From 55f1b4599da794904ec58720031c25f96547d637 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreas=20S=C3=B8gaard?= Date: Fri, 19 Jan 2024 05:29:09 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Aske-Ros?= =?UTF-8?q?ted/graphnet@f8d88b89d4e8078aa79e9d3ffe653212a3039cb8=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .buildinfo | 2 +- _modules/graphnet/data/constants.html | 3 +- _modules/graphnet/data/dataconverter.html | 13 +- _modules/graphnet/data/dataloader.html | 2 +- _modules/graphnet/data/dataset/dataset.html | 2 +- .../data/dataset/parquet/parquet_dataset.html | 2 +- .../data/dataset/sqlite/sqlite_dataset.html | 2 +- .../graphnet/data/extractors/i3extractor.html | 2 +- .../data/extractors/i3featureextractor.html | 35 +- .../data/extractors/i3genericextractor.html | 2 +- .../extractors/i3hybridrecoextractor.html | 2 +- .../extractors/i3ntmuonlabelsextractor.html | 2 +- .../data/extractors/i3particleextractor.html | 2 +- .../data/extractors/i3pisaextractor.html | 2 +- .../data/extractors/i3quesoextractor.html | 2 +- .../data/extractors/i3retroextractor.html | 2 +- .../data/extractors/i3splinempeextractor.html | 2 +- .../data/extractors/i3truthextractor.html | 16 +- .../data/extractors/i3tumextractor.html | 2 +- .../extractors/utilities/collections.html | 2 +- .../data/extractors/utilities/frames.html | 2 +- .../data/extractors/utilities/types.html | 2 +- _modules/graphnet/data/filters.html | 2 +- .../data/parquet/parquet_dataconverter.html | 2 +- _modules/graphnet/data/pipeline.html | 2 +- .../data/sqlite/sqlite_dataconverter.html | 2 +- .../data/sqlite/sqlite_utilities.html | 2 +- .../data/utilities/parquet_to_sqlite.html | 2 +- _modules/graphnet/data/utilities/random.html | 2 +- .../utilities/string_selection_resolver.html | 2 +- .../deployment/i3modules/graphnet_module.html | 4 +- _modules/graphnet/models/coarsening.html | 2 +- .../graphnet/models/components/layers.html | 2 +- _modules/graphnet/models/components/pool.html | 2 +- .../graphnet/models/detector/detector.html | 47 +- .../graphnet/models/detector/icecube.html | 20 +- .../graphnet/models/detector/prometheus.html | 30 +- _modules/graphnet/models/gnn/convnet.html | 2 +- _modules/graphnet/models/gnn/dynedge.html | 2 +- .../graphnet/models/gnn/dynedge_jinst.html | 2 +- .../models/gnn/dynedge_kaggle_tito.html | 4 +- _modules/graphnet/models/gnn/gnn.html | 2 +- .../graphnet/models/graphs/edges/edges.html | 2 +- .../models/graphs/edges/minkowski.html | 464 +++++++++++++ .../models/graphs/graph_definition.html | 129 +++- _modules/graphnet/models/graphs/graphs.html | 6 +- .../graphnet/models/graphs/nodes/nodes.html | 2 +- _modules/graphnet/models/graphs/utils.html | 2 +- _modules/graphnet/models/model.html | 290 +------- _modules/graphnet/models/standard_model.html | 338 ++++++++- .../graphnet/models/task/classification.html | 8 +- .../graphnet/models/task/reconstruction.html | 45 +- _modules/graphnet/models/task/task.html | 265 ++++++-- _modules/graphnet/models/utils.html | 2 +- _modules/graphnet/pisa/fitting.html | 2 +- _modules/graphnet/pisa/plotting.html | 2 +- _modules/graphnet/training/callbacks.html | 116 +++- _modules/graphnet/training/labels.html | 2 +- .../graphnet/training/loss_functions.html | 2 +- _modules/graphnet/training/utils.html | 2 +- .../graphnet/training/weight_fitting.html | 2 +- _modules/graphnet/utilities/argparse.html | 2 +- .../utilities/config/base_config.html | 2 +- .../utilities/config/configurable.html | 2 +- .../utilities/config/dataset_config.html | 2 +- .../utilities/config/model_config.html | 2 +- .../graphnet/utilities/config/parsing.html | 2 +- .../utilities/config/training_config.html | 2 +- .../graphnet/utilities/deprecation_tools.html | 396 +++++++++++ _modules/graphnet/utilities/filesys.html | 14 +- _modules/graphnet/utilities/imports.html | 2 +- _modules/graphnet/utilities/logging.html | 2 +- _modules/graphnet/utilities/maths.html | 2 +- _modules/index.html | 4 +- ...hnet.models.graphs.edges.minkowski.rst.txt | 8 + .../api/graphnet.models.graphs.edges.rst.txt | 1 + ...aphnet.utilities.deprecation_tools.rst.txt | 8 + _sources/api/graphnet.utilities.rst.txt | 1 + about.html | 2 +- api/graphnet.constants.html | 2 +- api/graphnet.data.constants.html | 8 +- api/graphnet.data.dataconverter.html | 24 +- api/graphnet.data.dataloader.html | 8 +- api/graphnet.data.dataset.dataset.html | 58 +- api/graphnet.data.dataset.html | 2 +- api/graphnet.data.dataset.parquet.html | 2 +- ....data.dataset.parquet.parquet_dataset.html | 36 +- api/graphnet.data.dataset.sqlite.html | 2 +- ...et.data.dataset.sqlite.sqlite_dataset.html | 36 +- api/graphnet.data.extractors.html | 2 +- api/graphnet.data.extractors.i3extractor.html | 8 +- ...et.data.extractors.i3featureextractor.html | 12 +- ...et.data.extractors.i3genericextractor.html | 6 +- ...data.extractors.i3hybridrecoextractor.html | 2 +- ...ta.extractors.i3ntmuonlabelsextractor.html | 2 +- ...t.data.extractors.i3particleextractor.html | 2 +- ...phnet.data.extractors.i3pisaextractor.html | 2 +- ...hnet.data.extractors.i3quesoextractor.html | 2 +- ...hnet.data.extractors.i3retroextractor.html | 2 +- ....data.extractors.i3splinempeextractor.html | 2 +- ...hnet.data.extractors.i3truthextractor.html | 8 +- ...aphnet.data.extractors.i3tumextractor.html | 2 +- ...data.extractors.utilities.collections.html | 14 +- ...hnet.data.extractors.utilities.frames.html | 10 +- api/graphnet.data.extractors.utilities.html | 2 +- ...phnet.data.extractors.utilities.types.html | 18 +- api/graphnet.data.filters.html | 2 +- api/graphnet.data.html | 2 +- api/graphnet.data.parquet.html | 2 +- ...et.data.parquet.parquet_dataconverter.html | 10 +- api/graphnet.data.pipeline.html | 20 +- api/graphnet.data.sqlite.html | 2 +- ...hnet.data.sqlite.sqlite_dataconverter.html | 22 +- ...graphnet.data.sqlite.sqlite_utilities.html | 38 +- api/graphnet.data.utilities.html | 2 +- ...hnet.data.utilities.parquet_to_sqlite.html | 8 +- api/graphnet.data.utilities.random.html | 4 +- ...a.utilities.string_selection_resolver.html | 4 +- api/graphnet.deployment.html | 2 +- ...raphnet.deployment.i3modules.deployer.html | 2 +- ....deployment.i3modules.graphnet_module.html | 48 +- api/graphnet.deployment.i3modules.html | 2 +- api/graphnet.html | 3 +- api/graphnet.models.coarsening.html | 4 +- api/graphnet.models.components.html | 2 +- api/graphnet.models.components.layers.html | 38 +- api/graphnet.models.components.pool.html | 50 +- api/graphnet.models.detector.detector.html | 76 ++- api/graphnet.models.detector.html | 3 +- api/graphnet.models.detector.icecube.html | 132 +++- api/graphnet.models.detector.prometheus.html | 113 +++- api/graphnet.models.gnn.convnet.html | 12 +- api/graphnet.models.gnn.dynedge.html | 20 +- api/graphnet.models.gnn.dynedge_jinst.html | 8 +- ...aphnet.models.gnn.dynedge_kaggle_tito.html | 16 +- api/graphnet.models.gnn.gnn.html | 4 +- api/graphnet.models.gnn.html | 2 +- api/graphnet.models.graphs.edges.edges.html | 25 +- api/graphnet.models.graphs.edges.html | 14 +- ...raphnet.models.graphs.edges.minkowski.html | 640 ++++++++++++++++++ ...aphnet.models.graphs.graph_definition.html | 40 +- api/graphnet.models.graphs.graphs.html | 18 +- api/graphnet.models.graphs.html | 3 +- api/graphnet.models.graphs.nodes.html | 8 +- api/graphnet.models.graphs.nodes.nodes.html | 20 +- api/graphnet.models.graphs.utils.html | 26 +- api/graphnet.models.html | 2 +- api/graphnet.models.model.html | 139 +--- api/graphnet.models.standard_model.html | 63 +- api/graphnet.models.task.classification.html | 86 +-- api/graphnet.models.task.html | 6 +- api/graphnet.models.task.reconstruction.html | 557 ++++----------- api/graphnet.models.task.task.html | 347 ++++++++-- api/graphnet.models.utils.html | 16 +- api/graphnet.pisa.fitting.html | 24 +- api/graphnet.pisa.html | 2 +- api/graphnet.pisa.plotting.html | 28 +- api/graphnet.training.callbacks.html | 171 ++++- api/graphnet.training.html | 3 +- api/graphnet.training.labels.html | 10 +- api/graphnet.training.loss_functions.html | 20 +- api/graphnet.training.utils.html | 12 +- api/graphnet.training.weight_fitting.html | 18 +- api/graphnet.utilities.argparse.html | 15 +- ...graphnet.utilities.config.base_config.html | 17 +- ...raphnet.utilities.config.configurable.html | 13 +- ...phnet.utilities.config.dataset_config.html | 13 +- api/graphnet.utilities.config.html | 9 +- ...raphnet.utilities.config.model_config.html | 13 +- api/graphnet.utilities.config.parsing.html | 21 +- ...hnet.utilities.config.training_config.html | 9 +- api/graphnet.utilities.decorators.html | 15 +- api/graphnet.utilities.deprecation_tools.html | 544 +++++++++++++++ api/graphnet.utilities.filesys.html | 28 +- api/graphnet.utilities.html | 13 +- api/graphnet.utilities.imports.html | 17 +- api/graphnet.utilities.logging.html | 25 +- api/graphnet.utilities.maths.html | 11 +- api/modules.html | 2 +- contribute.html | 2 +- genindex.html | 149 +++- index.html | 2 +- install.html | 2 +- objects.inv | Bin 6353 -> 6677 bytes py-modindex.html | 12 +- search.html | 2 +- searchindex.js | 2 +- sitemap.xml | 2 +- 188 files changed, 4818 insertions(+), 1718 deletions(-) create mode 100644 _modules/graphnet/models/graphs/edges/minkowski.html create mode 100644 _modules/graphnet/utilities/deprecation_tools.html create mode 100644 _sources/api/graphnet.models.graphs.edges.minkowski.rst.txt create mode 100644 _sources/api/graphnet.utilities.deprecation_tools.rst.txt create mode 100644 api/graphnet.models.graphs.edges.minkowski.html create mode 100644 api/graphnet.utilities.deprecation_tools.html diff --git a/.buildinfo b/.buildinfo index 4221c070d..67510ee00 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 13f414d23c87e7ab6845f74255c984e6 +config: 38e647a1ad5b910b669ed012bb05995f tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_modules/graphnet/data/constants.html b/_modules/graphnet/data/constants.html index b6526af39..9b2cbe685 100644 --- a/_modules/graphnet/data/constants.html +++ b/_modules/graphnet/data/constants.html @@ -367,6 +367,7 @@

Source code for graphnet.dat ICECUBE86 = [ "energy", "energy_track", + "energy_cascade", "position_x", "position_y", "position_z", @@ -432,7 +433,7 @@

Source code for graphnet.dat
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/dataconverter.html b/_modules/graphnet/data/dataconverter.html index 435040ba0..682033fb9 100644 --- a/_modules/graphnet/data/dataconverter.html +++ b/_modules/graphnet/data/dataconverter.html @@ -536,15 +536,22 @@

Source code for graphnet super().__init__(name=__name__, class_name=self.__class__.__name__) @final - def __call__(self, directories: Union[str, List[str]]) -> None: + def __call__( + self, + directories: Union[str, List[str]], + recursive: Optional[bool] = True, + ) -> None: """Convert I3-files in `directories. Args: directories: One or more directories, the I3 files within which should be converted to an intermediate file format. + recursive: Whether or not to search the directories recursively. """ # Find all I3 and GCD files in the specified directories. - i3_files, gcd_files = find_i3_files(directories, self._gcd_rescue) + i3_files, gcd_files = find_i3_files( + directories, self._gcd_rescue, recursive + ) if len(i3_files) == 0: self.error(f"No files found in {directories}.") return @@ -946,7 +953,7 @@

Source code for graphnet
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/dataloader.html b/_modules/graphnet/data/dataloader.html index 4de320872..6aa911562 100644 --- a/_modules/graphnet/data/dataloader.html +++ b/_modules/graphnet/data/dataloader.html @@ -439,7 +439,7 @@

Source code for graphnet.da
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/dataset/dataset.html b/_modules/graphnet/data/dataset/dataset.html index f3a1cfb4c..442af1a79 100644 --- a/_modules/graphnet/data/dataset/dataset.html +++ b/_modules/graphnet/data/dataset/dataset.html @@ -1071,7 +1071,7 @@

Source code for graphn
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/dataset/parquet/parquet_dataset.html b/_modules/graphnet/data/dataset/parquet/parquet_dataset.html index 35d6b34d7..10827b1ba 100644 --- a/_modules/graphnet/data/dataset/parquet/parquet_dataset.html +++ b/_modules/graphnet/data/dataset/parquet/parquet_dataset.html @@ -482,7 +482,7 @@

Source
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/dataset/sqlite/sqlite_dataset.html b/_modules/graphnet/data/dataset/sqlite/sqlite_dataset.html index a6734e1c8..ef174366f 100644 --- a/_modules/graphnet/data/dataset/sqlite/sqlite_dataset.html +++ b/_modules/graphnet/data/dataset/sqlite/sqlite_dataset.html @@ -497,7 +497,7 @@

Source c
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3extractor.html b/_modules/graphnet/data/extractors/i3extractor.html index 0680557f4..b19d0d2e8 100644 --- a/_modules/graphnet/data/extractors/i3extractor.html +++ b/_modules/graphnet/data/extractors/i3extractor.html @@ -460,7 +460,7 @@

Source code for
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3featureextractor.html b/_modules/graphnet/data/extractors/i3featureextractor.html index de384a97e..2b607b5df 100644 --- a/_modules/graphnet/data/extractors/i3featureextractor.html +++ b/_modules/graphnet/data/extractors/i3featureextractor.html @@ -391,7 +391,6 @@

Source c "event_time": [], "hlc": [], "awtd": [], - "fadc": [], "string": [], "pmt_number": [], "dom_number": [], @@ -489,16 +488,15 @@

Source c output["is_saturated_dom"].append(is_saturated_dom) output["is_errata_dom"].append(is_errata_dom) output["event_time"].append(event_time) + # Pulse flags flags = getattr(pulse, "flags", padding_value) if flags == padding_value: - output["hlc"].append(padding_value) # bit 0 - output["awtd"].append(padding_value) # bit 1 - output["fadc"].append(padding_value) # bit 2 + output["hlc"].append(padding_value) + output["awtd"].append(padding_value) else: output["hlc"].append((pulse.flags >> 0) & 0x1) # bit 0 - output["awtd"].append((pulse.flags >> 1) & 0x1) # bit 1 - output["fadc"].append((pulse.flags >> 2) & 0x1) # bit 2 + output["awtd"].append(self._parse_awtd_flag(pulse)) return output @@ -515,7 +513,28 @@

Source c rde = self._calibration.dom_cal[om_key].relative_dom_eff except: # noqa: E722 rde = padding_value - return rde

+ return rde + + def _parse_awtd_flag( + self, pulse: Any, fadc_min_width_ns: float = 6.0 + ) -> bool: + """Parse awtd flag from pulse width. + + Returns True if the pulse was readout using the awtd digitizer. + + Method by Tom Stuttard. + + Notes from Tom: + Function to read the bits of the pulse flags and unpack them into + meaningful info Using pulse width rather than flags to separate FADC vs + ATWD pulses, due to a known issue. + https://github.com/icecube/icetray/issues/2721 Note that the issue + states to use 8ns, but I have found that actually 6ns is correct. + """ + # Use pulse width to check whether a pulse is + # (a) FADC-only, or + # includes ATWD (and probably also FADC) + return pulse.width < (fadc_min_width_ns * icetray.I3Units.ns)
@@ -643,7 +662,7 @@

Source c
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3genericextractor.html b/_modules/graphnet/data/extractors/i3genericextractor.html index 9c7d786f3..f89909b49 100644 --- a/_modules/graphnet/data/extractors/i3genericextractor.html +++ b/_modules/graphnet/data/extractors/i3genericextractor.html @@ -632,7 +632,7 @@

Source c
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3hybridrecoextractor.html b/_modules/graphnet/data/extractors/i3hybridrecoextractor.html index ee80f730f..902eb16a5 100644 --- a/_modules/graphnet/data/extractors/i3hybridrecoextractor.html +++ b/_modules/graphnet/data/extractors/i3hybridrecoextractor.html @@ -396,7 +396,7 @@

Sourc
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3ntmuonlabelsextractor.html b/_modules/graphnet/data/extractors/i3ntmuonlabelsextractor.html index d295f81a2..60533192d 100644 --- a/_modules/graphnet/data/extractors/i3ntmuonlabelsextractor.html +++ b/_modules/graphnet/data/extractors/i3ntmuonlabelsextractor.html @@ -403,7 +403,7 @@

Sou
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3particleextractor.html b/_modules/graphnet/data/extractors/i3particleextractor.html index 60463b59a..fbbf1cec1 100644 --- a/_modules/graphnet/data/extractors/i3particleextractor.html +++ b/_modules/graphnet/data/extractors/i3particleextractor.html @@ -388,7 +388,7 @@

Source
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3pisaextractor.html b/_modules/graphnet/data/extractors/i3pisaextractor.html index 0752dc39a..98366db56 100644 --- a/_modules/graphnet/data/extractors/i3pisaextractor.html +++ b/_modules/graphnet/data/extractors/i3pisaextractor.html @@ -381,7 +381,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3quesoextractor.html b/_modules/graphnet/data/extractors/i3quesoextractor.html index 6ad9572a6..dba9d5e9f 100644 --- a/_modules/graphnet/data/extractors/i3quesoextractor.html +++ b/_modules/graphnet/data/extractors/i3quesoextractor.html @@ -391,7 +391,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3retroextractor.html b/_modules/graphnet/data/extractors/i3retroextractor.html index b76bed436..7a82ee826 100644 --- a/_modules/graphnet/data/extractors/i3retroextractor.html +++ b/_modules/graphnet/data/extractors/i3retroextractor.html @@ -463,7 +463,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3splinempeextractor.html b/_modules/graphnet/data/extractors/i3splinempeextractor.html index dd8d383bd..e527460ca 100644 --- a/_modules/graphnet/data/extractors/i3splinempeextractor.html +++ b/_modules/graphnet/data/extractors/i3splinempeextractor.html @@ -375,7 +375,7 @@

Source
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3truthextractor.html b/_modules/graphnet/data/extractors/i3truthextractor.html index 19d005d0d..c9659ce52 100644 --- a/_modules/graphnet/data/extractors/i3truthextractor.html +++ b/_modules/graphnet/data/extractors/i3truthextractor.html @@ -434,6 +434,7 @@

Source cod "track_length": padding_value, "stopped_muon": padding_value, "energy_track": padding_value, + "energy_cascade": padding_value, "inelasticity": padding_value, "DeepCoreFilter_13": padding_value, "CascadeFilter_13": padding_value, @@ -503,10 +504,15 @@

Source cod try: ( energy_track, + energy_cascade, inelasticity, ) = self._get_primary_track_energy_and_inelasticity(frame) except RuntimeError: # track energy fails on northeren tracks with ""Hadrons" has no mass implemented. Cannot get total energy." - energy_track, inelasticity = (padding_value, padding_value) + energy_track, energy_cascade, inelasticity = ( + padding_value, + padding_value, + padding_value, + ) output.update( { @@ -523,6 +529,7 @@

Source cod frame, padding_value ), "energy_track": energy_track, + "energy_cascade": energy_cascade, "inelasticity": inelasticity, } ) @@ -702,7 +709,7 @@

Source cod def _get_primary_track_energy_and_inelasticity( self, frame: "icetray.I3Frame", - ) -> Tuple[float, float]: + ) -> Tuple[float, float, float]: """Get the total energy of tracks from primary, and inelasticity. Args: @@ -725,9 +732,10 @@

Source cod energy_total = primary.total_energy energy_track = sum(track.total_energy for track in tracks) + energy_cascade = energy_total - energy_track inelasticity = 1.0 - energy_track / energy_total - return energy_track, inelasticity + return energy_track, energy_cascade, inelasticity # Utility methods def _find_data_type(self, mc: bool, input_file: str) -> str: @@ -777,7 +785,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/i3tumextractor.html b/_modules/graphnet/data/extractors/i3tumextractor.html index 89adb77c2..a923138fd 100644 --- a/_modules/graphnet/data/extractors/i3tumextractor.html +++ b/_modules/graphnet/data/extractors/i3tumextractor.html @@ -378,7 +378,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/utilities/collections.html b/_modules/graphnet/data/extractors/utilities/collections.html index 14835acc6..6bbe9e810 100644 --- a/_modules/graphnet/data/extractors/utilities/collections.html +++ b/_modules/graphnet/data/extractors/utilities/collections.html @@ -432,7 +432,7 @@

Sourc
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/utilities/frames.html b/_modules/graphnet/data/extractors/utilities/frames.html index 656cb6033..0bbf55a18 100644 --- a/_modules/graphnet/data/extractors/utilities/frames.html +++ b/_modules/graphnet/data/extractors/utilities/frames.html @@ -435,7 +435,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/extractors/utilities/types.html b/_modules/graphnet/data/extractors/utilities/types.html index 60de6d341..35d38c1fb 100644 --- a/_modules/graphnet/data/extractors/utilities/types.html +++ b/_modules/graphnet/data/extractors/utilities/types.html @@ -656,7 +656,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/filters.html b/_modules/graphnet/data/filters.html index d31dc2402..f4924a1a2 100644 --- a/_modules/graphnet/data/filters.html +++ b/_modules/graphnet/data/filters.html @@ -479,7 +479,7 @@

Source code for graphnet.data.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/parquet/parquet_dataconverter.html b/_modules/graphnet/data/parquet/parquet_dataconverter.html index 455153448..8c4c48d07 100644 --- a/_modules/graphnet/data/parquet/parquet_dataconverter.html +++ b/_modules/graphnet/data/parquet/parquet_dataconverter.html @@ -403,7 +403,7 @@

Source c
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/pipeline.html b/_modules/graphnet/data/pipeline.html index 90602e63c..9ed962b05 100644 --- a/_modules/graphnet/data/pipeline.html +++ b/_modules/graphnet/data/pipeline.html @@ -575,7 +575,7 @@

Source code for graphnet.data
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/sqlite/sqlite_dataconverter.html b/_modules/graphnet/data/sqlite/sqlite_dataconverter.html index 69fa21bbe..a0216ecf0 100644 --- a/_modules/graphnet/data/sqlite/sqlite_dataconverter.html +++ b/_modules/graphnet/data/sqlite/sqlite_dataconverter.html @@ -712,7 +712,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/sqlite/sqlite_utilities.html b/_modules/graphnet/data/sqlite/sqlite_utilities.html index a910e007d..8b726abf9 100644 --- a/_modules/graphnet/data/sqlite/sqlite_utilities.html +++ b/_modules/graphnet/data/sqlite/sqlite_utilities.html @@ -511,7 +511,7 @@

Source code fo
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/utilities/parquet_to_sqlite.html b/_modules/graphnet/data/utilities/parquet_to_sqlite.html index f3944caa0..2b5cf3919 100644 --- a/_modules/graphnet/data/utilities/parquet_to_sqlite.html +++ b/_modules/graphnet/data/utilities/parquet_to_sqlite.html @@ -529,7 +529,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/utilities/random.html b/_modules/graphnet/data/utilities/random.html index eb3a7f85c..3ccca08ca 100644 --- a/_modules/graphnet/data/utilities/random.html +++ b/_modules/graphnet/data/utilities/random.html @@ -371,7 +371,7 @@

Source code for graph
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/data/utilities/string_selection_resolver.html b/_modules/graphnet/data/utilities/string_selection_resolver.html index 361902f54..8b7938f75 100644 --- a/_modules/graphnet/data/utilities/string_selection_resolver.html +++ b/_modules/graphnet/data/utilities/string_selection_resolver.html @@ -672,7 +672,7 @@

So
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/deployment/i3modules/graphnet_module.html b/_modules/graphnet/deployment/i3modules/graphnet_module.html index 673e2979c..6a888402d 100644 --- a/_modules/graphnet/deployment/i3modules/graphnet_module.html +++ b/_modules/graphnet/deployment/i3modules/graphnet_module.html @@ -748,7 +748,7 @@

Source Arguments: frame: I3Frame (physics) - predictions: predictions from GNN + predictions: predictions from Model. Returns: predictions_map: a pulsemap from predictions @@ -799,7 +799,7 @@

Source
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/coarsening.html b/_modules/graphnet/models/coarsening.html index c9a941617..301d35d73 100644 --- a/_modules/graphnet/models/coarsening.html +++ b/_modules/graphnet/models/coarsening.html @@ -690,7 +690,7 @@

Source code for graphnet.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/components/layers.html b/_modules/graphnet/models/components/layers.html index 555ac857b..80fbc5ec0 100644 --- a/_modules/graphnet/models/components/layers.html +++ b/_modules/graphnet/models/components/layers.html @@ -561,7 +561,7 @@

Source code for gr
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/components/pool.html b/_modules/graphnet/models/components/pool.html index 2523dfb69..11ce78f3d 100644 --- a/_modules/graphnet/models/components/pool.html +++ b/_modules/graphnet/models/components/pool.html @@ -638,7 +638,7 @@

Source code for grap
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/detector/detector.html b/_modules/graphnet/models/detector/detector.html index 814e04815..be4d6bf1d 100644 --- a/_modules/graphnet/models/detector/detector.html +++ b/_modules/graphnet/models/detector/detector.html @@ -329,6 +329,7 @@

Source code for gr from torch_geometric.data import Data import torch +import pandas as pd from graphnet.models import Model from graphnet.utilities.decorators import final @@ -355,27 +356,57 @@

Source code for gr [docs] @final def forward( # type: ignore - self, node_features: torch.tensor, node_feature_names: List[str] + self, input_features: torch.tensor, input_feature_names: List[str] ) -> Data: """Pre-process graph `Data` features and build graph adjacency.""" - return self._standardize(node_features, node_feature_names)

+ return self._standardize(input_features, input_feature_names)
+ @property + def geometry_table(self) -> pd.DataFrame: + """Public get method for retrieving a `Detector`s geometry table.""" + if ~hasattr(self, "_geometry_table"): + try: + assert hasattr(self, "geometry_table_path") + except AssertionError as e: + self.error( + f"""{self.__class__.__name__} does not have class + variable `geometry_table_path` set.""" + ) + raise e + self._geometry_table = pd.read_parquet(self.geometry_table_path) + return self._geometry_table + + @property + def string_index_name(self) -> str: + """Public get method for retrieving the string index column name.""" + return self.string_id_column + + @property + def sensor_position_names(self) -> List[str]: + """Public get method for retrieving the xyz coordinate column names.""" + return self.xyz + + @property + def sensor_index_name(self) -> str: + """Public get method for retrieving the sensor id column name.""" + return self.sensor_id_column + @final def _standardize( - self, node_features: torch.tensor, node_feature_names: List[str] + self, input_features: torch.tensor, input_feature_names: List[str] ) -> Data: - for idx, feature in enumerate(node_feature_names): + for idx, feature in enumerate(input_feature_names): try: - node_features[:, idx] = self.feature_map()[feature]( # type: ignore - node_features[:, idx] + input_features[:, idx] = self.feature_map()[feature]( # type: ignore + input_features[:, idx] ) except KeyError as e: self.warning( f"""No Standardization function found for '{feature}'""" ) raise e - return node_features + return input_features def _identity(self, x: torch.tensor) -> torch.tensor: """Apply no standardization to input.""" @@ -401,7 +432,7 @@

Source code for gr
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/detector/icecube.html b/_modules/graphnet/models/detector/icecube.html index 76d9a8e16..fb38722fc 100644 --- a/_modules/graphnet/models/detector/icecube.html +++ b/_modules/graphnet/models/detector/icecube.html @@ -326,8 +326,10 @@

Source code for gra from typing import Dict, Callable import torch +import os from graphnet.models.detector.detector import Detector +from graphnet.constants import ICECUBE_GEOMETRY_TABLE_DIR
@@ -335,6 +337,13 @@

Source code for gra class IceCube86(Detector): """`Detector` class for IceCube-86.""" + geometry_table_path = os.path.join( + ICECUBE_GEOMETRY_TABLE_DIR, "icecube86.parquet" + ) + xyz = ["dom_x", "dom_y", "dom_z"] + string_id_column = "string" + sensor_id_column = "sensor_id" +
[docs] def feature_map(self) -> Dict[str, Callable]: @@ -401,7 +410,7 @@

Source code for gra
[docs] -class IceCubeDeepCore(Detector): +class IceCubeDeepCore(IceCube86): """`Detector` class for IceCube-DeepCore."""
@@ -442,6 +451,13 @@

Source code for gra class IceCubeUpgrade(Detector): """`Detector` class for IceCube-Upgrade.""" + geometry_table_path = os.path.join( + ICECUBE_GEOMETRY_TABLE_DIR, "icecube_upgrade.parquet" + ) + xyz = ["dom_x", "dom_y", "dom_z"] + string_id_column = "string" + sensor_id_column = "sensor_id" +
[docs] def feature_map(self) -> Dict[str, Callable]: @@ -510,7 +526,7 @@

