diff --git a/.zenodo.json b/.zenodo.json index ef1cfee2..d7163889 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -1,19 +1,20 @@ { "description": "A package for event file analysis and recasting of LHC results", "license": "GPL-3.0", - "title": "MadAnalysis/madanalysis5: v1.10.12", - "version": "v1.10.12", + "title": "MadAnalysis/madanalysis5: v1.11.0", + "version": "v1.11.0", "upload_type": "software", "creators": [ { - "affiliation": "Thomas Jefferson National Accelerator Facility", + "affiliation": "City St George's, University of London", "name": "Araz, Jack Y.", "orcid": "0000-0001-8721-8042" }, { - "affiliation": "Sorbonne University", - "name": "Fuks, Benjamin" - }, + "affiliation": "LPTHE / CNRS / Sorbonne Université", + "name": "Fuks, Benjamin", + "orcid": "0000-0002-0041-0566" + }, { "affiliation": "Institut Pluridisciplinaire Hubert Curien", "name": "Conte, Eric" @@ -29,7 +30,7 @@ "related_identifiers": [ { "scheme": "url", - "identifier": "https://github.com/MadAnalysis/madanalysis5/tree/v1.10.12", + "identifier": "https://github.com/MadAnalysis/madanalysis5/tree/v1.11.0", "relation": "isSupplementTo" }, { @@ -38,4 +39,4 @@ "relation": "isDocumentedBy" } ] -} \ No newline at end of file +} diff --git a/bin/ma5 b/bin/ma5 index a7974cf4..1d3e1b34 100755 --- a/bin/ma5 +++ b/bin/ma5 @@ -1,8 +1,8 @@ -#!/usr/bin/env python +#!/usr/bin/env python3 ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -33,7 +33,7 @@ This is the main executable, a simple frontend to set up the PYTHONPATH and call immediately the command line interface scripts """ -import importlib +from importlib import util import os import sys @@ -48,7 +48,7 @@ if sys.version_info[0] != 3 or sys.version_info[1] <= 6: ) # Checking that the 'six' package is present -if not importlib.util.find_spec("six"): +if not util.find_spec("six"): sys.exit( 'The python "six" module is not found on your system and it is required for MadAnalysis 5 for ' + "a question of Python 2/3 compatibility. Please install it with the following command:\n" @@ -74,8 +74,8 @@ sys.path.insert(0, servicedir) # Release version # Do not touch it !!!!! -version = "1.10.16" -date = "2024/08/26" +version = "1.11.0" +date = "2025/04/23" # Loading the MadAnalysis session import madanalysis.core.launcher diff --git a/doc/releases/changelog-dev.md b/doc/releases/changelog-dev.md deleted file mode 100644 index 9400eed3..00000000 --- a/doc/releases/changelog-dev.md +++ /dev/null @@ -1,11 +0,0 @@ -# Release 2.x-dev (development release) - -## New features since last release - -## Improvements - -## Bug fixes - -## Contributors - -This release contains contributions from (in alphabetical order): diff --git a/doc/releases/changelog-v1.10.md b/doc/releases/changelog-v1.10.md index 6d94a884..59639290 100644 --- a/doc/releases/changelog-v1.10.md +++ b/doc/releases/changelog-v1.10.md @@ -68,6 +68,9 @@ * Include c-tagging options. +* Integrating RestFrames as well as readers for CSV-encoded efficiencies provided on HEPData. + + ## Bug fixes * Permanently fix the zlib version to the latest. diff --git a/doc/releases/changelog-v1.11.md b/doc/releases/changelog-v1.11.md new file mode 100644 index 00000000..89c63ddd --- /dev/null +++ b/doc/releases/changelog-v1.11.md @@ -0,0 +1,18 @@ +# Release v1.11 + +## New features since last release + +* Restframes framework has been impelemented for expert mode + +## Improvements + +* Statistical inference has been moved to Spey + +## Bug fixes + +## Contributors + +This release contains contributions from (in alphabetical order): + +* Jack Y. Araz +* Benjamin Fuks \ No newline at end of file diff --git a/madanalysis/IOinterface/delphescard_checker.py b/madanalysis/IOinterface/delphescard_checker.py index 3cfc690d..b1092801 100644 --- a/madanalysis/IOinterface/delphescard_checker.py +++ b/madanalysis/IOinterface/delphescard_checker.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/folder_writer.py b/madanalysis/IOinterface/folder_writer.py index bb1905b4..c0b9a264 100644 --- a/madanalysis/IOinterface/folder_writer.py +++ b/madanalysis/IOinterface/folder_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/histo_matplotlib_producer.py b/madanalysis/IOinterface/histo_matplotlib_producer.py index c67796a3..c1859612 100644 --- a/madanalysis/IOinterface/histo_matplotlib_producer.py +++ b/madanalysis/IOinterface/histo_matplotlib_producer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/histo_root_producer.py b/madanalysis/IOinterface/histo_root_producer.py index 77aa04a1..5720a4c0 100644 --- a/madanalysis/IOinterface/histo_root_producer.py +++ b/madanalysis/IOinterface/histo_root_producer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/html_report_writer.py b/madanalysis/IOinterface/html_report_writer.py index 3b3c35a9..36271752 100644 --- a/madanalysis/IOinterface/html_report_writer.py +++ b/madanalysis/IOinterface/html_report_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/html_style_writer.py b/madanalysis/IOinterface/html_style_writer.py index 7c4c064a..b3086dbf 100644 --- a/madanalysis/IOinterface/html_style_writer.py +++ b/madanalysis/IOinterface/html_style_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/job_reader.py b/madanalysis/IOinterface/job_reader.py index 584994a5..fa40da36 100644 --- a/madanalysis/IOinterface/job_reader.py +++ b/madanalysis/IOinterface/job_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/job_writer.py b/madanalysis/IOinterface/job_writer.py index c5fae488..7e8642e6 100644 --- a/madanalysis/IOinterface/job_writer.py +++ b/madanalysis/IOinterface/job_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/latex_report_writer.py b/madanalysis/IOinterface/latex_report_writer.py index b88a7c0b..5d30c609 100644 --- a/madanalysis/IOinterface/latex_report_writer.py +++ b/madanalysis/IOinterface/latex_report_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/layout_writer.py b/madanalysis/IOinterface/layout_writer.py index d4f1cbcf..c19bb322 100644 --- a/madanalysis/IOinterface/layout_writer.py +++ b/madanalysis/IOinterface/layout_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/library_writer.py b/madanalysis/IOinterface/library_writer.py index eb3ff3ef..0b6a27c1 100644 --- a/madanalysis/IOinterface/library_writer.py +++ b/madanalysis/IOinterface/library_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/madgraph_interface.py b/madanalysis/IOinterface/madgraph_interface.py index 6e312a08..d87b96fd 100644 --- a/madanalysis/IOinterface/madgraph_interface.py +++ b/madanalysis/IOinterface/madgraph_interface.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/multiparticle_reader.py b/madanalysis/IOinterface/multiparticle_reader.py index 7c9453d5..54e667f5 100644 --- a/madanalysis/IOinterface/multiparticle_reader.py +++ b/madanalysis/IOinterface/multiparticle_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/particle_reader.py b/madanalysis/IOinterface/particle_reader.py index b1be375b..bdfdbea6 100644 --- a/madanalysis/IOinterface/particle_reader.py +++ b/madanalysis/IOinterface/particle_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/png_reader.py b/madanalysis/IOinterface/png_reader.py index 4c0b8a9c..deaa89ef 100644 --- a/madanalysis/IOinterface/png_reader.py +++ b/madanalysis/IOinterface/png_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/saf_block_status.py b/madanalysis/IOinterface/saf_block_status.py index 3a77aaf6..b6a07730 100644 --- a/madanalysis/IOinterface/saf_block_status.py +++ b/madanalysis/IOinterface/saf_block_status.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/text_file_reader.py b/madanalysis/IOinterface/text_file_reader.py index 369dbc9b..db381f6a 100644 --- a/madanalysis/IOinterface/text_file_reader.py +++ b/madanalysis/IOinterface/text_file_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/text_file_writer.py b/madanalysis/IOinterface/text_file_writer.py index b60c2286..bc8d4d83 100644 --- a/madanalysis/IOinterface/text_file_writer.py +++ b/madanalysis/IOinterface/text_file_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/text_report.py b/madanalysis/IOinterface/text_report.py index 130aad68..5a91e622 100644 --- a/madanalysis/IOinterface/text_report.py +++ b/madanalysis/IOinterface/text_report.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/IOinterface/ufo_reader.py b/madanalysis/IOinterface/ufo_reader.py index c207fce7..8bdde415 100644 --- a/madanalysis/IOinterface/ufo_reader.py +++ b/madanalysis/IOinterface/ufo_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/build/makefile_writer.py b/madanalysis/build/makefile_writer.py index 1c1513ba..c7a822b6 100644 --- a/madanalysis/build/makefile_writer.py +++ b/madanalysis/build/makefile_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/build/setup_writer.py b/madanalysis/build/setup_writer.py index a40a2a63..cf206fb9 100644 --- a/madanalysis/build/setup_writer.py +++ b/madanalysis/build/setup_writer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/beauty_identification.py b/madanalysis/configuration/beauty_identification.py index b15fca16..a13f440e 100644 --- a/madanalysis/configuration/beauty_identification.py +++ b/madanalysis/configuration/beauty_identification.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_antikt.py b/madanalysis/configuration/clustering_antikt.py index 464b91b7..e27397f0 100644 --- a/madanalysis/configuration/clustering_antikt.py +++ b/madanalysis/configuration/clustering_antikt.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_cambridge.py b/madanalysis/configuration/clustering_cambridge.py index 0277eb24..8c9a4ef3 100644 --- a/madanalysis/configuration/clustering_cambridge.py +++ b/madanalysis/configuration/clustering_cambridge.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_cdfjetclu.py b/madanalysis/configuration/clustering_cdfjetclu.py index 660e64f9..16676834 100644 --- a/madanalysis/configuration/clustering_cdfjetclu.py +++ b/madanalysis/configuration/clustering_cdfjetclu.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_cdfmidpoint.py b/madanalysis/configuration/clustering_cdfmidpoint.py index dee30945..c0c67c03 100644 --- a/madanalysis/configuration/clustering_cdfmidpoint.py +++ b/madanalysis/configuration/clustering_cdfmidpoint.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_configuration.py b/madanalysis/configuration/clustering_configuration.py index df7c8272..71ea99d0 100644 --- a/madanalysis/configuration/clustering_configuration.py +++ b/madanalysis/configuration/clustering_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_genkt.py b/madanalysis/configuration/clustering_genkt.py index 20ffc50d..07d51fb5 100644 --- a/madanalysis/configuration/clustering_genkt.py +++ b/madanalysis/configuration/clustering_genkt.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_gridjet.py b/madanalysis/configuration/clustering_gridjet.py index bacde565..ca8135a5 100644 --- a/madanalysis/configuration/clustering_gridjet.py +++ b/madanalysis/configuration/clustering_gridjet.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_kt.py b/madanalysis/configuration/clustering_kt.py index f91d493f..347bc204 100644 --- a/madanalysis/configuration/clustering_kt.py +++ b/madanalysis/configuration/clustering_kt.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/clustering_siscone.py b/madanalysis/configuration/clustering_siscone.py index ebcd6c88..3353a9e0 100644 --- a/madanalysis/configuration/clustering_siscone.py +++ b/madanalysis/configuration/clustering_siscone.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/delphesMA5tune_configuration.py b/madanalysis/configuration/delphesMA5tune_configuration.py index ef9a83db..3cab80f1 100644 --- a/madanalysis/configuration/delphesMA5tune_configuration.py +++ b/madanalysis/configuration/delphesMA5tune_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/delphes_configuration.py b/madanalysis/configuration/delphes_configuration.py index c57ad781..16670e17 100644 --- a/madanalysis/configuration/delphes_configuration.py +++ b/madanalysis/configuration/delphes_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/fastsim_configuration.py b/madanalysis/configuration/fastsim_configuration.py index b2e1c5ac..1996bb40 100644 --- a/madanalysis/configuration/fastsim_configuration.py +++ b/madanalysis/configuration/fastsim_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/fom_configuration.py b/madanalysis/configuration/fom_configuration.py index 6420f895..7f286d4a 100644 --- a/madanalysis/configuration/fom_configuration.py +++ b/madanalysis/configuration/fom_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/isolation_cone.py b/madanalysis/configuration/isolation_cone.py index bb082247..07db2b55 100644 --- a/madanalysis/configuration/isolation_cone.py +++ b/madanalysis/configuration/isolation_cone.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/isolation_configuration.py b/madanalysis/configuration/isolation_configuration.py index ddb00b69..664c9b8e 100644 --- a/madanalysis/configuration/isolation_configuration.py +++ b/madanalysis/configuration/isolation_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/isolation_sumpt.py b/madanalysis/configuration/isolation_sumpt.py index 8797a2ea..4c311bfe 100644 --- a/madanalysis/configuration/isolation_sumpt.py +++ b/madanalysis/configuration/isolation_sumpt.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/merging_configuration.py b/madanalysis/configuration/merging_configuration.py index d0725263..6058330f 100644 --- a/madanalysis/configuration/merging_configuration.py +++ b/madanalysis/configuration/merging_configuration.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/configuration/recast_configuration.py b/madanalysis/configuration/recast_configuration.py index 6980e309..fa875a6f 100644 --- a/madanalysis/configuration/recast_configuration.py +++ b/madanalysis/configuration/recast_configuration.py @@ -1,107 +1,126 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ from __future__ import absolute_import -from madanalysis.enumeration.ma5_running_type import MA5RunningType -import glob, logging, shutil, os, sys -from six.moves import range + +import glob +import logging +import os +import shutil + +from madanalysis.enumeration.ma5_running_type import MA5RunningType + +# pylint: disable=logging-fstring-interpolation, logging-not-lazy + class RecastConfiguration: - default_CLs_numofexps = 100000 - - userVariables ={ - "status" : ["on","off"],\ - "CLs_numofexps" : [str(default_CLs_numofexps)],\ - "card_path" : "",\ - "store_root" : ["True", "False"] , \ - "store_events" : ["True", "False"] , \ - "THerror_combination" : ["quadratic","linear"], \ - "error_extrapolation" : ["linear", "sqrt"],\ - "global_likelihoods" : ["on","off"],\ - "CLs_calculator_backend" : ["native", "pyhf"],\ - "simplify_likelihoods" : ["True", "False"],\ - "expectation_assumption" : ["apriori", "aposteriori"],\ - "TACO_output" : "" + userVariables = { + "status": ["on", "off"], + "card_path": "", + "store_root": ["True", "False"], + "store_events": ["True", "False"], + "THerror_combination": ["quadratic", "linear"], + "error_extrapolation": ["linear", "sqrt"], + "global_likelihoods": ["on", "off"], + "simplify_likelihoods": ["True", "False"], + "analysis_only_mode": ["True", "False"], +# "stat_only_mode": "", + "TACO_output": "" } def __init__(self): - self.status = "off" - self.delphes = False - self.ma5tune = False - self.pad = False - self.padtune = False - self.padsfs = False - self.store_root = False - self.store_events = False - self.TACO_output = "" - self.global_likelihoods_switch = True - self.CLs_calculator_backend = "native" - self.simplify_likelihoods = False - self.expectation_assumption = "apriori" - self.systematics = [] - self.extrapolated_luminosities = [] - self.THerror_combination = "linear" - self.error_extrapolation = "linear" - self.DelphesDic = { } - self.description = { } - self.ma5dir = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath( __file__ )), - os.pardir,os.pardir)) - for mypad in ['PAD', 'PADForMA5tune', 'PADForSFS']: - if os.path.isfile(os.path.join(self.ma5dir,'tools',mypad,'Input','recast_config.dat')): + self.status = "off" + self.delphes = False + self.ma5tune = False + self.pad = False + self.padtune = False + self.padsfs = False + self.store_root = False + self.store_events = False + self.TACO_output = "" + self.global_likelihoods_switch = True + self.simplify_likelihoods = False + self.systematics = [] + self.extrapolated_luminosities = [] + self.THerror_combination = "linear" + self.error_extrapolation = "linear" + self.stat_only_mode = False + self.analysis_only_mode = False + self.stat_only_dir = None + self.DelphesDic = {} + self.description = {} + self.ma5dir = os.path.abspath( + os.path.join( + os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir + ) + ) + for mypad in ["PAD", "PADForMA5tune", "PADForSFS"]: + if os.path.isfile( + os.path.join(self.ma5dir, "tools", mypad, "Input", "recast_config.dat") + ): dico_file = open( - os.path.join(self.ma5dir,'tools',mypad,'Input','recast_config.dat'), 'r' + os.path.join( + self.ma5dir, "tools", mypad, "Input", "recast_config.dat" + ), + "r", ) for line in dico_file: - if line.strip().startswith('#'): + if line.strip().startswith("#"): continue - self.DelphesDic[line.split('|')[0].strip()] = line.split('|')[1].split() + self.DelphesDic[line.split("|")[0].strip()] = line.split("|")[ + 1 + ].split() dico_file.close() - if os.path.isfile(os.path.join(self.ma5dir,'tools',mypad,'Input','analysis_description.dat')): + if os.path.isfile( + os.path.join( + self.ma5dir, "tools", mypad, "Input", "analysis_description.dat" + ) + ): dico_file = open( - os.path.join(self.ma5dir,'tools',mypad,'Input','analysis_description.dat'), - 'r' + os.path.join( + self.ma5dir, "tools", mypad, "Input", "analysis_description.dat" + ), + "r", ) for line in dico_file: - if line.strip().startswith('#'): + if line.strip().startswith("#"): continue - self.description[line.split('|')[0].strip()] = line.split('|')[1][:-1] + self.description[line.split("|")[0].strip()] = line.split("|")[1][:-1] dico_file.close() - self.CLs_numofexps= 100000 - self.card_path= "" - self.logger = logging.getLogger('MA5') + self.card_path = "" + self.logger = logging.getLogger("MA5") def Display(self): self.user_DisplayParameter("status") - if self.status=="on": + if self.status == "on": self.user_DisplayParameter("delphes") self.user_DisplayParameter("ma5tune") self.user_DisplayParameter("pad") self.user_DisplayParameter("padtune") self.user_DisplayParameter("padsfs") - self.user_DisplayParameter("CLs_numofexps") self.user_DisplayParameter("card_path") self.user_DisplayParameter("store_events") self.user_DisplayParameter("TACO_output") @@ -110,197 +129,227 @@ def Display(self): self.user_DisplayParameter("THerror_combination") self.user_DisplayParameter("error_extrapolation") self.user_DisplayParameter("global_likelihoods") - self.user_DisplayParameter("CLs_calculator_backend") - self.user_DisplayParameter("simplify_likelihoods") - self.user_DisplayParameter("expectation_assumption") - - def user_DisplayParameter(self,parameter): - if parameter=="status": - self.logger.info(" recasting mode: "+self.status) - return - elif parameter=="delphes": - if self.delphes: - self.logger.info(" * analyses based on delphes : allowed") - else: - self.logger.info(" * analyses based on delphes : not allowed") - return - elif parameter=="ma5tune": - if self.ma5tune: - self.logger.info(" * analyses based on the ma5tune: allowed") - else: - self.logger.info(" * analyses based on the ma5tune: not allowed") - return - elif parameter=="pad": - if self.pad: - self.logger.info(" * the PAD is : available") - else: - self.logger.info(" * the PAD is : not available") - return - elif parameter=="padtune": - if self.padtune: - self.logger.info(" * the PADForMa5tune is : available") - else: - self.logger.info(" * the PADForMa5tune is : not available") - return - elif parameter=="padsfs": - if self.padsfs: - self.logger.info(" * the PADForSFS is : available") - else: - self.logger.info(" * the PADForSFS is : not available") - return - elif parameter=="CLs_numofexps": - self.logger.info(" * Number of toy experiments for the CLs calculation: "+str(self.CLs_numofexps)) - return - elif parameter=="card_path": - self.logger.info(" * Path to a recasting card: "+str(self.card_path)) - return - elif parameter=="store_root" or parameter=="store_events": - self.logger.info(" * Keeping the event files: "+str(self.store_root or self.store_events)) - return - elif parameter=="TACO_output": - self.logger.info(" * Running in TACO mode and storing the results at " +str(self.TACO_output)); - return - elif parameter=="systematics": +# self.user_DisplayParameter("stat_only_mode") + self.user_DisplayParameter("analysis_only_mode") + + def user_DisplayParameter(self, parameter): + if parameter == "status": + self.logger.info(f" recasting mode: {self.status}") + elif parameter == "delphes": + self.logger.info( + f" * analyses based on delphes : {('not '*(not self.delphes)) + 'allowed'}" + ) + elif parameter == "ma5tune": + self.logger.info( + f" * analyses based on the ma5tune: {('not '*(not self.ma5tune)) + 'allowed'}" + ) + elif parameter == "pad": + self.logger.info( + f" * the PAD is : {('not '*(not self.pad)) + 'available'}" + ) + elif parameter == "padtune": + self.logger.info( + f" * the PADForMa5tune is : {('not '*(not self.padtune)) + 'available'}" + ) + elif parameter == "padsfs": + self.logger.info( + f" * the PADForSFS is : {('not '*(not self.padsfs)) + 'available'}" + ) + elif parameter == "card_path": + self.logger.info(" * Path to a recasting card: " + str(self.card_path)) + elif parameter in ["store_root", "store_events"]: + self.logger.info( + " * Keeping the event files: " + + str(self.store_root or self.store_events) + ) + elif parameter == "TACO_output": + self.logger.info( + " * Running in TACO mode and storing the results at " + + str(self.TACO_output) + ) + elif parameter == "systematics": if len(self.systematics) > 0: - for i in range(0,len(self.systematics)): - up, dn = self.systematics[i] - self.logger.info(" * Systematics "+str(i+1)+": [+{:.1%}, -{:.1%}]".format(up,dn)) - return - elif parameter=="extrapolated_luminosity": + for idx, syst in enumerate(self.systematics): + up, dn = syst + self.logger.info(f" * Systematics {idx}: [+{up:.1%}, -{dn:.1%}]") + elif parameter == "extrapolated_luminosity": if len(self.extrapolated_luminosities) > 0: - tmp = ["{:.1f}".format(x)+" fb^{-1}" for x in self.extrapolated_lumi] - self.logger.info(" * Results extrapolated for the luminosities: "+', '.join(tmp)) - return - elif parameter=="THerror_combination": - self.logger.info(" * Theory errors (if provided) are combined in a " + self.THerror_combination + " way") - return - elif parameter=="error_extrapolation": - if type(self.error_extrapolation) == str: - self.logger.info(" * Errors on the background extrapolated " + self.error_extrapolation + "ly (if necessary)") + tmp = [ + "{:.1f}".format(x) + " fb^{-1}" + for x in self.extrapolated_luminosities + ] + self.logger.info( + " * Results extrapolated for the luminosities: " + ", ".join(tmp) + ) + elif parameter == "THerror_combination": + self.logger.info( + " * Theory errors (if provided) are combined in a " + + self.THerror_combination + + " way" + ) + elif parameter == "error_extrapolation": + if isinstance(self.error_extrapolation, str): + self.logger.info( + " * Errors on the background extrapolated " + + self.error_extrapolation + + "ly (if necessary)" + ) else: - if self.error_extrapolation[1]==0: - self.logger.info(" * Relative error on the extrapolated background taken as"+\ - " {:.1%}".format(self.error_extrapolation[0])) + if self.error_extrapolation[1] == 0: + self.logger.info( + " * Relative error on the extrapolated background taken as" + + " {:.1%}".format(self.error_extrapolation[0]) + ) else: - self.logger.info(" * Relative error on the extrapolated background Nb taken as"+\ - " sqrt({:.2f}^2 + ({:.2f}/Nb)^2)".format(self.error_extrapolation[0],self.error_extrapolation[1])) - return - elif parameter=="global_likelihoods": - self.logger.info(" * Global-Likelihoods will"+(not self.global_likelihoods_switch)*' not'+\ - ' be calculated'+(self.global_likelihoods_switch)*', if available'+'.') - return - elif parameter=="CLs_calculator_backend": - self.logger.info(" * Exclusion limits will be calculated with " + - (self.CLs_calculator_backend == "native")*' MadAnalysis 5 native calculator'+ \ - (self.CLs_calculator_backend == "pyhf")*' pyhf (if available)'+'.') - return - elif parameter=="simplify_likelihoods": + self.logger.info( + " * Relative error on the extrapolated background Nb taken as" + + " sqrt({:.2f}^2 + ({:.2f}/Nb)^2)".format( + self.error_extrapolation[0], self.error_extrapolation[1] + ) + ) + elif parameter == "global_likelihoods": + self.logger.info( + " * Global-Likelihoods will" + + (not self.global_likelihoods_switch) * " not" + + " be calculated" + + (self.global_likelihoods_switch) * ", if available" + + "." + ) + elif parameter == "simplify_likelihoods": if self.simplify_likelihoods: - self.logger.debug(" * Simplified profile likelihoods will be used when available.") - return - elif parameter=="expectation_assumption": - self.logger.info(" * A "+self.expectation_assumption[1:]+ - " expected exclusion limits will be used.") - return + self.logger.info( + " * Simplified profile likelihoods will be used when available." + ) +# elif parameter == "stat_only_mode": +# if self.stat_only_mode: +# self.logger.info( +# " * Test statistics will be computed for the given analysis." +# ) + elif parameter == "analysis_only_mode": + if self.analysis_only_mode: + self.logger.info(" * MadAnalysis 5 will only compute the various signal region efficiencies (no statistical treatment).") return - def user_SetParameter(self,parameters,values,level,archi_info,session_info,datasets): + def user_SetParameter( + self, parameters, values, level, archi_info, session_info, datasets + ): # Make sure that previous features are unchanged: the 'add' keyword is properly dealt with if isinstance(parameters, list): parameter = parameters[0] value = values[0] else: - parameter=parameters - value=values + parameter = parameters + value = values + + if parameter != "status" and self.status != "on": + self.logger.error("Please first set the recasting mode to 'on'.") + return + # algorithm - if parameter=="status": + if parameter == "status": # Switch on the clustering - if value =="on": + if value == "on": # Only in reco mode - if level!=MA5RunningType.RECO: + if level != MA5RunningType.RECO: self.logger.error("recasting is only available in the RECO mode") return - # # Only if ROOT is install - # if not archi_info.has_root: - # self.logger.error("recasting is only available if ROOT is installed") - # return + # # Only if ROOT is install + # if not archi_info.has_root: + # self.logger.error("recasting is only available if ROOT is installed") + # return - canrecast=False + canrecast = False # Delphes and the PAD? if archi_info.has_root and archi_info.has_delphes: - self.delphes=True + self.delphes = True if archi_info.has_root and session_info.has_pad: - self.pad=True - if not archi_info.has_delphes or not session_info.has_pad: - self.logger.warning("Delphes and/or the PAD are not installed (or deactivated): " + \ - "the corresponding analyses will be unavailable") + self.pad = True + if ( + not archi_info.has_delphes + or not session_info.has_pad + or not archi_info.has_spey + ): + self.logger.warning( + "Delphes and/or the PAD are not installed (or deactivated): " + + "the corresponding analyses will be unavailable" + ) else: - canrecast=True + canrecast = True + + if not archi_info.has_spey: + self.logger.warning("Recast module requires Spey package.") + self.logger.warning( + "Installation instructions can be found at https://spey.readthedocs.io/" + ) + canrecast = False # DelphesMA5tune and the PADFor MA5TUne? if archi_info.has_root and archi_info.has_delphesMA5tune: - self.ma5tune=True + self.ma5tune = True if archi_info.has_root and session_info.has_padma5: - self.padtune=True - if not archi_info.has_delphesMA5tune or not session_info.has_padma5: - self.logger.warning("DelphesMA5tune and/or the PADForMA5tune are not installed " + \ - "(or deactivated): the corresponding analyses will be unavailable") + self.padtune = True + if ( + not archi_info.has_delphesMA5tune + or not session_info.has_padma5 + or not archi_info.has_spey + ): + self.logger.warning( + "DelphesMA5tune and/or the PADForMA5tune are not installed " + + "(or deactivated): the corresponding analyses will be unavailable" + ) else: - canrecast=True + canrecast = True # PADForSFS? if session_info.has_padsfs: - self.padsfs=True + self.padsfs = True if not self.padsfs: - self.logger.warning("PAD for Simplified-FastSim is not installed: " + \ - "the corresponding analyses will be unavailable") + self.logger.warning( + "PAD for Simplified-FastSim is not installed: " + + "the corresponding analyses will be unavailable" + ) else: - canrecast=True + canrecast = True # can we use the recasting mode if canrecast: - self.status="on" + self.status = "on" else: - self.logger.error("The recasting modules (PAD/Delphes, PADForMA5tune/DelphesMa5tune) " + \ - "are not available. The recasting mode cannot be activated") + self.logger.error( + "The recasting modules (PAD/Delphes, PADForMA5tune/DelphesMa5tune, Spey) " + + "are not available. The recasting mode cannot be activated" + ) return - elif value =="off": - test=True + elif value == "off": + test = True for dataset in datasets: if not test: break for file in dataset.filenames: - if file.endswith('hep') or \ - file.endswith('hep.gz') or \ - file.endswith('hepmc') or \ - file.endswith('hepmc.gz'): - test=False + if ( + file.endswith("hep") + or file.endswith("hep.gz") + or file.endswith("hepmc") + or file.endswith("hepmc.gz") + ): + test = False break if not test: - self.logger.error("some datasets have a hadronic file format. "+\ - "The recasting mode cannot be switched off.") + self.logger.error( + "some datasets have a hadronic file format. " + + "The recasting mode cannot be switched off." + ) return - self.status="off" + self.status = "off" else: self.logger.error("Recasting can only be set to 'on' or 'off'.") - # CLs module - elif parameter=="CLs_numofexps": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - self.CLs_numofexps = int(value) - # path to a recasting card - elif parameter=="card_path": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - import os + elif parameter == "card_path": if os.path.isfile(value): self.card_path = value else: @@ -308,65 +357,65 @@ def user_SetParameter(self,parameters,values,level,archi_info,session_info,datas return # Keeping the root files - elif parameter=="store_root" or parameter=="store_events": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - if value == 'True': - self.store_root = True + elif parameter == "store_root" or parameter == "store_events": + if value == "True": + self.store_root = True self.store_events = True - elif value == 'False': - self.store_root = False + elif value == "False": + self.store_root = False self.store_events = False else: self.logger.error("Do the root files need to be stored? (True/False)") return # Running in TACO mode - elif parameter=="TACO_output": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - self.TACO_output = value + elif parameter == "TACO_output": + self.TACO_output = value # Systematic uncertainties and Luminosity extrapolation - elif parameter=="add": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return + elif parameter == "add": ## Checking the values try: - vals = [float(x) for x in values if x!=','] + vals = [float(x) for x in values if x != ","] except: - self.logger.error("Values for the systematic uncertainties and extrapolated luminosities should be real") + self.logger.error( + "Values for the systematic uncertainties and extrapolated luminosities should be real" + ) return ## Systematics - if len(parameters)>1 and parameters[1]=='systematics': - if len(vals) == 1 and vals[0] >= 0. and vals[0] <= 1.: - self.systematics.append((vals[0],vals[0])) - elif len(vals) == 2 and vals[0]>=0. and vals[0]<=1. and vals[1]>= 0. and vals[1]<= 1.: - self.systematics.append((vals[0],vals[1])) + if len(parameters) > 1 and parameters[1] == "systematics": + if len(vals) == 1 and vals[0] >= 0.0 and vals[0] <= 1.0: + self.systematics.append((vals[0], vals[0])) + elif ( + len(vals) == 2 + and vals[0] >= 0.0 + and vals[0] <= 1.0 + and vals[1] >= 0.0 + and vals[1] <= 1.0 + ): + self.systematics.append((vals[0], vals[1])) else: - self.logger.error("Invalid syntax for adding systematics uncertainties.") + self.logger.error( + "Invalid syntax for adding systematics uncertainties." + ) return ## Extrapolated lumis - elif len(parameters)>1 and parameters[1]=='extrapolated_luminosity': + elif len(parameters) > 1 and parameters[1] == "extrapolated_luminosity": if len(vals) >= 1: self.extrapolated_luminosities += vals else: - self.logger.error("Invalid syntax for adding extrapolated luminosities.") + self.logger.error( + "Invalid syntax for adding extrapolated luminosities." + ) return ## protection else: - self.logger.error("Invalid syntax with the \'add\' keyword") + self.logger.error("Invalid syntax with the 'add' keyword") return # Error combination - elif parameter=="THerror_combination": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - if value in ["quadratic","linear"]: + elif parameter == "THerror_combination": + if value in ["quadratic", "linear"]: self.THerror_combination = value else: self.logger.error("Theoretical uncertainties can only be combined") @@ -374,153 +423,119 @@ def user_SetParameter(self,parameters,values,level,archi_info,session_info,datas return # Error extrapolation - elif parameter=="error_extrapolation": + elif parameter == "error_extrapolation": + def error_message(): - self.logger.error("When extrapolating to different luminosities, uncertainties") - self.logger.error("can only be extrapolated linearly [linear], sqrtly [sqrt], ") + self.logger.error( + "When extrapolating to different luminosities, uncertainties" + ) + self.logger.error( + "can only be extrapolated linearly [linear], sqrtly [sqrt], " + ) self.logger.error("overwriten by a single user-defined value (systs)") - self.logger.error("or taken as two comma-separated user-defined values (systs, stats)") - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return + self.logger.error( + "or taken as two comma-separated user-defined values (systs, stats)" + ) + if value in ["linear", "sqrt"]: self.error_extrapolation = value else: - all_values = [x for x in values if x !=',']; - if len(all_values)>2: - error_message(); - return; + all_values = [x for x in values if x != ","] + if len(all_values) > 2: + error_message() + return try: - if len(all_values)==1: - self.error_extrapolation = [float(value),0] + if len(all_values) == 1: + self.error_extrapolation = [float(value), 0] else: - self.error_extrapolation = [float(x) for x in all_values]; - except: - error_message(); + self.error_extrapolation = [float(x) for x in all_values] + except ValueError: + error_message() return # Switch to turn off the global likelihood calculations - elif parameter=="global_likelihoods": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return + elif parameter == "global_likelihoods": if value.lower() in ["on", "off"]: - self.global_likelihoods_switch = (value.lower()=="on") - else: - self.logger.error("You can only switch the global-likelihood machinery to 'on' or 'off'.") - return - - # Set exclusion limit calculator - elif parameter == "CLs_calculator_backend": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return - if value.lower() in ["native", "pyhf"]: - if value.lower() == "pyhf": - # if self.session_info.has_pyhf: - self.CLs_calculator_backend = "pyhf" - self.logger.warning("pyhf will be used as exclusion limit calculator, if available.") - # else: - # self.logger.error("Please install pyhf first by typing `install pyhf`") - # return - else: - self.CLs_calculator_backend = "native" + self.global_likelihoods_switch = value.lower() == "on" else: - self.logger.error("Unknown calculator "+str(value)+\ - ". Please choose between native or pyhf") + self.logger.error( + "You can only switch the global-likelihood machinery to 'on' or 'off'." + ) return - #Set simplified likelihoods + # Set simplified likelihoods elif parameter == "simplify_likelihoods": - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") - return if value.lower() in ["true", "false"]: - self.simplify_likelihoods = (value.lower() == "true") + self.simplify_likelihoods = value.lower() == "true" if self.simplify_likelihoods: - self.logger.warning("Please note that this method is currently under "+\ - "development and relies on third party software.") + self.logger.warning( + "Please note that this method is currently under " + + "development and relies on third party software." + ) else: self.logger.error("Please type either True or False.") return - #Set expectation assumption - elif parameter == "expectation_assumption": - if sys.version_info[0] != 3 and sys.version_info[1] < 6: - self.logger.error("This option is only available for python>=3.6") - return - if self.status!="on": - self.logger.error("Please first set the recasting mode to 'on'.") + elif parameter == "stat_only_mode": + if value.lower() == "false": + self.stat_only_mode = False + if os.path.isdir(os.path.join(value, "Output/SAF")): + self.stat_only_dir = value + self.stat_only_mode = True + else: + self.logger.error("{value} is not a valid directory.") return - if value.lower() in ["apriori", "aposteriori"]: - self.expectation_assumption = value.lower() - if self.expectation_assumption == "aposteriori": - self.logger.warning("A posteriori expected confidence limit calculation is " +\ - "only available with `pyhf` module.") - self.logger.warning("Setting CLs calculator to `pyhf`.") - self.CLs_calculator_backend = "pyhf" + elif parameter == "analysis_only_mode": + if value.lower() == "false": + self.analysis_only_mode = False + elif value.lower() == "true": + self.analysis_only_mode = True else: - self.logger.error("Expectation assumption can either be `apriori` or `aposteriori`.") + self.logger.error("analysis_only_mode can only be set to 'True' or 'False'.") return # other rejection if no algo specified else: - self.logger.error("the recast module has no parameter called '"+str(parameter)+"'") + self.logger.error(f"The recast module has no parameter called '{parameter}'") return - def user_GetParameters(self,var=''): - if self.status=="on": + def user_GetParameters(self, var=""): + if self.status == "on": if var == "add": table = ["extrapolated_luminosity", "systematics"] else: - table = ["CLs_numofexps", "card_path", "store_events", 'TACO_output', "add", - "THerror_combination", "error_extrapolation", "global_likelihoods", - "CLs_calculator_backend", "expectation_assumption"]#, "simplify_likelihoods" + table = [ + "card_path", + "store_events", + "TACO_output", + "add", + "THerror_combination", + "error_extrapolation", + "global_likelihoods", +# "stat_only_mode", + "analysis_only_mode" + ] # , "simplify_likelihoods" else: - table = [] + table = [] return table - - def user_GetValues(self,variable): + def user_GetValues(self, variable): table = [] - if variable=="status": - table.extend(RecastConfiguration.userVariables["status"]) - elif variable =="CLs_numofexps": - table.extend(RecastConfiguration.userVariables["CLs_numofexps"]) - elif variable =="card_path": - table.extend(RecastConfiguration.userVariables["card_path"]) - elif variable =="store_root": - table.extend(RecastConfiguration.userVariables["store_root"]) - elif variable =="store_events": - table.extend(RecastConfiguration.userVariables["store_events"]) - elif variable =="TACO_output": - table.extend(RecastConfiguration.userVariables["TACO_output"]) - elif variable =="THerror_combination": - table.extend(RecastConfiguration.userVariables["THerror_combination"]) - elif variable =="error_extrapolation": - table.extend(RecastConfiguration.userVariables["error_extrapolation"]) - elif variable =="global_likelihoods": - table.extend(RecastConfiguration.userVariables["global_likelihoods"]) - elif variable =="CLs_calculator_backend": - table.extend(RecastConfiguration.userVariables["CLs_calculator_backend"]) - elif variable =="simplify_likelihoods": - table.extend(RecastConfiguration.userVariables["simplify_likelihoods"]) - elif variable =="expectation_assumption": - table.extend(RecastConfiguration.userVariables["expectation_assumption"]) + if variable in RecastConfiguration.userVariables: + table.extend(RecastConfiguration.userVariables[variable]) return table - - def CreateCard(self,dirname,write=True): + def CreateCard(self, dirname, write=True): # using an existing card - if self.card_path=="": + if self.card_path == "": if self.padtune and self.ma5tune: - self.CreateMyCard(dirname,"PADForMA5tune",write) + self.CreateMyCard(dirname, "PADForMA5tune", write) if self.pad and self.delphes: - self.CreateMyCard(dirname,"PAD",write) + self.CreateMyCard(dirname, "PAD", write) if self.padsfs: - self.CreateMyCard(dirname,"PADForSFS",write) + self.CreateMyCard(dirname, "PADForSFS", write) return True - #using and checking an existing card + # using and checking an existing card else: if not os.path.isfile(self.card_path): self.logger.error("Invalid path to a recasting card.") @@ -530,179 +545,286 @@ def CreateCard(self,dirname,write=True): return False return True - def CheckCard(self,dirname): - self.logger.info(' Checking the recasting card...') + def CheckCard(self, dirname): + self.logger.info(" Checking the recasting card...") ToLoopOver = [] - padlist = [] - tunelist = [] - sfslist = [] + padlist = [] + tunelist = [] + sfslist = [] if self.pad: - padfile = open(os.path.normpath(os.path.join(self.ma5dir,"tools/PAD/Build/Main/main.cpp")), 'r') + padfile = open( + os.path.normpath( + os.path.join(self.ma5dir, "tools/PAD/Build/Main/main.cpp") + ), + "r", + ) ToLoopOver.append([padfile, padlist]) if self.padtune: - tunefile = open(os.path.normpath(os.path.join(self.ma5dir,"tools/PADForMA5tune/Build/Main/main.cpp")), 'r') + tunefile = open( + os.path.normpath( + os.path.join(self.ma5dir, "tools/PADForMA5tune/Build/Main/main.cpp") + ), + "r", + ) ToLoopOver.append([tunefile, tunelist]) if self.padsfs: # get the analysis list that is available in the folder - sfs_path = os.path.normpath(os.path.join(self.ma5dir,"tools/PADForSFS/Build/SampleAnalyzer/User/Analyzer")) - analysislist = [x.split('/')[-1].split('.cpp')[0] for x in glob.glob(sfs_path+'/*.cpp')]; + sfs_path = os.path.normpath( + os.path.join( + self.ma5dir, "tools/PADForSFS/Build/SampleAnalyzer/User/Analyzer" + ) + ) + analysislist = [ + x.split("/")[-1].split(".cpp")[0] for x in glob.glob(sfs_path + "/*.cpp") + ] # should check corresponding headers, keep only the analyses with headers - headerlist = [x.split('/')[-1].split('.h')[0] for x in glob.glob(sfs_path+'/*.h') if not x.startswith('analysisList')] + headerlist = [ + x.split("/")[-1].split(".h")[0] + for x in glob.glob(sfs_path + "/*.h") + if not x.startswith("analysisList") + ] analysislist = [i for i in analysislist if i in headerlist] # getting the list of available detector cards - sfs_path = os.path.normpath(os.path.join(self.ma5dir,"tools/PADForSFS/Input/Cards")) - cardlist = [x.split('/')[-1] for x in glob.glob(sfs_path+'/*.ma5')]; + sfs_path = os.path.normpath( + os.path.join(self.ma5dir, "tools/PADForSFS/Input/Cards") + ) + cardlist = [x.split("/")[-1] for x in glob.glob(sfs_path + "/*.ma5")] # final list with analyses for ma5card, analysis in self.DelphesDic.items(): for ana in analysis: if ana in analysislist and ma5card in cardlist: sfslist.append([ana, ma5card]) - for myfile,mylist in ToLoopOver: + for myfile, mylist in ToLoopOver: for line in myfile: if "manager.InitializeAnalyzer" in line: - analysis = str(line.split('\"')[1]) - mydelphes="UNKNOWN" - for mycard,alist in self.DelphesDic.items(): - if analysis in alist: - mydelphes=mycard - break - mylist.append([analysis,mydelphes]) + analysis = str(line.split('"')[1]) + mydelphes = "UNKNOWN" + for mycard, alist in self.DelphesDic.items(): + if analysis in alist: + mydelphes = mycard + break + mylist.append([analysis, mydelphes]) if self.pad: padfile.close() if self.padtune: tunefile.close() usercard = open(self.card_path) for line in usercard: - if len(line.strip())==0: + if len(line.strip()) == 0: continue - if line.lstrip()[0]=='#': + if line.lstrip()[0] == "#": continue - myline=line.split() + myline = line.split() myana = myline[0] myver = myline[1] mydelphes = myline[3] # checking the presence of the analysis and the delphes card - if myver=="v1.2": - if not myana in [x[0] for x in padlist]: - self.logger.error("Recasting card: invalid analysis (not present in the PAD): " + myana) + if myver == "v1.2": + if not myana in [x[0] for x in padlist]: + self.logger.error( + "Recasting card: invalid analysis (not present in the PAD): " + + myana + ) return False - if not os.path.isfile(os.path.normpath(os.path.join(self.ma5dir,'tools/PAD/Input/Cards',mydelphes))): - self.logger.error("Recasting card: PAD analysis linked to an invalid delphes card: " + myana + " - " + mydelphes) + if not os.path.isfile( + os.path.normpath( + os.path.join(self.ma5dir, "tools/PAD/Input/Cards", mydelphes) + ) + ): + self.logger.error( + "Recasting card: PAD analysis linked to an invalid delphes card: " + + myana + + " - " + + mydelphes + ) return False - elif myver=="v1.1": - if not myana in [x[0] for x in tunelist]: - self.logger.error("Recasting card: invalid analysis (not present in the PADForMA5tune): " + myana) + elif myver == "v1.1": + if not myana in [x[0] for x in tunelist]: + self.logger.error( + "Recasting card: invalid analysis (not present in the PADForMA5tune): " + + myana + ) return False - if not os.path.isfile(os.path.normpath(os.path.join(self.ma5dir,'tools/PADForMA5tune/Input/Cards',mydelphes))): - self.logger.error("Recasting card: PADForMA5tune analysis linked to an invalid delphes card: " +\ - myana + " - " + mydelphes) + if not os.path.isfile( + os.path.normpath( + os.path.join( + self.ma5dir, "tools/PADForMA5tune/Input/Cards", mydelphes + ) + ) + ): + self.logger.error( + "Recasting card: PADForMA5tune analysis linked to an invalid delphes card: " + + myana + + " - " + + mydelphes + ) return False - elif myver=="vSFS": - if not myana in [x[0] for x in sfslist]: - self.logger.error("Recasting card: invalid analysis (not present in PADForSFS): " + myana) + elif myver == "vSFS": + if not myana in [x[0] for x in sfslist]: + self.logger.error( + "Recasting card: invalid analysis (not present in PADForSFS): " + + myana + ) return False - if not os.path.isfile(os.path.normpath(os.path.join(self.ma5dir,'tools/PADForSFS/Input/Cards',mydelphes))): - self.logger.error("Recasting card: PADForSFS analysis linked to an invalid SFS card: " + myana + " - " + mydelphes) + if not os.path.isfile( + os.path.normpath( + os.path.join( + self.ma5dir, "tools/PADForSFS/Input/Cards", mydelphes + ) + ) + ): + self.logger.error( + "Recasting card: PADForSFS analysis linked to an invalid SFS card: " + + myana + + " - " + + mydelphes + ) return False else: - self.logger.error("Recasting card: invalid analysis (not present in the PAD, PADForMA5tune and PADForSFS): " + myana) + self.logger.error( + "Recasting card: invalid analysis (not present in the PAD, PADForMA5tune and PADForSFS): " + + myana + ) return False # checking the matching between the delphes card and the analysis - for mycard,alist in self.DelphesDic.items(): - if myana in alist and myver!='vSFS': - if mydelphes!=mycard: - self.logger.error("Invalid delphes card associated with the analysis: " + myana) + for mycard, alist in self.DelphesDic.items(): + if myana in alist and myver != "vSFS": + if mydelphes != mycard: + self.logger.error( + "Invalid delphes card associated with the analysis: " + myana + ) return False break usercard.close() try: - shutil.copy(self.card_path,dirname+'/Input/recasting_card.dat') + shutil.copy(self.card_path, dirname + "/Input/recasting_card.dat") except: - self.logger.error('impossible to copy the recasting card to the working directory') + self.logger.error( + "impossible to copy the recasting card to the working directory" + ) return False return True - - def CreateMyCard(self,dirname,padtype,write=True): - thecard=[] + def CreateMyCard(self, dirname, padtype, write=True): + thecard = [] if write: - exist=os.path.isfile(dirname+'/Input/recasting_card.dat') + exist = os.path.isfile(dirname + "/Input/recasting_card.dat") if not exist and write: - thecard.append('# Detector cards must be located in the PAD(ForMA5tune/ForSFS) directory') - thecard.append('# Switches must be on or off') - thecard.append('# AnalysisName PADType Switch DetectorCard') - if padtype in ['PAD','PADForMA5tune']: - mainfile = open(os.path.normpath(os.path.join(self.ma5dir,'tools',padtype,"Build/Main/main.cpp")), 'r') - if padtype=="PAD": - mytype="v1.2" + thecard.append( + "# Detector cards must be located in the PAD(ForMA5tune/ForSFS) directory" + ) + thecard.append("# Switches must be on or off") + thecard.append( + "# AnalysisName PADType Switch DetectorCard" + ) + if padtype in ["PAD", "PADForMA5tune"]: + mainfile = open( + os.path.normpath( + os.path.join(self.ma5dir, "tools", padtype, "Build/Main/main.cpp") + ), + "r", + ) + if padtype == "PAD": + mytype = "v1.2" else: - mytype="v1.1" + mytype = "v1.1" for line in mainfile: if "manager.InitializeAnalyzer" in line: - analysis = str(line.split('\"')[1]) - mydelphes="UNKNOWN" - descr="UNKNOWN" - for mycard,alist in self.DelphesDic.items(): - if analysis in alist: - mydelphes=mycard - break - for myana,mydesc in self.description.items(): - if analysis == myana: - descr=mydesc - break - thecard.append(analysis.ljust(30,' ') + mytype.ljust(12,' ') + 'on ' + mydelphes.ljust(50, ' ')+\ - ' # '+descr) + analysis = str(line.split('"')[1]) + mydelphes = "UNKNOWN" + descr = "UNKNOWN" + for mycard, alist in self.DelphesDic.items(): + if analysis in alist: + mydelphes = mycard + break + for myana, mydesc in self.description.items(): + if analysis == myana: + descr = mydesc + break + thecard.append( + analysis.ljust(30, " ") + + mytype.ljust(12, " ") + + "on " + + mydelphes.ljust(50, " ") + + " # " + + descr + ) mainfile.close() - elif padtype == 'PADForSFS': - sfs_path = os.path.normpath(os.path.join(self.ma5dir,"tools/PADForSFS/Build/SampleAnalyzer/User/Analyzer")) - analysislist = [x.split('/')[-1].split('.cpp')[0] for x in glob.glob(sfs_path+'/*.cpp')]; - for mycard,alist in self.DelphesDic.items(): + elif padtype == "PADForSFS": + sfs_path = os.path.normpath( + os.path.join( + self.ma5dir, "tools/PADForSFS/Build/SampleAnalyzer/User/Analyzer" + ) + ) + analysislist = [ + x.split("/")[-1].split(".cpp")[0] for x in glob.glob(sfs_path + "/*.cpp") + ] + for mycard, alist in self.DelphesDic.items(): # it the analysis name is the same skip the one which has delphes card - if mycard.endswith('tcl'): + if mycard.endswith("tcl"): continue for analysis in alist: if analysis not in analysislist: continue - descr = 'UNKNOWN' + descr = "UNKNOWN" if analysis in list(self.description.keys()): descr = self.description[analysis] - thecard.append(analysis.ljust(30,' ') + 'vSFS on ' + mycard.ljust(50, ' ')+\ - ' # '+descr) + thecard.append( + analysis.ljust(30, " ") + + "vSFS on " + + mycard.ljust(50, " ") + + " # " + + descr + ) thecard.sort() if write: - card = open(dirname+'/Input/recasting_card.dat','a') - card.write('\n'.join(thecard)) - card.write('#\n') + card = open(dirname + "/Input/recasting_card.dat", "a") + card.write("\n".join(thecard)) + card.write("#\n") card.close() else: return thecard - def CheckFile(self,dirname,dataset): - if self.CLs_numofexps <=0: return True; - filename=os.path.normpath(dirname+'/Output/SAF/'+dataset.name+'/CLs_output.dat') - self.logger.debug('Check file "'+filename+'"...') + def CheckFile(self, dirname, dataset): + dirname = self.stat_only_dir if self.stat_only_mode else dirname + filename = os.path.normpath( + dirname + "/Output/SAF/" + dataset.name + "/CLs_output.dat" + ) + self.logger.debug('Check file "' + filename + '"...') if not os.path.isfile(filename): - self.logger.error("The file '"+dirname+'/Output/SAF/'+dataset.name+'/CLs_output.dat" has not been found.') + self.logger.error( + "The file '" + + dirname + + "/Output/SAF/" + + dataset.name + + '/CLs_output.dat" has not been found.' + ) return False return True - def collect_outputs(self,dirname,datasets): - if self.CLs_numofexps <=0: return - filename=os.path.normpath(os.path.join(dirname,'Output/SAF/CLs_output_summary.dat')) - self.logger.debug('Check summary file "'+filename+'"...') - out = open(filename,'w') - counter=1 + def collect_outputs(self, dirname, datasets): + dirname = self.stat_only_dir if self.stat_only_mode else dirname + filename = os.path.normpath( + os.path.join(dirname, "Output/SAF/CLs_output_summary.dat") + ) + self.logger.debug('Check summary file "' + filename + '"...') + out = open(filename, "w") + counter = 1 for item in datasets: - outset=open(os.path.normpath(os.path.join(dirname,'Output','SAF',item.name,'CLs_output.dat'))) + outset = open( + os.path.normpath( + os.path.join(dirname, "Output", "SAF", item.name, "CLs_output.dat") + ) + ) for line in outset: - if counter==1 and '# analysis name' in line: - out.write('# dataset name'.ljust(30) + line[2:]) - counter+=1 - if len(line.lstrip())==0: - continue - if line.lstrip()[0]=='#': - continue - out.write(item.name.ljust(30)+line) + if counter == 1 and "# analysis name" in line: + out.write("# dataset name".ljust(30) + line[2:]) + counter += 1 + if len(line.lstrip()) == 0: + continue + if line.lstrip()[0] == "#": + continue + out.write(item.name.ljust(30) + line) outset.close() - out.write('\n') + out.write("\n") out.close() diff --git a/madanalysis/configuration/tau_identification.py b/madanalysis/configuration/tau_identification.py index df842928..37268611 100644 --- a/madanalysis/configuration/tau_identification.py +++ b/madanalysis/configuration/tau_identification.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/core/expert_mode.py b/madanalysis/core/expert_mode.py index 268470e9..2e546411 100644 --- a/madanalysis/core/expert_mode.py +++ b/madanalysis/core/expert_mode.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/core/launcher.py b/madanalysis/core/launcher.py index 3d6aefc0..33d500b3 100644 --- a/madanalysis/core/launcher.py +++ b/madanalysis/core/launcher.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/core/library_builder.py b/madanalysis/core/library_builder.py index cafab529..31142cd1 100644 --- a/madanalysis/core/library_builder.py +++ b/madanalysis/core/library_builder.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/core/main.py b/madanalysis/core/main.py index 8d417c65..9098a7a0 100644 --- a/madanalysis/core/main.py +++ b/madanalysis/core/main.py @@ -1,52 +1,52 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ from __future__ import absolute_import from madanalysis.multiparticle.multiparticle_collection import MultiParticleCollection -from madanalysis.dataset.dataset_collection import DatasetCollection -from madanalysis.selection.selection import Selection -from madanalysis.interpreter.cmd_base import CmdBase -from madanalysis.region.region_collection import RegionCollection -from madanalysis.fastsim.fastsim import SuperFastSim -from madanalysis.system.session_info import SessionInfo -from madanalysis.system.architecture_info import ArchitectureInfo -from madanalysis.core.library_builder import LibraryBuilder -from madanalysis.IOinterface.library_writer import LibraryWriter -from madanalysis.IOinterface.madgraph_interface import MadGraphInterface -from madanalysis.enumeration.ma5_running_type import MA5RunningType -from madanalysis.enumeration.stacking_method_type import StackingMethodType -from madanalysis.enumeration.uncertainty_type import UncertaintyType -from madanalysis.enumeration.normalize_type import NormalizeType -from madanalysis.enumeration.graphic_render_type import GraphicRenderType -from madanalysis.observable.observable_manager import ObservableManager -from madanalysis.configuration.recast_configuration import RecastConfiguration -from madanalysis.configuration.fastsim_configuration import FastsimConfiguration -from madanalysis.configuration.fom_configuration import FomConfiguration -from madanalysis.configuration.isolation_configuration import IsolationConfiguration -from madanalysis.configuration.merging_configuration import MergingConfiguration -from string_tools import StringTools -from madanalysis.system.checkup import CheckUp +from madanalysis.dataset.dataset_collection import DatasetCollection +from madanalysis.selection.selection import Selection +from madanalysis.interpreter.cmd_base import CmdBase +from madanalysis.region.region_collection import RegionCollection +from madanalysis.fastsim.fastsim import SuperFastSim +from madanalysis.system.session_info import SessionInfo +from madanalysis.system.architecture_info import ArchitectureInfo +from madanalysis.core.library_builder import LibraryBuilder +from madanalysis.IOinterface.library_writer import LibraryWriter +from madanalysis.IOinterface.madgraph_interface import MadGraphInterface +from madanalysis.enumeration.ma5_running_type import MA5RunningType +from madanalysis.enumeration.stacking_method_type import StackingMethodType +from madanalysis.enumeration.uncertainty_type import UncertaintyType +from madanalysis.enumeration.normalize_type import NormalizeType +from madanalysis.enumeration.graphic_render_type import GraphicRenderType +from madanalysis.observable.observable_manager import ObservableManager +from madanalysis.configuration.recast_configuration import RecastConfiguration +from madanalysis.configuration.fastsim_configuration import FastsimConfiguration +from madanalysis.configuration.fom_configuration import FomConfiguration +from madanalysis.configuration.isolation_configuration import IsolationConfiguration +from madanalysis.configuration.merging_configuration import MergingConfiguration +from string_tools import StringTools +from madanalysis.system.checkup import CheckUp import logging import os import sys @@ -54,7 +54,8 @@ from six.moves import range import traceback as tb -class Main(): + +class Main: userVariables = { "currentdir": [], "normalize": ["none", "lumi", "lumi_weight"], @@ -71,214 +72,253 @@ class Main(): date = "" def __init__(self): - self.currentdir = os.getcwd() - self.firstdir = os.getcwd() - self.archi_info = ArchitectureInfo() - self.session_info = SessionInfo() - self.mode = MA5RunningType.PARTON - self.forced = False + self.currentdir = os.getcwd() + self.firstdir = os.getcwd() + self.archi_info = ArchitectureInfo() + self.session_info = SessionInfo() + self.mode = MA5RunningType.PARTON + self.forced = False self.multiparticles = MultiParticleCollection() - self.datasets = DatasetCollection() - self.regions = RegionCollection() - self.selection = Selection() - self.script = False - self.observables = ObservableManager(self.mode) - self.expertmode = False - self.repeatSession = False + self.datasets = DatasetCollection() + self.regions = RegionCollection() + self.selection = Selection() + self.script = False + self.observables = ObservableManager(self.mode) + self.expertmode = False + self.repeatSession = False self.developer_mode = False - self.recast = "off" + self.recast = "off" self.ResetParameters() - self.madgraph = MadGraphInterface() - self.logger = logging.getLogger('MA5') + self.madgraph = MadGraphInterface() + self.logger = logging.getLogger("MA5") self.redirectSAlogger = False - self.random_seed = None - + self.random_seed = None def ResetParameters(self): - self.merging = MergingConfiguration() - self.fastsim = FastsimConfiguration() - self.superfastsim = SuperFastSim() - self.recasting = RecastConfiguration() - self.fom = FomConfiguration() - self.lumi = 10 - self.lastjob_name = '' + self.merging = MergingConfiguration() + self.fastsim = FastsimConfiguration() + self.superfastsim = SuperFastSim() + self.recasting = RecastConfiguration() + self.fom = FomConfiguration() + self.lumi = 10 + self.lastjob_name = "" self.lastjob_status = False - self.random_seed = None - self.stack = StackingMethodType.STACK - self.isolation = IsolationConfiguration() - self.output = "" + self.random_seed = None + self.stack = StackingMethodType.STACK + self.isolation = IsolationConfiguration() + self.output = "" self.graphic_render = GraphicRenderType.NONE - if self.mode==MA5RunningType.RECO: + if self.mode == MA5RunningType.RECO: self.normalize = NormalizeType.NONE else: self.normalize = NormalizeType.LUMI_WEIGHT self.superfastsim.InitObservables(self.observables) - - def InitObservables(self,mode): + def InitObservables(self, mode): self.observables = ObservableManager(mode) self.superfastsim.InitObservables(self.observables) - - def IsGoodFormat(self,file): + def IsGoodFormat(self, file): allowed, forbidden = self.GetSampleFormat() for item in allowed: if file.endswith(item): return True return False - def PrintErrorFormat(self,file): + def PrintErrorFormat(self, file): allowed, forbidden = self.GetSampleFormat() for item in forbidden: if file.endswith(item[0]): return item[1] return "The file format is unknown" - def GetSampleFormat(self): # Initializing containers - allowed = [] + allowed = [] forbidden = [] - errormsg = [] - + errormsg = [] + # Adding format according to MA5 level mode - if self.mode in [MA5RunningType.PARTON,MA5RunningType.HADRON]: - allowed.append('.lhe') - allowed.append('.hep') - allowed.append('.hepmc') - forbidden.append(['.root','ROOT format is only available at the reconstructed level of MA5']) - forbidden.append(['.lhco','LHCO format is only available at the reconstructed level of MA5']) + if self.mode in [MA5RunningType.PARTON, MA5RunningType.HADRON]: + allowed.append(".lhe") + allowed.append(".hep") + allowed.append(".hepmc") + forbidden.append( + [ + ".root", + "ROOT format is only available at the reconstructed level of MA5", + ] + ) + forbidden.append( + [ + ".lhco", + "LHCO format is only available at the reconstructed level of MA5", + ] + ) else: - if self.recasting.status=="on": - allowed.append('.hep') - allowed.append('.hepmc') - forbidden.append(['.lhe','LHE format cannot be used for recasting']) - forbidden.append(['.lhco','LHCO format cannot be used for recasting']) + if self.recasting.status == "on": + allowed.append(".hep") + allowed.append(".hepmc") + forbidden.append([".lhe", "LHE format cannot be used for recasting"]) + forbidden.append([".lhco", "LHCO format cannot be used for recasting"]) if self.archi_info.has_delphes or self.archi_info.has_delphesMA5tune: - allowed.append('.root') + allowed.append(".root") else: - forbidden.append(['.root','ROOT format requires the package ROOT']) - elif self.fastsim.package=="none": - allowed.append('.lhco') - forbidden.append(['.lhe','LHE format is only available at the parton or hadron level of MA5']) - forbidden.append(['.hep','HEP format is only available at the parton or hadron level of MA5']) - forbidden.append(['.hepmc','HEPMC format is only available at the parton or hadron level of MA5']) + forbidden.append([".root", "ROOT format requires the package ROOT"]) + elif self.fastsim.package == "none": + allowed.append(".lhco") + forbidden.append( + [ + ".lhe", + "LHE format is only available at the parton or hadron level of MA5", + ] + ) + forbidden.append( + [ + ".hep", + "HEP format is only available at the parton or hadron level of MA5", + ] + ) + forbidden.append( + [ + ".hepmc", + "HEPMC format is only available at the parton or hadron level of MA5", + ] + ) if self.archi_info.has_delphes or self.archi_info.has_delphesMA5tune: - allowed.append('.root') + allowed.append(".root") else: - forbidden.append(['.root','ROOT format is not supported. The ROOT package is required']) + forbidden.append( + [ + ".root", + "ROOT format is not supported. The ROOT package is required", + ] + ) else: - allowed.append('.lhe') - allowed.append('.hep') - allowed.append('.hepmc') - forbidden.append(['.root','ROOT format cannot be used when fastim package is applied']) - forbidden.append(['.lhco','LHCO format cannot be used when fastim package is applied']) + allowed.append(".lhe") + allowed.append(".hep") + allowed.append(".hepmc") + forbidden.append( + [".root", "ROOT format cannot be used when fastim package is applied"] + ) + forbidden.append( + [".lhco", "LHCO format cannot be used when fastim package is applied"] + ) # Adding gzip file if self.archi_info.has_zlib: - zipsamples=[] + zipsamples = [] for item in allowed: - zipsamples.append(item+'.gz') + zipsamples.append(item + ".gz") allowed.extend(zipsamples) - zipsamples=[] + zipsamples = [] for item in forbidden: - zipsamples.append([item[0]+'.gz',item[1]]) + zipsamples.append([item[0] + ".gz", item[1]]) forbidden.extend(zipsamples) else: - zipsamples=[] + zipsamples = [] for item in allowed: - zipsamples.append([item+'.gz','GZ format is not supported. The Zlib package is required']) + zipsamples.append( + [ + item + ".gz", + "GZ format is not supported. The Zlib package is required", + ] + ) for item in forbidden: - zipsamples.append([item[0]+'.gz','GZ format is not supported. The Zlib package is required']) + zipsamples.append( + [ + item[0] + ".gz", + "GZ format is not supported. The Zlib package is required", + ] + ) forbidden.extend(zipsamples) - + # fifo format fifosamples = [] for item in allowed: - fifosamples.append(item+'.fifo') + fifosamples.append(item + ".fifo") allowed.extend(fifosamples) fifosamples = [] for item in forbidden: - fifosamples.append([item[0]+'.fifo',item[1]]) + fifosamples.append([item[0] + ".fifo", item[1]]) forbidden.extend(fifosamples) return allowed, forbidden - def Display(self): - self.logger.info(" *********************************" ) - self.logger.info(" main program " ) - self.logger.info(" *********************************" ) + self.logger.info(" *********************************") + self.logger.info(" main program ") + self.logger.info(" *********************************") self.user_DisplayParameter("currentdir") self.user_DisplayParameter("graphic_render") self.user_DisplayParameter("normalize") self.user_DisplayParameter("lumi") self.user_DisplayParameter("outputfile") self.fom.Display() - self.logger.info(" *********************************" ) + self.logger.info(" *********************************") allowed, forbidden = self.GetSampleFormat() forbidden2 = [] for item in forbidden: - forbidden2.append(item[0]) - self.logger.info(" File extension readable in this session: "+ " ".join(allowed)) - self.logger.info(" File extension NOT readable in this session: "+ " ".join(forbidden2)) - self.logger.info(" *********************************" ) + forbidden2.append(item[0]) + self.logger.info(" File extension readable in this session: " + " ".join(allowed)) + self.logger.info( + " File extension NOT readable in this session: " + " ".join(forbidden2) + ) + self.logger.info(" *********************************") if self.archi_info.has_fastjet: self.merging.Display() self.fastsim.Display() self.isolation.Display() - self.logger.info(" *********************************" ) + self.logger.info(" *********************************") self.recasting.Display() - self.logger.info(" *********************************" ) + self.logger.info(" *********************************") - - def user_DisplayParameter(self,parameter): - if parameter=="currentdir": - self.logger.info(" currentdir = "+self.get_currentdir()) - elif parameter=="stacking_method": + def user_DisplayParameter(self, parameter): + if parameter == "currentdir": + self.logger.info(" currentdir = " + self.get_currentdir()) + elif parameter == "stacking_method": sentence = " stacking methode for histograms = " - if self.stack==StackingMethodType.STACK: - sentence+="stack" - elif self.stack==StackingMethodType.SUPERIMPOSE: - sentence+="superimpose" + if self.stack == StackingMethodType.STACK: + sentence += "stack" + elif self.stack == StackingMethodType.SUPERIMPOSE: + sentence += "superimpose" else: - sentence+="normalize2one" + sentence += "normalize2one" self.logger.info(sentence) - elif parameter=="normalize": - word="" - if self.normalize==NormalizeType.NONE: - word="none" - elif self.normalize==NormalizeType.LUMI: - word="lumi" - elif self.normalize==NormalizeType.LUMI_WEIGHT: - word="lumi_weight" + elif parameter == "normalize": + word = "" + if self.normalize == NormalizeType.NONE: + word = "none" + elif self.normalize == NormalizeType.LUMI: + word = "lumi" + elif self.normalize == NormalizeType.LUMI_WEIGHT: + word = "lumi_weight" self.logger.info(" histogram normalization mode = " + word) - elif parameter=="graphic_render": - word="" - if self.graphic_render==GraphicRenderType.NONE: - word="none" - elif self.graphic_render==GraphicRenderType.ROOT: - word="root" - elif self.graphic_render==GraphicRenderType.MATPLOTLIB: - word="matplotlib" + elif parameter == "graphic_render": + word = "" + if self.graphic_render == GraphicRenderType.NONE: + word = "none" + elif self.graphic_render == GraphicRenderType.ROOT: + word = "root" + elif self.graphic_render == GraphicRenderType.MATPLOTLIB: + word = "matplotlib" self.logger.info(" graphic renderer = " + word) - elif parameter=="outputfile": - if self.output=="": - msg="none" + elif parameter == "outputfile": + if self.output == "": + msg = "none" else: - msg='"'+self.output+'"' - self.logger.info(" output file = "+msg) - elif parameter=="lumi": - self.logger.info(" integrated luminosity = "+str(self.lumi)+" fb^{-1}" ) - elif parameter=="recast": + msg = '"' + self.output + '"' + self.logger.info(" output file = " + msg) + elif parameter == "lumi": + self.logger.info(" integrated luminosity = " + str(self.lumi) + " fb^{-1}") + elif parameter == "recast": self.logger.info(' Recasting mode = "' + self.recasting.status + '"') else: - self.logger.error("'main' has no parameter called '"+parameter+"'") - + self.logger.error("'main' has no parameter called '" + parameter + "'") - def user_GetValues(self,variable): - if variable=="currentdir": + def user_GetValues(self, variable): + if variable == "currentdir": return CmdBase.directory_complete() else: try: @@ -291,7 +331,7 @@ def user_GetParameters(self): def user_SetParameter(self, parameter, value): # currentdir - if parameter=="currentdir": + if parameter == "currentdir": self.set_currentdir(value) elif parameter == "random_seed": try: @@ -304,19 +344,21 @@ def user_SetParameter(self, parameter, value): self.logger.debug(f"Random seed has been set to {self.random_seed}") # stacked - elif parameter=="stacking_method": + elif parameter == "stacking_method": if value == "stack": - self.stack=StackingMethodType.STACK + self.stack = StackingMethodType.STACK elif value == "superimpose": - self.stack=StackingMethodType.SUPERIMPOSE + self.stack = StackingMethodType.SUPERIMPOSE elif value == "normalize2one": - self.stack=StackingMethodType.NORMALIZE2ONE + self.stack = StackingMethodType.NORMALIZE2ONE else: - self.logger.error("'stack' possible values are : 'stack', 'superimpose', 'normalize2one'") + self.logger.error( + "'stack' possible values are : 'stack', 'superimpose', 'normalize2one'" + ) return False # normalize - elif parameter=="normalize": + elif parameter == "normalize": if value == "none": self.normalize = NormalizeType.NONE elif value == "lumi": @@ -324,57 +366,67 @@ def user_SetParameter(self, parameter, value): elif value == "lumi_weight": self.normalize = NormalizeType.LUMI_WEIGHT else: - self.logger.error("'normalize' possible values are : 'none', 'lumi', 'lumi_weight'") + self.logger.error( + "'normalize' possible values are : 'none', 'lumi', 'lumi_weight'" + ) return False # graphic_render - elif parameter=="graphic_render": + elif parameter == "graphic_render": if value == "none": self.graphic_render = GraphicRenderType.NONE elif value == "root": if self.session_info.has_root: self.graphic_render = GraphicRenderType.ROOT else: - self.logger.error("Sorry but the Root package is not detected by MadAnalysis") + self.logger.error( + "Sorry but the Root package is not detected by MadAnalysis" + ) return False elif value == "matplotlib": if self.session_info.has_matplotlib: self.graphic_render = GraphicRenderType.MATPLOTLIB else: - self.logger.error("Sorry but the Matplotlib package is not detected by MadAnalysis") + self.logger.error( + "Sorry but the Matplotlib package is not detected by MadAnalysis" + ) return False else: - self.logger.error("'graphic_render' possible values are : 'none', 'root', 'matplotlib'") + self.logger.error( + "'graphic_render' possible values are : 'none', 'root', 'matplotlib'" + ) return False # lumi - elif (parameter=="lumi"): + elif parameter == "lumi": try: tmp = float(value) except: self.logger.error("'lumi' is a positive float value") return - if (tmp>0): - self.lumi=tmp + if tmp > 0: + self.lumi = tmp else: self.logger.error("'lumi' is a positive float value") return # output - elif (parameter=="outputfile"): - quoteTag=False + elif parameter == "outputfile": + quoteTag = False if value.startswith("'") and value.endswith("'"): - quoteTag=True + quoteTag = True if value.startswith('"') and value.endswith('"'): - quoteTag=True + quoteTag = True if quoteTag: - value=value[1:-1] + value = value[1:-1] valuemin = value.lower() # Compressed file if valuemin.endswith(".gz") and not self.archi_info.has_zlib: - self.logger.error("Compressed formats (*.gz) are not available. "\ - + "Please install zlib with the command line:") + self.logger.error( + "Compressed formats (*.gz) are not available. " + + "Please install zlib with the command line:" + ) self.logger.error(" install zlib") return False @@ -393,7 +445,9 @@ def user_SetParameter(self, parameter, value): return False elif self.mode == MA5RunningType.HADRON: if self.fastsim.package == "none": - self.logger.error("Please select a fast-simulation package before requesting a LHCO file output.") + self.logger.error( + "Please select a fast-simulation package before requesting a LHCO file output." + ) self.logger.error("Command: set main.fastsim.package = ... ") return False else: @@ -401,24 +455,25 @@ def user_SetParameter(self, parameter, value): return else: - self.logger.error("Output format is not available. Extension allowed: " +\ - ".lhe .lhe.gz .lhco .lhco.gz") + self.logger.error( + "Output format is not available. Extension allowed: " + + ".lhe .lhe.gz .lhco .lhco.gz" + ) return False # other else: - self.logger.error("'main' has no parameter called '"+parameter+"'") - + self.logger.error("'main' has no parameter called '" + parameter + "'") def get_currentdir(self): return os.getcwd() - def set_currentdir(self,dir): + def set_currentdir(self, dir): theDir = os.path.expanduser(dir) try: os.chdir(theDir) except: - self.logger.error("Impossible to access the directory : "+theDir) + self.logger.error("Impossible to access the directory : " + theDir) try: self.user_DisplayParameter("currentdir") except: @@ -427,22 +482,25 @@ def set_currentdir(self,dir): currentdir = property(get_currentdir, set_currentdir) def AutoSetGraphicalRenderer(self): - self.logger.debug('Function AutoSetGraphicalRenderer:') - self.logger.debug(' - ROOT is there: '+str(self.session_info.has_root)) - self.logger.debug(' - Matplotlib is there: '+str(self.session_info.has_matplotlib)) + self.logger.debug("Function AutoSetGraphicalRenderer:") + self.logger.debug(" - ROOT is there: " + str(self.session_info.has_root)) + self.logger.debug( + " - Matplotlib is there: " + str(self.session_info.has_matplotlib) + ) if self.session_info.has_root: self.graphic_render = GraphicRenderType.ROOT elif self.session_info.has_matplotlib: self.graphic_render = GraphicRenderType.MATPLOTLIB else: self.graphic_render = GraphicRenderType.NONE - self.logger.info("Package used for graphical rendering: "+\ - '\x1b[32m'+\ - GraphicRenderType.convert2string(self.graphic_render)+\ - '\x1b[0m') - - - def CheckConfig(self,debug=False): + self.logger.info( + "Package used for graphical rendering: " + + "\x1b[32m" + + GraphicRenderType.convert2string(self.graphic_render) + + "\x1b[0m" + ) + + def CheckConfig(self, debug=False): checkup = CheckUp(self.archi_info, self.session_info, debug, self.script) if not checkup.CheckArchitecture(): @@ -459,8 +517,7 @@ def CheckConfig(self,debug=False): return False return True - - def CheckConfig2(self,debug=False): + def CheckConfig2(self, debug=False): checkup = CheckUp(self.archi_info, self.session_info, debug, self.script) # Read user options @@ -476,28 +533,14 @@ def CheckConfig2(self,debug=False): return False self.AutoSetGraphicalRenderer() - try: - import datetime - # check for updates every afternoon between 1 PM and 3 PM - now = datetime.datetime.now() - if 13 < now.hour < 15: - checkup.check_updates() - except ModuleNotFoundError: - from random import random - if random() > 0.5: - checkup.check_updates() - except Exception as err: - self.logger.debug(f"Unable to check updates") - self.logger.debug(tb.format_exc()) - pass + checkup.check_updates() # Ok return True - - def BuildLibrary(self,forced=False): + def BuildLibrary(self, forced=False): builder = LibraryBuilder(self.archi_info) - UpdateNeed=False + UpdateNeed = False FirstUse, Missing = builder.checkMA5() if not FirstUse and not Missing: UpdateNeed = not builder.compare() @@ -505,142 +548,273 @@ def BuildLibrary(self,forced=False): rebuild = forced or FirstUse or UpdateNeed or Missing if not rebuild: - if not os.path.isfile(self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libprocess_for_ma5.so'): - FirstUse=True + if not os.path.isfile( + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Lib/libprocess_for_ma5.so" + ): + FirstUse = True rebuild = forced or FirstUse or UpdateNeed or Missing if not rebuild: - self.logger.info(' => MadAnalysis libraries found.') + self.logger.info(" => MadAnalysis libraries found.") # Test the program - if not os.path.isfile(self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestSampleAnalyzer'): - FirstUse=True - - precompiler = LibraryWriter('lib',self) - if not precompiler.Run('TestSampleAnalyzer',\ - [self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/Process/dummy_list.txt'],\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/',silent=True): - UpdateNeed=True - - if not precompiler.CheckRun('TestSampleAnalyzer',self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/',silent=True): - UpdateNeed=True + if not os.path.isfile( + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestSampleAnalyzer" + ): + FirstUse = True + + precompiler = LibraryWriter("lib", self) + if not precompiler.Run( + "TestSampleAnalyzer", + [ + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Test/Process/dummy_list.txt" + ], + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/", + silent=True, + ): + UpdateNeed = True + + if not precompiler.CheckRun( + "TestSampleAnalyzer", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/", + silent=True, + ): + UpdateNeed = True rebuild = forced or FirstUse or UpdateNeed or Missing if not rebuild: - self.logger.info(' => MadAnalysis test program works.') + self.logger.info(" => MadAnalysis test program works.") return True # Compile library if FirstUse: self.logger.info(" => First use of MadAnalysis (or the library is missing).") elif Missing: - self.logger.info(" => Libraries are missing or system configuration has changed. Need to rebuild the library.") + self.logger.info( + " => Libraries are missing or system configuration has changed. Need to rebuild the library." + ) elif UpdateNeed: - self.logger.info(" => System configuration has changed since the last use. Need to rebuild the library.") + self.logger.info( + " => System configuration has changed since the last use. Need to rebuild the library." + ) elif forced: self.logger.info(" => The user forces to rebuild the library.") # Initializing the JobWriter - compiler = LibraryWriter('lib',self) + compiler = LibraryWriter("lib", self) # Dumping architecture - if not self.archi_info.save(self.archi_info.ma5dir+'/tools/architecture.ma5'): + if not self.archi_info.save(self.archi_info.ma5dir + "/tools/architecture.ma5"): sys.exit() # Library to compiles # |- [0] = unique name # |- [1] = title of the library to display - # |- [2] = + # |- [2] = # |- [3] = output file to cross-check # |- [4] = folder # |- [5] = False=Library, True=Executable libraries = [] - libraries.append(['configuration','SampleAnalyzer configuration', 'configuration', \ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/PortabilityCheckup',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Configuration',True]) - libraries.append(['commons','SampleAnalyzer commons', 'commons', \ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libcommons_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Commons',False]) - libraries.append(['test_commons','SampleAnalyzer commons', 'test_commons', \ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestCommons',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "configuration", + "SampleAnalyzer configuration", + "configuration", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/PortabilityCheckup", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Configuration", + True, + ] + ) + libraries.append( + [ + "commons", + "SampleAnalyzer commons", + "commons", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libcommons_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Commons", + False, + ] + ) + libraries.append( + [ + "test_commons", + "SampleAnalyzer commons", + "test_commons", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestCommons", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # Zlib if self.archi_info.has_zlib: - libraries.append(['zlib', 'interface to zlib', 'zlib',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libzlib_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Interfaces',False]) - libraries.append(['test_zlib','interface to zlib', 'test_zlib',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestZlib',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "zlib", + "interface to zlib", + "zlib", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libzlib_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Interfaces", + False, + ] + ) + libraries.append( + [ + "test_zlib", + "interface to zlib", + "test_zlib", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestZlib", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # Fastjet if self.archi_info.has_fastjet: - libraries.append(['FastJet', 'interface to FastJet', 'fastjet',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libfastjet_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Interfaces',False]) - libraries.append(['test_fastjet','interface to Fastjet', 'test_fastjet',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestFastjet',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "FastJet", + "interface to FastJet", + "fastjet", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libfastjet_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Interfaces", + False, + ] + ) + libraries.append( + [ + "test_fastjet", + "interface to Fastjet", + "test_fastjet", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestFastjet", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # Delphes if self.archi_info.has_delphes: - libraries.append(['Delphes', 'interface to Delphes', 'delphes',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libdelphes_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Interfaces',False]) - libraries.append(['test_delphes','interface to Delphes', 'test_delphes',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestDelphes',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "Delphes", + "interface to Delphes", + "delphes", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libdelphes_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Interfaces", + False, + ] + ) + libraries.append( + [ + "test_delphes", + "interface to Delphes", + "test_delphes", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestDelphes", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # DelphesMA5tune if self.archi_info.has_delphesMA5tune: - libraries.append(['Delphes-MA5tune', 'interface to Delphes-MA5tune', 'delphesMA5tune',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libdelphesMA5tune_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Interfaces',False]) - libraries.append(['test_delphesMA5tune','interface to DelphesMA5tune', 'test_delphesMA5tune',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestDelphesMA5tune',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "Delphes-MA5tune", + "interface to Delphes-MA5tune", + "delphesMA5tune", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libdelphesMA5tune_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Interfaces", + False, + ] + ) + libraries.append( + [ + "test_delphesMA5tune", + "interface to DelphesMA5tune", + "test_delphesMA5tune", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Bin/TestDelphesMA5tune", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # Root if self.archi_info.has_root: - libraries.append(['Root', 'interface to Root', 'root',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libroot_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Interfaces',False]) - libraries.append(['test_root','interface to Root', 'test_root',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestRoot',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) + libraries.append( + [ + "Root", + "interface to Root", + "root", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libroot_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Interfaces", + False, + ] + ) + libraries.append( + [ + "test_root", + "interface to Root", + "test_root", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestRoot", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) # Process - libraries.append(['process', 'SampleAnalyzer core', 'process',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/libprocess_for_ma5.so',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Process',False]) - libraries.append(['test_process','SampleAnalyzer core', 'test_process',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/TestSampleAnalyzer',\ - self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/',True]) - - + libraries.append( + [ + "process", + "SampleAnalyzer core", + "process", + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Lib/libprocess_for_ma5.so", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Process", + False, + ] + ) + libraries.append( + [ + "test_process", + "SampleAnalyzer core", + "test_process", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/TestSampleAnalyzer", + self.archi_info.ma5dir + "/tools/SampleAnalyzer/Test/", + True, + ] + ) + # Writing the Makefiles self.logger.info("") self.logger.info(" **********************************************************") self.logger.info(" Building SampleAnalyzer libraries ") self.logger.info(" **********************************************************") - # Getting number of cores ncores = compiler.get_ncores2() # Chronometer start - from chronometer import Chronometer + from chronometer import Chronometer + chrono = Chronometer() chrono.Start() # Writing the main Makefile from madanalysis.build.makefile_writer import MakefileWriter - options=MakefileWriter.UserfriendlyMakefileOptions() - options.has_commons = True - options.has_process = True - options.has_test = True - options.has_zlib = self.archi_info.has_zlib - options.has_fastjet = self.archi_info.has_fastjet - options.has_delphes = self.archi_info.has_delphes + + options = MakefileWriter.UserfriendlyMakefileOptions() + options.has_commons = True + options.has_process = True + options.has_test = True + options.has_zlib = self.archi_info.has_zlib + options.has_fastjet = self.archi_info.has_fastjet + options.has_delphes = self.archi_info.has_delphes options.has_delphesMA5tune = self.archi_info.has_delphesMA5tune - #MakefileWriter.UserfriendlyMakefileForSampleAnalyzer(self.archi_info.ma5dir+'/tools/SampleAnalyzer/Makefile',options) + # MakefileWriter.UserfriendlyMakefileForSampleAnalyzer(self.archi_info.ma5dir+'/tools/SampleAnalyzer/Makefile',options) # Writing the setup self.logger.info(" Writing the setup files ...") @@ -648,61 +822,84 @@ def BuildLibrary(self,forced=False): # @Jack: git doesn't like empty folders so these have to be created before compilation if not os.path.isdir( - os.path.join(self.archi_info.ma5dir,"tools/SampleAnalyzer/","Bin") + os.path.join(self.archi_info.ma5dir, "tools/SampleAnalyzer/", "Bin") ): - os.mkdir(os.path.join(self.archi_info.ma5dir,"tools/SampleAnalyzer/","Bin")) - - SetupWriter.WriteSetupFile(True,self.archi_info.ma5dir+'/tools/SampleAnalyzer/',self.archi_info) - SetupWriter.WriteSetupFile(False,self.archi_info.ma5dir+'/tools/SampleAnalyzer/',self.archi_info) + os.mkdir(os.path.join(self.archi_info.ma5dir, "tools/SampleAnalyzer/", "Bin")) + + SetupWriter.WriteSetupFile( + True, self.archi_info.ma5dir + "/tools/SampleAnalyzer/", self.archi_info + ) + SetupWriter.WriteSetupFile( + False, self.archi_info.ma5dir + "/tools/SampleAnalyzer/", self.archi_info + ) # Writing the makefile self.logger.info(" Writing all the Makefiles ...") - for ind in range(0,len(libraries)): + for ind in range(0, len(libraries)): if not compiler.WriteMakefileForInterfaces(libraries[ind][2]): self.logger.error("library building aborted.") sys.exit() - if not compiler.WriteMakefileForInterfaces('test'): + if not compiler.WriteMakefileForInterfaces("test"): self.logger.error("test program building aborted.") sys.exit() # Compiling the libraries - for ind in range(0,len(libraries)): + for ind in range(0, len(libraries)): - isLibrary=not libraries[ind][5] + isLibrary = not libraries[ind][5] if isLibrary: - product='library' + product = "library" else: - product='test program' - - self.logger.info(" **********************************************************") - self.logger.info(" Component "+str(ind+1)+"/"+str(len(libraries))+" - "+product+": "+libraries[ind][1]) - - # Cleaning the project - self.logger.info(" - Cleaning the project before building the "+product+" ...") - if not compiler.MrProper(libraries[ind][2],libraries[ind][4]): - self.logger.error("The "+product+" building aborted.") + product = "test program" + + self.logger.info( + " **********************************************************" + ) + self.logger.info( + " Component " + + str(ind + 1) + + "/" + + str(len(libraries)) + + " - " + + product + + ": " + + libraries[ind][1] + ) + + # Cleaning the project + self.logger.info( + " - Cleaning the project before building the " + product + " ..." + ) + if not compiler.MrProper(libraries[ind][2], libraries[ind][4]): + self.logger.error("The " + product + " building aborted.") sys.exit() # Compiling self.logger.info(" - Compiling the source files ...") - if not compiler.Compile(ncores,libraries[ind][2],libraries[ind][4]): - self.logger.error("The "+product+" building aborted.") + if not compiler.Compile(ncores, libraries[ind][2], libraries[ind][4]): + self.logger.error("The " + product + " building aborted.") sys.exit() # Linking - self.logger.info(" - Linking the "+product+" ...") - if not compiler.Link(libraries[ind][2],libraries[ind][4]): - self.logger.error("The "+product+" building aborted.") + self.logger.info(" - Linking the " + product + " ...") + if not compiler.Link(libraries[ind][2], libraries[ind][4]): + self.logger.error("The " + product + " building aborted.") sys.exit() # Checking - self.logger.info(" - Checking that the "+product+" is properly built ...") + self.logger.info( + " - Checking that the " + product + " is properly built ..." + ) if not os.path.isfile(libraries[ind][3]): - self.logger.error("The "+product+" '"+libraries[ind][3]+"' is not produced.") + self.logger.error( + "The " + product + " '" + libraries[ind][3] + "' is not produced." + ) sys.exit() - # Cleaning the project - self.logger.info(" - Cleaning the project after building the "+product+" ...") - if not compiler.Clean(libraries[ind][2],libraries[ind][4]): + # Cleaning the project + self.logger.info( + " - Cleaning the project after building the " + product + " ..." + ) + if not compiler.Clean(libraries[ind][2], libraries[ind][4]): self.logger.error("library building aborted.") sys.exit() @@ -710,34 +907,43 @@ def BuildLibrary(self,forced=False): # Running the program test self.logger.info(" - Running the test program ...") - program=libraries[ind][3].split('/')[-1] + program = libraries[ind][3].split("/")[-1] argv = [] - if program=='TestSampleAnalyzer': - argv = [self.archi_info.ma5dir+'/tools/SampleAnalyzer/Test/Process/dummy_list.txt'] - if not compiler.Run(program,argv,self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/'): + if program == "TestSampleAnalyzer": + argv = [ + self.archi_info.ma5dir + + "/tools/SampleAnalyzer/Test/Process/dummy_list.txt" + ] + if not compiler.Run( + program, argv, self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/" + ): self.logger.error("the test failed.") sys.exit() # Checking the program output self.logger.info(" - Checking the program output...") - if libraries[ind][0]=="configuration": - if not compiler.CheckRunConfiguration(program,self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/'): + if libraries[ind][0] == "configuration": + if not compiler.CheckRunConfiguration( + program, self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/" + ): self.logger.error("the test failed.") sys.exit() - else: - if not compiler.CheckRun(program,self.archi_info.ma5dir+'/tools/SampleAnalyzer/Bin/'): + else: + if not compiler.CheckRun( + program, self.archi_info.ma5dir + "/tools/SampleAnalyzer/Bin/" + ): self.logger.error("the test failed.") sys.exit() # Print Ok - self.logger.info(' => Status: \x1b[32m'+'[OK]'+'\x1b[0m') + self.logger.info(" => Status: \x1b[32m" + "[OK]" + "\x1b[0m") self.logger.info(" **********************************************************") # Chrono end chrono.Stop() - self.logger.info(" Elapsed time = "+chrono.Display()) + self.logger.info(" Elapsed time = " + chrono.Display()) self.logger.info(" **********************************************************") self.logger.info("") diff --git a/madanalysis/core/script_stack.py b/madanalysis/core/script_stack.py index ee2f011d..9eb609a9 100644 --- a/madanalysis/core/script_stack.py +++ b/madanalysis/core/script_stack.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/dataset/dataset.py b/madanalysis/dataset/dataset.py index 63b438bd..573bd290 100644 --- a/madanalysis/dataset/dataset.py +++ b/madanalysis/dataset/dataset.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/dataset/dataset_collection.py b/madanalysis/dataset/dataset_collection.py index 3f680371..8deaf1ba 100644 --- a/madanalysis/dataset/dataset_collection.py +++ b/madanalysis/dataset/dataset_collection.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/dataset/sample_info.py b/madanalysis/dataset/sample_info.py index 2eb6e8c3..b8ac4782 100644 --- a/madanalysis/dataset/sample_info.py +++ b/madanalysis/dataset/sample_info.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/argument_type.py b/madanalysis/enumeration/argument_type.py index 64e8eda5..24b6fd0b 100644 --- a/madanalysis/enumeration/argument_type.py +++ b/madanalysis/enumeration/argument_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/backstyle_type.py b/madanalysis/enumeration/backstyle_type.py index 24744c10..49c77048 100644 --- a/madanalysis/enumeration/backstyle_type.py +++ b/madanalysis/enumeration/backstyle_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/color_hex.py b/madanalysis/enumeration/color_hex.py index d73c3ecb..89daffae 100644 --- a/madanalysis/enumeration/color_hex.py +++ b/madanalysis/enumeration/color_hex.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/color_type.py b/madanalysis/enumeration/color_type.py index d19227fc..a140f21b 100644 --- a/madanalysis/enumeration/color_type.py +++ b/madanalysis/enumeration/color_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/combination_type.py b/madanalysis/enumeration/combination_type.py index c9a47578..dbbef6aa 100644 --- a/madanalysis/enumeration/combination_type.py +++ b/madanalysis/enumeration/combination_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/connector_type.py b/madanalysis/enumeration/connector_type.py index 7aa4555b..b4958eb5 100644 --- a/madanalysis/enumeration/connector_type.py +++ b/madanalysis/enumeration/connector_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/cut_type.py b/madanalysis/enumeration/cut_type.py index 74284023..dae5b9db 100644 --- a/madanalysis/enumeration/cut_type.py +++ b/madanalysis/enumeration/cut_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/detect_status_type.py b/madanalysis/enumeration/detect_status_type.py index 9fdecd0d..66683c20 100644 --- a/madanalysis/enumeration/detect_status_type.py +++ b/madanalysis/enumeration/detect_status_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/font_type.py b/madanalysis/enumeration/font_type.py index a55bb49e..09690783 100644 --- a/madanalysis/enumeration/font_type.py +++ b/madanalysis/enumeration/font_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/graphic_render_type.py b/madanalysis/enumeration/graphic_render_type.py index 9db7ae5b..8ec6838f 100644 --- a/madanalysis/enumeration/graphic_render_type.py +++ b/madanalysis/enumeration/graphic_render_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/linestyle_type.py b/madanalysis/enumeration/linestyle_type.py index fe9b9cbb..7eec61fa 100644 --- a/madanalysis/enumeration/linestyle_type.py +++ b/madanalysis/enumeration/linestyle_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/ma5_running_type.py b/madanalysis/enumeration/ma5_running_type.py index d08ccef0..81b7961a 100644 --- a/madanalysis/enumeration/ma5_running_type.py +++ b/madanalysis/enumeration/ma5_running_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/normalize_type.py b/madanalysis/enumeration/normalize_type.py index 4487e2e4..0dbdab33 100644 --- a/madanalysis/enumeration/normalize_type.py +++ b/madanalysis/enumeration/normalize_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/observable_type.py b/madanalysis/enumeration/observable_type.py index 1b56c9e0..8b1d387f 100644 --- a/madanalysis/enumeration/observable_type.py +++ b/madanalysis/enumeration/observable_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/operator_type.py b/madanalysis/enumeration/operator_type.py index 837b7251..7683ec8f 100644 --- a/madanalysis/enumeration/operator_type.py +++ b/madanalysis/enumeration/operator_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/report_format_type.py b/madanalysis/enumeration/report_format_type.py index bf53902e..26e40643 100644 --- a/madanalysis/enumeration/report_format_type.py +++ b/madanalysis/enumeration/report_format_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/sb_ratio_type.py b/madanalysis/enumeration/sb_ratio_type.py index 11a24b9a..54a17988 100644 --- a/madanalysis/enumeration/sb_ratio_type.py +++ b/madanalysis/enumeration/sb_ratio_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/script_type.py b/madanalysis/enumeration/script_type.py index 1cf74b5f..316fa452 100644 --- a/madanalysis/enumeration/script_type.py +++ b/madanalysis/enumeration/script_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/special_particle.py b/madanalysis/enumeration/special_particle.py index 8366341b..4310bf9e 100644 --- a/madanalysis/enumeration/special_particle.py +++ b/madanalysis/enumeration/special_particle.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/stacking_method_type.py b/madanalysis/enumeration/stacking_method_type.py index ce8ad90d..baae11d1 100644 --- a/madanalysis/enumeration/stacking_method_type.py +++ b/madanalysis/enumeration/stacking_method_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/enumeration/uncertainty_type.py b/madanalysis/enumeration/uncertainty_type.py index bf8799e4..e595361f 100644 --- a/madanalysis/enumeration/uncertainty_type.py +++ b/madanalysis/enumeration/uncertainty_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/ast.py b/madanalysis/fastsim/ast.py index ab361b60..b08892b9 100644 --- a/madanalysis/fastsim/ast.py +++ b/madanalysis/fastsim/ast.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/ast_leaf.py b/madanalysis/fastsim/ast_leaf.py index aa04131d..41a026fd 100644 --- a/madanalysis/fastsim/ast_leaf.py +++ b/madanalysis/fastsim/ast_leaf.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/fastsim.py b/madanalysis/fastsim/fastsim.py index 9e53496b..6b0624c8 100644 --- a/madanalysis/fastsim/fastsim.py +++ b/madanalysis/fastsim/fastsim.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/recoefficiency.py b/madanalysis/fastsim/recoefficiency.py index e52d9721..4efd2e51 100644 --- a/madanalysis/fastsim/recoefficiency.py +++ b/madanalysis/fastsim/recoefficiency.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/scaling.py b/madanalysis/fastsim/scaling.py index 085c0e3c..af225579 100644 --- a/madanalysis/fastsim/scaling.py +++ b/madanalysis/fastsim/scaling.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/smearer.py b/madanalysis/fastsim/smearer.py index 20da8312..7ca9ed3f 100644 --- a/madanalysis/fastsim/smearer.py +++ b/madanalysis/fastsim/smearer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/fastsim/tagger.py b/madanalysis/fastsim/tagger.py index ad9644d9..d5be12c2 100644 --- a/madanalysis/fastsim/tagger.py +++ b/madanalysis/fastsim/tagger.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/detector_manager.py b/madanalysis/install/detector_manager.py index 3e3bc794..50279530 100644 --- a/madanalysis/install/detector_manager.py +++ b/madanalysis/install/detector_manager.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_delphes.py b/madanalysis/install/install_delphes.py index 331209f1..e54be120 100644 --- a/madanalysis/install/install_delphes.py +++ b/madanalysis/install/install_delphes.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_fastjet.py b/madanalysis/install/install_fastjet.py index 929c5777..baf9e4da 100644 --- a/madanalysis/install/install_fastjet.py +++ b/madanalysis/install/install_fastjet.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_fastjetcontrib.py b/madanalysis/install/install_fastjetcontrib.py index 89e92aa2..9a5a88c7 100644 --- a/madanalysis/install/install_fastjetcontrib.py +++ b/madanalysis/install/install_fastjetcontrib.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_gnuplot.py b/madanalysis/install/install_gnuplot.py index 51cb92f6..1f68add7 100644 --- a/madanalysis/install/install_gnuplot.py +++ b/madanalysis/install/install_gnuplot.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_manager.py b/madanalysis/install/install_manager.py index 17651f52..3b2d4929 100644 --- a/madanalysis/install/install_manager.py +++ b/madanalysis/install/install_manager.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_matplotlib.py b/madanalysis/install/install_matplotlib.py index d3cfb0cd..534cf430 100644 --- a/madanalysis/install/install_matplotlib.py +++ b/madanalysis/install/install_matplotlib.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_numpy.py b/madanalysis/install/install_numpy.py index 1a797a7f..bc65bad7 100644 --- a/madanalysis/install/install_numpy.py +++ b/madanalysis/install/install_numpy.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_pad.py b/madanalysis/install/install_pad.py index 6e69adcb..28bf0dd5 100644 --- a/madanalysis/install/install_pad.py +++ b/madanalysis/install/install_pad.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -55,13 +55,15 @@ def __init__(self,main, padname): self.files = { "padsfs.dat" : "https://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/padsfs3.dat", "bib_padsfs.dat" : "http://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/MA5SandBox/bib_pad3.dat", - "json_padsfs.dat": "http://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/json_padsfs3.dat" + "json_padsfs.dat": "http://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/json_padsfs3.dat", + "csv_padsfs.dat" : "http://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/SFS/csv_padsfs.dat" } self.analyses = [] self.analysis_files = [] self.pileup_files = [] self.delphes_cards = [] self.json_cards = [] + self.csv_cards = [] def Detect(self): @@ -137,6 +139,12 @@ def CreatePackageFolder(self): if not ok: return False + # CSV files + if "csv_padsfs.dat" in self.files.keys(): + logging.debug('Creating folder '+self.installdir+'/Input/CSV') + TheCommand = ['mkdir', self.installdir+'/Input/CSV'] + ok= ShellCommand.Execute(TheCommand,self.main.archi_info.ma5dir+'/tools') + # EXIT return True @@ -167,6 +175,24 @@ def Download(self): [ line.strip().split('|')[1].split(), line.strip().split('|')[2].split()]; json_input.close(); + # CSV files + csv_dictionary = {} + if self.padname in ['PADForSFS']: + csv_struct_name = [x for x in self.files.keys() if 'csv' in x] + if len(csv_struct_name) == 1: + logging.getLogger('MA5').debug(" ** Getting the list of data CSV files in " + self.downloaddir+"/"+csv_struct_name[0]) + csv_input = open(os.path.join(self.downloaddir,csv_struct_name[0])) + for line in csv_input: + if len(line.strip())==0 or line.strip().startswith('#'): continue + analysis_name = line.strip().split('|')[0].strip() + server_name = line.strip().split('|')[1].strip() + csv_name = line.strip().split('|')[2].strip() + tag = line.strip().split('|')[3].strip() + if not analysis_name in csv_dictionary.keys(): csv_dictionary[analysis_name] = {} + if not server_name in csv_dictionary[analysis_name].keys(): csv_dictionary[analysis_name][server_name] = {} + csv_dictionary[analysis_name][server_name][csv_name] = tag + csv_input.close(); + # Getting the analysis one by one (and creating first skeleton analyses for each of them) logging.getLogger('MA5').debug('Reading the analysis list in ' + \ os.path.join(self.downloaddir,self.padname.replace('For','').lower()+'.dat')) @@ -188,7 +214,11 @@ def Download(self): # getting the dataverse URLs delphes_url=''; if 'dataverse' in delphes: - delphes_url = 'https://dataverse.uclouvain.be/api/access/datafile/'+delphes.split()[-1][:-1]; + if len(delphes.split())==3: + delphes_url = 'https://dataverse.uclouvain.be/api/access/datafile/'+delphes.split()[-1][:-1]; + if len(delphes.split())==4: + delphes_url = 'https://dataverse.uclouvain.be/api/access/datafile/:persistentId?persistentId=doi:10.14428/DVN/' + \ + delphes.split()[2].strip() + '/' + delphes.split()[-1][:-1].strip(); if len(analysis)==0 and len(url)==0: delphes = delphes.split()[1] elif self.padname!='PADForSFS': @@ -234,15 +264,40 @@ def Download(self): if 'dataverse' in url: exts = ['cpp', 'h', 'info']; anl_files = url.split(']')[0].split()[1:]; + doi='' + if len(anl_files)==4: + doi=anl_files[0] + anl_files=anl_files[1:] for i in range(len(anl_files)): - files[analysis+'.'+exts[i]] = 'https://dataverse.uclouvain.be/api/access/datafile/'+anl_files[i]; + if doi=='': + files[analysis+'.'+exts[i]] = 'https://dataverse.uclouvain.be/api/access/datafile/'+anl_files[i]; + else: + files[analysis+'.'