.x_deepcore[:size]) + x_ic78=self._container.x_ic78[:size], + x_deepcore=self._container.x_deepcore[:size], + ) # Fix time offset if self.data_handler.relative_time_keys: - global_time_offset = \ - self._container.global_time_offset_batch[:size] + global_time_offset = self._container.global_time_offset_batch[ + :size + ] for i in self._time_indices: self.y_pred_batch[:, i] += global_time_offset if self._measure_time: - self._runtime_prediction = \ - (timeit.default_timer() - start_time) / size + self._runtime_prediction = ( + timeit.default_timer() - start_time + ) / size else: self.y_pred_batch = None self.y_unc_batch = None @@ -369,66 +425,71 @@ def _write_to_frame(self, frame, batch_event_index): # Write prediction and uncertainty estimate to frame results = {} for name, pred, unc, log_label in zip( - self._non_zero_labels, - self.y_pred_batch[batch_event_index][self._mask_labels], - self.y_unc_batch[batch_event_index][self._mask_labels], - self._non_zero_log_bins): + self._non_zero_labels, + self.y_pred_batch[batch_event_index][self._mask_labels], + self.y_unc_batch[batch_event_index][self._mask_labels], + self._non_zero_log_bins, + ): # save prediction results[name] = float(pred) # save uncertainty estimate if log_label: - results[name + '_log_uncertainty'] = float(unc) + results[name + "_log_uncertainty"] = float(unc) else: - results[name + '_uncertainty'] = float(unc) + results[name + "_uncertainty"] = float(unc) # Create combined I3Particle - if 'label_particle_keys' in self.config: - particle_keys = self.config['label_particle_keys'] + if "label_particle_keys" in self.config: + particle_keys = self.config["label_particle_keys"] particle = dataclasses.I3Particle() - if 'energy' in particle_keys: - if particle_keys['energy'] in self._non_zero_labels: - particle.energy = results[particle_keys['energy']] - if 'time' in particle_keys: - if particle_keys['time'] in self._non_zero_labels: - particle.time = results[particle_keys['time']] - if 'length' in particle_keys: - if particle_keys['length'] in self._non_zero_labels: - particle.length = results[particle_keys['length']] - if 'dir_x' in particle_keys: - if particle_keys['dir_x'] in self._non_zero_labels: + if "energy" in particle_keys: + if particle_keys["energy"] in self._non_zero_labels: + particle.energy = results[particle_keys["energy"]] + if "time" in particle_keys: + if particle_keys["time"] in self._non_zero_labels: + particle.time = results[particle_keys["time"]] + if "length" in particle_keys: + if particle_keys["length"] in self._non_zero_labels: + particle.length = results[particle_keys["length"]] + if "dir_x" in particle_keys: + if particle_keys["dir_x"] in self._non_zero_labels: particle.dir = dataclasses.I3Direction( - results[particle_keys['dir_x']], - results[particle_keys['dir_y']], - results[particle_keys['dir_z']]) - elif 'azimuth' in particle_keys: - if particle_keys['azimuth'] in self._non_zero_labels: + results[particle_keys["dir_x"]], + results[particle_keys["dir_y"]], + results[particle_keys["dir_z"]], + ) + elif "azimuth" in particle_keys: + if particle_keys["azimuth"] in self._non_zero_labels: particle.dir = dataclasses.I3Direction( - results[particle_keys['azimuth']], - results[particle_keys['zenith']]) + results[particle_keys["azimuth"]], + results[particle_keys["zenith"]], + ) - if 'pos_x' in particle_keys: - if particle_keys['pos_x'] in self._non_zero_labels: + if "pos_x" in particle_keys: + if particle_keys["pos_x"] in self._non_zero_labels: particle.pos = dataclasses.I3Position( - results[particle_keys['pos_x']], - results[particle_keys['pos_y']], - results[particle_keys['pos_z']]) + results[particle_keys["pos_x"]], + results[particle_keys["pos_y"]], + results[particle_keys["pos_z"]], + ) # transform zenith and azimuth to proper range: particle.dir = dataclasses.I3Direction( - particle.dir.x, particle.dir.y, particle.dir.z) + particle.dir.x, particle.dir.y, particle.dir.z + ) - frame[self._output_key + '_I3Particle'] = particle + frame[self._output_key + "_I3Particle"] = particle # write time measurement to frame if self._measure_time: - results['runtime_prediction'] = self._runtime_prediction - results['runtime_write'] = \ - timeit.default_timer() - start_time - results['runtime_preprocess'] = \ - self._container.runtime_batch[batch_event_index] + results["runtime_prediction"] = self._runtime_prediction + results["runtime_write"] = timeit.default_timer() - start_time + results["runtime_preprocess"] = self._container.runtime_batch[ + batch_event_index + ] # write to frame frame[self._output_key] = dataclasses.I3MapStringDouble(results) diff --git a/dnn_reco/ic3/segments.py b/dnn_reco/ic3/segments.py index 1916d7b..b9a9479 100644 --- a/dnn_reco/ic3/segments.py +++ b/dnn_reco/ic3/segments.py @@ -12,20 +12,21 @@ @icetray.traysegment def ApplyDNNRecos( - tray, name, - model_names, - pulse_key=None, - dom_exclusions=None, - partial_exclusion=None, - output_keys=None, - models_dir='/data/user/mhuennefeld/DNN_reco/models/exported_models', - cascade_key='MCCascade', - ignore_misconfigured_settings_list=None, - measure_time=True, - batch_size=1, - num_cpus=1, - verbose=False, - ): + tray, + name, + model_names, + pulse_key=None, + dom_exclusions=None, + partial_exclusion=None, + output_keys=None, + models_dir="/data/user/mhuennefeld/DNN_reco/models/exported_models", + cascade_key="MCCascade", + ignore_misconfigured_settings_list=None, + measure_time=True, + batch_size=1, + num_cpus=1, + verbose=False, +): """Apply DNN reco Parameters @@ -86,7 +87,7 @@ def ApplyDNNRecos( model_names = [model_names] if output_keys is None: - output_keys = ['DeepLearningReco_{}'.format(m) for m in model_names] + output_keys = ["DeepLearningReco_{}".format(m) for m in model_names] # create DNN data container object container = DNNDataContainer(batch_size=batch_size) @@ -100,18 +101,24 @@ def ApplyDNNRecos( dom_exclusions=dom_exclusions, partial_exclusion=partial_exclusion, cascade_key=cascade_key, - ignore_misconfigured_settings_list=ignore_misconfigured_settings_list) + ignore_misconfigured_settings_list=ignore_misconfigured_settings_list, + ) - tray.AddModule(DNNContainerHandler, 'DNNContainerHandler_' + name, - DNNDataContainer=container, Verbose=verbose) + tray.AddModule( + DNNContainerHandler, + "DNNContainerHandler_" + name, + DNNDataContainer=container, + Verbose=verbose, + ) for model_name, output_key in zip(model_names, output_keys): tray.AddModule( - DeepLearningReco, 'DeepLearningReco_'+model_name+name, + DeepLearningReco, + "DeepLearningReco_" + model_name + name, ModelPath=os.path.join(models_dir, model_name), DNNDataContainer=container, OutputBaseName=output_key, MeasureTime=measure_time, ParallelismThreads=num_cpus, IgnoreMisconfiguredSettingsList=ignore_misconfigured_settings_list, - ) + ) diff --git a/dnn_reco/misc.py b/dnn_reco/misc.py index 1e8f58b..9c43836 100644 --- a/dnn_reco/misc.py +++ b/dnn_reco/misc.py @@ -10,7 +10,7 @@ def print_warning(msg): msg : str String to print. """ - print('\033[93m' + msg + '\033[0m') + print("\033[93m" + msg + "\033[0m") def load_class(full_class_string): diff --git a/dnn_reco/model.py b/dnn_reco/model.py index a8b716e..9a332d8 100644 --- a/dnn_reco/model.py +++ b/dnn_reco/model.py @@ -34,8 +34,9 @@ class NNModel(object): on to sub modules. """ - def __init__(self, is_training, config, data_handler, data_transformer, - sess=None): + def __init__( + self, is_training, config, data_handler, data_transformer, sess=None + ): """Initializes neural network base class. Parameters @@ -84,34 +85,42 @@ def __init__(self, is_training, config, data_handler, data_transformer, if sess is None: sess = tf.compat.v1.get_default_session() if sess is None: - if 'tf_parallelism_threads' in self.config: - n_cpus = self.config['tf_parallelism_threads'] - sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': 1}, - intra_op_parallelism_threads=n_cpus, - inter_op_parallelism_threads=n_cpus, - )) + if "tf_parallelism_threads" in self.config: + n_cpus = self.config["tf_parallelism_threads"] + sess = tf.compat.v1.Session( + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions( + allow_growth=True + ), + device_count={"GPU": 1}, + intra_op_parallelism_threads=n_cpus, + inter_op_parallelism_threads=n_cpus, + ) + ) else: - sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto( - gpu_options=tf.compat.v1.GPUOptions(allow_growth=True), - device_count={'GPU': 1}, - )) + sess = tf.compat.v1.Session( + config=tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions( + allow_growth=True + ), + device_count={"GPU": 1}, + ) + ) self.sess = sess - tf.compat.v1.set_random_seed(self.config['tf_random_seed']) + tf.compat.v1.set_random_seed(self.config["tf_random_seed"]) def _setup_directories(self): - """Creates necessary directories - """ + """Creates necessary directories""" # Create directories - directories = [self.config['model_checkpoint_path'], - self.config['log_path'], - ] + directories = [ + self.config["model_checkpoint_path"], + self.config["log_path"], + ] for directory in directories: directory = os.path.dirname(directory) if not os.path.isdir(directory): os.makedirs(directory) - misc.print_warning('Creating directory: {}'.format(directory)) + misc.print_warning("Creating directory: {}".format(directory)) def _setup_training_config_saver(self): """Setup variables and check training step in order to save the @@ -122,29 +131,41 @@ def _setup_training_config_saver(self): in the model.fit method (further below). These will not yet be deleted here. """ - self._check_point_path = os.path.dirname(self.config[ - 'model_checkpoint_path']) - self._training_steps_file = os.path.join(self._check_point_path, - 'training_steps.yaml') + self._check_point_path = os.path.dirname( + self.config["model_checkpoint_path"] + ) + self._training_steps_file = os.path.join( + self._check_point_path, "training_steps.yaml" + ) # Load training iterations dict if os.path.isfile(self._training_steps_file): self._training_iterations_dict = yaml.safe_load( - open(self._training_steps_file)) + open(self._training_steps_file) + ) else: - misc.print_warning('Did not find {!r}. Creating new one'.format( - self._training_steps_file)) + misc.print_warning( + "Did not find {!r}. Creating new one".format( + self._training_steps_file + ) + ) self._training_iterations_dict = {} # get the training step number - if self.config['model_restore_model']: - files = glob.glob(os.path.join(self._check_point_path, - 'config_training_*.yaml')) + if self.config["model_restore_model"]: + files = glob.glob( + os.path.join(self._check_point_path, "config_training_*.yaml") + ) if files: max_file = os.path.basename(np.sort(files)[-1]) - self._training_step = int(max_file.replace( - 'config_training_', - '').replace('.yaml', '')) + 1 + self._training_step = ( + int( + max_file.replace("config_training_", "").replace( + ".yaml", "" + ) + ) + + 1 + ) else: self._training_step = 0 else: @@ -152,220 +173,259 @@ def _setup_training_config_saver(self): self._training_step = 0 self._training_config_file = os.path.join( - self._check_point_path, - 'config_training_{:04d}.yaml'.format(self._training_step)) + self._check_point_path, + "config_training_{:04d}.yaml".format(self._training_step), + ) def _setup_placeholders(self): - """Sets up placeholders for input data. - """ + """Sets up placeholders for input data.""" # define placeholders for keep probability for dropout - if 'keep_probability_list' in self.config: - keep_prob_list = [tf.compat.v1.placeholder( - self.config['tf_float_precision'], - name='keep_prob_{:0.2f}'.format(i)) - for i in self.config['keep_probability_list']] - self.shared_objects['keep_prob_list'] = keep_prob_list + if "keep_probability_list" in self.config: + keep_prob_list = [ + tf.compat.v1.placeholder( + self.config["tf_float_precision"], + name="keep_prob_{:0.2f}".format(i), + ) + for i in self.config["keep_probability_list"] + ] + self.shared_objects["keep_prob_list"] = keep_prob_list # IC78: main IceCube array - self.shared_objects['x_ic78'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=[None, 10, 10, 60, self.data_handler.num_bins], - name='x_ic78', - ) - self.shared_objects['x_ic78_trafo'] = self.data_transformer.transform( - self.shared_objects['x_ic78'], data_type='ic78') + self.shared_objects["x_ic78"] = tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=[None, 10, 10, 60, self.data_handler.num_bins], + name="x_ic78", + ) + self.shared_objects["x_ic78_trafo"] = self.data_transformer.transform( + self.shared_objects["x_ic78"], data_type="ic78" + ) # DeepCore - self.shared_objects['x_deepcore'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=[None, 8, 60, self.data_handler.num_bins], - name='x_deepcore', - ) - self.shared_objects['x_deepcore_trafo'] = \ - self.data_transformer.transform(self.shared_objects['x_deepcore'], - data_type='deepcore') + self.shared_objects["x_deepcore"] = tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=[None, 8, 60, self.data_handler.num_bins], + name="x_deepcore", + ) + self.shared_objects["x_deepcore_trafo"] = ( + self.data_transformer.transform( + self.shared_objects["x_deepcore"], data_type="deepcore" + ) + ) # labels - self.shared_objects['y_true'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=[None] + self.data_handler.label_shape, - name='y_true', - ) - self.shared_objects['y_true_trafo'] = self.data_transformer.transform( - self.shared_objects['y_true'], data_type='label') + self.shared_objects["y_true"] = tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=[None] + self.data_handler.label_shape, + name="y_true", + ) + self.shared_objects["y_true_trafo"] = self.data_transformer.transform( + self.shared_objects["y_true"], data_type="label" + ) # misc data if self.data_handler.misc_shape is not None: - self.shared_objects['x_misc'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=[None] + self.data_handler.misc_shape, - name='x_misc', - ) - self.shared_objects['x_misc_trafo'] = \ - self.data_transformer.transform(self.shared_objects['x_misc'], - data_type='misc') + self.shared_objects["x_misc"] = tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=[None] + self.data_handler.misc_shape, + name="x_misc", + ) + self.shared_objects["x_misc_trafo"] = ( + self.data_transformer.transform( + self.shared_objects["x_misc"], data_type="misc" + ) + ) def _build_model(self): - """Build neural network architecture. - """ - class_string = 'dnn_reco.modules.models.{}.{}'.format( - self.config['model_file'], - self.config['model_name'], - ) + """Build neural network architecture.""" + class_string = "dnn_reco.modules.models.{}.{}".format( + self.config["model_file"], + self.config["model_name"], + ) nn_model = misc.load_class(class_string) - print('\n----------------------') - print('Now Building Model ...') - print('----------------------\n') + print("\n----------------------") + print("Now Building Model ...") + print("----------------------\n") y_pred_trafo, y_unc_trafo, model_vars_pred, model_vars_unc = nn_model( - is_training=self.is_training, - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - shared_objects=self.shared_objects) + is_training=self.is_training, + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + shared_objects=self.shared_objects, + ) # transform back - y_pred = self.data_transformer.inverse_transform(y_pred_trafo, - data_type='label') - y_unc = self.data_transformer.inverse_transform(y_unc_trafo, - data_type='label', - bias_correction=False) - - self.shared_objects['y_pred_trafo'] = y_pred_trafo - self.shared_objects['y_unc_trafo'] = y_unc_trafo - self.shared_objects['y_pred'] = y_pred - self.shared_objects['y_unc'] = y_unc - self.shared_objects['model_vars_pred'] = model_vars_pred - self.shared_objects['model_vars_unc'] = model_vars_unc - self.shared_objects['model_vars'] = model_vars_pred + model_vars_unc - - y_pred_list = tf.unstack(self.shared_objects['y_pred'], axis=1) + y_pred = self.data_transformer.inverse_transform( + y_pred_trafo, data_type="label" + ) + y_unc = self.data_transformer.inverse_transform( + y_unc_trafo, data_type="label", bias_correction=False + ) + + self.shared_objects["y_pred_trafo"] = y_pred_trafo + self.shared_objects["y_unc_trafo"] = y_unc_trafo + self.shared_objects["y_pred"] = y_pred + self.shared_objects["y_unc"] = y_unc + self.shared_objects["model_vars_pred"] = model_vars_pred + self.shared_objects["model_vars_unc"] = model_vars_unc + self.shared_objects["model_vars"] = model_vars_pred + model_vars_unc + + y_pred_list = tf.unstack(self.shared_objects["y_pred"], axis=1) for i, name in enumerate(self.data_handler.label_names): - tf.compat.v1.summary.histogram('y_pred_' + name, y_pred_list[i]) + tf.compat.v1.summary.histogram("y_pred_" + name, y_pred_list[i]) # count number of trainable parameters - print('Number of free parameters in NN model: {}\n'.format( - self.count_parameters(self.shared_objects['model_vars']))) + print( + "Number of free parameters in NN model: {}\n".format( + self.count_parameters(self.shared_objects["model_vars"]) + ) + ) # create saver - self.saver = tf.compat.v1.train.Saver(self.shared_objects['model_vars']) + self.saver = tf.compat.v1.train.Saver( + self.shared_objects["model_vars"] + ) def _intialize_label_weights(self): - """Initialize label weights and non zero mask - """ + """Initialize label weights and non zero mask""" label_weight_config = np.ones(self.data_handler.label_shape) - label_weight_config *= self.config['label_weight_initialization'] + label_weight_config *= self.config["label_weight_initialization"] - if 'label_weight_dict' in self.config: - for key in self.config['label_weight_dict'].keys(): - label_weight_config[self.data_handler.get_label_index(key)] = \ - self.config['label_weight_dict'][key] - self.shared_objects['label_weight_config'] = label_weight_config - self.shared_objects['non_zero_mask'] = label_weight_config > 0 + if "label_weight_dict" in self.config: + for key in self.config["label_weight_dict"].keys(): + label_weight_config[self.data_handler.get_label_index(key)] = ( + self.config["label_weight_dict"][key] + ) + self.shared_objects["label_weight_config"] = label_weight_config + self.shared_objects["non_zero_mask"] = label_weight_config > 0 def _create_tukey_vars(self): - """Create variables required for tukey loss - """ - if self.config['label_scale_tukey']: + """Create variables required for tukey loss""" + if self.config["label_scale_tukey"]: median_abs_dev = tf.Variable( - np.ones(shape=self.data_handler.label_shape)*0.67449, - name='median_abs_dev', - trainable=False, - dtype=self.config['tf_float_precision']) - - self.shared_objects['new_median_abs_dev_values'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=self.data_handler.label_shape, - name='new_median_abs_dev_values') + np.ones(shape=self.data_handler.label_shape) * 0.67449, + name="median_abs_dev", + trainable=False, + dtype=self.config["tf_float_precision"], + ) + + self.shared_objects["new_median_abs_dev_values"] = ( + tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=self.data_handler.label_shape, + name="new_median_abs_dev_values", + ) + ) tukey_decay = 0.001 - self.shared_objects['assign_new_median_abs_dev_values'] = \ + self.shared_objects["assign_new_median_abs_dev_values"] = ( median_abs_dev.assign( - median_abs_dev * (1. - tukey_decay) + - self.shared_objects['new_median_abs_dev_values'] - * tukey_decay) + median_abs_dev * (1.0 - tukey_decay) + + self.shared_objects["new_median_abs_dev_values"] + * tukey_decay + ) + ) else: median_abs_dev = tf.constant( - np.ones(shape=self.data_handler.label_shape)*0.67449, - shape=self.data_handler.label_shape, - dtype=self.config['tf_float_precision']) + np.ones(shape=self.data_handler.label_shape) * 0.67449, + shape=self.data_handler.label_shape, + dtype=self.config["tf_float_precision"], + ) - self.shared_objects['median_abs_dev'] = median_abs_dev + self.shared_objects["median_abs_dev"] = median_abs_dev def _create_label_weights(self): - """Create label weights and update operation - """ - if self.config['label_update_weights']: + """Create label weights and update operation""" + if self.config["label_update_weights"]: label_weights = tf.Variable( - self.shared_objects['label_weight_config'], - name='label_weights', - trainable=False, - dtype=self.config['tf_float_precision']) - - self.shared_objects['new_label_weight_values'] = tf.compat.v1.placeholder( - self.config['tf_float_precision'], - shape=self.data_handler.label_shape, - name='new_label_weight_values') + self.shared_objects["label_weight_config"], + name="label_weights", + trainable=False, + dtype=self.config["tf_float_precision"], + ) + + self.shared_objects["new_label_weight_values"] = ( + tf.compat.v1.placeholder( + self.config["tf_float_precision"], + shape=self.data_handler.label_shape, + name="new_label_weight_values", + ) + ) label_weight_decay = 0.5 - self.shared_objects['assign_new_label_weights'] = \ + self.shared_objects["assign_new_label_weights"] = ( label_weights.assign( - label_weights * (1. - label_weight_decay) + - self.shared_objects['new_label_weight_values'] - * label_weight_decay) + label_weights * (1.0 - label_weight_decay) + + self.shared_objects["new_label_weight_values"] + * label_weight_decay + ) + ) # calculate MSE for each label y_diff_trafo = loss_utils.get_y_diff_trafo( - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - shared_objects=self.shared_objects) + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + shared_objects=self.shared_objects, + ) - self.shared_objects['y_diff_trafo'] = y_diff_trafo - self.shared_objects['mse_values_trafo'] = tf.reduce_mean( - input_tensor=tf.square(y_diff_trafo), axis=0) + self.shared_objects["y_diff_trafo"] = y_diff_trafo + self.shared_objects["mse_values_trafo"] = tf.reduce_mean( + input_tensor=tf.square(y_diff_trafo), axis=0 + ) else: label_weights = tf.constant( - self.shared_objects['label_weight_config'], - shape=self.data_handler.label_shape, - dtype=self.config['tf_float_precision']) - - self.shared_objects['label_weights'] = label_weights - self.shared_objects['label_weights_benchmark'] = tf.reduce_sum( - input_tensor=label_weights) - - tf.compat.v1.summary.histogram('label_weights', label_weights) - tf.compat.v1.summary.scalar('label_weights_benchmark', - self.shared_objects['label_weights_benchmark']) - - misc.print_warning('Total Benchmark should be: {:3.3f}'.format( - sum(self.shared_objects['label_weight_config']))) + self.shared_objects["label_weight_config"], + shape=self.data_handler.label_shape, + dtype=self.config["tf_float_precision"], + ) + + self.shared_objects["label_weights"] = label_weights + self.shared_objects["label_weights_benchmark"] = tf.reduce_sum( + input_tensor=label_weights + ) + + tf.compat.v1.summary.histogram("label_weights", label_weights) + tf.compat.v1.summary.scalar( + "label_weights_benchmark", + self.shared_objects["label_weights_benchmark"], + ) + + misc.print_warning( + "Total Benchmark should be: {:3.3f}".format( + sum(self.shared_objects["label_weight_config"]) + ) + ) def _create_event_weights(self): - """Create event weights - """ - if ('event_weight_file' in self.config and - self.config['event_weight_file'] is not None): + """Create event weights""" + if ( + "event_weight_file" in self.config + and self.config["event_weight_file"] is not None + ): # get event weight function - class_string = 'dnn_reco.modules.data.event_weights.{}.{}'.format( - self.config['event_weight_file'], - self.config['event_weight_name']) + class_string = "dnn_reco.modules.data.event_weights.{}.{}".format( + self.config["event_weight_file"], + self.config["event_weight_name"], + ) event_weight_function = misc.load_class(class_string) # compute loss - self.shared_objects['event_weights'] = event_weight_function( - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - shared_objects=self.shared_objects) - - shape = self.shared_objects['event_weights'].get_shape().as_list() - assert len(shape) == 2 and shape[1] == 1, \ - 'Expected shape [-1, 1] but got {!r}'.format(shape) + self.shared_objects["event_weights"] = event_weight_function( + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + shared_objects=self.shared_objects, + ) + + shape = self.shared_objects["event_weights"].get_shape().as_list() + assert ( + len(shape) == 2 and shape[1] == 1 + ), "Expected shape [-1, 1] but got {!r}".format(shape) def _get_optimizers_and_loss(self): """Get optimizers and loss terms as defined in config. @@ -375,48 +435,52 @@ def _get_optimizers_and_loss(self): ValueError Description """ - optimizer_dict = dict(self.config['model_optimizer_dict']) + optimizer_dict = dict(self.config["model_optimizer_dict"]) # create empty list to hold tensorflow optimizer operations optimizer_ops = [] # create empty dictionary to hold loss values - self.shared_objects['label_loss_dict'] = {} + self.shared_objects["label_loss_dict"] = {} # create each optimizer for name, opt_config in sorted(optimizer_dict.items()): # sanity check: make sure loss file and name have same length - if isinstance(opt_config['loss_file'], str): - assert isinstance(opt_config['loss_name'], str) - opt_config['loss_file'] = [opt_config['loss_file']] - opt_config['loss_name'] = [opt_config['loss_name']] + if isinstance(opt_config["loss_file"], str): + assert isinstance(opt_config["loss_name"], str) + opt_config["loss_file"] = [opt_config["loss_file"]] + opt_config["loss_name"] = [opt_config["loss_name"]] - assert len(opt_config['loss_file']) == len(opt_config['loss_name']) + assert len(opt_config["loss_file"]) == len(opt_config["loss_name"]) # aggregate over all defined loss functions label_loss = None - for file, name in zip(opt_config['loss_file'], - opt_config['loss_name']): + for file, name in zip( + opt_config["loss_file"], opt_config["loss_name"] + ): # get loss function - class_string = 'dnn_reco.modules.loss.{}.{}'.format(file, name) + class_string = "dnn_reco.modules.loss.{}.{}".format(file, name) loss_function = misc.load_class(class_string) # compute loss label_loss_i = loss_function( - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - shared_objects=self.shared_objects) + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + shared_objects=self.shared_objects, + ) # sanity check: make sure loss has expected shape loss_shape = label_loss_i.get_shape().as_list() if loss_shape != self.data_handler.label_shape: - error_msg = 'Shape of label loss {!r} does not match {!r}' - raise ValueError(error_msg.format( - loss_shape, - self.data_handler.label_shape)) + error_msg = "Shape of label loss {!r} does not match {!r}" + raise ValueError( + error_msg.format( + loss_shape, self.data_handler.label_shape + ) + ) # accumulate loss terms if label_loss is None: @@ -427,18 +491,22 @@ def _get_optimizers_and_loss(self): # weight label_losses # use nested where trick to avoid NaNs: # https://stackoverflow.com/questions/33712178/tensorflow-nan-bug - label_loss_safe = tf.where(self.shared_objects['non_zero_mask'], - label_loss, tf.zeros_like(label_loss)) + label_loss_safe = tf.where( + self.shared_objects["non_zero_mask"], + label_loss, + tf.zeros_like(label_loss), + ) weighted_label_loss = tf.where( - self.shared_objects['non_zero_mask'], - label_loss_safe * self.shared_objects['label_weights'], - tf.zeros_like(label_loss)) + self.shared_objects["non_zero_mask"], + label_loss_safe * self.shared_objects["label_weights"], + tf.zeros_like(label_loss), + ) weighted_loss_sum = tf.reduce_sum(input_tensor=weighted_label_loss) # create learning rate schedule if learning rate is a dict - optimizer_settings = dict(opt_config['optimizer_settings']) - if 'learning_rate' in optimizer_settings: - if isinstance(optimizer_settings['learning_rate'], dict): + optimizer_settings = dict(opt_config["optimizer_settings"]) + if "learning_rate" in optimizer_settings: + if isinstance(optimizer_settings["learning_rate"], dict): # assume that the learning rate dictionary defines a # schedule of learning rates @@ -448,42 +516,47 @@ def _get_optimizers_and_loss(self): # settings: dict # keyword arguments that are passed on to the # scheduler class. - lr_cfg = optimizer_settings.pop('learning_rate') + lr_cfg = optimizer_settings.pop("learning_rate") scheduler_class = misc.load_class( - lr_cfg['full_class_string']) - scheduler = scheduler_class(**lr_cfg['settings']) - optimizer_settings['learning_rate'] = scheduler + lr_cfg["full_class_string"] + ) + scheduler = scheduler_class(**lr_cfg["settings"]) + optimizer_settings["learning_rate"] = scheduler # get optimizer # check for old-style (tf < 2) optimizers in tf.train try: - optimizer_cls = getattr(tf.train, opt_config['optimizer']) + optimizer_cls = getattr(tf.train, opt_config["optimizer"]) except AttributeError: - optimizer_cls = getattr(tf.optimizers, opt_config['optimizer']) + optimizer_cls = getattr(tf.optimizers, opt_config["optimizer"]) optimizer = optimizer_cls(**optimizer_settings) # get variable list - if isinstance(opt_config['vars'], str): - opt_config['vars'] = [opt_config['vars']] + if isinstance(opt_config["vars"], str): + opt_config["vars"] = [opt_config["vars"]] var_list = [] - for var_name in opt_config['vars']: - var_list.extend(self.shared_objects['model_vars_' + var_name]) + for var_name in opt_config["vars"]: + var_list.extend(self.shared_objects["model_vars_" + var_name]) # apply regularization - if opt_config['l1_regularization'] > 0. or \ - opt_config['l2_regularization'] > 0.: + if ( + opt_config["l1_regularization"] > 0.0 + or opt_config["l2_regularization"] > 0.0 + ): - reg_loss = 0. + reg_loss = 0.0 # apply regularization - if opt_config['l1_regularization'] > 0.: + if opt_config["l1_regularization"] > 0.0: reg_loss += tf.add_n( - [tf.reduce_sum(tf.abs(v)) for v in var_list]) + [tf.reduce_sum(tf.abs(v)) for v in var_list] + ) - if opt_config['l2_regularization'] > 0.: + if opt_config["l2_regularization"] > 0.0: reg_loss += tf.add_n( - [tf.reduce_sum(v**2) for v in var_list]) + [tf.reduce_sum(v**2) for v in var_list] + ) total_loss = weighted_loss_sum + reg_loss @@ -491,63 +564,75 @@ def _get_optimizers_and_loss(self): total_loss = weighted_loss_sum # logging - self.shared_objects['label_loss_dict'].update({ - 'loss_label_' + name: weighted_label_loss, - 'loss_sum_' + name: weighted_loss_sum, - 'loss_sum_total_' + name: total_loss, - }) + self.shared_objects["label_loss_dict"].update( + { + "loss_label_" + name: weighted_label_loss, + "loss_sum_" + name: weighted_loss_sum, + "loss_sum_total_" + name: total_loss, + } + ) tf.compat.v1.summary.histogram( - 'loss_label_' + name, weighted_label_loss) - tf.compat.v1.summary.scalar('loss_sum_' + name, weighted_loss_sum) - tf.compat.v1.summary.scalar('loss_sum_total_' + name, total_loss) + "loss_label_" + name, weighted_label_loss + ) + tf.compat.v1.summary.scalar("loss_sum_" + name, weighted_loss_sum) + tf.compat.v1.summary.scalar("loss_sum_total_" + name, total_loss) # get gradients # compatibility mode for old and new tensorflow versions try: gvs = optimizer.compute_gradients( - total_loss, var_list=var_list) + total_loss, var_list=var_list + ) except AttributeError: gradients = tf.gradients(total_loss, var_list) gvs = zip(gradients, var_list) # remove nans in gradients and replace these with zeros - if 'remove_nan_gradients' in opt_config: - remove_nan_gradients = opt_config['remove_nan_gradients'] + if "remove_nan_gradients" in opt_config: + remove_nan_gradients = opt_config["remove_nan_gradients"] else: remove_nan_gradients = False if remove_nan_gradients: - gvs = [(tf.where( - tf.math.is_nan(grad), tf.zeros_like(grad), grad), - var) for grad, var in gvs if grad is not None] - - if 'clip_gradients_value' in opt_config: - clip_gradients_value = opt_config['clip_gradients_value'] + gvs = [ + ( + tf.where( + tf.math.is_nan(grad), tf.zeros_like(grad), grad + ), + var, + ) + for grad, var in gvs + if grad is not None + ] + + if "clip_gradients_value" in opt_config: + clip_gradients_value = opt_config["clip_gradients_value"] else: clip_gradients_value = None if clip_gradients_value is not None: gradients, variables = zip(*gvs) - gradients, _ = tf.clip_by_global_norm(gradients, - clip_gradients_value) + gradients, _ = tf.clip_by_global_norm( + gradients, clip_gradients_value + ) capped_gvs = zip(gradients, variables) else: capped_gvs = gvs optimizer_ops.append(optimizer.apply_gradients(capped_gvs)) - self.shared_objects['optimizer_ops'] = optimizer_ops + self.shared_objects["optimizer_ops"] = optimizer_ops def _merge_tensorboard_summaries(self): - """Merges summary variables for TensorBoard visualization and logging. - """ + """Merges summary variables for TensorBoard visualization and logging.""" self._merged_summary = tf.compat.v1.summary.merge_all() self._train_writer = tf.compat.v1.summary.FileWriter( - self.config['log_path'] + '_train') + self.config["log_path"] + "_train" + ) self._val_writer = tf.compat.v1.summary.FileWriter( - self.config['log_path'] + '_val') + self.config["log_path"] + "_val" + ) def _initialize_and_finalize_model(self): - """Initalize and finalize model weights - """ + """Initalize and finalize model weights""" # initialize variables self.sess.run(tf.compat.v1.global_variables_initializer()) @@ -580,19 +665,21 @@ def compile(self): # update importance def restore(self): - """Restore model weights from checkpoints - """ - latest_checkpoint = tf.train.latest_checkpoint(os.path.dirname( - self.config['model_checkpoint_path'])) + """Restore model weights from checkpoints""" + latest_checkpoint = tf.train.latest_checkpoint( + os.path.dirname(self.config["model_checkpoint_path"]) + ) if latest_checkpoint is None: misc.print_warning( - 'Could not find previous checkpoint. Creating new one!') + "Could not find previous checkpoint. Creating new one!" + ) else: - self._step_offset = int(latest_checkpoint.split('-')[-1]) + self._step_offset = int(latest_checkpoint.split("-")[-1]) self.saver.restore(sess=self.sess, save_path=latest_checkpoint) - def predict_batched(self, x_ic78, x_deepcore, max_size, transformed=False, - *args, **kwargs): + def predict_batched( + self, x_ic78, x_deepcore, max_size, transformed=False, *args, **kwargs + ): """Reconstruct events in multiple batches. Parameters @@ -618,15 +705,19 @@ def predict_batched(self, x_ic78, x_deepcore, max_size, transformed=False, y_pred_list = [] y_unc_list = [] - split_indices_list = np.array_split(np.arange(x_ic78.shape[0]), - np.ceil(x_ic78.shape[0]/max_size)) + split_indices_list = np.array_split( + np.arange(x_ic78.shape[0]), np.ceil(x_ic78.shape[0] / max_size) + ) for split_indices in split_indices_list: - y_pred, y_unc = self.predict(x_ic78[split_indices], - x_deepcore[split_indices], - transformed=transformed, - *args, **kwargs) + y_pred, y_unc = self.predict( + x_ic78[split_indices], + x_deepcore[split_indices], + transformed=transformed, + *args, + **kwargs + ) y_pred_list.append(y_pred) y_unc_list.append(y_unc) @@ -657,20 +748,24 @@ def predict(self, x_ic78, x_deepcore, transformed=False, *args, **kwargs): The prediction and estimated uncertainties """ feed_dict = { - self.shared_objects['x_ic78']: x_ic78, - self.shared_objects['x_deepcore']: x_deepcore, + self.shared_objects["x_ic78"]: x_ic78, + self.shared_objects["x_deepcore"]: x_deepcore, } # Fill in keep rates for dropout - for keep_prob in self.shared_objects['keep_prob_list']: + for keep_prob in self.shared_objects["keep_prob_list"]: feed_dict[keep_prob] = 1.0 if transformed: - vars_to_run = [self.shared_objects['y_pred_trafo'], - self.shared_objects['y_unc_trafo']] + vars_to_run = [ + self.shared_objects["y_pred_trafo"], + self.shared_objects["y_unc_trafo"], + ] else: - vars_to_run = [self.shared_objects['y_pred'], - self.shared_objects['y_unc']] + vars_to_run = [ + self.shared_objects["y_pred"], + self.shared_objects["y_unc"], + ] y_pred, y_unc = self.sess.run(vars_to_run, feed_dict=feed_dict) @@ -694,28 +789,35 @@ def _feed_placeholders(self, data_generator, is_validation): x_ic78, x_deepcore, labels, misc_data = next(data_generator) feed_dict = { - self.shared_objects['x_ic78']: x_ic78, - self.shared_objects['x_deepcore']: x_deepcore, - self.shared_objects['y_true']: labels, + self.shared_objects["x_ic78"]: x_ic78, + self.shared_objects["x_deepcore"]: x_deepcore, + self.shared_objects["y_true"]: labels, } if self.data_handler.misc_shape is not None: - feed_dict[self.shared_objects['x_misc']] = misc_data + feed_dict[self.shared_objects["x_misc"]] = misc_data # Fill in keep rates for dropout if is_validation: - for keep_prob in self.shared_objects['keep_prob_list']: + for keep_prob in self.shared_objects["keep_prob_list"]: feed_dict[keep_prob] = 1.0 else: - for keep_prob, prob in zip(self.shared_objects['keep_prob_list'], - self.config['keep_probability_list']): + for keep_prob, prob in zip( + self.shared_objects["keep_prob_list"], + self.config["keep_probability_list"], + ): feed_dict[keep_prob] = prob return feed_dict - def fit(self, num_training_iterations, train_data_generator, - val_data_generator, - evaluation_methods=None, - *args, **kwargs): + def fit( + self, + num_training_iterations, + train_data_generator, + val_data_generator, + evaluation_methods=None, + *args, + **kwargs + ): """Trains the NN model with the data provided by the data iterators. Parameters @@ -741,25 +843,29 @@ def fit(self, num_training_iterations, train_data_generator, """ if not self._model_is_compiled: raise ValueError( - 'Model must be compiled prior to call of fit method') + "Model must be compiled prior to call of fit method" + ) # training operations to run - train_ops = {'optimizer_{:03d}'.format(i): opt for i, opt in - enumerate(self.shared_objects['optimizer_ops'])} + train_ops = { + "optimizer_{:03d}".format(i): opt + for i, opt in enumerate(self.shared_objects["optimizer_ops"]) + } # add parameters and op if label weights are to be updated - if self.config['label_update_weights']: + if self.config["label_update_weights"]: - label_weight_n = 0. + label_weight_n = 0.0 label_weight_mean = np.zeros(self.data_handler.label_shape) label_weight_M2 = np.zeros(self.data_handler.label_shape) - train_ops['mse_values_trafo'] = \ - self.shared_objects['mse_values_trafo'] + train_ops["mse_values_trafo"] = self.shared_objects[ + "mse_values_trafo" + ] # add op if tukey scaling is to be applied - if self.config['label_scale_tukey']: - train_ops['y_diff_trafo'] = self.shared_objects['y_diff_trafo'] + if self.config["label_scale_tukey"]: + train_ops["y_diff_trafo"] = self.shared_objects["y_diff_trafo"] # ---------------- # training loop @@ -767,33 +873,37 @@ def fit(self, num_training_iterations, train_data_generator, start_time = timeit.default_timer() for i in range(num_training_iterations): - feed_dict = self._feed_placeholders(train_data_generator, - is_validation=False) - train_result = self.sess.run(train_ops, - feed_dict=feed_dict) + feed_dict = self._feed_placeholders( + train_data_generator, is_validation=False + ) + train_result = self.sess.run(train_ops, feed_dict=feed_dict) # ------------------------------------- # calculate variabels for tukey scaling # ------------------------------------- - if self.config['label_scale_tukey']: + if self.config["label_scale_tukey"]: batch_median_abs_dev = np.median( - np.abs(train_result['y_diff_trafo']), axis=0) + np.abs(train_result["y_diff_trafo"]), axis=0 + ) # assign new label weight updates feed_dict_assign = { - self.shared_objects['new_median_abs_dev_values']: - np.clip(batch_median_abs_dev, 1e-6, float('inf'))} + self.shared_objects["new_median_abs_dev_values"]: np.clip( + batch_median_abs_dev, 1e-6, float("inf") + ) + } self.sess.run( - self.shared_objects['assign_new_median_abs_dev_values'], - feed_dict=feed_dict_assign) + self.shared_objects["assign_new_median_abs_dev_values"], + feed_dict=feed_dict_assign, + ) # -------------------------------------------- # calculate online variabels for label weights # -------------------------------------------- - if self.config['label_update_weights']: - mse_values_trafo = train_result['mse_values_trafo'] - mse_values_trafo[~self.shared_objects['non_zero_mask']] = 1. + if self.config["label_update_weights"]: + mse_values_trafo = train_result["mse_values_trafo"] + mse_values_trafo[~self.shared_objects["non_zero_mask"]] = 1.0 if np.isfinite(mse_values_trafo).all(): label_weight_n += 1 @@ -802,148 +912,180 @@ def fit(self, num_training_iterations, train_data_generator, delta2 = mse_values_trafo - label_weight_mean label_weight_M2 += delta * delta2 else: - misc.print_warning('Found NaNs: {}'.format( - mse_values_trafo)) + misc.print_warning( + "Found NaNs: {}".format(mse_values_trafo) + ) for i, name in enumerate(self.data_handler.label_names): print(name, mse_values_trafo[i]) if not np.isfinite(label_weight_mean).all(): for i, name in enumerate(self.data_handler.label_names): - print('weight', name, label_weight_mean[i]) + print("weight", name, label_weight_mean[i]) if not np.isfinite(mse_values_trafo).all(): - raise ValueError('FOUND NANS!') + raise ValueError("FOUND NANS!") # every n steps: update label_weights - if i % self.config['validation_frequency'] == 0: - new_weights = 1.0 / (np.sqrt( - np.abs(label_weight_mean) + 1e-6) + 1e-3) + if i % self.config["validation_frequency"] == 0: + new_weights = 1.0 / ( + np.sqrt(np.abs(label_weight_mean) + 1e-6) + 1e-3 + ) new_weights[new_weights < 1] = 1 - new_weights *= self.shared_objects['label_weight_config'] + new_weights *= self.shared_objects["label_weight_config"] # assign new label weight updates feed_dict_assign = { - self.shared_objects['new_label_weight_values']: - new_weights} + self.shared_objects[ + "new_label_weight_values" + ]: new_weights + } self.sess.run( - self.shared_objects['assign_new_label_weights'], - feed_dict=feed_dict_assign) + self.shared_objects["assign_new_label_weights"], + feed_dict=feed_dict_assign, + ) # reset values - label_weight_n = 0. + label_weight_n = 0.0 label_weight_mean = np.zeros(self.data_handler.label_shape) label_weight_M2 = np.zeros(self.data_handler.label_shape) # ---------------- # validate performance # ---------------- - if i % self.config['validation_frequency'] == 0: + if i % self.config["validation_frequency"] == 0: updated_weights = self.sess.run( - self.shared_objects['label_weights']) + self.shared_objects["label_weights"] + ) eval_dict = { - 'merged_summary': self._merged_summary, - 'weights': self.shared_objects['label_weights_benchmark'], - 'rmse_trafo': self.shared_objects['rmse_values_trafo'], - 'y_pred': self.shared_objects['y_pred'], - 'y_unc': self.shared_objects['y_unc'], - 'y_pred_trafo': self.shared_objects['y_pred_trafo'], - 'y_unc_trafo': self.shared_objects['y_unc_trafo'], + "merged_summary": self._merged_summary, + "weights": self.shared_objects["label_weights_benchmark"], + "rmse_trafo": self.shared_objects["rmse_values_trafo"], + "y_pred": self.shared_objects["y_pred"], + "y_unc": self.shared_objects["y_unc"], + "y_pred_trafo": self.shared_objects["y_pred_trafo"], + "y_unc_trafo": self.shared_objects["y_unc_trafo"], } - result_msg = '' + result_msg = "" for k, loss in sorted( - self.shared_objects['label_loss_dict'].items()): - if k[:9] == 'loss_sum_': + self.shared_objects["label_loss_dict"].items() + ): + if k[:9] == "loss_sum_": eval_dict[k] = loss - result_msg += k + ': {' + k + ':2.3f} ' + result_msg += k + ": {" + k + ":2.3f} " # ------------------------------------- # Test performance on training data # ------------------------------------- - feed_dict_train = self._feed_placeholders(train_data_generator, - is_validation=True) - results_train = self.sess.run(eval_dict, - feed_dict=feed_dict_train) + feed_dict_train = self._feed_placeholders( + train_data_generator, is_validation=True + ) + results_train = self.sess.run( + eval_dict, feed_dict=feed_dict_train + ) # ------------------------------------- # Test performance on validation data # ------------------------------------- - feed_dict_val = self._feed_placeholders(val_data_generator, - is_validation=True) + feed_dict_val = self._feed_placeholders( + val_data_generator, is_validation=True + ) results_val = self.sess.run(eval_dict, feed_dict=feed_dict_val) - y_true_train = feed_dict_train[self.shared_objects['y_true']] - y_true_val = feed_dict_val[self.shared_objects['y_true']] + y_true_train = feed_dict_train[self.shared_objects["y_true"]] + y_true_val = feed_dict_val[self.shared_objects["y_true"]] y_true_trafo_train = self.data_transformer.transform( - y_true_train, data_type='label') + y_true_train, data_type="label" + ) y_true_trafo_val = self.data_transformer.transform( - y_true_val, data_type='label') + y_true_val, data_type="label" + ) self._train_writer.add_summary( - results_train['merged_summary'], i) - self._val_writer.add_summary(results_val['merged_summary'], i) - msg = 'Step: {:08d}, Runtime: {:2.2f}s, Benchmark: {:3.3f}' - print(msg.format(i, timeit.default_timer() - start_time, - np.sum(updated_weights))) - print('\t[Train] '+result_msg.format(**results_train)) - print('\t[Validation] '+result_msg.format(**results_val)) + results_train["merged_summary"], i + ) + self._val_writer.add_summary(results_val["merged_summary"], i) + msg = "Step: {:08d}, Runtime: {:2.2f}s, Benchmark: {:3.3f}" + print( + msg.format( + i, + timeit.default_timer() - start_time, + np.sum(updated_weights), + ) + ) + print("\t[Train] " + result_msg.format(**results_train)) + print("\t[Validation] " + result_msg.format(**results_val)) # print info for each label for name, index in sorted( - self.data_handler.label_name_dict.items()): + self.data_handler.label_name_dict.items() + ): if updated_weights[index] > 0: unc_pull_train = np.std( - (results_train['y_pred_trafo'][:, index] - - y_true_trafo_train[:, index]) / - results_train['y_unc_trafo'][:, index], ddof=1) + ( + results_train["y_pred_trafo"][:, index] + - y_true_trafo_train[:, index] + ) + / results_train["y_unc_trafo"][:, index], + ddof=1, + ) unc_pull_val = np.std( - (results_val['y_pred_trafo'][:, index] - - y_true_trafo_val[:, index]) / - results_val['y_unc_trafo'][:, index], ddof=1) - - msg = '\tweight: {weight:2.3f},' - msg += ' train: {train:2.3f} [{unc_pull_train:1.2f}],' - msg += 'val: {val:2.3f} [{unc_pull_val:2.2f}] [{name}' - msg += ', mean: {mean_train:2.3f} {mean_val:2.3f}]' - print(msg.format( - weight=updated_weights[index], - train=results_train['rmse_trafo'][index], - val=results_val['rmse_trafo'][index], - name=name, - mean_train=np.mean(y_true_train[:, index]), - mean_val=np.mean(y_true_val[:, index]), - unc_pull_train=unc_pull_train, - unc_pull_val=unc_pull_val, - )) + ( + results_val["y_pred_trafo"][:, index] + - y_true_trafo_val[:, index] + ) + / results_val["y_unc_trafo"][:, index], + ddof=1, + ) + + msg = "\tweight: {weight:2.3f}," + msg += " train: {train:2.3f} [{unc_pull_train:1.2f}]," + msg += "val: {val:2.3f} [{unc_pull_val:2.2f}] [{name}" + msg += ", mean: {mean_train:2.3f} {mean_val:2.3f}]" + print( + msg.format( + weight=updated_weights[index], + train=results_train["rmse_trafo"][index], + val=results_val["rmse_trafo"][index], + name=name, + mean_train=np.mean(y_true_train[:, index]), + mean_val=np.mean(y_true_val[:, index]), + unc_pull_train=unc_pull_train, + unc_pull_val=unc_pull_val, + ) + ) # Call user defined evaluation method - if self.config['evaluation_file'] is not None: - class_string = 'dnn_reco.modules.evaluation.