Source code for gra
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/detector/prometheus.html b/_modules/graphnet/models/detector/prometheus.html index 7f234af38..3674c8065 100644 --- a/_modules/graphnet/models/detector/prometheus.html +++ b/_modules/graphnet/models/detector/prometheus.html @@ -326,17 +326,26 @@

Source code for from typing import Dict, Callable import torch +import os from graphnet.models.detector.detector import Detector +from graphnet.constants import PROMETHEUS_GEOMETRY_TABLE_DIR -
-[docs] -class Prometheus(Detector): +
+[docs] +class ORCA150(Detector): """`Detector` class for Prometheus prototype.""" -
-[docs] + geometry_table_path = os.path.join( + PROMETHEUS_GEOMETRY_TABLE_DIR, "orca_150.parquet" + ) + xyz = ["sensor_pos_x", "sensor_pos_y", "sensor_pos_z"] + string_id_column = "sensor_string_id" + sensor_id_column = "sensor_id" + +
+[docs] def feature_map(self) -> Dict[str, Callable]: """Map standardization functions to each dimension.""" feature_map = { @@ -355,7 +364,14 @@

Source code for return (x + 350) / 100 def _t(self, x: torch.tensor) -> torch.tensor: - return ((x / 1.05e04) - 1.0) * 20.0

+ return x / 1.05e04
+ + + +
+[docs] +class Prometheus(ORCA150): + """Reference to ORCA150."""
@@ -377,7 +393,7 @@

Source code for
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/gnn/convnet.html b/_modules/graphnet/models/gnn/convnet.html index 61066449f..9c1b3f2f7 100644 --- a/_modules/graphnet/models/gnn/convnet.html +++ b/_modules/graphnet/models/gnn/convnet.html @@ -466,7 +466,7 @@

Source code for graphnet
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/gnn/dynedge.html b/_modules/graphnet/models/gnn/dynedge.html index 1bb966c10..90dbb8077 100644 --- a/_modules/graphnet/models/gnn/dynedge.html +++ b/_modules/graphnet/models/gnn/dynedge.html @@ -673,7 +673,7 @@

Source code for graphnet
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/gnn/dynedge_jinst.html b/_modules/graphnet/models/gnn/dynedge_jinst.html index 79fbe9a81..54e3e08fc 100644 --- a/_modules/graphnet/models/gnn/dynedge_jinst.html +++ b/_modules/graphnet/models/gnn/dynedge_jinst.html @@ -501,7 +501,7 @@

Source code for gr
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html b/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html index fe518f560..046822eaf 100644 --- a/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html +++ b/_modules/graphnet/models/gnn/dynedge_kaggle_tito.html @@ -341,7 +341,6 @@

Source code from torch_scatter import scatter_max, scatter_mean, scatter_min, scatter_sum from graphnet.models.components.layers import DynTrans -from graphnet.utilities.config import save_model_config from graphnet.models.gnn.gnn import GNN from graphnet.models.utils import calculate_xyzt_homophily @@ -358,7 +357,6 @@

Source code class DynEdgeTITO(GNN): """DynEdgeTITO (dynamical edge convolutional with Transformer) model.""" - @save_model_config def __init__( self, nb_inputs: int, @@ -617,7 +615,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/gnn/gnn.html b/_modules/graphnet/models/gnn/gnn.html index 1e183aaa6..4f0a11b6d 100644 --- a/_modules/graphnet/models/gnn/gnn.html +++ b/_modules/graphnet/models/gnn/gnn.html @@ -383,7 +383,7 @@

Source code for graphnet.mod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/graphs/edges/edges.html b/_modules/graphnet/models/graphs/edges/edges.html index 399251ee4..c8778c6a0 100644 --- a/_modules/graphnet/models/graphs/edges/edges.html +++ b/_modules/graphnet/models/graphs/edges/edges.html @@ -541,7 +541,7 @@

Source code for g
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/graphs/edges/minkowski.html b/_modules/graphnet/models/graphs/edges/minkowski.html new file mode 100644 index 000000000..e16af71d8 --- /dev/null +++ b/_modules/graphnet/models/graphs/edges/minkowski.html @@ -0,0 +1,464 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + graphnet.models.graphs.edges.minkowski — graphnet documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Skip to content +
+ +
+ + +
+ + + + +
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+ +
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+ +
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+ + +
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+ +
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+ +

Source code for graphnet.models.graphs.edges.minkowski

+"""Module containing EdgeDefinitions based on the Minkowski Metric."""
+from typing import Optional, List
+
+import torch
+from torch_geometric.data import Data
+from torch_geometric.utils import to_dense_batch
+from graphnet.models.graphs.edges.edges import EdgeDefinition
+
+
+

+[docs]
+def compute_minkowski_distance_mat(
+    x: torch.Tensor,
+    y: torch.Tensor,
+    c: float,
+    space_coords: Optional[List[int]] = None,
+    time_coord: Optional[int] = 3,
+) -> torch.Tensor:
+    """Compute all pairwise Minkowski distances.
+
+    Args:
+        x: First tensor of shape (n, d).
+        y: Second tensor of shape (m, d).
+        c: Speed of light, in scaled units.
+        space_coords: Indices of space coordinates.
+        time_coord: Index of time coordinate.
+
+    Returns: Matrix of shape (n, m) of all pairwise Minkowski distances.
+    """
+    space_coords = space_coords or [0, 1, 2]
+    assert x.dim() == 2, "x must be 2-dimensional"
+    assert y.dim() == 2, "x must be 2-dimensional"
+    dist = x[:, None] - y[None, :]
+    pos = dist[:, :, space_coords]
+    time = dist[:, :, time_coord] * c
+    return (pos**2).sum(dim=-1) - time**2

+
+
+
+

+[docs]
+class MinkowskiKNNEdges(EdgeDefinition):
+    """Builds edges between most light-like separated."""
+
+    def __init__(
+        self,
+        nb_nearest_neighbours: int,
+        c: float,
+        time_like_weight: float = 1.0,
+        space_coords: Optional[List[int]] = None,
+        time_coord: Optional[int] = 3,
+    ):
+        """Initialize MinkowskiKNNEdges.
+
+        Args:
+            nb_nearest_neighbours: Number of neighbours to connect to.
+            c: Speed of light, in scaled units.
+            time_like_weight: Preference to time-like over space-like edges.
+                Scales time_like distances by this value, before finding
+                nearest neighbours.
+            space_coords: Coordinates of x, y, z.
+            time_coord: Coordinate of time.
+        """
+        super().__init__(name=__name__, class_name=self.__class__.__name__)
+        self.nb_nearest_neighbours = nb_nearest_neighbours
+        self.c = c
+        self.time_like_weight = time_like_weight
+        self.space_coords = space_coords or [0, 1, 2]
+        self.time_coord = time_coord
+
+    def _construct_edges(self, graph: Data) -> Data:
+        x, mask = to_dense_batch(graph.x, graph.batch)
+        count = 0
+        row = []
+        col = []
+        for batch in range(x.shape[0]):
+            distance_mat = compute_minkowski_distance_mat(
+                x_masked := x[batch][mask[batch]],
+                x_masked,
+                self.c,
+                self.space_coords,
+                self.time_coord,
+            )
+            num_points = x_masked.shape[0]
+            num_edges = min(self.nb_nearest_neighbours, num_points)
+            col += [
+                c
+                for c in range(num_points)
+                for _ in range(count, count + num_edges)
+            ]
+            distance_mat[distance_mat < 0] *= -self.time_like_weight
+            distance_mat += (
+                torch.eye(distance_mat.shape[0]) * 1e9
+            )  # self-loops
+            distance_sorted = distance_mat.argsort(dim=1)
+            distance_sorted += count  # offset by previous events
+            row += distance_sorted[:num_edges].flatten().tolist()
+            count += num_points
+
+        graph.edge_index = torch.tensor(
+            [row, col], dtype=torch.long, device=graph.x.device
+        )
+        return graph

+
+
+ +
+
+
+
+
+
+
+ +
+
+
+
+
+ © Copyright 2021-2024, GraphNeT team. + +
+ Created using + Sphinx 7.2.6. + and + Material for + Sphinx +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/graphnet/models/graphs/graph_definition.html b/_modules/graphnet/models/graphs/graph_definition.html index 233ac8bdd..141e4a4e4 100644 --- a/_modules/graphnet/models/graphs/graph_definition.html +++ b/_modules/graphnet/models/graphs/graph_definition.html @@ -356,6 +356,10 @@

Source code dtype: Optional[torch.dtype] = torch.float, perturbation_dict: Optional[Dict[str, float]] = None, seed: Optional[Union[int, Generator]] = None, + add_inactive_sensors: bool = False, + sensor_mask: Optional[List[int]] = None, + string_mask: Optional[List[int]] = None, + sort_by: str = None, ): """Construct ´GraphDefinition´. The ´detector´ holds. @@ -381,6 +385,12 @@

Source code to None. seed: seed or Generator used to randomly sample perturbations. Defaults to None. + add_inactive_sensors: If True, inactive sensors will be appended + to the graph with padded pulse information. Defaults to False. + sensor_mask: A list of sensor id's to be masked from the graph. Any + sensor listed here will be removed from the graph. Defaults to None. + string_mask: A list of string id's to be masked from the graph. Defaults to None. + sort_by: Name of node feature to sort by. Defaults to None. """ # Base class constructor super().__init__(name=__name__, class_name=self.__class__.__name__) @@ -390,6 +400,16 @@

Source code self._edge_definition = edge_definition self._node_definition = node_definition self._perturbation_dict = perturbation_dict + self._sensor_mask = sensor_mask + self._string_mask = string_mask + self._add_inactive_sensors = add_inactive_sensors + + self._resolve_masks() + + if self._edge_definition is None: + self.warning_once( + """No EdgeDefinition given. Graphs will not have edges!""" + ) if input_feature_names is None: # Assume all features in Detector is used. @@ -400,7 +420,19 @@

Source code self._node_definition.set_output_feature_names( self._input_feature_names ) - + self.output_feature_names = self._node_definition._output_feature_names + + # Sorting + if sort_by is not None: + assert isinstance(sort_by, str) + try: + sort_by = self.output_feature_names.index(sort_by) # type: ignore + except ValueError as e: + self.error( + f"{sort_by} not in node features {self.output_feature_names}." + ) + raise e + self._sort_by = sort_by # Set data type self.to(dtype) @@ -466,6 +498,18 @@

Source code input_feature_names=input_feature_names, ) + # Add inactive sensors if `add_inactive_sensors = True` + if self._add_inactive_sensors: + input_features = self._attach_inactive_sensors( + input_features, input_feature_names + ) + + # Mask out sensors if `sensor_mask` is given + if self._sensor_mask is not None: + input_features = self._mask_sensors( + input_features, input_feature_names + ) + # Gaussian perturbation of each column if perturbation dict is given input_features = self._perturb_input(input_features) @@ -477,6 +521,8 @@

Source code # Create graph & get new node feature names graph, node_feature_names = self._node_definition(input_features) + if self._sort_by is not None: + graph.x = graph.x[graph.x[:, self._sort_by].sort()[1]] # Enforce dtype graph.x = graph.x.type(self.dtype) @@ -487,12 +533,6 @@

Source code # Assign edges if self._edge_definition is not None: graph = self._edge_definition(graph) - else: - - self.warning_once( - """No EdgeDefinition provided. - Graphs will not have edges defined!""" # noqa - ) # Attach data path - useful for Ensemble datasets. if data_path is not None: @@ -526,6 +566,79 @@

Source code return graph

+ def _resolve_masks(self) -> None: + """Handle cases with sensor/string masks.""" + if self._sensor_mask is not None: + if self._string_mask is not None: + assert ( + 1 == 2 + ), """Got arguments for both `sensor_mask`and `string_mask`. Please specify only one. """ + + if (self._sensor_mask is None) & (self._string_mask is not None): + self._sensor_mask = self._convert_string_to_sensor_mask() + + return + + def _convert_string_to_sensor_mask(self) -> List[int]: + """Convert a string mask to a sensor mask.""" + string_id_column = self._detector.string_id_column + sensor_id_column = self._detector.sensor_id_column + geometry_table = self._detector.geometry_table + idx = geometry_table[string_id_column].isin(self._string_mask) + return np.asarray(geometry_table.loc[idx, sensor_id_column]).tolist() + + def _attach_inactive_sensors( + self, input_features: np.ndarray, input_feature_names: List[str] + ) -> np.ndarray: + """Attach inactive sensors to `input_features`. + + This function will query the detector geometry table and add any sensor + in the geometry table that is not already present in `node_features`. + """ + lookup = self._geometry_table_lookup( + input_features, input_feature_names + ) + geometry_table = self._detector.geometry_table + unique_sensors = geometry_table.reset_index(drop=True) + + # multiple lines to avoid long line: + inactive_idx = ~geometry_table.index.isin(lookup) + inactive_sensors = unique_sensors.loc[ + inactive_idx, input_feature_names + ] + input_features = np.concatenate( + [input_features, inactive_sensors.to_numpy()], axis=0 + ) + return input_features + + def _mask_sensors( + self, input_features: np.ndarray, input_feature_names: List[str] + ) -> np.ndarray: + """Mask sensors according to `sensor_mask`.""" + sensor_id_column = self._detector.sensor_index_name + geometry_table = self._detector.geometry_table + + lookup = self._geometry_table_lookup( + input_features=input_features, + input_feature_names=input_feature_names, + ) + mask = ~geometry_table.loc[lookup, sensor_id_column].isin( + self._sensor_mask + ) + + return input_features[mask, :] + + def _geometry_table_lookup( + self, input_features: np.ndarray, input_feature_names: List[str] + ) -> np.ndarray: + """Convert xyz in `input_features` into a set of sensor ids.""" + lookup_columns = [ + input_feature_names.index(feature) + for feature in self._detector.sensor_position_names + ] + idx = [*zip(*[tuple(input_features[:, k]) for k in lookup_columns])] + return self._detector.geometry_table.loc[idx, :].index + def _validate_input( self, input_features: np.array, input_feature_names: List[str] ) -> None: @@ -684,7 +797,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/graphs/graphs.html b/_modules/graphnet/models/graphs/graphs.html index e616ca9e2..7e77b2ccc 100644 --- a/_modules/graphnet/models/graphs/graphs.html +++ b/_modules/graphnet/models/graphs/graphs.html @@ -342,7 +342,7 @@

Source code for graphn def __init__( self, detector: Detector, - node_definition: NodeDefinition = NodesAsPulses(), + node_definition: NodeDefinition = None, input_feature_names: Optional[List[str]] = None, dtype: Optional[torch.dtype] = torch.float, perturbation_dict: Optional[Dict[str, float]] = None, @@ -370,7 +370,7 @@

Source code for graphn # Base class constructor super().__init__( detector=detector, - node_definition=node_definition, + node_definition=node_definition or NodesAsPulses(), edge_definition=KNNEdges( nb_nearest_neighbours=nb_nearest_neighbours, columns=columns, @@ -401,7 +401,7 @@

Source code for graphn
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/graphs/nodes/nodes.html b/_modules/graphnet/models/graphs/nodes/nodes.html index f6a92c582..0a3da4c5c 100644 --- a/_modules/graphnet/models/graphs/nodes/nodes.html +++ b/_modules/graphnet/models/graphs/nodes/nodes.html @@ -573,7 +573,7 @@

Source code for g
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/graphs/utils.html b/_modules/graphnet/models/graphs/utils.html index b88ff658c..7081b3238 100644 --- a/_modules/graphnet/models/graphs/utils.html +++ b/_modules/graphnet/models/graphs/utils.html @@ -514,7 +514,7 @@

Source code for graphne
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/model.html b/_modules/graphnet/models/model.html index 2d895f29e..746e98773 100644 --- a/_modules/graphnet/models/model.html +++ b/_modules/graphnet/models/model.html @@ -324,30 +324,22 @@

Source code for graphnet.models.model

 """Base class(es) for building models."""
 
-from abc import ABC, abstractmethod
-from collections import OrderedDict
+from abc import ABC
 import dill
 import os.path
 from typing import Any, Dict, List, Optional, Union
 
-import numpy as np
-import pandas as pd
-from pytorch_lightning import Trainer, LightningModule
-from pytorch_lightning.callbacks.callback import Callback
-from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.loggers.logger import Logger as LightningLogger
+from pytorch_lightning import LightningModule
 import torch
-from torch import Tensor
-from torch.utils.data import DataLoader, SequentialSampler
 from torch_geometric.data import Data
 
+from graphnet.utilities.deprecation_tools import rename_state_dict_entries
 from graphnet.utilities.logging import Logger
 from graphnet.utilities.config import (
     Configurable,
     ModelConfig,
     ModelConfigSaverABC,
 )
-from graphnet.training.callbacks import ProgressBar
 
 
 

@@ -355,264 +347,11 @@ 
Source code for graphnet.model
 class Model(
     Logger, Configurable, LightningModule, ABC, metaclass=ModelConfigSaverABC
 ):
-    """Base class for all models in graphnet."""
-
-

-[docs]
-    @abstractmethod
-    def forward(self, x: Union[Tensor, Data]) -> Union[Tensor, Data]:
-        """Forward pass."""

-
+    """Base class for all components in graphnet."""
 
     @staticmethod
-    def _construct_trainer(
-        max_epochs: int = 10,
-        gpus: Optional[Union[List[int], int]] = None,
-        callbacks: Optional[List[Callback]] = None,
-        ckpt_path: Optional[str] = None,
-        logger: Optional[LightningLogger] = None,
-        log_every_n_steps: int = 1,
-        gradient_clip_val: Optional[float] = None,
-        distribution_strategy: Optional[str] = "ddp",
-        **trainer_kwargs: Any,
-    ) -> Trainer:
-
-        if gpus:
-            accelerator = "gpu"
-            devices = gpus
-        else:
-            accelerator = "cpu"
-            devices = 1
-
-        trainer = Trainer(
-            accelerator=accelerator,
-            devices=devices,
-            max_epochs=max_epochs,
-            callbacks=callbacks,
-            log_every_n_steps=log_every_n_steps,
-            logger=logger,
-            gradient_clip_val=gradient_clip_val,
-            strategy=distribution_strategy,
-            default_root_dir=ckpt_path,
-            **trainer_kwargs,
-        )
-
-        return trainer
-
-

-[docs]
-    def fit(
-        self,
-        train_dataloader: DataLoader,
-        val_dataloader: Optional[DataLoader] = None,
-        *,
-        max_epochs: int = 10,
-        gpus: Optional[Union[List[int], int]] = None,
-        callbacks: Optional[List[Callback]] = None,
-        ckpt_path: Optional[str] = None,
-        logger: Optional[LightningLogger] = None,
-        log_every_n_steps: int = 1,
-        gradient_clip_val: Optional[float] = None,
-        distribution_strategy: Optional[str] = "ddp",
-        **trainer_kwargs: Any,
-    ) -> None:
-        """Fit `Model` using `pytorch_lightning.Trainer`."""
-        # Checks
-        if callbacks is None:
-            callbacks = self._create_default_callbacks(
-                val_dataloader=val_dataloader,
-            )
-        elif val_dataloader is not None:
-            callbacks = self._add_early_stopping(
-                val_dataloader=val_dataloader, callbacks=callbacks
-            )
-
-        self.train(mode=True)
-        trainer = self._construct_trainer(
-            max_epochs=max_epochs,
-            gpus=gpus,
-            callbacks=callbacks,
-            ckpt_path=ckpt_path,
-            logger=logger,
-            log_every_n_steps=log_every_n_steps,
-            gradient_clip_val=gradient_clip_val,
-            distribution_strategy=distribution_strategy,
-            **trainer_kwargs,
-        )
-
-        try:
-            trainer.fit(
-                self, train_dataloader, val_dataloader, ckpt_path=ckpt_path
-            )
-        except KeyboardInterrupt:
-            self.warning("[ctrl+c] Exiting gracefully.")
-            pass

-
-
-    def _create_default_callbacks(self, val_dataloader: DataLoader) -> List:
-        callbacks = [ProgressBar()]
-        callbacks = self._add_early_stopping(
-            val_dataloader=val_dataloader, callbacks=callbacks
-        )
-        return callbacks
-
-    def _add_early_stopping(
-        self, val_dataloader: DataLoader, callbacks: List
-    ) -> List:
-        if val_dataloader is None:
-            return callbacks
-        has_early_stopping = False
-        assert isinstance(callbacks, list)
-        for callback in callbacks:
-            if isinstance(callback, EarlyStopping):
-                has_early_stopping = True
-
-        if not has_early_stopping:
-            callbacks.append(
-                EarlyStopping(
-                    monitor="val_loss",
-                    patience=5,
-                )
-            )
-            self.warning_once(
-                "Got validation dataloader but no EarlyStopping callback. An "
-                "EarlyStopping callback has been added automatically with "
-                "patience=5 and monitor = 'val_loss'."
-            )
-        return callbacks
-
-

-[docs]
-    def predict(
-        self,
-        dataloader: DataLoader,
-        gpus: Optional[Union[List[int], int]] = None,
-        distribution_strategy: Optional[str] = "auto",
-    ) -> List[Tensor]:
-        """Return predictions for `dataloader`.
-
-        Returns a list of Tensors, one for each model output.
-        """
-        self.train(mode=False)
-
-        callbacks = self._create_default_callbacks(
-            val_dataloader=None,
-        )
-
-        inference_trainer = self._construct_trainer(
-            gpus=gpus,
-            distribution_strategy=distribution_strategy,
-            callbacks=callbacks,
-        )
-
-        predictions_list = inference_trainer.predict(self, dataloader)
-        assert len(predictions_list), "Got no predictions"
-
-        nb_outputs = len(predictions_list[0])
-        predictions: List[Tensor] = [
-            torch.cat([preds[ix] for preds in predictions_list], dim=0)
-            for ix in range(nb_outputs)
-        ]
-
-        return predictions

-
-
-

-[docs]
-    def predict_as_dataframe(
-        self,
-        dataloader: DataLoader,
-        prediction_columns: List[str],
-        *,
-        additional_attributes: Optional[List[str]] = None,
-        gpus: Optional[Union[List[int], int]] = None,
-        distribution_strategy: Optional[str] = "auto",
-    ) -> pd.DataFrame:
-        """Return predictions for `dataloader` as a DataFrame.
-
-        Include `additional_attributes` as additional columns in the output
-        DataFrame.
-        """
-        # Check(s)
-        if additional_attributes is None:
-            additional_attributes = []
-        assert isinstance(additional_attributes, list)
-
-        if (
-            not isinstance(dataloader.sampler, SequentialSampler)
-            and additional_attributes
-        ):
-            print(dataloader.sampler)
-            raise UserWarning(
-                "DataLoader has a `sampler` that is not `SequentialSampler`, "
-                "indicating that shuffling is enabled. Using "
-                "`predict_as_dataframe` with `additional_attributes` assumes "
-                "that the sequence of batches in `dataloader` are "
-                "deterministic. Either call this method a `dataloader` which "
-                "doesn't resample batches; or do not request "
-                "`additional_attributes`."
-            )
-        self.info(f"Column names for predictions are: \n {prediction_columns}")
-        predictions_torch = self.predict(
-            dataloader=dataloader,
-            gpus=gpus,
-            distribution_strategy=distribution_strategy,
-        )
-        predictions = (
-            torch.cat(predictions_torch, dim=1).detach().cpu().numpy()
-        )
-        assert len(prediction_columns) == predictions.shape[1], (
-            f"Number of provided column names ({len(prediction_columns)}) and "
-            f"number of output columns ({predictions.shape[1]}) don't match."
-        )
-
-        # Get additional attributes
-        attributes: Dict[str, List[np.ndarray]] = OrderedDict(
-            [(attr, []) for attr in additional_attributes]
-        )
-
-        for batch in dataloader:
-            for attr in attributes:
-                attribute = batch[attr]
-                if isinstance(attribute, torch.Tensor):
-                    attribute = attribute.detach().cpu().numpy()
-
-                # Check if node level predictions
-                # If true, additional attributes are repeated
-                # to make dimensions fit
-                if len(predictions) != len(dataloader.dataset):
-                    if len(attribute) < np.sum(
-                        batch.n_pulses.detach().cpu().numpy()
-                    ):
-                        attribute = np.repeat(
-                            attribute, batch.n_pulses.detach().cpu().numpy()
-                        )
-                        try:
-                            assert len(attribute) == len(batch.x)
-                        except AssertionError:
-                            self.warning_once(
-                                "Could not automatically adjust length"
-                                f"of additional attribute {attr} to match length of"
-                                f"predictions. Make sure {attr} is a graph-level or"
-                                "node-level attribute. Attribute skipped."
-                            )
-                            pass
-                attributes[attr].extend(attribute)
-
-        data = np.concatenate(
-            [predictions]
-            + [
-                np.asarray(values)[:, np.newaxis]
-                for values in attributes.values()
-            ],
-            axis=1,
-        )
-
-        results = pd.DataFrame(
-            data, columns=prediction_columns + additional_attributes
-        )
-        return results

-
+    def _get_batch_size(data: List[Data]) -> int:
+        return sum([torch.numel(torch.unique(d.batch)) for d in data])
 
 

 [docs]
@@ -645,7 +384,10 @@ 
Source code for graphnet.model
             self.info(
                 "It is recommended to use the .pth suffix for state_dict files."
             )
-        torch.save(self.cpu().state_dict(), path)
+        state_dict = self.state_dict()
+        for key, value in state_dict.items():
+            state_dict[key] = value.cpu()
+        torch.save(state_dict, path)
         self.info(f"Model state_dict saved to {path}")

 
 
@@ -659,6 +401,16 @@ 
Source code for graphnet.model
             state_dict = torch.load(path)
         else:
             state_dict = path
+
+        # DEPRECATION UTILITY: REMOVE AT 2.0 LAUNCH
+        # See https://github.com/graphnet-team/graphnet/issues/647
+        state_dict, state_dict_altered = rename_state_dict_entries(
+            state_dict=state_dict, old_phrase="_gnn", new_phrase="backbone"
+        )
+        if state_dict_altered:
+            self.warning(
+                "DeprecationWarning: State dicts with `_gnn` entries will be deprecated in GraphNeT 2.0"
+            )
         return super().load_state_dict(state_dict, **kargs)

 
 
@@ -714,7 +466,7 @@ 
Source code for graphnet.model
       

         

           

-              © Copyright 2021-2023, GraphNeT team.
+              © Copyright 2021-2024, GraphNeT team.
               
           

             Created using
diff --git a/_modules/graphnet/models/standard_model.html b/_modules/graphnet/models/standard_model.html
index aba25e5f8..8d219a1e4 100644
--- a/_modules/graphnet/models/standard_model.html
+++ b/_modules/graphnet/models/standard_model.html
@@ -323,21 +323,26 @@
             
   
Source code for graphnet.models.standard_model
 """Standard model class(es)."""
+from collections import OrderedDict
+from typing import Any, Dict, List, Optional, Union, Type
 
-from typing import Any, Dict, List, Optional, Union
-
+import numpy as np
 import torch
+from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from torch import Tensor
 from torch.nn import ModuleList
 from torch.optim import Adam
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, SequentialSampler
 from torch_geometric.data import Data
 import pandas as pd
+from pytorch_lightning.loggers import Logger as LightningLogger
 
+from graphnet.training.callbacks import ProgressBar
 from graphnet.models.graphs import GraphDefinition
 from graphnet.models.gnn.gnn import GNN
 from graphnet.models.model import Model
-from graphnet.models.task import Task
+from graphnet.models.task import StandardLearnedTask
 
 
 

@@ -345,17 +350,18 @@ 
Source code for graph
 class StandardModel(Model):
     """Main class for standard models in graphnet.
 