+exts[i]] = 'https://dataverse.uclouvain.be/api/access/datafile/:persistentId?persistentId=doi:10.14428/DVN/'+\ + doi + '/' + anl_files[i]; ## json files if analysis in list(json_dictionary.keys()): for i_json in range(len(json_dictionary[analysis][0])): - files[analysis+'_'+json_dictionary[analysis][0][i_json]+'.json'] =\ - 'https://dataverse.uclouvain.be/api/access/datafile/' + json_dictionary[analysis][1][i_json] + if doi=='': + files[analysis+'_'+json_dictionary[analysis][0][i_json]+'.json'] =\ + 'https://dataverse.uclouvain.be/api/access/datafile/' + json_dictionary[analysis][1][i_json] + else: + files[analysis+'_'+json_dictionary[analysis][0][i_json]+'.json'] =\ + 'https://dataverse.uclouvain.be/api/access/datafile/:persistentId?persistentId=doi:10.14428/DVN/' + doi + '/' + json_dictionary[analysis][1][i_json] self.json_cards.append(analysis+'_'+json_dictionary[analysis][0][i_json]+'.json') self.analysis_files.append(analysis+'_'+json_dictionary[analysis][0][i_json]+'.json') + ## CSV files + if analysis in list(csv_dictionary.keys()): + TheCommand = ['mkdir', self.installdir+'/Input/CSV/'+ analysis.upper()] + ok= ShellCommand.Execute(TheCommand,self.main.archi_info.ma5dir+'/tools') + for k, v in csv_dictionary[analysis].items(): + if k != 'dataverse': continue + for file, tag in v.items(): + if doi=='': + files[file+'.csv'] = 'https://dataverse.uclouvain.be/api/access/datafile/' + tag + else: + files[file+'.csv'] = 'https://dataverse.uclouvain.be/api/access/datafile/:persistentId?persistentId=doi:10.14428/DVN/' +\ + doi + '/' + tag + self.csv_cards.append([analysis, file+'.csv']) else: if url=='MA5-local': url='http://madanalysis.irmp.ucl.ac.be/raw-attachment/wiki/' @@ -313,10 +368,14 @@ def Unpack(self): oldfile = os.path.join(self.downloaddir, analysis + '.'+extension) shutil.copy(oldfile,newfile) - # json files fopr pyhf + # json files for pyhf for json in self.json_cards: shutil.copy(os.path.join(self.downloaddir,json), self.PADdir) + # data files + for csv in self.csv_cards: + shutil.copy(os.path.join(self.downloaddir,csv[1]), self.installdir+'/Input/CSV/'+ csv[0].upper()) + # the delphes cards for myfile in self.delphes_cards: shutil.copy(os.path.join(self.downloaddir,myfile), self.delphesdir) diff --git a/madanalysis/install/install_pyhf.py b/madanalysis/install/install_pyhf.py index 5d17646b..771db5ca 100644 --- a/madanalysis/install/install_pyhf.py +++ b/madanalysis/install/install_pyhf.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_root.py b/madanalysis/install/install_root.py index a5361e9c..758fa16a 100644 --- a/madanalysis/install/install_root.py +++ b/madanalysis/install/install_root.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_samples.py b/madanalysis/install/install_samples.py index 20aee8ec..df30ef69 100644 --- a/madanalysis/install/install_samples.py +++ b/madanalysis/install/install_samples.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_service.py b/madanalysis/install/install_service.py index 90cf0d57..a81531cf 100644 --- a/madanalysis/install/install_service.py +++ b/madanalysis/install/install_service.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_simplify.py b/madanalysis/install/install_simplify.py index 03937403..139e2b3d 100644 --- a/madanalysis/install/install_simplify.py +++ b/madanalysis/install/install_simplify.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/install/install_zlib.py b/madanalysis/install/install_zlib.py index 1761a370..24f3a32a 100644 --- a/madanalysis/install/install_zlib.py +++ b/madanalysis/install/install_zlib.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_base.py b/madanalysis/interpreter/cmd_base.py index 9d4bdd1b..4da96229 100644 --- a/madanalysis/interpreter/cmd_base.py +++ b/madanalysis/interpreter/cmd_base.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_cut.py b/madanalysis/interpreter/cmd_cut.py index d4a6ff0d..53f9afe7 100644 --- a/madanalysis/interpreter/cmd_cut.py +++ b/madanalysis/interpreter/cmd_cut.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_define.py b/madanalysis/interpreter/cmd_define.py index dc4d3dd7..eaaad446 100644 --- a/madanalysis/interpreter/cmd_define.py +++ b/madanalysis/interpreter/cmd_define.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_define_region.py b/madanalysis/interpreter/cmd_define_region.py index 7f79dfdf..a0f0b54d 100644 --- a/madanalysis/interpreter/cmd_define_region.py +++ b/madanalysis/interpreter/cmd_define_region.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_display.py b/madanalysis/interpreter/cmd_display.py index 241140a2..b2558e8c 100644 --- a/madanalysis/interpreter/cmd_display.py +++ b/madanalysis/interpreter/cmd_display.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_display_datasets.py b/madanalysis/interpreter/cmd_display_datasets.py index d0dc93e1..4ce95479 100644 --- a/madanalysis/interpreter/cmd_display_datasets.py +++ b/madanalysis/interpreter/cmd_display_datasets.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_display_multiparticles.py b/madanalysis/interpreter/cmd_display_multiparticles.py index c17992d8..70f53bfc 100644 --- a/madanalysis/interpreter/cmd_display_multiparticles.py +++ b/madanalysis/interpreter/cmd_display_multiparticles.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_display_particles.py b/madanalysis/interpreter/cmd_display_particles.py index 847a64e5..f18e2248 100644 --- a/madanalysis/interpreter/cmd_display_particles.py +++ b/madanalysis/interpreter/cmd_display_particles.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_display_regions.py b/madanalysis/interpreter/cmd_display_regions.py index aea72933..515cb16d 100644 --- a/madanalysis/interpreter/cmd_display_regions.py +++ b/madanalysis/interpreter/cmd_display_regions.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_import.py b/madanalysis/interpreter/cmd_import.py index e98698b3..c33c3629 100644 --- a/madanalysis/interpreter/cmd_import.py +++ b/madanalysis/interpreter/cmd_import.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_install.py b/madanalysis/interpreter/cmd_install.py index 9168bd29..9ef2d7af 100644 --- a/madanalysis/interpreter/cmd_install.py +++ b/madanalysis/interpreter/cmd_install.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_open.py b/madanalysis/interpreter/cmd_open.py index 44c3a25d..33de8a63 100644 --- a/madanalysis/interpreter/cmd_open.py +++ b/madanalysis/interpreter/cmd_open.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_plot.py b/madanalysis/interpreter/cmd_plot.py index 0fa375a6..002a4a88 100644 --- a/madanalysis/interpreter/cmd_plot.py +++ b/madanalysis/interpreter/cmd_plot.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_remove.py b/madanalysis/interpreter/cmd_remove.py index 57ef8cf5..90f9e6df 100644 --- a/madanalysis/interpreter/cmd_remove.py +++ b/madanalysis/interpreter/cmd_remove.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_reset.py b/madanalysis/interpreter/cmd_reset.py index 4c81abc9..284ca369 100644 --- a/madanalysis/interpreter/cmd_reset.py +++ b/madanalysis/interpreter/cmd_reset.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_selection_base.py b/madanalysis/interpreter/cmd_selection_base.py index 4b7991b9..90996346 100644 --- a/madanalysis/interpreter/cmd_selection_base.py +++ b/madanalysis/interpreter/cmd_selection_base.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/cmd_set.py b/madanalysis/interpreter/cmd_set.py index 8bb1fb76..a3404966 100644 --- a/madanalysis/interpreter/cmd_set.py +++ b/madanalysis/interpreter/cmd_set.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -100,8 +100,7 @@ def do_main(self,args): object = object.replace('fastsim.bjet_id.','fastsim.bjet_idXXX') object = object.replace('fastsim.tau_id.','fastsim.tau_idXXX') objs = object.split('.') - for i in range(len(objs)): - objs[i] = objs[i].replace('XXX','.') + objs = [o.replace('XXX','.') for o in objs] if len(objs)==2 and objs[0].lower()=='main' and objs[1].lower()=='recast': user_info = UserInfo() diff --git a/madanalysis/interpreter/cmd_submit.py b/madanalysis/interpreter/cmd_submit.py index bb19f3ca..ee347ff5 100644 --- a/madanalysis/interpreter/cmd_submit.py +++ b/madanalysis/interpreter/cmd_submit.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -173,7 +173,7 @@ def do_submit(self,args,history): return # Checking if a dataset has been defined - if len(self.main.datasets)==0: + if len(self.main.datasets)==0 and not self.main.recasting.stat_only_mode: self.logger.error("no dataset found; please define a dataset (via the command import).") self.logger.error("job submission aborted.") return @@ -195,7 +195,10 @@ def do_submit(self,args,history): self.main.lastjob_status = False # Submission - self.logger.debug('Launching SampleAnalyzer ...') + if not self.main.recasting.stat_only_mode: + self.logger.debug('Launching SampleAnalyzer ...') + else: + self.logger.debug('Starting statistics calculations...') if not self.submit(filename,history): return @@ -385,11 +388,16 @@ def submit(self,dirname,history): self.logger.info(" Writing the command line history...") jobber.WriteHistory(history,self.main.firstdir) if self.main.recasting.status == "on": - self.main.recasting.collect_outputs(dirname,self.main.datasets) - self.logger.info(' -> the results can be found in:') - self.logger.info(' '+ dirname + '/Output/SAF/CLs_output_summary.dat') - for item in self.main.datasets: - self.logger.info(' '+ dirname + '/Output/SAF/'+ item.name + '/CLs_output.dat') + if not self.main.recasting.analysis_only_mode: + self.main.recasting.collect_outputs(dirname,self.main.datasets) + self.logger.info(' -> the results can be found in:') + self.logger.info(' '+ dirname + '/Output/SAF/CLs_output_summary.dat') + for item in self.main.datasets: + self.logger.info(' '+ dirname + '/Output/SAF/'+ item.name + '/CLs_output.dat') + else: + self.logger.info(' -> Efficiency results can be found in:') + for item in self.main.datasets: + self.logger.info(' '+ dirname + '/Output/SAF/' + item.name) else: layouter = LayoutWriter(self.main, dirname) layouter.WriteLayoutConfig() @@ -450,7 +458,7 @@ def extract(self,dirname,layout): for item in self.main.datasets: if self.main.recasting.status=='on': - if not self.main.recasting.CheckFile(dirname,item): + if not self.main.recasting.analysis_only_mode and not self.main.recasting.CheckFile(dirname,item): return False elif not jobber.CheckFile(item): self.logger.error("errors have occured during the analysis.") diff --git a/madanalysis/interpreter/cmd_swap.py b/madanalysis/interpreter/cmd_swap.py index e29f9389..f27bdb27 100644 --- a/madanalysis/interpreter/cmd_swap.py +++ b/madanalysis/interpreter/cmd_swap.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/history.py b/madanalysis/interpreter/history.py index ada71c85..833f84d4 100644 --- a/madanalysis/interpreter/history.py +++ b/madanalysis/interpreter/history.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/interpreter.py b/madanalysis/interpreter/interpreter.py index b812564d..1e1be060 100644 --- a/madanalysis/interpreter/interpreter.py +++ b/madanalysis/interpreter/interpreter.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/interpreter_base.py b/madanalysis/interpreter/interpreter_base.py index 2c04e99c..3aaa515b 100644 --- a/madanalysis/interpreter/interpreter_base.py +++ b/madanalysis/interpreter/interpreter_base.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/ma5_interpreter.py b/madanalysis/interpreter/ma5_interpreter.py index b0d486c0..23b719cd 100644 --- a/madanalysis/interpreter/ma5_interpreter.py +++ b/madanalysis/interpreter/ma5_interpreter.py @@ -2,7 +2,7 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/interpreter/timer.py b/madanalysis/interpreter/timer.py index 66c274b1..d3d5af7c 100644 --- a/madanalysis/interpreter/timer.py +++ b/madanalysis/interpreter/timer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_candidate_cut.py b/madanalysis/job/job_candidate_cut.py index a55f1580..597c2c5d 100644 --- a/madanalysis/job/job_candidate_cut.py +++ b/madanalysis/job/job_candidate_cut.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_event_cut.py b/madanalysis/job/job_event_cut.py index 41e3d3a1..d7644412 100644 --- a/madanalysis/job/job_event_cut.py +++ b/madanalysis/job/job_event_cut.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_execute.py b/madanalysis/job/job_execute.py index 4d61d949..3bedec74 100644 --- a/madanalysis/job/job_execute.py +++ b/madanalysis/job/job_execute.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_finalize.py b/madanalysis/job/job_finalize.py index a1afaa9d..408e2577 100644 --- a/madanalysis/job/job_finalize.py +++ b/madanalysis/job/job_finalize.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_header.py b/madanalysis/job/job_header.py index 6288fd23..fedcbabf 100644 --- a/madanalysis/job/job_header.py +++ b/madanalysis/job/job_header.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_initialize.py b/madanalysis/job/job_initialize.py index 9906257f..3758ae70 100644 --- a/madanalysis/job/job_initialize.py +++ b/madanalysis/job/job_initialize.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_main.py b/madanalysis/job/job_main.py index 0a526137..d5459970 100644 --- a/madanalysis/job/job_main.py +++ b/madanalysis/job/job_main.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_particle.py b/madanalysis/job/job_particle.py index 282597bf..d615196e 100644 --- a/madanalysis/job/job_particle.py +++ b/madanalysis/job/job_particle.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_plot.py b/madanalysis/job/job_plot.py index 4bcb3b4a..6d7cc346 100644 --- a/madanalysis/job/job_plot.py +++ b/madanalysis/job/job_plot.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_smearer_reco_header.py b/madanalysis/job/job_smearer_reco_header.py index 78c18fcf..32d1ff0f 100644 --- a/madanalysis/job/job_smearer_reco_header.py +++ b/madanalysis/job/job_smearer_reco_header.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_smearer_reco_main.py b/madanalysis/job/job_smearer_reco_main.py index a62fbe84..0fe47aa9 100644 --- a/madanalysis/job/job_smearer_reco_main.py +++ b/madanalysis/job/job_smearer_reco_main.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_tagger_header.py b/madanalysis/job/job_tagger_header.py index 85500b52..272c6cdb 100644 --- a/madanalysis/job/job_tagger_header.py +++ b/madanalysis/job/job_tagger_header.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/job/job_tagger_main.py b/madanalysis/job/job_tagger_main.py index aab7b1b5..b68e8e49 100644 --- a/madanalysis/job/job_tagger_main.py +++ b/madanalysis/job/job_tagger_main.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/cut_info.py b/madanalysis/layout/cut_info.py index bccd3b99..3cce20a9 100644 --- a/madanalysis/layout/cut_info.py +++ b/madanalysis/layout/cut_info.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/cutflow.py b/madanalysis/layout/cutflow.py index 6a220c48..f0548a07 100644 --- a/madanalysis/layout/cutflow.py +++ b/madanalysis/layout/cutflow.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/cutflow_for_dataset.py b/madanalysis/layout/cutflow_for_dataset.py index e262b435..e5df240e 100644 --- a/madanalysis/layout/cutflow_for_dataset.py +++ b/madanalysis/layout/cutflow_for_dataset.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/fom_calculation.py b/madanalysis/layout/fom_calculation.py index e41ead27..db3beda2 100644 --- a/madanalysis/layout/fom_calculation.py +++ b/madanalysis/layout/fom_calculation.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/histogram.py b/madanalysis/layout/histogram.py index c3a75701..19569b5c 100644 --- a/madanalysis/layout/histogram.py +++ b/madanalysis/layout/histogram.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/histogram_core.py b/madanalysis/layout/histogram_core.py index 21ece63f..bd986722 100644 --- a/madanalysis/layout/histogram_core.py +++ b/madanalysis/layout/histogram_core.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/histogram_frequency.py b/madanalysis/layout/histogram_frequency.py index 7bd882fa..4767a2ac 100644 --- a/madanalysis/layout/histogram_frequency.py +++ b/madanalysis/layout/histogram_frequency.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/histogram_frequency_core.py b/madanalysis/layout/histogram_frequency_core.py index 871aeb0f..ca0b0cdf 100644 --- a/madanalysis/layout/histogram_frequency_core.py +++ b/madanalysis/layout/histogram_frequency_core.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/histogram_logx.py b/madanalysis/layout/histogram_logx.py index 9602facf..f1c231cf 100644 --- a/madanalysis/layout/histogram_logx.py +++ b/madanalysis/layout/histogram_logx.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/layout.py b/madanalysis/layout/layout.py index 1e25ba63..7a7a2e47 100644 --- a/madanalysis/layout/layout.py +++ b/madanalysis/layout/layout.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/measure.py b/madanalysis/layout/measure.py index 10d98576..8e123a47 100644 --- a/madanalysis/layout/measure.py +++ b/madanalysis/layout/measure.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/merging_plots.py b/madanalysis/layout/merging_plots.py index 65f246bb..5d9747e7 100644 --- a/madanalysis/layout/merging_plots.py +++ b/madanalysis/layout/merging_plots.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/merging_plots_for_dataset.py b/madanalysis/layout/merging_plots_for_dataset.py index 5b177947..d6ba6d8c 100644 --- a/madanalysis/layout/merging_plots_for_dataset.py +++ b/madanalysis/layout/merging_plots_for_dataset.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/plotflow.py b/madanalysis/layout/plotflow.py index 6ef6f696..6b02aecb 100644 --- a/madanalysis/layout/plotflow.py +++ b/madanalysis/layout/plotflow.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/layout/plotflow_for_dataset.py b/madanalysis/layout/plotflow_for_dataset.py index 0d912b11..164dfaf9 100644 --- a/madanalysis/layout/plotflow_for_dataset.py +++ b/madanalysis/layout/plotflow_for_dataset.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/misc/freeze_environment.py b/madanalysis/misc/freeze_environment.py index 5d5607c4..cfe0b474 100644 --- a/madanalysis/misc/freeze_environment.py +++ b/madanalysis/misc/freeze_environment.py @@ -2,7 +2,7 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/misc/histfactory_reader.py b/madanalysis/misc/histfactory_reader.py index b4b42105..b7aeffb0 100644 --- a/madanalysis/misc/histfactory_reader.py +++ b/madanalysis/misc/histfactory_reader.py @@ -1,435 +1,650 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ - -from __future__ import absolute_import -import json, os, copy, math, logging -from six.moves import range - - -class HistFactory(object): - def __init__(self,pyhf_config): - self.pyhf_config = pyhf_config.get('SR' , {}) - self.lumi = pyhf_config.get('lumi', 1.) - self.path = pyhf_config.get('path', 'missing_path') - self.name = pyhf_config.get('name', 'missing_name') - self.logger = logging.getLogger('MA5') +import copy +import json +import logging +import math +import os +import sys +from collections import OrderedDict +from pathlib import Path +from typing import Tuple + +try: + from spey_pyhf.helper_functions import WorkspaceInterpreter +except ImportError: + WorkspaceInterpreter = None + + +class HistFactory: + def __init__(self, pyhf_config): + self.pyhf_config = pyhf_config.get("SR", {}) + self.lumi = pyhf_config.get("lumi", 1.0) + self.path = Path(pyhf_config.get("path", "missing_path")) + self.name = pyhf_config.get("name", "missing_name") + self.logger = logging.getLogger("MA5") if isinstance(self, HF_Background): - self.hf = {} - self.global_config = self.pyhf_config + self.hf = {} + self.global_config = self.pyhf_config elif isinstance(self, HF_Signal): - self.hf = [] + self.hf = [] - def __call__(self,lumi): + def __call__(self, lumi): return self.extrapolate(lumi) - def extrapolate(self,lumi): - """ To calculate the HL variables HF needs to be extrapolated. Expected - observables will be extrapolated and summed, summation is superseeded - to the observed values since there is no observation in HL. - - Modifiers are extrapolated with respect to their nature.""" + def extrapolate(self, lumi): + """To calculate the HL variables HF needs to be extrapolated. Expected + observables will be extrapolated and summed, summation is superseeded + to the observed values since there is no observation in HL. + + Modifiers are extrapolated with respect to their nature.""" lumi = float(lumi) - if lumi == self.lumi or self.hf in [{},[]]: + if lumi == self.lumi or self.hf in [{}, []]: return self.hf HF = copy.deepcopy(self.hf) - lumi_scale = round(lumi/self.lumi, 6) + lumi_scale = round(lumi / self.lumi, 6) if isinstance(self, HF_Background): # Background extrapolation total_expected = {} for SR, item in self.pyhf_config.items(): - if SR != 'lumi': - total_expected[SR] = [0.0]*len(item['data']) + if SR != "lumi": + total_expected[SR] = [0.0] * len(item["data"]) - for iSR in range(len(HF['channels'])): - self.logger.debug(' * Extrapolating channel '+ str(HF['channels'][iSR]['name'])) - if len(total_expected[HF['channels'][iSR]['name']]) == 0: + for iSR in range(len(HF["channels"])): + self.logger.debug( + " * Extrapolating channel " + str(HF["channels"][iSR]["name"]) + ) + if len(total_expected[HF["channels"][iSR]["name"]]) == 0: continue # modify the expected data of the sample - for sample in range(len(HF['channels'][iSR]['samples'])): - self.logger.debug(' * Extrapolating '+str(HF['channels'][iSR]['samples'][sample]['name'])+ ' sample') - for i in range(len(HF['channels'][iSR]['samples'][sample]['data'])): - HF['channels'][iSR]['samples'][sample]['data'][i] *= lumi_scale - total_expected[HF['channels'][iSR]['name']][i] += HF['channels'][iSR]['samples'][sample]['data'][i] - - for imod in range(len(HF['channels'][iSR]['samples'][sample]['modifiers'])): - mod_type = HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['type'] - if mod_type in ['normsys', 'normfactor', 'shapefactor', 'lumi']: + for sample in range(len(HF["channels"][iSR]["samples"])): + self.logger.debug( + " * Extrapolating " + + str(HF["channels"][iSR]["samples"][sample]["name"]) + + " sample" + ) + for i in range(len(HF["channels"][iSR]["samples"][sample]["data"])): + HF["channels"][iSR]["samples"][sample]["data"][i] *= lumi_scale + total_expected[HF["channels"][iSR]["name"]][i] += HF["channels"][ + iSR + ]["samples"][sample]["data"][i] + + for imod in range( + len(HF["channels"][iSR]["samples"][sample]["modifiers"]) + ): + mod_type = HF["channels"][iSR]["samples"][sample]["modifiers"][ + imod + ]["type"] + if mod_type in ["normsys", "normfactor", "shapefactor", "lumi"]: continue # extrapolate shape variables - elif mod_type == 'shapesys': - for i in range(len(HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data'])): - HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data'][i] *= lumi_scale + elif mod_type == "shapesys": + for i in range( + len( + HF["channels"][iSR]["samples"][sample]["modifiers"][ + imod + ]["data"] + ) + ): + HF["channels"][iSR]["samples"][sample]["modifiers"][imod][ + "data" + ][i] *= lumi_scale # extrapolate histo variables - elif mod_type == 'histosys': - for i in range(len(HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data']['hi_data'])): - HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data']['hi_data'][i] *= lumi_scale - for i in range(len(HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data']['lo_data'])): - HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data']['lo_data'][i] *= lumi_scale + elif mod_type == "histosys": + for i in range( + len( + HF["channels"][iSR]["samples"][sample]["modifiers"][ + imod + ]["data"]["hi_data"] + ) + ): + HF["channels"][iSR]["samples"][sample]["modifiers"][imod][ + "data" + ]["hi_data"][i] *= lumi_scale + for i in range( + len( + HF["channels"][iSR]["samples"][sample]["modifiers"][ + imod + ]["data"]["lo_data"] + ) + ): + HF["channels"][iSR]["samples"][sample]["modifiers"][imod][ + "data" + ]["lo_data"][i] *= lumi_scale # extrapolate stat variables - elif mod_type == 'staterror': - for i in range(len(HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data'])): - HF['channels'][iSR]['samples'][sample]['modifiers'][imod]['data'][i] *= math.sqrt(lumi_scale) + elif mod_type == "staterror": + for i in range( + len( + HF["channels"][iSR]["samples"][sample]["modifiers"][ + imod + ]["data"] + ) + ): + HF["channels"][iSR]["samples"][sample]["modifiers"][imod][ + "data" + ][i] *= math.sqrt(lumi_scale) # replace the observed bkg with total expected bkg for key, item in total_expected.items(): - for iobs in range(len(HF['observations'])): - if key == HF['observations'][iobs]['name']: + for iobs in range(len(HF["observations"])): + if key == HF["observations"][iobs]["name"]: if item != []: - HF['observations'][iobs]['data'] = item + HF["observations"][iobs]["data"] = item - elif isinstance(self, HF_Signal):#type(self) == HF_Signal: + elif isinstance(self, HF_Signal): # type(self) == HF_Signal: # Signal extrapolation for i in range(len(HF)): - if HF[i]['op'] == 'remove': + if HF[i]["op"] == "remove": continue - HF[i]['value']['data'] = [round(x*lumi_scale,6) for x in HF[i]['value']['data']] + HF[i]["value"]["data"] = [ + round(x * lumi_scale, 6) for x in HF[i]["value"]["data"] + ] # Extrapolate modifiers - for imod in range(len(HF[i]['value']['modifiers'])): - mod_type = HF[i]['value']['modifiers'][imod]['type'] - if mod_type in ['normsys', 'normfactor', 'shapefactor', 'lumi']: + for imod in range(len(HF[i]["value"]["modifiers"])): + mod_type = HF[i]["value"]["modifiers"][imod]["type"] + if mod_type in ["normsys", "normfactor", "shapefactor", "lumi"]: continue # extrapolate shape variables - elif mod_type == 'shapesys': - for i in range(len(HF[i]['value']['modifiers'][imod]['data'])): - HF[i]['value']['modifiers'][imod]['data'][i] *= lumi_scale + elif mod_type == "shapesys": + for i in range(len(HF[i]["value"]["modifiers"][imod]["data"])): + HF[i]["value"]["modifiers"][imod]["data"][i] *= lumi_scale # extrapolate histo variables - elif mod_type == 'histosys': - for i in range(len(HF[i]['value']['modifiers'][imod]['data']['hi_data'])): - HF[i]['value']['modifiers'][imod]['data']['hi_data'][i] *= lumi_scale - for i in range(len(HF[i]['value']['modifiers'][imod]['data']['lo_data'])): - HF[i]['value']['modifiers'][imod]['data']['lo_data'][i] *= lumi_scale + elif mod_type == "histosys": + for i in range( + len(HF[i]["value"]["modifiers"][imod]["data"]["hi_data"]) + ): + HF[i]["value"]["modifiers"][imod]["data"]["hi_data"][ + i + ] *= lumi_scale + for i in range( + len(HF[i]["value"]["modifiers"][imod]["data"]["lo_data"]) + ): + HF[i]["value"]["modifiers"][imod]["data"]["lo_data"][ + i + ] *= lumi_scale # extrapolate statistical variables - elif mod_type == 'staterror': - for i in range(len(HF[i]['value']['modifiers'][imod]['data'])): - HF[i]['value']['modifiers'][imod]['data'][i] *= math.sqrt(lumi_scale) + elif mod_type == "staterror": + for i in range(len(HF[i]["value"]["modifiers"][imod]["data"])): + HF[i]["value"]["modifiers"][imod]["data"][i] *= math.sqrt( + lumi_scale + ) return HF class HF_Background(HistFactory): - def __init__(self, pyhf_config, expected=False): - super(HF_Background, self).__init__(pyhf_config) - self.logger.debug('Reading : '+os.path.join(self.path,self.name)) - if os.path.isfile(os.path.join(self.path,self.name)): - with open(os.path.join(self.path,self.name),'r') as json_file: + def __init__(self, pyhf_config: dict, expected: bool = False): + super().__init__(pyhf_config) + + if WorkspaceInterpreter is None: + raise ImportError( + "The 'spey_pyhf' package is required for the HistFactory class." + ) + + bkg_file = self.path.joinpath(self.name) + self.logger.debug("Reading : %s", bkg_file) + if bkg_file.is_file(): + with bkg_file.open("r") as json_file: self.hf = json.load(json_file) else: - self.logger.warning('Can not find file : '+ os.path.join(self.path,self.name)) + self.logger.warning("Can not find file : %s", bkg_file) if expected: self.hf = self.impose_expected() def size(self): # The number of SRs in the likelihood profile - return [len(x.get('data',[])) for x in self.get_observed()] + return list(WorkspaceInterpreter(self.hf).bin_map.values()) def impose_expected(self): """ - To switch observed data with total expected data per SR bin. + To switch observed data with total expected data per SR bin. """ total_expected = {} - HF = copy.deepcopy(self.hf) - for i in range(len(HF.get('observations',[]))): - total_expected[HF['observations'][i]['name']] = [0.0]*len(HF['observations'][i]['data']) - - for iSR in range(len(HF['channels'])): - for sample in range(len(HF['channels'][iSR]['samples'])): - for SRbin in range(len(HF['channels'][iSR]['samples'][sample]['data'])): - total_expected[HF['channels'][iSR]['name']][SRbin] += \ - HF['channels'][iSR]['samples'][sample]['data'][SRbin] + HF = copy.deepcopy(self.hf) + for i in range(len(HF.get("observations", []))): + total_expected[HF["observations"][i]["name"]] = [0.0] * len( + HF["observations"][i]["data"] + ) + + for iSR in range(len(HF["channels"])): + for sample in range(len(HF["channels"][iSR]["samples"])): + for SRbin in range(len(HF["channels"][iSR]["samples"][sample]["data"])): + total_expected[HF["channels"][iSR]["name"]][SRbin] += HF["channels"][ + iSR + ]["samples"][sample]["data"][SRbin] # replace the observed bkg with total expected bkg for key, item in total_expected.items(): - for iobs in range(len(HF['observations'])): - if key == HF['observations'][iobs]['name']: - HF['observations'][iobs]['data'] = [round(x,5) for x in item] + for iobs in range(len(HF["observations"])): + if key == HF["observations"][iobs]["name"]: + HF["observations"][iobs]["data"] = [round(x, 5) for x in item] return HF def get_expected(self): - return self.impose_expected().get('observations',[]) + return self.impose_expected().get("observations", []) def get_observed(self): - return self.hf.get('observations',[]) + return self.hf.get("observations", []) def get_sample_names(self): samples = {} - HF = copy.deepcopy(self.hf) - for iSR in range(len(HF.get('channels',[]))): - samples[HF['channels'][iSR]['name']] = [] - for sample in range(len(HF['channels'][iSR]['samples'])): - samples[HF['channels'][iSR]['name']].append(HF['channels'][iSR]['samples'][sample]['name']) + HF = copy.deepcopy(self.hf) + for iSR in range(len(HF.get("channels", []))): + samples[HF["channels"][iSR]["name"]] = [] + for sample in range(len(HF["channels"][iSR]["samples"])): + samples[HF["channels"][iSR]["name"]].append( + HF["channels"][iSR]["samples"][sample]["name"] + ) return samples - - - - class HF_Signal(HistFactory): """ - HistFactory requires a jsonpathch file to be attached to the bkg. - BKG histfactory includes a configuration file which is necessary to - construct the signal patch. - - **kwargs are for initialization of uncertainties in the future - also background can be inputted for simultaneous validation of the profile. - - validate = True will initiate a mock validation sequence to ensure that - the construction of pyhf_config is correct. The validation requires the - background sample to be completed. self.hf == [] means that validation - is failed and correct pyhf_config is needed. + HistFactory requires a jsonpathch file to be attached to the bkg. + BKG histfactory includes a configuration file which is necessary to + construct the signal patch. + + **kwargs are for initialization of uncertainties in the future + also background can be inputted for simultaneous validation of the profile. + + validate = True will initiate a mock validation sequence to ensure that + the construction of pyhf_config is correct. The validation requires the + background sample to be completed. self.hf == [] means that validation + is failed and correct pyhf_config is needed. """ - def __init__(self,pyhf_config, regiondata, xsection=-1, **kwargs): - super(HF_Signal, self).__init__(pyhf_config) + + def __init__(self, pyhf_config, regiondata, xsection=-1, **kwargs): + super().__init__(pyhf_config) self.signal_config = {} - with open(os.path.join(self.path, self.name), 'r') as json_file: - tmp_bkg = json.load(json_file) + if WorkspaceInterpreter is None: + raise ImportError( + "The 'spey_pyhf' package is required for the HistFactory class." + ) - bin_sizes = [len(x.get('data', [])) for x in tmp_bkg.get('observations',[])] + with self.path.joinpath(self.name).open("r") as json_file: + tmp_bkg = WorkspaceInterpreter(json.load(json_file)) + + bin_map = tmp_bkg.bin_map + self.poi_name = tmp_bkg.poi_name[0][1] for key, item in self.pyhf_config.items(): - if key != 'lumi': + if key != "lumi": self.signal_config[key] = {} - if not item['is_included']: - self.signal_config[key]['op'] = 'remove' - self.signal_config[key]["path"] = '/channels/' + str(item['channels']) + if not item["is_included"]: + self.signal_config[key]["op"] = "remove" + self.signal_config[key]["path"] = "/channels/" + str(item["channels"]) else: - self.signal_config[key]['op'] = 'add' - self.signal_config[key]["path"] = \ - '/channels/' + str(item['channels']) + '/samples/' + \ - str(len(tmp_bkg["channels"][int(item['channels'])]["samples"])-1) - self.signal_config[key]["bin_size"] = \ - bin_sizes[int(self.signal_config[key]["path"].split('/')[2])] - - self.signal_config[key]['data'] = [] - for SRname in item['data']: - if kwargs.get('validate',False): + self.signal_config[key]["op"] = "add" + self.signal_config[key]["path"] = ( + "/channels/" + + str(item["channels"]) + + "/samples/" + + str( + len(tmp_bkg["channels"][int(item["channels"])]["samples"]) - 1 + ) + ) + self.signal_config[key]["bin_size"] = bin_map[key] + + self.signal_config[key]["data"] = [] + for SRname in item["data"]: + if kwargs.get("validate", False): # initiate mock validation sequence, this requires the # background to be given in kwargs - self.signal_config[key]['data'].append(1.) + self.signal_config[key]["data"].append(1.0) else: - self.signal_config[key]['data'].append( - regiondata[SRname]['Nf']/regiondata[SRname]['N0'] + self.signal_config[key]["data"].append( + regiondata[SRname]["Nf"] / regiondata[SRname]["N0"] ) - self.hf = self.set_HF(xsection, background = kwargs.get('background', {}), - add_normsys = kwargs.get('add_normsys', []), - add_histosys = kwargs.get('add_histosys',[]),) + self.hf = self.set_HF( + xsection, + background=kwargs.get("background", {}), + add_normsys=kwargs.get("add_normsys", []), + add_histosys=kwargs.get("add_histosys", []), + ) def set_HF(self, xsection, **kwargs): HF = [] - if xsection<=0.: + if xsection <= 0.0: return HF toRemove = [] for ix, SR in enumerate(self.signal_config.keys()): - if self.signal_config[SR]['op'] != 'remove': + if self.signal_config[SR]["op"] != "remove": SR_tmp = { - 'op' : self.signal_config[SR]['op'], - 'path' : self.signal_config[SR]['path'], - 'value' : { - 'name' : 'MA5_signal_' + str(ix), - 'data' : [ - eff * xsection * self.lumi * 1000. - for eff in self.signal_config[SR]['data'] + "op": self.signal_config[SR]["op"], + "path": self.signal_config[SR]["path"], + "value": { + "name": "MA5_signal_" + str(ix), + "data": [ + eff * xsection * self.lumi * 1000.0 + for eff in self.signal_config[SR]["data"] ], - 'modifiers' : [ - {u'data': None, u'name': u'lumi', u'type': u'lumi'}, - {u'data': None, u'name': u'mu_SIG', u'type': u'normfactor'}, - ] - } + "modifiers": [ + {"data": None, "name": "lumi", "type": "lumi"}, + {"data": None, "name": self.poi_name, "type": "normfactor"}, + ], + }, } if len(SR_tmp["value"]["data"]) == 0: - SR_tmp["value"]["data"] = [0.0] * self.signal_config[SR]['bin_size'] + SR_tmp["value"]["data"] = [0.0] * self.signal_config[SR]["bin_size"] HF.append(SR_tmp) else: toRemove.append( - {'op' : self.signal_config[SR]['op'], - 'path' : self.signal_config[SR]['path']} + { + "op": self.signal_config[SR]["op"], + "path": self.signal_config[SR]["path"], + } ) # Need to sort correctly the paths to the channels to be removed - toRemove.sort(key = lambda p : p["path"].split("/")[-1], reverse = True) + toRemove.sort(key=lambda p: p["path"].split("/")[-1], reverse=True) for d in toRemove: HF.append(d) - for sys in kwargs.get('add_normsys',[]): - HF = self.add_normsys(HF,sys['hi'],sys['lo'],sys['name']) - for sys in kwargs.get('add_histosys',[]): - HF = self.add_normsys(HF,sys['hi_data'],sys['lo_data'],sys['name']) + for sys in kwargs.get("add_normsys", []): + HF = self.add_normsys(HF, sys["hi"], sys["lo"], sys["name"]) + for sys in kwargs.get("add_histosys", []): + HF = self.add_normsys(HF, sys["hi_data"], sys["lo_data"], sys["name"]) - background = kwargs.get('background',{}) + background = kwargs.get("background", {}) if isinstance(background, HF_Background): if not self.validate_bins(background, HF): - self.logger.warning('Signal HistFactory validation failed.') + self.logger.warning("Signal HistFactory validation failed.") return [] return HF - def validate_bins(self, background, HF = []): - if HF == []: + def validate_bins(self, background, HF: list = None): + if HF is None: HF = self.hf - bkg_bins = background.size() - to_validate = [False]*len(bkg_bins) + bkg_bins = background.size() + to_validate = [False] * len(bkg_bins) if HF == {}: return all(to_validate) try: for sample in HF: # check if the size of the bins in the data matches the background - if sample['op'] == 'remove': - to_validate[int(sample['path'].split('/')[2])] = True + if sample["op"] == "remove": + to_validate[int(sample["path"].split("/")[2])] = True continue - elif len(sample['value']['data']) == bkg_bins[int(sample['path'].split('/')[2])]: - to_validate[int(sample['path'].split('/')[2])] = True + elif ( + len(sample["value"]["data"]) + == bkg_bins[int(sample["path"].split("/")[2])] + ): + to_validate[int(sample["path"].split("/")[2])] = True # also check if the modifier data size matches with the background - for modifier in sample['value']['modifiers']: - if modifier['type'] == 'histosys': - if len(modifier['data']['hi_data']) != bkg_bins[int(sample['path'].split('/')[2])]: - to_validate[int(sample['path'].split('/')[2])] = False - if len(modifier['data']['lo_data']) != bkg_bins[int(sample['path'].split('/')[2])]: - to_validate[int(sample['path'].split('/')[2])] = False + for modifier in sample["value"]["modifiers"]: + if modifier["type"] == "histosys": + if ( + len(modifier["data"]["hi_data"]) + != bkg_bins[int(sample["path"].split("/")[2])] + ): + to_validate[int(sample["path"].split("/")[2])] = False + if ( + len(modifier["data"]["lo_data"]) + != bkg_bins[int(sample["path"].split("/")[2])] + ): + to_validate[int(sample["path"].split("/")[2])] = False except: - self.logger.debug('Signal HistFactory : Key error in dictionary...') + self.logger.debug("Signal HistFactory : Key error in dictionary...") return False return all(to_validate) def isAlive(self): for sample in self.hf: - if sample['op'] != "remove": - if any([s>0 for s in sample['value']['data']]): + if sample["op"] != "remove": + if any([s > 0 for s in sample["value"]["data"]]): return True return False - def add_normsys(self,HF, hi, lo, name): + def add_normsys(self, HF, hi, lo, name): # systematic unc: name has to be MA5_scale, MA5_PDF, MA5_TH or MA5_sys # hi = 1.XX lo = 0.XX for i in range(len(HF)): - if HF[i]['op'] == 'remove': + if HF[i]["op"] == "remove": continue - HF[i]['value']['modifiers'].append({ "name": name, - "type": "normsys", - "data": {"hi": hi, - "lo": lo} - }) + HF[i]["value"]["modifiers"].append( + {"name": name, "type": "normsys", "data": {"hi": hi, "lo": lo}} + ) return HF - def add_histosys(self,HF,hi_data,lo_data,name): + def add_histosys(self, HF, hi_data, lo_data, name): # scale and TH uncertainties: name has to be MA5_scale, MA5_PDF, MA5_TH or MA5_sys # hi_data,lo_data are list!! for i in range(len(HF)): - if HF[i]['op'] == 'remove': + if HF[i]["op"] == "remove": continue - HF[i]['value']['modifiers'].append({ "name": name, - "type": "histosys", - "data": {"hi_data": hi_data, - "lo_data": lo_data} - }) + HF[i]["value"]["modifiers"].append( + { + "name": name, + "type": "histosys", + "data": {"hi_data": hi_data, "lo_data": lo_data}, + } + ) return HF def clear_modifiers(self): for i in range(len(self.hf)): - self.hf[i]['value']['modifiers'] = [ - {'data': None, - 'name': 'lumi', - 'type': 'lumi'}, - {'data': None, - 'name': 'mu_SIG', - 'type': 'normfactor'} - ] + self.hf[i]["value"]["modifiers"] = [ + {"data": None, "name": "lumi", "type": "lumi"}, + {"data": None, "name": "mu_SIG", "type": "normfactor"}, + ] - - - -def get_HFID(file,SRname): +def get_HFID(file, SRname): """ - Extract the location of the profiles within the JSON file. + Extract the location of the profiles within the JSON file. """ if os.path.isfile(file): - with open(file, 'r') as json_file: + with open(file, "r") as json_file: HF = json.load(json_file) else: - return 'Can not find background file: '+file - for ch in HF['channels']: - if ch['name'] == SRname: - return HF['channels'].index(ch) - return 'Invalid or corrupted info file.' - - - - -#def merge_backgrounds(Background1,Background2): -# """ -# Merging method for two bakcground JSON file. It merges "only" the files -# with same POI and version. -# """ -# if {} in [Background1.hf, Background2.hf] or type(Background1) != type(Background2): -# return Background1, 0 -# logging.getLogger('MA5').debug('merging :'+', '.join(list(Background1.global_config.keys())+\ -# list(Background2.pyhf_config.keys()))) -# measurements = [] -# # merge common measurements -# for measurement1 in Background1.hf.get('measurements',[]): -# poi = measurement1['config']['poi'] -# param = measurement1['config']['parameters'] -# for measurement2 in Background2.hf.get('measurements',[]): -# if poi == measurement2['config']['poi']: -# for parameter in measurement2['config']['parameters'] : -# if parameter not in param: -# param += parameter -# measurements.append({'name' : measurement1['name'], -# 'config' : {'parameters' : param, -# 'poi' : poi -# }}) -# -# if measurements == [] or Background1.hf['version'] != Background2.hf['version'] : -# logging.getLogger('MA5').warning('Merging failed: Either measurements or versions does not match...') -# logging.getLogger('MA5').warning(', '.join(list(Background2.pyhf_config.keys()))+' will not be added.') -# return Background1, 0 # only get uncontradctory poi -# -# logging.getLogger('MA5').debug('measurements are matching...') -# extend = len(Background1.get_observed()) -# for profile, info in Background2.pyhf_config.items(): -# check = [x for x in Background1.global_config.keys() if x.startswith(profile)] -# profile += ''+(len(check)>0)*('_ma5_'+str(len(check))) -# Background1.global_config[profile] = {} -# Background1.global_config[profile]['channels'] = str(int(info['channels'])+extend) -# #print str(int(info['channels'])+extend) -# Background1.global_config[profile]['data' ] = info['data'] -# -# for ch in Background2.hf['channels']: -# Background1.hf['channels'].append(ch) -# for obs in Background2.hf['observations']: -# Background1.hf['observations'].append(obs) -# Background1.hf['measurements'] = measurements -# return Background1, 1 - + return "Can not find background file: " + file + for ch in HF["channels"]: + if ch["name"] == SRname: + return HF["channels"].index(ch) + return "Invalid or corrupted info file." + + +def construct_histfactory_dictionary(info_root, run_recast_session) -> Tuple[dict, list]: + """Read the info file and construct histfactory dictionary""" + pyhf_config = OrderedDict() + nprofile, default_lumi = 0, 0 + to_remove = [] + run_recast_session.logger.debug(" === Reading info file for pyhf ===") + for child in info_root: + if child.tag == "lumi": + default_lumi = float(child.text) + if child.tag == "pyhf": + likelihood_profile = child.attrib.get("id", "HF-Likelihood-" + str(nprofile)) + if likelihood_profile == "HF-Likelihood-" + str(nprofile): + nprofile += 1 + if not likelihood_profile in list(pyhf_config.keys()): + pyhf_config[likelihood_profile] = { + "name": "No File name in info file...", + "path": os.path.join( + run_recast_session.pad, "Build/SampleAnalyzer/User/Analyzer" + ), + "lumi": default_lumi, + "SR": OrderedDict(), + } + for subchild in child: + if subchild.tag == "name": + if ( + run_recast_session.main.recasting.simplify_likelihoods + and run_recast_session.main.session_info.has_simplify + ): + main_path = pyhf_config[likelihood_profile]["path"] + full = str(subchild.text) + simplified = full.split(".json")[0] + "_simplified.json" + if os.path.isfile(os.path.join(main_path, simplified)): + pyhf_config[likelihood_profile]["name"] = simplified + else: + simplify_path = os.path.join( + run_recast_session.main.archi_info.ma5dir, + "tools/simplify/simplify-master/src", + ) + try: + if ( + os.path.isdir(simplify_path) + and simplify_path not in sys.path + ): + sys.path.insert(0, simplify_path) + import simplify + + run_recast_session.logger.debug( + "simplify has been imported from " + + " ".join(simplify.__path__) + ) + run_recast_session.logger.debug("simplifying " + full) + with open(os.path.join(main_path, full), "r") as f: + spec = json.load(f) + # Get model and data + poi_name = "lumi" + try: + original_poi = spec["measurements"][0]["config"][ + "poi" + ] + spec["measurements"][0]["config"]["poi"] = poi_name + except IndexError: + raise simplify.exceptions.InvalidMeasurement( + "The measurement index 0 is out of bounds." + ) + model, data = simplify.model_tools.model_and_data(spec) + + fixed_params = model.config.suggested_fixed() + init_pars = model.config.suggested_init() + # Fit the model to data + fit_result = simplify.fitter.fit( + model, + data, + init_pars=init_pars, + fixed_pars=fixed_params, + ) + # Get yields + ylds = simplify.yields.get_yields(spec, fit_result, []) + newspec = simplify.simplified.get_simplified_spec( + spec, + ylds, + allowed_modifiers=[], + prune_channels=[], + include_signal=False, + ) + newspec["measurements"][0]["config"]["poi"] = original_poi + with open( + os.path.join(main_path, simplified), "w+" + ) as out_file: + json.dump(newspec, out_file, indent=4, sort_keys=True) + pyhf_config[likelihood_profile]["name"] = simplified + except ImportError: + run_recast_session.logger.warning( + "To use simplified likelihoods, please install simplify" + ) + pyhf_config[likelihood_profile]["name"] = str( + subchild.text + ) + except ( + Exception, + simplify.exceptions.InvalidMeasurement, + ) as err: + run_recast_session.logger.warning( + "Can not simplify " + full + ) + run_recast_session.logger.debug(str(err)) + pyhf_config[likelihood_profile]["name"] = str( + subchild.text + ) + else: + pyhf_config[likelihood_profile]["name"] = str(subchild.text) + run_recast_session.logger.debug( + pyhf_config[likelihood_profile]["name"] + " file will be used." + ) + elif subchild.tag == "regions": + for channel in subchild: + if channel.tag == "channel": + if not channel.attrib.get("name", False): + run_recast_session.logger.warning( + "Invalid or corrupted info file" + ) + run_recast_session.logger.warning( + "Please check " + likelihood_profile + ) + to_remove.append(likelihood_profile) + else: + data = [] + if channel.text != None: + data = channel.text.split() + pyhf_config[likelihood_profile]["SR"][ + channel.attrib["name"] + ] = { + "channels": channel.get("id", default=-1), + "data": data, + } + is_included = ( + ( + channel.get("is_included", default=0) + in ["True", "1", "yes"] + ) + if len(data) == 0 + else True + ) + pyhf_config[likelihood_profile]["SR"][ + channel.attrib["name"] + ].update({"is_included": is_included}) + if ( + pyhf_config[likelihood_profile]["SR"][ + channel.attrib["name"] + ]["channels"] + == -1 + ): + file = os.path.join( + pyhf_config[likelihood_profile]["path"], + pyhf_config[likelihood_profile]["name"], + ) + ID = get_HFID(file, channel.attrib["name"]) + if not isinstance(ID, str): + pyhf_config[likelihood_profile]["SR"][ + channel.attrib["name"] + ]["channels"] = str(ID) + else: + run_recast_session.logger.warning(ID) + run_recast_session.logger.warning( + "Please check " + + likelihood_profile + + "and/or " + + channel.attrib["name"] + ) + to_remove.append(likelihood_profile) + + return pyhf_config, to_remove diff --git a/madanalysis/misc/run_recast.py b/madanalysis/misc/run_recast.py index 2fa6a4b5..c1275c9f 100644 --- a/madanalysis/misc/run_recast.py +++ b/madanalysis/misc/run_recast.py @@ -1,24 +1,24 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ @@ -32,128 +32,118 @@ import shutil import sys import time -from collections import OrderedDict -import numpy as np +import numpy as np from shell_command import ShellCommand -from six.moves import input, map, range +from six.moves import input, range from string_tools import StringTools -from madanalysis.configuration.delphes_configuration import \ - DelphesConfiguration -from madanalysis.configuration.delphesMA5tune_configuration import \ - DelphesMA5tuneConfiguration +from madanalysis.configuration.delphes_configuration import DelphesConfiguration +from madanalysis.configuration.delphesMA5tune_configuration import ( + DelphesMA5tuneConfiguration, +) from madanalysis.install.detector_manager import DetectorManager from madanalysis.IOinterface.folder_writer import FolderWriter from madanalysis.IOinterface.job_writer import JobWriter from madanalysis.IOinterface.library_writer import LibraryWriter from madanalysis.misc.histfactory_reader import ( - HF_Background, HF_Signal,get_HFID + HF_Background, + HF_Signal, + construct_histfactory_dictionary, ) +from madanalysis.misc.theoretical_error_setup import error_dict_setup +# pylint: disable=logging-fstring-interpolation,import-outside-toplevel -class RunRecast(): +class RunRecast: def __init__(self, main, dirname): - self.dirname = dirname - self.main = main - self.delphes_runcard = [] + self.dirname = dirname + self.main = main + self.delphes_runcard = [] self.analysis_runcard = [] - self.forced = self.main.forced - self.detector = "" - self.pad = "" - self.first11 = True - self.first12 = True - self.ntoys = self.main.recasting.CLs_numofexps - self.pyhf_config = {} # initialize and configure histfactory - self.cov_config = {} - self.logger = logging.getLogger('MA5') - self.is_apriori = True - self.cls_calculator = cls - self.TACO_output = self.main.recasting.TACO_output + self.forced = self.main.forced + self.detector = "" + self.pad = "" + self.first11 = True + self.first12 = True + self.pyhf_config = {} # initialize and configure histfactory + self.cov_config = {} + self.logger = logging.getLogger("MA5") + self.TACO_output = self.main.recasting.TACO_output def init(self): ### First, the analyses to take care off - logging.getLogger("MA5").debug(" Inviting the user to edit the recasting card...") + logging.getLogger("MA5").debug( + " Inviting the user to edit the recasting card..." + ) self.edit_recasting_card() ### Getting the list of analyses to recast self.logger.info(" Getting the list of delphes simulation to be performed...") self.get_runs() ### Check if we have anything to do - if len(self.delphes_runcard)==0: - self.logger.warning('No recasting to do... Please check the recasting card') + if len(self.delphes_runcard) == 0: + self.logger.warning("No recasting to do... Please check the recasting card") return False ### Exit return True - - def SetCLsCalculator(self): - if self.main.session_info.has_pyhf and self.main.recasting.CLs_calculator_backend == "pyhf": - self.cls_calculator = pyhf_wrapper - elif not self.main.session_info.has_pyhf: - self.main.recasting.CLs_calculator_backend = "native" - - if self.main.session_info.has_pyhf and self.main.recasting.expectation_assumption == "aposteriori": - self.cls_calculator = pyhf_wrapper - self.main.recasting.CLs_calculator_backend = "pyhf" - self.is_apriori = False - elif not self.main.session_info.has_pyhf and self.main.recasting.expectation_assumption == "aposteriori": - self.main.recasting.expectation_assumption = "apriori" - self.main.recasting.CLs_calculator_backend = "native" - self.is_apriori = True - self.logger.warning("A posteriori expectation calculation is not available, " + \ - "a priori limits will be calculated.") - ################################################ ### GENERAL METHODS ################################################ ## Running the machinery def execute(self): - self.main.forced=True + self.main.forced = True for delphescard in list(set(sorted(self.delphes_runcard))): ## Extracting run infos and checks version = delphescard[:4] - card = delphescard[5:] + card = delphescard[5:] if not self.check_run(version): - self.main.forced=self.forced + self.main.forced = self.forced return False ## Running the fastsim if not self.fastsim_single(version, card): - self.main.forced=self.forced + self.main.forced = self.forced return False self.main.fastsim.package = self.detector ## Running the analyses if not self.analysis_single(version, card): - self.main.forced=self.forced + self.main.forced = self.forced return False ## Cleaning - if not FolderWriter.RemoveDirectory(os.path.normpath(self.dirname+'_RecastRun')): + if not FolderWriter.RemoveDirectory( + os.path.normpath(self.dirname + "_RecastRun") + ): return False # exit - self.main.forced=self.forced + self.main.forced = self.forced return True - ## Prompt to edit the recasting card def edit_recasting_card(self): if self.forced or self.main.script: return self.logger.info("Would you like to edit the recasting Card ? (Y/N)") - allowed_answers=['n','no','y','yes'] - answer="" - while answer not in allowed_answers: - answer=input("Answer: ") - answer=answer.lower() - if answer=="no" or answer=="n": + allowed_answers = ["n", "no", "y", "yes"] + answer = "" + while answer not in allowed_answers: + answer = input("Answer: ") + answer = answer.lower() + if answer in ["no", "n"]: return else: - err = os.system(self.main.session_info.editor+" "+self.dirname+"/Input/recasting_card.dat") + err = os.system( + self.main.session_info.editor + + " " + + self.dirname + + "/Input/recasting_card.dat" + ) return @@ -162,71 +152,92 @@ def get_runs(self): del_runs = [] ana_runs = [] ## decoding the card - runcard = open(self.dirname+"/Input/recasting_card.dat",'r') + runcard = open(self.dirname + "/Input/recasting_card.dat", "r") for line in runcard: - if len(line.strip())==0 or line.strip().startswith('#'): + if len(line.strip()) == 0 or line.strip().startswith("#"): continue - myline=line.split() - if myline[2].lower() =='on' and myline[3] not in del_runs: - del_runs.append(myline[1]+'_'+myline[3]) - if myline[2].lower() =='on': - ana_runs.append(myline[1]+'_'+myline[0]) + myline = line.split() + if myline[2].lower() == "on" and myline[3] not in del_runs: + del_runs.append(myline[1] + "_" + myline[3]) + if myline[2].lower() == "on": + ana_runs.append(myline[1] + "_" + myline[0]) ## saving the information and exti self.delphes_runcard = del_runs self.analysis_runcard = ana_runs return - def check_run(self,version): + def check_run(self, version): ## setup + check = False if version == "v1.1": self.detector = "delphesMA5tune" - self.pad = self.main.archi_info.ma5dir+'/tools/PADForMA5tune' - check = self.main.recasting.ma5tune + self.pad = self.main.archi_info.ma5dir + "/tools/PADForMA5tune" + check = self.main.recasting.ma5tune elif version == "v1.2": self.detector = "delphes" - self.pad = self.main.archi_info.ma5dir+'/tools/PAD' - check = self.main.recasting.delphes + self.pad = self.main.archi_info.ma5dir + "/tools/PAD" + check = self.main.recasting.delphes elif version == "vSFS": self.detector = "fastjet" - self.pad = self.main.archi_info.ma5dir+'/tools/PADForSFS' - check = True + self.pad = self.main.archi_info.ma5dir + "/tools/PADForSFS" + check = True ## Check and exit if not check: - self.logger.error('The ' + self.detector + ' library is not present -> the associated analyses cannot be used') - return False + self.logger.error( + "The %s library is not present -> the associated analyses cannot be used", + self.detector, + ) + return False return True ################################################ ### DELPHES RUN ################################################ - def fastsim_single(self,version,delphescard): - self.logger.debug('Launch a bunch of fastsim with the delphes card: '+delphescard) + def fastsim_single(self, version, delphescard): + self.logger.debug( + "Launch a bunch of fastsim with the delphes card: %s", delphescard + ) # Init and header self.fastsim_header(version) # Activating the right delphes - if self.detector!="fastjet": - self.logger.debug('Activating the detector (switch delphes/delphesMA5tune)') + if self.detector != "fastjet": + self.logger.debug("Activating the detector (switch delphes/delphesMA5tune)") self.main.fastsim.package = self.detector detector_handler = DetectorManager(self.main) if not detector_handler.manage(self.detector): - self.logger.error('Problem with the activation of delphesMA5tune') + self.logger.error("Problem with the activation of delphesMA5tune") return False # Checking whether events have already been generated and if not, event generation - self.logger.debug('Loop over the datasets...') + self.logger.debug("Loop over the datasets...") + evtfile = None for item in self.main.datasets: - if self.detector=="delphesMA5tune": - evtfile = self.dirname+'/Output/SAF/'+item.name+'/RecoEvents/RecoEvents_v1x1_'+delphescard.replace('.tcl','')+'.root' - elif self.detector=="delphes": - evtfile = self.dirname+'/Output/SAF/'+item.name+'/RecoEvents/RecoEvents_v1x2_'+delphescard.replace('.tcl','')+'.root' - elif self.detector=="fastjet": + if self.detector == "delphesMA5tune": + evtfile = ( + self.dirname + + "/Output/SAF/" + + item.name + + "/RecoEvents/RecoEvents_v1x1_" + + delphescard.replace(".tcl", "") + + ".root" + ) + elif self.detector == "delphes": + evtfile = ( + self.dirname + + "/Output/SAF/" + + item.name + + "/RecoEvents/RecoEvents_v1x2_" + + delphescard.replace(".tcl", "") + + ".root" + ) + elif self.detector == "fastjet": return True - self.logger.debug('- applying fastsim and producing '+evtfile+'...') + self.logger.debug("- applying fastsim and producing %s ...", evtfile) if not os.path.isfile(os.path.normpath(evtfile)): - if not self.generate_events(item,delphescard): + if not self.generate_events(item, delphescard): return False # Exit @@ -235,29 +246,40 @@ def fastsim_single(self,version,delphescard): def fastsim_header(self, version): ## Gettign the version dependent stuff to_print = False - if version=="v1.1" and self.first11: + tag = None + if version == "v1.1" and self.first11: to_print = True tag = version self.first11 = False - elif version!="v1.1" and self.first12: + elif version != "v1.1" and self.first12: to_print = True tag = "v1.2+" self.first12 = False ## Printing if to_print: - self.logger.info(" **********************************************************") - self.logger.info(" "+StringTools.Center(tag+' detector simulations',57)) - self.logger.info(" **********************************************************") - - def run_delphes(self,dataset,card): + self.logger.info( + " **********************************************************" + ) + self.logger.info( + " %s", StringTools.Center(f"{tag} detector simulations", 57) + ) + self.logger.info( + " **********************************************************" + ) + + def run_delphes(self, dataset, card): # Initializing the JobWriter - if os.path.isdir(self.dirname+'_RecastRun'): - if not FolderWriter.RemoveDirectory(os.path.normpath(self.dirname+'_RecastRun')): + if os.path.isdir(self.dirname + "_RecastRun"): + if not FolderWriter.RemoveDirectory( + os.path.normpath(self.dirname + "_RecastRun") + ): return False - jobber = JobWriter(self.main,self.dirname+'_RecastRun') + jobber = JobWriter(self.main, self.dirname + "_RecastRun") # Writing process - self.logger.info(" Creating folder '"+self.dirname.split('/')[-1] + "_RecastRun'...") + self.logger.info( + " Creating folder '" + self.dirname.split("/")[-1] + "_RecastRun'..." + ) if not jobber.Open(): return False self.logger.info(" Copying 'SampleAnalyzer' source files...") @@ -276,7 +298,7 @@ def run_delphes(self,dataset,card): if not jobber.WriteMakefiles(): return False self.logger.debug(" Fixing the pileup path...") - self.fix_pileup(self.dirname+'_RecastRun/Input/'+card) + self.fix_pileup(self.dirname + "_RecastRun/Input/" + card) # Creating executable self.logger.info(" Compiling 'SampleAnalyzer'...") @@ -287,17 +309,18 @@ def run_delphes(self,dataset,card): return False # Running - self.logger.info(" Running 'SampleAnalyzer' over dataset '" +dataset.name+"'...") + self.logger.info( + " Running 'SampleAnalyzer' over dataset '" + dataset.name + "'..." + ) self.logger.info(" *******************************************************") if not jobber.RunJob(dataset): - self.logger.error("run over '"+dataset.name+"' aborted.") + self.logger.error("run over '" + dataset.name + "' aborted.") self.logger.info(" *******************************************************") # Exit return True - - def run_SimplifiedFastSim(self,dataset,card,analysislist): + def run_SimplifiedFastSim(self, dataset, card, analysislist): """ Parameters @@ -315,46 +338,51 @@ def run_SimplifiedFastSim(self,dataset,card,analysislist): SFS run correctly (True), there was a mistake (False) """ - if any([(x.endswith('root')) or (x.endswith('lhco')) or (x.endswith('lhco.gz')) for x in dataset.filenames]): + if any( + any(x.endswith(y) for y in ["root", "lhco", "lhco.gz"]) + for x in dataset.filenames + ): self.logger.error(" Dataset can not contain reconstructed file type.") return False # Load the analysis card from madanalysis.core.script_stack import ScriptStack + ScriptStack.AddScript(card) - self.main.recasting.status="off" + self.main.recasting.status = "off" self.main.superfastsim.Reset() script_mode = self.main.script self.main.script = True from madanalysis.interpreter.interpreter import Interpreter + interpreter = Interpreter(self.main) interpreter.load(verbose=self.main.developer_mode) self.main.script = script_mode old_fastsim = self.main.fastsim.package - self.main.fastsim.package="fastjet" + self.main.fastsim.package = "fastjet" if self.main.recasting.store_events: output_name = "SFS_events.lhe" if self.main.archi_info.has_zlib: output_name += ".gz" - self.logger.debug(" Setting the output LHE file :"+output_name) + self.logger.debug(" Setting the output LHE file :" + output_name) # Initializing the JobWriter - jobber = JobWriter(self.main,self.dirname+'_SFSRun') + jobber = JobWriter(self.main, self.dirname + "_SFSRun") # Writing process - self.logger.info(" Creating folder '"+self.dirname.split('/')[-1] + "'...") + self.logger.info(" Creating folder '" + self.dirname.split("/")[-1] + "'...") if not jobber.Open(): return False self.logger.info(" Copying 'SampleAnalyzer' source files...") if not jobber.CopyLHEAnalysis(): return False - if not jobber.CreateBldDir(analysisName="SFSRun",outputName="SFSRun.saf"): + if not jobber.CreateBldDir(analysisName="SFSRun", outputName="SFSRun.saf"): return False if not jobber.WriteSelectionHeader(self.main): return False - os.remove(self.dirname+'_SFSRun/Build/SampleAnalyzer/User/Analyzer/user.h') + os.remove(self.dirname + "_SFSRun/Build/SampleAnalyzer/User/Analyzer/user.h") if not jobber.WriteSelectionSource(self.main): return False - os.remove(self.dirname+'_SFSRun/Build/SampleAnalyzer/User/Analyzer/user.cpp') + os.remove(self.dirname + "_SFSRun/Build/SampleAnalyzer/User/Analyzer/user.cpp") ####### self.logger.info(" Writing the list of datasets...") jobber.WriteDatasetList(dataset) @@ -362,83 +390,130 @@ def run_SimplifiedFastSim(self,dataset,card,analysislist): if not jobber.WriteMakefiles(): return False # Copying the analysis files - analysisList = open(self.dirname+'_SFSRun/Build/SampleAnalyzer/User/Analyzer/analysisList.h','w') + analysisList = open( + self.dirname + "_SFSRun/Build/SampleAnalyzer/User/Analyzer/analysisList.h", + "w", + ) for ana in analysislist: - analysisList.write('#include "SampleAnalyzer/User/Analyzer/'+ana+'.h"\n') - analysisList.write('#include "SampleAnalyzer/Process/Analyzer/AnalyzerManager.h"\n') + analysisList.write('#include "SampleAnalyzer/User/Analyzer/' + ana + '.h"\n') + analysisList.write( + '#include "SampleAnalyzer/Process/Analyzer/AnalyzerManager.h"\n' + ) analysisList.write('#include "SampleAnalyzer/Commons/Service/LogStream.h"\n\n') - analysisList.write('// -----------------------------------------------------------------------------\n') - analysisList.write('// BuildUserTable\n') - analysisList.write('// -----------------------------------------------------------------------------\n') - analysisList.write('void BuildUserTable(MA5::AnalyzerManager& manager)\n') - analysisList.write('{\n') - analysisList.write(' using namespace MA5;\n') + analysisList.write( + "// -----------------------------------------------------------------------------\n" + ) + analysisList.write("// BuildUserTable\n") + analysisList.write( + "// -----------------------------------------------------------------------------\n" + ) + analysisList.write("void BuildUserTable(MA5::AnalyzerManager& manager)\n") + analysisList.write("{\n") + analysisList.write(" using namespace MA5;\n") try: for ana in analysislist: - shutil.copyfile\ - (self.pad+'/Build/SampleAnalyzer/User/Analyzer/'+ana+'.cpp',\ - self.dirname+'_SFSRun/Build/SampleAnalyzer/User/Analyzer/'+ana+'.cpp') - shutil.copyfile\ - (self.pad+'/Build/SampleAnalyzer/User/Analyzer/'+ana+'.h',\ - self.dirname+'_SFSRun/Build/SampleAnalyzer/User/Analyzer/'+ana+'.h') - analysisList.write(' manager.Add("'+ana+'", new '+ana+');\n') + shutil.copyfile( + self.pad + "/Build/SampleAnalyzer/User/Analyzer/" + ana + ".cpp", + self.dirname + + "_SFSRun/Build/SampleAnalyzer/User/Analyzer/" + + ana + + ".cpp", + ) + shutil.copyfile( + self.pad + "/Build/SampleAnalyzer/User/Analyzer/" + ana + ".h", + self.dirname + + "_SFSRun/Build/SampleAnalyzer/User/Analyzer/" + + ana + + ".h", + ) + analysisList.write(' manager.Add("' + ana + '", new ' + ana + ");\n") except Exception as err: self.logger.debug(str(err)) - self.logger.error('Cannot copy the analysis: '+ana) - self.logger.error('Please make sure that corresponding analysis downloaded propoerly.') + self.logger.error("Cannot copy the analysis: " + ana) + self.logger.error( + "Please make sure that corresponding analysis downloaded propoerly." + ) return False - analysisList.write('}\n') + analysisList.write("}\n") analysisList.close() # Update Main self.logger.info(" Updating the main executable") - shutil.move(self.dirname+'_SFSRun/Build/Main/main.cpp',\ - self.dirname+'_SFSRun/Build/Main/main.bak') - mainfile = open(self.dirname+"_SFSRun/Build/Main/main.bak",'r') - newfile = open(self.dirname+"_SFSRun/Build/Main/main.cpp",'w') + shutil.move( + self.dirname + "_SFSRun/Build/Main/main.cpp", + self.dirname + "_SFSRun/Build/Main/main.bak", + ) + mainfile = open(self.dirname + "_SFSRun/Build/Main/main.bak", "r") + newfile = open(self.dirname + "_SFSRun/Build/Main/main.cpp", "w") ignore = False for line in mainfile: - if '// Getting pointer to the analyzer' in line: + if "// Getting pointer to the analyzer" in line: ignore = True newfile.write(line) for analysis in analysislist: - newfile.write(' std::map prm'+analysis+';\n') - newfile.write(' AnalyzerBase* analyzer_'+analysis+'=\n') - newfile.write(' manager.InitializeAnalyzer(\"'+analysis+'\",\"'+analysis+'.saf\",'+\ - 'prm'+analysis+');\n') - newfile.write( ' if (analyzer_'+analysis+'==0) return 1;\n\n') + newfile.write( + " std::map prm" + analysis + ";\n" + ) + newfile.write(" AnalyzerBase* analyzer_" + analysis + "=\n") + newfile.write( + ' manager.InitializeAnalyzer("' + + analysis + + '","' + + analysis + + '.saf",' + + "prm" + + analysis + + ");\n" + ) + newfile.write(" if (analyzer_" + analysis + "==0) return 1;\n\n") if self.main.recasting.store_events: - newfile.write(' //Getting pointer to the writer\n') - newfile.write(' WriterBase* writer1 = \n') - newfile.write(' manager.InitializeWriter("lhe","'+output_name+'");\n') - newfile.write(' if (writer1==0) return 1;\n\n') - elif '// Post initialization (creates the new output directory structure)' in line and self.TACO_output!='': - newfile.write(' std::ofstream out;\n out.open(\"../Output/' + self.TACO_output+'\");\n') - newfile.write('\n manager.HeadSR(out);\n out << std::endl;\n'); - elif '//Getting pointer to the clusterer' in line: - ignore=False + newfile.write(" //Getting pointer to the writer\n") + newfile.write(" WriterBase* writer1 = \n") + newfile.write( + ' manager.InitializeWriter("lhe","' + output_name + '");\n' + ) + newfile.write(" if (writer1==0) return 1;\n\n") + elif ( + "// Post initialization (creates the new output directory structure)" + in line + and self.TACO_output != "" + ): + newfile.write( + ' std::ofstream out;\n out.open("../Output/' + + self.TACO_output + + '");\n' + ) + newfile.write("\n manager.HeadSR(out);\n out << std::endl;\n") + elif "//Getting pointer to the clusterer" in line: + ignore = False newfile.write(line) - elif '!analyzer1' in line and not ignore: - ignore=True + elif "!analyzer1" in line and not ignore: + ignore = True if self.main.recasting.store_events: - newfile.write(' writer1->WriteEvent(myEvent,mySample);\n') + newfile.write(" writer1->WriteEvent(myEvent,mySample);\n") for analysis in analysislist: - newfile.write(' if (!analyzer_'+analysis+'->Execute(mySample,myEvent)) continue;\n') - if self.TACO_output!='': - newfile.write('\n manager.DumpSR(out);\n'); - elif ' }' in line: + newfile.write( + " if (!analyzer_" + + analysis + + "->Execute(mySample,myEvent)) continue;\n" + ) + if self.TACO_output != "": + newfile.write("\n manager.DumpSR(out);\n") + elif " }" in line: + newfile.write(line) + ignore = False + elif ( + "manager.Finalize(mySamples,myEvent);" in line and self.TACO_output != "" + ): newfile.write(line) - ignore=False - elif 'manager.Finalize(mySamples,myEvent);' in line and self.TACO_output!='': - newfile.write(line); - newfile.write(' out.close();\n'); + newfile.write(" out.close();\n") elif not ignore: newfile.write(line) mainfile.close() newfile.close() - #restore + # restore self.main.recasting.status = "on" - self.main.fastsim.package = old_fastsim + self.main.fastsim.package = old_fastsim # Creating executable self.logger.info(" Compiling 'SampleAnalyzer'...") if not jobber.CompileJob(): @@ -449,94 +524,250 @@ def run_SimplifiedFastSim(self,dataset,card,analysislist): self.logger.error("job submission aborted.") return False # Running - self.logger.info(" Running 'SampleAnalyzer' over dataset '" +dataset.name+"'...") + self.logger.info( + " Running 'SampleAnalyzer' over dataset '" + dataset.name + "'..." + ) self.logger.info(" *******************************************************") if not jobber.RunJob(dataset): - self.logger.error("run over '"+dataset.name+"' aborted.") + self.logger.error("run over '" + dataset.name + "' aborted.") return False self.logger.info(" *******************************************************") - - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name) + + if not os.path.isdir(self.dirname + "/Output/SAF/" + dataset.name): + os.mkdir(self.dirname + "/Output/SAF/" + dataset.name) for analysis in analysislist: - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis) - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/CutFlows'): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/Cutflows') - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/Histograms'): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/Histograms') - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/RecoEvents') and \ - self.main.recasting.store_events : - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/RecoEvents') - cutflow_list = os.listdir(self.dirname+'_SFSRun/Output/SAF/_'+ dataset.name+'/'+analysis+'_0/Cutflows') - histogram_list = os.listdir(self.dirname+'_SFSRun/Output/SAF/_'+ dataset.name+'/'+analysis+'_0/Histograms') + if not os.path.isdir( + self.dirname + "/Output/SAF/" + dataset.name + "/" + analysis + ): + os.mkdir(self.dirname + "/Output/SAF/" + dataset.name + "/" + analysis) + if not os.path.isdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/CutFlows" + ): + os.mkdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Cutflows" + ) + if not os.path.isdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Histograms" + ): + os.mkdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Histograms" + ) + if ( + not os.path.isdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/RecoEvents" + ) + and self.main.recasting.store_events + ): + os.mkdir( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/RecoEvents" + ) + cutflow_list = os.listdir( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/" + + analysis + + "_0/Cutflows" + ) + histogram_list = os.listdir( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/" + + analysis + + "_0/Histograms" + ) # Copy dataset info file - if os.path.isfile(self.dirname+'_SFSRun/Output/SAF/_'+ dataset.name+'/_'+ dataset.name+'.saf'): - shutil.move(self.dirname+'_SFSRun/Output/SAF/_'+ dataset.name+'/_'+ dataset.name+'.saf',\ - self.dirname+'/Output/SAF/'+dataset.name+'/'+ dataset.name+'.saf') + if os.path.isfile( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/_" + + dataset.name + + ".saf" + ): + shutil.move( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/_" + + dataset.name + + ".saf", + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + dataset.name + + ".saf", + ) for cutflow in cutflow_list: - shutil.move(self.dirname+'_SFSRun/Output/SAF/_'+\ - dataset.name+'/'+analysis+'_0/Cutflows/'+cutflow,\ - self.dirname+'/Output/SAF/'+dataset.name+'/'+\ - analysis+'/Cutflows/'+cutflow) + shutil.move( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/" + + analysis + + "_0/Cutflows/" + + cutflow, + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Cutflows/" + + cutflow, + ) for histos in histogram_list: - shutil.move(self.dirname+'_SFSRun/Output/SAF/_'+\ - dataset.name+'/'+analysis+'_0/Histograms/'+histos,\ - self.dirname+'/Output/SAF/'+dataset.name+'/'+\ - analysis+'/Histograms/'+histos) + shutil.move( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/" + + analysis + + "_0/Histograms/" + + histos, + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Histograms/" + + histos, + ) if self.main.recasting.store_events: - event_list = os.listdir(self.dirname+'_SFSRun/Output/SAF/_'+ dataset.name+'/lheEvents0_0/') + event_list = os.listdir( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/lheEvents0_0/" + ) if len(event_list) > 0: - shutil.move(self.dirname+'_SFSRun/Output/SAF/_'+dataset.name+\ - '/lheEvents0_0/'+event_list[0], self.dirname+\ - '/Output/SAF/'+dataset.name+'/'+analysis+'/RecoEvents/'+\ - event_list[0]) - if self.TACO_output!='': - filename = '.'.join(self.TACO_output.split('.')[:-1]) + '_' + \ - card.split('/')[-1].replace('ma5','') + self.TACO_output.split('.')[-1] - shutil.move(self.dirname+'_SFSRun/Output/'+self.TACO_output,self.dirname+'/Output/SAF/'+dataset.name+'/'+filename) + shutil.move( + self.dirname + + "_SFSRun/Output/SAF/_" + + dataset.name + + "/lheEvents0_0/" + + event_list[0], + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/RecoEvents/" + + event_list[0], + ) + if self.TACO_output != "": + filename = ( + ".".join(self.TACO_output.split(".")[:-1]) + + "_" + + card.split("/")[-1].replace("ma5", "") + + self.TACO_output.split(".")[-1] + ) + shutil.move( + self.dirname + "_SFSRun/Output/" + self.TACO_output, + self.dirname + "/Output/SAF/" + dataset.name + "/" + filename, + ) if not self.main.developer_mode: # Remove the analysis folder - if not FolderWriter.RemoveDirectory(os.path.normpath(self.dirname+'_SFSRun')): - self.logger.error("Cannot remove directory: "+self.dirname+'_SFSRun') + if not FolderWriter.RemoveDirectory( + os.path.normpath(self.dirname + "_SFSRun") + ): + self.logger.error("Cannot remove directory: " + self.dirname + "_SFSRun") else: - self.logger.debug("Analysis kept in "+self.dirname+'_SFSRun folder.') + self.logger.debug("Analysis kept in " + self.dirname + "_SFSRun folder.") return True - - def generate_events(self,dataset,card): + def generate_events(self, dataset, card): # Preparing the run - self.main.recasting.status="off" - self.main.fastsim.package=self.detector - self.main.fastsim.clustering=0 - if self.detector=="delphesMA5tune": - self.main.fastsim.delphes=0 + self.main.recasting.status = "off" + self.main.fastsim.package = self.detector + self.main.fastsim.clustering = 0 + if self.detector == "delphesMA5tune": + self.main.fastsim.delphes = 0 self.main.fastsim.delphesMA5tune = DelphesMA5tuneConfiguration() - self.main.fastsim.delphesMA5tune.card = os.path.normpath("../../../../tools/PADForMA5tune/Input/Cards/"+card) - elif self.detector=="delphes": + self.main.fastsim.delphesMA5tune.card = os.path.normpath( + "../../../../tools/PADForMA5tune/Input/Cards/" + card + ) + elif self.detector == "delphes": self.main.fastsim.delphesMA5tune = 0 - self.main.fastsim.delphes = DelphesConfiguration() - self.main.fastsim.delphes.card = os.path.normpath("../../../../tools/PAD/Input/Cards/"+card) + self.main.fastsim.delphes = DelphesConfiguration() + self.main.fastsim.delphes.card = os.path.normpath( + "../../../../tools/PAD/Input/Cards/" + card + ) # Execution - if not self.run_delphes(dataset,card): - self.logger.error('The '+self.detector+' problem with the running of the fastsim') + if not self.run_delphes(dataset, card): + self.logger.error( + "The " + self.detector + " problem with the running of the fastsim" + ) return False # Restoring the run - self.main.recasting.status="on" - self.main.fastsim.package="none" + self.main.recasting.status = "on" + self.main.fastsim.package = "none" ## Saving the output - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name) - if not os.path.isdir(self.dirname+'/Output/SAF/'+dataset.name+'/RecoEvents'): - os.mkdir(self.dirname+'/Output/SAF/'+dataset.name+'/RecoEvents') - if self.detector=="delphesMA5tune": - shutil.move(self.dirname+'_RecastRun/Output/SAF/_'+dataset.name+'/RecoEvents0_0/DelphesMA5tuneEvents.root',\ - self.dirname+'/Output/SAF/'+dataset.name+'/RecoEvents/RecoEvents_v1x1_'+card.replace('.tcl','')+'.root') - elif self.detector=="delphes": - shutil.move(self.dirname+'_RecastRun/Output/SAF/_'+dataset.name+'/RecoEvents0_0/DelphesEvents.root',\ - self.dirname+'/Output/SAF/'+dataset.name+'/RecoEvents/RecoEvents_v1x2_'+card.replace('.tcl','')+'.root') + if not os.path.isdir(self.dirname + "/Output/SAF/" + dataset.name): + os.mkdir(self.dirname + "/Output/SAF/" + dataset.name) + if not os.path.isdir( + self.dirname + "/Output/SAF/" + dataset.name + "/RecoEvents" + ): + os.mkdir(self.dirname + "/Output/SAF/" + dataset.name + "/RecoEvents") + if self.detector == "delphesMA5tune": + shutil.move( + self.dirname + + "_RecastRun/Output/SAF/_" + + dataset.name + + "/RecoEvents0_0/DelphesMA5tuneEvents.root", + self.dirname + + "/Output/SAF/" + + dataset.name + + "/RecoEvents/RecoEvents_v1x1_" + + card.replace(".tcl", "") + + ".root", + ) + elif self.detector == "delphes": + shutil.move( + self.dirname + + "_RecastRun/Output/SAF/_" + + dataset.name + + "/RecoEvents0_0/DelphesEvents.root", + self.dirname + + "/Output/SAF/" + + dataset.name + + "/RecoEvents/RecoEvents_v1x2_" + + card.replace(".tcl", "") + + ".root", + ) ## Exit return True @@ -550,56 +781,77 @@ def analysis_single(self, version, card): # Activating the right delphes detector_handler = DetectorManager(self.main) if not detector_handler.manage(self.detector): - self.logger.error('Problem with the activation of delphesMA5tune') + self.logger.error("Problem with the activation of delphesMA5tune") return False ## Getting the analyses associated with the given card - analyses = [ x.replace(version+'_','') for x in self.analysis_runcard if version in x ] - for del_card,ana_list in self.main.recasting.DelphesDic.items(): + analyses = [ + x.replace(version + "_", "") for x in self.analysis_runcard if version in x + ] + for del_card, ana_list in self.main.recasting.DelphesDic.items(): if card == del_card: - analyses = [ x for x in analyses if x in ana_list] + analyses = [x for x in analyses if x in ana_list] break # Executing the PAD for myset in self.main.datasets: - if version in ['v1.1', 'v1.2']: - ## Preparing the PAD - self.update_pad_main(analyses) - if not self.make_pad(): - self.main.forced=self.forced - return False - ## Getting the file name corresponding to the events - eventfile = os.path.normpath(self.dirname + '/Output/SAF/' + myset.name + '/RecoEvents/RecoEvents_' +\ - version.replace('.','x')+'_' + card.replace('.tcl','')+'.root') - if not os.path.isfile(eventfile): - self.logger.error('The file called '+eventfile+' is not found...') - return False - ## Running the PAD - if not self.run_pad(eventfile): - self.main.forced=self.forced - return False - ## Saving the output and cleaning - if not self.save_output('\"'+eventfile+'\"', myset.name, analyses, card): - self.main.forced=self.forced - return False - if not self.main.recasting.store_root: - os.remove(eventfile) + if not self.main.recasting.stat_only_mode: + if version in ["v1.1", "v1.2"]: + ## Preparing the PAD + self.update_pad_main(analyses) + if not self.make_pad(): + self.main.forced = self.forced + return False + ## Getting the file name corresponding to the events + eventfile = os.path.normpath( + self.dirname + + "/Output/SAF/" + + myset.name + + "/RecoEvents/RecoEvents_" + + version.replace(".", "x") + + "_" + + card.replace(".tcl", "") + + ".root" + ) + if not os.path.isfile(eventfile): + self.logger.error(f"The file called {eventfile} is not found...") + return False + ## Running the PAD + if not self.run_pad(eventfile): + self.main.forced = self.forced + return False + ## Saving the output and cleaning + if not self.save_output( + '"' + eventfile + '"', myset.name, analyses, card + ): + self.main.forced = self.forced + return False + if not self.main.recasting.store_root: + os.remove(eventfile) + else: + time.sleep(1.0) else: - time.sleep(1.); + # Run SFS + if not self.run_SimplifiedFastSim( + myset, + self.main.archi_info.ma5dir + + "/tools/PADForSFS/Input/Cards/" + + card, + analyses, + ): + return False + if self.main.recasting.store_root: + self.logger.warning( + "Simplified-FastSim does not use root, hence file will not be stored." + ) else: - # Run SFS - if not self.run_SimplifiedFastSim(myset,self.main.archi_info.ma5dir+\ - '/tools/PADForSFS/Input/Cards/'+\ - card,analyses): - return False - if self.main.recasting.store_root: - self.logger.warning("Simplified-FastSim does not use root, hence file will not be stored.") - + self.dirname = self.main.recasting.stat_only_dir ## Running the CLs exclusion script (if available) - self.logger.debug('Compute CLs exclusion for '+myset.name) - if self.ntoys>0 and not self.compute_cls(analyses,myset): - self.main.forced=self.forced - return False + if not self.main.recasting.analysis_only_mode: + self.logger.debug(f"Compute CLs exclusion for {myset.name}") + if not self.compute_cls(analyses, myset): + self.main.forced = self.forced + return False # Exit return True @@ -607,40 +859,75 @@ def analysis_single(self, version, card): def analysis_header(self, version, card): ## Printing self.logger.info(" **********************************************************") - self.logger.info(" "+StringTools.Center(version+' running of the PAD'+\ - ' on events generated with',57)) - self.logger.info(" "+StringTools.Center(card,57)) + self.logger.info( + " " + + StringTools.Center( + version + " running of the PAD" + " on events generated with", 57 + ) + ) + self.logger.info(" " + StringTools.Center(card, 57)) self.logger.info(" **********************************************************") - def update_pad_main(self,analysislist): + def update_pad_main(self, analysislist): ## Migrating the necessary files to the working directory self.logger.info(" Writing the PAD analyses") ## Safety (for backwards compatibility) - if not os.path.isfile(self.pad+'/Build/Main/main.bak'): - shutil.copy(self.pad+'/Build/Main/main.cpp',self.pad+'/Build/Main/main.bak') - mainfile = open(self.pad+"/Build/Main/main.bak",'r') - newfile = open(self.dirname+"_RecastRun/Build/Main/main.cpp",'w') + if not os.path.isfile(self.pad + "/Build/Main/main.bak"): + shutil.copy( + self.pad + "/Build/Main/main.cpp", self.pad + "/Build/Main/main.bak" + ) + mainfile = open(self.pad + "/Build/Main/main.bak", "r") + newfile = open(self.dirname + "_RecastRun/Build/Main/main.cpp", "w") # Clean the analyzer folder - if not FolderWriter.RemoveDirectory(os.path.normpath(self.dirname+'_RecastRun/Build/SampleAnalyzer/User/Analyzer')): + if not FolderWriter.RemoveDirectory( + os.path.normpath( + self.dirname + "_RecastRun/Build/SampleAnalyzer/User/Analyzer" + ) + ): return False - os.mkdir(os.path.normpath(self.dirname+'_RecastRun/Build/SampleAnalyzer/User/Analyzer')) + os.mkdir( + os.path.normpath( + self.dirname + "_RecastRun/Build/SampleAnalyzer/User/Analyzer" + ) + ) # Including the necessary analyses - analysisList = open(self.dirname+"_RecastRun/Build/SampleAnalyzer/User/Analyzer/analysisList.h",'w') - analysisList_header = '#include "SampleAnalyzer/Process/Analyzer/AnalyzerManager.h"\n'+\ - '#include "SampleAnalyzer/Commons/Service/LogStream.h"\n' - analysisList_body = '\n// -----------------------------------------------------------------------------\n'+\ - '// BuildTable\n'+\ - '// -----------------------------------------------------------------------------\n'+\ - 'void BuildUserTable(MA5::AnalyzerManager& manager)\n{\n using namespace MA5;\n' + analysisList = open( + self.dirname + "_RecastRun/Build/SampleAnalyzer/User/Analyzer/analysisList.h", + "w", + ) + analysisList_header = ( + '#include "SampleAnalyzer/Process/Analyzer/AnalyzerManager.h"\n' + + '#include "SampleAnalyzer/Commons/Service/LogStream.h"\n' + ) + analysisList_body = ( + "\n// -----------------------------------------------------------------------------\n" + + "// BuildTable\n" + + "// -----------------------------------------------------------------------------\n" + + "void BuildUserTable(MA5::AnalyzerManager& manager)\n{\n using namespace MA5;\n" + ) for analysis in analysislist: - analysisList_header += '#include "SampleAnalyzer/User/Analyzer/'+analysis+'.h"\n' - analysisList_body += ' manager.Add("'+analysis+'",new '+analysis+');\n' - shutil.copy(self.pad+'/Build/SampleAnalyzer/User/Analyzer/'+analysis+'.cpp', - self.dirname+"_RecastRun/Build/SampleAnalyzer/User/Analyzer/"+analysis+".cpp") - shutil.copy(self.pad+'/Build/SampleAnalyzer/User/Analyzer/'+analysis+'.h', - self.dirname+"_RecastRun/Build/SampleAnalyzer/User/Analyzer/"+analysis+".h") + analysisList_header += ( + '#include "SampleAnalyzer/User/Analyzer/' + analysis + '.h"\n' + ) + analysisList_body += ( + ' manager.Add("' + analysis + '",new ' + analysis + ");\n" + ) + shutil.copy( + self.pad + "/Build/SampleAnalyzer/User/Analyzer/" + analysis + ".cpp", + self.dirname + + "_RecastRun/Build/SampleAnalyzer/User/Analyzer/" + + analysis + + ".cpp", + ) + shutil.copy( + self.pad + "/Build/SampleAnalyzer/User/Analyzer/" + analysis + ".h", + self.dirname + + "_RecastRun/Build/SampleAnalyzer/User/Analyzer/" + + analysis + + ".h", + ) # Finalisation - analysisList_body += '}\n' + analysisList_body += "}\n" analysisList.write(analysisList_header) analysisList.write(analysisList_body) analysisList.close() @@ -648,118 +935,176 @@ def update_pad_main(self,analysislist): ## creating the main file with the desired analyses inside for line in mainfile: - if '// Getting pointer to the analyzer' in line: + if "// Getting pointer to the analyzer" in line: ignore = True newfile.write(line) for analysis in analysislist: - newfile.write(' std::map prm'+analysis+';\n') - newfile.write(' AnalyzerBase* analyzer_'+analysis+'=\n') - newfile.write(' manager.InitializeAnalyzer(\"'+analysis+'\",\"'+analysis+'.saf\",'+\ - 'prm'+analysis+');\n') - newfile.write( ' if (analyzer_'+analysis+'==0) return 1;\n\n') - elif '// Post initialization (creates the new output directory structure)' in line: - ignore=False + newfile.write( + " std::map prm" + analysis + ";\n" + ) + newfile.write(" AnalyzerBase* analyzer_" + analysis + "=\n") + newfile.write( + ' manager.InitializeAnalyzer("' + + analysis + + '","' + + analysis + + '.saf",' + + "prm" + + analysis + + ");\n" + ) + newfile.write(" if (analyzer_" + analysis + "==0) return 1;\n\n") + elif ( + "// Post initialization (creates the new output directory structure)" + in line + ): + ignore = False newfile.write(line) - if self.TACO_output!='': - newfile.write(' std::ofstream out;\n out.open(\"../Output/' + self.TACO_output+'\");\n') - newfile.write(' manager.HeadSR(out);\n out << std::endl;\n'); - elif '!analyzer_' in line and not ignore: - ignore=True + if self.TACO_output != "": + newfile.write( + ' std::ofstream out;\n out.open("../Output/' + + self.TACO_output + + '");\n' + ) + newfile.write(" manager.HeadSR(out);\n out << std::endl;\n") + elif "!analyzer_" in line and not ignore: + ignore = True for analysis in analysislist: - newfile.write(' if (!analyzer_'+analysis+'->Execute(mySample,myEvent)) continue;\n') - elif '!analyzer1' in line: - if self.TACO_output!='': - newfile.write('\nmanager.DumpSR(out);\n'); - ignore=False - elif 'manager.Finalize(mySamples,myEvent);' in line and self.TACO_output!='': - newfile.write(line); - newfile.write(' out.close();\n'); + newfile.write( + " if (!analyzer_" + + analysis + + "->Execute(mySample,myEvent)) continue;\n" + ) + elif "!analyzer1" in line: + if self.TACO_output != "": + newfile.write("\nmanager.DumpSR(out);\n") + ignore = False + elif ( + "manager.Finalize(mySamples,myEvent);" in line and self.TACO_output != "" + ): + newfile.write(line) + newfile.write(" out.close();\n") elif not ignore: newfile.write(line) ## exit mainfile.close() newfile.close() - time.sleep(1.); + time.sleep(1.0) return True def make_pad(self): # Initializing the compiler - self.logger.info(' Compiling the PAD located in ' +self.dirname+'_RecastRun'); - compiler = LibraryWriter('lib',self.main) + self.logger.info(" Compiling the PAD located in " + self.dirname + "_RecastRun") + compiler = LibraryWriter("lib", self.main) ncores = compiler.get_ncores2() # compiling - command = ['make'] - strcores='' #ERIC - if ncores>1: - strcores='-j'+str(ncores) + command = ["make"] + strcores = "" # ERIC + if ncores > 1: + strcores = "-j" + str(ncores) command.append(strcores) - logfile = self.dirname+'_RecastRun/Build/Log/PADcompilation.log' - result, out = ShellCommand.ExecuteWithLog(command,logfile,self.dirname+'_RecastRun/Build') - time.sleep(1.); + logfile = self.dirname + "_RecastRun/Build/Log/PADcompilation.log" + result, out = ShellCommand.ExecuteWithLog( + command, logfile, self.dirname + "_RecastRun/Build" + ) + time.sleep(1.0) # Checks and exit if not result: - self.logger.error('Impossible to compile the PAD. For more details, see the log file:') + self.logger.error( + "Impossible to compile the PAD. For more details, see the log file:" + ) self.logger.error(logfile) return False return True - def run_pad(self,eventfile): + def run_pad(self, eventfile): ## input file - if os.path.isfile(self.dirname+'_RecastRun/Input/PADevents.list'): - os.remove(self.dirname+'_RecastRun/Input/PADevents.list') - infile = open(self.dirname+'_RecastRun/Input/PADevents.list','w') + if os.path.isfile(self.dirname + "_RecastRun/Input/PADevents.list"): + os.remove(self.dirname + "_RecastRun/Input/PADevents.list") + infile = open(self.dirname + "_RecastRun/Input/PADevents.list", "w") infile.write(eventfile) infile.close() ## cleaning the output directory - if os.path.isdir(os.path.normpath(self.dirname+'_RecastRun/Output/SAF/PADevents')): - if not FolderWriter.RemoveDirectory(os.path.normpath(self.dirname+'_RecastRun/Output/SAF/PADevents')): + if os.path.isdir( + os.path.normpath(self.dirname + "_RecastRun/Output/SAF/PADevents") + ): + if not FolderWriter.RemoveDirectory( + os.path.normpath(self.dirname + "_RecastRun/Output/SAF/PADevents") + ): return False ## running - command = ['./MadAnalysis5job', '../Input/PADevents.list'] - ok = ShellCommand.Execute(command,self.dirname+'_RecastRun/Build') + command = ["./MadAnalysis5job", "../Input/PADevents.list"] + ok = ShellCommand.Execute(command, self.dirname + "_RecastRun/Build") ## checks if not ok: - self.logger.error('Problem with the run of the PAD on the file: '+ eventfile) + self.logger.error("Problem with the run of the PAD on the file: " + eventfile) return False - os.remove(self.dirname+'_RecastRun/Input/PADevents.list') + os.remove(self.dirname + "_RecastRun/Input/PADevents.list") ## exit - time.sleep(1.); + time.sleep(1.0) return True def save_output(self, eventfile, setname, analyses, card): - outfile = self.dirname+'/Output/SAF/'+setname+'/'+setname+'.saf' + outfile = self.dirname + "/Output/SAF/" + setname + "/" + setname + ".saf" if not os.path.isfile(outfile): - shutil.move(self.dirname+'_RecastRun/Output/SAF/PADevents/PADevents.saf',outfile) + shutil.move( + self.dirname + "_RecastRun/Output/SAF/PADevents/PADevents.saf", outfile + ) else: - inp = open(outfile, 'r') - out = open(outfile+'.2', 'w') - intag = False - stack = [] - maxl = len(eventfile) + inp = open(outfile, "r") + out = open(outfile + ".2", "w") + intag = False + stack = [] + maxl = len(eventfile) for line in inp: - if '' in line: + if "" in line: out.write(line) intag = True - elif '' in line: + elif "" in line: for i in range(len(stack)): - out.write(stack[i].ljust(maxl)+ ' # file ' + str(i+1) + '/' + str(len(stack)+1) + '\n') - out.write(eventfile.ljust(maxl)+' # file ' + str(len(stack)+1)+'/'+str(len(stack)+1)+'\n') + out.write( + stack[i].ljust(maxl) + + " # file " + + str(i + 1) + + "/" + + str(len(stack) + 1) + + "\n" + ) + out.write( + eventfile.ljust(maxl) + + " # file " + + str(len(stack) + 1) + + "/" + + str(len(stack) + 1) + + "\n" + ) out.write(line) intag = False elif intag: - stack.append(line.strip().split('#')[0]) - maxl = max(maxl,len(line.strip().split('#')[0])) + stack.append(line.strip().split("#")[0]) + maxl = max(maxl, len(line.strip().split("#")[0])) else: out.write(line) inp.close() out.close() - shutil.move(outfile+'.2', outfile) + shutil.move(outfile + ".2", outfile) for analysis in analyses: - shutil.move(self.dirname+'_RecastRun/Output/SAF/PADevents/'+analysis+'_0',self.dirname+'/Output/SAF/'+setname+'/'+analysis) - if self.TACO_output!='': - filename = '.'.join(self.TACO_output.split('.')[:-1]) + '_' + card.replace('tcl','') + self.TACO_output.split('.')[-1] - shutil.move(self.dirname+'_RecastRun/Output/'+self.TACO_output,self.dirname+'/Output/SAF/'+setname+'/'+filename) + shutil.move( + self.dirname + "_RecastRun/Output/SAF/PADevents/" + analysis + "_0", + self.dirname + "/Output/SAF/" + setname + "/" + analysis, + ) + if self.TACO_output != "": + filename = ( + ".".join(self.TACO_output.split(".")[:-1]) + + "_" + + card.replace("tcl", "") + + self.TACO_output.split(".")[-1] + ) + shutil.move( + self.dirname + "_RecastRun/Output/" + self.TACO_output, + self.dirname + "/Output/SAF/" + setname + "/" + filename, + ) return True ################################################ @@ -767,225 +1112,323 @@ def save_output(self, eventfile, setname, analyses, card): ################################################ def compute_cls(self, analyses, dataset): + import spey + from spey.system.webutils import get_bibtex + + from .statistical_models import ( + compute_poi_upper_limits, + initialise_statistical_models, + ) + ## Checking whether the CLs module can be used - ET = self.check_xml_scipy_methods() + ET = self.check_xml_scipy_methods() if not ET: return False - self.SetCLsCalculator() - print_gl_citation = self.main.recasting.global_likelihoods_switch or (self.main.recasting.CLs_calculator_backend == "pyhf") - if len(self.main.recasting.extrapolated_luminosities)>0 or \ - any([x!=None for x in [dataset.scaleup,dataset.scaledn, dataset.pdfup, dataset.pdfdn]]) or \ - any([a+b>0. for a,b in self.main.recasting.systematics]): - self.logger.info("\033[1m * Using Uncertainties and Higher-Luminosity Estimates\033[0m") + self.logger.info( + "\033[1m * Exclusion limit computation uses Spey package\033[0m" + ) + self.logger.info("\033[1m Please cite arXiv:2307.06996 [hep-ph]\033[0m") + + # Bibliography + bibfile = os.path.join(self.dirname, "bibliography.bib") + print_gl_citation = self.main.recasting.global_likelihoods_switch + with open(bibfile, "w") as bib: + bib.write(spey.cite() + "\n") + if self.pyhf_config: + pyhfbib = spey.get_backend_bibtex("pyhf") + for _, item in pyhfbib.items(): + for it in item: + bib.write(it + "\n") + try: + for arxiv in [ + "1910.11418", + "2303.03427", + "2206.14870", + "2112.05163", + "2006.09387", + ]: + bib.write(get_bibtex("inspire/arxiv", arxiv) + "\n") + except Exception: + pass + if ( + len(self.main.recasting.extrapolated_luminosities) > 0 + or any( + x is not None + for x in [ + dataset.scaleup, + dataset.scaledn, + dataset.pdfup, + dataset.pdfdn, + ] + ) + or any(a + b > 0.0 for a, b in self.main.recasting.systematics) + ): + self.logger.info( + "\033[1m * Using Uncertainties and Higher-Luminosity Estimates\033[0m" + ) self.logger.info("\033[1m Please cite arXiv:1910.11418 [hep-ph]\033[0m") - ## Running over all luminosities to extrapolate - for extrapolated_lumi in ['default']+self.main.recasting.extrapolated_luminosities: - self.logger.info(' Calculation of the exclusion CLs for a lumi of ' + - str(extrapolated_lumi)) + for extrapolated_lumi in [ + "default" + ] + self.main.recasting.extrapolated_luminosities: + self.logger.info( + f" Calculation of the exclusion CLs for a lumi of {extrapolated_lumi}" + ) ## Preparing the output file and checking whether a cross section has been defined - outext = "" if extrapolated_lumi == 'default' else "_lumi_{:.3f}".format(extrapolated_lumi) - outfile = os.path.join(self.dirname, 'Output/SAF', - dataset.name, 'CLs_output'+outext+'.dat') + outext = ( + "" + if extrapolated_lumi == "default" + else "_lumi_{:.3f}".format(extrapolated_lumi) + ) + outfile = os.path.join( + self.dirname, "Output/SAF", dataset.name, "CLs_output" + outext + ".dat" + ) if os.path.isfile(outfile): - mysummary=open(outfile,'a+') + mysummary = open(outfile, "a+") mysummary.write("\n") else: - mysummary=open(outfile,'w') + mysummary = open(outfile, "w") self.write_cls_header(dataset.xsection, mysummary) ## running over all analysis for analysis in analyses: - self.logger.debug('Running CLs exclusion calculation for '+analysis) + self.logger.debug(f"Running CLs exclusion calculation for {analysis}") # Getting the info file information (possibly rescaled) - lumi, regions, regiondata = self.parse_info_file(ET,analysis,extrapolated_lumi) - self.logger.debug('lumi = ' + str(lumi)); - self.logger.debug('regions = ' + str(regions)); - self.logger.debug('regiondata = ' + str(regiondata)); - if lumi==-1 or regions==-1 or regiondata==-1: - self.logger.warning('Info file for '+analysis+' missing or corrupted. Skipping the CLs calculation.') + lumi, regions, regiondata = self.parse_info_file( + ET, analysis, extrapolated_lumi + ) + self.logger.debug(f"lumi = {str(lumi)}") + self.logger.debug(f"regions = {str(regions)}") + self.logger.debug(f"regiondata = {str(regiondata)}") + if lumi == -1 or regions == -1 or regiondata == -1: + self.logger.warning( + f"Info file for {analysis} missing or corrupted. Skipping the CLs calculation." + ) return False # Citation notifications for Global Likelihoods - if (self.cov_config != {} or self.pyhf_config!={}) and print_gl_citation: - print_gl_citation = False - self.logger.info("\033[1m * Using global likelihoods to improve CLs calculations\033[0m") - self.logger.info("\033[1m Please cite arXiv:2206.14870 [hep-ph]\033[0m") - if self.pyhf_config!={}: - self.logger.info("\033[1m pyhf DOI:10.5281/zenodo.1169739\033[0m") - self.logger.info("\033[1m For more details see https://scikit-hep.org/pyhf/\033[0m") - if sys.version_info[0]==2: - self.logger.warning("Please note that recent pyhf releases no longer support Python 2."+\ - " An older version has been used. Results may be impacted.") - if self.main.recasting.simplify_likelihoods and self.main.session_info.has_simplify: - self.logger.info("\033[1m using simplify: ATL-PHYS-PUB-2021-038\033[0m") - self.logger.info("\033[1m For more details see https://github.com/eschanet/simplify\033[0m") - elif self.cov_config != {}: - self.logger.info("\033[1m CMS-NOTE-2017-001\033[0m") - elif print_gl_citation and self.main.recasting.CLs_calculator_backend == "pyhf": + if (self.cov_config or self.pyhf_config) and print_gl_citation: print_gl_citation = False - self.logger.info("\033[1m * Using `pyhf` for CLs calculation\033[0m") - self.logger.info("\033[1m DOI:10.5281/zenodo.1169739\033[0m") - self.logger.info("\033[1m For more details see https://scikit-hep.org/pyhf/\033[0m") - self.logger.info("\033[1m Please cite arXiv:2206.14870 [hep-ph]\033[0m") - - - ## Reading the cutflow information - regiondata=self.read_cutflows( - self.dirname+'/Output/SAF/'+dataset.name+'/'+analysis+'/Cutflows', - regions, regiondata + self.logger.info( + "\033[1m * Using global likelihoods to improve CLs calculations\033[0m" + ) + self.logger.info( + "\033[1m Please cite arXiv:2206.14870 [hep-ph]\033[0m" + ) + if self.pyhf_config != {}: + self.logger.info( + "\033[1m pyhf DOI:10.5281/zenodo.1169739\033[0m" + ) + self.logger.info( + "\033[1m For more details see https://scikit-hep.org/pyhf/\033[0m" + ) + if ( + self.main.recasting.simplify_likelihoods + and self.main.session_info.has_simplify + ): + self.logger.info( + "\033[1m using simplify: ATL-PHYS-PUB-2021-038\033[0m" + ) + self.logger.info( + "\033[1m For more details see https://github.com/eschanet/simplify\033[0m" + ) + elif self.cov_config: + self.logger.info( + "\033[1m CMS-NOTE-2017-001\033[0m" + ) + + ## Reading the cutflow information + regiondata = self.read_cutflows( + self.dirname + + "/Output/SAF/" + + dataset.name + + "/" + + analysis + + "/Cutflows", + regions, + regiondata, ) - if regiondata==-1: - self.logger.warning('Info file for '+analysis+' corrupted. Skipping the CLs calculation.') + if regiondata == -1: + self.logger.warning( + f"Info file for {analysis} corrupted. Skipping the CLs calculation." + ) + return False + + if dataset.xsection <= 0: + self.logger.error( + f"Cross section for {dataset.name} is not defined. Skipping the CLs calculation." + ) return False + # Setup statistical models + statistical_models = initialise_statistical_models( + regiondata=regiondata, + regions=regions, + xsection=dataset.xsection, + lumi=lumi, + simplified_model_config=self.cov_config if self.cov_config else None, + full_statistical_model_config=self.pyhf_config + if self.pyhf_config + else None, + ) ## Performing the CLS calculation - regiondata=self.extract_sig_cls(regiondata,regions,lumi,"exp") - if self.cov_config != {}: - regiondata=self.extract_sig_lhcls(regiondata,lumi,"exp") - # CLs calculation for pyhf - regiondata = self.pyhf_sig95Wrapper(lumi, regiondata, "exp") - - if extrapolated_lumi=='default': - if self.cov_config != {}: - regiondata=self.extract_sig_lhcls(regiondata,lumi,"obs") - regiondata = self.extract_sig_cls(regiondata,regions,lumi,"obs") - regiondata = self.pyhf_sig95Wrapper(lumi,regiondata,'obs') - else: - for reg in regions: - regiondata[reg]["nobs"]=regiondata[reg]["nb"] - xsflag=True + model_types = [ + "uncorrelated_background", + "simplified_likelihoods", + "full_likelihoods", + ] + for model_type, record in zip(model_types, [None, "cov_subset", "pyhf"]): + models = statistical_models[model_type] + if models: + regiondata = compute_poi_upper_limits( + regiondata, + models, + dataset.xsection, + is_extrapolated=extrapolated_lumi != "default", + record_to=record, + ) + + xsflag = True if dataset.xsection > 0: - xsflag=False - regiondata=self.extract_cls(regiondata,regions,dataset.xsection,lumi) + xsflag = False + regiondata = self.extract_cls( + regiondata, + statistical_models, + dataset.xsection, + lumi, + is_extrapolated=extrapolated_lumi != "default", + ) ## Uncertainties on the rates - Error_dict = {} - if dataset.scaleup != None: - Error_dict['scale_up'] = round(dataset.scaleup,8) - Error_dict['scale_dn'] = -round(dataset.scaledn,8) - else: - Error_dict['scale_up'], Error_dict['scale_dn'] = 0., 0. - if dataset.pdfup != None: - Error_dict['pdf_up'] = round(dataset.pdfup,8) - Error_dict['pdf_dn'] = -round(dataset.pdfdn,8) - else: - Error_dict['pdf_up'], Error_dict['pdf_dn'] = 0., 0. - if self.main.recasting.THerror_combination == 'linear': - Error_dict['TH_up'] = round(Error_dict['scale_up'] + Error_dict['pdf_up'],8) - Error_dict['TH_dn'] = round(Error_dict['scale_dn'] + Error_dict['pdf_dn'],8) - else: - Error_dict['TH_up'] = round(math.sqrt(Error_dict['pdf_up']**2 + Error_dict['scale_up']**2),8) - Error_dict['TH_dn'] = -round(math.sqrt(Error_dict['pdf_dn']**2 + Error_dict['scale_dn']**2),8) - for i in range(0,len(self.main.recasting.systematics)): - for unc in self.main.recasting.systematics: - Error_dict['sys'+str(i)+'_up'] =\ - round(math.sqrt(Error_dict['TH_up']**2+self.main.recasting.systematics[i][0]**2),8) - Error_dict['sys'+str(i)+'_dn'] =\ - -round(math.sqrt(Error_dict['TH_dn']**2+self.main.recasting.systematics[i][1]**2),8) + Error_dict = error_dict_setup( + dataset=dataset, + systematics=self.main.recasting.systematics, + linear_comb=self.main.recasting.THerror_combination == "linear", + ) ## Computation of the uncertainties on the limits regiondata_errors = {} - if dataset.xsection > 0. and any([x!=0 for x in Error_dict.values()]): + if dataset.xsection > 0.0 and any(x != 0 for x in Error_dict.values()): for error_key, error_value in Error_dict.items(): - varied_xsec = max(round(dataset.xsection*(1.0+error_value),10),0.0) + varied_xsec = max( + round(dataset.xsection * (1.0 + error_value), 10), 0.0 + ) if varied_xsec > 0: - xsflag=False + xsflag = False + varied_statistical_models = initialise_statistical_models( + regiondata=regiondata, + regions=regions, + xsection=varied_xsec, + lumi=lumi, + simplified_model_config=self.cov_config + if self.cov_config + else None, + full_statistical_model_config=self.pyhf_config + if self.pyhf_config + else None, + ) regiondata_errors[error_key] = copy.deepcopy(regiondata) - if error_value!