{}.{}'.format( - self.config['evaluation_file'], - self.config['evaluation_name'], - ) + if self.config["evaluation_file"] is not None: + class_string = "dnn_reco.modules.evaluation.{}.{}".format( + self.config["evaluation_file"], + self.config["evaluation_name"], + ) eval_func = misc.load_class(class_string) - eval_func(feed_dict_train=feed_dict_train, - feed_dict_val=feed_dict_val, - results_train=results_train, - results_val=results_val, - config=self.config, - data_handler=self.data_handler, - data_transformer=self.data_transformer, - shared_objects=self.shared_objects) + eval_func( + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + results_train=results_train, + results_val=results_val, + config=self.config, + data_handler=self.data_handler, + data_transformer=self.data_transformer, + shared_objects=self.shared_objects, + ) # ---------------- # save models # ---------------- - if i % self.config['save_frequency'] == 0: - if self.config['model_save_model']: + if i % self.config["save_frequency"] == 0: + if self.config["model_save_model"]: self._save_training_config(i) self.saver.save( - sess=self.sess, - global_step=self._step_offset + i, - save_path=self.config['model_checkpoint_path']) + sess=self.sess, + global_step=self._step_offset + i, + save_path=self.config["model_checkpoint_path"], + ) # ---------------- def _save_training_config(self, iteration): @@ -960,38 +1102,48 @@ def _save_training_config(self, iteration): Description """ if iteration == 0: - if not self.config['model_restore_model']: + if not self.config["model_restore_model"]: # Delete old training config files and create a new and empty # training_steps.txt, since we are training a new model # from scratch and overwriting the old one # delete all previous training config files (if they exist) - files = glob.glob(os.path.join(self._check_point_path, - 'config_training_*.yaml')) + files = glob.glob( + os.path.join( + self._check_point_path, "config_training_*.yaml" + ) + ) if files: - misc.print_warning("Please confirm the deletion of the " - "previous trainin configs:") + misc.print_warning( + "Please confirm the deletion of the " + "previous trainin configs:" + ) for file in files: - if click.confirm('Delete {!r}?'.format(file), - default=True): + if click.confirm( + "Delete {!r}?".format(file), default=True + ): os.remove(file) else: raise ValueError( - 'Old training configs must be deleted!') + "Old training configs must be deleted!" + ) # save training step config under appropriate name training_config = dict(self.config) - del training_config['np_float_precision'] - del training_config['tf_float_precision'] + del training_config["np_float_precision"] + del training_config["tf_float_precision"] - with open(self._training_config_file, 'w') as yaml_file: + with open(self._training_config_file, "w") as yaml_file: yaml.dump(training_config, yaml_file, default_flow_style=False) # update number of training iterations in training_steps.yaml self._training_iterations_dict[self._training_step] = iteration - with open(self._training_steps_file, 'w') as yaml_file: - yaml.dump(self._training_iterations_dict, yaml_file, - default_flow_style=False) + with open(self._training_steps_file, "w") as yaml_file: + yaml.dump( + self._training_iterations_dict, + yaml_file, + default_flow_style=False, + ) def count_parameters(self, var_list=None): """Count number of trainable parameters diff --git a/dnn_reco/modules/data/event_weights/event_weights.py b/dnn_reco/modules/data/event_weights/event_weights.py index 189b970..58b45e0 100644 --- a/dnn_reco/modules/data/event_weights/event_weights.py +++ b/dnn_reco/modules/data/event_weights/event_weights.py @@ -32,8 +32,9 @@ """ -def event_selection_weight(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def event_selection_weight( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Event weights for event selection models. Parameters @@ -61,24 +62,24 @@ def event_selection_weight(config, data_handler, data_transformer, Shape: label_shape (same shape as labels) """ - event_weights = tf.zeros_like(shared_objects['x_misc'][:, 0]) + event_weights = tf.zeros_like(shared_objects["x_misc"][:, 0]) # get Corsika weights - for key in config['event_weights_corsika_keys']: - weights = shared_objects['x_misc'][:, data_handler.get_misc_index(key)] - weights /= config['event_weights_num_corsika_files'] + for key in config["event_weights_corsika_keys"]: + weights = shared_objects["x_misc"][:, data_handler.get_misc_index(key)] + weights /= config["event_weights_num_corsika_files"] event_weights += weights # get MuonGun weights - for key in config['event_weights_muongun_keys']: - weights = shared_objects['x_misc'][:, data_handler.get_misc_index(key)] - weights /= config['event_weights_num_muongun_files'] + for key in config["event_weights_muongun_keys"]: + weights = shared_objects["x_misc"][:, data_handler.get_misc_index(key)] + weights /= config["event_weights_num_muongun_files"] event_weights += weights # get NuGen weights - for key in config['event_weights_nugen_keys']: - weights = shared_objects['x_misc'][:, data_handler.get_misc_index(key)] - weights /= config['event_weights_num_nugen_files'] + for key in config["event_weights_nugen_keys"]: + weights = shared_objects["x_misc"][:, data_handler.get_misc_index(key)] + weights /= config["event_weights_num_nugen_files"] event_weights += weights # weight for 1 year of livetime @@ -87,8 +88,9 @@ def event_selection_weight(config, data_handler, data_transformer, return tf.expand_dims(event_weights, axis=-1) -def clipped_astroness_weights(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def clipped_astroness_weights( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Clipped "Astroness" Weights All events are assigned an event weight of 1 plus a possible addition, if @@ -119,13 +121,19 @@ def clipped_astroness_weights(config, data_handler, data_transformer, Shape: label_shape (same shape as labels) """ - event_weights = tf.ones_like(shared_objects['x_misc'][:, 0]) + event_weights = tf.ones_like(shared_objects["x_misc"][:, 0]) # get astro and conv weights - astro = shared_objects['x_misc'][:, data_handler.get_misc_index( - config['event_weights_nugen_astro_weight'])] - conv = shared_objects['x_misc'][:, data_handler.get_misc_index( - config['event_weights_nugen_conv_weight'])] + astro = shared_objects["x_misc"][ + :, + data_handler.get_misc_index( + config["event_weights_nugen_astro_weight"] + ), + ] + conv = shared_objects["x_misc"][ + :, + data_handler.get_misc_index(config["event_weights_nugen_conv_weight"]), + ] eps = 1e-32 @@ -135,6 +143,6 @@ def clipped_astroness_weights(config, data_handler, data_transformer, # offset the Point Source energy llh term by 7.65 # --> events will obtain maximum boosting factor of 7.65 llh_energy = tf.math.log(astro / (astro + conv + eps) + eps) - event_weights += tf.clip_by_value(7.65 + llh_energy, 0., 10) + event_weights += tf.clip_by_value(7.65 + llh_energy, 0.0, 10) return tf.expand_dims(event_weights, axis=-1) diff --git a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py index ac82e63..f20e1f7 100644 --- a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py +++ b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py @@ -32,8 +32,9 @@ """ -def nersc_gnn_weight(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def nersc_gnn_weight( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Event weight as calculated for nersc gnn comparison. Parameters @@ -62,17 +63,17 @@ def nersc_gnn_weight(config, data_handler, data_transformer, shared_objects, """ # get indices - index_weight = data_handler.get_misc_index('nersc_gnn_info_weight') - index_signal = data_handler.get_label_index('is_signal') + index_weight = data_handler.get_misc_index("nersc_gnn_info_weight") + index_signal = data_handler.get_label_index("is_signal") # weight for 10 years of livetime - weight = shared_objects['x_misc'][:, index_weight] * 86400 * 365 * 10 - is_nugen = shared_objects['y_true'][:, index_signal] > .5 + weight = shared_objects["x_misc"][:, index_weight] * 86400 * 365 * 10 + is_nugen = shared_objects["y_true"][:, index_signal] > 0.5 event_weights = tf.where( is_nugen, - weight / config['nersc_gnn_weight_num_nugen_files'], - weight / config['nersc_gnn_weight_num_corsika_files'], - ) + weight / config["nersc_gnn_weight_num_nugen_files"], + weight / config["nersc_gnn_weight_num_corsika_files"], + ) return tf.expand_dims(event_weights, axis=-1) diff --git a/dnn_reco/modules/data/filter/default_filter.py b/dnn_reco/modules/data/filter/default_filter.py index 37ab934..29876f3 100644 --- a/dnn_reco/modules/data/filter/default_filter.py +++ b/dnn_reco/modules/data/filter/default_filter.py @@ -1,5 +1,6 @@ from __future__ import division, print_function import numpy as np + """ All filter functions must have the following parameters and return values: @@ -40,8 +41,18 @@ """ -def dummy_filter(data_handler, input_data, config, x_ic70, x_deepcore, labels, - misc_data, time_range_start, *args, **kwargs): +def dummy_filter( + data_handler, + input_data, + config, + x_ic70, + x_deepcore, + labels, + misc_data, + time_range_start, + *args, + **kwargs +): """Dummy filter that accepts all events. Parameters @@ -83,8 +94,18 @@ def dummy_filter(data_handler, input_data, config, x_ic70, x_deepcore, labels, return np.ones(len(time_range_start), dtype=bool) -def general_filter(data_handler, input_data, config, x_ic70, x_deepcore, - labels, misc_data, time_range_start, *args, **kwargs): +def general_filter( + data_handler, + input_data, + config, + x_ic70, + x_deepcore, + labels, + misc_data, + time_range_start, + *args, + **kwargs +): """A general filter. Filters events according to the key value pairs defined in the config: @@ -147,32 +168,35 @@ def general_filter(data_handler, input_data, config, x_ic70, x_deepcore, mask_true = np.ones(len(time_range_start), dtype=bool) # go through equal mask - if 'filter_equal' in config: - for key, value in config['filter_equal'].items(): + if "filter_equal" in config: + for key, value in config["filter_equal"].items(): mask_true = np.logical_and( mask_true, - misc_data[:, data_handler.misc_name_dict[key]] == value) + misc_data[:, data_handler.misc_name_dict[key]] == value, + ) # go through greater than mask - if 'filter_greater_than' in config: - for key, value in config['filter_greater_than'].items(): + if "filter_greater_than" in config: + for key, value in config["filter_greater_than"].items(): mask_true = np.logical_and( mask_true, - misc_data[:, data_handler.misc_name_dict[key]] > value) + misc_data[:, data_handler.misc_name_dict[key]] > value, + ) # go through less than mask - if 'filter_less_than' in config: - for key, value in config['filter_less_than'].items(): + if "filter_less_than" in config: + for key, value in config["filter_less_than"].items(): mask_true = np.logical_and( mask_true, - misc_data[:, data_handler.misc_name_dict[key]] < value) + misc_data[:, data_handler.misc_name_dict[key]] < value, + ) # Only run the filter on events with MC Primaries of specified PDG encoding - if 'filter_apply_on_pdg_encodings' in config: - pdg_encodings = config['filter_apply_on_pdg_encodings'] + if "filter_apply_on_pdg_encodings" in config: + pdg_encodings = config["filter_apply_on_pdg_encodings"] if pdg_encodings: - pdg_key = 'MCPrimary_pdg_encoding' + pdg_key = "MCPrimary_pdg_encoding" pdg_values = misc_data[:, data_handler.misc_name_dict[pdg_key]] mask_pdg = np.array([p not in pdg_encodings for p in pdg_values]) diff --git a/dnn_reco/modules/data/labels/biased_selection_labels.py b/dnn_reco/modules/data/labels/biased_selection_labels.py index 8af7ffe..20892c8 100644 --- a/dnn_reco/modules/data/labels/biased_selection_labels.py +++ b/dnn_reco/modules/data/labels/biased_selection_labels.py @@ -4,6 +4,7 @@ import numpy as np from dnn_reco.modules.data.labels.default_labels import simple_label_loader + """ All label functions must have the following parameters and return values: @@ -62,21 +63,23 @@ def biased_muongun(input_data, config, label_names=None, *args, **kwargs): """ # define keys to load - if 'labels_biased_selection_filter_keys' in config: - filter_mask_keys = config['labels_biased_selection_filter_keys'] + if "labels_biased_selection_filter_keys" in config: + filter_mask_keys = config["labels_biased_selection_filter_keys"] else: - filter_mask_keys = ['CascadeFilter', 'MuonFilter'] + filter_mask_keys = ["CascadeFilter", "MuonFilter"] - if 'labels_biased_selection_dnn_label_names' in config: - dnn_labels_names = config['labels_biased_selection_dnn_label_names'] + if "labels_biased_selection_dnn_label_names" in config: + dnn_labels_names = config["labels_biased_selection_dnn_label_names"] else: - base = 'DeepLearningReco_' + base = "DeepLearningReco_" dnn_labels_names = { - base+'event_selection_cscdl3_300m_01': [ - ('p_starting_300m', 0.8), + base + + "event_selection_cscdl3_300m_01": [ + ("p_starting_300m", 0.8), ], - base+'event_selection_dnn_cscd_l3a_starting_events_03': [ - ('p_starting_300m', 0.5), + base + + "event_selection_dnn_cscd_l3a_starting_events_03": [ + ("p_starting_300m", 0.5), ], } @@ -87,35 +90,38 @@ def biased_muongun(input_data, config, label_names=None, *args, **kwargs): # special handling for FilterMask keys # FilterMask columns often have the year appended, e.g. _12 # Here, we map a filter_name_12 to filter_name - with h5py.File(input_data, 'r') as f: - _mask = f['FilterMask'][:] + with h5py.File(input_data, "r") as f: + _mask = f["FilterMask"][:] for filter_mask_key in filter_mask_keys: for filter_name in _mask.dtype.fields.keys(): if filter_mask_key in filter_name: - label_dict['p_passed_'+filter_mask_key] = \ - _mask[filter_name][:, 1] + label_dict["p_passed_" + filter_mask_key] = _mask[ + filter_name + ][:, 1] # load CscdL3 and CscdL3_Cont_Tag and all defined dnn_labels - with pd.HDFStore(input_data, mode='r') as f: + with pd.HDFStore(input_data, mode="r") as f: - _cscd_l3 = f['CscdL3']['value'] - _cscd_l3_cont_tag = f['CscdL3_Cont_Tag']['value'] - label_dict['p_passed_cscdl3'] = _cscd_l3 - label_dict['p_passed_cscdl3_cont'] = \ - np.logical_and(_cscd_l3_cont_tag > 0, _cscd_l3) + _cscd_l3 = f["CscdL3"]["value"] + _cscd_l3_cont_tag = f["CscdL3_Cont_Tag"]["value"] + label_dict["p_passed_cscdl3"] = _cscd_l3 + label_dict["p_passed_cscdl3_cont"] = np.logical_and( + _cscd_l3_cont_tag > 0, _cscd_l3 + ) for model_name, values in dnn_labels_names.items(): _dnn = f[model_name] for name, cut in values: - label_name = '{}_{}'.format( - model_name.replace('DeepLearningReco_', ''), name) + label_name = "{}_{}".format( + model_name.replace("DeepLearningReco_", ""), name + ) score = _dnn[name] - label_dict['p_passed_' + label_name] = score > cut + label_dict["p_passed_" + label_name] = score > cut label_dict[label_name] = score except KeyError as k: print(k) - print('Skipping file') + print("Skipping file") if label_names is None: label_names = sorted(label_dict.keys()) @@ -124,6 +130,6 @@ def biased_muongun(input_data, config, label_names=None, *args, **kwargs): for name in label_names: labels.append(label_dict[name]) - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names diff --git a/dnn_reco/modules/data/labels/default_labels.py b/dnn_reco/modules/data/labels/default_labels.py index 425501a..a55e172 100644 --- a/dnn_reco/modules/data/labels/default_labels.py +++ b/dnn_reco/modules/data/labels/default_labels.py @@ -1,6 +1,7 @@ from __future__ import division, print_function import pandas as pd import numpy as np + """ All label functions must have the following parameters and return values: @@ -67,67 +68,73 @@ def simple_label_loader(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ - with pd.HDFStore(input_data, mode='r') as f: - _labels = f[config['data_handler_label_key']] - time_offset = f[config['data_handler_time_offset_name']]['value'] + with pd.HDFStore(input_data, mode="r") as f: + _labels = f[config["data_handler_label_key"]] + time_offset = f[config["data_handler_time_offset_name"]]["value"] - ignore_columns = ['Run', 'Event', 'SubEvent', 'SubEventStream', 'exists'] + ignore_columns = ["Run", "Event", "SubEvent", "SubEventStream", "exists"] - if config['label_add_dir_vec']: - ignore_columns.extend(['direction_x', 'direction_y', 'direction_z']) + if config["label_add_dir_vec"]: + ignore_columns.extend(["direction_x", "direction_y", "direction_z"]) - if 'label_position_at_rel_time' in config and \ - config['label_position_at_rel_time'] is not None: - ignore_columns.extend(['rel_pos_x', 'rel_pos_y', 'rel_pos_z']) + if ( + "label_position_at_rel_time" in config + and config["label_position_at_rel_time"] is not None + ): + ignore_columns.extend(["rel_pos_x", "rel_pos_y", "rel_pos_z"]) if label_names is None: - if 'label_keys_to_load' in config: - name_list = config['label_keys_to_load'] + if "label_keys_to_load" in config: + name_list = config["label_keys_to_load"] else: name_list = _labels.keys().tolist() label_names = [n for n in name_list if n not in ignore_columns] - labels = [_labels[name] for name in label_names - if name not in ignore_columns] + labels = [ + _labels[name] for name in label_names if name not in ignore_columns + ] # calculate direction vector components on the fly - if config['label_add_dir_vec']: + if config["label_add_dir_vec"]: # get azimuth and zenith - azimuth = _labels[config['label_azimuth_key']] - zenith = _labels[config['label_zenith_key']] + azimuth = _labels[config["label_azimuth_key"]] + zenith = _labels[config["label_zenith_key"]] # calculate direction vector components # We need the negative values here, since (azimuth, zenith) points # towards the source, whereas the direction vector points in the # direction of the moving particle - dir_x = -np.sin(zenith)*np.cos(azimuth) - dir_y = -np.sin(zenith)*np.sin(azimuth) + dir_x = -np.sin(zenith) * np.cos(azimuth) + dir_y = -np.sin(zenith) * np.sin(azimuth) dir_z = -np.cos(zenith) # add direction vector components to labels - label_names.extend(['direction_x', 'direction_y', 'direction_z']) + label_names.extend(["direction_x", "direction_y", "direction_z"]) labels.extend([dir_x, dir_y, dir_z]) # calculate position at relative time t (only makes sense for tracks) - if 'label_position_at_rel_time' in config and \ - config['label_position_at_rel_time'] is not None: - dir_x = _labels[config['label_dir_x_key']] - dir_y = _labels[config['label_dir_y_key']] - dir_z = _labels[config['label_dir_z_key']] - - delta_t = (time_offset + config['label_position_at_rel_time']) - \ - _labels['VertexTime'] + if ( + "label_position_at_rel_time" in config + and config["label_position_at_rel_time"] is not None + ): + dir_x = _labels[config["label_dir_x_key"]] + dir_y = _labels[config["label_dir_y_key"]] + dir_z = _labels[config["label_dir_z_key"]] + + delta_t = ( + time_offset + config["label_position_at_rel_time"] + ) - _labels["VertexTime"] c = 0.299792458 # m / ns length = c * delta_t - x_at_t = _labels['VertexX'] + length * dir_x - y_at_t = _labels['VertexY'] + length * dir_y - z_at_t = _labels['VertexZ'] + length * dir_z + x_at_t = _labels["VertexX"] + length * dir_x + y_at_t = _labels["VertexY"] + length * dir_y + z_at_t = _labels["VertexZ"] + length * dir_z # add position at relative time to labels - label_names.extend(['rel_pos_x', 'rel_pos_y', 'rel_pos_z']) + label_names.extend(["rel_pos_x", "rel_pos_y", "rel_pos_z"]) labels.extend([x_at_t, y_at_t, z_at_t]) - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names diff --git a/dnn_reco/modules/data/labels/event_selection_labels.py b/dnn_reco/modules/data/labels/event_selection_labels.py index f11c1bd..e2de864 100644 --- a/dnn_reco/modules/data/labels/event_selection_labels.py +++ b/dnn_reco/modules/data/labels/event_selection_labels.py @@ -1,6 +1,7 @@ from __future__ import division, print_function import pandas as pd import numpy as np + """ All label functions must have the following parameters and return values: @@ -64,48 +65,49 @@ def astroness(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ - if 'labels_starting_cascades_lengths' in config: - lengths = [int(l) for l in config['labels_starting_cascades_lengths']] + if "labels_starting_cascades_lengths" in config: + lengths = [int(l) for l in config["labels_starting_cascades_lengths"]] else: lengths = [25, 50, 100] - with pd.HDFStore(input_data, mode='r') as f: - _primary = f['MCPrimary'] + with pd.HDFStore(input_data, mode="r") as f: + _primary = f["MCPrimary"] try: - _weights = f['weights_mese'] + _weights = f["weights_mese"] except KeyError as error: # load dummy weights, this should only happen with Corsika files, # e.g. astroness should be all zero, which is checked by assert - _weights = f['weights'] + _weights = f["weights"] # check if event is a neutrino - abs_pdg_code = np.abs(_primary['pdg_encoding']) + abs_pdg_code = np.abs(_primary["pdg_encoding"]) mask = np.logical_or(abs_pdg_code == 12, abs_pdg_code == 14) is_neutrino = np.logical_or(mask, abs_pdg_code == 16) - if 'weight_E250' in _weights: - astroness = (_weights['weight_E250'] / - (_weights['weight_E250'] + _weights['weight_conv'])) - assert (is_neutrino).all(), 'Expected only Neutrinos!' - elif 'weights_mese_flux' in _weights: - astroness = ( - _weights['weights_mese_flux'] / - (_weights['weights_mese_flux'] + - _weights['weights_honda2006_gaisserH4a_elbert_conv_NNFlux'])) - assert (is_neutrino).all(), 'Expected only Neutrinos!' + if "weight_E250" in _weights: + astroness = _weights["weight_E250"] / ( + _weights["weight_E250"] + _weights["weight_conv"] + ) + assert (is_neutrino).all(), "Expected only Neutrinos!" + elif "weights_mese_flux" in _weights: + astroness = _weights["weights_mese_flux"] / ( + _weights["weights_mese_flux"] + + _weights["weights_honda2006_gaisserH4a_elbert_conv_NNFlux"] + ) + assert (is_neutrino).all(), "Expected only Neutrinos!" else: astroness = np.zeros_like(is_neutrino) - assert np.sum(is_neutrino) == 0, 'Expected no Neutrinos!' + assert np.sum(is_neutrino) == 0, "Expected no Neutrinos!" if label_names is None: - label_names = ['astroness'] + label_names = ["astroness"] - if label_names != ['astroness']: - raise ValueError('Labels {!r} != [astroness]'.format(label_names)) + if label_names != ["astroness"]: + raise ValueError("Labels {!r} != [astroness]".format(label_names)) labels = [astroness] - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names @@ -158,38 +160,39 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ - if 'labels_starting_cascades_lengths' in config: - lengths = [int(l) for l in config['labels_starting_cascades_lengths']] + if "labels_starting_cascades_lengths" in config: + lengths = [int(l) for l in config["labels_starting_cascades_lengths"]] else: lengths = [25, 50, 100] - if 'labels_starting_cascades_distances' in config: + if "labels_starting_cascades_distances" in config: distances = [ - float(l) for l in config['labels_starting_cascades_distances']] + float(l) for l in config["labels_starting_cascades_distances"] + ] else: - distances = [-60., 0., 60, 300., float('inf')] + distances = [-60.0, 0.0, 60, 300.0, float("inf")] # make sure the provdied distances are allowed - allowed_distances = [-60., 0., 60, 150., 300., float('inf')] + allowed_distances = [-60.0, 0.0, 60, 150.0, 300.0, float("inf")] assert np.all([d in allowed_distances for d in distances]), distances - with pd.HDFStore(input_data, mode='r') as f: - if 60. in distances: - _labels_p60 = f['LabelsDeepLearning_p60'] - if -60. in distances: - _labels_m60 = f['LabelsDeepLearning_m60'] - if 150. in distances: - _labels_p150 = f['LabelsDeepLearning_p150'] - _labels = f['LabelsDeepLearning'] - _primary = f['MCPrimary'] + with pd.HDFStore(input_data, mode="r") as f: + if 60.0 in distances: + _labels_p60 = f["LabelsDeepLearning_p60"] + if -60.0 in distances: + _labels_m60 = f["LabelsDeepLearning_m60"] + if 150.0 in distances: + _labels_p150 = f["LabelsDeepLearning_p150"] + _labels = f["LabelsDeepLearning"] + _primary = f["MCPrimary"] # check if event is a neutrino - abs_pdg_code = np.abs(_primary['pdg_encoding']) + abs_pdg_code = np.abs(_primary["pdg_encoding"]) mask = np.logical_or(abs_pdg_code == 12, abs_pdg_code == 14) is_neutrino = np.logical_or(mask, abs_pdg_code == 16) - cascade_lengths = _labels['Length'] + cascade_lengths = _labels["Length"] - label_name_base = 'p_starting_cascade_L{}_D{}' + label_name_base = "p_starting_cascade_L{}_D{}" label_dict = {} label_names_list = [] for l in lengths: @@ -197,38 +200,38 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): mask = np.logical_and(is_neutrino, cascade_lengths <= l) # get hull distance -60m - if -60. in distances: - name = label_name_base.format(l, 'm60') + if -60.0 in distances: + name = label_name_base.format(l, "m60") label_names_list.append(name) - label_dict[name] = np.logical_and(mask, _labels_m60['p_starting']) + label_dict[name] = np.logical_and(mask, _labels_m60["p_starting"]) # get hull distance 0m - if 0. in distances: - name = label_name_base.format(l, '0') + if 0.0 in distances: + name = label_name_base.format(l, "0") label_names_list.append(name) - label_dict[name] = np.logical_and(mask, _labels['p_starting']) + label_dict[name] = np.logical_and(mask, _labels["p_starting"]) # get hull distance 60m - if 60. in distances: - name = label_name_base.format(l, '60') + if 60.0 in distances: + name = label_name_base.format(l, "60") label_names_list.append(name) - label_dict[name] = np.logical_and(mask, _labels_p60['p_starting']) + label_dict[name] = np.logical_and(mask, _labels_p60["p_starting"]) # get hull distance 150m - if 150. in distances: - name = label_name_base.format(l, '150') + if 150.0 in distances: + name = label_name_base.format(l, "150") label_names_list.append(name) - label_dict[name] = np.logical_and(mask, _labels_p150['p_starting']) + label_dict[name] = np.logical_and(mask, _labels_p150["p_starting"]) # get hull distance 300m - if 300. in distances: - name = label_name_base.format(l, '300') + if 300.0 in distances: + name = label_name_base.format(l, "300") label_names_list.append(name) - label_dict[name] = np.logical_and(mask, _labels['p_starting_300m']) + label_dict[name] = np.logical_and(mask, _labels["p_starting_300m"]) # get inf hull distance - if float('inf') in distances: - name = label_name_base.format(l, 'inf') + if float("inf") in distances: + name = label_name_base.format(l, "inf") label_names_list.append(name) label_dict[name] = mask @@ -240,9 +243,9 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): if name in label_dict: labels.append(label_dict[name]) else: - raise KeyError('Label {!r} does not exist!'.format(name)) + raise KeyError("Label {!r} does not exist!".format(name)) - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names @@ -286,44 +289,51 @@ def upgoing_tracks(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ - with pd.HDFStore(input_data, mode='r') as f: - _labels = f[config['data_handler_label_key']] - _primary = f['MCPrimary'] + with pd.HDFStore(input_data, mode="r") as f: + _labels = f[config["data_handler_label_key"]] + _primary = f["MCPrimary"] # check if event is a neutrino - abs_pdg_code = np.abs(_primary['pdg_encoding']) + abs_pdg_code = np.abs(_primary["pdg_encoding"]) mask = np.logical_or(abs_pdg_code == 12, abs_pdg_code == 14) is_neutrino = np.logical_or(mask, abs_pdg_code == 16) - mask = _labels['PrimaryZenith'] > np.deg2rad(85) - mask = np.logical_and(mask, _labels['LengthInDetector'] > 100) - is_numu = np.logical_or(_primary['pdg_encoding'] == -14, - _primary['pdg_encoding'] == 14) + mask = _labels["PrimaryZenith"] > np.deg2rad(85) + mask = np.logical_and(mask, _labels["LengthInDetector"] > 100) + is_numu = np.logical_or( + _primary["pdg_encoding"] == -14, _primary["pdg_encoding"] == 14 + ) is_upgoing_track = np.logical_and(mask, is_numu) - is_starting = _labels['p_starting_300m'] == 1 + is_starting = _labels["p_starting_300m"] == 1 is_neutrino_selection = np.logical_or(is_upgoing_track, is_starting) if label_names is None: label_names = [ - 'is_upgoing_track', 'is_neutrino_selection', - 'is_numu', 'is_neutrino' + "is_upgoing_track", + "is_neutrino_selection", + "is_numu", + "is_neutrino", ] labels = [ - is_upgoing_track, is_neutrino_selection, is_numu, is_neutrino] + is_upgoing_track, + is_neutrino_selection, + is_numu, + is_neutrino, + ] else: labels = [] for name in label_names: - if name == 'is_upgoing_track': + if name == "is_upgoing_track": labels.append(is_upgoing_track) - elif name == 'is_neutrino_selection': + elif name == "is_neutrino_selection": labels.append(is_neutrino_selection) - elif name == 'is_numu': + elif name == "is_numu": labels.append(is_numu) - elif name == 'is_neutrino': + elif name == "is_neutrino": labels.append(is_neutrino) else: - raise KeyError('Label {!r} does not exist!'.format(name)) + raise KeyError("Label {!r} does not exist!".format(name)) - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names diff --git a/dnn_reco/modules/data/labels/muon_scattering_labels.py b/dnn_reco/modules/data/labels/muon_scattering_labels.py index 623363a..27ba7d8 100644 --- a/dnn_reco/modules/data/labels/muon_scattering_labels.py +++ b/dnn_reco/modules/data/labels/muon_scattering_labels.py @@ -3,6 +3,7 @@ import numpy as np from dnn_reco.modules.data.labels.default_labels import simple_label_loader + """ All label functions must have the following parameters and return values: @@ -60,17 +61,19 @@ def muon_scattering(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ labels, label_names_def = simple_label_loader( - input_data, config, label_names=None, *args, **kwargs) + input_data, config, label_names=None, *args, **kwargs + ) - if 'labels_muon_scattering_defs' not in config: - config['labels_muon_scattering_defs'] = {} + if "labels_muon_scattering_defs" not in config: + config["labels_muon_scattering_defs"] = {} label_dict = {} for i, name in enumerate(label_names_def): label_dict[name] = labels[:, i] - for def_name, scattering_def in \ - config['labels_muon_scattering_defs'].items(): + for def_name, scattering_def in config[ + "labels_muon_scattering_defs" + ].items(): passed_cuts = np.ones_like(labels[:, 0], dtype=bool) for key, value in scattering_def.items(): passed_cuts = np.logical_and(passed_cuts, label_dict[key] >= value) @@ -83,6 +86,6 @@ def muon_scattering(input_data, config, label_names=None, *args, **kwargs): for name in label_names: labels.append(label_dict[name]) - labels = np.array(labels, dtype=config['np_float_precision']).T + labels = np.array(labels, dtype=config["np_float_precision"]).T return labels, label_names diff --git a/dnn_reco/modules/data/misc/default_misc.py b/dnn_reco/modules/data/misc/default_misc.py index d058f4f..5646742 100644 --- a/dnn_reco/modules/data/misc/default_misc.py +++ b/dnn_reco/modules/data/misc/default_misc.py @@ -2,6 +2,7 @@ import h5py import pandas as pd import numpy as np + """ All misc functions must have the following parameters and return values: @@ -95,21 +96,21 @@ def general_misc_loader(input_data, config, misc_names=None, *args, **kwargs): # create list of misc names if misc_names is None: misc_names = [] - for key, col_list in config['misc_load_dict'].items(): + for key, col_list in config["misc_load_dict"].items(): if not isinstance(col_list, list): col_list = [col_list] for col in col_list: - misc_names.append(key + '_' + col) + misc_names.append(key + "_" + col) misc_names = sorted(misc_names) if len(misc_names) == 0: misc_values = None else: - with pd.HDFStore(input_data, mode='r') as f: - num_events = len(f[config['data_handler_time_offset_name']]) + with pd.HDFStore(input_data, mode="r") as f: + num_events = len(f[config["data_handler_time_offset_name"]]) misc_dict = {} - for key, col_list in config['misc_load_dict'].items(): + for key, col_list in config["misc_load_dict"].items(): if not isinstance(col_list, list): col_list = [col_list] for col in col_list: @@ -117,31 +118,34 @@ def general_misc_loader(input_data, config, misc_names=None, *args, **kwargs): # special handling for FilterMask keys # FilterMask columns often have the year appended, e.g. _12 # Here, we map a filter_name_12 to filter_name - if key == 'FilterMask' or key == 'QFilterMask': - with h5py.File(input_data, 'r') as f: + if key == "FilterMask" or key == "QFilterMask": + with h5py.File(input_data, "r") as f: _mask = f[key][:] for filter_name in _mask.dtype.fields.keys(): if col in filter_name: - misc_dict[key + '_' + col] = \ - _mask[filter_name][:, 1] + misc_dict[key + "_" + col] = _mask[filter_name][ + :, 1 + ] # Standard hdf5 keys else: try: - with pd.HDFStore(input_data, mode='r') as f: - misc_dict[key + '_' + col] = f[key][col].values + with pd.HDFStore(input_data, mode="r") as f: + misc_dict[key + "_" + col] = f[key][col].values except KeyError as e: # check if a fill value is defined - if key + '_' + col in config['misc_fill_values']: - misc_dict[key + '_' + col] = np.tile( - config['misc_fill_values'][key + '_' + col], - num_events) + if key + "_" + col in config["misc_fill_values"]: + misc_dict[key + "_" + col] = np.tile( + config["misc_fill_values"][key + "_" + col], + num_events, + ) else: raise e misc_values = [misc_dict[k] for k in misc_names] - misc_values = np.array(misc_values, - dtype=config['np_float_precision']).T + misc_values = np.array( + misc_values, dtype=config["np_float_precision"] + ).T return misc_values, misc_names diff --git a/dnn_reco/modules/evaluation/default_evaluation.py b/dnn_reco/modules/evaluation/default_evaluation.py index 99dec17..74c6964 100644 --- a/dnn_reco/modules/evaluation/default_evaluation.py +++ b/dnn_reco/modules/evaluation/default_evaluation.py @@ -38,9 +38,18 @@ """ -def eval_direction(feed_dict_train, feed_dict_val, results_train, results_val, - config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def eval_direction( + feed_dict_train, + feed_dict_val, + results_train, + results_val, + config, + data_handler, + data_transformer, + shared_objects, + *args, + **kwargs +): """Evaluation method to compute angular resolution. Parameters @@ -74,71 +83,105 @@ def eval_direction(feed_dict_train, feed_dict_val, results_train, results_val, Arbitrary keyword arguments. """ - y_true_train = feed_dict_train[shared_objects['y_true']] - y_true_val = feed_dict_val[shared_objects['y_true']] - - y_pred_train = results_train['y_pred'] - y_pred_val = results_val['y_pred'] - y_unc_train = results_train['y_unc'] - y_unc_val = results_val['y_unc'] - - index_azimuth = data_handler.get_label_index(config['label_azimuth_key']) - index_zenith = data_handler.get_label_index(config['label_zenith_key']) - - angle_train = get_angle_deviation(azimuth1=y_true_train[:, index_azimuth], - zenith1=y_true_train[:, index_zenith], - azimuth2=y_pred_train[:, index_azimuth], - zenith2=y_pred_train[:, index_zenith]) - - angle_val = get_angle_deviation(azimuth1=y_true_val[:, index_azimuth], - zenith1=y_true_val[:, index_zenith], - azimuth2=y_pred_val[:, index_azimuth], - zenith2=y_pred_val[:, index_zenith]) - - print('\t[Train] Opening Angle: mean {:3.1f}, median {:3.1f}'.format( - np.mean(np.rad2deg(angle_train)), np.median(np.rad2deg(angle_train)))) - print('\t[Validation] Opening Angle: mean {:3.1f}, median {:3.1f}'.format( - np.mean(np.rad2deg(angle_val)), np.median(np.rad2deg(angle_val)))) - - if 'label_dir_x_key' in config and config['label_dir_x_key'] is not None: - - index_dir_x = data_handler.get_label_index(config['label_dir_x_key']) - index_dir_y = data_handler.get_label_index(config['label_dir_y_key']) - index_dir_z = data_handler.get_label_index(config['label_dir_z_key']) - - vec1_train = np.stack([y_true_train[:, index_dir_x], - y_true_train[:, index_dir_y], - y_true_train[:, index_dir_z], - ], axis=1) - vec2_train = np.stack([y_pred_train[:, index_dir_x], - y_pred_train[:, index_dir_y], - y_pred_train[:, index_dir_z], - ], axis=1) - vec1_val = np.stack([y_true_val[:, index_dir_x], - y_true_val[:, index_dir_y], - y_true_val[:, index_dir_z], - ], axis=1) - vec2_val = np.stack([y_pred_val[:, index_dir_x], - y_pred_val[:, index_dir_y], - y_pred_val[:, index_dir_z], - ], axis=1) + y_true_train = feed_dict_train[shared_objects["y_true"]] + y_true_val = feed_dict_val[shared_objects["y_true"]] + + y_pred_train = results_train["y_pred"] + y_pred_val = results_val["y_pred"] + y_unc_train = results_train["y_unc"] + y_unc_val = results_val["y_unc"] + + index_azimuth = data_handler.get_label_index(config["label_azimuth_key"]) + index_zenith = data_handler.get_label_index(config["label_zenith_key"]) + + angle_train = get_angle_deviation( + azimuth1=y_true_train[:, index_azimuth], + zenith1=y_true_train[:, index_zenith], + azimuth2=y_pred_train[:, index_azimuth], + zenith2=y_pred_train[:, index_zenith], + ) + + angle_val = get_angle_deviation( + azimuth1=y_true_val[:, index_azimuth], + zenith1=y_true_val[:, index_zenith], + azimuth2=y_pred_val[:, index_azimuth], + zenith2=y_pred_val[:, index_zenith], + ) + + print( + "\t[Train] Opening Angle: mean {:3.1f}, median {:3.1f}".format( + np.mean(np.rad2deg(angle_train)), + np.median(np.rad2deg(angle_train)), + ) + ) + print( + "\t[Validation] Opening Angle: mean {:3.1f}, median {:3.1f}".format( + np.mean(np.rad2deg(angle_val)), np.median(np.rad2deg(angle_val)) + ) + ) + + if "label_dir_x_key" in config and config["label_dir_x_key"] is not None: + + index_dir_x = data_handler.get_label_index(config["label_dir_x_key"]) + index_dir_y = data_handler.get_label_index(config["label_dir_y_key"]) + index_dir_z = data_handler.get_label_index(config["label_dir_z_key"]) + + vec1_train = np.stack( + [ + y_true_train[:, index_dir_x], + y_true_train[:, index_dir_y], + y_true_train[:, index_dir_z], + ], + axis=1, + ) + vec2_train = np.stack( + [ + y_pred_train[:, index_dir_x], + y_pred_train[:, index_dir_y], + y_pred_train[:, index_dir_z], + ], + axis=1, + ) + vec1_val = np.stack( + [ + y_true_val[:, index_dir_x], + y_true_val[:, index_dir_y], + y_true_val[:, index_dir_z], + ], + axis=1, + ) + vec2_val = np.stack( + [ + y_pred_val[:, index_dir_x], + y_pred_val[:, index_dir_y], + y_pred_val[:, index_dir_z], + ], + axis=1, + ) angle_dir_train = get_angle(vec1_train, vec2_train) angle_dir_val = get_angle(vec1_val, vec2_val) - print('\t[Train] {}: mean {:3.1f}, median {:3.1f} [{}]'.format( - 'Opening Angle', - np.mean(np.rad2deg(angle_dir_train)), - np.median(np.rad2deg(angle_dir_train)), - 'Over Direction Vector')) - print('\t[Validation] {}: mean {:3.1f}, median {:3.1f} [{}]'.format( - 'Opening Angle', - np.mean(np.rad2deg(angle_dir_val)), - np.median(np.rad2deg(angle_dir_val)), - 'Over Direction Vector')) + print( + "\t[Train] {}: mean {:3.1f}, median {:3.1f} [{}]".format( + "Opening Angle", + np.mean(np.rad2deg(angle_dir_train)), + np.median(np.rad2deg(angle_dir_train)), + "Over Direction Vector", + ) + ) + print( + "\t[Validation] {}: mean {:3.1f}, median {:3.1f} [{}]".format( + "Opening Angle", + np.mean(np.rad2deg(angle_dir_val)), + np.median(np.rad2deg(angle_dir_val)), + "Over Direction Vector", + ) + ) # Test weighted corection: [very much beta version] - def get_weighted_normed_dir_vector(dir_x, dir_y, dir_z, - dir_x_unc, dir_y_unc, dir_z_unc): + def get_weighted_normed_dir_vector( + dir_x, dir_y, dir_z, dir_x_unc, dir_y_unc, dir_z_unc + ): # get direction of normalization scaling d1 = dir_x * dir_x_unc @@ -150,7 +193,7 @@ def get_weighted_normed_dir_vector(dir_x, dir_y, dir_z, d3 /= norm # calculate scaling length - p = d1*dir_x + d2*dir_y + d3*dir_z + p = d1 * dir_x + d2 * dir_y + d3 * dir_z q = dir_x**2 + dir_y**2 + dir_z**2 - 1 L = -p + np.sqrt(p**2 - q) @@ -166,33 +209,45 @@ def get_weighted_normed_dir_vector(dir_x, dir_y, dir_z, test_dir_vector_scaling = False if test_dir_vector_scaling: - vec2_train = np.stack(get_weighted_normed_dir_vector( - dir_x=y_pred_train[:, index_dir_x], - dir_y=y_pred_train[:, index_dir_y], - dir_z=y_pred_train[:, index_dir_z], - dir_x_unc=y_unc_train[:, index_dir_x], - dir_y_unc=y_unc_train[:, index_dir_y], - dir_z_unc=y_unc_train[:, index_dir_z]), - axis=1) - vec2_val = np.stack(get_weighted_normed_dir_vector( - dir_x=y_pred_val[:, index_dir_x], - dir_y=y_pred_val[:, index_dir_y], - dir_z=y_pred_val[:, index_dir_z], - dir_x_unc=y_unc_val[:, index_dir_x], - dir_y_unc=y_unc_val[:, index_dir_y], - dir_z_unc=y_unc_val[:, index_dir_z]), - axis=1) + vec2_train = np.stack( + get_weighted_normed_dir_vector( + dir_x=y_pred_train[:, index_dir_x], + dir_y=y_pred_train[:, index_dir_y], + dir_z=y_pred_train[:, index_dir_z], + dir_x_unc=y_unc_train[:, index_dir_x], + dir_y_unc=y_unc_train[:, index_dir_y], + dir_z_unc=y_unc_train[:, index_dir_z], + ), + axis=1, + ) + vec2_val = np.stack( + get_weighted_normed_dir_vector( + dir_x=y_pred_val[:, index_dir_x], + dir_y=y_pred_val[:, index_dir_y], + dir_z=y_pred_val[:, index_dir_z], + dir_x_unc=y_unc_val[:, index_dir_x], + dir_y_unc=y_unc_val[:, index_dir_y], + dir_z_unc=y_unc_val[:, index_dir_z], + ), + axis=1, + ) angle_dir_train = get_angle(vec1_train, vec2_train) angle_dir_val = get_angle(vec1_val, vec2_val) - print('\t[Train] {}: mean {:3.1f}, median {:3.1f} [{}]'.format( - 'Opening Angle', - np.mean(np.rad2deg(angle_dir_train)), - np.median(np.rad2deg(angle_dir_train)), - 'Weighted dir vector')) - print('\t[Validation] {}: mean {:3.1f}, median {:3.1f} [{}]'.format( - 'Opening Angle', - np.mean(np.rad2deg(angle_dir_val)), - np.median(np.rad2deg(angle_dir_val)), - 'Weighted dir vector')) + print( + "\t[Train] {}: mean {:3.1f}, median {:3.1f} [{}]".format( + "Opening Angle", + np.mean(np.rad2deg(angle_dir_train)), + np.median(np.rad2deg(angle_dir_train)), + "Weighted dir vector", + ) + ) + print( + "\t[Validation] {}: mean {:3.1f}, median {:3.1f} [{}]".format( + "Opening Angle", + np.mean(np.rad2deg(angle_dir_val)), + np.median(np.rad2deg(angle_dir_val)), + "Weighted dir vector", + ) + ) diff --git a/dnn_reco/modules/loss/default_loss.py b/dnn_reco/modules/loss/default_loss.py index 1e7017b..3ed4348 100644 --- a/dnn_reco/modules/loss/default_loss.py +++ b/dnn_reco/modules/loss/default_loss.py @@ -37,8 +37,9 @@ """ -def weighted_mse(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def weighted_mse( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Weighted mean squared error of transformed prediction and true values. The MSE is weighted by the per event uncertainty estimate. @@ -70,20 +71,22 @@ def weighted_mse(config, data_handler, data_transformer, shared_objects, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) - unc_trafo = tf.stop_gradient(shared_objects['y_unc_trafo']) - unc_trafo = tf.clip_by_value(unc_trafo, 1e-3, float('inf')) + unc_trafo = tf.stop_gradient(shared_objects["y_unc_trafo"]) + unc_trafo = tf.clip_by_value(unc_trafo, 1e-3, float("inf")) loss_event = tf.square(y_diff_trafo / unc_trafo) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] - mse_values_trafo = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / \ - tf.reduce_sum(input_tensor=weights, axis=0) + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] + mse_values_trafo = tf.reduce_sum( + input_tensor=loss_event * weights, axis=0 + ) / tf.reduce_sum(input_tensor=weights, axis=0) else: mse_values_trafo = tf.reduce_mean(input_tensor=loss_event, axis=0) @@ -92,8 +95,9 @@ def weighted_mse(config, data_handler, data_transformer, shared_objects, return mse_values_trafo -def mse(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def mse( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Mean squared error of transformed prediction and true values. Parameters @@ -123,21 +127,28 @@ def mse(config, data_handler, data_transformer, shared_objects, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) loss_event = tf.square(y_diff_trafo) - unc_diff = shared_objects['y_unc_trafo'] - \ - tf.stop_gradient(tf.abs(y_diff_trafo)) + unc_diff = shared_objects["y_unc_trafo"] - tf.stop_gradient( + tf.abs(y_diff_trafo) + ) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - mse_values_trafo = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum - mse_unc_values_trafo = tf.reduce_sum(input_tensor=unc_diff**2 * weights, axis=0) / \ - weight_sum + mse_values_trafo = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) + mse_unc_values_trafo = ( + tf.reduce_sum(input_tensor=unc_diff**2 * weights, axis=0) + / weight_sum + ) else: mse_values_trafo = tf.reduce_mean(input_tensor=loss_event, axis=0) mse_unc_values_trafo = tf.reduce_mean(input_tensor=unc_diff**2, axis=0) @@ -147,8 +158,9 @@ def mse(config, data_handler, data_transformer, shared_objects, return mse_values_trafo + mse_unc_values_trafo -def abs(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def abs( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Absolute error of transformed prediction and true values. Parameters @@ -177,32 +189,42 @@ def abs(config, data_handler, data_transformer, shared_objects, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) loss_event = tf.abs(y_diff_trafo) - unc_diff = shared_objects['y_unc_trafo'] - \ - tf.stop_gradient(tf.abs(y_diff_trafo)) + unc_diff = shared_objects["y_unc_trafo"] - tf.stop_gradient( + tf.abs(y_diff_trafo) + ) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - abs_values_trafo = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum - abs_unc_values_trafo = tf.reduce_sum(input_tensor=tf.abs(unc_diff) * weights, axis=0) / \ - weight_sum + abs_values_trafo = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) + abs_unc_values_trafo = ( + tf.reduce_sum(input_tensor=tf.abs(unc_diff) * weights, axis=0) + / weight_sum + ) else: abs_values_trafo = tf.reduce_mean(input_tensor=loss_event, axis=0) - abs_unc_values_trafo = tf.reduce_mean(input_tensor=tf.abs(unc_diff), axis=0) + abs_unc_values_trafo = tf.reduce_mean( + input_tensor=tf.abs(unc_diff), axis=0 + ) loss_utils.add_logging_info(data_handler, shared_objects) return abs_values_trafo + abs_unc_values_trafo -def gaussian_likelihood(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def gaussian_likelihood( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Gaussian likelhood of transformed prediction and true values. Parameters @@ -231,23 +253,27 @@ def gaussian_likelihood(config, data_handler, data_transformer, shared_objects, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) # small float to prevent division by zero eps = 1e-6 # uncertainty estimate on prediction - unc = tf.clip_by_value(shared_objects['y_unc_trafo'], eps, float('inf')) + unc = tf.clip_by_value(shared_objects["y_unc_trafo"], eps, float("inf")) - loss_event = 2*tf.math.log(unc) + (y_diff_trafo / unc)**2 + loss_event = 2 * tf.math.log(unc) + (y_diff_trafo / unc) ** 2 - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - loss = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum + loss = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) else: loss = tf.reduce_mean(input_tensor=loss_event, axis=0) @@ -256,8 +282,9 @@ def gaussian_likelihood(config, data_handler, data_transformer, shared_objects, return loss -def pull_distribution_scale(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def pull_distribution_scale( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """This loss penalized the standard deviation of the pull distribution. This is meant to run for a few steps with very high batch size at the very @@ -290,36 +317,42 @@ def pull_distribution_scale(config, data_handler, data_transformer, Shape: label_shape (same shape as labels) """ - if 'event_weights' in shared_objects: - misc.print_warning("Event weights will be ignored for loss function " - "'pull_distribution_scale'") - - y_diff_trafo = tf.stop_gradient(loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects)) + if "event_weights" in shared_objects: + misc.print_warning( + "Event weights will be ignored for loss function " + "'pull_distribution_scale'" + ) + + y_diff_trafo = tf.stop_gradient( + loss_utils.get_y_diff_trafo( + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) + ) # small float to prevent division by zero eps = 1e-6 # uncertainty estimate on prediction - unc = tf.clip_by_value(shared_objects['y_unc_trafo'], eps, float('inf')) + unc = tf.clip_by_value(shared_objects["y_unc_trafo"], eps, float("inf")) pull = y_diff_trafo / unc # get variance mean, var = tf.nn.moments(x=pull, axes=[0]) - loss = (var - 1.)**2 + loss = (var - 1.0) ** 2 loss_utils.add_logging_info(data_handler, shared_objects) return loss -def mse_and_cross_entropy(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def mse_and_cross_entropy( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Mean squared error of transformed prediction and true values. Cross entropy loss will be applied to labels for which logit tensors are defined in shared_objects[logit_tensors]. These logit tensors must be @@ -354,37 +387,44 @@ def mse_and_cross_entropy(config, data_handler, data_transformer, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) loss_event = tf.square(y_diff_trafo) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - mse_values_trafo = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum + mse_values_trafo = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) else: mse_values_trafo = tf.reduce_mean(input_tensor=loss_event, axis=0) - logit_tensors = shared_objects['logit_tensors'] + logit_tensors = shared_objects["logit_tensors"] label_loss = [] for i, name in enumerate(data_handler.label_names): # sanity check for correct ordering of labels index = data_handler.get_label_index(name) - assert i == index, '{!r} != {!r}'.format(i, index) + assert i == index, "{!r} != {!r}".format(i, index) # apply cross entropy if logits are provided if name in logit_tensors: loss_i = tf.nn.sigmoid_cross_entropy_with_logits( - labels=shared_objects['y_true'][:, i], - logits=logit_tensors[name]) - if 'event_weights' in shared_objects: + labels=shared_objects["y_true"][:, i], + logits=logit_tensors[name], + ) + if "event_weights" in shared_objects: label_loss.append( - tf.reduce_sum(input_tensor=loss_i * weights[:, 0], axis=0) / weight_sum[0]) + tf.reduce_sum(input_tensor=loss_i * weights[:, 0], axis=0) + / weight_sum[0] + ) else: label_loss.append(tf.reduce_mean(input_tensor=loss_i)) else: @@ -397,8 +437,9 @@ def mse_and_cross_entropy(config, data_handler, data_transformer, return label_loss -def mse_and_weighted_cross_entropy(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def mse_and_weighted_cross_entropy( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Mean squared error of transformed prediction and true values. Weighted cross entroy loss will be applied to labels for which logit tensors are defined in shared_objects[logit_tensors]. @@ -441,46 +482,50 @@ def mse_and_weighted_cross_entropy(config, data_handler, data_transformer, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) loss_event = tf.square(y_diff_trafo) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - mse_values_trafo = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum + mse_values_trafo = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) else: mse_values_trafo = tf.reduce_mean(input_tensor=loss_event, axis=0) weights = tf.expand_dims(tf.ones_like(loss_event[:, 0]), axis=-1) - logit_tensors = shared_objects['logit_tensors'] + logit_tensors = shared_objects["logit_tensors"] label_loss = [] for i, name in enumerate(data_handler.label_names): # sanity check for correct ordering of labels index = data_handler.get_label_index(name) - assert i == index, '{!r} != {!r}'.format(i, index) + assert i == index, "{!r} != {!r}".format(i, index) # calculate average precision if logits are provided if name in logit_tensors: - labels_i = shared_objects['y_true'][:, i] + labels_i = shared_objects["y_true"][:, i] predictions_i = logit_tensors[name] loss_i = tf.nn.sigmoid_cross_entropy_with_logits( - labels=labels_i, - logits=predictions_i) + labels=labels_i, logits=predictions_i + ) # ------------------------------ # compute weights for each event # ------------------------------ # Here we assume that background class has value 0 and signal 1 - signal_weights = tf.where(labels_i > 0.5, - weights[:, 0], - tf.zeros_like(weights[:, 0])) + signal_weights = tf.where( + labels_i > 0.5, weights[:, 0], tf.zeros_like(weights[:, 0]) + ) # sort events according to the classification score sorted_indices = tf.argsort(predictions_i) @@ -495,8 +540,9 @@ def mse_and_weighted_cross_entropy(config, data_handler, data_transformer, eps = 1e-6 label_loss.append( - tf.reduce_sum(input_tensor=loss_i_sorted * loss_weight, axis=0) / - (tf.reduce_sum(input_tensor=loss_weight) + eps)) + tf.reduce_sum(input_tensor=loss_i_sorted * loss_weight, axis=0) + / (tf.reduce_sum(input_tensor=loss_weight) + eps) + ) # ------------------------------ else: label_loss.append(mse_values_trafo[i]) @@ -508,8 +554,9 @@ def mse_and_weighted_cross_entropy(config, data_handler, data_transformer, return label_loss -def tukey(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def tukey( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Tukey loss of transformed prediction and true values. A robust loss measure that is equivalent to MSE for small residuals, but has constant loss for very large residuals. This reduces the effect of @@ -543,25 +590,31 @@ def tukey(config, data_handler, data_transformer, shared_objects, """ y_diff_trafo = loss_utils.get_y_diff_trafo( - config=config, - data_handler=data_handler, - data_transformer=data_transformer, - shared_objects=shared_objects) + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + shared_objects=shared_objects, + ) - y_diff_trafo_scaled = \ - y_diff_trafo / (1.4826 * shared_objects['median_abs_dev']) + y_diff_trafo_scaled = y_diff_trafo / ( + 1.4826 * shared_objects["median_abs_dev"] + ) c = 4.6851 loss_event = tf.where( tf.less(tf.abs(y_diff_trafo_scaled), c), - (c**2/6) * (1 - (1 - (y_diff_trafo_scaled/c)**2)**3), - tf.zeros_like(y_diff_trafo_scaled) + (c**2/6), - name='tukey_loss') + (c**2 / 6) * (1 - (1 - (y_diff_trafo_scaled / c) ** 2) ** 3), + tf.zeros_like(y_diff_trafo_scaled) + (c**2 / 6), + name="tukey_loss", + ) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - tukey_loss = tf.reduce_sum(input_tensor=loss_event * weights, axis=0) / weight_sum + tukey_loss = ( + tf.reduce_sum(input_tensor=loss_event * weights, axis=0) + / weight_sum + ) else: tukey_loss = tf.reduce_mean(input_tensor=loss_event, axis=0) @@ -570,8 +623,9 @@ def tukey(config, data_handler, data_transformer, shared_objects, return tukey_loss -def opening_angle(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def opening_angle( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Error of opening angle between true and predicted angle. The MSE of the opening angle will be predicted as uncertainty. @@ -602,34 +656,41 @@ def opening_angle(config, data_handler, data_transformer, shared_objects, Shape: label_shape (same shape as labels) """ - index_azimuth = data_handler.get_label_index(config['label_azimuth_key']) - index_zenith = data_handler.get_label_index(config['label_zenith_key']) + index_azimuth = data_handler.get_label_index(config["label_azimuth_key"]) + index_zenith = data_handler.get_label_index(config["label_zenith_key"]) - azimuth_true = shared_objects['y_true'][:, index_azimuth] - zenith_true = shared_objects['y_true'][:, index_zenith] + azimuth_true = shared_objects["y_true"][:, index_azimuth] + zenith_true = shared_objects["y_true"][:, index_zenith] - azimuth_pred = shared_objects['y_pred'][:, index_azimuth] - zenith_pred = shared_objects['y_pred'][:, index_zenith] + azimuth_pred = shared_objects["y_pred"][:, index_azimuth] + zenith_pred = shared_objects["y_pred"][:, index_zenith] - azimuth_unc = shared_objects['y_unc'][:, index_azimuth] - zenith_unc = shared_objects['y_unc'][:, index_zenith] + azimuth_unc = shared_objects["y_unc"][:, index_azimuth] + zenith_unc = shared_objects["y_unc"][:, index_zenith] - angle = angle_utils.tf_get_angle_deviation(azimuth1=azimuth_true, - zenith1=zenith_true, - azimuth2=azimuth_pred, - zenith2=zenith_pred) + angle = angle_utils.tf_get_angle_deviation( + azimuth1=azimuth_true, + zenith1=zenith_true, + azimuth2=azimuth_pred, + zenith2=zenith_pred, + ) # use zenith true here, even though it will hae to be predicted value - sigma = tf.sqrt(zenith_unc**2 + azimuth_unc**2 * tf.sin(zenith_true)**2) + sigma = tf.sqrt(zenith_unc**2 + azimuth_unc**2 * tf.sin(zenith_true) ** 2) sigma /= np.sqrt(2) unc_diff = tf.stop_gradient(angle) - sigma - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - loss_angle = tf.reduce_sum(input_tensor=angle * weights, axis=0) / weight_sum - loss_unc = tf.reduce_sum(input_tensor=unc_diff**2 * weights, axis=0) / weight_sum + loss_angle = ( + tf.reduce_sum(input_tensor=angle * weights, axis=0) / weight_sum + ) + loss_unc = ( + tf.reduce_sum(input_tensor=unc_diff**2 * weights, axis=0) + / weight_sum + ) else: loss_angle = tf.reduce_mean(input_tensor=angle, axis=0) loss_unc = tf.reduce_mean(input_tensor=unc_diff**2, axis=0) @@ -639,7 +700,7 @@ def opening_angle(config, data_handler, data_transformer, shared_objects, loss_all_list = [] for label in data_handler.label_names: - if label in [config['label_azimuth_key'], config['label_zenith_key']]: + if label in [config["label_azimuth_key"], config["label_zenith_key"]]: loss_all_list.append(loss) else: loss_all_list.append(zeros) @@ -651,8 +712,9 @@ def opening_angle(config, data_handler, data_transformer, shared_objects, return loss_all -def opening_angle_raleigh(config, data_handler, data_transformer, - shared_objects, *args, **kwargs): +def opening_angle_raleigh( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """Raleigh loss of opening angle between true and predicted angle. This loss only applies to label_azimuth_key and label_zenith_key! @@ -683,38 +745,46 @@ def opening_angle_raleigh(config, data_handler, data_transformer, """ - index_azimuth = data_handler.get_label_index(config['label_azimuth_key']) - index_zenith = data_handler.get_label_index(config['label_zenith_key']) + index_azimuth = data_handler.get_label_index(config["label_azimuth_key"]) + index_zenith = data_handler.get_label_index(config["label_zenith_key"]) - azimuth_true = shared_objects['y_true'][:, index_azimuth] - zenith_true = shared_objects['y_true'][:, index_zenith] + azimuth_true = shared_objects["y_true"][:, index_azimuth] + zenith_true = shared_objects["y_true"][:, index_zenith] - azimuth_pred = shared_objects['y_pred'][:, index_azimuth] - zenith_pred = shared_objects['y_pred'][:, index_zenith] + azimuth_pred = shared_objects["y_pred"][:, index_azimuth] + zenith_pred = shared_objects["y_pred"][:, index_zenith] - azimuth_unc = shared_objects['y_unc'][:, index_azimuth] - zenith_unc = shared_objects['y_unc'][:, index_zenith] + azimuth_unc = shared_objects["y_unc"][:, index_azimuth] + zenith_unc = shared_objects["y_unc"][:, index_zenith] - angle = angle_utils.tf_get_angle_deviation(azimuth1=azimuth_true, - zenith1=zenith_true, - azimuth2=azimuth_pred, - zenith2=zenith_pred) + angle = angle_utils.tf_get_angle_deviation( + azimuth1=azimuth_true, + zenith1=zenith_true, + azimuth2=azimuth_pred, + zenith2=zenith_pred, + ) # use zenith true here, even though it will hae to be predicted value - sigma = tf.sqrt(zenith_unc**2 + azimuth_unc**2 * tf.sin(zenith_true)**2) + sigma = tf.sqrt(zenith_unc**2 + azimuth_unc**2 * tf.sin(zenith_true) ** 2) sigma /= np.sqrt(2) # small float to prevent division by zero eps = 1e-6 - sigma = tf.clip_by_value(sigma, eps, float('inf')) + sigma = tf.clip_by_value(sigma, eps, float("inf")) - raleigh = (angle / sigma)**2 + 4*tf.math.log(sigma) - 2*tf.math.log(angle) + raleigh = ( + (angle / sigma) ** 2 + 4 * tf.math.log(sigma) - 2 * tf.math.log(angle) + ) - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] weight_sum = tf.reduce_sum(input_tensor=weights, axis=0) - loss_angle = tf.reduce_sum(input_tensor=angle * weights, axis=0) / weight_sum - raleigh_loss = tf.reduce_sum(input_tensor=raleigh * weights, axis=0) / weight_sum + loss_angle = ( + tf.reduce_sum(input_tensor=angle * weights, axis=0) / weight_sum + ) + raleigh_loss = ( + tf.reduce_sum(input_tensor=raleigh * weights, axis=0) / weight_sum + ) else: loss_angle = tf.reduce_mean(input_tensor=angle, axis=0) raleigh_loss = tf.reduce_mean(input_tensor=raleigh, axis=0) @@ -724,7 +794,7 @@ def opening_angle_raleigh(config, data_handler, data_transformer, loss_all_list = [] for label in data_handler.label_names: - if label in [config['label_azimuth_key'], config['label_zenith_key']]: + if label in [config["label_azimuth_key"], config["label_zenith_key"]]: loss_all_list.append(loss) else: loss_all_list.append(zeros) diff --git a/dnn_reco/modules/loss/track_loss.py b/dnn_reco/modules/loss/track_loss.py index 57ea013..4583bee 100644 --- a/dnn_reco/modules/loss/track_loss.py +++ b/dnn_reco/modules/loss/track_loss.py @@ -36,8 +36,9 @@ """ -def track_pos_mse(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def track_pos_mse( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """The MSE of the 4-vector distance of the predicted vertex (x, y, z, t) and the infinite track given by the true direction. @@ -70,37 +71,41 @@ def track_pos_mse(config, data_handler, data_transformer, shared_objects, Shape: label_shape (same shape as labels) """ - index_dir_x = data_handler.get_label_index(config['label_dir_x_key']) - index_dir_y = data_handler.get_label_index(config['label_dir_y_key']) - index_dir_z = data_handler.get_label_index(config['label_dir_z_key']) + index_dir_x = data_handler.get_label_index(config["label_dir_x_key"]) + index_dir_y = data_handler.get_label_index(config["label_dir_y_key"]) + index_dir_z = data_handler.get_label_index(config["label_dir_z_key"]) index_pos_x = data_handler.get_label_index( - config['label_particle_keys']['pos_x']) + config["label_particle_keys"]["pos_x"] + ) index_pos_y = data_handler.get_label_index( - config['label_particle_keys']['pos_y']) + config["label_particle_keys"]["pos_y"] + ) index_pos_z = data_handler.get_label_index( - config['label_particle_keys']['pos_z']) + config["label_particle_keys"]["pos_z"] + ) index_time = data_handler.get_label_index( - config['label_particle_keys']['time']) + config["label_particle_keys"]["time"] + ) - dir_x_true = shared_objects['y_true'][:, index_dir_x] - dir_y_true = shared_objects['y_true'][:, index_dir_y] - dir_z_true = shared_objects['y_true'][:, index_dir_z] + dir_x_true = shared_objects["y_true"][:, index_dir_x] + dir_y_true = shared_objects["y_true"][:, index_dir_y] + dir_z_true = shared_objects["y_true"][:, index_dir_z] - x_true = shared_objects['y_true'][:, index_pos_x] - y_true = shared_objects['y_true'][:, index_pos_y] - z_true = shared_objects['y_true'][:, index_pos_z] - time_true = shared_objects['y_true'][:, index_time] + x_true = shared_objects["y_true"][:, index_pos_x] + y_true = shared_objects["y_true"][:, index_pos_y] + z_true = shared_objects["y_true"][:, index_pos_z] + time_true = shared_objects["y_true"][:, index_time] - x_pred = shared_objects['y_pred'][:, index_pos_x] - y_pred = shared_objects['y_pred'][:, index_pos_y] - z_pred = shared_objects['y_pred'][:, index_pos_z] - time_pred = shared_objects['y_pred'][:, index_time] + x_pred = shared_objects["y_pred"][:, index_pos_x] + y_pred = shared_objects["y_pred"][:, index_pos_y] + z_pred = shared_objects["y_pred"][:, index_pos_z] + time_pred = shared_objects["y_pred"][:, index_time] - x_unc = shared_objects['y_unc'][:, index_pos_x] - y_unc = shared_objects['y_unc'][:, index_pos_y] - z_unc = shared_objects['y_unc'][:, index_pos_z] - time_unc = shared_objects['y_unc'][:, index_time] + x_unc = shared_objects["y_unc"][:, index_pos_x] + y_unc = shared_objects["y_unc"][:, index_pos_y] + z_unc = shared_objects["y_unc"][:, index_pos_z] + time_unc = shared_objects["y_unc"][:, index_time] # x: predicted point, p: true point on track, d: true unit direction vector # calculate a = x - p @@ -109,12 +114,12 @@ def track_pos_mse(config, data_handler, data_transformer, shared_objects, a3 = z_pred - z_true # scalar product s = a*d, s is distance to closest point on infinite track - s = a1*dir_x_true + a2*dir_y_true + a3*dir_z_true + s = a1 * dir_x_true + a2 * dir_y_true + a3 * dir_z_true # caculate r = s*d -a = (p + s*d) - x - r1 = s*dir_x_true - a1 - r2 = s*dir_y_true - a2 - r3 = s*dir_z_true - a3 + r1 = s * dir_x_true - a1 + r2 = s * dir_y_true - a2 + r3 = s * dir_z_true - a3 # calculate time diff [meter] at closest approach point on infinite track c = 0.299792458 # in m /ns @@ -125,13 +130,33 @@ def track_pos_mse(config, data_handler, data_transformer, shared_objects, unc_diff_z = tf.stop_gradient(r3) - z_unc unc_diff_t = tf.stop_gradient(rt) - time_unc - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] w_sum = tf.reduce_sum(input_tensor=weights, axis=0) - loss_x = tf.reduce_sum(input_tensor=(r1**2 + unc_diff_x**2) * weights, axis=0) / w_sum - loss_y = tf.reduce_sum(input_tensor=(r2**2 + unc_diff_y**2) * weights, axis=0) / w_sum - loss_z = tf.reduce_sum(input_tensor=(r3**2 + unc_diff_z**2) * weights, axis=0) / w_sum - loss_t = tf.reduce_sum(input_tensor=(rt**2 + unc_diff_t**2) * weights, axis=0) / w_sum + loss_x = ( + tf.reduce_sum( + input_tensor=(r1**2 + unc_diff_x**2) * weights, axis=0 + ) + / w_sum + ) + loss_y = ( + tf.reduce_sum( + input_tensor=(r2**2 + unc_diff_y**2) * weights, axis=0 + ) + / w_sum + ) + loss_z = ( + tf.reduce_sum( + input_tensor=(r3**2 + unc_diff_z**2) * weights, axis=0 + ) + / w_sum + ) + loss_t = ( + tf.reduce_sum( + input_tensor=(rt**2 + unc_diff_t**2) * weights, axis=0 + ) + / w_sum + ) else: loss_x = tf.reduce_mean(input_tensor=r1**2 + unc_diff_x**2, axis=0) loss_y = tf.reduce_mean(input_tensor=r2**2 + unc_diff_y**2, axis=0) @@ -143,16 +168,16 @@ def track_pos_mse(config, data_handler, data_transformer, shared_objects, loss_all_list = [] for label in data_handler.label_names: - if label == config['label_particle_keys']['pos_x']: + if label == config["label_particle_keys"]["pos_x"]: loss_all_list.append(loss_x) - elif label == config['label_particle_keys']['pos_y']: + elif label == config["label_particle_keys"]["pos_y"]: loss_all_list.append(loss_y) - elif label == config['label_particle_keys']['pos_z']: + elif label == config["label_particle_keys"]["pos_z"]: loss_all_list.append(loss_z) - elif label == config['label_particle_keys']['time']: + elif label == config["label_particle_keys"]["time"]: loss_all_list.append(loss_t) else: @@ -165,8 +190,9 @@ def track_pos_mse(config, data_handler, data_transformer, shared_objects, return loss_all -def track_pos_gaussian(config, data_handler, data_transformer, shared_objects, - *args, **kwargs): +def track_pos_gaussian( + config, data_handler, data_transformer, shared_objects, *args, **kwargs +): """The Gaussian Likelihood loss of the 4-vector distance of the predicted vertex (x, y, z, t) and the infinite track given by the true direction. @@ -199,37 +225,41 @@ def track_pos_gaussian(config, data_handler, data_transformer, shared_objects, Shape: label_shape (same shape as labels) """ - index_dir_x = data_handler.get_label_index(config['label_dir_x_key']) - index_dir_y = data_handler.get_label_index(config['label_dir_y_key']) - index_dir_z = data_handler.get_label_index(config['label_dir_z_key']) + index_dir_x = data_handler.get_label_index(config["label_dir_x_key"]) + index_dir_y = data_handler.get_label_index(config["label_dir_y_key"]) + index_dir_z = data_handler.get_label_index(config["label_dir_z_key"]) index_pos_x = data_handler.get_label_index( - config['label_particle_keys']['pos_x']) + config["label_particle_keys"]["pos_x"] + ) index_pos_y = data_handler.get_label_index( - config['label_particle_keys']['pos_y']) + config["label_particle_keys"]["pos_y"] + ) index_pos_z = data_handler.get_label_index( - config['label_particle_keys']['pos_z']) + config["label_particle_keys"]["pos_z"] + ) index_time = data_handler.get_label_index( - config['label_particle_keys']['time']) + config["label_particle_keys"]["time"] + ) - dir_x_true = shared_objects['y_true'][:, index_dir_x] - dir_y_true = shared_objects['y_true'][:, index_dir_y] - dir_z_true = shared_objects['y_true'][:, index_dir_z] + dir_x_true = shared_objects["y_true"][:, index_dir_x] + dir_y_true = shared_objects["y_true"][:, index_dir_y] + dir_z_true = shared_objects["y_true"][:, index_dir_z] - x_true = shared_objects['y_true'][:, index_pos_x] - y_true = shared_objects['y_true'][:, index_pos_y] - z_true = shared_objects['y_true'][:, index_pos_z] - time_true = shared_objects['y_true'][:, index_time] + x_true = shared_objects["y_true"][:, index_pos_x] + y_true = shared_objects["y_true"][:, index_pos_y] + z_true = shared_objects["y_true"][:, index_pos_z] + time_true = shared_objects["y_true"][:, index_time] - x_pred = shared_objects['y_pred'][:, index_pos_x] - y_pred = shared_objects['y_pred'][:, index_pos_y] - z_pred = shared_objects['y_pred'][:, index_pos_z] - time_pred = shared_objects['y_pred'][:, index_time] + x_pred = shared_objects["y_pred"][:, index_pos_x] + y_pred = shared_objects["y_pred"][:, index_pos_y] + z_pred = shared_objects["y_pred"][:, index_pos_z] + time_pred = shared_objects["y_pred"][:, index_time] - x_unc = shared_objects['y_unc'][:, index_pos_x] - y_unc = shared_objects['y_unc'][:, index_pos_y] - z_unc = shared_objects['y_unc'][:, index_pos_z] - time_unc = shared_objects['y_unc'][:, index_time] + x_unc = shared_objects["y_unc"][:, index_pos_x] + y_unc = shared_objects["y_unc"][:, index_pos_y] + z_unc = shared_objects["y_unc"][:, index_pos_z] + time_unc = shared_objects["y_unc"][:, index_time] # x: predicted point, p: true point on track, d: true unit direction vector # calculate a = x - p @@ -238,24 +268,24 @@ def track_pos_gaussian(config, data_handler, data_transformer, shared_objects, a3 = z_pred - z_true # scalar product s = a*d, s is distance to closest point on infinite track - s = a1*dir_x_true + a2*dir_y_true + a3*dir_z_true + s = a1 * dir_x_true + a2 * dir_y_true + a3 * dir_z_true # caculate r = s*d -a = (p + s*d) - x - r1 = s*dir_x_true - a1 - r2 = s*dir_y_true - a2 - r3 = s*dir_z_true - a3 + r1 = s * dir_x_true - a1 + r2 = s * dir_y_true - a2 + r3 = s * dir_z_true - a3 # calculate time diff [meter] at closest approach point on infinite track c = 0.299792458 # in m /ns rt = (time_true + (s / c) - time_pred) * c - gl_x = 2*tf.math.log(x_unc) + (r1 / x_unc)**2 - gl_y = 2*tf.math.log(y_unc) + (r2 / y_unc)**2 - gl_z = 2*tf.math.log(z_unc) + (r3 / z_unc)**2 - gl_t = 2*tf.math.log(time_unc) + (rt / time_unc)**2 + gl_x = 2 * tf.math.log(x_unc) + (r1 / x_unc) ** 2 + gl_y = 2 * tf.math.log(y_unc) + (r2 / y_unc) ** 2 + gl_z = 2 * tf.math.log(z_unc) + (r3 / z_unc) ** 2 + gl_t = 2 * tf.math.log(time_unc) + (rt / time_unc) ** 2 - if 'event_weights' in shared_objects: - weights = shared_objects['event_weights'] + if "event_weights" in shared_objects: + weights = shared_objects["event_weights"] w_sum = tf.reduce_sum(input_tensor=weights, axis=0) loss_x = tf.reduce_sum(input_tensor=gl_x * weights, axis=0) / w_sum loss_y = tf.reduce_sum(input_tensor=gl_y * weights, axis=0) / w_sum @@ -272,16 +302,16 @@ def track_pos_gaussian(config, data_handler, data_transformer, shared_objects, loss_all_list = [] for label in data_handler.label_names: - if label == config['label_particle_keys']['pos_x']: + if label == config["label_particle_keys"]["pos_x"]: loss_all_list.append(loss_x) - elif label == config['label_particle_keys']['pos_y']: + elif label == config["label_particle_keys"]["pos_y"]: loss_all_list.append(loss_y) - elif label == config['label_particle_keys']['pos_z']: + elif label == config["label_particle_keys"]["pos_z"]: loss_all_list.append(loss_z) - elif label == config['label_particle_keys']['time']: + elif label == config["label_particle_keys"]["time"]: loss_all_list.append(loss_t) else: diff --git a/dnn_reco/modules/loss/utils/loss_utils.py b/dnn_reco/modules/loss/utils/loss_utils.py index d217995..3422c38 100644 --- a/dnn_reco/modules/loss/utils/loss_utils.py +++ b/dnn_reco/modules/loss/utils/loss_utils.py @@ -25,29 +25,35 @@ def add_logging_info(data_handler, shared_objects): """ # check if 'add_logging_info' has already been called - if 'rmse_values_trafo' not in shared_objects: + if "rmse_values_trafo" not in shared_objects: # calculate RMSE of untransformed values - y_diff = shared_objects['y_pred'] - shared_objects['y_true'] + y_diff = shared_objects["y_pred"] - shared_objects["y_true"] mse_values = tf.reduce_mean(input_tensor=tf.square(y_diff), axis=0) rmse_values = tf.sqrt(mse_values) # calcuate RMSE of transformed values - y_diff_trafo = (shared_objects['y_pred_trafo'] - - shared_objects['y_true_trafo']) - mse_values_trafo = tf.reduce_mean(input_tensor=tf.square(y_diff_trafo), axis=0) + y_diff_trafo = ( + shared_objects["y_pred_trafo"] - shared_objects["y_true_trafo"] + ) + mse_values_trafo = tf.reduce_mean( + input_tensor=tf.square(y_diff_trafo), axis=0 + ) rmse_values_trafo = tf.sqrt(mse_values_trafo) - shared_objects['rmse_values_trafo'] = rmse_values_trafo + shared_objects["rmse_values_trafo"] = rmse_values_trafo # add the RMSE of each label as a tf.summary.scalar for i, name in enumerate(data_handler.label_names): - tf.compat.v1.summary.scalar('RMSE_' + name, rmse_values[i]) + tf.compat.v1.summary.scalar("RMSE_" + name, rmse_values[i]) - tf.compat.v1.summary.scalar('Benchmark', tf.reduce_sum(input_tensor=rmse_values_trafo, axis=0)) + tf.compat.v1.summary.scalar( + "Benchmark", tf.reduce_sum(input_tensor=rmse_values_trafo, axis=0) + ) -def correct_azimuth_residual(y_diff_trafo, config, data_handler, - data_transformer, name_pattern): +def correct_azimuth_residual( + y_diff_trafo, config, data_handler, data_transformer, name_pattern +): """Correct azimuth residuals for two pi periodicity. Parameters @@ -72,32 +78,46 @@ def correct_azimuth_residual(y_diff_trafo, config, data_handler, Same shape and type as y_diff_trafo """ misc.print_warning( - 'Correcting azimuth residuals for name pattern: {!r}'.format( - name_pattern)) + "Correcting azimuth residuals for name pattern: {!r}".format( + name_pattern + ) + ) # Assumes labels to be a vector, e.g. 1-dimensional assert len(data_handler.label_shape) == 1 - pi = np.ones([1] + data_handler.label_shape, - dtype=config['np_float_precision']) * np.pi - pi_trafo = tf.squeeze(data_transformer.transform( - pi, data_type='label', bias_correction=False)) - two_pi_trafo = tf.squeeze(data_transformer.transform( - 2*pi, data_type='label', bias_correction=False)) + pi = ( + np.ones( + [1] + data_handler.label_shape, dtype=config["np_float_precision"] + ) + * np.pi + ) + pi_trafo = tf.squeeze( + data_transformer.transform( + pi, data_type="label", bias_correction=False + ) + ) + two_pi_trafo = tf.squeeze( + data_transformer.transform( + 2 * pi, data_type="label", bias_correction=False + ) + ) # Get correct y_diff_trafo that accounts for 2pi periodicity y_diff_list = tf.unstack(y_diff_trafo, axis=-1) for i, name in enumerate(data_handler.label_names): if name_pattern in name: # sanity check: this correction does not work if log is applied - assert bool(data_transformer.trafo_model['log_label_bins'][i]) \ + assert ( + bool(data_transformer.trafo_model["log_label_bins"][i]) is False + ) # Found an azimuth label: correct for 2 pi periodicity abs_diff = tf.abs(y_diff_list[i]) - y_diff_list[i] = tf.where(abs_diff < pi_trafo[i], - abs_diff, - two_pi_trafo[i] - abs_diff) + y_diff_list[i] = tf.where( + abs_diff < pi_trafo[i], abs_diff, two_pi_trafo[i] - abs_diff + ) return tf.stack(y_diff_list, axis=-1) @@ -125,15 +145,17 @@ def get_y_diff_trafo(config, data_handler, data_transformer, shared_objects): A tensorflow tensor containing the loss for each label. Shape: label_shape (same shape as labels) """ - y_diff_trafo = (shared_objects['y_pred_trafo'] - - shared_objects['y_true_trafo']) + y_diff_trafo = ( + shared_objects["y_pred_trafo"] - shared_objects["y_true_trafo"] + ) # correct azimuth residual for 2pi periodicity - if config['label_azimuth_key']: + if config["label_azimuth_key"]: y_diff_trafo = correct_azimuth_residual( - config=config, - y_diff_trafo=y_diff_trafo, - data_handler=data_handler, - data_transformer=data_transformer, - name_pattern=config['label_azimuth_key']) + config=config, + y_diff_trafo=y_diff_trafo, + data_handler=data_handler, + data_transformer=data_transformer, + name_pattern=config["label_azimuth_key"], + ) return y_diff_trafo diff --git a/dnn_reco/modules/models/general_IC86_models.py b/dnn_reco/modules/models/general_IC86_models.py index ab3b903..48f12a7 100644 --- a/dnn_reco/modules/models/general_IC86_models.py +++ b/dnn_reco/modules/models/general_IC86_models.py @@ -47,8 +47,15 @@ """ -def general_model_IC86(is_training, config, data_handler, - data_transformer, shared_objects, *args, **kwargs): +def general_model_IC86( + is_training, + config, + data_handler, + data_transformer, + shared_objects, + *args, + **kwargs +): """A general NN model for the IceCube IC86 configuration. Very similar to general_model_IC86_opt4, but uncertainty fc layers get @@ -85,67 +92,76 @@ def general_model_IC86(is_training, config, data_handler, list of tf.Tensor The trainable parameters of the uncertainty sub network. """ - keep_prob_list = shared_objects['keep_prob_list'] + keep_prob_list = shared_objects["keep_prob_list"] num_labels = data_handler.label_shape[-1] - with tf.compat.v1.variable_scope('model_pred'): + with tf.compat.v1.variable_scope("model_pred"): # apply DOM dropout, split and reshape DeepCore input X_IC78, X_DeepCore_upper, X_DeepCore_lower = preprocess_icecube_data( - is_training, shared_objects) + is_training, shared_objects + ) # ----------------------------------- # convolutional layers over DeepCore_1 : upper part # ----------------------------------- conv2d_1_layers, kernels, biases = tfs.new_conv_nd_layers( - X_DeepCore_upper, - is_training=is_training, - name='Upper DeepCore', - method_list='convolution', - keep_prob=keep_prob_list[1], - **config['conv_upper_DeepCore_settings'] - ) + X_DeepCore_upper, + is_training=is_training, + name="Upper DeepCore", + method_list="convolution", + keep_prob=keep_prob_list[1], + **config["conv_upper_DeepCore_settings"] + ) # ----------------------------------- # convolutional layers over DeepCore_2 : lower part # ----------------------------------- conv2d_2_layers, kernels, biases = tfs.new_conv_nd_layers( - X_DeepCore_lower, - is_training=is_training, - name='Lower DeepCore', - method_list='convolution', - keep_prob=keep_prob_list[1], - **config['conv_lower_DeepCore_settings'] - ) + X_DeepCore_lower, + is_training=is_training, + name="Lower DeepCore", + method_list="convolution", + keep_prob=keep_prob_list[1], + **config["conv_lower_DeepCore_settings"] + ) # ----------------------------------- # convolutional hex3d layers over X_IC78 data # ----------------------------------- conv_hex3d_layers, kernels, biases = tfs.new_conv_nd_layers( - X_IC78, - name='IC78', - is_training=is_training, - method_list='hex_convolution', - keep_prob=keep_prob_list[1], - **config['conv_IC78_settings'] - ) + X_IC78, + name="IC78", + is_training=is_training, + method_list="hex_convolution", + keep_prob=keep_prob_list[1], + **config["conv_IC78_settings"] + ) # ----------------------------------- # combine results of convolution and flatten # ----------------------------------- # flatten layer layer_flat_IC78, num_features_IC78 = tfs.flatten_layer( - conv_hex3d_layers[-1]) + conv_hex3d_layers[-1] + ) layer_flat_DeepCore_1, num_features_DeepCore_1 = tfs.flatten_layer( - conv2d_1_layers[-1]) + conv2d_1_layers[-1] + ) layer_flat_DeepCore_2, num_features_DeepCore_2 = tfs.flatten_layer( - conv2d_2_layers[-1]) + conv2d_2_layers[-1] + ) # combine layers - num_features = (num_features_DeepCore_1 + num_features_DeepCore_2 - + num_features_IC78) - layer_flat = tf.concat([layer_flat_IC78, layer_flat_DeepCore_1, - layer_flat_DeepCore_2], axis=1) + num_features = ( + num_features_DeepCore_1 + + num_features_DeepCore_2 + + num_features_IC78 + ) + layer_flat = tf.concat( + [layer_flat_IC78, layer_flat_DeepCore_1, layer_flat_DeepCore_2], + axis=1, + ) # dropout layer_flat = tf.nn.dropout(layer_flat, rate=1 - (keep_prob_list[2])) @@ -153,14 +169,15 @@ def general_model_IC86(is_training, config, data_handler, # ----------------------------------- # fully connected layers # ----------------------------------- - fc_settings = dict(config['fc_settings']) - fc_settings['fc_sizes'][-1] = num_labels + fc_settings = dict(config["fc_settings"]) + fc_settings["fc_sizes"][-1] = num_labels layers, weights, biases = tfs.new_fc_layers( - input=layer_flat, - keep_prob=keep_prob_list[3], - is_training=is_training, - **fc_settings) + input=layer_flat, + keep_prob=keep_prob_list[3], + is_training=is_training, + **fc_settings + ) y_pred_trafo_orig = layers[-1] @@ -170,49 +187,63 @@ def general_model_IC86(is_training, config, data_handler, assert len(y_pred_trafo_orig.get_shape().as_list()) == 2 # transform back - y_pred = data_transformer.inverse_transform(y_pred_trafo_orig, - data_type='label') + y_pred = data_transformer.inverse_transform( + y_pred_trafo_orig, data_type="label" + ) y_pred_list = tf.unstack(y_pred, axis=1) - if 'model_enforce_direction_norm' not in config: + if "model_enforce_direction_norm" not in config: # backward compatibility enforce_direction_norm = True else: - enforce_direction_norm = config['model_enforce_direction_norm'] + enforce_direction_norm = config["model_enforce_direction_norm"] if enforce_direction_norm: - index_dir_x = \ - data_handler.get_label_index(config['label_dir_x_key']) - index_dir_y = \ - data_handler.get_label_index(config['label_dir_y_key']) - index_dir_z = \ - data_handler.get_label_index(config['label_dir_z_key']) - index_zenith = \ - data_handler.get_label_index(config['label_zenith_key']) - index_azimuth = \ - data_handler.get_label_index(config['label_azimuth_key']) - - trafo_indices = [index_dir_x, index_dir_y, index_dir_z, - index_azimuth, index_zenith] - - norm = tf.sqrt(y_pred_list[index_dir_x]**2 + - y_pred_list[index_dir_y]**2 + - y_pred_list[index_dir_z]**2) + index_dir_x = data_handler.get_label_index( + config["label_dir_x_key"] + ) + index_dir_y = data_handler.get_label_index( + config["label_dir_y_key"] + ) + index_dir_z = data_handler.get_label_index( + config["label_dir_z_key"] + ) + index_zenith = data_handler.get_label_index( + config["label_zenith_key"] + ) + index_azimuth = data_handler.get_label_index( + config["label_azimuth_key"] + ) + + trafo_indices = [ + index_dir_x, + index_dir_y, + index_dir_z, + index_azimuth, + index_zenith, + ] + + norm = tf.sqrt( + y_pred_list[index_dir_x] ** 2 + + y_pred_list[index_dir_y] ** 2 + + y_pred_list[index_dir_z] ** 2 + ) y_pred_list[index_dir_x] /= norm y_pred_list[index_dir_y] /= norm y_pred_list[index_dir_z] /= norm # calculate zenith - y_pred_list[index_zenith] = tf.acos(tf.clip_by_value( - -y_pred_list[index_dir_z], - -1, 1)) + y_pred_list[index_zenith] = tf.acos( + tf.clip_by_value(-y_pred_list[index_dir_z], -1, 1) + ) # calculate azimuth - y_pred_list[index_azimuth] = (tf.atan2(-y_pred_list[index_dir_y], - -y_pred_list[index_dir_x]) - + 2 * np.pi) % (2 * np.pi) + y_pred_list[index_azimuth] = ( + tf.atan2(-y_pred_list[index_dir_y], -y_pred_list[index_dir_x]) + + 2 * np.pi + ) % (2 * np.pi) else: trafo_indices = [] @@ -220,11 +251,11 @@ def general_model_IC86(is_training, config, data_handler, # safety check for k in data_handler.label_names: - if k[0:2] == 'p_' and k not in config['label_pid_keys']: - raise ValueError('Did you forget about {!r}?'