-    This class chains together the different elements of a complete GNN-based
-    model (detector read-in, GNN architecture, and task-specific read-outs).
+    This class chains together the different elements of a complete GNN- based
+    model (detector read-in, GNN backbone, and task-specific read-outs).
     """
 
     def __init__(
         self,
         *,
         graph_definition: GraphDefinition,
-        gnn: GNN,
-        tasks: Union[Task, List[Task]],
-        optimizer_class: type = Adam,
+        backbone: GNN = None,
+        gnn: Optional[GNN] = None,
+        tasks: Union[StandardLearnedTask, List[StandardLearnedTask]],
+        optimizer_class: Type[torch.optim.Optimizer] = Adam,
         optimizer_kwargs: Optional[Dict] = None,
         scheduler_class: Optional[type] = None,
         scheduler_kwargs: Optional[Dict] = None,
@@ -366,16 +372,29 @@ 
Source code for graph
         super().__init__(name=__name__, class_name=self.__class__.__name__)
 
         # Check(s)
-        if isinstance(tasks, Task):
+        if isinstance(tasks, StandardLearnedTask):
             tasks = [tasks]
         assert isinstance(tasks, (list, tuple))
-        assert all(isinstance(task, Task) for task in tasks)
+        assert all(isinstance(task, StandardLearnedTask) for task in tasks)
         assert isinstance(graph_definition, GraphDefinition)
-        assert isinstance(gnn, GNN)
+
+        # deprecation warnings
+        if (backbone is None) & (gnn is not None):
+            backbone = gnn
+            # Code continues after warning
+            self.warning(
+                """DeprecationWarning: Argument `gnn` will be deprecated in GraphNeT 2.0. Please use `backbone` instead."""
+            )
+        elif (backbone is None) & (gnn is None):
+            # Code stops
+            raise TypeError(
+                "__init__() missing 1 required keyword-only argument: 'backbone'"
+            )
+        assert isinstance(backbone, GNN)
 
         # Member variable(s)
         self._graph_definition = graph_definition
-        self._gnn = gnn
+        self.backbone = backbone
         self._tasks = ModuleList(tasks)
         self._optimizer_class = optimizer_class
         self._optimizer_kwargs = optimizer_kwargs or dict()
@@ -384,7 +403,129 @@ 
Source code for graph
         self._scheduler_config = scheduler_config or dict()
 
         # set dtype of GNN from graph_definition
-        self._gnn.type(self._graph_definition._dtype)
+        self.backbone.type(self._graph_definition._dtype)
+
+    @staticmethod
+    def _construct_trainer(
+        max_epochs: int = 10,
+        gpus: Optional[Union[List[int], int]] = None,
+        callbacks: Optional[List[Callback]] = None,
+        logger: Optional[LightningLogger] = None,
+        log_every_n_steps: int = 1,
+        gradient_clip_val: Optional[float] = None,
+        distribution_strategy: Optional[str] = "ddp",
+        **trainer_kwargs: Any,
+    ) -> Trainer:
+        if gpus:
+            accelerator = "gpu"
+            devices = gpus
+        else:
+            accelerator = "cpu"
+            devices = 1
+
+        trainer = Trainer(
+            accelerator=accelerator,
+            devices=devices,
+            max_epochs=max_epochs,
+            callbacks=callbacks,
+            log_every_n_steps=log_every_n_steps,
+            logger=logger,
+            gradient_clip_val=gradient_clip_val,
+            strategy=distribution_strategy,
+            **trainer_kwargs,
+        )
+
+        return trainer
+
+

+[docs]
+    def fit(
+        self,
+        train_dataloader: DataLoader,
+        val_dataloader: Optional[DataLoader] = None,
+        *,
+        max_epochs: int = 10,
+        early_stopping_patience: int = 5,
+        gpus: Optional[Union[List[int], int]] = None,
+        callbacks: Optional[List[Callback]] = None,
+        ckpt_path: Optional[str] = None,
+        logger: Optional[LightningLogger] = None,
+        log_every_n_steps: int = 1,
+        gradient_clip_val: Optional[float] = None,
+        distribution_strategy: Optional[str] = "ddp",
+        **trainer_kwargs: Any,
+    ) -> None:
+        """Fit `StandardModel` using `pytorch_lightning.Trainer`."""
+        # Checks
+        if callbacks is None:
+            # We create the bare-minimum callbacks for you.
+            callbacks = self._create_default_callbacks(
+                val_dataloader=val_dataloader,
+                early_stopping_patience=early_stopping_patience,
+            )
+            self.debug("No Callbacks specified. Default callbacks added.")
+        else:
+            # You are on your own!
+            self.debug("Initializing training with user-provided callbacks.")
+            pass
+        self._print_callbacks(callbacks)
+        has_early_stopping = self._contains_callback(callbacks, EarlyStopping)
+        has_model_checkpoint = self._contains_callback(
+            callbacks, ModelCheckpoint
+        )
+
+        if (has_early_stopping) & (has_model_checkpoint is False):
+            self.warning(
+                """No ModelCheckpoint found in callbacks. Best-fit model will not automatically be loaded after training!"""
+            )
+
+        self.train(mode=True)
+        trainer = self._construct_trainer(
+            max_epochs=max_epochs,
+            gpus=gpus,
+            callbacks=callbacks,
+            logger=logger,
+            log_every_n_steps=log_every_n_steps,
+            gradient_clip_val=gradient_clip_val,
+            distribution_strategy=distribution_strategy,
+            **trainer_kwargs,
+        )
+
+        try:
+            trainer.fit(
+                self, train_dataloader, val_dataloader, ckpt_path=ckpt_path
+            )
+        except KeyboardInterrupt:
+            self.warning("[ctrl+c] Exiting gracefully.")
+            pass
+
+        # Load weights from best-fit model after training if possible
+        if has_early_stopping & has_model_checkpoint:
+            for callback in callbacks:
+                if isinstance(callback, ModelCheckpoint):
+                    checkpoint_callback = callback
+            self.load_state_dict(
+                torch.load(checkpoint_callback.best_model_path)["state_dict"]
+            )
+            self.info("Best-fit weights from EarlyStopping loaded.")

+
+
+    def _print_callbacks(self, callbacks: List[Callback]) -> None:
+        callback_names = []
+        for cbck in callbacks:
+            callback_names.append(cbck.__class__.__name__)
+        self.info(
+            f"Training initiated with callbacks: {', '.join(callback_names)}"
+        )
+
+    def _contains_callback(
+        self, callbacks: List[Callback], callback: Callback
+    ) -> bool:
+        """Check if `callback` is in `callbacks`."""
+        for cbck in callbacks:
+            if isinstance(cbck, callback):
+                return True
+        return False
 
     @property
     def target_labels(self) -> List[str]:
@@ -433,7 +574,7 @@ 
Source code for graph
             data = [data]
         x_list = []
         for d in data:
-            x = self._gnn(d)
+            x = self.backbone(d)
             x_list.append(x)
         x = torch.cat(x_list, dim=0)
 
@@ -524,9 +665,6 @@ 
Source code for graph
         return torch.sum(torch.stack(losses))

 
 
-    def _get_batch_size(self, data: List[Data]) -> int:
-        return sum([torch.numel(torch.unique(d.batch)) for d in data])
-
 

 [docs]
     def inference(self) -> None:
@@ -556,11 +694,27 @@ 
Source code for graph
     ) -> List[Tensor]:
         """Return predictions for `dataloader`."""
         self.inference()
-        return super().predict(
-            dataloader=dataloader,
+        self.train(mode=False)
+
+        callbacks = self._create_default_callbacks(
+            val_dataloader=None,
+        )
+
+        inference_trainer = self._construct_trainer(
             gpus=gpus,
             distribution_strategy=distribution_strategy,
-        )

+            callbacks=callbacks,
+        )
+
+        predictions_list = inference_trainer.predict(self, dataloader)
+        assert len(predictions_list), "Got no predictions"
+
+        nb_outputs = len(predictions_list[0])
+        predictions: List[Tensor] = [
+            torch.cat([preds[ix] for preds in predictions_list], dim=0)
+            for ix in range(nb_outputs)
+        ]
+        return predictions

 
 
 

@@ -581,14 +735,144 @@ 
Source code for graph
         """
         if prediction_columns is None:
             prediction_columns = self.prediction_labels
-        return super().predict_as_dataframe(
+
+        if additional_attributes is None:
+            additional_attributes = []
+        assert isinstance(additional_attributes, list)
+
+        if (
+            not isinstance(dataloader.sampler, SequentialSampler)
+            and additional_attributes
+        ):
+            print(dataloader.sampler)
+            raise UserWarning(
+                "DataLoader has a `sampler` that is not `SequentialSampler`, "
+                "indicating that shuffling is enabled. Using "
+                "`predict_as_dataframe` with `additional_attributes` assumes "
+                "that the sequence of batches in `dataloader` are "
+                "deterministic. Either call this method a `dataloader` which "
+                "doesn't resample batches; or do not request "
+                "`additional_attributes`."
+            )
+        self.info(f"Column names for predictions are: \n {prediction_columns}")
+        predictions_torch = self.predict(
             dataloader=dataloader,
-            prediction_columns=prediction_columns,
-            additional_attributes=additional_attributes,
             gpus=gpus,
             distribution_strategy=distribution_strategy,
-        )

-

+        )
+        predictions = (
+            torch.cat(predictions_torch, dim=1).detach().cpu().numpy()
+        )
+        assert len(prediction_columns) == predictions.shape[1], (
+            f"Number of provided column names ({len(prediction_columns)}) and "
+            f"number of output columns ({predictions.shape[1]}) don't match."
+        )
+
+        # Get additional attributes
+        attributes: Dict[str, List[np.ndarray]] = OrderedDict(
+            [(attr, []) for attr in additional_attributes]
+        )
+        for batch in dataloader:
+            for attr in attributes:
+                attribute = batch[attr]
+                if isinstance(attribute, torch.Tensor):
+                    attribute = attribute.detach().cpu().numpy()
+
+                # Check if node level predictions
+                # If true, additional attributes are repeated
+                # to make dimensions fit
+                if len(predictions) != len(dataloader.dataset):
+                    if len(attribute) < np.sum(
+                        batch.n_pulses.detach().cpu().numpy()
+                    ):
+                        attribute = np.repeat(
+                            attribute, batch.n_pulses.detach().cpu().numpy()
+                        )
+                        try:
+                            assert len(attribute) == len(batch.x)
+                        except AssertionError:
+                            self.warning_once(
+                                "Could not automatically adjust length"
+                                f"of additional attribute {attr} to match length of"
+                                f"predictions. Make sure {attr} is a graph-level or"
+                                "node-level attribute. Attribute skipped."
+                            )
+                            pass
+                attributes[attr].extend(attribute)
+
+        data = np.concatenate(
+            [predictions]
+            + [
+                np.asarray(values)[:, np.newaxis]
+                for values in attributes.values()
+            ],
+            axis=1,
+        )
+
+        results = pd.DataFrame(
+            data, columns=prediction_columns + additional_attributes
+        )
+        return results

+
+
+    def _create_default_callbacks(
+        self,
+        val_dataloader: DataLoader,
+        early_stopping_patience: Optional[int] = None,
+    ) -> List:
+        """Create default callbacks.
+
+        Used in cases where no callbacks are specified by the user in .fit
+        """
+        callbacks = [ProgressBar()]
+        if val_dataloader is not None:
+            assert early_stopping_patience is not None
+            # Add Early Stopping
+            callbacks.append(
+                EarlyStopping(
+                    monitor="val_loss",
+                    patience=early_stopping_patience,
+                )
+            )
+            # Add Model Check Point
+            callbacks.append(
+                ModelCheckpoint(
+                    save_top_k=1,
+                    monitor="val_loss",
+                    mode="min",
+                    filename=f"{self.backbone.__class__.__name__}"
+                    + "-{epoch}-{val_loss:.2f}-{train_loss:.2f}",
+                )
+            )
+            self.info(
+                f"EarlyStopping has been added with a patience of {early_stopping_patience}."
+            )
+        return callbacks
+
+    def _add_early_stopping(
+        self, val_dataloader: DataLoader, callbacks: List
+    ) -> List:
+        if val_dataloader is None:
+            return callbacks
+        has_early_stopping = False
+        assert isinstance(callbacks, list)
+        for callback in callbacks:
+            if isinstance(callback, EarlyStopping):
+                has_early_stopping = True
+
+        if not has_early_stopping:
+            callbacks.append(
+                EarlyStopping(
+                    monitor="val_loss",
+                    patience=5,
+                )
+            )
+            self.warning_once(
+                "Got validation dataloader but no EarlyStopping callback. An "
+                "EarlyStopping callback has been added automatically with "
+                "patience=5 and monitor = 'val_loss'."
+            )
+        return callbacks
@@ -610,7 +894,7 @@

Source code for graph
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/task/classification.html b/_modules/graphnet/models/task/classification.html index b3c6c9440..cc251f57b 100644 --- a/_modules/graphnet/models/task/classification.html +++ b/_modules/graphnet/models/task/classification.html @@ -329,7 +329,7 @@

Source code for import torch from torch import Tensor -from graphnet.models.task import Task, IdentityTask +from graphnet.models.task import IdentityTask, StandardLearnedTask
@@ -346,7 +346,7 @@

Source code for
[docs] -class BinaryClassificationTask(Task): +class BinaryClassificationTask(StandardLearnedTask): """Performs binary classification.""" # Requires one feature, logit for being signal class. @@ -362,7 +362,7 @@

Source code for
[docs] -class BinaryClassificationTaskLogits(Task): +class BinaryClassificationTaskLogits(StandardLearnedTask): """Performs binary classification form logits.""" # Requires one feature, logit for being signal class. @@ -393,7 +393,7 @@

Source code for
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/task/reconstruction.html b/_modules/graphnet/models/task/reconstruction.html index ccb644a97..381966505 100644 --- a/_modules/graphnet/models/task/reconstruction.html +++ b/_modules/graphnet/models/task/reconstruction.html @@ -328,13 +328,13 @@

Source code for import torch from torch import Tensor -from graphnet.models.task import Task +from graphnet.models.task import StandardLearnedTask from graphnet.utilities.maths import eps_like
[docs] -class AzimuthReconstructionWithKappa(Task): +class AzimuthReconstructionWithKappa(StandardLearnedTask): """Reconstructs azimuthal angle and associated kappa (1/var).""" # Requires two features: untransformed points in (x,y)-space. @@ -378,7 +378,7 @@

Source code for
[docs] -class DirectionReconstructionWithKappa(Task): +class DirectionReconstructionWithKappa(StandardLearnedTask): """Reconstructs direction with kappa from the 3D-vMF distribution.""" # Requires three features: untransformed points in (x,y,z)-space. @@ -405,7 +405,7 @@

Source code for
[docs] -class ZenithReconstruction(Task): +class ZenithReconstruction(StandardLearnedTask): """Reconstructs zenith angle.""" # Requires two features: zenith angle itself. @@ -439,7 +439,7 @@

Source code for
[docs] -class EnergyReconstruction(Task): +class EnergyReconstruction(StandardLearnedTask): """Reconstructs energy using stable method.""" # Requires one feature: untransformed energy @@ -456,7 +456,7 @@

Source code for
[docs] -class EnergyReconstructionWithPower(Task): +class EnergyReconstructionWithPower(StandardLearnedTask): """Reconstructs energy.""" # Requires one feature: untransformed energy @@ -470,6 +470,29 @@

Source code for +
+[docs] +class EnergyTCReconstruction(StandardLearnedTask): + """Reconstructs track and cascade energies using stable method.""" + + # Requires two features: untransformed energy for track and cascade + default_target_labels = ["energy_track", "energy_cascade"] + default_prediction_labels = ["energy_track_pred", "energy_cascade_pred"] + nb_inputs = 2 + + def _forward(self, x: Tensor) -> Tensor: + # Transform to positive energy domain avoiding `-inf` in `log10` + # Transform, thereby preventing overflow and underflow error. + x[:, 0] = torch.nn.functional.softplus( + x[:, 0].clone(), beta=0.05 + ) + eps_like(x[:, 0].clone()) + x[:, 1] = torch.nn.functional.softplus( + x[:, 1].clone(), beta=0.05 + ) + eps_like(x[:, 1].clone()) + return x
+ + +
[docs] class EnergyReconstructionWithUncertainty(EnergyReconstruction): @@ -491,7 +514,7 @@

Source code for
[docs] -class VertexReconstruction(Task): +class VertexReconstruction(StandardLearnedTask): """Reconstructs vertex position and time.""" # Requires four features, x, y, z, and t. @@ -516,7 +539,7 @@

Source code for
[docs] -class PositionReconstruction(Task): +class PositionReconstruction(StandardLearnedTask): """Reconstructs vertex position.""" # Requires three features, x, y, and z. @@ -540,7 +563,7 @@

Source code for
[docs] -class TimeReconstruction(Task): +class TimeReconstruction(StandardLearnedTask): """Reconstructs time.""" # Requires one feature, time. @@ -556,7 +579,7 @@

Source code for
[docs] -class InelasticityReconstruction(Task): +class InelasticityReconstruction(StandardLearnedTask): """Reconstructs interaction inelasticity. That is, 1-(track energy / hadronic energy). @@ -591,7 +614,7 @@

Source code for
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/task/task.html b/_modules/graphnet/models/task/task.html index f592e588a..97f4dce52 100644 --- a/_modules/graphnet/models/task/task.html +++ b/_modules/graphnet/models/task/task.html @@ -345,7 +345,7 @@

Source code for graphnet.m
[docs] class Task(Model): - """Base class for all reconstruction and classification tasks.""" + """Base class for Tasks in GraphNeT.""" @property @abstractmethod @@ -353,13 +353,11 @@

Source code for graphnet.m """Return number of inputs assumed by task.""" @property - @abstractmethod def default_target_labels(self) -> List[str]: """Return default target labels.""" return self._default_target_labels @property - @abstractmethod def default_prediction_labels(self) -> List[str]: """Return default prediction labels.""" return self._default_prediction_labels @@ -367,7 +365,6 @@

Source code for graphnet.m def __init__( self, *, - hidden_size: int, loss_function: "LossFunction", target_labels: Optional[Union[str, List[str]]] = None, prediction_labels: Optional[Union[str, List[str]]] = None, @@ -380,9 +377,6 @@

Source code for graphnet.m """Construct `Task`. Args: - hidden_size: The number of nodes in the layer feeding into this - tasks, used to construct the affine transformation to the - predicted quantity. loss_function: Loss function appropriate to the task. target_labels: Name(s) of the quantity/-ies being predicted, used to extract the target tensor(s) from the `Data` object in @@ -451,20 +445,6 @@

Source code for graphnet.m transform_support, ) - # Mapping from last hidden layer to required size of input - self._affine = Linear(hidden_size, self.nb_inputs) - -
-[docs] - @final - def forward(self, x: Union[Tensor, Data]) -> Union[Tensor, Data]: - """Forward pass.""" - self._regularisation_loss = 0 # Reset - x = self._affine(x) - x = self._forward(x) - return self._transform_prediction(x)
- - @final def _transform_prediction( self, prediction: Union[Tensor, Data] @@ -474,33 +454,6 @@

Source code for graphnet.m else: return self._transform_prediction_training(prediction) - @abstractmethod - def _forward(self, x: Union[Tensor, Data]) -> Union[Tensor, Data]: - """Syntax like `.forward`, for implentation in inheriting classes.""" - -
-[docs] - @final - def compute_loss(self, pred: Union[Tensor, Data], data: Data) -> Tensor: - """Compute loss of `pred` wrt. - - target labels in `data`. - """ - target = torch.stack( - [data[label] for label in self._target_labels], dim=1 - ) - target = self._transform_target(target) - if self._loss_weight is not None: - weights = data[self._loss_weight] - else: - weights = None - loss = ( - self._loss_function(pred, target, weights=weights) - + self._regularisation_loss - ) - return loss
- -
[docs] @final @@ -598,9 +551,137 @@

Source code for graphnet.m +
+[docs] +class LearnedTask(Task): + """Task class with a learned mapping. + + Applies a learned mapping between the last latent layer of `Model` and + target space. E.g. the `LearnedTask` contains learnable parameters that + acts like a prediction head. + """ + + def __init__( + self, + hidden_size: int, + **task_kwargs: Any, + ): + """Construct `LearnedTask`. + + Args: + hidden_size: The number of columns in the output of + the last latent layer of `Model` using this Task. + Available through `Model.nb_outputs` + """ + # Base class constructor + super().__init__(**task_kwargs) + + # Mapping from last hidden layer to required size of input + self._affine = Linear(hidden_size, self.nb_inputs) + + @abstractmethod + def _forward( # type: ignore + self, x: Union[Tensor, Data] + ) -> Union[Tensor, Data]: + """Syntax like `.forward`, for implentation in inheriting classes.""" + raise NotImplementedError + +
+[docs] + @abstractmethod + def compute_loss(self, pred: Union[Tensor, Data], data: Data) -> Tensor: + """Compute loss of `pred` wrt. + + target labels in `data`. + """
+ + + @property + @abstractmethod + def nb_inputs(self) -> int: + """Return number of inputs assumed by task.""" + +
+[docs] + @final + def forward( # type: ignore + self, x: Union[Tensor, Data] + ) -> Union[Tensor, Data]: + """Forward call for `LearnedTask`. + + The learned embedding transforms last latent layer of Model to meet + target dimensions. + """ + self._regularisation_loss = 0 # Reset + x = self._affine(x) + x = self._forward(x=x) + return self._transform_prediction(x)
+
+ + + +
+[docs] +class StandardLearnedTask(LearnedTask): + """Standard class for classification and reconstruction in GraphNeT. + + This class comes with a definition of `compute_loss` that is compatible + with the vast majority of supervised learning tasks. + """ + + def __init__( + self, + hidden_size: int, + **task_kwargs: Any, + ): + """Construct `StandardLearnedTask`. + + Args: + hidden_size: The number of columns in the output of + the last latent layer of `Model` using this Task. + Available through `Model.nb_outputs` + """ + # Base class constructor + super().__init__(hidden_size=hidden_size, **task_kwargs) + + @property + @abstractmethod + def nb_inputs(self) -> int: + """Return number of inputs assumed by task.""" + + @abstractmethod + def _forward(self, x: Union[Tensor, Data]) -> Union[Tensor, Data]: + """Syntax like `.forward`, for implentation in inheriting classes.""" + +
+[docs] + @final + def compute_loss(self, pred: Union[Tensor, Data], data: Data) -> Tensor: + """Compute supervised learning loss. + + Grabs truth labels in `data` and sends both `pred` and `target` to loss + function for evaluation. Suits most supervised learning `Task`s. + """ + target = torch.stack( + [data[label] for label in self._target_labels], dim=1 + ) + target = self._transform_target(target) + if self._loss_weight is not None: + weights = data[self._loss_weight] + else: + weights = None + loss = ( + self._loss_function(pred, target, weights=weights) + + self._regularisation_loss + ) + return loss
+
+ + +
[docs] -class IdentityTask(Task): +class IdentityTask(StandardLearnedTask): """Identity, or trivial, task.""" def __init__( @@ -612,8 +693,8 @@

Source code for graphnet.m ): """Construct IdentityTask. - Return the `nb_outputs` as a direct, affine transformation of the last - hidden layer. + A task that does not apply a learned embedding to the input. It returns + the direct inputs from `Model`. """ self._nb_inputs = nb_outputs self._default_target_labels = ( @@ -643,10 +724,88 @@

Source code for graphnet.m """Return number of inputs assumed by task.""" return self._nb_inputs - def _forward(self, x: Tensor) -> Tensor: + def _forward(self, x: Union[Tensor, Data]) -> Tensor: # type: ignore # Leave it as is. return x

+ + +
+[docs] +class StandardFlowTask(Task): + """A `Task` for `NormalizingFlow`s in GraphNeT.""" + + def __init__( + self, + target_labels: List[str], + **task_kwargs: Any, + ): + """Construct `StandardLearnedTask`. + + Args: + target_labels: A list of names for the targets of this Task. + hidden_size: The number of columns in the output of + the last latent layer of `Model` using this Task. + Available through `Model.nb_outputs` + """ + # Base class constructor + super().__init__(target_labels=target_labels, **task_kwargs) + +
+[docs] + def nb_inputs(self) -> int: + """Return number of inputs assumed by task.""" + return len(self._target_labels)
+ + + def _forward(self, x: Tensor, jacobian: Tensor) -> Tensor: # type: ignore + # Leave it as is. + return x + +
+[docs] + @final + def forward( + self, x: Union[Tensor, Data], jacobian: Optional[Tensor] + ) -> Union[Tensor, Data]: + """Forward pass.""" + self._regularisation_loss = 0 # Reset + x = self._forward(x, jacobian) + return self._transform_prediction(x)
+ + +
+[docs] + @final + def compute_loss( + self, prediction: Tensor, jacobian: Tensor, data: Data + ) -> Tensor: + """Compute loss for normalizing flow tasks. + + Args: + prediction: transformed sample in latent distribution space. + jacobian: the jacobian associated with the transformation. + data: the graph object. + + Returns: + the loss associated with the transformation. + """ + if self._loss_weight is not None: + weights = data[self._loss_weight] + else: + weights = None + loss = ( + self._loss_function( + prediction=prediction, + jacobian=jacobian, + weights=weights, + target=None, + ) + + self._regularisation_loss + ) + return loss
+
+

@@ -667,7 +826,7 @@

Source code for graphnet.m
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/models/utils.html b/_modules/graphnet/models/utils.html index 35c1b3460..977a318ec 100644 --- a/_modules/graphnet/models/utils.html +++ b/_modules/graphnet/models/utils.html @@ -412,7 +412,7 @@

Source code for graphnet.model
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/pisa/fitting.html b/_modules/graphnet/pisa/fitting.html index 1865353e4..a0e1059cc 100644 --- a/_modules/graphnet/pisa/fitting.html +++ b/_modules/graphnet/pisa/fitting.html @@ -1165,7 +1165,7 @@

Source code for graphnet.pisa.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/pisa/plotting.html b/_modules/graphnet/pisa/plotting.html index 6f31c1d90..530683a56 100644 --- a/_modules/graphnet/pisa/plotting.html +++ b/_modules/graphnet/pisa/plotting.html @@ -524,7 +524,7 @@

Source code for graphnet.pisa
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/training/callbacks.html b/_modules/graphnet/training/callbacks.html index fcf981d92..7bea62b10 100644 --- a/_modules/graphnet/training/callbacks.html +++ b/_modules/graphnet/training/callbacks.html @@ -325,20 +325,26 @@

Source code for graphnet """Callback class(es) for using during model training.""" import logging -from typing import Dict, List +import os +from typing import Dict, List, TYPE_CHECKING, Any, Optional import warnings import numpy as np +import torch from tqdm.std import Bar from pytorch_lightning import LightningModule, Trainer -from pytorch_lightning.callbacks import TQDMProgressBar +from pytorch_lightning.callbacks import TQDMProgressBar, EarlyStopping from pytorch_lightning.utilities import rank_zero_only from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from graphnet.utilities.logging import Logger +if TYPE_CHECKING: + from graphnet.models import Model + import pytorch_lightning as pl +
[docs] @@ -506,6 +512,110 @@

Source code for graphnet h.setLevel(level)

+ + +
+[docs] +class GraphnetEarlyStopping(EarlyStopping): + """Early stopping callback for graphnet.""" + + def __init__(self, save_dir: str, **kwargs: Dict[str, Any]) -> None: + """Construct `GraphnetEarlyStopping` Callback. + + Args: + save_dir: Path to directory to save best model and config. + **kwargs: Keyword arguments to pass to `EarlyStopping`. See + `pytorch_lightning.callbacks.EarlyStopping` for details. + """ + self.save_dir = save_dir + super().__init__(**kwargs) + +
+[docs] + def setup( + self, + trainer: "pl.Trainer", + graphnet_model: "Model", + stage: Optional[str] = None, + ) -> None: + """Call at setup stage of training. + + Args: + trainer: The trainer. + graphnet_model: The model. + stage: The stage of training. + """ + super().setup(trainer, graphnet_model, stage) + os.makedirs(self.save_dir, exist_ok=True) + graphnet_model.save_config(os.path.join(self.save_dir, "config.yml"))
+ + +
+[docs] + def on_train_epoch_end( + self, trainer: "pl.Trainer", graphnet_model: "Model" + ) -> None: + """Call after each train epoch. + + Args: + trainer: Trainer object. + graphnet_model: Graphnet Model. + + Returns: None. + """ + if not self._check_on_train_epoch_end or self._should_skip_check( + trainer + ): + return + current_best = self.best_score + self._run_early_stopping_check(trainer) + if self.best_score != current_best: + graphnet_model.save_state_dict( + os.path.join(self.save_dir, "best_model.pth") + )
+ + +
+[docs] + def on_validation_end( + self, trainer: "pl.Trainer", graphnet_model: "Model" + ) -> None: + """Call after each validation epoch. + + Args: + trainer: Trainer object. + graphnet_model: Graphnet Model. + + Returns: None. + """ + if self._check_on_train_epoch_end or self._should_skip_check(trainer): + return + current_best = self.best_score + self._run_early_stopping_check(trainer) + if self.best_score != current_best: + graphnet_model.save_state_dict( + os.path.join(self.save_dir, "best_model.pth") + )
+ + +
+[docs] + def on_fit_end( + self, trainer: "pl.Trainer", graphnet_model: "Model" + ) -> None: + """Call at the end of training. + + Args: + trainer: Trainer object. + graphnet_model: Graphnet Model. + + Returns: None. + """ + graphnet_model.load_state_dict( + os.path.join(self.save_dir, "best_model.pth") + )
+
+
@@ -526,7 +636,7 @@

Source code for graphnet
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/training/labels.html b/_modules/graphnet/training/labels.html index b2dd18a77..0da6b1526 100644 --- a/_modules/graphnet/training/labels.html +++ b/_modules/graphnet/training/labels.html @@ -418,7 +418,7 @@

Source code for graphnet.tr
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/training/loss_functions.html b/_modules/graphnet/training/loss_functions.html index d1b35f50a..a5dab2a8f 100644 --- a/_modules/graphnet/training/loss_functions.html +++ b/_modules/graphnet/training/loss_functions.html @@ -838,7 +838,7 @@

Source code for gra
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/training/utils.html b/_modules/graphnet/training/utils.html index 3d113e303..547bf5619 100644 --- a/_modules/graphnet/training/utils.html +++ b/_modules/graphnet/training/utils.html @@ -679,7 +679,7 @@

Source code for graphnet.tra
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/training/weight_fitting.html b/_modules/graphnet/training/weight_fitting.html index 909eb3dbe..221af1e4c 100644 --- a/_modules/graphnet/training/weight_fitting.html +++ b/_modules/graphnet/training/weight_fitting.html @@ -552,7 +552,7 @@

Source code for gra
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/argparse.html b/_modules/graphnet/utilities/argparse.html index a68f077b2..f46e09075 100644 --- a/_modules/graphnet/utilities/argparse.html +++ b/_modules/graphnet/utilities/argparse.html @@ -511,7 +511,7 @@

Source code for graphnet
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/base_config.html b/_modules/graphnet/utilities/config/base_config.html index f57c1f185..32d6f50c1 100644 --- a/_modules/graphnet/utilities/config/base_config.html +++ b/_modules/graphnet/utilities/config/base_config.html @@ -431,7 +431,7 @@

Source code fo
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/configurable.html b/_modules/graphnet/utilities/config/configurable.html index cfe023b65..eb58aed73 100644 --- a/_modules/graphnet/utilities/config/configurable.html +++ b/_modules/graphnet/utilities/config/configurable.html @@ -390,7 +390,7 @@

Source code f
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/dataset_config.html b/_modules/graphnet/utilities/config/dataset_config.html index 3b7b036b5..f5390b772 100644 --- a/_modules/graphnet/utilities/config/dataset_config.html +++ b/_modules/graphnet/utilities/config/dataset_config.html @@ -618,7 +618,7 @@

Source code
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/model_config.html b/_modules/graphnet/utilities/config/model_config.html index 0aa31c78e..03fb80c8d 100644 --- a/_modules/graphnet/utilities/config/model_config.html +++ b/_modules/graphnet/utilities/config/model_config.html @@ -687,7 +687,7 @@

Source code f
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/parsing.html b/_modules/graphnet/utilities/config/parsing.html index e1bff4682..0bdfbcebc 100644 --- a/_modules/graphnet/utilities/config/parsing.html +++ b/_modules/graphnet/utilities/config/parsing.html @@ -457,7 +457,7 @@

Source code for gr
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/config/training_config.html b/_modules/graphnet/utilities/config/training_config.html index add0468a2..dda012503 100644 --- a/_modules/graphnet/utilities/config/training_config.html +++ b/_modules/graphnet/utilities/config/training_config.html @@ -360,7 +360,7 @@