=0.0: + if error_value != 0.0: regiondata_errors[error_key] = self.extract_cls( - regiondata_errors[error_key], regions, varied_xsec, lumi + regiondata_errors[error_key], + varied_statistical_models, + varied_xsec, + lumi, + is_extrapolated=extrapolated_lumi != "default", ) ## writing the output file self.write_cls_output( - analysis, regions, regiondata, regiondata_errors, mysummary, xsflag, lumi + analysis, + regions, + regiondata, + regiondata_errors, + mysummary, + xsflag, + lumi, ) - mysummary.write('\n') + mysummary.write("\n") ## Closing the output file mysummary.close() return True def check_xml_scipy_methods(self): - ## Checking whether scipy is installed - if not self.main.session_info.has_scipy: - self.logger.warning('scipy is not installed... the CLs module cannot be used.') - self.logger.warning('Please install scipy.') - return False - else: - import scipy.stats ## Checking XML parsers try: from lxml import ET - except Exception as err: + except ImportError as err: self.logger.debug(str(err)) try: import xml.etree.ElementTree as ET - except Exception as err: - self.logger.warning('lxml or xml not available... the CLs module cannot be used') - self.logger.debug(str(err)) + except ImportError as err2: + self.logger.warning( + "lxml or xml not available... the CLs module cannot be used" + ) + self.logger.debug(str(err2)) return False # exit return ET def parse_info_file(self, etree, analysis, extrapolated_lumi): ## Is file existing? - filename=self.pad+'/Build/SampleAnalyzer/User/Analyzer/'+analysis+'.info' #ERIC + filename = ( + self.pad + "/Build/SampleAnalyzer/User/Analyzer/" + analysis + ".info" + ) # ERIC if not os.path.isfile(filename): - self.logger.warning('Info '+filename+' does not exist...') - return -1,-1, -1 + self.logger.warning("Info " + filename + " does not exist...") + return -1, -1, -1 ## Getting the XML information try: with open(filename, "r") as info_input: info_tree = etree.parse(info_input) except Exception as err: - self.logger.warning("Error during XML parsing: "+str(err)) - self.logger.warning('Cannot parse the info file') - return -1,-1, -1 + self.logger.warning("Error during XML parsing: " + str(err)) + self.logger.warning("Cannot parse the info file") + return -1, -1, -1 try: - results = self.header_info_file(info_tree,analysis,extrapolated_lumi) + results = self.header_info_file(info_tree, analysis, extrapolated_lumi) return results except Exception as err: - self.logger.warning("Error during extracting header info file: "+str(err)) - self.logger.warning('Cannot parse the info file') - return -1,-1, -1 + self.logger.warning("Error during extracting header info file: " + str(err)) + self.logger.warning("Cannot parse the info file") + return -1, -1, -1 - def fix_pileup(self,filename): - #x - self.logger.debug('delphes card is here: '+filename) + def fix_pileup(self, filename): + # x + self.logger.debug("delphes card is here: " + filename) # Container for pileup - FoundPileup=[] + FoundPileup = [] # Safe if not os.path.isfile(filename): - self.logger.error('internal error: file '+filename+' is not found') + self.logger.error("internal error: file " + filename + " is not found") return False # Estimate the newpath of pileup - if self.detector=="delphesMA5tune": - newpath=self.main.archi_info.ma5dir+'/tools/PADForMA5tune/Input/Pileup' + if self.detector == "delphesMA5tune": + newpath = self.main.archi_info.ma5dir + "/tools/PADForMA5tune/Input/Pileup" else: - newpath=self.main.archi_info.ma5dir+'/tools/PAD/Input/Pileup' + newpath = self.main.archi_info.ma5dir + "/tools/PAD/Input/Pileup" # Safe copy - shutil.copyfile(filename,filename+'.original') - input = open(filename+'.original','r') - output = open(filename,'w') + shutil.copyfile(filename, filename + ".original") + input = open(filename + ".original", "r") + output = open(filename, "w") # Loop on lines for line in input: - line2=line.lstrip() - line2=line2.rstrip() - words=line2.split() - if len(words)>=3 and words[0]=='set' and words[1]=='PileUpFile': - pileup=words[2].split('/')[-1] - newfilename = os.path.normpath(newpath+'/'+pileup) - output.write(line.replace(words[2],newfilename)) + line2 = line.lstrip() + line2 = line2.rstrip() + words = line2.split() + if len(words) >= 3 and words[0] == "set" and words[1] == "PileUpFile": + pileup = words[2].split("/")[-1] + newfilename = os.path.normpath(newpath + "/" + pileup) + output.write(line.replace(words[2], newfilename)) FoundPileup.append(newfilename) else: output.write(line) @@ -995,144 +1438,210 @@ def fix_pileup(self,filename): output.close() # Found pileup? - logging.getLogger("MA5").debug(str(len(FoundPileup))+' pile-up samples has been declared') + logging.getLogger("MA5").debug( + str(len(FoundPileup)) + " pile-up samples has been declared" + ) for item in FoundPileup: if not os.path.isfile(item): - logging.getLogger("MA5").warning("Problem with Delphes card: pile-up sample is not found: "+item) + logging.getLogger("MA5").warning( + "Problem with Delphes card: pile-up sample is not found: " + item + ) return False return True - def header_info_file(self, etree, analysis, extrapolated_lumi): - self.logger.debug('Reading info from the file related to '+analysis + '...') + self.logger.debug("Reading info from the file related to " + analysis + "...") ## checking the header of the file info_root = etree.getroot() if info_root.tag != "analysis": - self.logger.warning('Invalid info file (' + analysis+ '): tag.') - return -1,-1,-1 + self.logger.warning("Invalid info file (" + analysis + "): tag.") + return -1, -1, -1 if info_root.attrib["id"].lower() != analysis.lower(): - self.logger.warning('Invalid info file (' + analysis+ '): tag.') - return -1,-1,-1 + self.logger.warning( + "Invalid info file (" + analysis + "): tag." + ) + return -1, -1, -1 ## extracting the information - lumi = 0 - lumi_scaling = 1. - regions = [] - self.cov_config = {} + lumi = 0 + lumi_scaling = 1.0 + regions = [] + self.cov_config = {} self.pyhf_config = {} - regiondata = {} + regiondata = {} # Getting the description of the subset of SRs having covariances # Now the cov_switch is activated here - if "cov_subset" in info_root.attrib and self.main.recasting.global_likelihoods_switch: - self.cov_config[info_root.attrib["cov_subset"]] = dict(cov_regions = [], - covariance = []) + if ( + "cov_subset" in info_root.attrib + and self.main.recasting.global_likelihoods_switch + ): + self.cov_config[info_root.attrib["cov_subset"]] = dict( + cov_regions=[], covariance=[] + ) # activate pyhf - if self.main.recasting.global_likelihoods_switch and self.main.session_info.has_pyhf and self.cov_config == {}: + if ( + self.main.recasting.global_likelihoods_switch + and self.main.session_info.has_pyhf + and self.cov_config == {} + ): try: self.pyhf_config = self.pyhf_info_file(info_root) self.logger.debug(str(self.pyhf_config)) except Exception as err: - self.logger.debug('Check pyhf_info_file function!\n' + str(err)) + self.logger.debug("Check pyhf_info_file function!\n" + str(err)) self.pyhf_config = {} - ## first we need to get the number of regions for child in info_root: # Luminosity if child.tag == "lumi": try: lumi = float(child.text) - if extrapolated_lumi!='default': - lumi_scaling = round(extrapolated_lumi/lumi,8) - lumi=lumi*lumi_scaling + if extrapolated_lumi != "default": + lumi_scaling = round(extrapolated_lumi / lumi, 8) + lumi = lumi * lumi_scaling except Exception as err: - self.logger.warning('Invalid info file (' + analysis+ '): ill-defined lumi') + self.logger.warning( + "Invalid info file (" + analysis + "): ill-defined lumi" + ) self.logger.debug(str(err)) return -1, -1, -1 - self.logger.debug('The luminosity of ' + analysis + ' is ' + str(lumi) + ' fb-1.') + self.logger.debug( + "The luminosity of " + analysis + " is " + str(lumi) + " fb-1." + ) # regions - if child.tag == "region" and ("type" not in child.attrib or child.attrib["type"] == "signal"): + if child.tag == "region" and ( + "type" not in child.attrib or child.attrib["type"] == "signal" + ): if "id" not in child.attrib: - self.logger.warning('Invalid info file (' + analysis+ '): tag.') + self.logger.warning( + "Invalid info file (" + analysis + "): tag." + ) return -1, -1, -1 if child.attrib["id"] in regions: - self.logger.warning('Invalid info file (' + analysis+ '): doubly-defined region.') + self.logger.warning( + "Invalid info file (" + analysis + "): doubly-defined region." + ) return -1, -1, -1 regions.append(child.attrib["id"]) # If one covariance entry is found, the covariance switch is turned on if self.main.recasting.global_likelihoods_switch: for grand_child in child.findall("covariance"): if "cov_subset" in info_root.attrib: - if grand_child.attrib.get("cov_subset", "default") in [info_root.attrib["cov_subset"], "default"]: - if child.attrib["id"] not in self.cov_config[info_root.attrib["cov_subset"]]["cov_regions"]: - self.cov_config[info_root.attrib["cov_subset"]]["cov_regions"].append(child.attrib["id"]) + if grand_child.attrib.get("cov_subset", "default") in [ + info_root.attrib["cov_subset"], + "default", + ]: + if ( + child.attrib["id"] + not in self.cov_config[ + info_root.attrib["cov_subset"] + ]["cov_regions"] + ): + self.cov_config[info_root.attrib["cov_subset"]][ + "cov_regions" + ].append(child.attrib["id"]) else: if grand_child.attrib.get("cov_subset", False): subsetID = grand_child.attrib["cov_subset"] if subsetID not in self.cov_config.keys(): - self.cov_config[subsetID] = dict(cov_regions = [], - covariance = [] ) - if child.attrib["id"] not in self.cov_config[subsetID]["cov_regions"]: - self.cov_config[subsetID]["cov_regions"].append(child.attrib["id"]) + self.cov_config[subsetID] = dict( + cov_regions=[], covariance=[] + ) + if ( + child.attrib["id"] + not in self.cov_config[subsetID]["cov_regions"] + ): + self.cov_config[subsetID]["cov_regions"].append( + child.attrib["id"] + ) if self.cov_config: for cov_subset, subset in self.cov_config.items(): length = len(subset["cov_regions"]) self.cov_config[cov_subset]["covariance"] = [ - [0. for i in range(length)] for j in range(length) + [0.0 for i in range(length)] for j in range(length) ] ## getting the region information for child in info_root: - if child.tag == "region" and ("type" not in child.attrib or child.attrib["type"] == "signal"): - nobs, nb, deltanb, syst, stat = [-1]*5 + if child.tag == "region" and ( + "type" not in child.attrib or child.attrib["type"] == "signal" + ): + nobs, nb, deltanb, syst, stat = [-1] * 5 for rchild in child: # self.logger.debug(rchild.tag) # self.logger.debug(str(lumi)+' '+str(regions)+ ' '+str(regiondata)) try: - myval=float(rchild.text) + myval = float(rchild.text) except ValueError as err: - self.logger.warning('Invalid info file (' + analysis+ '): region data ill-defined.') + self.logger.warning( + "Invalid info file (" + + analysis + + "): region data ill-defined." + ) self.logger.debug(str(err)) - return -1,-1,-1 - if rchild.tag=="nobs": + return -1, -1, -1 + if rchild.tag == "nobs": nobs = myval - elif rchild.tag=="nb": + elif rchild.tag == "nb": nb = myval - elif rchild.tag=="deltanb": + elif rchild.tag == "deltanb": deltanb = myval - elif rchild.tag=="deltanb_syst": + elif rchild.tag == "deltanb_syst": syst = myval - elif rchild.tag=="deltanb_stat": + elif rchild.tag == "deltanb_stat": stat = myval - elif rchild.tag=="covariance": + elif rchild.tag == "covariance": if self.cov_config: for cov_subset, item in self.cov_config.items(): - if child.attrib["id"] not in item["cov_regions"] or \ - rchild.attrib["region"] not in item["cov_regions"]: + if ( + child.attrib["id"] not in item["cov_regions"] + or rchild.attrib["region"] not in item["cov_regions"] + ): continue i = item["cov_regions"].index(child.attrib["id"]) j = item["cov_regions"].index(rchild.attrib["region"]) self.cov_config[cov_subset]["covariance"][i][j] = myval else: - self.logger.warning('Invalid info file (' + analysis+ '): unknown region subtag.') - return -1,-1,-1 + self.logger.warning( + "Invalid info file (" + analysis + "): unknown region subtag." + ) + return -1, -1, -1 if syst == -1 and stat == -1: - if self.main.recasting.error_extrapolation=='sqrt': - deltanb = round(deltanb * math.sqrt(lumi_scaling),8) - elif self.main.recasting.error_extrapolation=='linear': - deltanb = round(deltanb * lumi_scaling,8) + if self.main.recasting.error_extrapolation == "sqrt": + deltanb = round(deltanb * math.sqrt(lumi_scaling), 8) + elif self.main.recasting.error_extrapolation == "linear": + deltanb = round(deltanb * lumi_scaling, 8) else: - nb_new = nb*lumi_scaling - deltanb = round(math.sqrt(self.main.recasting.error_extrapolation[0]**2*nb_new**2 \ - + self.main.recasting.error_extrapolation[1]**2*nb_new), 8); + nb_new = nb * lumi_scaling + deltanb = round( + math.sqrt( + self.main.recasting.error_extrapolation[0] ** 2 + * nb_new**2 + + self.main.recasting.error_extrapolation[1] ** 2 * nb_new + ), + 8, + ) else: - if syst==-1: - syst=0. - if stat==-1: - stat=0. - deltanb = round(math.sqrt( (syst/nb)**2 + (stat/(nb*math.sqrt(lumi_scaling)))**2 )*nb*lumi_scaling,8) - regiondata[child.attrib["id"]] = { "nobs":nobs*lumi_scaling, "nb":nb*lumi_scaling, "deltanb":deltanb} + if syst == -1: + syst = 0.0 + if stat == -1: + stat = 0.0 + deltanb = round( + math.sqrt( + (syst / nb) ** 2 + + (stat / (nb * math.sqrt(lumi_scaling))) ** 2 + ) + * nb + * lumi_scaling, + 8, + ) + regiondata[child.attrib["id"]] = { + "nobs": nobs * lumi_scaling, + "nb": nb * lumi_scaling, + "deltanb": deltanb, + } tmp = {} for cov_subset, item in self.cov_config.items(): @@ -1141,14 +1650,20 @@ def header_info_file(self, etree, analysis, extrapolated_lumi): sigma = np.sqrt(np.diag(cov)) invsigma = np.linalg.inv(np.diag(sigma)) corr = invsigma @ cov @ invsigma - - if self.main.recasting.error_extrapolation=='sqrt': - new_sigma = round(math.sqrt(sigma)*lumi_scaling,8) - elif self.main.recasting.error_extrapolation=='linear': + + if self.main.recasting.error_extrapolation == "sqrt": + new_sigma = round(math.sqrt(sigma) * lumi_scaling, 8) + elif self.main.recasting.error_extrapolation == "linear": new_sigma = sigma * lumi_scaling**2 else: - new_sigma = sigma * lumi_scaling**2 * self.main.recasting.error_extrapolation[0]**2 + \ - np.sqrt(sigma) * lumi_scaling * self.main.recasting.error_extrapolation[1]**2 + new_sigma = ( + sigma + * lumi_scaling**2 + * self.main.recasting.error_extrapolation[0] ** 2 + + np.sqrt(sigma) + * lumi_scaling + * self.main.recasting.error_extrapolation[1] ** 2 + ) new_sigma_matrix = np.diag(new_sigma) new_cov = new_sigma_matrix @ corr @ new_sigma_matrix @@ -1158,209 +1673,141 @@ def header_info_file(self, etree, analysis, extrapolated_lumi): return lumi, regions, regiondata - - def pyhf_info_file(self,info_root): + def pyhf_info_file(self, info_root): """In order to make use of HistFactory, we need some pieces of information. First, - the location of the specific background-only likelihood json files that are given - in the info file. The collection of SR contributing to a given profile must be - provided. One can process multiple likelihood profiles dedicated to different sets - of SRs. + the location of the specific background-only likelihood json files that are given + in the info file. The collection of SR contributing to a given profile must be + provided. One can process multiple likelihood profiles dedicated to different sets + of SRs. """ - self.pyhf_config = {} # reset - if any([x.tag=='pyhf' for x in info_root]): + self.pyhf_config = {} # reset + if any(x.tag == "pyhf" for x in info_root): # pyhf_path = os.path.join(self.main.archi_info.ma5dir, 'tools/pyhf/pyhf-master/src') try: # if os.path.isdir(pyhf_path) and pyhf_path not in sys.path: # sys.path.insert(0, pyhf_path) - import pyhf - self.logger.debug('Pyhf v'+str(pyhf.__version__)) - self.logger.debug("pyhf has been imported from "+" ".join(pyhf.__path__)) + import spey_pyhf + + self.logger.debug("spey_pyhf v" + str(spey_pyhf.__version__)) + self.logger.debug( + "spey_pyhf has been imported from " + " ".join(spey_pyhf.__path__) + ) except ImportError: - self.logger.warning('To use the global likelihood PYHF machinery, please type "install pyhf"') + self.logger.warning( + "To use the global likelihood machinery, please install spey-pyhf" + ) return {} except Exception as err: - self.logger.debug('Problem with pyhf_info_file function!!') + self.logger.debug("Problem with pyhf_info_file function!!") self.logger.debug(str(err)) return {} else: return {} - pyhf_config = OrderedDict() - analysis = info_root.attrib['id'] - nprofile = 0 - to_remove = [] - self.logger.debug(' === Reading info file for pyhf ===') - for child in info_root: - if child.tag == 'lumi': - default_lumi = float(child.text) - if child.tag == 'pyhf': - likelihood_profile = child.attrib.get('id','HF-Likelihood-'+str(nprofile)) - if likelihood_profile == 'HF-Likelihood-'+str(nprofile): - nprofile += 1 - if not likelihood_profile in list(pyhf_config.keys()): - pyhf_config[likelihood_profile] = { - 'name' : 'No File name in info file...', - 'path' : os.path.join(self.pad, 'Build/SampleAnalyzer/User/Analyzer'), - 'lumi' : default_lumi, - 'SR' : OrderedDict() - } - for subchild in child: - if subchild.tag == 'name': - if self.main.recasting.simplify_likelihoods and self.main.session_info.has_simplify: - main_path = pyhf_config[likelihood_profile]["path"] - full = str(subchild.text) - simplified = full.split(".json")[0] + "_simplified.json" - if os.path.isfile(os.path.join(main_path, simplified)): - pyhf_config[likelihood_profile]['name'] = simplified - else: - simplify_path = os.path.join(self.main.archi_info.ma5dir, - 'tools/simplify/simplify-master/src') - try: - if os.path.isdir(simplify_path) and simplify_path not in sys.path: - sys.path.insert(0, simplify_path) - import simplify - self.logger.debug("simplify has been imported from "+\ - " ".join(simplify.__path__)) - self.logger.debug("simplifying "+full) - with open(os.path.join(main_path, full), "r") as f: - spec = json.load(f) - # Get model and data - poi_name = "lumi" - try: - original_poi = spec['measurements'][0]["config"]["poi"] - spec['measurements'][0]["config"]["poi"] = poi_name - except IndexError: - raise simplify.exceptions.InvalidMeasurement( - "The measurement index 0 is out of bounds." - ) - model, data = simplify.model_tools.model_and_data(spec) - - fixed_params = model.config.suggested_fixed() - init_pars = model.config.suggested_init() - # Fit the model to data - fit_result = simplify.fitter.fit( - model, data, init_pars=init_pars, fixed_pars=fixed_params - ) - # Get yields - ylds = simplify.yields.get_yields(spec, fit_result, []) - newspec = simplify.simplified.get_simplified_spec( - spec, ylds, allowed_modifiers=[], - prune_channels=[], include_signal=False - ) - newspec['measurements'][0]["config"]["poi"] = original_poi - with open(os.path.join(main_path,simplified), "w+") as out_file: - json.dump(newspec, out_file, indent=4, sort_keys=True) - pyhf_config[likelihood_profile]['name'] = simplified - except ImportError: - self.logger.warning('To use simplified likelihoods, please install simplify') - pyhf_config[likelihood_profile]['name'] = str(subchild.text) - except (Exception, simplify.exceptions.InvalidMeasurement) as err: - self.logger.warning('Can not simplify '+full) - self.logger.debug(str(err)) - pyhf_config[likelihood_profile]['name'] = str(subchild.text) - else: - pyhf_config[likelihood_profile]['name'] = str(subchild.text) - self.logger.debug(pyhf_config[likelihood_profile]['name'] + " file will be used.") - elif subchild.tag == 'regions': - for channel in subchild: - if channel.tag == 'channel': - if not channel.attrib.get('name',False): - self.logger.warning('Invalid or corrupted info file') - self.logger.warning('Please check '+likelihood_profile) - to_remove.append(likelihood_profile) - else: - data = [] - if channel.text != None: - data = channel.text.split() - pyhf_config[likelihood_profile]['SR'][channel.attrib['name']] = { - "channels" : channel.get('id', default = -1), - "data" : data, - } - is_included = ( - channel.get("is_included", default = 0) in ["True", "1", "yes"] - ) if len(data) == 0 else True - pyhf_config[likelihood_profile]['SR'][channel.attrib['name']].update( - {"is_included" : is_included} - ) - if pyhf_config[likelihood_profile]['SR'][channel.attrib['name']]['channels'] == -1: - file = os.path.join( - pyhf_config[likelihood_profile]['path'], - pyhf_config[likelihood_profile]['name'] - ) - ID = get_HFID(file, channel.attrib['name']) - if not isinstance(ID, str): - pyhf_config[likelihood_profile]['SR'][channel.attrib['name']]['channels'] = str(ID) - else: - self.logger.warning(ID) - self.logger.warning( - 'Please check '+likelihood_profile + \ - 'and/or '+channel.attrib['name'] - ) - to_remove.append(likelihood_profile) - + analysis = info_root.attrib["id"] + pyhf_config, to_remove = construct_histfactory_dictionary(info_root, self) # validate for likelihood_profile, config in pyhf_config.items(): if likelihood_profile in to_remove: continue # validat pyhf config background = HF_Background(config) - signal = HF_Signal(config, {}, xsection=1., - background = background, - validate = True) + signal = HF_Signal( + config, {}, xsection=1.0, background=background, validate=True + ) if signal.hf != []: - self.logger.debug('Likelihood profile "'+str(likelihood_profile)+'" is valid.') + self.logger.debug( + 'Likelihood profile "' + str(likelihood_profile) + '" is valid.' + ) else: - self.logger.warning('Invalid profile in '+analysis+' ignoring :'+\ - str(likelihood_profile)) + self.logger.warning( + "Invalid profile in " + + analysis + + " ignoring :" + + str(likelihood_profile) + ) to_remove.append(likelihood_profile) - #remove invalid profiles + # remove invalid profiles for rm in to_remove: pyhf_config.pop(rm) return pyhf_config - - def write_cls_header(self, xs, out): - if xs <=0: - self.logger.info(' Signal xsection not defined. The 95% excluded xsection will be calculated.') - out.write("# analysis name".ljust(30, ' ') + "signal region".ljust(60,' ') + \ - 'sig95(exp)'.ljust(15, ' ') + 'sig95(obs)'.ljust(10, ' ') +' || ' + 'efficiency'.ljust(15,' ') +\ - "stat".ljust(15,' ')); - for i in range(0,len(self.main.recasting.systematics)): - out.write(("syst" + str(i+1) + "(" + str(self.main.recasting.systematics[i][0]*100) + "%)").ljust(15," ")) - out.write('\n'); + if xs <= 0: + self.logger.info( + " Signal xsection not defined. The 95% excluded xsection will be calculated." + ) + out.write( + "# analysis name".ljust(30, " ") + + "signal region".ljust(60, " ") + + "sig95(exp)".ljust(15, " ") + + "sig95(obs)".ljust(10, " ") + + " || " + + "efficiency".ljust(15, " ") + + "stat".ljust(15, " ") + ) + for i in range(0, len(self.main.recasting.systematics)): + out.write( + ( + "syst" + + str(i + 1) + + "(" + + str(self.main.recasting.systematics[i][0] * 100) + + "%)" + ).ljust(15, " ") + ) + out.write("\n") else: - out.write("# analysis name".ljust(30, ' ') + "signal region".ljust(60,' ') + \ - "best?".ljust(10,' ') + 'sig95(exp)'.ljust(20,' ') + 'sig95(obs)'.ljust(20, ' ') +\ - '1-CLs'.ljust(10,' ') + ' || ' + 'efficiency'.ljust(15,' ') +\ - "stat".ljust(15,' ')); - for i in range(0,len(self.main.recasting.systematics)): - out.write(("syst" + str(i+1) + "(" + str(self.main.recasting.systematics[i][0]*100) + "%)").ljust(15," ")) - out.write('\n'); - + out.write( + "# analysis name".ljust(30, " ") + + "signal region".ljust(60, " ") + + "best?".ljust(10, " ") + + "sig95(exp)".ljust(20, " ") + + "sig95(obs)".ljust(20, " ") + + "1-CLs".ljust(10, " ") + + " || " + + "efficiency".ljust(15, " ") + + "stat".ljust(15, " ") + ) + for i in range(0, len(self.main.recasting.systematics)): + out.write( + ( + "syst" + + str(i + 1) + + "(" + + str(self.main.recasting.systematics[i][0] * 100) + + "%)" + ).ljust(15, " ") + ) + out.write("\n") def read_cutflows(self, path, regions, regiondata): - self.logger.debug('Read the cutflow from the files:') + self.logger.debug("Read the cutflow from the files:") for reg in regions: regname = clean_region_name(reg) ## getting the initial and final number of events IsInitial = False IsCounter = False - N0 = 0. - Nf = 0. + N0 = 0.0 + Nf = 0.0 ## checking if regions must be combined - theregs=regname.split(';') + theregs = regname.split(";") for regiontocombine in theregs: - filename=path+'/'+regiontocombine+'.saf' - self.logger.debug('+ '+filename) + filename = path + "/" + regiontocombine + ".saf" + self.logger.debug("+ " + filename) if not os.path.isfile(filename): - self.logger.warning('Cannot find a cutflow for the region '+regiontocombine+' in ' + path) - self.logger.warning('Skipping the CLs calculation.') + self.logger.warning( + "Cannot find a cutflow for the region " + + regiontocombine + + " in " + + path + ) + self.logger.warning("Skipping the CLs calculation.") return -1 mysaffile = open(filename) - myN0=-1 - myNf=-1 + myN0 = -1 + myNf = -1 for line in mysaffile: if "" in line: IsInitial = True @@ -1374,336 +1821,169 @@ def read_cutflows(self, path, regions, regiondata): elif "" in line: IsCounter = False continue - if IsInitial and "sum of weights" in line and not '^2' in line: - myN0 = float(line.split()[0])+float(line.split()[1]) - if IsCounter and "sum of weights" in line and not '^2' in line: - myNf = float(line.split()[0])+float(line.split()[1]) + if IsInitial and "sum of weights" in line and not "^2" in line: + myN0 = float(line.split()[0]) + float(line.split()[1]) + if IsCounter and "sum of weights" in line and not "^2" in line: + myNf = float(line.split()[0]) + float(line.split()[1]) mysaffile.close() - if myNf==-1 or myN0==-1: - self.logger.warning('Invalid cutflow for the region ' + reg +'('+regname+') in ' + path) - self.logger.warning('Skipping the CLs calculation.') + if myNf == -1 or myN0 == -1: + self.logger.warning( + "Invalid cutflow for the region " + + reg + + "(" + + regname + + ") in " + + path + ) + self.logger.warning("Skipping the CLs calculation.") return -1 - Nf+=myNf - N0+=myN0 - if Nf==0 and N0==0: - self.logger.warning('Invalid cutflow for the region ' + reg +'('+regname+') in ' + path) - self.logger.warning('Skipping the CLs calculation.') + Nf += myNf + N0 += myN0 + if Nf == 0 and N0 == 0: + self.logger.warning( + "Invalid cutflow for the region " + + reg + + "(" + + regname + + ") in " + + path + ) + self.logger.warning("Skipping the CLs calculation.") return -1 - regiondata[reg]["N0"]=N0 - regiondata[reg]["Nf"]=Nf + regiondata[reg]["N0"] = N0 + regiondata[reg]["Nf"] = Nf return regiondata - def extract_cls(self,regiondata,regions,xsection,lumi): - self.logger.debug('Compute CLs...') + def extract_cls( + self, + regiondata: dict, + stat_models: dict, + xsection: float, + lumi: float, + is_extrapolated: bool, + ) -> dict: + from .statistical_models import APRIORI, OBSERVED + + self.logger.debug("Compute CLs...") ## computing fi a region belongs to the best expected ones, and derive the CLs in all cases - bestreg=[] + idx = 2 if is_extrapolated else 0 + expected = APRIORI if is_extrapolated else OBSERVED + bestreg = [] rMax = -1 - for reg in regions: - nsignal = xsection * lumi * 1000. * regiondata[reg]["Nf"] / regiondata[reg]["N0"] - nb = regiondata[reg]["nb"] - nobs = regiondata[reg]["nobs"] - deltanb = regiondata[reg]["deltanb"] - if nsignal<=0: - rSR = -1 + for reg, stat_model in stat_models["uncorrelated_background"].items(): + nsignal = ( + xsection * lumi * 1000.0 * regiondata[reg]["Nf"] / regiondata[reg]["N0"] + ) + if nsignal <= 0: + rSR = -1 myCLs = 0 else: - n95 = float(regiondata[reg]["s95exp"]) * lumi * 1000. * regiondata[reg]["Nf"] / regiondata[reg]["N0"] - rSR = nsignal/n95 - myCLs = self.cls_calculator(nobs, nb, deltanb, nsignal, self.ntoys, CLs_obs = True) + n95 = ( + float(regiondata[reg]["s95exp"]) + * lumi + * 1000.0 + * regiondata[reg]["Nf"] + / regiondata[reg]["N0"] + ) + rSR = nsignal / n95 + myCLs = stat_model.exclusion_confidence_level(expected=expected)[idx] regiondata[reg]["rSR"] = rSR regiondata[reg]["CLs"] = myCLs if rSR > rMax: - regiondata[reg]["best"]=1 + regiondata[reg]["best"] = 1 for mybr in bestreg: - regiondata[mybr]["best"]=0 + regiondata[mybr]["best"] = 0 bestreg = [reg] rMax = rSR else: - regiondata[reg]["best"]=0 + regiondata[reg]["best"] = 0 if self.cov_config: minsig95, bestreg = 1e99, [] - for cov_subset, subset in self.cov_config.items(): - cov_regions = subset["cov_regions"] - covariance = subset["covariance" ] - if all(s <= 0. for s in [regiondata[reg]["Nf"] for reg in cov_regions]): - regiondata["cov_subset"][cov_subset]["CLs"]= 0. - continue - CLs = self.slhCLs(regiondata,cov_regions,xsection,lumi,covariance, ntoys = self.ntoys) + for cov_subset, stat_model in stat_models["simplified_likelihoods"].items(): + CLs = stat_model.exclusion_confidence_level(expected=expected) s95 = float(regiondata["cov_subset"][cov_subset]["s95exp"]) - regiondata["cov_subset"][cov_subset]["CLs"] = CLs - if 0. < s95 < minsig95: - regiondata['cov_subset'][cov_subset]["best"] = 1 + regiondata["cov_subset"][cov_subset]["CLs"] = CLs[idx] + if expected != OBSERVED: + regiondata["cov_subset"][cov_subset]["full_CLs_output"] = CLs + if 0.0 < s95 < minsig95: + regiondata["cov_subset"][cov_subset]["best"] = 1 for mybr in bestreg: - regiondata['cov_subset'][mybr]["best"]=0 + regiondata["cov_subset"][mybr]["best"] = 0 bestreg = [cov_subset] minsig95 = s95 else: - regiondata['cov_subset'][cov_subset]["best"]=0 - - #initialize pyhf for cls calculation - iterator = [] if not self.pyhf_config else copy.deepcopy(self.pyhf_config).items() - minsig95, bestreg = 1e99, [] - for likelihood_profile, config in iterator: - self.logger.debug(' * Running CLs for '+likelihood_profile) - # safety check, just in case - if regiondata.get('pyhf',{}).get(likelihood_profile, False) is False: - continue - background = HF_Background(config) - self.logger.debug('current pyhf Configuration = '+str(config)) - signal = HF_Signal(config,regiondata,xsection=xsection) - is_not_extrapolated = signal.lumi == lumi - CLs = -1 - if signal.isAlive(): - sig_HF = signal(lumi) - bkg_HF = background(lumi) - if self.main.developer_mode: - setattr(self, "hf_sig_test", sig_HF) - setattr(self, "hf_bkg_test", bkg_HF) - CLs = pyhf_wrapper(bkg_HF, sig_HF) - # Take observed if default lumi used, use expected if extrapolated - CLs_out = CLs['CLs_obs'] if is_not_extrapolated else CLs['CLs_exp'][2] - regiondata['pyhf'][likelihood_profile]['full_CLs_output'] = CLs - if CLs_out >= 0.: - regiondata['pyhf'][likelihood_profile]['CLs'] = CLs_out - s95 = float(regiondata['pyhf'][likelihood_profile]['s95exp']) - if 0. < s95 < minsig95: - regiondata['pyhf'][likelihood_profile]["best"] = 1 + regiondata["cov_subset"][cov_subset]["best"] = 0 + + if self.pyhf_config: + minsig95, bestreg = 1e99, [] + for llhd_profile, stat_model in stat_models["full_likelihoods"].items(): + CLs = stat_model.exclusion_confidence_level(expected=expected) + regiondata["pyhf"][llhd_profile]["CLs"] = CLs[idx] + if expected != OBSERVED: + regiondata["pyhf"][llhd_profile]["full_CLs_output"] = CLs + s95 = float(regiondata["pyhf"][llhd_profile]["s95exp"]) + if 0.0 < s95 < minsig95: + regiondata["pyhf"][llhd_profile]["best"] = 1 for mybr in bestreg: - regiondata['pyhf'][mybr]["best"]=0 - bestreg = [likelihood_profile] + regiondata["pyhf"][mybr]["best"] = 0 + bestreg = [llhd_profile] minsig95 = s95 else: - regiondata['pyhf'][likelihood_profile]["best"]=0 - return regiondata - - - @staticmethod - def slhCLs(regiondata,cov_regions,xsection,lumi,covariance,expected=False, ntoys = 10000): - """ (slh for simplified likelihood) - Compute a global CLs combining the different region yields by using a simplified - likelihood method (see CMS-NOTE-2017-001 for more information). It relies on the - simplifiedLikelihood.py code designed by Wolfgang Waltenberger. The method - returns the computed CLs value. """ - observed, backgrounds, nsignal = [], [], [] - # Collect the input data necessary for the simplified_likelyhood.py method - for reg in cov_regions: - nsignal.append(xsection*lumi*1000.*regiondata[reg]["Nf"]/regiondata[reg]["N0"]) - backgrounds.append(regiondata[reg]["nb"]) - observed.append(regiondata[reg]["nobs"]) - # data - from madanalysis.misc.simplified_likelihood import Data - LHdata = Data(observed, backgrounds, covariance, None, nsignal) - from madanalysis.misc.simplified_likelihood import CLsComputer - computer = CLsComputer(ntoys = ntoys, cl = .95) - # calculation and output - try: - return computer.computeCLs(LHdata, expected=expected) - except Exception as err: - logging.getLogger('MA5').debug("slhCLs : " + str(err)) - return 0.0 - - - def extract_sig_cls(self,regiondata,regions,lumi,tag): - self.logger.debug('Compute signal CL...') - for reg in regions: - nb = regiondata[reg]["nb"] - nobs = regiondata[reg]["nobs"] - if tag == "exp" and self.is_apriori: - nobs = regiondata[reg]["nb"] - deltanb = regiondata[reg]["deltanb"] - - def sig95(xsection): - if regiondata[reg]["Nf"]<=0.: - return 0 - nsignal=xsection * lumi * 1000. * regiondata[reg]["Nf"] / regiondata[reg]["N0"] - return self.cls_calculator( - nobs, nb, deltanb, nsignal, self.ntoys, **{"CLs_"+tag : True} - ) - 0.95 - - nslow = lumi * 1000. * regiondata[reg]["Nf"] / regiondata[reg]["N0"] - nshig = lumi * 1000. * regiondata[reg]["Nf"] / regiondata[reg]["N0"] - - if nslow <= 0 and nshig <= 0: - regiondata[reg]["s95"+tag]="-1" - continue - - low,hig = 1., 1. - while self.cls_calculator(nobs,nb,deltanb,nslow,self.ntoys, **{"CLs_"+tag : True})>0.95: - self.logger.debug('region ' + reg + ', lower bound = ' + str(low)) - nslow*=0.1; low *=0.1 - while self.cls_calculator(nobs,nb,deltanb,nshig,self.ntoys, **{"CLs_"+tag : True})<0.95: - self.logger.debug('region ' + reg + ', upper bound = ' + str(hig)) - nshig*=10.; hig *=10. - - try: - import scipy - s95 = scipy.optimize.brentq(sig95,low,hig,xtol=low/100.) - except ImportError as err: - self.logger.debug("Can't import scipy"); s95=-1 - except Exception as err: - self.logger.debug(str(err)); s95=-1 - - self.logger.debug('region ' + reg + ', s95 = ' + str(s95) + ' pb') - regiondata[reg]["s95"+tag] = ("%-20.7f" % s95) - - return regiondata - - # Calculating the upper limits on sigma with simplified likelihood - def extract_sig_lhcls(self,regiondata,lumi,tag): - """ - Compute gloabal upper limit on cross section. - - Parameters - ---------- - regiondata : Dict - Dictionary including all the information about SR yields - lumi : float - luminosity - tag : str - expected or observed - """ - self.logger.debug('Compute signal CL...') - if "cov_subset" not in regiondata.keys(): - regiondata["cov_subset"] = {} - - def get_s95(regs, matrix): - def sig95(xsection): - return self.slhCLs(regiondata,regs,xsection,lumi,matrix,(tag=="exp"), ntoys = self.ntoys)-0.95 - return sig95 - - for cov_subset in self.cov_config.keys(): - cov_regions = self.cov_config[cov_subset]["cov_regions"] - covariance = self.cov_config[cov_subset]["covariance" ] - if cov_subset not in regiondata["cov_subset"].keys(): - regiondata["cov_subset"][cov_subset] = {} - if all(s <= 0. for s in [regiondata[reg]["Nf"] for reg in cov_regions]): - regiondata["cov_subset"][cov_subset]["s95"+tag]= "-1" - continue - - low, hig = 1., 1. - while self.slhCLs(regiondata,cov_regions,low,lumi,covariance,(tag=="exp"), ntoys = self.ntoys)>0.95: - self.logger.debug('lower bound = ' + str(low)) - low *= 0.1 - if low < 1e-10: break - while self.slhCLs(regiondata,cov_regions,hig,lumi,covariance,(tag=="exp"), ntoys = self.ntoys)<0.95: - self.logger.debug('upper bound = ' + str(hig)) - hig *= 10. - if hig > 1e10: break - - try: - import scipy - sig95 = get_s95(cov_regions, covariance) - s95 = scipy.optimize.brentq(sig95,low,hig,xtol=low/100.) - except ImportError as err: - self.logger.debug("Can't import scipy") - s95=-1 - except Exception as err: - self.logger.debug(str(err)) - s95=-1 - - self.logger.debug('s95 = ' + str(s95) + ' pb') - regiondata["cov_subset"][cov_subset]["s95"+tag] = ("%-20.7f" % s95) - - return regiondata - + regiondata["pyhf"][llhd_profile]["best"] = 0 - def pyhf_sig95Wrapper(self, lumi, regiondata, tag): - if self.pyhf_config == {}: - return regiondata - if 'pyhf' not in list(regiondata.keys()): - regiondata['pyhf'] = {} - - def get_pyhf_result(*args): - rslt = pyhf_wrapper(*args) - if tag == "exp" and not self.is_apriori: - return rslt["CLs_exp"][2] - return rslt['CLs_obs'] - - def sig95(conf, regdat, bkg): - def CLs(xsec): - signal = HF_Signal(conf, regdat, xsection=xsec) - return get_pyhf_result(bkg(lumi), signal(lumi))-0.95 - return CLs - - iterator = [] if self.pyhf_config=={} else copy.deepcopy(list(self.pyhf_config.items())) - for n, (likelihood_profile, config) in enumerate(iterator): - self.logger.debug(' * Running sig95'+tag+' for '+likelihood_profile) - if likelihood_profile not in list(regiondata['pyhf'].keys()): - regiondata['pyhf'][likelihood_profile] = {} - background = HF_Background(config, expected=(tag=='exp' and self.is_apriori)) - self.logger.debug('Config : '+str(config)) - if not HF_Signal(config, regiondata, xsection=1., background=background).isAlive(): - self.logger.debug(likelihood_profile+' has no signal event.') - regiondata['pyhf'][likelihood_profile]["s95"+tag] = "-1" - continue - - low, hig = 1., 1. - while get_pyhf_result(background(lumi),\ - HF_Signal(config, regiondata,xsection=low)(lumi)) > 0.95: - self.logger.debug(tag+': profile '+likelihood_profile+\ - ', lower bound = '+str(low)) - low *= 0.1 - if low < 1e-10: break - while get_pyhf_result(background(lumi),\ - HF_Signal(config, regiondata,xsection=hig)(lumi)) < 0.95: - self.logger.debug(tag+': profile '+likelihood_profile+\ - ', higher bound = '+str(hig)) - hig *= 10. - if hig > 1e10: break - try: - import scipy - s95 = scipy.optimize.brentq( - sig95(config, regiondata, background),low,hig,xtol=low/100. - ) - except Exception as err: - self.logger.debug(str(err)) - self.logger.debug('Can not calculate sig95'+tag+' for '+likelihood_profile) - s95=-1 - regiondata['pyhf'][likelihood_profile]["s95"+tag] = "{:.7f}".format(s95) - self.logger.debug(likelihood_profile+' sig95'+tag+' = {:.7f} pb'.format(s95)) return regiondata - - def write_cls_output(self, analysis, regions, regiondata, errordata, summary, xsflag, lumi): - self.logger.debug('Write CLs...') + def write_cls_output( + self, analysis, regions, regiondata, errordata, summary, xsflag, lumi + ): + self.logger.debug("Write CLs...") if self.main.developer_mode: - to_save = {analysis : {'regiondata' : regiondata, 'errordata' : errordata}} - name = summary.name.split('.dat')[0] + '.json' + to_save = {analysis: {"regiondata": regiondata, "errordata": errordata}} + name = summary.name.split(".dat")[0] + ".json" if os.path.isfile(name): - with open(name,'r') as json_file: + with open(name, "r") as json_file: past = json.load(json_file) - for key, item in [(k,i) for k,i in past.items() if k not in list(to_save.keys())]: + for key, item in [ + (k, i) for k, i in past.items() if k not in list(to_save.keys()) + ]: to_save[key] = item - self.logger.debug('Saving dictionary : '+name) - with open(name,'w+') as results: + self.logger.debug("Saving dictionary : " + name) + with open(name, "w+") as results: json.dump(to_save, results, indent=4) ################################################################################### # @Jack : For debugging purposes in the future. This slice of code # prints the Json file for signal WITH XSEC=1 !!! - if self.pyhf_config!={}: + if self.pyhf_config != {}: iterator = copy.deepcopy(list(self.pyhf_config.items())) for n, (likelihood_profile, config) in enumerate(iterator): - if regiondata.get('pyhf',{}).get(likelihood_profile, False) == False: - continue - signal = HF_Signal(config,regiondata,xsection=1.) - name = summary.name.split('.dat')[0] - with open(name+'_'+likelihood_profile+'_sig.json','w+') as out_file: - json.dump(signal(lumi), out_file, indent=4) + if regiondata.get("pyhf", {}).get(likelihood_profile, False) == False: + continue + signal = HF_Signal(config, regiondata, xsection=1.0) + name = summary.name.split(".dat")[0] + with open( + name + "_" + likelihood_profile + "_sig.json", "w+" + ) as out_file: + json.dump(signal(lumi), out_file, indent=4) ################################################################################### - err_sets = [ ['scale_up', 'scale_dn', 'Scale var.'], ['TH_up', 'TH_dn', 'TH error'] ] + err_sets = [ + ["scale_up", "scale_dn", "Scale var."], + ["TH_up", "TH_dn", "TH error"], + ] for reg in regions: - eff = (regiondata[reg]["Nf"] / regiondata[reg]["N0"]) + eff = regiondata[reg]["Nf"] / regiondata[reg]["N0"] if eff < 0: eff = 0 - stat = round(math.sqrt(eff*(1-eff)/(abs(regiondata[reg]["N0"])*lumi)),10) - syst = [] - if len(self.main.recasting.systematics)>0: + stat = round( + math.sqrt(eff * (1 - eff) / (abs(regiondata[reg]["N0"]) * lumi)), 10 + ) + syst = [] + if len(self.main.recasting.systematics) > 0: for unc in self.main.recasting.systematics: - syst.append(round(.5*(unc[0]+unc[1])*eff,8)) + syst.append(round(0.5 * (unc[0] + unc[1]) * eff, 8)) else: syst = [0] - myeff = "%.7f" % eff + myeff = "%.7f" % eff mystat = "%.7f" % stat mysyst = ["%.7f" % x for x in syst] myxsexp = regiondata[reg]["s95exp"] @@ -1712,298 +1992,248 @@ def write_cls_output(self, analysis, regions, regiondata, errordata, summary, xs else: myxsobs = "-1" if not xsflag: - mycls = "%.10f" % regiondata[reg]["CLs"] - summary.write(analysis.ljust(30,' ') + reg.ljust(60,' ') +\ - str(regiondata[reg]["best"]).ljust(10, ' ') +\ - myxsexp.ljust(20,' ') + myxsobs.ljust(20,' ') + mycls.ljust(10,' ') + \ - ' || ' + myeff.ljust(15,' ') + mystat.ljust(15,' ')); + mycls = "%.10f" % regiondata[reg]["CLs"] + summary.write( + analysis.ljust(30, " ") + + reg.ljust(60, " ") + + str(regiondata[reg]["best"]).ljust(10, " ") + + myxsexp.ljust(20, " ") + + myxsobs.ljust(20, " ") + + mycls.ljust(10, " ") + + " || " + + myeff.ljust(15, " ") + + mystat.ljust(15, " ") + ) for onesyst in mysyst: - summary.write(onesyst.ljust(15, ' ')) - summary.write('\n') + summary.write(onesyst.ljust(15, " ")) + summary.write("\n") band = [] for error_set in err_sets: - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]][reg]['CLs'], errordata[error_set[1]][reg]['CLs'], regiondata[reg]['CLs'] ] - if len(set(band))==1: + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]][reg]["CLs"], + errordata[error_set[1]][reg]["CLs"], + regiondata[reg]["CLs"], + ] + if len(set(band)) == 1: continue - summary.write(''.ljust(90,' ') + error_set[2] + ' band: [' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') - for i in range(0, len(self.main.recasting.systematics)): - error_set = [ 'sys'+str(i)+'_up', 'sys'+str(i)+'_dn' ] - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]][reg]['CLs'], errordata[error_set[1]][reg]['CLs'], regiondata[reg]['CLs'] ] - if len(set(band))==1: + summary.write( + "".ljust(90, " ") + + error_set[2] + + " band: [" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) + for i, sys in enumerate(self.main.recasting.systematics): + error_set = ["sys" + str(i) + "_up", "sys" + str(i) + "_dn"] + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]][reg]["CLs"], + errordata[error_set[1]][reg]["CLs"], + regiondata[reg]["CLs"], + ] + if len(set(band)) == 1: continue - up, dn = self.main.recasting.systematics[i] - summary.write(''.ljust(90,' ') + '+{:.1f}% -{:.1f}% syst:'.format(up*100.,dn*100.).ljust(25,' ') + '[' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') + up, dn = sys + summary.write( + "".ljust(90, " ") + + "+{:.1f}% -{:.1f}% syst:".format( + up * 100.0, dn * 100.0 + ).ljust(25, " ") + + "[" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) else: - summary.write(analysis.ljust(30,' ') + reg.ljust(60,' ') +\ - myxsexp.ljust(20,' ') + myxsobs.ljust(20,' ') + \ - ' || ' + myeff.ljust(15,' ') + mystat.ljust(15,' ')) - if syst!=[0]: + summary.write( + analysis.ljust(30, " ") + + reg.ljust(60, " ") + + myxsexp.ljust(20, " ") + + myxsobs.ljust(20, " ") + + " || " + + myeff.ljust(15, " ") + + mystat.ljust(15, " ") + ) + if syst != [0]: for onesyst in mysyst: - summary.write(onesyst.ljust(15, ' ')) - summary.write('\n') + summary.write(onesyst.ljust(15, " ")) + summary.write("\n") # Adding the global CLs from simplified likelihood - for cov_subset in self.cov_config.keys(): + for cov_subset in self.cov_config: if not xsflag: myxsexp = regiondata["cov_subset"][cov_subset].get("s95exp", "-1") myxsobs = regiondata["cov_subset"][cov_subset].get("s95obs", "-1") - best = str(regiondata["cov_subset"][cov_subset].get("best", 0)) + best = str(regiondata["cov_subset"][cov_subset].get("best", 0)) myglobalcls = "%.4f" % regiondata["cov_subset"][cov_subset]["CLs"] - description = "[SL]-"+cov_subset - summary.write(analysis.ljust(30,' ') + description.ljust(60,' ') + best.ljust(10, ' ') + - myxsexp.ljust(15,' ') + myxsobs.ljust(15,' ') + - myglobalcls.ljust(7, ' ') + ' || \n') + description = "[SL]-" + cov_subset + summary.write( + analysis.ljust(30, " ") + + description.ljust(60, " ") + + best.ljust(10, " ") + + myxsexp.ljust(15, " ") + + myxsobs.ljust(15, " ") + + myglobalcls.ljust(7, " ") + + " || \n" + ) band = [] for error_set in err_sets: - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]]["cov_subset"][cov_subset]["CLs"], - errordata[error_set[1]]["cov_subset"][cov_subset]["CLs"], - regiondata["cov_subset"][cov_subset]["CLs"] ] - if len(set(band))==1: + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]]["cov_subset"][cov_subset]["CLs"], + errordata[error_set[1]]["cov_subset"][cov_subset]["CLs"], + regiondata["cov_subset"][cov_subset]["CLs"], + ] + if len(set(band)) == 1: continue - summary.write(''.ljust(90,' ') + error_set[2] + ' band: [' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') - for i in range(0, len(self.main.recasting.systematics)): - error_set = [ 'sys'+str(i)+'_up', 'sys'+str(i)+'_dn' ] - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]]["cov_subset"][cov_subset]["CLs"], - errordata[error_set[1]]["cov_subset"][cov_subset]["CLs"], - regiondata["cov_subset"][cov_subset]["CLs"] ] - if len(set(band))==1: + summary.write( + "".ljust(90, " ") + + error_set[2] + + " band: [" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) + for i, sys in enumerate(self.main.recasting.systematics): + error_set = ["sys" + str(i) + "_up", "sys" + str(i) + "_dn"] + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]]["cov_subset"][cov_subset]["CLs"], + errordata[error_set[1]]["cov_subset"][cov_subset]["CLs"], + regiondata["cov_subset"][cov_subset]["CLs"], + ] + if len(set(band)) == 1: continue - up, dn = self.main.recasting.systematics[i] - summary.write(''.ljust(90,' ') + '+{:.1f}% -{:.1f}% syst:'.format(up*100.,dn*100.).ljust(25,' ') + '[' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') + up, dn = sys + summary.write( + "".ljust(90, " ") + + "+{:.1f}% -{:.1f}% syst:".format( + up * 100.0, dn * 100.0 + ).ljust(25, " ") + + "[" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) else: myxsexp = regiondata["cov_subset"][cov_subset]["s95exp"] myxsobs = regiondata["cov_subset"][cov_subset]["s95obs"] - description = "[SL]-"+cov_subset - summary.write(analysis.ljust(30,' ') + description.ljust(60,' ') +\ - myxsexp.ljust(15,' ') + myxsobs.ljust(15,' ') + \ - ' || \n') + description = "[SL]-" + cov_subset + summary.write( + analysis.ljust(30, " ") + + description.ljust(60, " ") + + myxsexp.ljust(15, " ") + + myxsobs.ljust(15, " ") + + " || \n" + ) # pyhf results - pyhf_data = regiondata.get('pyhf',{}) + pyhf_data = regiondata.get("pyhf", {}) for likelihood_profile in list(self.pyhf_config.keys()): if likelihood_profile not in list(pyhf_data.keys()): continue - myxsexp = pyhf_data.get(likelihood_profile, {}).get('s95exp', "-1") - myxsobs = pyhf_data.get(likelihood_profile, {}).get('s95obs', "-1") + myxsexp = pyhf_data.get(likelihood_profile, {}).get("s95exp", "-1") + myxsobs = pyhf_data.get(likelihood_profile, {}).get("s95obs", "-1") if not xsflag: self.logger.debug(str(pyhf_data)) - mycls = '{:.4f}'.format(pyhf_data.get(likelihood_profile,{}).get('CLs', 0.)) - best = str(pyhf_data.get(likelihood_profile,{}).get('best', 0)) - summary.write(analysis.ljust(30,' ') + ('[pyhf]-'+likelihood_profile+'-profile').ljust(60,' ') +\ - best.ljust(10, ' ') +myxsexp.ljust(15,' ') + myxsobs.ljust(15,' ') +\ - mycls.ljust( 7,' ') + ' || ' + ''.ljust(15,' ') + ''.ljust(15,' ')); - summary.write('\n') + mycls = "{:.4f}".format( + pyhf_data.get(likelihood_profile, {}).get("CLs", 0.0) + ) + best = str(pyhf_data.get(likelihood_profile, {}).get("best", 0)) + summary.write( + analysis.ljust(30, " ") + + ("[pyhf]-" + likelihood_profile + "-profile").ljust(60, " ") + + best.ljust(10, " ") + + myxsexp.ljust(15, " ") + + myxsobs.ljust(15, " ") + + mycls.ljust(7, " ") + + " || " + + "".ljust(15, " ") + + "".ljust(15, " ") + ) + summary.write("\n") band = [] for error_set in err_sets: - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]].get('pyhf',{}).get(likelihood_profile,{}).get('CLs',0.0), - errordata[error_set[1]].get('pyhf',{}).get(likelihood_profile,{}).get('CLs',0.0), - pyhf_data.get(likelihood_profile,{}).get('CLs', 0.)] - if len(set(band))==1: + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]] + .get("pyhf", {}) + .get(likelihood_profile, {}) + .get("CLs", 0.0), + errordata[error_set[1]] + .get("pyhf", {}) + .get(likelihood_profile, {}) + .get("CLs", 0.0), + pyhf_data.get(likelihood_profile, {}).get("CLs", 0.0), + ] + if len(set(band)) == 1: continue - summary.write(''.ljust(90,' ') + error_set[2] + ' band: [' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') - for i in range(0, len(self.main.recasting.systematics)): - error_set = [ 'sys'+str(i)+'_up', 'sys'+str(i)+'_dn' ] - if len([ x for x in error_set if x in list(errordata.keys()) ])==2: - band = band + [errordata[error_set[0]].get('pyhf',{}).get(likelihood_profile,{}).get('CLs',0.0), - errordata[error_set[1]].get('pyhf',{}).get(likelihood_profile,{}).get('CLs',0.0), - pyhf_data.get(likelihood_profile,{}).get('CLs', 0.)] - if len(set(band))==1: + summary.write( + "".ljust(90, " ") + + error_set[2] + + " band: [" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) + for i, sys in enumerate(self.main.recasting.systematics): + error_set = ["sys" + str(i) + "_up", "sys" + str(i) + "_dn"] + if len([x for x in error_set if x in list(errordata.keys())]) == 2: + band = band + [ + errordata[error_set[0]] + .get("pyhf", {}) + .get(likelihood_profile, {}) + .get("CLs", 0.0), + errordata[error_set[1]] + .get("pyhf", {}) + .get(likelihood_profile, {}) + .get("CLs", 0.0), + pyhf_data.get(likelihood_profile, {}).get("CLs", 0.0), + ] + if len(set(band)) == 1: continue - up, dn = self.main.recasting.systematics[i] - summary.write(''.ljust(90,' ') + '+{:.1f}% -{:.1f}% syst:'.format(up*100.,dn*100.).ljust(25,' ') + '[' + \ - ("%.4f" % min(band)) + ', ' + ("%.4f" % max(band)) + ']\n') + up, dn = sys + summary.write( + "".ljust(90, " ") + + "+{:.1f}% -{:.1f}% syst:".format( + up * 100.0, dn * 100.0 + ).ljust(25, " ") + + "[" + + ("%.4f" % min(band)) + + ", " + + ("%.4f" % max(band)) + + "]\n" + ) else: - summary.write(analysis.ljust(30,' ') + ('[pyhf]-'+likelihood_profile+'-profile').ljust(60,' ') +\ - myxsexp.ljust(15,' ') + myxsobs.ljust(15,' ') + \ - ' || ' + ''.ljust(15,' ') + ''.ljust(15,' ')) - summary.write('\n') + summary.write( + analysis.ljust(30, " ") + + ("[pyhf]-" + likelihood_profile + "-profile").ljust(60, " ") + + myxsexp.ljust(15, " ") + + myxsobs.ljust(15, " ") + + " || " + + "".ljust(15, " ") + + "".ljust(15, " ") + ) + summary.write("\n") def clean_region_name(mystr): - newstr = mystr.replace("/", "_slash_") + newstr = mystr.replace("/", "_slash_") newstr = newstr.replace("->", "_to_") newstr = newstr.replace(">=", "_greater_than_or_equal_to_") - newstr = newstr.replace(">", "_greater_than_") + newstr = newstr.replace(">", "_greater_than_") newstr = newstr.replace("<=", "_smaller_than_or_equal_to_") - newstr = newstr.replace("<", "_smaller_than_") - newstr = newstr.replace(" ", "_") - newstr = newstr.replace(",", "_") - newstr = newstr.replace("+", "_") - newstr = newstr.replace("-", "_") - newstr = newstr.replace("(", "_lp_") - newstr = newstr.replace(")", "_rp_") + newstr = newstr.replace("<", "_smaller_than_") + newstr = newstr.replace(" ", "_") + newstr = newstr.replace(",", "_") + newstr = newstr.replace("+", "_") + newstr = newstr.replace("-", "_") + newstr = newstr.replace("(", "_lp_") + newstr = newstr.replace(")", "_rp_") return newstr - - -def pyhf_wrapper(*args, **kwargs): - """ - Computes CLs values via pyhf interface - - :param args: input arguments `nobs, nb, deltanb, nsignal` or `bkg_HF, sig_HF` - function will decide for the correct action depending on number of arguments - :param kwargs: - CLs_exp: bool - return expected values - CLs_obs: bool - return obs values - """ - import pyhf - from numpy import isnan, ndarray, warnings - from pyhf.optimize import mixins - - # Scilence pyhf's messages - pyhf.pdf.log.setLevel(logging.CRITICAL) - pyhf.workspace.log.setLevel(logging.CRITICAL) - mixins.log.setLevel(logging.CRITICAL) - pyhf.set_backend('numpy', precision="64b") - - with warnings.catch_warnings(): - warnings.filterwarnings('ignore') - try: - if len(args) == 2 and all([isinstance(x, (dict, list)) for x in args]): - background, signal = args - workspace = pyhf.Workspace(background) - model = workspace.model( - patches=[signal], - modifier_settings={'normsys': {'interpcode': 'code4'}, - 'histosys': {'interpcode': 'code4p'}} - ) - - data = workspace.data(model) - - elif len(args) == 5 and all([isinstance(x, (float, int)) for x in args]): - NumObserved, ExpectedBG, BGError, SigHypothesis, _ = args - model = pyhf.simplemodels.uncorrelated_background( - [max(SigHypothesis, 0.0)], [ExpectedBG], [BGError] - ) - data = [NumObserved] + model.config.auxdata - - except (pyhf.exceptions.InvalidSpecification, KeyError) as err: - logging.getLogger('MA5').error("Invalid JSON file!! "+str(err)) - if kwargs.get("CLs_exp", False) or kwargs.get("CLs_obs", False): - return -1 - return {'CLs_obs':-1 , 'CLs_exp' : [-1]*5} - except Exception as err: - logging.getLogger('MA5').debug("Unknown error, check pyhf_wrapper_py3 "+ str(err)) - if kwargs.get("CLs_exp", False) or kwargs.get("CLs_obs", False): - return -1 - return {'CLs_obs':-1 , 'CLs_exp' : [-1]*5} - - def get_CLs(**kwargs): - try: - CLs_obs, CLs_exp = pyhf.infer.hypotest( - 1., data, model, - test_stat=kwargs.get("stats", "qtilde"), - par_bounds=kwargs.get('bounds', model.config.suggested_bounds()), - return_expected_set=True - ) - - except (AssertionError, pyhf.exceptions.FailedMinimization, ValueError) as err: - logging.getLogger('MA5').debug(str(err)) - # dont use false here 1.-CLs = 0 can be interpreted as false - return 'update bounds' - - # if isnan(float(CLs_obs)) or any([isnan(float(x)) for x in CLs_exp]): - # return "update mu" - CLs_obs = float(CLs_obs[0]) if isinstance(CLs_obs, (list, tuple)) else float(CLs_obs) - - return { - 'CLs_obs' : 1. - CLs_obs , - 'CLs_exp' : list(map(lambda x : float(1. - x), CLs_exp)) - } - - - #pyhf can raise an error if the poi_test bounds are too stringent - #they need to be updated dynamically. - arguments = dict(bounds=model.config.suggested_bounds(), stats="qtilde") - iteration_limit = 0 - while True: - CLs = get_CLs(**arguments) - if CLs == 'update bounds': - arguments["bounds"][model.config.poi_index] = ( - arguments["bounds"][model.config.poi_index][0], - 2*arguments["bounds"][model.config.poi_index][1] - ) - logging.getLogger("MA5").debug( - "Hypothesis test inference integration bounds has been increased to " + \ - str(arguments["bounds"][model.config.poi_index]) - ) - iteration_limit += 1 - elif isinstance(CLs, dict): - if isnan(CLs["CLs_obs"]) or any([isnan(x) for x in CLs["CLs_exp"]]): - arguments["stats"] = "q" - arguments["bounds"][model.config.poi_index] = ( - arguments["bounds"][model.config.poi_index][0]-5, - arguments["bounds"][model.config.poi_index][1] - ) - logging.getLogger("MA5").debug( - "Hypothesis test inference integration bounds has been increased to " + \ - str(arguments["bounds"][model.config.poi_index]) - ) - else: - break - else: - iteration_limit += 1 - # hard limit on iteration required if it exceeds this value it means - # Nsig >>>>> Nobs - if iteration_limit>=3: - if kwargs.get("CLs_exp", False) or kwargs.get("CLs_obs", False): - return 1 - return {'CLs_obs':1. , 'CLs_exp' : [1.]*5} - - if kwargs.get("CLs_exp", False): - return CLs["CLs_exp"][2] - elif kwargs.get("CLs_obs", False): - return CLs["CLs_obs"] - - return CLs - - -def cls(NumObserved, ExpectedBG, BGError, SigHypothesis, NumToyExperiments, **kwargs): - import scipy.stats - - # generate a set of expected-number-of-background-events, one for each toy - # experiment, distributed according to a Gaussian with the specified mean - # and uncertainty - ExpectedBGs = scipy.stats.norm.rvs(loc=ExpectedBG, scale=BGError, size=NumToyExperiments) - - # Ignore values in the tail of the Gaussian extending to negative numbers - ExpectedBGs = [value for value in ExpectedBGs if value > 0] - - # For each toy experiment, get the actual number of background events by - # taking one value from a Poisson distribution created using the expected - # number of events. - ToyBGs = scipy.stats.poisson.rvs(ExpectedBGs) - ToyBGs = list(map(float, ToyBGs)) - - # The probability for the background alone to fluctutate as LOW as - # observed = the fraction of the toy experiments with backgrounds as low as - # observed = p_b. - # NB (1 - this p_b) corresponds to what is usually called p_b for CLs. - p_b = scipy.stats.percentileofscore(ToyBGs, NumObserved, kind='weak')*.01 - - # Toy MC for background+signal - ExpectedBGandS = [expectedbg + SigHypothesis for expectedbg in ExpectedBGs] - ExpectedBGandS = [x for x in ExpectedBGandS if x > 0] - if len(ExpectedBGandS)==0: - return 0. - ToyBplusS = scipy.stats.poisson.rvs(ExpectedBGandS) - ToyBplusS = list(map(float, ToyBplusS)) - - # Calculate the fraction of these that are >= the number observed, - # giving p_(S+B). Divide by (1 - p_b) a la the CLs prescription. - p_SplusB = scipy.stats.percentileofscore(ToyBplusS, NumObserved, kind='weak')*.01 - - if p_SplusB>p_b: - return 0. - else: - return 1.-(p_SplusB / p_b) # 1 - CLs diff --git a/madanalysis/misc/simplified_likelihood.py b/madanalysis/misc/simplified_likelihood.py deleted file mode 100644 index e5795924..00000000 --- a/madanalysis/misc/simplified_likelihood.py +++ /dev/null @@ -1,961 +0,0 @@ -################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks -# The MadAnalysis development team, email: -# -# This file is part of MadAnalysis 5. -# Official website: -# -# MadAnalysis 5 is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# MadAnalysis 5 is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with MadAnalysis 5. If not, see -# -################################################################################ - - -#!/usr/bin/env python3 -""" -.. module:: simplified_likelihood - :synopsis: Code that implements the simplified likelihoods as presented - in CMS-NOTE-2017-001, see https://cds.cern.ch/record/2242860. - Copied from the SModelS v1.2 simplifiedLikelihood.py module (arXiv:1811.10624) - -.. moduleauthor:: Wolfgang Waltenberger - -""" - -from __future__ import print_function -from __future__ import absolute_import -from scipy import stats, optimize, integrate, special -from scipy import __version__ as scipy_version -from numpy import sqrt, exp, log, sign, array, ndarray -from functools import reduce -import numpy as NP -import math, copy, logging -import copy -from six.moves import range -from six.moves import zip - -logger = logging.getLogger('MA5') - -class Data: - """ A very simple observed container to collect all the data - needed to fully define a specific statistical model """ - - def __init__(self, observed, backgrounds, covariance, third_moment=None, - nsignal=None, name="model", deltas_rel = 0.2): - """ - :param observed: number of observed events per dataset - :param backgrounds: expected bg per dataset - :param covariance: uncertainty in background, as a covariance matrix - :param nsignal: number of signal events in each dataset - :param name: give the model a name, just for convenience - :param deltas_rel: the assumed relative error on the signal hypotheses. - The default is 20%. - """ - self.observed = NP.around(self.convert(observed)) #Make sure observed number of events are integers - self.backgrounds = self.convert(backgrounds) - self.n = len(self.observed) - self.covariance = self._convertCov(covariance) - self.nsignal = self.convert(nsignal) - if self.nsignal is None: - self.signal_rel = self.convert(1.) - elif self.nsignal.sum(): - self.signal_rel = self.nsignal/self.nsignal.sum() - else: - self.signal_rel = array([0.]*len(self.nsignal)) - - self.third_moment = self.convert(third_moment) - if type(self.third_moment) != type(None) and NP.sum([ abs(x) for x in self.third_moment ]) < 1e-10: - self.third_moment = None - self.name = name - self.deltas_rel = deltas_rel - self._computeABC() - # Checking the scip version - v = float(scipy_version.split(".")[0]) - if v < 1.0: - logger.warning("You're using an old version of scipy. This might impact the global CLs calculation.") - - def totalCovariance ( self, nsig ): - """ get the total covariance matrix, taking into account - also signal uncertainty for the signal hypothesis . - If nsig is None, the predefined signal hypothesis is taken. - """ - if self.isLinear(): - cov_tot = self.V + self.var_s( nsig ) - else: - cov_tot = self.covariance+ self.var_s(nsig) - return cov_tot - - - def zeroSignal(self): - """ - Is the total number of signal events zero? - """ - - return len(self.nsignal[self.nsignal>0.]) == 0 - - def var_s(self,nsig=None): - """ - The signal variances. Convenience function. - - :param nsig: If None, it will use the model expected number of signal events, - otherwise will return the variances for the input value using the relative - signal uncertainty defined for the model. - - """ - - if nsig is None: - nsig = self.nsignal - else: - nsig = self.convert(nsig) - return NP.diag((nsig*self.deltas_rel)**2) - - def isScalar(self, obj): - """ - Determine if obj is a scalar (float or int) - """ - - if type(obj) == ndarray: - ## need to treat separately since casting array([0.]) to float works - return False - try: - _ = float(obj) - return True - except: - pass - return False - - def convert(self, obj): - """ - Convert object to numpy arrays. - If object is a float or int, it is converted to a one element - array. - """ - - if type(obj) == type(None): - return obj - if self.isScalar(obj): - return array([obj]) - return array(obj) - - def __str__(self): - return self.name + " (%d dims)" % self.n - - def _convertCov(self, obj): - - if self.isScalar(obj): - return array ( [ [ obj ] ] ) - if type(obj[0]) == list: - return array ( obj ) - if type(obj[0]) == float: - ## if the matrix is flattened, unflatten it. - return array([ obj[self.n*i:self.n*(i+1)] for i in range(self.n)]) - - return obj - - def _computeABC( self ): - """ - Compute the terms A, B, C, rho, V. Corresponds with - Eqs. 1.27-1.30 in the second paper. - """ - self.V = self.covariance - if self.third_moment is None: - self.A = None - self.B = None - self.C = None - return - - covD = self.diagCov() - C=[] - for m2,m3 in zip(covD, self.third_moment): - if m3 == 0.: - m3 = 1e-30 - k = -NP.sign(m3)*sqrt(2.*m2 ) - dm = sqrt ( 8.*m2**3/m3**2 - 1. ) - C.append( k*NP.cos ( 4.*NP.pi/3. + NP.arctan(dm) / 3. )) - - self.C=NP.array(C) ## C, as define in Eq. 1.27 (?) in the second paper - self.B = sqrt( covD - 2*self.C**2 ) ## B, as defined in Eq. 1.28(?) - self.A = self.backgrounds - self.C ## A, Eq. 1.30(?) - self.rho = NP.array( [ [0.]*self.n ]*self.n ) ## Eq. 1.29 (?) - for x in range(self.n): - for y in range(x,self.n): - bxby=self.B[x]*self.B[y] - cxcy=self.C[x]*self.C[y] - e=(4.*cxcy)**(-1)*(sqrt( bxby**2+8*cxcy*self.covariance[x][y])-bxby) - self.rho[x][y]=e - self.rho[y][x]=e - - self.sandwich() - # self.V = sandwich ( self.B, self.rho ) - - def sandwich( self ): - """ - Sandwich product - """ - - ret = NP.array ( [ [0.]*len(self.B) ]*len(self.B) ) - for x in range(len(self.B)): - for y in range(x,len(self.B)): - T=self.B[x]*self.B[y]*self.rho[x][y] - ret[x][y]=T - ret[y][x]=T - self.V = ret - - def isLinear(self): - """ - Statistical model is linear, i.e. no quadratic term in poissonians - """ - - return type(self.C) == type(None) - - def diagCov(self): - """ - Diagonal elements of covariance matrix. Convenience function. - """ - - return NP.diag( self.covariance ) - - def correlations(self): - """ - Correlation matrix, computed from covariance matrix. - Convenience function. - """ - - if hasattr(self, "corr"): - return self.corr - - self.corr = copy.deepcopy(self.covariance) - for x in range(self.n): - self.corr[x][x]=1. - for y in range(x+1,self.n): - rho=self.corr[x][y]/sqrt(self.covariance[x][x]*self.covariance[y][y]) - self.corr[x][y]=rho - self.corr[y][x]=rho - return self.corr - - def signals(self, mu): - """ - Returns the number of expected signal events, for all datasets, - given total signal strength mu. - - :param mu: Total number of signal events summed over all datasets. - """ - - return (mu*self.nsignal) - -class LikelihoodComputer: - def __init__(self, data, ntoys = 10000 ): - """ - :param data: a Data object. - :param ntoys: number of toys when marginalizing - """ - - self.model = data - self.ntoys = ntoys - - def dLdMu(self, mu, signal_rel, theta_hat): - """ - d (ln L)/d mu, if L is the likelihood. The function - whose root gives us muhat, i.e. the mu that maximizes - the likelihood. - - :param mu: total number of signal events - :param signal_rel: array with the relative signal strengths for each dataset (signal region) - :param theta_hat: array with nuisance parameters - - """ - - #Define relative signal strengths: - denominator = mu*signal_rel + self.model.backgrounds + theta_hat - - for ctr,d in enumerate(denominator): - if d == 0.: - if (self.model.observed[ctr]*signal_rel[ctr]) == 0.: - # logger.debug("zero denominator, but numerator also zero, so we set denom to 1.") - denominator[ctr]=1. - else: - raise Exception("we have a zero value in the denominator at pos "+\ - "%d, with a non-zero numerator. dont know how to handle." % ctr) - ret = self.model.observed*signal_rel/denominator - signal_rel - - if type(ret) in [ array, ndarray, list ]: - ret = sum(ret) - return ret - - def findMuHat(self, signal_rel): - """ - Find the most likely signal strength mu - given the relative signal strengths in each dataset (signal region). - - :param signal_rel: array with relative signal strengths - - :returns: mu_hat, the total signal yield. - """ - - if (self.model.backgrounds == self.model.observed).all(): - return 0. - - if type(signal_rel) in [list, ndarray]: - signal_rel = array(signal_rel) - - signal_rel[signal_rel==0.] = 1e-20 - if sum(signal_rel<0.): - raise Exception("Negative relative signal strengths!") - - ## we need a very rough initial guess for mu(hat), to come - ## up with a first theta - self.nsig = array([0.]*len(self.model.observed)) - ## we start with theta_hat being all zeroes - theta_hat = array([0.]*len(self.model.observed)) - mu_hat_old, mu_hat = 0., 1. - ctr=0 - widener=3. - while abs(mu_hat - mu_hat_old)>1e-10 and abs(mu_hat - mu_hat_old )/(mu_hat+mu_hat_old) > .5e-2 and ctr < 20: - ctr+=1 - mu_hat_old = mu_hat - #logger.info ( "theta hat[%d]=%s" % (ctr,list( theta_hat[:11] ) ) ) - #logger.info ( " mu hat[%d]=%s" % (ctr, mu_hat ) ) - mu_c = NP.abs(self.model.observed - self.model.backgrounds - theta_hat)/signal_rel - ## find mu_hat by finding the root of 1/L dL/dmu. We know - ## that the zero has to be between min(mu_c) and max(mu_c). - lower,upper = 0.,widener*max(mu_c) - lower_v = self.dLdMu(lower, signal_rel, theta_hat) - upper_v = self.dLdMu(upper, signal_rel, theta_hat) - total_sign = NP.sign(lower_v * upper_v) - if total_sign > -.5: - if upper_v < lower_v < 0.: - ## seems like we really want to go for mu_hat = 0. - return 0. - logger.debug ( "weird. cant find a zero in the Brent bracket "\ - "for finding mu(hat). Let me try with a very small" - " value." ) - lower = 1e-4*max(mu_c) - lower_v = self.dLdMu( lower, signal_rel, theta_hat ) - total_sign = NP.sign( lower_v * upper_v ) - if total_sign > -.5: - logger.debug ( "cant find zero in Brentq bracket. l,u,ctr=%s,%s,%s" % \ - ( lower, upper, ctr ) ) - widener=widener*1.5 - continue - mu_hat = optimize.brentq ( self.dLdMu, lower, upper, args=(signal_rel, theta_hat ) ) - theta_hat,_ = self.findThetaHat( mu_hat*signal_rel) - ctr+=1 - - return mu_hat - - def getSigmaMu(self, signal_rel): - """ - Get a rough estimate for the variance of mu around mu_max. - - :param signal_rel: array with relative signal strengths in each dataset (signal region) - """ - if type(signal_rel) in [ list, ndarray ]: - s_effs = sum(signal_rel) - - sgm_mu = sqrt(sum(self.model.observed) + sum(NP.diag(self.model.covariance)))/s_effs - - return sgm_mu - - #Define integrand (gaussian_(bg+signal)*poisson(nobs)): - # def prob(x0, x1 ) - - def probMV(self, nll, theta ): - """ probability, for nuicance parameters theta - :params nll: if True, compute negative log likelihood """ - # theta = array ( thetaA ) - # ntot = self.model.backgrounds + self.nsig - # lmbda = theta + self.ntot ## the lambda for the Poissonian - if self.model.isLinear(): - lmbda = self.model.backgrounds + self.nsig + theta - else: - lmbda = self.nsig + self.model.A + theta + self.model.C * theta**2 / self.model.B**2 - lmbda[lmbda<=0.] = 1e-30 ## turn zeroes to small values - if nll: - #poisson = self.model.observed * log ( lmbda ) - lmbda #- self.gammaln - poisson = stats.poisson.logpmf( self.model.observed, lmbda ) - #print ( "p",poisson,poisson2 ) - else: - poisson = stats.poisson.pmf( self.model.observed, lmbda ) - #print ( "nonll",poisson ) - try: - M = [0.]*len(theta) - C = self.model.V - if self.model.n == 1: - C = self.model.totalCovariance(self.nsig) - if nll: - gaussian = stats.multivariate_normal.logpdf(theta,mean=M,cov=C) - ret = - gaussian - sum(poisson) - else: - gaussian = stats.multivariate_normal.pdf(theta,mean=M,cov=C) - ret = gaussian * ( reduce(lambda x, y: x*y, poisson) ) - return ret - except ValueError as e: - raise Exception("ValueError %s, %s" % ( e, self.model.totalCovariance(self.nsig) )) - # raise Exception("ValueError %s, %s" % ( e, self.model.V )) - - def nll( self, theta ): - """ probability, for nuicance parameters theta, - as a negative log likelihood. """ - return self.probMV(True,theta) - - def nllprime( self, theta ): - """ the derivative of nll as a function of the thetas. - Makes it easier to find the maximum likelihood. """ - if self.model.isLinear(): - xtot = theta + self.model.backgrounds + self.nsig - xtot[xtot<=0.] = 1e-30 ## turn zeroes to small values - nllp_ = self.ones - self.model.observed / xtot + NP.dot( theta , self.weight ) - return nllp_ - lmbda = self.nsig + self.model.A + theta + self.model.C * theta**2 / self.model.B**2 - lmbda[lmbda<=0.] = 1e-30 ## turn zeroes to small values - # nllp_ = ( self.ones - self.model.observed / lmbda + NP.dot( theta , self.weight ) ) \ - # * ( self.ones + 2*self.model.C * theta / self.model.B**2 ) - T=self.ones + 2*self.model.C/self.model.B**2*theta - nllp_ = T - self.model.observed / lmbda * ( T ) + NP.dot( theta , self.weight ) - return nllp_ - - def nllHess( self, theta ): - """ the Hessian of nll as a function of the thetas. - Makes it easier to find the maximum likelihood. """ - # xtot = theta + self.ntot - if self.model.isLinear(): - xtot = theta + self.model.backgrounds + self.nsig - xtot[xtot<=0.] = 1e-30 ## turn zeroes to small values - nllh_ = self.weight + NP.diag ( self.model.observed / (xtot**2) ) - return nllh_ - lmbda = self.nsig + self.model.A + theta + self.model.C * theta**2 / self.model.B**2 - lmbda[lmbda<=0.] = 1e-30 ## turn zeroes to small values - T=self.ones + 2*self.model.C/self.model.B**2*theta - # T_i = 1_i + 2*c_i/B_i**2 * theta_i - nllh_ = self.weight + NP.diag ( self.model.observed * T**2 / (lmbda**2) ) - \ - NP.diag ( self.model.observed / lmbda * 2 * self.model.C / self.model.B**2 ) + \ - NP.diag ( 2*self.model.C/self.model.B**2 ) - return nllh_ - - def getThetaHat(self, nobs, nb, nsig, covb, max_iterations ): - """ Compute nuisance parameter theta that - maximizes our likelihood (poisson*gauss). """ - self.nsig = nsig - sigma2 = covb + self.model.var_s(nsig) ## NP.diag ( (self.model.deltas)**2 ) - ## for now deal with variances only - ntot = nb + nsig - cov = NP.array(sigma2) - weight = NP.linalg.inv(cov) ## weight matrix - diag_cov = NP.diag(cov) - # first: no covariances: - q = diag_cov * ( ntot - nobs ) - p = ntot + diag_cov - thetamaxes = [] - thetamax = -p/2. * ( 1 - sign(p) * sqrt ( 1. - 4*q / p**2 ) ) - thetamaxes.append ( thetamax ) - ndims = len(p) - def distance ( theta1, theta2 ): - for ctr,i in enumerate ( theta1 ): - if i == 0.: - theta1[ctr]=1e-20 - for ctr,i in enumerate ( theta2 ): - if i == 0.: - theta2[ctr]=1e-20 - return sum ( NP.abs(theta1 - theta2) / NP.abs ( theta1+theta2 ) ) - - ictr = 0 - while ictr < max_iterations: - ictr += 1 - q = diag_cov * ( ntot - nobs ) - p = ntot + diag_cov - for i in range(ndims): - #q[i] = diag_cov[i] * ( ntot[i] - nobs[i] ) - #p[i] = ntot[i] + diag_cov[i] - for j in range(ndims): - if i==j: continue - dq = thetamax[j]*ntot[i]*diag_cov[i]*weight[i,j] - dp = thetamax[j]*weight[i,j]*diag_cov[i] - if abs ( dq / q[i] ) > .3: - #logger.warning ( "too big a change in iteration." ) - dq=NP.abs( .3 * q[i] ) * NP.sign ( dq ) - if abs ( dp / p[i] ) > .3: - #logger.warning ( "too big a change in iteration." ) - dp=NP.abs( .3 * p[i] ) * NP.sign ( dp ) - q[i] += dq - p[i] += dp - thetamax = -p/2. * ( 1 - sign(p) * sqrt ( 1. - 4*q / p**2 ) ) - thetamaxes.append ( thetamax ) - if len(thetamaxes)>2: - d1 = distance ( thetamaxes[-2], thetamax ) - d2 = distance ( thetamaxes[-3], thetamaxes[-2] ) - if d1 > d2: - raise Exception("diverging when computing thetamax: %f > %f" % ( d1, d2 )) - if d1 < 1e-5: - return thetamax - return thetamax - - def findThetaHat(self, nsig): - """ Compute nuisance parameter theta that maximizes our likelihood - (poisson*gauss). - """ - - ## first step is to disregard the covariances and solve the - ## quadratic equations - ini = self.getThetaHat ( self.model.observed, self.model.backgrounds, nsig, self.model.covariance, 0 ) - self.cov_tot = self.model.V - if self.model.n == 1: - self.cov_tot = self.model.totalCovariance ( nsig ) - # self.ntot = self.model.backgrounds + self.nsig - # if not self.model.isLinear(): - # self.cov_tot = self.model.V + self.model.var_s(nsig) - # self.cov_tot = self.model.totalCovariance (nsig) - #self.ntot = None - self.weight = NP.linalg.inv(self.cov_tot) - self.ones = 1. - if type ( self.model.observed) in [ list, ndarray ]: - self.ones = NP.ones ( len (self.model.observed) ) - self.gammaln = special.gammaln(self.model.observed + 1) - try: - ret_c = optimize.fmin_ncg ( self.nll, ini, fprime=self.nllprime, - fhess=self.nllHess, full_output=True, disp=0 ) - # then always continue with TNC - if type ( self.model.observed ) in [ int, float ]: - bounds = [ ( -10*self.model.observed, 10*self.model.observed ) ] - else: - bounds = [ ( -10*x, 10*x ) for x in self.model.observed ] - ini = ret_c - ret_c = optimize.fmin_tnc ( self.nll, ret_c[0], fprime=self.nllprime, - disp=0, bounds=bounds ) - # print ( "[findThetaHat] mu=%s bg=%s observed=%s V=%s, nsig=%s theta=%s, nll=%s"\ - #% ( self.nsig[0]/self.model.efficiencies[0], self.model.backgrounds, \ - #self.model.observed,self.model.covariance, self.nsig, ret_c[0], self.nll(ret_c[0]) ) ) - if ret_c[-1] not in [ 0, 1, 2 ]: - return ret_c[0],ret_c[-1] - else: - return ret_c[0],0 - logger.debug ( "tnc worked." ) - - ret = ret_c[0] - return ret,-2 - except Exception as e: - logger.error("exception: %s. ini[-3:]=%s" % (e,ini[-3:]) ) - raise Exception("cov-1=%s" % (self.model.covariance+self.model.var_s(nsig))**(-1)) - return ini,-1 - - def marginalizedLLHD1D(self, nsig, nll): - """ - Return the likelihood (of 1 signal region) to observe nobs events given the - predicted background nb, error on this background (deltab), - expected number of signal events nsig and the relative error on the signal (deltas_rel). - - :param nsig: predicted signal (float) - :param nobs: number of observed events (float) - :param nb: predicted background (float) - :param deltab: uncertainty on background (float) - - :return: likelihood to observe nobs events (float) - - """ - self.sigma2 = self.model.covariance + self.model.var_s(nsig)## (self.model.deltas)**2 - self.sigma_tot = sqrt(self.sigma2) - self.lngamma = math.lgamma(self.model.observed[0] + 1) - # Why not a simple gamma function for the factorial: - # ----------------------------------------------------- - # The scipy.stats.poisson.pmf probability mass function - # for the Poisson distribution only works for discrete - # numbers. The gamma distribution is used to create a - # continuous Poisson distribution. - # - # Why not a simple gamma function for the factorial: - # ----------------------------------------------------- - # The gamma function does not yield results for integers - # larger than 170. Since the expression for the Poisson - # probability mass function as a whole should not be huge, - # the exponent of the log of this expression is calculated - # instead to avoid using large numbers. - - - #Define integrand (gaussian_(bg+signal)*poisson(nobs)): - def prob( x, nsig ): - poisson = exp(self.model.observed*log(x) - x - self.lngamma ) - gaussian = stats.norm.pdf(x,loc=self.model.backgrounds+nsig,scale=self.sigma_tot) - - return poisson*gaussian - - #Compute maximum value for the integrand: - xm = self.model.backgrounds + nsig - self.sigma2 - #If nb + nsig = sigma2, shift the values slightly: - if xm == 0.: - xm = 0.001 - xmax = xm*(1.+sign(xm)*sqrt(1. + 4.*self.model.observed*self.sigma2/xm**2))/2. - - #Define initial integration range: - nrange = 5. - a = max(0.,xmax-nrange*sqrt(self.sigma2)) - b = xmax+nrange*self.sigma_tot - like = integrate.quad(prob,a,b,(nsig), epsabs=0.,epsrel=1e-3)[0] - if like==0.: - return 0. - - #Increase integration range until integral converges - err = 1. - ctr=0 - while err > 0.01: - ctr+=1 - if ctr > 10.: - raise Exception("Could not compute likelihood within required precision") - - like_old = like - nrange = nrange*2 - a = max(0.,(xmax-nrange*self.sigma_tot)[0][0] ) - b = (xmax+nrange*self.sigma_tot)[0][0] - like = integrate.quad(prob,a,b,(nsig), - epsabs=0.,epsrel=1e-3)[0] - if like == 0.: - continue - err = abs(like_old-like)/like - - #Renormalize the likelihood to account for the cut at x = 0. - #The integral of the gaussian from 0 to infinity gives: - #(1/2)*(1 + Erf(mu/sqrt(2*sigma2))), so we need to divide by it - #(for mu - sigma >> 0, the normalization gives 1.) - norm = (1./2.)*(1. + special.erf((self.model.backgrounds+nsig)/sqrt(2.*self.sigma2))) - like = like/norm - - if nll: - like = - log ( like ) - - return like[0][0] - - def marginalizedLikelihood(self, nsig, nll ): - """ compute the marginalized likelihood of observing nsig signal event""" - if self.model.isLinear() and self.model.n == 1: ## 1-dimensional non-skewed llhds we can integrate analytically - return self.marginalizedLLHD1D ( nsig, nll ) - - vals=[] - self.gammaln = special.gammaln(self.model.observed + 1) - thetas = stats.multivariate_normal.rvs(mean=[0.]*self.model.n, - # cov=(self.model.totalCovariance(nsig)), - cov=self.model.V, - size=self.ntoys ) ## get ntoys values - for theta in thetas : - if self.model.isLinear(): - lmbda = nsig + self.model.backgrounds + theta - else: - lmbda = nsig + self.model.A + theta + self.model.C*theta**2/self.model.B**2 - if self.model.isScalar( lmbda ): - lmbda = array([lmbda]) - for ctr,v in enumerate( lmbda ): - if v<=0.: lmbda[ctr]=1e-30 -# print ( "lmbda=",lmbda ) - poisson = self.model.observed*NP.log(lmbda) - lmbda - self.gammaln - # poisson = NP.exp(self.model.observed*NP.log(lmbda) - lmbda - self.model.backgrounds - self.gammaln) - vals.append( NP.exp ( sum(poisson) ) ) - #vals.append ( reduce(lambda x, y: x*y, poisson) ) - mean = NP.mean( vals ) - if nll: - if mean == 0.: - mean = 1e-100 - mean = - log ( mean ) - return mean - - - def profileLikelihood( self, nsig, nll ): - """ compute the profiled likelihood for nsig. - Warning: not normalized. - Returns profile likelihood and error code (0=no error) - """ - # compute the profiled (not normalized) likelihood of observing - # nsig signal events - theta_hat,_ = self.findThetaHat ( nsig ) - ret = self.probMV ( nll, theta_hat ) - - return ret - - def likelihood(self, nsig, marginalize=False, nll=False ): - """ compute likelihood for nsig, profiling the nuisances - :param marginalize: if true, marginalize, if false, profile - :param nll: return nll instead of likelihood - """ - nsig = self.model.convert(nsig) - self.ntot = self.model.backgrounds + nsig - if marginalize: - # p,err = self.profileLikelihood ( nsig, deltas ) - return self.marginalizedLikelihood(nsig, nll) - # print ( "p,l=",p,l,p/l ) - else: - return self.profileLikelihood(nsig, nll) - - def chi2(self, nsig, marginalize=False): - """ - Computes the chi2 for a given number of observed events nobs given - the predicted background nb, error on this background deltab, - expected number of signal events nsig and the relative error on - signal (deltas_rel). - :param marginalize: if true, marginalize, if false, profile - :param nsig: number of signal events - :return: chi2 (float) - - """ - nsig = self.model.convert(nsig) - - # Compute the likelhood for the null hypothesis (signal hypothesis) H0: - llhd = self.likelihood(nsig, marginalize=marginalize, nll=True) - - # Compute the maximum likelihood H1, which sits at nsig = nobs - nb - # (keeping the same % error on signal): - dn = self.model.observed-self.model.backgrounds - maxllhd = self.likelihood(dn, marginalize=marginalize, nll=True ) - - chi2=2*(llhd-maxllhd) - - if not NP.isfinite ( chi2 ): - logger.error("chi2 is not a finite number! %s,%s,%s" % \ - (chi2, llhd,maxllhd)) - # Return the test statistic -2log(H0/H1) - return chi2 - -class CLsComputer: - def __init__(self, ntoys=10000, cl=.95): - - """ - :param ntoys: number of toys when marginalizing - :param cl: desired quantile for limits - """ - self.ntoys = ntoys - self.cl = cl - - def ulSigma(self, model, marginalize=False, toys=None, expected=False ): - """ upper limit obtained from the defined Data (using the signal prediction - for each signal regio/dataset), by using - the q_mu test statistic from the CCGV paper (arXiv:1007.1727). - - :params marginalize: if true, marginalize nuisances, else profile them - :params toys: specify number of toys. Use default is none - :params expected: compute the expected value, not the observed. - :returns: upper limit on *production* xsec (efficiencies unfolded) - """ - if model.zeroSignal(): - """ only zeroes in efficiencies? cannot give a limit! """ - return None - if toys==None: - toys=self.ntoys - oldmodel = model - if expected: - model = copy.deepcopy(oldmodel) - #model.observed = model.backgrounds - for i,d in enumerate(model.backgrounds): - model.observed[i]=int(NP.round(d)) - computer = LikelihoodComputer(model, toys) - mu_hat = computer.findMuHat(model.signal_rel) - theta_hat0,_ = computer.findThetaHat(0*model.signal_rel) - sigma_mu = computer.getSigmaMu(model.signal_rel) - - aModel = copy.deepcopy(model) - aModel.observed = array([NP.round(x+y) for x,y in zip(model.backgrounds,theta_hat0)]) - #print ( "aModeldata=", aModel.observed ) - #aModel.observed = array ( [ round(x) for x in model.backgrounds ] ) - aModel.name = aModel.name + "A" - compA = LikelihoodComputer(aModel, toys) - ## compute - mu_hatA = compA.findMuHat(aModel.signal_rel) - if mu_hat < 0.: - mu_hat = 0. - nll0 = computer.likelihood(model.signals(mu_hat), - marginalize=marginalize, - nll=True) - if NP.isinf(nll0) and marginalize==False: - logger.warning("nll is infinite in profiling! we switch to marginalization, but only for this one!" ) - marginalize=True - nll0 = computer.likelihood(model.signals(mu_hat), - marginalize=True, - nll=True) - if NP.isinf(nll0): - logger.warning("marginalization didnt help either. switch back.") - marginalize=False - else: - logger.warning("marginalization worked.") - nll0A = compA.likelihood(aModel.signals(mu_hatA), - marginalize=marginalize, - nll=True) - - def root_func(mu): - ## the function to minimize. - nsig = model.signals(mu) - computer.ntot = model.backgrounds + nsig - nll = computer.likelihood(nsig, marginalize=marginalize, nll=True ) - nllA = compA.likelihood(nsig, marginalize=marginalize, nll=True ) - qmu = 2*( nll - nll0 ) - if qmu<0.: qmu=0. - sqmu = sqrt (qmu) - qA = 2*( nllA - nll0A ) - # print ( "mu: %s, qMu: %s, qA: %s nll0A: %s nllA: %s" % ( mu, qmu, qA, nll0A, nllA ) ) - if qA<0.: - qA=0. - sqA = sqrt(qA) - CLsb = 1. - stats.multivariate_normal.cdf(sqmu) - CLb = 0. - if qA >= qmu: - CLb = stats.multivariate_normal.cdf(sqA - sqmu) - else: - if qA == 0.: - CLsb = 1. - CLb = 1. - else: - CLsb = 1. - stats.multivariate_normal.cdf( (qmu + qA)/(2*sqA) ) - CLb = 1. - stats.multivariate_normal.cdf( (qmu - qA)/(2*sqA) ) - CLs = 0. - if CLb > 0.: - CLs = CLsb/CLb - root = CLs - 1. + self.cl - return root - - - a,b=1.5*mu_hat,2.5*mu_hat+2*sigma_mu - ctr=0 - while True: - while ( NP.sign ( root_func(a)* root_func(b) ) > -.5 ): - b=1.4*b ## widen bracket FIXME make a linear extrapolation! - a=a-(b-a)*.3 ## widen bracket - if a < 0.: a=0. - ctr+=1 - if ctr>20: ## but stop after 20 trials - if toys > 2000: - logger.error("cannot find brent bracket after 20 trials. a,b=%s(%s),%s(%s)" % ( root_func(a),a,root_func(b),b ) ) - return None - else: - logger.debug("cannot find brent bracket after 20 trials. but very low number of toys") - return self.ulSigma ( model, marginalize, 4*toys ) - try: - mu_lim = optimize.brentq ( root_func, a, b, rtol=1e-03, xtol=1e-06 ) - return mu_lim - except ValueError as e: ## it could still be that the signs arent opposite - # in that case, try again - pass - - """ - .. method:: computeCLs - :synopsis: Method that implements CLs calculation from simplified likelihoods as presented - in CMS-NOTE-2017-001. It basically consist in fixing mu to 1.0 instead of varying it - like it is done in the previous ulSigma method - - .. methodauthor:: Gael Alguero - - """ - - def computeCLs(self, model, marginalize=False, toys=None, expected=False ): - """ exclusion confidence level obtained from the defined Data (using the signal prediction - for each signal regio/dataset), by using - the q_mu test statistic from the CCGV paper (arXiv:1007.1727). - - :params marginalize: if true, marginalize nuisances, else profile them - :params toys: specify number of toys. Use default is none - :params expected: compute the expected value, not the observed. - :returns: exclusion confidence level (1-CLs) - """ - if model.zeroSignal(): - """ only zeroes in efficiencies? cannot give a limit! """ - return None - if toys==None: - toys=self.ntoys - oldmodel = model - if expected: - model = copy.deepcopy(oldmodel) - #model.observed = model.backgrounds - for i,d in enumerate(model.backgrounds): - model.observed[i]=int(NP.round(d)) - computer = LikelihoodComputer(model, toys) - mu_hat = computer.findMuHat(model.nsignal) - theta_hat0,_ = computer.findThetaHat(0*model.nsignal) - sigma_mu = computer.getSigmaMu(model.nsignal) - - aModel = copy.deepcopy(model) - aModel.observed = array([NP.round(x+y) for x,y in zip(model.backgrounds,theta_hat0)]) - #print ( "aModeldata=", aModel.observed ) - #aModel.observed = array ( [ round(x) for x in model.backgrounds ] ) - aModel.name = aModel.name + "A" - compA = LikelihoodComputer(aModel, toys) - ## compute - mu_hatA = compA.findMuHat(aModel.nsignal) - # if mu_hat < 0.: - # mu_hat = 0. - # -log L(mu_hat, theta_hat(mu_hat)) - nll0 = computer.likelihood(model.signals(mu_hat), - marginalize=marginalize, - nll=True) - if NP.isinf(nll0) and marginalize==False: - logger.warning("nll is infinite in profiling! we switch to marginalization, but only for this one!" ) - marginalize=True - nll0 = computer.likelihood(model.signals(mu_hat), - marginalize=True, - nll=True) - if NP.isinf(nll0): - logger.warning("marginalization didnt help either. switch back.") - marginalize=False - else: - logger.warning("marginalization worked.") - nll0A = compA.likelihood(aModel.signals(mu_hatA), - marginalize=marginalize, - nll=True) - - # nsig = model.signals(1.) - computer.ntot = model.backgrounds + model.nsignal - # -log L(mu = 1, theta(1)) - nll = computer.likelihood(model.nsignal, marginalize=marginalize, nll=True ) - nllA = compA.likelihood(model.nsignal, marginalize=marginalize, nll=True ) - qmu = 2*( nll - nll0 ) - if qmu<0.: qmu=0. - sqmu = sqrt (qmu) - qA = 2*( nllA - nll0A ) - # print ( "mu: %s, qMu: %s, qA: %s nll0A: %s nllA: %s" % ( mu, qmu, qA, nll0A, nllA ) ) - if qA<0.: - qA=0. - sqA = sqrt(qA) - if qA >= qmu: - CLsb = 1. - stats.multivariate_normal.cdf(sqmu) - CLb = stats.multivariate_normal.cdf(sqA - sqmu) - else: - if qA == 0.: - CLsb = 1. - CLb = 1. - else: - CLsb = 1. - stats.multivariate_normal.cdf( (qmu + qA)/(2*sqA) ) - CLb = 1. - stats.multivariate_normal.cdf( (qmu - qA)/(2*sqA) ) - # CLs = 0. - # if CLb > 0.: - CLs = CLsb/CLb if CLb > 0. else 1. - return 1 - CLs - - -if __name__ == "__main__": - C = [ 18774.2, -2866.97, -5807.3, -4460.52, -2777.25, -1572.97, -846.653, -442.531, - -2866.97, 496.273, 900.195, 667.591, 403.92, 222.614, 116.779, 59.5958, - -5807.3, 900.195, 1799.56, 1376.77, 854.448, 482.435, 258.92, 134.975, - -4460.52, 667.591, 1376.77, 1063.03, 664.527, 377.714, 203.967, 106.926, - -2777.25, 403.92, 854.448, 664.527, 417.837, 238.76, 129.55, 68.2075, - -1572.97, 222.614, 482.435, 377.714, 238.76, 137.151, 74.7665, 39.5247, - -846.653, 116.779, 258.92, 203.967, 129.55, 74.7665, 40.9423, 21.7285, - -442.531, 59.5958, 134.975, 106.926, 68.2075, 39.5247, 21.7285, 11.5732] - nsignal = [ x/100. for x in [47,29.4,21.1,14.3,9.4,7.1,4.7,4.3] ] - m=Data( observed=[1964,877,354,182,82,36,15,11], - backgrounds=[2006.4,836.4,350.,147.1,62.0,26.2,11.1,4.7], - covariance= C, -# third_moment = [ 0.1, 0.02, 0.1, 0.1, 0.003, 0.0001, 0.0002, 0.0005 ], - third_moment = [ 0. ] * 8, - nsignal = nsignal, - name="CMS-NOTE-2017-001 model" ) - ulComp = UpperLimitComputer(ntoys=500, cl=.95) - #uls = ulComp.ulSigma ( Data ( 15,17.5,3.2,0.00454755 ) ) - #print ( "uls=", uls ) - ul_old = 131.828*sum(nsignal) #With respect to the older refernece value one must normalize the xsec - print ( "old ul=", ul_old ) - ul = ulComp.ulSigma ( m ) - print ( "ul (marginalized)", ul ) - ul = ulComp.ulSigma ( m, marginalize=False ) - print ( "ul (profiled)", ul ) diff --git a/madanalysis/misc/statistical_models.py b/madanalysis/misc/statistical_models.py new file mode 100644 index 00000000..8663403b --- /dev/null +++ b/madanalysis/misc/statistical_models.py @@ -0,0 +1,144 @@ +import logging + +import spey +from numpy import isinf, isnan + +from .histfactory_reader import HF_Background, HF_Signal + +APRIORI = spey.ExpectationType.apriori +APOSTERIORI = spey.ExpectationType.aposteriori +OBSERVED = spey.ExpectationType.observed + +logger = logging.getLogger("MA5") + + +def initialise_statistical_models( + regiondata: dict, + regions: list[str], + xsection: float, + lumi: float, + simplified_model_config: dict = None, + full_statistical_model_config: dict = None, +) -> dict[str, dict[str, spey.StatisticalModel]]: + """ + initialise statistical models + + Args: + regiondata (``dict``): data per region + regions (``list[str]``): region list + xsection (``float``): cross section + lumi (``float``): luminosity + simplified_model_config (``dict``, default ``None``): simplified model configuration + full_statistical_model_config (``dict``, default ``None``): full statistical model configuration + + Returns: + ``dict[str, dict[str, spey.StatisticalModel]]``: + Statistical model dictionary + """ + uncorrelated_background = {} + simplified_likelihoods = {} + full_likelihoods = {} + + signal_yields_per_region = {} + + # Uncorrelated background + pdf_wrapper = spey.get_backend("default.uncorrelated_background") + for reg in regions: + signal_yields_per_region[reg] = ( + xsection * lumi * 1000.0 * regiondata[reg]["Nf"] / regiondata[reg]["N0"] + ) + uncorrelated_background[reg] = pdf_wrapper( + signal_yields=[signal_yields_per_region[reg]], + background_yields=[regiondata[reg]["nb"]], + data=[regiondata[reg]["nobs"]], + absolute_uncertainties=[regiondata[reg]["deltanb"]], + analysis=reg, + ) + + # Simplified likelihoods + if simplified_model_config is not None: + pdf_wrapper = spey.get_backend("default.correlated_background") + for cov_subset, item in simplified_model_config.items(): + cov_regions, covariance = item["cov_regions"], item["covariance"] + + observed, backgrounds, nsignal = [], [], [] + for reg in cov_regions: + nsignal.append(signal_yields_per_region[reg]) + backgrounds.append(regiondata[reg]["nb"]) + observed.append(regiondata[reg]["nobs"]) + + simplified_likelihoods[cov_subset] = pdf_wrapper( + signal_yields=nsignal, + background_yields=backgrounds, + data=observed, + covariance_matrix=covariance, + ) + + # Full likelihoods + if full_statistical_model_config is not None: + pdf_wrapper = spey.get_backend("pyhf") + for llhd_profile, config in full_statistical_model_config.items(): + background = HF_Background(config)(lumi) + signal = HF_Signal( + config, + regiondata, + xsection=xsection, + background=background, + )(lumi) + full_likelihoods[llhd_profile] = pdf_wrapper( + signal_patch=signal, background_only_model=background + ) + + return { + "uncorrelated_background": uncorrelated_background, + "simplified_likelihoods": simplified_likelihoods, + "full_likelihoods": full_likelihoods, + } + + +def compute_poi_upper_limits( + regiondata: dict, + stat_models: dict, + xsection: float, + is_extrapolated: bool, + record_to: str = None, +) -> dict: # pylint: disable=too-many-arguments + """ + Compute upper limit on cross section. + + Args: + regiondata (``dict``): data for each region + regions (``list[str]``): list of regions + xsection (``float``): cross section + lumi (``float``): luminosity + is_extrapolated (``bool``): extrapolated luminosity + record_to (``str``): record to a specific section in regiondata + + Returns: + ``dict``: + regiondata + """ + logger.debug("Computing upper limits...") + if record_to is not None: + if record_to not in regiondata: + regiondata[record_to] = {} + tags = ( + [[APRIORI], ["exp"]] + if is_extrapolated + else [[APOSTERIORI, OBSERVED], ["exp", "obs"]] + ) + + for tag, label in zip(*tags): + for reg, stat_model in stat_models.items(): + logger.debug("running %s for %s", reg, record_to) + s95 = stat_model.poi_upper_limit(expected=tag) * xsection + s95 = -1 if isinf(s95) or isnan(s95) else s95 + if record_to is None: + logger.debug("region %s s95%s = %.5f pb", reg, label, s95) + regiondata[reg][f"s95{label}"] = f"{s95:20.7f}" + else: + if reg not in regiondata[record_to]: + regiondata[record_to][reg] = {} + logger.debug("%s:: region %s s95%s = %.5f pb", record_to, reg, label, s95) + regiondata[record_to][reg][f"s95{label}"] = f"{s95:20.7f}" + return regiondata diff --git a/madanalysis/misc/theoretical_error_setup.py b/madanalysis/misc/theoretical_error_setup.py new file mode 100644 index 00000000..bbe63ca0 --- /dev/null +++ b/madanalysis/misc/theoretical_error_setup.py @@ -0,0 +1,57 @@ +from math import sqrt + + +def comb_sqr(*args, rnd: int = None) -> float: + """Combine squared values""" + val = sqrt(sum(x**2 for x in args)) + if rnd is not None: + return round(val, rnd) + return val + + +def error_dict_setup( + dataset, systematics: list[list[float]], linear_comb: bool +) -> dict[str, float]: + """ + Setup the error dictionary for a given dataset. + + Args: + dataset (``_type_``): dataset description + systematics (``list[list[float]]``): systematics description + + Returns: + ``dict[str, float]``: + error dictionary + """ + + def comb(*args, rnd=8): + if linear_comb: + return round(sum(args), rnd) + return comb_sqr(*args, rnd=rnd) + + err_dict = { + "scale_up": 0.0, + "scale_dn": 0.0, + "pdf_up": 0.0, + "pdf_dn": 0.0, + } + if dataset.scaleup is not None: + err_dict["scale_up"] = round(dataset.scaleup, 8) + err_dict["scale_dn"] = -round(dataset.scaledn, 8) + if dataset.pdfup is not None: + err_dict["pdf_up"] = round(dataset.pdfup, 8) + err_dict["pdf_dn"] = -round(dataset.pdfdn, 8) + err_dict.update( + { + "TH_up": comb(err_dict["scale_up"], err_dict["pdf_up"]), + "TH_dn": -comb(err_dict["scale_dn"], err_dict["pdf_dn"]), + } + ) + for idx, syst in enumerate(systematics): + err_dict.update( + { + f"sys{idx}_up": comb_sqr(err_dict["TH_up"], syst[0], rnd=8), + f"sys{idx}_dn": -comb_sqr(err_dict["TH_dn"], syst[1], rnd=8), + } + ) + return err_dict diff --git a/madanalysis/multiparticle/extraparticle.py b/madanalysis/multiparticle/extraparticle.py index 4f614a8d..ac91f099 100644 --- a/madanalysis/multiparticle/extraparticle.py +++ b/madanalysis/multiparticle/extraparticle.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/multiparticle/multiparticle.py b/madanalysis/multiparticle/multiparticle.py index 3267613e..82ae5c17 100644 --- a/madanalysis/multiparticle/multiparticle.py +++ b/madanalysis/multiparticle/multiparticle.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/multiparticle/multiparticle_collection.py b/madanalysis/multiparticle/multiparticle_collection.py index 93d9f265..e307bef3 100644 --- a/madanalysis/multiparticle/multiparticle_collection.py +++ b/madanalysis/multiparticle/multiparticle_collection.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/multiparticle/particle_combination.py b/madanalysis/multiparticle/particle_combination.py index d3eeccf7..a3db6f2b 100644 --- a/madanalysis/multiparticle/particle_combination.py +++ b/madanalysis/multiparticle/particle_combination.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/multiparticle/particle_object.py b/madanalysis/multiparticle/particle_object.py index 1759e930..347fd75f 100644 --- a/madanalysis/multiparticle/particle_object.py +++ b/madanalysis/multiparticle/particle_object.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/observable/observable_base.py b/madanalysis/observable/observable_base.py index 0f48529c..ee0603c0 100644 --- a/madanalysis/observable/observable_base.py +++ b/madanalysis/observable/observable_base.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/observable/observable_list.py b/madanalysis/observable/observable_list.py index 4708c4f0..fc70ec32 100644 --- a/madanalysis/observable/observable_list.py +++ b/madanalysis/observable/observable_list.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/observable/observable_manager.py b/madanalysis/observable/observable_manager.py index c077361d..3da1c866 100644 --- a/madanalysis/observable/observable_manager.py +++ b/madanalysis/observable/observable_manager.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/region/region.py b/madanalysis/region/region.py index 943f92d0..191e253b 100644 --- a/madanalysis/region/region.py +++ b/madanalysis/region/region.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/region/region_collection.py b/madanalysis/region/region_collection.py index dc1d4d56..6f8bf83c 100644 --- a/madanalysis/region/region_collection.py +++ b/madanalysis/region/region_collection.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/condition_connector.py b/madanalysis/selection/condition_connector.py index d3a70b82..13e06f99 100644 --- a/madanalysis/selection/condition_connector.py +++ b/madanalysis/selection/condition_connector.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/condition_sequence.py b/madanalysis/selection/condition_sequence.py index 58d17481..0ebc12f1 100644 --- a/madanalysis/selection/condition_sequence.py +++ b/madanalysis/selection/condition_sequence.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/condition_type.py b/madanalysis/selection/condition_type.py index 4f7ff16c..91ae797e 100644 --- a/madanalysis/selection/condition_type.py +++ b/madanalysis/selection/condition_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/cut.py b/madanalysis/selection/cut.py index 759154c9..34c67ecd 100644 --- a/madanalysis/selection/cut.py +++ b/madanalysis/selection/cut.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/histogram.py b/madanalysis/selection/histogram.py index 8c09b1a7..393bd165 100644 --- a/madanalysis/selection/histogram.py +++ b/madanalysis/selection/histogram.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/instance_name.py b/madanalysis/selection/instance_name.py index 4ac5aac1..ea78dc65 100644 --- a/madanalysis/selection/instance_name.py +++ b/madanalysis/selection/instance_name.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/selection/selection.py b/madanalysis/selection/selection.py index 81d7ccc3..05bb0776 100644 --- a/madanalysis/selection/selection.py +++ b/madanalysis/selection/selection.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/architecture_info.py b/madanalysis/system/architecture_info.py index 6106ad05..01b23ad8 100644 --- a/madanalysis/system/architecture_info.py +++ b/madanalysis/system/architecture_info.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -44,6 +44,7 @@ def __init__(self): self.has_zlib = False self.has_delphes = False self.has_delphesMA5tune = False + self.has_spey = False # Library to put before all the others? self.root_priority = False diff --git a/madanalysis/system/checkup.py b/madanalysis/system/checkup.py index b12c7e94..60af93d2 100644 --- a/madanalysis/system/checkup.py +++ b/madanalysis/system/checkup.py @@ -1,71 +1,79 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ from __future__ import absolute_import -from madanalysis.system.user_info import UserInfo -from madanalysis.system.config_checker import ConfigChecker -from madanalysis.system.detect_manager import DetectManager -from string_tools import StringTools -from shell_command import ShellCommand + import logging -import os, json +import os -class CheckUp(): +from string_tools import StringTools - def __init__(self,archi_info,session_info,debug,script): - self.user_info = UserInfo() - self.archi_info = archi_info - self.session_info = session_info - self.debug = debug - self.script = script - self.checker = DetectManager(self.archi_info, self.user_info, self.session_info, self.script, self.debug) - self.logger = logging.getLogger('MA5') +from madanalysis.system.config_checker import ConfigChecker +from madanalysis.system.detect_manager import DetectManager +from madanalysis.system.user_info import UserInfo +class CheckUp: + def __init__(self, archi_info, session_info, debug, script): + self.user_info = UserInfo() + self.archi_info = archi_info + self.session_info = session_info + self.debug = debug + self.script = script + self.checker = DetectManager( + self.archi_info, self.user_info, self.session_info, self.script, self.debug + ) + self.logger = logging.getLogger("MA5") + def CheckArchitecture(self): # Fill with Python info import sys - self.archi_info.python_version = sys.version.replace('\n','') + + self.archi_info.python_version = sys.version.replace("\n", "") # Fill with Platform info import platform + self.archi_info.platform = platform.system() - self.archi_info.release = platform.release() + self.archi_info.release = platform.release() # Fill with number of cores import multiprocessing + self.archi_info.ncores = multiprocessing.cpu_count() # Is Mac - platform_text= "Platform: "+self.archi_info.platform+" "+self.archi_info.release+" " - if self.archi_info.platform.lower() in ['darwin','mac','macosx']: + platform_text = ( + "Platform: " + self.archi_info.platform + " " + self.archi_info.release + " " + ) + if self.archi_info.platform.lower() in ["darwin", "mac", "macosx"]: self.archi_info.isMac = True - platform_text+='\x1b[32m'+'[MAC/OSX mode]'+'\x1b[0m' + platform_text += "\x1b[32m" + "[MAC/OSX mode]" + "\x1b[0m" else: self.archi_info.isMac = False - platform_text+='\x1b[32m'+'[Linux mode]'+'\x1b[0m' + platform_text += "\x1b[32m" + "[Linux mode]" + "\x1b[0m" self.logger.info(platform_text) # Info for debug mode @@ -73,206 +81,246 @@ def CheckArchitecture(self): # Machine general import platform + self.logger.debug("") self.logger.debug("Machine - Cross platform information") - self.logger.debug(StringTools.Left(" Machine type: ",28) + str(platform.machine())) - self.logger.debug(StringTools.Left(" Processor name: ",28) + str(platform.processor())) - self.logger.debug(StringTools.Left(" Platform: ",28) + str(platform.platform())) - self.logger.debug(StringTools.Left(" Platform release: ",28) + str(platform.release())) - self.logger.debug(StringTools.Left(" System: ",28) + str(platform.system())) - self.logger.debug(StringTools.Left(" Node: ",28) + str(platform.node())) - self.logger.debug(StringTools.Left(" Number of cores: ",28) + str(self.archi_info.ncores)) + self.logger.debug( + StringTools.Left(" Machine type: ", 28) + str(platform.machine()) + ) + self.logger.debug( + StringTools.Left(" Processor name: ", 28) + str(platform.processor()) + ) + self.logger.debug( + StringTools.Left(" Platform: ", 28) + str(platform.platform()) + ) + self.logger.debug( + StringTools.Left(" Platform release: ", 28) + str(platform.release()) + ) + self.logger.debug( + StringTools.Left(" System: ", 28) + str(platform.system()) + ) + self.logger.debug( + StringTools.Left(" Node: ", 28) + str(platform.node()) + ) + self.logger.debug( + StringTools.Left(" Number of cores: ", 28) + str(self.archi_info.ncores) + ) self.logger.debug("") # Machine OS self.logger.debug("Machine - OS-specific information") try: - tmp=platform.java_ver() + tmp = platform.java_ver() except: - tmp='' - self.logger.debug(StringTools.Left(" Java version: ",28) + str(tmp)) + tmp = "" + self.logger.debug(StringTools.Left(" Java version: ", 28) + str(tmp)) try: - tmp=platform.win32_ver() + tmp = platform.win32_ver() except: - tmp='' - self.logger.debug(StringTools.Left(" Windows version: ",28) + str(tmp)) + tmp = "" + self.logger.debug(StringTools.Left(" Windows version: ", 28) + str(tmp)) try: - tmp=platform.mac_ver() + tmp = platform.mac_ver() except: - tmp='' - self.logger.debug(StringTools.Left(" Mac Os version: ",28) + str(tmp)) + tmp = "" + self.logger.debug(StringTools.Left(" Mac Os version: ", 28) + str(tmp)) try: - tmp=platform.dist() + tmp = platform.dist() except: - tmp='' - self.logger.debug(StringTools.Left(" Unix distribution:",28) + str(platform.platform())) + tmp = "" + self.logger.debug( + StringTools.Left(" Unix distribution:", 28) + str(platform.platform()) + ) self.logger.debug("") return True - def CheckSessionInfo(self): # Fill with user name try: import getpass + self.session_info.username = getpass.getuser() except: - self.session_info.username = 'anonymous' + self.session_info.username = "anonymous" # Display user info if self.debug: - self.logger.debug('') + self.logger.debug("") self.logger.debug("User") - self.logger.debug(StringTools.Left(" User name:",28)+str(self.session_info.username)) + self.logger.debug( + StringTools.Left(" User name:", 28) + str(self.session_info.username) + ) import os - self.logger.debug(StringTools.Left(" User ID:",28)+str(os.getuid())) - self.logger.debug(StringTools.Left(" Expanding folder ~/:",28)+str(os.path.expanduser("~/"))) - for name in ["USER","USERNAME","LNAME","LOGNAME","HOME","HOMEDRIVE","posix","HOMEPATH"]: + + self.logger.debug(StringTools.Left(" User ID:", 28) + str(os.getuid())) + self.logger.debug( + StringTools.Left(" Expanding folder ~/:", 28) + + str(os.path.expanduser("~/")) + ) + for name in [ + "USER", + "USERNAME", + "LNAME", + "LOGNAME", + "HOME", + "HOMEDRIVE", + "posix", + "HOMEPATH", + ]: if name in os.environ: - tmp=os.environ[name] + tmp = os.environ[name] else: - tmp='' - self.logger.debug(StringTools.Left(" Variable $"+name+":",28)+ str(tmp)) - self.logger.debug('') + tmp = "" + self.logger.debug( + StringTools.Left(" Variable $" + name + ":", 28) + str(tmp) + ) + self.logger.debug("") # Web access self.logger.debug("Web access") if self.user_info.webaccess_veto: - self.session_info.has_web=False - self.logger.debug(' disable') + self.session_info.has_web = False + self.logger.debug(" disable") else: - self.session_info.has_web=True - self.logger.debug(' enable') - self.logger.debug('') - + self.session_info.has_web = True + self.logger.debug(" enable") + self.logger.debug("") + # Fill with tmp folder import os + self.logger.debug("Temporary folder") - tmpdir='' + tmpdir = "" # force by the user? - if self.user_info.tmp_dir!=None: - self.logger.debug(' Folder forced by the user: '+str(self.user_info.tmp_dir)) - tmpdir=os.path.normpath(self.user_info.tmp_dir) + if self.user_info.tmp_dir != None: + self.logger.debug( + " Folder forced by the user: " + str(self.user_info.tmp_dir) + ) + tmpdir = os.path.normpath(self.user_info.tmp_dir) if os.path.isdir(tmpdir): - self.logger.debug('-> found') + self.logger.debug("-> found") else: - self.logger.debug('-> not found') - self.logger.debug('Try to create the folder '+tmpdir+' ...') + self.logger.debug("-> not found") + self.logger.debug("Try to create the folder " + tmpdir + " ...") try: os.mkdir(tmpdir) except: - self.logger.debug('-> impossible to create it') - tmpdir='' + self.logger.debug("-> impossible to create it") + tmpdir = "" # environment variable - if tmpdir=='': - for name in ["TMPDIR","TMP","TEMP"]: + if tmpdir == "": + for name in ["TMPDIR", "TMP", "TEMP"]: if name in os.environ: - tmp=os.environ[name] + tmp = os.environ[name] else: - tmp='' - self.logger.debug(StringTools.Left(" Variable $"+name+":",28)+ str(tmp)) - if tmp!