.format(k)) + if k[0:2] == "p_" and k not in config["label_pid_keys"]: + raise ValueError("Did you forget about {!r}?".format(k)) logit_tensors = {} - for pid_key in config['label_pid_keys']: + for pid_key in config["label_pid_keys"]: if pid_key in data_handler.label_names: index_pid = data_handler.get_label_index(pid_key) trafo_indices.append(index_pid) @@ -235,13 +266,13 @@ def general_model_IC86(is_training, config, data_handler, # cross entropy as a loss function. Tensorflow's cross entropy # functions use logits as input as opposed to sigmoid(logits) due # to numerical stability. - shared_objects['logit_tensors'] = logit_tensors + shared_objects["logit_tensors"] = logit_tensors # put it back together y_pred = tf.stack(y_pred_list, axis=1) # transform - y_pred_trafo = data_transformer.transform(y_pred, data_type='label') + y_pred_trafo = data_transformer.transform(y_pred, data_type="label") # Only do inv_trafo(trafo(y)) if necessary (numerical problems ...) y_pred_trafo_orig_list = tf.unstack(y_pred_trafo_orig, axis=1) @@ -264,48 +295,58 @@ def general_model_IC86(is_training, config, data_handler, # put it back together y_pred_trafo = tf.stack(y_pred_trafo_final_list, axis=1) - with tf.compat.v1.variable_scope('model_unc'): + with tf.compat.v1.variable_scope("model_unc"): # ----------------------------------- # Uncertainty estimate # ----------------------------------- - fc_unc_settings = dict(config['fc_unc_settings']) - fc_unc_settings['fc_sizes'][-1] = num_labels + fc_unc_settings = dict(config["fc_unc_settings"]) + fc_unc_settings["fc_sizes"][-1] = num_labels - unc_input = tf.stop_gradient(tf.concat((y_pred_trafo, layer_flat), - axis=-1)) + unc_input = tf.stop_gradient( + tf.concat((y_pred_trafo, layer_flat), axis=-1) + ) uncertainty_layers, weights, biases = tfs.new_fc_layers( - input=unc_input, - is_training=is_training, - keep_prob=keep_prob_list[3], - **fc_unc_settings - ) + input=unc_input, + is_training=is_training, + keep_prob=keep_prob_list[3], + **fc_unc_settings + ) y_unc_pred_trafo = uncertainty_layers[-1] y_unc_pred_trafo = tf.abs(y_unc_pred_trafo) + 1e-3 # ----------------------------------- # print architecture # ----------------------------------- - print('flat IC78:', layer_flat_IC78) - print('layer_flat:', layer_flat) - print('unc_input:', unc_input) - print('y_pred_trafo:', y_pred_trafo) - print('y_unc_pred_trafo:', y_unc_pred_trafo) + print("flat IC78:", layer_flat_IC78) + print("layer_flat:", layer_flat) + print("unc_input:", unc_input) + print("y_pred_trafo:", y_pred_trafo) + print("y_unc_pred_trafo:", y_unc_pred_trafo) # ----------------------------------- # collect model variables that need to be saved # ----------------------------------- - model_vars_pred = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, - 'model_pred') - model_vars_unc = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, - 'model_unc') + model_vars_pred = tf.compat.v1.get_collection( + tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, "model_pred" + ) + model_vars_unc = tf.compat.v1.get_collection( + tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, "model_unc" + ) return y_pred_trafo, y_unc_pred_trafo, model_vars_pred, model_vars_unc -def general_model_IC86_opt4(is_training, config, data_handler, - data_transformer, shared_objects, *args, **kwargs): +def general_model_IC86_opt4( + is_training, + config, + data_handler, + data_transformer, + shared_objects, + *args, + **kwargs +): """A general NN model for the IceCube IC86 configuration. Parameters @@ -339,67 +380,76 @@ def general_model_IC86_opt4(is_training, config, data_handler, list of tf.Tensor The trainable parameters of the uncertainty sub network. """ - keep_prob_list = shared_objects['keep_prob_list'] + keep_prob_list = shared_objects["keep_prob_list"] num_labels = data_handler.label_shape[-1] - with tf.compat.v1.variable_scope('model_pred'): + with tf.compat.v1.variable_scope("model_pred"): # apply DOM dropout, split and reshape DeepCore input X_IC78, X_DeepCore_upper, X_DeepCore_lower = preprocess_icecube_data( - is_training, shared_objects) + is_training, shared_objects + ) # ----------------------------------- # convolutional layers over DeepCore_1 : upper part # ----------------------------------- conv2d_1_layers, kernels, biases = tfs.new_conv_nd_layers( - X_DeepCore_upper, - is_training=is_training, - name='Upper DeepCore', - method_list='convolution', - keep_prob=keep_prob_list[1], - **config['conv_upper_DeepCore_settings'] - ) + X_DeepCore_upper, + is_training=is_training, + name="Upper DeepCore", + method_list="convolution", + keep_prob=keep_prob_list[1], + **config["conv_upper_DeepCore_settings"] + ) # ----------------------------------- # convolutional layers over DeepCore_2 : lower part # ----------------------------------- conv2d_2_layers, kernels, biases = tfs.new_conv_nd_layers( - X_DeepCore_lower, - is_training=is_training, - name='Lower DeepCore', - method_list='convolution', - keep_prob=keep_prob_list[1], - **config['conv_lower_DeepCore_settings'] - ) + X_DeepCore_lower, + is_training=is_training, + name="Lower DeepCore", + method_list="convolution", + keep_prob=keep_prob_list[1], + **config["conv_lower_DeepCore_settings"] + ) # ----------------------------------- # convolutional hex3d layers over X_IC78 data # ----------------------------------- conv_hex3d_layers, kernels, biases = tfs.new_conv_nd_layers( - X_IC78, - name='IC78', - is_training=is_training, - method_list='hex_convolution', - keep_prob=keep_prob_list[1], - **config['conv_IC78_settings'] - ) + X_IC78, + name="IC78", + is_training=is_training, + method_list="hex_convolution", + keep_prob=keep_prob_list[1], + **config["conv_IC78_settings"] + ) # ----------------------------------- # combine results of convolution and flatten # ----------------------------------- # flatten layer layer_flat_IC78, num_features_IC78 = tfs.flatten_layer( - conv_hex3d_layers[-1]) + conv_hex3d_layers[-1] + ) layer_flat_DeepCore_1, num_features_DeepCore_1 = tfs.flatten_layer( - conv2d_1_layers[-1]) + conv2d_1_layers[-1] + ) layer_flat_DeepCore_2, num_features_DeepCore_2 = tfs.flatten_layer( - conv2d_2_layers[-1]) + conv2d_2_layers[-1] + ) # combine layers - num_features = (num_features_DeepCore_1 + num_features_DeepCore_2 - + num_features_IC78) - layer_flat = tf.concat([layer_flat_IC78, layer_flat_DeepCore_1, - layer_flat_DeepCore_2], axis=1) + num_features = ( + num_features_DeepCore_1 + + num_features_DeepCore_2 + + num_features_IC78 + ) + layer_flat = tf.concat( + [layer_flat_IC78, layer_flat_DeepCore_1, layer_flat_DeepCore_2], + axis=1, + ) # dropout layer_flat = tf.nn.dropout(layer_flat, rate=1 - (keep_prob_list[2])) @@ -407,14 +457,15 @@ def general_model_IC86_opt4(is_training, config, data_handler, # ----------------------------------- # fully connected layers # ----------------------------------- - fc_settings = dict(config['fc_settings']) - fc_settings['fc_sizes'][-1] = num_labels + fc_settings = dict(config["fc_settings"]) + fc_settings["fc_sizes"][-1] = num_labels layers, weights, biases = tfs.new_fc_layers( - input=layer_flat, - keep_prob=keep_prob_list[3], - is_training=is_training, - **fc_settings) + input=layer_flat, + keep_prob=keep_prob_list[3], + is_training=is_training, + **fc_settings + ) y_pred_trafo_orig = layers[-1] @@ -424,52 +475,69 @@ def general_model_IC86_opt4(is_training, config, data_handler, assert len(y_pred_trafo_orig.get_shape().as_list()) == 2 # transform back - y_pred = data_transformer.inverse_transform(y_pred_trafo_orig, - data_type='label') + y_pred = data_transformer.inverse_transform( + y_pred_trafo_orig, data_type="label" + ) y_pred_list = tf.unstack(y_pred, axis=1) - if 'model_enforce_direction_norm' not in config: + if "model_enforce_direction_norm" not in config: # backward compatibility enforce_direction_norm = True else: - enforce_direction_norm = config['model_enforce_direction_norm'] + enforce_direction_norm = config["model_enforce_direction_norm"] - if 'model_dir_z_independent' not in config: + if "model_dir_z_independent" not in config: # backward compatibility model_dir_z_independent = False else: - model_dir_z_independent = config['model_dir_z_independent'] + model_dir_z_independent = config["model_dir_z_independent"] - if 'model_limit_dir_vec' not in config: + if "model_limit_dir_vec" not in config: # backward compatibility model_limit_dir_vec = None else: - model_limit_dir_vec = config['model_limit_dir_vec'] + model_limit_dir_vec = config["model_limit_dir_vec"] if enforce_direction_norm or model_limit_dir_vec: - index_dir_x = \ - data_handler.get_label_index(config['label_dir_x_key']) - index_dir_y = \ - data_handler.get_label_index(config['label_dir_y_key']) - index_dir_z = \ - data_handler.get_label_index(config['label_dir_z_key']) - index_zenith = \ - data_handler.get_label_index(config['label_zenith_key']) - index_azimuth = \ - data_handler.get_label_index(config['label_azimuth_key']) + index_dir_x = data_handler.get_label_index( + config["label_dir_x_key"] + ) + index_dir_y = data_handler.get_label_index( + config["label_dir_y_key"] + ) + index_dir_z = data_handler.get_label_index( + config["label_dir_z_key"] + ) + index_zenith = data_handler.get_label_index( + config["label_zenith_key"] + ) + index_azimuth = data_handler.get_label_index( + config["label_azimuth_key"] + ) # limit direction vector components to certain value range if model_limit_dir_vec is not None: - y_pred_list[index_dir_x] = tf.math.tanh( - y_pred_list[index_dir_x]) * model_limit_dir_vec - y_pred_list[index_dir_y] = tf.math.tanh( - y_pred_list[index_dir_y]) * model_limit_dir_vec - y_pred_list[index_dir_z] = tf.math.tanh( - y_pred_list[index_dir_z]) * model_limit_dir_vec - - trafo_indices = [index_dir_x, index_dir_y, index_dir_z, - index_azimuth, index_zenith] + y_pred_list[index_dir_x] = ( + tf.math.tanh(y_pred_list[index_dir_x]) + * model_limit_dir_vec + ) + y_pred_list[index_dir_y] = ( + tf.math.tanh(y_pred_list[index_dir_y]) + * model_limit_dir_vec + ) + y_pred_list[index_dir_z] = ( + tf.math.tanh(y_pred_list[index_dir_z]) + * model_limit_dir_vec + ) + + trafo_indices = [ + index_dir_x, + index_dir_y, + index_dir_z, + index_azimuth, + index_zenith, + ] if enforce_direction_norm: if model_dir_z_independent: @@ -480,30 +548,35 @@ def general_model_IC86_opt4(is_training, config, data_handler, # of tanh assert np.abs(model_limit_dir_vec - 1) < 0.1 norm_xy = tf.math.sqrt( - (y_pred_list[index_dir_x]**2 + - y_pred_list[index_dir_y]**2) / - (1 - tf.stop_gradient(y_pred_list[index_dir_z])**2) + ( + y_pred_list[index_dir_x] ** 2 + + y_pred_list[index_dir_y] ** 2 + ) + / (1 - tf.stop_gradient(y_pred_list[index_dir_z]) ** 2) ) y_pred_list[index_dir_x] /= norm_xy y_pred_list[index_dir_y] /= norm_xy else: - norm = tf.sqrt(y_pred_list[index_dir_x]**2 + - y_pred_list[index_dir_y]**2 + - y_pred_list[index_dir_z]**2) + norm = tf.sqrt( + y_pred_list[index_dir_x] ** 2 + + y_pred_list[index_dir_y] ** 2 + + y_pred_list[index_dir_z] ** 2 + ) y_pred_list[index_dir_x] /= norm y_pred_list[index_dir_y] /= norm y_pred_list[index_dir_z] /= norm # calculate zenith - y_pred_list[index_zenith] = tf.acos(tf.clip_by_value( - -y_pred_list[index_dir_z], - -1, 1)) + y_pred_list[index_zenith] = tf.acos( + tf.clip_by_value(-y_pred_list[index_dir_z], -1, 1) + ) # calculate azimuth - y_pred_list[index_azimuth] = (tf.atan2(-y_pred_list[index_dir_y], - -y_pred_list[index_dir_x]) - + 2 * np.pi) % (2 * np.pi) + y_pred_list[index_azimuth] = ( + tf.atan2(-y_pred_list[index_dir_y], -y_pred_list[index_dir_x]) + + 2 * np.pi + ) % (2 * np.pi) else: trafo_indices = [] @@ -513,11 +586,11 @@ def general_model_IC86_opt4(is_training, config, data_handler, # safety check for k in data_handler.label_names: - if k[0:2] == 'p_' and k not in config['label_pid_keys']: - raise ValueError('Did you forget about {!r}?'.format(k)) + if k[0:2] == "p_" and k not in config["label_pid_keys"]: + raise ValueError("Did you forget about {!r}?".format(k)) logit_tensors = {} - for pid_key in config['label_pid_keys']: + for pid_key in config["label_pid_keys"]: if pid_key in data_handler.label_names: index_pid = data_handler.get_label_index(pid_key) trafo_indices.append(index_pid) @@ -528,13 +601,13 @@ def general_model_IC86_opt4(is_training, config, data_handler, # cross entropy as a loss function. Tensorflow's cross entropy # functions use logits as input as opposed to sigmoid(logits) due # to numerical stability. - shared_objects['logit_tensors'] = logit_tensors + shared_objects["logit_tensors"] = logit_tensors # put it back together y_pred = tf.stack(y_pred_list, axis=1) # transform - y_pred_trafo = data_transformer.transform(y_pred, data_type='label') + y_pred_trafo = data_transformer.transform(y_pred, data_type="label") # Only do inv_trafo(trafo(y)) if necessary (numerical problems ...) y_pred_trafo_orig_list = tf.unstack(y_pred_trafo_orig, axis=1) @@ -557,37 +630,39 @@ def general_model_IC86_opt4(is_training, config, data_handler, # put it back together y_pred_trafo = tf.stack(y_pred_trafo_final_list, axis=1) - with tf.compat.v1.variable_scope('model_unc'): + with tf.compat.v1.variable_scope("model_unc"): # ----------------------------------- # Uncertainty estimate # ----------------------------------- - fc_unc_settings = dict(config['fc_unc_settings']) - fc_unc_settings['fc_sizes'][-1] = num_labels + fc_unc_settings = dict(config["fc_unc_settings"]) + fc_unc_settings["fc_sizes"][-1] = num_labels uncertainty_layers, weights, biases = tfs.new_fc_layers( - input=tf.stop_gradient(layer_flat), - is_training=is_training, - keep_prob=keep_prob_list[3], - **fc_unc_settings - ) + input=tf.stop_gradient(layer_flat), + is_training=is_training, + keep_prob=keep_prob_list[3], + **fc_unc_settings + ) y_unc_pred_trafo = uncertainty_layers[-1] y_unc_pred_trafo = tf.abs(y_unc_pred_trafo) + 1e-3 # ----------------------------------- # print architecture # ----------------------------------- - print('flat IC78:', layer_flat_IC78) - print('layer_flat:', layer_flat) - print('y_pred_trafo:', y_pred_trafo) - print('y_unc_pred_trafo:', y_unc_pred_trafo) + print("flat IC78:", layer_flat_IC78) + print("layer_flat:", layer_flat) + print("y_pred_trafo:", y_pred_trafo) + print("y_unc_pred_trafo:", y_unc_pred_trafo) # ----------------------------------- # collect model variables that need to be saved # ----------------------------------- - model_vars_pred = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, - 'model_pred') - model_vars_unc = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, - 'model_unc') + model_vars_pred = tf.compat.v1.get_collection( + tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, "model_pred" + ) + model_vars_unc = tf.compat.v1.get_collection( + tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, "model_unc" + ) return y_pred_trafo, y_unc_pred_trafo, model_vars_pred, model_vars_unc diff --git a/dnn_reco/modules/models/utils/model_utils.py b/dnn_reco/modules/models/utils/model_utils.py index 5fcf86e..2eeefa5 100644 --- a/dnn_reco/modules/models/utils/model_utils.py +++ b/dnn_reco/modules/models/utils/model_utils.py @@ -38,42 +38,55 @@ def preprocess_icecube_data(is_training, shared_objects): Lower DeepCore array shape: [none, 1, 50, 8 * num_bins] """ - X_IC78 = shared_objects['x_ic78_trafo'] - X_DeepCore = shared_objects['x_deepcore_trafo'] - keep_prob_list = shared_objects['keep_prob_list'] + X_IC78 = shared_objects["x_ic78_trafo"] + X_DeepCore = shared_objects["x_deepcore_trafo"] + keep_prob_list = shared_objects["keep_prob_list"] # ----------------------------------- # DropOut on whole DOMs # ----------------------------------- if is_training: - noise_shape_IC78 = [tf.shape(input=X_IC78)[0]] + \ - X_IC78.get_shape().as_list()[1:-1] + [1] - noise_shape_DeepCore = [tf.shape(input=X_DeepCore)[0]] + \ - X_DeepCore.get_shape().as_list()[1:-1] + [1] + noise_shape_IC78 = ( + [tf.shape(input=X_IC78)[0]] + + X_IC78.get_shape().as_list()[1:-1] + + [1] + ) + noise_shape_DeepCore = ( + [tf.shape(input=X_DeepCore)[0]] + + X_DeepCore.get_shape().as_list()[1:-1] + + [1] + ) - X_IC78 = tf.nn.dropout(X_IC78, - rate=1 - (keep_prob_list[0]), - noise_shape=noise_shape_IC78, - ) + X_IC78 = tf.nn.dropout( + X_IC78, + rate=1 - (keep_prob_list[0]), + noise_shape=noise_shape_IC78, + ) - X_DeepCore = tf.nn.dropout(X_DeepCore, - rate=1 - (keep_prob_list[0]), - noise_shape=noise_shape_DeepCore, - ) + X_DeepCore = tf.nn.dropout( + X_DeepCore, + rate=1 - (keep_prob_list[0]), + noise_shape=noise_shape_DeepCore, + ) # ----------------------------------- # Reshape DeepCore: # Move strings in channel dimension: # only convolve over DOMs dimension # ----------------------------------- - X_DeepCore_upper = tf.transpose(a=X_DeepCore[:, :, 0:10, :], - perm=[0, 2, 3, 1]) - X_DeepCore_lower = tf.transpose(a=X_DeepCore[:, :, 10:, :], perm=[0, 2, 3, 1]) + X_DeepCore_upper = tf.transpose( + a=X_DeepCore[:, :, 0:10, :], perm=[0, 2, 3, 1] + ) + X_DeepCore_lower = tf.transpose( + a=X_DeepCore[:, :, 10:, :], perm=[0, 2, 3, 1] + ) input_ch_size = X_DeepCore.get_shape().as_list()[-1] - X_DeepCore_upper = tf.reshape(X_DeepCore_upper, - [-1, 1, 10, input_ch_size*8]) - X_DeepCore_lower = tf.reshape(X_DeepCore_lower, - [-1, 1, 50, input_ch_size*8]) + X_DeepCore_upper = tf.reshape( + X_DeepCore_upper, [-1, 1, 10, input_ch_size * 8] + ) + X_DeepCore_lower = tf.reshape( + X_DeepCore_lower, [-1, 1, 50, input_ch_size * 8] + ) return X_IC78, X_DeepCore_upper, X_DeepCore_lower diff --git a/dnn_reco/setup_manager.py b/dnn_reco/setup_manager.py index bcf134f..8a512f7 100644 --- a/dnn_reco/setup_manager.py +++ b/dnn_reco/setup_manager.py @@ -11,7 +11,7 @@ import warnings from tables import NaturalNameWarning -warnings.filterwarnings('ignore', category=NaturalNameWarning) +warnings.filterwarnings("ignore", category=NaturalNameWarning) class SetupManager: @@ -182,12 +182,12 @@ class SetupManager: # define default config _default_config = { - 'float_precision': 'float32', - 'DOM_init_values': 0.0, - 'trafo_norm_constant': 1e-6, - 'data_handler_nan_fill_value': None, - 'data_handler_num_splits': None, - } + "float_precision": "float32", + "DOM_init_values": 0.0, + "trafo_norm_constant": 1e-6, + "data_handler_nan_fill_value": None, + "data_handler_num_splits": None, + } def __init__(self, config_files): """Initializes the DNN reco Setup Manager @@ -220,7 +220,7 @@ def _setup_config(self): If a setting is defined in multiplie config files. """ if len(self._config_files) == 0: - raise ValueError('You must specify at least one config file!') + raise ValueError("You must specify at least one config file!") # ---------------------------------- # load config @@ -230,42 +230,45 @@ def _setup_config(self): for config_file in self._config_files: # append yaml file to config_name - file_base_name = os.path.basename(config_file).replace('.yaml', '') + file_base_name = os.path.basename(config_file).replace(".yaml", "") if config_name is None: config_name = file_base_name else: - config_name += '__' + file_base_name + config_name += "__" + file_base_name config_update = yaml.safe_load(open(config_file)) duplicates = set(new_config.keys()).intersection( - set(config_update.keys())) + set(config_update.keys()) + ) # make sure no options are defined multiple times if duplicates: - raise ValueError('Keys are defined multiple times {!r}'.format( - duplicates)) + raise ValueError( + "Keys are defined multiple times {!r}".format(duplicates) + ) # update config new_config.update(config_update) config = dict(self._default_config) config.update(new_config) # define numpy and tensorflow float precision - config['tf_float_precision'] = getattr(tf, config['float_precision']) - config['np_float_precision'] = getattr(np, config['float_precision']) + config["tf_float_precision"] = getattr(tf, config["float_precision"]) + config["np_float_precision"] = getattr(np, config["float_precision"]) import tfscripts as tfs - tfs.FLOAT_PRECISION = config['tf_float_precision'] + + tfs.FLOAT_PRECISION = config["tf_float_precision"] # get git repo information - config['git_short_sha'] = str(version_control.short_sha) - config['git_sha'] = str(version_control.sha) - config['git_origin'] = str(version_control.origin) - config['git_uncommited_changes'] = version_control.uncommitted_changes - config['pip_installed_packages'] = version_control.installed_packages + config["git_short_sha"] = str(version_control.short_sha) + config["git_sha"] = str(version_control.sha) + config["git_origin"] = str(version_control.origin) + config["git_uncommited_changes"] = version_control.uncommitted_changes + config["pip_installed_packages"] = version_control.installed_packages # ---------------------------------- # expand all strings with variables # ---------------------------------- - config['config_name'] = str(config_name) + config["config_name"] = str(config_name) for key in config: if isinstance(config[key], str): config[key] = config[key].format(**config) diff --git a/dnn_reco/train_model.py b/dnn_reco/train_model.py index 93b7094..d4efdbe 100644 --- a/dnn_reco/train_model.py +++ b/dnn_reco/train_model.py @@ -12,7 +12,7 @@ @click.command() -@click.argument('config_files', type=click.Path(exists=True), nargs=-1) +@click.argument("config_files", type=click.Path(exists=True), nargs=-1) def main(config_files): """Script to train the NN model. @@ -29,80 +29,85 @@ def main(config_files): setup_manager = SetupManager(config_files) config = setup_manager.get_config() - if not config['model_is_training']: - raise ValueError('Model must be in training mode!') + if not config["model_is_training"]: + raise ValueError("Model must be in training mode!") # Create Data Handler object data_handler = DataHandler(config) - data_handler.setup_with_test_data(config['training_data_file']) + data_handler.setup_with_test_data(config["training_data_file"]) # Create Data iterators for training and validation data train_data_generator = data_handler.get_batch_generator( - input_data=config['training_data_file'], - batch_size=config['batch_size'], - sample_randomly=True, - pick_random_files_forever=True, - file_capacity=config['file_capacity'], - batch_capacity=config['batch_capacity'], - num_jobs=config['num_jobs'], - num_add_files=config['num_add_files'], - num_repetitions=config['num_repetitions'], - num_splits=config['data_handler_num_splits'], - init_values=config['DOM_init_values'], - nan_fill_value=config['data_handler_nan_fill_value'], - ) + input_data=config["training_data_file"], + batch_size=config["batch_size"], + sample_randomly=True, + pick_random_files_forever=True, + file_capacity=config["file_capacity"], + batch_capacity=config["batch_capacity"], + num_jobs=config["num_jobs"], + num_add_files=config["num_add_files"], + num_repetitions=config["num_repetitions"], + num_splits=config["data_handler_num_splits"], + init_values=config["DOM_init_values"], + nan_fill_value=config["data_handler_nan_fill_value"], + ) val_data_generator = data_handler.get_batch_generator( - input_data=config['validation_data_file'], - batch_size=config['batch_size'], - sample_randomly=True, - pick_random_files_forever=True, - file_capacity=1, - batch_capacity=5, - num_jobs=1, - num_add_files=1, - num_repetitions=1, - num_splits=config['data_handler_num_splits'], - init_values=config['DOM_init_values'], - nan_fill_value=config['data_handler_nan_fill_value'], - ) + input_data=config["validation_data_file"], + batch_size=config["batch_size"], + sample_randomly=True, + pick_random_files_forever=True, + file_capacity=1, + batch_capacity=5, + num_jobs=1, + num_add_files=1, + num_repetitions=1, + num_splits=config["data_handler_num_splits"], + init_values=config["DOM_init_values"], + nan_fill_value=config["data_handler_nan_fill_value"], + ) # create data transformer data_transformer = DataTransformer( - data_handler=data_handler, - treat_doms_equally=config['trafo_treat_doms_equally'], - normalize_dom_data=config['trafo_normalize_dom_data'], - normalize_label_data=config['trafo_normalize_label_data'], - normalize_misc_data=config['trafo_normalize_misc_data'], - log_dom_bins=config['trafo_log_dom_bins'], - log_label_bins=config['trafo_log_label_bins'], - log_misc_bins=config['trafo_log_misc_bins'], - norm_constant=config['trafo_norm_constant']) + data_handler=data_handler, + treat_doms_equally=config["trafo_treat_doms_equally"], + normalize_dom_data=config["trafo_normalize_dom_data"], + normalize_label_data=config["trafo_normalize_label_data"], + normalize_misc_data=config["trafo_normalize_misc_data"], + log_dom_bins=config["trafo_log_dom_bins"], + log_label_bins=config["trafo_log_label_bins"], + log_misc_bins=config["trafo_log_misc_bins"], + norm_constant=config["trafo_norm_constant"], + ) # load trafo model from file - data_transformer.load_trafo_model(config['trafo_model_path']) + data_transformer.load_trafo_model(config["trafo_model_path"]) with tf.Graph().as_default(): # create NN model - model = NNModel(is_training=True, - config=config, - data_handler=data_handler, - data_transformer=data_transformer) + model = NNModel( + is_training=True, + config=config, + data_handler=data_handler, + data_transformer=data_transformer, + ) # compile model: define loss function and optimizer model.compile() # restore model weights - if config['model_restore_model']: + if config["model_restore_model"]: model.restore() # train model - model.fit(num_training_iterations=config['num_training_iterations'], - train_data_generator=train_data_generator, - val_data_generator=val_data_generator, - evaluation_methods=None,) + model.fit( + num_training_iterations=config["num_training_iterations"], + train_data_generator=train_data_generator, + val_data_generator=val_data_generator, + evaluation_methods=None, + ) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/dnn_reco/utils/angles.py b/dnn_reco/utils/angles.py index 12ff590..9030a4e 100644 --- a/dnn_reco/utils/angles.py +++ b/dnn_reco/utils/angles.py @@ -23,10 +23,10 @@ def get_angle_deviation(azimuth1, zenith1, azimuth2, zenith2): The opening angle in rad between the vector 1 and 2. Same shape as input vectors. """ - cos_dist = (np.cos(azimuth1 - azimuth2) * - np.sin(zenith1) * np.sin(zenith2) + - np.cos(zenith1) * np.cos(zenith2)) - cos_dist = np.clip(cos_dist, -1., 1.) + cos_dist = np.cos(azimuth1 - azimuth2) * np.sin(zenith1) * np.sin( + zenith2 + ) + np.cos(zenith1) * np.cos(zenith2) + cos_dist = np.clip(cos_dist, -1.0, 1.0) return np.arccos(cos_dist) @@ -50,10 +50,10 @@ def tf_get_angle_deviation(azimuth1, zenith1, azimuth2, zenith2): The opening angle in rad between the vector 1 and 2. Same shape as input vectors. """ - cos_dist = (tf.cos(azimuth1 - azimuth2) * - tf.sin(zenith1) * tf.sin(zenith2) + - tf.cos(zenith1) * tf.cos(zenith2)) - cos_dist = tf.clip_by_value(cos_dist, -1., 1.) + cos_dist = tf.cos(azimuth1 - azimuth2) * tf.sin(zenith1) * tf.sin( + zenith2 + ) + tf.cos(zenith1) * tf.cos(zenith2) + cos_dist = tf.clip_by_value(cos_dist, -1.0, 1.0) return tf.acos(cos_dist) @@ -84,10 +84,12 @@ def get_angle(vec1, vec2, dtype=np.float64): vec1 = np.array(vec1, dtype=dtype) vec2 = np.array(vec2, dtype=dtype) - assert vec1.shape[-1] == 3, \ - "Expect shape [?,3] or [3], but got {}".format(vec1.shape) - assert vec2.shape[-1] == 3, \ - "Expect shape [?,3] or [3], but got {}".format(vec2.shape) + assert vec1.shape[-1] == 3, "Expect shape [?,3] or [3], but got {}".format( + vec1.shape + ) + assert vec2.shape[-1] == 3, "Expect shape [?,3] or [3], but got {}".format( + vec2.shape + ) norm1 = np.linalg.norm(vec1, axis=-1, keepdims=True) norm2 = np.linalg.norm(vec2, axis=-1, keepdims=True) @@ -97,6 +99,6 @@ def get_angle(vec1, vec2, dtype=np.float64): tmp3 = np.linalg.norm(tmp1 - tmp2, axis=-1) tmp4 = np.linalg.norm(tmp1 + tmp2, axis=-1) - theta = 2*np.arctan2(tmp3, tmp4) + theta = 2 * np.arctan2(tmp3, tmp4) return theta diff --git a/dnn_reco/utils/learning_rate.py b/dnn_reco/utils/learning_rate.py index b863cf9..4a37339 100644 --- a/dnn_reco/utils/learning_rate.py +++ b/dnn_reco/utils/learning_rate.py @@ -12,8 +12,7 @@ class MultiLearningRateScheduler(LearningRateSchedule): - """A LearningRateSchedule that combines multiple schedulers - """ + """A LearningRateSchedule that combines multiple schedulers""" def __init__(self, boundaries, scheduler_settings, name=None): """MultiLearningRateScheduler @@ -48,18 +47,19 @@ def __init__(self, boundaries, scheduler_settings, name=None): if len(boundaries) != len(scheduler_settings) - 1: raise ValueError( - "The length of boundaries should be 1 less than the length " - "of scheduler_settings") + "The length of boundaries should be 1 less than the length " + "of scheduler_settings" + ) # create schedulers schedulers = [] for settings in scheduler_settings: - scheduler_class = misc.load_class(settings['full_class_string']) - scheduler = scheduler_class(**settings['settings']) + scheduler_class = misc.load_class(settings["full_class_string"]) + scheduler = scheduler_class(**settings["settings"]) schedulers.append(scheduler) if name is None: - name = 'MultiLearningRateScheduler' + name = "MultiLearningRateScheduler" self.boundaries = tf.convert_to_tensor(boundaries) self.scheduler_settings = scheduler_settings @@ -75,23 +75,29 @@ def __call__(self, step): # create a list of (boolean, callable) pairs pred_fn_pairs = [] - pred_fn_pairs.append(( - step <= boundaries[0], - lambda: self.schedulers[0](step), - )) - pred_fn_pairs.append(( - step > boundaries[-1], - lambda: self.schedulers[-1](step - boundaries[-1]), - )) + pred_fn_pairs.append( + ( + step <= boundaries[0], + lambda: self.schedulers[0](step), + ) + ) + pred_fn_pairs.append( + ( + step > boundaries[-1], + lambda: self.schedulers[-1](step - boundaries[-1]), + ) + ) for index in range(len(self.schedulers) - 2): low = boundaries[index] high = boundaries[index + 1] scheduler = self.schedulers[index + 1] - pred_fn_pairs.append(( - step > low and step <= high, - lambda: scheduler(step - low), - )) + pred_fn_pairs.append( + ( + step > low and step <= high, + lambda: scheduler(step - low), + ) + ) return tf.case(pred_fn_pairs, exclusive=True) @@ -99,5 +105,5 @@ def get_config(self): return { "boundaries": self.boundaries, "scheduler_settings": self.scheduler_settings, - "name": self.name + "name": self.name, } diff --git a/dnn_reco/version_control.py b/dnn_reco/version_control.py index 628e509..7bcaf6f 100644 --- a/dnn_reco/version_control.py +++ b/dnn_reco/version_control.py @@ -14,7 +14,8 @@ def get_git_infos(): short_sha = str(repo.git.rev_parse(sha, short=7)) try: origin = str( - repo.git.execute(["git", "config", "--get", "remote.origin.url"])) + repo.git.execute(["git", "config", "--get", "remote.origin.url"]) + ) except git.exc.GitCommandError: origin = None uncommitted_changes = repo.is_dirty() @@ -23,5 +24,6 @@ def get_git_infos(): short_sha, sha, origin, uncommitted_changes = get_git_infos() -installed_packages = [[d.project_name, d.version] for - d in pkg_resources.working_set] +installed_packages = [ + [d.project_name, d.version] for d in pkg_resources.working_set +] diff --git a/docs/Makefile b/docs/Makefile index 2a13486..528da73 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -17,4 +17,4 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/source/conf.py b/docs/source/conf.py index 68e966d..83b3007 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -30,41 +30,43 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.autodoc', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.autosectionlabel', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.viewcode', - 'sphinx.ext.githubpages'] +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "sphinx.ext.autosectionlabel", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.viewcode", + "sphinx.ext.githubpages", +] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] -source_suffix = '.rst' +source_suffix = ".rst" # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'IceCube DNN Reconstruction' -copyright = u'2019, Mirco Huennefeld' -author = u'Mirco Huennefeld' +project = "IceCube DNN Reconstruction" +copyright = "2019, Mirco Huennefeld" +author = "Mirco Huennefeld" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = u'0.0' +version = "0.0" # The full version, including alpha/beta/rc tags. -release = u'0.0.1' +release = "0.0.1" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -79,7 +81,7 @@ exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True @@ -90,7 +92,7 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the @@ -101,7 +103,7 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] # Custom sidebar templates, must be a dictionary that maps document names # to template names. @@ -109,9 +111,9 @@ # This is required for the alabaster theme # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars html_sidebars = { - '**': [ - 'relations.html', # needs 'show_related': True theme option to display - 'searchbox.html', + "**": [ + "relations.html", # needs 'show_related': True theme option to display + "searchbox.html", ] } @@ -119,7 +121,7 @@ # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. -htmlhelp_basename = 'IceCubeDNNreconstructiondoc' +htmlhelp_basename = "IceCubeDNNreconstructiondoc" # -- Options for LaTeX output --------------------------------------------- @@ -128,15 +130,12 @@ # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. # # 'preamble': '', - # Latex figure (float) alignment # # 'figure_align': 'htbp', @@ -146,8 +145,13 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'IceCubeDNNreconstruction.tex', u'IceCube DNN reconstruction Documentation', - u'Mirco Huennefeld', 'manual'), + ( + master_doc, + "IceCubeDNNreconstruction.tex", + "IceCube DNN reconstruction Documentation", + "Mirco Huennefeld", + "manual", + ), ] @@ -156,8 +160,13 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - (master_doc, 'icecubednnreconstruction', u'IceCube DNN reconstruction Documentation', - [author], 1) + ( + master_doc, + "icecubednnreconstruction", + "IceCube DNN reconstruction Documentation", + [author], + 1, + ) ] @@ -167,16 +176,20 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'IceCubeDNNreconstruction', u'IceCube DNN reconstruction Documentation', - author, 'IceCubeDNNreconstruction', 'One line description of project.', - 'Miscellaneous'), + ( + master_doc, + "IceCubeDNNreconstruction", + "IceCube DNN reconstruction Documentation", + author, + "IceCubeDNNreconstruction", + "One line description of project.", + "Miscellaneous", + ), ] - - # Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} +intersphinx_mapping = {"https://docs.python.org/": None} # Define global substitutions diff --git a/docs/source/index.rst b/docs/source/index.rst index f121b4f..4f72705 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -52,4 +52,3 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` - diff --git a/docs/source/pages/bootcamp.rst b/docs/source/pages/bootcamp.rst index 1e0045f..fcc09d7 100644 --- a/docs/source/pages/bootcamp.rst +++ b/docs/source/pages/bootcamp.rst @@ -43,4 +43,4 @@ it enters the convex hull around the IceCube detector. bootcamp/01_bootcamp_train bootcamp/02_bootcamp_monitor bootcamp/03_bootcamp_apply - bootcamp/04_bootcamp_visualize \ No newline at end of file + bootcamp/04_bootcamp_visualize diff --git a/docs/source/pages/bootcamp/00_bootcamp_setup.rst b/docs/source/pages/bootcamp/00_bootcamp_setup.rst index e7aa92b..e71aff3 100644 --- a/docs/source/pages/bootcamp/00_bootcamp_setup.rst +++ b/docs/source/pages/bootcamp/00_bootcamp_setup.rst @@ -129,4 +129,4 @@ Try to create a tensorflow session and to import |dnn_reco|. python -c 'import tensorflow as tf; print(tf.__version__); tf.Session()' # try to import dnn_reco (This should run without giving any output) - python -c 'import dnn_reco; import tfscripts; import ic3_labels; import ic3_data' \ No newline at end of file + python -c 'import dnn_reco; import tfscripts; import ic3_labels; import ic3_data' diff --git a/docs/source/pages/bootcamp/01_bootcamp_train.rst b/docs/source/pages/bootcamp/01_bootcamp_train.rst index 324875e..5bfb717 100644 --- a/docs/source/pages/bootcamp/01_bootcamp_train.rst +++ b/docs/source/pages/bootcamp/01_bootcamp_train.rst @@ -253,4 +253,3 @@ each of your models. architecture here as well as the option to add custom loss functions. More information on the exchangable modules is provided in :ref:`Code Documentation`. - diff --git a/docs/source/pages/bootcamp/03_bootcamp_apply.rst b/docs/source/pages/bootcamp/03_bootcamp_apply.rst index e1859e5..d508c37 100644 --- a/docs/source/pages/bootcamp/03_bootcamp_apply.rst +++ b/docs/source/pages/bootcamp/03_bootcamp_apply.rst @@ -42,89 +42,105 @@ following: @click.command() - @click.argument('input_file_pattern', type=click.Path(exists=True), - required=True, nargs=-1) - @click.option('-o', '--outfile', default='dnn_output', - help='Name of output file without file ending.') - @click.option('-m', '--model_names', - default='getting_started_model', - help='Parent directory of exported models.') - @click.option('-d', '--models_dir', - default='{DNN_HOME}/exported_models', - help='Parent directory of exported models.') - @click.option('-g', '--gcd_file', - default='/shared/dnn_reco_tutorial/i3_files/GeoCalibDetectorStatus_2012.56063_V1.i3.gz', - help='GCD File to use.') - @click.option('--i3/--no-i3', default=True) - @click.option('--hdf5/--no-hdf5', default=True) - def main(input_file_pattern, outfile, model_names, models_dir, gcd_file, - i3, hdf5): + @click.argument("input_file_pattern", type=click.Path(exists=True), required=True, nargs=-1) + @click.option( + "-o", "--outfile", default="dnn_output", help="Name of output file without file ending." + ) + @click.option( + "-m", + "--model_names", + default="getting_started_model", + help="Parent directory of exported models.", + ) + @click.option( + "-d", + "--models_dir", + default="{DNN_HOME}/exported_models", + help="Parent directory of exported models.", + ) + @click.option( + "-g", + "--gcd_file", + default="/shared/dnn_reco_tutorial/i3_files/GeoCalibDetectorStatus_2012.56063_V1.i3.gz", + help="GCD File to use.", + ) + @click.option("--i3/--no-i3", default=True) + @click.option("--hdf5/--no-hdf5", default=True) + def main(input_file_pattern, outfile, model_names, models_dir, gcd_file, i3, hdf5): # create output directory if necessary base_path = os.path.dirname(outfile) if not os.path.isdir(base_path): - print('\nCreating directory: {}\n'.format(base_path)) + print("\nCreating directory: {}\n".format(base_path)) os.makedirs(base_path) # expand models_dir with environment variable - models_dir = models_dir.format(DNN_HOME=os.environ['DNN_HOME']) + models_dir = models_dir.format(DNN_HOME=os.environ["DNN_HOME"]) - HDF_keys = ['LabelsDeepLearning', 'MCPrimary', - 'OnlineL2_PoleL2MPEFit_MuEx', - 'OnlineL2_PoleL2MPEFit_TruncatedEnergy_AllBINS_Muon'] + HDF_keys = [ + "LabelsDeepLearning", + "MCPrimary", + "OnlineL2_PoleL2MPEFit_MuEx", + "OnlineL2_PoleL2MPEFit_TruncatedEnergy_AllBINS_Muon", + ] tray = I3Tray() # read in files file_name_list = [str(gcd_file)] file_name_list.extend(list(input_file_pattern)) - tray.AddModule('I3Reader', 'reader', Filenamelist=file_name_list) + tray.AddModule("I3Reader", "reader", Filenamelist=file_name_list) # Add labels - tray.AddModule(get_weighted_primary, 'getWeightedPrimary', - If=lambda f: not f.Has('MCPrimary')) - tray.AddModule(MCLabelsCascades, 'MCLabelsCascades', - PulseMapString='InIceDSTPulses', - PrimaryKey='MCPrimary', - ExtendBoundary=0., - OutputKey='LabelsDeepLearning') + tray.AddModule(get_weighted_primary, "getWeightedPrimary", If=lambda f: not f.Has("MCPrimary")) + tray.AddModule( + MCLabelsCascades, + "MCLabelsCascades", + PulseMapString="InIceDSTPulses", + PrimaryKey="MCPrimary", + ExtendBoundary=0.0, + OutputKey="LabelsDeepLearning", + ) # collect model and output names if isinstance(model_names, (str, unicode)): model_names = [str(model_names)] - output_names = ['DeepLearningReco_{}'.format(m) for m - in model_names] + output_names = ["DeepLearningReco_{}".format(m) for m in model_names] # Make sure DNN reco will be writen to hdf5 file for outbox in output_names: if outbox not in HDF_keys: HDF_keys.append(outbox) - HDF_keys.append(outbox + '_I3Particle') + HDF_keys.append(outbox + "_I3Particle") # Apply DNN Reco - tray.AddSegment(ApplyDNNRecos, 'ApplyDNNRecos', - pulse_key='InIceDSTPulses', - model_names=model_names, - output_keys=output_names, - models_dir=models_dir, - ) + tray.AddSegment( + ApplyDNNRecos, + "ApplyDNNRecos", + pulse_key="InIceDSTPulses", + model_names=model_names, + output_keys=output_names, + models_dir=models_dir, + ) # Write output if i3: - tray.AddModule("I3Writer", "EventWriter", - filename='{}.i3.bz2'.format(outfile)) + tray.AddModule("I3Writer", "EventWriter", filename="{}.i3.bz2".format(outfile)) if hdf5: - tray.AddSegment(hdfwriter.I3HDFWriter, 'hdf', - Output='{}.hdf5'.format(outfile), - CompressionLevel=9, - Keys=HDF_keys, - SubEventStreams=['InIceSplit']) - tray.AddModule('TrashCan', 'YesWeCan') + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(outfile), + CompressionLevel=9, + Keys=HDF_keys, + SubEventStreams=["InIceSplit"], + ) + tray.AddModule("TrashCan", "YesWeCan") tray.Execute() - if __name__ == '__main__': + if __name__ == "__main__": main() @@ -155,6 +171,3 @@ This will create an hdf5 and an i3 file with the specified file names: and run them (no need to run dagman for just one file, we can simply execute the ) - - - diff --git a/docs/source/pages/bootcamp/04_bootcamp_visualize.rst b/docs/source/pages/bootcamp/04_bootcamp_visualize.rst index 626e060..eeaf9f7 100644 --- a/docs/source/pages/bootcamp/04_bootcamp_visualize.rst +++ b/docs/source/pages/bootcamp/04_bootcamp_visualize.rst @@ -33,4 +33,4 @@ Here are the result plots of a model that was run for 44500 iterations: .. figure:: correlation_dnn.png - This is the correlation plot for DNN reco. \ No newline at end of file + This is the correlation plot for DNN reco. diff --git a/docs/source/pages/code.rst b/docs/source/pages/code.rst index 0b7f777..cdcfe12 100644 --- a/docs/source/pages/code.rst +++ b/docs/source/pages/code.rst @@ -69,4 +69,4 @@ Misc Data Loading Event Filter ============ .. automodule:: dnn_reco.modules.data.filter.default_filter - :members: \ No newline at end of file + :members: diff --git a/docs/source/pages/code/03_classes.rst b/docs/source/pages/code/03_classes.rst index 13443c0..b7c0541 100644 --- a/docs/source/pages/code/03_classes.rst +++ b/docs/source/pages/code/03_classes.rst @@ -12,4 +12,4 @@ DataTrafo NNModel SetupManager -references to API and source code \ No newline at end of file +references to API and source code diff --git a/docs/source/pages/documentation.rst b/docs/source/pages/documentation.rst index 0f21af7..d819676 100644 --- a/docs/source/pages/documentation.rst +++ b/docs/source/pages/documentation.rst @@ -25,4 +25,3 @@ where we will focus on: code/01_scripts code/02_modules code/03_classes - diff --git a/docs/source/pages/getting_started/00_general_remarks.rst b/docs/source/pages/getting_started/00_general_remarks.rst index 3a6553f..e2ef8a8 100644 --- a/docs/source/pages/getting_started/00_general_remarks.rst +++ b/docs/source/pages/getting_started/00_general_remarks.rst @@ -41,4 +41,4 @@ created during the installation steps (see :ref:`Installation and Requirements`) activate the virtual environment. If done the other way around, the environment variables defined by the virtual environment will be overwritten by the icecube env-shell.sh. As a result, a different - python version will be used than the one from the virtual environment. \ No newline at end of file + python version will be used than the one from the virtual environment. diff --git a/docs/source/pages/getting_started/01_create_training_data.rst b/docs/source/pages/getting_started/01_create_training_data.rst index 7a015e1..ac7ff37 100644 --- a/docs/source/pages/getting_started/01_create_training_data.rst +++ b/docs/source/pages/getting_started/01_create_training_data.rst @@ -62,11 +62,14 @@ The settings we will use to achieve this are: from ic3_data.segments import CreateDNNData - tray.AddSegment(CreateDNNData, 'CreateDNNData', - NumDataBins=9, - RelativeTimeMethod='time_range', - DataFormat='pulse_summmary_clipped', - PulseKey='InIceDSTPulses') + tray.AddSegment( + CreateDNNData, + "CreateDNNData", + NumDataBins=9, + RelativeTimeMethod="time_range", + DataFormat="pulse_summmary_clipped", + PulseKey="InIceDSTPulses", + ) To be able to train our neural network, we must also define labels. In this tutorial we will use: @@ -75,11 +78,14 @@ In this tutorial we will use: from ic3_labels.labels.modules import MCLabelsCascades - tray.AddModule(MCLabelsCascades, 'MCLabelsCascades', - PulseMapString='InIceDSTPulses', - PrimaryKey='MCPrimary', - ExtendBoundary=0., - OutputKey='LabelsDeepLearning') + tray.AddModule( + MCLabelsCascades, + "MCLabelsCascades", + PulseMapString="InIceDSTPulses", + PrimaryKey="MCPrimary", + ExtendBoundary=0.0, + OutputKey="LabelsDeepLearning", + ) This will create a number of different labels and write them to an I3MapStringDouble. @@ -105,14 +111,19 @@ Now we are ready to save the training data to hdf5 files: from icecube import hdfwriter - tray.AddSegment(hdfwriter.I3HDFWriter, 'hdf', - Output='name_of_output_file.hdf5', - CompressionLevel=9, - Keys=['dnn_data_bin_values', - 'dnn_data_bin_indices', - 'dnn_data_global_time_offset', - 'LabelsDeepLearning'], - SubEventStreams=['InIceSplit']) + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="name_of_output_file.hdf5", + CompressionLevel=9, + Keys=[ + "dnn_data_bin_values", + "dnn_data_bin_indices", + "dnn_data_global_time_offset", + "LabelsDeepLearning", + ], + SubEventStreams=["InIceSplit"], + ) We can now put these modules together in a script and process the dataset 11883. To facilitate this process, we will use the mentioned `processing scripts `_, in which this is already done. @@ -174,4 +185,3 @@ To test