Source cod
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/deprecation_tools.html b/_modules/graphnet/utilities/deprecation_tools.html new file mode 100644 index 000000000..df8e2670d --- /dev/null +++ b/_modules/graphnet/utilities/deprecation_tools.html @@ -0,0 +1,396 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + graphnet.utilities.deprecation_tools — graphnet documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Skip to content +
+ +
+ + +
+ + + + +
+
+ +
+
+
+ +
+
+
+
+
+
+ + +
+
+
+ +
+
+ +

Source code for graphnet.utilities.deprecation_tools

+"""Utility functions for handling deprecation transitions."""
+from typing import Dict, Tuple
+from copy import deepcopy
+from torch import Tensor
+
+
+

+[docs]
+def rename_state_dict_entries(
+    state_dict: Dict[str, Tensor], old_phrase: str, new_phrase: str
+) -> Tuple[Dict[str, Tensor], bool]:
+    """Replace `old_phrase` in state dict fields with `new_phrase`.
+
+    Returned state dict is a deepcopy of the input.
+
+    Args:
+        state_dict: The state dict whos fields need renaming.
+        old_phrase: Phrase in state dict field that needs to be replaced.
+        new_phrase: Phrase to add in place of `old_phrase` in state dict.
+    """
+    assert isinstance(old_phrase, str)
+    assert isinstance(new_phrase, str)
+
+    # Make a carbon-copy
+    new_state_dict = deepcopy(state_dict)
+
+    # Replace old entries in copy
+    state_dict_altered = False
+    for key in state_dict.keys():
+        if old_phrase in key:
+            new_key = key.replace(old_phrase, new_phrase)
+            new_state_dict[new_key] = new_state_dict.pop(key)
+            state_dict_altered = True
+
+    return new_state_dict, state_dict_altered

+
+
+ +
+
+
+
+
+
+
+ +
+
+
+
+
+ © Copyright 2021-2024, GraphNeT team. + +
+ Created using + Sphinx 7.2.6. + and + Material for + Sphinx +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/graphnet/utilities/filesys.html b/_modules/graphnet/utilities/filesys.html index 121c9bfe5..5a4640792 100644 --- a/_modules/graphnet/utilities/filesys.html +++ b/_modules/graphnet/utilities/filesys.html @@ -366,7 +366,9 @@

Source code for graphnet.
[docs] def find_i3_files( - directories: Union[str, List[str]], gcd_rescue: Optional[str] = None + directories: Union[str, List[str]], + gcd_rescue: Optional[str] = None, + recursive: Optional[bool] = True, ) -> Tuple[List[str], List[str]]: """Find I3 files and corresponding GCD files in `directories`. @@ -378,6 +380,7 @@

Source code for graphnet. directories: Directories to search recursively for I3 files. gcd_rescue: Path to the GCD that will be default if no GCD is present in the directory. + recursive: Whether or not to search the directories recursively. Returns: i3_list: Paths to I3 files in `directories` @@ -392,11 +395,14 @@

Source code for graphnet. for directory in directories: - # Recursively find all I3-like files in `directory`. + # Find all I3-like files in `directory`, may or may not be recursively. paths = [] i3_patterns = ["*.bz2", "*.zst", "*.gz"] for i3_pattern in i3_patterns: - paths.extend(list(Path(directory).rglob(i3_pattern))) + if recursive: + paths.extend(list(Path(directory).rglob(i3_pattern))) + else: + paths.extend(list(Path(directory).glob(i3_pattern))) # Loop over all folders containing such I3-like files. folders = sorted(set([path.parent for path in paths])) @@ -443,7 +449,7 @@

Source code for graphnet.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/imports.html b/_modules/graphnet/utilities/imports.html index 80debdadf..67929efcd 100644 --- a/_modules/graphnet/utilities/imports.html +++ b/_modules/graphnet/utilities/imports.html @@ -416,7 +416,7 @@

Source code for graphnet.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/logging.html b/_modules/graphnet/utilities/logging.html index aa1b8a091..f4b14db28 100644 --- a/_modules/graphnet/utilities/logging.html +++ b/_modules/graphnet/utilities/logging.html @@ -626,7 +626,7 @@

Source code for graphnet.
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/graphnet/utilities/maths.html b/_modules/graphnet/utilities/maths.html index 607211db6..cff801128 100644 --- a/_modules/graphnet/utilities/maths.html +++ b/_modules/graphnet/utilities/maths.html @@ -353,7 +353,7 @@

Source code for graphnet.ut
- © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
Created using diff --git a/_modules/index.html b/_modules/index.html index 74441c4d6..b5d45ae22 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -363,6 +363,7 @@

All modules for which code is available

  • graphnet.models.gnn.dynedge_kaggle_tito
  • graphnet.models.gnn.gnn
  • graphnet.models.graphs.edges.edges
    • +
  • graphnet.models.graphs.edges.minkowski
  • graphnet.models.graphs.graph_definition
  • graphnet.models.graphs.graphs
  • graphnet.models.graphs.nodes.nodes
    • @@ -387,6 +388,7 @@

    All modules for which code is available

  • graphnet.utilities.config.model_config
  • graphnet.utilities.config.parsing
  • graphnet.utilities.config.training_config
    • +
  • graphnet.utilities.deprecation_tools
  • graphnet.utilities.filesys
  • graphnet.utilities.imports
  • graphnet.utilities.logging
    • @@ -411,7 +413,7 @@

    All modules for which code is available

    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/_sources/api/graphnet.models.graphs.edges.minkowski.rst.txt b/_sources/api/graphnet.models.graphs.edges.minkowski.rst.txt new file mode 100644 index 000000000..6f59c777c --- /dev/null +++ b/_sources/api/graphnet.models.graphs.edges.minkowski.rst.txt @@ -0,0 +1,8 @@ + +minkowski +========= + +.. automodule:: graphnet.models.graphs.edges.minkowski + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/api/graphnet.models.graphs.edges.rst.txt b/_sources/api/graphnet.models.graphs.edges.rst.txt index 39abd0423..0e88d9d2b 100644 --- a/_sources/api/graphnet.models.graphs.edges.rst.txt +++ b/_sources/api/graphnet.models.graphs.edges.rst.txt @@ -16,6 +16,7 @@ edges :maxdepth: 2 graphnet.models.graphs.edges.edges + graphnet.models.graphs.edges.minkowski diff --git a/_sources/api/graphnet.utilities.deprecation_tools.rst.txt b/_sources/api/graphnet.utilities.deprecation_tools.rst.txt new file mode 100644 index 000000000..bf515165f --- /dev/null +++ b/_sources/api/graphnet.utilities.deprecation_tools.rst.txt @@ -0,0 +1,8 @@ + +deprecation\_tools +================== + +.. automodule:: graphnet.utilities.deprecation_tools + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/api/graphnet.utilities.rst.txt b/_sources/api/graphnet.utilities.rst.txt index 5b90da9de..d65d66838 100644 --- a/_sources/api/graphnet.utilities.rst.txt +++ b/_sources/api/graphnet.utilities.rst.txt @@ -24,6 +24,7 @@ utilities graphnet.utilities.argparse graphnet.utilities.decorators + graphnet.utilities.deprecation_tools graphnet.utilities.filesys graphnet.utilities.imports graphnet.utilities.logging diff --git a/about.html b/about.html index 6422d9cf3..709cfe0de 100644 --- a/about.html +++ b/about.html @@ -388,7 +388,7 @@

    Acknowledgements
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.constants.html b/api/graphnet.constants.html index 0a215dbf4..8b00f9333 100644 --- a/api/graphnet.constants.html +++ b/api/graphnet.constants.html @@ -418,7 +418,7 @@
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.data.constants.html b/api/graphnet.data.constants.html index 98b2c312c..11f7344a0 100644 --- a/api/graphnet.data.constants.html +++ b/api/graphnet.data.constants.html @@ -639,15 +639,15 @@

    Namespace for standard names working with I3TruthExtractor.

    -ICECUBE86 = ['energy', 'energy_track', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    +ICECUBE86 = ['energy', 'energy_track', 'energy_cascade', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    -DEEPCORE = ['energy', 'energy_track', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    +DEEPCORE = ['energy', 'energy_track', 'energy_cascade', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    -UPGRADE = ['energy', 'energy_track', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    +UPGRADE = ['energy', 'energy_track', 'energy_cascade', 'position_x', 'position_y', 'position_z', 'azimuth', 'zenith', 'pid', 'elasticity', 'sim_type', 'interaction_type', 'interaction_time', 'inelasticity', 'stopped_muon']
    @@ -703,7 +703,7 @@
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.data.dataconverter.html b/api/graphnet.data.dataconverter.html index bf883f9f6..148415a35 100644 --- a/api/graphnet.data.dataconverter.html +++ b/api/graphnet.data.dataconverter.html @@ -622,7 +622,7 @@

    Make global_index available to pool workers.

    Return type:
    -

    None

    +

    None

    Parameters:
      @@ -638,7 +638,7 @@

      Decorate process_method to cache output file names.

      Return type:
      -

      TypeVar(F, bound= Callable[..., Any])

      +

      TypeVar(F, bound= Callable[..., Any])

      Parameters:

      process_method (F) –

      @@ -717,10 +717,10 @@ intermediate file format.

      Parameters:
      -

      filesets (List[FileSet]) – List of paths to I3 and corresponding GCD files.

      +

      filesets (List[FileSet]) – List of paths to I3 and corresponding GCD files.

      Return type:
      -

      None

      +

      None

      @@ -731,12 +731,12 @@
      Parameters:
        -

      • data (List[OrderedDict]) – List of extracted features.

        • -

      • output_file (str) – Name of output file.

        • +

      • data (List[OrderedDict]) – List of extracted features.

        • +

      • output_file (str) – Name of output file.

      Return type:
      -

      None

      +

      None

    @@ -747,8 +747,8 @@
    Parameters:
      -

    • output_file (str) – Name of the output file containing the merged results.

      • -

    • input_files (Optional[List[str]], default: None) – Intermediate files to be merged, according to the +

    • output_file (str) – Name of the output file containing the merged results.

      • +

    • input_files (Optional[List[str]], default: None) – Intermediate files to be merged, according to the specific implementation. Default to None, meaning that all files output by the current instance are merged.

    @@ -758,7 +758,7 @@ backend in question.

    Return type:
    -

    None

    +

    None

    @@ -768,7 +768,7 @@

    Identify map function to use (pure python or multiprocess).

    Return type:
    -

    Tuple[Any, Optional[Pool]]

    +

    Tuple[Any, Optional[Pool]]

    Parameters:
      @@ -824,7 +824,7 @@
      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
      Created using diff --git a/api/graphnet.data.dataloader.html b/api/graphnet.data.dataloader.html index f29c90a88..7da1e8cf0 100644 --- a/api/graphnet.data.dataloader.html +++ b/api/graphnet.data.dataloader.html @@ -521,7 +521,7 @@

      Should not occur in “production.

      Return type:
      -

      Batch

      +

      Batch

      Parameters:

      graphs (List[Data]) –

      @@ -534,7 +534,7 @@

      Check whether to shuffle selection with name selection_name.

      Return type:
      -

      bool

      +

      bool

      Parameters:

      selection_name (str) –

      @@ -567,7 +567,7 @@

      Construct DataLoader`s based on selections in `DatasetConfig.

      Return type:
      -

      Union[DataLoader, Dict[str, DataLoader]]

      +

      Union[DataLoader, Dict[str, DataLoader]]

      Parameters:
        @@ -623,7 +623,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.data.dataset.dataset.html b/api/graphnet.data.dataset.dataset.html index 2b83ce83b..fae36ebe0 100644 --- a/api/graphnet.data.dataset.dataset.html +++ b/api/graphnet.data.dataset.dataset.html @@ -625,10 +625,10 @@

        Load graphnet module from string name.

        Parameters:
        -

        class_name (str) – name of class

        +

        class_name (str) – name of class

        Return type:
        -

        Type

        +

        Type

        Returns:

        graphnet module.

        @@ -641,7 +641,7 @@

        Construct GraphDefinition from DatasetConfig.

        Return type:
        -

        GraphDefinition

        +

        GraphDefinition

        Parameters:

        cfg (dict) –

        @@ -657,48 +657,48 @@
        Parameters:
          -

        • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

          • -

        • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to +

        • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

          • +

        • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to construct the nodes on the individual graph objects, and their features. Multiple pulse series maps can be used, e.g., when different DOM types are stored in different maps.

          • -

        • features (List[str]) – List of columns in the input files that should be used as +

        • features (List[str]) – List of columns in the input files that should be used as node features on the graph objects.

          • -

        • truth (List[str]) – List of event-level columns in the input files that should +

        • truth (List[str]) – List of event-level columns in the input files that should be used added as attributes on the graph objects.

          • -

        • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that +

        • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that should be used added as attributes on the graph objects.

          • -

        • index_column (str, default: 'event_no') – Name of the column in the input files that contains +

        • index_column (str, default: 'event_no') – Name of the column in the input files that contains unique indicies to identify and map events across tables.

          • -

        • truth_table (str, default: 'truth') – Name of the table containing event-level truth +

        • truth_table (str, default: 'truth') – Name of the table containing event-level truth information.

          • -

        • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth +

        • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth information.

          • -

        • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read +

        • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read and used to construct graph objects. Defaults to None, meaning all strings for which data exists are used.

          • -

        • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either +

        • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either as list of indicies (in index_column); or a string-based selection used to query the Dataset for events passing the selection. Defaults to None, meaning that all events in the input files are read.

          • -

        • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

          • -

        • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss +

        • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

          • +

        • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss weights.

          • -

        • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table +

        • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table containing per-event loss weights. This is also the name of the corresponding attribute assigned to the graph object.

          • -

        • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. +

        • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. NOTE: This default value is only applied when loss_weight_table and loss_weight_column are specified, and in this case to events with no value in the corresponding table/column. That is, if no per-event loss weight table/column is provided, this value is ignored. Defaults to None.

          • -

        • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random +

        • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random subset of events when resolving a string-based selection (e.g., “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

          • -

        • graph_definition (GraphDefinition) – Method that defines the graph representation.

          • +

        • graph_definition (GraphDefinition) – Method that defines the graph representation.

        • args (Any) –

        • kwargs (Any) –

        @@ -713,7 +713,7 @@

        Construct Dataset instance from source configuration.

        Return type:
        -

        Union[Dataset, EnsembleDataset, Dict[str, Dataset], Dict[str, EnsembleDataset]]

        +

        Union[Dataset, EnsembleDataset, Dict[str, Dataset], Dict[str, EnsembleDataset]]

        Parameters:

        source (DatasetConfig | str) –

        @@ -726,7 +726,7 @@

        Concatenate multiple `Dataset`s into one instance.

        Return type:
        -

        EnsembleDataset

        +

        EnsembleDataset

        Parameters:

        datasets (List[Dataset]) –

        @@ -750,18 +750,18 @@
        Parameters:
          -

        • table (str) – Table to be queried.

          • -

        • columns (Union[List[str], str]) – Columns to read out.

          • -

        • sequential_index (Optional[int], default: None) – Sequentially numbered index +

        • table (str) – Table to be queried.

          • +

        • columns (Union[List[str], str]) – Columns to read out.

          • +

        • sequential_index (Optional[int], default: None) – Sequentially numbered index (i.e. in [0,len(self))) of the event to query. This _may_ differ from the indexation used in self._indices. If no value is provided, the entire column is returned.

          • -

        • selection (Optional[str], default: None) – Selection to be imposed before reading out data. +

        • selection (Optional[str], default: None) – Selection to be imposed before reading out data. Defaults to None.

        Return type:
        -

        List[Tuple[Any, ...]]

        +

        List[Tuple[Any, ...]]

        Returns:

        @@ -783,7 +783,7 @@

        Add custom graph label define using function fn.

        Return type:
        -

        None

        +

        None

        Parameters:
          @@ -802,7 +802,7 @@

          Construct a single dataset from a collection of datasets.

          Parameters:
          -

          datasets (Iterable[Dataset]) – A collection of Datasets

          +

          datasets (Iterable[Dataset]) – A collection of Datasets

        @@ -851,7 +851,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.data.dataset.html b/api/graphnet.data.dataset.html index e842ecaf1..4dc0f578f 100644 --- a/api/graphnet.data.dataset.html +++ b/api/graphnet.data.dataset.html @@ -548,7 +548,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.data.dataset.parquet.html b/api/graphnet.data.dataset.parquet.html index 0eef0edb3..d66b67d51 100644 --- a/api/graphnet.data.dataset.parquet.html +++ b/api/graphnet.data.dataset.parquet.html @@ -539,7 +539,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.data.dataset.parquet.parquet_dataset.html b/api/graphnet.data.dataset.parquet.parquet_dataset.html index 09ccbf46a..349f42c6b 100644 --- a/api/graphnet.data.dataset.parquet.parquet_dataset.html +++ b/api/graphnet.data.dataset.parquet.parquet_dataset.html @@ -531,48 +531,48 @@
        Parameters:
          -

        • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

          • -

        • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to +

        • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

          • +

        • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to construct the nodes on the individual graph objects, and their features. Multiple pulse series maps can be used, e.g., when different DOM types are stored in different maps.

          • -

        • features (List[str]) – List of columns in the input files that should be used as +

        • features (List[str]) – List of columns in the input files that should be used as node features on the graph objects.

          • -

        • truth (List[str]) – List of event-level columns in the input files that should +

        • truth (List[str]) – List of event-level columns in the input files that should be used added as attributes on the graph objects.

          • -

        • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that +

        • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that should be used added as attributes on the graph objects.

          • -

        • index_column (str, default: 'event_no') – Name of the column in the input files that contains +

        • index_column (str, default: 'event_no') – Name of the column in the input files that contains unique indicies to identify and map events across tables.

          • -

        • truth_table (str, default: 'truth') – Name of the table containing event-level truth +

        • truth_table (str, default: 'truth') – Name of the table containing event-level truth information.

          • -

        • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth +

        • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth information.

          • -

        • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read +

        • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read and used to construct graph objects. Defaults to None, meaning all strings for which data exists are used.

          • -

        • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either +

        • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either as list of indicies (in index_column); or a string-based selection used to query the Dataset for events passing the selection. Defaults to None, meaning that all events in the input files are read.

          • -

        • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

          • -

        • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss +

        • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

          • +

        • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss weights.

          • -

        • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table +

        • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table containing per-event loss weights. This is also the name of the corresponding attribute assigned to the graph object.

          • -

        • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. +

        • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. NOTE: This default value is only applied when loss_weight_table and loss_weight_column are specified, and in this case to events with no value in the corresponding table/column. That is, if no per-event loss weight table/column is provided, this value is ignored. Defaults to None.

          • -

        • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random +

        • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random subset of events when resolving a string-based selection (e.g., “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

          • -

        • graph_definition (GraphDefinition) – Method that defines the graph representation.

          • +

        • graph_definition (GraphDefinition) – Method that defines the graph representation.

        • args (Any) –

        • kwargs (Any) –

        @@ -587,7 +587,7 @@

        Query table at a specific index, optionally with some selection.

        Return type:
        -

        List[Tuple[Any, ...]]

        +

        List[Tuple[Any, ...]]

        Parameters:
          @@ -645,7 +645,7 @@
          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
          Created using diff --git a/api/graphnet.data.dataset.sqlite.html b/api/graphnet.data.dataset.sqlite.html index ba090aafe..8c8466a90 100644 --- a/api/graphnet.data.dataset.sqlite.html +++ b/api/graphnet.data.dataset.sqlite.html @@ -539,7 +539,7 @@
          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
          Created using diff --git a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html index e9ad8aeec..e7c699424 100644 --- a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html +++ b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html @@ -531,48 +531,48 @@
          Parameters:
            -

          • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

            • -

          • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to +

          • path (Union[str, List[str]]) – Path to the file(s) from which this Dataset should read.

            • +

          • pulsemaps (Union[str, List[str]]) – Name(s) of the pulse map series that should be used to construct the nodes on the individual graph objects, and their features. Multiple pulse series maps can be used, e.g., when different DOM types are stored in different maps.

            • -

          • features (List[str]) – List of columns in the input files that should be used as +

          • features (List[str]) – List of columns in the input files that should be used as node features on the graph objects.

            • -

          • truth (List[str]) – List of event-level columns in the input files that should +

          • truth (List[str]) – List of event-level columns in the input files that should be used added as attributes on the graph objects.

            • -

          • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that +

          • node_truth (Optional[List[str]], default: None) – List of node-level columns in the input files that should be used added as attributes on the graph objects.

            • -

          • index_column (str, default: 'event_no') – Name of the column in the input files that contains +

          • index_column (str, default: 'event_no') – Name of the column in the input files that contains unique indicies to identify and map events across tables.

            • -

          • truth_table (str, default: 'truth') – Name of the table containing event-level truth +

          • truth_table (str, default: 'truth') – Name of the table containing event-level truth information.

            • -

          • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth +

          • node_truth_table (Optional[str], default: None) – Name of the table containing node-level truth information.

            • -

          • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read +

          • string_selection (Optional[List[int]], default: None) – Subset of strings for which data should be read and used to construct graph objects. Defaults to None, meaning all strings for which data exists are used.

            • -

          • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either +

          • selection (Union[str, List[int], List[List[int]], None], default: None) – The events that should be read. This can be given either as list of indicies (in index_column); or a string-based selection used to query the Dataset for events passing the selection. Defaults to None, meaning that all events in the input files are read.

            • -

          • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

            • -

          • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss +

          • dtype (dtype, default: torch.float32) – Type of the feature tensor on the graph objects returned.

            • +

          • loss_weight_table (Optional[str], default: None) – Name of the table containing per-event loss weights.

            • -

          • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table +

          • loss_weight_column (Optional[str], default: None) – Name of the column in loss_weight_table containing per-event loss weights. This is also the name of the corresponding attribute assigned to the graph object.

            • -

          • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. +

          • loss_weight_default_value (Optional[float], default: None) – Default per-event loss weight. NOTE: This default value is only applied when loss_weight_table and loss_weight_column are specified, and in this case to events with no value in the corresponding table/column. That is, if no per-event loss weight table/column is provided, this value is ignored. Defaults to None.

            • -

          • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random +

          • seed (Optional[int], default: None) – Random number generator seed, used for selecting a random subset of events when resolving a string-based selection (e.g., “10000 random events ~ event_no % 5 > 0” or “20% random events ~ event_no % 5 > 0”).

            • -

          • graph_definition (GraphDefinition) – Method that defines the graph representation.

            • +

          • graph_definition (GraphDefinition) – Method that defines the graph representation.

          • args (Any) –

          • kwargs (Any) –

          @@ -587,7 +587,7 @@

          Query table at a specific index, optionally with some selection.

          Return type:
          -

          List[Tuple[Any, ...]]

          +

          List[Tuple[Any, ...]]

          Parameters:
            @@ -645,7 +645,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.data.extractors.html b/api/graphnet.data.extractors.html index 60983d4bb..602f50a76 100644 --- a/api/graphnet.data.extractors.html +++ b/api/graphnet.data.extractors.html @@ -661,7 +661,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.data.extractors.i3extractor.html b/api/graphnet.data.extractors.i3extractor.html index 4c997c333..088aa5ada 100644 --- a/api/graphnet.data.extractors.i3extractor.html +++ b/api/graphnet.data.extractors.i3extractor.html @@ -632,7 +632,7 @@

            Construct I3Extractor.

            Parameters:
            -

            name (str) – Name of the I3Extractor instance. Used to keep track of the +

            name (str) – Name of the I3Extractor instance. Used to keep track of the provenance of different data, and to name tables to which this data is saved.

            @@ -643,7 +643,7 @@

            Store references to the I3- and GCD-files being processed.

            Return type:
            -

            None

            +

            None

            Parameters:
              @@ -679,7 +679,7 @@

              Store references to the I3- and GCD-files being processed.

              Return type:
              -

              None

              +

              None

              Parameters:
                @@ -735,7 +735,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3featureextractor.html b/api/graphnet.data.extractors.i3featureextractor.html index af4e8a44a..ac3be3fbb 100644 --- a/api/graphnet.data.extractors.i3featureextractor.html +++ b/api/graphnet.data.extractors.i3featureextractor.html @@ -621,7 +621,7 @@

                Construct I3FeatureExtractor.

                Parameters:
                -

                pulsemap (str) – Name of the pulse (series) map for which to extract +

                pulsemap (str) – Name of the pulse (series) map for which to extract reconstructed features.

                @@ -634,7 +634,7 @@

                Construct I3FeatureExtractor.

                Parameters:
                -

                pulsemap (str) – Name of the pulse (series) map for which to extract +

                pulsemap (str) – Name of the pulse (series) map for which to extract reconstructed features.

                @@ -647,7 +647,7 @@

                Construct I3FeatureExtractor.

                Parameters:
                -

                pulsemap (str) – Name of the pulse (series) map for which to extract +

                pulsemap (str) – Name of the pulse (series) map for which to extract reconstructed features.

                @@ -660,7 +660,7 @@

                Construct I3FeatureExtractor.

                Parameters:
                -

                pulsemap (str) – Name of the pulse (series) map for which to extract +

                pulsemap (str) – Name of the pulse (series) map for which to extract reconstructed features.

                @@ -673,7 +673,7 @@

                Construct I3FeatureExtractor.

                Parameters:
                -

                pulsemap (str) – Name of the pulse (series) map for which to extract +

                pulsemap (str) – Name of the pulse (series) map for which to extract reconstructed features.

                @@ -723,7 +723,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3genericextractor.html b/api/graphnet.data.extractors.i3genericextractor.html index 95f90241e..ace919977 100644 --- a/api/graphnet.data.extractors.i3genericextractor.html +++ b/api/graphnet.data.extractors.i3genericextractor.html @@ -588,8 +588,8 @@
                Parameters:
                  -

                • keys (Union[str, List[str], None], default: None) – List of keys in I3Frame to be parsed. Defaults to all keys.

                  • -

                • exclude_keys (Union[str, List[str], None], default: None) – List of keys in I3Frame to exclude while parsing.

                  • +

                • keys (Union[str, List[str], None], default: None) – List of keys in I3Frame to be parsed. Defaults to all keys.

                  • +

                • exclude_keys (Union[str, List[str], None], default: None) – List of keys in I3Frame to exclude while parsing.

                Raises:
                @@ -642,7 +642,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3hybridrecoextractor.html b/api/graphnet.data.extractors.i3hybridrecoextractor.html index 0c7c194a4..2219ddab5 100644 --- a/api/graphnet.data.extractors.i3hybridrecoextractor.html +++ b/api/graphnet.data.extractors.i3hybridrecoextractor.html @@ -626,7 +626,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html b/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html index 0963d0958..c8a12e6b6 100644 --- a/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html +++ b/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html @@ -629,7 +629,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3particleextractor.html b/api/graphnet.data.extractors.i3particleextractor.html index 63a412059..d0d8cf1bc 100644 --- a/api/graphnet.data.extractors.i3particleextractor.html +++ b/api/graphnet.data.extractors.i3particleextractor.html @@ -628,7 +628,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3pisaextractor.html b/api/graphnet.data.extractors.i3pisaextractor.html index 7e39bcfb5..6bbf3f02b 100644 --- a/api/graphnet.data.extractors.i3pisaextractor.html +++ b/api/graphnet.data.extractors.i3pisaextractor.html @@ -626,7 +626,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3quesoextractor.html b/api/graphnet.data.extractors.i3quesoextractor.html index 58803e2c5..42497984b 100644 --- a/api/graphnet.data.extractors.i3quesoextractor.html +++ b/api/graphnet.data.extractors.i3quesoextractor.html @@ -629,7 +629,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3retroextractor.html b/api/graphnet.data.extractors.i3retroextractor.html index e61476306..f7aee6ee2 100644 --- a/api/graphnet.data.extractors.i3retroextractor.html +++ b/api/graphnet.data.extractors.i3retroextractor.html @@ -626,7 +626,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3splinempeextractor.html b/api/graphnet.data.extractors.i3splinempeextractor.html index 86ae4d368..f9755a130 100644 --- a/api/graphnet.data.extractors.i3splinempeextractor.html +++ b/api/graphnet.data.extractors.i3splinempeextractor.html @@ -626,7 +626,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3truthextractor.html b/api/graphnet.data.extractors.i3truthextractor.html index c75ce93d8..ded247fae 100644 --- a/api/graphnet.data.extractors.i3truthextractor.html +++ b/api/graphnet.data.extractors.i3truthextractor.html @@ -578,12 +578,12 @@
                Parameters:
                  -

                • name (str, default: 'truth') – Name of the I3Extractor instance.

                  • -

                • borders (Optional[List[ndarray]], default: None) – Array of boundaries of the detector volume as ((x,y),z)- +

                • name (str, default: 'truth') – Name of the I3Extractor instance.

                  • +

                • borders (Optional[List[ndarray]], default: None) – Array of boundaries of the detector volume as ((x,y),z)- coordinates, for identifying, e.g., particles starting and stopping within the detector. Defaults to hard-coded boundary coordinates.

                  • -

                • mctree (Optional[str], default: 'I3MCTree') – Str of which MCTree to use for truth values.

                  • +

                • mctree (Optional[str], default: 'I3MCTree') – Str of which MCTree to use for truth values.

                @@ -633,7 +633,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.i3tumextractor.html b/api/graphnet.data.extractors.i3tumextractor.html index 89cf8e966..2bda092dc 100644 --- a/api/graphnet.data.extractors.i3tumextractor.html +++ b/api/graphnet.data.extractors.i3tumextractor.html @@ -626,7 +626,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.data.extractors.utilities.collections.html b/api/graphnet.data.extractors.utilities.collections.html index 5d712156b..2ba4e4c1a 100644 --- a/api/graphnet.data.extractors.utilities.collections.html +++ b/api/graphnet.data.extractors.utilities.collections.html @@ -624,13 +624,13 @@
                Parameters:
                  -

                • obj (Union[Dict, Any]) – The object that should be flattened, if applicable.

                  • -

                • parent_key (str, default: '') – The combined name of the parent key(s) containing obj.

                  • -

                • separator (str, default: '__') – The string used to concatenate nester parent keys.

                  • +

                • obj (Union[Dict, Any]) – The object that should be flattened, if applicable.

                  • +

                • parent_key (str, default: '') – The combined name of the parent key(s) containing obj.

                  • +

                • separator (str, default: '__') – The string used to concatenate nester parent keys.

                Return type:
                -

                Union[Dict, Any]

                +

                Union[Dict, Any]

              @@ -646,7 +646,7 @@ file.