='' and tmpdir=='': - tmp=os.path.normpath(tmp) - self.logger.debug('Check if the folder '+tmp+' exists ...') + tmp = "" + self.logger.debug( + StringTools.Left(" Variable $" + name + ":", 28) + str(tmp) + ) + if tmp != "" and tmpdir == "": + tmp = os.path.normpath(tmp) + self.logger.debug("Check if the folder " + tmp + " exists ...") if os.path.isdir(tmp): - self.logger.debug('-> found') - tmpdir=tmp + self.logger.debug("-> found") + tmpdir = tmp else: - self.logger.debug('-> not found') - self.logger.debug('Try to create this folder ...') + self.logger.debug("-> not found") + self.logger.debug("Try to create this folder ...") try: os.mkdir(tmp) - self.logger.debug('-> ok') - tmpdir=tmp + self.logger.debug("-> ok") + tmpdir = tmp except: - self.logger.debug('-> impossible to create it') - + self.logger.debug("-> impossible to create it") + # /tmp/ + username - if tmpdir=='': - pathname = os.path.normpath('/tmp/'+self.session_info.username) - self.logger.debug('Check if the folder '+pathname+' exists ...') + if tmpdir == "": + pathname = os.path.normpath("/tmp/" + self.session_info.username) + self.logger.debug("Check if the folder " + pathname + " exists ...") if os.path.isdir(pathname): - self.logger.debug('-> found') - tmpdir=pathname + self.logger.debug("-> found") + tmpdir = pathname else: - self.logger.debug('-> not found') - self.logger.debug('Try to create the folder '+pathname+' ...') + self.logger.debug("-> not found") + self.logger.debug("Try to create the folder " + pathname + " ...") try: os.mkdir(pathname) - tmpdir=pathname + tmpdir = pathname except: - self.logger.debug('-> impossible to create it') + self.logger.debug("-> impossible to create it") - if tmpdir!='': + if tmpdir != "": self.session_info.tmpdir = tmpdir - self.logger.debug('temporary folder will be used for MA5: '+tmpdir) + self.logger.debug("temporary folder will be used for MA5: " + tmpdir) else: - self.logger.error('Impossible to create a tmp folder') + self.logger.error("Impossible to create a tmp folder") return False - self.logger.debug('') + self.logger.debug("") # Download dir self.logger.debug("Download dir") - tmpdir='' + tmpdir = "" # -> forced by the user? - if self.user_info.download_dir!=None: - self.logger.debug(' Folder forced by the user: '+str(self.user_info.download_dir)) - tmpdir=os.path.normpath(self.user_info.download_dir) + if self.user_info.download_dir != None: + self.logger.debug( + " Folder forced by the user: " + str(self.user_info.download_dir) + ) + tmpdir = os.path.normpath(self.user_info.download_dir) if os.path.isdir(tmpdir): - self.logger.debug('-> found') + self.logger.debug("-> found") else: - self.logger.debug('-> not found') - self.logger.debug('Try to create the folder '+tmpdir+' ...') + self.logger.debug("-> not found") + self.logger.debug("Try to create the folder " + tmpdir + " ...") try: os.mkdir(tmpdir) except: - self.logger.debug('-> impossible to create it') - tmpdir='' - + self.logger.debug("-> impossible to create it") + tmpdir = "" + # -> temporary folder + 'MA5_download' - if tmpdir=='': - pathname = os.path.normpath(self.session_info.tmpdir+'/MA5_downloads/') - self.logger.debug('Check if the folder '+pathname+' exists ...') + if tmpdir == "": + pathname = os.path.normpath(self.session_info.tmpdir + "/MA5_downloads/") + self.logger.debug("Check if the folder " + pathname + " exists ...") if os.path.isdir(pathname): - self.logger.debug('-> found') - tmpdir=pathname + self.logger.debug("-> found") + tmpdir = pathname else: - self.logger.debug('-> not found') - self.logger.debug('Try to create the folder '+pathname+' ...') + self.logger.debug("-> not found") + self.logger.debug("Try to create the folder " + pathname + " ...") try: os.mkdir(pathname) - tmpdir=pathname + tmpdir = pathname except: - self.logger.debug('-> impossible to create it') + self.logger.debug("-> impossible to create it") - if tmpdir!='': + if tmpdir != "": self.session_info.downloaddir = tmpdir - self.logger.debug('download folder will be used for MA5: '+tmpdir) + self.logger.debug("download folder will be used for MA5: " + tmpdir) else: - self.logger.error('Impossible to create a download folder') + self.logger.error("Impossible to create a download folder") return False - self.logger.debug('') - + self.logger.debug("") # Fill with editor program self.logger.debug("Text editor") self.logger.debug("Look for the global variable $EDITOR ...") - if 'EDITOR' in os.environ: - self.session_info.editor = os.environ['EDITOR'] + if "EDITOR" in os.environ: + self.session_info.editor = os.environ["EDITOR"] self.session_info.editor = self.session_info.editor.lstrip() self.session_info.editor = self.session_info.editor.rstrip() - self.logger.debug("-> variable $EDITOR found : "+self.session_info.editor) + self.logger.debug("-> variable $EDITOR found : " + self.session_info.editor) else: - self.session_info.editor = 'vi' - self.logger.debug('-> variable not found. VI editor is set by default.') - if self.session_info.editor=='': - self.session_info.editor = 'vi' - self.logger.debug('-> variable empty. VI editor is set by default.') - self.logger.debug('') + self.session_info.editor = "vi" + self.logger.debug("-> variable not found. VI editor is set by default.") + if self.session_info.editor == "": + self.session_info.editor = "vi" + self.logger.debug("-> variable empty. VI editor is set by default.") + self.logger.debug("") # Ok return True @@ -280,7 +328,7 @@ def CheckSessionInfo(self): def ReadUserOptions(self): # Reading user options self.logger.info("Reading user settings ...") - filename = self.archi_info.ma5dir+'/madanalysis/input/installation_options.dat' + filename = self.archi_info.ma5dir + "/madanalysis/input/installation_options.dat" if not self.user_info.ReadUserOptions(filename): return False return True @@ -289,11 +337,11 @@ def CheckMandatoryPackages(self): # Mandatory packages self.logger.info("Checking mandatory packages:") - if not self.checker.Execute('python'): + if not self.checker.Execute("python"): return False - if not self.checker.Execute('gpp'): + if not self.checker.Execute("gpp"): return False - if not self.checker.Execute('make'): + if not self.checker.Execute("make"): return False return True @@ -301,142 +349,145 @@ def CheckOptionalGraphicalPackages(self): # Optional packages self.logger.info("Checking optional packages devoted to histogramming:") - if not self.checker.Execute('root_graphical'): + if not self.checker.Execute("root_graphical"): return False - if not self.checker.Execute('matplotlib'): + if not self.checker.Execute("matplotlib"): return False - if not self.checker.Execute('gnuplot'): + if not self.checker.Execute("gnuplot"): return False - if not self.checker.Execute('pdflatex'): + if not self.checker.Execute("pdflatex"): return False - if not self.checker.Execute('latex'): + if not self.checker.Execute("latex"): return False return True def CheckOptionalProcessingPackages(self): # Optional packages self.logger.info("Checking optional packages devoted to data processing:") - checker2 = ConfigChecker(self.archi_info, self.user_info, self.session_info, self.script, self.debug) + checker2 = ConfigChecker( + self.archi_info, self.user_info, self.session_info, self.script, self.debug + ) - if not self.checker.Execute('zlib'): + if not self.checker.Execute("zlib"): return False - if not self.checker.Execute('fastjet'): + if not self.checker.Execute("fastjet"): return False - if not self.checker.Execute('root'): + if not self.checker.Execute("root"): return False - self.archi_info.has_delphes = checker2.checkDelphes() - self.archi_info.has_delphesMA5tune = checker2.checkDelphesMA5tune() + self.archi_info.has_delphes = checker2.checkDelphes() + self.archi_info.has_delphesMA5tune = checker2.checkDelphesMA5tune() return True - def CheckOptionalReinterpretationPackages(self): # Optional packages self.logger.info("Checking optional packages devoted to reinterpretation:") - - if not self.checker.Execute('scipy'): - return False - if not self.checker.Execute('pad'): - return False - if not self.checker.Execute('padma5'): - return False - if not self.checker.Execute('padsfs'): - return False - if not self.checker.Execute('pyhf'): - return False - if not self.checker.Execute('simplify'): - return False + for package in ["scipy", "spey", "pyhf", "pad", "padma5", "padsfs", "simplify"]: + if not self.checker.Execute(package): + return False return True - - def CreateSymLink(self,source,destination): + def CreateSymLink(self, source, destination): # Is it a good source - if source=='': - self.logger.error('source empty for creating symbolic link: '+source) + if source == "": + self.logger.error("source empty for creating symbolic link: " + source) # Is there a previous link? if os.path.islink(destination): try: os.remove(destination) except: - self.logger.error('impossible to remove the file '+destination) + self.logger.error("impossible to remove the file " + destination) return False # Does the destination exist but it is a file or a folder elif os.path.isfile(destination): - self.logger.error('creating symbolic link: destination already exist and it is file') + self.logger.error( + "creating symbolic link: destination already exist and it is file" + ) return False elif os.path.isdir(destination): - self.logger.error('creating symbolic link: destination already exist and it is folder') + self.logger.error( + "creating symbolic link: destination already exist and it is folder" + ) return False # Creating a link - logging.getLogger('MA5').debug('Creating symbolic link from '+source) - logging.getLogger('MA5').debug(' to '+destination+' ...') + logging.getLogger("MA5").debug("Creating symbolic link from " + source) + logging.getLogger("MA5").debug( + " to " + destination + " ..." + ) try: - os.symlink(source,destination) + os.symlink(source, destination) except: - self.logger.error('impossible to create the link '+destination) + self.logger.error("impossible to create the link " + destination) return False return True - def SetFolder(self): # Reset the pieces of environment variables - self.archi_info.toPATH1=[] # First in PATH variable - self.archi_info.toPATH2=[] # Last in PATH variable - self.archi_info.toLDPATH1=[] # First in (DY)LD_LIBRARY_PATH - self.archi_info.toLDPATH2=[] # Last in (DY)LD_LIBRARY_PATH + self.archi_info.toPATH1 = [] # First in PATH variable + self.archi_info.toPATH2 = [] # Last in PATH variable + self.archi_info.toLDPATH1 = [] # First in (DY)LD_LIBRARY_PATH + self.archi_info.toLDPATH2 = [] # Last in (DY)LD_LIBRARY_PATH # Creating folder Lib if not found - folder=os.path.normpath(self.archi_info.ma5dir+'/tools/SampleAnalyzer/Lib/') + folder = os.path.normpath(self.archi_info.ma5dir + "/tools/SampleAnalyzer/Lib/") if not os.path.isdir(folder): try: os.mkdir(folder) except: - self.logger.error('impossible to create the folder '+folder) + self.logger.error("impossible to create the folder " + folder) self.archi_info.toLDPATH1.append(folder) - + # Creating folder ExternalSymLink if not found - folder=os.path.normpath(self.archi_info.ma5dir+'/tools/SampleAnalyzer/ExternalSymLink') + folder = os.path.normpath( + self.archi_info.ma5dir + "/tools/SampleAnalyzer/ExternalSymLink" + ) if not os.path.isdir(folder): try: os.mkdir(folder) except: - self.logger.error('impossible to create the folder '+folder) + self.logger.error("impossible to create the folder " + folder) # Creating folder ExternalSymLink/Lib if not found - folder=os.path.normpath(self.archi_info.ma5dir+'/tools/SampleAnalyzer/ExternalSymLink/Lib') + folder = os.path.normpath( + self.archi_info.ma5dir + "/tools/SampleAnalyzer/ExternalSymLink/Lib" + ) if not os.path.isdir(folder): try: os.mkdir(folder) except: - self.logger.error('impossible to create the folder '+folder) + self.logger.error("impossible to create the folder " + folder) self.archi_info.toLDPATH1.append(folder) - folderSymLinkLib=folder + folderSymLinkLib = folder # Creating folder ExternalSymLink/Bin if not found - folder=os.path.normpath(self.archi_info.ma5dir+'/tools/SampleAnalyzer/ExternalSymLink/Bin') + folder = os.path.normpath( + self.archi_info.ma5dir + "/tools/SampleAnalyzer/ExternalSymLink/Bin" + ) if not os.path.isdir(folder): try: os.mkdir(folder) except: - self.logger.error('impossible to create the folder '+folder) + self.logger.error("impossible to create the folder " + folder) self.archi_info.toPATH1.append(folder) - folderSymLinkBin=folder + folderSymLinkBin = folder # ROOT if self.archi_info.has_root: for source in self.archi_info.root_original_bins: - destination=os.path.normpath(folderSymLinkBin+'/'+source.split('/')[-1]) - self.CreateSymLink(source,destination) + destination = os.path.normpath( + folderSymLinkBin + "/" + source.split("/")[-1] + ) + self.CreateSymLink(source, destination) - if 1: #self.archi_info.root_priority: + if 1: # self.archi_info.root_priority: self.archi_info.toLDPATH1.append(self.archi_info.root_lib_path) self.archi_info.toPATH1.append(self.archi_info.root_bin_path) else: @@ -446,10 +497,12 @@ def SetFolder(self): # FASTJET if self.archi_info.has_fastjet: for source in self.archi_info.fastjet_original_bins: - destination=os.path.normpath(folderSymLinkBin+'/'+source.split('/')[-1]) - self.CreateSymLink(source,destination) + destination = os.path.normpath( + folderSymLinkBin + "/" + source.split("/")[-1] + ) + self.CreateSymLink(source, destination) - if 1: #self.archi_info.fastjet_priority: + if 1: # self.archi_info.fastjet_priority: self.archi_info.toPATH1.append(self.archi_info.fastjet_bin_path) for path in self.archi_info.fastjet_lib_paths: self.archi_info.toLDPATH1.append(path) @@ -461,122 +514,130 @@ def SetFolder(self): # ZLIB if self.archi_info.has_zlib: for source in self.archi_info.zlib_original_libs: - destination=os.path.normpath(folderSymLinkLib+'/'+source.split('/')[-1]) - self.CreateSymLink(source,destination) + destination = os.path.normpath( + folderSymLinkLib + "/" + source.split("/")[-1] + ) + self.CreateSymLink(source, destination) -# if self.archi_info.zlib_priority: -# self.archi_info.toLDPATH1.append(self.archi_info.zlib_lib_path) -# else: -# self.archi_info.toLDPATH2.append(self.archi_info.zlib_lib_path) + # if self.archi_info.zlib_priority: + # self.archi_info.toLDPATH1.append(self.archi_info.zlib_lib_path) + # else: + # self.archi_info.toLDPATH2.append(self.archi_info.zlib_lib_path) # DELPHES if self.archi_info.has_delphes: for source in self.archi_info.delphes_original_libs: - destination=os.path.normpath(folderSymLinkLib+'/'+source.split('/')[-1]) - self.CreateSymLink(source,destination) - -# if self.archi_info.delphes_priority: -# for path in self.archi_info.delphes_lib_paths: -# self.archi_info.toLDPATH1.append(path) -# else: -# for path in self.archi_info.delphes_lib_paths: -# self.archi_info.toLDPATH2.append(path) + destination = os.path.normpath( + folderSymLinkLib + "/" + source.split("/")[-1] + ) + self.CreateSymLink(source, destination) + + # if self.archi_info.delphes_priority: + # for path in self.archi_info.delphes_lib_paths: + # self.archi_info.toLDPATH1.append(path) + # else: + # for path in self.archi_info.delphes_lib_paths: + # self.archi_info.toLDPATH2.append(path) # DELPHES MA5tune if self.archi_info.has_delphesMA5tune: for source in self.archi_info.delphesMA5tune_original_libs: - destination=os.path.normpath(folderSymLinkLib+'/'+source.split('/')[-1]) - self.CreateSymLink(source,destination) -# if self.archi_info.delphesMA5tune_priority: -# for path in self.archi_info.delphesMA5tune_lib_paths: -# self.archi_info.toLDPATH1.append(path) -# else: -# for path in self.archi_info.delphesMA5tune_lib_paths: -# self.archi_info.toLDPATH2.append(path) - + destination = os.path.normpath( + folderSymLinkLib + "/" + source.split("/")[-1] + ) + self.CreateSymLink(source, destination) + # if self.archi_info.delphesMA5tune_priority: + # for path in self.archi_info.delphesMA5tune_lib_paths: + # self.archi_info.toLDPATH1.append(path) + # else: + # for path in self.archi_info.delphesMA5tune_lib_paths: + # self.archi_info.toLDPATH2.append(path) # Setting environment variables - self.logger.debug('-------- BEGIN: set environment variables --------') + self.logger.debug("-------- BEGIN: set environment variables --------") # - PATH - if 'PATH' not in os.environ: - os.environ['PATH'] = '' - self.logger.debug('before PATH='+str(os.environ['PATH'])) - self.logger.debug('--------') - os.environ['PATH'] = ':'.join(self.archi_info.toPATH1) + ":" + \ - os.environ['PATH'] + ":" + \ - ':'.join(self.archi_info.toPATH2) - self.logger.debug('after PATH='+str(os.environ['PATH'])) - self.logger.debug('--------') - - # - LD_LIBRARY_PATH - if 'LD_LIBRARY_PATH' not in os.environ: - os.environ['LD_LIBRARY_PATH'] = '' - self.logger.debug('before LD_LIBRARY_PATH='+str(os.environ['LD_LIBRARY_PATH'])) - self.logger.debug('--------') - os.environ['LD_LIBRARY_PATH'] = ':'.join(self.archi_info.toLDPATH1) + ":" + \ - os.environ['LD_LIBRARY_PATH'] + ":" + \ - ':'.join(self.archi_info.toLDPATH2) - self.logger.debug('after LD_LIBRARY_PATH='+str(os.environ['LD_LIBRARY_PATH'])) - self.logger.debug('--------') - - # - DYLD_LIBRARY_PATH - if 'DYLD_LIBRARY_PATH' not in os.environ: - os.environ['DYLD_LIBRARY_PATH'] = '' - self.logger.debug('before DYLD_LIBRARY_PATH='+str(os.environ['DYLD_LIBRARY_PATH'])) - self.logger.debug('--------') + if "PATH" not in os.environ: + os.environ["PATH"] = "" + self.logger.debug("before PATH=" + str(os.environ["PATH"])) + self.logger.debug("--------") + os.environ["PATH"] = ( + ":".join(self.archi_info.toPATH1) + + ":" + + os.environ["PATH"] + + ":" + + ":".join(self.archi_info.toPATH2) + ) + self.logger.debug("after PATH=" + str(os.environ["PATH"])) + self.logger.debug("--------") + + # - LD_LIBRARY_PATH + if "LD_LIBRARY_PATH" not in os.environ: + os.environ["LD_LIBRARY_PATH"] = "" + self.logger.debug("before LD_LIBRARY_PATH=" + str(os.environ["LD_LIBRARY_PATH"])) + self.logger.debug("--------") + os.environ["LD_LIBRARY_PATH"] = ( + ":".join(self.archi_info.toLDPATH1) + + ":" + + os.environ["LD_LIBRARY_PATH"] + + ":" + + ":".join(self.archi_info.toLDPATH2) + ) + self.logger.debug("after LD_LIBRARY_PATH=" + str(os.environ["LD_LIBRARY_PATH"])) + self.logger.debug("--------") + + # - DYLD_LIBRARY_PATH + if "DYLD_LIBRARY_PATH" not in os.environ: + os.environ["DYLD_LIBRARY_PATH"] = "" + self.logger.debug( + "before DYLD_LIBRARY_PATH=" + str(os.environ["DYLD_LIBRARY_PATH"]) + ) + self.logger.debug("--------") if self.archi_info.isMac: - os.environ['DYLD_LIBRARY_PATH'] = ':'.join(self.archi_info.toLDPATH1) + ":" + \ - os.environ['DYLD_LIBRARY_PATH'] + ":" + \ - ':'.join(self.archi_info.toLDPATH2) - self.logger.debug('after DYLD_LIBRARY_PATH='+str(os.environ['DYLD_LIBRARY_PATH'])) - self.logger.debug('--------') + os.environ["DYLD_LIBRARY_PATH"] = ( + ":".join(self.archi_info.toLDPATH1) + + ":" + + os.environ["DYLD_LIBRARY_PATH"] + + ":" + + ":".join(self.archi_info.toLDPATH2) + ) + self.logger.debug( + "after DYLD_LIBRARY_PATH=" + str(os.environ["DYLD_LIBRARY_PATH"]) + ) + self.logger.debug("--------") # ROOT INCLUDE PATH if self.archi_info.has_delphes: - os.environ['ROOT_INCLUDE_PATH']=os.path.join(self.archi_info.ma5dir,'tools','delphes','external'); + os.environ["ROOT_INCLUDE_PATH"] = os.path.join( + self.archi_info.ma5dir, "tools", "delphes", "external" + ) if self.archi_info.has_delphesMA5tune: - os.environ['ROOT_INCLUDE_PATH']=os.path.join(self.archi_info.ma5dir,'tools','delphesMA5tune','external'); - + os.environ["ROOT_INCLUDE_PATH"] = os.path.join( + self.archi_info.ma5dir, "tools", "delphesMA5tune", "external" + ) - self.logger.debug('-------- END: set environment variables --------') + self.logger.debug("-------- END: set environment variables --------") return True - def check_updates(self): - github_repo = "https://api.github.com/repos/MadAnalysis/madanalysis5/releases/latest" - theCommands=['curl', '-s', github_repo] - ok, out = ShellCommand.ExecuteWithLog( - theCommands, os.path.normpath(self.archi_info.ma5dir + "/.github_meta.log"), ".", silent=True - ) - if not ok: - self.logger.debug("Unable to retrieve information from GitHub...") - self.logger.debug(f"see the log file in : " - f"{os.path.normpath(self.archi_info.ma5dir + '/.github_meta.log')}") - return True - - meta = json.loads(out) - - latest_version = [int(x) for x in meta['tag_name'][1:].split(".")] - current_version = [int(x) for x in self.archi_info.ma5_version.split(".")] - - def compare_versions(version1, version2): - if version1 == version2: - return False - if version1[0] > version2[0]: - return True - elif version1[0] == version2[0] and version1[1] > version2[1]: - return True - elif version1[0] == version2[0] and version1[1] == version2[1] and version1[2] > version2[2]: - return True - else: - return False - - if compare_versions(latest_version, current_version): - self.logger.warning(f"A new version of MadAnalysis 5 is available ({meta['tag_name']}).") - self.logger.warning(f"The latest version can be downloaded from : ") - self.logger.warning(f"{meta['html_url']}") - elif compare_versions(current_version, latest_version): - self.logger.warning(f"A non stable version of MadAnalysis 5 is in use (v{self.archi_info.ma5_version}).") - self.logger.warning(f"The latest stable version can be downloaded from :") - self.logger.warning(f"{meta['html_url']}") + def check_updates(self) -> None: + try: + import requests + from semantic_version import Version + + response = requests.get( + "https://api.github.com/repos/MadAnalysis/madanalysis5/releases/latest", + timeout=1, + ) + response.encoding = "utf-8" + info = response.json() + github_version = info["name"][1:] + if Version(github_version) > Version(self.archi_info.ma5_version): + self.logger.warning( + f"A new version of MadAnalysis 5 is available ({github_version})." + ) + self.logger.warning(f"The latest version can be downloaded from : ") + self.logger.warning(f"{info['html_url']}") + except (requests.exceptions.ConnectionError, ImportError) as e: + self.logger.debug("Cannot check updates...") + pass diff --git a/madanalysis/system/config_checker.py b/madanalysis/system/config_checker.py index a166aa07..f6da0727 100644 --- a/madanalysis/system/config_checker.py +++ b/madanalysis/system/config_checker.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_dvipdf.py b/madanalysis/system/detect_dvipdf.py index 4311baf2..fdadad0f 100644 --- a/madanalysis/system/detect_dvipdf.py +++ b/madanalysis/system/detect_dvipdf.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_fastjet.py b/madanalysis/system/detect_fastjet.py index 1ed907a3..5991d4ac 100644 --- a/madanalysis/system/detect_fastjet.py +++ b/madanalysis/system/detect_fastjet.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_gnuplot.py b/madanalysis/system/detect_gnuplot.py index 1b1aff78..1379fe04 100644 --- a/madanalysis/system/detect_gnuplot.py +++ b/madanalysis/system/detect_gnuplot.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_gpp.py b/madanalysis/system/detect_gpp.py index abae6cb5..fa7b55ea 100644 --- a/madanalysis/system/detect_gpp.py +++ b/madanalysis/system/detect_gpp.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_latex.py b/madanalysis/system/detect_latex.py index b5db4177..99174620 100644 --- a/madanalysis/system/detect_latex.py +++ b/madanalysis/system/detect_latex.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_make.py b/madanalysis/system/detect_make.py index 7dbf8dbb..cffb9367 100644 --- a/madanalysis/system/detect_make.py +++ b/madanalysis/system/detect_make.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_manager.py b/madanalysis/system/detect_manager.py index 6b1edd9a..68811819 100644 --- a/madanalysis/system/detect_manager.py +++ b/madanalysis/system/detect_manager.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -111,6 +111,9 @@ def Execute(self, rawpackage): elif package=='pyhf': from madanalysis.system.detect_pyhf import Detectpyhf checker=Detectpyhf(self.archi_info, self.user_info, self.session_info, self.debug) + elif package=='spey': + from madanalysis.system.detect_spey import DetectSpey + checker=DetectSpey(self.archi_info, self.user_info, self.session_info, self.debug) elif package=='simplify': from madanalysis.system.detect_simplify import DetectSimplify checker=DetectSimplify(self.archi_info, self.user_info, self.session_info, self.debug) diff --git a/madanalysis/system/detect_matplotlib.py b/madanalysis/system/detect_matplotlib.py index 07e40222..83c4bcb7 100644 --- a/madanalysis/system/detect_matplotlib.py +++ b/madanalysis/system/detect_matplotlib.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_pad.py b/madanalysis/system/detect_pad.py index e49d63e6..90d8dd94 100644 --- a/madanalysis/system/detect_pad.py +++ b/madanalysis/system/detect_pad.py @@ -1,56 +1,54 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ -from __future__ import absolute_import -from shell_command import ShellCommand +from __future__ import absolute_import +from shell_command import ShellCommand from madanalysis.enumeration.detect_status_type import DetectStatusType -from madanalysis.system.config_checker import ConfigChecker +from madanalysis.system.config_checker import ConfigChecker import logging, os class DetectPAD: - - def __init__(self,archi_info, user_info, session_info, debug, padtype=''): - self.archi_info = archi_info - self.user_info = user_info + def __init__(self, archi_info, user_info, session_info, debug, padtype=""): + self.archi_info = archi_info + self.user_info = user_info self.session_info = session_info - self.debug = debug - self.ma5tune = (padtype=="ma5") - self.sfs = (padtype=="sfs") + self.debug = debug + self.ma5tune = padtype == "ma5" + self.sfs = padtype == "sfs" if self.ma5tune: - self.name = 'PADForMA5tune' + self.name = "PADForMA5tune" elif self.sfs: - self.name = 'PADForSFS' + self.name = "PADForSFS" else: - self.name = 'PAD' - self.mandatory = False - self.force = False - self.build_file = '' - self.build_path = '' - self.version = '' - self.logger = logging.getLogger('MA5') - + self.name = "PAD" + self.mandatory = False + self.force = False + self.build_file = "" + self.build_path = "" + self.version = "" + self.logger = logging.getLogger("MA5") def IsItVetoed(self): if self.ma5tune: @@ -75,9 +73,10 @@ def IsItVetoed(self): self.logger.debug("no user veto") return False - def AreDependenciesInstalled(self): - checker = ConfigChecker(self.archi_info, self.user_info, self.session_info, False, False) + checker = ConfigChecker( + self.archi_info, self.user_info, self.session_info, False, False + ) if self.ma5tune: if not checker.checkDelphesMA5tune(True): self.logger.debug("dependency 'DelphesMA5tune' is not installed") @@ -89,11 +88,11 @@ def AreDependenciesInstalled(self): return True def ManualDetection(self): - msg = '' + msg = "" if self.ma5tune: # User setting - if self.user_info.padma5_build_path==None: + if self.user_info.padma5_build_path == None: return DetectStatusType.UNFOUND, msg self.logger.debug("User setting: PADForMA5Tune build path is specified.") @@ -103,7 +102,10 @@ def ManualDetection(self): if self.sfs: # User setting - if self.user_info.padsfs_build_path==None or not self.archi_info.has_fastjet: + if ( + self.user_info.padsfs_build_path == None + or not self.archi_info.has_fastjet + ): return DetectStatusType.UNFOUND, msg self.logger.debug("User setting: PADForSFS build path is specified.") @@ -113,7 +115,7 @@ def ManualDetection(self): else: # User setting - if self.user_info.pad_build_path==None: + if self.user_info.pad_build_path == None: return DetectStatusType.UNFOUND, msg self.logger.debug("User setting: PAD build path is specified.") @@ -121,76 +123,90 @@ def ManualDetection(self): # Folder name folder = os.path.normpath(self.user_info.pad_build_path) - filename = folder+'/MadAnalysis5job' + filename = folder + "/MadAnalysis5job" # Detection of the PAD exectuable - self.logger.debug("Detecting MadAnalysis5job in the path specified by the user ...") + self.logger.debug( + "Detecting MadAnalysis5job in the path specified by the user ..." + ) if not os.path.isfile(filename) and not self.sfs: - logging.getLogger('MA5').debug('-> not found') - msg = "MadAnalysis5job program is not found in folder: "+folder+"\n" - msg += "Please check that "+self.name+" is properly installed." + logging.getLogger("MA5").debug("-> not found") + msg = "MadAnalysis5job program is not found in folder: " + folder + "\n" + msg += "Please check that " + self.name + " is properly installed." return DetectStatusType.UNFOUND, msg - self.build_file=filename - self.build_path=folder + self.build_file = filename + self.build_path = folder - self.logger.debug("MadAnalysis5job program found in: "+self.build_path) + self.logger.debug("MadAnalysis5job program found in: " + self.build_path) # Ok return DetectStatusType.FOUND, msg - def ToolsDetection(self): - msg = '' + msg = "" + if not self.archi_info.has_spey: + msg = ( + "Spey is not installed. Please install it before using " + self.name + "." + ) + return DetectStatusType.UNFOUND, msg if self.ma5tune: - thefolder = 'PADForMA5tune' + thefolder = "PADForMA5tune" elif self.sfs: - thefolder = 'PADForSFS' + thefolder = "PADForSFS" if not self.archi_info.has_fastjet: return DetectStatusType.UNFOUND, msg else: - thefolder = 'PAD' + thefolder = "PAD" - filename = os.path.normpath(self.archi_info.ma5dir+'/tools/'+thefolder+'/Build/MadAnalysis5job') + filename = os.path.normpath( + self.archi_info.ma5dir + "/tools/" + thefolder + "/Build/MadAnalysis5job" + ) if self.sfs: - filename = os.path.normpath(self.archi_info.ma5dir+'/tools/'+thefolder+'/Build/SampleAnalyzer/User/Analyzer/analysisList.h') - - self.logger.debug("Look for "+self.name+" in the folder here :"+filename+" ...") + filename = os.path.normpath( + self.archi_info.ma5dir + + "/tools/" + + thefolder + + "/Build/SampleAnalyzer/User/Analyzer/analysisList.h" + ) + + self.logger.debug( + "Look for " + self.name + " in the folder here :" + filename + " ..." + ) if os.path.isfile(filename): self.logger.debug("-> found") - self.build_file=filename - self.build_path=os.path.dirname(self.build_file) + self.build_file = filename + self.build_path = os.path.dirname(self.build_file) else: self.logger.debug("-> not found") return DetectStatusType.UNFOUND, msg return DetectStatusType.FOUND, msg - def ExtractInfo(self): if self.sfs: return True - theCommands = [self.build_file,'--info'] - ok, out, err = ShellCommand.ExecuteWithCapture(theCommands,'./') + theCommands = [self.build_file, "--info"] + ok, out, err = ShellCommand.ExecuteWithCapture(theCommands, "./") if not ok: self.logger.debug("->ERROR: MadAnalyis5job program does not work properly.") self.logger.debug(str(out)) self.logger.debug(str(err)) return False - lines=out.split('\n') + lines = out.split("\n") ok = False nbAnalysis = 0 for line in lines: - line=line.lstrip() - line=line.rstrip() - if line.startswith('BEGIN '): + line = line.lstrip() + line = line.rstrip() + if line.startswith("BEGIN "): self.logger.debug(" MA5 stamp found!") ok = True continue if ok: nbAnalysis += 1 - if line.endswith('END '): + if line.endswith("END "): break if self.debug: self.logger.debug(" number of recast analyses: " + str(nbAnalysis)) @@ -198,23 +214,20 @@ def ExtractInfo(self): # Ok return ok - def SaveInfo(self): # archi_info if self.ma5tune: - self.session_info.has_padma5 = True - self.session_info.padma5_build_path = self.build_path - self.session_info.padma5_original_bins = [self.build_file] + self.session_info.has_padma5 = True + self.session_info.padma5_build_path = self.build_path + self.session_info.padma5_original_bins = [self.build_file] elif self.sfs: - self.session_info.has_padsfs = True - self.session_info.padsfs_build_path = self.build_path - self.session_info.padsfs_original_bins = [self.build_file] + self.session_info.has_padsfs = True + self.session_info.padsfs_build_path = self.build_path + self.session_info.padsfs_original_bins = [self.build_file] else: - self.session_info.has_pad = True - self.session_info.pad_build_path = self.build_path - self.session_info.pad_original_bins = [self.build_file] - + self.session_info.has_pad = True + self.session_info.pad_build_path = self.build_path + self.session_info.pad_original_bins = [self.build_file] + # Ok return True - - diff --git a/madanalysis/system/detect_pdflatex.py b/madanalysis/system/detect_pdflatex.py index 2a2e8f9e..11025cc7 100644 --- a/madanalysis/system/detect_pdflatex.py +++ b/madanalysis/system/detect_pdflatex.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_pyhf.py b/madanalysis/system/detect_pyhf.py index 8247e426..a4e56d29 100644 --- a/madanalysis/system/detect_pyhf.py +++ b/madanalysis/system/detect_pyhf.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_python.py b/madanalysis/system/detect_python.py index d3e7bd85..251a1be0 100644 --- a/madanalysis/system/detect_python.py +++ b/madanalysis/system/detect_python.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. @@ -66,16 +66,16 @@ def ExtractInfo(self): # Which python if self.debug: self.logger.debug(" sys.executable: " + str(sys.executable)) - result = ShellCommand.Which('python',all=False,mute=True) + result = ShellCommand.Which('python3',all=False,mute=True) if len(result)==0: - self.logger.error('python compiler not found. Please install it before ' + \ + self.logger.error('python3 compiler not found. Please install it before ' + \ 'using MadAnalysis 5') return False self.logger.debug(" which: " + str(result[0])) # Which all if self.debug: - result = ShellCommand.Which('python',all=True,mute=True) + result = ShellCommand.Which('python3',all=True,mute=True) if len(result)==0: self.logger.error('g++ compiler not found. Please install it before ' + \ 'using MadAnalysis 5') diff --git a/madanalysis/system/detect_root.py b/madanalysis/system/detect_root.py index 059fa8f9..9fc14b55 100644 --- a/madanalysis/system/detect_root.py +++ b/madanalysis/system/detect_root.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_root_graphical.py b/madanalysis/system/detect_root_graphical.py index 3d88f54c..dc8fd762 100644 --- a/madanalysis/system/detect_root_graphical.py +++ b/madanalysis/system/detect_root_graphical.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_scipy.py b/madanalysis/system/detect_scipy.py index 950f6eff..4cfacd1d 100644 --- a/madanalysis/system/detect_scipy.py +++ b/madanalysis/system/detect_scipy.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_simplify.py b/madanalysis/system/detect_simplify.py index cf3abfe6..8292c5fa 100644 --- a/madanalysis/system/detect_simplify.py +++ b/madanalysis/system/detect_simplify.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/detect_spey.py b/madanalysis/system/detect_spey.py new file mode 100644 index 00000000..166ed19a --- /dev/null +++ b/madanalysis/system/detect_spey.py @@ -0,0 +1,67 @@ +################################################################################ +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks +# The MadAnalysis development team, email: +# +# This file is part of MadAnalysis 5. +# Official website: +# +# MadAnalysis 5 is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# MadAnalysis 5 is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with MadAnalysis 5. If not, see +# +################################################################################ + + +from __future__ import absolute_import + +import logging + +from madanalysis.enumeration.detect_status_type import DetectStatusType + + +class DetectSpey: + def __init__(self, archi_info, user_info, session_info, debug): + # mandatory options + self.archi_info = archi_info + self.user_info = user_info + self.session_info = session_info + self.debug = debug + self.name = "Spey" + self.mandatory = False + self.log = [] + self.logger = logging.getLogger("MA5") + self.moreInfo = "For more details see https://spey.readthedocs.io" + # adding what you want here + + def IsItVetoed(self): + return False + + def AutoDetection(self): + try: + import spey + import spey_pyhf + except ModuleNotFoundError as err: + self.logger.debug(str(err)) + return DetectStatusType.UNFOUND, "" + + # Checking release + self.logger.debug(" release = " + spey.version()) + self.logger.debug(" where? = " + spey.__file__) + + # Ok + return DetectStatusType.FOUND, "" + + def SaveInfo(self): + self.session_info.has_spey = True + self.archi_info.has_spey = True + return True diff --git a/madanalysis/system/detect_zlib.py b/madanalysis/system/detect_zlib.py index 5974db60..92df22bf 100644 --- a/madanalysis/system/detect_zlib.py +++ b/madanalysis/system/detect_zlib.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/madanalysis/system/session_info.py b/madanalysis/system/session_info.py index e43adf2a..16ebfa2c 100644 --- a/madanalysis/system/session_info.py +++ b/madanalysis/system/session_info.py @@ -1,110 +1,115 @@ ################################################################################ -# -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: -# +# # This file is part of MadAnalysis 5. # Official website: -# +# # MadAnalysis 5 is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. -# +# # MadAnalysis 5 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. -# +# # You should have received a copy of the GNU General Public License # along with MadAnalysis 5. If not, see -# +# ################################################################################ from __future__ import absolute_import import logging -class SessionInfo(): + +class SessionInfo: def __init__(self): - self.editor = "" - self.username = "" - self.tmpdir = "" - self.downloaddir = "" - self.has_root = False - self.has_matplotlib = False - self.has_pyhf = False - self.has_simplify = False - self.has_scipy = False - self.has_gnuplot = False - self.has_pdflatex = False - self.has_latex = False - self.has_dvipdf = False - self.has_web = True - self.has_pad = False - self.has_padsfs = False - self.has_padma5 = False - self.gcc_header_search_path = [] + self.editor = "" + self.username = "" + self.tmpdir = "" + self.downloaddir = "" + self.has_root = False + self.has_matplotlib = False + self.has_spey = False + self.has_simplify = False + self.has_scipy = False + self.has_gnuplot = False + self.has_pdflatex = False + self.has_latex = False + self.has_dvipdf = False + self.has_web = True + self.has_pad = False + self.has_padsfs = False + self.has_padma5 = False + self.gcc_header_search_path = [] self.gcc_library_search_path = [] self.padma5_build_path = "" self.padma5_original_bins = [] self.pad_build_path = "" self.pad_original_bins = [] - self.logger = logging.getLogger('MA5') + self.logger = logging.getLogger("MA5") def dump(self): for item in self.__dict__: - self.logger.debug(item+'\t'+str(self.__dict__[item])) + self.logger.debug(item + "\t" + str(self.__dict__[item])) - def __eq__(self,other): - return self.__dict__==other.__dict__ + def __eq__(self, other): + return self.__dict__ == other.__dict__ - def __neq__(self,other): + def __neq__(self, other): return not self.__eq__(other) - def save(self,filename): + def save(self, filename): # Open the file try: - file = open(filename,"w") + file = open(filename, "w") except: - self.logger.error("impossible to write the configuration file '" + \ - filename + "'") + self.logger.error( + "impossible to write the configuration file '" + filename + "'" + ) return False # Dump data import pickle + try: - pickle.dump(self,file) - test=True + pickle.dump(self, file) + test = True except: - self.logger.error("error occured during saving data to "+filename) - test=False + self.logger.error("error occured during saving data to " + filename) + test = False # Close the file file.close() # Return the operation status return test - - def load(self,filename): + + def load(self, filename): # Open the file try: - file = open(filename,"r") + file = open(filename, "r") except: - self.logger.error("impossible to read the configuration file '" + \ - filename + "'") + self.logger.error( + "impossible to read the configuration file '" + filename + "'" + ) return False # Import data import pickle + try: newone = pickle.load(file) - test=True + test = True except: - self.logger.warning("error occured during reading data from "+filename) - test=False + self.logger.warning("error occured during reading data from " + filename) + test = False # Close the file file.close() @@ -114,12 +119,13 @@ def load(self,filename): # Fill the class variables import copy + try: for item in self.__dict__: - self.__dict__[item]=copy.copy(newone.__dict__[item]) + self.__dict__[item] = copy.copy(newone.__dict__[item]) except: - self.logger.error("error occured during copying data from "+filename) - test=False + self.logger.error("error occured during copying data from " + filename) + test = False # Return the operation status return test diff --git a/madanalysis/system/user_info.py b/madanalysis/system/user_info.py index 1ee5ec9c..7241db7b 100644 --- a/madanalysis/system/user_info.py +++ b/madanalysis/system/user_info.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/requirements.txt b/requirements.txt index 3662162c..1e323baa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ matplotlib>=3.4.2 scipy>=1.7.1 -numpy>=1.19.5 -pyhf==0.7.3 +numpy>=1.19.5, <2.0.0 lxml>=4.6.2 +spey>=0.2.0 +spey_pyhf>=0.2.0 diff --git a/tools/ReportGenerator/Core/job_reader.py b/tools/ReportGenerator/Core/job_reader.py index 1ed1b5d8..4aa96f5e 100644 --- a/tools/ReportGenerator/Core/job_reader.py +++ b/tools/ReportGenerator/Core/job_reader.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Core/layout.py b/tools/ReportGenerator/Core/layout.py index 4a1ab81d..881c8e1c 100644 --- a/tools/ReportGenerator/Core/layout.py +++ b/tools/ReportGenerator/Core/layout.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Core/main.py b/tools/ReportGenerator/Core/main.py index 75dfc08f..4e81e8ac 100644 --- a/tools/ReportGenerator/Core/main.py +++ b/tools/ReportGenerator/Core/main.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Services/chronometer.py b/tools/ReportGenerator/Services/chronometer.py index 8da8ef72..f9fe051d 100644 --- a/tools/ReportGenerator/Services/chronometer.py +++ b/tools/ReportGenerator/Services/chronometer.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Services/colored_log.py b/tools/ReportGenerator/Services/colored_log.py index 5f5a32a1..850b8744 100644 --- a/tools/ReportGenerator/Services/colored_log.py +++ b/tools/ReportGenerator/Services/colored_log.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Services/fom_type.py b/tools/ReportGenerator/Services/fom_type.py index ceb5c122..b399b5ef 100644 --- a/tools/ReportGenerator/Services/fom_type.py +++ b/tools/ReportGenerator/Services/fom_type.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Services/shell_command.py b/tools/ReportGenerator/Services/shell_command.py index 4d773f91..85b5bc7d 100644 --- a/tools/ReportGenerator/Services/shell_command.py +++ b/tools/ReportGenerator/Services/shell_command.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/ReportGenerator/Services/string_tools.py b/tools/ReportGenerator/Services/string_tools.py index 5bbee1bd..82728610 100644 --- a/tools/ReportGenerator/Services/string_tools.py +++ b/tools/ReportGenerator/Services/string_tools.py @@ -1,6 +1,6 @@ ################################################################################ # -# Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +# Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks # The MadAnalysis development team, email: # # This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/ClusterAlgoBase.h b/tools/SampleAnalyzer/Commons/Base/ClusterAlgoBase.h index b930a281..9994afee 100644 --- a/tools/SampleAnalyzer/Commons/Base/ClusterAlgoBase.h +++ b/tools/SampleAnalyzer/Commons/Base/ClusterAlgoBase.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/Configuration.cpp b/tools/SampleAnalyzer/Commons/Base/Configuration.cpp index ab867acc..9d1692bb 100644 --- a/tools/SampleAnalyzer/Commons/Base/Configuration.cpp +++ b/tools/SampleAnalyzer/Commons/Base/Configuration.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. @@ -37,8 +37,8 @@ using namespace MA5; // Initializing static data members // ----------------------------------------------------------------------------- // DO NOT TOUCH THESE LINES -const std::string Configuration::sampleanalyzer_version_ = "1.10.16"; -const std::string Configuration::sampleanalyzer_date_ = "2024/08/26"; +const std::string Configuration::sampleanalyzer_version_ = "1.11.0"; +const std::string Configuration::sampleanalyzer_date_ = "2025/04/23"; // DO NOT TOUCH THESE LINES // ----------------------------------------------------------------------------- diff --git a/tools/SampleAnalyzer/Commons/Base/Configuration.h b/tools/SampleAnalyzer/Commons/Base/Configuration.h index 9d025ccf..304166c3 100644 --- a/tools/SampleAnalyzer/Commons/Base/Configuration.h +++ b/tools/SampleAnalyzer/Commons/Base/Configuration.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/DetectorBase.h b/tools/SampleAnalyzer/Commons/Base/DetectorBase.h index 5d7cf78e..26b85074 100644 --- a/tools/SampleAnalyzer/Commons/Base/DetectorBase.h +++ b/tools/SampleAnalyzer/Commons/Base/DetectorBase.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/PortableDatatypes.h b/tools/SampleAnalyzer/Commons/Base/PortableDatatypes.h index 2968eceb..6f2c4c37 100644 --- a/tools/SampleAnalyzer/Commons/Base/PortableDatatypes.h +++ b/tools/SampleAnalyzer/Commons/Base/PortableDatatypes.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/ReaderBase.h b/tools/SampleAnalyzer/Commons/Base/ReaderBase.h index 37e17943..0e49c504 100644 --- a/tools/SampleAnalyzer/Commons/Base/ReaderBase.h +++ b/tools/SampleAnalyzer/Commons/Base/ReaderBase.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/SmearerBase.cpp b/tools/SampleAnalyzer/Commons/Base/SmearerBase.cpp index 7674208c..04ce1be6 100644 --- a/tools/SampleAnalyzer/Commons/Base/SmearerBase.cpp +++ b/tools/SampleAnalyzer/Commons/Base/SmearerBase.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/SmearerBase.h b/tools/SampleAnalyzer/Commons/Base/SmearerBase.h index 25be44c4..7d2a5015 100644 --- a/tools/SampleAnalyzer/Commons/Base/SmearerBase.h +++ b/tools/SampleAnalyzer/Commons/Base/SmearerBase.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/StatusCode.h b/tools/SampleAnalyzer/Commons/Base/StatusCode.h index e67b50b1..46377434 100644 --- a/tools/SampleAnalyzer/Commons/Base/StatusCode.h +++ b/tools/SampleAnalyzer/Commons/Base/StatusCode.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/TaggerBase.cpp b/tools/SampleAnalyzer/Commons/Base/TaggerBase.cpp index 7b4928c7..5acccb76 100644 --- a/tools/SampleAnalyzer/Commons/Base/TaggerBase.cpp +++ b/tools/SampleAnalyzer/Commons/Base/TaggerBase.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/TaggerBase.h b/tools/SampleAnalyzer/Commons/Base/TaggerBase.h index ca58dc1f..e31c4977 100644 --- a/tools/SampleAnalyzer/Commons/Base/TaggerBase.h +++ b/tools/SampleAnalyzer/Commons/Base/TaggerBase.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Base/TestBase.cpp b/tools/SampleAnalyzer/Commons/Base/TestBase.cpp index 6414bb23..3b7a9089 100644 --- a/tools/SampleAnalyzer/Commons/Base/TestBase.cpp +++ b/tools/SampleAnalyzer/Commons/Base/TestBase.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/EventFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/EventFormat.h index 886036e0..b158e9bf 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/EventFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/EventFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/GeneratorInfo.h b/tools/SampleAnalyzer/Commons/DataFormat/GeneratorInfo.h index 2847357b..26a76ca2 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/GeneratorInfo.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/GeneratorInfo.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/IsolationConeType.h b/tools/SampleAnalyzer/Commons/DataFormat/IsolationConeType.h index 5f02c38f..9288cf50 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/IsolationConeType.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/IsolationConeType.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.cpp b/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.cpp index 19812a85..673526dc 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.cpp +++ b/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.h index a9eac6a2..6234b7b2 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/MCEventFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/MCParticleFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/MCParticleFormat.h index ea650b30..799a4771 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/MCParticleFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/MCParticleFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/MCProcessFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/MCProcessFormat.h index 03227fe2..6f58f288 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/MCProcessFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/MCProcessFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/MCSampleFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/MCSampleFormat.h index 532e1c30..4a202bfe 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/MCSampleFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/MCSampleFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/ParticleBaseFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/ParticleBaseFormat.h index d55b1433..e1d03f90 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/ParticleBaseFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/ParticleBaseFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.cpp b/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.cpp index e52ff3cd..e2f91314 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.cpp +++ b/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.h index fb4c3b8f..cd7afd87 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/PdgDataFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.cpp b/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.cpp index 527bc92d..1f5442ac 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.cpp +++ b/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.h b/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.h index c90cea07..a28b24f1 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/PdgTable.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecEventFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecEventFormat.h index 4b427340..ae587476 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecEventFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecEventFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecJetFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecJetFormat.h index cf71ebff..d2a9b90b 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecJetFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecJetFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecLeptonFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecLeptonFormat.h index 75b4499d..f87b386d 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecLeptonFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecLeptonFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecParticleFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecParticleFormat.h index dd6d893c..61c1738e 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecParticleFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecParticleFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecPhotonFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecPhotonFormat.h index 953636a2..9d950043 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecPhotonFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecPhotonFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecSampleFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecSampleFormat.h index 94c0630e..aca3fa4b 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecSampleFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecSampleFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecTauFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecTauFormat.h index 1b5c31c6..faa084f5 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecTauFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecTauFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecTowerFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecTowerFormat.h index 008fb035..edf39a75 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecTowerFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecTowerFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecTrackFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecTrackFormat.h index 86548a45..1f4175e2 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecTrackFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecTrackFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/RecVertexFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/RecVertexFormat.h index 78686635..5a150623 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/RecVertexFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/RecVertexFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/SampleFormat.h b/tools/SampleAnalyzer/Commons/DataFormat/SampleFormat.h index 624c795c..cdec6b77 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/SampleFormat.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/SampleFormat.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.cpp b/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.cpp index 4bbc440b..ecb0664d 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.cpp +++ b/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.h b/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.h index 9de39994..64a6e046 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/WeightCollection.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.cpp b/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.cpp index 0ec623a0..812edf12 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.cpp +++ b/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.cpp @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.h b/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.h index 5d7ebfd5..f382c994 100644 --- a/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.h +++ b/tools/SampleAnalyzer/Commons/DataFormat/WeightDefinition.h @@ -1,6 +1,6 @@ //////////////////////////////////////////////////////////////////////////////// // -// Copyright (C) 2012-2023 Jack Araz, Eric Conte & Benjamin Fuks +// Copyright (C) 2012-2025 Jack Araz, Eric Conte & Benjamin Fuks // The MadAnalysis development team, email: // // This file is part of MadAnalysis 5. diff --git a/tools/SampleAnalyzer/Commons/Eigen/Cholesky b/tools/SampleAnalyzer/Commons/Eigen/Cholesky new file mode 100644 index 00000000..a318ceb7 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Cholesky @@ -0,0 +1,45 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CHOLESKY_MODULE_H +#define EIGEN_CHOLESKY_MODULE_H + +#include "Core" +#include "Jacobi" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup Cholesky_Module Cholesky module + * + * + * + * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices. + * Those decompositions are also accessible via the following methods: + * - MatrixBase::llt() + * - MatrixBase::ldlt() + * - SelfAdjointView::llt() + * - SelfAdjointView::ldlt() + * + * \code + * #include + * \endcode + */ + +#include "src/Cholesky/LLT.h" +#include "src/Cholesky/LDLT.h" +#ifdef EIGEN_USE_LAPACKE +#ifdef EIGEN_USE_MKL +#include "mkl_lapacke.h" +#else +#include "src/misc/lapacke.h" +#endif +#include "src/Cholesky/LLT_LAPACKE.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_CHOLESKY_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/CholmodSupport b/tools/SampleAnalyzer/Commons/Eigen/CholmodSupport new file mode 100644 index 00000000..bed8924d --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/CholmodSupport @@ -0,0 +1,48 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H +#define EIGEN_CHOLMODSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +extern "C" { + #include +} + +/** \ingroup Support_modules + * \defgroup CholmodSupport_Module CholmodSupport module + * + * This module provides an interface to the Cholmod library which is part of the suitesparse package. + * It provides the two following main factorization classes: + * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization. + * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial). + * + * For the sake of completeness, this module also propose the two following classes: + * - class CholmodSimplicialLLT + * - class CholmodSimplicialLDLT + * Note that these classes does not bring any particular advantage compared to the built-in + * SimplicialLLT and SimplicialLDLT factorization classes. + * + * \code + * #include + * \endcode + * + * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies. + * The dependencies depend on how cholmod has been compiled. + * For a cmake based project, you can use our FindCholmod.cmake module to help you in this task. + * + */ + +#include "src/CholmodSupport/CholmodSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_CHOLMODSUPPORT_MODULE_H + diff --git a/tools/SampleAnalyzer/Commons/Eigen/Core b/tools/SampleAnalyzer/Commons/Eigen/Core new file mode 100644 index 00000000..5921e15f --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Core @@ -0,0 +1,384 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2007-2011 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CORE_H +#define EIGEN_CORE_H + +// first thing Eigen does: stop the compiler from reporting useless warnings. +#include "src/Core/util/DisableStupidWarnings.h" + +// then include this file where all our macros are defined. It's really important to do it first because +// it's where we do all the compiler/OS/arch detections and define most defaults. +#include "src/Core/util/Macros.h" + +// This detects SSE/AVX/NEON/etc. and configure alignment settings +#include "src/Core/util/ConfigureVectorization.h" + +// We need cuda_runtime.h/hip_runtime.h to ensure that +// the EIGEN_USING_STD macro works properly on the device side +#if defined(EIGEN_CUDACC) + #include +#elif defined(EIGEN_HIPCC) + #include +#endif + + +#ifdef EIGEN_EXCEPTIONS + #include +#endif + +// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3) +// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details. +#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6) && EIGEN_GNUC_AT_MOST(5,5) + #pragma GCC optimize ("-fno-ipa-cp-clone") +#endif + +// Prevent ICC from specializing std::complex operators that silently fail +// on device. This allows us to use our own device-compatible specializations +// instead. +#if defined(EIGEN_COMP_ICC) && defined(EIGEN_GPU_COMPILE_PHASE) \ + && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_) +#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1 +#endif +#include + +// this include file manages BLAS and MKL related macros +// and inclusion of their respective header files +#include "src/Core/util/MKL_support.h" + + +#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16) + #define EIGEN_HAS_GPU_FP16 +#endif + +#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16) + #define EIGEN_HAS_GPU_BF16 +#endif + +#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE) + #define EIGEN_HAS_OPENMP +#endif + +#ifdef EIGEN_HAS_OPENMP +#include +#endif + +// MSVC for windows mobile does not have the errno.h file +#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM +#define EIGEN_HAS_ERRNO +#endif + +#ifdef EIGEN_HAS_ERRNO +#include +#endif +#include +#include +#include +#include +#include +#include +#ifndef EIGEN_NO_IO + #include +#endif +#include +#include +#include +#include // for CHAR_BIT +// for min/max: +#include + +#if EIGEN_HAS_CXX11 +#include +#endif + +// for std::is_nothrow_move_assignable +#ifdef EIGEN_INCLUDE_TYPE_TRAITS +#include +#endif + +// for outputting debug info +#ifdef EIGEN_DEBUG_ASSIGN +#include +#endif + +// required for __cpuid, needs to be included after cmath +#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE + #include +#endif + +#if defined(EIGEN_USE_SYCL) + #undef min + #undef max + #undef isnan + #undef isinf + #undef isfinite + #include + #include + #include + #include + #include + #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0 + #define EIGEN_SYCL_LOCAL_THREAD_DIM0 16 + #endif + #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1 + #define EIGEN_SYCL_LOCAL_THREAD_DIM1 16 + #endif +#endif + + +#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT +// This will generate an error message: +#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information +#endif + +namespace Eigen { + +// we use size_t frequently and we'll never remember to prepend it with std:: every time just to +// ensure QNX/QCC support +using std::size_t; +// gcc 4.6.0 wants std:: for ptrdiff_t +using std::ptrdiff_t; + +} + +/** \defgroup Core_Module Core module + * This is the main module of Eigen providing dense matrix and vector support + * (both fixed and dynamic size) with all the features corresponding to a BLAS library + * and much more... + * + * \code + * #include + * \endcode + */ + +#include "src/Core/util/Constants.h" +#include "src/Core/util/Meta.h" +#include "src/Core/util/ForwardDeclarations.h" +#include "src/Core/util/StaticAssert.h" +#include "src/Core/util/XprHelper.h" +#include "src/Core/util/Memory.h" +#include "src/Core/util/IntegralConstant.h" +#include "src/Core/util/SymbolicIndex.h" + +#include "src/Core/NumTraits.h" +#include "src/Core/MathFunctions.h" +#include "src/Core/GenericPacketMath.h" +#include "src/Core/MathFunctionsImpl.h" +#include "src/Core/arch/Default/ConjHelper.h" +// Generic half float support +#include "src/Core/arch/Default/Half.h" +#include "src/Core/arch/Default/BFloat16.h" +#include "src/Core/arch/Default/TypeCasting.h" +#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h" + +#if defined EIGEN_VECTORIZE_AVX512 + #include "src/Core/arch/SSE/PacketMath.h" + #include "src/Core/arch/SSE/TypeCasting.h" + #include "src/Core/arch/SSE/Complex.h" + #include "src/Core/arch/AVX/PacketMath.h" + #include "src/Core/arch/AVX/TypeCasting.h" + #include "src/Core/arch/AVX/Complex.h" + #include "src/Core/arch/AVX512/PacketMath.h" + #include "src/Core/arch/AVX512/TypeCasting.h" + #include "src/Core/arch/AVX512/Complex.h" + #include "src/Core/arch/SSE/MathFunctions.h" + #include "src/Core/arch/AVX/MathFunctions.h" + #include "src/Core/arch/AVX512/MathFunctions.h" +#elif defined EIGEN_VECTORIZE_AVX + // Use AVX for floats and doubles, SSE for integers + #include "src/Core/arch/SSE/PacketMath.h" + #include "src/Core/arch/SSE/TypeCasting.h" + #include "src/Core/arch/SSE/Complex.h" + #include "src/Core/arch/AVX/PacketMath.h" + #include "src/Core/arch/AVX/TypeCasting.h" + #include "src/Core/arch/AVX/Complex.h" + #include "src/Core/arch/SSE/MathFunctions.h" + #include "src/Core/arch/AVX/MathFunctions.h" +#elif defined EIGEN_VECTORIZE_SSE + #include "src/Core/arch/SSE/PacketMath.h" + #include "src/Core/arch/SSE/TypeCasting.h" + #include "src/Core/arch/SSE/MathFunctions.h" + #include "src/Core/arch/SSE/Complex.h" +#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX) + #include "src/Core/arch/AltiVec/PacketMath.h" + #include "src/Core/arch/AltiVec/MathFunctions.h" + #include "src/Core/arch/AltiVec/Complex.h" +#elif defined EIGEN_VECTORIZE_NEON + #include "src/Core/arch/NEON/PacketMath.h" + #include "src/Core/arch/NEON/TypeCasting.h" + #include "src/Core/arch/NEON/MathFunctions.h" + #include "src/Core/arch/NEON/Complex.h" +#elif defined EIGEN_VECTORIZE_SVE + #include "src/Core/arch/SVE/PacketMath.h" + #include "src/Core/arch/SVE/TypeCasting.h" + #include "src/Core/arch/SVE/MathFunctions.h" +#elif defined EIGEN_VECTORIZE_ZVECTOR + #include "src/Core/arch/ZVector/PacketMath.h" + #include "src/Core/arch/ZVector/MathFunctions.h" + #include "src/Core/arch/ZVector/Complex.h" +#elif defined EIGEN_VECTORIZE_MSA + #include "src/Core/arch/MSA/PacketMath.h" + #include "src/Core/arch/MSA/MathFunctions.h" + #include "src/Core/arch/MSA/Complex.h" +#endif + +#if defined EIGEN_VECTORIZE_GPU + #include "src/Core/arch/GPU/PacketMath.h" + #include "src/Core/arch/GPU/MathFunctions.h" + #include "src/Core/arch/GPU/TypeCasting.h" +#endif + +#if defined(EIGEN_USE_SYCL) + #include "src/Core/arch/SYCL/SyclMemoryModel.h" + #include "src/Core/arch/SYCL/InteropHeaders.h" +#if !defined(EIGEN_DONT_VECTORIZE_SYCL) + #include "src/Core/arch/SYCL/PacketMath.h" + #include "src/Core/arch/SYCL/MathFunctions.h" + #include "src/Core/arch/SYCL/TypeCasting.h" +#endif +#endif + +#include "src/Core/arch/Default/Settings.h" +// This file provides generic implementations valid for scalar as well +#include "src/Core/arch/Default/GenericPacketMathFunctions.h" + +#include "src/Core/functors/TernaryFunctors.h" +#include "src/Core/functors/BinaryFunctors.h" +#include "src/Core/functors/UnaryFunctors.h" +#include "src/Core/functors/NullaryFunctors.h" +#include "src/Core/functors/StlFunctors.h" +#include "src/Core/functors/AssignmentFunctors.h" + +// Specialized functors to enable the processing of complex numbers +// on CUDA devices +#ifdef EIGEN_CUDACC +#include "src/Core/arch/CUDA/Complex.h" +#endif + +#include "src/Core/util/IndexedViewHelper.h" +#include "src/Core/util/ReshapedHelper.h" +#include "src/Core/ArithmeticSequence.h" +#ifndef EIGEN_NO_IO + #include "src/Core/IO.h" +#endif +#include "src/Core/DenseCoeffsBase.h" +#include "src/Core/DenseBase.h" +#include "src/Core/MatrixBase.h" +#include "src/Core/EigenBase.h" + +#include "src/Core/Product.h" +#include "src/Core/CoreEvaluators.h" +#include "src/Core/AssignEvaluator.h" + +#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874 + // at least confirmed with Doxygen 1.5.5 and 1.5.6 + #include "src/Core/Assign.h" +#endif + +#include "src/Core/ArrayBase.h" +#include "src/Core/util/BlasUtil.h" +#include "src/Core/DenseStorage.h" +#include "src/Core/NestByValue.h" + +// #include "src/Core/ForceAlignedAccess.h" + +#include "src/Core/ReturnByValue.h" +#include "src/Core/NoAlias.h" +#include "src/Core/PlainObjectBase.h" +#include "src/Core/Matrix.h" +#include "src/Core/Array.h" +#include "src/Core/CwiseTernaryOp.h" +#include "src/Core/CwiseBinaryOp.h" +#include "src/Core/CwiseUnaryOp.h" +#include "src/Core/CwiseNullaryOp.h" +#include "src/Core/CwiseUnaryView.h" +#include "src/Core/SelfCwiseBinaryOp.h" +#include "src/Core/Dot.h" +#include "src/Core/StableNorm.h" +#include "src/Core/Stride.h" +#include "src/Core/MapBase.h" +#include "src/Core/Map.h" +#include "src/Core/Ref.h" +#include "src/Core/Block.h" +#include "src/Core/VectorBlock.h" +#include "src/Core/IndexedView.h" +#include "src/Core/Reshaped.h" +#include "src/Core/Transpose.h" +#include "src/Core/DiagonalMatrix.h" +#include "src/Core/Diagonal.h" +#include "src/Core/DiagonalProduct.h" +#include "src/Core/Redux.h" +#include "src/Core/Visitor.h" +#include "src/Core/Fuzzy.h" +#include "src/Core/Swap.h" +#include "src/Core/CommaInitializer.h" +#include "src/Core/GeneralProduct.h" +#include "src/Core/Solve.h" +#include "src/Core/Inverse.h" +#include "src/Core/SolverBase.h" +#include "src/Core/PermutationMatrix.h" +#include "src/Core/Transpositions.h" +#include "src/Core/TriangularMatrix.h" +#include "src/Core/SelfAdjointView.h" +#include "src/Core/products/GeneralBlockPanelKernel.h" +#include "src/Core/products/Parallelizer.h" +#include "src/Core/ProductEvaluators.h" +#include "src/Core/products/GeneralMatrixVector.h" +#include "src/Core/products/GeneralMatrixMatrix.h" +#include "src/Core/SolveTriangular.h" +#include "src/Core/products/GeneralMatrixMatrixTriangular.h" +#include "src/Core/products/SelfadjointMatrixVector.h" +#include "src/Core/products/SelfadjointMatrixMatrix.h" +#include "src/Core/products/SelfadjointProduct.h" +#include "src/Core/products/SelfadjointRank2Update.h" +#include "src/Core/products/TriangularMatrixVector.h" +#include "src/Core/products/TriangularMatrixMatrix.h" +#include "src/Core/products/TriangularSolverMatrix.h" +#include "src/Core/products/TriangularSolverVector.h" +#include "src/Core/BandMatrix.h" +#include "src/Core/CoreIterators.h" +#include "src/Core/ConditionEstimator.h" + +#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX) + #include "src/Core/arch/AltiVec/MatrixProduct.h" +#elif defined EIGEN_VECTORIZE_NEON + #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h" +#endif + +#include "src/Core/BooleanRedux.h" +#include "src/Core/Select.h" +#include "src/Core/VectorwiseOp.h" +#include "src/Core/PartialReduxEvaluator.h" +#include "src/Core/Random.h" +#include "src/Core/Replicate.h" +#include "src/Core/Reverse.h" +#include "src/Core/ArrayWrapper.h" +#include "src/Core/StlIterators.h" + +#ifdef EIGEN_USE_BLAS +#include "src/Core/products/GeneralMatrixMatrix_BLAS.h" +#include "src/Core/products/GeneralMatrixVector_BLAS.h" +#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h" +#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h" +#include "src/Core/products/SelfadjointMatrixVector_BLAS.h" +#include "src/Core/products/TriangularMatrixMatrix_BLAS.h" +#include "src/Core/products/TriangularMatrixVector_BLAS.h" +#include "src/Core/products/TriangularSolverMatrix_BLAS.h" +#endif // EIGEN_USE_BLAS + +#ifdef EIGEN_USE_MKL_VML +#include "src/Core/Assign_MKL.h" +#endif + +#include "src/Core/GlobalFunctions.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_CORE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/Dense b/tools/SampleAnalyzer/Commons/Eigen/Dense new file mode 100644 index 00000000..5768910b --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Dense @@ -0,0 +1,7 @@ +#include "Core" +#include "LU" +#include "Cholesky" +#include "QR" +#include "SVD" +#include "Geometry" +#include "Eigenvalues" diff --git a/tools/SampleAnalyzer/Commons/Eigen/Eigen b/tools/SampleAnalyzer/Commons/Eigen/Eigen new file mode 100644 index 00000000..654c8dc6 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Eigen @@ -0,0 +1,2 @@ +#include "Dense" +#include "Sparse" diff --git a/tools/SampleAnalyzer/Commons/Eigen/Eigenvalues b/tools/SampleAnalyzer/Commons/Eigen/Eigenvalues new file mode 100644 index 00000000..5467a2e7 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Eigenvalues @@ -0,0 +1,60 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_EIGENVALUES_MODULE_H +#define EIGEN_EIGENVALUES_MODULE_H + +#include "Core" + +#include "Cholesky" +#include "Jacobi" +#include "Householder" +#include "LU" +#include "Geometry" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup Eigenvalues_Module Eigenvalues module + * + * + * + * This module mainly provides various eigenvalue solvers. + * This module also provides some MatrixBase methods, including: + * - MatrixBase::eigenvalues(), + * - MatrixBase::operatorNorm() + * + * \code + * #include + * \endcode + */ + +#include "src/misc/RealSvd2x2.h" +#include "src/Eigenvalues/Tridiagonalization.h" +#include "src/Eigenvalues/RealSchur.h" +#include "src/Eigenvalues/EigenSolver.h" +#include "src/Eigenvalues/SelfAdjointEigenSolver.h" +#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h" +#include "src/Eigenvalues/HessenbergDecomposition.h" +#include "src/Eigenvalues/ComplexSchur.h" +#include "src/Eigenvalues/ComplexEigenSolver.h" +#include "src/Eigenvalues/RealQZ.h" +#include "src/Eigenvalues/GeneralizedEigenSolver.h" +#include "src/Eigenvalues/MatrixBaseEigenvalues.h" +#ifdef EIGEN_USE_LAPACKE +#ifdef EIGEN_USE_MKL +#include "mkl_lapacke.h" +#else +#include "src/misc/lapacke.h" +#endif +#include "src/Eigenvalues/RealSchur_LAPACKE.h" +#include "src/Eigenvalues/ComplexSchur_LAPACKE.h" +#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_EIGENVALUES_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/Geometry b/tools/SampleAnalyzer/Commons/Eigen/Geometry new file mode 100644 index 00000000..bc78110a --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Geometry @@ -0,0 +1,59 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_GEOMETRY_MODULE_H +#define EIGEN_GEOMETRY_MODULE_H + +#include "Core" + +#include "SVD" +#include "LU" +#include + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup Geometry_Module Geometry module + * + * This module provides support for: + * - fixed-size homogeneous transformations + * - translation, scaling, 2D and 3D rotations + * - \link Quaternion quaternions \endlink + * - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3) + * - orthognal vector generation (\ref MatrixBase::unitOrthogonal) + * - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink + * - \link AlignedBox axis aligned bounding boxes \endlink + * - \link umeyama least-square transformation fitting \endlink + * + * \code + * #include + * \endcode + */ + +#include "src/Geometry/OrthoMethods.h" +#include "src/Geometry/EulerAngles.h" + +#include "src/Geometry/Homogeneous.h" +#include "src/Geometry/RotationBase.h" +#include "src/Geometry/Rotation2D.h" +#include "src/Geometry/Quaternion.h" +#include "src/Geometry/AngleAxis.h" +#include "src/Geometry/Transform.h" +#include "src/Geometry/Translation.h" +#include "src/Geometry/Scaling.h" +#include "src/Geometry/Hyperplane.h" +#include "src/Geometry/ParametrizedLine.h" +#include "src/Geometry/AlignedBox.h" +#include "src/Geometry/Umeyama.h" + +// Use the SSE optimized version whenever possible. +#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON) +#include "src/Geometry/arch/Geometry_SIMD.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_GEOMETRY_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/Householder b/tools/SampleAnalyzer/Commons/Eigen/Householder new file mode 100644 index 00000000..f2fa7996 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Householder @@ -0,0 +1,29 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_HOUSEHOLDER_MODULE_H +#define EIGEN_HOUSEHOLDER_MODULE_H + +#include "Core" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup Householder_Module Householder module + * This module provides Householder transformations. + * + * \code + * #include + * \endcode + */ + +#include "src/Householder/Householder.h" +#include "src/Householder/HouseholderSequence.h" +#include "src/Householder/BlockHouseholder.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_HOUSEHOLDER_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/IterativeLinearSolvers b/tools/SampleAnalyzer/Commons/Eigen/IterativeLinearSolvers new file mode 100644 index 00000000..957d5750 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/IterativeLinearSolvers @@ -0,0 +1,48 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H +#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H + +#include "SparseCore" +#include "OrderingMethods" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** + * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module + * + * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse. + * Those solvers are accessible via the following classes: + * - ConjugateGradient for selfadjoint (hermitian) matrices, + * - LeastSquaresConjugateGradient for rectangular least-square problems, + * - BiCGSTAB for general square matrices. + * + * These iterative solvers are associated with some preconditioners: + * - IdentityPreconditioner - not really useful + * - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices. + * - IncompleteLUT - incomplete LU factorization with dual thresholding + * + * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport. + * + \code + #include + \endcode + */ + +#include "src/IterativeLinearSolvers/SolveWithGuess.h" +#include "src/IterativeLinearSolvers/IterativeSolverBase.h" +#include "src/IterativeLinearSolvers/BasicPreconditioners.h" +#include "src/IterativeLinearSolvers/ConjugateGradient.h" +#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h" +#include "src/IterativeLinearSolvers/BiCGSTAB.h" +#include "src/IterativeLinearSolvers/IncompleteLUT.h" +#include "src/IterativeLinearSolvers/IncompleteCholesky.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/Jacobi b/tools/SampleAnalyzer/Commons/Eigen/Jacobi new file mode 100644 index 00000000..43edc7a1 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Jacobi @@ -0,0 +1,32 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_JACOBI_MODULE_H +#define EIGEN_JACOBI_MODULE_H + +#include "Core" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup Jacobi_Module Jacobi module + * This module provides Jacobi and Givens rotations. + * + * \code + * #include + * \endcode + * + * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation: + * - MatrixBase::applyOnTheLeft() + * - MatrixBase::applyOnTheRight(). + */ + +#include "src/Jacobi/Jacobi.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_JACOBI_MODULE_H + diff --git a/tools/SampleAnalyzer/Commons/Eigen/KLUSupport b/tools/SampleAnalyzer/Commons/Eigen/KLUSupport new file mode 100644 index 00000000..b23d9053 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/KLUSupport @@ -0,0 +1,41 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_KLUSUPPORT_MODULE_H +#define EIGEN_KLUSUPPORT_MODULE_H + +#include + +#include + +extern "C" { +#include +#include + } + +/** \ingroup Support_modules + * \defgroup KLUSupport_Module KLUSupport module + * + * This module provides an interface to the KLU library which is part of the suitesparse package. + * It provides the following factorization class: + * - class KLU: a sparse LU factorization, well-suited for circuit simulation. + * + * \code + * #include + * \endcode + * + * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must be linked to the klu library and its dependencies. + * The dependencies depend on how umfpack has been compiled. + * For a cmake based project, you can use our FindKLU.cmake module to help you in this task. + * + */ + +#include "src/KLUSupport/KLUSupport.h" + +#include + +#endif // EIGEN_KLUSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/LU b/tools/SampleAnalyzer/Commons/Eigen/LU new file mode 100644 index 00000000..1236ceb0 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/LU @@ -0,0 +1,47 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_LU_MODULE_H +#define EIGEN_LU_MODULE_H + +#include "Core" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup LU_Module LU module + * This module includes %LU decomposition and related notions such as matrix inversion and determinant. + * This module defines the following MatrixBase methods: + * - MatrixBase::inverse() + * - MatrixBase::determinant() + * + * \code + * #include + * \endcode + */ + +#include "src/misc/Kernel.h" +#include "src/misc/Image.h" +#include "src/LU/FullPivLU.h" +#include "src/LU/PartialPivLU.h" +#ifdef EIGEN_USE_LAPACKE +#ifdef EIGEN_USE_MKL +#include "mkl_lapacke.h" +#else +#include "src/misc/lapacke.h" +#endif +#include "src/LU/PartialPivLU_LAPACKE.h" +#endif +#include "src/LU/Determinant.h" +#include "src/LU/InverseImpl.h" + +#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON + #include "src/LU/arch/InverseSize4.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_LU_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/MetisSupport b/tools/SampleAnalyzer/Commons/Eigen/MetisSupport new file mode 100644 index 00000000..85c41bf3 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/MetisSupport @@ -0,0 +1,35 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_METISSUPPORT_MODULE_H +#define EIGEN_METISSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +extern "C" { +#include +} + + +/** \ingroup Support_modules + * \defgroup MetisSupport_Module MetisSupport module + * + * \code + * #include + * \endcode + * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis). + * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink + */ + + +#include "src/MetisSupport/MetisSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_METISSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/OrderingMethods b/tools/SampleAnalyzer/Commons/Eigen/OrderingMethods new file mode 100644 index 00000000..29691a62 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/OrderingMethods @@ -0,0 +1,70 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ORDERINGMETHODS_MODULE_H +#define EIGEN_ORDERINGMETHODS_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** + * \defgroup OrderingMethods_Module OrderingMethods module + * + * This module is currently for internal use only + * + * It defines various built-in and external ordering methods for sparse matrices. + * They are typically used to reduce the number of elements during + * the sparse matrix decomposition (LLT, LU, QR). + * Precisely, in a preprocessing step, a permutation matrix P is computed using + * those ordering methods and applied to the columns of the matrix. + * Using for instance the sparse Cholesky decomposition, it is expected that + * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A). + * + * + * Usage : + * \code + * #include + * \endcode + * + * A simple usage is as a template parameter in the sparse decomposition classes : + * + * \code + * SparseLU > solver; + * \endcode + * + * \code + * SparseQR > solver; + * \endcode + * + * It is possible as well to call directly a particular ordering method for your own purpose, + * \code + * AMDOrdering ordering; + * PermutationMatrix perm; + * SparseMatrix A; + * //Fill the matrix ... + * + * ordering(A, perm); // Call AMD + * \endcode + * + * \note Some of these methods (like AMD or METIS), need the sparsity pattern + * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, + * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method. + * If your matrix is already symmetric (at leat in structure), you can avoid that + * by calling the method with a SelfAdjointView type. + * + * \code + * // Call the ordering on the pattern of the lower triangular matrix A + * ordering(A.selfadjointView(), perm); + * \endcode + */ + +#include "src/OrderingMethods/Amd.h" +#include "src/OrderingMethods/Ordering.h" +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_ORDERINGMETHODS_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/PaStiXSupport b/tools/SampleAnalyzer/Commons/Eigen/PaStiXSupport new file mode 100644 index 00000000..234619ac --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/PaStiXSupport @@ -0,0 +1,49 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_PASTIXSUPPORT_MODULE_H +#define EIGEN_PASTIXSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +extern "C" { +#include +#include +} + +#ifdef complex +#undef complex +#endif + +/** \ingroup Support_modules + * \defgroup PaStiXSupport_Module PaStiXSupport module + * + * This module provides an interface to the PaSTiX library. + * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver. + * It provides the two following main factorization classes: + * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization. + * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization. + * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern). + * + * \code + * #include + * \endcode + * + * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies. + * This wrapper resuires PaStiX version 5.x compiled without MPI support. + * The dependencies depend on how PaSTiX has been compiled. + * For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task. + * + */ + +#include "src/PaStiXSupport/PaStiXSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_PASTIXSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/PardisoSupport b/tools/SampleAnalyzer/Commons/Eigen/PardisoSupport new file mode 100644 index 00000000..340edf51 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/PardisoSupport @@ -0,0 +1,35 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_PARDISOSUPPORT_MODULE_H +#define EIGEN_PARDISOSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +#include + +/** \ingroup Support_modules + * \defgroup PardisoSupport_Module PardisoSupport module + * + * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers. + * + * \code + * #include + * \endcode + * + * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies. + * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration. + * + */ + +#include "src/PardisoSupport/PardisoSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_PARDISOSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/QR b/tools/SampleAnalyzer/Commons/Eigen/QR new file mode 100644 index 00000000..8465b62c --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/QR @@ -0,0 +1,50 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_QR_MODULE_H +#define EIGEN_QR_MODULE_H + +#include "Core" + +#include "Cholesky" +#include "Jacobi" +#include "Householder" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup QR_Module QR module + * + * + * + * This module provides various QR decompositions + * This module also provides some MatrixBase methods, including: + * - MatrixBase::householderQr() + * - MatrixBase::colPivHouseholderQr() + * - MatrixBase::fullPivHouseholderQr() + * + * \code + * #include + * \endcode + */ + +#include "src/QR/HouseholderQR.h" +#include "src/QR/FullPivHouseholderQR.h" +#include "src/QR/ColPivHouseholderQR.h" +#include "src/QR/CompleteOrthogonalDecomposition.h" +#ifdef EIGEN_USE_LAPACKE +#ifdef EIGEN_USE_MKL +#include "mkl_lapacke.h" +#else +#include "src/misc/lapacke.h" +#endif +#include "src/QR/HouseholderQR_LAPACKE.h" +#include "src/QR/ColPivHouseholderQR_LAPACKE.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_QR_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/QtAlignedMalloc b/tools/SampleAnalyzer/Commons/Eigen/QtAlignedMalloc new file mode 100644 index 00000000..6fe82374 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/QtAlignedMalloc @@ -0,0 +1,39 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_QTMALLOC_MODULE_H +#define EIGEN_QTMALLOC_MODULE_H + +#include "Core" + +#if (!EIGEN_MALLOC_ALREADY_ALIGNED) + +#include "src/Core/util/DisableStupidWarnings.h" + +void *qMalloc(std::size_t size) +{ + return Eigen::internal::aligned_malloc(size); +} + +void qFree(void *ptr) +{ + Eigen::internal::aligned_free(ptr); +} + +void *qRealloc(void *ptr, std::size_t size) +{ + void* newPtr = Eigen::internal::aligned_malloc(size); + std::memcpy(newPtr, ptr, size); + Eigen::internal::aligned_free(ptr); + return newPtr; +} + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif + +#endif // EIGEN_QTMALLOC_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/SPQRSupport b/tools/SampleAnalyzer/Commons/Eigen/SPQRSupport new file mode 100644 index 00000000..f70390c1 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SPQRSupport @@ -0,0 +1,34 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPQRSUPPORT_MODULE_H +#define EIGEN_SPQRSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +#include "SuiteSparseQR.hpp" + +/** \ingroup Support_modules + * \defgroup SPQRSupport_Module SuiteSparseQR module + * + * This module provides an interface to the SPQR library, which is part of the suitesparse package. + * + * \code + * #include + * \endcode + * + * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...). + * For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules + * + */ + +#include "src/CholmodSupport/CholmodSupport.h" +#include "src/SPQRSupport/SuiteSparseQRSupport.h" + +#endif diff --git a/tools/SampleAnalyzer/Commons/Eigen/SVD b/tools/SampleAnalyzer/Commons/Eigen/SVD new file mode 100644 index 00000000..34517949 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SVD @@ -0,0 +1,50 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SVD_MODULE_H +#define EIGEN_SVD_MODULE_H + +#include "QR" +#include "Householder" +#include "Jacobi" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup SVD_Module SVD module + * + * + * + * This module provides SVD decomposition for matrices (both real and complex). + * Two decomposition algorithms are provided: + * - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones. + * - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems. + * These decompositions are accessible via the respective classes and following MatrixBase methods: + * - MatrixBase::jacobiSvd() + * - MatrixBase::bdcSvd() + * + * \code + * #include + * \endcode + */ + +#include "src/misc/RealSvd2x2.h" +#include "src/SVD/UpperBidiagonalization.h" +#include "src/SVD/SVDBase.h" +#include "src/SVD/JacobiSVD.h" +#include "src/SVD/BDCSVD.h" +#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT) +#ifdef EIGEN_USE_MKL +#include "mkl_lapacke.h" +#else +#include "src/misc/lapacke.h" +#endif +#include "src/SVD/JacobiSVD_LAPACKE.h" +#endif + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_SVD_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/Sparse b/tools/SampleAnalyzer/Commons/Eigen/Sparse new file mode 100644 index 00000000..a2ef7a66 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/Sparse @@ -0,0 +1,34 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPARSE_MODULE_H +#define EIGEN_SPARSE_MODULE_H + +/** \defgroup Sparse_Module Sparse meta-module + * + * Meta-module including all related modules: + * - \ref SparseCore_Module + * - \ref OrderingMethods_Module + * - \ref SparseCholesky_Module + * - \ref SparseLU_Module + * - \ref SparseQR_Module + * - \ref IterativeLinearSolvers_Module + * + \code + #include + \endcode + */ + +#include "SparseCore" +#include "OrderingMethods" +#include "SparseCholesky" +#include "SparseLU" +#include "SparseQR" +#include "IterativeLinearSolvers" + +#endif // EIGEN_SPARSE_MODULE_H + diff --git a/tools/SampleAnalyzer/Commons/Eigen/SparseCholesky b/tools/SampleAnalyzer/Commons/Eigen/SparseCholesky new file mode 100644 index 00000000..d2b1f127 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SparseCholesky @@ -0,0 +1,37 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2013 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPARSECHOLESKY_MODULE_H +#define EIGEN_SPARSECHOLESKY_MODULE_H + +#include "SparseCore" +#include "OrderingMethods" + +#include "src/Core/util/DisableStupidWarnings.h" + +/** + * \defgroup SparseCholesky_Module SparseCholesky module + * + * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices. + * Those decompositions are accessible via the following classes: + * - SimplicialLLt, + * - SimplicialLDLt + * + * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module. + * + * \code + * #include + * \endcode + */ + +#include "src/SparseCholesky/SimplicialCholesky.h" +#include "src/SparseCholesky/SimplicialCholesky_impl.h" +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_SPARSECHOLESKY_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/SparseCore b/tools/SampleAnalyzer/Commons/Eigen/SparseCore new file mode 100644 index 00000000..76966c4c --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SparseCore @@ -0,0 +1,69 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPARSECORE_MODULE_H +#define EIGEN_SPARSECORE_MODULE_H + +#include "Core" + +#include "src/Core/util/DisableStupidWarnings.h" + +#include +#include +#include +#include +#include + +/** + * \defgroup SparseCore_Module SparseCore module + * + * This module provides a sparse matrix representation, and basic associated matrix manipulations + * and operations. + * + * See the \ref TutorialSparse "Sparse tutorial" + * + * \code + * #include + * \endcode + * + * This module depends on: Core. + */ + +#include "src/SparseCore/SparseUtil.h" +#include "src/SparseCore/SparseMatrixBase.h" +#include "src/SparseCore/SparseAssign.h" +#include "src/SparseCore/CompressedStorage.h" +#include "src/SparseCore/AmbiVector.h" +#include "src/SparseCore/SparseCompressedBase.h" +#include "src/SparseCore/SparseMatrix.h" +#include "src/SparseCore/SparseMap.h" +#include "src/SparseCore/MappedSparseMatrix.h" +#include "src/SparseCore/SparseVector.h" +#include "src/SparseCore/SparseRef.h" +#include "src/SparseCore/SparseCwiseUnaryOp.h" +#include "src/SparseCore/SparseCwiseBinaryOp.h" +#include "src/SparseCore/SparseTranspose.h" +#include "src/SparseCore/SparseBlock.h" +#include "src/SparseCore/SparseDot.h" +#include "src/SparseCore/SparseRedux.h" +#include "src/SparseCore/SparseView.h" +#include "src/SparseCore/SparseDiagonalProduct.h" +#include "src/SparseCore/ConservativeSparseSparseProduct.h" +#include "src/SparseCore/SparseSparseProductWithPruning.h" +#include "src/SparseCore/SparseProduct.h" +#include "src/SparseCore/SparseDenseProduct.h" +#include "src/SparseCore/SparseSelfAdjointView.h" +#include "src/SparseCore/SparseTriangularView.h" +#include "src/SparseCore/TriangularSolver.h" +#include "src/SparseCore/SparsePermutation.h" +#include "src/SparseCore/SparseFuzzy.h" +#include "src/SparseCore/SparseSolverBase.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_SPARSECORE_MODULE_H + diff --git a/tools/SampleAnalyzer/Commons/Eigen/SparseLU b/tools/SampleAnalyzer/Commons/Eigen/SparseLU new file mode 100644 index 00000000..37c4a5c5 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SparseLU @@ -0,0 +1,50 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2012 Désiré Nuentsa-Wakam +// Copyright (C) 2012 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPARSELU_MODULE_H +#define EIGEN_SPARSELU_MODULE_H + +#include "SparseCore" + +/** + * \defgroup SparseLU_Module SparseLU module + * This module defines a supernodal factorization of general sparse matrices. + * The code is fully optimized for supernode-panel updates with specialized kernels. + * Please, see the documentation of the SparseLU class for more details. + */ + +// Ordering interface +#include "OrderingMethods" + +#include "src/Core/util/DisableStupidWarnings.h" + +#include "src/SparseLU/SparseLU_gemm_kernel.h" + +#include "src/SparseLU/SparseLU_Structs.h" +#include "src/SparseLU/SparseLU_SupernodalMatrix.h" +#include "src/SparseLU/SparseLUImpl.h" +#include "src/SparseCore/SparseColEtree.h" +#include "src/SparseLU/SparseLU_Memory.h" +#include "src/SparseLU/SparseLU_heap_relax_snode.h" +#include "src/SparseLU/SparseLU_relax_snode.h" +#include "src/SparseLU/SparseLU_pivotL.h" +#include "src/SparseLU/SparseLU_panel_dfs.h" +#include "src/SparseLU/SparseLU_kernel_bmod.h" +#include "src/SparseLU/SparseLU_panel_bmod.h" +#include "src/SparseLU/SparseLU_column_dfs.h" +#include "src/SparseLU/SparseLU_column_bmod.h" +#include "src/SparseLU/SparseLU_copy_to_ucol.h" +#include "src/SparseLU/SparseLU_pruneL.h" +#include "src/SparseLU/SparseLU_Utils.h" +#include "src/SparseLU/SparseLU.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_SPARSELU_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/SparseQR b/tools/SampleAnalyzer/Commons/Eigen/SparseQR new file mode 100644 index 00000000..f5fc5fa7 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SparseQR @@ -0,0 +1,36 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SPARSEQR_MODULE_H +#define EIGEN_SPARSEQR_MODULE_H + +#include "SparseCore" +#include "OrderingMethods" +#include "src/Core/util/DisableStupidWarnings.h" + +/** \defgroup SparseQR_Module SparseQR module + * \brief Provides QR decomposition for sparse matrices + * + * This module provides a simplicial version of the left-looking Sparse QR decomposition. + * The columns of the input matrix should be reordered to limit the fill-in during the + * decomposition. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end. + * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list + * of built-in and external ordering methods. + * + * \code + * #include + * \endcode + * + * + */ + +#include "src/SparseCore/SparseColEtree.h" +#include "src/SparseQR/SparseQR.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif diff --git a/tools/SampleAnalyzer/Commons/Eigen/StdDeque b/tools/SampleAnalyzer/Commons/Eigen/StdDeque new file mode 100644 index 00000000..bc68397b --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/StdDeque @@ -0,0 +1,27 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// Copyright (C) 2009 Hauke Heibel +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_STDDEQUE_MODULE_H +#define EIGEN_STDDEQUE_MODULE_H + +#include "Core" +#include + +#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */ + +#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) + +#else + +#include "src/StlSupport/StdDeque.h" + +#endif + +#endif // EIGEN_STDDEQUE_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/StdList b/tools/SampleAnalyzer/Commons/Eigen/StdList new file mode 100644 index 00000000..4c6262c0 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/StdList @@ -0,0 +1,26 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Hauke Heibel +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_STDLIST_MODULE_H +#define EIGEN_STDLIST_MODULE_H + +#include "Core" +#include + +#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */ + +#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) + +#else + +#include "src/StlSupport/StdList.h" + +#endif + +#endif // EIGEN_STDLIST_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/StdVector b/tools/SampleAnalyzer/Commons/Eigen/StdVector new file mode 100644 index 00000000..0c4697ad --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/StdVector @@ -0,0 +1,27 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// Copyright (C) 2009 Hauke Heibel +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_STDVECTOR_MODULE_H +#define EIGEN_STDVECTOR_MODULE_H + +#include "Core" +#include + +#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */ + +#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) + +#else + +#include "src/StlSupport/StdVector.h" + +#endif + +#endif // EIGEN_STDVECTOR_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/SuperLUSupport b/tools/SampleAnalyzer/Commons/Eigen/SuperLUSupport new file mode 100644 index 00000000..59312a82 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/SuperLUSupport @@ -0,0 +1,64 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H +#define EIGEN_SUPERLUSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +#ifdef EMPTY +#define EIGEN_EMPTY_WAS_ALREADY_DEFINED +#endif + +typedef int int_t; +#include +#include +#include + +// slu_util.h defines a preprocessor token named EMPTY which is really polluting, +// so we remove it in favor of a SUPERLU_EMPTY token. +// If EMPTY was already defined then we don't undef it. + +#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED) +# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED +#elif defined(EMPTY) +# undef EMPTY +#endif + +#define SUPERLU_EMPTY (-1) + +namespace Eigen { struct SluMatrix; } + +/** \ingroup Support_modules + * \defgroup SuperLUSupport_Module SuperLUSupport module + * + * This module provides an interface to the SuperLU library. + * It provides the following factorization class: + * - class SuperLU: a supernodal sequential LU factorization. + * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods). + * + * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported. + * + * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting. + * + * \code + * #include + * \endcode + * + * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be linked to the superlu library and its dependencies. + * The dependencies depend on how superlu has been compiled. + * For a cmake based project, you can use our FindSuperLU.cmake module to help you in this task. + * + */ + +#include "src/SuperLUSupport/SuperLUSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_SUPERLUSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/UmfPackSupport b/tools/SampleAnalyzer/Commons/Eigen/UmfPackSupport new file mode 100644 index 00000000..00eec808 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/UmfPackSupport @@ -0,0 +1,40 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H +#define EIGEN_UMFPACKSUPPORT_MODULE_H + +#include "SparseCore" + +#include "src/Core/util/DisableStupidWarnings.h" + +extern "C" { +#include +} + +/** \ingroup Support_modules + * \defgroup UmfPackSupport_Module UmfPackSupport module + * + * This module provides an interface to the UmfPack library which is part of the suitesparse package. + * It provides the following factorization class: + * - class UmfPackLU: a multifrontal sequential LU factorization. + * + * \code + * #include + * \endcode + * + * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be linked to the umfpack library and its dependencies. + * The dependencies depend on how umfpack has been compiled. + * For a cmake based project, you can use our FindUmfPack.cmake module to help you in this task. + * + */ + +#include "src/UmfPackSupport/UmfPackSupport.h" + +#include "src/Core/util/ReenableStupidWarnings.h" + +#endif // EIGEN_UMFPACKSUPPORT_MODULE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LDLT.h b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LDLT.h new file mode 100644 index 00000000..1013ca04 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LDLT.h @@ -0,0 +1,688 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2011 Gael Guennebaud +// Copyright (C) 2009 Keir Mierle +// Copyright (C) 2009 Benoit Jacob +// Copyright (C) 2011 Timothy E. Holy +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_LDLT_H +#define EIGEN_LDLT_H + +namespace Eigen { + +namespace internal { + template struct traits > + : traits<_MatrixType> + { + typedef MatrixXpr XprKind; + typedef SolverStorage StorageKind; + typedef int StorageIndex; + enum { Flags = 0 }; + }; + + template struct LDLT_Traits; + + // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef + enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite }; +} + +/** \ingroup Cholesky_Module + * + * \class LDLT + * + * \brief Robust Cholesky decomposition of a matrix with pivoting + * + * \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition + * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. + * The other triangular part won't be read. + * + * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite + * matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L + * is lower triangular with a unit diagonal and D is a diagonal matrix. + * + * The decomposition uses pivoting to ensure stability, so that D will have + * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root + * on D also stabilizes the computation. + * + * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky + * decomposition to determine whether a system of equations has a solution. + * + * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism. + * + * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT + */ +template class LDLT + : public SolverBase > +{ + public: + typedef _MatrixType MatrixType; + typedef SolverBase Base; + friend class SolverBase; + + EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT) + enum { + MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, + UpLo = _UpLo + }; + typedef Matrix TmpMatrixType; + + typedef Transpositions TranspositionType; + typedef PermutationMatrix PermutationType; + + typedef internal::LDLT_Traits Traits; + + /** \brief Default Constructor. + * + * The default constructor is useful in cases in which the user intends to + * perform decompositions via LDLT::compute(const MatrixType&). + */ + LDLT() + : m_matrix(), + m_transpositions(), + m_sign(internal::ZeroSign), + m_isInitialized(false) + {} + + /** \brief Default Constructor with memory preallocation + * + * Like the default constructor but with preallocation of the internal data + * according to the specified problem \a size. + * \sa LDLT() + */ + explicit LDLT(Index size) + : m_matrix(size, size), + m_transpositions(size), + m_temporary(size), + m_sign(internal::ZeroSign), + m_isInitialized(false) + {} + + /** \brief Constructor with decomposition + * + * This calculates the decomposition for the input \a matrix. + * + * \sa LDLT(Index size) + */ + template + explicit LDLT(const EigenBase& matrix) + : m_matrix(matrix.rows(), matrix.cols()), + m_transpositions(matrix.rows()), + m_temporary(matrix.rows()), + m_sign(internal::ZeroSign), + m_isInitialized(false) + { + compute(matrix.derived()); + } + + /** \brief Constructs a LDLT factorization from a given matrix + * + * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref. + * + * \sa LDLT(const EigenBase&) + */ + template + explicit LDLT(EigenBase& matrix) + : m_matrix(matrix.derived()), + m_transpositions(matrix.rows()), + m_temporary(matrix.rows()), + m_sign(internal::ZeroSign), + m_isInitialized(false) + { + compute(matrix.derived()); + } + + /** Clear any existing decomposition + * \sa rankUpdate(w,sigma) + */ + void setZero() + { + m_isInitialized = false; + } + + /** \returns a view of the upper triangular matrix U */ + inline typename Traits::MatrixU matrixU() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return Traits::getU(m_matrix); + } + + /** \returns a view of the lower triangular matrix L */ + inline typename Traits::MatrixL matrixL() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return Traits::getL(m_matrix); + } + + /** \returns the permutation matrix P as a transposition sequence. + */ + inline const TranspositionType& transpositionsP() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_transpositions; + } + + /** \returns the coefficients of the diagonal matrix D */ + inline Diagonal vectorD() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_matrix.diagonal(); + } + + /** \returns true if the matrix is positive (semidefinite) */ + inline bool isPositive() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign; + } + + /** \returns true if the matrix is negative (semidefinite) */ + inline bool isNegative(void) const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign; + } + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A. + * + * This function also supports in-place solves using the syntax x = decompositionObject.solve(x) . + * + * \note_about_checking_solutions + * + * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$ + * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$, + * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then + * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the + * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function + * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular. + * + * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt() + */ + template + inline const Solve + solve(const MatrixBase& b) const; + #endif + + template + bool solveInPlace(MatrixBase &bAndX) const; + + template + LDLT& compute(const EigenBase& matrix); + + /** \returns an estimate of the reciprocal condition number of the matrix of + * which \c *this is the LDLT decomposition. + */ + RealScalar rcond() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return internal::rcond_estimate_helper(m_l1_norm, *this); + } + + template + LDLT& rankUpdate(const MatrixBase& w, const RealScalar& alpha=1); + + /** \returns the internal LDLT decomposition matrix + * + * TODO: document the storage layout + */ + inline const MatrixType& matrixLDLT() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_matrix; + } + + MatrixType reconstructedMatrix() const; + + /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint. + * + * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as: + * \code x = decomposition.adjoint().solve(b) \endcode + */ + const LDLT& adjoint() const { return *this; }; + + EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); } + EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); } + + /** \brief Reports whether previous computation was successful. + * + * \returns \c Success if computation was successful, + * \c NumericalIssue if the factorization failed because of a zero pivot. + */ + ComputationInfo info() const + { + eigen_assert(m_isInitialized && "LDLT is not initialized."); + return m_info; + } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + void _solve_impl(const RhsType &rhs, DstType &dst) const; + + template + void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const; + #endif + + protected: + + static void check_template_parameters() + { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar); + } + + /** \internal + * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U. + * The strict upper part is used during the decomposition, the strict lower + * part correspond to the coefficients of L (its diagonal is equal to 1 and + * is not stored), and the diagonal entries correspond to D. + */ + MatrixType m_matrix; + RealScalar m_l1_norm; + TranspositionType m_transpositions; + TmpMatrixType m_temporary; + internal::SignMatrix m_sign; + bool m_isInitialized; + ComputationInfo m_info; +}; + +namespace internal { + +template struct ldlt_inplace; + +template<> struct ldlt_inplace +{ + template + static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) + { + using std::abs; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + typedef typename TranspositionType::StorageIndex IndexType; + eigen_assert(mat.rows()==mat.cols()); + const Index size = mat.rows(); + bool found_zero_pivot = false; + bool ret = true; + + if (size <= 1) + { + transpositions.setIdentity(); + if(size==0) sign = ZeroSign; + else if (numext::real(mat.coeff(0,0)) > static_cast(0) ) sign = PositiveSemiDef; + else if (numext::real(mat.coeff(0,0)) < static_cast(0)) sign = NegativeSemiDef; + else sign = ZeroSign; + return true; + } + + for (Index k = 0; k < size; ++k) + { + // Find largest diagonal element + Index index_of_biggest_in_corner; + mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner); + index_of_biggest_in_corner += k; + + transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner); + if(k != index_of_biggest_in_corner) + { + // apply the transposition while taking care to consider only + // the lower triangular part + Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element + mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k)); + mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s)); + std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner)); + for(Index i=k+1;i::IsComplex) + mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k)); + } + + // partition the matrix: + // A00 | - | - + // lu = A10 | A11 | - + // A20 | A21 | A22 + Index rs = size - k - 1; + Block A21(mat,k+1,k,rs,1); + Block A10(mat,k,0,1,k); + Block A20(mat,k+1,0,rs,k); + + if(k>0) + { + temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint(); + mat.coeffRef(k,k) -= (A10 * temp.head(k)).value(); + if(rs>0) + A21.noalias() -= A20 * temp.head(k); + } + + // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot + // was smaller than the cutoff value. However, since LDLT is not rank-revealing + // we should only make sure that we do not introduce INF or NaN values. + // Remark that LAPACK also uses 0 as the cutoff value. + RealScalar realAkk = numext::real(mat.coeffRef(k,k)); + bool pivot_is_valid = (abs(realAkk) > RealScalar(0)); + + if(k==0 && !pivot_is_valid) + { + // The entire diagonal is zero, there is nothing more to do + // except filling the transpositions, and checking whether the matrix is zero. + sign = ZeroSign; + for(Index j = 0; j0) && pivot_is_valid) + A21 /= realAkk; + else if(rs>0) + ret = ret && (A21.array()==Scalar(0)).all(); + + if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed + else if(!pivot_is_valid) found_zero_pivot = true; + + if (sign == PositiveSemiDef) { + if (realAkk < static_cast(0)) sign = Indefinite; + } else if (sign == NegativeSemiDef) { + if (realAkk > static_cast(0)) sign = Indefinite; + } else if (sign == ZeroSign) { + if (realAkk > static_cast(0)) sign = PositiveSemiDef; + else if (realAkk < static_cast(0)) sign = NegativeSemiDef; + } + } + + return ret; + } + + // Reference for the algorithm: Davis and Hager, "Multiple Rank + // Modifications of a Sparse Cholesky Factorization" (Algorithm 1) + // Trivial rearrangements of their computations (Timothy E. Holy) + // allow their algorithm to work for rank-1 updates even if the + // original matrix is not of full rank. + // Here only rank-1 updates are implemented, to reduce the + // requirement for intermediate storage and improve accuracy + template + static bool updateInPlace(MatrixType& mat, MatrixBase& w, const typename MatrixType::RealScalar& sigma=1) + { + using numext::isfinite; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + + const Index size = mat.rows(); + eigen_assert(mat.cols() == size && w.size()==size); + + RealScalar alpha = 1; + + // Apply the update + for (Index j = 0; j < size; j++) + { + // Check for termination due to an original decomposition of low-rank + if (!