the rest of the tutorial, it is enough to process one file from the training and validation set. However, the network will overfit on the training data which then only consists of about 700 events. - diff --git a/docs/source/pages/getting_started/04_apply_model.rst b/docs/source/pages/getting_started/04_apply_model.rst index 7e2dfd0..62a0688 100644 --- a/docs/source/pages/getting_started/04_apply_model.rst +++ b/docs/source/pages/getting_started/04_apply_model.rst @@ -42,93 +42,109 @@ following: @click.command() - @click.argument('input_file_pattern', type=click.Path(exists=True), - required=True, nargs=-1) - @click.option('-o', '--outfile', default='dnn_output', - help='Name of output file without file ending.') - @click.option('-m', '--model_names', - default='getting_started_model', - help='Parent directory of exported models.') - @click.option('-d', '--models_dir', - default='{DNN_HOME}/exported_models', - help='Parent directory of exported models.') - @click.option('-g', '--gcd_file', - default='/cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2012.56063_V1.i3.gz', - help='GCD File to use.') - @click.option('-j', '--num_cpus', - default=8, - help='Number of CPUs to use if run on CPU instead of GPU') - @click.option('--i3/--no-i3', default=True) - @click.option('--hdf5/--no-hdf5', default=True) - def main(input_file_pattern, outfile, model_names, models_dir, gcd_file, - num_cpus, i3, hdf5): + @click.argument("input_file_pattern", type=click.Path(exists=True), required=True, nargs=-1) + @click.option( + "-o", "--outfile", default="dnn_output", help="Name of output file without file ending." + ) + @click.option( + "-m", + "--model_names", + default="getting_started_model", + help="Parent directory of exported models.", + ) + @click.option( + "-d", + "--models_dir", + default="{DNN_HOME}/exported_models", + help="Parent directory of exported models.", + ) + @click.option( + "-g", + "--gcd_file", + default="/cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2012.56063_V1.i3.gz", + help="GCD File to use.", + ) + @click.option( + "-j", "--num_cpus", default=8, help="Number of CPUs to use if run on CPU instead of GPU" + ) + @click.option("--i3/--no-i3", default=True) + @click.option("--hdf5/--no-hdf5", default=True) + def main(input_file_pattern, outfile, model_names, models_dir, gcd_file, num_cpus, i3, hdf5): # create output directory if necessary base_path = os.path.dirname(outfile) if not os.path.isdir(base_path): - print('\nCreating directory: {}\n'.format(base_path)) + print("\nCreating directory: {}\n".format(base_path)) os.makedirs(base_path) # expand models_dir with environment variable - models_dir = models_dir.format(DNN_HOME=os.environ['DNN_HOME']) + models_dir = models_dir.format(DNN_HOME=os.environ["DNN_HOME"]) - HDF_keys = ['LabelsDeepLearning', 'MCPrimary', - 'OnlineL2_PoleL2MPEFit_MuEx', - 'OnlineL2_PoleL2MPEFit_TruncatedEnergy_AllBINS_Muon'] + HDF_keys = [ + "LabelsDeepLearning", + "MCPrimary", + "OnlineL2_PoleL2MPEFit_MuEx", + "OnlineL2_PoleL2MPEFit_TruncatedEnergy_AllBINS_Muon", + ] tray = I3Tray() # read in files file_name_list = [str(gcd_file)] file_name_list.extend(list(input_file_pattern)) - tray.AddModule('I3Reader', 'reader', Filenamelist=file_name_list) + tray.AddModule("I3Reader", "reader", Filenamelist=file_name_list) # Add labels - tray.AddModule(get_weighted_primary, 'getWeightedPrimary', - If=lambda f: not f.Has('MCPrimary')) - tray.AddModule(MCLabelsCascades, 'MCLabelsCascades', - PulseMapString='InIceDSTPulses', - PrimaryKey='MCPrimary', - ExtendBoundary=0., - OutputKey='LabelsDeepLearning') + tray.AddModule(get_weighted_primary, "getWeightedPrimary", If=lambda f: not f.Has("MCPrimary")) + tray.AddModule( + MCLabelsCascades, + "MCLabelsCascades", + PulseMapString="InIceDSTPulses", + PrimaryKey="MCPrimary", + ExtendBoundary=0.0, + OutputKey="LabelsDeepLearning", + ) # collect model and output names if isinstance(model_names, (str, unicode)): model_names = [str(model_names)] - output_names = ['DeepLearningReco_{}'.format(m) for m - in model_names] + output_names = ["DeepLearningReco_{}".format(m) for m in model_names] # Make sure DNN reco will be writen to hdf5 file for outbox in output_names: if outbox not in HDF_keys: HDF_keys.append(outbox) - HDF_keys.append(outbox + '_I3Particle') + HDF_keys.append(outbox + "_I3Particle") # Apply DNN Reco - tray.AddSegment(ApplyDNNRecos, 'ApplyDNNRecos', - pulse_key='InIceDSTPulses', - model_names=model_names, - output_keys=output_names, - models_dir=models_dir, - num_cpus=num_cpus, - ) + tray.AddSegment( + ApplyDNNRecos, + "ApplyDNNRecos", + pulse_key="InIceDSTPulses", + model_names=model_names, + output_keys=output_names, + models_dir=models_dir, + num_cpus=num_cpus, + ) # Write output if i3: - tray.AddModule("I3Writer", "EventWriter", - filename='{}.i3.bz2'.format(outfile)) + tray.AddModule("I3Writer", "EventWriter", filename="{}.i3.bz2".format(outfile)) if hdf5: - tray.AddSegment(hdfwriter.I3HDFWriter, 'hdf', - Output='{}.hdf5'.format(outfile), - CompressionLevel=9, - Keys=HDF_keys, - SubEventStreams=['InIceSplit']) - tray.AddModule('TrashCan', 'YesWeCan') + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(outfile), + CompressionLevel=9, + Keys=HDF_keys, + SubEventStreams=["InIceSplit"], + ) + tray.AddModule("TrashCan", "YesWeCan") tray.Execute() - if __name__ == '__main__': + if __name__ == "__main__": main() diff --git a/docs/source/pages/installation.rst b/docs/source/pages/installation.rst index 91865fc..429e537 100644 --- a/docs/source/pages/installation.rst +++ b/docs/source/pages/installation.rst @@ -269,4 +269,4 @@ try to create a tensorflow session and to import |dnn_reco|. newer machines: cobalt 5 through 8. On NPX, if running CPU jobs, you can request nodes with avx2 and ssse3 support by adding: ``requirements = (TARGET.has_avx2) && (TARGET.has_ssse3)``. This is only necessary for CPU jobs. For GPU jobs, - these requirements should not be set. \ No newline at end of file + these requirements should not be set. diff --git a/docs/source/pages/tutorial.rst b/docs/source/pages/tutorial.rst index 9adf1e0..b3fc874 100644 --- a/docs/source/pages/tutorial.rst +++ b/docs/source/pages/tutorial.rst @@ -37,4 +37,4 @@ it enters the convex hull around the IceCube detector. getting_started/01_create_training_data getting_started/02_train_model getting_started/03_monitor_progress - getting_started/04_apply_model \ No newline at end of file + getting_started/04_apply_model diff --git a/tests/README.txt b/tests/README.txt index 8c57c71..10ad0cd 100644 --- a/tests/README.txt +++ b/tests/README.txt @@ -30,6 +30,3 @@ rsync -avP madisonData:/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test # run test python test.py - - - diff --git a/tests/test.py b/tests/test.py index 359c08f..c632817 100644 --- a/tests/test.py +++ b/tests/test.py @@ -6,14 +6,14 @@ class bcolors: - HEADER = '\033[95m' - OKBLUE = '\033[94m' - OKGREEN = '\033[92m' - WARNING = '\033[93m' - FAIL = '\033[91m' - ENDC = '\033[0m' - BOLD = '\033[1m' - UNDERLINE = '\033[4m' + HEADER = "\033[95m" + OKBLUE = "\033[94m" + OKGREEN = "\033[92m" + WARNING = "\033[93m" + FAIL = "\033[91m" + ENDC = "\033[0m" + BOLD = "\033[1m" + UNDERLINE = "\033[4m" def warning(msg): @@ -25,89 +25,119 @@ def error(msg): files = [ - 'NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5', - 'NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5', - 'NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5', - 'NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5', - 'NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5', - 'NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5', + "NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5", + "NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5", + "NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5", + "NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5", + "NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5", + "NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5", ] keys = [ - 'dnn_data_bin_values', - 'dnn_data_bin_indices', - 'dnn_data_bin_exclusions', - 'dnn_data_global_time_offset', - 'LabelsDeepLearning', - 'DeepLearningReco_event_selection_cscdl3_300m_01', - 'DeepLearningReco_event_selection_cascade_monopod_starting_events_big_kernel_02', - 'DeepLearningReco_mese_v2__all_gl_both2', - 'DeepLearningReco_dnn_reco_paper_hese__m7_after_sys', + "dnn_data_bin_values", + "dnn_data_bin_indices", + "dnn_data_bin_exclusions", + "dnn_data_global_time_offset", + "LabelsDeepLearning", + "DeepLearningReco_event_selection_cscdl3_300m_01", + "DeepLearningReco_event_selection_cascade_monopod_starting_events_big_kernel_02", + "DeepLearningReco_mese_v2__all_gl_both2", + "DeepLearningReco_dnn_reco_paper_hese__m7_after_sys", ] -dir_original = 'test_data/dnn_reco_test_01_base_v0.2.0' -test_dirs = glob.glob('test_data/*') +dir_original = "test_data/dnn_reco_test_01_base_v0.2.0" +test_dirs = glob.glob("test_data/*") test_dirs.remove(dir_original) got_warning = False passed_test = True for dir_test in test_dirs: - print('\nNow testing {!r} against {!r}'.format(dir_test, dir_original)) + print("\nNow testing {!r} against {!r}".format(dir_test, dir_original)) for file_name in files: - print('\n\tNow testing {!r}'.format(file_name)) + print("\n\tNow testing {!r}".format(file_name)) for key in keys: try: df_original = pd.read_hdf( - os.path.join(dir_original, file_name), key=key) + os.path.join(dir_original, file_name), key=key + ) df_test = pd.read_hdf( - os.path.join(dir_test, file_name), key=key) + os.path.join(dir_test, file_name), key=key + ) except Exception as e: - warning('\t\tProblem with key {!r}'.format(key)) - warning('\t\t', e) + warning("\t\tProblem with key {!r}".format(key)) + warning("\t\t", e) got_warning = True assert (df_original.columns == df_test.columns).all() for k in df_original.columns: - if 'runtime' not in k: + if "runtime" not in k: if not np.allclose( - df_original[k].values, df_test[k].values, - atol=5e-6, rtol=5e-4): - if key == 'LabelsDeepLearning': - warning('\t\tWarning: mis-match for {}'.format(k)) + df_original[k].values, + df_test[k].values, + atol=5e-6, + rtol=5e-4, + ): + if key == "LabelsDeepLearning": + warning("\t\tWarning: mis-match for {}".format(k)) got_warning = True else: - error('\t\tError: mis-match for {}'.format(k)) + error("\t\tError: mis-match for {}".format(k)) passed_test = False - print('\t\t', key, k, - (df_original[k].values - df_test[k].values)) - print('\t\t', df_original[k].values) - print('\t\t', df_test[k].values) + print( + "\t\t", + key, + k, + (df_original[k].values - df_test[k].values), + ) + print("\t\t", df_original[k].values) + print("\t\t", df_test[k].values) else: - runtime_orig = np.mean(df_original[k].values) * 1000. - runtime_orig_std = np.std(df_original[k].values) * 1000. - runtime_test = np.mean(df_test[k].values) * 1000. - runtime_test_std = np.std(df_test[k].values) * 1000. - max_dev = max(2 * runtime_orig_std, 0.5*runtime_orig) + runtime_orig = np.mean(df_original[k].values) * 1000.0 + runtime_orig_std = np.std(df_original[k].values) * 1000.0 + runtime_test = np.mean(df_test[k].values) * 1000.0 + runtime_test_std = np.std(df_test[k].values) * 1000.0 + max_dev = max(2 * runtime_orig_std, 0.5 * runtime_orig) if np.abs(runtime_orig - runtime_test) > max_dev: - msg = '\t\t Runtimes: {:3.3f} +- {:3.3f}ms [base] ' - msg += '{:3.3f} +- {:3.3f}ms [test]' - print(msg.format( - runtime_orig, runtime_orig_std, - runtime_test, runtime_test_std)) + msg = "\t\t Runtimes: {:3.3f} +- {:3.3f}ms [base] " + msg += "{:3.3f} +- {:3.3f}ms [test]" + print( + msg.format( + runtime_orig, + runtime_orig_std, + runtime_test, + runtime_test_std, + ) + ) -print('\n====================') -print('=== Summary ========') -print('====================') +print("\n====================") +print("=== Summary ========") +print("====================") if got_warning: - print('=== ' + bcolors.WARNING + 'Warnings: {}'.format(got_warning) - + bcolors.ENDC) + print( + "=== " + + bcolors.WARNING + + "Warnings: {}".format(got_warning) + + bcolors.ENDC + ) else: - print('===' + bcolors.OKGREEN + ' Warnings: {}'.format(got_warning) - + bcolors.ENDC) + print( + "===" + + bcolors.OKGREEN + + " Warnings: {}".format(got_warning) + + bcolors.ENDC + ) if passed_test: - print('===' + bcolors.OKGREEN + ' Passed: {}'.format(passed_test) - + bcolors.ENDC) + print( + "===" + + bcolors.OKGREEN + + " Passed: {}".format(passed_test) + + bcolors.ENDC + ) else: - print('===' + bcolors.FAIL + ' Passed: {}'.format(passed_test) - + bcolors.ENDC) -print('====================\n') + print( + "===" + + bcolors.FAIL + + " Passed: {}".format(passed_test) + + bcolors.ENDC + ) +print("====================\n") diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh index 984d7e7..d355dc0 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh index 44aee13..224dd35 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh index 4391fd3..cbca599 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh index 6b75ba7..86b4aac 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh index bce520a..5da90fd 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh index d80efa3..7fef5c5 100755 --- a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ b/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -81,4 +81,3 @@ else fi fi exit $ICETRAY_RC - diff --git a/tests/test_trafo_model.py b/tests/test_trafo_model.py index fffbfd2..cee5cce 100644 --- a/tests/test_trafo_model.py +++ b/tests/test_trafo_model.py @@ -10,7 +10,7 @@ @click.command() -@click.argument('config_files', type=click.Path(exists=True), nargs=-1) +@click.argument("config_files", type=click.Path(exists=True), nargs=-1) def main(config_files): """Script to generate trafo model. @@ -29,36 +29,37 @@ def main(config_files): # Create Data Handler object data_handler = DataHandler(config) - data_handler.setup_with_test_data(config['trafo_data_file']) + data_handler.setup_with_test_data(config["trafo_data_file"]) settings_trafo = { - 'input_data': config['training_data_file'], - 'batch_size': config['batch_size'], - 'sample_randomly': True, - 'pick_random_files_forever': False, - 'file_capacity': 1, - 'batch_capacity': 2, - 'num_jobs': config['num_jobs'], - 'num_add_files': 0, - 'num_repetitions': 1, - 'init_values': config['DOM_init_values'], - } + "input_data": config["training_data_file"], + "batch_size": config["batch_size"], + "sample_randomly": True, + "pick_random_files_forever": False, + "file_capacity": 1, + "batch_capacity": 2, + "num_jobs": config["num_jobs"], + "num_add_files": 0, + "num_repetitions": 1, + "init_values": config["DOM_init_values"], + } trafo_data_generator = data_handler.get_batch_generator(**settings_trafo) # create TrafoModel data_transformer = DataTransformer( - data_handler=data_handler, - treat_doms_equally=config['trafo_treat_doms_equally'], - normalize_dom_data=config['trafo_normalize_dom_data'], - normalize_label_data=config['trafo_normalize_label_data'], - normalize_misc_data=config['trafo_normalize_misc_data'], - log_dom_bins=config['trafo_log_dom_bins'], - log_label_bins=config['trafo_log_label_bins'], - log_misc_bins=config['trafo_log_misc_bins'], - norm_constant=config['trafo_norm_constant']) + data_handler=data_handler, + treat_doms_equally=config["trafo_treat_doms_equally"], + normalize_dom_data=config["trafo_normalize_dom_data"], + normalize_label_data=config["trafo_normalize_label_data"], + normalize_misc_data=config["trafo_normalize_misc_data"], + log_dom_bins=config["trafo_log_dom_bins"], + log_label_bins=config["trafo_log_label_bins"], + log_misc_bins=config["trafo_log_misc_bins"], + norm_constant=config["trafo_norm_constant"], + ) # load trafo model from file - data_transformer.load_trafo_model(config['trafo_model_path']) + data_transformer.load_trafo_model(config["trafo_model_path"]) # Perform some basic tests for i in range(3): @@ -66,65 +67,90 @@ def main(config_files): # get a new batch batch = next(trafo_data_generator) - for test_data, data_type in zip(batch, - ['ic78', 'deepcore', - 'label', 'misc']): + for test_data, data_type in zip( + batch, ["ic78", "deepcore", "label", "misc"] + ): if test_data is None: continue test_data_trafo = data_transformer.transform(test_data, data_type) test_data_inv_trafo = data_transformer.inverse_transform( - test_data_trafo, data_type) - deviations = np.reshape(np.abs(test_data_inv_trafo - test_data), - [-1]) - - print('Checking numpy trafor for {}:'.format(data_type)) - print('\tChecking Mean:') - print('\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}'.format( + test_data_trafo, data_type + ) + deviations = np.reshape( + np.abs(test_data_inv_trafo - test_data), [-1] + ) + + print("Checking numpy trafor for {}:".format(data_type)) + print("\tChecking Mean:") + print( + "\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}".format( np.mean(test_data), np.mean(test_data_trafo), - np.mean(test_data_inv_trafo))) - print('\tChecking Standard Deviation:') - print('\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}'.format( + np.mean(test_data_inv_trafo), + ) + ) + print("\tChecking Standard Deviation:") + print( + "\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}".format( np.std(test_data, ddof=1), np.std(test_data_trafo, ddof=1), - np.std(test_data_inv_trafo, ddof=1))) - print('\tOriginal is same as inv-trafo(trafo(original)): ', - np.allclose(test_data, test_data_inv_trafo), - (test_data_inv_trafo == test_data).all()) - print('\tResiduals: min {:2.8f}, max {:2.8f}, mean {:2.8f}'.format( - np.min(deviations), - np.max(deviations), - np.mean(deviations))) + np.std(test_data_inv_trafo, ddof=1), + ) + ) + print( + "\tOriginal is same as inv-trafo(trafo(original)): ", + np.allclose(test_data, test_data_inv_trafo), + (test_data_inv_trafo == test_data).all(), + ) + print( + "\tResiduals: min {:2.8f}, max {:2.8f}, mean {:2.8f}".format( + np.min(deviations), np.max(deviations), np.mean(deviations) + ) + ) test_data = tf.constant(test_data) test_data_trafo = data_transformer.transform(test_data, data_type) test_data_inv_trafo = data_transformer.inverse_transform( - test_data_trafo, data_type) + test_data_trafo, data_type + ) sess = tf.Session() test_data, test_data_trafo, test_data_inv_trafo = sess.run( - [test_data, test_data_trafo, test_data_inv_trafo]) - deviations = np.reshape(np.abs(test_data_inv_trafo - test_data), - [-1]) - - print('Checking tensorflow trafor for {}:'.format(data_type)) - print('\tChecking Mean:') - print('\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}'.format( + [test_data, test_data_trafo, test_data_inv_trafo] + ) + deviations = np.reshape( + np.abs(test_data_inv_trafo - test_data), [-1] + ) + + print("Checking tensorflow trafor for {}:".format(data_type)) + print("\tChecking Mean:") + print( + "\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}".format( np.mean(test_data), np.mean(test_data_trafo), - np.mean(test_data_inv_trafo))) - print('\tChecking Standard Deviation:') - print('\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}'.format( + np.mean(test_data_inv_trafo), + ) + ) + print("\tChecking Standard Deviation:") + print( + "\tOrig: {:2.5f}, Trafo: {:2.5f}, Inv-Trafo: {:2.5f}".format( np.std(test_data, ddof=1), np.std(test_data_trafo, ddof=1), - np.std(test_data_inv_trafo, ddof=1))) - print('\tOriginal is same as inv-trafo(trafo(original)): ', - np.allclose(test_data, test_data_inv_trafo), - (test_data_inv_trafo == test_data).all()) - print('\tResiduals: min {:2.8f}, max {:2.8f}, mean {:2.8f}'.format( - np.min(deviations), np.max(deviations), np.mean(deviations))) - - -if __name__ == '__main__': + np.std(test_data_inv_trafo, ddof=1), + ) + ) + print( + "\tOriginal is same as inv-trafo(trafo(original)): ", + np.allclose(test_data, test_data_inv_trafo), + (test_data_inv_trafo == test_data).all(), + ) + print( + "\tResiduals: min {:2.8f}, max {:2.8f}, mean {:2.8f}".format( + np.min(deviations), np.max(deviations), np.mean(deviations) + ) + ) + + +if __name__ == "__main__": main() From 0deeae7b6b2d6dcd1b8c94399db65d8b4b376443 Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 14:06:42 +0200 Subject: [PATCH 3/9] Fix ruff/black errors --- dnn_reco/count_number_of_events.py | 5 -- dnn_reco/create_trafo_model.py | 0 dnn_reco/export_model.py | 1 - dnn_reco/ic3/llh.py | 2 - dnn_reco/ic3/llh_base.py | 3 - dnn_reco/ic3/modules.py | 11 +-- dnn_reco/ic3/segments.py | 2 - .../data/event_weights/event_weights.py | 7 +- .../event_weights/nersc_gnn_event_weights.py | 7 +- .../modules/data/filter/default_filter.py | 6 +- .../data/labels/biased_selection_labels.py | 12 ++- .../modules/data/labels/default_labels.py | 8 +- .../data/labels/event_selection_labels.py | 39 +++++----- .../data/labels/muon_scattering_labels.py | 11 ++- dnn_reco/modules/data/misc/default_misc.py | 10 +-- .../modules/evaluation/default_evaluation.py | 10 +-- dnn_reco/modules/loss/default_loss.py | 16 ++-- dnn_reco/modules/loss/track_loss.py | 12 ++- dnn_reco/modules/loss/utils/loss_utils.py | 8 +- .../modules/models/general_IC86_models.py | 34 +++------ dnn_reco/modules/models/utils/model_utils.py | 11 +-- dnn_reco/setup_manager.py | 1 - dnn_reco/train_model.py | 1 - dnn_reco/version_control.py | 2 - docs/make.bat | 72 +++++++++--------- docs/source/index.rst | 2 +- pyproject.toml | 1 - {tests => tests_manual}/README.txt | 0 {tests => tests_manual}/test.py | 0 .../l5/1/DNN_l5_00000002.hdf5 | Bin .../l5/1/DNN_l5_00000001.hdf5 | Bin .../l5/1/DNN_l5_00000002.hdf5 | Bin .../l5/1/DNN_l5_00000001.hdf5 | Bin .../l5/1/DNN_l5_00000002.hdf5 | Bin .../l5/1/DNN_l5_00000001.hdf5 | Bin .../l5/dnn_reco_test_01_base.yaml_0000 | 0 .../l5/jobs/1/job_DNN_l5_00000002.sh | 0 .../l5/dnn_reco_test_01_base.yaml_0000 | 0 .../l5/jobs/1/job_DNN_l5_00000001.sh | 0 .../l5/dnn_reco_test_01_base.yaml_0002 | 0 .../l5/jobs/1/job_DNN_l5_00000002.sh | 0 .../l5/dnn_reco_test_01_base.yaml_0002 | 0 .../l5/jobs/1/job_DNN_l5_00000001.sh | 0 .../l5/dnn_reco_test_01_base.yaml_0001 | 0 .../l5/jobs/1/job_DNN_l5_00000002.sh | 0 .../l5/dnn_reco_test_01_base.yaml_0001 | 0 .../l5/jobs/1/job_DNN_l5_00000001.sh | 0 {tests => tests_manual}/test_trafo_model.py | 1 - 48 files changed, 124 insertions(+), 171 deletions(-) mode change 100644 => 100755 dnn_reco/count_number_of_events.py mode change 100644 => 100755 dnn_reco/create_trafo_model.py mode change 100644 => 100755 dnn_reco/export_model.py mode change 100644 => 100755 dnn_reco/train_model.py rename {tests => tests_manual}/README.txt (100%) rename {tests => tests_manual}/test.py (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 (100%) rename {tests => tests_manual}/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh (100%) rename {tests => tests_manual}/test_trafo_model.py (99%) diff --git a/dnn_reco/count_number_of_events.py b/dnn_reco/count_number_of_events.py old mode 100644 new mode 100755 index 6684da4..c469909 --- a/dnn_reco/count_number_of_events.py +++ b/dnn_reco/count_number_of_events.py @@ -1,18 +1,13 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import division, print_function -import os import click -import ruamel.yaml as yaml from tqdm import tqdm import glob import pandas as pd from multiprocessing import Pool -from dnn_reco import misc from dnn_reco.setup_manager import SetupManager -from dnn_reco.data_handler import DataHandler -from dnn_reco.data_trafo import DataTransformer def count_num_events(data): diff --git a/dnn_reco/create_trafo_model.py b/dnn_reco/create_trafo_model.py old mode 100644 new mode 100755 diff --git a/dnn_reco/export_model.py b/dnn_reco/export_model.py old mode 100644 new mode 100755 index 8f5e1f0..d6f16fb --- a/dnn_reco/export_model.py +++ b/dnn_reco/export_model.py @@ -8,7 +8,6 @@ import ruamel.yaml as yaml import tensorflow as tf -from dnn_reco import misc from dnn_reco.setup_manager import SetupManager from dnn_reco.data_handler import DataHandler from dnn_reco.data_trafo import DataTransformer diff --git a/dnn_reco/ic3/llh.py b/dnn_reco/ic3/llh.py index 521cc90..c890e9d 100644 --- a/dnn_reco/ic3/llh.py +++ b/dnn_reco/ic3/llh.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- from __future__ import division, print_function import numpy as np import healpy as hp diff --git a/dnn_reco/ic3/llh_base.py b/dnn_reco/ic3/llh_base.py index 9a883d7..d50b875 100644 --- a/dnn_reco/ic3/llh_base.py +++ b/dnn_reco/ic3/llh_base.py @@ -1,12 +1,9 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- from __future__ import division, print_function import numpy as np import healpy as hp from uncertainties import unumpy, umath from scipy.stats import multivariate_normal from scipy.optimize import minimize -from tqdm import tqdm class DNN_LLH_Base(object): diff --git a/dnn_reco/ic3/modules.py b/dnn_reco/ic3/modules.py index 3544edf..3112c1b 100644 --- a/dnn_reco/ic3/modules.py +++ b/dnn_reco/ic3/modules.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- from __future__ import division, print_function import os import logging @@ -12,9 +10,6 @@ from icecube import icetray, dataclasses import ruamel.yaml as yaml -from ic3_data.container import DNNDataContainer -from ic3_data.data import DNNContainerHandler - from dnn_reco.setup_manager import SetupManager from dnn_reco.data_handler import DataHandler from dnn_reco.data_trafo import DataTransformer @@ -296,12 +291,12 @@ def Configure(self): if b ] self._non_zero_log_bins = [ - l - for l, b in zip( + log_bin + for log_bin, bin in zip( self.data_transformer.trafo_model["log_label_bins"], self._mask_labels, ) - if b + if bin ] def Process(self): diff --git a/dnn_reco/ic3/segments.py b/dnn_reco/ic3/segments.py index b9a9479..8d5b0fc 100644 --- a/dnn_reco/ic3/segments.py +++ b/dnn_reco/ic3/segments.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- from __future__ import division, print_function import os diff --git a/dnn_reco/modules/data/event_weights/event_weights.py b/dnn_reco/modules/data/event_weights/event_weights.py index 58b45e0..b2552e7 100644 --- a/dnn_reco/modules/data/event_weights/event_weights.py +++ b/dnn_reco/modules/data/event_weights/event_weights.py @@ -1,7 +1,3 @@ -from __future__ import division, print_function -import tensorflow as tf - - """ All event weighting functions must have the following signature: @@ -31,6 +27,9 @@ Shape: [batch_size, 1] """ +from __future__ import division, print_function +import tensorflow as tf + def event_selection_weight( config, data_handler, data_transformer, shared_objects, *args, **kwargs diff --git a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py index f20e1f7..dedd91c 100644 --- a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py +++ b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py @@ -1,7 +1,3 @@ -from __future__ import division, print_function -import tensorflow as tf - - """ All event weighting functions must have the following signature: @@ -31,6 +27,9 @@ Shape: [batch_size, 1] """ +from __future__ import division, print_function +import tensorflow as tf + def nersc_gnn_weight( config, data_handler, data_transformer, shared_objects, *args, **kwargs diff --git a/dnn_reco/modules/data/filter/default_filter.py b/dnn_reco/modules/data/filter/default_filter.py index 29876f3..a37d837 100644 --- a/dnn_reco/modules/data/filter/default_filter.py +++ b/dnn_reco/modules/data/filter/default_filter.py @@ -1,6 +1,3 @@ -from __future__ import division, print_function -import numpy as np - """ All filter functions must have the following parameters and return values: @@ -40,6 +37,9 @@ shape: [batch_size] """ +from __future__ import division, print_function +import numpy as np + def dummy_filter( data_handler, diff --git a/dnn_reco/modules/data/labels/biased_selection_labels.py b/dnn_reco/modules/data/labels/biased_selection_labels.py index 20892c8..63f0ddc 100644 --- a/dnn_reco/modules/data/labels/biased_selection_labels.py +++ b/dnn_reco/modules/data/labels/biased_selection_labels.py @@ -1,10 +1,3 @@ -from __future__ import division, print_function -import h5py -import pandas as pd -import numpy as np - -from dnn_reco.modules.data.labels.default_labels import simple_label_loader - """ All label functions must have the following parameters and return values: @@ -34,6 +27,11 @@ The names of the labels """ +from __future__ import division, print_function +import h5py +import pandas as pd +import numpy as np + def biased_muongun(input_data, config, label_names=None, *args, **kwargs): """Default muon scattering labels in addtion to relaxed thresholds for diff --git a/dnn_reco/modules/data/labels/default_labels.py b/dnn_reco/modules/data/labels/default_labels.py index a55e172..42888e8 100644 --- a/dnn_reco/modules/data/labels/default_labels.py +++ b/dnn_reco/modules/data/labels/default_labels.py @@ -1,7 +1,3 @@ -from __future__ import division, print_function -import pandas as pd -import numpy as np - """ All label functions must have the following parameters and return values: @@ -31,6 +27,10 @@ The names of the labels """ +from __future__ import division, print_function +import pandas as pd +import numpy as np + def simple_label_loader(input_data, config, label_names=None, *args, **kwargs): """Simple Label Loader. diff --git a/dnn_reco/modules/data/labels/event_selection_labels.py b/dnn_reco/modules/data/labels/event_selection_labels.py index e2de864..9644273 100644 --- a/dnn_reco/modules/data/labels/event_selection_labels.py +++ b/dnn_reco/modules/data/labels/event_selection_labels.py @@ -1,7 +1,3 @@ -from __future__ import division, print_function -import pandas as pd -import numpy as np - """ All label functions must have the following parameters and return values: @@ -31,6 +27,10 @@ The names of the labels """ +from __future__ import division, print_function +import pandas as pd +import numpy as np + def astroness(input_data, config, label_names=None, *args, **kwargs): """Astroness of an event. @@ -65,16 +65,11 @@ def astroness(input_data, config, label_names=None, *args, **kwargs): The names of the labels """ - if "labels_starting_cascades_lengths" in config: - lengths = [int(l) for l in config["labels_starting_cascades_lengths"]] - else: - lengths = [25, 50, 100] - with pd.HDFStore(input_data, mode="r") as f: _primary = f["MCPrimary"] try: _weights = f["weights_mese"] - except KeyError as error: + except KeyError: # load dummy weights, this should only happen with Corsika files, # e.g. astroness should be all zero, which is checked by assert _weights = f["weights"] @@ -161,13 +156,17 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): """ if "labels_starting_cascades_lengths" in config: - lengths = [int(l) for l in config["labels_starting_cascades_lengths"]] + lengths = [ + int(cascade_len) + for cascade_len in config["labels_starting_cascades_lengths"] + ] else: lengths = [25, 50, 100] if "labels_starting_cascades_distances" in config: distances = [ - float(l) for l in config["labels_starting_cascades_distances"] + float(dist) + for dist in config["labels_starting_cascades_distances"] ] else: distances = [-60.0, 0.0, 60, 300.0, float("inf")] @@ -195,43 +194,43 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): label_name_base = "p_starting_cascade_L{}_D{}" label_dict = {} label_names_list = [] - for l in lengths: + for length in lengths: - mask = np.logical_and(is_neutrino, cascade_lengths <= l) + mask = np.logical_and(is_neutrino, cascade_lengths <= length) # get hull distance -60m if -60.0 in distances: - name = label_name_base.format(l, "m60") + name = label_name_base.format(length, "m60") label_names_list.append(name) label_dict[name] = np.logical_and(mask, _labels_m60["p_starting"]) # get hull distance 0m if 0.0 in distances: - name = label_name_base.format(l, "0") + name = label_name_base.format(length, "0") label_names_list.append(name) label_dict[name] = np.logical_and(mask, _labels["p_starting"]) # get hull distance 60m if 60.0 in distances: - name = label_name_base.format(l, "60") + name = label_name_base.format(length, "60") label_names_list.append(name) label_dict[name] = np.logical_and(mask, _labels_p60["p_starting"]) # get hull distance 150m if 150.0 in distances: - name = label_name_base.format(l, "150") + name = label_name_base.format(length, "150") label_names_list.append(name) label_dict[name] = np.logical_and(mask, _labels_p150["p_starting"]) # get hull distance 300m if 300.0 in distances: - name = label_name_base.format(l, "300") + name = label_name_base.format(length, "300") label_names_list.append(name) label_dict[name] = np.logical_and(mask, _labels["p_starting_300m"]) # get inf hull distance if float("inf") in distances: - name = label_name_base.format(l, "inf") + name = label_name_base.format(length, "inf") label_names_list.append(name) label_dict[name] = mask diff --git a/dnn_reco/modules/data/labels/muon_scattering_labels.py b/dnn_reco/modules/data/labels/muon_scattering_labels.py index 27ba7d8..42afd3c 100644 --- a/dnn_reco/modules/data/labels/muon_scattering_labels.py +++ b/dnn_reco/modules/data/labels/muon_scattering_labels.py @@ -1,9 +1,3 @@ -from __future__ import division, print_function -import pandas as pd -import numpy as np - -from dnn_reco.modules.data.labels.default_labels import simple_label_loader - """ All label functions must have the following parameters and return values: @@ -33,6 +27,11 @@ The names of the labels """ +from __future__ import division, print_function +import numpy as np + +from dnn_reco.modules.data.labels.default_labels import simple_label_loader + def muon_scattering(input_data, config, label_names=None, *args, **kwargs): """Default muon scattering labels in addtion to relaxed thresholds for diff --git a/dnn_reco/modules/data/misc/default_misc.py b/dnn_reco/modules/data/misc/default_misc.py index 5646742..b67497d 100644 --- a/dnn_reco/modules/data/misc/default_misc.py +++ b/dnn_reco/modules/data/misc/default_misc.py @@ -1,8 +1,3 @@ -from __future__ import division, print_function -import h5py -import pandas as pd -import numpy as np - """ All misc functions must have the following parameters and return values: @@ -32,6 +27,11 @@ The names of the misc variables. """ +from __future__ import division, print_function +import h5py +import pandas as pd +import numpy as np + def dummy_misc_loader(input_data, config, misc_names=None, *args, **kwargs): """Dummy misc loader. diff --git a/dnn_reco/modules/evaluation/default_evaluation.py b/dnn_reco/modules/evaluation/default_evaluation.py index 74c6964..277e0b4 100644 --- a/dnn_reco/modules/evaluation/default_evaluation.py +++ b/dnn_reco/modules/evaluation/default_evaluation.py @@ -1,8 +1,3 @@ -from __future__ import division, print_function -import numpy as np - -from dnn_reco.utils.angles import get_angle_deviation, get_angle - """ All defined evaluation functions must have the following signature: @@ -37,6 +32,11 @@ Arbitrary keyword arguments. """ +from __future__ import division, print_function +import numpy as np + +from dnn_reco.utils.angles import get_angle_deviation, get_angle + def eval_direction( feed_dict_train, diff --git a/dnn_reco/modules/loss/default_loss.py b/dnn_reco/modules/loss/default_loss.py index 3ed4348..9d0f56c 100644 --- a/dnn_reco/modules/loss/default_loss.py +++ b/dnn_reco/modules/loss/default_loss.py @@ -1,11 +1,3 @@ -from __future__ import division, print_function -import tensorflow as tf -import numpy as np - -from dnn_reco import misc -from dnn_reco.utils import angles as angle_utils -from dnn_reco.modules.loss.utils import loss_utils - """ All defined models must have the following signature: @@ -36,6 +28,14 @@ Shape: label_shape (same shape as labels) """ +from __future__ import division, print_function +import tensorflow as tf +import numpy as np + +from dnn_reco import misc +from dnn_reco.utils import angles as angle_utils +from dnn_reco.modules.loss.utils import loss_utils + def weighted_mse( config, data_handler, data_transformer, shared_objects, *args, **kwargs diff --git a/dnn_reco/modules/loss/track_loss.py b/dnn_reco/modules/loss/track_loss.py index 4583bee..d2b6c54 100644 --- a/dnn_reco/modules/loss/track_loss.py +++ b/dnn_reco/modules/loss/track_loss.py @@ -1,10 +1,3 @@ -from __future__ import division, print_function -import tensorflow as tf -import numpy as np - -from dnn_reco import misc -from dnn_reco.modules.loss.utils import loss_utils - """ All defined models must have the following signature: @@ -35,6 +28,11 @@ Shape: label_shape (same shape as labels) """ +from __future__ import division, print_function +import tensorflow as tf + +from dnn_reco.modules.loss.utils import loss_utils + def track_pos_mse( config, data_handler, data_transformer, shared_objects, *args, **kwargs diff --git a/dnn_reco/modules/loss/utils/loss_utils.py b/dnn_reco/modules/loss/utils/loss_utils.py index 3422c38..b4c1775 100644 --- a/dnn_reco/modules/loss/utils/loss_utils.py +++ b/dnn_reco/modules/loss/utils/loss_utils.py @@ -1,13 +1,13 @@ +""" +This file contains commonly used utility functions to compute loss. +""" + from __future__ import division, print_function import numpy as np import tensorflow as tf from dnn_reco import misc -""" -This file contains commonly used utility functions to compute loss. -""" - def add_logging_info(data_handler, shared_objects): """Add some TensorBoard logging infor for the labels. diff --git a/dnn_reco/modules/models/general_IC86_models.py b/dnn_reco/modules/models/general_IC86_models.py index 48f12a7..27f8f1a 100644 --- a/dnn_reco/modules/models/general_IC86_models.py +++ b/dnn_reco/modules/models/general_IC86_models.py @@ -1,15 +1,3 @@ -from __future__ import division, print_function -import numpy as np -import tensorflow as tf - -# Check and allow for newer TFScripts versions -try: - from tfscripts.compat.v1 import layers as tfs -except ImportError: - from tfscripts import layers as tfs - -from dnn_reco.modules.models.utils.model_utils import preprocess_icecube_data - """ All defined models must have the following signature: @@ -46,6 +34,18 @@ Can optionally be an empty list. """ +from __future__ import division, print_function +import numpy as np +import tensorflow as tf + +# Check and allow for newer TFScripts versions +try: + from tfscripts.compat.v1 import layers as tfs +except ImportError: + from tfscripts import layers as tfs + +from dnn_reco.modules.models.utils.model_utils import preprocess_icecube_data + def general_model_IC86( is_training, @@ -153,11 +153,6 @@ def general_model_IC86( ) # combine layers - num_features = ( - num_features_DeepCore_1 - + num_features_DeepCore_2 - + num_features_IC78 - ) layer_flat = tf.concat( [layer_flat_IC78, layer_flat_DeepCore_1, layer_flat_DeepCore_2], axis=1, @@ -441,11 +436,6 @@ def general_model_IC86_opt4( ) # combine layers - num_features = ( - num_features_DeepCore_1 - + num_features_DeepCore_2 - + num_features_IC78 - ) layer_flat = tf.concat( [layer_flat_IC78, layer_flat_DeepCore_1, layer_flat_DeepCore_2], axis=1, diff --git a/dnn_reco/modules/models/utils/model_utils.py b/dnn_reco/modules/models/utils/model_utils.py index 2eeefa5..a07ad3f 100644 --- a/dnn_reco/modules/models/utils/model_utils.py +++ b/dnn_reco/modules/models/utils/model_utils.py @@ -1,15 +1,10 @@ -from __future__ import division, print_function -import tensorflow as tf - -# Check and allow for newer TFScripts versions -try: - from tfscripts.compat.v1 import layers as tfs -except ImportError: - from tfscripts import layers as tfs """ This file contains commonly used utility functions to build icecube nn models. """ +from __future__ import division, print_function +import tensorflow as tf + def preprocess_icecube_data(is_training, shared_objects): """Performs some basic preprocessing of IceCube input data. diff --git a/dnn_reco/setup_manager.py b/dnn_reco/setup_manager.py index 8a512f7..4b39097 100644 --- a/dnn_reco/setup_manager.py +++ b/dnn_reco/setup_manager.py @@ -4,7 +4,6 @@ import tensorflow as tf import ruamel.yaml as yaml -from dnn_reco import misc from dnn_reco import version_control # suppress natural naming warnings diff --git a/dnn_reco/train_model.py b/dnn_reco/train_model.py old mode 100644 new mode 100755 index d4efdbe..fc5f1fe --- a/dnn_reco/train_model.py +++ b/dnn_reco/train_model.py @@ -4,7 +4,6 @@ import click import tensorflow as tf -from dnn_reco import misc from dnn_reco.setup_manager import SetupManager from dnn_reco.data_handler import DataHandler from dnn_reco.data_trafo import DataTransformer diff --git a/dnn_reco/version_control.py b/dnn_reco/version_control.py index 7bcaf6f..59c943c 100644 --- a/dnn_reco/version_control.py +++ b/dnn_reco/version_control.