              Return type:
              -

              Union[Dict, Any]

              +

              Union[Dict, Any]

              Parameters:

              obj (Dict | Any) –

              @@ -659,7 +659,7 @@

              Transpose a list of dicts to a dict of lists.

              Return type:
              -

              Dict[str, List[Any]]

              +

              Dict[str, List[Any]]

              Parameters:

              array (List[Dict[str, Any]]) –

              @@ -711,7 +711,7 @@
              - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
              Created using diff --git a/api/graphnet.data.extractors.utilities.frames.html b/api/graphnet.data.extractors.utilities.frames.html index 565c37851..596e21fca 100644 --- a/api/graphnet.data.extractors.utilities.frames.html +++ b/api/graphnet.data.extractors.utilities.frames.html @@ -619,7 +619,7 @@

              Check whether frame is from Monte Carlo simulation.

              Return type:
              -

              bool

              +

              bool

              Parameters:
                @@ -635,7 +635,7 @@

                Check whether frame is from noise.

                Return type:
                -

                bool

                +

                bool

                Parameters:
                  @@ -652,14 +652,14 @@
                  Parameters:
                    -

                  • frame (I3Frame) – Physics (P) I3-frame from which to extract OM keys and pulse +

                  • frame (I3Frame) – Physics (P) I3-frame from which to extract OM keys and pulse series

                  • pulseseries (str) –

                  • calibration (icecube.dataclasses.I3Calibration | None) –

                  Return type:
                  -

                  Tuple[Any, Any]

                  +

                  Tuple[Any, Any]

                  Returns:

                  Tuple containing the OM keys/indicesfor the GCD dictionary, and the @@ -712,7 +712,7 @@

                  - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                  Created using diff --git a/api/graphnet.data.extractors.utilities.html b/api/graphnet.data.extractors.utilities.html index 2b944221a..4240ace5f 100644 --- a/api/graphnet.data.extractors.utilities.html +++ b/api/graphnet.data.extractors.utilities.html @@ -643,7 +643,7 @@
                  - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                  Created using diff --git a/api/graphnet.data.extractors.utilities.types.html b/api/graphnet.data.extractors.utilities.types.html index f101a55fb..cbf5c13bb 100644 --- a/api/graphnet.data.extractors.utilities.types.html +++ b/api/graphnet.data.extractors.utilities.types.html @@ -685,7 +685,7 @@

                  Check whether obj inherits from Boost.Python.enum.

                  Return type:
                  -

                  bool

                  +

                  bool

                  Parameters:

                  obj (Any) –

                  @@ -698,7 +698,7 @@

                  Check whether obj is instance of Boost.Python.enum.

                  Return type:
                  -

                  bool

                  +

                  bool

                  Parameters:

                  obj (Any) –

                  @@ -711,7 +711,7 @@

                  Check whether obj is an IceCube-specific class.

                  Return type:
                  -

                  bool

                  +

                  bool

                  Parameters:

                  obj (Any) –

                  @@ -724,7 +724,7 @@

                  Check whether obj is a type, and not an instance.

                  Return type:
                  -

                  bool

                  +

                  bool

                  Parameters:

                  obj (Any) –

                  @@ -737,7 +737,7 @@

                  Check whether obj is a method.

                  Return type:
                  -

                  bool

                  +

                  bool

                  Parameters:

                  obj (Any) –

                  @@ -750,7 +750,7 @@

                  Ensure that method isn’t called recursively on the same object.

                  Return type:
                  -

                  Callable

                  +

                  Callable

                  Parameters:

                  fn (Callable) –

                  @@ -764,7 +764,7 @@

                  Ignoring mangled (__*) variables, types, methods, and Boost enums.

                  Return type:
                  -

                  Union[List[str], Tuple[List[str], Dict[str, List[str]]]]

                  +

                  Union[List[str], Tuple[List[str], Dict[str, List[str]]]]

                  Parameters:
                    @@ -792,7 +792,7 @@ object itself if it deemed “pythonic” in this way.

                    Return type:
                    -

                    Any

                    +

                    Any

                    Parameters:

                    obj (Any) –

                    @@ -870,7 +870,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.filters.html b/api/graphnet.data.filters.html index 47f8b2372..ab303e539 100644 --- a/api/graphnet.data.filters.html +++ b/api/graphnet.data.filters.html @@ -606,7 +606,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.html b/api/graphnet.data.html index b39013ac1..5e2ae42f4 100644 --- a/api/graphnet.data.html +++ b/api/graphnet.data.html @@ -577,7 +577,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.parquet.html b/api/graphnet.data.parquet.html index 65a99817e..2d1cba88a 100644 --- a/api/graphnet.data.parquet.html +++ b/api/graphnet.data.parquet.html @@ -517,7 +517,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.parquet.parquet_dataconverter.html b/api/graphnet.data.parquet.parquet_dataconverter.html index 5714050d2..e2c6fb03c 100644 --- a/api/graphnet.data.parquet.parquet_dataconverter.html +++ b/api/graphnet.data.parquet.parquet_dataconverter.html @@ -591,7 +591,7 @@

                    Save data to parquet file.

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:
                      @@ -608,8 +608,8 @@
                      Parameters:
                        -

                      • output_file (str) – Name of the output file containing the merged results.

                        • -

                      • input_files (Optional[List[str]], default: None) – Intermediate files to be merged, according to the +

                      • output_file (str) – Name of the output file containing the merged results.

                        • +

                      • input_files (Optional[List[str]], default: None) – Intermediate files to be merged, according to the specific implementation. Default to None, meaning that all files output by the current instance are merged.

                      @@ -619,7 +619,7 @@ Parquet backend.

                      Return type:
                      -

                      None

                      +

                      None

                    @@ -669,7 +669,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.pipeline.html b/api/graphnet.data.pipeline.html index 90980d6e2..4564c978d 100644 --- a/api/graphnet.data.pipeline.html +++ b/api/graphnet.data.pipeline.html @@ -488,17 +488,17 @@
                    Parameters:
                      -

                    • module_dict (Dict) – A dictionary with GNN modules from GraphNet. E.g. +

                    • module_dict (Dict) – A dictionary with GNN modules from GraphNet. E.g. {‘energy’: gnn_module_for_energy_regression}

                      • -

                    • features (List[str]) – List of input features for the GNN modules.

                      • -

                    • truth (List[str]) – List of truth for the GNN ModuleList.

                      • -

                    • device (device) – The device used for computation.

                      • -

                    • retro_table_name (str, default: 'retro') – Name of the retro table for.

                      • -

                    • outdir (Optional[str], default: None) – the directory in which the pipeline database will be +

                    • features (List[str]) – List of input features for the GNN modules.

                      • +

                    • truth (List[str]) – List of truth for the GNN ModuleList.

                      • +

                    • device (device) – The device used for computation.

                      • +

                    • retro_table_name (str, default: 'retro') – Name of the retro table for.

                      • +

                    • outdir (Optional[str], default: None) – the directory in which the pipeline database will be stored.

                      • -

                    • batch_size (int, default: 100) – Batch size for inference.

                      • -

                    • n_workers (int, default: 10) – Number of workers used in dataloading.

                      • -

                    • pipeline_name (str, default: 'pipeline') – Name of the pipeline. If such a pipeline already +

                    • batch_size (int, default: 100) – Batch size for inference.

                      • +

                    • n_workers (int, default: 10) – Number of workers used in dataloading.

                      • +

                    • pipeline_name (str, default: 'pipeline') – Name of the pipeline. If such a pipeline already exists, an error will be prompted to avoid overwriting.

                    @@ -549,7 +549,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.sqlite.html b/api/graphnet.data.sqlite.html index 280a62c62..adf845d97 100644 --- a/api/graphnet.data.sqlite.html +++ b/api/graphnet.data.sqlite.html @@ -537,7 +537,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.sqlite.sqlite_dataconverter.html b/api/graphnet.data.sqlite.sqlite_dataconverter.html index e06986d99..ab813ff41 100644 --- a/api/graphnet.data.sqlite.sqlite_dataconverter.html +++ b/api/graphnet.data.sqlite.sqlite_dataconverter.html @@ -642,7 +642,7 @@

                    Save data to SQLite database.

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:
                      @@ -659,17 +659,17 @@
                      Parameters:
                        -

                      • output_file (str) – Name of the output file containing the merged results.

                        • -

                      • input_files (Optional[List[str]], default: None) – Intermediate files/databases to be merged, according +

                      • output_file (str) – Name of the output file containing the merged results.

                        • +

                      • input_files (Optional[List[str]], default: None) – Intermediate files/databases to be merged, according to the specific implementation. Default to None, meaning that all files/databases output by the current instance are merged.

                        • -

                      • max_table_size (Optional[int], default: None) – The maximum number of rows in any given table. +

                      • max_table_size (Optional[int], default: None) – The maximum number of rows in any given table. If any one table exceed this limit, a new database will be created.

                      Return type:
                      -

                      None

                      +

                      None

                    @@ -685,7 +685,7 @@ True.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:

                    data_dict (Dict[str, Dict]) –

                    @@ -699,10 +699,10 @@

                    Convert extraction to pandas.DataFrame.

                    Parameters:
                    -

                    extraction (Dict[str, Any]) – Dictionary with the extracted data.

                    +

                    extraction (Dict[str, Any]) – Dictionary with the extracted data.

                    Return type:
                    -

                    DataFrame

                    +

                    DataFrame

                    Returns:

                    Extraction as pandas.DataFrame.

                    @@ -715,7 +715,7 @@

                    Check whether table_name corresponds to a pulse map.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:

                    table_name (str) –

                    @@ -728,7 +728,7 @@

                    Check whether table_name corresponds to an MC tree.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:

                    table_name (str) –

                    @@ -780,7 +780,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.sqlite.sqlite_utilities.html b/api/graphnet.data.sqlite.sqlite_utilities.html index 358694949..c7ceb8c00 100644 --- a/api/graphnet.data.sqlite.sqlite_utilities.html +++ b/api/graphnet.data.sqlite.sqlite_utilities.html @@ -564,7 +564,7 @@

                    Check whether database exists at database_path.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:

                    database_path (str) –

                    @@ -577,7 +577,7 @@

                    Check whether table_name exists in database at database_path.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:
                      @@ -594,12 +594,12 @@
                      Parameters:
                        -

                      • database_path (str) – Path to databases

                        • -

                      • code (str) – SQLite code

                        • +

                      • database_path (str) – Path to databases

                        • +

                      • code (str) – SQLite code

                      Return type:
                      -

                      None

                      +

                      None

                    @@ -611,13 +611,13 @@
                    Parameters:
                      -

                    • df (DataFrame) – Dataframe with data to be stored in sqlite table

                      • -

                    • table_name (str) – Name of table. Must exist already

                      • -

                    • database_path (str) – Path to SQLite database

                      • +

                    • df (DataFrame) – Dataframe with data to be stored in sqlite table

                      • +

                    • table_name (str) – Name of table. Must exist already

                      • +

                    • database_path (str) – Path to SQLite database

                    Return type:
                    -

                    None

                    +

                    None

                    @@ -628,7 +628,7 @@

                    Important for query times!

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:
                      @@ -646,12 +646,12 @@
                      Parameters:
                        -

                      • columns (List[str]) – Column names to be created in table.

                        • -

                      • table_name (str) – Name of the table.

                        • -

                      • database_path (str) – Path to the database.

                        • -

                      • index_column (str, default: 'event_no') – Name of the index column.

                        • -

                      • default_type (str, default: 'NOT NULL') – The type used for all non-index columns.

                        • -

                      • integer_primary_key (bool, default: True) – Whether or not to create the index_column with +

                      • columns (List[str]) – Column names to be created in table.

                        • +

                      • table_name (str) – Name of the table.

                        • +

                      • database_path (str) – Path to the database.

                        • +

                      • index_column (str, default: 'event_no') – Name of the index column.

                        • +

                      • default_type (str, default: 'NOT NULL') – The type used for all non-index columns.

                        • +

                      • integer_primary_key (bool, default: True) – Whether or not to create the index_column with the INTEGER PRIMARY KEY type. Such a column is required to have unique, integer values for each row. This is appropriate when the table has one row per event, e.g., event-level MC truth. It is not @@ -661,7 +661,7 @@

                      Return type:
                      -

                      None

                      +

                      None

                    @@ -671,7 +671,7 @@

                    Create table if it doesn’t exist and save dataframe to it.

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:
                      @@ -730,7 +730,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.data.utilities.html b/api/graphnet.data.utilities.html index 613317a7c..b6d615fe9 100644 --- a/api/graphnet.data.utilities.html +++ b/api/graphnet.data.utilities.html @@ -539,7 +539,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.data.utilities.parquet_to_sqlite.html b/api/graphnet.data.utilities.parquet_to_sqlite.html index 1c7325eec..e2305f498 100644 --- a/api/graphnet.data.utilities.parquet_to_sqlite.html +++ b/api/graphnet.data.utilities.parquet_to_sqlite.html @@ -539,12 +539,12 @@
                      Parameters:
                        -

                      • outdir (str) – Output directory for SQLite database.

                        • -

                      • database_name (str) – Name of output SQLite database.

                        • +

                      • outdir (str) – Output directory for SQLite database.

                        • +

                      • database_name (str) – Name of output SQLite database.

                      Return type:
                      -

                      None

                      +

                      None

                    @@ -594,7 +594,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.utilities.random.html b/api/graphnet.data.utilities.random.html index 42300b87c..87750af0c 100644 --- a/api/graphnet.data.utilities.random.html +++ b/api/graphnet.data.utilities.random.html @@ -508,7 +508,7 @@
                    Parameters:

                    • files_list – List of I3 file paths.

                      • -

                    • gcd_list (List[str]) – List of corresponding gcd file paths.

                      • +

                    • gcd_list (List[str]) – List of corresponding gcd file paths.

                    • i3_list (List[str]) –

                    @@ -566,7 +566,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.data.utilities.string_selection_resolver.html b/api/graphnet.data.utilities.string_selection_resolver.html index 9b3328447..2abdce906 100644 --- a/api/graphnet.data.utilities.string_selection_resolver.html +++ b/api/graphnet.data.utilities.string_selection_resolver.html @@ -576,7 +576,7 @@ events ~ event_no % 5 > 0" "20% random events ~ event_no % 5 > 0" `

                    Return type:
                    -

                    List[int]

                    +

                    List[int]

                    Parameters:

                    selection (str) –

                    @@ -629,7 +629,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.deployment.html b/api/graphnet.deployment.html index a21590d71..7ac7c8dc4 100644 --- a/api/graphnet.deployment.html +++ b/api/graphnet.deployment.html @@ -449,7 +449,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.deployment.i3modules.deployer.html b/api/graphnet.deployment.i3modules.deployer.html index c29615e41..9ebe17073 100644 --- a/api/graphnet.deployment.i3modules.deployer.html +++ b/api/graphnet.deployment.i3modules.deployer.html @@ -452,7 +452,7 @@

                    deployer
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.deployment.i3modules.graphnet_module.html b/api/graphnet.deployment.i3modules.graphnet_module.html index ac93cd1ea..27b6641c3 100644 --- a/api/graphnet.deployment.i3modules.graphnet_module.html +++ b/api/graphnet.deployment.i3modules.graphnet_module.html @@ -462,13 +462,13 @@
                    Parameters:
                      -

                    • graph_definition (GraphDefinition) – An instance of GraphDefinition. E.g. KNNGraph.

                      • -

                    • pulsemap (str) – the pulse map on which the module functions

                      • -

                    • features (List[str]) – the features that is used from the pulse map. +

                    • graph_definition (GraphDefinition) – An instance of GraphDefinition. E.g. KNNGraph.

                      • +

                    • pulsemap (str) – the pulse map on which the module functions

                      • +

                    • features (List[str]) – the features that is used from the pulse map. E.g. [dom_x, dom_y, dom_z, charge]

                      • -

                    • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The I3FeatureExtractor used to extract the +

                    • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The I3FeatureExtractor used to extract the pulsemap from the I3Frames

                      • -

                    • gcd_file (str) – Path to the associated gcd-file.

                      • +

                    • gcd_file (str) – Path to the associated gcd-file.

                    @@ -482,16 +482,16 @@
                    Parameters:
                      -

                    • pulsemap (str) – the pulsmap that the model is expecting as input.

                      • -

                    • features (List[str]) – the features of the pulsemap that the model is expecting.

                      • -

                    • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The extractor used to extract the pulsemap.

                      • -

                    • model_config (Union[ModelConfig, str]) – The ModelConfig (or path to it) that summarizes the +

                    • pulsemap (str) – the pulsmap that the model is expecting as input.

                      • +

                    • features (List[str]) – the features of the pulsemap that the model is expecting.

                      • +

                    • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The extractor used to extract the pulsemap.

                      • +

                    • model_config (Union[ModelConfig, str]) – The ModelConfig (or path to it) that summarizes the model used for inference.

                      • -

                    • state_dict (str) – Path to state_dict containing the learned weights.

                      • -

                    • model_name (str) – The name used for the model. Will help define the +

                    • state_dict (str) – Path to state_dict containing the learned weights.

                      • +

                    • model_name (str) – The name used for the model. Will help define the named entry in the I3Frame. E.g. “dynedge”.

                      • -

                    • gcd_file (str) – path to associated gcd file.

                      • -

                    • prediction_columns (Union[str, List[str], None], default: None) –

                      column names for the predictions of the model. +

                    • gcd_file (str) – path to associated gcd file.

                      • +

                    • prediction_columns (Union[str, List[str], None], default: None) –

                      column names for the predictions of the model. Will help define the named entry in the I3Frame.

                      E.g. [‘energy_reco’]. Optional.

                      @@ -511,25 +511,25 @@
                      Parameters:
                        -

                      • pulsemap (str) – the pulsmap that the model is expecting as input +

                      • pulsemap (str) – the pulsmap that the model is expecting as input (the one that is being cleaned).

                        • -

                      • features (List[str]) – the features of the pulsemap that the model is expecting.

                        • -

                      • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The extractor used to extract the pulsemap.

                        • -

                      • model_config (str) – The ModelConfig (or path to it) that summarizes the +

                      • features (List[str]) – the features of the pulsemap that the model is expecting.

                        • +

                      • pulsemap_extractor (Union[List[I3FeatureExtractor], I3FeatureExtractor]) – The extractor used to extract the pulsemap.

                        • +

                      • model_config (str) – The ModelConfig (or path to it) that summarizes the model used for inference.

                        • -

                      • state_dict (str) – Path to state_dict containing the learned weights.

                        • -

                      • model_name (str) – The name used for the model. Will help define the named +

                      • state_dict (str) – Path to state_dict containing the learned weights.

                        • +

                      • model_name (str) – The name used for the model. Will help define the named entry in the I3Frame. E.g. “dynedge”.

                        • -

                      • gcd_file (str) – path to associated gcd file.

                        • -

                      • threshold (float, default: 0.7) – the threshold for being considered a positive case. +

                      • gcd_file (str) – path to associated gcd file.

                        • +

                      • threshold (float, default: 0.7) – the threshold for being considered a positive case. E.g., predictions >= threshold will be considered to be signal, all else noise.

                        • -

                      • discard_empty_events (bool, default: False) – When true, this flag will eliminate events +

                      • discard_empty_events (bool, default: False) – When true, this flag will eliminate events whose cleaned pulse series are empty. Can be used to speed up processing especially for noise simulation, since it will not do any writing or further calculations.

                        • -

                      • prediction_columns (Union[str, List[str], None], default: None) – column names for the predictions of the model. +

                      • prediction_columns (Union[str, List[str], None], default: None) – column names for the predictions of the model. Will help define the named entry in the I3Frame. E.g. [‘energy_reco’]. Optional.

                      @@ -581,7 +581,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.deployment.i3modules.html b/api/graphnet.deployment.i3modules.html index 975ce8207..434164c3f 100644 --- a/api/graphnet.deployment.i3modules.html +++ b/api/graphnet.deployment.i3modules.html @@ -463,7 +463,7 @@

                      i3modules
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.html b/api/graphnet.html index fbb608bff..88c1336e9 100644 --- a/api/graphnet.html +++ b/api/graphnet.html @@ -460,6 +460,7 @@
                    • config
                    • argparse
                    • decorators
                      • +
                    • deprecation_tools
                    • filesys
                    • imports
                    • logging
                      • @@ -519,7 +520,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.models.coarsening.html b/api/graphnet.models.coarsening.html index 4257e142b..602eed114 100644 --- a/api/graphnet.models.coarsening.html +++ b/api/graphnet.models.coarsening.html @@ -597,7 +597,7 @@

                      Perform coarsening operation.

                      Return type:
                      -

                      Union[Data, Batch]

                      +

                      Union[Data, Batch]

                      Parameters:

                      data (Data | Batch) –

                      @@ -722,7 +722,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.models.components.html b/api/graphnet.models.components.html index a46e005c6..073301a16 100644 --- a/api/graphnet.models.components.html +++ b/api/graphnet.models.components.html @@ -532,7 +532,7 @@
                      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                      Created using diff --git a/api/graphnet.models.components.layers.html b/api/graphnet.models.components.layers.html index 06e348db9..f7cebd134 100644 --- a/api/graphnet.models.components.layers.html +++ b/api/graphnet.models.components.layers.html @@ -583,14 +583,14 @@
                      Parameters:
                        -

                      • nn (Callable) – The MLP/torch.Module to be used within the EdgeConv.

                        • -

                      • aggr (str, default: 'max') – Aggregation method to be used with EdgeConv.

                        • -

                      • nb_neighbors (int, default: 8) – Number of neighbours to be clustered after the +

                      • nn (Callable) – The MLP/torch.Module to be used within the EdgeConv.

                        • +

                      • aggr (str, default: 'max') – Aggregation method to be used with EdgeConv.

                        • +

                      • nb_neighbors (int, default: 8) – Number of neighbours to be clustered after the EdgeConv operation.

                        • -

                      • features_subset (Union[Sequence[int], slice, None], default: None) – Subset of features in Data.x that should be used +

                      • features_subset (Union[Sequence[int], slice, None], default: None) – Subset of features in Data.x that should be used when dynamically performing the new graph clustering after the EdgeConv operation. Defaults to all features.

                        • -

                      • **kwargs (Any) – Additional features to be passed to EdgeConv.

                        • +

                      • **kwargs (Any) – Additional features to be passed to EdgeConv.

                      @@ -600,7 +600,7 @@

                      Forward pass.

                      Return type:
                      -

                      Tensor

                      +

                      Tensor

                      Parameters:
                        @@ -622,9 +622,9 @@
                        Parameters:
                          -

                        • nn (Callable) – The MLP/torch.Module to be used within the EdgeConvTito.

                          • -

                        • aggr (str, default: 'max') – Aggregation method to be used with EdgeConvTito.

                          • -

                        • **kwargs (Any) – Additional features to be passed to EdgeConvTito.

                          • +

                        • nn (Callable) – The MLP/torch.Module to be used within the EdgeConvTito.

                          • +

                        • aggr (str, default: 'max') – Aggregation method to be used with EdgeConvTito.

                          • +

                        • **kwargs (Any) – Additional features to be passed to EdgeConvTito.

                        @@ -634,7 +634,7 @@

                        Reset all learnable parameters of the module.

                        Return type:
                        -

                        None

                        +

                        None

                      @@ -644,7 +644,7 @@

                      Forward pass.

                      Return type:
                      -

                      Tensor

                      +

                      Tensor

                      Parameters:
                        @@ -660,7 +660,7 @@

                        Edgeconvtito message passing.

                        Return type:
                        -

                        Tensor

                        +

                        Tensor

                        Parameters:
                          @@ -682,13 +682,13 @@
                          Parameters:

                          • nn – The MLP/torch.Module to be used within the DynTrans.

                            • -

                          • layer_sizes (Optional[List[int]], default: None) – List of layer sizes to be used in DynTrans.

                            • -

                          • aggr (str, default: 'max') – Aggregation method to be used with DynTrans.

                            • -

                          • features_subset (Union[Sequence[int], slice, None], default: None) – Subset of features in Data.x that should be used +

                          • layer_sizes (Optional[List[int]], default: None) – List of layer sizes to be used in DynTrans.

                            • +

                          • aggr (str, default: 'max') – Aggregation method to be used with DynTrans.

                            • +

                          • features_subset (Union[Sequence[int], slice, None], default: None) – Subset of features in Data.x that should be used when dynamically performing the new graph clustering after the EdgeConv operation. Defaults to all features.

                            • -

                          • n_head (int, default: 8) – Number of heads to be used in the multiheadattention models.

                            • -

                          • **kwargs (Any) – Additional features to be passed to DynTrans.

                            • +

                          • n_head (int, default: 8) – Number of heads to be used in the multiheadattention models.

                            • +

                          • **kwargs (Any) – Additional features to be passed to DynTrans.

                        @@ -698,7 +698,7 @@

                        Forward pass.

                        Return type:
                        -

                        Tensor

                        +

                        Tensor

                        Parameters:
                          @@ -755,7 +755,7 @@
                          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                          Created using diff --git a/api/graphnet.models.components.pool.html b/api/graphnet.models.components.pool.html index 16bcb18ac..00ca61435 100644 --- a/api/graphnet.models.components.pool.html +++ b/api/graphnet.models.components.pool.html @@ -592,7 +592,7 @@

                          Like max_pool, just negating `data.x.

                          Return type:
                          -

                          Data

                          +

                          Data

                          Parameters:
                            @@ -610,7 +610,7 @@

                            Like max_pool_x, just negating `x.

                            Return type:
                            -

                            Tensor

                            +

                            Tensor

                            Parameters:
                              @@ -628,7 +628,7 @@

                              Sum-pool values and distribute result to the individual nodes.

                              Return type:
                              -

                              Tensor

                              +

                              Tensor

                              Parameters:
                                @@ -650,7 +650,7 @@ method.

                                Return type:
                                -

                                LongTensor

                                +

                                LongTensor

                                Parameters:
                                  @@ -676,7 +676,7 @@

                                  Group pulses on the same DOM, using DOM and string number.

                                  Return type:
                                  -

                                  Data

                                  +

                                  Data

                                  Parameters:

                                  data (Data) –

                                  @@ -689,7 +689,7 @@

                                  Group pulses on the same PMT, using PMT, DOM, and string number.

                                  Return type:
                                  -

                                  Data

                                  +

                                  Data

                                  Parameters:

                                  data (Data) –

                                  @@ -703,13 +703,13 @@
                                  Parameters:
                                    -

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, +

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, \ldots, N - 1 \}^N\), which assigns each node to a specific cluster.

                                    • -

                                  • x (Tensor) – Node feature matrix +

                                  • x (Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}\).

                                    • -

                                  • batch (LongTensor) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, +

                                  • batch (LongTensor) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each node to a specific example.

                                    • -

                                  • size (Optional[int], default: None) – The maximum number of clusters in a single +

                                  • size (Optional[int], default: None) – The maximum number of clusters in a single example. This property is useful to obtain a batch-wise dense representation, e.g. for applying FC layers, but should only be used if the size of the maximum number of clusters per example is @@ -717,7 +717,7 @@

                                  Return type:
                                  -

                                  Tensor

                                  +

                                  Tensor

                                  @@ -728,13 +728,13 @@
                                  Parameters:
                                    -

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, +

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, \ldots, N - 1 \}^N\), which assigns each node to a specific cluster.

                                    • -

                                  • x (Tensor) – Node feature matrix +

                                  • x (Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}\).

                                    • -

                                  • batch (LongTensor) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, +

                                  • batch (LongTensor) – Batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each node to a specific example.

                                    • -

                                  • size (Optional[int], default: None) – The maximum number of clusters in a single +

                                  • size (Optional[int], default: None) – The maximum number of clusters in a single example. This property is useful to obtain a batch-wise dense representation, e.g. for applying FC layers, but should only be used if the size of the maximum number of clusters per example is @@ -742,7 +742,7 @@

                                  Return type:
                                  -

                                  Tensor

                                  +

                                  Tensor

                                  @@ -758,16 +758,16 @@
                                  Parameters:
                                    -

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, +

                                  • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, \ldots, N - 1 \}^N\), which assigns each node to a specific cluster.

                                    • -

                                  • data (Data) – Graph data object.

                                    • -

                                  • transform (Optional[Callable], default: None) – A function/transform that takes in the +

                                  • data (Data) – Graph data object.

                                    • +

                                  • transform (Optional[Callable], default: None) – A function/transform that takes in the coarsened and pooled torch_geometric.data.Data object and returns a transformed version.

                                  Return type:
                                  -

                                  Data

                                  +

                                  Data

                                @@ -783,16 +783,16 @@
                                Parameters:
                                  -

                                • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, +

                                • cluster (LongTensor) – Cluster vector \(\mathbf{c} \in \{ 0, \ldots, N - 1 \}^N\), which assigns each node to a specific cluster.

                                  • -

                                • data (Data) – Graph data object.

                                  • -

                                • transform (Optional[Callable], default: None) – A function/transform that takes in the +

                                • data (Data) – Graph data object.

                                  • +

                                • transform (Optional[Callable], default: None) – A function/transform that takes in the coarsened and pooled torch_geometric.data.Data object and returns a transformed version.

                                Return type:
                                -

                                Data

                                +

                                Data

                              @@ -841,7 +841,7 @@
                              - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                              Created using diff --git a/api/graphnet.models.detector.detector.html b/api/graphnet.models.detector.detector.html index 1ba47c47f..026afc282 100644 --- a/api/graphnet.models.detector.detector.html +++ b/api/graphnet.models.detector.detector.html @@ -352,6 +352,14 @@
                            • feature_map()
                            • forward() +
                              • +
                            • geometry_table +
                              • +
                            • string_index_name +
                              • +
                            • sensor_position_names +
                              • +
                            • sensor_index_name
                          @@ -378,6 +386,34 @@ forward() + +
                        • + + + geometry_table + + +
                          • +
                        • + + + string_index_name + + +
                          • +
                        • + + + sensor_position_names + + +
                          • +
                        • + + + sensor_index_name + +
                      @@ -501,6 +537,14 @@
                    • feature_map()
                    • forward() +
                      • +
                    • geometry_table +
                      • +
                    • string_index_name +
                      • +
                    • sensor_position_names +
                      • +
                    • sensor_index_name

                  @@ -541,26 +585,46 @@

                  List of features used/assumed by inheriting Detector objects.