(isfinite)(alpha)) + break; + + // Update the diagonal terms + RealScalar dj = numext::real(mat.coeff(j,j)); + Scalar wj = w.coeff(j); + RealScalar swj2 = sigma*numext::abs2(wj); + RealScalar gamma = dj*alpha + swj2; + + mat.coeffRef(j,j) += swj2/alpha; + alpha += swj2/dj; + + + // Update the terms of L + Index rs = size-j-1; + w.tail(rs) -= wj * mat.col(j).tail(rs); + if(gamma != 0) + mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs); + } + return true; + } + + template + static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1) + { + // Apply the permutation to the input w + tmp = transpositions * w; + + return ldlt_inplace::updateInPlace(mat,tmp,sigma); + } +}; + +template<> struct ldlt_inplace +{ + template + static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) + { + Transpose matt(mat); + return ldlt_inplace::unblocked(matt, transpositions, temp, sign); + } + + template + static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1) + { + Transpose matt(mat); + return ldlt_inplace::update(matt, transpositions, tmp, w.conjugate(), sigma); + } +}; + +template struct LDLT_Traits +{ + typedef const TriangularView MatrixL; + typedef const TriangularView MatrixU; + static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); } + static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); } +}; + +template struct LDLT_Traits +{ + typedef const TriangularView MatrixL; + typedef const TriangularView MatrixU; + static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); } + static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); } +}; + +} // end namespace internal + +/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix + */ +template +template +LDLT& LDLT::compute(const EigenBase& a) +{ + check_template_parameters(); + + eigen_assert(a.rows()==a.cols()); + const Index size = a.rows(); + + m_matrix = a.derived(); + + // Compute matrix L1 norm = max abs column sum. + m_l1_norm = RealScalar(0); + // TODO move this code to SelfAdjointView + for (Index col = 0; col < size; ++col) { + RealScalar abs_col_sum; + if (_UpLo == Lower) + abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>(); + else + abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>(); + if (abs_col_sum > m_l1_norm) + m_l1_norm = abs_col_sum; + } + + m_transpositions.resize(size); + m_isInitialized = false; + m_temporary.resize(size); + m_sign = internal::ZeroSign; + + m_info = internal::ldlt_inplace::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue; + + m_isInitialized = true; + return *this; +} + +/** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T. + * \param w a vector to be incorporated into the decomposition. + * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1. + * \sa setZero() + */ +template +template +LDLT& LDLT::rankUpdate(const MatrixBase& w, const typename LDLT::RealScalar& sigma) +{ + typedef typename TranspositionType::StorageIndex IndexType; + const Index size = w.rows(); + if (m_isInitialized) + { + eigen_assert(m_matrix.rows()==size); + } + else + { + m_matrix.resize(size,size); + m_matrix.setZero(); + m_transpositions.resize(size); + for (Index i = 0; i < size; i++) + m_transpositions.coeffRef(i) = IndexType(i); + m_temporary.resize(size); + m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef; + m_isInitialized = true; + } + + internal::ldlt_inplace::update(m_matrix, m_transpositions, m_temporary, w, sigma); + + return *this; +} + +#ifndef EIGEN_PARSED_BY_DOXYGEN +template +template +void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const +{ + _solve_impl_transposed(rhs, dst); +} + +template +template +void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const +{ + // dst = P b + dst = m_transpositions * rhs; + + // dst = L^-1 (P b) + // dst = L^-*T (P b) + matrixL().template conjugateIf().solveInPlace(dst); + + // dst = D^-* (L^-1 P b) + // dst = D^-1 (L^-*T P b) + // more precisely, use pseudo-inverse of D (see bug 241) + using std::abs; + const typename Diagonal::RealReturnType vecD(vectorD()); + // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min()) + // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS: + // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits::epsilon(),RealScalar(1) / NumTraits::highest()); + // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest + // diagonal element is not well justified and leads to numerical issues in some cases. + // Moreover, Lapack's xSYTRS routines use 0 for the tolerance. + // Using numeric_limits::min() gives us more robustness to denormals. + RealScalar tolerance = (std::numeric_limits::min)(); + for (Index i = 0; i < vecD.size(); ++i) + { + if(abs(vecD(i)) > tolerance) + dst.row(i) /= vecD(i); + else + dst.row(i).setZero(); + } + + // dst = L^-* (D^-* L^-1 P b) + // dst = L^-T (D^-1 L^-*T P b) + matrixL().transpose().template conjugateIf().solveInPlace(dst); + + // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b + // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b + dst = m_transpositions.transpose() * dst; +} +#endif + +/** \internal use x = ldlt_object.solve(x); + * + * This is the \em in-place version of solve(). + * + * \param bAndX represents both the right-hand side matrix b and result x. + * + * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD. + * + * This version avoids a copy when the right hand side matrix b is not + * needed anymore. + * + * \sa LDLT::solve(), MatrixBase::ldlt() + */ +template +template +bool LDLT::solveInPlace(MatrixBase &bAndX) const +{ + eigen_assert(m_isInitialized && "LDLT is not initialized."); + eigen_assert(m_matrix.rows() == bAndX.rows()); + + bAndX = this->solve(bAndX); + + return true; +} + +/** \returns the matrix represented by the decomposition, + * i.e., it returns the product: P^T L D L^* P. + * This function is provided for debug purpose. */ +template +MatrixType LDLT::reconstructedMatrix() const +{ + eigen_assert(m_isInitialized && "LDLT is not initialized."); + const Index size = m_matrix.rows(); + MatrixType res(size,size); + + // P + res.setIdentity(); + res = transpositionsP() * res; + // L^* P + res = matrixU() * res; + // D(L^*P) + res = vectorD().real().asDiagonal() * res; + // L(DL^*P) + res = matrixL() * res; + // P^T (LDL^*P) + res = transpositionsP().transpose() * res; + + return res; +} + +/** \cholesky_module + * \returns the Cholesky decomposition with full pivoting without square root of \c *this + * \sa MatrixBase::ldlt() + */ +template +inline const LDLT::PlainObject, UpLo> +SelfAdjointView::ldlt() const +{ + return LDLT(m_matrix); +} + +/** \cholesky_module + * \returns the Cholesky decomposition with full pivoting without square root of \c *this + * \sa SelfAdjointView::ldlt() + */ +template +inline const LDLT::PlainObject> +MatrixBase::ldlt() const +{ + return LDLT(derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_LDLT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT.h b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT.h new file mode 100644 index 00000000..8c9b2b39 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT.h @@ -0,0 +1,558 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_LLT_H +#define EIGEN_LLT_H + +namespace Eigen { + +namespace internal{ + +template struct traits > + : traits<_MatrixType> +{ + typedef MatrixXpr XprKind; + typedef SolverStorage StorageKind; + typedef int StorageIndex; + enum { Flags = 0 }; +}; + +template struct LLT_Traits; +} + +/** \ingroup Cholesky_Module + * + * \class LLT + * + * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features + * + * \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition + * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. + * The other triangular part won't be read. + * + * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite + * matrix A such that A = LL^* = U^*U, where L is lower triangular. + * + * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b, + * for that purpose, we recommend the Cholesky decomposition without square root which is more stable + * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other + * situations like generalised eigen problems with hermitian matrices. + * + * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices, + * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations + * has a solution. + * + * Example: \include LLT_example.cpp + * Output: \verbinclude LLT_example.out + * + * \b Performance: for best performance, it is recommended to use a column-major storage format + * with the Lower triangular part (the default), or, equivalently, a row-major storage format + * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization + * step, and rank-updates can be up to 3 times slower. + * + * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism. + * + * Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered. + * Therefore, the strict lower part does not have to store correct values. + * + * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT + */ +template class LLT + : public SolverBase > +{ + public: + typedef _MatrixType MatrixType; + typedef SolverBase Base; + friend class SolverBase; + + EIGEN_GENERIC_PUBLIC_INTERFACE(LLT) + enum { + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; + + enum { + PacketSize = internal::packet_traits::size, + AlignmentMask = int(PacketSize)-1, + UpLo = _UpLo + }; + + typedef internal::LLT_Traits Traits; + + /** + * \brief Default Constructor. + * + * The default constructor is useful in cases in which the user intends to + * perform decompositions via LLT::compute(const MatrixType&). + */ + LLT() : m_matrix(), m_isInitialized(false) {} + + /** \brief Default Constructor with memory preallocation + * + * Like the default constructor but with preallocation of the internal data + * according to the specified problem \a size. + * \sa LLT() + */ + explicit LLT(Index size) : m_matrix(size, size), + m_isInitialized(false) {} + + template + explicit LLT(const EigenBase& matrix) + : m_matrix(matrix.rows(), matrix.cols()), + m_isInitialized(false) + { + compute(matrix.derived()); + } + + /** \brief Constructs a LLT factorization from a given matrix + * + * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when + * \c MatrixType is a Eigen::Ref. + * + * \sa LLT(const EigenBase&) + */ + template + explicit LLT(EigenBase& matrix) + : m_matrix(matrix.derived()), + m_isInitialized(false) + { + compute(matrix.derived()); + } + + /** \returns a view of the upper triangular matrix U */ + inline typename Traits::MatrixU matrixU() const + { + eigen_assert(m_isInitialized && "LLT is not initialized."); + return Traits::getU(m_matrix); + } + + /** \returns a view of the lower triangular matrix L */ + inline typename Traits::MatrixL matrixL() const + { + eigen_assert(m_isInitialized && "LLT is not initialized."); + return Traits::getL(m_matrix); + } + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. + * + * Since this LLT class assumes anyway that the matrix A is invertible, the solution + * theoretically exists and is unique regardless of b. + * + * Example: \include LLT_solve.cpp + * Output: \verbinclude LLT_solve.out + * + * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt() + */ + template + inline const Solve + solve(const MatrixBase& b) const; + #endif + + template + void solveInPlace(const MatrixBase &bAndX) const; + + template + LLT& compute(const EigenBase& matrix); + + /** \returns an estimate of the reciprocal condition number of the matrix of + * which \c *this is the Cholesky decomposition. + */ + RealScalar rcond() const + { + eigen_assert(m_isInitialized && "LLT is not initialized."); + eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative"); + return internal::rcond_estimate_helper(m_l1_norm, *this); + } + + /** \returns the LLT decomposition matrix + * + * TODO: document the storage layout + */ + inline const MatrixType& matrixLLT() const + { + eigen_assert(m_isInitialized && "LLT is not initialized."); + return m_matrix; + } + + MatrixType reconstructedMatrix() const; + + + /** \brief Reports whether previous computation was successful. + * + * \returns \c Success if computation was successful, + * \c NumericalIssue if the matrix.appears not to be positive definite. + */ + ComputationInfo info() const + { + eigen_assert(m_isInitialized && "LLT is not initialized."); + return m_info; + } + + /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint. + * + * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as: + * \code x = decomposition.adjoint().solve(b) \endcode + */ + const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; }; + + inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); } + inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); } + + template + LLT & rankUpdate(const VectorType& vec, const RealScalar& sigma = 1); + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + void _solve_impl(const RhsType &rhs, DstType &dst) const; + + template + void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const; + #endif + + protected: + + static void check_template_parameters() + { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar); + } + + /** \internal + * Used to compute and store L + * The strict upper part is not used and even not initialized. + */ + MatrixType m_matrix; + RealScalar m_l1_norm; + bool m_isInitialized; + ComputationInfo m_info; +}; + +namespace internal { + +template struct llt_inplace; + +template +static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) +{ + using std::sqrt; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + typedef typename MatrixType::ColXpr ColXpr; + typedef typename internal::remove_all::type ColXprCleaned; + typedef typename ColXprCleaned::SegmentReturnType ColXprSegment; + typedef Matrix TempVectorType; + typedef typename TempVectorType::SegmentReturnType TempVecSegment; + + Index n = mat.cols(); + eigen_assert(mat.rows()==n && vec.size()==n); + + TempVectorType temp; + + if(sigma>0) + { + // This version is based on Givens rotations. + // It is faster than the other one below, but only works for updates, + // i.e., for sigma > 0 + temp = sqrt(sigma) * vec; + + for(Index i=0; i g; + g.makeGivens(mat(i,i), -temp(i), &mat(i,i)); + + Index rs = n-i-1; + if(rs>0) + { + ColXprSegment x(mat.col(i).tail(rs)); + TempVecSegment y(temp.tail(rs)); + apply_rotation_in_the_plane(x, y, g); + } + } + } + else + { + temp = vec; + RealScalar beta = 1; + for(Index j=0; j struct llt_inplace +{ + typedef typename NumTraits::Real RealScalar; + template + static Index unblocked(MatrixType& mat) + { + using std::sqrt; + + eigen_assert(mat.rows()==mat.cols()); + const Index size = mat.rows(); + for(Index k = 0; k < size; ++k) + { + Index rs = size-k-1; // remaining size + + Block A21(mat,k+1,k,rs,1); + Block A10(mat,k,0,1,k); + Block A20(mat,k+1,0,rs,k); + + RealScalar x = numext::real(mat.coeff(k,k)); + if (k>0) x -= A10.squaredNorm(); + if (x<=RealScalar(0)) + return k; + mat.coeffRef(k,k) = x = sqrt(x); + if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint(); + if (rs>0) A21 /= x; + } + return -1; + } + + template + static Index blocked(MatrixType& m) + { + eigen_assert(m.rows()==m.cols()); + Index size = m.rows(); + if(size<32) + return unblocked(m); + + Index blockSize = size/8; + blockSize = (blockSize/16)*16; + blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128)); + + for (Index k=0; k A11(m,k, k, bs,bs); + Block A21(m,k+bs,k, rs,bs); + Block A22(m,k+bs,k+bs,rs,rs); + + Index ret; + if((ret=unblocked(A11))>=0) return k+ret; + if(rs>0) A11.adjoint().template triangularView().template solveInPlace(A21); + if(rs>0) A22.template selfadjointView().rankUpdate(A21,typename NumTraits::Literal(-1)); // bottleneck + } + return -1; + } + + template + static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) + { + return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); + } +}; + +template struct llt_inplace +{ + typedef typename NumTraits::Real RealScalar; + + template + static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat) + { + Transpose matt(mat); + return llt_inplace::unblocked(matt); + } + template + static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat) + { + Transpose matt(mat); + return llt_inplace::blocked(matt); + } + template + static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) + { + Transpose matt(mat); + return llt_inplace::rankUpdate(matt, vec.conjugate(), sigma); + } +}; + +template struct LLT_Traits +{ + typedef const TriangularView MatrixL; + typedef const TriangularView MatrixU; + static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); } + static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); } + static bool inplace_decomposition(MatrixType& m) + { return llt_inplace::blocked(m)==-1; } +}; + +template struct LLT_Traits +{ + typedef const TriangularView MatrixL; + typedef const TriangularView MatrixU; + static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); } + static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); } + static bool inplace_decomposition(MatrixType& m) + { return llt_inplace::blocked(m)==-1; } +}; + +} // end namespace internal + +/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix + * + * \returns a reference to *this + * + * Example: \include TutorialLinAlgComputeTwice.cpp + * Output: \verbinclude TutorialLinAlgComputeTwice.out + */ +template +template +LLT& LLT::compute(const EigenBase& a) +{ + check_template_parameters(); + + eigen_assert(a.rows()==a.cols()); + const Index size = a.rows(); + m_matrix.resize(size, size); + if (!internal::is_same_dense(m_matrix, a.derived())) + m_matrix = a.derived(); + + // Compute matrix L1 norm = max abs column sum. + m_l1_norm = RealScalar(0); + // TODO move this code to SelfAdjointView + for (Index col = 0; col < size; ++col) { + RealScalar abs_col_sum; + if (_UpLo == Lower) + abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>(); + else + abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>(); + if (abs_col_sum > m_l1_norm) + m_l1_norm = abs_col_sum; + } + + m_isInitialized = true; + bool ok = Traits::inplace_decomposition(m_matrix); + m_info = ok ? Success : NumericalIssue; + + return *this; +} + +/** Performs a rank one update (or dowdate) of the current decomposition. + * If A = LL^* before the rank one update, + * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector + * of same dimension. + */ +template +template +LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType); + eigen_assert(v.size()==m_matrix.cols()); + eigen_assert(m_isInitialized); + if(internal::llt_inplace::rankUpdate(m_matrix,v,sigma)>=0) + m_info = NumericalIssue; + else + m_info = Success; + + return *this; +} + +#ifndef EIGEN_PARSED_BY_DOXYGEN +template +template +void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const +{ + _solve_impl_transposed(rhs, dst); +} + +template +template +void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const +{ + dst = rhs; + + matrixL().template conjugateIf().solveInPlace(dst); + matrixU().template conjugateIf().solveInPlace(dst); +} +#endif + +/** \internal use x = llt_object.solve(x); + * + * This is the \em in-place version of solve(). + * + * \param bAndX represents both the right-hand side matrix b and result x. + * + * This version avoids a copy when the right hand side matrix b is not needed anymore. + * + * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here. + * This function will const_cast it, so constness isn't honored here. + * + * \sa LLT::solve(), MatrixBase::llt() + */ +template +template +void LLT::solveInPlace(const MatrixBase &bAndX) const +{ + eigen_assert(m_isInitialized && "LLT is not initialized."); + eigen_assert(m_matrix.rows()==bAndX.rows()); + matrixL().solveInPlace(bAndX); + matrixU().solveInPlace(bAndX); +} + +/** \returns the matrix represented by the decomposition, + * i.e., it returns the product: L L^*. + * This function is provided for debug purpose. */ +template +MatrixType LLT::reconstructedMatrix() const +{ + eigen_assert(m_isInitialized && "LLT is not initialized."); + return matrixL() * matrixL().adjoint().toDenseMatrix(); +} + +/** \cholesky_module + * \returns the LLT decomposition of \c *this + * \sa SelfAdjointView::llt() + */ +template +inline const LLT::PlainObject> +MatrixBase::llt() const +{ + return LLT(derived()); +} + +/** \cholesky_module + * \returns the LLT decomposition of \c *this + * \sa SelfAdjointView::llt() + */ +template +inline const LLT::PlainObject, UpLo> +SelfAdjointView::llt() const +{ + return LLT(m_matrix); +} + +} // end namespace Eigen + +#endif // EIGEN_LLT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT_LAPACKE.h b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT_LAPACKE.h new file mode 100644 index 00000000..bc6489e6 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Cholesky/LLT_LAPACKE.h @@ -0,0 +1,99 @@ +/* + Copyright (c) 2011, Intel Corporation. All rights reserved. + + Redistribution and use in source and binary forms, with or without modification, + are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + * Neither the name of Intel Corporation nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON + ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ******************************************************************************** + * Content : Eigen bindings to LAPACKe + * LLt decomposition based on LAPACKE_?potrf function. + ******************************************************************************** +*/ + +#ifndef EIGEN_LLT_LAPACKE_H +#define EIGEN_LLT_LAPACKE_H + +namespace Eigen { + +namespace internal { + +template struct lapacke_llt; + +#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \ +template<> struct lapacke_llt \ +{ \ + template \ + static inline Index potrf(MatrixType& m, char uplo) \ + { \ + lapack_int matrix_order; \ + lapack_int size, lda, info, StorageOrder; \ + EIGTYPE* a; \ + eigen_assert(m.rows()==m.cols()); \ + /* Set up parameters for ?potrf */ \ + size = convert_index(m.rows()); \ + StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \ + matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ + a = &(m.coeffRef(0,0)); \ + lda = convert_index(m.outerStride()); \ +\ + info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \ + info = (info==0) ? -1 : info>0 ? info-1 : size; \ + return info; \ + } \ +}; \ +template<> struct llt_inplace \ +{ \ + template \ + static Index blocked(MatrixType& m) \ + { \ + return lapacke_llt::potrf(m, 'L'); \ + } \ + template \ + static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ + { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \ +}; \ +template<> struct llt_inplace \ +{ \ + template \ + static Index blocked(MatrixType& m) \ + { \ + return lapacke_llt::potrf(m, 'U'); \ + } \ + template \ + static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ + { \ + Transpose matt(mat); \ + return llt_inplace::rankUpdate(matt, vec.conjugate(), sigma); \ + } \ +}; + +EIGEN_LAPACKE_LLT(double, double, d) +EIGEN_LAPACKE_LLT(float, float, s) +EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z) +EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c) + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_LLT_LAPACKE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/CholmodSupport/CholmodSupport.h b/tools/SampleAnalyzer/Commons/Eigen/src/CholmodSupport/CholmodSupport.h new file mode 100644 index 00000000..adaf5285 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/CholmodSupport/CholmodSupport.h @@ -0,0 +1,682 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CHOLMODSUPPORT_H +#define EIGEN_CHOLMODSUPPORT_H + +namespace Eigen { + +namespace internal { + +template struct cholmod_configure_matrix; + +template<> struct cholmod_configure_matrix { + template + static void run(CholmodType& mat) { + mat.xtype = CHOLMOD_REAL; + mat.dtype = CHOLMOD_DOUBLE; + } +}; + +template<> struct cholmod_configure_matrix > { + template + static void run(CholmodType& mat) { + mat.xtype = CHOLMOD_COMPLEX; + mat.dtype = CHOLMOD_DOUBLE; + } +}; + +// Other scalar types are not yet supported by Cholmod +// template<> struct cholmod_configure_matrix { +// template +// static void run(CholmodType& mat) { +// mat.xtype = CHOLMOD_REAL; +// mat.dtype = CHOLMOD_SINGLE; +// } +// }; +// +// template<> struct cholmod_configure_matrix > { +// template +// static void run(CholmodType& mat) { +// mat.xtype = CHOLMOD_COMPLEX; +// mat.dtype = CHOLMOD_SINGLE; +// } +// }; + +} // namespace internal + +/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object. + * Note that the data are shared. + */ +template +cholmod_sparse viewAsCholmod(Ref > mat) +{ + cholmod_sparse res; + res.nzmax = mat.nonZeros(); + res.nrow = mat.rows(); + res.ncol = mat.cols(); + res.p = mat.outerIndexPtr(); + res.i = mat.innerIndexPtr(); + res.x = mat.valuePtr(); + res.z = 0; + res.sorted = 1; + if(mat.isCompressed()) + { + res.packed = 1; + res.nz = 0; + } + else + { + res.packed = 0; + res.nz = mat.innerNonZeroPtr(); + } + + res.dtype = 0; + res.stype = -1; + + if (internal::is_same<_StorageIndex,int>::value) + { + res.itype = CHOLMOD_INT; + } + else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value) + { + res.itype = CHOLMOD_LONG; + } + else + { + eigen_assert(false && "Index type not supported yet"); + } + + // setup res.xtype + internal::cholmod_configure_matrix<_Scalar>::run(res); + + res.stype = 0; + + return res; +} + +template +const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat) +{ + cholmod_sparse res = viewAsCholmod(Ref >(mat.const_cast_derived())); + return res; +} + +template +const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat) +{ + cholmod_sparse res = viewAsCholmod(Ref >(mat.const_cast_derived())); + return res; +} + +/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix. + * The data are not copied but shared. */ +template +cholmod_sparse viewAsCholmod(const SparseSelfAdjointView, UpLo>& mat) +{ + cholmod_sparse res = viewAsCholmod(Ref >(mat.matrix().const_cast_derived())); + + if(UpLo==Upper) res.stype = 1; + if(UpLo==Lower) res.stype = -1; + // swap stype for rowmajor matrices (only works for real matrices) + EIGEN_STATIC_ASSERT((_Options & RowMajorBit) == 0 || NumTraits<_Scalar>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); + if(_Options & RowMajorBit) res.stype *=-1; + + return res; +} + +/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix. + * The data are not copied but shared. */ +template +cholmod_dense viewAsCholmod(MatrixBase& mat) +{ + EIGEN_STATIC_ASSERT((internal::traits::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); + typedef typename Derived::Scalar Scalar; + + cholmod_dense res; + res.nrow = mat.rows(); + res.ncol = mat.cols(); + res.nzmax = res.nrow * res.ncol; + res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride(); + res.x = (void*)(mat.derived().data()); + res.z = 0; + + internal::cholmod_configure_matrix::run(res); + + return res; +} + +/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix. + * The data are not copied but shared. */ +template +MappedSparseMatrix viewAsEigen(cholmod_sparse& cm) +{ + return MappedSparseMatrix + (cm.nrow, cm.ncol, static_cast(cm.p)[cm.ncol], + static_cast(cm.p), static_cast(cm.i),static_cast(cm.x) ); +} + +namespace internal { + +// template specializations for int and long that call the correct cholmod method + +#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name) \ + template inline ret cm_ ## name (cholmod_common &Common) { return cholmod_ ## name (&Common); } \ + template<> inline ret cm_ ## name (cholmod_common &Common) { return cholmod_l_ ## name (&Common); } + +#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1) \ + template inline ret cm_ ## name (t1& a1, cholmod_common &Common) { return cholmod_ ## name (&a1, &Common); } \ + template<> inline ret cm_ ## name (t1& a1, cholmod_common &Common) { return cholmod_l_ ## name (&a1, &Common); } + +EIGEN_CHOLMOD_SPECIALIZE0(int, start) +EIGEN_CHOLMOD_SPECIALIZE0(int, finish) + +EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L) +EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X) +EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A) + +EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A) + +template inline cholmod_dense* cm_solve (int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common &Common) { return cholmod_solve (sys, &L, &B, &Common); } +template<> inline cholmod_dense* cm_solve (int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common &Common) { return cholmod_l_solve (sys, &L, &B, &Common); } + +template inline cholmod_sparse* cm_spsolve (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve (sys, &L, &B, &Common); } +template<> inline cholmod_sparse* cm_spsolve (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_l_spsolve (sys, &L, &B, &Common); } + +template +inline int cm_factorize_p (cholmod_sparse* A, double beta[2], _StorageIndex* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p (A, beta, fset, fsize, L, &Common); } +template<> +inline int cm_factorize_p (cholmod_sparse* A, double beta[2], SuiteSparse_long* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_l_factorize_p (A, beta, fset, fsize, L, &Common); } + +#undef EIGEN_CHOLMOD_SPECIALIZE0 +#undef EIGEN_CHOLMOD_SPECIALIZE1 + +} // namespace internal + + +enum CholmodMode { + CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt +}; + + +/** \ingroup CholmodSupport_Module + * \class CholmodBase + * \brief The base class for the direct Cholesky factorization of Cholmod + * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT + */ +template +class CholmodBase : public SparseSolverBase +{ + protected: + typedef SparseSolverBase Base; + using Base::derived; + using Base::m_isInitialized; + public: + typedef _MatrixType MatrixType; + enum { UpLo = _UpLo }; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + typedef MatrixType CholMatrixType; + typedef typename MatrixType::StorageIndex StorageIndex; + enum { + ColsAtCompileTime = MatrixType::ColsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; + + public: + + CholmodBase() + : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) + { + EIGEN_STATIC_ASSERT((internal::is_same::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY); + m_shiftOffset[0] = m_shiftOffset[1] = 0.0; + internal::cm_start(m_cholmod); + } + + explicit CholmodBase(const MatrixType& matrix) + : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) + { + EIGEN_STATIC_ASSERT((internal::is_same::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY); + m_shiftOffset[0] = m_shiftOffset[1] = 0.0; + internal::cm_start(m_cholmod); + compute(matrix); + } + + ~CholmodBase() + { + if(m_cholmodFactor) + internal::cm_free_factor(m_cholmodFactor, m_cholmod); + internal::cm_finish(m_cholmod); + } + + inline StorageIndex cols() const { return internal::convert_index(m_cholmodFactor->n); } + inline StorageIndex rows() const { return internal::convert_index(m_cholmodFactor->n); } + + /** \brief Reports whether previous computation was successful. + * + * \returns \c Success if computation was successful, + * \c NumericalIssue if the matrix.appears to be negative. + */ + ComputationInfo info() const + { + eigen_assert(m_isInitialized && "Decomposition is not initialized."); + return m_info; + } + + /** Computes the sparse Cholesky decomposition of \a matrix */ + Derived& compute(const MatrixType& matrix) + { + analyzePattern(matrix); + factorize(matrix); + return derived(); + } + + /** Performs a symbolic decomposition on the sparsity pattern of \a matrix. + * + * This function is particularly useful when solving for several problems having the same structure. + * + * \sa factorize() + */ + void analyzePattern(const MatrixType& matrix) + { + if(m_cholmodFactor) + { + internal::cm_free_factor(m_cholmodFactor, m_cholmod); + m_cholmodFactor = 0; + } + cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView()); + m_cholmodFactor = internal::cm_analyze(A, m_cholmod); + + this->m_isInitialized = true; + this->m_info = Success; + m_analysisIsOk = true; + m_factorizationIsOk = false; + } + + /** Performs a numeric decomposition of \a matrix + * + * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed. + * + * \sa analyzePattern() + */ + void factorize(const MatrixType& matrix) + { + eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); + cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView()); + internal::cm_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod); + + // If the factorization failed, minor is the column at which it did. On success minor == n. + this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue); + m_factorizationIsOk = true; + } + + /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations. + * See the Cholmod user guide for details. */ + cholmod_common& cholmod() { return m_cholmod; } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal */ + template + void _solve_impl(const MatrixBase &b, MatrixBase &dest) const + { + eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); + const Index size = m_cholmodFactor->n; + EIGEN_UNUSED_VARIABLE(size); + eigen_assert(size==b.rows()); + + // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref. + Ref > b_ref(b.derived()); + + cholmod_dense b_cd = viewAsCholmod(b_ref); + cholmod_dense* x_cd = internal::cm_solve(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod); + if(!x_cd) + { + this->m_info = NumericalIssue; + return; + } + // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) + // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve + dest = Matrix::Map(reinterpret_cast(x_cd->x),b.rows(),b.cols()); + internal::cm_free_dense(x_cd, m_cholmod); + } + + /** \internal */ + template + void _solve_impl(const SparseMatrixBase &b, SparseMatrixBase &dest) const + { + eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); + const Index size = m_cholmodFactor->n; + EIGEN_UNUSED_VARIABLE(size); + eigen_assert(size==b.rows()); + + // note: cs stands for Cholmod Sparse + Ref > b_ref(b.const_cast_derived()); + cholmod_sparse b_cs = viewAsCholmod(b_ref); + cholmod_sparse* x_cs = internal::cm_spsolve(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod); + if(!x_cs) + { + this->m_info = NumericalIssue; + return; + } + // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) + // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's sparse solver) + dest.derived() = viewAsEigen(*x_cs); + internal::cm_free_sparse(x_cs, m_cholmod); + } + #endif // EIGEN_PARSED_BY_DOXYGEN + + + /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization. + * + * During the numerical factorization, an offset term is added to the diagonal coefficients:\n + * \c d_ii = \a offset + \c d_ii + * + * The default is \a offset=0. + * + * \returns a reference to \c *this. + */ + Derived& setShift(const RealScalar& offset) + { + m_shiftOffset[0] = double(offset); + return derived(); + } + + /** \returns the determinant of the underlying matrix from the current factorization */ + Scalar determinant() const + { + using std::exp; + return exp(logDeterminant()); + } + + /** \returns the log determinant of the underlying matrix from the current factorization */ + Scalar logDeterminant() const + { + using std::log; + using numext::real; + eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); + + RealScalar logDet = 0; + Scalar *x = static_cast(m_cholmodFactor->x); + if (m_cholmodFactor->is_super) + { + // Supernodal factorization stored as a packed list of dense column-major blocs, + // as described by the following structure: + + // super[k] == index of the first column of the j-th super node + StorageIndex *super = static_cast(m_cholmodFactor->super); + // pi[k] == offset to the description of row indices + StorageIndex *pi = static_cast(m_cholmodFactor->pi); + // px[k] == offset to the respective dense block + StorageIndex *px = static_cast(m_cholmodFactor->px); + + Index nb_super_nodes = m_cholmodFactor->nsuper; + for (Index k=0; k < nb_super_nodes; ++k) + { + StorageIndex ncols = super[k + 1] - super[k]; + StorageIndex nrows = pi[k + 1] - pi[k]; + + Map, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1)); + logDet += sk.real().log().sum(); + } + } + else + { + // Simplicial factorization stored as standard CSC matrix. + StorageIndex *p = static_cast(m_cholmodFactor->p); + Index size = m_cholmodFactor->n; + for (Index k=0; kis_ll) + logDet *= 2.0; + return logDet; + }; + + template + void dumpMemory(Stream& /*s*/) + {} + + protected: + mutable cholmod_common m_cholmod; + cholmod_factor* m_cholmodFactor; + double m_shiftOffset[2]; + mutable ComputationInfo m_info; + int m_factorizationIsOk; + int m_analysisIsOk; +}; + +/** \ingroup CholmodSupport_Module + * \class CholmodSimplicialLLT + * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod + * + * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization + * using the Cholmod library. + * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest. + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices + * X and B can be either dense or sparse. + * + * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> + * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower + * or Upper. Default is Lower. + * + * \implsparsesolverconcept + * + * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. + * + * \warning Only double precision real and complex scalar types are supported by Cholmod. + * + * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT + */ +template +class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> > +{ + typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base; + using Base::m_cholmod; + + public: + + typedef _MatrixType MatrixType; + + CholmodSimplicialLLT() : Base() { init(); } + + CholmodSimplicialLLT(const MatrixType& matrix) : Base() + { + init(); + this->compute(matrix); + } + + ~CholmodSimplicialLLT() {} + protected: + void init() + { + m_cholmod.final_asis = 0; + m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; + m_cholmod.final_ll = 1; + } +}; + + +/** \ingroup CholmodSupport_Module + * \class CholmodSimplicialLDLT + * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod + * + * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization + * using the Cholmod library. + * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest. + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices + * X and B can be either dense or sparse. + * + * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> + * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower + * or Upper. Default is Lower. + * + * \implsparsesolverconcept + * + * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. + * + * \warning Only double precision real and complex scalar types are supported by Cholmod. + * + * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT + */ +template +class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> > +{ + typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base; + using Base::m_cholmod; + + public: + + typedef _MatrixType MatrixType; + + CholmodSimplicialLDLT() : Base() { init(); } + + CholmodSimplicialLDLT(const MatrixType& matrix) : Base() + { + init(); + this->compute(matrix); + } + + ~CholmodSimplicialLDLT() {} + protected: + void init() + { + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; + } +}; + +/** \ingroup CholmodSupport_Module + * \class CholmodSupernodalLLT + * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod + * + * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization + * using the Cholmod library. + * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM. + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices + * X and B can be either dense or sparse. + * + * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> + * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower + * or Upper. Default is Lower. + * + * \implsparsesolverconcept + * + * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. + * + * \warning Only double precision real and complex scalar types are supported by Cholmod. + * + * \sa \ref TutorialSparseSolverConcept + */ +template +class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> > +{ + typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base; + using Base::m_cholmod; + + public: + + typedef _MatrixType MatrixType; + + CholmodSupernodalLLT() : Base() { init(); } + + CholmodSupernodalLLT(const MatrixType& matrix) : Base() + { + init(); + this->compute(matrix); + } + + ~CholmodSupernodalLLT() {} + protected: + void init() + { + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_SUPERNODAL; + } +}; + +/** \ingroup CholmodSupport_Module + * \class CholmodDecomposition + * \brief A general Cholesky factorization and solver based on Cholmod + * + * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization + * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices + * X and B can be either dense or sparse. + * + * This variant permits to change the underlying Cholesky method at runtime. + * On the other hand, it does not provide access to the result of the factorization. + * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization. + * + * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> + * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower + * or Upper. Default is Lower. + * + * \implsparsesolverconcept + * + * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. + * + * \warning Only double precision real and complex scalar types are supported by Cholmod. + * + * \sa \ref TutorialSparseSolverConcept + */ +template +class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> > +{ + typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base; + using Base::m_cholmod; + + public: + + typedef _MatrixType MatrixType; + + CholmodDecomposition() : Base() { init(); } + + CholmodDecomposition(const MatrixType& matrix) : Base() + { + init(); + this->compute(matrix); + } + + ~CholmodDecomposition() {} + + void setMode(CholmodMode mode) + { + switch(mode) + { + case CholmodAuto: + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_AUTO; + break; + case CholmodSimplicialLLt: + m_cholmod.final_asis = 0; + m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; + m_cholmod.final_ll = 1; + break; + case CholmodSupernodalLLt: + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_SUPERNODAL; + break; + case CholmodLDLt: + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; + break; + default: + break; + } + } + protected: + void init() + { + m_cholmod.final_asis = 1; + m_cholmod.supernodal = CHOLMOD_AUTO; + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CHOLMODSUPPORT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArithmeticSequence.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArithmeticSequence.h new file mode 100644 index 00000000..b6200fac --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArithmeticSequence.h @@ -0,0 +1,413 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2017 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ARITHMETIC_SEQUENCE_H +#define EIGEN_ARITHMETIC_SEQUENCE_H + +namespace Eigen { + +namespace internal { + +#if (!EIGEN_HAS_CXX11) || !((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48) +template struct aseq_negate {}; + +template<> struct aseq_negate { + typedef Index type; +}; + +template struct aseq_negate > { + typedef FixedInt<-N> type; +}; + +// Compilation error in the following case: +template<> struct aseq_negate > {}; + +template::value, + bool SizeIsSymbolic =symbolic::is_symbolic::value> +struct aseq_reverse_first_type { + typedef Index type; +}; + +template +struct aseq_reverse_first_type { + typedef symbolic::AddExpr > >, + symbolic::ValueExpr > + > type; +}; + +template +struct aseq_reverse_first_type_aux { + typedef Index type; +}; + +template +struct aseq_reverse_first_type_aux::type> { + typedef FixedInt<(SizeType::value-1)*IncrType::value> type; +}; + +template +struct aseq_reverse_first_type { + typedef typename aseq_reverse_first_type_aux::type Aux; + typedef symbolic::AddExpr > type; +}; + +template +struct aseq_reverse_first_type { + typedef symbolic::AddExpr > >, + symbolic::ValueExpr >, + symbolic::ValueExpr<> > type; +}; +#endif + +// Helper to cleanup the type of the increment: +template struct cleanup_seq_incr { + typedef typename cleanup_index_type::type type; +}; + +} + +//-------------------------------------------------------------------------------- +// seq(first,last,incr) and seqN(first,size,incr) +//-------------------------------------------------------------------------------- + +template > +class ArithmeticSequence; + +template +ArithmeticSequence::type, + typename internal::cleanup_index_type::type, + typename internal::cleanup_seq_incr::type > +seqN(FirstType first, SizeType size, IncrType incr); + +/** \class ArithmeticSequence + * \ingroup Core_Module + * + * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by + * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride) + * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i. + * + * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments + * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the + * only way it is used. + * + * \tparam FirstType type of the first element, usually an Index, + * but internally it can be a symbolic expression + * \tparam SizeType type representing the size of the sequence, usually an Index + * or a compile time integral constant. Internally, it can also be a symbolic expression + * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is compile-time 1) + * + * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView + */ +template +class ArithmeticSequence +{ +public: + ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {} + ArithmeticSequence(FirstType first, SizeType size, IncrType incr) : m_first(first), m_size(size), m_incr(incr) {} + + enum { + SizeAtCompileTime = internal::get_fixed_value::value, + IncrAtCompileTime = internal::get_fixed_value::value + }; + + /** \returns the size, i.e., number of elements, of the sequence */ + Index size() const { return m_size; } + + /** \returns the first element \f$ a_0 \f$ in the sequence */ + Index first() const { return m_first; } + + /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */ + Index operator[](Index i) const { return m_first + i * m_incr; } + + const FirstType& firstObject() const { return m_first; } + const SizeType& sizeObject() const { return m_size; } + const IncrType& incrObject() const { return m_incr; } + +protected: + FirstType m_first; + SizeType m_size; + IncrType m_incr; + +public: + +#if EIGEN_HAS_CXX11 && ((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48) + auto reverse() const -> decltype(Eigen::seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr)) { + return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr); + } +#else +protected: + typedef typename internal::aseq_negate::type ReverseIncrType; + typedef typename internal::aseq_reverse_first_type::type ReverseFirstType; +public: + ArithmeticSequence + reverse() const { + return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr); + } +#endif +}; + +/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr + * + * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */ +template +ArithmeticSequence::type,typename internal::cleanup_index_type::type,typename internal::cleanup_seq_incr::type > +seqN(FirstType first, SizeType size, IncrType incr) { + return ArithmeticSequence::type,typename internal::cleanup_index_type::type,typename internal::cleanup_seq_incr::type>(first,size,incr); +} + +/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment + * + * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */ +template +ArithmeticSequence::type,typename internal::cleanup_index_type::type > +seqN(FirstType first, SizeType size) { + return ArithmeticSequence::type,typename internal::cleanup_index_type::type>(first,size); +} + +#ifdef EIGEN_PARSED_BY_DOXYGEN + +/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a incr + * + * It is essentially an alias to: + * \code + * seqN(f, (l-f+incr)/incr, incr); + * \endcode + * + * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) + */ +template +auto seq(FirstType f, LastType l, IncrType incr); + +/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment + * + * It is essentially an alias to: + * \code + * seqN(f,l-f+1); + * \endcode + * + * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) + */ +template +auto seq(FirstType f, LastType l); + +#else // EIGEN_PARSED_BY_DOXYGEN + +#if EIGEN_HAS_CXX11 +template +auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type::type(f), + ( typename internal::cleanup_index_type::type(l) + - typename internal::cleanup_index_type::type(f)+fix<1>()))) +{ + return seqN(typename internal::cleanup_index_type::type(f), + (typename internal::cleanup_index_type::type(l) + -typename internal::cleanup_index_type::type(f)+fix<1>())); +} + +template +auto seq(FirstType f, LastType l, IncrType incr) + -> decltype(seqN(typename internal::cleanup_index_type::type(f), + ( typename internal::cleanup_index_type::type(l) + - typename internal::cleanup_index_type::type(f)+typename internal::cleanup_seq_incr::type(incr) + ) / typename internal::cleanup_seq_incr::type(incr), + typename internal::cleanup_seq_incr::type(incr))) +{ + typedef typename internal::cleanup_seq_incr::type CleanedIncrType; + return seqN(typename internal::cleanup_index_type::type(f), + ( typename internal::cleanup_index_type::type(l) + -typename internal::cleanup_index_type::type(f)+CleanedIncrType(incr)) / CleanedIncrType(incr), + CleanedIncrType(incr)); +} + +#else // EIGEN_HAS_CXX11 + +template +typename internal::enable_if::value || symbolic::is_symbolic::value), + ArithmeticSequence::type,Index> >::type +seq(FirstType f, LastType l) +{ + return seqN(typename internal::cleanup_index_type::type(f), + Index((typename internal::cleanup_index_type::type(l)-typename internal::cleanup_index_type::type(f)+fix<1>()))); +} + +template +typename internal::enable_if::value, + ArithmeticSequence,symbolic::ValueExpr<> >, + symbolic::ValueExpr > > > >::type +seq(const symbolic::BaseExpr &f, LastType l) +{ + return seqN(f.derived(),(typename internal::cleanup_index_type::type(l)-f.derived()+fix<1>())); +} + +template +typename internal::enable_if::value, + ArithmeticSequence::type, + symbolic::AddExpr >, + symbolic::ValueExpr > > > >::type +seq(FirstType f, const symbolic::BaseExpr &l) +{ + return seqN(typename internal::cleanup_index_type::type(f),(l.derived()-typename internal::cleanup_index_type::type(f)+fix<1>())); +} + +template +ArithmeticSequence >,symbolic::ValueExpr > > > +seq(const symbolic::BaseExpr &f, const symbolic::BaseExpr &l) +{ + return seqN(f.derived(),(l.derived()-f.derived()+fix<1>())); +} + + +template +typename internal::enable_if::value || symbolic::is_symbolic::value), + ArithmeticSequence::type,Index,typename internal::cleanup_seq_incr::type> >::type +seq(FirstType f, LastType l, IncrType incr) +{ + typedef typename internal::cleanup_seq_incr::type CleanedIncrType; + return seqN(typename internal::cleanup_index_type::type(f), + Index((typename internal::cleanup_index_type::type(l)-typename internal::cleanup_index_type::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr)), incr); +} + +template +typename internal::enable_if::value, + ArithmeticSequence, + symbolic::ValueExpr<> >, + symbolic::ValueExpr::type> >, + symbolic::ValueExpr::type> >, + typename internal::cleanup_seq_incr::type> >::type +seq(const symbolic::BaseExpr &f, LastType l, IncrType incr) +{ + typedef typename internal::cleanup_seq_incr::type CleanedIncrType; + return seqN(f.derived(),(typename internal::cleanup_index_type::type(l)-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr); +} + +template +typename internal::enable_if::value, + ArithmeticSequence::type, + symbolic::QuotientExpr >, + symbolic::ValueExpr::type> >, + symbolic::ValueExpr::type> >, + typename internal::cleanup_seq_incr::type> >::type +seq(FirstType f, const symbolic::BaseExpr &l, IncrType incr) +{ + typedef typename internal::cleanup_seq_incr::type CleanedIncrType; + return seqN(typename internal::cleanup_index_type::type(f), + (l.derived()-typename internal::cleanup_index_type::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr), incr); +} + +template +ArithmeticSequence >, + symbolic::ValueExpr::type> >, + symbolic::ValueExpr::type> >, + typename internal::cleanup_seq_incr::type> +seq(const symbolic::BaseExpr &f, const symbolic::BaseExpr &l, IncrType incr) +{ + typedef typename internal::cleanup_seq_incr::type CleanedIncrType; + return seqN(f.derived(),(l.derived()-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr); +} +#endif // EIGEN_HAS_CXX11 + +#endif // EIGEN_PARSED_BY_DOXYGEN + + +#if EIGEN_HAS_CXX11 || defined(EIGEN_PARSED_BY_DOXYGEN) +/** \cpp11 + * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr. + * + * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode + * + * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */ +template +auto lastN(SizeType size, IncrType incr) +-> decltype(seqN(Eigen::last-(size-fix<1>())*incr, size, incr)) +{ + return seqN(Eigen::last-(size-fix<1>())*incr, size, incr); +} + +/** \cpp11 + * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment. + * + * It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode + * + * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */ +template +auto lastN(SizeType size) +-> decltype(seqN(Eigen::last+fix<1>()-size, size)) +{ + return seqN(Eigen::last+fix<1>()-size, size); +} +#endif + +namespace internal { + +// Convert a symbolic span into a usable one (i.e., remove last/end "keywords") +template +struct make_size_type { + typedef typename internal::conditional::value, Index, T>::type type; +}; + +template +struct IndexedViewCompatibleType, XprSize> { + typedef ArithmeticSequence::type,IncrType> type; +}; + +template +ArithmeticSequence::type,IncrType> +makeIndexedViewCompatible(const ArithmeticSequence& ids, Index size,SpecializedType) { + return ArithmeticSequence::type,IncrType>( + eval_expr_given_size(ids.firstObject(),size),eval_expr_given_size(ids.sizeObject(),size),ids.incrObject()); +} + +template +struct get_compile_time_incr > { + enum { value = get_fixed_value::value }; +}; + +} // end namespace internal + +/** \namespace Eigen::indexing + * \ingroup Core_Module + * + * The sole purpose of this namespace is to be able to import all functions + * and symbols that are expected to be used within operator() for indexing + * and slicing. If you already imported the whole Eigen namespace: + * \code using namespace Eigen; \endcode + * then you are already all set. Otherwise, if you don't want/cannot import + * the whole Eigen namespace, the following line: + * \code using namespace Eigen::indexing; \endcode + * is equivalent to: + * \code + using Eigen::all; + using Eigen::seq; + using Eigen::seqN; + using Eigen::lastN; // c++11 only + using Eigen::last; + using Eigen::lastp1; + using Eigen::fix; + \endcode + */ +namespace indexing { + using Eigen::all; + using Eigen::seq; + using Eigen::seqN; + #if EIGEN_HAS_CXX11 + using Eigen::lastN; + #endif + using Eigen::last; + using Eigen::lastp1; + using Eigen::fix; +} + +} // end namespace Eigen + +#endif // EIGEN_ARITHMETIC_SEQUENCE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Array.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Array.h new file mode 100644 index 00000000..20c789b1 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Array.h @@ -0,0 +1,417 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ARRAY_H +#define EIGEN_ARRAY_H + +namespace Eigen { + +namespace internal { +template +struct traits > : traits > +{ + typedef ArrayXpr XprKind; + typedef ArrayBase > XprBase; +}; +} + +/** \class Array + * \ingroup Core_Module + * + * \brief General-purpose arrays with easy API for coefficient-wise operations + * + * The %Array class is very similar to the Matrix class. It provides + * general-purpose one- and two-dimensional arrays. The difference between the + * %Array and the %Matrix class is primarily in the API: the API for the + * %Array class provides easy access to coefficient-wise operations, while the + * API for the %Matrix class provides easy access to linear-algebra + * operations. + * + * See documentation of class Matrix for detailed information on the template parameters + * storage layout. + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN. + * + * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy + */ +template +class Array + : public PlainObjectBase > +{ + public: + + typedef PlainObjectBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Array) + + enum { Options = _Options }; + typedef typename Base::PlainObject PlainObject; + + protected: + template + friend struct internal::conservative_resize_like_impl; + + using Base::m_storage; + + public: + + using Base::base; + using Base::coeff; + using Base::coeffRef; + + /** + * The usage of + * using Base::operator=; + * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped + * the usage of 'using'. This should be done only for operator=. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array& operator=(const EigenBase &other) + { + return Base::operator=(other); + } + + /** Set all the entries to \a value. + * \sa DenseBase::setConstant(), DenseBase::fill() + */ + /* This overload is needed because the usage of + * using Base::operator=; + * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped + * the usage of 'using'. This should be done only for operator=. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array& operator=(const Scalar &value) + { + Base::setConstant(value); + return *this; + } + + /** Copies the value of the expression \a other into \c *this with automatic resizing. + * + * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), + * it will be initialized. + * + * Note that copying a row-vector into a vector (and conversely) is allowed. + * The resizing, if any, is then done in the appropriate way so that row-vectors + * remain row-vectors and vectors remain vectors. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array& operator=(const DenseBase& other) + { + return Base::_set(other); + } + + /** This is a special case of the templated operator=. Its purpose is to + * prevent a default operator= from hiding the templated operator=. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array& operator=(const Array& other) + { + return Base::_set(other); + } + + /** Default constructor. + * + * For fixed-size matrices, does nothing. + * + * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix + * is called a null matrix. This constructor is the unique way to create null matrices: resizing + * a matrix to 0 is not supported. + * + * \sa resize(Index,Index) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array() : Base() + { + Base::_check_template_params(); + EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } + +#ifndef EIGEN_PARSED_BY_DOXYGEN + // FIXME is it still needed ?? + /** \internal */ + EIGEN_DEVICE_FUNC + Array(internal::constructor_without_unaligned_array_assert) + : Base(internal::constructor_without_unaligned_array_assert()) + { + Base::_check_template_params(); + EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } +#endif + +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC + Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible::value) + : Base(std::move(other)) + { + Base::_check_template_params(); + } + EIGEN_DEVICE_FUNC + Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable::value) + { + Base::operator=(std::move(other)); + return *this; + } +#endif + + #if EIGEN_HAS_CXX11 + /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + * + * Example: \include Array_variadic_ctor_cxx11.cpp + * Output: \verbinclude Array_variadic_ctor_cxx11.out + * + * \sa Array(const std::initializer_list>&) + * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&) + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + : Base(a0, a1, a2, a3, args...) {} + + /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11 + * + * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients: + * + * Example: \include Array_initializer_list_23_cxx11.cpp + * Output: \verbinclude Array_initializer_list_23_cxx11.out + * + * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered. + * + * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed. + * Therefore Array{{1,2,3,4,5}} is legal and the more verbose syntax + * Array{{1},{2},{3},{4},{5}} can be avoided: + * + * Example: \include Array_initializer_list_vector_cxx11.cpp + * Output: \verbinclude Array_initializer_list_vector_cxx11.out + * + * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes, + * and implicit transposition is allowed for compile-time 1D arrays only. + * + * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const std::initializer_list>& list) : Base(list) {} + #endif // end EIGEN_HAS_CXX11 + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE explicit Array(const T& x) + { + Base::_check_template_params(); + Base::template _init1(x); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) + { + Base::_check_template_params(); + this->template _init2(val0, val1); + } + + #else + /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */ + EIGEN_DEVICE_FUNC explicit Array(const Scalar *data); + /** Constructs a vector or row-vector with given dimension. \only_for_vectors + * + * Note that this is only useful for dynamic-size vectors. For fixed-size vectors, + * it is redundant to pass the dimension here, so it makes more sense to use the default + * constructor Array() instead. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE explicit Array(Index dim); + /** constructs an initialized 1x1 Array with the given coefficient + * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */ + Array(const Scalar& value); + /** constructs an uninitialized array with \a rows rows and \a cols columns. + * + * This is useful for dynamic-size arrays. For fixed-size arrays, + * it is redundant to pass these parameters, so one should use the default constructor + * Array() instead. */ + Array(Index rows, Index cols); + /** constructs an initialized 2D vector with given coefficients + * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */ + Array(const Scalar& val0, const Scalar& val1); + #endif // end EIGEN_PARSED_BY_DOXYGEN + + /** constructs an initialized 3D vector with given coefficients + * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) + { + Base::_check_template_params(); + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3) + m_storage.data()[0] = val0; + m_storage.data()[1] = val1; + m_storage.data()[2] = val2; + } + /** constructs an initialized 4D vector with given coefficients + * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3) + { + Base::_check_template_params(); + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4) + m_storage.data()[0] = val0; + m_storage.data()[1] = val1; + m_storage.data()[2] = val2; + m_storage.data()[3] = val3; + } + + /** Copy constructor */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const Array& other) + : Base(other) + { } + + private: + struct PrivateType {}; + public: + + /** \sa MatrixBase::operator=(const EigenBase&) */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Array(const EigenBase &other, + typename internal::enable_if::value, + PrivateType>::type = PrivateType()) + : Base(other.derived()) + { } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT{ return 1; } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); } + + #ifdef EIGEN_ARRAY_PLUGIN + #include EIGEN_ARRAY_PLUGIN + #endif + + private: + + template + friend struct internal::matrix_swap_impl; +}; + +/** \defgroup arraytypedefs Global array typedefs + * \ingroup Core_Module + * + * %Eigen defines several typedef shortcuts for most common 1D and 2D array types. + * + * The general patterns are the following: + * + * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, + * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd + * for complex double. + * + * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats. + * + * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is + * a fixed-size 1D array of 4 complex floats. + * + * With \cpp11, template alias are also defined for common sizes. + * They follow the same pattern as above except that the scalar type suffix is replaced by a + * template parameter, i.e.: + * - `ArrayRowsCols` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size. + * - `ArraySize` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays. + * + * \sa class Array + */ + +#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ +/** \ingroup arraytypedefs */ \ +typedef Array Array##SizeSuffix##SizeSuffix##TypeSuffix; \ +/** \ingroup arraytypedefs */ \ +typedef Array Array##SizeSuffix##TypeSuffix; + +#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ +/** \ingroup arraytypedefs */ \ +typedef Array Array##Size##X##TypeSuffix; \ +/** \ingroup arraytypedefs */ \ +typedef Array Array##X##Size##TypeSuffix; + +#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ +EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \ +EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \ +EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \ +EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4) + +EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i) +EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f) +EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d) +EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex, cf) +EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex, cd) + +#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES +#undef EIGEN_MAKE_ARRAY_TYPEDEFS +#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS + +#if EIGEN_HAS_CXX11 + +#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix) \ +/** \ingroup arraytypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Array##SizeSuffix##SizeSuffix = Array; \ +/** \ingroup arraytypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Array##SizeSuffix = Array; + +#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size) \ +/** \ingroup arraytypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Array##Size##X = Array; \ +/** \ingroup arraytypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Array##X##Size = Array; + +EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2) +EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3) +EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4) +EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X) +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2) +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3) +EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4) + +#undef EIGEN_MAKE_ARRAY_TYPEDEFS +#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS + +#endif // EIGEN_HAS_CXX11 + +#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \ +using Eigen::Matrix##SizeSuffix##TypeSuffix; \ +using Eigen::Vector##SizeSuffix##TypeSuffix; \ +using Eigen::RowVector##SizeSuffix##TypeSuffix; + +#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \ + +#define EIGEN_USING_ARRAY_TYPEDEFS \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \ +EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd) + +} // end namespace Eigen + +#endif // EIGEN_ARRAY_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayBase.h new file mode 100644 index 00000000..ea3dd1c3 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayBase.h @@ -0,0 +1,226 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ARRAYBASE_H +#define EIGEN_ARRAYBASE_H + +namespace Eigen { + +template class MatrixWrapper; + +/** \class ArrayBase + * \ingroup Core_Module + * + * \brief Base class for all 1D and 2D array, and related expressions + * + * An array is similar to a dense vector or matrix. While matrices are mathematical + * objects with well defined linear algebra operators, an array is just a collection + * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence, + * all operations applied to an array are performed coefficient wise. Furthermore, + * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient + * constructors allowing to easily write generic code working for both scalar values + * and arrays. + * + * This class is the base that is inherited by all array expression types. + * + * \tparam Derived is the derived type, e.g., an array or an expression type. + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN. + * + * \sa class MatrixBase, \ref TopicClassHierarchy + */ +template class ArrayBase + : public DenseBase +{ + public: +#ifndef EIGEN_PARSED_BY_DOXYGEN + /** The base class for a given storage type. */ + typedef ArrayBase StorageBaseType; + + typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl; + + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::packet_traits::type PacketScalar; + typedef typename NumTraits::Real RealScalar; + + typedef DenseBase Base; + using Base::RowsAtCompileTime; + using Base::ColsAtCompileTime; + using Base::SizeAtCompileTime; + using Base::MaxRowsAtCompileTime; + using Base::MaxColsAtCompileTime; + using Base::MaxSizeAtCompileTime; + using Base::IsVectorAtCompileTime; + using Base::Flags; + + using Base::derived; + using Base::const_cast_derived; + using Base::rows; + using Base::cols; + using Base::size; + using Base::coeff; + using Base::coeffRef; + using Base::lazyAssign; + using Base::operator-; + using Base::operator=; + using Base::operator+=; + using Base::operator-=; + using Base::operator*=; + using Base::operator/=; + + typedef typename Base::CoeffReturnType CoeffReturnType; + +#endif // not EIGEN_PARSED_BY_DOXYGEN + +#ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename Base::PlainObject PlainObject; + + /** \internal Represents a matrix with all coefficients equal to one another*/ + typedef CwiseNullaryOp,PlainObject> ConstantReturnType; +#endif // not EIGEN_PARSED_BY_DOXYGEN + +#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase +#define EIGEN_DOC_UNARY_ADDONS(X,Y) +# include "../plugins/MatrixCwiseUnaryOps.h" +# include "../plugins/ArrayCwiseUnaryOps.h" +# include "../plugins/CommonCwiseBinaryOps.h" +# include "../plugins/MatrixCwiseBinaryOps.h" +# include "../plugins/ArrayCwiseBinaryOps.h" +# ifdef EIGEN_ARRAYBASE_PLUGIN +# include EIGEN_ARRAYBASE_PLUGIN +# endif +#undef EIGEN_CURRENT_STORAGE_BASE_CLASS +#undef EIGEN_DOC_UNARY_ADDONS + + /** Special case of the template operator=, in order to prevent the compiler + * from generating a default operator= (issue hit with g++ 4.1) + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const ArrayBase& other) + { + internal::call_assignment(derived(), other.derived()); + return derived(); + } + + /** Set all the entries to \a value. + * \sa DenseBase::setConstant(), DenseBase::fill() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const Scalar &value) + { Base::setConstant(value); return derived(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator+=(const Scalar& scalar); + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator-=(const Scalar& scalar); + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator+=(const ArrayBase& other); + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator-=(const ArrayBase& other); + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator*=(const ArrayBase& other); + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator/=(const ArrayBase& other); + + public: + EIGEN_DEVICE_FUNC + ArrayBase& array() { return *this; } + EIGEN_DEVICE_FUNC + const ArrayBase& array() const { return *this; } + + /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array + * \sa MatrixBase::array() */ + EIGEN_DEVICE_FUNC + MatrixWrapper matrix() { return MatrixWrapper(derived()); } + EIGEN_DEVICE_FUNC + const MatrixWrapper matrix() const { return MatrixWrapper(derived()); } + +// template +// inline void evalTo(Dest& dst) const { dst = matrix(); } + + protected: + EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase) + EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase) + + private: + explicit ArrayBase(Index); + ArrayBase(Index,Index); + template explicit ArrayBase(const ArrayBase&); + protected: + // mixing arrays and matrices is not legal + template Derived& operator+=(const MatrixBase& ) + {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} + // mixing arrays and matrices is not legal + template Derived& operator-=(const MatrixBase& ) + {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} +}; + +/** replaces \c *this by \c *this - \a other. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +ArrayBase::operator-=(const ArrayBase &other) +{ + call_assignment(derived(), other.derived(), internal::sub_assign_op()); + return derived(); +} + +/** replaces \c *this by \c *this + \a other. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +ArrayBase::operator+=(const ArrayBase& other) +{ + call_assignment(derived(), other.derived(), internal::add_assign_op()); + return derived(); +} + +/** replaces \c *this by \c *this * \a other coefficient wise. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +ArrayBase::operator*=(const ArrayBase& other) +{ + call_assignment(derived(), other.derived(), internal::mul_assign_op()); + return derived(); +} + +/** replaces \c *this by \c *this / \a other coefficient wise. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +ArrayBase::operator/=(const ArrayBase& other) +{ + call_assignment(derived(), other.derived(), internal::div_assign_op()); + return derived(); +} + +} // end namespace Eigen + +#endif // EIGEN_ARRAYBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayWrapper.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayWrapper.h new file mode 100644 index 00000000..2e9555b5 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ArrayWrapper.h @@ -0,0 +1,209 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ARRAYWRAPPER_H +#define EIGEN_ARRAYWRAPPER_H + +namespace Eigen { + +/** \class ArrayWrapper + * \ingroup Core_Module + * + * \brief Expression of a mathematical vector or matrix as an array object + * + * This class is the return type of MatrixBase::array(), and most of the time + * this is the only way it is use. + * + * \sa MatrixBase::array(), class MatrixWrapper + */ + +namespace internal { +template +struct traits > + : public traits::type > +{ + typedef ArrayXpr XprKind; + // Let's remove NestByRefBit + enum { + Flags0 = traits::type >::Flags, + LvalueBitFlag = is_lvalue::value ? LvalueBit : 0, + Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag + }; +}; +} + +template +class ArrayWrapper : public ArrayBase > +{ + public: + typedef ArrayBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper) + typedef typename internal::remove_all::type NestedExpression; + + typedef typename internal::conditional< + internal::is_lvalue::value, + Scalar, + const Scalar + >::type ScalarWithConstIfNotLvalue; + + typedef typename internal::ref_selector::non_const_type NestedExpressionType; + + using Base::coeffRef; + + EIGEN_DEVICE_FUNC + explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {} + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); } + + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); } + EIGEN_DEVICE_FUNC + inline const Scalar* data() const { return m_expression.data(); } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index rowId, Index colId) const + { + return m_expression.coeffRef(rowId, colId); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index index) const + { + return m_expression.coeffRef(index); + } + + template + EIGEN_DEVICE_FUNC + inline void evalTo(Dest& dst) const { dst = m_expression; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all::type& + nestedExpression() const + { + return m_expression; + } + + /** Forwards the resizing request to the nested expression + * \sa DenseBase::resize(Index) */ + EIGEN_DEVICE_FUNC + void resize(Index newSize) { m_expression.resize(newSize); } + /** Forwards the resizing request to the nested expression + * \sa DenseBase::resize(Index,Index)*/ + EIGEN_DEVICE_FUNC + void resize(Index rows, Index cols) { m_expression.resize(rows,cols); } + + protected: + NestedExpressionType m_expression; +}; + +/** \class MatrixWrapper + * \ingroup Core_Module + * + * \brief Expression of an array as a mathematical vector or matrix + * + * This class is the return type of ArrayBase::matrix(), and most of the time + * this is the only way it is use. + * + * \sa MatrixBase::matrix(), class ArrayWrapper + */ + +namespace internal { +template +struct traits > + : public traits::type > +{ + typedef MatrixXpr XprKind; + // Let's remove NestByRefBit + enum { + Flags0 = traits::type >::Flags, + LvalueBitFlag = is_lvalue::value ? LvalueBit : 0, + Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag + }; +}; +} + +template +class MatrixWrapper : public MatrixBase > +{ + public: + typedef MatrixBase > Base; + EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper) + typedef typename internal::remove_all::type NestedExpression; + + typedef typename internal::conditional< + internal::is_lvalue::value, + Scalar, + const Scalar + >::type ScalarWithConstIfNotLvalue; + + typedef typename internal::ref_selector::non_const_type NestedExpressionType; + + using Base::coeffRef; + + EIGEN_DEVICE_FUNC + explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {} + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); } + + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); } + EIGEN_DEVICE_FUNC + inline const Scalar* data() const { return m_expression.data(); } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index rowId, Index colId) const + { + return m_expression.derived().coeffRef(rowId, colId); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index index) const + { + return m_expression.coeffRef(index); + } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all::type& + nestedExpression() const + { + return m_expression; + } + + /** Forwards the resizing request to the nested expression + * \sa DenseBase::resize(Index) */ + EIGEN_DEVICE_FUNC + void resize(Index newSize) { m_expression.resize(newSize); } + /** Forwards the resizing request to the nested expression + * \sa DenseBase::resize(Index,Index)*/ + EIGEN_DEVICE_FUNC + void resize(Index rows, Index cols) { m_expression.resize(rows,cols); } + + protected: + NestedExpressionType m_expression; +}; + +} // end namespace Eigen + +#endif // EIGEN_ARRAYWRAPPER_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign.h new file mode 100644 index 00000000..655412ef --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign.h @@ -0,0 +1,90 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2007 Michael Olbrich +// Copyright (C) 2006-2010 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ASSIGN_H +#define EIGEN_ASSIGN_H + +namespace Eigen { + +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase + ::lazyAssign(const DenseBase& other) +{ + enum{ + SameType = internal::is_same::value + }; + + EIGEN_STATIC_ASSERT_LVALUE(Derived) + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived) + EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) + + eigen_assert(rows() == other.rows() && cols() == other.cols()); + internal::call_assignment_no_alias(derived(),other.derived()); + + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& DenseBase::operator=(const DenseBase& other) +{ + internal::call_assignment(derived(), other.derived()); + return derived(); +} + +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& DenseBase::operator=(const DenseBase& other) +{ + internal::call_assignment(derived(), other.derived()); + return derived(); +} + +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const MatrixBase& other) +{ + internal::call_assignment(derived(), other.derived()); + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const DenseBase& other) +{ + internal::call_assignment(derived(), other.derived()); + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const EigenBase& other) +{ + internal::call_assignment(derived(), other.derived()); + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const ReturnByValue& other) +{ + other.derived().evalTo(derived()); + return derived(); +} + +} // end namespace Eigen + +#endif // EIGEN_ASSIGN_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/AssignEvaluator.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/AssignEvaluator.h new file mode 100644 index 00000000..7d76f0c2 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/AssignEvaluator.h @@ -0,0 +1,1010 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2011 Benoit Jacob +// Copyright (C) 2011-2014 Gael Guennebaud +// Copyright (C) 2011-2012 Jitse Niesen +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ASSIGN_EVALUATOR_H +#define EIGEN_ASSIGN_EVALUATOR_H + +namespace Eigen { + +// This implementation is based on Assign.h + +namespace internal { + +/*************************************************************************** +* Part 1 : the logic deciding a strategy for traversal and unrolling * +***************************************************************************/ + +// copy_using_evaluator_traits is based on assign_traits + +template +struct copy_using_evaluator_traits +{ + typedef typename DstEvaluator::XprType Dst; + typedef typename Dst::Scalar DstScalar; + + enum { + DstFlags = DstEvaluator::Flags, + SrcFlags = SrcEvaluator::Flags + }; + +public: + enum { + DstAlignment = DstEvaluator::Alignment, + SrcAlignment = SrcEvaluator::Alignment, + DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit, + JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment) + }; + +private: + enum { + InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime) + : int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime) + : int(Dst::RowsAtCompileTime), + InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime) + : int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime) + : int(Dst::MaxRowsAtCompileTime), + RestrictedInnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(InnerSize,MaxPacketSize), + RestrictedLinearSize = EIGEN_SIZE_MIN_PREFER_FIXED(Dst::SizeAtCompileTime,MaxPacketSize), + OuterStride = int(outer_stride_at_compile_time::ret), + MaxSizeAtCompileTime = Dst::SizeAtCompileTime + }; + + // TODO distinguish between linear traversal and inner-traversals + typedef typename find_best_packet::type LinearPacketType; + typedef typename find_best_packet::type InnerPacketType; + + enum { + LinearPacketSize = unpacket_traits::size, + InnerPacketSize = unpacket_traits::size + }; + +public: + enum { + LinearRequiredAlignment = unpacket_traits::alignment, + InnerRequiredAlignment = unpacket_traits::alignment + }; + +private: + enum { + DstIsRowMajor = DstFlags&RowMajorBit, + SrcIsRowMajor = SrcFlags&RowMajorBit, + StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)), + MightVectorize = bool(StorageOrdersAgree) + && (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit) + && bool(functor_traits::PacketAccess), + MayInnerVectorize = MightVectorize + && int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0 + && int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0 + && (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)), + MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit), + MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess) + && (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic), + /* If the destination isn't aligned, we have to do runtime checks and we don't unroll, + so it's only good for large enough sizes. */ + MaySliceVectorize = bool(MightVectorize) && bool(DstHasDirectAccess) + && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize))) + /* slice vectorization can be slow, so we only want it if the slices are big, which is + indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block + in a fixed-size matrix + However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */ + }; + +public: + enum { + Traversal = int(Dst::SizeAtCompileTime) == 0 ? int(AllAtOnceTraversal) // If compile-size is zero, traversing will fail at compile-time. + : (int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize)) ? int(LinearVectorizedTraversal) + : int(MayInnerVectorize) ? int(InnerVectorizedTraversal) + : int(MayLinearVectorize) ? int(LinearVectorizedTraversal) + : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) + : int(MayLinearize) ? int(LinearTraversal) + : int(DefaultTraversal), + Vectorized = int(Traversal) == InnerVectorizedTraversal + || int(Traversal) == LinearVectorizedTraversal + || int(Traversal) == SliceVectorizedTraversal + }; + + typedef typename conditional::type PacketType; + +private: + enum { + ActualPacketSize = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize + : Vectorized ? InnerPacketSize + : 1, + UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize, + MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic + && int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit), + MayUnrollInner = int(InnerSize) != Dynamic + && int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit) + }; + +public: + enum { + Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal)) + ? ( + int(MayUnrollCompletely) ? int(CompleteUnrolling) + : int(MayUnrollInner) ? int(InnerUnrolling) + : int(NoUnrolling) + ) + : int(Traversal) == int(LinearVectorizedTraversal) + ? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment))) + ? int(CompleteUnrolling) + : int(NoUnrolling) ) + : int(Traversal) == int(LinearTraversal) + ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) + : int(NoUnrolling) ) +#if EIGEN_UNALIGNED_VECTORIZE + : int(Traversal) == int(SliceVectorizedTraversal) + ? ( bool(MayUnrollInner) ? int(InnerUnrolling) + : int(NoUnrolling) ) +#endif + : int(NoUnrolling) + }; + +#ifdef EIGEN_DEBUG_ASSIGN + static void debug() + { + std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl; + std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl; + std::cerr.setf(std::ios::hex, std::ios::basefield); + std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl; + std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl; + std::cerr.unsetf(std::ios::hex); + EIGEN_DEBUG_VAR(DstAlignment) + EIGEN_DEBUG_VAR(SrcAlignment) + EIGEN_DEBUG_VAR(LinearRequiredAlignment) + EIGEN_DEBUG_VAR(InnerRequiredAlignment) + EIGEN_DEBUG_VAR(JointAlignment) + EIGEN_DEBUG_VAR(InnerSize) + EIGEN_DEBUG_VAR(InnerMaxSize) + EIGEN_DEBUG_VAR(LinearPacketSize) + EIGEN_DEBUG_VAR(InnerPacketSize) + EIGEN_DEBUG_VAR(ActualPacketSize) + EIGEN_DEBUG_VAR(StorageOrdersAgree) + EIGEN_DEBUG_VAR(MightVectorize) + EIGEN_DEBUG_VAR(MayLinearize) + EIGEN_DEBUG_VAR(MayInnerVectorize) + EIGEN_DEBUG_VAR(MayLinearVectorize) + EIGEN_DEBUG_VAR(MaySliceVectorize) + std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl; + EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost) + EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost) + EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime) + EIGEN_DEBUG_VAR(UnrollingLimit) + EIGEN_DEBUG_VAR(MayUnrollCompletely) + EIGEN_DEBUG_VAR(MayUnrollInner) + std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl; + std::cerr << std::endl; + } +#endif +}; + +/*************************************************************************** +* Part 2 : meta-unrollers +***************************************************************************/ + +/************************ +*** Default traversal *** +************************/ + +template +struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling +{ + // FIXME: this is not very clean, perhaps this information should be provided by the kernel? + typedef typename Kernel::DstEvaluatorType DstEvaluatorType; + typedef typename DstEvaluatorType::XprType DstXprType; + + enum { + outer = Index / DstXprType::InnerSizeAtCompileTime, + inner = Index % DstXprType::InnerSizeAtCompileTime + }; + + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + kernel.assignCoeffByOuterInner(outer, inner); + copy_using_evaluator_DefaultTraversal_CompleteUnrolling::run(kernel); + } +}; + +template +struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { } +}; + +template +struct copy_using_evaluator_DefaultTraversal_InnerUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer) + { + kernel.assignCoeffByOuterInner(outer, Index_); + copy_using_evaluator_DefaultTraversal_InnerUnrolling::run(kernel, outer); + } +}; + +template +struct copy_using_evaluator_DefaultTraversal_InnerUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { } +}; + +/*********************** +*** Linear traversal *** +***********************/ + +template +struct copy_using_evaluator_LinearTraversal_CompleteUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) + { + kernel.assignCoeff(Index); + copy_using_evaluator_LinearTraversal_CompleteUnrolling::run(kernel); + } +}; + +template +struct copy_using_evaluator_LinearTraversal_CompleteUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { } +}; + +/************************** +*** Inner vectorization *** +**************************/ + +template +struct copy_using_evaluator_innervec_CompleteUnrolling +{ + // FIXME: this is not very clean, perhaps this information should be provided by the kernel? + typedef typename Kernel::DstEvaluatorType DstEvaluatorType; + typedef typename DstEvaluatorType::XprType DstXprType; + typedef typename Kernel::PacketType PacketType; + + enum { + outer = Index / DstXprType::InnerSizeAtCompileTime, + inner = Index % DstXprType::InnerSizeAtCompileTime, + SrcAlignment = Kernel::AssignmentTraits::SrcAlignment, + DstAlignment = Kernel::AssignmentTraits::DstAlignment + }; + + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + kernel.template assignPacketByOuterInner(outer, inner); + enum { NextIndex = Index + unpacket_traits::size }; + copy_using_evaluator_innervec_CompleteUnrolling::run(kernel); + } +}; + +template +struct copy_using_evaluator_innervec_CompleteUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { } +}; + +template +struct copy_using_evaluator_innervec_InnerUnrolling +{ + typedef typename Kernel::PacketType PacketType; + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer) + { + kernel.template assignPacketByOuterInner(outer, Index_); + enum { NextIndex = Index_ + unpacket_traits::size }; + copy_using_evaluator_innervec_InnerUnrolling::run(kernel, outer); + } +}; + +template +struct copy_using_evaluator_innervec_InnerUnrolling +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { } +}; + +/*************************************************************************** +* Part 3 : implementation of all cases +***************************************************************************/ + +// dense_assignment_loop is based on assign_impl + +template +struct dense_assignment_loop; + +/************************ +***** Special Cases ***** +************************/ + +// Zero-sized assignment is a no-op. +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel& /*kernel*/) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + EIGEN_STATIC_ASSERT(int(DstXprType::SizeAtCompileTime) == 0, + EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT) + } +}; + +/************************ +*** Default traversal *** +************************/ + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel) + { + for(Index outer = 0; outer < kernel.outerSize(); ++outer) { + for(Index inner = 0; inner < kernel.innerSize(); ++inner) { + kernel.assignCoeffByOuterInner(outer, inner); + } + } + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + copy_using_evaluator_DefaultTraversal_CompleteUnrolling::run(kernel); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + + const Index outerSize = kernel.outerSize(); + for(Index outer = 0; outer < outerSize; ++outer) + copy_using_evaluator_DefaultTraversal_InnerUnrolling::run(kernel, outer); + } +}; + +/*************************** +*** Linear vectorization *** +***************************/ + + +// The goal of unaligned_dense_assignment_loop is simply to factorize the handling +// of the non vectorizable beginning and ending parts + +template +struct unaligned_dense_assignment_loop +{ + // if IsAligned = true, then do nothing + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {} +}; + +template <> +struct unaligned_dense_assignment_loop +{ + // MSVC must not inline this functions. If it does, it fails to optimize the + // packet access path. + // FIXME check which version exhibits this issue +#if EIGEN_COMP_MSVC + template + static EIGEN_DONT_INLINE void run(Kernel &kernel, + Index start, + Index end) +#else + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, + Index start, + Index end) +#endif + { + for (Index index = start; index < end; ++index) + kernel.assignCoeff(index); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + const Index size = kernel.size(); + typedef typename Kernel::Scalar Scalar; + typedef typename Kernel::PacketType PacketType; + enum { + requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment, + packetSize = unpacket_traits::size, + dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment), + dstAlignment = packet_traits::AlignedOnScalar ? int(requestedAlignment) + : int(Kernel::AssignmentTraits::DstAlignment), + srcAlignment = Kernel::AssignmentTraits::JointAlignment + }; + const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned(kernel.dstDataPtr(), size); + const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize; + + unaligned_dense_assignment_loop::run(kernel, 0, alignedStart); + + for(Index index = alignedStart; index < alignedEnd; index += packetSize) + kernel.template assignPacket(index); + + unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + typedef typename Kernel::PacketType PacketType; + + enum { size = DstXprType::SizeAtCompileTime, + packetSize =unpacket_traits::size, + alignedSize = (int(size)/packetSize)*packetSize }; + + copy_using_evaluator_innervec_CompleteUnrolling::run(kernel); + copy_using_evaluator_DefaultTraversal_CompleteUnrolling::run(kernel); + } +}; + +/************************** +*** Inner vectorization *** +**************************/ + +template +struct dense_assignment_loop +{ + typedef typename Kernel::PacketType PacketType; + enum { + SrcAlignment = Kernel::AssignmentTraits::SrcAlignment, + DstAlignment = Kernel::AssignmentTraits::DstAlignment + }; + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + const Index innerSize = kernel.innerSize(); + const Index outerSize = kernel.outerSize(); + const Index packetSize = unpacket_traits::size; + for(Index outer = 0; outer < outerSize; ++outer) + for(Index inner = 0; inner < innerSize; inner+=packetSize) + kernel.template assignPacketByOuterInner(outer, inner); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + copy_using_evaluator_innervec_CompleteUnrolling::run(kernel); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + typedef typename Kernel::AssignmentTraits Traits; + const Index outerSize = kernel.outerSize(); + for(Index outer = 0; outer < outerSize; ++outer) + copy_using_evaluator_innervec_InnerUnrolling::run(kernel, outer); + } +}; + +/*********************** +*** Linear traversal *** +***********************/ + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + const Index size = kernel.size(); + for(Index i = 0; i < size; ++i) + kernel.assignCoeff(i); + } +}; + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + copy_using_evaluator_LinearTraversal_CompleteUnrolling::run(kernel); + } +}; + +/************************** +*** Slice vectorization *** +***************************/ + +template +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::Scalar Scalar; + typedef typename Kernel::PacketType PacketType; + enum { + packetSize = unpacket_traits::size, + requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment), + alignable = packet_traits::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar), + dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment), + dstAlignment = alignable ? int(requestedAlignment) + : int(Kernel::AssignmentTraits::DstAlignment) + }; + const Scalar *dst_ptr = kernel.dstDataPtr(); + if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0) + { + // the pointer is not aligned-on scalar, so alignment is not possible + return dense_assignment_loop::run(kernel); + } + const Index packetAlignedMask = packetSize - 1; + const Index innerSize = kernel.innerSize(); + const Index outerSize = kernel.outerSize(); + const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0; + Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned(dst_ptr, innerSize); + + for(Index outer = 0; outer < outerSize; ++outer) + { + const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask); + // do the non-vectorizable part of the assignment + for(Index inner = 0; inner(outer, inner); + + // do the non-vectorizable part of the assignment + for(Index inner = alignedEnd; inner +struct dense_assignment_loop +{ + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel) + { + typedef typename Kernel::DstEvaluatorType::XprType DstXprType; + typedef typename Kernel::PacketType PacketType; + + enum { innerSize = DstXprType::InnerSizeAtCompileTime, + packetSize =unpacket_traits::size, + vectorizableSize = (int(innerSize) / int(packetSize)) * int(packetSize), + size = DstXprType::SizeAtCompileTime }; + + for(Index outer = 0; outer < kernel.outerSize(); ++outer) + { + copy_using_evaluator_innervec_InnerUnrolling::run(kernel, outer); + copy_using_evaluator_DefaultTraversal_InnerUnrolling::run(kernel, outer); + } + } +}; +#endif + + +/*************************************************************************** +* Part 4 : Generic dense assignment kernel +***************************************************************************/ + +// This class generalize the assignment of a coefficient (or packet) from one dense evaluator +// to another dense writable evaluator. +// It is parametrized by the two evaluators, and the actual assignment functor. +// This abstraction level permits to keep the evaluation loops as simple and as generic as possible. +// One can customize the assignment using this generic dense_assignment_kernel with different +// functors, or by completely overloading it, by-passing a functor. +template +class generic_dense_assignment_kernel +{ +protected: + typedef typename DstEvaluatorTypeT::XprType DstXprType; + typedef typename SrcEvaluatorTypeT::XprType SrcXprType; +public: + + typedef DstEvaluatorTypeT DstEvaluatorType; + typedef SrcEvaluatorTypeT SrcEvaluatorType; + typedef typename DstEvaluatorType::Scalar Scalar; + typedef copy_using_evaluator_traits AssignmentTraits; + typedef typename AssignmentTraits::PacketType PacketType; + + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr) + : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr) + { + #ifdef EIGEN_DEBUG_ASSIGN + AssignmentTraits::debug(); + #endif + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_dstExpr.size(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index innerSize() const EIGEN_NOEXCEPT { return m_dstExpr.innerSize(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerSize() const EIGEN_NOEXCEPT { return m_dstExpr.outerSize(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dstExpr.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_dstExpr.cols(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT { return m_dstExpr.outerStride(); } + + EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() EIGEN_NOEXCEPT { return m_dst; } + EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const EIGEN_NOEXCEPT { return m_src; } + + /// Assign src(row,col) to dst(row,col) through the assignment functor. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col) + { + m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col)); + } + + /// \sa assignCoeff(Index,Index) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index) + { + m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index)); + } + + /// \sa assignCoeff(Index,Index) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner) + { + Index row = rowIndexByOuterInner(outer, inner); + Index col = colIndexByOuterInner(outer, inner); + assignCoeff(row, col); + } + + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col) + { + m_functor.template assignPacket(&m_dst.coeffRef(row,col), m_src.template packet(row,col)); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index) + { + m_functor.template assignPacket(&m_dst.coeffRef(index), m_src.template packet(index)); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner) + { + Index row = rowIndexByOuterInner(outer, inner); + Index col = colIndexByOuterInner(outer, inner); + assignPacket(row, col); + } + + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) + { + typedef typename DstEvaluatorType::ExpressionTraits Traits; + return int(Traits::RowsAtCompileTime) == 1 ? 0 + : int(Traits::ColsAtCompileTime) == 1 ? inner + : int(DstEvaluatorType::Flags)&RowMajorBit ? outer + : inner; + } + + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) + { + typedef typename DstEvaluatorType::ExpressionTraits Traits; + return int(Traits::ColsAtCompileTime) == 1 ? 