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- import os import git import pkg_resources diff --git a/docs/make.bat b/docs/make.bat index 47965cc..e0ca019 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -1,36 +1,36 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build -set SPHINXPROJ=IceCubeDNNreconstruction - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build +set SPHINXPROJ=IceCubeDNNreconstruction + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docs/source/index.rst b/docs/source/index.rst index 4f72705..eda2282 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,7 +1,7 @@ .. IceCube DNN reconstruction documentation master file, created by sphinx-quickstart on Wed Apr 17 21:56:02 2019. You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. + contain the root ``toctree`` directive. Welcome to IceCube DNN reconstruction's documentation! ****************************************************** diff --git a/pyproject.toml b/pyproject.toml index 0be614c..84d5b01 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,6 @@ lint.ignore = [ "TCH", # flake8-type-checking "S101", # assert-used "COM812", # trailing comma to not insert after **kwargs, which is a syntax error prior to py3.6 - #"F401", # imported but unused. NOTE: sooner or later, we should not ignore this ] line-length = 79 target-version = "py38" diff --git a/tests/README.txt b/tests_manual/README.txt similarity index 100% rename from tests/README.txt rename to tests_manual/README.txt diff --git a/tests/test.py b/tests_manual/test.py similarity index 100% rename from tests/test.py rename to tests_manual/test.py diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001.hdf5 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 rename to 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tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 diff --git a/tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh similarity index 100% rename from tests/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh rename to tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh diff --git a/tests/test_trafo_model.py b/tests_manual/test_trafo_model.py similarity index 99% rename from tests/test_trafo_model.py rename to tests_manual/test_trafo_model.py index cee5cce..8f6ca8c 100644 --- a/tests/test_trafo_model.py +++ b/tests_manual/test_trafo_model.py @@ -3,7 +3,6 @@ import numpy as np import tensorflow as tf -from dnn_reco import misc from dnn_reco.setup_manager import SetupManager from dnn_reco.data_handler import DataHandler from dnn_reco.data_trafo import DataTransformer From da334d69de76de4d688762bd6bbd5d0d7c0a1b44 Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 15:36:59 +0200 Subject: [PATCH 4/9] Fix spelling --- .pre-commit-config.yaml | 4 +++- configs/biased_selection/biased_muongun.yaml | 2 +- ...ascade_monopod_cascade_dir_big_kernel.yaml | 2 +- ..._monopod_starting_cascades_big_kernel.yaml | 2 +- .../cascade_monopod_starting_events.yaml | 2 +- ...de_monopod_starting_events_big_kernel.yaml | 2 +- ...scade_monopod_starting_pid_big_kernel.yaml | 2 +- .../dnn_cscd_l3a_starting_events.yaml | 2 +- ...cut2_starting_events_300m_fast_medium.yaml | 2 +- ..._cut2_starting_events_300m_fast_small.yaml | 2 +- ...cscd_l3b_starting_cascades_big_kernel.yaml | 6 ++--- ...b_starting_cascades_big_kernel_strict.yaml | 6 ++--- .../dnn_cscd_l3b_starting_events.yaml | 2 +- ...n_cscd_l3b_starting_events_big_kernel.yaml | 6 ++--- ...scd_l3b_starting_events_default_model.yaml | 2 +- ...rting_events_default_model_unweighted.yaml | 2 +- ...n_cscd_l3b_starting_events_unweighted.yaml | 2 +- ...cut2_starting_events_300m_fast_medium.yaml | 2 +- .../dnn_cscd_l3c_pid_nutau_cc_vs_nue_cc.yaml | 2 +- ...dnn_cscd_l3c_track_in_detector_length.yaml | 2 +- .../dnn_cscd_l3c_track_numu_cc_vs_nue_cc.yaml | 2 +- ...nn_cscd_l3c_track_numu_cc_vs_starting.yaml | 2 +- .../egen_seed/event_selection_egen_seed.yaml | 10 ++++----- .../event_selection_egen_seed_dir.yaml | 10 ++++----- .../event_selection_egen_seed_energy.yaml | 10 ++++----- .../event_selection_egen_seed_pos.yaml | 10 ++++----- .../event_selection_egen_seed_time.yaml | 10 ++++----- .../egen_vertex/egen_vertex_pid.yaml | 2 +- .../egen_vertex_pid_nue_cc_vs_nue_nc.yaml | 2 +- .../egen_vertex_pid_nue_cc_vs_nutau_cc.yaml | 2 +- ...rtex_starting_events_300m_fast_medium.yaml | 2 +- ..._vertex_starting_nue_300m_fast_medium.yaml | 2 +- .../egen_vertex_track_in_detector_length.yaml | 2 +- .../egen_vertex_track_numu_cc_vs_nue_cc.yaml | 2 +- ...egen_vertex_track_numu_cc_vs_starting.yaml | 2 +- .../event_selection_astroness.yaml | 2 +- .../event_selection_cascade_dir.yaml | 2 +- .../event_selection_cascade_energy.yaml | 2 +- .../event_selection_cascade_pos.yaml | 2 +- .../event_selection_cascade_t.yaml | 2 +- .../event_selection_entering.yaml | 2 +- ...ent_selection_nugen_l2_reconstruction.yaml | 2 +- ...selection_nugen_l2_reconstruction_big.yaml | 2 +- .../event_selection/event_selection_pid.yaml | 2 +- .../event_selection_starting_cascades.yaml | 2 +- .../event_selection_starting_events.yaml | 2 +- .../event_selection_starting_events_big.yaml | 2 +- .../event_selection_track_detectorlength.yaml | 2 +- .../event_selection_track_dir.yaml | 2 +- .../event_selection_track_energy.yaml | 2 +- .../event_selection_track_length.yaml | 2 +- .../event_selection_track_pos.yaml | 2 +- .../event_selection_upgoing_tracks.yaml | 4 ++-- ..._cascades_150m_red_summary_stats_fast.yaml | 2 +- ...ng_events_150m_red_summary_stats_fast.yaml | 2 +- ...ng_events_300m_red_summary_stats_fast.yaml | 2 +- ...tarting_events_red_summary_stats_fast.yaml | 2 +- ...starting_events_total_dom_charge_fast.yaml | 2 +- ..._selection_veto_classifier__downgoing.yaml | 2 +- ...election_veto_classifier_vertex_early.yaml | 2 +- ...to_classifier_vertex_early__downgoing.yaml | 2 +- ...o_classifier_vertex_early_with_energy.yaml | 2 +- configs/muon_scattering/muon_scattering.yaml | 2 +- ...scattering_L200_Lb100_La100_E10_r0_40.yaml | 2 +- .../muon_scattering_energy.yaml | 2 +- .../muon_scattering_tracks.yaml | 2 +- .../stopping_muons_hoinka_no_filter.yaml | 2 +- ...ing_muons_hoinka_no_filter__direction.yaml | 2 +- ...opping_muons_hoinka_no_filter__energy.yaml | 2 +- ...g_muons_hoinka_no_filter__stopping_rz.yaml | 2 +- dnn_reco/data_handler.py | 14 ++++++------ dnn_reco/data_trafo.py | 14 ++++++------ dnn_reco/ic3/llh.py | 8 +++---- dnn_reco/ic3/llh_base.py | 12 +++++----- dnn_reco/ic3/modules.py | 14 ++++++------ dnn_reco/ic3/segments.py | 2 +- dnn_reco/model.py | 12 +++++----- .../data/event_weights/event_weights.py | 6 ++--- .../event_weights/nersc_gnn_event_weights.py | 4 ++-- .../data/labels/biased_selection_labels.py | 4 ++-- .../modules/data/labels/default_labels.py | 2 +- .../data/labels/event_selection_labels.py | 4 ++-- .../data/labels/muon_scattering_labels.py | 4 ++-- dnn_reco/modules/data/misc/default_misc.py | 6 ++--- .../modules/evaluation/default_evaluation.py | 6 ++--- dnn_reco/modules/loss/default_loss.py | 22 +++++++++---------- dnn_reco/modules/loss/track_loss.py | 10 ++++----- dnn_reco/modules/loss/utils/loss_utils.py | 8 +++---- .../modules/models/general_IC86_models.py | 6 ++--- dnn_reco/modules/models/utils/model_utils.py | 2 +- dnn_reco/setup_manager.py | 6 ++--- docs/source/pages/bootcamp.rst | 2 +- .../pages/bootcamp/00_bootcamp_setup.rst | 2 +- .../pages/bootcamp/01_bootcamp_train.rst | 10 ++++----- .../pages/bootcamp/02_bootcamp_monitor.rst | 4 ++-- .../pages/bootcamp/03_bootcamp_apply.rst | 2 +- docs/source/pages/code.rst | 8 +++---- docs/source/pages/config.rst | 6 ++--- .../01_create_training_data.rst | 8 +++---- .../pages/getting_started/02_train_model.rst | 10 ++++----- .../getting_started/03_monitor_progress.rst | 4 ++-- .../pages/getting_started/04_apply_model.rst | 2 +- docs/source/pages/installation.rst | 4 ++-- docs/source/pages/models.rst | 2 +- pyproject.toml | 3 +++ tests_manual/test.py | 4 ++-- 106 files changed, 219 insertions(+), 214 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f2d4143..62eebfa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,7 +26,9 @@ repos: rev: v2.2.6 hooks: - id: codespell - args: [-L, livetime] + args: [--toml, pyproject.toml] + additional_dependencies: + - tomli - repo: https://github.com/pre-commit/pygrep-hooks rev: v1.10.0 hooks: diff --git a/configs/biased_selection/biased_muongun.yaml b/configs/biased_selection/biased_muongun.yaml index 3655a00..16d1637 100644 --- a/configs/biased_selection/biased_muongun.yaml +++ b/configs/biased_selection/biased_muongun.yaml @@ -162,7 +162,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/cascade_monopod_cascade_dir_big_kernel.yaml b/configs/event_selection/cascade_monopod_cascade_dir_big_kernel.yaml index 1977115..ea5eb57 100644 --- a/configs/event_selection/cascade_monopod_cascade_dir_big_kernel.yaml +++ b/configs/event_selection/cascade_monopod_cascade_dir_big_kernel.yaml @@ -278,7 +278,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/cascade_monopod_starting_cascades_big_kernel.yaml b/configs/event_selection/cascade_monopod_starting_cascades_big_kernel.yaml index 2fe4385..9fab5f5 100644 --- a/configs/event_selection/cascade_monopod_starting_cascades_big_kernel.yaml +++ b/configs/event_selection/cascade_monopod_starting_cascades_big_kernel.yaml @@ -278,7 +278,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/cascade_monopod_starting_events.yaml b/configs/event_selection/cascade_monopod_starting_events.yaml index 88a9f52..32fdd78 100644 --- a/configs/event_selection/cascade_monopod_starting_events.yaml +++ b/configs/event_selection/cascade_monopod_starting_events.yaml @@ -278,7 +278,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/cascade_monopod_starting_events_big_kernel.yaml b/configs/event_selection/cascade_monopod_starting_events_big_kernel.yaml index 0d41e63..87667a9 100644 --- a/configs/event_selection/cascade_monopod_starting_events_big_kernel.yaml +++ b/configs/event_selection/cascade_monopod_starting_events_big_kernel.yaml @@ -278,7 +278,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/cascade_monopod_starting_pid_big_kernel.yaml b/configs/event_selection/cascade_monopod_starting_pid_big_kernel.yaml index 54d9e96..0e508bc 100644 --- a/configs/event_selection/cascade_monopod_starting_pid_big_kernel.yaml +++ b/configs/event_selection/cascade_monopod_starting_pid_big_kernel.yaml @@ -278,7 +278,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3a_starting_events.yaml b/configs/event_selection/dnn_cscd_l3a_starting_events.yaml index ad692b9..ae106ce 100644 --- a/configs/event_selection/dnn_cscd_l3a_starting_events.yaml +++ b/configs/event_selection/dnn_cscd_l3a_starting_events.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_medium.yaml b/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_medium.yaml index 3faa6f4..fe8d83e 100644 --- a/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_medium.yaml +++ b/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_medium.yaml @@ -202,7 +202,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_small.yaml b/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_small.yaml index 284f8e6..ea98219 100644 --- a/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_small.yaml +++ b/configs/event_selection/dnn_cscd_l3b/dnn_cscd_l3b_cut2_starting_events_300m_fast_small.yaml @@ -202,7 +202,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel.yaml b/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel.yaml index 7629de7..2f8097f 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel.yaml @@ -91,7 +91,7 @@ # Additional MuonGun at CscdL3 '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # Manuel Silva's MuonGun + # Manuel's MuonGun '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # Additional MuonGun biased simulation: corner clippers @@ -177,7 +177,7 @@ # # Additional MuonGun at CscdL3 # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # # Manuel Silva's MuonGun + # # Manuel's MuonGun # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # # Additional MuonGun biased simulation: corner clippers @@ -323,7 +323,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel_strict.yaml b/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel_strict.yaml index 1180b25..6c1aae1 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel_strict.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_cascades_big_kernel_strict.yaml @@ -91,7 +91,7 @@ # Additional MuonGun at CscdL3 '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # Manuel Silva's MuonGun + # Manuel's MuonGun '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # Additional MuonGun biased simulation: corner clippers @@ -177,7 +177,7 @@ # # Additional MuonGun at CscdL3 # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # # Manuel Silva's MuonGun + # # Manuel's MuonGun # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # # Additional MuonGun biased simulation: corner clippers @@ -323,7 +323,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_events.yaml b/configs/event_selection/dnn_cscd_l3b_starting_events.yaml index e7b3604..4ea1fe1 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_events.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_events.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_events_big_kernel.yaml b/configs/event_selection/dnn_cscd_l3b_starting_events_big_kernel.yaml index 797194a..3d854f2 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_events_big_kernel.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_events_big_kernel.yaml @@ -91,7 +91,7 @@ # Additional MuonGun at CscdL3 '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # Manuel Silva's MuonGun + # Manuel's MuonGun '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # Additional MuonGun biased simulation: corner clippers @@ -177,7 +177,7 @@ # # Additional MuonGun at CscdL3 # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/12*/0.99_eff/00*000-00*999/*.hdf5', - # # Manuel Silva's MuonGun + # # Manuel's MuonGun # '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/dnn_cscd_l3b/MuonGun/2131*/00*000-00*999/*.hdf5', # # Additional MuonGun biased simulation: corner clippers @@ -323,7 +323,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_events_default_model.yaml b/configs/event_selection/dnn_cscd_l3b_starting_events_default_model.yaml index bfcd15a..b1fbedf 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_events_default_model.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_events_default_model.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_events_default_model_unweighted.yaml b/configs/event_selection/dnn_cscd_l3b_starting_events_default_model_unweighted.yaml index 129a42b..ac55784 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_events_default_model_unweighted.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_events_default_model_unweighted.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3b_starting_events_unweighted.yaml b/configs/event_selection/dnn_cscd_l3b_starting_events_unweighted.yaml index 2738b62..c0cdb77 100644 --- a/configs/event_selection/dnn_cscd_l3b_starting_events_unweighted.yaml +++ b/configs/event_selection/dnn_cscd_l3b_starting_events_unweighted.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_cut2_starting_events_300m_fast_medium.yaml b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_cut2_starting_events_300m_fast_medium.yaml index 1b6c3bd..dc43687 100644 --- a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_cut2_starting_events_300m_fast_medium.yaml +++ b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_cut2_starting_events_300m_fast_medium.yaml @@ -246,7 +246,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_pid_nutau_cc_vs_nue_cc.yaml b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_pid_nutau_cc_vs_nue_cc.yaml index 039fc06..9d4a2ea 100644 --- a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_pid_nutau_cc_vs_nue_cc.yaml +++ b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_pid_nutau_cc_vs_nue_cc.yaml @@ -227,7 +227,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_in_detector_length.yaml b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_in_detector_length.yaml index afa6cb3..29239fd 100644 --- a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_in_detector_length.yaml +++ b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_in_detector_length.yaml @@ -209,7 +209,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_nue_cc.yaml b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_nue_cc.yaml index 8b9c7ae..dd14b63 100644 --- a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_nue_cc.yaml +++ b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_nue_cc.yaml @@ -225,7 +225,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_starting.yaml b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_starting.yaml index 7655aae..e149d7b 100644 --- a/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_starting.yaml +++ b/configs/event_selection/dnn_cscd_l3c/dnn_cscd_l3c_track_numu_cc_vs_starting.yaml @@ -208,7 +208,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_seed/event_selection_egen_seed.yaml b/configs/event_selection/egen_seed/event_selection_egen_seed.yaml index 9dff1c1..e7ab352 100644 --- a/configs/event_selection/egen_seed/event_selection_egen_seed.yaml +++ b/configs/event_selection/egen_seed/event_selection_egen_seed.yaml @@ -18,7 +18,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -30,7 +30,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -39,13 +39,13 @@ ] 'validation_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] 'test_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] @@ -166,7 +166,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_seed/event_selection_egen_seed_dir.yaml b/configs/event_selection/egen_seed/event_selection_egen_seed_dir.yaml index 1e9e46d..a5b124a 100644 --- a/configs/event_selection/egen_seed/event_selection_egen_seed_dir.yaml +++ b/configs/event_selection/egen_seed/event_selection_egen_seed_dir.yaml @@ -18,7 +18,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -30,7 +30,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -39,13 +39,13 @@ ] 'validation_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] 'test_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] @@ -166,7 +166,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_seed/event_selection_egen_seed_energy.yaml b/configs/event_selection/egen_seed/event_selection_egen_seed_energy.yaml index 37ec87c..2c45445 100644 --- a/configs/event_selection/egen_seed/event_selection_egen_seed_energy.yaml +++ b/configs/event_selection/egen_seed/event_selection_egen_seed_energy.yaml @@ -18,7 +18,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -30,7 +30,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -39,13 +39,13 @@ ] 'validation_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] 'test_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] @@ -166,7 +166,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_seed/event_selection_egen_seed_pos.yaml b/configs/event_selection/egen_seed/event_selection_egen_seed_pos.yaml index 55de13e..29c44cc 100644 --- a/configs/event_selection/egen_seed/event_selection_egen_seed_pos.yaml +++ b/configs/event_selection/egen_seed/event_selection_egen_seed_pos.yaml @@ -18,7 +18,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -30,7 +30,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -39,13 +39,13 @@ ] 'validation_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] 'test_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] @@ -166,7 +166,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_seed/event_selection_egen_seed_time.yaml b/configs/event_selection/egen_seed/event_selection_egen_seed_time.yaml index 0e4edc5..7e76f7d 100644 --- a/configs/event_selection/egen_seed/event_selection_egen_seed_time.yaml +++ b/configs/event_selection/egen_seed/event_selection_egen_seed_time.yaml @@ -18,7 +18,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -30,7 +30,7 @@ '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/00000-00999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/new/Nu*/*_energy/*/l2/01000-01999/*.hdf5', - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0000000-0000999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0001000-0001999/*.hdf5', '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0002000-0002999/*.hdf5', @@ -39,13 +39,13 @@ ] 'validation_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] 'test_data_file' : [ - # Manuels NuGen Spice3.2.1 (with coincident events) + # Manuel's NuGen Spice3.2.1 (with coincident events) '/net/big-tank/POOL/users/mhuennefeld/data/event_selection/training_data/egen_seed/NuGen/2121*/0004000-0004999/*.hdf5', ] @@ -166,7 +166,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_pid.yaml b/configs/event_selection/egen_vertex/egen_vertex_pid.yaml index 872d3cc..8876331 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_pid.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_pid.yaml @@ -209,7 +209,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nue_nc.yaml b/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nue_nc.yaml index 9864b4b..6c4c84b 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nue_nc.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nue_nc.yaml @@ -214,7 +214,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nutau_cc.yaml b/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nutau_cc.yaml index 1fa5f32..3a17c88 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nutau_cc.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_pid_nue_cc_vs_nutau_cc.yaml @@ -220,7 +220,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_starting_events_300m_fast_medium.yaml b/configs/event_selection/egen_vertex/egen_vertex_starting_events_300m_fast_medium.yaml index 0b42033..6bf6198 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_starting_events_300m_fast_medium.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_starting_events_300m_fast_medium.yaml @@ -246,7 +246,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_starting_nue_300m_fast_medium.yaml b/configs/event_selection/egen_vertex/egen_vertex_starting_nue_300m_fast_medium.yaml index d8abcd7..6a58069 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_starting_nue_300m_fast_medium.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_starting_nue_300m_fast_medium.yaml @@ -251,7 +251,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_track_in_detector_length.yaml b/configs/event_selection/egen_vertex/egen_vertex_track_in_detector_length.yaml index 0bad073..55cdaa7 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_track_in_detector_length.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_track_in_detector_length.yaml @@ -209,7 +209,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_nue_cc.yaml b/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_nue_cc.yaml index 137b4d6..14d48d4 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_nue_cc.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_nue_cc.yaml @@ -225,7 +225,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_starting.yaml b/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_starting.yaml index 426ae95..960465c 100644 --- a/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_starting.yaml +++ b/configs/event_selection/egen_vertex/egen_vertex_track_numu_cc_vs_starting.yaml @@ -208,7 +208,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_astroness.yaml b/configs/event_selection/event_selection_astroness.yaml index 85447a9..8334cc1 100644 --- a/configs/event_selection/event_selection_astroness.yaml +++ b/configs/event_selection/event_selection_astroness.yaml @@ -307,7 +307,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_cascade_dir.yaml b/configs/event_selection/event_selection_cascade_dir.yaml index 5391a8d..73eb7da 100644 --- a/configs/event_selection/event_selection_cascade_dir.yaml +++ b/configs/event_selection/event_selection_cascade_dir.yaml @@ -310,7 +310,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_cascade_energy.yaml b/configs/event_selection/event_selection_cascade_energy.yaml index c910497..ff63fb7 100644 --- a/configs/event_selection/event_selection_cascade_energy.yaml +++ b/configs/event_selection/event_selection_cascade_energy.yaml @@ -309,7 +309,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_cascade_pos.yaml b/configs/event_selection/event_selection_cascade_pos.yaml index e4c4c0b..7f76a26 100644 --- a/configs/event_selection/event_selection_cascade_pos.yaml +++ b/configs/event_selection/event_selection_cascade_pos.yaml @@ -308,7 +308,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_cascade_t.yaml b/configs/event_selection/event_selection_cascade_t.yaml index 5084bf4..d374cf5 100644 --- a/configs/event_selection/event_selection_cascade_t.yaml +++ b/configs/event_selection/event_selection_cascade_t.yaml @@ -308,7 +308,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_entering.yaml b/configs/event_selection/event_selection_entering.yaml index e2f542e..78df72e 100644 --- a/configs/event_selection/event_selection_entering.yaml +++ b/configs/event_selection/event_selection_entering.yaml @@ -307,7 +307,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_nugen_l2_reconstruction.yaml b/configs/event_selection/event_selection_nugen_l2_reconstruction.yaml index 3cdc4f1..0c8c6b2 100644 --- a/configs/event_selection/event_selection_nugen_l2_reconstruction.yaml +++ b/configs/event_selection/event_selection_nugen_l2_reconstruction.yaml @@ -133,7 +133,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_nugen_l2_reconstruction_big.yaml b/configs/event_selection/event_selection_nugen_l2_reconstruction_big.yaml index 36b1201..24c59f5 100644 --- a/configs/event_selection/event_selection_nugen_l2_reconstruction_big.yaml +++ b/configs/event_selection/event_selection_nugen_l2_reconstruction_big.yaml @@ -307,7 +307,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_pid.yaml b/configs/event_selection/event_selection_pid.yaml index dd15821..e81b854 100644 --- a/configs/event_selection/event_selection_pid.yaml +++ b/configs/event_selection/event_selection_pid.yaml @@ -312,7 +312,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_starting_cascades.yaml b/configs/event_selection/event_selection_starting_cascades.yaml index 4266c42..68f7073 100644 --- a/configs/event_selection/event_selection_starting_cascades.yaml +++ b/configs/event_selection/event_selection_starting_cascades.yaml @@ -307,7 +307,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_starting_events.yaml b/configs/event_selection/event_selection_starting_events.yaml index ee0502d..38fdccc 100644 --- a/configs/event_selection/event_selection_starting_events.yaml +++ b/configs/event_selection/event_selection_starting_events.yaml @@ -306,7 +306,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_starting_events_big.yaml b/configs/event_selection/event_selection_starting_events_big.yaml index 2d15402..667e73a 100644 --- a/configs/event_selection/event_selection_starting_events_big.yaml +++ b/configs/event_selection/event_selection_starting_events_big.yaml @@ -271,7 +271,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_track_detectorlength.yaml b/configs/event_selection/event_selection_track_detectorlength.yaml index ac8ab4c..1cb722c 100644 --- a/configs/event_selection/event_selection_track_detectorlength.yaml +++ b/configs/event_selection/event_selection_track_detectorlength.yaml @@ -308,7 +308,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_track_dir.yaml b/configs/event_selection/event_selection_track_dir.yaml index a30723b..4fa15a7 100644 --- a/configs/event_selection/event_selection_track_dir.yaml +++ b/configs/event_selection/event_selection_track_dir.yaml @@ -309,7 +309,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_track_energy.yaml b/configs/event_selection/event_selection_track_energy.yaml index b3da131..068c1ea 100644 --- a/configs/event_selection/event_selection_track_energy.yaml +++ b/configs/event_selection/event_selection_track_energy.yaml @@ -308,7 +308,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_track_length.yaml b/configs/event_selection/event_selection_track_length.yaml index 1012141..c6dd2f2 100644 --- a/configs/event_selection/event_selection_track_length.yaml +++ b/configs/event_selection/event_selection_track_length.yaml @@ -305,7 +305,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_track_pos.yaml b/configs/event_selection/event_selection_track_pos.yaml index c89343f..c88b89d 100644 --- a/configs/event_selection/event_selection_track_pos.yaml +++ b/configs/event_selection/event_selection_track_pos.yaml @@ -307,7 +307,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/event_selection_upgoing_tracks.yaml b/configs/event_selection/event_selection_upgoing_tracks.yaml index 581f35e..3ec3df6 100644 --- a/configs/event_selection/event_selection_upgoing_tracks.yaml +++ b/configs/event_selection/event_selection_upgoing_tracks.yaml @@ -317,7 +317,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. @@ -345,7 +345,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_cascades_150m_red_summary_stats_fast.yaml b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_cascades_150m_red_summary_stats_fast.yaml index be9e98f..04709f0 100644 --- a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_cascades_150m_red_summary_stats_fast.yaml +++ b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_cascades_150m_red_summary_stats_fast.yaml @@ -194,7 +194,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_150m_red_summary_stats_fast.yaml b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_150m_red_summary_stats_fast.yaml index 82ef732..c2785ff 100644 --- a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_150m_red_summary_stats_fast.yaml +++ b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_150m_red_summary_stats_fast.yaml @@ -193,7 +193,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_300m_red_summary_stats_fast.yaml b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_300m_red_summary_stats_fast.yaml index 60c14c1..fc54677 100644 --- a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_300m_red_summary_stats_fast.yaml +++ b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_300m_red_summary_stats_fast.yaml @@ -193,7 +193,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_red_summary_stats_fast.yaml b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_red_summary_stats_fast.yaml index 1b44b62..1b45860 100644 --- a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_red_summary_stats_fast.yaml +++ b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_red_summary_stats_fast.yaml @@ -173,7 +173,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_total_dom_charge_fast.yaml b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_total_dom_charge_fast.yaml index e9b7e70..f1e0e27 100644 --- a/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_total_dom_charge_fast.yaml +++ b/configs/event_selection/pre_selection/dnn_cscd_l3a_starting_events_total_dom_charge_fast.yaml @@ -173,7 +173,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/veto_classifier/event_selection_veto_classifier__downgoing.yaml b/configs/event_selection/veto_classifier/event_selection_veto_classifier__downgoing.yaml index 5b02468..c2cb676 100644 --- a/configs/event_selection/veto_classifier/event_selection_veto_classifier__downgoing.yaml +++ b/configs/event_selection/veto_classifier/event_selection_veto_classifier__downgoing.yaml @@ -185,7 +185,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early.yaml b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early.yaml index 529d33e..37382d1 100644 --- a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early.yaml +++ b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early.yaml @@ -239,7 +239,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early__downgoing.yaml b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early__downgoing.yaml index 9331495..d19427e 100644 --- a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early__downgoing.yaml +++ b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early__downgoing.yaml @@ -185,7 +185,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early_with_energy.yaml b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early_with_energy.yaml index d17cb62..ab8849d 100644 --- a/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early_with_energy.yaml +++ b/configs/event_selection/veto_classifier/event_selection_veto_classifier_vertex_early_with_energy.yaml @@ -239,7 +239,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/muon_scattering/muon_scattering.yaml b/configs/muon_scattering/muon_scattering.yaml index 192ec6a..b61e6d7 100644 --- a/configs/muon_scattering/muon_scattering.yaml +++ b/configs/muon_scattering/muon_scattering.yaml @@ -191,7 +191,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/muon_scattering/muon_scattering_L200_Lb100_La100_E10_r0_40.yaml b/configs/muon_scattering/muon_scattering_L200_Lb100_La100_E10_r0_40.yaml index 9e67dc6..28df28a 100644 --- a/configs/muon_scattering/muon_scattering_L200_Lb100_La100_E10_r0_40.yaml +++ b/configs/muon_scattering/muon_scattering_L200_Lb100_La100_E10_r0_40.yaml @@ -209,7 +209,7 @@ data_handler_nan_fill_value: 0. #----------------------- Use this with CAUTION! # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/muon_scattering/muon_scattering_energy.yaml b/configs/muon_scattering/muon_scattering_energy.yaml index 16663af..7fc3d2d 100644 --- a/configs/muon_scattering/muon_scattering_energy.yaml +++ b/configs/muon_scattering/muon_scattering_energy.yaml @@ -209,7 +209,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/muon_scattering/muon_scattering_tracks.yaml b/configs/muon_scattering/muon_scattering_tracks.yaml index d6e9deb..b5a4ed4 100644 --- a/configs/muon_scattering/muon_scattering_tracks.yaml +++ b/configs/muon_scattering/muon_scattering_tracks.yaml @@ -191,7 +191,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/stopping_muons/stopping_muons_hoinka_no_filter.yaml b/configs/stopping_muons/stopping_muons_hoinka_no_filter.yaml index 411b153..798582c 100644 --- a/configs/stopping_muons/stopping_muons_hoinka_no_filter.yaml +++ b/configs/stopping_muons/stopping_muons_hoinka_no_filter.yaml @@ -158,7 +158,7 @@ data_handler_nan_fill_value : 0. # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/stopping_muons/stopping_muons_hoinka_no_filter__direction.yaml b/configs/stopping_muons/stopping_muons_hoinka_no_filter__direction.yaml index 40f57dd..0915657 100644 --- a/configs/stopping_muons/stopping_muons_hoinka_no_filter__direction.yaml +++ b/configs/stopping_muons/stopping_muons_hoinka_no_filter__direction.yaml @@ -159,7 +159,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/stopping_muons/stopping_muons_hoinka_no_filter__energy.yaml b/configs/stopping_muons/stopping_muons_hoinka_no_filter__energy.yaml index 354b95b..86c91fe 100644 --- a/configs/stopping_muons/stopping_muons_hoinka_no_filter__energy.yaml +++ b/configs/stopping_muons/stopping_muons_hoinka_no_filter__energy.yaml @@ -159,7 +159,7 @@ # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/configs/stopping_muons/stopping_muons_hoinka_no_filter__stopping_rz.yaml b/configs/stopping_muons/stopping_muons_hoinka_no_filter__stopping_rz.yaml index 839bd76..e1f4b2a 100644 --- a/configs/stopping_muons/stopping_muons_hoinka_no_filter__stopping_rz.yaml +++ b/configs/stopping_muons/stopping_muons_hoinka_no_filter__stopping_rz.yaml @@ -161,7 +161,7 @@ data_handler_nan_fill_value : 0. # ---------------- # Biased Selection # ---------------- -# Filter events based on the curent reconstruction performance on these +# Filter events based on the current reconstruction performance on these # by defining key, value pairs and a biased fraction. # Events will be put in queues, such that biased_fraction of all events # passes one of the defined cuts. diff --git a/dnn_reco/data_handler.py b/dnn_reco/data_handler.py index b96264c..633ff0a 100644 --- a/dnn_reco/data_handler.py +++ b/dnn_reco/data_handler.py @@ -37,7 +37,7 @@ class DataHandler(object): misc_data_exists : bool If true, misc data exists and is != None. misc_names : list of str - Names of misc names. If no misc data exists, this is an emtpy list. + Names of misc names. If no misc data exists, this is an empty list. misc_shape : list of int Shape of misc data without batch dimension. num_bins : int @@ -243,7 +243,7 @@ def read_icecube_data( Entries with nan values will be replaced by this value. If None, no replacement will be performed. init_values : float, optional - The x_ic78 array will be initalized with these values via: + The x_ic78 array will be initialized with these values via: np.zeros_like(x_ic78) * np.array(init_values) verbose : bool, optional Print out additional information on runtimes for loading and @@ -521,7 +521,7 @@ def get_batch_generator( 2) Another worker aggregates the events of several files (number of files defined by 'num_add_files') together - by dequeing elements from the 'data_batch_queue'. + by dequeuing elements from the 'data_batch_queue'. It then creates batches from these events (randomly if sample_randomly == True ). These batches are then put onto the 'final_batch_queue'. @@ -559,16 +559,16 @@ def get_batch_generator( of size 'batch_size'. This queue is what is used to obtain the final batches, which the generator yields. num_jobs : int, optional - Number of jobs to run in parrallel to load and process input files. + Number of jobs to run in parallel to load and process input files. num_add_files : int, optional - Defines how many files are additionaly loaded at once. + Defines how many files are additionally loaded at once. Batches will be generated among events of these (1 + num_add_files) files num_repetitions : int, optional Number of times the events in a loaded file are to be used, before new files are loaded. init_values : float, optional - The x_ic78 array will be initalized with these values via: + The x_ic78 array will be initialized with these values via: np.zeros_like(x_ic78) * np.array(init_values) num_splits : int, optional If num_splits is given, the loaded file will be divided into @@ -1067,7 +1067,7 @@ def _create_model(self, cfg, cfg_sel): sess=sess, ) - # compile model: initalize and finalize graph + # compile model: initialize and finalize graph model.compile() # restore model weights diff --git a/dnn_reco/data_trafo.py b/dnn_reco/data_trafo.py index 896bece..2ac67ae 100644 --- a/dnn_reco/data_trafo.py +++ b/dnn_reco/data_trafo.py @@ -68,7 +68,7 @@ def __init__( The natural logarithm is applied to the misc data bins prior to normalization. If a list is given, the length of the list must match the number of - misc variabels misc_shape[-1]. The logarithm is applied to bin i + misc variables misc_shape[-1]. The logarithm is applied to bin i if the ith entry of the log_misc_bins list is True. If a dictionary is provided, a list of length label_shape[-1] will be initialized with False and only the values of the labels as @@ -200,7 +200,7 @@ def _perform_update_step(self, log_bins, data_batch, n, mean, M2): Parameters ---------- log_bins : list of bool - Defines whether the natural logarithm is appllied to bins along + Defines whether the natural logarithm is applied to bins along last axis. Must have same length as data_batch.shape[-1]. data_batch : numpy ndarray A batch of data for which to update the variance variables of the @@ -308,7 +308,7 @@ def create_trafo_model_iteratively(self, data_iterator, num_batches): # combine DOM data over all DOMs if desired if self.trafo_model["treat_doms_equally"]: - # initalize with zeros + # initialize with zeros self.trafo_model["ic78_mean"] = np.zeros(self._ic78_shape) self.trafo_model["ic78_std"] = np.zeros(self._ic78_shape) @@ -425,7 +425,7 @@ def _check_settings(self, data, data_type): ------ ValueError If DataTransformer object has not created or loaded a trafo model. - If provided data_type is unkown. + If provided data_type is unknown. """ dtype = data.dtype data_type = data_type.lower() @@ -506,7 +506,7 @@ def transform(self, data, data_type, bias_correction=True): ---------------- ValueError If DataTransformer object has not created or loaded a trafo model. - If provided data_type is unkown. + If provided data_type is unknown. """ data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = ( self._check_settings(data, data_type) @@ -570,14 +570,14 @@ def inverse_transform(self, data, data_type, bias_correction=True): Returns ------- type(data) - Returns the inverse transformed DOM respones and + Returns the inverse transformed DOM responses and cascade_parameters. No Longer Raises ---------------- ValueError If DataTransformer object has not created or loaded a trafo model. - If provided data_type is unkown. + If provided data_type is unknown. """ data, log_name, normalize_name, log_func, exp_func, is_tf, dtype = ( self._check_settings(data, data_type) diff --git a/dnn_reco/ic3/llh.py b/dnn_reco/ic3/llh.py index c890e9d..49de083 100644 --- a/dnn_reco/ic3/llh.py +++ b/dnn_reco/ic3/llh.py @@ -700,7 +700,7 @@ def cdf_dir(self, dir_x, dir_y, dir_z): Returns ------- np.array - The cumulative probabilty for the given direction vectors. + The cumulative probability for the given direction vectors. """ if not self.is_normalized(dir_x, dir_y, dir_z): print("cdf_dir is normalizing direction vectors") @@ -714,7 +714,7 @@ def cdf_dir(self, dir_x, dir_y, dir_z): return cdf def _get_level_indices(self, level=0.5, delta=0.01): - """Get indices of sampled diections, which belong to the specified + """Get indices of sampled directions, which belong to the specified contour as defined by: level +- delta. Parameters @@ -1047,7 +1047,7 @@ def cdf_dir(self, dir_x, dir_y, dir_z): Returns ------- np.array - The cumulative probabilty for the given direction vectors. + The cumulative probability for the given direction vectors. """ if not self.is_normalized(dir_x, dir_y, dir_z): print("cdf_dir is normalizing direction vectors") @@ -1059,7 +1059,7 @@ def cdf_dir(self, dir_x, dir_y, dir_z): return cdf def _get_level_indices(self, level=0.5, delta=0.001): - """Get indices of sampled diections, which belong to the specified + """Get indices of sampled directions, which belong to the specified contour as defined by: level +- delta. Parameters diff --git a/dnn_reco/ic3/llh_base.py b/dnn_reco/ic3/llh_base.py index d50b875..fa8b049 100644 --- a/dnn_reco/ic3/llh_base.py +++ b/dnn_reco/ic3/llh_base.py @@ -420,7 +420,7 @@ def cdf(self, zenith, azimuth): Returns ------- np.array - The cumulative probabilty for the given zenith/azimuth pairs. + The cumulative probability for the given zenith/azimuth pairs. """ dir_x, dir_y, dir_z = self.get_dir_vec(zenith, azimuth) return self.cdf_dir(dir_x, dir_y, dir_z) @@ -440,12 +440,12 @@ def cdf_dir(self, dir_x, dir_y, dir_z): Returns ------- np.array - The cumulative probabilty for the given direction vectors. + The cumulative probability for the given direction vectors. """ raise NotImplementedError def _get_level_indices(self, level=0.5, delta=0.01): - """Get indices of sampled diections, which belong to the specified + """Get indices of sampled directions, which belong to the specified contour as defined by: level +- delta. Parameters @@ -824,7 +824,7 @@ def cdf(self, zenith, azimuth): Returns ------- np.array - The cumulative probabilty for the given zenith/azimuth pairs. + The cumulative probability for the given zenith/azimuth pairs. """ neg_llh = -self.log_prob(zenith, azimuth) pos = np.searchsorted(self.neg_llh_values, neg_llh) @@ -846,12 +846,12 @@ def cdf_dir(self, dir_x, dir_y, dir_z): Returns ------- np.array - The cumulative probabilty for the given direction vectors. + The cumulative probability for the given direction vectors. """ return self.cdf(*self.get_zenith_azimuth(dir_x, dir_y, dir_z)) def _get_level_indices(self, level=0.5, delta=0.01): - """Get indices of sampled diections, which belong to the specified + """Get indices of sampled directions, which belong to the specified contour as defined by: level +- delta. Parameters diff --git a/dnn_reco/ic3/modules.py b/dnn_reco/ic3/modules.py index 3112c1b..e8fcabe 100644 --- a/dnn_reco/ic3/modules.py +++ b/dnn_reco/ic3/modules.py @@ -23,7 +23,7 @@ class DeepLearningReco(icetray.I3ConditionalModule): ---------- batch_size : int, optional The number of events to accumulate and pass through the network in - parallel. A higher batch size than 1 can usually improve recontruction + parallel. A higher batch size than 1 can usually improve reconstruction runtime, but will also increase the memory footprint. config : dict Dictionary with configuration settings @@ -94,11 +94,11 @@ def Configure(self): self._output_key = self.GetParameter("OutputBaseName") self._measure_time = self.GetParameter("MeasureTime") self._parallelism_threads = self.GetParameter("ParallelismThreads") - self._ingore_list = self.GetParameter( + self._ignore_list = self.GetParameter( "IgnoreMisconfiguredSettingsList" ) - if self._ingore_list is None: - self._ingore_list = [] + if self._ignore_list is None: + self._ignore_list = [] # read in and combine config files and set up training_files = glob.glob( @@ -176,10 +176,10 @@ def Configure(self): continue if not self._container.config[k] == data_config[k]: - if k in self._ingore_list: + if k in self._ignore_list: msg = "Warning: parameter {!r} is set to {!r} which " msg += "differs from the model [{!r}] default value {!r}. " - msg += "This mismatch will be ingored since the parameter " + msg += "This mismatch will be ignored since the parameter " msg += "is in the IgnoreMisconfiguredSettingsList. " msg += "Make sure this is what you intend to do!" logging.warning( @@ -273,7 +273,7 @@ def Configure(self): sess=sess, ) - # compile model: initalize and finalize graph + # compile model: initialize and finalize graph self.model.compile() # restore model weights diff --git a/dnn_reco/ic3/segments.py b/dnn_reco/ic3/segments.py index 8d5b0fc..353e757 100644 --- a/dnn_reco/ic3/segments.py +++ b/dnn_reco/ic3/segments.py @@ -74,7 +74,7 @@ def ApplyDNNRecos( If True, the run-time will be measured. batch_size : int, optional The number of events to accumulate and pass through the network in - parallel. A higher batch size than 1 can usually improve recontruction + parallel. A higher batch size than 1 can usually improve reconstruction runtime, but will also increase the memory footprint. num_cpus : int, optional Number of CPU cores to use if CPUs are used instead of a GPU. diff --git a/dnn_reco/model.py b/dnn_reco/model.py index 9a332d8..cf0025d 100644 --- a/dnn_reco/model.py +++ b/dnn_reco/model.py @@ -30,7 +30,7 @@ class NNModel(object): saver : tensorflow.train.Saver A tensorflow saver used to save and load model weights. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. """ @@ -75,7 +75,7 @@ def __init__( # create tensorflow placeholders for input data self._setup_placeholders() - # initalize label weights and non zero mask + # initialize label weights and non zero mask self._intialize_label_weights() # build NN architecture @@ -632,7 +632,7 @@ def _merge_tensorboard_summaries(self): ) def _initialize_and_finalize_model(self): - """Initalize and finalize model weights""" + """Initialize and finalize model weights""" # initialize variables self.sess.run(tf.compat.v1.global_variables_initializer()) @@ -879,7 +879,7 @@ def fit( train_result = self.sess.run(train_ops, feed_dict=feed_dict) # ------------------------------------- - # calculate variabels for tukey scaling + # calculate variables for tukey scaling # ------------------------------------- if self.config["label_scale_tukey"]: batch_median_abs_dev = np.median( @@ -899,7 +899,7 @@ def fit( ) # -------------------------------------------- - # calculate online variabels for label weights + # calculate online variables for label weights # -------------------------------------------- if self.config["label_update_weights"]: mse_values_trafo = train_result["mse_values_trafo"] @@ -1151,7 +1151,7 @@ def count_parameters(self, var_list=None): Parameters ---------- var_list : list of tf.Tensors, optional - A list of tensorflow tensors for which to calculate the nubmer of + A list of tensorflow tensors for which to calculate the number of trainable parameters. If None, then all trainable parameters available will be counted. diff --git a/dnn_reco/modules/data/event_weights/event_weights.py b/dnn_reco/modules/data/event_weights/event_weights.py index b2552e7..182fdb5 100644 --- a/dnn_reco/modules/data/event_weights/event_weights.py +++ b/dnn_reco/modules/data/event_weights/event_weights.py @@ -12,7 +12,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -47,7 +47,7 @@ def event_selection_weight( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -106,7 +106,7 @@ def clipped_astroness_weights( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. diff --git a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py index dedd91c..de819a2 100644 --- a/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py +++ b/dnn_reco/modules/data/event_weights/nersc_gnn_event_weights.py @@ -12,7 +12,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -47,7 +47,7 @@ def nersc_gnn_weight( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. diff --git a/dnn_reco/modules/data/labels/biased_selection_labels.py b/dnn_reco/modules/data/labels/biased_selection_labels.py index 63f0ddc..b5fdbc2 100644 --- a/dnn_reco/modules/data/labels/biased_selection_labels.py +++ b/dnn_reco/modules/data/labels/biased_selection_labels.py @@ -11,7 +11,7 @@ label_names : None, optional The names of the labels. This defines which labels to include as well as the ordering. - If label_names is None (e.g. first call to initate name list), then + If label_names is None (e.g. first call to initiate name list), then a list of label names needs to be created and returned. *args Variable length argument list. @@ -34,7 +34,7 @@ def biased_muongun(input_data, config, label_names=None, *args, **kwargs): - """Default muon scattering labels in addtion to relaxed thresholds for + """Default muon scattering labels in addition to relaxed thresholds for classification. Parameters diff --git a/dnn_reco/modules/data/labels/default_labels.py b/dnn_reco/modules/data/labels/default_labels.py index 42888e8..9443189 100644 --- a/dnn_reco/modules/data/labels/default_labels.py +++ b/dnn_reco/modules/data/labels/default_labels.py @@ -11,7 +11,7 @@ label_names : None, optional The names of the labels. This defines which labels to include as well as the ordering. - If label_names is None (e.g. first call to initate name list), then + If label_names is None (e.g. first call to initiate name list), then a list of label names needs to be created and returned. *args Variable length argument list. diff --git a/dnn_reco/modules/data/labels/event_selection_labels.py b/dnn_reco/modules/data/labels/event_selection_labels.py index 9644273..71fdf64 100644 --- a/dnn_reco/modules/data/labels/event_selection_labels.py +++ b/dnn_reco/modules/data/labels/event_selection_labels.py @@ -11,7 +11,7 @@ label_names : None, optional The names of the labels. This defines which labels to include as well as the ordering. - If label_names is None (e.g. first call to initate name list), then + If label_names is None (e.g. first call to initiate name list), then a list of label names needs to be created and returned. *args Variable length argument list. @@ -171,7 +171,7 @@ def starting_cascades(input_data, config, label_names=None, *args, **kwargs): else: distances = [-60.0, 0.0, 60, 300.0, float("inf")] - # make sure the provdied distances are allowed + # make sure the provided distances are allowed allowed_distances = [-60.0, 0.0, 60, 150.0, 300.0, float("inf")] assert np.all([d in allowed_distances for d in distances]), distances diff --git a/dnn_reco/modules/data/labels/muon_scattering_labels.py b/dnn_reco/modules/data/labels/muon_scattering_labels.py index 42afd3c..0ff3453 100644 --- a/dnn_reco/modules/data/labels/muon_scattering_labels.py +++ b/dnn_reco/modules/data/labels/muon_scattering_labels.py @@ -11,7 +11,7 @@ label_names : None, optional The names of the labels. This defines which labels to include as well as the ordering. - If label_names is None (e.g. first call to initate name list), then + If label_names is None (e.g. first call to initiate name list), then a list of label names needs to be created and returned. *args Variable length argument list. @@ -34,7 +34,7 @@ def muon_scattering(input_data, config, label_names=None, *args, **kwargs): - """Default muon scattering labels in addtion to relaxed thresholds for + """Default muon scattering labels in addition to relaxed thresholds for classification. Parameters diff --git a/dnn_reco/modules/data/misc/default_misc.py b/dnn_reco/modules/data/misc/default_misc.py index b67497d..3b66861 100644 --- a/dnn_reco/modules/data/misc/default_misc.py +++ b/dnn_reco/modules/data/misc/default_misc.py @@ -11,7 +11,7 @@ misc_names : None, optional The names of the misc variables. This defines which variables to include as well as the ordering. - If misc_names is None (e.g. first call to initate name list), then + If misc_names is None (e.g. first call to initiate name list), then a list of misc names needs to be created and returned. *args Variable length argument list. @@ -47,7 +47,7 @@ def dummy_misc_loader(input_data, config, misc_names=None, *args, **kwargs): misc_names : None, optional The names of the misc variables. This defines which variables to include as well as the ordering. - If misc_names is None (e.g. first call to initate name list), then + If misc_names is None (e.g. first call to initiate name list), then a list of misc names needs to be created and returned. *args Variable length argument list. @@ -78,7 +78,7 @@ def general_misc_loader(input_data, config, misc_names=None, *args, **kwargs): misc_names : None, optional The names of the misc variables. This defines which variables to include as well as the ordering. - If misc_names is None (e.g. first call to initate name list), then + If misc_names is None (e.g. first call to initiate name list), then a list of misc names needs to be created and returned. *args Variable length argument list. diff --git a/dnn_reco/modules/evaluation/default_evaluation.py b/dnn_reco/modules/evaluation/default_evaluation.py index 277e0b4..e96a718 100644 --- a/dnn_reco/modules/evaluation/default_evaluation.py +++ b/dnn_reco/modules/evaluation/default_evaluation.py @@ -24,7 +24,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -75,7 +75,7 @@ def eval_direction( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -178,7 +178,7 @@ def eval_direction( ) ) - # Test weighted corection: [very much beta version] + # Test weighted correction: [very much beta version] def get_weighted_normed_dir_vector( dir_x, dir_y, dir_z, dir_x_unc, dir_y_unc, dir_z_unc ): diff --git a/dnn_reco/modules/loss/default_loss.py b/dnn_reco/modules/loss/default_loss.py index 9d0f56c..484eefe 100644 --- a/dnn_reco/modules/loss/default_loss.py +++ b/dnn_reco/modules/loss/default_loss.py @@ -12,7 +12,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -55,7 +55,7 @@ def weighted_mse( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -111,7 +111,7 @@ def mse( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -174,7 +174,7 @@ def abs( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -238,7 +238,7 @@ def gaussian_likelihood( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -303,7 +303,7 @@ def pull_distribution_scale( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -371,7 +371,7 @@ def mse_and_cross_entropy( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -466,7 +466,7 @@ def mse_and_weighted_cross_entropy( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -575,7 +575,7 @@ def tukey( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -642,7 +642,7 @@ def opening_angle( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -730,7 +730,7 @@ def opening_angle_raleigh( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. diff --git a/dnn_reco/modules/loss/track_loss.py b/dnn_reco/modules/loss/track_loss.py index d2b6c54..1532e32 100644 --- a/dnn_reco/modules/loss/track_loss.py +++ b/dnn_reco/modules/loss/track_loss.py @@ -12,7 +12,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -55,7 +55,7 @@ def track_pos_mse( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -114,7 +114,7 @@ def track_pos_mse( # scalar product s = a*d, s is distance to closest point on infinite track s = a1 * dir_x_true + a2 * dir_y_true + a3 * dir_z_true - # caculate r = s*d -a = (p + s*d) - x + # calculate r = s*d -a = (p + s*d) - x r1 = s * dir_x_true - a1 r2 = s * dir_y_true - a2 r3 = s * dir_z_true - a3 @@ -209,7 +209,7 @@ def track_pos_gaussian( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -268,7 +268,7 @@ def track_pos_gaussian( # scalar product s = a*d, s is distance to closest point on infinite track s = a1 * dir_x_true + a2 * dir_y_true + a3 * dir_z_true - # caculate r = s*d -a = (p + s*d) - x + # calculate r = s*d -a = (p + s*d) - x r1 = s * dir_x_true - a1 r2 = s * dir_y_true - a2 r3 = s * dir_z_true - a3 diff --git a/dnn_reco/modules/loss/utils/loss_utils.py b/dnn_reco/modules/loss/utils/loss_utils.py index b4c1775..22ceada 100644 --- a/dnn_reco/modules/loss/utils/loss_utils.py +++ b/dnn_reco/modules/loss/utils/loss_utils.py @@ -10,7 +10,7 @@ def add_logging_info(data_handler, shared_objects): - """Add some TensorBoard logging infor for the labels. + """Add some TensorBoard logging info for the labels. Adds Tf.summary.scalars of the RMSE for each label. @@ -20,7 +20,7 @@ def add_logging_info(data_handler, shared_objects): An instance of the DataHandler class. The object is used to obtain meta data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. """ @@ -32,7 +32,7 @@ def add_logging_info(data_handler, shared_objects): mse_values = tf.reduce_mean(input_tensor=tf.square(y_diff), axis=0) rmse_values = tf.sqrt(mse_values) - # calcuate RMSE of transformed values + # calculate RMSE of transformed values y_diff_trafo = ( shared_objects["y_pred_trafo"] - shared_objects["y_true_trafo"] ) @@ -136,7 +136,7 @@ def get_y_diff_trafo(config, data_handler, data_transformer, shared_objects): An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. Returns diff --git a/dnn_reco/modules/models/general_IC86_models.py b/dnn_reco/modules/models/general_IC86_models.py index 27f8f1a..3d3d307 100644 --- a/dnn_reco/modules/models/general_IC86_models.py +++ b/dnn_reco/modules/models/general_IC86_models.py @@ -14,7 +14,7 @@ An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -74,7 +74,7 @@ def general_model_IC86( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. @@ -357,7 +357,7 @@ def general_model_IC86_opt4( An instance of the DataTransformer class. The object is used to transform data. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. *args Variable length argument list. diff --git a/dnn_reco/modules/models/utils/model_utils.py b/dnn_reco/modules/models/utils/model_utils.py index a07ad3f..0a3efdb 100644 --- a/dnn_reco/modules/models/utils/model_utils.py +++ b/dnn_reco/modules/models/utils/model_utils.py @@ -18,7 +18,7 @@ def preprocess_icecube_data(is_training, shared_objects): is_training : bool True if model is in training mode, false if in inference mode. shared_objects : dict - A dictionary containg settings and objects that are shared and passed + A dictionary containing settings and objects that are shared and passed on to sub modules. Returns diff --git a/dnn_reco/setup_manager.py b/dnn_reco/setup_manager.py index 4b39097..3e469c7 100644 --- a/dnn_reco/setup_manager.py +++ b/dnn_reco/setup_manager.py @@ -58,7 +58,7 @@ class SetupManager: The shape of the DOM response tensor excluding the batch dimension. E.g.: [x_dim, y_dim, z_dim, num_bins] DOM_init_values: float or array-like - The x_ic78 and deepcore array will be initalized with these + The x_ic78 and deepcore array will be initialized with these values via: np.zeros_like(x_ic78) * np.array(init_values) batch_size : int @@ -111,7 +111,7 @@ class SetupManager: specified by the 'trafo_data' key. trafo_save_model : bool If true, the transformation model will be saved to the file - specfied by the 'trafo_model_path' key. + specified by the 'trafo_model_path' key. Note: This will overwrite the file! trafo_normalize : bool If true, data will be normalized to have a mean of 0 and a variance @@ -172,7 +172,7 @@ class SetupManager: config : dictionary Dictionary with defined settings. shared_objects : dictionary - Dictionary with additional objects that are availabe in all modules. + Dictionary with additional objects that are available in all modules. Keys: 'data_transformer' : DataTransformer object used to transform data. 'keep_prob_list' : Tensorflow placeholders for keep probabilities diff --git a/docs/source/pages/bootcamp.rst b/docs/source/pages/bootcamp.rst index fcc09d7..47ee4ec 100644 --- a/docs/source/pages/bootcamp.rst +++ b/docs/source/pages/bootcamp.rst @@ -10,7 +10,7 @@ IceCube DNN Bootcamp ******************** -This tutorial is a simplifed version of the :ref:`Getting Started tutorial` +This tutorial is a simplified version of the :ref:`Getting Started tutorial` where we will focus on: diff --git a/docs/source/pages/bootcamp/00_bootcamp_setup.rst b/docs/source/pages/bootcamp/00_bootcamp_setup.rst index e71aff3..d0d42c5 100644 --- a/docs/source/pages/bootcamp/00_bootcamp_setup.rst +++ b/docs/source/pages/bootcamp/00_bootcamp_setup.rst @@ -102,7 +102,7 @@ We are now ready to install the necessary prerequisites and |dnn_reco|. git clone https://github.com/mhuen/dnn_reco.git # make sure that your virtualenv is activated - # you can check this by exectuting + # you can check this by executing which pip # It should point to: echo ${DNN_HOME}/dnn_reco_env/bin/pip diff --git a/docs/source/pages/bootcamp/01_bootcamp_train.rst b/docs/source/pages/bootcamp/01_bootcamp_train.rst index 5bfb717..fe25d66 100644 --- a/docs/source/pages/bootcamp/01_bootcamp_train.rst +++ b/docs/source/pages/bootcamp/01_bootcamp_train.rst @@ -96,7 +96,7 @@ We are going to highlight a few options in the following: Path to which the transformation model will be saved. ``trafo_normalize_dom_data``/ ``trafo_normalize_label_data``/ ``trafo_normalize_misc_data``: - If true, the input data per DOM, labels, and miscellanous data will be + If true, the input data per DOM, labels, and miscellaneous data will be normalized to have a mean of zero and a standard deviation of one. ``trafo_log_dom_bins``: @@ -130,7 +130,7 @@ because the mean and standard deviation depend on the data. # create the transformation Model python create_trafo_model.py $CONFIG_DIR/getting_started.yaml -Upon succesful completion this should print: +Upon successful completion this should print: .. code-block:: php @@ -197,7 +197,7 @@ For now we will use a simple Mean Squared Error (MSE) for the prediction and uncertainty estimate. The structure of the setting ``model_optimizer_dict`` is a bit complicated, but it is very powerful. -We can define as many optimizers with as many loss funtions as we like. +We can define as many optimizers with as many loss functions as we like. A few basic loss functions are already implemented in ``dnn_reco.modules.loss``. You are free to add your custom loss functions by adding a file/function in @@ -231,7 +231,7 @@ This means that you do not have to keep track yourself. Moreover, the currently installed python packages and the git revision is logged. This information will be exported together with the model, to ensure -reproducability. +reproducibility. The keys ``model_checkpoint_path`` and ``log_path`` define where the model checkpoints and the tensorboard log files will be saved to. The ``model_checkpoint_path`` also defines the path from which the weights of @@ -251,5 +251,5 @@ each of your models. Therefore you can easily swap out modules and create custom ones. We have briefly touched the option to create your own neural network architecture here as well as the option to add custom loss functions. - More information on the exchangable modules is provided in + More information on the exchangeable modules is provided in :ref:`Code Documentation`. diff --git a/docs/source/pages/bootcamp/02_bootcamp_monitor.rst b/docs/source/pages/bootcamp/02_bootcamp_monitor.rst index ba48246..5a45171 100644 --- a/docs/source/pages/bootcamp/02_bootcamp_monitor.rst +++ b/docs/source/pages/bootcamp/02_bootcamp_monitor.rst @@ -35,7 +35,7 @@ The input pipeline consists of three steps: 2. Another worker aggregates the events of several files (number of files defined by ``num_add_files``) together - by dequeing elements from the 'data_batch_queue'. + by dequeuing elements from the 'data_batch_queue'. It then creates batches from these events (randomly if 'sample_randomly' == True ). These batches are then put onto the 'final_batch_queue'. @@ -102,7 +102,7 @@ We can then use tensorboard to render these logs. # to forward this port in the ssh connection tensorboard --logdir=PATH/TO/MY/LOGS --port 7475 -If the port forwading is correctly set up, you can then point your browser to +If the port forwarding is correctly set up, you can then point your browser to to the appropriate address. More info on tensorboard is provided here: https://www.tensorflow.org/guide/summaries_and_tensorboard. diff --git a/docs/source/pages/bootcamp/03_bootcamp_apply.rst b/docs/source/pages/bootcamp/03_bootcamp_apply.rst index d508c37..5e56b68 100644 --- a/docs/source/pages/bootcamp/03_bootcamp_apply.rst +++ b/docs/source/pages/bootcamp/03_bootcamp_apply.rst @@ -107,7 +107,7 @@ following: model_names = [str(model_names)] output_names = ["DeepLearningReco_{}".format(m) for m in model_names] - # Make sure DNN reco will be writen to hdf5 file + # Make sure DNN reco will be written to hdf5 file for outbox in output_names: if outbox not in HDF_keys: HDF_keys.append(outbox) diff --git a/docs/source/pages/code.rst b/docs/source/pages/code.rst index cdcfe12..c3abdee 100644 --- a/docs/source/pages/code.rst +++ b/docs/source/pages/code.rst @@ -18,8 +18,8 @@ IceCube Detector Specifics .. automodule:: dnn_reco.detector :members: -Miscellanous Module -=================== +Miscellaneous Module +==================== .. automodule:: dnn_reco.misc :members: @@ -51,8 +51,8 @@ Loss Functions .. automodule:: dnn_reco.modules.loss.utils.loss_utils :members: -Evalution Functions -=================== +Evaluation Functions +==================== .. automodule:: dnn_reco.modules.evaluation.default_evaluation :members: diff --git a/docs/source/pages/config.rst b/docs/source/pages/config.rst index 9cfedb7..f51991b 100644 --- a/docs/source/pages/config.rst +++ b/docs/source/pages/config.rst @@ -32,7 +32,7 @@ General Settings validation set. ``test_data_file``: - If the data handler object is not instanciated with a config, it will load + If the data handler object is not instantiated with a config, it will load one of these files to obtain meta data, such as the labels and misc data names. @@ -352,7 +352,7 @@ Trafo Settings Path to which the transformation model will be saved. ``trafo_normalize_dom_data``/ ``trafo_normalize_label_data``/ ``trafo_normalize_misc_data``: - If true, the input data per DOM, labels, and miscellanous data will be + If true, the input data per DOM, labels, and miscellaneous data will be normalized to have a mean of zero and a standard deviation of one. ``trafo_log_dom_bins``: @@ -447,7 +447,7 @@ NN Model Architecture The network architecture that will be used is defined by the ``model_file`` and ``model_name`` keys. The ``model_name`` defines the function that will be used to - create and buld the model. + create and build the model. Default: `general_model_IC86` ``model_is_training``: diff --git a/docs/source/pages/getting_started/01_create_training_data.rst b/docs/source/pages/getting_started/01_create_training_data.rst index ac7ff37..635e847 100644 --- a/docs/source/pages/getting_started/01_create_training_data.rst +++ b/docs/source/pages/getting_started/01_create_training_data.rst @@ -10,14 +10,14 @@ Create Training Data ******************** To create the training data we will use the tray -segment ``ic3_data.segements.CreateDNNData`` from the |ic3_data| project +segment ``ic3_data.segments.CreateDNNData`` from the |ic3_data| project and for the labels we will use ``ic3_labels.labels.modules.MCLabelsCascades`` from the |ic3_labels| repository. You are free to include these modules in your processing set up of choice. Here we will use `these processing scripts `_. -The tray segment ``ic3_data.segements.CreateDNNData`` can write out different +The tray segment ``ic3_data.segments.CreateDNNData`` can write out different types of input data. Options include (more available): @@ -29,7 +29,7 @@ Options include (more available): If your application needs different input data, you can easily add a function in ``ic3_data.data_formats``. -The 'DataFormat' key of the tray segment ``ic3_data.segements.CreateDNNData`` +The 'DataFormat' key of the tray segment ``ic3_data.segments.CreateDNNData`` defines which function in ``ic3_data.data_formats`` will be used to create the input data. The only requirement is that the input data must be a vector of length n for @@ -155,7 +155,7 @@ Create the job files via: # create job files (--help for more options) python create_job_files.py configs/tutorial_dnn_reco/getting_started/create_training_data_01_py3-v4.1.1.yaml -d $DNN_HOME/training_data/ -This will write the exectuable job files and the configuration file that was used +This will write the executable job files and the configuration file that was used to the directory ``$DNN_HOME/training_data/processing``. The output files will be written to ``$DNN_HOME/training_data/datasets``. You may also write DAGMan files if you pass the option ``--dagman``. diff --git a/docs/source/pages/getting_started/02_train_model.rst b/docs/source/pages/getting_started/02_train_model.rst index ba9da73..c5322d6 100644 --- a/docs/source/pages/getting_started/02_train_model.rst +++ b/docs/source/pages/getting_started/02_train_model.rst @@ -138,7 +138,7 @@ We are going to highlight a few options in the following: Path to which the transformation model will be saved. ``trafo_normalize_dom_data``/ ``trafo_normalize_label_data``/ ``trafo_normalize_misc_data``: - If true, the input data per DOM, labels, and miscellanous data will be + If true, the input data per DOM, labels, and miscellaneous data will be normalized to have a mean of zero and a standard deviation of one. ``trafo_log_dom_bins``: @@ -183,7 +183,7 @@ because the mean and standard deviation depend on the data. ``$CONFIG_DIR/getting_started.yaml`` to a lower value such as 20. -Upon succesful completion this should print: +Upon successful completion this should print: .. code-block:: php @@ -243,7 +243,7 @@ in the ``model_optimizer_dict``. Here, we will use a Gaussian Likelihood as the loss function for the prediction and uncertainty estimate. The structure of the setting ``model_optimizer_dict`` is a bit complicated, but it is very powerful. -We can define as many optimizers with as many loss funtions as we like. +We can define as many optimizers with as many loss functions as we like. A few basic loss functions are already implemented in ``dnn_reco.modules.loss``. Amongst others, these include the Mean Squared Error (MSE) and cross-entropy @@ -302,7 +302,7 @@ This means that you do not have to keep track yourself. Moreover, the currently installed python packages and the git revision is logged. This information will be exported together with the model, to ensure -reproducability. +reproducibility. The keys ``model_checkpoint_path`` and ``log_path`` define where the model checkpoints and the tensorboard log files will be saved to. The ``model_checkpoint_path`` also defines the path from which the weights of @@ -322,7 +322,7 @@ each of your models. Therefore you can easily swap out modules and create custom ones. We have briefly touched the option to create your own neural network architecture here as well as the option to add custom loss functions. - More information on the exchangable modules is provided in + More information on the exchangeable modules is provided in :ref:`Code Documentation`. diff --git a/docs/source/pages/getting_started/03_monitor_progress.rst b/docs/source/pages/getting_started/03_monitor_progress.rst index fa51968..07afaf3 100644 --- a/docs/source/pages/getting_started/03_monitor_progress.rst +++ b/docs/source/pages/getting_started/03_monitor_progress.rst @@ -35,7 +35,7 @@ The input pipeline consists of three steps: 2. Another worker aggregates the events of several files (number of files defined by ``num_add_files``) together - by dequeing elements from the 'data_batch_queue'. + by dequeuing elements from the 'data_batch_queue'. It then creates batches from these events (randomly if 'sample_randomly' == True ). These batches are then put onto the 'final_batch_queue'. @@ -102,7 +102,7 @@ We can then use tensorboard to render these logs. # to forward this port in the ssh connection tensorboard --logdir=PATH/TO/MY/LOGS --port 7475 -If the port forwading is correctly set up, you can then point your browser to +If the port forwarding is correctly set up, you can then point your browser to to the appropriate address. More info on tensorboard is provided here: https://www.tensorflow.org/guide/summaries_and_tensorboard. diff --git a/docs/source/pages/getting_started/04_apply_model.rst b/docs/source/pages/getting_started/04_apply_model.rst index 62a0688..ff0203e 100644 --- a/docs/source/pages/getting_started/04_apply_model.rst +++ b/docs/source/pages/getting_started/04_apply_model.rst @@ -110,7 +110,7 @@ following: model_names = [str(model_names)] output_names = ["DeepLearningReco_{}".format(m) for m in model_names] - # Make sure DNN reco will be writen to hdf5 file + # Make sure DNN reco will be written to hdf5 file for outbox in output_names: if outbox not in HDF_keys: HDF_keys.append(outbox) diff --git a/docs/source/pages/installation.rst b/docs/source/pages/installation.rst index 429e537..b0c9a2f 100644 --- a/docs/source/pages/installation.rst +++ b/docs/source/pages/installation.rst @@ -71,7 +71,7 @@ directories to find packages before it looks inside the virtual environment. We want to avoid this and enforce that packages from our virtual environment are used first. To do this we need to prepend the path to our virtual environment in the ``$PYTHONPATH``. Since we don't want to do this manually -everytime we load the environment, we can add this to the ``activate`` shell +every time we load the environment, we can add this to the ``activate`` shell script that starts the virtual environment. We will need to add the following to the ``deactivate ()`` function @@ -111,7 +111,7 @@ pip is being used, you can execute the following. .. code-block:: bash # make sure that your virtualenv is activated - # you can check this by exectuting + # you can check this by executing which pip # It should point to: echo ${DNN_HOME}/py3-v4.1.1_tensorflow2.3/bin/pip diff --git a/docs/source/pages/models.rst b/docs/source/pages/models.rst index ae73f05..e4018ad 100644 --- a/docs/source/pages/models.rst +++ b/docs/source/pages/models.rst @@ -32,7 +32,7 @@ If run on a CPU, the number of CPUs to run the model on may be passed via ``num_cpus``. Especially if on a GPU, it is advisable to run the |dnn_reco| on batches of events at a time. This can be controlled via ``batch_size`` which defines the -number of events to reconstruct simulatenously. +number of events to reconstruct simulateneously. The best settings depend on the hardware setup. A good staring point could be 32 or 64. diff --git a/pyproject.toml b/pyproject.toml index 84d5b01..56a99cf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,5 +81,8 @@ lint.ignore = [ line-length = 79 target-version = "py38" +[tool.codespell] +ignore-words-list = "hese,mese,livetime" + [tool.ruff.lint.pydocstyle] convention = "numpy" diff --git a/tests_manual/test.py b/tests_manual/test.py index c632817..6034e8c 100644 --- a/tests_manual/test.py +++ b/tests_manual/test.py @@ -78,10 +78,10 @@ def error(msg): rtol=5e-4, ): if key == "LabelsDeepLearning": - warning("\t\tWarning: mis-match for {}".format(k)) + warning("\t\tWarning: mismatch for {}".format(k)) got_warning = True else: - error("\t\tError: mis-match for {}".format(k)) + error("\t\tError: mismatch for {}".format(k)) passed_test = False print( "\t\t", From a149f215e20fb65d18b8d177703bfa2822103e38 Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 15:41:32 +0200 Subject: [PATCH 5/9] Update badges --- README.md | 6 +++--- pyproject.toml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 36a0b77..a918df3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -| Documentation | DOI | -| :-----------: | :-----: | -| [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://user-web.icecube.wisc.edu/~mhuennefeld/docs/dnn_reco/html/) | [![DOI](https://zenodo.org/badge/144996337.svg)](https://zenodo.org/badge/latestdoi/144996337) | +| Testing | Coverage | Documentation | DOI | +| :-----: | :------: | :-----------: | :-----: | +| [![Unit Tests](https://github.com/icecube/dnn_reco/actions/workflows/test_suite.yml/badge.svg)](https://github.com/icecube/dnn_reco/actions/workflows/test_suite.yml) | [![codecov](https://codecov.io/github/icecube/dnn_reco/graph/badge.svg?token=ZQ6K8V9F4U)](https://codecov.io/github/icecube/dnn_reco) | [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://user-web.icecube.wisc.edu/~mhuennefeld/docs/dnn_reco/html/) | [![DOI](https://zenodo.org/badge/144996337.svg)](https://zenodo.org/badge/latestdoi/144996337) | # dnn_reco IceCube DNN reconstruction diff --git a/pyproject.toml b/pyproject.toml index 56a99cf..6b4ea59 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,7 @@ requires-python = ">=2.7" # Note: unfortunately need python >=2.7 dependencies = [ 'numpy', 'pandas', 'click', 'ruamel.yaml', 'gitpython', 'tfscripts', 'h5py', 'tqdm', - 'uncertainties', 'tables', + 'uncertainties', 'tables', 'tensorflow', ] classifiers = [ From 7828d66fcca652669b3dc0ce0aa3057b56cdcfe3 Mon Sep 17 00:00:00 2001 From: mhuen Date: Thu, 11 Apr 2024 15:47:46 +0200 Subject: [PATCH 6/9] Add dependencies for docs --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6b4ea59..261af1c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ classifiers = [ [project.optional-dependencies] dev = ["pre-commit","black","ruff"] test = ["pytest", "coverage", "codecov"] +docs = ["sphinx-rtd-theme"] icecube = [ "ic3-labels @ git+ssh://git@github.com/icecube/ic3-labels", "ic3-data @ git+ssh://git@github.com/icecube/ic3-data", From 4dd50455867be1191b8c93c58a57f761e45da15a Mon Sep 17 00:00:00 2001 From: mhuen Date: Mon, 15 Apr 2024 15:44:58 +0200 Subject: [PATCH 7/9] Update manual tests to use ic3-processing --- .pre-commit-config.yaml | 5 +- pyproject.toml | 9 +- tests_manual/README.txt | 22 +- tests_manual/test.py | 23 +- .../l5/1/DNN_l5_00000002.hdf5 | Bin 251256 -> 0 bytes .../l5/1/DNN_l5_00000001.hdf5 | Bin 2540154 -> 0 bytes .../l5/1/DNN_l5_00000002.hdf5 | Bin 231435 -> 0 bytes .../l5/1/DNN_l5_00000001.hdf5 | Bin 552468 -> 0 bytes .../l5/1/DNN_l5_00000002.hdf5 | Bin 231659 -> 0 bytes .../l5/1/DNN_l5_00000001.hdf5 | Bin 646190 -> 0 bytes .../l5/dnn_reco_test_01_base.yaml_0000 | 328 ---------------- 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tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py create mode 100644 tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml create mode 100644 tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml create mode 100644 tests_manual/test_data_cfg.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 62eebfa..a6d4e47 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,9 @@ repos: rev: 24.3.0 hooks: - id: black + exclude: ^tests_manual/test_data/.* - id: black-jupyter + exclude: ^tests_manual/test_data/.* - repo: https://github.com/asottile/blacken-docs rev: "1.16.0" hooks: @@ -48,6 +50,7 @@ repos: rev: v4.5.0 hooks: - id: check-added-large-files + exclude: ^tests_manual/test_data/.* - id: check-ast - id: check-builtin-literals - id: check-case-conflict @@ -66,4 +69,4 @@ repos: - id: name-tests-test args: [--pytest-test-first] - id: trailing-whitespace - exclude: \.svg$ + exclude: ^(tests_manual/test_data/.*|\.svg$) diff --git a/pyproject.toml b/pyproject.toml index 261af1c..99465c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,11 +36,14 @@ classifiers = [ [project.optional-dependencies] dev = ["pre-commit","black","ruff"] -test = ["pytest", "coverage", "codecov"] +test = [ + "pytest", "coverage", "codecov", + "ic3_processing @ git+ssh://git@github.com/mhuen/ic3-processing", +] docs = ["sphinx-rtd-theme"] icecube = [ - "ic3-labels @ git+ssh://git@github.com/icecube/ic3-labels", - "ic3-data @ git+ssh://git@github.com/icecube/ic3-data", + "ic3_labels @ git+ssh://git@github.com/icecube/ic3-labels", + "ic3_data @ git+ssh://git@github.com/icecube/ic3-data", ] [project.urls] diff --git a/tests_manual/README.txt b/tests_manual/README.txt index 10ad0cd..aaec6f5 100644 --- a/tests_manual/README.txt +++ b/tests_manual/README.txt @@ -4,17 +4,19 @@ # Test: # - pulse summary values data # - DNN reco based on summary -# Note: the tests here use the processing scripts from -# https://code.icecube.wisc.edu/projects/icecube/browser/IceCube/sandbox/mhuennefeld/processing_scripts/trunk/ -# and more specifically this config: -# https://code.icecube.wisc.edu/projects/icecube/browser/IceCube/sandbox/mhuennefeld/processing_scripts/trunk/processing/configs/unit_tests/dnn_reco_test_01.yaml +# Note: the tests here use the processing framework `ic3_processing` from +https://github.com/mhuen/ic3-processing +# and the config located in this directory: +./test_data_cfg.yaml # --------------------------------------- -# Define directory for output -export output_dir=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01 +# Define directory for output, which we will directly place into the +# test_data directory of dnn_reco +export dnn_reco_dir=/INSERT/PATH/TO/DNN_RECO/DIRECTORY +export output_dir=${dnn_reco_dir}/tests_manual/test_data/dnn_reco_test_01 # Create jobs [adjust the python env in config prior to job creation] -python create_job_files.py configs/unit_tests/dnn_reco_test_01.yaml -d ${output_dir} +ic3_create_job_files ${dnn_reco_dir}/tests_manual/test_data_cfg.yaml -d ${output_dir} # Run scripts on NPX GPU/CPU ${output_dir}/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -24,9 +26,5 @@ ${output_dir}/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs ${output_dir}/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh ${output_dir}/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh - -# copy files over to location where test is being run -rsync -avP madisonData:/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01 ./test_data/ - # run test -python test.py +python ${dnn_reco_dir}/tests_manual/test.py diff --git a/tests_manual/test.py b/tests_manual/test.py index 6034e8c..01e35d9 100644 --- a/tests_manual/test.py +++ b/tests_manual/test.py @@ -34,18 +34,31 @@ def error(msg): ] keys = [ - "dnn_data_bin_values", - "dnn_data_bin_indices", - "dnn_data_bin_exclusions", - "dnn_data_global_time_offset", + # sanity checks + "I3EventHeader", + # ic3-data input data to dnn_reco + "dnn_data__charge_bins_bin_values", + "dnn_data__charge_bins_bin_indices", + "dnn_data__charge_bins_bin_exclusions", + "dnn_data__charge_bins_global_time_offset", + "dnn_data_inputs3_InIceDSTPulses_bin_values", + "dnn_data_inputs3_InIceDSTPulses_bin_indices", + "dnn_data_inputs3_InIceDSTPulses_global_time_offset", + "dnn_data_inputs9_InIceDSTPulses_bin_values", + "dnn_data_inputs9_InIceDSTPulses_bin_indices", + "dnn_data_inputs9_InIceDSTPulses_global_time_offset", + # ic3-labels labels "LabelsDeepLearning", + "LabelsMCCascade", + "MCCascade", + # dnn_reco results "DeepLearningReco_event_selection_cscdl3_300m_01", "DeepLearningReco_event_selection_cascade_monopod_starting_events_big_kernel_02", "DeepLearningReco_mese_v2__all_gl_both2", "DeepLearningReco_dnn_reco_paper_hese__m7_after_sys", ] -dir_original = "test_data/dnn_reco_test_01_base_v0.2.0" +dir_original = "test_data/dnn_reco_test_01_base_v1_0_1_dev" test_dirs = glob.glob("test_data/*") test_dirs.remove(dir_original) diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002.hdf5 deleted file mode 100644 index e526a7b3bdacca94c7edd7ea10e873146d442cbe..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 251256 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-Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_low -datasets: - Nugen_low: - cycler: *id001 - data_type: NuGen - n_events_per_run: 50000 - runs_range: &id002 - - 2 - - 3 - Nugen_medium: - cycler: - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 10000 - runs_range: - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: low_energy -flavor: NuE -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 50000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh deleted file mode 100755 index d355dc0..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 2 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 2 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - if [ "$WRITE_HDF5" = "True" ]; then - rm *.hdf5 - fi - if [ "$WRITE_I3" = "True" ]; then - rm *.i3.bz2 - fi -fi -exit $ICETRAY_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 deleted file mode 100644 index 4849a28..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 +++ /dev/null @@ -1,328 +0,0 @@ -Cgenerator_add_mc_cascade: true -Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_medium -datasets: - Nugen_low: - cycler: - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 50000 - runs_range: - - 2 - - 3 - Nugen_medium: - cycler: *id001 - data_type: NuGen - n_events_per_run: 10000 - runs_range: &id002 - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: medium_energy -flavor: NuE -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 10000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh deleted file mode 100755 index 224dd35..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 1 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0000 1 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - if [ "$WRITE_HDF5" = "True" ]; then - rm *.hdf5 - fi - if [ "$WRITE_I3" = "True" ]; then - rm *.i3.bz2 - fi -fi -exit $ICETRAY_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 deleted file mode 100644 index befa109..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 +++ /dev/null @@ -1,328 +0,0 @@ -Cgenerator_add_mc_cascade: true -Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_low -datasets: - Nugen_low: - cycler: *id001 - data_type: NuGen - n_events_per_run: 50000 - runs_range: &id002 - - 2 - - 3 - Nugen_medium: - cycler: - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 10000 - runs_range: - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: low_energy -flavor: NuMu -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 50000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh deleted file mode 100755 index cbca599..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 2 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 2 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - if [ "$WRITE_HDF5" = "True" ]; then - rm *.hdf5 - fi - if [ "$WRITE_I3" = "True" ]; then - rm *.i3.bz2 - fi -fi -exit $ICETRAY_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 deleted file mode 100644 index 0775c0d..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 +++ /dev/null @@ -1,328 +0,0 @@ -Cgenerator_add_mc_cascade: true -Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_medium -datasets: - Nugen_low: - cycler: - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 50000 - runs_range: - - 2 - - 3 - Nugen_medium: - cycler: *id001 - data_type: NuGen - n_events_per_run: 10000 - runs_range: &id002 - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: medium_energy -flavor: NuMu -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 10000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh deleted file mode 100755 index 86b4aac..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 1 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0002 1 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - if [ "$WRITE_HDF5" = "True" ]; then - rm *.hdf5 - fi - if [ "$WRITE_I3" = "True" ]; then - rm *.i3.bz2 - fi -fi -exit $ICETRAY_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 deleted file mode 100644 index 82d5a90..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 +++ /dev/null @@ -1,328 +0,0 @@ -Cgenerator_add_mc_cascade: true -Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_low -datasets: - Nugen_low: - cycler: *id001 - data_type: NuGen - n_events_per_run: 50000 - runs_range: &id002 - - 2 - - 3 - Nugen_medium: - cycler: - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 10000 - runs_range: - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: low_energy -flavor: NuTau -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 50000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh deleted file mode 100755 index 5da90fd..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_2_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 2 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 2 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - if [ "$WRITE_HDF5" = "True" ]; then - rm *.hdf5 - fi - if [ "$WRITE_I3" = "True" ]; then - rm *.i3.bz2 - fi -fi -exit $ICETRAY_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 deleted file mode 100644 index 60a38fc..