                  Return type:
                  -

                  Dict[str, Callable]

                  +

                  Dict[str, Callable]

                  -forward(node_features, node_feature_names)[source]
                  +forward(input_features, input_feature_names)[source]

                  Pre-process graph Data features and build graph adjacency.

                  Return type:
                  -

                  Data

                  +

                  Data

                  Parameters:
                    -

                  • node_features (tensor) –

                    • -

                  • node_feature_names (List[str]) –

                    • +

                  • input_features (tensor) –

                    • +

                  • input_feature_names (List[str]) –

                  +
                  +
                  +property geometry_table: DataFrame
                  +

                  Public get method for retrieving a `Detector`s geometry table.

                  +
                  +
                  +
                  +property string_index_name: str
                  +

                  Public get method for retrieving the string index column name.

                  +
                  +
                  +
                  +property sensor_position_names: List[str]
                  +

                  Public get method for retrieving the xyz coordinate column names.

                  +
                  +
                  +
                  +property sensor_index_name: str
                  +

                  Public get method for retrieving the sensor id column name.

                  +
                  @@ -607,7 +671,7 @@
                  - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                  Created using diff --git a/api/graphnet.models.detector.html b/api/graphnet.models.detector.html index d42b5269f..24b7f1cd9 100644 --- a/api/graphnet.models.detector.html +++ b/api/graphnet.models.detector.html @@ -485,6 +485,7 @@
              • prometheus
                • @@ -535,7 +536,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.models.detector.icecube.html b/api/graphnet.models.detector.icecube.html index 11d972fa9..6edfda5dc 100644 --- a/api/graphnet.models.detector.icecube.html +++ b/api/graphnet.models.detector.icecube.html @@ -356,6 +356,14 @@
                +
                +
                +geometry_table_path = '/home/runner/.local/lib/python3.10/data/geometry_tables/icecube/icecube86.parquet'
                +
                +
                +
                +xyz = ['dom_x', 'dom_y', 'dom_z']
                +
                +
                +
                +string_id_column = 'string'
                +
                +
                +
                +sensor_id_column = 'sensor_id'
                +
                feature_map()[source]

                Map standardization functions to each dimension of input data.

                Return type:
                -

                Dict[str, Callable]

                +

                Dict[str, Callable]

                @@ -636,7 +740,7 @@

                Map standardization functions to each dimension of input data.

                Return type:
                -

                Dict[str, Callable]

                +

                Dict[str, Callable]

              @@ -644,7 +748,7 @@
              class graphnet.models.detector.icecube.IceCubeDeepCore(*args, **kwargs)[source]
              -

              Bases: Detector

              +

              Bases: IceCube86

              Detector class for IceCube-DeepCore.

              Construct Detector.

              @@ -664,7 +768,7 @@

              Map standardization functions to each dimension of input data.

              Return type:
              -

              Dict[str, Callable]

              +

              Dict[str, Callable]

              @@ -686,13 +790,29 @@

              object

              +
              +
              +geometry_table_path = '/home/runner/.local/lib/python3.10/data/geometry_tables/icecube/icecube_upgrade.parquet'
              +
              +
              +
              +xyz = ['dom_x', 'dom_y', 'dom_z']
              +
              +
              +
              +string_id_column = 'string'
              +
              +
              +
              +sensor_id_column = 'sensor_id'
              +
              feature_map()[source]

              Map standardization functions to each dimension of input data.

              Return type:
              -

              Dict[str, Callable]

              +

              Dict[str, Callable]

              @@ -742,7 +862,7 @@
              - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
              Created using diff --git a/api/graphnet.models.detector.prometheus.html b/api/graphnet.models.detector.prometheus.html index 8aa25084d..1b350bab8 100644 --- a/api/graphnet.models.detector.prometheus.html +++ b/api/graphnet.models.detector.prometheus.html @@ -361,11 +361,21 @@
            @@ -487,11 +532,21 @@
            • prometheus
              • @@ -508,8 +563,8 @@

              prometheus

              Prometheus-specific Detector class(es).

              -
              -class graphnet.models.detector.prometheus.Prometheus(*args, **kwargs)[source]
              +
              +class graphnet.models.detector.prometheus.ORCA150(*args, **kwargs)[source]

              Bases: Detector

              Detector class for Prometheus prototype.

              Construct Detector.

              @@ -524,17 +579,51 @@

              object

              +
              +
              +geometry_table_path = '/home/runner/.local/lib/python3.10/data/geometry_tables/prometheus/orca_150.parquet'
              +
              +
              +
              +xyz = ['sensor_pos_x', 'sensor_pos_y', 'sensor_pos_z']
              +
              +
              +
              +string_id_column = 'sensor_string_id'
              +
              +
              +
              +sensor_id_column = 'sensor_id'
              +
              -
              -feature_map()[source]
              +
              +feature_map()[source]

              Map standardization functions to each dimension.

              Return type:
              -

              Dict[str, Callable]

              +

              Dict[str, Callable]

            +
            +
            +class graphnet.models.detector.prometheus.Prometheus(*args, **kwargs)[source]
            +

            Bases: ORCA150

            +

            Reference to ORCA150.

            +

            Construct Detector.

            +
            +
            Parameters:
            +
              +

            • args (Any) –

              • +

            • kwargs (Any) –

              • +
            +
            +
            Return type:
            +

            object

            +
            +
            +
            @@ -580,7 +669,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.gnn.convnet.html b/api/graphnet.models.gnn.convnet.html index 9ba1f86af..6b11359f2 100644 --- a/api/graphnet.models.gnn.convnet.html +++ b/api/graphnet.models.gnn.convnet.html @@ -531,12 +531,12 @@
            Parameters:
              -

            • nb_inputs (int) – Number of input features, i.e. dimension of input +

            • nb_inputs (int) – Number of input features, i.e. dimension of input layer.

              • -

            • nb_outputs (int) – Number of prediction labels, i.e. dimension of +

            • nb_outputs (int) – Number of prediction labels, i.e. dimension of output layer.

              • -

            • nb_intermediate (int, default: 128) – Number of nodes in intermediate layer(s).

              • -

            • dropout_ratio (float, default: 0.3) – Fraction of nodes to drop.

              • +

            • nb_intermediate (int, default: 128) – Number of nodes in intermediate layer(s).

              • +

            • dropout_ratio (float, default: 0.3) – Fraction of nodes to drop.

            • args (Any) –

            • kwargs (Any) –

            @@ -551,7 +551,7 @@

            Apply learnable forward pass.

            Return type:
            -

            Tensor

            +

            Tensor

            Parameters:

            data (Data) –

            @@ -604,7 +604,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.gnn.dynedge.html b/api/graphnet.models.gnn.dynedge.html index 836c517ca..ec76a893b 100644 --- a/api/graphnet.models.gnn.dynedge.html +++ b/api/graphnet.models.gnn.dynedge.html @@ -530,14 +530,14 @@
            Parameters:
              -

            • nb_inputs (int) – Number of input features on each node.

              • -

            • nb_neighbours (int, default: 8) – Number of neighbours to used in the k-nearest +

            • nb_inputs (int) – Number of input features on each node.

              • +

            • nb_neighbours (int, default: 8) – Number of neighbours to used in the k-nearest neighbour clustering which is performed after each (dynamical) edge convolution.

              • -

            • features_subset (Union[List[int], slice, None], default: None) – The subset of latent features on each node that +

            • features_subset (Union[List[int], slice, None], default: None) – The subset of latent features on each node that are used as metric dimensions when performing the k-nearest neighbours clustering. Defaults to [0,1,2].

              • -

            • dynedge_layer_sizes (Optional[List[Tuple[int, ...]]], default: None) – The layer sizes, or latent feature dimenions, +

            • dynedge_layer_sizes (Optional[List[Tuple[int, ...]]], default: None) – The layer sizes, or latent feature dimenions, used in the DynEdgeConv layer. Each entry in dynedge_layer_sizes corresponds to a single DynEdgeConv layer; the integers in the corresponding tuple corresponds to @@ -546,16 +546,16 @@ size-two tuples means that all DynEdgeConv layers contain a two-layer MLP. Defaults to [(128, 256), (336, 256), (336, 256), (336, 256)].

              • -

            • post_processing_layer_sizes (Optional[List[int]], default: None) – Hidden layer sizes in the MLP +

            • post_processing_layer_sizes (Optional[List[int]], default: None) – Hidden layer sizes in the MLP following the skip-concatenation of the outputs of each DynEdgeConv layer. Defaults to [336, 256].

              • -

            • readout_layer_sizes (Optional[List[int]], default: None) – Hidden layer sizes in the MLP following the +

            • readout_layer_sizes (Optional[List[int]], default: None) – Hidden layer sizes in the MLP following the post-processing _and_ optional global pooling. As this is the last layer(s) in the model, the last layer in the read-out yields the output of the DynEdge model. Defaults to [128,].

              • -

            • global_pooling_schemes (Union[str, List[str], None], default: None) – The list global pooling schemes to use. +

            • global_pooling_schemes (Union[str, List[str], None], default: None) – The list global pooling schemes to use. Options are: “min”, “max”, “mean”, and “sum”.

              • -

            • add_global_variables_after_pooling (bool, default: False) – Whether to add global variables +

            • add_global_variables_after_pooling (bool, default: False) – Whether to add global variables after global pooling. The alternative is to added (distribute) them to the individual nodes before any convolutional operations.

              • @@ -573,7 +573,7 @@

              Apply learnable forward pass.

              Return type:
              -

              Tensor

              +

              Tensor

              Parameters:

              data (Data) –

              @@ -626,7 +626,7 @@
              - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
              Created using diff --git a/api/graphnet.models.gnn.dynedge_jinst.html b/api/graphnet.models.gnn.dynedge_jinst.html index 17e28429c..562835544 100644 --- a/api/graphnet.models.gnn.dynedge_jinst.html +++ b/api/graphnet.models.gnn.dynedge_jinst.html @@ -531,9 +531,9 @@
              Parameters:
                -

              • nb_inputs (int) – Number of input features.

                • +

              • nb_inputs (int) – Number of input features.

              • nb_outputs – Number of output features.

                • -

              • layer_size_scale (int, default: 4) – Integer that scales the size of hidden layers.

                • +

              • layer_size_scale (int, default: 4) – Integer that scales the size of hidden layers.

              • args (Any) –

              • kwargs (Any) –

              @@ -548,7 +548,7 @@

              Apply learnable forward pass.

              Return type:
              -

              Tensor

              +

              Tensor

              Parameters:

              data (Data) –

              @@ -601,7 +601,7 @@
              - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
              Created using diff --git a/api/graphnet.models.gnn.dynedge_kaggle_tito.html b/api/graphnet.models.gnn.dynedge_kaggle_tito.html index 9b5b08b45..bf31a35ba 100644 --- a/api/graphnet.models.gnn.dynedge_kaggle_tito.html +++ b/api/graphnet.models.gnn.dynedge_kaggle_tito.html @@ -536,17 +536,17 @@
              Parameters:
                -

              • nb_inputs (int) – Number of input features on each node.

                • -

              • features_subset (Optional[List[int]], default: None) – The subset of latent features on each node that +

              • nb_inputs (int) – Number of input features on each node.

                • +

              • features_subset (Optional[List[int]], default: None) – The subset of latent features on each node that are used as metric dimensions when performing the k-nearest neighbours clustering. Defaults to [0,1,2,3].

                • -

              • dyntrans_layer_sizes (Optional[List[Tuple[int, ...]]], default: None) – The layer sizes, or latent feature dimenions, +

              • dyntrans_layer_sizes (Optional[List[Tuple[int, ...]]], default: None) – The layer sizes, or latent feature dimenions, used in the DynTrans layer. Defaults to [(256, 256), (256, 256), (256, 256), (256, 256)].

                • -

              • global_pooling_schemes (List[str], default: ['max']) – The list global pooling schemes to use. +

              • global_pooling_schemes (List[str], default: ['max']) – The list global pooling schemes to use. Options are: “min”, “max”, “mean”, and “sum”.

                • -

              • use_global_features (bool, default: True) – Whether to use global features after pooling.

                • -

              • use_post_processing_layers (bool, default: True) – Whether to use post-processing layers +

              • use_global_features (bool, default: True) – Whether to use global features after pooling.

                • +

              • use_post_processing_layers (bool, default: True) – Whether to use post-processing layers after the DynTrans layers.

              • args (Any) –

              • kwargs (Any) –

                • @@ -562,7 +562,7 @@

                Apply learnable forward pass.

                Return type:
                -

                Tensor

                +

                Tensor

                Parameters:

                data (Data) –

                @@ -615,7 +615,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.models.gnn.gnn.html b/api/graphnet.models.gnn.gnn.html index 537c08b73..4d1518d3a 100644 --- a/api/graphnet.models.gnn.gnn.html +++ b/api/graphnet.models.gnn.gnn.html @@ -576,7 +576,7 @@

                Apply learnable forward pass in model.

                Return type:
                -

                Tensor

                +

                Tensor

                Parameters:

                data (Data) –

                @@ -629,7 +629,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.models.gnn.html b/api/graphnet.models.gnn.html index be8f47a7f..b84087b08 100644 --- a/api/graphnet.models.gnn.html +++ b/api/graphnet.models.gnn.html @@ -554,7 +554,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.models.graphs.edges.edges.html b/api/graphnet.models.graphs.edges.edges.html index 95b4b4261..c638b906f 100644 --- a/api/graphnet.models.graphs.edges.edges.html +++ b/api/graphnet.models.graphs.edges.edges.html @@ -129,7 +129,7 @@ - + @@ -420,6 +420,13 @@
              + +
            • + + + minkowski + +
            @@ -586,7 +593,7 @@

            ´_construct_edges´

            Parameters:
            -

            graph (Data) – a graph without edges

            +

            graph (Data) – a graph without edges

            Returns:

            a graph with edges

            @@ -608,8 +615,8 @@
            Parameters:
            +
          • minkowski +
          @@ -552,7 +564,7 @@
          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
          Created using diff --git a/api/graphnet.models.graphs.edges.minkowski.html b/api/graphnet.models.graphs.edges.minkowski.html new file mode 100644 index 000000000..b94c54396 --- /dev/null +++ b/api/graphnet.models.graphs.edges.minkowski.html @@ -0,0 +1,640 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + minkowski — graphnet documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Skip to content +
          + +
          + + +
          + + + + +
          +
          + +
          +
          +
          + +
          +
          +
          +
          +
          +
          + + +
          +
          +
          + +
          +
          + +
          +

          minkowski

          +

          Module containing EdgeDefinitions based on the Minkowski Metric.

          +
          +
          +graphnet.models.graphs.edges.minkowski.compute_minkowski_distance_mat(x, y, c, space_coords, time_coord)[source]
          +

          Compute all pairwise Minkowski distances.

          +
          +
          Parameters:
          +
            +

          • x (Tensor) – First tensor of shape (n, d).

            • +

          • y (Tensor) – Second tensor of shape (m, d).

            • +

          • c (float) – Speed of light, in scaled units.

            • +

          • space_coords (Optional[List[int]], default: None) – Indices of space coordinates.

            • +

          • time_coord (Optional[int], default: 3) – Index of time coordinate.

            • +
          +
          +
          Return type:
          +

          Tensor

          +
          +
          +

          Returns: Matrix of shape (n, m) of all pairwise Minkowski distances.

          +
          +
          +
          +class graphnet.models.graphs.edges.minkowski.MinkowskiKNNEdges(*args, **kwargs)[source]
          +

          Bases: EdgeDefinition

          +

          Builds edges between most light-like separated.

          +

          Initialize MinkowskiKNNEdges.

          +
          +
          Parameters:
          +
            +

          • nb_nearest_neighbours (int) – Number of neighbours to connect to.

            • +

          • c (float) – Speed of light, in scaled units.

            • +

          • time_like_weight (float, default: 1.0) – Preference to time-like over space-like edges. +Scales time_like distances by this value, before finding +nearest neighbours.

            • +

          • space_coords (Optional[List[int]], default: None) – Coordinates of x, y, z.

            • +

          • time_coord (Optional[int], default: 3) – Coordinate of time.

            • +

          • args (Any) –

            • +

          • kwargs (Any) –

            • +
          +
          +
          Return type:
          +

          object

          +
          +
          +
          +
          + + +
          +
          +
          +
          +
          + + + + + \ No newline at end of file diff --git a/api/graphnet.models.graphs.graph_definition.html b/api/graphnet.models.graphs.graph_definition.html index 48d5e5056..85be967ee 100644 --- a/api/graphnet.models.graphs.graph_definition.html +++ b/api/graphnet.models.graphs.graph_definition.html @@ -537,20 +537,26 @@
          Parameters:
            -

          • detector (Detector) – The corresponding ´Detector´ representing the data.

            • -

          • node_definition (NodeDefinition, default: NodesAsPulses()) – Definition of nodes. Defaults to NodesAsPulses.

            • -

          • edge_definition (Optional[EdgeDefinition], default: None) – Definition of edges. Defaults to None.

            • -

          • input_feature_names (Optional[List[str]], default: None) – Names of each column in expected input data +

          • detector (Detector) – The corresponding ´Detector´ representing the data.

            • +

          • node_definition (NodeDefinition, default: NodesAsPulses()) – Definition of nodes. Defaults to NodesAsPulses.

            • +

          • edge_definition (Optional[EdgeDefinition], default: None) – Definition of edges. Defaults to None.

            • +

          • input_feature_names (Optional[List[str]], default: None) – Names of each column in expected input data that will be built into a graph. If not provided, it is automatically assumed that all features in Detector is used.

            • -

          • dtype (Optional[dtype], default: torch.float32) – data type used for node features. e.g. ´torch.float´

            • -

          • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard +

          • dtype (Optional[dtype], default: torch.float32) – data type used for node features. e.g. ´torch.float´

            • +

          • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard deviation according to which the values for this feature should be randomly perturbed. Defaults to None.

            • -

          • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. +

          • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. Defaults to None.

            • +

          • add_inactive_sensors (bool, default: False) – If True, inactive sensors will be appended +to the graph with padded pulse information. Defaults to False.

            • +

          • sensor_mask (Optional[List[int]], default: None) – A list of sensor id’s to be masked from the graph. Any +sensor listed here will be removed from the graph. Defaults to None.

            • +

          • string_mask (Optional[List[int]], default: None) – A list of string id’s to be masked from the graph. Defaults to None.

            • +

          • sort_by (Optional[str], default: None) – Name of node feature to sort by. Defaults to None.

          • args (Any) –

          • kwargs (Any) –

          @@ -566,21 +572,21 @@
          Parameters:
            -

          • input_features (ndarray) – Input features for graph construction. Shape ´[num_rows, d]´

            • -

          • input_feature_names (List[str]) – name of each column. Shape ´[,d]´.

            • -

          • truth_dicts (Optional[List[Dict[str, Any]]], default: None) – Dictionary containing truth labels.

            • -

          • custom_label_functions (Optional[Dict[str, Callable[..., Any]]], default: None) – Custom label functions. See https://github.com/graphnet-team/graphnet/blob/main/GETTING_STARTED.md#adding-custom-truth-labels.

            • -

          • loss_weight_column (Optional[str], default: None) – Name of column that holds loss weight. +

          • input_features (ndarray) – Input features for graph construction. Shape ´[num_rows, d]´

            • +

          • input_feature_names (List[str]) – name of each column. Shape ´[,d]´.

            • +

          • truth_dicts (Optional[List[Dict[str, Any]]], default: None) – Dictionary containing truth labels.

            • +

          • custom_label_functions (Optional[Dict[str, Callable[..., Any]]], default: None) – Custom label functions. See https://github.com/graphnet-team/graphnet/blob/main/GETTING_STARTED.md#adding-custom-truth-labels.

            • +

          • loss_weight_column (Optional[str], default: None) – Name of column that holds loss weight. Defaults to None.

            • -

          • loss_weight (Optional[float], default: None) – Loss weight associated with event. Defaults to None.

            • -

          • loss_weight_default_value (Optional[float], default: None) – default value for loss weight. +

          • loss_weight (Optional[float], default: None) – Loss weight associated with event. Defaults to None.

            • +

          • loss_weight_default_value (Optional[float], default: None) – default value for loss weight. Used in instances where some events have no pre-defined loss weight. Defaults to None.

            • -

          • data_path (Optional[str], default: None) – Path to dataset data files. Defaults to None.

            • +

          • data_path (Optional[str], default: None) – Path to dataset data files. Defaults to None.

          Return type:
          -

          Data

          +

          Data

          Returns:

          graph

          @@ -633,7 +639,7 @@
          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
          Created using diff --git a/api/graphnet.models.graphs.graphs.html b/api/graphnet.models.graphs.graphs.html index c32567771..81c36ff78 100644 --- a/api/graphnet.models.graphs.graphs.html +++ b/api/graphnet.models.graphs.graphs.html @@ -515,18 +515,18 @@
          Parameters:
            -

          • detector (Detector) – Detector that represents your data.

            • -

          • node_definition (NodeDefinition, default: NodesAsPulses()) – Definition of nodes in the graph.

            • -

          • input_feature_names (Optional[List[str]], default: None) – Name of input feature columns.

            • -

          • dtype (Optional[dtype], default: torch.float32) – data type for node features.

            • -

          • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard +

          • detector (Detector) – Detector that represents your data.

            • +

          • node_definition (Optional[NodeDefinition], default: None) – Definition of nodes in the graph.

            • +

          • input_feature_names (Optional[List[str]], default: None) – Name of input feature columns.

            • +

          • dtype (Optional[dtype], default: torch.float32) – data type for node features.

            • +

          • perturbation_dict (Optional[Dict[str, float]], default: None) – Dictionary mapping a feature name to a standard deviation according to which the values for this feature should be randomly perturbed. Defaults to None.

            • -

          • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. +

          • seed (Union[int, Generator, None], default: None) – seed or Generator used to randomly sample perturbations. Defaults to None.

            • -

          • nb_nearest_neighbours (int, default: 8) – Number of edges for each node. Defaults to 8.

            • -

          • columns (List[int], default: [0, 1, 2]) – node feature columns used for distance calculation

            • +

          • nb_nearest_neighbours (int, default: 8) – Number of edges for each node. Defaults to 8.

            • +

          • columns (List[int], default: [0, 1, 2]) – node feature columns used for distance calculation

          • [0 (. Defaults to) –

          • 1 –

          • 2]. –

            • @@ -584,7 +584,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.graphs.html b/api/graphnet.models.graphs.html index 77895550c..8e26eaac0 100644 --- a/api/graphnet.models.graphs.html +++ b/api/graphnet.models.graphs.html @@ -492,6 +492,7 @@
            • edges
            • nodes
                @@ -565,7 +566,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.models.graphs.nodes.html b/api/graphnet.models.graphs.nodes.html index 0f4f9b699..3796f586d 100644 --- a/api/graphnet.models.graphs.nodes.html +++ b/api/graphnet.models.graphs.nodes.html @@ -130,7 +130,7 @@ - + @@ -518,7 +518,7 @@
          @@ -689,10 +689,10 @@
          Parameters:
            -

          • cluster_on (List[str]) – Names of features to create clusters from.

            • -

          • percentiles (List[int]) – List of percentiles. E.g. [10, 50, 90].

            • -

          • add_counts (bool, default: True) – If True, number of duplicates is added to output array.

            • -

          • input_feature_names (Optional[List[str]], default: None) – (Optional) column names for input features.

            • +

          • cluster_on (List[str]) – Names of features to create clusters from.

            • +

          • percentiles (List[int]) – List of percentiles. E.g. [10, 50, 90].

            • +

          • add_counts (bool, default: True) – If True, number of duplicates is added to output array.

            • +

          • input_feature_names (Optional[List[str]], default: None) – (Optional) column names for input features.

          • args (Any) –

          • kwargs (Any) –

          @@ -747,7 +747,7 @@
          - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
          Created using diff --git a/api/graphnet.models.graphs.utils.html b/api/graphnet.models.graphs.utils.html index 6ad7ef7d8..779b94300 100644 --- a/api/graphnet.models.graphs.utils.html +++ b/api/graphnet.models.graphs.utils.html @@ -549,12 +549,12 @@
          Parameters:
            -

          • x (array) – array to be sorted.

            • -

          • cluster_columns (List[int]) – Columns of x to be sorted along.

            • +

          • x (array) – array to be sorted.

            • +

          • cluster_columns (List[int]) – Columns of x to be sorted along.

          Return type:
          -

          ndarray

          +

          ndarray

          Returns:

          A sorted version of x.

          @@ -579,10 +579,10 @@
          Parameters:
            -

          • x (ndarray) – Array for clustering

            • -

          • feature_idx (int) – Index of the feature in x to +

          • x (ndarray) – Array for clustering

            • +

          • feature_idx (int) – Index of the feature in x to be gathered for each cluster.

            • -

          • cluster_columns (List[int]) – Index in x from which to build clusters.

            • +

          • cluster_columns (List[int]) – Index in x from which to build clusters.

          Returns:
          @@ -600,7 +600,7 @@

          Identify indices for clustering and summarization.

          Return type:
          -

          Tuple[List[int], List[int], List[str]]

          +

          Tuple[List[int], List[int], List[str]]

          Parameters:
            @@ -630,17 +630,17 @@
            Parameters:
              -

            • x (ndarray) – Array to be clustered

              • -

            • summarization_indices (List[int]) – List of column indices that defines features +

            • x (ndarray) – Array to be clustered

              • +

            • summarization_indices (List[int]) – List of column indices that defines features that will be summarized with percentiles.

              • -

            • cluster_indices (List[int]) – List of column indices on which the clusters +

            • cluster_indices (List[int]) – List of column indices on which the clusters are constructed.

              • -

            • percentiles (List[int]) – percentiles used to summarize x. E.g. [10,50,90].

              • +

            • percentiles (List[int]) – percentiles used to summarize x. E.g. [10,50,90].

            • add_counts (bool) –

            Return type:
            -

            ndarray

            +

            ndarray

            Returns:

            Percentile-summarized array

            @@ -692,7 +692,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.html b/api/graphnet.models.html index 1add6eb32..6ef893d4d 100644 --- a/api/graphnet.models.html +++ b/api/graphnet.models.html @@ -564,7 +564,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.model.html b/api/graphnet.models.model.html index cee61ff8f..3e841f294 100644 --- a/api/graphnet.models.model.html +++ b/api/graphnet.models.model.html @@ -378,14 +378,6 @@
            -
            -abstract forward(x)[source]
            -

            Forward pass.

            -
            -
            Return type:
            -

            Union[Tensor, Data]

            -
            -
            Parameters:
            -

            x (Tensor | Data) –

            -
            -
            -
            -
            -
            -fit(train_dataloader, val_dataloader, *, max_epochs, gpus, callbacks, ckpt_path, logger, log_every_n_steps, gradient_clip_val, distribution_strategy, **trainer_kwargs)[source]
            -

            Fit Model using pytorch_lightning.Trainer.

            -
            -
            Return type:
            -

            None

            -
            -
            Parameters:
            -
              -

            • train_dataloader (DataLoader) –

              • -

            • val_dataloader (DataLoader | None) –

              • -

            • max_epochs (int) –

              • -

            • gpus (List[int] | int | None) –

              • -

            • callbacks (List[Callback] | None) –

              • -

            • ckpt_path (str | None) –

              • -

            • logger (Logger | None) –

              • -

            • log_every_n_steps (int) –

              • -

            • gradient_clip_val (float | None) –

              • -

            • distribution_strategy (str | None) –

              • -

            • trainer_kwargs (Any) –

              • -
            -
            -
            -
            -
            -
            -predict(dataloader, gpus, distribution_strategy)[source]
            -

            Return predictions for dataloader.

            -

            Returns a list of Tensors, one for each model output.

            -
            -
            Return type:
            -

            List[Tensor]

            -
            -
            Parameters:
            -
              -

            • dataloader (DataLoader) –

              • -

            • gpus (List[int] | int | None) –

              • -

            • distribution_strategy (str | None) –

              • -
            -
            -
            -
            -
            -
            -predict_as_dataframe(dataloader, prediction_columns, *, additional_attributes, gpus, distribution_strategy)[source]
            -

            Return predictions for dataloader as a DataFrame.

            -

            Include additional_attributes as additional columns in the output -DataFrame.

            -
            -
            Return type:
            -

            DataFrame

            -
            -
            Parameters:
            -
              -

            • dataloader (DataLoader) –

              • -

            • prediction_columns (List[str]) –

              • -

            • additional_attributes (List[str] | None) –

              • -

            • gpus (List[int] | int | None) –

              • -

            • distribution_strategy (str | None) –

              • -
            -
            -
            -
            -
            save(path)[source]

            Save entire model to path.

            Return type:
            -

            None

            +

            None

            Parameters:

            path (str) –

            @@ -686,7 +565,7 @@

            Load entire model from path.

            Return type:
            -

            Model

            +

            Model

            Parameters:

            path (str) –

            @@ -699,7 +578,7 @@

            Save model state_dict to path.

            Return type:
            -

            None

            +

            None

            Parameters:

            path (str) –

            @@ -712,7 +591,7 @@

            Load model state_dict from path.

            Return type:
            -

            Model

            +

            Model

            Parameters:
              @@ -729,9 +608,9 @@
              Parameters:
                -

              • trust (bool, default: False) – Whether to trust the ModelConfig file enough to eval(…) +

              • trust (bool, default: False) – Whether to trust the ModelConfig file enough to eval(…) any lambda function expressions contained.

                • -

              • load_modules (Optional[List[str]], default: None) – List of modules used in the definition of the model +

              • load_modules (Optional[List[str]], default: None) – List of modules used in the definition of the model which, as a consequence, need to be loaded into the global namespace. Defaults to loading torch.

              • source (ModelConfig | str) –

                • @@ -742,7 +621,7 @@ trust = False.