0 + : int(Traits::RowsAtCompileTime) == 1 ? inner + : int(DstEvaluatorType::Flags)&RowMajorBit ? inner + : outer; + } + + EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const + { + return m_dstExpr.data(); + } + +protected: + DstEvaluatorType& m_dst; + const SrcEvaluatorType& m_src; + const Functor &m_functor; + // TODO find a way to avoid the needs of the original expression + DstXprType& m_dstExpr; +}; + +// Special kernel used when computing small products whose operands have dynamic dimensions. It ensures that the +// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used +// when computing the product. + +template +class restricted_packet_dense_assignment_kernel : public generic_dense_assignment_kernel +{ +protected: + typedef generic_dense_assignment_kernel Base; + public: + typedef typename Base::Scalar Scalar; + typedef typename Base::DstXprType DstXprType; + typedef copy_using_evaluator_traits AssignmentTraits; + typedef typename AssignmentTraits::PacketType PacketType; + + EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr) + : Base(dst, src, func, dstExpr) + { + } + }; + +/*************************************************************************** +* Part 5 : Entry point for dense rectangular assignment +***************************************************************************/ + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/) +{ + EIGEN_ONLY_USED_FOR_DEBUG(dst); + EIGEN_ONLY_USED_FOR_DEBUG(src); + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op &/*func*/) +{ + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols))) + dst.resize(dstRows, dstCols); + eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func) +{ + typedef evaluator DstEvaluatorType; + typedef evaluator SrcEvaluatorType; + + SrcEvaluatorType srcEvaluator(src); + + // NOTE To properly handle A = (A*A.transpose())/s with A rectangular, + // we need to resize the destination after the source evaluator has been created. + resize_if_allowed(dst, src, func); + + DstEvaluatorType dstEvaluator(dst); + + typedef generic_dense_assignment_kernel Kernel; + Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived()); + + dense_assignment_loop::run(kernel); +} + +// Specialization for filling the destination with a constant value. +#ifndef EIGEN_GPU_COMPILE_PHASE +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const Eigen::CwiseNullaryOp, DstXprType>& src, const internal::assign_op& func) +{ + resize_if_allowed(dst, src, func); + std::fill_n(dst.data(), dst.size(), src.functor()()); +} +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src) +{ + call_dense_assignment_loop(dst, src, internal::assign_op()); +} + +/*************************************************************************** +* Part 6 : Generic assignment +***************************************************************************/ + +// Based on the respective shapes of the destination and source, +// the class AssignmentKind determine the kind of assignment mechanism. +// AssignmentKind must define a Kind typedef. +template struct AssignmentKind; + +// Assignment kind defined in this file: +struct Dense2Dense {}; +struct EigenBase2EigenBase {}; + +template struct AssignmentKind { typedef EigenBase2EigenBase Kind; }; +template<> struct AssignmentKind { typedef Dense2Dense Kind; }; + +// This is the main assignment class +template< typename DstXprType, typename SrcXprType, typename Functor, + typename Kind = typename AssignmentKind< typename evaluator_traits::Shape , typename evaluator_traits::Shape >::Kind, + typename EnableIf = void> +struct Assignment; + + +// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition. +// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated. +// So this intermediate function removes everything related to "assume-aliasing" such that Assignment +// does not has to bother about these annoying details. + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment(Dst& dst, const Src& src) +{ + call_assignment(dst, src, internal::assign_op()); +} +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment(const Dst& dst, const Src& src) +{ + call_assignment(dst, src, internal::assign_op()); +} + +// Deal with "assume-aliasing" +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing::value, void*>::type = 0) +{ + typename plain_matrix_type::type tmp(src); + call_assignment_no_alias(dst, tmp, func); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if::value, void*>::type = 0) +{ + call_assignment_no_alias(dst, src, func); +} + +// by-pass "assume-aliasing" +// When there is no aliasing, we require that 'dst' has been properly resized +template class StorageBase, typename Src, typename Func> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment(NoAlias& dst, const Src& src, const Func& func) +{ + call_assignment_no_alias(dst.expression(), src, func); +} + + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func) +{ + enum { + NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) + || (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1) + ) && int(Dst::SizeAtCompileTime) != 1 + }; + + typedef typename internal::conditional, Dst>::type ActualDstTypeCleaned; + typedef typename internal::conditional, Dst&>::type ActualDstType; + ActualDstType actualDst(dst); + + // TODO check whether this is the right place to perform these checks: + EIGEN_STATIC_ASSERT_LVALUE(Dst) + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src) + EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar); + + Assignment::run(actualDst, src, func); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src, const Func& func) +{ + typedef evaluator DstEvaluatorType; + typedef evaluator SrcEvaluatorType; + typedef restricted_packet_dense_assignment_kernel Kernel; + + EIGEN_STATIC_ASSERT_LVALUE(Dst) + EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar); + + SrcEvaluatorType srcEvaluator(src); + resize_if_allowed(dst, src, func); + + DstEvaluatorType dstEvaluator(dst); + Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived()); + + dense_assignment_loop::run(kernel); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment_no_alias(Dst& dst, const Src& src) +{ + call_assignment_no_alias(dst, src, internal::assign_op()); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func) +{ + // TODO check whether this is the right place to perform these checks: + EIGEN_STATIC_ASSERT_LVALUE(Dst) + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src) + EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar); + + Assignment::run(dst, src, func); +} +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src) +{ + call_assignment_no_alias_no_transpose(dst, src, internal::assign_op()); +} + +// forward declaration +template void check_for_aliasing(const Dst &dst, const Src &src); + +// Generic Dense to Dense assignment +// Note that the last template argument "Weak" is needed to make it possible to perform +// both partial specialization+SFINAE without ambiguous specialization +template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak> +struct Assignment +{ + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func) + { +#ifndef EIGEN_NO_DEBUG + internal::check_for_aliasing(dst, src); +#endif + + call_dense_assignment_loop(dst, src, func); + } +}; + +// Generic assignment through evalTo. +// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism. +// Note that the last template argument "Weak" is needed to make it possible to perform +// both partial specialization+SFINAE without ambiguous specialization +template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak> +struct Assignment +{ + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op &/*func*/) + { + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if((dst.rows()!=dstRows) || (dst.cols()!=dstCols)) + dst.resize(dstRows, dstCols); + + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); + src.evalTo(dst); + } + + // NOTE The following two functions are templated to avoid their instantiation if not needed + // This is needed because some expressions supports evalTo only and/or have 'void' as scalar type. + template + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op &/*func*/) + { + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if((dst.rows()!=dstRows) || (dst.cols()!=dstCols)) + dst.resize(dstRows, dstCols); + + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); + src.addTo(dst); + } + + template + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op &/*func*/) + { + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if((dst.rows()!=dstRows) || (dst.cols()!=dstCols)) + dst.resize(dstRows, dstCols); + + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); + src.subTo(dst); + } +}; + +} // namespace internal + +} // end namespace Eigen + +#endif // EIGEN_ASSIGN_EVALUATOR_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign_MKL.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign_MKL.h new file mode 100755 index 00000000..c6140d18 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Assign_MKL.h @@ -0,0 +1,178 @@ +/* + Copyright (c) 2011, Intel Corporation. All rights reserved. + Copyright (C) 2015 Gael Guennebaud + + Redistribution and use in source and binary forms, with or without modification, + are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + * Neither the name of Intel Corporation nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON + ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ******************************************************************************** + * Content : Eigen bindings to Intel(R) MKL + * MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin() + ******************************************************************************** +*/ + +#ifndef EIGEN_ASSIGN_VML_H +#define EIGEN_ASSIGN_VML_H + +namespace Eigen { + +namespace internal { + +template +class vml_assign_traits +{ + private: + enum { + DstHasDirectAccess = Dst::Flags & DirectAccessBit, + SrcHasDirectAccess = Src::Flags & DirectAccessBit, + StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)), + InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime) + : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime) + : int(Dst::RowsAtCompileTime), + InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime) + : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime) + : int(Dst::MaxRowsAtCompileTime), + MaxSizeAtCompileTime = Dst::SizeAtCompileTime, + + MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1, + MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit), + VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize, + LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD + }; + public: + enum { + EnableVml = MightEnableVml && LargeEnough, + Traversal = MightLinearize ? LinearTraversal : DefaultTraversal + }; +}; + +#define EIGEN_PP_EXPAND(ARG) ARG +#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1) +#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA +#else +#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA +#endif + +#define EIGEN_VMLMODE_EXPAND_x_ + +#define EIGEN_VMLMODE_PREFIX_xLA vm +#define EIGEN_VMLMODE_PREFIX_x_ v +#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x,VMLMODE) + +#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \ + template< typename DstXprType, typename SrcXprNested> \ + struct Assignment, SrcXprNested>, assign_op, \ + Dense2Dense, typename enable_if::EnableVml>::type> { \ + typedef CwiseUnaryOp, SrcXprNested> SrcXprType; \ + static void run(DstXprType &dst, const SrcXprType &src, const assign_op &func) { \ + resize_if_allowed(dst, src, func); \ + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \ + if(vml_assign_traits::Traversal==LinearTraversal) { \ + VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \ + (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) ); \ + } else { \ + const Index outerSize = dst.outerSize(); \ + for(Index outer = 0; outer < outerSize; ++outer) { \ + const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : \ + &(src.nestedExpression().coeffRef(0, outer)); \ + EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \ + VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, \ + (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE)); \ + } \ + } \ + } \ + }; \ + + +#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE) + +#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE) + +#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \ + EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE) + + +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA) +// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs, _) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _) + +EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _) +EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _) + +#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \ + template< typename DstXprType, typename SrcXprNested, typename Plain> \ + struct Assignment, SrcXprNested, \ + const CwiseNullaryOp,Plain> >, assign_op, \ + Dense2Dense, typename enable_if::EnableVml>::type> { \ + typedef CwiseBinaryOp, SrcXprNested, \ + const CwiseNullaryOp,Plain> > SrcXprType; \ + static void run(DstXprType &dst, const SrcXprType &src, const assign_op &func) { \ + resize_if_allowed(dst, src, func); \ + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \ + VMLTYPE exponent = reinterpret_cast(src.rhs().functor().m_other); \ + if(vml_assign_traits::Traversal==LinearTraversal) \ + { \ + VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent, \ + (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) ); \ + } else { \ + const Index outerSize = dst.outerSize(); \ + for(Index outer = 0; outer < outerSize; ++outer) { \ + const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) : \ + &(src.lhs().coeffRef(0, outer)); \ + EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \ + VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent, \ + (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE)); \ + } \ + } \ + } \ + }; + +EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA) +EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA) +EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA) +EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA) + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_ASSIGN_VML_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/BandMatrix.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/BandMatrix.h new file mode 100644 index 00000000..878c0240 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/BandMatrix.h @@ -0,0 +1,353 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_BANDMATRIX_H +#define EIGEN_BANDMATRIX_H + +namespace Eigen { + +namespace internal { + +template +class BandMatrixBase : public EigenBase +{ + public: + + enum { + Flags = internal::traits::Flags, + CoeffReadCost = internal::traits::CoeffReadCost, + RowsAtCompileTime = internal::traits::RowsAtCompileTime, + ColsAtCompileTime = internal::traits::ColsAtCompileTime, + MaxRowsAtCompileTime = internal::traits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = internal::traits::MaxColsAtCompileTime, + Supers = internal::traits::Supers, + Subs = internal::traits::Subs, + Options = internal::traits::Options + }; + typedef typename internal::traits::Scalar Scalar; + typedef Matrix DenseMatrixType; + typedef typename DenseMatrixType::StorageIndex StorageIndex; + typedef typename internal::traits::CoefficientsType CoefficientsType; + typedef EigenBase Base; + + protected: + enum { + DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) + ? 1 + Supers + Subs + : Dynamic, + SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime) + }; + + public: + + using Base::derived; + using Base::rows; + using Base::cols; + + /** \returns the number of super diagonals */ + inline Index supers() const { return derived().supers(); } + + /** \returns the number of sub diagonals */ + inline Index subs() const { return derived().subs(); } + + /** \returns an expression of the underlying coefficient matrix */ + inline const CoefficientsType& coeffs() const { return derived().coeffs(); } + + /** \returns an expression of the underlying coefficient matrix */ + inline CoefficientsType& coeffs() { return derived().coeffs(); } + + /** \returns a vector expression of the \a i -th column, + * only the meaningful part is returned. + * \warning the internal storage must be column major. */ + inline Block col(Index i) + { + EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); + Index start = 0; + Index len = coeffs().rows(); + if (i<=supers()) + { + start = supers()-i; + len = (std::min)(rows(),std::max(0,coeffs().rows() - (supers()-i))); + } + else if (i>=rows()-subs()) + len = std::max(0,coeffs().rows() - (i + 1 - rows() + subs())); + return Block(coeffs(), start, i, len, 1); + } + + /** \returns a vector expression of the main diagonal */ + inline Block diagonal() + { return Block(coeffs(),supers(),0,1,(std::min)(rows(),cols())); } + + /** \returns a vector expression of the main diagonal (const version) */ + inline const Block diagonal() const + { return Block(coeffs(),supers(),0,1,(std::min)(rows(),cols())); } + + template struct DiagonalIntReturnType { + enum { + ReturnOpposite = (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)), + Conjugate = ReturnOpposite && NumTraits::IsComplex, + ActualIndex = ReturnOpposite ? -Index : Index, + DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic) + ? Dynamic + : (ActualIndex<0 + ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex) + : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex)) + }; + typedef Block BuildType; + typedef typename internal::conditional,BuildType >, + BuildType>::type Type; + }; + + /** \returns a vector expression of the \a N -th sub or super diagonal */ + template inline typename DiagonalIntReturnType::Type diagonal() + { + return typename DiagonalIntReturnType::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N)); + } + + /** \returns a vector expression of the \a N -th sub or super diagonal */ + template inline const typename DiagonalIntReturnType::Type diagonal() const + { + return typename DiagonalIntReturnType::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N)); + } + + /** \returns a vector expression of the \a i -th sub or super diagonal */ + inline Block diagonal(Index i) + { + eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers())); + return Block(coeffs(), supers()-i, std::max(0,i), 1, diagonalLength(i)); + } + + /** \returns a vector expression of the \a i -th sub or super diagonal */ + inline const Block diagonal(Index i) const + { + eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers())); + return Block(coeffs(), supers()-i, std::max(0,i), 1, diagonalLength(i)); + } + + template inline void evalTo(Dest& dst) const + { + dst.resize(rows(),cols()); + dst.setZero(); + dst.diagonal() = diagonal(); + for (Index i=1; i<=supers();++i) + dst.diagonal(i) = diagonal(i); + for (Index i=1; i<=subs();++i) + dst.diagonal(-i) = diagonal(-i); + } + + DenseMatrixType toDenseMatrix() const + { + DenseMatrixType res(rows(),cols()); + evalTo(res); + return res; + } + + protected: + + inline Index diagonalLength(Index i) const + { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); } +}; + +/** + * \class BandMatrix + * \ingroup Core_Module + * + * \brief Represents a rectangular matrix with a banded storage + * + * \tparam _Scalar Numeric type, i.e. float, double, int + * \tparam _Rows Number of rows, or \b Dynamic + * \tparam _Cols Number of columns, or \b Dynamic + * \tparam _Supers Number of super diagonal + * \tparam _Subs Number of sub diagonal + * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint + * The former controls \ref TopicStorageOrders "storage order", and defaults to + * column-major. The latter controls whether the matrix represents a selfadjoint + * matrix in which case either Supers of Subs have to be null. + * + * \sa class TridiagonalMatrix + */ + +template +struct traits > +{ + typedef _Scalar Scalar; + typedef Dense StorageKind; + typedef Eigen::Index StorageIndex; + enum { + CoeffReadCost = NumTraits::ReadCost, + RowsAtCompileTime = _Rows, + ColsAtCompileTime = _Cols, + MaxRowsAtCompileTime = _Rows, + MaxColsAtCompileTime = _Cols, + Flags = LvalueBit, + Supers = _Supers, + Subs = _Subs, + Options = _Options, + DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic + }; + typedef Matrix CoefficientsType; +}; + +template +class BandMatrix : public BandMatrixBase > +{ + public: + + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::traits::StorageIndex StorageIndex; + typedef typename internal::traits::CoefficientsType CoefficientsType; + + explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs) + : m_coeffs(1+supers+subs,cols), + m_rows(rows), m_supers(supers), m_subs(subs) + { + } + + /** \returns the number of columns */ + inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); } + + /** \returns the number of rows */ + inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); } + + /** \returns the number of super diagonals */ + inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); } + + /** \returns the number of sub diagonals */ + inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); } + + inline const CoefficientsType& coeffs() const { return m_coeffs; } + inline CoefficientsType& coeffs() { return m_coeffs; } + + protected: + + CoefficientsType m_coeffs; + internal::variable_if_dynamic m_rows; + internal::variable_if_dynamic m_supers; + internal::variable_if_dynamic m_subs; +}; + +template +class BandMatrixWrapper; + +template +struct traits > +{ + typedef typename _CoefficientsType::Scalar Scalar; + typedef typename _CoefficientsType::StorageKind StorageKind; + typedef typename _CoefficientsType::StorageIndex StorageIndex; + enum { + CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost, + RowsAtCompileTime = _Rows, + ColsAtCompileTime = _Cols, + MaxRowsAtCompileTime = _Rows, + MaxColsAtCompileTime = _Cols, + Flags = LvalueBit, + Supers = _Supers, + Subs = _Subs, + Options = _Options, + DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic + }; + typedef _CoefficientsType CoefficientsType; +}; + +template +class BandMatrixWrapper : public BandMatrixBase > +{ + public: + + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::traits::CoefficientsType CoefficientsType; + typedef typename internal::traits::StorageIndex StorageIndex; + + explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs) + : m_coeffs(coeffs), + m_rows(rows), m_supers(supers), m_subs(subs) + { + EIGEN_UNUSED_VARIABLE(cols); + //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows()); + } + + /** \returns the number of columns */ + inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); } + + /** \returns the number of rows */ + inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); } + + /** \returns the number of super diagonals */ + inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); } + + /** \returns the number of sub diagonals */ + inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); } + + inline const CoefficientsType& coeffs() const { return m_coeffs; } + + protected: + + const CoefficientsType& m_coeffs; + internal::variable_if_dynamic m_rows; + internal::variable_if_dynamic m_supers; + internal::variable_if_dynamic m_subs; +}; + +/** + * \class TridiagonalMatrix + * \ingroup Core_Module + * + * \brief Represents a tridiagonal matrix with a compact banded storage + * + * \tparam Scalar Numeric type, i.e. float, double, int + * \tparam Size Number of rows and cols, or \b Dynamic + * \tparam Options Can be 0 or \b SelfAdjoint + * + * \sa class BandMatrix + */ +template +class TridiagonalMatrix : public BandMatrix +{ + typedef BandMatrix Base; + typedef typename Base::StorageIndex StorageIndex; + public: + explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {} + + inline typename Base::template DiagonalIntReturnType<1>::Type super() + { return Base::template diagonal<1>(); } + inline const typename Base::template DiagonalIntReturnType<1>::Type super() const + { return Base::template diagonal<1>(); } + inline typename Base::template DiagonalIntReturnType<-1>::Type sub() + { return Base::template diagonal<-1>(); } + inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const + { return Base::template diagonal<-1>(); } + protected: +}; + + +struct BandShape {}; + +template +struct evaluator_traits > + : public evaluator_traits_base > +{ + typedef BandShape Shape; +}; + +template +struct evaluator_traits > + : public evaluator_traits_base > +{ + typedef BandShape Shape; +}; + +template<> struct AssignmentKind { typedef EigenBase2EigenBase Kind; }; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_BANDMATRIX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Block.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Block.h new file mode 100644 index 00000000..3206d663 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Block.h @@ -0,0 +1,448 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_BLOCK_H +#define EIGEN_BLOCK_H + +namespace Eigen { + +namespace internal { +template +struct traits > : traits +{ + typedef typename traits::Scalar Scalar; + typedef typename traits::StorageKind StorageKind; + typedef typename traits::XprKind XprKind; + typedef typename ref_selector::type XprTypeNested; + typedef typename remove_reference::type _XprTypeNested; + enum{ + MatrixRows = traits::RowsAtCompileTime, + MatrixCols = traits::ColsAtCompileTime, + RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows, + ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols, + MaxRowsAtCompileTime = BlockRows==0 ? 0 + : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) + : int(traits::MaxRowsAtCompileTime), + MaxColsAtCompileTime = BlockCols==0 ? 0 + : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) + : int(traits::MaxColsAtCompileTime), + + XprTypeIsRowMajor = (int(traits::Flags)&RowMajorBit) != 0, + IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 + : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 + : XprTypeIsRowMajor, + HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor), + InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime), + InnerStrideAtCompileTime = HasSameStorageOrderAsXprType + ? int(inner_stride_at_compile_time::ret) + : int(outer_stride_at_compile_time::ret), + OuterStrideAtCompileTime = HasSameStorageOrderAsXprType + ? int(outer_stride_at_compile_time::ret) + : int(inner_stride_at_compile_time::ret), + + // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further + FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, + FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0, + Flags = (traits::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit, + // FIXME DirectAccessBit should not be handled by expressions + // + // Alignment is needed by MapBase's assertions + // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator + Alignment = 0 + }; +}; + +template::ret> class BlockImpl_dense; + +} // end namespace internal + +template class BlockImpl; + +/** \class Block + * \ingroup Core_Module + * + * \brief Expression of a fixed-size or dynamic-size block + * + * \tparam XprType the type of the expression in which we are taking a block + * \tparam BlockRows the number of rows of the block we are taking at compile time (optional) + * \tparam BlockCols the number of columns of the block we are taking at compile time (optional) + * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or + * to set of columns of a column major matrix (optional). The parameter allows to determine + * at compile time whether aligned access is possible on the block expression. + * + * This class represents an expression of either a fixed-size or dynamic-size block. It is the return + * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block(Index,Index) and + * most of the time this is the only way it is used. + * + * However, if you want to directly maniputate block expressions, + * for instance if you want to write a function returning such an expression, you + * will need to use this class. + * + * Here is an example illustrating the dynamic case: + * \include class_Block.cpp + * Output: \verbinclude class_Block.out + * + * \note Even though this expression has dynamic size, in the case where \a XprType + * has fixed size, this expression inherits a fixed maximal size which means that evaluating + * it does not cause a dynamic memory allocation. + * + * Here is an example illustrating the fixed-size case: + * \include class_FixedBlock.cpp + * Output: \verbinclude class_FixedBlock.out + * + * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock + */ +template class Block + : public BlockImpl::StorageKind> +{ + typedef BlockImpl::StorageKind> Impl; + public: + //typedef typename Impl::Base Base; + typedef Impl Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(Block) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block) + + typedef typename internal::remove_all::type NestedExpression; + + /** Column or Row constructor + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Block(XprType& xpr, Index i) : Impl(xpr,i) + { + eigen_assert( (i>=0) && ( + ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows() + && startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols()); + } + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Block(XprType& xpr, + Index startRow, Index startCol, + Index blockRows, Index blockCols) + : Impl(xpr, startRow, startCol, blockRows, blockCols) + { + eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows) + && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols)); + eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows + && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols); + } +}; + +// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense +// that must be specialized for direct and non-direct access... +template +class BlockImpl + : public internal::BlockImpl_dense +{ + typedef internal::BlockImpl_dense Impl; + typedef typename XprType::StorageIndex StorageIndex; + public: + typedef Impl Base; + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {} + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols) + : Impl(xpr, startRow, startCol, blockRows, blockCols) {} +}; + +namespace internal { + +/** \internal Internal implementation of dense Blocks in the general case. */ +template class BlockImpl_dense + : public internal::dense_xpr_base >::type +{ + typedef Block BlockType; + typedef typename internal::ref_selector::non_const_type XprTypeNested; + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(BlockType) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense) + + // class InnerIterator; // FIXME apparently never used + + /** Column or Row constructor + */ + EIGEN_DEVICE_FUNC + inline BlockImpl_dense(XprType& xpr, Index i) + : m_xpr(xpr), + // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime, + // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1, + // all other cases are invalid. + // The case a 1x1 matrix seems ambiguous, but the result is the same anyway. + m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0), + m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0), + m_blockRows(BlockRows==1 ? 1 : xpr.rows()), + m_blockCols(BlockCols==1 ? 1 : xpr.cols()) + {} + + /** Fixed-size constructor + */ + EIGEN_DEVICE_FUNC + inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol) + : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), + m_blockRows(BlockRows), m_blockCols(BlockCols) + {} + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC + inline BlockImpl_dense(XprType& xpr, + Index startRow, Index startCol, + Index blockRows, Index blockCols) + : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), + m_blockRows(blockRows), m_blockCols(blockCols) + {} + + EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); } + EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index rowId, Index colId) + { + EIGEN_STATIC_ASSERT_LVALUE(XprType) + return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value()); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index rowId, Index colId) const + { + return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const + { + return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value()); + } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index index) + { + EIGEN_STATIC_ASSERT_LVALUE(XprType) + return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), + m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index index) const + { + return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), + m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); + } + + EIGEN_DEVICE_FUNC + inline const CoeffReturnType coeff(Index index) const + { + return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), + m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); + } + + template + inline PacketScalar packet(Index rowId, Index colId) const + { + return m_xpr.template packet(rowId + m_startRow.value(), colId + m_startCol.value()); + } + + template + inline void writePacket(Index rowId, Index colId, const PacketScalar& val) + { + m_xpr.template writePacket(rowId + m_startRow.value(), colId + m_startCol.value(), val); + } + + template + inline PacketScalar packet(Index index) const + { + return m_xpr.template packet + (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), + m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); + } + + template + inline void writePacket(Index index, const PacketScalar& val) + { + m_xpr.template writePacket + (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), + m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val); + } + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** \sa MapBase::data() */ + EIGEN_DEVICE_FUNC inline const Scalar* data() const; + EIGEN_DEVICE_FUNC inline Index innerStride() const; + EIGEN_DEVICE_FUNC inline Index outerStride() const; + #endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const typename internal::remove_all::type& nestedExpression() const + { + return m_xpr; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + XprType& nestedExpression() { return m_xpr; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + StorageIndex startRow() const EIGEN_NOEXCEPT + { + return m_startRow.value(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + StorageIndex startCol() const EIGEN_NOEXCEPT + { + return m_startCol.value(); + } + + protected: + + XprTypeNested m_xpr; + const internal::variable_if_dynamic m_startRow; + const internal::variable_if_dynamic m_startCol; + const internal::variable_if_dynamic m_blockRows; + const internal::variable_if_dynamic m_blockCols; +}; + +/** \internal Internal implementation of dense Blocks in the direct access case.*/ +template +class BlockImpl_dense + : public MapBase > +{ + typedef Block BlockType; + typedef typename internal::ref_selector::non_const_type XprTypeNested; + enum { + XprTypeIsRowMajor = (int(traits::Flags)&RowMajorBit) != 0 + }; + public: + + typedef MapBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(BlockType) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense) + + /** Column or Row constructor + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + BlockImpl_dense(XprType& xpr, Index i) + : Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor)) + || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()), + BlockRows==1 ? 1 : xpr.rows(), + BlockCols==1 ? 1 : xpr.cols()), + m_xpr(xpr), + m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0), + m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0) + { + init(); + } + + /** Fixed-size constructor + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + BlockImpl_dense(XprType& xpr, Index startRow, Index startCol) + : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)), + m_xpr(xpr), m_startRow(startRow), m_startCol(startCol) + { + init(); + } + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + BlockImpl_dense(XprType& xpr, + Index startRow, Index startCol, + Index blockRows, Index blockCols) + : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols), + m_xpr(xpr), m_startRow(startRow), m_startCol(startCol) + { + init(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const typename internal::remove_all::type& nestedExpression() const EIGEN_NOEXCEPT + { + return m_xpr; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + XprType& nestedExpression() { return m_xpr; } + + /** \sa MapBase::innerStride() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index innerStride() const EIGEN_NOEXCEPT + { + return internal::traits::HasSameStorageOrderAsXprType + ? m_xpr.innerStride() + : m_xpr.outerStride(); + } + + /** \sa MapBase::outerStride() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index outerStride() const EIGEN_NOEXCEPT + { + return internal::traits::HasSameStorageOrderAsXprType + ? m_xpr.outerStride() + : m_xpr.innerStride(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + StorageIndex startRow() const EIGEN_NOEXCEPT { return m_startRow.value(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + StorageIndex startCol() const EIGEN_NOEXCEPT { return m_startCol.value(); } + + #ifndef __SUNPRO_CC + // FIXME sunstudio is not friendly with the above friend... + // META-FIXME there is no 'friend' keyword around here. Is this obsolete? + protected: + #endif + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal used by allowAligned() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols) + : Base(data, blockRows, blockCols), m_xpr(xpr) + { + init(); + } + #endif + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void init() + { + m_outerStride = internal::traits::HasSameStorageOrderAsXprType + ? m_xpr.outerStride() + : m_xpr.innerStride(); + } + + XprTypeNested m_xpr; + const internal::variable_if_dynamic m_startRow; + const internal::variable_if_dynamic m_startCol; + Index m_outerStride; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_BLOCK_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/BooleanRedux.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/BooleanRedux.h new file mode 100644 index 00000000..852de8b9 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/BooleanRedux.h @@ -0,0 +1,162 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_ALLANDANY_H +#define EIGEN_ALLANDANY_H + +namespace Eigen { + +namespace internal { + +template +struct all_unroller +{ + enum { + col = (UnrollCount-1) / Rows, + row = (UnrollCount-1) % Rows + }; + + EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat) + { + return all_unroller::run(mat) && mat.coeff(row, col); + } +}; + +template +struct all_unroller +{ + EIGEN_DEVICE_FUNC static inline bool run(const Derived &/*mat*/) { return true; } +}; + +template +struct all_unroller +{ + EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; } +}; + +template +struct any_unroller +{ + enum { + col = (UnrollCount-1) / Rows, + row = (UnrollCount-1) % Rows + }; + + EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat) + { + return any_unroller::run(mat) || mat.coeff(row, col); + } +}; + +template +struct any_unroller +{ + EIGEN_DEVICE_FUNC static inline bool run(const Derived & /*mat*/) { return false; } +}; + +template +struct any_unroller +{ + EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; } +}; + +} // end namespace internal + +/** \returns true if all coefficients are true + * + * Example: \include MatrixBase_all.cpp + * Output: \verbinclude MatrixBase_all.out + * + * \sa any(), Cwise::operator<() + */ +template +EIGEN_DEVICE_FUNC inline bool DenseBase::all() const +{ + typedef internal::evaluator Evaluator; + enum { + unroll = SizeAtCompileTime != Dynamic + && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits::AddCost)) <= EIGEN_UNROLLING_LIMIT + }; + Evaluator evaluator(derived()); + if(unroll) + return internal::all_unroller::RowsAtCompileTime>::run(evaluator); + else + { + for(Index j = 0; j < cols(); ++j) + for(Index i = 0; i < rows(); ++i) + if (!evaluator.coeff(i, j)) return false; + return true; + } +} + +/** \returns true if at least one coefficient is true + * + * \sa all() + */ +template +EIGEN_DEVICE_FUNC inline bool DenseBase::any() const +{ + typedef internal::evaluator Evaluator; + enum { + unroll = SizeAtCompileTime != Dynamic + && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits::AddCost)) <= EIGEN_UNROLLING_LIMIT + }; + Evaluator evaluator(derived()); + if(unroll) + return internal::any_unroller::RowsAtCompileTime>::run(evaluator); + else + { + for(Index j = 0; j < cols(); ++j) + for(Index i = 0; i < rows(); ++i) + if (evaluator.coeff(i, j)) return true; + return false; + } +} + +/** \returns the number of coefficients which evaluate to true + * + * \sa all(), any() + */ +template +EIGEN_DEVICE_FUNC inline Eigen::Index DenseBase::count() const +{ + return derived().template cast().template cast().sum(); +} + +/** \returns true is \c *this contains at least one Not A Number (NaN). + * + * \sa allFinite() + */ +template +inline bool DenseBase::hasNaN() const +{ +#if EIGEN_COMP_MSVC || (defined __FAST_MATH__) + return derived().array().isNaN().any(); +#else + return !((derived().array()==derived().array()).all()); +#endif +} + +/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values. + * + * \sa hasNaN() + */ +template +inline bool DenseBase::allFinite() const +{ +#if EIGEN_COMP_MSVC || (defined __FAST_MATH__) + return derived().array().isFinite().all(); +#else + return !((derived()-derived()).hasNaN()); +#endif +} + +} // end namespace Eigen + +#endif // EIGEN_ALLANDANY_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CommaInitializer.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CommaInitializer.h new file mode 100644 index 00000000..c0e29c75 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CommaInitializer.h @@ -0,0 +1,164 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_COMMAINITIALIZER_H +#define EIGEN_COMMAINITIALIZER_H + +namespace Eigen { + +/** \class CommaInitializer + * \ingroup Core_Module + * + * \brief Helper class used by the comma initializer operator + * + * This class is internally used to implement the comma initializer feature. It is + * the return type of MatrixBase::operator<<, and most of the time this is the only + * way it is used. + * + * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished() + */ +template +struct CommaInitializer +{ + typedef typename XprType::Scalar Scalar; + + EIGEN_DEVICE_FUNC + inline CommaInitializer(XprType& xpr, const Scalar& s) + : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) + { + eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0 + && "Cannot comma-initialize a 0x0 matrix (operator<<)"); + m_xpr.coeffRef(0,0) = s; + } + + template + EIGEN_DEVICE_FUNC + inline CommaInitializer(XprType& xpr, const DenseBase& other) + : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) + { + eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols() + && "Cannot comma-initialize a 0x0 matrix (operator<<)"); + m_xpr.block(0, 0, other.rows(), other.cols()) = other; + } + + /* Copy/Move constructor which transfers ownership. This is crucial in + * absence of return value optimization to avoid assertions during destruction. */ + // FIXME in C++11 mode this could be replaced by a proper RValue constructor + EIGEN_DEVICE_FUNC + inline CommaInitializer(const CommaInitializer& o) + : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) { + // Mark original object as finished. In absence of R-value references we need to const_cast: + const_cast(o).m_row = m_xpr.rows(); + const_cast(o).m_col = m_xpr.cols(); + const_cast(o).m_currentBlockRows = 0; + } + + /* inserts a scalar value in the target matrix */ + EIGEN_DEVICE_FUNC + CommaInitializer& operator,(const Scalar& s) + { + if (m_col==m_xpr.cols()) + { + m_row+=m_currentBlockRows; + m_col = 0; + m_currentBlockRows = 1; + eigen_assert(m_row + EIGEN_DEVICE_FUNC + CommaInitializer& operator,(const DenseBase& other) + { + if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows)) + { + m_row+=m_currentBlockRows; + m_col = 0; + m_currentBlockRows = other.rows(); + eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows() + && "Too many rows passed to comma initializer (operator<<)"); + } + eigen_assert((m_col + other.cols() <= m_xpr.cols()) + && "Too many coefficients passed to comma initializer (operator<<)"); + eigen_assert(m_currentBlockRows==other.rows()); + m_xpr.template block + (m_row, m_col, other.rows(), other.cols()) = other; + m_col += other.cols(); + return *this; + } + + EIGEN_DEVICE_FUNC + inline ~CommaInitializer() +#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS + EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception) +#endif + { + finished(); + } + + /** \returns the built matrix once all its coefficients have been set. + * Calling finished is 100% optional. Its purpose is to write expressions + * like this: + * \code + * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished()); + * \endcode + */ + EIGEN_DEVICE_FUNC + inline XprType& finished() { + eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0) + && m_col == m_xpr.cols() + && "Too few coefficients passed to comma initializer (operator<<)"); + return m_xpr; + } + + XprType& m_xpr; // target expression + Index m_row; // current row id + Index m_col; // current col id + Index m_currentBlockRows; // current block height +}; + +/** \anchor MatrixBaseCommaInitRef + * Convenient operator to set the coefficients of a matrix. + * + * The coefficients must be provided in a row major order and exactly match + * the size of the matrix. Otherwise an assertion is raised. + * + * Example: \include MatrixBase_set.cpp + * Output: \verbinclude MatrixBase_set.out + * + * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order. + * + * \sa CommaInitializer::finished(), class CommaInitializer + */ +template +EIGEN_DEVICE_FUNC inline CommaInitializer DenseBase::operator<< (const Scalar& s) +{ + return CommaInitializer(*static_cast(this), s); +} + +/** \sa operator<<(const Scalar&) */ +template +template +EIGEN_DEVICE_FUNC inline CommaInitializer +DenseBase::operator<<(const DenseBase& other) +{ + return CommaInitializer(*static_cast(this), other); +} + +} // end namespace Eigen + +#endif // EIGEN_COMMAINITIALIZER_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ConditionEstimator.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ConditionEstimator.h new file mode 100644 index 00000000..51a2e5f1 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ConditionEstimator.h @@ -0,0 +1,175 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com) +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CONDITIONESTIMATOR_H +#define EIGEN_CONDITIONESTIMATOR_H + +namespace Eigen { + +namespace internal { + +template +struct rcond_compute_sign { + static inline Vector run(const Vector& v) { + const RealVector v_abs = v.cwiseAbs(); + return (v_abs.array() == static_cast(0)) + .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs)); + } +}; + +// Partial specialization to avoid elementwise division for real vectors. +template +struct rcond_compute_sign { + static inline Vector run(const Vector& v) { + return (v.array() < static_cast(0)) + .select(-Vector::Ones(v.size()), Vector::Ones(v.size())); + } +}; + +/** + * \returns an estimate of ||inv(matrix)||_1 given a decomposition of + * \a matrix that implements .solve() and .adjoint().solve() methods. + * + * This function implements Algorithms 4.1 and 5.1 from + * http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf + * which also forms the basis for the condition number estimators in + * LAPACK. Since at most 10 calls to the solve method of dec are + * performed, the total cost is O(dims^2), as opposed to O(dims^3) + * needed to compute the inverse matrix explicitly. + * + * The most common usage is in estimating the condition number + * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be + * computed directly in O(n^2) operations. + * + * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and + * LLT. + * + * \sa FullPivLU, PartialPivLU, LDLT, LLT. + */ +template +typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec) +{ + typedef typename Decomposition::MatrixType MatrixType; + typedef typename Decomposition::Scalar Scalar; + typedef typename Decomposition::RealScalar RealScalar; + typedef typename internal::plain_col_type::type Vector; + typedef typename internal::plain_col_type::type RealVector; + const bool is_complex = (NumTraits::IsComplex != 0); + + eigen_assert(dec.rows() == dec.cols()); + const Index n = dec.rows(); + if (n == 0) + return 0; + + // Disable Index to float conversion warning +#ifdef __INTEL_COMPILER + #pragma warning push + #pragma warning ( disable : 2259 ) +#endif + Vector v = dec.solve(Vector::Ones(n) / Scalar(n)); +#ifdef __INTEL_COMPILER + #pragma warning pop +#endif + + // lower_bound is a lower bound on + // ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1 + // and is the objective maximized by the ("super-") gradient ascent + // algorithm below. + RealScalar lower_bound = v.template lpNorm<1>(); + if (n == 1) + return lower_bound; + + // Gradient ascent algorithm follows: We know that the optimum is achieved at + // one of the simplices v = e_i, so in each iteration we follow a + // super-gradient to move towards the optimal one. + RealScalar old_lower_bound = lower_bound; + Vector sign_vector(n); + Vector old_sign_vector; + Index v_max_abs_index = -1; + Index old_v_max_abs_index = v_max_abs_index; + for (int k = 0; k < 4; ++k) + { + sign_vector = internal::rcond_compute_sign::run(v); + if (k > 0 && !is_complex && sign_vector == old_sign_vector) { + // Break if the solution stagnated. + break; + } + // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )| + v = dec.adjoint().solve(sign_vector); + v.real().cwiseAbs().maxCoeff(&v_max_abs_index); + if (v_max_abs_index == old_v_max_abs_index) { + // Break if the solution stagnated. + break; + } + // Move to the new simplex e_j, where j = v_max_abs_index. + v = dec.solve(Vector::Unit(n, v_max_abs_index)); // v = inv(matrix) * e_j. + lower_bound = v.template lpNorm<1>(); + if (lower_bound <= old_lower_bound) { + // Break if the gradient step did not increase the lower_bound. + break; + } + if (!is_complex) { + old_sign_vector = sign_vector; + } + old_v_max_abs_index = v_max_abs_index; + old_lower_bound = lower_bound; + } + // The following calculates an independent estimate of ||matrix||_1 by + // multiplying matrix by a vector with entries of slowly increasing + // magnitude and alternating sign: + // v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1. + // This improvement to Hager's algorithm above is due to Higham. It was + // added to make the algorithm more robust in certain corner cases where + // large elements in the matrix might otherwise escape detection due to + // exact cancellation (especially when op and op_adjoint correspond to a + // sequence of backsubstitutions and permutations), which could cause + // Hager's algorithm to vastly underestimate ||matrix||_1. + Scalar alternating_sign(RealScalar(1)); + for (Index i = 0; i < n; ++i) { + // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates + v[i] = alternating_sign * static_cast(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1)))); + alternating_sign = -alternating_sign; + } + v = dec.solve(v); + const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n)); + return numext::maxi(lower_bound, alternate_lower_bound); +} + +/** \brief Reciprocal condition number estimator. + * + * Computing a decomposition of a dense matrix takes O(n^3) operations, while + * this method estimates the condition number quickly and reliably in O(n^2) + * operations. + * + * \returns an estimate of the reciprocal condition number + * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and + * its decomposition. Supports the following decompositions: FullPivLU, + * PartialPivLU, LDLT, and LLT. + * + * \sa FullPivLU, PartialPivLU, LDLT, LLT. + */ +template +typename Decomposition::RealScalar +rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec) +{ + typedef typename Decomposition::RealScalar RealScalar; + eigen_assert(dec.rows() == dec.cols()); + if (dec.rows() == 0) return NumTraits::infinity(); + if (matrix_norm == RealScalar(0)) return RealScalar(0); + if (dec.rows() == 1) return RealScalar(1); + const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec); + return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0) + : (RealScalar(1) / inverse_matrix_norm) / matrix_norm); +} + +} // namespace internal + +} // namespace Eigen + +#endif diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreEvaluators.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreEvaluators.h new file mode 100644 index 00000000..0ff8c8de --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreEvaluators.h @@ -0,0 +1,1741 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2011 Benoit Jacob +// Copyright (C) 2011-2014 Gael Guennebaud +// Copyright (C) 2011-2012 Jitse Niesen +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +#ifndef EIGEN_COREEVALUATORS_H +#define EIGEN_COREEVALUATORS_H + +namespace Eigen { + +namespace internal { + +// This class returns the evaluator kind from the expression storage kind. +// Default assumes index based accessors +template +struct storage_kind_to_evaluator_kind { + typedef IndexBased Kind; +}; + +// This class returns the evaluator shape from the expression storage kind. +// It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc. +template struct storage_kind_to_shape; + +template<> struct storage_kind_to_shape { typedef DenseShape Shape; }; +template<> struct storage_kind_to_shape { typedef SolverShape Shape; }; +template<> struct storage_kind_to_shape { typedef PermutationShape Shape; }; +template<> struct storage_kind_to_shape { typedef TranspositionsShape Shape; }; + +// Evaluators have to be specialized with respect to various criteria such as: +// - storage/structure/shape +// - scalar type +// - etc. +// Therefore, we need specialization of evaluator providing additional template arguments for each kind of evaluators. +// We currently distinguish the following kind of evaluators: +// - unary_evaluator for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose, MatrixWrapper, ArrayWrapper, Reverse, Replicate) +// - binary_evaluator for expression taking two arguments (CwiseBinaryOp) +// - ternary_evaluator for expression taking three arguments (CwiseTernaryOp) +// - product_evaluator for linear algebra products (Product); special case of binary_evaluator because it requires additional tags for dispatching. +// - mapbase_evaluator for Map, Block, Ref +// - block_evaluator for Block (special dispatching to a mapbase_evaluator or unary_evaluator) + +template< typename T, + typename Arg1Kind = typename evaluator_traits::Kind, + typename Arg2Kind = typename evaluator_traits::Kind, + typename Arg3Kind = typename evaluator_traits::Kind, + typename Arg1Scalar = typename traits::Scalar, + typename Arg2Scalar = typename traits::Scalar, + typename Arg3Scalar = typename traits::Scalar> struct ternary_evaluator; + +template< typename T, + typename LhsKind = typename evaluator_traits::Kind, + typename RhsKind = typename evaluator_traits::Kind, + typename LhsScalar = typename traits::Scalar, + typename RhsScalar = typename traits::Scalar> struct binary_evaluator; + +template< typename T, + typename Kind = typename evaluator_traits::Kind, + typename Scalar = typename T::Scalar> struct unary_evaluator; + +// evaluator_traits contains traits for evaluator + +template +struct evaluator_traits_base +{ + // by default, get evaluator kind and shape from storage + typedef typename storage_kind_to_evaluator_kind::StorageKind>::Kind Kind; + typedef typename storage_kind_to_shape::StorageKind>::Shape Shape; +}; + +// Default evaluator traits +template +struct evaluator_traits : public evaluator_traits_base +{ +}; + +template::Shape > +struct evaluator_assume_aliasing { + static const bool value = false; +}; + +// By default, we assume a unary expression: +template +struct evaluator : public unary_evaluator +{ + typedef unary_evaluator Base; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const T& xpr) : Base(xpr) {} +}; + + +// TODO: Think about const-correctness +template +struct evaluator + : evaluator +{ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const T& xpr) : evaluator(xpr) {} +}; + +// ---------- base class for all evaluators ---------- + +template +struct evaluator_base +{ + // TODO that's not very nice to have to propagate all these traits. They are currently only needed to handle outer,inner indices. + typedef traits ExpressionTraits; + + enum { + Alignment = 0 + }; + // noncopyable: + // Don't make this class inherit noncopyable as this kills EBO (Empty Base Optimization) + // and make complex evaluator much larger than then should do. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE evaluator_base() {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~evaluator_base() {} +private: + EIGEN_DEVICE_FUNC evaluator_base(const evaluator_base&); + EIGEN_DEVICE_FUNC const evaluator_base& operator=(const evaluator_base&); +}; + +// -------------------- Matrix and Array -------------------- +// +// evaluator is a common base class for the +// Matrix and Array evaluators. +// Here we directly specialize evaluator. This is not really a unary expression, and it is, by definition, dense, +// so no need for more sophisticated dispatching. + +// this helper permits to completely eliminate m_outerStride if it is known at compiletime. +template class plainobjectbase_evaluator_data { +public: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr) + { +#ifndef EIGEN_INTERNAL_DEBUGGING + EIGEN_UNUSED_VARIABLE(outerStride); +#endif + eigen_internal_assert(outerStride==OuterStride); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index outerStride() const EIGEN_NOEXCEPT { return OuterStride; } + const Scalar *data; +}; + +template class plainobjectbase_evaluator_data { +public: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr), m_outerStride(outerStride) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Index outerStride() const { return m_outerStride; } + const Scalar *data; +protected: + Index m_outerStride; +}; + +template +struct evaluator > + : evaluator_base +{ + typedef PlainObjectBase PlainObjectType; + typedef typename PlainObjectType::Scalar Scalar; + typedef typename PlainObjectType::CoeffReturnType CoeffReturnType; + + enum { + IsRowMajor = PlainObjectType::IsRowMajor, + IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime, + RowsAtCompileTime = PlainObjectType::RowsAtCompileTime, + ColsAtCompileTime = PlainObjectType::ColsAtCompileTime, + + CoeffReadCost = NumTraits::ReadCost, + Flags = traits::EvaluatorFlags, + Alignment = traits::Alignment + }; + enum { + // We do not need to know the outer stride for vectors + OuterStrideAtCompileTime = IsVectorAtCompileTime ? 0 + : int(IsRowMajor) ? ColsAtCompileTime + : RowsAtCompileTime + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + evaluator() + : m_d(0,OuterStrideAtCompileTime) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const PlainObjectType& m) + : m_d(m.data(),IsVectorAtCompileTime ? 0 : m.outerStride()) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + if (IsRowMajor) + return m_d.data[row * m_d.outerStride() + col]; + else + return m_d.data[row + col * m_d.outerStride()]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_d.data[index]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + if (IsRowMajor) + return const_cast(m_d.data)[row * m_d.outerStride() + col]; + else + return const_cast(m_d.data)[row + col * m_d.outerStride()]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return const_cast(m_d.data)[index]; + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + if (IsRowMajor) + return ploadt(m_d.data + row * m_d.outerStride() + col); + else + return ploadt(m_d.data + row + col * m_d.outerStride()); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return ploadt(m_d.data + index); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + if (IsRowMajor) + return pstoret + (const_cast(m_d.data) + row * m_d.outerStride() + col, x); + else + return pstoret + (const_cast(m_d.data) + row + col * m_d.outerStride(), x); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + return pstoret(const_cast(m_d.data) + index, x); + } + +protected: + + plainobjectbase_evaluator_data m_d; +}; + +template +struct evaluator > + : evaluator > > +{ + typedef Matrix XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + evaluator() {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& m) + : evaluator >(m) + { } +}; + +template +struct evaluator > + : evaluator > > +{ + typedef Array XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + evaluator() {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& m) + : evaluator >(m) + { } +}; + +// -------------------- Transpose -------------------- + +template +struct unary_evaluator, IndexBased> + : evaluator_base > +{ + typedef Transpose XprType; + + enum { + CoeffReadCost = evaluator::CoeffReadCost, + Flags = evaluator::Flags ^ RowMajorBit, + Alignment = evaluator::Alignment + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& t) : m_argImpl(t.nestedExpression()) {} + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_argImpl.coeff(col, row); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_argImpl.coeff(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_argImpl.coeffRef(col, row); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + typename XprType::Scalar& coeffRef(Index index) + { + return m_argImpl.coeffRef(index); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_argImpl.template packet(col, row); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return m_argImpl.template packet(index); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + m_argImpl.template writePacket(col, row, x); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + m_argImpl.template writePacket(index, x); + } + +protected: + evaluator m_argImpl; +}; + +// -------------------- CwiseNullaryOp -------------------- +// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator. +// Likewise, there is not need to more sophisticated dispatching here. + +template::value, + bool has_unary = has_unary_operator::value, + bool has_binary = has_binary_operator::value> +struct nullary_wrapper +{ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { return op(i,j); } + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); } + + template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { return op.template packetOp(i,j); } + template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp(i); } +}; + +template +struct nullary_wrapper +{ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType=0, IndexType=0) const { return op(); } + template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType=0, IndexType=0) const { return op.template packetOp(); } +}; + +template +struct nullary_wrapper +{ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j=0) const { return op(i,j); } + template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j=0) const { return op.template packetOp(i,j); } +}; + +// We need the following specialization for vector-only functors assigned to a runtime vector, +// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd. +// In this case, i==0 and j is used for the actual iteration. +template +struct nullary_wrapper +{ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { + eigen_assert(i==0 || j==0); + return op(i+j); + } + template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { + eigen_assert(i==0 || j==0); + return op.template packetOp(i+j); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); } + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp(i); } +}; + +template +struct nullary_wrapper {}; + +#if 0 && EIGEN_COMP_MSVC>0 +// Disable this ugly workaround. This is now handled in traits::match, +// but this piece of code might still become handly if some other weird compilation +// erros pop up again. + +// MSVC exhibits a weird compilation error when +// compiling: +// Eigen::MatrixXf A = MatrixXf::Random(3,3); +// Ref R = 2.f*A; +// and that has_*ary_operator> have not been instantiated yet. +// The "problem" is that evaluator<2.f*A> is instantiated by traits::match<2.f*A> +// and at that time has_*ary_operator returns true regardless of T. +// Then nullary_wrapper is badly instantiated as nullary_wrapper<.,.,true,true,true>. +// The trick is thus to defer the proper instantiation of nullary_wrapper when coeff(), +// and packet() are really instantiated as implemented below: + +// This is a simple wrapper around Index to enforce the re-instantiation of +// has_*ary_operator when needed. +template struct nullary_wrapper_workaround_msvc { + nullary_wrapper_workaround_msvc(const T&); + operator T()const; +}; + +template +struct nullary_wrapper +{ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { + return nullary_wrapper >::value, + has_unary_operator >::value, + has_binary_operator >::value>().operator()(op,i,j); + } + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { + return nullary_wrapper >::value, + has_unary_operator >::value, + has_binary_operator >::value>().operator()(op,i); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { + return nullary_wrapper >::value, + has_unary_operator >::value, + has_binary_operator >::value>().template packetOp(op,i,j); + } + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { + return nullary_wrapper >::value, + has_unary_operator >::value, + has_binary_operator >::value>().template packetOp(op,i); + } +}; +#endif // MSVC workaround + +template +struct evaluator > + : evaluator_base > +{ + typedef CwiseNullaryOp XprType; + typedef typename internal::remove_all::type PlainObjectTypeCleaned; + + enum { + CoeffReadCost = internal::functor_traits::Cost, + + Flags = (evaluator::Flags + & ( HereditaryBits + | (functor_has_linear_access::ret ? LinearAccessBit : 0) + | (functor_traits::PacketAccess ? PacketAccessBit : 0))) + | (functor_traits::IsRepeatable ? 0 : EvalBeforeNestingBit), + Alignment = AlignedMax + }; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& n) + : m_functor(n.functor()), m_wrapper() + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(IndexType row, IndexType col) const + { + return m_wrapper(m_functor, row, col); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(IndexType index) const + { + return m_wrapper(m_functor,index); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(IndexType row, IndexType col) const + { + return m_wrapper.template packetOp(m_functor, row, col); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(IndexType index) const + { + return m_wrapper.template packetOp(m_functor, index); + } + +protected: + const NullaryOp m_functor; + const internal::nullary_wrapper m_wrapper; +}; + +// -------------------- CwiseUnaryOp -------------------- + +template +struct unary_evaluator, IndexBased > + : evaluator_base > +{ + typedef CwiseUnaryOp XprType; + + enum { + CoeffReadCost = int(evaluator::CoeffReadCost) + int(functor_traits::Cost), + + Flags = evaluator::Flags + & (HereditaryBits | LinearAccessBit | (functor_traits::PacketAccess ? PacketAccessBit : 0)), + Alignment = evaluator::Alignment + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& op) : m_d(op) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits::Cost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_d.func()(m_d.argImpl.coeff(row, col)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_d.func()(m_d.argImpl.coeff(index)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_d.func().packetOp(m_d.argImpl.template packet(row, col)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return m_d.func().packetOp(m_d.argImpl.template packet(index)); + } + +protected: + + // this helper permits to completely eliminate the functor if it is empty + struct Data + { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const UnaryOp& func() const { return op; } + UnaryOp op; + evaluator argImpl; + }; + + Data m_d; +}; + +// -------------------- CwiseTernaryOp -------------------- + +// this is a ternary expression +template +struct evaluator > + : public ternary_evaluator > +{ + typedef CwiseTernaryOp XprType; + typedef ternary_evaluator > Base; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {} +}; + +template +struct ternary_evaluator, IndexBased, IndexBased> + : evaluator_base > +{ + typedef CwiseTernaryOp XprType; + + enum { + CoeffReadCost = int(evaluator::CoeffReadCost) + int(evaluator::CoeffReadCost) + int(evaluator::CoeffReadCost) + int(functor_traits::Cost), + + Arg1Flags = evaluator::Flags, + Arg2Flags = evaluator::Flags, + Arg3Flags = evaluator::Flags, + SameType = is_same::value && is_same::value, + StorageOrdersAgree = (int(Arg1Flags)&RowMajorBit)==(int(Arg2Flags)&RowMajorBit) && (int(Arg1Flags)&RowMajorBit)==(int(Arg3Flags)&RowMajorBit), + Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) & ( + HereditaryBits + | (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) & + ( (StorageOrdersAgree ? LinearAccessBit : 0) + | (functor_traits::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0) + ) + ) + ), + Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit), + Alignment = EIGEN_PLAIN_ENUM_MIN( + EIGEN_PLAIN_ENUM_MIN(evaluator::Alignment, evaluator::Alignment), + evaluator::Alignment) + }; + + EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr) : m_d(xpr) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits::Cost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_d.func()(m_d.arg1Impl.coeff(row, col), m_d.arg2Impl.coeff(row, col), m_d.arg3Impl.coeff(row, col)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_d.func()(m_d.arg1Impl.coeff(index), m_d.arg2Impl.coeff(index), m_d.arg3Impl.coeff(index)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_d.func().packetOp(m_d.arg1Impl.template packet(row, col), + m_d.arg2Impl.template packet(row, col), + m_d.arg3Impl.template packet(row, col)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return m_d.func().packetOp(m_d.arg1Impl.template packet(index), + m_d.arg2Impl.template packet(index), + m_d.arg3Impl.template packet(index)); + } + +protected: + // this helper permits to completely eliminate the functor if it is empty + struct Data + { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Data(const XprType& xpr) : op(xpr.functor()), arg1Impl(xpr.arg1()), arg2Impl(xpr.arg2()), arg3Impl(xpr.arg3()) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TernaryOp& func() const { return op; } + TernaryOp op; + evaluator arg1Impl; + evaluator arg2Impl; + evaluator arg3Impl; + }; + + Data m_d; +}; + +// -------------------- CwiseBinaryOp -------------------- + +// this is a binary expression +template +struct evaluator > + : public binary_evaluator > +{ + typedef CwiseBinaryOp XprType; + typedef binary_evaluator > Base; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& xpr) : Base(xpr) {} +}; + +template +struct binary_evaluator, IndexBased, IndexBased> + : evaluator_base > +{ + typedef CwiseBinaryOp XprType; + + enum { + CoeffReadCost = int(evaluator::CoeffReadCost) + int(evaluator::CoeffReadCost) + int(functor_traits::Cost), + + LhsFlags = evaluator::Flags, + RhsFlags = evaluator::Flags, + SameType = is_same::value, + StorageOrdersAgree = (int(LhsFlags)&RowMajorBit)==(int(RhsFlags)&RowMajorBit), + Flags0 = (int(LhsFlags) | int(RhsFlags)) & ( + HereditaryBits + | (int(LhsFlags) & int(RhsFlags) & + ( (StorageOrdersAgree ? LinearAccessBit : 0) + | (functor_traits::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0) + ) + ) + ), + Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit), + Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator::Alignment,evaluator::Alignment) + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit binary_evaluator(const XprType& xpr) : m_d(xpr) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits::Cost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_d.func()(m_d.lhsImpl.coeff(row, col), m_d.rhsImpl.coeff(row, col)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_d.func()(m_d.lhsImpl.coeff(index), m_d.rhsImpl.coeff(index)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_d.func().packetOp(m_d.lhsImpl.template packet(row, col), + m_d.rhsImpl.template packet(row, col)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return m_d.func().packetOp(m_d.lhsImpl.template packet(index), + m_d.rhsImpl.template packet(index)); + } + +protected: + + // this helper permits to completely eliminate the functor if it is empty + struct Data + { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Data(const XprType& xpr) : op(xpr.functor()), lhsImpl(xpr.lhs()), rhsImpl(xpr.rhs()) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const BinaryOp& func() const { return op; } + BinaryOp op; + evaluator lhsImpl; + evaluator rhsImpl; + }; + + Data m_d; +}; + +// -------------------- CwiseUnaryView -------------------- + +template +struct unary_evaluator, IndexBased> + : evaluator_base > +{ + typedef CwiseUnaryView XprType; + + enum { + CoeffReadCost = int(evaluator::CoeffReadCost) + int(functor_traits::Cost), + + Flags = (evaluator::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit)), + + Alignment = 0 // FIXME it is not very clear why alignment is necessarily lost... + }; + + EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op) : m_d(op) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits::Cost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_d.func()(m_d.argImpl.coeff(row, col)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_d.func()(m_d.argImpl.coeff(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_d.func()(m_d.argImpl.coeffRef(row, col)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return m_d.func()(m_d.argImpl.coeffRef(index)); + } + +protected: + + // this helper permits to completely eliminate the functor if it is empty + struct Data + { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {} + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const UnaryOp& func() const { return op; } + UnaryOp op; + evaluator argImpl; + }; + + Data m_d; +}; + +// -------------------- Map -------------------- + +// FIXME perhaps the PlainObjectType could be provided by Derived::PlainObject ? +// but that might complicate template specialization +template +struct mapbase_evaluator; + +template +struct mapbase_evaluator : evaluator_base +{ + typedef Derived XprType; + typedef typename XprType::PointerType PointerType; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + enum { + IsRowMajor = XprType::RowsAtCompileTime, + ColsAtCompileTime = XprType::ColsAtCompileTime, + CoeffReadCost = NumTraits::ReadCost + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit mapbase_evaluator(const XprType& map) + : m_data(const_cast(map.data())), + m_innerStride(map.innerStride()), + m_outerStride(map.outerStride()) + { + EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(evaluator::Flags&PacketAccessBit, internal::inner_stride_at_compile_time::ret==1), + PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_data[col * colStride() + row * rowStride()]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_data[index * m_innerStride.value()]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_data[col * colStride() + row * rowStride()]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return m_data[index * m_innerStride.value()]; + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + PointerType ptr = m_data + row * rowStride() + col * colStride(); + return internal::ploadt(ptr); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return internal::ploadt(m_data + index * m_innerStride.value()); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + PointerType ptr = m_data + row * rowStride() + col * colStride(); + return internal::pstoret(ptr, x); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + internal::pstoret(m_data + index * m_innerStride.value(), x); + } +protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rowStride() const EIGEN_NOEXCEPT { + return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value(); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index colStride() const EIGEN_NOEXCEPT { + return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value(); + } + + PointerType m_data; + const internal::variable_if_dynamic m_innerStride; + const internal::variable_if_dynamic m_outerStride; +}; + +template +struct evaluator > + : public mapbase_evaluator, PlainObjectType> +{ + typedef Map XprType; + typedef typename XprType::Scalar Scalar; + // TODO: should check for smaller packet types once we can handle multi-sized packet types + typedef typename packet_traits::type PacketScalar; + + enum { + InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0 + ? int(PlainObjectType::InnerStrideAtCompileTime) + : int(StrideType::InnerStrideAtCompileTime), + OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0 + ? int(PlainObjectType::OuterStrideAtCompileTime) + : int(StrideType::OuterStrideAtCompileTime), + HasNoInnerStride = InnerStrideAtCompileTime == 1, + HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0, + HasNoStride = HasNoInnerStride && HasNoOuterStride, + IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic, + + PacketAccessMask = bool(HasNoInnerStride) ? ~int(0) : ~int(PacketAccessBit), + LinearAccessMask = bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime) ? ~int(0) : ~int(LinearAccessBit), + Flags = int( evaluator::Flags) & (LinearAccessMask&PacketAccessMask), + + Alignment = int(MapOptions)&int(AlignedMask) + }; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& map) + : mapbase_evaluator(map) + { } +}; + +// -------------------- Ref -------------------- + +template +struct evaluator > + : public mapbase_evaluator, PlainObjectType> +{ + typedef Ref XprType; + + enum { + Flags = evaluator >::Flags, + Alignment = evaluator >::Alignment + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& ref) + : mapbase_evaluator(ref) + { } +}; + +// -------------------- Block -------------------- + +template::ret> struct block_evaluator; + +template +struct evaluator > + : block_evaluator +{ + typedef Block XprType; + typedef typename XprType::Scalar Scalar; + // TODO: should check for smaller packet types once we can handle multi-sized packet types + typedef typename packet_traits::type PacketScalar; + + enum { + CoeffReadCost = evaluator::CoeffReadCost, + + RowsAtCompileTime = traits::RowsAtCompileTime, + ColsAtCompileTime = traits::ColsAtCompileTime, + MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = traits::MaxColsAtCompileTime, + + ArgTypeIsRowMajor = (int(evaluator::Flags)&RowMajorBit) != 0, + IsRowMajor = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? 1 + : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0 + : ArgTypeIsRowMajor, + HasSameStorageOrderAsArgType = (IsRowMajor == ArgTypeIsRowMajor), + InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime), + InnerStrideAtCompileTime = HasSameStorageOrderAsArgType + ? int(inner_stride_at_compile_time::ret) + : int(outer_stride_at_compile_time::ret), + OuterStrideAtCompileTime = HasSameStorageOrderAsArgType + ? int(outer_stride_at_compile_time::ret) + : int(inner_stride_at_compile_time::ret), + MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0, + + FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator::Flags&LinearAccessBit))) ? LinearAccessBit : 0, + FlagsRowMajorBit = XprType::Flags&RowMajorBit, + Flags0 = evaluator::Flags & ( (HereditaryBits & ~RowMajorBit) | + DirectAccessBit | + MaskPacketAccessBit), + Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit, + + PacketAlignment = unpacket_traits::alignment, + Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) + && (OuterStrideAtCompileTime!=0) + && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0, + Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator::Alignment, Alignment0) + }; + typedef block_evaluator block_evaluator_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& block) : block_evaluator_type(block) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } +}; + +// no direct-access => dispatch to a unary evaluator +template +struct block_evaluator + : unary_evaluator > +{ + typedef Block XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit block_evaluator(const XprType& block) + : unary_evaluator(block) + {} +}; + +template +struct unary_evaluator, IndexBased> + : evaluator_base > +{ + typedef Block XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& block) + : m_argImpl(block.nestedExpression()), + m_startRow(block.startRow()), + m_startCol(block.startCol()), + m_linear_offset(ForwardLinearAccess?(ArgType::IsRowMajor ? block.startRow()*block.nestedExpression().cols() + block.startCol() : block.startCol()*block.nestedExpression().rows() + block.startRow()):0) + { } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + enum { + RowsAtCompileTime = XprType::RowsAtCompileTime, + ForwardLinearAccess = (InnerPanel || int(XprType::IsRowMajor)==int(ArgType::IsRowMajor)) && bool(evaluator::Flags&LinearAccessBit) + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return linear_coeff_impl(index, bool_constant()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return linear_coeffRef_impl(index, bool_constant()); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_argImpl.template packet(m_startRow.value() + row, m_startCol.value() + col); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + if (ForwardLinearAccess) + return m_argImpl.template packet(m_linear_offset.value() + index); + else + return packet(RowsAtCompileTime == 1 ? 0 : index, + RowsAtCompileTime == 1 ? index : 0); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + return m_argImpl.template writePacket(m_startRow.value() + row, m_startCol.value() + col, x); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + if (ForwardLinearAccess) + return m_argImpl.template writePacket(m_linear_offset.value() + index, x); + else + return writePacket(RowsAtCompileTime == 1 ? 0 : index, + RowsAtCompileTime == 1 ? index : 0, + x); + } + +protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType linear_coeff_impl(Index index, internal::true_type /* ForwardLinearAccess */) const + { + return m_argImpl.coeff(m_linear_offset.value() + index); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType linear_coeff_impl(Index index, internal::false_type /* not ForwardLinearAccess */) const + { + return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& linear_coeffRef_impl(Index index, internal::true_type /* ForwardLinearAccess */) + { + return m_argImpl.coeffRef(m_linear_offset.value() + index); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& linear_coeffRef_impl(Index index, internal::false_type /* not ForwardLinearAccess */) + { + return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0); + } + + evaluator m_argImpl; + const variable_if_dynamic m_startRow; + const variable_if_dynamic m_startCol; + const variable_if_dynamic m_linear_offset; +}; + +// TODO: This evaluator does not actually use the child evaluator; +// all action is via the data() as returned by the Block expression. + +template +struct block_evaluator + : mapbase_evaluator, + typename Block::PlainObject> +{ + typedef Block XprType; + typedef typename XprType::Scalar Scalar; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit block_evaluator(const XprType& block) + : mapbase_evaluator(block) + { + // TODO: for the 3.3 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime + eigen_assert(((internal::UIntPtr(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator::Alignment)) == 0) && "data is not aligned"); + } +}; + + +// -------------------- Select -------------------- +// NOTE shall we introduce a ternary_evaluator? + +// TODO enable vectorization for Select +template +struct evaluator > + : evaluator_base > +{ + typedef Select XprType; + enum { + CoeffReadCost = evaluator::CoeffReadCost + + EIGEN_PLAIN_ENUM_MAX(evaluator::CoeffReadCost, + evaluator::CoeffReadCost), + + Flags = (unsigned int)evaluator::Flags & evaluator::Flags & HereditaryBits, + + Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator::Alignment, evaluator::Alignment) + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& select) + : m_conditionImpl(select.conditionMatrix()), + m_thenImpl(select.thenMatrix()), + m_elseImpl(select.elseMatrix()) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + if (m_conditionImpl.coeff(row, col)) + return m_thenImpl.coeff(row, col); + else + return m_elseImpl.coeff(row, col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + if (m_conditionImpl.coeff(index)) + return m_thenImpl.coeff(index); + else + return m_elseImpl.coeff(index); + } + +protected: + evaluator m_conditionImpl; + evaluator m_thenImpl; + evaluator m_elseImpl; +}; + + +// -------------------- Replicate -------------------- + +template +struct unary_evaluator > + : evaluator_base > +{ + typedef Replicate XprType; + typedef typename XprType::CoeffReturnType CoeffReturnType; + enum { + Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor + }; + typedef typename internal::nested_eval::type ArgTypeNested; + typedef typename internal::remove_all::type ArgTypeNestedCleaned; + + enum { + CoeffReadCost = evaluator::CoeffReadCost, + LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0, + Flags = (evaluator::Flags & (HereditaryBits|LinearAccessMask) & ~RowMajorBit) | (traits::Flags & RowMajorBit), + + Alignment = evaluator::Alignment + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& replicate) + : m_arg(replicate.nestedExpression()), + m_argImpl(m_arg), + m_rows(replicate.nestedExpression().rows()), + m_cols(replicate.nestedExpression().cols()) + {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + // try to avoid using modulo; this is a pure optimization strategy + const Index actual_row = internal::traits::RowsAtCompileTime==1 ? 0 + : RowFactor==1 ? row + : row % m_rows.value(); + const Index actual_col = internal::traits::ColsAtCompileTime==1 ? 0 + : ColFactor==1 ? col + : col % m_cols.value(); + + return m_argImpl.coeff(actual_row, actual_col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + // try to avoid using modulo; this is a pure optimization strategy + const Index actual_index = internal::traits::RowsAtCompileTime==1 + ? (ColFactor==1 ? index : index%m_cols.value()) + : (RowFactor==1 ? index : index%m_rows.value()); + + return m_argImpl.coeff(actual_index); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + const Index actual_row = internal::traits::RowsAtCompileTime==1 ? 0 + : RowFactor==1 ? row + : row % m_rows.value(); + const Index actual_col = internal::traits::ColsAtCompileTime==1 ? 0 + : ColFactor==1 ? col + : col % m_cols.value(); + + return m_argImpl.template packet(actual_row, actual_col); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + const Index actual_index = internal::traits::RowsAtCompileTime==1 + ? (ColFactor==1 ? index : index%m_cols.value()) + : (RowFactor==1 ? index : index%m_rows.value()); + + return m_argImpl.template packet(actual_index); + } + +protected: + const ArgTypeNested m_arg; + evaluator m_argImpl; + const variable_if_dynamic m_rows; + const variable_if_dynamic m_cols; +}; + +// -------------------- MatrixWrapper and ArrayWrapper -------------------- +// +// evaluator_wrapper_base is a common base class for the +// MatrixWrapper and ArrayWrapper evaluators. + +template +struct evaluator_wrapper_base + : evaluator_base +{ + typedef typename remove_all::type ArgType; + enum { + CoeffReadCost = evaluator::CoeffReadCost, + Flags = evaluator::Flags, + Alignment = evaluator::Alignment + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator_wrapper_base(const ArgType& arg) : m_argImpl(arg) {} + + typedef typename ArgType::Scalar Scalar; + typedef typename ArgType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_argImpl.coeff(row, col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_argImpl.coeff(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_argImpl.coeffRef(row, col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return m_argImpl.coeffRef(index); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + return m_argImpl.template packet(row, col); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + return m_argImpl.template packet(index); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + m_argImpl.template writePacket(row, col, x); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + m_argImpl.template writePacket(index, x); + } + +protected: + evaluator m_argImpl; +}; + +template +struct unary_evaluator > + : evaluator_wrapper_base > +{ + typedef MatrixWrapper XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& wrapper) + : evaluator_wrapper_base >(wrapper.nestedExpression()) + { } +}; + +template +struct unary_evaluator > + : evaluator_wrapper_base > +{ + typedef ArrayWrapper XprType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& wrapper) + : evaluator_wrapper_base >(wrapper.nestedExpression()) + { } +}; + + +// -------------------- Reverse -------------------- + +// defined in Reverse.h: +template struct reverse_packet_cond; + +template +struct unary_evaluator > + : evaluator_base > +{ + typedef Reverse XprType; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + enum { + IsRowMajor = XprType::IsRowMajor, + IsColMajor = !IsRowMajor, + ReverseRow = (Direction == Vertical) || (Direction == BothDirections), + ReverseCol = (Direction == Horizontal) || (Direction == BothDirections), + ReversePacket = (Direction == BothDirections) + || ((Direction == Vertical) && IsColMajor) + || ((Direction == Horizontal) && IsRowMajor), + + CoeffReadCost = evaluator::CoeffReadCost, + + // let's enable LinearAccess only with vectorization because of the product overhead + // FIXME enable DirectAccess with negative strides? + Flags0 = evaluator::Flags, + LinearAccess = ( (Direction==BothDirections) && (int(Flags0)&PacketAccessBit) ) + || ((ReverseRow && XprType::ColsAtCompileTime==1) || (ReverseCol && XprType::RowsAtCompileTime==1)) + ? LinearAccessBit : 0, + + Flags = int(Flags0) & (HereditaryBits | PacketAccessBit | LinearAccess), + + Alignment = 0 // FIXME in some rare cases, Alignment could be preserved, like a Vector4f. + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit unary_evaluator(const XprType& reverse) + : m_argImpl(reverse.nestedExpression()), + m_rows(ReverseRow ? reverse.nestedExpression().rows() : 1), + m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row, + ReverseCol ? m_cols.value() - col - 1 : col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row, + ReverseCol ? m_cols.value() - col - 1 : col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index row, Index col) const + { + enum { + PacketSize = unpacket_traits::size, + OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, + OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1 + }; + typedef internal::reverse_packet_cond reverse_packet; + return reverse_packet::run(m_argImpl.template packet( + ReverseRow ? m_rows.value() - row - OffsetRow : row, + ReverseCol ? m_cols.value() - col - OffsetCol : col)); + } + + template + EIGEN_STRONG_INLINE + PacketType packet(Index index) const + { + enum { PacketSize = unpacket_traits::size }; + return preverse(m_argImpl.template packet(m_rows.value() * m_cols.value() - index - PacketSize)); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index row, Index col, const PacketType& x) + { + // FIXME we could factorize some code with packet(i,j) + enum { + PacketSize = unpacket_traits::size, + OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, + OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1 + }; + typedef internal::reverse_packet_cond reverse_packet; + m_argImpl.template writePacket( + ReverseRow ? m_rows.value() - row - OffsetRow : row, + ReverseCol ? m_cols.value() - col - OffsetCol : col, + reverse_packet::run(x)); + } + + template + EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketType& x) + { + enum { PacketSize = unpacket_traits::size }; + m_argImpl.template writePacket + (m_rows.value() * m_cols.value() - index - PacketSize, preverse(x)); + } + +protected: + evaluator m_argImpl; + + // If we do not reverse rows, then we do not need to know the number of rows; same for columns + // Nonetheless, in this case it is important to set to 1 such that the coeff(index) method works fine for vectors. + const variable_if_dynamic m_rows; + const variable_if_dynamic m_cols; +}; + + +// -------------------- Diagonal -------------------- + +template +struct evaluator > + : evaluator_base > +{ + typedef Diagonal XprType; + + enum { + CoeffReadCost = evaluator::CoeffReadCost, + + Flags = (unsigned int)(evaluator::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit, + + Alignment = 0 + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit evaluator(const XprType& diagonal) + : m_argImpl(diagonal.nestedExpression()), + m_index(diagonal.index()) + { } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index) const + { + return m_argImpl.coeff(row + rowOffset(), row + colOffset()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index index) const + { + return m_argImpl.coeff(index + rowOffset(), index + colOffset()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index) + { + return m_argImpl.coeffRef(row + rowOffset(), row + colOffset()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + return m_argImpl.coeffRef(index + rowOffset(), index + colOffset()); + } + +protected: + evaluator m_argImpl; + const internal::variable_if_dynamicindex m_index; + +private: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; } +}; + + +//---------------------------------------------------------------------- +// deprecated code +//---------------------------------------------------------------------- + +// -------------------- EvalToTemp -------------------- + +// expression class for evaluating nested expression to a temporary + +template class EvalToTemp; + +template +struct traits > + : public traits +{ }; + +template +class EvalToTemp + : public dense_xpr_base >::type +{ + public: + + typedef typename dense_xpr_base::type Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp) + + explicit EvalToTemp(const ArgType& arg) + : m_arg(arg) + { } + + const ArgType& arg() const + { + return m_arg; + } + + EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT + { + return m_arg.rows(); + } + + EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT + { + return m_arg.cols(); + } + + private: + const ArgType& m_arg; +}; + +template +struct evaluator > + : public evaluator +{ + typedef EvalToTemp XprType; + typedef typename ArgType::PlainObject PlainObject; + typedef evaluator Base; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) + : m_result(xpr.arg()) + { + ::new (static_cast(this)) Base(m_result); + } + + // This constructor is used when nesting an EvalTo evaluator in another evaluator + EIGEN_DEVICE_FUNC evaluator(const ArgType& arg) + : m_result(arg) + { + ::new (static_cast(this)) Base(m_result); + } + +protected: + PlainObject m_result; +}; + +} // namespace internal + +} // end namespace Eigen + +#endif // EIGEN_COREEVALUATORS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreIterators.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreIterators.h new file mode 100644 index 00000000..b9671968 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CoreIterators.h @@ -0,0 +1,132 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2014 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_COREITERATORS_H +#define EIGEN_COREITERATORS_H + +namespace Eigen { + +/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core + */ + +namespace internal { + +template +class inner_iterator_selector; + +} + +/** \class InnerIterator + * \brief An InnerIterator allows to loop over the element of any matrix expression. + * + * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed. + * + * TODO: add a usage example + */ +template +class InnerIterator +{ +protected: + typedef internal::inner_iterator_selector::Kind> IteratorType; + typedef internal::evaluator EvaluatorType; + typedef typename internal::traits::Scalar Scalar; +public: + /** Construct an iterator over the \a outerId -th row or column of \a xpr */ + InnerIterator(const XprType &xpr, const Index &outerId) + : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize()) + {} + + /// \returns the value of the current coefficient. + EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); } + /** Increment the iterator \c *this to the next non-zero coefficient. + * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView + */ + EIGEN_STRONG_INLINE InnerIterator& operator++() { m_iter.operator++(); return *this; } + EIGEN_STRONG_INLINE InnerIterator& operator+=(Index i) { m_iter.operator+=(i); return *this; } + EIGEN_STRONG_INLINE InnerIterator operator+(Index i) + { InnerIterator result(*this); result+=i; return result; } + + + /// \returns the column or row index of the current coefficient. + EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); } + /// \returns the row index of the current coefficient. + EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); } + /// \returns the column index of the current coefficient. + EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); } + /// \returns \c true if the iterator \c *this still references a valid coefficient. + EIGEN_STRONG_INLINE operator bool() const { return m_iter; } + +protected: + EvaluatorType m_eval; + IteratorType m_iter; +private: + // If you get here, then you're not using the right InnerIterator type, e.g.: + // SparseMatrix A; + // SparseMatrix::InnerIterator it(A,0); + template InnerIterator(const EigenBase&,Index outer); +}; + +namespace internal { + +// Generic inner iterator implementation for dense objects +template +class inner_iterator_selector +{ +protected: + typedef evaluator EvaluatorType; + typedef typename traits::Scalar Scalar; + enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit }; + +public: + EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize) + : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize) + {} + + EIGEN_STRONG_INLINE Scalar value() const + { + return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner) + : m_eval.coeff(m_inner, m_outer); + } + + EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; } + + EIGEN_STRONG_INLINE Index index() const { return m_inner; } + inline Index row() const { return IsRowMajor ? m_outer : index(); } + inline Index col() const { return IsRowMajor ? index() : m_outer; } + + EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; } + +protected: + const EvaluatorType& m_eval; + Index m_inner; + const Index m_outer; + const Index m_end; +}; + +// For iterator-based evaluator, inner-iterator is already implemented as +// evaluator<>::InnerIterator +template +class inner_iterator_selector + : public evaluator::InnerIterator +{ +protected: + typedef typename evaluator::InnerIterator Base; + typedef evaluator EvaluatorType; + +public: + EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/) + : Base(eval, outerId) + {} +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_COREITERATORS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseBinaryOp.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseBinaryOp.h new file mode 100644 index 00000000..2202b1cc --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseBinaryOp.h @@ -0,0 +1,183 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2014 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CWISE_BINARY_OP_H +#define EIGEN_CWISE_BINARY_OP_H + +namespace Eigen { + +namespace internal { +template +struct traits > +{ + // we must not inherit from traits since it has + // the potential to cause problems with MSVC + typedef typename remove_all::type Ancestor; + typedef typename traits::XprKind XprKind; + enum { + RowsAtCompileTime = traits::RowsAtCompileTime, + ColsAtCompileTime = traits::ColsAtCompileTime, + MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = traits::MaxColsAtCompileTime + }; + + // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor), + // we still want to handle the case when the result type is different. + typedef typename result_of< + BinaryOp( + const typename Lhs::Scalar&, + const typename Rhs::Scalar& + ) + >::type Scalar; + typedef typename cwise_promote_storage_type::StorageKind, + typename traits::StorageKind, + BinaryOp>::ret StorageKind; + typedef typename promote_index_type::StorageIndex, + typename traits::StorageIndex>::type StorageIndex; + typedef typename Lhs::Nested LhsNested; + typedef typename Rhs::Nested RhsNested; + typedef typename remove_reference::type _LhsNested; + typedef typename remove_reference::type _RhsNested; + enum { + Flags = cwise_promote_storage_order::StorageKind,typename traits::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value + }; +}; +} // end namespace internal + +template +class CwiseBinaryOpImpl; + +/** \class CwiseBinaryOp + * \ingroup Core_Module + * + * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions + * + * \tparam BinaryOp template functor implementing the operator + * \tparam LhsType the type of the left-hand side + * \tparam RhsType the type of the right-hand side + * + * This class represents an expression where a coefficient-wise binary operator is applied to two expressions. + * It is the return type of binary operators, by which we mean only those binary operators where + * both the left-hand side and the right-hand side are Eigen expressions. + * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp. + * + * Most of the time, this is the only way that it is used, so you typically don't have to name + * CwiseBinaryOp types explicitly. + * + * \sa MatrixBase::binaryExpr(const MatrixBase &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp + */ +template +class CwiseBinaryOp : + public CwiseBinaryOpImpl< + BinaryOp, LhsType, RhsType, + typename internal::cwise_promote_storage_type::StorageKind, + typename internal::traits::StorageKind, + BinaryOp>::ret>, + internal::no_assignment_operator +{ + public: + + typedef typename internal::remove_all::type Functor; + typedef typename internal::remove_all::type Lhs; + typedef typename internal::remove_all::type Rhs; + + typedef typename CwiseBinaryOpImpl< + BinaryOp, LhsType, RhsType, + typename internal::cwise_promote_storage_type::StorageKind, + typename internal::traits::StorageKind, + BinaryOp>::ret>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp) + + typedef typename internal::ref_selector::type LhsNested; + typedef typename internal::ref_selector::type RhsNested; + typedef typename internal::remove_reference::type _LhsNested; + typedef typename internal::remove_reference::type _RhsNested; + +#if EIGEN_COMP_MSVC && EIGEN_HAS_CXX11 + //Required for Visual Studio or the Copy constructor will probably not get inlined! + EIGEN_STRONG_INLINE + CwiseBinaryOp(const CwiseBinaryOp&) = default; +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp()) + : m_lhs(aLhs), m_rhs(aRhs), m_functor(func) + { + EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar); + // require the sizes to match + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs) + eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const EIGEN_NOEXCEPT { + // return the fixed size type if available to enable compile time optimizations + return internal::traits::type>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows(); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const EIGEN_NOEXCEPT { + // return the fixed size type if available to enable compile time optimizations + return internal::traits::type>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols(); + } + + /** \returns the left hand side nested expression */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const _LhsNested& lhs() const { return m_lhs; } + /** \returns the right hand side nested expression */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const _RhsNested& rhs() const { return m_rhs; } + /** \returns the functor representing the binary operation */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const BinaryOp& functor() const { return m_functor; } + + protected: + LhsNested m_lhs; + RhsNested m_rhs; + const BinaryOp m_functor; +}; + +// Generic API dispatcher +template +class CwiseBinaryOpImpl + : public internal::generic_xpr_base >::type +{ +public: + typedef typename internal::generic_xpr_base >::type Base; +}; + +/** replaces \c *this by \c *this - \a other. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +MatrixBase::operator-=(const MatrixBase &other) +{ + call_assignment(derived(), other.derived(), internal::sub_assign_op()); + return derived(); +} + +/** replaces \c *this by \c *this + \a other. + * + * \returns a reference to \c *this + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived & +MatrixBase::operator+=(const MatrixBase& other) +{ + call_assignment(derived(), other.derived(), internal::add_assign_op()); + return derived(); +} + +} // end namespace Eigen + +#endif // EIGEN_CWISE_BINARY_OP_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseNullaryOp.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseNullaryOp.h new file mode 100644 index 00000000..289ec510 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseNullaryOp.h @@ -0,0 +1,1001 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CWISE_NULLARY_OP_H +#define EIGEN_CWISE_NULLARY_OP_H + +namespace Eigen { + +namespace internal { +template +struct traits > : traits +{ + enum { + Flags = traits::Flags & RowMajorBit + }; +}; + +} // namespace internal + +/** \class CwiseNullaryOp + * \ingroup Core_Module + * + * \brief Generic expression of a matrix where all coefficients are defined by a functor + * + * \tparam NullaryOp template functor implementing the operator + * \tparam PlainObjectType the underlying plain matrix/array type + * + * This class represents an expression of a generic nullary operator. + * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods, + * and most of the time this is the only way it is used. + * + * However, if you want to write a function returning such an expression, you + * will need to use this class. + * + * The functor NullaryOp must expose one of the following method: + + + + +
\c operator()() if the procedural generation does not depend on the coefficient entries (e.g., random numbers)
\c operator()(Index i)if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace)
\c operator()(Index i,Index j)if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)