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 +++ /dev/null @@ -1,328 +0,0 @@ -Cgenerator_add_mc_cascade: true -Cgenerator_cascade_seed: L5MonopodFit4 -Cgenerator_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -Cgenerator_model_names: -- cascades_1000X_charge_bins_177_vertex_implicit_autoencoder_all_doms -Cgenerator_models_dir: /data/user/mhuennefeld/exported_models/cgenerator -Cgenerator_partial_exclusion: true -Cgenerator_pulse_key: InIceDSTPulses -DNN_cascade_key: cscd_classification_base_L5MonopodFit4 -DNN_excluded_doms: -- BrightDOMs -- SaturationWindows -- BadDomsList -- CalibrationErrata -DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models -DNN_partial_exclusion: true -DNN_reco_configs: -- DNN_model_names: - - dnn_reco_paper_hese__m7_after_sys - - mese_v2__all_gl_both2 - - event_selection_cscdl3_300m_01 -- DNN_model_names: - - event_selection_cascade_monopod_starting_events_big_kernel_02 -HDF_SubEventStreams: -- in_ice -- InIceSplit -- Final -- topological_split -HDF_keys: -- dnn_data_bin_values -- dnn_data_bin_indices -- dnn_data_bin_exclusions -- dnn_data_global_time_offset -- LabelsDeepLearning -- LabelsDeepLearning_new -- weights -- weights_mese -- weights_meta_info -add_cgenerator_reco: false -add_cscd_l3_tag: false -add_cscd_l3_tag_discard_non_l2: false -add_dnn_input_data: true -add_dnn_reco: true -add_labels: true -add_mese_weights: true -add_weights: true -apply_final_cascade_l5_cuts: false -autoencoder_encoder_name: null -autoencoder_settings: null -config_base_name: !!python/unicode 'dnn_reco_test_01_base.yaml' -create_cascade_classification_base_cascades: -- I3ParticleBase: L5MonopodFit4 - VertexTime_unc: 30 - VertexX_unc: 15 - VertexY_unc: 15 - VertexZ_unc: 15 -cycler: &id001 - energy: - - medium_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 -dagman_max_jobs: 5000 -dagman_scan_interval: 1 -dagman_submit_delay: 0 -dagman_submits_interval: 500 -data_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base' -data_type: NuGen -dataset_name: Nugen_medium -datasets: - Nugen_low: - cycler: - energy: - - low_energy - flavor: - - NuE - - NuTau - - NuMu - level: - - l5 - systematic_addition: - - _holeice_30_v4 - year: - - IC86_2013 - data_type: NuGen - n_events_per_run: 50000 - runs_range: - - 2 - - 3 - Nugen_medium: - cycler: *id001 - data_type: NuGen - n_events_per_run: 10000 - runs_range: &id002 - - 1 - - 2 -dnn_data_dom_exclusions: -- SaturationWindows -- BadDomsList -- CalibrationErrata -dnn_data_output_key: dnn_data -dnn_data_partial_exclusion: true -energy: medium_energy -flavor: NuTau -folder_offset: 1 -folder_pattern: '{folder_num:d}' -gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz -in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 -job_template: job_templates/cascade_l5_cuts.sh -keep_crashed_files: false -label_output_key: LabelsDeepLearning -level: l5 -merge_files: false -n_events_per_run: 10000 -num_data_bins: 177 -out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' -out_file_pattern: DNN_{level}_{run_number:08d} -processing_folder: !!python/unicode '/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/' -pulse_data_format: charge_bins -pulse_map_string: InIceDSTPulses -pulse_time_binning: -- -inf -- -500.0 -- -480.0 -- -460.0 -- -440.0 -- -420.0 -- -400.0 -- -380.0 -- -360.0 -- -340.0 -- -320.0 -- -300.0 -- -280.0 -- -260.0 -- -240.0 -- -220.0 -- -200.0 -- -180.0 -- -160.0 -- -140.0 -- -120.0 -- -100.0 -- -80.0 -- -60.0 -- -40.0 -- -20.0 -- 0.0 -- 20.0 -- 40.0 -- 60.0 -- 80.0 -- 100.0 -- 120.0 -- 140.0 -- 160.0 -- 180.0 -- 200.0 -- 220.0 -- 240.0 -- 260.0 -- 280.0 -- 300.0 -- 320.0 -- 340.0 -- 360.0 -- 380.0 -- 400.0 -- 420.0 -- 440.0 -- 460.0 -- 480.0 -- 500.0 -- 520.0 -- 540.0 -- 560.0 -- 580.0 -- 600.0 -- 620.0 -- 640.0 -- 660.0 -- 680.0 -- 700.0 -- 720.0 -- 740.0 -- 760.0 -- 780.0 -- 800.0 -- 820.0 -- 840.0 -- 860.0 -- 880.0 -- 900.0 -- 920.0 -- 940.0 -- 960.0 -- 980.0 -- 1000.0 -- 1020.0 -- 1040.0 -- 1060.0 -- 1080.0 -- 1100.0 -- 1120.0 -- 1140.0 -- 1160.0 -- 1180.0 -- 1200.0 -- 1220.0 -- 1240.0 -- 1260.0 -- 1280.0 -- 1300.0 -- 1320.0 -- 1340.0 -- 1360.0 -- 1380.0 -- 1400.0 -- 1420.0 -- 1440.0 -- 1460.0 -- 1480.0 -- 1500.0 -- 1520.0 -- 1540.0 -- 1560.0 -- 1580.0 -- 1600.0 -- 1620.0 -- 1640.0 -- 1660.0 -- 1680.0 -- 1700.0 -- 1720.0 -- 1740.0 -- 1760.0 -- 1780.0 -- 1800.0 -- 1820.0 -- 1840.0 -- 1860.0 -- 1880.0 -- 1900.0 -- 1920.0 -- 1940.0 -- 1960.0 -- 1980.0 -- 2000.0 -- 2020.0 -- 2040.0 -- 2060.0 -- 2080.0 -- 2100.0 -- 2120.0 -- 2140.0 -- 2160.0 -- 2180.0 -- 2200.0 -- 2220.0 -- 2240.0 -- 2260.0 -- 2280.0 -- 2300.0 -- 2320.0 -- 2340.0 -- 2360.0 -- 2380.0 -- 2400.0 -- 2420.0 -- 2440.0 -- 2460.0 -- 2480.0 -- 2500.0 -- 2520.0 -- 2540.0 -- 2560.0 -- 2580.0 -- 2600.0 -- 2620.0 -- 2640.0 -- 2660.0 -- 2680.0 -- 2700.0 -- 2720.0 -- 2740.0 -- 2760.0 -- 2780.0 -- 2800.0 -- 2820.0 -- 2840.0 -- 2860.0 -- 2880.0 -- 2900.0 -- 2920.0 -- 2940.0 -- 2960.0 -- 2980.0 -- 3000.0 -- inf -pulse_time_quantiles: null -python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_cpu_py2-v3.0.1 -python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -relative_time_method: cascade_vertex -resources: - cpus: 1 - gpus: 1 - memory: 2gb -runs_range: *id002 -scratchfile_pattern: DNN_{level}_{run_number:08d} -script_folder: /data/user/mhuennefeld/scripts/processing_scripts/processing -script_name: dnn_reco_create_data.py -systematic_addition: _holeice_30_v4 -write_hdf5: true -write_i3: false -yaml_copy: /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 -year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh deleted file mode 100755 index 7fef5c5..0000000 --- a/tests_manual/test_data/dnn_reco_test_01_base_v0.2.0/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/bin/bash -#PBS -l nodes=1:ppn={cpus} -#PBS -l pmem={memory} -#PBS -l mem={memory} -#PBS -l vmem={memory} -#PBS -l pvmem={memory} -#PBS -l walltime={walltime} -#PBS -o /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.out -#PBS -e /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/logs/1/run_1_${PBS_JOBID}.err -#PBS -q long -#PBS -S /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start -FINAL_OUT=/data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 -KEEP_CRASHED_FILES=False -WRITE_HDF5=True -WRITE_I3=False - -echo 'Loading py2-v3.0.1' -eval `/cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/setup.sh` -export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py2-v3.0.1 -echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} - - -export MPLBACKEND=agg -export PATH=$PYTHONUSERBASE/bin:$PATH -export PYTHONPATH=$PYTHONUSERBASE/lib/python2.7/site-packages:$PYTHONPATH - -#----------------------------------------------------------- -# Work-around for nodes which do not have correct paths set -#----------------------------------------------------------- -export CUDA_HOME=/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0; -export PATH=$PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/bin -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/mhuennefeld/software/condor_cuda3/cuda-8.0/lib64 - -# check if CUDA_HOME is set -if [ -z ${CUDA_HOME+x} ]; then - export CUDA_HOME=/usr/local/cuda-8.0; - export PATH=$PATH:/usr/local/cuda-8.0/bin:/usr/local/cuda/bin - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 -fi -#----------------------------------------------------------- - - -echo $FINAL_OUT -if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] -then - echo 'Running Script w/o temporary scratch' - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 1 --no-scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then - echo 'Deleting partially processed file! ' $FINAL_OUT - rm ${FINAL_OUT}.i3.bz2 - rm ${FINAL_OUT}.hdf5 - fi -else - echo 'Running Script w/ temporary scratch' - if [ -z ${_CONDOR_SCRATCH_DIR} ] - then - cd /scratch/${USER} - else - cd ${_CONDOR_SCRATCH_DIR} - fi - /data/user/mhuennefeld/scripts/processing_scripts/processing/scripts/dnn_reco_create_data.py /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/dnn_reco_test_01_base.yaml_0001 1 --scratch - ICETRAY_RC=$? - echo 'IceTray finished with Exit Code: ' $ICETRAY_RC - if [ $ICETRAY_RC -eq 0 ] || [ $KEEP_CRASHED_FILES -eq 1 ]; then - if [ "$WRITE_HDF5" = "True" ]; then - cp *.hdf5 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - if [ "$WRITE_I3" = "True" ]; then - cp *.i3.bz2 /data/user/mhuennefeld/to_delete/unit_tests/dnn_reco_test_01_base/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 - fi - fi - - # Clean Up - 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z^R#GJuPl$DA#PS?Slt(gwlZz8csDh7clWZ%XObE$Ef)50DrebQ8_5V{u9S1qB5uPBEplL{}xHq&BTaz@eckiv8!{b!@m=rS$ydH?14`AQ*9KhyJ7UnQ-%#pvaN!BUds07OB2A1wMQ7oB}T2YU9uAMJ{2!p)elzx4vy-E6Qq%4Z$gi5zR9%jisY= zUiQNVm`?$C`B}d^eE9EA4+FoAb)B~HJYIire0tlOF>NF0#}t1L9~ej_I7#iqq~sHY za$c>-lh^YoeV#z)_9YY;naYoHNaalS7k|K?d|Z^L zN%#o#;S#5-zNZdIwG#iH$G_*yuf_|0rC)H+z>OFDdwK`1rZ@sP0yqLV0yqNS3H-`l lpg;L*`4>=jnm^kMxb=&F0fdX<2;d0d2;d0d2>b&H{0FuRc}f5P literal 0 HcmV?d00001 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh new file mode 100755 index 0000000..baee680 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000*' +rm ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh new file mode 100755 index 0000000..31d58e6 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002_step0000.hdf5 ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002_step0000 ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh new file mode 100755 index 0000000..3004945 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..c6132fc --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuE +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..6d1254c --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuE +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh new file mode 100755 index 0000000..f5b67cd --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000*' +rm ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh new file mode 100755 index 0000000..912eaa6 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001_step0000.hdf5 ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001_step0000 ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh new file mode 100755 index 0000000..32cca82 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..8d94758 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuE +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..5080d41 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuE +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuE/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0000 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh new file mode 100755 index 0000000..314315f --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000*' +rm ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh new file mode 100755 index 0000000..8763411 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002_step0000.hdf5 ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002_step0000 ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh new file mode 100755 index 0000000..adbc1b5 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..86d06cd --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuMu +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..a766754 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuMu +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh new file mode 100755 index 0000000..83533a4 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000*' +rm ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh new file mode 100755 index 0000000..f75c3e3 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001_step0000.hdf5 ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001_step0000 ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh new file mode 100755 index 0000000..c860960 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..5d0fc5f --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuMu +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..4e1ac3a --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuMu +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuMu/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0002 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh new file mode 100755 index 0000000..d8b1f6d --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000002.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000002_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000*' +rm ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh new file mode 100755 index 0000000..007679c --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002_step0000.hdf5 ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002_step0000 ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh new file mode 100755 index 0000000..3a7b326 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000002/job_DNN_l5_00000002_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000002 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml 2 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml 2 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000002.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000002.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000002 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000002.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000002.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000002 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..bd929f7 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuTau +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..c0c1771 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_low +datasets: + Nugen_low: + cycler: *id001 + data_type: NuGen + n_events_per_run: 50000 + runs_range: &id002 + - 2 + - 3 + Nugen_medium: + cycler: + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 10000 + runs_range: + - 1 + - 2 +energy: low_energy +exclude_corrupted_input_files: false +flavor: NuTau +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 50000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/low_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh new file mode 100755 index 0000000..3248a9c --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/job_DNN_l5_00000001.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Shell-script wrapper to execute indidividual steps + +# Start Timer +# Note: SECONDS is a bash special variable that returns the seconds +# since it was set. +SECONDS=0 + +# Create array and index counter to keep track of time per-step +declare -a times +times[0]=$SECONDS +step_counter=1 +OUT_DIR=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001 + +echo && +echo '=========================' && +echo '==> Starting step 000 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0000.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + +echo && +echo '=========================' && +echo '==> Starting step 001 <==' && +echo '=========================' && +echo && +eval "${OUT_DIR}/job_DNN_l5_00000001_step_0001.sh" && +times[$step_counter]=$SECONDS && +((step_counter++)) && + + + echo && + echo '=========================================' && + echo '==> Successfully processed all steps! <==' && + echo '=========================================' + + RET=$? + if [ $RET -ne 0 ] ; then + echo + echo '=======================' + echo '==> Error occurred! <==' + echo '=======================' + echo + fi + + echo + echo 'Runtime for each processed step:' + + for (( i=1; i<$step_counter; i++ )) + do + printf " %-13s --> " "Step $i" + printf "%+8u s +" $(( ${times[$i]} - ${times[$i-1]})) + done + printf " %13s --> %+8u s +" "Total runtime" $SECONDS + echo + + echo Cleaning up intermediate files ... +echo ' ... removing ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000*' +rm ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000* +echo ' ... checking if temp_step_files exist' +if [ -d temp_step_files ]; then + echo ' ... checking if temp_step_files is empty' + lines=$(find temp_step_files/ -type f | wc -l) + if [ $lines -eq 0 ]; then + echo ' ... removing directory temp_step_files' + rm -r temp_step_files + else + echo ' ... Not deleting directory as it still contains files.' + fi +else + echo ' ... temp_step_files does not exist' +fi +exit $RET diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh new file mode 100755 index 0000000..3bd125b --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0000.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001_step0000 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=False +WRITE_I3=True +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py2-v3.0.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001_step0000.hdf5 ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001_step0000.${I3_ENDING} ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001_step0000 ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001_step0000.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001_step0000.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001_step0000 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh new file mode 100755 index 0000000..d0ecae8 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/jobs/1/steps_DNN_l5_00000001/job_DNN_l5_00000001_step_0001.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Gather keys +FINAL_OUT=/data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1/DNN_l5_00000001 +I3_ENDING=i3.bz2 +KEEP_CRASHED_FILES=False +WRITE_HDF5=True +WRITE_I3=False +CUDA_HOME=/data/user/mhuennefeld/software/cuda/cuda-11.2 +LD_LIBRARY_PATH_PREPENDS={ld_library_path_prepends} +CVMFS_PYTHON=py3-v4.2.1 + + +# load environment +echo 'Starting job on Host: '$HOSTNAME +echo 'Loading: ' ${CVMFS_PYTHON} +eval `/cvmfs/icecube.opensciencegrid.org/${CVMFS_PYTHON}/setup.sh` +export PYTHONUSERBASE=/data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +echo 'Using PYTHONUSERBASE: '${PYTHONUSERBASE} + +export ENV_SITE_PACKGES=$(find ${PYTHONUSERBASE}/lib* -maxdepth 2 -type d -name "site-packages") +export PYTHONPATH=$ENV_SITE_PACKGES:$PYTHONPATH +export PATH=$PYTHONUSERBASE/bin:$PATH +echo 'Using PYTHONPATH: '${PYTHONPATH} + +# set MPL backend for Matplotlib +export MPLBACKEND=agg + +# add cuda directory +if [ "$(echo "$CUDA_HOME" | sed 's/^.\(.*\).$/\1/')" = "cuda_home" ]; then + echo 'No cuda home provided. Not adding cuda to path.' +else + echo 'Adding cuda dir: '$CUDA_HOME + export PATH=$CUDA_HOME/bin:$PATH + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +fi + +# add additional LD_LIBRARY_PATH additions if we have them +if [ "$(echo "$LD_LIBRARY_PATH_PREPENDS" | sed 's/^.\(.*\).$/\1/')" != "ld_library_path_prepends" ]; then + echo 'Prepending to LD_LIBRARY_PATH: '$LD_LIBRARY_PATH_PREPENDS + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH_PREPENDS:$LD_LIBRARY_PATH +fi + +# start python script +echo 'Starting process for output file: '$FINAL_OUT +if [ -z ${PBS_JOBID} ] && [ -z ${_CONDOR_SCRATCH_DIR} ] +then + echo 'Running Script w/o temporary scratch' + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml 1 --no-scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -ne 0 ] && [ "$KEEP_CRASHED_FILES" = "False" ] ; then + echo 'Deleting partially processed file! ' $FINAL_OUT + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm ${FINAL_OUT}.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm ${FINAL_OUT}.i3.bz2 + fi + if [ -f "$FINAL_OUT" ]; then + rm $FINAL_OUT + fi + + fi +else + echo 'Running Script w/ temporary scratch' + if [ -z ${_CONDOR_SCRATCH_DIR} ] + then + cd /scratch/${USER} + else + cd ${_CONDOR_SCRATCH_DIR} + fi + /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml 1 --scratch + JOB_RC=$? + echo 'Job finished with Exit Code: ' $JOB_RC + if [ $JOB_RC -eq 0 ] || [ "$KEEP_CRASHED_FILES" = "True" ]; then + + # create output folder + mkdir -p /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + + if [ "$WRITE_HDF5" = "True" ]; then + cp DNN_l5_00000001.hdf5 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ "$WRITE_I3" = "True" ]; then + cp DNN_l5_00000001.${I3_ENDING} /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + if [ -f "$FINAL_OUT" ]; then + cp DNN_l5_00000001 /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/1 + fi + fi + + # Clean Up + if [ "$WRITE_HDF5" = "True" ]; then + rm DNN_l5_00000001.hdf5 + fi + if [ "$WRITE_I3" = "True" ]; then + rm DNN_l5_00000001.${I3_ENDING} + fi + if [ -f "$FINAL_OUT" ]; then + rm DNN_l5_00000001 + fi +fi +exit $JOB_RC diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py new file mode 100755 index 0000000..c3ccc80 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0000.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py2-v3.0.1/icetray-start +#METAPROJECT simulation/V06-01-01 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py new file mode 100755 index 0000000..ab66664 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/general_i3_processing_step_0001.py @@ -0,0 +1,199 @@ +#!/bin/sh /cvmfs/icecube.opensciencegrid.org/py3-v4.2.1/icetray-start +#METAPROJECT icetray/v1.5.1 +# Note: this file must unfortunately be python 2.7 compatible! +from __future__ import print_function, division +import timeit +import click + +from I3Tray import I3Tray +from icecube import icetray, hdfwriter + +from ic3_labels.weights.segments import AddWeightMetaData, UpdateMergedWeights + + +from ic3_processing.utils.exp_data import livetime +from ic3_processing.utils import setup +from ic3_processing.modules.utils import tray_timer + + +@click.command() +@click.argument("cfg", type=click.Path(exists=True)) +@click.argument("run_number", type=int) +@click.option("--scratch/--no-scratch", default=True) +def main(cfg, run_number, scratch): + # start timer + start_time = timeit.default_timer() + + # -------------------------------- + # load configuration and setup job + # -------------------------------- + cfg, context = setup.setup_job_and_config(cfg, run_number, scratch) + # -------------------------------- + + tray = I3Tray() + + tray.context["ic3_processing"] = context + tray.context["ic3_processing"]["HDF_keys"] = [] + + tray.Add("I3Reader", FilenameList=context["infiles"]) + + # ----------------------------------------- + # Write livetime data for experimental data + # ----------------------------------------- + livetime.write_exp_livetime_data( + tray, + name="write_exp_livetime_data", + cfg=cfg, + ) + + # ---------------------------------------------- + # Add tray segments defined in configuration cfg + # ---------------------------------------------- + for i, settings in enumerate(cfg["tray_segments"]): + # get module/segment class + if "." not in settings["ModuleClass"]: + module_class = settings["ModuleClass"] + else: + module_class = setup.load_class(settings["ModuleClass"]) + + # sanity check to make sure the user didn't have a typo + allowed_keys = ["ModuleClass", "ModuleKwargs", "ModuleTimer"] + for key in settings.keys(): + if key not in allowed_keys: + msg = "The key '{}'' is not in the allowed keys: {}!" + raise KeyError(msg.format(key, allowed_keys)) + + # dynamically replace values of the form + # context-->a.b.c + # with tray.context[a][b][c] + search_key = "context-->" + kwargs = {} + if "ModuleKwargs" in settings: + for key, value in settings["ModuleKwargs"].items(): + # dynamically replace key + if isinstance(value, str): + if search_key in value: + context_keys = value.replace(search_key, "").split(".") + value = tray.context + for context_key in context_keys: + value = value[context_key] + + elif value == "": + value = cfg + + else: + # expand with parameters in config + value = value.format(**cfg) + + kwargs[key] = value + + if "OutputKey" in kwargs: + tray.context["ic3_processing"]["HDF_keys"].append( + kwargs["OutputKey"] + ) + + # get name of module under which it will be added to the tray + name = settings["ModuleClass"].split(".")[-1] + "_{:05d}".format(i) + + # start timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStart, TimerName=name + "_timer") + tray.context["ic3_processing"]["HDF_keys"].append("DurationQ") + tray.context["ic3_processing"]["HDF_keys"].append("DurationP") + + # add module + tray.Add(module_class, name, **kwargs) + + # stop timer if specified + if "ModuleTimer" in settings and settings["ModuleTimer"]: + tray.Add(tray_timer.TimerStop, TimerName=name + "_timer") + + # ----------------------------------------------------------- + # keep track of merged files and update weights if they exist + # ----------------------------------------------------------- + if cfg["data_type"] != "exp": + # Official simulation sets save the n_event_per_run information + # in keys such as I3MCWeightDict or CorsikaWeightMap. However, + # there are some older custom datasets that lack this information. + if "dataset_n_events_per_run" in cfg: + n_evets_per_run = cfg["dataset_n_events_per_run"] + else: + n_evets_per_run = -1 + + if not context["weights_meta_info_exists"]: + # check if "AddWeightMetaData" was already added to the I3Tray + if "AddWeightMetaData" not in tray.TrayInfo().modules_in_order: + tray.AddModule( + AddWeightMetaData, + "AddWeightMetaData", + NFiles=tray.context["ic3_processing"]["total_n_files"], + NEventsPerRun=n_evets_per_run, + ) + + tray.AddModule( + UpdateMergedWeights, + "UpdateMergedWeights", + TotalNFiles=tray.context["ic3_processing"]["total_n_files"], + ) + + # -------------------------------------------------- + # Write output + # -------------------------------------------------- + if cfg["write_i3"]: + if "i3_streams" in cfg["write_i3_kwargs"]: + i3_streams = [ + icetray.I3Frame.Stream(s) + for s in cfg["write_i3_kwargs"].pop("i3_streams") + ] + else: + i3_streams = [ + icetray.I3Frame.DAQ, + icetray.I3Frame.Physics, + icetray.I3Frame.TrayInfo, + icetray.I3Frame.Simulation, + icetray.I3Frame.Stream("S"), + icetray.I3Frame.Stream("M"), + icetray.I3Frame.Stream("m"), + icetray.I3Frame.Stream("W"), + icetray.I3Frame.Stream("X"), + ] + print("Only writing the following streams:", i3_streams) + + tray.AddModule( + "I3Writer", + "EventWriter", + filename="{}.{}".format(context["outfile"], cfg["i3_ending"]), + Streams=i3_streams, + **cfg["write_i3_kwargs"] + ) + + if cfg["write_hdf5"]: + keys = cfg["write_hdf5_kwargs"].pop("Keys") + keys += tray.context["ic3_processing"]["HDF_keys"] + tray.AddSegment( + hdfwriter.I3HDFWriter, + "hdf", + Output="{}.hdf5".format(context["outfile"]), + Keys=[k for k in set(keys)], + **cfg["write_hdf5_kwargs"] + ) + # -------------------------------------------------- + + tray.AddModule("TrashCan", "the can") + tray.Execute() + tray.Finish() + + print("\nUsage:") + print("------") + msg = " systime: {:7.2f} | usertime {:7.2f} | ncall: {:5d} | {}" + for entry in tray.Usage(): + key = entry.key() + usage = entry.data() + print(msg.format(usage.systime, usage.usertime, usage.ncall, key)) + + end_time = timeit.default_timer() + print("\nDuration: {:5.3f}s".format(end_time - start_time)) + + +if __name__ == "__main__": + main() diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml new file mode 100644 index 0000000..338be86 --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0000.yaml @@ -0,0 +1,240 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py2-v3.0.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: ./temp_step_files +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuTau +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: simulation/V06-01-01 +in_file_pattern: /data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_files_is_n_runs: true +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d}_step0000 +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: false +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: true +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml new file mode 100644 index 0000000..ce2231a --- /dev/null +++ b/tests_manual/test_data/dnn_reco_test_01_base_v1_0_1_dev/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001/test_data_cfg_step_0001.yaml @@ -0,0 +1,239 @@ +config_base_name: test_data_cfg +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 +cvmfs_python: py3-v4.2.1 +cycler: &id001 + energy: + - medium_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 +dagman_max_jobs: 5000 +dagman_scan_interval: 1 +dagman_submit_delay: 0 +dagman_submits_interval: 500 +data_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01 +data_type: NuGen +dataset_name: Nugen_medium +datasets: + Nugen_low: + cycler: + energy: + - low_energy + flavor: + - NuE + - NuTau + - NuMu + level: + - l5 + systematic_addition: + - _holeice_30_v4 + year: + - IC86_2013 + data_type: NuGen + n_events_per_run: 50000 + runs_range: + - 2 + - 3 + Nugen_medium: + cycler: *id001 + data_type: NuGen + n_events_per_run: 10000 + runs_range: &id002 + - 1 + - 2 +energy: medium_energy +exclude_corrupted_input_files: false +flavor: NuTau +folder_offset: 1 +folder_pattern: '{folder_num:d}' +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +i3_ending: i3.bz2 +ic3_processing_version: '0.1' +icetray_metaproject: icetray/v1.5.1 +in_file_pattern: ./temp_step_files/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/{folder_num:d}/DNN_{level}_{run_number:08d}_step0000.i3.bz2 +job_template: job_templates/cvmfs_python.sh +keep_crashed_files: false +ld_library_path_prepends: '{ld_library_path_prepends}' +level: l5 +n_events_per_run: 10000 +n_jobs_per_folder: 1000 +n_runs_per_merge: 1 +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: DNN_{level}_{run_number:08d} +processing_folder: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/ +processing_steps: +- cvmfs_python: py2-v3.0.1 + icetray_metaproject: simulation/V06-01-01 + n_files_is_n_runs: true + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1 + tray_segments: + - ModuleClass: ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal + ModuleKwargs: {} + ModuleTimer: true +- cvmfs_python: py3-v4.2.1 + icetray_metaproject: icetray/v1.5.1 + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 + tray_segments: &id003 + - ModuleClass: ic3_processing.modules.labels.primary.add_weighted_primary + ModuleKwargs: {} + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascades + ModuleKwargs: + ExtendBoundary: -60 + OutputKey: LabelsDeepLearning + PrimaryKey: MCPrimary + PulseMapString: InIceDSTPulses + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_labels.labels.modules.modules.MCLabelsCascadeParameters + ModuleKwargs: + OutputKey: LabelsMCCascade + PrimaryKey: MCPrimary + PulseMapString: null + RunOnDAQFrames: true + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades + ModuleKwargs: + cscd_base_configs: + - I3ParticleBase: L5MonopodFit4 + VertexTime_unc: 30 + VertexX_unc: 15 + VertexY_unc: 15 + VertexZ_unc: 15 + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: charge_bins + NumDataBins: 177 + OutputKey: dnn_data__charge_bins + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: cascade_vertex + TimeBins: + - -inf + - -500.0 + - -250.0 + - 0.0 + - 500.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2500.0 + - 3000.0 + - inf + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: pulse_summmary_clipped + NumDataBins: 9 + OutputKey: dnn_data_inputs9_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: time_range + ModuleTimer: true + - ModuleClass: ic3_data.segments.CreateDNNData + ModuleKwargs: + DOMExclusions: + - SaturationWindows + - BadDomsList + - CalibrationErrata + DataFormat: reduced_summary_statistics_data + NumDataBins: 3 + OutputKey: dnn_data_inputs3_InIceDSTPulses + PartialExclusion: true + PulseKey: InIceDSTPulses + RelativeTimeMethod: null + ModuleTimer: true + - ModuleClass: ic3_processing.modules.reco.reco.apply_dnn_reco + ModuleKwargs: + cfg: + DNN_batch_size: 32 + DNN_excluded_doms: + - BrightDOMs + - SaturationWindows + - BadDomsList + - CalibrationErrata + DNN_partial_exclusion: true + DNN_reco_configs: + - DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - dnn_reco_paper_hese__m7_after_sys + - mese_v2__all_gl_both2 + - event_selection_cscdl3_300m_01 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + - DNN_cascade_key: cscd_classification_base_L5MonopodFit4 + DNN_ignore_misconfigured_settings_list: [] + DNN_model_names: + - event_selection_cascade_monopod_starting_events_big_kernel_02 + DNN_models_dir: /data/user/mhuennefeld/DNN_reco/models/exported_models + pulse_map_string: InIceDSTPulses + add_dnn_reco: true + ModuleTimer: true +python_user_base: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1 +resources: + cpus: 1 + gpus: 0 + has_avx2: true + memory: 3gb +runs_range: *id002 +script_folder: /mnt/ceph1-npx/user/mhuennefeld/software/repositories/ic3-processing/ic3_processing/cli +script_name: general_i3_processing.py +set_env_vars_from_python: {} +sub_process_dir: /data/user/mhuennefeld/software/repositories/dnn_recoV2compatV1/tests_manual/test_data/dnn_reco_test_01/processing/NuGen/NuTau/medium_energy/IC86_2013_holeice_30_v4/l5/processing_steps_0001 +systematic_addition: _holeice_30_v4 +tray_segments: *id003 +write_hdf5: true +write_hdf5_kwargs: + Keys: + - I3EventHeader + - DurationQ + - DurationP + - dnn_data__charge_bins_bin_values + - dnn_data__charge_bins_bin_indices + - dnn_data__charge_bins_bin_exclusions + - dnn_data__charge_bins_global_time_offset + - dnn_data_inputs3_InIceDSTPulses_bin_values + - dnn_data_inputs3_InIceDSTPulses_bin_indices + - dnn_data_inputs3_InIceDSTPulses_global_time_offset + - dnn_data_inputs9_InIceDSTPulses_bin_values + - dnn_data_inputs9_InIceDSTPulses_bin_indices + - dnn_data_inputs9_InIceDSTPulses_global_time_offset + - LabelsDeepLearning + - LabelsMCCascade + - MCCascade + SubEventStreams: + - in_ice + - InIceSplit + - Final + - topological_split +write_i3: false +write_i3_kwargs: + i3_streams: + - Q + - P + - I + - S + - M + - m + - W + - X +year: IC86_2013 diff --git a/tests_manual/test_data_cfg.yaml b/tests_manual/test_data_cfg.yaml new file mode 100644 index 0000000..1c43fa1 --- /dev/null +++ b/tests_manual/test_data_cfg.yaml @@ -0,0 +1,367 @@ +# Example config file to process Level 2 MC i3-files +# +# Directory structure: +# At prompt of create_job_files.py a data_folder +# will be asked for in which the files are to be +# saved +# +# Files are then stored as: +# +# data_folder: +# processing: +# out_dir_pattern: +# jobs: (job files are stored here) +# logs: (log files are stored here) +# +# out_dir_pattern: +# +# run_folder: +# out_file_pattern + '.' + i3_ending +# out_file_pattern + '.hdf5' +# +# +# Where the run folder is given by: +# run_folder = folder_pattern.format( +# folder_num=folder_num, +# folder_num_pre_offset=folder_num_pre_offset, +# folder_num_n_merged=folder_num_n_merged, +# folder_num_pre_offset_n_merged=folder_num_pre_offset_n_merged, +# **cfg +# ) +# +# The following variables are computed and can be used in input/output patterns +# +# folder_num = folder_offset + run_number // n_jobs_per_folder +# folder_num_pre_offset = run_number // n_jobs_per_folder +# folder_num_n_merged = folder_offset + (n_runs_per_merge * run_number) // n_jobs_per_folder +# folder_num_pre_offset_n_merged = (n_runs_per_merge * run_number) // n_jobs_per_folder +# + +#------------------------------ +# General job submission config +#------------------------------ + +keep_crashed_files: False + +resources: + # If gpus == 1 this will be run on a GPU with + gpus: 0 + cpus: 1 + memory: 3gb # runs with 1gb, but sometimes crashes + has_avx2: True + # has_ssse3: True + +dagman_max_jobs: 5000 +dagman_submits_interval: 500 +dagman_scan_interval: 1 +dagman_submit_delay: 0 + +# If true, the input files will first be checked for corruption. +# Note that this will take a while, since each input file has to be +# iterated through. You generally only want to set this to true if you +# are merging a number of input files of which some are known to be corrupt. +exclude_corrupted_input_files: False + +#------------------------------ +# Define Datasets to process +#------------------------------ + +#------ +# common settings shared by all datasets +#------ +i3_ending: 'i3.bz2' +n_runs_per_merge: 1 +n_jobs_per_folder: 1000 +in_file_pattern: '/data/ana/Cscd/StartingEvents/{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/{folder_num:d}/{level:.2s}_{run_number:08d}.i3.bz2' +out_dir_pattern: '{data_type}/{flavor}/{energy}/{year}{systematic_addition}/{level}/' +out_file_pattern: 'DNN_{level}_{run_number:08d}' +folder_pattern: '{folder_num:d}' +folder_offset: 1 +#gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_IC86_Merged.i3.gz +gcd: /cvmfs/icecube.opensciencegrid.org/data/GCD/GeoCalibDetectorStatus_2013.56429_V1.i3.gz +#------ + + +datasets: + + # NuGen without Cgenerator added + Nugen_low: + + cycler: + year: ['IC86_2013'] + energy: ['low_energy'] + flavor: ['NuE','NuTau','NuMu'] + systematic_addition: ['_holeice_30_v4'] + level: ['l5'] + + runs_range: [2, 3] + data_type: 'NuGen' + n_events_per_run: 50000 + + Nugen_medium: + + cycler: + year: ['IC86_2013'] + energy: ['medium_energy'] + flavor: ['NuE','NuTau','NuMu'] + systematic_addition: ['_holeice_30_v4'] + level: ['l5'] + + runs_range: [1, 2] + data_type: 'NuGen' + n_events_per_run: 10000 + +# ------------------------------------------------------------- +# Define environment information shared across processing steps +# ------------------------------------------------------------- +job_template: job_templates/cvmfs_python.sh +script_name: general_i3_processing.py +cuda_home: /data/user/mhuennefeld/software/cuda/cuda-11.2 + +# add optional additions to the LD_LIBRARY_PATH +# Note: '{ld_library_path_prepends}' is the default which does not add anything +ld_library_path_prepends: '{ld_library_path_prepends}' + +# Defines environment variables that are set from python +set_env_vars_from_python: { + # 'TF_DETERMINISTIC_OPS': '1', +} + +#----------------------------------------------- +# Define I3Traysegments for each processing step +#----------------------------------------------- + +# a list of processing steps. Each processing step contains +# information on the python and cvmfs environment as well as +# a list of I3TraySegments/Modules that will be added to the I3Tray. +# Any options defined in these nested dictionaries will supersede the +# ones defined globally in this config. +# Tray context can be accessed via "context-->key". +# For nested dictionaries it's possible to do: "context-->key.key2.key3" +# The configuration dictionary of the job can be passed via "" +# Special keys for the tray_segments: +# ModuleClass: str +# The module/segment to run. +# ModuleKwargs: dict +# The parameters for the specified module. +# ModuleTimer: str +# If provided, a timer for this module will be added. +# Results of all timers are saved in the frame key "Duration". +processing_steps: [ + # ---------------------- + # Re-create MMCTrackList + # ---------------------- + { + # Define environment for this processing step + cvmfs_python: py2-v3.0.1, + icetray_metaproject: simulation/V06-01-01, + python_user_base_cpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1, + python_user_base_gpu: /data/user/pgutjahr/software/virtual_envs/py2-v3.0.1, + + n_files_is_n_runs: True, + + # define a list of tray segments to run + tray_segments: [ + { + # Re-create I3MCTree + ModuleClass: 'ic3_processing.modules.labels.recreate_and_add_mmc_tracklist.RerunProposal', + ModuleKwargs: {}, + ModuleTimer: True, + }, + ], + }, + # ---------------------------------- + # Run DNN-reco, ic3-labels, ic3-data + # ---------------------------------- + { + # Define environment for this processing step + cvmfs_python: py3-v4.2.1, + icetray_metaproject: icetray/v1.5.1, + python_user_base_cpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1, + python_user_base_gpu: /data/user/mhuennefeld/DNN_reco/virtualenvs/tensorflow_gpu_py3-v4.2.1, + + # define a list of tray segments to run + tray_segments: [ + { + # add weighted primary + ModuleClass: 'ic3_processing.modules.labels.primary.add_weighted_primary', + ModuleKwargs: {}, + }, + { + # add labels + ModuleClass: 'ic3_labels.labels.modules.modules.MCLabelsCascades', + ModuleKwargs: { + PulseMapString: InIceDSTPulses, + PrimaryKey: 'MCPrimary', + RunOnDAQFrames: True, + ExtendBoundary: -60, + OutputKey: 'LabelsDeepLearning', + }, + ModuleTimer: True, + }, + { + # add MCCascade + ModuleClass: 'ic3_labels.labels.modules.modules.MCLabelsCascadeParameters', + ModuleKwargs: { + PulseMapString: , + PrimaryKey: 'MCPrimary', + RunOnDAQFrames: True, + OutputKey: 'LabelsMCCascade', + }, + ModuleTimer: True, + }, + { + # add base cascade parameters to frame + ModuleClass: 'ic3_processing.modules.reco.combine_i3_particle.create_cascade_classification_base_cascades', + ModuleKwargs: { + cscd_base_configs: [ + { + 'I3ParticleBase': 'L5MonopodFit4', + 'VertexX_unc': 15, + 'VertexY_unc': 15, + 'VertexZ_unc': 15, + 'VertexTime_unc': 30, + }, + ], + }, + }, + { + # write DNN-reco training data to file [177 charge bins] + ModuleClass: 'ic3_data.segments.CreateDNNData', + ModuleKwargs: { + NumDataBins: 177, + RelativeTimeMethod: cascade_vertex, + DataFormat: charge_bins, + PulseKey: InIceDSTPulses, + DOMExclusions: ['BrightDOMs', 'SaturationWindows','BadDomsList','CalibrationErrata'], + PartialExclusion: True, + OutputKey: 'dnn_data__charge_bins', + TimeBins: [ + '-inf', -500., -250., 0., 500., 1000., 1500., + 2000., 2500., 3000., 'inf', + ], + }, + ModuleTimer: True, + }, + { + # write DNN-reco training data to file [9 inputs, un-cleaned] + ModuleClass: 'ic3_data.segments.CreateDNNData', + ModuleKwargs: { + NumDataBins: 9, + RelativeTimeMethod: time_range, + DataFormat: pulse_summmary_clipped, + PulseKey: InIceDSTPulses, + DOMExclusions: ['SaturationWindows','BadDomsList','CalibrationErrata'], + PartialExclusion: True, + OutputKey: 'dnn_data_inputs9_InIceDSTPulses', + }, + ModuleTimer: True, + }, + { + # write DNN-reco training data to file [3 inputs, un-cleaned] + ModuleClass: 'ic3_data.segments.CreateDNNData', + ModuleKwargs: { + NumDataBins: 3, + RelativeTimeMethod: , + DataFormat: reduced_summary_statistics_data, + PulseKey: InIceDSTPulses, + DOMExclusions: ['SaturationWindows','BadDomsList','CalibrationErrata'], + PartialExclusion: True, + OutputKey: 'dnn_data_inputs3_InIceDSTPulses', + }, + ModuleTimer: True, + }, + { + # run dnn-reco + ModuleClass: 'ic3_processing.modules.reco.reco.apply_dnn_reco', + ModuleTimer: True, + ModuleKwargs: { + cfg: { + # global settings shared for all DNN-reco modules + add_dnn_reco: True, + DNN_batch_size: 32, + DNN_excluded_doms: [ + 'BrightDOMs', 'SaturationWindows', 'BadDomsList', 'CalibrationErrata', + ], + DNN_partial_exclusion: True, + + DNN_reco_configs: [ + { + pulse_map_string: InIceDSTPulses, + DNN_model_names: [ + 'dnn_reco_paper_hese__m7_after_sys', # Real-time cascades + 'mese_v2__all_gl_both2', + 'event_selection_cscdl3_300m_01', + ], + DNN_ignore_misconfigured_settings_list: [], + DNN_models_dir: '/data/user/mhuennefeld/DNN_reco/models/exported_models', + }, + { + pulse_map_string: InIceDSTPulses, + DNN_model_names: [ + 'event_selection_cascade_monopod_starting_events_big_kernel_02', # Real-time cascades + ], + DNN_ignore_misconfigured_settings_list: [], + DNN_models_dir: '/data/user/mhuennefeld/DNN_reco/models/exported_models', + DNN_cascade_key: 'cscd_classification_base_L5MonopodFit4', + }, + ], + }, + }, + }, + ], + }, +] + +#-------------------- +# File output options +#-------------------- + +# write output as i3 files via the I3Writer +write_i3: False +write_i3_kwargs: { + + # only write these stream types, + # i.e ['Q', 'P', 'I', 'S', 'M', 'm', 'W', 'X'] + 'i3_streams': ['Q', 'P', 'I', 'S', 'M', 'm', 'W', 'X'], +} + +# write output as hdf5 files via the I3HDFWriter +write_hdf5: True +write_hdf5_kwargs: { + + # sub event streams to write + 'SubEventStreams': ['in_ice', + 'InIceSplit', + 'Final', + 'topological_split'], + + # HDF keys to write (in addition to the ones in + # tray.context['ic3_processing']['HDF_keys']) + # Note: added tray segments should add outputs that should be written + # to hdf5 to the tray context. + 'Keys': [ + + # general + 'I3EventHeader', + 'DurationQ', + 'DurationP', + + # ic3-data input data creation for dnn_reco + 'dnn_data__charge_bins_bin_values', + 'dnn_data__charge_bins_bin_indices', + 'dnn_data__charge_bins_bin_exclusions', + 'dnn_data__charge_bins_global_time_offset', + 'dnn_data_inputs3_InIceDSTPulses_bin_values', + 'dnn_data_inputs3_InIceDSTPulses_bin_indices', + 'dnn_data_inputs3_InIceDSTPulses_global_time_offset', + 'dnn_data_inputs9_InIceDSTPulses_bin_values', + 'dnn_data_inputs9_InIceDSTPulses_bin_indices', + 'dnn_data_inputs9_InIceDSTPulses_global_time_offset', + + # labels + 'LabelsDeepLearning', + 'LabelsMCCascade', + 'MCCascade', + ], +} From 1da78a49ba17e04ad2fe1f9bad2413a491c8aa67 Mon Sep 17 00:00:00 2001 From: mhuen Date: Mon, 15 Apr 2024 15:47:56 +0200 Subject: [PATCH 8/9] Add error if no test directories are found --- tests_manual/test.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests_manual/test.py b/tests_manual/test.py index 01e35d9..334adfc 100644 --- a/tests_manual/test.py +++ b/tests_manual/test.py @@ -62,6 +62,9 @@ def error(msg): test_dirs = glob.glob("test_data/*") test_dirs.remove(dir_original) +if len(test_dirs) == 0: + raise ValueError("No test directories found!") + got_warning = False passed_test = True for dir_test in test_dirs: From ad078d36eb4b845533d9255edaac2f77d1eecdbe Mon Sep 17 00:00:00 2001 From: mhuen Date: Mon, 15 Apr 2024 15:52:51 +0200 Subject: [PATCH 9/9] move optional dependency --- pyproject.toml | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 99465c6..f5d9177 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,14 +36,12 @@ classifiers = [ [project.optional-dependencies] dev = ["pre-commit","black","ruff"] -test = [ - "pytest", "coverage", "codecov", - "ic3_processing @ git+ssh://git@github.com/mhuen/ic3-processing", -] +test = ["pytest", "coverage", "codecov"] docs = ["sphinx-rtd-theme"] icecube = [ "ic3_labels @ git+ssh://git@github.com/icecube/ic3-labels", "ic3_data @ git+ssh://git@github.com/icecube/ic3-data", + "ic3_processing @ git+ssh://git@github.com/mhuen/ic3-processing", ] [project.urls]

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