              Return type:
              -

              Model

              +

              Model

            @@ -792,7 +671,7 @@
            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
            Created using diff --git a/api/graphnet.models.standard_model.html b/api/graphnet.models.standard_model.html index 4b068d66d..ed04dde69 100644 --- a/api/graphnet.models.standard_model.html +++ b/api/graphnet.models.standard_model.html @@ -385,6 +385,8 @@
            • StandardModel
              • +
            • EnergyTCReconstruction +
            • EnergyReconstructionWithUncertainty
              • +
            • LearnedTask +
              • +
            • StandardLearnedTask +
            • IdentityTask +
              • +
            • StandardFlowTask
            @@ -444,28 +468,63 @@
          • - forward() + inference()
          • - compute_loss() + train_eval() +
          +
        • - inference() + LearnedTask + + +
          • +
        • + + + StandardLearnedTask + @@ -496,6 +555,34 @@ nb_inputs +
        + + +
      • + + + StandardFlowTask +
      @@ -587,16 +674,30 @@
    • default_prediction_labels
      • -
    • forward() -
      • -
    • compute_loss() -
    • inference()
    • train_eval()
    +
  • LearnedTask +
    • +
  • StandardLearnedTask +
  • IdentityTask +
    • +
  • StandardFlowTask
    • @@ -625,27 +736,24 @@
    class graphnet.models.task.task.Task(*args, **kwargs)[source]

    Bases: Model

    -

    Base class for all reconstruction and classification tasks.

    +

    Base class for Tasks in GraphNeT.

    Construct Task.

    Parameters:
      -

    • hidden_size (int) – The number of nodes in the layer feeding into this -tasks, used to construct the affine transformation to the -predicted quantity.

      • -

    • loss_function (LossFunction) – Loss function appropriate to the task.

      • -

    • target_labels (Union[str, List[str], None], default: None) – Name(s) of the quantity/-ies being predicted, used +

    • loss_function (LossFunction) – Loss function appropriate to the task.

      • +

    • target_labels (Union[str, List[str], None], default: None) – Name(s) of the quantity/-ies being predicted, used to extract the target tensor(s) from the Data object in .compute_loss(…).

      • -

    • prediction_labels (Union[str, List[str], None], default: None) – The name(s) of each column that is predicted by +

    • prediction_labels (Union[str, List[str], None], default: None) – The name(s) of each column that is predicted by the model during inference. If not given, the name will auto matically be set to target_label + _pred.

      • -

    • transform_prediction_and_target (Optional[Callable], default: None) – Optional function to transform +

    • transform_prediction_and_target (Optional[Callable], default: None) – Optional function to transform both the predicted and target tensor before passing them to the loss function. Useful e.g. for having the model predict quantities on a physical scale, but transforming this scale to O(1) for a numerically stable loss computation.

      • -

    • transform_target (Optional[Callable], default: None) – Optional function to transform only the target +

    • transform_target (Optional[Callable], default: None) – Optional function to transform only the target tensor before passing it, and the predicted tensor, to the loss function. Useful e.g. for having the model predict a transformed version of the target quantity, e.g. the log10- @@ -653,15 +761,15 @@ in conjunction with transform_inference to perform the inverse transform on the predicted quantity to recover the physical scale.

      • -

    • transform_inference (Optional[Callable], default: None) – Optional function to inverse-transform the +

    • transform_inference (Optional[Callable], default: None) – Optional function to inverse-transform the model prediction to recover a physical scale. Used in conjunction with transform_target.

      • -

    • transform_support (Optional[Tuple], default: None) – Optional tuple to specify minimum and maximum +

    • transform_support (Optional[Tuple], default: None) – Optional tuple to specify minimum and maximum of the range of validity for the inverse transforms transform_target and transform_inference in case this is restricted. By default the invertibility of transform_target is tested on the range [-1e6, 1e6].

      • -

    • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event +

    • loss_weight (Optional[str], default: None) – Name of the attribute in data containing per-event loss weights.

    • args (Any) –

    • kwargs (Any) –

      • @@ -678,35 +786,66 @@
    -abstract property default_target_labels: List[str]
    +property default_target_labels: List[str]

    Return default target labels.

    -abstract property default_prediction_labels: List[str]
    +property default_prediction_labels: List[str]

    Return default prediction labels.

    -
    -forward(x)[source]
    -

    Forward pass.

    +
    +inference()[source]
    +

    Activate inference mode.

    Return type:
    -

    Union[Tensor, Data]

    +

    None

    -
    Parameters:
    -

    x (Tensor | Data) –

    +
    +
    +
    +
    +train_eval()[source]
    +

    Deactivate inference mode.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    +class graphnet.models.task.task.LearnedTask(*args, **kwargs)[source]
    +

    Bases: Task

    +

    Task class with a learned mapping.

    +

    Applies a learned mapping between the last latent layer of Model and +target space. E.g. the LearnedTask contains learnable parameters that +acts like a prediction head.

    +

    Construct LearnedTask.

    +
    +
    Parameters:
    +
      +

    • hidden_size (int) – The number of columns in the output of +the last latent layer of Model using this Task. +Available through Model.nb_outputs

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    +
    -
    -compute_loss(pred, data)[source]
    +
    +abstract compute_loss(pred, data)[source]

    Compute loss of pred wrt.

    target labels in data.

    Return type:
    -

    Tensor

    +

    Tensor

    Parameters:
      @@ -716,35 +855,81 @@
    +
    +
    +abstract property nb_inputs: int
    +

    Return number of inputs assumed by task.

    +
    -
    -inference()[source]
    -

    Activate inference mode.

    +
    +forward(x)[source]
    +

    Forward call for LearnedTask.

    +

    The learned embedding transforms last latent layer of Model to meet +target dimensions.

    Return type:
    -

    None

    +

    Union[Tensor, Data]

    +
    +
    Parameters:
    +

    x (Tensor | Data) –

    +
    +
    +
    +class graphnet.models.task.task.StandardLearnedTask(*args, **kwargs)[source]
    +

    Bases: LearnedTask

    +

    Standard class for classification and reconstruction in GraphNeT.

    +

    This class comes with a definition of compute_loss that is compatible +with the vast majority of supervised learning tasks.

    +

    Construct StandardLearnedTask.

    +
    +
    Parameters:
    +
      +

    • hidden_size (int) – The number of columns in the output of +the last latent layer of Model using this Task. +Available through Model.nb_outputs

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    +
    +
    +
    +abstract property nb_inputs: int
    +

    Return number of inputs assumed by task.

    +
    -
    -train_eval()[source]
    -

    Deactivate inference mode.

    +
    +compute_loss(pred, data)[source]
    +

    Compute supervised learning loss.

    Return type:
    -

    None

    +

    Tensor

    +
    +
    Parameters:
    +
      +

    • pred (Tensor | Data) –

      • +

    • data (Data) –

      • +
    +

    Grabs truth labels in data and sends both pred and target to loss +function for evaluation. Suits most supervised learning `Task`s.

    class graphnet.models.task.task.IdentityTask(*args, **kwargs)[source]
    -

    Bases: Task

    +

    Bases: StandardLearnedTask

    Identity, or trivial, task.

    Construct IdentityTask.

    -

    Return the nb_outputs as a direct, affine transformation of the last -hidden layer.

    +

    A task that does not apply a learned embedding to the input. It returns +the direct inputs from Model.

    Parameters:
      @@ -772,6 +957,74 @@

      Return number of inputs assumed by task.

    +
    +
    +class graphnet.models.task.task.StandardFlowTask(*args, **kwargs)[source]
    +

    Bases: Task

    +

    A Task for `NormalizingFlow`s in GraphNeT.

    +

    Construct StandardLearnedTask.

    +
    +
    Parameters:
    +
      +

    • target_labels (List[str]) – A list of names for the targets of this Task.

      • +

    • hidden_size – The number of columns in the output of +the last latent layer of Model using this Task. +Available through Model.nb_outputs

      • +

    • args (Any) –

      • +

    • kwargs (Any) –

      • +
    +
    +
    Return type:
    +

    object

    +
    +
    +
    +
    +nb_inputs()[source]
    +

    Return number of inputs assumed by task.

    +
    +
    Return type:
    +

    int

    +
    +
    +
    +
    +
    +forward(x, jacobian)[source]
    +

    Forward pass.

    +
    +
    Return type:
    +

    Union[Tensor, Data]

    +
    +
    Parameters:
    +
      +

    • x (Tensor | Data) –

      • +

    • jacobian (Tensor | None) –

      • +
    +
    +
    +
    +
    +
    +compute_loss(prediction, jacobian, data)[source]
    +

    Compute loss for normalizing flow tasks.

    +
    +
    Parameters:
    +
      +

    • prediction (Tensor) – transformed sample in latent distribution space.

      • +

    • jacobian (Tensor) – the jacobian associated with the transformation.

      • +

    • data (Data) – the graph object.

      • +
    +
    +
    Return type:
    +

    Tensor

    +
    +
    Returns:
    +

    the loss associated with the transformation.

    +
    +
    +
    +
    @@ -817,7 +1070,7 @@
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.models.utils.html b/api/graphnet.models.utils.html index 475f25794..1499ab427 100644 --- a/api/graphnet.models.utils.html +++ b/api/graphnet.models.utils.html @@ -501,7 +501,7 @@ input features in x.

    Return type:
    -

    Tuple[Tensor, Tensor, Tensor, Tensor]

    +

    Tuple[Tensor, Tensor, Tensor, Tensor]

    Returns:

    Tuple, each element with shape [batch_size,1].

    @@ -521,10 +521,10 @@

    Calculate the matrix of pairwise distances between pulses.

    Parameters:
    -

    xyz_coords (Tensor) – (x,y,z)-coordinates of pulses, of shape [nb_doms, 3].

    +

    xyz_coords (Tensor) – (x,y,z)-coordinates of pulses, of shape [nb_doms, 3].

    Return type:
    -

    Tensor

    +

    Tensor

    Returns:

    Matrix of pairwise distances, of shape [nb_doms, nb_doms]

    @@ -538,14 +538,14 @@
    Parameters:
      -

    • batch (Batch) – Batch of events.

      • -

    • k (List[int]) – A list of k’s.

      • -

    • columns (List[int]) – The columns of Data.x used for computing the distances. E.g., +

    • batch (Batch) – Batch of events.

      • +

    • k (List[int]) – A list of k’s.

      • +

    • columns (List[int]) – The columns of Data.x used for computing the distances. E.g., Data.x[:,[0,1,2]]

    Return type:
    -

    Batch

    +

    Batch

    Returns:

    Returns the same batch of events, but with updated edges.

    @@ -597,7 +597,7 @@
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.pisa.fitting.html b/api/graphnet.pisa.fitting.html index 8bc401462..c40fe61bf 100644 --- a/api/graphnet.pisa.fitting.html +++ b/api/graphnet.pisa.fitting.html @@ -491,9 +491,9 @@
    Parameters:
      -

    • config_path (str) – Path to original config file.

      • -

    • new_config_path (Optional[str], default: None) – Path to save updated config file.

      • -

    • dummy_section (str, default: 'temp') – Dummy section name to use for config files without +

    • config_path (str) – Path to original config file.

      • +

    • new_config_path (Optional[str], default: None) – Path to save updated config file.

      • +

    • dummy_section (str, default: 'temp') – Dummy section name to use for config files without section headers.

    @@ -501,7 +501,7 @@

    ConfigUpdater instance for programatically updating config file.

    Return type:
    -

    ConfigUpdater

    +

    ConfigUpdater

    @@ -531,19 +531,19 @@
    Parameters:
      -

    • config_outdir (str) – The output directory in which to store the +

    • config_outdir (str) – The output directory in which to store the configuration.

      • -

    • weight_name (str, default: '') – The name of the weight. If add_to_database=True, +

    • weight_name (str, default: '') – The name of the weight. If add_to_database=True, this will be the name of the table.

      • -

    • pisa_config_dict (Optional[Dict], default: None) – The dictionary of PISA configurations. Can be +

    • pisa_config_dict (Optional[Dict], default: None) – The dictionary of PISA configurations. Can be used to change assumptions regarding the fit.

      • -

    • add_to_database (bool, default: False) – If True, a table will be added to the database +

    • add_to_database (bool, default: False) – If True, a table will be added to the database called weight_name with two columns: [index_column, weight_name]

    Return type:
    -

    DataFrame

    +

    DataFrame

    Returns:

    A dataframe with columns [index_column, weight_name].

    @@ -575,7 +575,7 @@

    Fit 1D contours.

    Return type:
    -

    None

    +

    None

    Parameters:
      @@ -595,7 +595,7 @@

      Fit 2D contours.

      Return type:
      -

      None

      +

      None

      Parameters:
        @@ -655,7 +655,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.pisa.html b/api/graphnet.pisa.html index 77788eee8..a411dfc87 100644 --- a/api/graphnet.pisa.html +++ b/api/graphnet.pisa.html @@ -461,7 +461,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.pisa.plotting.html b/api/graphnet.pisa.plotting.html index fd463920f..8d9b9e9e8 100644 --- a/api/graphnet.pisa.plotting.html +++ b/api/graphnet.pisa.plotting.html @@ -449,7 +449,7 @@

        Parse the contents of entry.

        Return type:
        -

        Tuple[Any, ...]

        +

        Tuple[Any, ...]

        Parameters:

        entry (Dict) –

        @@ -463,7 +463,7 @@
        Parameters:
          -

        • contour_data (List[Dict]) –

          List of dictionaries with plotting information. Format is +

        • contour_data (List[Dict]) –

          List of dictionaries with plotting information. Format is for each dictionary is:

          @@ -475,16 +475,16 @@ legend label, “color” - the color of the contour, “linestyle” - the style of the contour line.

          • -

        • xlim (Tuple[float, float], default: (0.4, 0.6)) – Lower and upper bound of x-axis.

          • -

        • ylim (Tuple[float, float], default: (0.0023799999999999997, 0.0025499999999999997)) – Lower and upper bound of y-axis.

          • -

        • chi2_critical_value (float, default: 4.605) – The critical value of the chi2 fits. Defaults to +

        • xlim (Tuple[float, float], default: (0.4, 0.6)) – Lower and upper bound of x-axis.

          • +

        • ylim (Tuple[float, float], default: (0.0023799999999999997, 0.0025499999999999997)) – Lower and upper bound of y-axis.

          • +

        • chi2_critical_value (float, default: 4.605) – The critical value of the chi2 fits. Defaults to 4.605 (90% CL). @NOTE: This, and the below, can’t both be right.

          • -

        • width (float, default: 3.176) – width of figure in inches.

          • -

        • height (float, default: 2.388) – height of figure in inches.

          • +

        • width (float, default: 3.176) – width of figure in inches.

          • +

        • height (float, default: 2.388) – height of figure in inches.

        Return type:
        -

        Figure

        +

        Figure

        Returns:

        The figure with contours.

        @@ -498,7 +498,7 @@
        Parameters:
          -

        • contour_data (List[Dict]) –

          List of dictionaries with plotting information. Format is +

        • contour_data (List[Dict]) –

          List of dictionaries with plotting information. Format is for each dictionary is:

          @@ -510,14 +510,14 @@ legend label, “color” - the color of the contour, “linestyle” - the style of the contour line.

          • -

        • chi2_critical_value (float, default: 2.706) – The critical value of the chi2 fits. Defaults to +

        • chi2_critical_value (float, default: 2.706) – The critical value of the chi2 fits. Defaults to 2.706 (90% CL). @NOTE: This, and the above, can’t both be right.

          • -

        • width (float, default: 6.352) – width of figure in inches.

          • -

        • height (float, default: 2.388) – height of figure in inches.

          • +

        • width (float, default: 6.352) – width of figure in inches.

          • +

        • height (float, default: 2.388) – height of figure in inches.

        Return type:
        -

        Figure

        +

        Figure

        Returns:

        The figure with contours.

        @@ -569,7 +569,7 @@
        - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
        Created using diff --git a/api/graphnet.training.callbacks.html b/api/graphnet.training.callbacks.html index 2d3ffa1f3..30f76fb50 100644 --- a/api/graphnet.training.callbacks.html +++ b/api/graphnet.training.callbacks.html @@ -369,6 +369,18 @@
      • on_train_epoch_start()
      • on_train_epoch_end() +
      + + +
    • GraphnetEarlyStopping
    @@ -444,6 +456,41 @@ on_train_epoch_end() + + + +
  • + + + GraphnetEarlyStopping +
    • @@ -532,6 +579,18 @@
  • on_train_epoch_start()
  • on_train_epoch_end() +
    • + + +
  • GraphnetEarlyStopping
    • @@ -564,12 +623,12 @@
    Parameters:
      -

    • optimizer (Optimizer) – Wrapped optimizer.

      • -

    • milestones (List[int]) – List of step indices. Must be increasing.

      • -

    • factors (List[float]) – List of multiplicative factors. Must be same length as +

    • optimizer (Optimizer) – Wrapped optimizer.

      • +

    • milestones (List[int]) – List of step indices. Must be increasing.

      • +

    • factors (List[float]) – List of multiplicative factors. Must be same length as milestones.

      • -

    • last_epoch (int, default: -1) – The index of the last epoch.

      • -

    • verbose (bool, default: False) – If True, prints a message to stdout for each update.

      • +

    • last_epoch (int, default: -1) – The index of the last epoch.

      • +

    • verbose (bool, default: False) – If True, prints a message to stdout for each update.

    @@ -579,7 +638,7 @@

    Get effective learning rate(s) for each optimizer.

    Return type:
    -

    List[float]

    +

    List[float]

    @@ -604,7 +663,7 @@

    Override for customisation.

    Return type:
    -

    Bar

    +

    Bar

    @@ -614,7 +673,7 @@

    Override for customisation.

    Return type:
    -

    Bar

    +

    Bar

    @@ -624,7 +683,7 @@

    Override for customisation.

    Return type:
    -

    Bar

    +

    Bar

    @@ -634,7 +693,7 @@

    Override for customisation.

    Return type:
    -

    Bar

    +

    Bar

    @@ -644,7 +703,7 @@

    Override to not show the version number in the logging.

    Return type:
    -

    Dict

    +

    Dict

    Parameters:
      @@ -663,7 +722,7 @@ lightning is to overwrite the progress bar from previous epochs.

      Return type:
      -

      None

      +

      None

      Parameters:
        @@ -680,7 +739,7 @@

        Don’t duplciate to stdout.

        Return type:
        -

        None

        +

        None

        Parameters:
          @@ -691,6 +750,90 @@
    +
    +
    +class graphnet.training.callbacks.GraphnetEarlyStopping(save_dir, **kwargs)[source]
    +

    Bases: EarlyStopping

    +

    Early stopping callback for graphnet.

    +

    Construct GraphnetEarlyStopping Callback.

    +
    +
    Parameters:
    +
      +

    • save_dir (str) – Path to directory to save best model and config.

      • +

    • **kwargs (Dict[str, Any]) – Keyword arguments to pass to EarlyStopping. See +pytorch_lightning.callbacks.EarlyStopping for details.

      • +
    +
    +
    +
    +
    +setup(trainer, graphnet_model, stage)[source]
    +

    Call at setup stage of training.

    +
    +
    Parameters:
    +
      +

    • trainer (Trainer) – The trainer.

      • +

    • graphnet_model (Model) – The model.

      • +

    • stage (Optional[str], default: None) – The stage of training.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +
    +
    +
    +on_train_epoch_end(trainer, graphnet_model)[source]
    +

    Call after each train epoch.

    +
    +
    Parameters:
    +
      +

    • trainer (Trainer) – Trainer object.

      • +

    • graphnet_model (Model) – Graphnet Model.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +

    Returns: None.

    +
    +
    +
    +on_validation_end(trainer, graphnet_model)[source]
    +

    Call after each validation epoch.

    +
    +
    Parameters:
    +
      +

    • trainer (Trainer) – Trainer object.

      • +

    • graphnet_model (Model) – Graphnet Model.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +

    Returns: None.

    +
    +
    +
    +on_fit_end(trainer, graphnet_model)[source]
    +

    Call at the end of training.

    +
    +
    Parameters:
    +
      +

    • trainer (Trainer) – Trainer object.

      • +

    • graphnet_model (Model) – Graphnet Model.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +

    Returns: None.

    +
    +
    @@ -736,7 +879,7 @@
    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
    Created using diff --git a/api/graphnet.training.html b/api/graphnet.training.html index 7b9e19bdf..d33bd01fc 100644 --- a/api/graphnet.training.html +++ b/api/graphnet.training.html @@ -427,6 +427,7 @@
  • callbacks
  • labels
      @@ -510,7 +511,7 @@
      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
      Created using diff --git a/api/graphnet.training.labels.html b/api/graphnet.training.labels.html index 6514d41a8..95e4c5d5b 100644 --- a/api/graphnet.training.labels.html +++ b/api/graphnet.training.labels.html @@ -476,7 +476,7 @@

      Construct Label.

      Parameters:
      -

      key (str) – The name of the field in Data where the label will be +

      key (str) – The name of the field in Data where the label will be stored. That is, graph[key] = label.

      @@ -495,12 +495,12 @@
      Parameters:
        -

      • key (str, default: 'direction') – The name of the field in Data where the label will be +

      • key (str, default: 'direction') – The name of the field in Data where the label will be stored. That is, graph[key] = label.

        • -

      • azimuth_key (str, default: 'azimuth') – The name of the pre-existing key in graph that will +

      • azimuth_key (str, default: 'azimuth') – The name of the pre-existing key in graph that will be used to access the azimiuth angle, used when calculating the direction.

        • -

      • zenith_key (str, default: 'zenith') – The name of the pre-existing key in graph that will +

      • zenith_key (str, default: 'zenith') – The name of the pre-existing key in graph that will be used to access the zenith angle, used when calculating the direction.

      @@ -552,7 +552,7 @@
      - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
      Created using diff --git a/api/graphnet.training.loss_functions.html b/api/graphnet.training.loss_functions.html index 0dd1cc788..bcf94e1bb 100644 --- a/api/graphnet.training.loss_functions.html +++ b/api/graphnet.training.loss_functions.html @@ -656,15 +656,15 @@
      Parameters:
        -

      • prediction (Tensor) – Tensor containing predictions. Shape [N,P]

        • -

      • target (Tensor) – Tensor containing targets. Shape [N,T]

        • -

      • return_elements (bool, default: False) – Whether elementwise loss terms should be returned. +

      • prediction (Tensor) – Tensor containing predictions. Shape [N,P]

        • +

      • target (Tensor) – Tensor containing targets. Shape [N,T]

        • +

      • return_elements (bool, default: False) – Whether elementwise loss terms should be returned. The alternative is to return the averaged loss across examples.

      • weights (Tensor | None) –

      Return type:
      -

      Tensor

      +

      Tensor

      Returns:

      Loss, either averaged to a scalar (if return_elements = False) or @@ -802,7 +802,7 @@

      Forward pass.

      Return type:
      -

      Tensor

      +

      Tensor

      Parameters:
        @@ -819,7 +819,7 @@

        Backward pass.

        Return type:
        -

        Tensor

        +

        Tensor

        Parameters:
          @@ -855,7 +855,7 @@

          Calculate $log C_{m}(k)$ term in von Mises-Fisher loss exactly.

          Return type:
          -

          Tensor

          +

          Tensor

          Parameters:
            @@ -872,7 +872,7 @@

            [https://arxiv.org/abs/1812.04616] Sec. 8.2 with additional minus sign.

            Return type:
            -

            Tensor

            +

            Tensor

            Parameters:
              @@ -892,7 +892,7 @@ ensuring continuity at this point.

              Return type:
              -

              Tensor

              +

              Tensor

              Parameters:
                @@ -1003,7 +1003,7 @@
                - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                Created using diff --git a/api/graphnet.training.utils.html b/api/graphnet.training.utils.html index 0c320984b..a2ddce336 100644 --- a/api/graphnet.training.utils.html +++ b/api/graphnet.training.utils.html @@ -504,7 +504,7 @@

                Should not occur in “production”.

                Return type:
                -

                Batch

                +

                Batch

                Parameters:

                graphs (List[Data]) –

                @@ -532,7 +532,7 @@

                Construct DataLoader instance.

                Return type:
                -

                DataLoader

                +

                DataLoader

                Parameters:
                  @@ -564,7 +564,7 @@

                  Construct train and test DataLoader instances.

                  Return type:
                  -

                  Tuple[DataLoader, DataLoader]

                  +

                  Tuple[DataLoader, DataLoader]

                  Parameters:
                    @@ -598,7 +598,7 @@

                    Get model predictions on dataloader.

                    Return type:
                    -

                    DataFrame

                    +

                    DataFrame

                    Parameters:
                      @@ -618,7 +618,7 @@

                      Save trained model and prediction results in db.

                      Return type:
                      -

                      None

                      +

                      None

                      Parameters:
                      @@ -515,7 +522,7 @@

                      Check whether the pisa package is available.

                      Return type:
                      -

                      bool

                      +

                      bool

                    @@ -525,7 +532,7 @@

                    Decorate test_function for use only if icecube module is present.

                    Return type:
                    -

                    Callable

                    +

                    Callable

                    Parameters:

                    test_function (Callable) –

                    @@ -577,7 +584,7 @@
                    - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                    Created using diff --git a/api/graphnet.utilities.logging.html b/api/graphnet.utilities.logging.html index 7972b4bad..af5990025 100644 --- a/api/graphnet.utilities.logging.html +++ b/api/graphnet.utilities.logging.html @@ -361,6 +361,13 @@ decorators + +
                  • + + + deprecation_tools + +
                  • @@ -625,7 +632,7 @@

                    Filter messages printed more than nb_repeats_allowed times.

                    Return type:
                    -

                    bool

                    +

                    bool

                    Parameters:

                    record (LogRecord) –

                    @@ -661,7 +668,7 @@

                    Delegate setLevel call to member logger.

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:

                    level (int) –

                    @@ -674,7 +681,7 @@

                    Delegate a critical call to member logger.

                    Return type:
                    -

                    None

                    +

                    None

                    Parameters:
                      @@ -691,7 +698,7 @@

                      Delegate an error call to member logger.

                      Return type:
                      -

                      None

                      +

                      None

                      Parameters:
                        @@ -708,7 +715,7 @@

                        Delegate a warning call to member logger.

                        Return type:
                        -

                        None

                        +

                        None

                        Parameters:
                          @@ -725,7 +732,7 @@

                          Delegate an info call to member logger.

                          Return type:
                          -

                          None

                          +

                          None

                          Parameters:
                            @@ -742,7 +749,7 @@

                            Delegate a debug call to member logger.

                            Return type:
                            -

                            None

                            +

                            None

                            Parameters:
                          • FEATURES (class in graphnet.data.constants) @@ -668,7 +681,7 @@

                            F

                          • fit (graphnet.utilities.config.training_config.TrainingConfig attribute)
                            • -
                          • fit() (graphnet.models.model.Model method) +
                          • fit() (graphnet.models.standard_model.StandardModel method)
                            • +
                          • GraphnetEarlyStopping (class in graphnet.training.callbacks) +
                          • GraphNeTI3Module (class in graphnet.deployment.i3modules.graphnet_module)
                          • group_by() (in module graphnet.models.components.pool) @@ -1608,6 +1647,8 @@

                            L

                            - +
                            -
                            +

                            X

                            + + +
                            +

                            Z

                            + + + + + +
                                graphnet.models.graphs.edges.edges
                                + graphnet.models.graphs.edges.minkowski +
                                @@ -790,6 +795,11 @@

                            Python Module Index

                            		    graphnet.utilities.decorators
                                + graphnet.utilities.deprecation_tools +
                                @@ -831,7 +841,7 @@

                            Python Module Index

                            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                            Created using diff --git a/search.html b/search.html index 4199f1026..ec2b51087 100644 --- a/search.html +++ b/search.html @@ -357,7 +357,7 @@

                            Search

                            - © Copyright 2021-2023, GraphNeT team. + © Copyright 2021-2024, GraphNeT team.
                            Created using diff --git a/searchindex.js b/searchindex.js index 325baca70..228159cba 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["about", "api/graphnet", "api/graphnet.constants", "api/graphnet.data", "api/graphnet.data.constants", "api/graphnet.data.dataconverter", "api/graphnet.data.dataloader", "api/graphnet.data.dataset", "api/graphnet.data.dataset.dataset", "api/graphnet.data.dataset.parquet", "api/graphnet.data.dataset.parquet.parquet_dataset", "api/graphnet.data.dataset.sqlite", "api/graphnet.data.dataset.sqlite.sqlite_dataset", "api/graphnet.data.extractors", "api/graphnet.data.extractors.i3extractor", "api/graphnet.data.extractors.i3featureextractor", "api/graphnet.data.extractors.i3genericextractor", "api/graphnet.data.extractors.i3hybridrecoextractor", "api/graphnet.data.extractors.i3ntmuonlabelsextractor", "api/graphnet.data.extractors.i3particleextractor", 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"graphnet.data.extractors.utilities": [[26, "module-graphnet.data.extractors.utilities"]], "flatten_nested_dictionary() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.flatten_nested_dictionary"]], "graphnet.data.extractors.utilities.collections": [[27, "module-graphnet.data.extractors.utilities.collections"]], "serialise() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.serialise"]], "transpose_list_of_dicts() (in module graphnet.data.extractors.utilities.collections)": [[27, "graphnet.data.extractors.utilities.collections.transpose_list_of_dicts"]], "frame_is_montecarlo() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.frame_is_montecarlo"]], "frame_is_noise() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.frame_is_noise"]], "get_om_keys_and_pulseseries() (in module graphnet.data.extractors.utilities.frames)": [[28, "graphnet.data.extractors.utilities.frames.get_om_keys_and_pulseseries"]], "graphnet.data.extractors.utilities.frames": [[28, "module-graphnet.data.extractors.utilities.frames"]], "break_cyclic_recursion() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.break_cyclic_recursion"]], "cast_object_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.cast_object_to_pure_python"]], "cast_pulse_series_to_pure_python() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.cast_pulse_series_to_pure_python"]], "get_member_variables() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.get_member_variables"]], "graphnet.data.extractors.utilities.types": [[29, "module-graphnet.data.extractors.utilities.types"]], "is_boost_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_class"]], "is_boost_enum() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_enum"]], "is_icecube_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_icecube_class"]], "is_method() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_method"]], "is_type() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_type"]], "i3filter (class in graphnet.data.filters)": [[30, "graphnet.data.filters.I3Filter"]], "i3filtermask (class in graphnet.data.filters)": [[30, "graphnet.data.filters.I3FilterMask"]], "nullspliti3filter (class in graphnet.data.filters)": [[30, "graphnet.data.filters.NullSplitI3Filter"]], "graphnet.data.filters": [[30, "module-graphnet.data.filters"]], "graphnet.data.parquet": [[31, "module-graphnet.data.parquet"]], "parquetdataconverter (class in graphnet.data.parquet.parquet_dataconverter)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter"]], "file_suffix (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter attribute)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.file_suffix"]], "graphnet.data.parquet.parquet_dataconverter": [[32, "module-graphnet.data.parquet.parquet_dataconverter"]], "merge_files() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.merge_files"]], "save_data() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.save_data"]], "insqlitepipeline (class in graphnet.data.pipeline)": [[33, "graphnet.data.pipeline.InSQLitePipeline"]], "graphnet.data.pipeline": [[33, "module-graphnet.data.pipeline"]], "graphnet.data.sqlite": [[34, "module-graphnet.data.sqlite"]], "sqlitedataconverter (class in graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter"]], "any_pulsemap_is_non_empty() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.any_pulsemap_is_non_empty"]], "construct_dataframe() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.construct_dataframe"]], "file_suffix (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter attribute)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.file_suffix"]], "graphnet.data.sqlite.sqlite_dataconverter": [[35, "module-graphnet.data.sqlite.sqlite_dataconverter"]], "is_mc_tree() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_mc_tree"]], "is_pulse_map() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_pulse_map"]], "merge_files() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.merge_files"]], "save_data() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.save_data"]], "attach_index() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.attach_index"]], "create_table() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table"]], "create_table_and_save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table_and_save_to_sql"]], "database_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_exists"]], "database_table_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_table_exists"]], "graphnet.data.sqlite.sqlite_utilities": [[36, "module-graphnet.data.sqlite.sqlite_utilities"]], "run_sql_code() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.run_sql_code"]], "save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.save_to_sql"]], "graphnet.data.utilities": [[37, "module-graphnet.data.utilities"]], "parquettosqliteconverter (class in graphnet.data.utilities.parquet_to_sqlite)": [[38, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter"]], "graphnet.data.utilities.parquet_to_sqlite": [[38, "module-graphnet.data.utilities.parquet_to_sqlite"]], "run() (graphnet.data.utilities.parquet_to_sqlite.parquettosqliteconverter method)": [[38, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter.run"]], "graphnet.data.utilities.random": [[39, "module-graphnet.data.utilities.random"]], "pairwise_shuffle() (in module graphnet.data.utilities.random)": [[39, "graphnet.data.utilities.random.pairwise_shuffle"]], "stringselectionresolver (class in graphnet.data.utilities.string_selection_resolver)": [[40, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver"]], "graphnet.data.utilities.string_selection_resolver": [[40, "module-graphnet.data.utilities.string_selection_resolver"]], "resolve() (graphnet.data.utilities.string_selection_resolver.stringselectionresolver method)": [[40, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver.resolve"]], "graphnet.deployment": [[41, "module-graphnet.deployment"]], "graphneti3module (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.GraphNeTI3Module"]], "i3inferencemodule (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.I3InferenceModule"]], "i3pulsecleanermodule (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.I3PulseCleanerModule"]], "graphnet.deployment.i3modules.graphnet_module": [[44, "module-graphnet.deployment.i3modules.graphnet_module"]], "graphnet.models": [[45, "module-graphnet.models"]], "attributecoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.AttributeCoarsening"]], "coarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.Coarsening"]], "customdomcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.CustomDOMCoarsening"]], "domandtimewindowcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.DOMAndTimeWindowCoarsening"]], "domcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.DOMCoarsening"]], "forward() (graphnet.models.coarsening.coarsening method)": [[46, "graphnet.models.coarsening.Coarsening.forward"]], "graphnet.models.coarsening": [[46, "module-graphnet.models.coarsening"]], "reduce_options (graphnet.models.coarsening.coarsening attribute)": [[46, "graphnet.models.coarsening.Coarsening.reduce_options"]], "unbatch_edge_index() (in module graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.unbatch_edge_index"]], "graphnet.models.components": [[47, "module-graphnet.models.components"]], "dynedgeconv (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.DynEdgeConv"]], "dyntrans (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.DynTrans"]], "edgeconvtito (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.EdgeConvTito"]], "forward() (graphnet.models.components.layers.dynedgeconv method)": [[48, "graphnet.models.components.layers.DynEdgeConv.forward"]], "forward() (graphnet.models.components.layers.dyntrans method)": [[48, "graphnet.models.components.layers.DynTrans.forward"]], "forward() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.forward"]], "graphnet.models.components.layers": [[48, "module-graphnet.models.components.layers"]], "message() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.message"]], "reset_parameters() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.reset_parameters"]], "graphnet.models.components.pool": [[49, "module-graphnet.models.components.pool"]], "group_by() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_by"]], "group_pulses_to_dom() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_pulses_to_dom"]], "group_pulses_to_pmt() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_pulses_to_pmt"]], "min_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.min_pool"]], "min_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.min_pool_x"]], "std_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.std_pool"]], "std_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.std_pool_x"]], "sum_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool"]], "sum_pool_and_distribute() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool_and_distribute"]], "sum_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool_x"]], "graphnet.models.detector": [[50, "module-graphnet.models.detector"]], "detector (class in graphnet.models.detector.detector)": [[51, "graphnet.models.detector.detector.Detector"]], "feature_map() (graphnet.models.detector.detector.detector method)": [[51, "graphnet.models.detector.detector.Detector.feature_map"]], "forward() (graphnet.models.detector.detector.detector method)": [[51, "graphnet.models.detector.detector.Detector.forward"]], "graphnet.models.detector.detector": [[51, "module-graphnet.models.detector.detector"]], "icecube86 (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCube86"]], "icecubedeepcore (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeDeepCore"]], "icecubekaggle (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeKaggle"]], "icecubeupgrade (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade"]], "feature_map() (graphnet.models.detector.icecube.icecube86 method)": [[52, "graphnet.models.detector.icecube.IceCube86.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubedeepcore method)": [[52, "graphnet.models.detector.icecube.IceCubeDeepCore.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubekaggle method)": [[52, "graphnet.models.detector.icecube.IceCubeKaggle.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubeupgrade method)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.feature_map"]], "graphnet.models.detector.icecube": [[52, "module-graphnet.models.detector.icecube"]], "prometheus (class in graphnet.models.detector.prometheus)": [[53, "graphnet.models.detector.prometheus.Prometheus"]], "feature_map() (graphnet.models.detector.prometheus.prometheus method)": [[53, "graphnet.models.detector.prometheus.Prometheus.feature_map"]], "graphnet.models.detector.prometheus": [[53, "module-graphnet.models.detector.prometheus"]], "graphnet.models.gnn": [[54, "module-graphnet.models.gnn"]], "convnet (class in graphnet.models.gnn.convnet)": [[55, "graphnet.models.gnn.convnet.ConvNet"]], "forward() (graphnet.models.gnn.convnet.convnet method)": [[55, "graphnet.models.gnn.convnet.ConvNet.forward"]], "graphnet.models.gnn.convnet": [[55, "module-graphnet.models.gnn.convnet"]], "dynedge (class in graphnet.models.gnn.dynedge)": [[56, "graphnet.models.gnn.dynedge.DynEdge"]], "forward() (graphnet.models.gnn.dynedge.dynedge method)": [[56, "graphnet.models.gnn.dynedge.DynEdge.forward"]], "graphnet.models.gnn.dynedge": [[56, "module-graphnet.models.gnn.dynedge"]], "dynedgejinst (class in graphnet.models.gnn.dynedge_jinst)": [[57, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST"]], "forward() (graphnet.models.gnn.dynedge_jinst.dynedgejinst method)": [[57, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST.forward"]], "graphnet.models.gnn.dynedge_jinst": [[57, "module-graphnet.models.gnn.dynedge_jinst"]], "dynedgetito (class in graphnet.models.gnn.dynedge_kaggle_tito)": [[58, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO"]], "forward() (graphnet.models.gnn.dynedge_kaggle_tito.dynedgetito method)": [[58, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO.forward"]], "graphnet.models.gnn.dynedge_kaggle_tito": [[58, "module-graphnet.models.gnn.dynedge_kaggle_tito"]], "gnn (class in graphnet.models.gnn.gnn)": [[59, "graphnet.models.gnn.gnn.GNN"]], "forward() (graphnet.models.gnn.gnn.gnn method)": [[59, "graphnet.models.gnn.gnn.GNN.forward"]], "graphnet.models.gnn.gnn": [[59, "module-graphnet.models.gnn.gnn"]], "nb_inputs (graphnet.models.gnn.gnn.gnn property)": [[59, "graphnet.models.gnn.gnn.GNN.nb_inputs"]], "nb_outputs (graphnet.models.gnn.gnn.gnn property)": [[59, "graphnet.models.gnn.gnn.GNN.nb_outputs"]], "graphnet.models.graphs": [[60, "module-graphnet.models.graphs"]], "graphnet.models.graphs.edges": [[61, "module-graphnet.models.graphs.edges"]], "edgedefinition (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.EdgeDefinition"]], "euclideanedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.EuclideanEdges"]], "knnedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.KNNEdges"]], "radialedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.RadialEdges"]], "forward() (graphnet.models.graphs.edges.edges.edgedefinition method)": [[62, "graphnet.models.graphs.edges.edges.EdgeDefinition.forward"]], "graphnet.models.graphs.edges.edges": [[62, "module-graphnet.models.graphs.edges.edges"]], "graphdefinition (class in graphnet.models.graphs.graph_definition)": [[63, "graphnet.models.graphs.graph_definition.GraphDefinition"]], "forward() (graphnet.models.graphs.graph_definition.graphdefinition method)": [[63, "graphnet.models.graphs.graph_definition.GraphDefinition.forward"]], "graphnet.models.graphs.graph_definition": [[63, "module-graphnet.models.graphs.graph_definition"]], "knngraph (class in graphnet.models.graphs.graphs)": [[64, "graphnet.models.graphs.graphs.KNNGraph"]], "graphnet.models.graphs.graphs": [[64, "module-graphnet.models.graphs.graphs"]], "graphnet.models.graphs.nodes": [[65, "module-graphnet.models.graphs.nodes"]], "nodedefinition (class in graphnet.models.graphs.nodes.nodes)": [[66, "graphnet.models.graphs.nodes.nodes.NodeDefinition"]], "nodesaspulses (class in graphnet.models.graphs.nodes.nodes)": [[66, "graphnet.models.graphs.nodes.nodes.NodesAsPulses"]], "percentileclusters (class in graphnet.models.graphs.nodes.nodes)": [[66, "graphnet.models.graphs.nodes.nodes.PercentileClusters"]], "forward() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[66, "graphnet.models.graphs.nodes.nodes.NodeDefinition.forward"]], "graphnet.models.graphs.nodes.nodes": [[66, "module-graphnet.models.graphs.nodes.nodes"]], "nb_outputs (graphnet.models.graphs.nodes.nodes.nodedefinition property)": [[66, "graphnet.models.graphs.nodes.nodes.NodeDefinition.nb_outputs"]], "set_number_of_inputs() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[66, "graphnet.models.graphs.nodes.nodes.NodeDefinition.set_number_of_inputs"]], "set_output_feature_names() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[66, "graphnet.models.graphs.nodes.nodes.NodeDefinition.set_output_feature_names"]], "cluster_summarize_with_percentiles() (in module graphnet.models.graphs.utils)": [[67, "graphnet.models.graphs.utils.cluster_summarize_with_percentiles"]], "gather_cluster_sequence() (in module graphnet.models.graphs.utils)": [[67, "graphnet.models.graphs.utils.gather_cluster_sequence"]], "graphnet.models.graphs.utils": [[67, "module-graphnet.models.graphs.utils"]], "identify_indices() (in module graphnet.models.graphs.utils)": [[67, "graphnet.models.graphs.utils.identify_indices"]], "lex_sort() (in module graphnet.models.graphs.utils)": [[67, "graphnet.models.graphs.utils.lex_sort"]], "model (class in graphnet.models.model)": [[68, "graphnet.models.model.Model"]], "fit() (graphnet.models.model.model method)": [[68, "graphnet.models.model.Model.fit"]], "forward() (graphnet.models.model.model method)": [[68, "graphnet.models.model.Model.forward"]], "from_config() (graphnet.models.model.model class method)": [[68, "graphnet.models.model.Model.from_config"]], "graphnet.models.model": [[68, "module-graphnet.models.model"]], "load() (graphnet.models.model.model class method)": [[68, "graphnet.models.model.Model.load"]], "load_state_dict() (graphnet.models.model.model method)": [[68, "graphnet.models.model.Model.load_state_dict"]], "predict() (graphnet.models.model.model 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"graphnet.data.extractors.utilities.types.cast_pulse_series_to_pure_python"]], "get_member_variables() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.get_member_variables"]], "graphnet.data.extractors.utilities.types": [[29, "module-graphnet.data.extractors.utilities.types"]], "is_boost_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_class"]], "is_boost_enum() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_boost_enum"]], "is_icecube_class() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_icecube_class"]], "is_method() (in module graphnet.data.extractors.utilities.types)": [[29, "graphnet.data.extractors.utilities.types.is_method"]], "is_type() (in module graphnet.data.extractors.utilities.types)": [[29, 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method)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.merge_files"]], "save_data() (graphnet.data.parquet.parquet_dataconverter.parquetdataconverter method)": [[32, "graphnet.data.parquet.parquet_dataconverter.ParquetDataConverter.save_data"]], "insqlitepipeline (class in graphnet.data.pipeline)": [[33, "graphnet.data.pipeline.InSQLitePipeline"]], "graphnet.data.pipeline": [[33, "module-graphnet.data.pipeline"]], "graphnet.data.sqlite": [[34, "module-graphnet.data.sqlite"]], "sqlitedataconverter (class in graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter"]], "any_pulsemap_is_non_empty() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.any_pulsemap_is_non_empty"]], "construct_dataframe() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.construct_dataframe"]], "file_suffix (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter attribute)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.file_suffix"]], "graphnet.data.sqlite.sqlite_dataconverter": [[35, "module-graphnet.data.sqlite.sqlite_dataconverter"]], "is_mc_tree() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_mc_tree"]], "is_pulse_map() (in module graphnet.data.sqlite.sqlite_dataconverter)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.is_pulse_map"]], "merge_files() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.merge_files"]], "save_data() (graphnet.data.sqlite.sqlite_dataconverter.sqlitedataconverter method)": [[35, "graphnet.data.sqlite.sqlite_dataconverter.SQLiteDataConverter.save_data"]], "attach_index() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.attach_index"]], "create_table() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table"]], "create_table_and_save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.create_table_and_save_to_sql"]], "database_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_exists"]], "database_table_exists() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.database_table_exists"]], "graphnet.data.sqlite.sqlite_utilities": [[36, "module-graphnet.data.sqlite.sqlite_utilities"]], "run_sql_code() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.run_sql_code"]], "save_to_sql() (in module graphnet.data.sqlite.sqlite_utilities)": [[36, "graphnet.data.sqlite.sqlite_utilities.save_to_sql"]], "graphnet.data.utilities": [[37, "module-graphnet.data.utilities"]], "parquettosqliteconverter (class in graphnet.data.utilities.parquet_to_sqlite)": [[38, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter"]], "graphnet.data.utilities.parquet_to_sqlite": [[38, "module-graphnet.data.utilities.parquet_to_sqlite"]], "run() (graphnet.data.utilities.parquet_to_sqlite.parquettosqliteconverter method)": [[38, "graphnet.data.utilities.parquet_to_sqlite.ParquetToSQLiteConverter.run"]], "graphnet.data.utilities.random": [[39, "module-graphnet.data.utilities.random"]], "pairwise_shuffle() (in module graphnet.data.utilities.random)": [[39, "graphnet.data.utilities.random.pairwise_shuffle"]], "stringselectionresolver (class in graphnet.data.utilities.string_selection_resolver)": [[40, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver"]], "graphnet.data.utilities.string_selection_resolver": [[40, "module-graphnet.data.utilities.string_selection_resolver"]], "resolve() (graphnet.data.utilities.string_selection_resolver.stringselectionresolver method)": [[40, "graphnet.data.utilities.string_selection_resolver.StringSelectionResolver.resolve"]], "graphnet.deployment": [[41, "module-graphnet.deployment"]], "graphneti3module (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.GraphNeTI3Module"]], "i3inferencemodule (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.I3InferenceModule"]], "i3pulsecleanermodule (class in graphnet.deployment.i3modules.graphnet_module)": [[44, "graphnet.deployment.i3modules.graphnet_module.I3PulseCleanerModule"]], "graphnet.deployment.i3modules.graphnet_module": [[44, "module-graphnet.deployment.i3modules.graphnet_module"]], "graphnet.models": [[45, "module-graphnet.models"]], "attributecoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.AttributeCoarsening"]], "coarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.Coarsening"]], "customdomcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.CustomDOMCoarsening"]], "domandtimewindowcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.DOMAndTimeWindowCoarsening"]], "domcoarsening (class in graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.DOMCoarsening"]], "forward() (graphnet.models.coarsening.coarsening method)": [[46, "graphnet.models.coarsening.Coarsening.forward"]], "graphnet.models.coarsening": [[46, "module-graphnet.models.coarsening"]], "reduce_options (graphnet.models.coarsening.coarsening attribute)": [[46, "graphnet.models.coarsening.Coarsening.reduce_options"]], "unbatch_edge_index() (in module graphnet.models.coarsening)": [[46, "graphnet.models.coarsening.unbatch_edge_index"]], "graphnet.models.components": [[47, "module-graphnet.models.components"]], "dynedgeconv (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.DynEdgeConv"]], "dyntrans (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.DynTrans"]], "edgeconvtito (class in graphnet.models.components.layers)": [[48, "graphnet.models.components.layers.EdgeConvTito"]], "forward() (graphnet.models.components.layers.dynedgeconv method)": [[48, "graphnet.models.components.layers.DynEdgeConv.forward"]], "forward() (graphnet.models.components.layers.dyntrans method)": [[48, "graphnet.models.components.layers.DynTrans.forward"]], "forward() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.forward"]], "graphnet.models.components.layers": [[48, "module-graphnet.models.components.layers"]], "message() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.message"]], "reset_parameters() (graphnet.models.components.layers.edgeconvtito method)": [[48, "graphnet.models.components.layers.EdgeConvTito.reset_parameters"]], "graphnet.models.components.pool": [[49, "module-graphnet.models.components.pool"]], "group_by() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_by"]], "group_pulses_to_dom() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_pulses_to_dom"]], "group_pulses_to_pmt() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.group_pulses_to_pmt"]], "min_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.min_pool"]], "min_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.min_pool_x"]], "std_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.std_pool"]], "std_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.std_pool_x"]], "sum_pool() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool"]], "sum_pool_and_distribute() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool_and_distribute"]], "sum_pool_x() (in module graphnet.models.components.pool)": [[49, "graphnet.models.components.pool.sum_pool_x"]], "graphnet.models.detector": [[50, "module-graphnet.models.detector"]], "detector (class in graphnet.models.detector.detector)": [[51, "graphnet.models.detector.detector.Detector"]], "feature_map() (graphnet.models.detector.detector.detector method)": [[51, "graphnet.models.detector.detector.Detector.feature_map"]], "forward() (graphnet.models.detector.detector.detector method)": [[51, "graphnet.models.detector.detector.Detector.forward"]], "geometry_table (graphnet.models.detector.detector.detector property)": [[51, "graphnet.models.detector.detector.Detector.geometry_table"]], "graphnet.models.detector.detector": [[51, "module-graphnet.models.detector.detector"]], "sensor_index_name (graphnet.models.detector.detector.detector property)": [[51, "graphnet.models.detector.detector.Detector.sensor_index_name"]], "sensor_position_names (graphnet.models.detector.detector.detector property)": [[51, "graphnet.models.detector.detector.Detector.sensor_position_names"]], "string_index_name (graphnet.models.detector.detector.detector property)": [[51, "graphnet.models.detector.detector.Detector.string_index_name"]], "icecube86 (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCube86"]], "icecubedeepcore (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeDeepCore"]], "icecubekaggle (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeKaggle"]], "icecubeupgrade (class in graphnet.models.detector.icecube)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade"]], "feature_map() (graphnet.models.detector.icecube.icecube86 method)": [[52, "graphnet.models.detector.icecube.IceCube86.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubedeepcore method)": [[52, "graphnet.models.detector.icecube.IceCubeDeepCore.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubekaggle method)": [[52, "graphnet.models.detector.icecube.IceCubeKaggle.feature_map"]], "feature_map() (graphnet.models.detector.icecube.icecubeupgrade method)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.feature_map"]], "geometry_table_path (graphnet.models.detector.icecube.icecube86 attribute)": [[52, "graphnet.models.detector.icecube.IceCube86.geometry_table_path"]], "geometry_table_path (graphnet.models.detector.icecube.icecubeupgrade attribute)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.geometry_table_path"]], "graphnet.models.detector.icecube": [[52, "module-graphnet.models.detector.icecube"]], "sensor_id_column (graphnet.models.detector.icecube.icecube86 attribute)": [[52, "graphnet.models.detector.icecube.IceCube86.sensor_id_column"]], "sensor_id_column (graphnet.models.detector.icecube.icecubeupgrade attribute)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.sensor_id_column"]], "string_id_column (graphnet.models.detector.icecube.icecube86 attribute)": [[52, "graphnet.models.detector.icecube.IceCube86.string_id_column"]], "string_id_column (graphnet.models.detector.icecube.icecubeupgrade attribute)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.string_id_column"]], "xyz (graphnet.models.detector.icecube.icecube86 attribute)": [[52, "graphnet.models.detector.icecube.IceCube86.xyz"]], "xyz (graphnet.models.detector.icecube.icecubeupgrade attribute)": [[52, "graphnet.models.detector.icecube.IceCubeUpgrade.xyz"]], "orca150 (class in graphnet.models.detector.prometheus)": [[53, "graphnet.models.detector.prometheus.ORCA150"]], "prometheus (class in graphnet.models.detector.prometheus)": [[53, "graphnet.models.detector.prometheus.Prometheus"]], "feature_map() (graphnet.models.detector.prometheus.orca150 method)": [[53, "graphnet.models.detector.prometheus.ORCA150.feature_map"]], "geometry_table_path (graphnet.models.detector.prometheus.orca150 attribute)": [[53, "graphnet.models.detector.prometheus.ORCA150.geometry_table_path"]], "graphnet.models.detector.prometheus": [[53, "module-graphnet.models.detector.prometheus"]], "sensor_id_column (graphnet.models.detector.prometheus.orca150 attribute)": [[53, "graphnet.models.detector.prometheus.ORCA150.sensor_id_column"]], "string_id_column (graphnet.models.detector.prometheus.orca150 attribute)": [[53, "graphnet.models.detector.prometheus.ORCA150.string_id_column"]], "xyz (graphnet.models.detector.prometheus.orca150 attribute)": [[53, "graphnet.models.detector.prometheus.ORCA150.xyz"]], "graphnet.models.gnn": [[54, "module-graphnet.models.gnn"]], "convnet (class in graphnet.models.gnn.convnet)": [[55, "graphnet.models.gnn.convnet.ConvNet"]], "forward() (graphnet.models.gnn.convnet.convnet method)": [[55, "graphnet.models.gnn.convnet.ConvNet.forward"]], "graphnet.models.gnn.convnet": [[55, "module-graphnet.models.gnn.convnet"]], "dynedge (class in graphnet.models.gnn.dynedge)": [[56, "graphnet.models.gnn.dynedge.DynEdge"]], "forward() (graphnet.models.gnn.dynedge.dynedge method)": [[56, "graphnet.models.gnn.dynedge.DynEdge.forward"]], "graphnet.models.gnn.dynedge": [[56, "module-graphnet.models.gnn.dynedge"]], "dynedgejinst (class in graphnet.models.gnn.dynedge_jinst)": [[57, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST"]], "forward() (graphnet.models.gnn.dynedge_jinst.dynedgejinst method)": [[57, "graphnet.models.gnn.dynedge_jinst.DynEdgeJINST.forward"]], "graphnet.models.gnn.dynedge_jinst": [[57, "module-graphnet.models.gnn.dynedge_jinst"]], "dynedgetito (class in graphnet.models.gnn.dynedge_kaggle_tito)": [[58, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO"]], "forward() (graphnet.models.gnn.dynedge_kaggle_tito.dynedgetito method)": [[58, "graphnet.models.gnn.dynedge_kaggle_tito.DynEdgeTITO.forward"]], "graphnet.models.gnn.dynedge_kaggle_tito": [[58, "module-graphnet.models.gnn.dynedge_kaggle_tito"]], "gnn (class in graphnet.models.gnn.gnn)": [[59, "graphnet.models.gnn.gnn.GNN"]], "forward() (graphnet.models.gnn.gnn.gnn method)": [[59, "graphnet.models.gnn.gnn.GNN.forward"]], "graphnet.models.gnn.gnn": [[59, "module-graphnet.models.gnn.gnn"]], "nb_inputs (graphnet.models.gnn.gnn.gnn property)": [[59, "graphnet.models.gnn.gnn.GNN.nb_inputs"]], "nb_outputs (graphnet.models.gnn.gnn.gnn property)": [[59, "graphnet.models.gnn.gnn.GNN.nb_outputs"]], "graphnet.models.graphs": [[60, "module-graphnet.models.graphs"]], "graphnet.models.graphs.edges": [[61, "module-graphnet.models.graphs.edges"]], "edgedefinition (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.EdgeDefinition"]], "euclideanedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.EuclideanEdges"]], "knnedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.KNNEdges"]], "radialedges (class in graphnet.models.graphs.edges.edges)": [[62, "graphnet.models.graphs.edges.edges.RadialEdges"]], "forward() (graphnet.models.graphs.edges.edges.edgedefinition method)": [[62, "graphnet.models.graphs.edges.edges.EdgeDefinition.forward"]], "graphnet.models.graphs.edges.edges": [[62, "module-graphnet.models.graphs.edges.edges"]], "minkowskiknnedges (class in graphnet.models.graphs.edges.minkowski)": [[63, "graphnet.models.graphs.edges.minkowski.MinkowskiKNNEdges"]], "compute_minkowski_distance_mat() (in module graphnet.models.graphs.edges.minkowski)": [[63, "graphnet.models.graphs.edges.minkowski.compute_minkowski_distance_mat"]], "graphnet.models.graphs.edges.minkowski": [[63, "module-graphnet.models.graphs.edges.minkowski"]], "graphdefinition (class in graphnet.models.graphs.graph_definition)": [[64, "graphnet.models.graphs.graph_definition.GraphDefinition"]], "forward() (graphnet.models.graphs.graph_definition.graphdefinition method)": [[64, "graphnet.models.graphs.graph_definition.GraphDefinition.forward"]], "graphnet.models.graphs.graph_definition": [[64, "module-graphnet.models.graphs.graph_definition"]], "knngraph (class in graphnet.models.graphs.graphs)": [[65, "graphnet.models.graphs.graphs.KNNGraph"]], "graphnet.models.graphs.graphs": [[65, "module-graphnet.models.graphs.graphs"]], "graphnet.models.graphs.nodes": [[66, "module-graphnet.models.graphs.nodes"]], "nodedefinition (class in graphnet.models.graphs.nodes.nodes)": [[67, "graphnet.models.graphs.nodes.nodes.NodeDefinition"]], "nodesaspulses (class in graphnet.models.graphs.nodes.nodes)": [[67, "graphnet.models.graphs.nodes.nodes.NodesAsPulses"]], "percentileclusters (class in graphnet.models.graphs.nodes.nodes)": [[67, "graphnet.models.graphs.nodes.nodes.PercentileClusters"]], "forward() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[67, "graphnet.models.graphs.nodes.nodes.NodeDefinition.forward"]], "graphnet.models.graphs.nodes.nodes": [[67, "module-graphnet.models.graphs.nodes.nodes"]], "nb_outputs (graphnet.models.graphs.nodes.nodes.nodedefinition property)": [[67, "graphnet.models.graphs.nodes.nodes.NodeDefinition.nb_outputs"]], "set_number_of_inputs() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[67, "graphnet.models.graphs.nodes.nodes.NodeDefinition.set_number_of_inputs"]], "set_output_feature_names() (graphnet.models.graphs.nodes.nodes.nodedefinition method)": [[67, "graphnet.models.graphs.nodes.nodes.NodeDefinition.set_output_feature_names"]], "cluster_summarize_with_percentiles() (in module graphnet.models.graphs.utils)": [[68, "graphnet.models.graphs.utils.cluster_summarize_with_percentiles"]], "gather_cluster_sequence() (in module graphnet.models.graphs.utils)": [[68, "graphnet.models.graphs.utils.gather_cluster_sequence"]], "graphnet.models.graphs.utils": [[68, "module-graphnet.models.graphs.utils"]], "identify_indices() (in module graphnet.models.graphs.utils)": [[68, "graphnet.models.graphs.utils.identify_indices"]], "lex_sort() (in module graphnet.models.graphs.utils)": [[68, "graphnet.models.graphs.utils.lex_sort"]], "model (class in graphnet.models.model)": [[69, "graphnet.models.model.Model"]], "from_config() (graphnet.models.model.model class method)": [[69, "graphnet.models.model.Model.from_config"]], "graphnet.models.model": [[69, "module-graphnet.models.model"]], "load() (graphnet.models.model.model class method)": [[69, "graphnet.models.model.Model.load"]], "load_state_dict() (graphnet.models.model.model method)": [[69, "graphnet.models.model.Model.load_state_dict"]], "save() (graphnet.models.model.model method)": [[69, "graphnet.models.model.Model.save"]], "save_state_dict() (graphnet.models.model.model method)": [[69, "graphnet.models.model.Model.save_state_dict"]], "standardmodel (class in graphnet.models.standard_model)": [[70, "graphnet.models.standard_model.StandardModel"]], "compute_loss() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.compute_loss"]], "configure_optimizers() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.configure_optimizers"]], "fit() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.fit"]], "forward() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.forward"]], "graphnet.models.standard_model": [[70, "module-graphnet.models.standard_model"]], "inference() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.inference"]], "predict() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.predict"]], "predict_as_dataframe() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.predict_as_dataframe"]], "prediction_labels (graphnet.models.standard_model.standardmodel property)": [[70, "graphnet.models.standard_model.StandardModel.prediction_labels"]], "shared_step() (graphnet.models.standard_model.standardmodel method)": [[70, "graphnet.models.standard_model.StandardModel.shared_step"]], "target_labels (graphnet.models.standard_model.standardmodel property)": [[70, "graphnet.models.standard_model.StandardModel.target_labels"]], 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