+ * It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors. + * + * See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding + * C++11 random number generators. + * + * A nullary expression can also be used to implement custom sophisticated matrix manipulations + * that cannot be covered by the existing set of natively supported matrix manipulations. + * See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations + * on the behavior of CwiseNullaryOp. + * + * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr + */ +template +class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp >::type, internal::no_assignment_operator +{ + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp) + + EIGEN_DEVICE_FUNC + CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp()) + : m_rows(rows), m_cols(cols), m_functor(func) + { + eigen_assert(rows >= 0 + && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) + && cols >= 0 + && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const { return m_rows.value(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const { return m_cols.value(); } + + /** \returns the functor representing the nullary operation */ + EIGEN_DEVICE_FUNC + const NullaryOp& functor() const { return m_functor; } + + protected: + const internal::variable_if_dynamic m_rows; + const internal::variable_if_dynamic m_cols; + const NullaryOp m_functor; +}; + + +/** \returns an expression of a matrix defined by a custom functor \a func + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this MatrixBase type. + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used + * instead. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * \sa class CwiseNullaryOp + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +#ifndef EIGEN_PARSED_BY_DOXYGEN +const CwiseNullaryOp::PlainObject> +#else +const CwiseNullaryOp +#endif +DenseBase::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func) +{ + return CwiseNullaryOp(rows, cols, func); +} + +/** \returns an expression of a matrix defined by a custom functor \a func + * + * The parameter \a size is the size of the returned vector. + * Must be compatible with this MatrixBase type. + * + * \only_for_vectors + * + * This variant is meant to be used for dynamic-size vector types. For fixed-size types, + * it is redundant to pass \a size as argument, so Zero() should be used + * instead. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * Here is an example with C++11 random generators: \include random_cpp11.cpp + * Output: \verbinclude random_cpp11.out + * + * \sa class CwiseNullaryOp + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +#ifndef EIGEN_PARSED_BY_DOXYGEN +const CwiseNullaryOp::PlainObject> +#else +const CwiseNullaryOp +#endif +DenseBase::NullaryExpr(Index size, const CustomNullaryOp& func) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + if(RowsAtCompileTime == 1) return CwiseNullaryOp(1, size, func); + else return CwiseNullaryOp(size, 1, func); +} + +/** \returns an expression of a matrix defined by a custom functor \a func + * + * This variant is only for fixed-size DenseBase types. For dynamic-size types, you + * need to use the variants taking size arguments. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * \sa class CwiseNullaryOp + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +#ifndef EIGEN_PARSED_BY_DOXYGEN +const CwiseNullaryOp::PlainObject> +#else +const CwiseNullaryOp +#endif +DenseBase::NullaryExpr(const CustomNullaryOp& func) +{ + return CwiseNullaryOp(RowsAtCompileTime, ColsAtCompileTime, func); +} + +/** \returns an expression of a constant matrix of value \a value + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this DenseBase type. + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used + * instead. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * \sa class CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Constant(Index rows, Index cols, const Scalar& value) +{ + return DenseBase::NullaryExpr(rows, cols, internal::scalar_constant_op(value)); +} + +/** \returns an expression of a constant matrix of value \a value + * + * The parameter \a size is the size of the returned vector. + * Must be compatible with this DenseBase type. + * + * \only_for_vectors + * + * This variant is meant to be used for dynamic-size vector types. For fixed-size types, + * it is redundant to pass \a size as argument, so Zero() should be used + * instead. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * \sa class CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Constant(Index size, const Scalar& value) +{ + return DenseBase::NullaryExpr(size, internal::scalar_constant_op(value)); +} + +/** \returns an expression of a constant matrix of value \a value + * + * This variant is only for fixed-size DenseBase types. For dynamic-size types, you + * need to use the variants taking size arguments. + * + * The template parameter \a CustomNullaryOp is the type of the functor. + * + * \sa class CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Constant(const Scalar& value) +{ + EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) + return DenseBase::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op(value)); +} + +/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&) + * + * \only_for_vectors + * + * Example: \include DenseBase_LinSpaced_seq_deprecated.cpp + * Output: \verbinclude DenseBase_LinSpaced_seq_deprecated.out + * + * \sa LinSpaced(Index,const Scalar&, const Scalar&), setLinSpaced(Index,const Scalar&,const Scalar&) + */ +template +EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType +DenseBase::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return DenseBase::NullaryExpr(size, internal::linspaced_op(low,high,size)); +} + +/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&) + * + * \sa LinSpaced(const Scalar&, const Scalar&) + */ +template +EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType +DenseBase::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) + return DenseBase::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op(low,high,Derived::SizeAtCompileTime)); +} + +/** + * \brief Sets a linearly spaced vector. + * + * The function generates 'size' equally spaced values in the closed interval [low,high]. + * When size is set to 1, a vector of length 1 containing 'high' is returned. + * + * \only_for_vectors + * + * Example: \include DenseBase_LinSpaced.cpp + * Output: \verbinclude DenseBase_LinSpaced.out + * + * For integer scalar types, an even spacing is possible if and only if the length of the range, + * i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the + * number of values \c high-low+1 (meaning each value can be repeated the same number of time). + * If one of these two considions is not satisfied, then \c high is lowered to the largest value + * satisfying one of this constraint. + * Here are some examples: + * + * Example: \include DenseBase_LinSpacedInt.cpp + * Output: \verbinclude DenseBase_LinSpacedInt.out + * + * \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType +DenseBase::LinSpaced(Index size, const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return DenseBase::NullaryExpr(size, internal::linspaced_op(low,high,size)); +} + +/** + * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&) + * Special version for fixed size types which does not require the size parameter. + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType +DenseBase::LinSpaced(const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) + return DenseBase::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op(low,high,Derived::SizeAtCompileTime)); +} + +/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */ +template +EIGEN_DEVICE_FUNC bool DenseBase::isApproxToConstant +(const Scalar& val, const RealScalar& prec) const +{ + typename internal::nested_eval::type self(derived()); + for(Index j = 0; j < cols(); ++j) + for(Index i = 0; i < rows(); ++i) + if(!internal::isApprox(self.coeff(i, j), val, prec)) + return false; + return true; +} + +/** This is just an alias for isApproxToConstant(). + * + * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */ +template +EIGEN_DEVICE_FUNC bool DenseBase::isConstant +(const Scalar& val, const RealScalar& prec) const +{ + return isApproxToConstant(val, prec); +} + +/** Alias for setConstant(): sets all coefficients in this expression to \a val. + * + * \sa setConstant(), Constant(), class CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase::fill(const Scalar& val) +{ + setConstant(val); +} + +/** Sets all coefficients in this expression to value \a val. + * + * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase::setConstant(const Scalar& val) +{ + return derived() = Constant(rows(), cols(), val); +} + +/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val. + * + * \only_for_vectors + * + * Example: \include Matrix_setConstant_int.cpp + * Output: \verbinclude Matrix_setConstant_int.out + * + * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setConstant(Index size, const Scalar& val) +{ + resize(size); + return setConstant(val); +} + +/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val. + * + * \param rows the new number of rows + * \param cols the new number of columns + * \param val the value to which all coefficients are set + * + * Example: \include Matrix_setConstant_int_int.cpp + * Output: \verbinclude Matrix_setConstant_int_int.out + * + * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setConstant(Index rows, Index cols, const Scalar& val) +{ + resize(rows, cols); + return setConstant(val); +} + +/** Resizes to the given size, changing only the number of columns, and sets all + * coefficients in this expression to the given value \a val. For the parameter + * of type NoChange_t, just pass the special value \c NoChange. + * + * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setConstant(NoChange_t, Index cols, const Scalar& val) +{ + return setConstant(rows(), cols, val); +} + +/** Resizes to the given size, changing only the number of rows, and sets all + * coefficients in this expression to the given value \a val. For the parameter + * of type NoChange_t, just pass the special value \c NoChange. + * + * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setConstant(Index rows, NoChange_t, const Scalar& val) +{ + return setConstant(rows, cols(), val); +} + + +/** + * \brief Sets a linearly spaced vector. + * + * The function generates 'size' equally spaced values in the closed interval [low,high]. + * When size is set to 1, a vector of length 1 containing 'high' is returned. + * + * \only_for_vectors + * + * Example: \include DenseBase_setLinSpaced.cpp + * Output: \verbinclude DenseBase_setLinSpaced.out + * + * For integer scalar types, do not miss the explanations on the definition + * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink. + * + * \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op(low,high,newSize)); +} + +/** + * \brief Sets a linearly spaced vector. + * + * The function fills \c *this with equally spaced values in the closed interval [low,high]. + * When size is set to 1, a vector of length 1 containing 'high' is returned. + * + * \only_for_vectors + * + * For integer scalar types, do not miss the explanations on the definition + * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink. + * + * \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase::setLinSpaced(const Scalar& low, const Scalar& high) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return setLinSpaced(size(), low, high); +} + +// zero: + +/** \returns an expression of a zero matrix. + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this MatrixBase type. + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used + * instead. + * + * Example: \include MatrixBase_zero_int_int.cpp + * Output: \verbinclude MatrixBase_zero_int_int.out + * + * \sa Zero(), Zero(Index) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Zero(Index rows, Index cols) +{ + return Constant(rows, cols, Scalar(0)); +} + +/** \returns an expression of a zero vector. + * + * The parameter \a size is the size of the returned vector. + * Must be compatible with this MatrixBase type. + * + * \only_for_vectors + * + * This variant is meant to be used for dynamic-size vector types. For fixed-size types, + * it is redundant to pass \a size as argument, so Zero() should be used + * instead. + * + * Example: \include MatrixBase_zero_int.cpp + * Output: \verbinclude MatrixBase_zero_int.out + * + * \sa Zero(), Zero(Index,Index) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Zero(Index size) +{ + return Constant(size, Scalar(0)); +} + +/** \returns an expression of a fixed-size zero matrix or vector. + * + * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you + * need to use the variants taking size arguments. + * + * Example: \include MatrixBase_zero.cpp + * Output: \verbinclude MatrixBase_zero.out + * + * \sa Zero(Index), Zero(Index,Index) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Zero() +{ + return Constant(Scalar(0)); +} + +/** \returns true if *this is approximately equal to the zero matrix, + * within the precision given by \a prec. + * + * Example: \include MatrixBase_isZero.cpp + * Output: \verbinclude MatrixBase_isZero.out + * + * \sa class CwiseNullaryOp, Zero() + */ +template +EIGEN_DEVICE_FUNC bool DenseBase::isZero(const RealScalar& prec) const +{ + typename internal::nested_eval::type self(derived()); + for(Index j = 0; j < cols(); ++j) + for(Index i = 0; i < rows(); ++i) + if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast(1), prec)) + return false; + return true; +} + +/** Sets all coefficients in this expression to zero. + * + * Example: \include MatrixBase_setZero.cpp + * Output: \verbinclude MatrixBase_setZero.out + * + * \sa class CwiseNullaryOp, Zero() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase::setZero() +{ + return setConstant(Scalar(0)); +} + +/** Resizes to the given \a size, and sets all coefficients in this expression to zero. + * + * \only_for_vectors + * + * Example: \include Matrix_setZero_int.cpp + * Output: \verbinclude Matrix_setZero_int.out + * + * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setZero(Index newSize) +{ + resize(newSize); + return setConstant(Scalar(0)); +} + +/** Resizes to the given size, and sets all coefficients in this expression to zero. + * + * \param rows the new number of rows + * \param cols the new number of columns + * + * Example: \include Matrix_setZero_int_int.cpp + * Output: \verbinclude Matrix_setZero_int_int.out + * + * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setZero(Index rows, Index cols) +{ + resize(rows, cols); + return setConstant(Scalar(0)); +} + +/** Resizes to the given size, changing only the number of columns, and sets all + * coefficients in this expression to zero. For the parameter of type NoChange_t, + * just pass the special value \c NoChange. + * + * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Zero() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setZero(NoChange_t, Index cols) +{ + return setZero(rows(), cols); +} + +/** Resizes to the given size, changing only the number of rows, and sets all + * coefficients in this expression to zero. For the parameter of type NoChange_t, + * just pass the special value \c NoChange. + * + * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Zero() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setZero(Index rows, NoChange_t) +{ + return setZero(rows, cols()); +} + +// ones: + +/** \returns an expression of a matrix where all coefficients equal one. + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this MatrixBase type. + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used + * instead. + * + * Example: \include MatrixBase_ones_int_int.cpp + * Output: \verbinclude MatrixBase_ones_int_int.out + * + * \sa Ones(), Ones(Index), isOnes(), class Ones + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Ones(Index rows, Index cols) +{ + return Constant(rows, cols, Scalar(1)); +} + +/** \returns an expression of a vector where all coefficients equal one. + * + * The parameter \a newSize is the size of the returned vector. + * Must be compatible with this MatrixBase type. + * + * \only_for_vectors + * + * This variant is meant to be used for dynamic-size vector types. For fixed-size types, + * it is redundant to pass \a size as argument, so Ones() should be used + * instead. + * + * Example: \include MatrixBase_ones_int.cpp + * Output: \verbinclude MatrixBase_ones_int.out + * + * \sa Ones(), Ones(Index,Index), isOnes(), class Ones + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Ones(Index newSize) +{ + return Constant(newSize, Scalar(1)); +} + +/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one. + * + * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you + * need to use the variants taking size arguments. + * + * Example: \include MatrixBase_ones.cpp + * Output: \verbinclude MatrixBase_ones.out + * + * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType +DenseBase::Ones() +{ + return Constant(Scalar(1)); +} + +/** \returns true if *this is approximately equal to the matrix where all coefficients + * are equal to 1, within the precision given by \a prec. + * + * Example: \include MatrixBase_isOnes.cpp + * Output: \verbinclude MatrixBase_isOnes.out + * + * \sa class CwiseNullaryOp, Ones() + */ +template +EIGEN_DEVICE_FUNC bool DenseBase::isOnes +(const RealScalar& prec) const +{ + return isApproxToConstant(Scalar(1), prec); +} + +/** Sets all coefficients in this expression to one. + * + * Example: \include MatrixBase_setOnes.cpp + * Output: \verbinclude MatrixBase_setOnes.out + * + * \sa class CwiseNullaryOp, Ones() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase::setOnes() +{ + return setConstant(Scalar(1)); +} + +/** Resizes to the given \a newSize, and sets all coefficients in this expression to one. + * + * \only_for_vectors + * + * Example: \include Matrix_setOnes_int.cpp + * Output: \verbinclude Matrix_setOnes_int.out + * + * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setOnes(Index newSize) +{ + resize(newSize); + return setConstant(Scalar(1)); +} + +/** Resizes to the given size, and sets all coefficients in this expression to one. + * + * \param rows the new number of rows + * \param cols the new number of columns + * + * Example: \include Matrix_setOnes_int_int.cpp + * Output: \verbinclude Matrix_setOnes_int_int.out + * + * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setOnes(Index rows, Index cols) +{ + resize(rows, cols); + return setConstant(Scalar(1)); +} + +/** Resizes to the given size, changing only the number of rows, and sets all + * coefficients in this expression to one. For the parameter of type NoChange_t, + * just pass the special value \c NoChange. + * + * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(NoChange_t, Index), class CwiseNullaryOp, MatrixBase::Ones() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setOnes(Index rows, NoChange_t) +{ + return setOnes(rows, cols()); +} + +/** Resizes to the given size, changing only the number of columns, and sets all + * coefficients in this expression to one. For the parameter of type NoChange_t, + * just pass the special value \c NoChange. + * + * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(Index, NoChange_t) class CwiseNullaryOp, MatrixBase::Ones() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setOnes(NoChange_t, Index cols) +{ + return setOnes(rows(), cols); +} + +// Identity: + +/** \returns an expression of the identity matrix (not necessarily square). + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this MatrixBase type. + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used + * instead. + * + * Example: \include MatrixBase_identity_int_int.cpp + * Output: \verbinclude MatrixBase_identity_int_int.out + * + * \sa Identity(), setIdentity(), isIdentity() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::IdentityReturnType +MatrixBase::Identity(Index rows, Index cols) +{ + return DenseBase::NullaryExpr(rows, cols, internal::scalar_identity_op()); +} + +/** \returns an expression of the identity matrix (not necessarily square). + * + * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you + * need to use the variant taking size arguments. + * + * Example: \include MatrixBase_identity.cpp + * Output: \verbinclude MatrixBase_identity.out + * + * \sa Identity(Index,Index), setIdentity(), isIdentity() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::IdentityReturnType +MatrixBase::Identity() +{ + EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) + return MatrixBase::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op()); +} + +/** \returns true if *this is approximately equal to the identity matrix + * (not necessarily square), + * within the precision given by \a prec. + * + * Example: \include MatrixBase_isIdentity.cpp + * Output: \verbinclude MatrixBase_isIdentity.out + * + * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity() + */ +template +bool MatrixBase::isIdentity +(const RealScalar& prec) const +{ + typename internal::nested_eval::type self(derived()); + for(Index j = 0; j < cols(); ++j) + { + for(Index i = 0; i < rows(); ++i) + { + if(i == j) + { + if(!internal::isApprox(self.coeff(i, j), static_cast(1), prec)) + return false; + } + else + { + if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast(1), prec)) + return false; + } + } + } + return true; +} + +namespace internal { + +template=16)> +struct setIdentity_impl +{ + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE Derived& run(Derived& m) + { + return m = Derived::Identity(m.rows(), m.cols()); + } +}; + +template +struct setIdentity_impl +{ + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE Derived& run(Derived& m) + { + m.setZero(); + const Index size = numext::mini(m.rows(), m.cols()); + for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1); + return m; + } +}; + +} // end namespace internal + +/** Writes the identity expression (not necessarily square) into *this. + * + * Example: \include MatrixBase_setIdentity.cpp + * Output: \verbinclude MatrixBase_setIdentity.out + * + * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase::setIdentity() +{ + return internal::setIdentity_impl::run(derived()); +} + +/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this. + * + * \param rows the new number of rows + * \param cols the new number of columns + * + * Example: \include Matrix_setIdentity_int_int.cpp + * Output: \verbinclude Matrix_setIdentity_int_int.out + * + * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase::setIdentity(Index rows, Index cols) +{ + derived().resize(rows, cols); + return setIdentity(); +} + +/** \returns an expression of the i-th unit (basis) vector. + * + * \only_for_vectors + * + * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::Unit(Index newSize, Index i) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i); +} + +/** \returns an expression of the i-th unit (basis) vector. + * + * \only_for_vectors + * + * This variant is for fixed-size vector only. + * + * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::Unit(Index i) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + return BasisReturnType(SquareMatrixType::Identity(),i); +} + +/** \returns an expression of the X axis unit vector (1{,0}^*) + * + * \only_for_vectors + * + * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitX() +{ return Derived::Unit(0); } + +/** \returns an expression of the Y axis unit vector (0,1{,0}^*) + * + * \only_for_vectors + * + * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitY() +{ return Derived::Unit(1); } + +/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*) + * + * \only_for_vectors + * + * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitZ() +{ return Derived::Unit(2); } + +/** \returns an expression of the W axis unit vector (0,0,0,1) + * + * \only_for_vectors + * + * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitW() +{ return Derived::Unit(3); } + +/** \brief Set the coefficients of \c *this to the i-th unit (basis) vector + * + * \param i index of the unique coefficient to be set to 1 + * + * \only_for_vectors + * + * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index) + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase::setUnit(Index i) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived); + eigen_assert(i +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase::setUnit(Index newSize, Index i) +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived); + eigen_assert(i +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2016 Eugene Brevdo +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CWISE_TERNARY_OP_H +#define EIGEN_CWISE_TERNARY_OP_H + +namespace Eigen { + +namespace internal { +template +struct traits > { + // we must not inherit from traits since it has + // the potential to cause problems with MSVC + typedef typename remove_all::type Ancestor; + typedef typename traits::XprKind XprKind; + enum { + RowsAtCompileTime = traits::RowsAtCompileTime, + ColsAtCompileTime = traits::ColsAtCompileTime, + MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = traits::MaxColsAtCompileTime + }; + + // even though we require Arg1, Arg2, and Arg3 to have the same scalar type + // (see CwiseTernaryOp constructor), + // we still want to handle the case when the result type is different. + typedef typename result_of::type Scalar; + + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::StorageIndex StorageIndex; + + typedef typename Arg1::Nested Arg1Nested; + typedef typename Arg2::Nested Arg2Nested; + typedef typename Arg3::Nested Arg3Nested; + typedef typename remove_reference::type _Arg1Nested; + typedef typename remove_reference::type _Arg2Nested; + typedef typename remove_reference::type _Arg3Nested; + enum { Flags = _Arg1Nested::Flags & RowMajorBit }; +}; +} // end namespace internal + +template +class CwiseTernaryOpImpl; + +/** \class CwiseTernaryOp + * \ingroup Core_Module + * + * \brief Generic expression where a coefficient-wise ternary operator is + * applied to two expressions + * + * \tparam TernaryOp template functor implementing the operator + * \tparam Arg1Type the type of the first argument + * \tparam Arg2Type the type of the second argument + * \tparam Arg3Type the type of the third argument + * + * This class represents an expression where a coefficient-wise ternary + * operator is applied to three expressions. + * It is the return type of ternary operators, by which we mean only those + * ternary operators where + * all three arguments are Eigen expressions. + * For example, the return type of betainc(matrix1, matrix2, matrix3) is a + * CwiseTernaryOp. + * + * Most of the time, this is the only way that it is used, so you typically + * don't have to name + * CwiseTernaryOp types explicitly. + * + * \sa MatrixBase::ternaryExpr(const MatrixBase &, const + * MatrixBase &, const CustomTernaryOp &) const, class CwiseBinaryOp, + * class CwiseUnaryOp, class CwiseNullaryOp + */ +template +class CwiseTernaryOp : public CwiseTernaryOpImpl< + TernaryOp, Arg1Type, Arg2Type, Arg3Type, + typename internal::traits::StorageKind>, + internal::no_assignment_operator +{ + public: + typedef typename internal::remove_all::type Arg1; + typedef typename internal::remove_all::type Arg2; + typedef typename internal::remove_all::type Arg3; + + typedef typename CwiseTernaryOpImpl< + TernaryOp, Arg1Type, Arg2Type, Arg3Type, + typename internal::traits::StorageKind>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp) + + typedef typename internal::ref_selector::type Arg1Nested; + typedef typename internal::ref_selector::type Arg2Nested; + typedef typename internal::ref_selector::type Arg3Nested; + typedef typename internal::remove_reference::type _Arg1Nested; + typedef typename internal::remove_reference::type _Arg2Nested; + typedef typename internal::remove_reference::type _Arg3Nested; + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2, + const Arg3& a3, + const TernaryOp& func = TernaryOp()) + : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) { + // require the sizes to match + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2) + EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3) + + // The index types should match + EIGEN_STATIC_ASSERT((internal::is_same< + typename internal::traits::StorageKind, + typename internal::traits::StorageKind>::value), + STORAGE_KIND_MUST_MATCH) + EIGEN_STATIC_ASSERT((internal::is_same< + typename internal::traits::StorageKind, + typename internal::traits::StorageKind>::value), + STORAGE_KIND_MUST_MATCH) + + eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() && + a1.rows() == a3.rows() && a1.cols() == a3.cols()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index rows() const { + // return the fixed size type if available to enable compile time + // optimizations + if (internal::traits::type>:: + RowsAtCompileTime == Dynamic && + internal::traits::type>:: + RowsAtCompileTime == Dynamic) + return m_arg3.rows(); + else if (internal::traits::type>:: + RowsAtCompileTime == Dynamic && + internal::traits::type>:: + RowsAtCompileTime == Dynamic) + return m_arg2.rows(); + else + return m_arg1.rows(); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index cols() const { + // return the fixed size type if available to enable compile time + // optimizations + if (internal::traits::type>:: + ColsAtCompileTime == Dynamic && + internal::traits::type>:: + ColsAtCompileTime == Dynamic) + return m_arg3.cols(); + else if (internal::traits::type>:: + ColsAtCompileTime == Dynamic && + internal::traits::type>:: + ColsAtCompileTime == Dynamic) + return m_arg2.cols(); + else + return m_arg1.cols(); + } + + /** \returns the first argument nested expression */ + EIGEN_DEVICE_FUNC + const _Arg1Nested& arg1() const { return m_arg1; } + /** \returns the first argument nested expression */ + EIGEN_DEVICE_FUNC + const _Arg2Nested& arg2() const { return m_arg2; } + /** \returns the third argument nested expression */ + EIGEN_DEVICE_FUNC + const _Arg3Nested& arg3() const { return m_arg3; } + /** \returns the functor representing the ternary operation */ + EIGEN_DEVICE_FUNC + const TernaryOp& functor() const { return m_functor; } + + protected: + Arg1Nested m_arg1; + Arg2Nested m_arg2; + Arg3Nested m_arg3; + const TernaryOp m_functor; +}; + +// Generic API dispatcher +template +class CwiseTernaryOpImpl + : public internal::generic_xpr_base< + CwiseTernaryOp >::type { + public: + typedef typename internal::generic_xpr_base< + CwiseTernaryOp >::type Base; +}; + +} // end namespace Eigen + +#endif // EIGEN_CWISE_TERNARY_OP_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryOp.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryOp.h new file mode 100644 index 00000000..e68c4f74 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryOp.h @@ -0,0 +1,103 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2014 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CWISE_UNARY_OP_H +#define EIGEN_CWISE_UNARY_OP_H + +namespace Eigen { + +namespace internal { +template +struct traits > + : traits +{ + typedef typename result_of< + UnaryOp(const typename XprType::Scalar&) + >::type Scalar; + typedef typename XprType::Nested XprTypeNested; + typedef typename remove_reference::type _XprTypeNested; + enum { + Flags = _XprTypeNested::Flags & RowMajorBit + }; +}; +} + +template +class CwiseUnaryOpImpl; + +/** \class CwiseUnaryOp + * \ingroup Core_Module + * + * \brief Generic expression where a coefficient-wise unary operator is applied to an expression + * + * \tparam UnaryOp template functor implementing the operator + * \tparam XprType the type of the expression to which we are applying the unary operator + * + * This class represents an expression where a unary operator is applied to an expression. + * It is the return type of all operations taking exactly 1 input expression, regardless of the + * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix + * is considered unary, because only the right-hand side is an expression, and its + * return type is a specialization of CwiseUnaryOp. + * + * Most of the time, this is the only way that it is used, so you typically don't have to name + * CwiseUnaryOp types explicitly. + * + * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp + */ +template +class CwiseUnaryOp : public CwiseUnaryOpImpl::StorageKind>, internal::no_assignment_operator +{ + public: + + typedef typename CwiseUnaryOpImpl::StorageKind>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp) + typedef typename internal::ref_selector::type XprTypeNested; + typedef typename internal::remove_all::type NestedExpression; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp()) + : m_xpr(xpr), m_functor(func) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); } + + /** \returns the functor representing the unary operation */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const UnaryOp& functor() const { return m_functor; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const typename internal::remove_all::type& + nestedExpression() const { return m_xpr; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + typename internal::remove_all::type& + nestedExpression() { return m_xpr; } + + protected: + XprTypeNested m_xpr; + const UnaryOp m_functor; +}; + +// Generic API dispatcher +template +class CwiseUnaryOpImpl + : public internal::generic_xpr_base >::type +{ +public: + typedef typename internal::generic_xpr_base >::type Base; +}; + +} // end namespace Eigen + +#endif // EIGEN_CWISE_UNARY_OP_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryView.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryView.h new file mode 100644 index 00000000..a06d7621 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/CwiseUnaryView.h @@ -0,0 +1,132 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CWISE_UNARY_VIEW_H +#define EIGEN_CWISE_UNARY_VIEW_H + +namespace Eigen { + +namespace internal { +template +struct traits > + : traits +{ + typedef typename result_of< + ViewOp(const typename traits::Scalar&) + >::type Scalar; + typedef typename MatrixType::Nested MatrixTypeNested; + typedef typename remove_all::type _MatrixTypeNested; + enum { + FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, + Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions + MatrixTypeInnerStride = inner_stride_at_compile_time::ret, + // need to cast the sizeof's from size_t to int explicitly, otherwise: + // "error: no integral type can represent all of the enumerator values + InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic + ? int(Dynamic) + : int(MatrixTypeInnerStride) * int(sizeof(typename traits::Scalar) / sizeof(Scalar)), + OuterStrideAtCompileTime = outer_stride_at_compile_time::ret == Dynamic + ? int(Dynamic) + : outer_stride_at_compile_time::ret * int(sizeof(typename traits::Scalar) / sizeof(Scalar)) + }; +}; +} + +template +class CwiseUnaryViewImpl; + +/** \class CwiseUnaryView + * \ingroup Core_Module + * + * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector + * + * \tparam ViewOp template functor implementing the view + * \tparam MatrixType the type of the matrix we are applying the unary operator + * + * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector. + * It is the return type of real() and imag(), and most of the time this is the only way it is used. + * + * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp + */ +template +class CwiseUnaryView : public CwiseUnaryViewImpl::StorageKind> +{ + public: + + typedef typename CwiseUnaryViewImpl::StorageKind>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView) + typedef typename internal::ref_selector::non_const_type MatrixTypeNested; + typedef typename internal::remove_all::type NestedExpression; + + explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp()) + : m_matrix(mat), m_functor(func) {} + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); } + + /** \returns the functor representing unary operation */ + EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC const typename internal::remove_all::type& + nestedExpression() const { return m_matrix; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC typename internal::remove_reference::type& + nestedExpression() { return m_matrix; } + + protected: + MatrixTypeNested m_matrix; + ViewOp m_functor; +}; + +// Generic API dispatcher +template +class CwiseUnaryViewImpl + : public internal::generic_xpr_base >::type +{ +public: + typedef typename internal::generic_xpr_base >::type Base; +}; + +template +class CwiseUnaryViewImpl + : public internal::dense_xpr_base< CwiseUnaryView >::type +{ + public: + + typedef CwiseUnaryView Derived; + typedef typename internal::dense_xpr_base< CwiseUnaryView >::type Base; + + EIGEN_DENSE_PUBLIC_INTERFACE(Derived) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl) + + EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); } + EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const + { + return derived().nestedExpression().innerStride() * sizeof(typename internal::traits::Scalar) / sizeof(Scalar); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const + { + return derived().nestedExpression().outerStride() * sizeof(typename internal::traits::Scalar) / sizeof(Scalar); + } + protected: + EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl) +}; + +} // end namespace Eigen + +#endif // EIGEN_CWISE_UNARY_VIEW_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseBase.h new file mode 100644 index 00000000..9b16db68 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseBase.h @@ -0,0 +1,701 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2007-2010 Benoit Jacob +// Copyright (C) 2008-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DENSEBASE_H +#define EIGEN_DENSEBASE_H + +namespace Eigen { + +namespace internal { + +// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type. +// This dummy function simply aims at checking that at compile time. +static inline void check_DenseIndex_is_signed() { + EIGEN_STATIC_ASSERT(NumTraits::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE) +} + +} // end namespace internal + +/** \class DenseBase + * \ingroup Core_Module + * + * \brief Base class for all dense matrices, vectors, and arrays + * + * This class is the base that is inherited by all dense objects (matrix, vector, arrays, + * and related expression types). The common Eigen API for dense objects is contained in this class. + * + * \tparam Derived is the derived type, e.g., a matrix type or an expression. + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN. + * + * \sa \blank \ref TopicClassHierarchy + */ +template class DenseBase +#ifndef EIGEN_PARSED_BY_DOXYGEN + : public DenseCoeffsBase::value> +#else + : public DenseCoeffsBase +#endif // not EIGEN_PARSED_BY_DOXYGEN +{ + public: + + /** Inner iterator type to iterate over the coefficients of a row or column. + * \sa class InnerIterator + */ + typedef Eigen::InnerIterator InnerIterator; + + typedef typename internal::traits::StorageKind StorageKind; + + /** + * \brief The type used to store indices + * \details This typedef is relevant for types that store multiple indices such as + * PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index + * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase. + */ + typedef typename internal::traits::StorageIndex StorageIndex; + + /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex, etc. */ + typedef typename internal::traits::Scalar Scalar; + + /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex, etc. + * + * It is an alias for the Scalar type */ + typedef Scalar value_type; + + typedef typename NumTraits::Real RealScalar; + typedef DenseCoeffsBase::value> Base; + + using Base::derived; + using Base::const_cast_derived; + using Base::rows; + using Base::cols; + using Base::size; + using Base::rowIndexByOuterInner; + using Base::colIndexByOuterInner; + using Base::coeff; + using Base::coeffByOuterInner; + using Base::operator(); + using Base::operator[]; + using Base::x; + using Base::y; + using Base::z; + using Base::w; + using Base::stride; + using Base::innerStride; + using Base::outerStride; + using Base::rowStride; + using Base::colStride; + typedef typename Base::CoeffReturnType CoeffReturnType; + + enum { + + RowsAtCompileTime = internal::traits::RowsAtCompileTime, + /**< The number of rows at compile-time. This is just a copy of the value provided + * by the \a Derived type. If a value is not known at compile-time, + * it is set to the \a Dynamic constant. + * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */ + + ColsAtCompileTime = internal::traits::ColsAtCompileTime, + /**< The number of columns at compile-time. This is just a copy of the value provided + * by the \a Derived type. If a value is not known at compile-time, + * it is set to the \a Dynamic constant. + * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */ + + + SizeAtCompileTime = (internal::size_at_compile_time::RowsAtCompileTime, + internal::traits::ColsAtCompileTime>::ret), + /**< This is equal to the number of coefficients, i.e. the number of + * rows times the number of columns, or to \a Dynamic if this is not + * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */ + + MaxRowsAtCompileTime = internal::traits::MaxRowsAtCompileTime, + /**< This value is equal to the maximum possible number of rows that this expression + * might have. If this expression might have an arbitrarily high number of rows, + * this value is set to \a Dynamic. + * + * This value is useful to know when evaluating an expression, in order to determine + * whether it is possible to avoid doing a dynamic memory allocation. + * + * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime + */ + + MaxColsAtCompileTime = internal::traits::MaxColsAtCompileTime, + /**< This value is equal to the maximum possible number of columns that this expression + * might have. If this expression might have an arbitrarily high number of columns, + * this value is set to \a Dynamic. + * + * This value is useful to know when evaluating an expression, in order to determine + * whether it is possible to avoid doing a dynamic memory allocation. + * + * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime + */ + + MaxSizeAtCompileTime = (internal::size_at_compile_time::MaxRowsAtCompileTime, + internal::traits::MaxColsAtCompileTime>::ret), + /**< This value is equal to the maximum possible number of coefficients that this expression + * might have. If this expression might have an arbitrarily high number of coefficients, + * this value is set to \a Dynamic. + * + * This value is useful to know when evaluating an expression, in order to determine + * whether it is possible to avoid doing a dynamic memory allocation. + * + * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime + */ + + IsVectorAtCompileTime = internal::traits::RowsAtCompileTime == 1 + || internal::traits::ColsAtCompileTime == 1, + /**< This is set to true if either the number of rows or the number of + * columns is known at compile-time to be equal to 1. Indeed, in that case, + * we are dealing with a column-vector (if there is only one column) or with + * a row-vector (if there is only one row). */ + + NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2, + /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors, + * and 2 for matrices. + */ + + Flags = internal::traits::Flags, + /**< This stores expression \ref flags flags which may or may not be inherited by new expressions + * constructed from this one. See the \ref flags "list of flags". + */ + + IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */ + + InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime) + : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime), + + InnerStrideAtCompileTime = internal::inner_stride_at_compile_time::ret, + OuterStrideAtCompileTime = internal::outer_stride_at_compile_time::ret + }; + + typedef typename internal::find_best_packet::type PacketScalar; + + enum { IsPlainObjectBase = 0 }; + + /** The plain matrix type corresponding to this expression. + * \sa PlainObject */ + typedef Matrix::Scalar, + internal::traits::RowsAtCompileTime, + internal::traits::ColsAtCompileTime, + AutoAlign | (internal::traits::Flags&RowMajorBit ? RowMajor : ColMajor), + internal::traits::MaxRowsAtCompileTime, + internal::traits::MaxColsAtCompileTime + > PlainMatrix; + + /** The plain array type corresponding to this expression. + * \sa PlainObject */ + typedef Array::Scalar, + internal::traits::RowsAtCompileTime, + internal::traits::ColsAtCompileTime, + AutoAlign | (internal::traits::Flags&RowMajorBit ? RowMajor : ColMajor), + internal::traits::MaxRowsAtCompileTime, + internal::traits::MaxColsAtCompileTime + > PlainArray; + + /** \brief The plain matrix or array type corresponding to this expression. + * + * This is not necessarily exactly the return type of eval(). In the case of plain matrices, + * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed + * that the return type of eval() is either PlainObject or const PlainObject&. + */ + typedef typename internal::conditional::XprKind,MatrixXpr >::value, + PlainMatrix, PlainArray>::type PlainObject; + + /** \returns the number of nonzero coefficients which is in practice the number + * of stored coefficients. */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index nonZeros() const { return size(); } + + /** \returns the outer size. + * + * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension + * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a + * column-major matrix, and the number of rows for a row-major matrix. */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + Index outerSize() const + { + return IsVectorAtCompileTime ? 1 + : int(IsRowMajor) ? this->rows() : this->cols(); + } + + /** \returns the inner size. + * + * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension + * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a + * column-major matrix, and the number of columns for a row-major matrix. */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + Index innerSize() const + { + return IsVectorAtCompileTime ? this->size() + : int(IsRowMajor) ? this->cols() : this->rows(); + } + + /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are + * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does + * nothing else. + */ + EIGEN_DEVICE_FUNC + void resize(Index newSize) + { + EIGEN_ONLY_USED_FOR_DEBUG(newSize); + eigen_assert(newSize == this->size() + && "DenseBase::resize() does not actually allow to resize."); + } + /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are + * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does + * nothing else. + */ + EIGEN_DEVICE_FUNC + void resize(Index rows, Index cols) + { + EIGEN_ONLY_USED_FOR_DEBUG(rows); + EIGEN_ONLY_USED_FOR_DEBUG(cols); + eigen_assert(rows == this->rows() && cols == this->cols() + && "DenseBase::resize() does not actually allow to resize."); + } + +#ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal Represents a matrix with all coefficients equal to one another*/ + typedef CwiseNullaryOp,PlainObject> ConstantReturnType; + /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */ + EIGEN_DEPRECATED typedef CwiseNullaryOp,PlainObject> SequentialLinSpacedReturnType; + /** \internal Represents a vector with linearly spaced coefficients that allows random access. */ + typedef CwiseNullaryOp,PlainObject> RandomAccessLinSpacedReturnType; + /** \internal the return type of MatrixBase::eigenvalues() */ + typedef Matrix::Scalar>::Real, internal::traits::ColsAtCompileTime, 1> EigenvaluesReturnType; + +#endif // not EIGEN_PARSED_BY_DOXYGEN + + /** Copies \a other into *this. \returns a reference to *this. */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const DenseBase& other); + + /** Special case of the template operator=, in order to prevent the compiler + * from generating a default operator= (issue hit with g++ 4.1) + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const DenseBase& other); + + template + EIGEN_DEVICE_FUNC + Derived& operator=(const EigenBase &other); + + template + EIGEN_DEVICE_FUNC + Derived& operator+=(const EigenBase &other); + + template + EIGEN_DEVICE_FUNC + Derived& operator-=(const EigenBase &other); + + template + EIGEN_DEVICE_FUNC + Derived& operator=(const ReturnByValue& func); + + /** \internal + * Copies \a other into *this without evaluating other. \returns a reference to *this. */ + template + /** \deprecated */ + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC + Derived& lazyAssign(const DenseBase& other); + + EIGEN_DEVICE_FUNC + CommaInitializer operator<< (const Scalar& s); + + template + /** \deprecated it now returns \c *this */ + EIGEN_DEPRECATED + const Derived& flagged() const + { return derived(); } + + template + EIGEN_DEVICE_FUNC + CommaInitializer operator<< (const DenseBase& other); + + typedef Transpose TransposeReturnType; + EIGEN_DEVICE_FUNC + TransposeReturnType transpose(); + typedef typename internal::add_const >::type ConstTransposeReturnType; + EIGEN_DEVICE_FUNC + ConstTransposeReturnType transpose() const; + EIGEN_DEVICE_FUNC + void transposeInPlace(); + + EIGEN_DEVICE_FUNC static const ConstantReturnType + Constant(Index rows, Index cols, const Scalar& value); + EIGEN_DEVICE_FUNC static const ConstantReturnType + Constant(Index size, const Scalar& value); + EIGEN_DEVICE_FUNC static const ConstantReturnType + Constant(const Scalar& value); + + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType + LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high); + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType + LinSpaced(Sequential_t, const Scalar& low, const Scalar& high); + + EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType + LinSpaced(Index size, const Scalar& low, const Scalar& high); + EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType + LinSpaced(const Scalar& low, const Scalar& high); + + template EIGEN_DEVICE_FUNC + static const CwiseNullaryOp + NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func); + template EIGEN_DEVICE_FUNC + static const CwiseNullaryOp + NullaryExpr(Index size, const CustomNullaryOp& func); + template EIGEN_DEVICE_FUNC + static const CwiseNullaryOp + NullaryExpr(const CustomNullaryOp& func); + + EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols); + EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size); + EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(); + EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols); + EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size); + EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(); + + EIGEN_DEVICE_FUNC void fill(const Scalar& value); + EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value); + EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high); + EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high); + EIGEN_DEVICE_FUNC Derived& setZero(); + EIGEN_DEVICE_FUNC Derived& setOnes(); + EIGEN_DEVICE_FUNC Derived& setRandom(); + + template EIGEN_DEVICE_FUNC + bool isApprox(const DenseBase& other, + const RealScalar& prec = NumTraits::dummy_precision()) const; + EIGEN_DEVICE_FUNC + bool isMuchSmallerThan(const RealScalar& other, + const RealScalar& prec = NumTraits::dummy_precision()) const; + template EIGEN_DEVICE_FUNC + bool isMuchSmallerThan(const DenseBase& other, + const RealScalar& prec = NumTraits::dummy_precision()) const; + + EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits::dummy_precision()) const; + EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits::dummy_precision()) const; + EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits::dummy_precision()) const; + EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits::dummy_precision()) const; + + inline bool hasNaN() const; + inline bool allFinite() const; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator*=(const Scalar& other); + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator/=(const Scalar& other); + + typedef typename internal::add_const_on_value_type::type>::type EvalReturnType; + /** \returns the matrix or vector obtained by evaluating this expression. + * + * Notice that in the case of a plain matrix or vector (not an expression) this function just returns + * a const reference, in order to avoid a useless copy. + * + * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE EvalReturnType eval() const + { + // Even though MSVC does not honor strong inlining when the return type + // is a dynamic matrix, we desperately need strong inlining for fixed + // size types on MSVC. + return typename internal::eval::type(derived()); + } + + /** swaps *this with the expression \a other. + * + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void swap(const DenseBase& other) + { + EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY); + eigen_assert(rows()==other.rows() && cols()==other.cols()); + call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op()); + } + + /** swaps *this with the matrix or array \a other. + * + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void swap(PlainObjectBase& other) + { + eigen_assert(rows()==other.rows() && cols()==other.cols()); + call_assignment(derived(), other.derived(), internal::swap_assign_op()); + } + + EIGEN_DEVICE_FUNC inline const NestByValue nestByValue() const; + EIGEN_DEVICE_FUNC inline const ForceAlignedAccess forceAlignedAccess() const; + EIGEN_DEVICE_FUNC inline ForceAlignedAccess forceAlignedAccess(); + template EIGEN_DEVICE_FUNC + inline const typename internal::conditional,Derived&>::type forceAlignedAccessIf() const; + template EIGEN_DEVICE_FUNC + inline typename internal::conditional,Derived&>::type forceAlignedAccessIf(); + + EIGEN_DEVICE_FUNC Scalar sum() const; + EIGEN_DEVICE_FUNC Scalar mean() const; + EIGEN_DEVICE_FUNC Scalar trace() const; + + EIGEN_DEVICE_FUNC Scalar prod() const; + + template + EIGEN_DEVICE_FUNC typename internal::traits::Scalar minCoeff() const; + template + EIGEN_DEVICE_FUNC typename internal::traits::Scalar maxCoeff() const; + + + // By default, the fastest version with undefined NaN propagation semantics is + // used. + // TODO(rmlarsen): Replace with default template argument when we move to + // c++11 or beyond. + EIGEN_DEVICE_FUNC inline typename internal::traits::Scalar minCoeff() const { + return minCoeff(); + } + EIGEN_DEVICE_FUNC inline typename internal::traits::Scalar maxCoeff() const { + return maxCoeff(); + } + + template + EIGEN_DEVICE_FUNC + typename internal::traits::Scalar minCoeff(IndexType* row, IndexType* col) const; + template + EIGEN_DEVICE_FUNC + typename internal::traits::Scalar maxCoeff(IndexType* row, IndexType* col) const; + template + EIGEN_DEVICE_FUNC + typename internal::traits::Scalar minCoeff(IndexType* index) const; + template + EIGEN_DEVICE_FUNC + typename internal::traits::Scalar maxCoeff(IndexType* index) const; + + // TODO(rmlarsen): Replace these methods with a default template argument. + template + EIGEN_DEVICE_FUNC inline + typename internal::traits::Scalar minCoeff(IndexType* row, IndexType* col) const { + return minCoeff(row, col); + } + template + EIGEN_DEVICE_FUNC inline + typename internal::traits::Scalar maxCoeff(IndexType* row, IndexType* col) const { + return maxCoeff(row, col); + } + template + EIGEN_DEVICE_FUNC inline + typename internal::traits::Scalar minCoeff(IndexType* index) const { + return minCoeff(index); + } + template + EIGEN_DEVICE_FUNC inline + typename internal::traits::Scalar maxCoeff(IndexType* index) const { + return maxCoeff(index); + } + + template + EIGEN_DEVICE_FUNC + Scalar redux(const BinaryOp& func) const; + + template + EIGEN_DEVICE_FUNC + void visit(Visitor& func) const; + + /** \returns a WithFormat proxy object allowing to print a matrix the with given + * format \a fmt. + * + * See class IOFormat for some examples. + * + * \sa class IOFormat, class WithFormat + */ + inline const WithFormat format(const IOFormat& fmt) const + { + return WithFormat(derived(), fmt); + } + + /** \returns the unique coefficient of a 1x1 expression */ + EIGEN_DEVICE_FUNC + CoeffReturnType value() const + { + EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) + eigen_assert(this->rows() == 1 && this->cols() == 1); + return derived().coeff(0,0); + } + + EIGEN_DEVICE_FUNC bool all() const; + EIGEN_DEVICE_FUNC bool any() const; + EIGEN_DEVICE_FUNC Index count() const; + + typedef VectorwiseOp RowwiseReturnType; + typedef const VectorwiseOp ConstRowwiseReturnType; + typedef VectorwiseOp ColwiseReturnType; + typedef const VectorwiseOp ConstColwiseReturnType; + + /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions + * + * Example: \include MatrixBase_rowwise.cpp + * Output: \verbinclude MatrixBase_rowwise.out + * + * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting + */ + //Code moved here due to a CUDA compiler bug + EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const { + return ConstRowwiseReturnType(derived()); + } + EIGEN_DEVICE_FUNC RowwiseReturnType rowwise(); + + /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions + * + * Example: \include MatrixBase_colwise.cpp + * Output: \verbinclude MatrixBase_colwise.out + * + * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting + */ + EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const { + return ConstColwiseReturnType(derived()); + } + EIGEN_DEVICE_FUNC ColwiseReturnType colwise(); + + typedef CwiseNullaryOp,PlainObject> RandomReturnType; + static const RandomReturnType Random(Index rows, Index cols); + static const RandomReturnType Random(Index size); + static const RandomReturnType Random(); + + template + inline EIGEN_DEVICE_FUNC const Select + select(const DenseBase& thenMatrix, + const DenseBase& elseMatrix) const; + + template + inline EIGEN_DEVICE_FUNC const Select + select(const DenseBase& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const; + + template + inline EIGEN_DEVICE_FUNC const Select + select(const typename ElseDerived::Scalar& thenScalar, const DenseBase& elseMatrix) const; + + template RealScalar lpNorm() const; + + template + EIGEN_DEVICE_FUNC + const Replicate replicate() const; + /** + * \return an expression of the replication of \c *this + * + * Example: \include MatrixBase_replicate_int_int.cpp + * Output: \verbinclude MatrixBase_replicate_int_int.out + * + * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate + */ + //Code moved here due to a CUDA compiler bug + EIGEN_DEVICE_FUNC + const Replicate replicate(Index rowFactor, Index colFactor) const + { + return Replicate(derived(), rowFactor, colFactor); + } + + typedef Reverse ReverseReturnType; + typedef const Reverse ConstReverseReturnType; + EIGEN_DEVICE_FUNC ReverseReturnType reverse(); + /** This is the const version of reverse(). */ + //Code moved here due to a CUDA compiler bug + EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const + { + return ConstReverseReturnType(derived()); + } + EIGEN_DEVICE_FUNC void reverseInPlace(); + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** STL-like RandomAccessIterator + * iterator type as returned by the begin() and end() methods. + */ + typedef random_access_iterator_type iterator; + /** This is the const version of iterator (aka read-only) */ + typedef random_access_iterator_type const_iterator; + #else + typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit, + internal::pointer_based_stl_iterator, + internal::generic_randaccess_stl_iterator + >::type iterator_type; + + typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit, + internal::pointer_based_stl_iterator, + internal::generic_randaccess_stl_iterator + >::type const_iterator_type; + + // Stl-style iterators are supported only for vectors. + + typedef typename internal::conditional< IsVectorAtCompileTime, + iterator_type, + void + >::type iterator; + + typedef typename internal::conditional< IsVectorAtCompileTime, + const_iterator_type, + void + >::type const_iterator; + #endif + + inline iterator begin(); + inline const_iterator begin() const; + inline const_iterator cbegin() const; + inline iterator end(); + inline const_iterator end() const; + inline const_iterator cend() const; + +#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase +#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL +#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND) +#define EIGEN_DOC_UNARY_ADDONS(X,Y) +# include "../plugins/CommonCwiseUnaryOps.h" +# include "../plugins/BlockMethods.h" +# include "../plugins/IndexedViewMethods.h" +# include "../plugins/ReshapedMethods.h" +# ifdef EIGEN_DENSEBASE_PLUGIN +# include EIGEN_DENSEBASE_PLUGIN +# endif +#undef EIGEN_CURRENT_STORAGE_BASE_CLASS +#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL +#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF +#undef EIGEN_DOC_UNARY_ADDONS + + // disable the use of evalTo for dense objects with a nice compilation error + template + EIGEN_DEVICE_FUNC + inline void evalTo(Dest& ) const + { + EIGEN_STATIC_ASSERT((internal::is_same::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS); + } + + protected: + EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase) + /** Default constructor. Do nothing. */ + EIGEN_DEVICE_FUNC DenseBase() + { + /* Just checks for self-consistency of the flags. + * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down + */ +#ifdef EIGEN_INTERNAL_DEBUGGING + EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor)) + && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))), + INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION) +#endif + } + + private: + EIGEN_DEVICE_FUNC explicit DenseBase(int); + EIGEN_DEVICE_FUNC DenseBase(int,int); + template EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase&); +}; + +} // end namespace Eigen + +#endif // EIGEN_DENSEBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseCoeffsBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseCoeffsBase.h new file mode 100644 index 00000000..37fcdb59 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseCoeffsBase.h @@ -0,0 +1,685 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DENSECOEFFSBASE_H +#define EIGEN_DENSECOEFFSBASE_H + +namespace Eigen { + +namespace internal { +template struct add_const_on_value_type_if_arithmetic +{ + typedef typename conditional::value, T, typename add_const_on_value_type::type>::type type; +}; +} + +/** \brief Base class providing read-only coefficient access to matrices and arrays. + * \ingroup Core_Module + * \tparam Derived Type of the derived class + * + * \note #ReadOnlyAccessors Constant indicating read-only access + * + * This class defines the \c operator() \c const function and friends, which can be used to read specific + * entries of a matrix or array. + * + * \sa DenseCoeffsBase, DenseCoeffsBase, + * \ref TopicClassHierarchy + */ +template +class DenseCoeffsBase : public EigenBase +{ + public: + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::packet_traits::type PacketScalar; + + // Explanation for this CoeffReturnType typedef. + // - This is the return type of the coeff() method. + // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references + // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value). + // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems + // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is + // not possible, since the underlying expressions might not offer a valid address the reference could be referring to. + typedef typename internal::conditional::Flags&LvalueBit), + const Scalar&, + typename internal::conditional::value, Scalar, const Scalar>::type + >::type CoeffReturnType; + + typedef typename internal::add_const_on_value_type_if_arithmetic< + typename internal::packet_traits::type + >::type PacketReturnType; + + typedef EigenBase Base; + using Base::rows; + using Base::cols; + using Base::size; + using Base::derived; + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const + { + return int(Derived::RowsAtCompileTime) == 1 ? 0 + : int(Derived::ColsAtCompileTime) == 1 ? inner + : int(Derived::Flags)&RowMajorBit ? outer + : inner; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const + { + return int(Derived::ColsAtCompileTime) == 1 ? 0 + : int(Derived::RowsAtCompileTime) == 1 ? inner + : int(Derived::Flags)&RowMajorBit ? inner + : outer; + } + + /** Short version: don't use this function, use + * \link operator()(Index,Index) const \endlink instead. + * + * Long version: this function is similar to + * \link operator()(Index,Index) const \endlink, but without the assertion. + * Use this for limiting the performance cost of debugging code when doing + * repeated coefficient access. Only use this when it is guaranteed that the + * parameters \a row and \a col are in range. + * + * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this + * function equivalent to \link operator()(Index,Index) const \endlink. + * + * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const + { + eigen_internal_assert(row >= 0 && row < rows() + && col >= 0 && col < cols()); + return internal::evaluator(derived()).coeff(row,col); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const + { + return coeff(rowIndexByOuterInner(outer, inner), + colIndexByOuterInner(outer, inner)); + } + + /** \returns the coefficient at given the given row and column. + * + * \sa operator()(Index,Index), operator[](Index) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const + { + eigen_assert(row >= 0 && row < rows() + && col >= 0 && col < cols()); + return coeff(row, col); + } + + /** Short version: don't use this function, use + * \link operator[](Index) const \endlink instead. + * + * Long version: this function is similar to + * \link operator[](Index) const \endlink, but without the assertion. + * Use this for limiting the performance cost of debugging code when doing + * repeated coefficient access. Only use this when it is guaranteed that the + * parameter \a index is in range. + * + * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this + * function equivalent to \link operator[](Index) const \endlink. + * + * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + coeff(Index index) const + { + EIGEN_STATIC_ASSERT(internal::evaluator::Flags & LinearAccessBit, + THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS) + eigen_internal_assert(index >= 0 && index < size()); + return internal::evaluator(derived()).coeff(index); + } + + + /** \returns the coefficient at given index. + * + * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. + * + * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, + * z() const, w() const + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + operator[](Index index) const + { + EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, + THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) + eigen_assert(index >= 0 && index < size()); + return coeff(index); + } + + /** \returns the coefficient at given index. + * + * This is synonymous to operator[](Index) const. + * + * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. + * + * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, + * z() const, w() const + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + operator()(Index index) const + { + eigen_assert(index >= 0 && index < size()); + return coeff(index); + } + + /** equivalent to operator[](0). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + x() const { return (*this)[0]; } + + /** equivalent to operator[](1). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + y() const + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS); + return (*this)[1]; + } + + /** equivalent to operator[](2). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + z() const + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS); + return (*this)[2]; + } + + /** equivalent to operator[](3). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE CoeffReturnType + w() const + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS); + return (*this)[3]; + } + + /** \internal + * \returns the packet of coefficients starting at the given row and column. It is your responsibility + * to ensure that a packet really starts there. This method is only available on expressions having the + * PacketAccessBit. + * + * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select + * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets + * starting at an address which is a multiple of the packet size. + */ + + template + EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const + { + typedef typename internal::packet_traits::type DefaultPacketType; + eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); + return internal::evaluator(derived()).template packet(row,col); + } + + + /** \internal */ + template + EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const + { + return packet(rowIndexByOuterInner(outer, inner), + colIndexByOuterInner(outer, inner)); + } + + /** \internal + * \returns the packet of coefficients starting at the given index. It is your responsibility + * to ensure that a packet really starts there. This method is only available on expressions having the + * PacketAccessBit and the LinearAccessBit. + * + * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select + * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets + * starting at an address which is a multiple of the packet size. + */ + + template + EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT(internal::evaluator::Flags & LinearAccessBit, + THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS) + typedef typename internal::packet_traits::type DefaultPacketType; + eigen_internal_assert(index >= 0 && index < size()); + return internal::evaluator(derived()).template packet(index); + } + + protected: + // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase. + // But some methods are only available in the DirectAccess case. + // So we add dummy methods here with these names, so that "using... " doesn't fail. + // It's not private so that the child class DenseBase can access them, and it's not public + // either since it's an implementation detail, so has to be protected. + void coeffRef(); + void coeffRefByOuterInner(); + void writePacket(); + void writePacketByOuterInner(); + void copyCoeff(); + void copyCoeffByOuterInner(); + void copyPacket(); + void copyPacketByOuterInner(); + void stride(); + void innerStride(); + void outerStride(); + void rowStride(); + void colStride(); +}; + +/** \brief Base class providing read/write coefficient access to matrices and arrays. + * \ingroup Core_Module + * \tparam Derived Type of the derived class + * + * \note #WriteAccessors Constant indicating read/write access + * + * This class defines the non-const \c operator() function and friends, which can be used to write specific + * entries of a matrix or array. This class inherits DenseCoeffsBase which + * defines the const variant for reading specific entries. + * + * \sa DenseCoeffsBase, \ref TopicClassHierarchy + */ +template +class DenseCoeffsBase : public DenseCoeffsBase +{ + public: + + typedef DenseCoeffsBase Base; + + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::packet_traits::type PacketScalar; + typedef typename NumTraits::Real RealScalar; + + using Base::coeff; + using Base::rows; + using Base::cols; + using Base::size; + using Base::derived; + using Base::rowIndexByOuterInner; + using Base::colIndexByOuterInner; + using Base::operator[]; + using Base::operator(); + using Base::x; + using Base::y; + using Base::z; + using Base::w; + + /** Short version: don't use this function, use + * \link operator()(Index,Index) \endlink instead. + * + * Long version: this function is similar to + * \link operator()(Index,Index) \endlink, but without the assertion. + * Use this for limiting the performance cost of debugging code when doing + * repeated coefficient access. Only use this when it is guaranteed that the + * parameters \a row and \a col are in range. + * + * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this + * function equivalent to \link operator()(Index,Index) \endlink. + * + * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) + { + eigen_internal_assert(row >= 0 && row < rows() + && col >= 0 && col < cols()); + return internal::evaluator(derived()).coeffRef(row,col); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + coeffRefByOuterInner(Index outer, Index inner) + { + return coeffRef(rowIndexByOuterInner(outer, inner), + colIndexByOuterInner(outer, inner)); + } + + /** \returns a reference to the coefficient at given the given row and column. + * + * \sa operator[](Index) + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + operator()(Index row, Index col) + { + eigen_assert(row >= 0 && row < rows() + && col >= 0 && col < cols()); + return coeffRef(row, col); + } + + + /** Short version: don't use this function, use + * \link operator[](Index) \endlink instead. + * + * Long version: this function is similar to + * \link operator[](Index) \endlink, but without the assertion. + * Use this for limiting the performance cost of debugging code when doing + * repeated coefficient access. Only use this when it is guaranteed that the + * parameters \a row and \a col are in range. + * + * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this + * function equivalent to \link operator[](Index) \endlink. + * + * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index) + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + coeffRef(Index index) + { + EIGEN_STATIC_ASSERT(internal::evaluator::Flags & LinearAccessBit, + THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS) + eigen_internal_assert(index >= 0 && index < size()); + return internal::evaluator(derived()).coeffRef(index); + } + + /** \returns a reference to the coefficient at given index. + * + * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. + * + * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + operator[](Index index) + { + EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, + THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) + eigen_assert(index >= 0 && index < size()); + return coeffRef(index); + } + + /** \returns a reference to the coefficient at given index. + * + * This is synonymous to operator[](Index). + * + * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. + * + * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() + */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + operator()(Index index) + { + eigen_assert(index >= 0 && index < size()); + return coeffRef(index); + } + + /** equivalent to operator[](0). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + x() { return (*this)[0]; } + + /** equivalent to operator[](1). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + y() + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS); + return (*this)[1]; + } + + /** equivalent to operator[](2). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + z() + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS); + return (*this)[2]; + } + + /** equivalent to operator[](3). */ + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& + w() + { + EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS); + return (*this)[3]; + } +}; + +/** \brief Base class providing direct read-only coefficient access to matrices and arrays. + * \ingroup Core_Module + * \tparam Derived Type of the derived class + * + * \note #DirectAccessors Constant indicating direct access + * + * This class defines functions to work with strides which can be used to access entries directly. This class + * inherits DenseCoeffsBase which defines functions to access entries read-only using + * \c operator() . + * + * \sa \blank \ref TopicClassHierarchy + */ +template +class DenseCoeffsBase : public DenseCoeffsBase +{ + public: + + typedef DenseCoeffsBase Base; + typedef typename internal::traits::Scalar Scalar; + typedef typename NumTraits::Real RealScalar; + + using Base::rows; + using Base::cols; + using Base::size; + using Base::derived; + + /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. + * + * \sa outerStride(), rowStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const + { + return derived().innerStride(); + } + + /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns + * in a column-major matrix). + * + * \sa innerStride(), rowStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const + { + return derived().outerStride(); + } + + // FIXME shall we remove it ? + EIGEN_CONSTEXPR inline Index stride() const + { + return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); + } + + /** \returns the pointer increment between two consecutive rows. + * + * \sa innerStride(), outerStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rowStride() const + { + return Derived::IsRowMajor ? outerStride() : innerStride(); + } + + /** \returns the pointer increment between two consecutive columns. + * + * \sa innerStride(), outerStride(), rowStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index colStride() const + { + return Derived::IsRowMajor ? innerStride() : outerStride(); + } +}; + +/** \brief Base class providing direct read/write coefficient access to matrices and arrays. + * \ingroup Core_Module + * \tparam Derived Type of the derived class + * + * \note #DirectWriteAccessors Constant indicating direct access + * + * This class defines functions to work with strides which can be used to access entries directly. This class + * inherits DenseCoeffsBase which defines functions to access entries read/write using + * \c operator(). + * + * \sa \blank \ref TopicClassHierarchy + */ +template +class DenseCoeffsBase + : public DenseCoeffsBase +{ + public: + + typedef DenseCoeffsBase Base; + typedef typename internal::traits::Scalar Scalar; + typedef typename NumTraits::Real RealScalar; + + using Base::rows; + using Base::cols; + using Base::size; + using Base::derived; + + /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. + * + * \sa outerStride(), rowStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT + { + return derived().innerStride(); + } + + /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns + * in a column-major matrix). + * + * \sa innerStride(), rowStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT + { + return derived().outerStride(); + } + + // FIXME shall we remove it ? + EIGEN_CONSTEXPR inline Index stride() const EIGEN_NOEXCEPT + { + return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); + } + + /** \returns the pointer increment between two consecutive rows. + * + * \sa innerStride(), outerStride(), colStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rowStride() const EIGEN_NOEXCEPT + { + return Derived::IsRowMajor ? outerStride() : innerStride(); + } + + /** \returns the pointer increment between two consecutive columns. + * + * \sa innerStride(), outerStride(), rowStride() + */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index colStride() const EIGEN_NOEXCEPT + { + return Derived::IsRowMajor ? innerStride() : outerStride(); + } +}; + +namespace internal { + +template +struct first_aligned_impl +{ + static EIGEN_CONSTEXPR inline Index run(const Derived&) EIGEN_NOEXCEPT + { return 0; } +}; + +template +struct first_aligned_impl +{ + static inline Index run(const Derived& m) + { + return internal::first_aligned(m.data(), m.size()); + } +}; + +/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization. + * + * \tparam Alignment requested alignment in Bytes. + * + * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more + * documentation. + */ +template +static inline Index first_aligned(const DenseBase& m) +{ + enum { ReturnZero = (int(evaluator::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) }; + return first_aligned_impl::run(m.derived()); +} + +template +static inline Index first_default_aligned(const DenseBase& m) +{ + typedef typename Derived::Scalar Scalar; + typedef typename packet_traits::type DefaultPacketType; + return internal::first_aligned::alignment),Derived>(m); +} + +template::ret> +struct inner_stride_at_compile_time +{ + enum { ret = traits::InnerStrideAtCompileTime }; +}; + +template +struct inner_stride_at_compile_time +{ + enum { ret = 0 }; +}; + +template::ret> +struct outer_stride_at_compile_time +{ + enum { ret = traits::OuterStrideAtCompileTime }; +}; + +template +struct outer_stride_at_compile_time +{ + enum { ret = 0 }; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_DENSECOEFFSBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseStorage.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseStorage.h new file mode 100644 index 00000000..08ef6c53 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DenseStorage.h @@ -0,0 +1,652 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2009 Benoit Jacob +// Copyright (C) 2010-2013 Hauke Heibel +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATRIXSTORAGE_H +#define EIGEN_MATRIXSTORAGE_H + +#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN; +#else + #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) +#endif + +namespace Eigen { + +namespace internal { + +struct constructor_without_unaligned_array_assert {}; + +template +EIGEN_DEVICE_FUNC +void check_static_allocation_size() +{ + // if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit + #if EIGEN_STACK_ALLOCATION_LIMIT + EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG); + #endif +} + +/** \internal + * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned: + * to 16 bytes boundary if the total size is a multiple of 16 bytes. + */ +template ::value > +struct plain_array +{ + T array[Size]; + + EIGEN_DEVICE_FUNC + plain_array() + { + check_static_allocation_size(); + } + + EIGEN_DEVICE_FUNC + plain_array(constructor_without_unaligned_array_assert) + { + check_static_allocation_size(); + } +}; + +#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT) + #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) +#elif EIGEN_GNUC_AT_LEAST(4,7) + // GCC 4.7 is too aggressive in its optimizations and remove the alignment test based on the fact the array is declared to be aligned. + // See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900 + // Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined: + template + EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; } + #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \ + eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \ + && "this assertion is explained here: " \ + "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \ + " **** READ THIS WEB PAGE !!! ****"); +#else + #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \ + eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \ + && "this assertion is explained here: " \ + "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \ + " **** READ THIS WEB PAGE !!! ****"); +#endif + +template +struct plain_array +{ + EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size]; + + EIGEN_DEVICE_FUNC + plain_array() + { + EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7); + check_static_allocation_size(); + } + + EIGEN_DEVICE_FUNC + plain_array(constructor_without_unaligned_array_assert) + { + check_static_allocation_size(); + } +}; + +template +struct plain_array +{ + EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size]; + + EIGEN_DEVICE_FUNC + plain_array() + { + EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15); + check_static_allocation_size(); + } + + EIGEN_DEVICE_FUNC + plain_array(constructor_without_unaligned_array_assert) + { + check_static_allocation_size(); + } +}; + +template +struct plain_array +{ + EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size]; + + EIGEN_DEVICE_FUNC + plain_array() + { + EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31); + check_static_allocation_size(); + } + + EIGEN_DEVICE_FUNC + plain_array(constructor_without_unaligned_array_assert) + { + check_static_allocation_size(); + } +}; + +template +struct plain_array +{ + EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size]; + + EIGEN_DEVICE_FUNC + plain_array() + { + EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63); + check_static_allocation_size(); + } + + EIGEN_DEVICE_FUNC + plain_array(constructor_without_unaligned_array_assert) + { + check_static_allocation_size(); + } +}; + +template +struct plain_array +{ + T array[1]; + EIGEN_DEVICE_FUNC plain_array() {} + EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {} +}; + +struct plain_array_helper { + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + static void copy(const plain_array& src, const Eigen::Index size, + plain_array& dst) { + smart_copy(src.array, src.array + size, dst.array); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + static void swap(plain_array& a, const Eigen::Index a_size, + plain_array& b, const Eigen::Index b_size) { + if (a_size < b_size) { + std::swap_ranges(b.array, b.array + a_size, a.array); + smart_move(b.array + a_size, b.array + b_size, a.array + a_size); + } else if (a_size > b_size) { + std::swap_ranges(a.array, a.array + b_size, b.array); + smart_move(a.array + b_size, a.array + a_size, b.array + b_size); + } else { + std::swap_ranges(a.array, a.array + a_size, b.array); + } + } +}; + +} // end namespace internal + +/** \internal + * + * \class DenseStorage + * \ingroup Core_Module + * + * \brief Stores the data of a matrix + * + * This class stores the data of fixed-size, dynamic-size or mixed matrices + * in a way as compact as possible. + * + * \sa Matrix + */ +template class DenseStorage; + +// purely fixed-size matrix +template class DenseStorage +{ + internal::plain_array m_data; + public: + EIGEN_DEVICE_FUNC DenseStorage() { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size) + } + EIGEN_DEVICE_FUNC + explicit DenseStorage(internal::constructor_without_unaligned_array_assert) + : m_data(internal::constructor_without_unaligned_array_assert()) {} +#if !EIGEN_HAS_CXX11 || defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN) + EIGEN_DEVICE_FUNC + DenseStorage(const DenseStorage& other) : m_data(other.m_data) { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size) + } +#else + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) = default; +#endif +#if !EIGEN_HAS_CXX11 + EIGEN_DEVICE_FUNC + DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) m_data = other.m_data; + return *this; + } +#else + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) = default; +#endif +#if EIGEN_HAS_RVALUE_REFERENCES +#if !EIGEN_HAS_CXX11 + EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT + : m_data(std::move(other.m_data)) + { + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT + { + if (this != &other) + m_data = std::move(other.m_data); + return *this; + } +#else + EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&&) = default; + EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&&) = default; +#endif +#endif + EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols); + EIGEN_UNUSED_VARIABLE(size); + EIGEN_UNUSED_VARIABLE(rows); + EIGEN_UNUSED_VARIABLE(cols); + } + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { + numext::swap(m_data, other.m_data); + } + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;} + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {} + EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {} + EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } + EIGEN_DEVICE_FUNC T *data() { return m_data.array; } +}; + +// null matrix +template class DenseStorage +{ + public: + EIGEN_DEVICE_FUNC DenseStorage() {} + EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {} + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {} + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; } + EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {} + EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {} + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;} + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {} + EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {} + EIGEN_DEVICE_FUNC const T *data() const { return 0; } + EIGEN_DEVICE_FUNC T *data() { return 0; } +}; + +// more specializations for null matrices; these are necessary to resolve ambiguities +template class DenseStorage +: public DenseStorage { }; + +template class DenseStorage +: public DenseStorage { }; + +template class DenseStorage +: public DenseStorage { }; + +// dynamic-size matrix with fixed-size storage +template class DenseStorage +{ + internal::plain_array m_data; + Index m_rows; + Index m_cols; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {} + EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) + : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {} + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols) + { + internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data); + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + m_rows = other.m_rows; + m_cols = other.m_cols; + internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data); + } + return *this; + } + EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {} + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) + { + internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols); + numext::swap(m_rows,other.m_rows); + numext::swap(m_cols,other.m_cols); + } + EIGEN_DEVICE_FUNC Index rows() const {return m_rows;} + EIGEN_DEVICE_FUNC Index cols() const {return m_cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; } + EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; } + EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } + EIGEN_DEVICE_FUNC T *data() { return m_data.array; } +}; + +// dynamic-size matrix with fixed-size storage and fixed width +template class DenseStorage +{ + internal::plain_array m_data; + Index m_rows; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {} + EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) + : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {} + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows) + { + internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data); + } + + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + m_rows = other.m_rows; + internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data); + } + return *this; + } + EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {} + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) + { + internal::plain_array_helper::swap(m_data, m_rows * _Cols, other.m_data, other.m_rows * _Cols); + numext::swap(m_rows, other.m_rows); + } + EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;} + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return _Cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; } + EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; } + EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } + EIGEN_DEVICE_FUNC T *data() { return m_data.array; } +}; + +// dynamic-size matrix with fixed-size storage and fixed height +template class DenseStorage +{ + internal::plain_array m_data; + Index m_cols; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {} + EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) + : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {} + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols) + { + internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data); + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + m_cols = other.m_cols; + internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data); + } + return *this; + } + EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {} + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { + internal::plain_array_helper::swap(m_data, _Rows * m_cols, other.m_data, _Rows * other.m_cols); + numext::swap(m_cols, other.m_cols); + } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return _Rows;} + EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) { m_cols = cols; } + EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) { m_cols = cols; } + EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } + EIGEN_DEVICE_FUNC T *data() { return m_data.array; } +}; + +// purely dynamic matrix. +template class DenseStorage +{ + T *m_data; + Index m_rows; + Index m_cols; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {} + EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) + : m_data(0), m_rows(0), m_cols(0) {} + EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) + : m_data(internal::conditional_aligned_new_auto(size)), m_rows(rows), m_cols(cols) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0); + } + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::conditional_aligned_new_auto(other.m_rows*other.m_cols)) + , m_rows(other.m_rows) + , m_cols(other.m_cols) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols) + internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data); + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + DenseStorage tmp(other); + this->swap(tmp); + } + return *this; + } +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC + DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT + : m_data(std::move(other.m_data)) + , m_rows(std::move(other.m_rows)) + , m_cols(std::move(other.m_cols)) + { + other.m_data = nullptr; + other.m_rows = 0; + other.m_cols = 0; + } + EIGEN_DEVICE_FUNC + DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT + { + numext::swap(m_data, other.m_data); + numext::swap(m_rows, other.m_rows); + numext::swap(m_cols, other.m_cols); + return *this; + } +#endif + EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, m_rows*m_cols); } + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) + { + numext::swap(m_data,other.m_data); + numext::swap(m_rows,other.m_rows); + numext::swap(m_cols,other.m_cols); + } + EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;} + EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;} + void conservativeResize(Index size, Index rows, Index cols) + { + m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, m_rows*m_cols); + m_rows = rows; + m_cols = cols; + } + EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols) + { + if(size != m_rows*m_cols) + { + internal::conditional_aligned_delete_auto(m_data, m_rows*m_cols); + if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative + m_data = internal::conditional_aligned_new_auto(size); + else + m_data = 0; + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + } + m_rows = rows; + m_cols = cols; + } + EIGEN_DEVICE_FUNC const T *data() const { return m_data; } + EIGEN_DEVICE_FUNC T *data() { return m_data; } +}; + +// matrix with dynamic width and fixed height (so that matrix has dynamic size). +template class DenseStorage +{ + T *m_data; + Index m_cols; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {} + explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {} + EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto(size)), m_cols(cols) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0); + EIGEN_UNUSED_VARIABLE(rows); + } + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::conditional_aligned_new_auto(_Rows*other.m_cols)) + , m_cols(other.m_cols) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows) + internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data); + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + DenseStorage tmp(other); + this->swap(tmp); + } + return *this; + } +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC + DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT + : m_data(std::move(other.m_data)) + , m_cols(std::move(other.m_cols)) + { + other.m_data = nullptr; + other.m_cols = 0; + } + EIGEN_DEVICE_FUNC + DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT + { + numext::swap(m_data, other.m_data); + numext::swap(m_cols, other.m_cols); + return *this; + } +#endif + EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, _Rows*m_cols); } + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { + numext::swap(m_data,other.m_data); + numext::swap(m_cols,other.m_cols); + } + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;} + EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;} + EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols) + { + m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, _Rows*m_cols); + m_cols = cols; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols) + { + if(size != _Rows*m_cols) + { + internal::conditional_aligned_delete_auto(m_data, _Rows*m_cols); + if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative + m_data = internal::conditional_aligned_new_auto(size); + else + m_data = 0; + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + } + m_cols = cols; + } + EIGEN_DEVICE_FUNC const T *data() const { return m_data; } + EIGEN_DEVICE_FUNC T *data() { return m_data; } +}; + +// matrix with dynamic height and fixed width (so that matrix has dynamic size). +template class DenseStorage +{ + T *m_data; + Index m_rows; + public: + EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {} + explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {} + EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto(size)), m_rows(rows) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols); + EIGEN_UNUSED_VARIABLE(cols); + } + EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) + : m_data(internal::conditional_aligned_new_auto(other.m_rows*_Cols)) + , m_rows(other.m_rows) + { + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols) + internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data); + } + EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) + { + if (this != &other) + { + DenseStorage tmp(other); + this->swap(tmp); + } + return *this; + } +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC + DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT + : m_data(std::move(other.m_data)) + , m_rows(std::move(other.m_rows)) + { + other.m_data = nullptr; + other.m_rows = 0; + } + EIGEN_DEVICE_FUNC + DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT + { + numext::swap(m_data, other.m_data); + numext::swap(m_rows, other.m_rows); + return *this; + } +#endif + EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, _Cols*m_rows); } + EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { + numext::swap(m_data,other.m_data); + numext::swap(m_rows,other.m_rows); + } + EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;} + EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) {return _Cols;} + void conservativeResize(Index size, Index rows, Index) + { + m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, m_rows*_Cols); + m_rows = rows; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index) + { + if(size != m_rows*_Cols) + { + internal::conditional_aligned_delete_auto(m_data, _Cols*m_rows); + if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative + m_data = internal::conditional_aligned_new_auto(size); + else + m_data = 0; + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + } + m_rows = rows; + } + EIGEN_DEVICE_FUNC const T *data() const { return m_data; } + EIGEN_DEVICE_FUNC T *data() { return m_data; } +}; + +} // end namespace Eigen + +#endif // EIGEN_MATRIX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Diagonal.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Diagonal.h new file mode 100644 index 00000000..3112d2c1 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Diagonal.h @@ -0,0 +1,258 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2007-2009 Benoit Jacob +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DIAGONAL_H +#define EIGEN_DIAGONAL_H + +namespace Eigen { + +/** \class Diagonal + * \ingroup Core_Module + * + * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix + * + * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal + * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal. + * A positive value means a superdiagonal, a negative value means a subdiagonal. + * You can also use DynamicIndex so the index can be set at runtime. + * + * The matrix is not required to be square. + * + * This class represents an expression of the main diagonal, or any sub/super diagonal + * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the + * time this is the only way it is used. + * + * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index) + */ + +namespace internal { +template +struct traits > + : traits +{ + typedef typename ref_selector::type MatrixTypeNested; + typedef typename remove_reference::type _MatrixTypeNested; + typedef typename MatrixType::StorageKind StorageKind; + enum { + RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic + : (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0), + MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))), + ColsAtCompileTime = 1, + MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic + : DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, + MatrixType::MaxColsAtCompileTime) + : (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0), + MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))), + MaxColsAtCompileTime = 1, + MaskLvalueBit = is_lvalue::value ? LvalueBit : 0, + Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions + MatrixTypeOuterStride = outer_stride_at_compile_time::ret, + InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1, + OuterStrideAtCompileTime = 0 + }; +}; +} + +template class Diagonal + : public internal::dense_xpr_base< Diagonal >::type +{ + public: + + enum { DiagIndex = _DiagIndex }; + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal) + + EIGEN_DEVICE_FUNC + explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) + { + eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() ); + } + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal) + + EIGEN_DEVICE_FUNC + inline Index rows() const + { + return m_index.value()<0 ? numext::mini(m_matrix.cols(),m_matrix.rows()+m_index.value()) + : numext::mini(m_matrix.rows(),m_matrix.cols()-m_index.value()); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return 1; } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT { + return m_matrix.outerStride() + 1; + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return 0; } + + typedef typename internal::conditional< + internal::is_lvalue::value, + Scalar, + const Scalar + >::type ScalarWithConstIfNotLvalue; + + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); } + EIGEN_DEVICE_FUNC + inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index row, Index) + { + EIGEN_STATIC_ASSERT_LVALUE(MatrixType) + return m_matrix.coeffRef(row+rowOffset(), row+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index row, Index) const + { + return m_matrix.coeffRef(row+rowOffset(), row+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline CoeffReturnType coeff(Index row, Index) const + { + return m_matrix.coeff(row+rowOffset(), row+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index idx) + { + EIGEN_STATIC_ASSERT_LVALUE(MatrixType) + return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index idx) const + { + return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline CoeffReturnType coeff(Index idx) const + { + return m_matrix.coeff(idx+rowOffset(), idx+colOffset()); + } + + EIGEN_DEVICE_FUNC + inline const typename internal::remove_all::type& + nestedExpression() const + { + return m_matrix; + } + + EIGEN_DEVICE_FUNC + inline Index index() const + { + return m_index.value(); + } + + protected: + typename internal::ref_selector::non_const_type m_matrix; + const internal::variable_if_dynamicindex m_index; + + private: + // some compilers may fail to optimize std::max etc in case of compile-time constants... + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index absDiagIndex() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : -m_index.value(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rowOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? 0 : -m_index.value(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index colOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : 0; } + // trigger a compile-time error if someone try to call packet + template typename MatrixType::PacketReturnType packet(Index) const; + template typename MatrixType::PacketReturnType packet(Index,Index) const; +}; + +/** \returns an expression of the main diagonal of the matrix \c *this + * + * \c *this is not required to be square. + * + * Example: \include MatrixBase_diagonal.cpp + * Output: \verbinclude MatrixBase_diagonal.out + * + * \sa class Diagonal */ +template +EIGEN_DEVICE_FUNC inline typename MatrixBase::DiagonalReturnType +MatrixBase::diagonal() +{ + return DiagonalReturnType(derived()); +} + +/** This is the const version of diagonal(). */ +template +EIGEN_DEVICE_FUNC inline typename MatrixBase::ConstDiagonalReturnType +MatrixBase::diagonal() const +{ + return ConstDiagonalReturnType(derived()); +} + +/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this + * + * \c *this is not required to be square. + * + * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 + * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. + * + * Example: \include MatrixBase_diagonal_int.cpp + * Output: \verbinclude MatrixBase_diagonal_int.out + * + * \sa MatrixBase::diagonal(), class Diagonal */ +template +EIGEN_DEVICE_FUNC inline typename MatrixBase::DiagonalDynamicIndexReturnType +MatrixBase::diagonal(Index index) +{ + return DiagonalDynamicIndexReturnType(derived(), index); +} + +/** This is the const version of diagonal(Index). */ +template +EIGEN_DEVICE_FUNC inline typename MatrixBase::ConstDiagonalDynamicIndexReturnType +MatrixBase::diagonal(Index index) const +{ + return ConstDiagonalDynamicIndexReturnType(derived(), index); +} + +/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this + * + * \c *this is not required to be square. + * + * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 + * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. + * + * Example: \include MatrixBase_diagonal_template_int.cpp + * Output: \verbinclude MatrixBase_diagonal_template_int.out + * + * \sa MatrixBase::diagonal(), class Diagonal */ +template +template +EIGEN_DEVICE_FUNC +inline typename MatrixBase::template DiagonalIndexReturnType::Type +MatrixBase::diagonal() +{ + return typename DiagonalIndexReturnType::Type(derived()); +} + +/** This is the const version of diagonal(). */ +template +template +EIGEN_DEVICE_FUNC +inline typename MatrixBase::template ConstDiagonalIndexReturnType::Type +MatrixBase::diagonal() const +{ + return typename ConstDiagonalIndexReturnType::Type(derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_DIAGONAL_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalMatrix.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalMatrix.h new file mode 100644 index 00000000..542685c6 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalMatrix.h @@ -0,0 +1,391 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// Copyright (C) 2007-2009 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DIAGONALMATRIX_H +#define EIGEN_DIAGONALMATRIX_H + +namespace Eigen { + +#ifndef EIGEN_PARSED_BY_DOXYGEN +template +class DiagonalBase : public EigenBase +{ + public: + typedef typename internal::traits::DiagonalVectorType DiagonalVectorType; + typedef typename DiagonalVectorType::Scalar Scalar; + typedef typename DiagonalVectorType::RealScalar RealScalar; + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::StorageIndex StorageIndex; + + enum { + RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, + ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, + MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, + MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, + IsVectorAtCompileTime = 0, + Flags = NoPreferredStorageOrderBit + }; + + typedef Matrix DenseMatrixType; + typedef DenseMatrixType DenseType; + typedef DiagonalMatrix PlainObject; + + EIGEN_DEVICE_FUNC + inline const Derived& derived() const { return *static_cast(this); } + EIGEN_DEVICE_FUNC + inline Derived& derived() { return *static_cast(this); } + + EIGEN_DEVICE_FUNC + DenseMatrixType toDenseMatrix() const { return derived(); } + + EIGEN_DEVICE_FUNC + inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); } + EIGEN_DEVICE_FUNC + inline DiagonalVectorType& diagonal() { return derived().diagonal(); } + + EIGEN_DEVICE_FUNC + inline Index rows() const { return diagonal().size(); } + EIGEN_DEVICE_FUNC + inline Index cols() const { return diagonal().size(); } + + template + EIGEN_DEVICE_FUNC + const Product + operator*(const MatrixBase &matrix) const + { + return Product(derived(),matrix.derived()); + } + + typedef DiagonalWrapper, const DiagonalVectorType> > InverseReturnType; + EIGEN_DEVICE_FUNC + inline const InverseReturnType + inverse() const + { + return InverseReturnType(diagonal().cwiseInverse()); + } + + EIGEN_DEVICE_FUNC + inline const DiagonalWrapper + operator*(const Scalar& scalar) const + { + return DiagonalWrapper(diagonal() * scalar); + } + EIGEN_DEVICE_FUNC + friend inline const DiagonalWrapper + operator*(const Scalar& scalar, const DiagonalBase& other) + { + return DiagonalWrapper(scalar * other.diagonal()); + } + + template + EIGEN_DEVICE_FUNC + #ifdef EIGEN_PARSED_BY_DOXYGEN + inline unspecified_expression_type + #else + inline const DiagonalWrapper + #endif + operator+(const DiagonalBase& other) const + { + return (diagonal() + other.diagonal()).asDiagonal(); + } + + template + EIGEN_DEVICE_FUNC + #ifdef EIGEN_PARSED_BY_DOXYGEN + inline unspecified_expression_type + #else + inline const DiagonalWrapper + #endif + operator-(const DiagonalBase& other) const + { + return (diagonal() - other.diagonal()).asDiagonal(); + } +}; + +#endif + +/** \class DiagonalMatrix + * \ingroup Core_Module + * + * \brief Represents a diagonal matrix with its storage + * + * \param _Scalar the type of coefficients + * \param SizeAtCompileTime the dimension of the matrix, or Dynamic + * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults + * to SizeAtCompileTime. Most of the time, you do not need to specify it. + * + * \sa class DiagonalWrapper + */ + +namespace internal { +template +struct traits > + : traits > +{ + typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType; + typedef DiagonalShape StorageKind; + enum { + Flags = LvalueBit | NoPreferredStorageOrderBit + }; +}; +} +template +class DiagonalMatrix + : public DiagonalBase > +{ + public: + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename internal::traits::DiagonalVectorType DiagonalVectorType; + typedef const DiagonalMatrix& Nested; + typedef _Scalar Scalar; + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::StorageIndex StorageIndex; + #endif + + protected: + + DiagonalVectorType m_diagonal; + + public: + + /** const version of diagonal(). */ + EIGEN_DEVICE_FUNC + inline const DiagonalVectorType& diagonal() const { return m_diagonal; } + /** \returns a reference to the stored vector of diagonal coefficients. */ + EIGEN_DEVICE_FUNC + inline DiagonalVectorType& diagonal() { return m_diagonal; } + + /** Default constructor without initialization */ + EIGEN_DEVICE_FUNC + inline DiagonalMatrix() {} + + /** Constructs a diagonal matrix with given dimension */ + EIGEN_DEVICE_FUNC + explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {} + + /** 2D constructor. */ + EIGEN_DEVICE_FUNC + inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {} + + /** 3D constructor. */ + EIGEN_DEVICE_FUNC + inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {} + + #if EIGEN_HAS_CXX11 + /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients. \cpp11 + * + * There exists C++98 anologue constructors for fixed-size diagonal matrices having 2 or 3 coefficients. + * + * \warning To construct a diagonal matrix of fixed size, the number of values passed to this + * constructor must match the fixed dimension of \c *this. + * + * \sa DiagonalMatrix(const Scalar&, const Scalar&) + * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&) + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const ArgTypes&... args) + : m_diagonal(a0, a1, a2, args...) {} + + /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer + * lists \cpp11 + */ + EIGEN_DEVICE_FUNC + explicit EIGEN_STRONG_INLINE DiagonalMatrix(const std::initializer_list>& list) + : m_diagonal(list) {} + #endif // EIGEN_HAS_CXX11 + + /** Copy constructor. */ + template + EIGEN_DEVICE_FUNC + inline DiagonalMatrix(const DiagonalBase& other) : m_diagonal(other.diagonal()) {} + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */ + inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {} + #endif + + /** generic constructor from expression of the diagonal coefficients */ + template + EIGEN_DEVICE_FUNC + explicit inline DiagonalMatrix(const MatrixBase& other) : m_diagonal(other) + {} + + /** Copy operator. */ + template + EIGEN_DEVICE_FUNC + DiagonalMatrix& operator=(const DiagonalBase& other) + { + m_diagonal = other.diagonal(); + return *this; + } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** This is a special case of the templated operator=. Its purpose is to + * prevent a default operator= from hiding the templated operator=. + */ + EIGEN_DEVICE_FUNC + DiagonalMatrix& operator=(const DiagonalMatrix& other) + { + m_diagonal = other.diagonal(); + return *this; + } + #endif + + /** Resizes to given size. */ + EIGEN_DEVICE_FUNC + inline void resize(Index size) { m_diagonal.resize(size); } + /** Sets all coefficients to zero. */ + EIGEN_DEVICE_FUNC + inline void setZero() { m_diagonal.setZero(); } + /** Resizes and sets all coefficients to zero. */ + EIGEN_DEVICE_FUNC + inline void setZero(Index size) { m_diagonal.setZero(size); } + /** Sets this matrix to be the identity matrix of the current size. */ + EIGEN_DEVICE_FUNC + inline void setIdentity() { m_diagonal.setOnes(); } + /** Sets this matrix to be the identity matrix of the given size. */ + EIGEN_DEVICE_FUNC + inline void setIdentity(Index size) { m_diagonal.setOnes(size); } +}; + +/** \class DiagonalWrapper + * \ingroup Core_Module + * + * \brief Expression of a diagonal matrix + * + * \param _DiagonalVectorType the type of the vector of diagonal coefficients + * + * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients, + * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal() + * and most of the time this is the only way that it is used. + * + * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal() + */ + +namespace internal { +template +struct traits > +{ + typedef _DiagonalVectorType DiagonalVectorType; + typedef typename DiagonalVectorType::Scalar Scalar; + typedef typename DiagonalVectorType::StorageIndex StorageIndex; + typedef DiagonalShape StorageKind; + typedef typename traits::XprKind XprKind; + enum { + RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, + ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, + MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, + MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, + Flags = (traits::Flags & LvalueBit) | NoPreferredStorageOrderBit + }; +}; +} + +template +class DiagonalWrapper + : public DiagonalBase >, internal::no_assignment_operator +{ + public: + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef _DiagonalVectorType DiagonalVectorType; + typedef DiagonalWrapper Nested; + #endif + + /** Constructor from expression of diagonal coefficients to wrap. */ + EIGEN_DEVICE_FUNC + explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {} + + /** \returns a const reference to the wrapped expression of diagonal coefficients. */ + EIGEN_DEVICE_FUNC + const DiagonalVectorType& diagonal() const { return m_diagonal; } + + protected: + typename DiagonalVectorType::Nested m_diagonal; +}; + +/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients + * + * \only_for_vectors + * + * Example: \include MatrixBase_asDiagonal.cpp + * Output: \verbinclude MatrixBase_asDiagonal.out + * + * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal() + **/ +template +EIGEN_DEVICE_FUNC inline const DiagonalWrapper +MatrixBase::asDiagonal() const +{ + return DiagonalWrapper(derived()); +} + +/** \returns true if *this is approximately equal to a diagonal matrix, + * within the precision given by \a prec. + * + * Example: \include MatrixBase_isDiagonal.cpp + * Output: \verbinclude MatrixBase_isDiagonal.out + * + * \sa asDiagonal() + */ +template +bool MatrixBase::isDiagonal(const RealScalar& prec) const +{ + if(cols() != rows()) return false; + RealScalar maxAbsOnDiagonal = static_cast(-1); + for(Index j = 0; j < cols(); ++j) + { + RealScalar absOnDiagonal = numext::abs(coeff(j,j)); + if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal; + } + for(Index j = 0; j < cols(); ++j) + for(Index i = 0; i < j; ++i) + { + if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false; + if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false; + } + return true; +} + +namespace internal { + +template<> struct storage_kind_to_shape { typedef DiagonalShape Shape; }; + +struct Diagonal2Dense {}; + +template<> struct AssignmentKind { typedef Diagonal2Dense Kind; }; + +// Diagonal matrix to Dense assignment +template< typename DstXprType, typename SrcXprType, typename Functor> +struct Assignment +{ + static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op &/*func*/) + { + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if((dst.rows()!=dstRows) || (dst.cols()!=dstCols)) + dst.resize(dstRows, dstCols); + + dst.setZero(); + dst.diagonal() = src.diagonal(); + } + + static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op &/*func*/) + { dst.diagonal() += src.diagonal(); } + + static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op &/*func*/) + { dst.diagonal() -= src.diagonal(); } +}; + +} // namespace internal + +} // end namespace Eigen + +#endif // EIGEN_DIAGONALMATRIX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalProduct.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalProduct.h new file mode 100644 index 00000000..7911d1cd --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/DiagonalProduct.h @@ -0,0 +1,28 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2007-2009 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DIAGONALPRODUCT_H +#define EIGEN_DIAGONALPRODUCT_H + +namespace Eigen { + +/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal. + */ +template +template +EIGEN_DEVICE_FUNC inline const Product +MatrixBase::operator*(const DiagonalBase &a_diagonal) const +{ + return Product(derived(),a_diagonal.derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_DIAGONALPRODUCT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Dot.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Dot.h new file mode 100644 index 00000000..5c3441b9 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Dot.h @@ -0,0 +1,318 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008, 2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DOT_H +#define EIGEN_DOT_H + +namespace Eigen { + +namespace internal { + +// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot +// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE +// looking at the static assertions. Thus this is a trick to get better compile errors. +template +struct dot_nocheck +{ + typedef scalar_conj_product_op::Scalar,typename traits::Scalar> conj_prod; + typedef typename conj_prod::result_type ResScalar; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE + static ResScalar run(const MatrixBase& a, const MatrixBase& b) + { + return a.template binaryExpr(b).sum(); + } +}; + +template +struct dot_nocheck +{ + typedef scalar_conj_product_op::Scalar,typename traits::Scalar> conj_prod; + typedef typename conj_prod::result_type ResScalar; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE + static ResScalar run(const MatrixBase& a, const MatrixBase& b) + { + return a.transpose().template binaryExpr(b).sum(); + } +}; + +} // end namespace internal + +/** \fn MatrixBase::dot + * \returns the dot product of *this with other. + * + * \only_for_vectors + * + * \note If the scalar type is complex numbers, then this function returns the hermitian + * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the + * second variable. + * + * \sa squaredNorm(), norm() + */ +template +template +EIGEN_DEVICE_FUNC +EIGEN_STRONG_INLINE +typename ScalarBinaryOpTraits::Scalar,typename internal::traits::Scalar>::ReturnType +MatrixBase::dot(const MatrixBase& other) const +{ + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) + EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) +#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG)) + typedef internal::scalar_conj_product_op func; + EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar); +#endif + + eigen_assert(size() == other.size()); + + return internal::dot_nocheck::run(*this, other); +} + +//---------- implementation of L2 norm and related functions ---------- + +/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm. + * In both cases, it consists in the sum of the square of all the matrix entries. + * For vectors, this is also equals to the dot product of \c *this with itself. + * + * \sa dot(), norm(), lpNorm() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits::Scalar>::Real MatrixBase::squaredNorm() const +{ + return numext::real((*this).cwiseAbs2().sum()); +} + +/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm. + * In both cases, it consists in the square root of the sum of the square of all the matrix entries. + * For vectors, this is also equals to the square root of the dot product of \c *this with itself. + * + * \sa lpNorm(), dot(), squaredNorm() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits::Scalar>::Real MatrixBase::norm() const +{ + return numext::sqrt(squaredNorm()); +} + +/** \returns an expression of the quotient of \c *this by its own norm. + * + * \warning If the input vector is too small (i.e., this->norm()==0), + * then this function returns a copy of the input. + * + * \only_for_vectors + * + * \sa norm(), normalize() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::PlainObject +MatrixBase::normalized() const +{ + typedef typename internal::nested_eval::type _Nested; + _Nested n(derived()); + RealScalar z = n.squaredNorm(); + // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU + if(z>RealScalar(0)) + return n / numext::sqrt(z); + else + return n; +} + +/** Normalizes the vector, i.e. divides it by its own norm. + * + * \only_for_vectors + * + * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged. + * + * \sa norm(), normalized() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase::normalize() +{ + RealScalar z = squaredNorm(); + // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU + if(z>RealScalar(0)) + derived() /= numext::sqrt(z); +} + +/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow. + * + * \only_for_vectors + * + * This method is analogue to the normalized() method, but it reduces the risk of + * underflow and overflow when computing the norm. + * + * \warning If the input vector is too small (i.e., this->norm()==0), + * then this function returns a copy of the input. + * + * \sa stableNorm(), stableNormalize(), normalized() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase::PlainObject +MatrixBase::stableNormalized() const +{ + typedef typename internal::nested_eval::type _Nested; + _Nested n(derived()); + RealScalar w = n.cwiseAbs().maxCoeff(); + RealScalar z = (n/w).squaredNorm(); + if(z>RealScalar(0)) + return n / (numext::sqrt(z)*w); + else + return n; +} + +/** Normalizes the vector while avoid underflow and overflow + * + * \only_for_vectors + * + * This method is analogue to the normalize() method, but it reduces the risk of + * underflow and overflow when computing the norm. + * + * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged. + * + * \sa stableNorm(), stableNormalized(), normalize() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase::stableNormalize() +{ + RealScalar w = cwiseAbs().maxCoeff(); + RealScalar z = (derived()/w).squaredNorm(); + if(z>RealScalar(0)) + derived() /= numext::sqrt(z)*w; +} + +//---------- implementation of other norms ---------- + +namespace internal { + +template +struct lpNorm_selector +{ + typedef typename NumTraits::Scalar>::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const MatrixBase& m) + { + EIGEN_USING_STD(pow) + return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p); + } +}; + +template +struct lpNorm_selector +{ + EIGEN_DEVICE_FUNC + static inline typename NumTraits::Scalar>::Real run(const MatrixBase& m) + { + return m.cwiseAbs().sum(); + } +}; + +template +struct lpNorm_selector +{ + EIGEN_DEVICE_FUNC + static inline typename NumTraits::Scalar>::Real run(const MatrixBase& m) + { + return m.norm(); + } +}; + +template +struct lpNorm_selector +{ + typedef typename NumTraits::Scalar>::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const MatrixBase& m) + { + if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0)) + return RealScalar(0); + return m.cwiseAbs().maxCoeff(); + } +}; + +} // end namespace internal + +/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values + * of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$ + * norm, that is the maximum of the absolute values of the coefficients of \c *this. + * + * In all cases, if \c *this is empty, then the value 0 is returned. + * + * \note For matrices, this function does not compute the operator-norm. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink. + * + * \sa norm() + */ +template +template +#ifndef EIGEN_PARSED_BY_DOXYGEN +EIGEN_DEVICE_FUNC inline typename NumTraits::Scalar>::Real +#else +EIGEN_DEVICE_FUNC MatrixBase::RealScalar +#endif +MatrixBase::lpNorm() const +{ + return internal::lpNorm_selector::run(*this); +} + +//---------- implementation of isOrthogonal / isUnitary ---------- + +/** \returns true if *this is approximately orthogonal to \a other, + * within the precision given by \a prec. + * + * Example: \include MatrixBase_isOrthogonal.cpp + * Output: \verbinclude MatrixBase_isOrthogonal.out + */ +template +template +bool MatrixBase::isOrthogonal +(const MatrixBase& other, const RealScalar& prec) const +{ + typename internal::nested_eval::type nested(derived()); + typename internal::nested_eval::type otherNested(other.derived()); + return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm(); +} + +/** \returns true if *this is approximately an unitary matrix, + * within the precision given by \a prec. In the case where the \a Scalar + * type is real numbers, a unitary matrix is an orthogonal matrix, whence the name. + * + * \note This can be used to check whether a family of vectors forms an orthonormal basis. + * Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an + * orthonormal basis. + * + * Example: \include MatrixBase_isUnitary.cpp + * Output: \verbinclude MatrixBase_isUnitary.out + */ +template +bool MatrixBase::isUnitary(const RealScalar& prec) const +{ + typename internal::nested_eval::type self(derived()); + for(Index i = 0; i < cols(); ++i) + { + if(!internal::isApprox(self.col(i).squaredNorm(), static_cast(1), prec)) + return false; + for(Index j = 0; j < i; ++j) + if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast(1), prec)) + return false; + } + return true; +} + +} // end namespace Eigen + +#endif // EIGEN_DOT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/EigenBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/EigenBase.h new file mode 100644 index 00000000..6b3c7d37 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/EigenBase.h @@ -0,0 +1,160 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Benoit Jacob +// Copyright (C) 2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_EIGENBASE_H +#define EIGEN_EIGENBASE_H + +namespace Eigen { + +/** \class EigenBase + * \ingroup Core_Module + * + * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T). + * + * In other words, an EigenBase object is an object that can be copied into a MatrixBase. + * + * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc. + * + * Notice that this class is trivial, it is only used to disambiguate overloaded functions. + * + * \sa \blank \ref TopicClassHierarchy + */ +template struct EigenBase +{ +// typedef typename internal::plain_matrix_type::type PlainObject; + + /** \brief The interface type of indices + * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE. + * \sa StorageIndex, \ref TopicPreprocessorDirectives. + * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead. + * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation attribute. + */ + typedef Eigen::Index Index; + + // FIXME is it needed? + typedef typename internal::traits::StorageKind StorageKind; + + /** \returns a reference to the derived object */ + EIGEN_DEVICE_FUNC + Derived& derived() { return *static_cast(this); } + /** \returns a const reference to the derived object */ + EIGEN_DEVICE_FUNC + const Derived& derived() const { return *static_cast(this); } + + EIGEN_DEVICE_FUNC + inline Derived& const_cast_derived() const + { return *static_cast(const_cast(this)); } + EIGEN_DEVICE_FUNC + inline const Derived& const_derived() const + { return *static_cast(this); } + + /** \returns the number of rows. \sa cols(), RowsAtCompileTime */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); } + /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); } + /** \returns the number of coefficients, which is rows()*cols(). + * \sa rows(), cols(), SizeAtCompileTime. */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index size() const EIGEN_NOEXCEPT { return rows() * cols(); } + + /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */ + template + EIGEN_DEVICE_FUNC + inline void evalTo(Dest& dst) const + { derived().evalTo(dst); } + + /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */ + template + EIGEN_DEVICE_FUNC + inline void addTo(Dest& dst) const + { + // This is the default implementation, + // derived class can reimplement it in a more optimized way. + typename Dest::PlainObject res(rows(),cols()); + evalTo(res); + dst += res; + } + + /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */ + template + EIGEN_DEVICE_FUNC + inline void subTo(Dest& dst) const + { + // This is the default implementation, + // derived class can reimplement it in a more optimized way. + typename Dest::PlainObject res(rows(),cols()); + evalTo(res); + dst -= res; + } + + /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */ + template + EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const + { + // This is the default implementation, + // derived class can reimplement it in a more optimized way. + dst = dst * this->derived(); + } + + /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */ + template + EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const + { + // This is the default implementation, + // derived class can reimplement it in a more optimized way. + dst = this->derived() * dst; + } + +}; + +/*************************************************************************** +* Implementation of matrix base methods +***************************************************************************/ + +/** \brief Copies the generic expression \a other into *this. + * + * \details The expression must provide a (templated) evalTo(Derived& dst) const + * function which does the actual job. In practice, this allows any user to write + * its own special matrix without having to modify MatrixBase + * + * \returns a reference to *this. + */ +template +template +EIGEN_DEVICE_FUNC +Derived& DenseBase::operator=(const EigenBase &other) +{ + call_assignment(derived(), other.derived()); + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +Derived& DenseBase::operator+=(const EigenBase &other) +{ + call_assignment(derived(), other.derived(), internal::add_assign_op()); + return derived(); +} + +template +template +EIGEN_DEVICE_FUNC +Derived& DenseBase::operator-=(const EigenBase &other) +{ + call_assignment(derived(), other.derived(), internal::sub_assign_op()); + return derived(); +} + +} // end namespace Eigen + +#endif // EIGEN_EIGENBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ForceAlignedAccess.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ForceAlignedAccess.h new file mode 100644 index 00000000..817a43af --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ForceAlignedAccess.h @@ -0,0 +1,150 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_FORCEALIGNEDACCESS_H +#define EIGEN_FORCEALIGNEDACCESS_H + +namespace Eigen { + +/** \class ForceAlignedAccess + * \ingroup Core_Module + * + * \brief Enforce aligned packet loads and stores regardless of what is requested + * + * \param ExpressionType the type of the object of which we are forcing aligned packet access + * + * This class is the return type of MatrixBase::forceAlignedAccess() + * and most of the time this is the only way it is used. + * + * \sa MatrixBase::forceAlignedAccess() + */ + +namespace internal { +template +struct traits > : public traits +{}; +} + +template class ForceAlignedAccess + : public internal::dense_xpr_base< ForceAlignedAccess >::type +{ + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess) + + EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {} + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); } + + EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const + { + return m_expression.coeff(row, col); + } + + EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) + { + return m_expression.const_cast_derived().coeffRef(row, col); + } + + EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const + { + return m_expression.coeff(index); + } + + EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) + { + return m_expression.const_cast_derived().coeffRef(index); + } + + template + inline const PacketScalar packet(Index row, Index col) const + { + return m_expression.template packet(row, col); + } + + template + inline void writePacket(Index row, Index col, const PacketScalar& x) + { + m_expression.const_cast_derived().template writePacket(row, col, x); + } + + template + inline const PacketScalar packet(Index index) const + { + return m_expression.template packet(index); + } + + template + inline void writePacket(Index index, const PacketScalar& x) + { + m_expression.const_cast_derived().template writePacket(index, x); + } + + EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; } + + protected: + const ExpressionType& m_expression; + + private: + ForceAlignedAccess& operator=(const ForceAlignedAccess&); +}; + +/** \returns an expression of *this with forced aligned access + * \sa forceAlignedAccessIf(),class ForceAlignedAccess + */ +template +inline const ForceAlignedAccess +MatrixBase::forceAlignedAccess() const +{ + return ForceAlignedAccess(derived()); +} + +/** \returns an expression of *this with forced aligned access + * \sa forceAlignedAccessIf(), class ForceAlignedAccess + */ +template +inline ForceAlignedAccess +MatrixBase::forceAlignedAccess() +{ + return ForceAlignedAccess(derived()); +} + +/** \returns an expression of *this with forced aligned access if \a Enable is true. + * \sa forceAlignedAccess(), class ForceAlignedAccess + */ +template +template +inline typename internal::add_const_on_value_type,Derived&>::type>::type +MatrixBase::forceAlignedAccessIf() const +{ + return derived(); // FIXME This should not work but apparently is never used +} + +/** \returns an expression of *this with forced aligned access if \a Enable is true. + * \sa forceAlignedAccess(), class ForceAlignedAccess + */ +template +template +inline typename internal::conditional,Derived&>::type +MatrixBase::forceAlignedAccessIf() +{ + return derived(); // FIXME This should not work but apparently is never used +} + +} // end namespace Eigen + +#endif // EIGEN_FORCEALIGNEDACCESS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Fuzzy.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Fuzzy.h new file mode 100644 index 00000000..43aa49b2 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Fuzzy.h @@ -0,0 +1,155 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_FUZZY_H +#define EIGEN_FUZZY_H + +namespace Eigen { + +namespace internal +{ + +template::IsInteger> +struct isApprox_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) + { + typename internal::nested_eval::type nested(x); + typename internal::nested_eval::type otherNested(y); + return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum()); + } +}; + +template +struct isApprox_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&) + { + return x.matrix() == y.matrix(); + } +}; + +template::IsInteger> +struct isMuchSmallerThan_object_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) + { + return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum(); + } +}; + +template +struct isMuchSmallerThan_object_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&) + { + return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix(); + } +}; + +template::IsInteger> +struct isMuchSmallerThan_scalar_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec) + { + return x.cwiseAbs2().sum() <= numext::abs2(prec * y); + } +}; + +template +struct isMuchSmallerThan_scalar_selector +{ + EIGEN_DEVICE_FUNC + static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&) + { + return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix(); + } +}; + +} // end namespace internal + + +/** \returns \c true if \c *this is approximately equal to \a other, within the precision + * determined by \a prec. + * + * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$ + * are considered to be approximately equal within precision \f$ p \f$ if + * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f] + * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm + * L2 norm). + * + * \note Because of the multiplicativeness of this comparison, one can't use this function + * to check whether \c *this is approximately equal to the zero matrix or vector. + * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix + * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const + * RealScalar&, RealScalar) instead. + * + * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const + */ +template +template +EIGEN_DEVICE_FUNC bool DenseBase::isApprox( + const DenseBase& other, + const RealScalar& prec +) const +{ + return internal::isApprox_selector::run(derived(), other.derived(), prec); +} + +/** \returns \c true if the norm of \c *this is much smaller than \a other, + * within the precision determined by \a prec. + * + * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is + * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if + * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f] + * + * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason, + * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm + * of a reference matrix of same dimensions. + * + * \sa isApprox(), isMuchSmallerThan(const DenseBase&, RealScalar) const + */ +template +EIGEN_DEVICE_FUNC bool DenseBase::isMuchSmallerThan( + const typename NumTraits::Real& other, + const RealScalar& prec +) const +{ + return internal::isMuchSmallerThan_scalar_selector::run(derived(), other, prec); +} + +/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other, + * within the precision determined by \a prec. + * + * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is + * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if + * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f] + * For matrices, the comparison is done using the Hilbert-Schmidt norm. + * + * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const + */ +template +template +EIGEN_DEVICE_FUNC bool DenseBase::isMuchSmallerThan( + const DenseBase& other, + const RealScalar& prec +) const +{ + return internal::isMuchSmallerThan_object_selector::run(derived(), other.derived(), prec); +} + +} // end namespace Eigen + +#endif // EIGEN_FUZZY_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/GeneralProduct.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GeneralProduct.h new file mode 100644 index 00000000..6906aa75 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GeneralProduct.h @@ -0,0 +1,465 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2008-2011 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_GENERAL_PRODUCT_H +#define EIGEN_GENERAL_PRODUCT_H + +namespace Eigen { + +enum { + Large = 2, + Small = 3 +}; + +// Define the threshold value to fallback from the generic matrix-matrix product +// implementation (heavy) to the lightweight coeff-based product one. +// See generic_product_impl +// in products/GeneralMatrixMatrix.h for more details. +// TODO This threshold should also be used in the compile-time selector below. +#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD +// This default value has been obtained on a Haswell architecture. +#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20 +#endif + +namespace internal { + +template struct product_type_selector; + +template struct product_size_category +{ + enum { + #ifndef EIGEN_GPU_COMPILE_PHASE + is_large = MaxSize == Dynamic || + Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD || + (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD), + #else + is_large = 0, + #endif + value = is_large ? Large + : Size == 1 ? 1 + : Small + }; +}; + +template struct product_type +{ + typedef typename remove_all::type _Lhs; + typedef typename remove_all::type _Rhs; + enum { + MaxRows = traits<_Lhs>::MaxRowsAtCompileTime, + Rows = traits<_Lhs>::RowsAtCompileTime, + MaxCols = traits<_Rhs>::MaxColsAtCompileTime, + Cols = traits<_Rhs>::ColsAtCompileTime, + MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime, + traits<_Rhs>::MaxRowsAtCompileTime), + Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime, + traits<_Rhs>::RowsAtCompileTime) + }; + + // the splitting into different lines of code here, introducing the _select enums and the typedef below, + // is to work around an internal compiler error with gcc 4.1 and 4.2. +private: + enum { + rows_select = product_size_category::value, + cols_select = product_size_category::value, + depth_select = product_size_category::value + }; + typedef product_type_selector selector; + +public: + enum { + value = selector::ret, + ret = selector::ret + }; +#ifdef EIGEN_DEBUG_PRODUCT + static void debug() + { + EIGEN_DEBUG_VAR(Rows); + EIGEN_DEBUG_VAR(Cols); + EIGEN_DEBUG_VAR(Depth); + EIGEN_DEBUG_VAR(rows_select); + EIGEN_DEBUG_VAR(cols_select); + EIGEN_DEBUG_VAR(depth_select); + EIGEN_DEBUG_VAR(value); + } +#endif +}; + +/* The following allows to select the kind of product at compile time + * based on the three dimensions of the product. + * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */ +// FIXME I'm not sure the current mapping is the ideal one. +template struct product_type_selector { enum { ret = OuterProduct }; }; +template struct product_type_selector { enum { ret = LazyCoeffBasedProductMode }; }; +template struct product_type_selector<1, N, 1> { enum { ret = LazyCoeffBasedProductMode }; }; +template struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; }; +template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector<1, Small,Small> { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = LazyCoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = LazyCoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = LazyCoeffBasedProductMode }; }; +template<> struct product_type_selector<1, Large,Small> { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector<1, Large,Large> { enum { ret = GemvProduct }; }; +template<> struct product_type_selector<1, Small,Large> { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = GemvProduct }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = GemmProduct }; }; +template<> struct product_type_selector { enum { ret = GemmProduct }; }; +template<> struct product_type_selector { enum { ret = GemmProduct }; }; +template<> struct product_type_selector { enum { ret = GemmProduct }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = CoeffBasedProductMode }; }; +template<> struct product_type_selector { enum { ret = GemmProduct }; }; + +} // end namespace internal + +/*********************************************************************** +* Implementation of Inner Vector Vector Product +***********************************************************************/ + +// FIXME : maybe the "inner product" could return a Scalar +// instead of a 1x1 matrix ?? +// Pro: more natural for the user +// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix +// product ends up to a row-vector times col-vector product... To tackle this use +// case, we could have a specialization for Block with: operator=(Scalar x); + +/*********************************************************************** +* Implementation of Outer Vector Vector Product +***********************************************************************/ + +/*********************************************************************** +* Implementation of General Matrix Vector Product +***********************************************************************/ + +/* According to the shape/flags of the matrix we have to distinghish 3 different cases: + * 1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine + * 2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine + * 3 - all other cases are handled using a simple loop along the outer-storage direction. + * Therefore we need a lower level meta selector. + * Furthermore, if the matrix is the rhs, then the product has to be transposed. + */ +namespace internal { + +template +struct gemv_dense_selector; + +} // end namespace internal + +namespace internal { + +template struct gemv_static_vector_if; + +template +struct gemv_static_vector_if +{ + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; } +}; + +template +struct gemv_static_vector_if +{ + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; } +}; + +template +struct gemv_static_vector_if +{ + enum { + ForceAlignment = internal::packet_traits::Vectorizable, + PacketSize = internal::packet_traits::size + }; + #if EIGEN_MAX_STATIC_ALIGN_BYTES!=0 + internal::plain_array m_data; + EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; } + #else + // Some architectures cannot align on the stack, + // => let's manually enforce alignment by allocating more data and return the address of the first aligned element. + internal::plain_array m_data; + EIGEN_STRONG_INLINE Scalar* data() { + return ForceAlignment + ? reinterpret_cast((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES) + : m_data.array; + } + #endif +}; + +// The vector is on the left => transposition +template +struct gemv_dense_selector +{ + template + static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) + { + Transpose destT(dest); + enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor }; + gemv_dense_selector + ::run(rhs.transpose(), lhs.transpose(), destT, alpha); + } +}; + +template<> struct gemv_dense_selector +{ + template + static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) + { + typedef typename Lhs::Scalar LhsScalar; + typedef typename Rhs::Scalar RhsScalar; + typedef typename Dest::Scalar ResScalar; + typedef typename Dest::RealScalar RealScalar; + + typedef internal::blas_traits LhsBlasTraits; + typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; + typedef internal::blas_traits RhsBlasTraits; + typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; + + typedef Map, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits::size)> MappedDest; + + ActualLhsType actualLhs = LhsBlasTraits::extract(lhs); + ActualRhsType actualRhs = RhsBlasTraits::extract(rhs); + + ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs); + + // make sure Dest is a compile-time vector type (bug 1166) + typedef typename conditional::type ActualDest; + + enum { + // FIXME find a way to allow an inner stride on the result if packet_traits::size==1 + // on, the other hand it is good for the cache to pack the vector anyways... + EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1), + ComplexByReal = (NumTraits::IsComplex) && (!NumTraits::IsComplex), + MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime!=0) + }; + + typedef const_blas_data_mapper LhsMapper; + typedef const_blas_data_mapper RhsMapper; + RhsScalar compatibleAlpha = get_factor::run(actualAlpha); + + if(!MightCannotUseDest) + { + // shortcut if we are sure to be able to use dest directly, + // this ease the compiler to generate cleaner and more optimzized code for most common cases + general_matrix_vector_product + ::run( + actualLhs.rows(), actualLhs.cols(), + LhsMapper(actualLhs.data(), actualLhs.outerStride()), + RhsMapper(actualRhs.data(), actualRhs.innerStride()), + dest.data(), 1, + compatibleAlpha); + } + else + { + gemv_static_vector_if static_dest; + + const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0)); + const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; + + ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), + evalToDest ? dest.data() : static_dest.data()); + + if(!evalToDest) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + Index size = dest.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + if(!alphaIsCompatible) + { + MappedDest(actualDestPtr, dest.size()).setZero(); + compatibleAlpha = RhsScalar(1); + } + else + MappedDest(actualDestPtr, dest.size()) = dest; + } + + general_matrix_vector_product + ::run( + actualLhs.rows(), actualLhs.cols(), + LhsMapper(actualLhs.data(), actualLhs.outerStride()), + RhsMapper(actualRhs.data(), actualRhs.innerStride()), + actualDestPtr, 1, + compatibleAlpha); + + if (!evalToDest) + { + if(!alphaIsCompatible) + dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size()); + else + dest = MappedDest(actualDestPtr, dest.size()); + } + } + } +}; + +template<> struct gemv_dense_selector +{ + template + static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) + { + typedef typename Lhs::Scalar LhsScalar; + typedef typename Rhs::Scalar RhsScalar; + typedef typename Dest::Scalar ResScalar; + + typedef internal::blas_traits LhsBlasTraits; + typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; + typedef internal::blas_traits RhsBlasTraits; + typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; + typedef typename internal::remove_all::type ActualRhsTypeCleaned; + + typename add_const::type actualLhs = LhsBlasTraits::extract(lhs); + typename add_const::type actualRhs = RhsBlasTraits::extract(rhs); + + ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs); + + enum { + // FIXME find a way to allow an inner stride on the result if packet_traits::size==1 + // on, the other hand it is good for the cache to pack the vector anyways... + DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime==0 + }; + + gemv_static_vector_if static_rhs; + + ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), + DirectlyUseRhs ? const_cast(actualRhs.data()) : static_rhs.data()); + + if(!DirectlyUseRhs) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + Index size = actualRhs.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + Map(actualRhsPtr, actualRhs.size()) = actualRhs; + } + + typedef const_blas_data_mapper LhsMapper; + typedef const_blas_data_mapper RhsMapper; + general_matrix_vector_product + ::run( + actualLhs.rows(), actualLhs.cols(), + LhsMapper(actualLhs.data(), actualLhs.outerStride()), + RhsMapper(actualRhsPtr, 1), + dest.data(), dest.col(0).innerStride(), //NOTE if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166) + actualAlpha); + } +}; + +template<> struct gemv_dense_selector +{ + template + static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) + { + EIGEN_STATIC_ASSERT((!nested_eval::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE); + // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp + typename nested_eval::type actual_rhs(rhs); + const Index size = rhs.rows(); + for(Index k=0; k struct gemv_dense_selector +{ + template + static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) + { + EIGEN_STATIC_ASSERT((!nested_eval::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE); + typename nested_eval::type actual_rhs(rhs); + const Index rows = dest.rows(); + for(Index i=0; i +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +const Product +MatrixBase::operator*(const MatrixBase &other) const +{ + // A note regarding the function declaration: In MSVC, this function will sometimes + // not be inlined since DenseStorage is an unwindable object for dynamic + // matrices and product types are holding a member to store the result. + // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. + enum { + ProductIsValid = Derived::ColsAtCompileTime==Dynamic + || OtherDerived::RowsAtCompileTime==Dynamic + || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), + AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, + SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) + }; + // note to the lost user: + // * for a dot product use: v1.dot(v2) + // * for a coeff-wise product use: v1.cwiseProduct(v2) + EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), + INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) + EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), + INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) + EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) +#ifdef EIGEN_DEBUG_PRODUCT + internal::product_type::debug(); +#endif + + return Product(derived(), other.derived()); +} + +/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation. + * + * The returned product will behave like any other expressions: the coefficients of the product will be + * computed once at a time as requested. This might be useful in some extremely rare cases when only + * a small and no coherent fraction of the result's coefficients have to be computed. + * + * \warning This version of the matrix product can be much much slower. So use it only if you know + * what you are doing and that you measured a true speed improvement. + * + * \sa operator*(const MatrixBase&) + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +const Product +MatrixBase::lazyProduct(const MatrixBase &other) const +{ + enum { + ProductIsValid = Derived::ColsAtCompileTime==Dynamic + || OtherDerived::RowsAtCompileTime==Dynamic + || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), + AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, + SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) + }; + // note to the lost user: + // * for a dot product use: v1.dot(v2) + // * for a coeff-wise product use: v1.cwiseProduct(v2) + EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), + INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) + EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), + INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) + EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) + + return Product(derived(), other.derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_PRODUCT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/GenericPacketMath.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GenericPacketMath.h new file mode 100644 index 00000000..cf677a19 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GenericPacketMath.h @@ -0,0 +1,1040 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_GENERIC_PACKET_MATH_H +#define EIGEN_GENERIC_PACKET_MATH_H + +namespace Eigen { + +namespace internal { + +/** \internal + * \file GenericPacketMath.h + * + * Default implementation for types not supported by the vectorization. + * In practice these functions are provided to make easier the writing + * of generic vectorized code. + */ + +#ifndef EIGEN_DEBUG_ALIGNED_LOAD +#define EIGEN_DEBUG_ALIGNED_LOAD +#endif + +#ifndef EIGEN_DEBUG_UNALIGNED_LOAD +#define EIGEN_DEBUG_UNALIGNED_LOAD +#endif + +#ifndef EIGEN_DEBUG_ALIGNED_STORE +#define EIGEN_DEBUG_ALIGNED_STORE +#endif + +#ifndef EIGEN_DEBUG_UNALIGNED_STORE +#define EIGEN_DEBUG_UNALIGNED_STORE +#endif + +struct default_packet_traits +{ + enum { + HasHalfPacket = 0, + + HasAdd = 1, + HasSub = 1, + HasShift = 1, + HasMul = 1, + HasNegate = 1, + HasAbs = 1, + HasArg = 0, + HasAbs2 = 1, + HasAbsDiff = 0, + HasMin = 1, + HasMax = 1, + HasConj = 1, + HasSetLinear = 1, + HasBlend = 0, + // This flag is used to indicate whether packet comparison is supported. + // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true. + HasCmp = 0, + + HasDiv = 0, + HasSqrt = 0, + HasRsqrt = 0, + HasExp = 0, + HasExpm1 = 0, + HasLog = 0, + HasLog1p = 0, + HasLog10 = 0, + HasPow = 0, + + HasSin = 0, + HasCos = 0, + HasTan = 0, + HasASin = 0, + HasACos = 0, + HasATan = 0, + HasSinh = 0, + HasCosh = 0, + HasTanh = 0, + HasLGamma = 0, + HasDiGamma = 0, + HasZeta = 0, + HasPolygamma = 0, + HasErf = 0, + HasErfc = 0, + HasNdtri = 0, + HasBessel = 0, + HasIGamma = 0, + HasIGammaDerA = 0, + HasGammaSampleDerAlpha = 0, + HasIGammac = 0, + HasBetaInc = 0, + + HasRound = 0, + HasRint = 0, + HasFloor = 0, + HasCeil = 0, + HasSign = 0 + }; +}; + +template struct packet_traits : default_packet_traits +{ + typedef T type; + typedef T half; + enum { + Vectorizable = 0, + size = 1, + AlignedOnScalar = 0, + HasHalfPacket = 0 + }; + enum { + HasAdd = 0, + HasSub = 0, + HasMul = 0, + HasNegate = 0, + HasAbs = 0, + HasAbs2 = 0, + HasMin = 0, + HasMax = 0, + HasConj = 0, + HasSetLinear = 0 + }; +}; + +template struct packet_traits : packet_traits { }; + +template struct unpacket_traits +{ + typedef T type; + typedef T half; + enum + { + size = 1, + alignment = 1, + vectorizable = false, + masked_load_available=false, + masked_store_available=false + }; +}; + +template struct unpacket_traits : unpacket_traits { }; + +template struct type_casting_traits { + enum { + VectorizedCast = 0, + SrcCoeffRatio = 1, + TgtCoeffRatio = 1 + }; +}; + +/** \internal Wrapper to ensure that multiple packet types can map to the same + same underlying vector type. */ +template +struct eigen_packet_wrapper +{ + EIGEN_ALWAYS_INLINE operator T&() { return m_val; } + EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; } + EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {} + EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {} + EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) { + m_val = v; + return *this; + } + + T m_val; +}; + + +/** \internal A convenience utility for determining if the type is a scalar. + * This is used to enable some generic packet implementations. + */ +template +struct is_scalar { + typedef typename unpacket_traits::type Scalar; + enum { + value = internal::is_same::value + }; +}; + +/** \internal \returns static_cast(a) (coeff-wise) */ +template +EIGEN_DEVICE_FUNC inline TgtPacket +pcast(const SrcPacket& a) { + return static_cast(a); +} +template +EIGEN_DEVICE_FUNC inline TgtPacket +pcast(const SrcPacket& a, const SrcPacket& /*b*/) { + return static_cast(a); +} +template +EIGEN_DEVICE_FUNC inline TgtPacket +pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) { + return static_cast(a); +} +template +EIGEN_DEVICE_FUNC inline TgtPacket +pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/, + const SrcPacket& /*e*/, const SrcPacket& /*f*/, const SrcPacket& /*g*/, const SrcPacket& /*h*/) { + return static_cast(a); +} + +/** \internal \returns reinterpret_cast(a) */ +template +EIGEN_DEVICE_FUNC inline Target +preinterpret(const Packet& a); /* { return reinterpret_cast(a); } */ + +/** \internal \returns a + b (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +padd(const Packet& a, const Packet& b) { return a+b; } +// Avoid compiler warning for boolean algebra. +template<> EIGEN_DEVICE_FUNC inline bool +padd(const bool& a, const bool& b) { return a || b; } + +/** \internal \returns a - b (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +psub(const Packet& a, const Packet& b) { return a-b; } + +/** \internal \returns -a (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pnegate(const Packet& a) { return -a; } + +template<> EIGEN_DEVICE_FUNC inline bool +pnegate(const bool& a) { return !a; } + +/** \internal \returns conj(a) (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pconj(const Packet& a) { return numext::conj(a); } + +/** \internal \returns a * b (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pmul(const Packet& a, const Packet& b) { return a*b; } +// Avoid compiler warning for boolean algebra. +template<> EIGEN_DEVICE_FUNC inline bool +pmul(const bool& a, const bool& b) { return a && b; } + +/** \internal \returns a / b (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pdiv(const Packet& a, const Packet& b) { return a/b; } + +// In the generic case, memset to all one bits. +template +struct ptrue_impl { + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/){ + Packet b; + memset(static_cast(&b), 0xff, sizeof(Packet)); + return b; + } +}; + +// For non-trivial scalars, set to Scalar(1) (i.e. a non-zero value). +// Although this is technically not a valid bitmask, the scalar path for pselect +// uses a comparison to zero, so this should still work in most cases. We don't +// have another option, since the scalar type requires initialization. +template +struct ptrue_impl::value && NumTraits::RequireInitialization>::type > { + static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){ + return T(1); + } +}; + +/** \internal \returns one bits. */ +template EIGEN_DEVICE_FUNC inline Packet +ptrue(const Packet& a) { + return ptrue_impl::run(a); +} + +// In the general case, memset to zero. +template +struct pzero_impl { + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) { + Packet b; + memset(static_cast(&b), 0x00, sizeof(Packet)); + return b; + } +}; + +// For scalars, explicitly set to Scalar(0), since the underlying representation +// for zero may not consist of all-zero bits. +template +struct pzero_impl::value>::type> { + static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) { + return T(0); + } +}; + +/** \internal \returns packet of zeros */ +template EIGEN_DEVICE_FUNC inline Packet +pzero(const Packet& a) { + return pzero_impl::run(a); +} + +/** \internal \returns a <= b as a bit mask */ +template EIGEN_DEVICE_FUNC inline Packet +pcmp_le(const Packet& a, const Packet& b) { return a<=b ? ptrue(a) : pzero(a); } + +/** \internal \returns a < b as a bit mask */ +template EIGEN_DEVICE_FUNC inline Packet +pcmp_lt(const Packet& a, const Packet& b) { return a EIGEN_DEVICE_FUNC inline Packet +pcmp_eq(const Packet& a, const Packet& b) { return a==b ? ptrue(a) : pzero(a); } + +/** \internal \returns a < b or a==NaN or b==NaN as a bit mask */ +template EIGEN_DEVICE_FUNC inline Packet +pcmp_lt_or_nan(const Packet& a, const Packet& b) { return a>=b ? pzero(a) : ptrue(a); } + +template +struct bit_and { + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { + return a & b; + } +}; + +template +struct bit_or { + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { + return a | b; + } +}; + +template +struct bit_xor { + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { + return a ^ b; + } +}; + +template +struct bit_not { + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a) const { + return ~a; + } +}; + +// Use operators &, |, ^, ~. +template +struct operator_bitwise_helper { + EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { return bit_and()(a, b); } + EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return bit_or()(a, b); } + EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { return bit_xor()(a, b); } + EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return bit_not()(a); } +}; + +// Apply binary operations byte-by-byte +template +struct bytewise_bitwise_helper { + EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { + return binary(a, b, bit_and()); + } + EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { + return binary(a, b, bit_or()); + } + EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { + return binary(a, b, bit_xor()); + } + EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { + return unary(a,bit_not()); + } + + private: + template + EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) { + const unsigned char* a_ptr = reinterpret_cast(&a); + T c; + unsigned char* c_ptr = reinterpret_cast(&c); + for (size_t i = 0; i < sizeof(T); ++i) { + *c_ptr++ = op(*a_ptr++); + } + return c; + } + + template + EIGEN_DEVICE_FUNC static inline T binary(const T& a, const T& b, Op op) { + const unsigned char* a_ptr = reinterpret_cast(&a); + const unsigned char* b_ptr = reinterpret_cast(&b); + T c; + unsigned char* c_ptr = reinterpret_cast(&c); + for (size_t i = 0; i < sizeof(T); ++i) { + *c_ptr++ = op(*a_ptr++, *b_ptr++); + } + return c; + } +}; + +// In the general case, use byte-by-byte manipulation. +template +struct bitwise_helper : public bytewise_bitwise_helper {}; + +// For integers or non-trivial scalars, use binary operators. +template +struct bitwise_helper::value && (NumTraits::IsInteger || NumTraits::RequireInitialization)>::type + > : public operator_bitwise_helper {}; + +/** \internal \returns the bitwise and of \a a and \a b */ +template EIGEN_DEVICE_FUNC inline Packet +pand(const Packet& a, const Packet& b) { + return bitwise_helper::bitwise_and(a, b); +} + +/** \internal \returns the bitwise or of \a a and \a b */ +template EIGEN_DEVICE_FUNC inline Packet +por(const Packet& a, const Packet& b) { + return bitwise_helper::bitwise_or(a, b); +} + +/** \internal \returns the bitwise xor of \a a and \a b */ +template EIGEN_DEVICE_FUNC inline Packet +pxor(const Packet& a, const Packet& b) { + return bitwise_helper::bitwise_xor(a, b); +} + +/** \internal \returns the bitwise not of \a a */ +template EIGEN_DEVICE_FUNC inline Packet +pnot(const Packet& a) { + return bitwise_helper::bitwise_not(a); +} + +/** \internal \returns the bitwise and of \a a and not \a b */ +template EIGEN_DEVICE_FUNC inline Packet +pandnot(const Packet& a, const Packet& b) { return pand(a, pnot(b)); } + +// In the general case, use bitwise select. +template +struct pselect_impl { + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) { + return por(pand(a,mask),pandnot(b,mask)); + } +}; + +// For scalars, use ternary select. +template +struct pselect_impl::value>::type > { + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) { + return numext::equal_strict(mask, Packet(0)) ? b : a; + } +}; + +/** \internal \returns \a or \b for each field in packet according to \mask */ +template EIGEN_DEVICE_FUNC inline Packet +pselect(const Packet& mask, const Packet& a, const Packet& b) { + return pselect_impl::run(mask, a, b); +} + +template<> EIGEN_DEVICE_FUNC inline bool pselect( + const bool& cond, const bool& a, const bool& b) { + return cond ? a : b; +} + +/** \internal \returns the min or of \a a and \a b (coeff-wise) + If either \a a or \a b are NaN, the result is implementation defined. */ +template +struct pminmax_impl { + template + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) { + return op(a,b); + } +}; + +/** \internal \returns the min or max of \a a and \a b (coeff-wise) + If either \a a or \a b are NaN, NaN is returned. */ +template<> +struct pminmax_impl { + template + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) { + Packet not_nan_mask_a = pcmp_eq(a, a); + Packet not_nan_mask_b = pcmp_eq(b, b); + return pselect(not_nan_mask_a, + pselect(not_nan_mask_b, op(a, b), b), + a); + } +}; + +/** \internal \returns the min or max of \a a and \a b (coeff-wise) + If both \a a and \a b are NaN, NaN is returned. + Equivalent to std::fmin(a, b). */ +template<> +struct pminmax_impl { + template + static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) { + Packet not_nan_mask_a = pcmp_eq(a, a); + Packet not_nan_mask_b = pcmp_eq(b, b); + return pselect(not_nan_mask_a, + pselect(not_nan_mask_b, op(a, b), a), + b); + } +}; + + +#ifndef SYCL_DEVICE_ONLY +#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) Func +#else +#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) \ +[](const Type& a, const Type& b) { \ + return Func(a, b);} +#endif + +/** \internal \returns the min of \a a and \a b (coeff-wise). + If \a a or \b b is NaN, the return value is implementation defined. */ +template EIGEN_DEVICE_FUNC inline Packet +pmin(const Packet& a, const Packet& b) { return numext::mini(a,b); } + +/** \internal \returns the min of \a a and \a b (coeff-wise). + NaNPropagation determines the NaN propagation semantics. */ +template +EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) { + return pminmax_impl::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmin))); +} + +/** \internal \returns the max of \a a and \a b (coeff-wise) + If \a a or \b b is NaN, the return value is implementation defined. */ +template EIGEN_DEVICE_FUNC inline Packet +pmax(const Packet& a, const Packet& b) { return numext::maxi(a, b); } + +/** \internal \returns the max of \a a and \a b (coeff-wise). + NaNPropagation determines the NaN propagation semantics. */ +template +EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) { + return pminmax_impl::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet,(pmax))); +} + +/** \internal \returns the absolute value of \a a */ +template EIGEN_DEVICE_FUNC inline Packet +pabs(const Packet& a) { return numext::abs(a); } +template<> EIGEN_DEVICE_FUNC inline unsigned int +pabs(const unsigned int& a) { return a; } +template<> EIGEN_DEVICE_FUNC inline unsigned long +pabs(const unsigned long& a) { return a; } +template<> EIGEN_DEVICE_FUNC inline unsigned long long +pabs(const unsigned long long& a) { return a; } + +/** \internal \returns the addsub value of \a a,b */ +template EIGEN_DEVICE_FUNC inline Packet +paddsub(const Packet& a, const Packet& b) { + return pselect(peven_mask(a), padd(a, b), psub(a, b)); + } + +/** \internal \returns the phase angle of \a a */ +template EIGEN_DEVICE_FUNC inline Packet +parg(const Packet& a) { using numext::arg; return arg(a); } + + +/** \internal \returns \a a logically shifted by N bits to the right */ +template EIGEN_DEVICE_FUNC inline int +parithmetic_shift_right(const int& a) { return a >> N; } +template EIGEN_DEVICE_FUNC inline long int +parithmetic_shift_right(const long int& a) { return a >> N; } + +/** \internal \returns \a a arithmetically shifted by N bits to the right */ +template EIGEN_DEVICE_FUNC inline int +plogical_shift_right(const int& a) { return static_cast(static_cast(a) >> N); } +template EIGEN_DEVICE_FUNC inline long int +plogical_shift_right(const long int& a) { return static_cast(static_cast(a) >> N); } + +/** \internal \returns \a a shifted by N bits to the left */ +template EIGEN_DEVICE_FUNC inline int +plogical_shift_left(const int& a) { return a << N; } +template EIGEN_DEVICE_FUNC inline long int +plogical_shift_left(const long int& a) { return a << N; } + +/** \internal \returns the significant and exponent of the underlying floating point numbers + * See https://en.cppreference.com/w/cpp/numeric/math/frexp + */ +template +EIGEN_DEVICE_FUNC inline Packet pfrexp(const Packet& a, Packet& exponent) { + int exp; + EIGEN_USING_STD(frexp); + Packet result = static_cast(frexp(a, &exp)); + exponent = static_cast(exp); + return result; +} + +/** \internal \returns a * 2^((int)exponent) + * See https://en.cppreference.com/w/cpp/numeric/math/ldexp + */ +template EIGEN_DEVICE_FUNC inline Packet +pldexp(const Packet &a, const Packet &exponent) { + EIGEN_USING_STD(ldexp) + return static_cast(ldexp(a, static_cast(exponent))); +} + +/** \internal \returns the min of \a a and \a b (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pabsdiff(const Packet& a, const Packet& b) { return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b)); } + +/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */ +template EIGEN_DEVICE_FUNC inline Packet +pload(const typename unpacket_traits::type* from) { return *from; } + +/** \internal \returns a packet version of \a *from, (un-aligned load) */ +template EIGEN_DEVICE_FUNC inline Packet +ploadu(const typename unpacket_traits::type* from) { return *from; } + +/** \internal \returns a packet version of \a *from, (un-aligned masked load) + * There is no generic implementation. We only have implementations for specialized + * cases. Generic case should not be called. + */ +template EIGEN_DEVICE_FUNC inline +typename enable_if::masked_load_available, Packet>::type +ploadu(const typename unpacket_traits::type* from, typename unpacket_traits::mask_t umask); + +/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */ +template EIGEN_DEVICE_FUNC inline Packet +pset1(const typename unpacket_traits::type& a) { return a; } + +/** \internal \returns a packet with constant coefficients set from bits */ +template EIGEN_DEVICE_FUNC inline Packet +pset1frombits(BitsType a); + +/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */ +template EIGEN_DEVICE_FUNC inline Packet +pload1(const typename unpacket_traits::type *a) { return pset1(*a); } + +/** \internal \returns a packet with elements of \a *from duplicated. + * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and + * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]} + * Currently, this function is only used for scalar * complex products. + */ +template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet +ploaddup(const typename unpacket_traits::type* from) { return *from; } + +/** \internal \returns a packet with elements of \a *from quadrupled. + * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and + * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]} + * Currently, this function is only used in matrix products. + * For packet-size smaller or equal to 4, this function is equivalent to pload1 + */ +template EIGEN_DEVICE_FUNC inline Packet +ploadquad(const typename unpacket_traits::type* from) +{ return pload1(from); } + +/** \internal equivalent to + * \code + * a0 = pload1(a+0); + * a1 = pload1(a+1); + * a2 = pload1(a+2); + * a3 = pload1(a+3); + * \endcode + * \sa pset1, pload1, ploaddup, pbroadcast2 + */ +template EIGEN_DEVICE_FUNC +inline void pbroadcast4(const typename unpacket_traits::type *a, + Packet& a0, Packet& a1, Packet& a2, Packet& a3) +{ + a0 = pload1(a+0); + a1 = pload1(a+1); + a2 = pload1(a+2); + a3 = pload1(a+3); +} + +/** \internal equivalent to + * \code + * a0 = pload1(a+0); + * a1 = pload1(a+1); + * \endcode + * \sa pset1, pload1, ploaddup, pbroadcast4 + */ +template EIGEN_DEVICE_FUNC +inline void pbroadcast2(const typename unpacket_traits::type *a, + Packet& a0, Packet& a1) +{ + a0 = pload1(a+0); + a1 = pload1(a+1); +} + +/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */ +template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet +plset(const typename unpacket_traits::type& a) { return a; } + +/** \internal \returns a packet with constant coefficients \a a, e.g.: (x, 0, x, 0), + where x is the value of all 1-bits. */ +template EIGEN_DEVICE_FUNC inline Packet +peven_mask(const Packet& /*a*/) { + typedef typename unpacket_traits::type Scalar; + const size_t n = unpacket_traits::size; + EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n]; + for(size_t i = 0; i < n; ++i) { + memset(elements+i, ((i & 1) == 0 ? 0xff : 0), sizeof(Scalar)); + } + return ploadu(elements); +} + + +/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */ +template EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from) +{ (*to) = from; } + +/** \internal copy the packet \a from to \a *to, (un-aligned store) */ +template EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from) +{ (*to) = from; } + +/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask) + * There is no generic implementation. We only have implementations for specialized + * cases. Generic case should not be called. + */ +template +EIGEN_DEVICE_FUNC inline +typename enable_if::masked_store_available, void>::type +pstoreu(Scalar* to, const Packet& from, typename unpacket_traits::mask_t umask); + + template EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/) + { return ploadu(from); } + + template EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/) + { pstore(to, from); } + +/** \internal tries to do cache prefetching of \a addr */ +template EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr) +{ +#if defined(EIGEN_HIP_DEVICE_COMPILE) + // do nothing +#elif defined(EIGEN_CUDA_ARCH) +#if defined(__LP64__) || EIGEN_OS_WIN64 + // 64-bit pointer operand constraint for inlined asm + asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr)); +#else + // 32-bit pointer operand constraint for inlined asm + asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr)); +#endif +#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC) + __builtin_prefetch(addr); +#endif +} + +/** \internal \returns the reversed elements of \a a*/ +template EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a) +{ return a; } + +/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */ +template EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a) +{ + return Packet(numext::imag(a),numext::real(a)); +} + +/************************** +* Special math functions +***************************/ + +/** \internal \returns the sine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet psin(const Packet& a) { EIGEN_USING_STD(sin); return sin(a); } + +/** \internal \returns the cosine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pcos(const Packet& a) { EIGEN_USING_STD(cos); return cos(a); } + +/** \internal \returns the tan of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet ptan(const Packet& a) { EIGEN_USING_STD(tan); return tan(a); } + +/** \internal \returns the arc sine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pasin(const Packet& a) { EIGEN_USING_STD(asin); return asin(a); } + +/** \internal \returns the arc cosine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pacos(const Packet& a) { EIGEN_USING_STD(acos); return acos(a); } + +/** \internal \returns the arc tangent of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet patan(const Packet& a) { EIGEN_USING_STD(atan); return atan(a); } + +/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet psinh(const Packet& a) { EIGEN_USING_STD(sinh); return sinh(a); } + +/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pcosh(const Packet& a) { EIGEN_USING_STD(cosh); return cosh(a); } + +/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet ptanh(const Packet& a) { EIGEN_USING_STD(tanh); return tanh(a); } + +/** \internal \returns the exp of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pexp(const Packet& a) { EIGEN_USING_STD(exp); return exp(a); } + +/** \internal \returns the expm1 of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pexpm1(const Packet& a) { return numext::expm1(a); } + +/** \internal \returns the log of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet plog(const Packet& a) { EIGEN_USING_STD(log); return log(a); } + +/** \internal \returns the log1p of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet plog1p(const Packet& a) { return numext::log1p(a); } + +/** \internal \returns the log10 of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); } + +/** \internal \returns the log10 of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet plog2(const Packet& a) { + typedef typename internal::unpacket_traits::type Scalar; + return pmul(pset1(Scalar(EIGEN_LOG2E)), plog(a)); +} + +/** \internal \returns the square-root of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet psqrt(const Packet& a) { return numext::sqrt(a); } + +/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet prsqrt(const Packet& a) { + typedef typename internal::unpacket_traits::type Scalar; + return pdiv(pset1(Scalar(1)), psqrt(a)); +} + +/** \internal \returns the rounded value of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pround(const Packet& a) { using numext::round; return round(a); } + +/** \internal \returns the floor of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pfloor(const Packet& a) { using numext::floor; return floor(a); } + +/** \internal \returns the rounded value of \a a (coeff-wise) with current + * rounding mode */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet print(const Packet& a) { using numext::rint; return rint(a); } + +/** \internal \returns the ceil of \a a (coeff-wise) */ +template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS +Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); } + +/** \internal \returns the first element of a packet */ +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type +pfirst(const Packet& a) +{ return a; } + +/** \internal \returns the sum of the elements of upper and lower half of \a a if \a a is larger than 4. + * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7} + * For packet-size smaller or equal to 4, this boils down to a noop. + */ +template +EIGEN_DEVICE_FUNC inline typename conditional<(unpacket_traits::size%8)==0,typename unpacket_traits::half,Packet>::type +predux_half_dowto4(const Packet& a) +{ return a; } + +// Slow generic implementation of Packet reduction. +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type +predux_helper(const Packet& a, Op op) { + typedef typename unpacket_traits::type Scalar; + const size_t n = unpacket_traits::size; + EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n]; + pstoreu(elements, a); + for(size_t k = n / 2; k > 0; k /= 2) { + for(size_t i = 0; i < k; ++i) { + elements[i] = op(elements[i], elements[i + k]); + } + } + return elements[0]; +} + +/** \internal \returns the sum of the elements of \a a*/ +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type +predux(const Packet& a) +{ + return a; +} + +/** \internal \returns the product of the elements of \a a */ +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type predux_mul( + const Packet& a) { + typedef typename unpacket_traits::type Scalar; + return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul))); +} + +/** \internal \returns the min of the elements of \a a */ +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type predux_min( + const Packet &a) { + typedef typename unpacket_traits::type Scalar; + return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin))); +} + +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type predux_min( + const Packet& a) { + typedef typename unpacket_traits::type Scalar; + return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin))); +} + +/** \internal \returns the min of the elements of \a a */ +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type predux_max( + const Packet &a) { + typedef typename unpacket_traits::type Scalar; + return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax))); +} + +template +EIGEN_DEVICE_FUNC inline typename unpacket_traits::type predux_max( + const Packet& a) { + typedef typename unpacket_traits::type Scalar; + return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax))); +} + +#undef EIGEN_BINARY_OP_NAN_PROPAGATION + +/** \internal \returns true if all coeffs of \a a means "true" + * It is supposed to be called on values returned by pcmp_*. + */ +// not needed yet +// template EIGEN_DEVICE_FUNC inline bool predux_all(const Packet& a) +// { return bool(a); } + +/** \internal \returns true if any coeffs of \a a means "true" + * It is supposed to be called on values returned by pcmp_*. + */ +template EIGEN_DEVICE_FUNC inline bool predux_any(const Packet& a) +{ + // Dirty but generic implementation where "true" is assumed to be non 0 and all the sames. + // It is expected that "true" is either: + // - Scalar(1) + // - bits full of ones (NaN for floats), + // - or first bit equals to 1 (1 for ints, smallest denormal for floats). + // For all these cases, taking the sum is just fine, and this boils down to a no-op for scalars. + typedef typename unpacket_traits::type Scalar; + return numext::not_equal_strict(predux(a), Scalar(0)); +} + +/*************************************************************************** +* The following functions might not have to be overwritten for vectorized types +***************************************************************************/ + +/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */ +// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type) +template +inline void pstore1(typename unpacket_traits::type* to, const typename unpacket_traits::type& a) +{ + pstore(to, pset1(a)); +} + +/** \internal \returns a * b + c (coeff-wise) */ +template EIGEN_DEVICE_FUNC inline Packet +pmadd(const Packet& a, + const Packet& b, + const Packet& c) +{ return padd(pmul(a, b),c); } + +/** \internal \returns a packet version of \a *from. + * The pointer \a from must be aligned on a \a Alignment bytes boundary. */ +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits::type* from) +{ + if(Alignment >= unpacket_traits::alignment) + return pload(from); + else + return ploadu(from); +} + +/** \internal copy the packet \a from to \a *to. + * The pointer \a from must be aligned on a \a Alignment bytes boundary. */ +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from) +{ + if(Alignment >= unpacket_traits::alignment) + pstore(to, from); + else + pstoreu(to, from); +} + +/** \internal \returns a packet version of \a *from. + * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the + * hardware if available to speedup the loading of data that won't be modified + * by the current computation. + */ +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits::type* from) +{ + return ploadt(from); +} + +/*************************************************************************** +* Fast complex products (GCC generates a function call which is very slow) +***************************************************************************/ + +// Eigen+CUDA does not support complexes. +#if !defined(EIGEN_GPUCC) + +template<> inline std::complex pmul(const std::complex& a, const std::complex& b) +{ return std::complex(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); } + +template<> inline std::complex pmul(const std::complex& a, const std::complex& b) +{ return std::complex(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); } + +#endif + + +/*************************************************************************** + * PacketBlock, that is a collection of N packets where the number of words + * in the packet is a multiple of N. +***************************************************************************/ +template ::size> struct PacketBlock { + Packet packet[N]; +}; + +template EIGEN_DEVICE_FUNC inline void +ptranspose(PacketBlock& /*kernel*/) { + // Nothing to do in the scalar case, i.e. a 1x1 matrix. +} + +/*************************************************************************** + * Selector, i.e. vector of N boolean values used to select (i.e. blend) + * words from 2 packets. +***************************************************************************/ +template struct Selector { + bool select[N]; +}; + +template EIGEN_DEVICE_FUNC inline Packet +pblend(const Selector::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) { + return ifPacket.select[0] ? thenPacket : elsePacket; +} + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_GENERIC_PACKET_MATH_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/GlobalFunctions.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GlobalFunctions.h new file mode 100644 index 00000000..629af94b --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/GlobalFunctions.h @@ -0,0 +1,194 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2010-2016 Gael Guennebaud +// Copyright (C) 2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_GLOBAL_FUNCTIONS_H +#define EIGEN_GLOBAL_FUNCTIONS_H + +#ifdef EIGEN_PARSED_BY_DOXYGEN + +#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \ + /** \returns an expression of the coefficient-wise DOC_OP of \a x + + DOC_DETAILS + + \sa Math functions, class CwiseUnaryOp + */ \ + template \ + inline const Eigen::CwiseUnaryOp, const Derived> \ + NAME(const Eigen::ArrayBase& x); + +#else + +#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \ + template \ + inline const Eigen::CwiseUnaryOp, const Derived> \ + (NAME)(const Eigen::ArrayBase& x) { \ + return Eigen::CwiseUnaryOp, const Derived>(x.derived()); \ + } + +#endif // EIGEN_PARSED_BY_DOXYGEN + +#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \ + \ + template \ + struct NAME##_retval > \ + { \ + typedef const Eigen::CwiseUnaryOp, const Derived> type; \ + }; \ + template \ + struct NAME##_impl > \ + { \ + static inline typename NAME##_retval >::type run(const Eigen::ArrayBase& x) \ + { \ + return typename NAME##_retval >::type(x.derived()); \ + } \ + }; + +namespace Eigen +{ + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh) +#if EIGEN_HAS_CXX11_MATH + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh,scalar_asinh_op,inverse hyperbolic sine,\sa ArrayBase::asinh) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh,scalar_acosh_op,inverse hyperbolic cosine,\sa ArrayBase::acosh) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh,scalar_atanh_op,inverse hyperbolic tangent,\sa ArrayBase::atanh) +#endif + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri,scalar_ndtri_op,inverse normal distribution function,\sa ArrayBase::ndtri) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1,scalar_expm1_op,exponential of a value minus 1,\sa ArrayBase::expm1) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2,scalar_log2_op,base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint,scalar_rint_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite) + EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign) + + /** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent. + * + * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar). + * + * \sa ArrayBase::pow() + * + * \relates ArrayBase + */ +#ifdef EIGEN_PARSED_BY_DOXYGEN + template + inline const CwiseBinaryOp,Derived,Constant > + pow(const Eigen::ArrayBase& x, const ScalarExponent& exponent); +#else + template + EIGEN_DEVICE_FUNC inline + EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE( + const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg::type,pow)) + pow(const Eigen::ArrayBase& x, const ScalarExponent& exponent) + { + typedef typename internal::promote_scalar_arg::type PromotedExponent; + return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedExponent,pow)(x.derived(), + typename internal::plain_constant_type::type(x.derived().rows(), x.derived().cols(), internal::scalar_constant_op(exponent))); + } +#endif + + /** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents. + * + * This function computes the coefficient-wise power. + * + * Example: \include Cwise_array_power_array.cpp + * Output: \verbinclude Cwise_array_power_array.out + * + * \sa ArrayBase::pow() + * + * \relates ArrayBase + */ + template + inline const Eigen::CwiseBinaryOp, const Derived, const ExponentDerived> + pow(const Eigen::ArrayBase& x, const Eigen::ArrayBase& exponents) + { + return Eigen::CwiseBinaryOp, const Derived, const ExponentDerived>( + x.derived(), + exponents.derived() + ); + } + + /** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents. + * + * This function computes the coefficient-wise power between a scalar and an array of exponents. + * + * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar). + * + * Example: \include Cwise_scalar_power_array.cpp + * Output: \verbinclude Cwise_scalar_power_array.out + * + * \sa ArrayBase::pow() + * + * \relates ArrayBase + */ +#ifdef EIGEN_PARSED_BY_DOXYGEN + template + inline const CwiseBinaryOp,Constant,Derived> + pow(const Scalar& x,const Eigen::ArrayBase& x); +#else + template + EIGEN_DEVICE_FUNC inline + EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE( + const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg::type,Derived,pow)) + pow(const Scalar& x, const Eigen::ArrayBase& exponents) { + typedef typename internal::promote_scalar_arg::type PromotedScalar; + return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar,Derived,pow)( + typename internal::plain_constant_type::type(exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op(x)), exponents.derived()); + } +#endif + + + namespace internal + { + EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op) + EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op) + EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op) + } +} + +// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...) + +#endif // EIGEN_GLOBAL_FUNCTIONS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/IO.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/IO.h new file mode 100644 index 00000000..e81c3152 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/IO.h @@ -0,0 +1,258 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_IO_H +#define EIGEN_IO_H + +namespace Eigen { + +enum { DontAlignCols = 1 }; +enum { StreamPrecision = -1, + FullPrecision = -2 }; + +namespace internal { +template +std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt); +} + +/** \class IOFormat + * \ingroup Core_Module + * + * \brief Stores a set of parameters controlling the way matrices are printed + * + * List of available parameters: + * - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision. + * The default is the special value \c StreamPrecision which means to use the + * stream's own precision setting, as set for instance using \c cout.precision(3). The other special value + * \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point + * type. + * - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which + * allows to disable the alignment of columns, resulting in faster code. + * - \b coeffSeparator string printed between two coefficients of the same row + * - \b rowSeparator string printed between two rows + * - \b rowPrefix string printed at the beginning of each row + * - \b rowSuffix string printed at the end of each row + * - \b matPrefix string printed at the beginning of the matrix + * - \b matSuffix string printed at the end of the matrix + * - \b fill character printed to fill the empty space in aligned columns + * + * Example: \include IOFormat.cpp + * Output: \verbinclude IOFormat.out + * + * \sa DenseBase::format(), class WithFormat + */ +struct IOFormat +{ + /** Default constructor, see class IOFormat for the meaning of the parameters */ + IOFormat(int _precision = StreamPrecision, int _flags = 0, + const std::string& _coeffSeparator = " ", + const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="", + const std::string& _matPrefix="", const std::string& _matSuffix="", const char _fill=' ') + : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator), + rowSpacer(""), coeffSeparator(_coeffSeparator), fill(_fill), precision(_precision), flags(_flags) + { + // TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline + // don't add rowSpacer if columns are not to be aligned + if((flags & DontAlignCols)) + return; + int i = int(matSuffix.length())-1; + while (i>=0 && matSuffix[i]!='\n') + { + rowSpacer += ' '; + i--; + } + } + std::string matPrefix, matSuffix; + std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer; + std::string coeffSeparator; + char fill; + int precision; + int flags; +}; + +/** \class WithFormat + * \ingroup Core_Module + * + * \brief Pseudo expression providing matrix output with given format + * + * \tparam ExpressionType the type of the object on which IO stream operations are performed + * + * This class represents an expression with stream operators controlled by a given IOFormat. + * It is the return type of DenseBase::format() + * and most of the time this is the only way it is used. + * + * See class IOFormat for some examples. + * + * \sa DenseBase::format(), class IOFormat + */ +template +class WithFormat +{ + public: + + WithFormat(const ExpressionType& matrix, const IOFormat& format) + : m_matrix(matrix), m_format(format) + {} + + friend std::ostream & operator << (std::ostream & s, const WithFormat& wf) + { + return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format); + } + + protected: + typename ExpressionType::Nested m_matrix; + IOFormat m_format; +}; + +namespace internal { + +// NOTE: This helper is kept for backward compatibility with previous code specializing +// this internal::significant_decimals_impl structure. In the future we should directly +// call digits10() which has been introduced in July 2016 in 3.3. +template +struct significant_decimals_impl +{ + static inline int run() + { + return NumTraits::digits10(); + } +}; + +/** \internal + * print the matrix \a _m to the output stream \a s using the output format \a fmt */ +template +std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt) +{ + using internal::is_same; + using internal::conditional; + + if(_m.size() == 0) + { + s << fmt.matPrefix << fmt.matSuffix; + return s; + } + + typename Derived::Nested m = _m; + typedef typename Derived::Scalar Scalar; + typedef typename + conditional< + is_same::value || + is_same::value || + is_same::value || + is_same::value, + int, + typename conditional< + is_same >::value || + is_same >::value || + is_same >::value || + is_same >::value, + std::complex, + const Scalar& + >::type + >::type PrintType; + + Index width = 0; + + std::streamsize explicit_precision; + if(fmt.precision == StreamPrecision) + { + explicit_precision = 0; + } + else if(fmt.precision == FullPrecision) + { + if (NumTraits::IsInteger) + { + explicit_precision = 0; + } + else + { + explicit_precision = significant_decimals_impl::run(); + } + } + else + { + explicit_precision = fmt.precision; + } + + std::streamsize old_precision = 0; + if(explicit_precision) old_precision = s.precision(explicit_precision); + + bool align_cols = !(fmt.flags & DontAlignCols); + if(align_cols) + { + // compute the largest width + for(Index j = 0; j < m.cols(); ++j) + for(Index i = 0; i < m.rows(); ++i) + { + std::stringstream sstr; + sstr.copyfmt(s); + sstr << static_cast(m.coeff(i,j)); + width = std::max(width, Index(sstr.str().length())); + } + } + std::streamsize old_width = s.width(); + char old_fill_character = s.fill(); + s << fmt.matPrefix; + for(Index i = 0; i < m.rows(); ++i) + { + if (i) + s << fmt.rowSpacer; + s << fmt.rowPrefix; + if(width) { + s.fill(fmt.fill); + s.width(width); + } + s << static_cast(m.coeff(i, 0)); + for(Index j = 1; j < m.cols(); ++j) + { + s << fmt.coeffSeparator; + if(width) { + s.fill(fmt.fill); + s.width(width); + } + s << static_cast(m.coeff(i, j)); + } + s << fmt.rowSuffix; + if( i < m.rows() - 1) + s << fmt.rowSeparator; + } + s << fmt.matSuffix; + if(explicit_precision) s.precision(old_precision); + if(width) { + s.fill(old_fill_character); + s.width(old_width); + } + return s; +} + +} // end namespace internal + +/** \relates DenseBase + * + * Outputs the matrix, to the given stream. + * + * If you wish to print the matrix with a format different than the default, use DenseBase::format(). + * + * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers. + * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters. + * + * \sa DenseBase::format() + */ +template +std::ostream & operator << +(std::ostream & s, + const DenseBase & m) +{ + return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT); +} + +} // end namespace Eigen + +#endif // EIGEN_IO_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/IndexedView.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/IndexedView.h new file mode 100644 index 00000000..08476251 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/IndexedView.h @@ -0,0 +1,237 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2017 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_INDEXED_VIEW_H +#define EIGEN_INDEXED_VIEW_H + +namespace Eigen { + +namespace internal { + +template +struct traits > + : traits +{ + enum { + RowsAtCompileTime = int(array_size::value), + ColsAtCompileTime = int(array_size::value), + MaxRowsAtCompileTime = RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) : Dynamic, + MaxColsAtCompileTime = ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) : Dynamic, + + XprTypeIsRowMajor = (int(traits::Flags)&RowMajorBit) != 0, + IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 + : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 + : XprTypeIsRowMajor, + + RowIncr = int(get_compile_time_incr::value), + ColIncr = int(get_compile_time_incr::value), + InnerIncr = IsRowMajor ? ColIncr : RowIncr, + OuterIncr = IsRowMajor ? RowIncr : ColIncr, + + HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor), + XprInnerStride = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time::ret) : int(outer_stride_at_compile_time::ret), + XprOuterstride = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time::ret) : int(inner_stride_at_compile_time::ret), + + InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime, + IsBlockAlike = InnerIncr==1 && OuterIncr==1, + IsInnerPannel = HasSameStorageOrderAsXprType && is_same,typename conditional::type>::value, + + InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic ? Dynamic : XprInnerStride * InnerIncr, + OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic ? Dynamic : XprOuterstride * OuterIncr, + + ReturnAsScalar = is_same::value && is_same::value, + ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike, + ReturnAsIndexedView = (!ReturnAsScalar) && (!ReturnAsBlock), + + // FIXME we deal with compile-time strides if and only if we have DirectAccessBit flag, + // but this is too strict regarding negative strides... + DirectAccessMask = (int(InnerIncr)!=UndefinedIncr && int(OuterIncr)!=UndefinedIncr && InnerIncr>=0 && OuterIncr>=0) ? DirectAccessBit : 0, + FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0, + FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, + FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0, + Flags = (traits::Flags & (HereditaryBits | DirectAccessMask )) | FlagsLvalueBit | FlagsRowMajorBit | FlagsLinearAccessBit + }; + + typedef Block BlockType; +}; + +} + +template +class IndexedViewImpl; + + +/** \class IndexedView + * \ingroup Core_Module + * + * \brief Expression of a non-sequential sub-matrix defined by arbitrary sequences of row and column indices + * + * \tparam XprType the type of the expression in which we are taking the intersections of sub-rows and sub-columns + * \tparam RowIndices the type of the object defining the sequence of row indices + * \tparam ColIndices the type of the object defining the sequence of column indices + * + * This class represents an expression of a sub-matrix (or sub-vector) defined as the intersection + * of sub-sets of rows and columns, that are themself defined by generic sequences of row indices \f$ \{r_0,r_1,..r_{m-1}\} \f$ + * and column indices \f$ \{c_0,c_1,..c_{n-1} \}\f$. Let \f$ A \f$ be the nested matrix, then the resulting matrix \f$ B \f$ has \c m + * rows and \c n columns, and its entries are given by: \f$ B(i,j) = A(r_i,c_j) \f$. + * + * The \c RowIndices and \c ColIndices types must be compatible with the following API: + * \code + * operator[](Index) const; + * Index size() const; + * \endcode + * + * Typical supported types thus include: + * - std::vector + * - std::valarray + * - std::array + * - Plain C arrays: int[N] + * - Eigen::ArrayXi + * - decltype(ArrayXi::LinSpaced(...)) + * - Any view/expressions of the previous types + * - Eigen::ArithmeticSequence + * - Eigen::internal::AllRange (helper for Eigen::all) + * - Eigen::internal::SingleRange (helper for single index) + * - etc. + * + * In typical usages of %Eigen, this class should never be used directly. It is the return type of + * DenseBase::operator()(const RowIndices&, const ColIndices&). + * + * \sa class Block + */ +template +class IndexedView : public IndexedViewImpl::StorageKind> +{ +public: + typedef typename IndexedViewImpl::StorageKind>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(IndexedView) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView) + + typedef typename internal::ref_selector::non_const_type MatrixTypeNested; + typedef typename internal::remove_all::type NestedExpression; + + template + IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices) + : m_xpr(xpr), m_rowIndices(rowIndices), m_colIndices(colIndices) + {} + + /** \returns number of rows */ + Index rows() const { return internal::size(m_rowIndices); } + + /** \returns number of columns */ + Index cols() const { return internal::size(m_colIndices); } + + /** \returns the nested expression */ + const typename internal::remove_all::type& + nestedExpression() const { return m_xpr; } + + /** \returns the nested expression */ + typename internal::remove_reference::type& + nestedExpression() { return m_xpr; } + + /** \returns a const reference to the object storing/generating the row indices */ + const RowIndices& rowIndices() const { return m_rowIndices; } + + /** \returns a const reference to the object storing/generating the column indices */ + const ColIndices& colIndices() const { return m_colIndices; } + +protected: + MatrixTypeNested m_xpr; + RowIndices m_rowIndices; + ColIndices m_colIndices; +}; + + +// Generic API dispatcher +template +class IndexedViewImpl + : public internal::generic_xpr_base >::type +{ +public: + typedef typename internal::generic_xpr_base >::type Base; +}; + +namespace internal { + + +template +struct unary_evaluator, IndexBased> + : evaluator_base > +{ + typedef IndexedView XprType; + + enum { + CoeffReadCost = evaluator::CoeffReadCost /* TODO + cost of row/col index */, + + FlagsLinearAccessBit = (traits::RowsAtCompileTime == 1 || traits::ColsAtCompileTime == 1) ? LinearAccessBit : 0, + + FlagsRowMajorBit = traits::FlagsRowMajorBit, + + Flags = (evaluator::Flags & (HereditaryBits & ~RowMajorBit /*| LinearAccessBit | DirectAccessBit*/)) | FlagsLinearAccessBit | FlagsRowMajorBit, + + Alignment = 0 + }; + + EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeff(Index row, Index col) const + { + return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index row, Index col) + { + return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Scalar& coeffRef(Index index) + { + EIGEN_STATIC_ASSERT_LVALUE(XprType) + Index row = XprType::RowsAtCompileTime == 1 ? 0 : index; + Index col = XprType::RowsAtCompileTime == 1 ? index : 0; + return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Scalar& coeffRef(Index index) const + { + Index row = XprType::RowsAtCompileTime == 1 ? 0 : index; + Index col = XprType::RowsAtCompileTime == 1 ? index : 0; + return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const CoeffReturnType coeff(Index index) const + { + Index row = XprType::RowsAtCompileTime == 1 ? 0 : index; + Index col = XprType::RowsAtCompileTime == 1 ? index : 0; + return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]); + } + +protected: + + evaluator m_argImpl; + const XprType& m_xpr; + +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_INDEXED_VIEW_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Inverse.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Inverse.h new file mode 100644 index 00000000..c514438c --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Inverse.h @@ -0,0 +1,117 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014-2019 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_INVERSE_H +#define EIGEN_INVERSE_H + +namespace Eigen { + +template class InverseImpl; + +namespace internal { + +template +struct traits > + : traits +{ + typedef typename XprType::PlainObject PlainObject; + typedef traits BaseTraits; + enum { + Flags = BaseTraits::Flags & RowMajorBit + }; +}; + +} // end namespace internal + +/** \class Inverse + * + * \brief Expression of the inverse of another expression + * + * \tparam XprType the type of the expression we are taking the inverse + * + * This class represents an abstract expression of A.inverse() + * and most of the time this is the only way it is used. + * + */ +template +class Inverse : public InverseImpl::StorageKind> +{ +public: + typedef typename XprType::StorageIndex StorageIndex; + typedef typename XprType::Scalar Scalar; + typedef typename internal::ref_selector::type XprTypeNested; + typedef typename internal::remove_all::type XprTypeNestedCleaned; + typedef typename internal::ref_selector::type Nested; + typedef typename internal::remove_all::type NestedExpression; + + explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr) + : m_xpr(xpr) + {} + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.cols(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.rows(); } + + EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; } + +protected: + XprTypeNested m_xpr; +}; + +// Generic API dispatcher +template +class InverseImpl + : public internal::generic_xpr_base >::type +{ +public: + typedef typename internal::generic_xpr_base >::type Base; + typedef typename XprType::Scalar Scalar; +private: + + Scalar coeff(Index row, Index col) const; + Scalar coeff(Index i) const; +}; + +namespace internal { + +/** \internal + * \brief Default evaluator for Inverse expression. + * + * This default evaluator for Inverse expression simply evaluate the inverse into a temporary + * by a call to internal::call_assignment_no_alias. + * Therefore, inverse implementers only have to specialize Assignment, ...> for + * there own nested expression. + * + * \sa class Inverse + */ +template +struct unary_evaluator > + : public evaluator::PlainObject> +{ + typedef Inverse InverseType; + typedef typename InverseType::PlainObject PlainObject; + typedef evaluator Base; + + enum { Flags = Base::Flags | EvalBeforeNestingBit }; + + unary_evaluator(const InverseType& inv_xpr) + : m_result(inv_xpr.rows(), inv_xpr.cols()) + { + ::new (static_cast(this)) Base(m_result); + internal::call_assignment_no_alias(m_result, inv_xpr); + } + +protected: + PlainObject m_result; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_INVERSE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Map.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Map.h new file mode 100644 index 00000000..218cc157 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Map.h @@ -0,0 +1,171 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2007-2010 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MAP_H +#define EIGEN_MAP_H + +namespace Eigen { + +namespace internal { +template +struct traits > + : public traits +{ + typedef traits TraitsBase; + enum { + PlainObjectTypeInnerSize = ((traits::Flags&RowMajorBit)==RowMajorBit) + ? PlainObjectType::ColsAtCompileTime + : PlainObjectType::RowsAtCompileTime, + + InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0 + ? int(PlainObjectType::InnerStrideAtCompileTime) + : int(StrideType::InnerStrideAtCompileTime), + OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0 + ? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic + ? Dynamic + : int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize)) + : int(StrideType::OuterStrideAtCompileTime), + Alignment = int(MapOptions)&int(AlignedMask), + Flags0 = TraitsBase::Flags & (~NestByRefBit), + Flags = is_lvalue::value ? int(Flags0) : (int(Flags0) & ~LvalueBit) + }; +private: + enum { Options }; // Expressions don't have Options +}; +} + +/** \class Map + * \ingroup Core_Module + * + * \brief A matrix or vector expression mapping an existing array of data. + * + * \tparam PlainObjectType the equivalent matrix type of the mapped data + * \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned. + * The default is \c #Unaligned. + * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout + * of an ordinary, contiguous array. This can be overridden by specifying strides. + * The type passed here must be a specialization of the Stride template, see examples below. + * + * This class represents a matrix or vector expression mapping an existing array of data. + * It can be used to let Eigen interface without any overhead with non-Eigen data structures, + * such as plain C arrays or structures from other libraries. By default, it assumes that the + * data is laid out contiguously in memory. You can however override this by explicitly specifying + * inner and outer strides. + * + * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix: + * \include Map_simple.cpp + * Output: \verbinclude Map_simple.out + * + * If you need to map non-contiguous arrays, you can do so by specifying strides: + * + * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer + * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time + * fixed value. + * \include Map_inner_stride.cpp + * Output: \verbinclude Map_inner_stride.out + * + * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping + * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns. + * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is + * a short version of \c OuterStride because the default template parameter of OuterStride + * is \c Dynamic + * \include Map_outer_stride.cpp + * Output: \verbinclude Map_outer_stride.out + * + * For more details and for an example of specifying both an inner and an outer stride, see class Stride. + * + * \b Tip: to change the array of data mapped by a Map object, you can use the C++ + * placement new syntax: + * + * Example: \include Map_placement_new.cpp + * Output: \verbinclude Map_placement_new.out + * + * This class is the return type of PlainObjectBase::Map() but can also be used directly. + * + * \sa PlainObjectBase::Map(), \ref TopicStorageOrders + */ +template class Map + : public MapBase > +{ + public: + + typedef MapBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Map) + + typedef typename Base::PointerType PointerType; + typedef PointerType PointerArgType; + EIGEN_DEVICE_FUNC + inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const + { + return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1; + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const + { + return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer() + : internal::traits::OuterStrideAtCompileTime != Dynamic ? Index(internal::traits::OuterStrideAtCompileTime) + : IsVectorAtCompileTime ? (this->size() * innerStride()) + : int(Flags)&RowMajorBit ? (this->cols() * innerStride()) + : (this->rows() * innerStride()); + } + + /** Constructor in the fixed-size case. + * + * \param dataPtr pointer to the array to map + * \param stride optional Stride object, passing the strides. + */ + EIGEN_DEVICE_FUNC + explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType()) + : Base(cast_to_pointer_type(dataPtr)), m_stride(stride) + { + PlainObjectType::Base::_check_template_params(); + } + + /** Constructor in the dynamic-size vector case. + * + * \param dataPtr pointer to the array to map + * \param size the size of the vector expression + * \param stride optional Stride object, passing the strides. + */ + EIGEN_DEVICE_FUNC + inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType()) + : Base(cast_to_pointer_type(dataPtr), size), m_stride(stride) + { + PlainObjectType::Base::_check_template_params(); + } + + /** Constructor in the dynamic-size matrix case. + * + * \param dataPtr pointer to the array to map + * \param rows the number of rows of the matrix expression + * \param cols the number of columns of the matrix expression + * \param stride optional Stride object, passing the strides. + */ + EIGEN_DEVICE_FUNC + inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType()) + : Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride) + { + PlainObjectType::Base::_check_template_params(); + } + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map) + + protected: + StrideType m_stride; +}; + + +} // end namespace Eigen + +#endif // EIGEN_MAP_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/MapBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MapBase.h new file mode 100644 index 00000000..d856447f --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MapBase.h @@ -0,0 +1,310 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2007-2010 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MAPBASE_H +#define EIGEN_MAPBASE_H + +#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \ + EIGEN_STATIC_ASSERT((int(internal::evaluator::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \ + YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT) + +namespace Eigen { + +/** \ingroup Core_Module + * + * \brief Base class for dense Map and Block expression with direct access + * + * This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense + * Map and Block objects with direct access. + * Typical users do not have to directly deal with this class. + * + * This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN. + * See \link TopicCustomizing_Plugins customizing Eigen \endlink for details. + * + * The \c Derived class has to provide the following two methods describing the memory layout: + * \code Index innerStride() const; \endcode + * \code Index outerStride() const; \endcode + * + * \sa class Map, class Block + */ +template class MapBase + : public internal::dense_xpr_base::type +{ + public: + + typedef typename internal::dense_xpr_base::type Base; + enum { + RowsAtCompileTime = internal::traits::RowsAtCompileTime, + ColsAtCompileTime = internal::traits::ColsAtCompileTime, + InnerStrideAtCompileTime = internal::traits::InnerStrideAtCompileTime, + SizeAtCompileTime = Base::SizeAtCompileTime + }; + + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::packet_traits::type PacketScalar; + typedef typename NumTraits::Real RealScalar; + typedef typename internal::conditional< + bool(internal::is_lvalue::value), + Scalar *, + const Scalar *>::type + PointerType; + + using Base::derived; +// using Base::RowsAtCompileTime; +// using Base::ColsAtCompileTime; +// using Base::SizeAtCompileTime; + using Base::MaxRowsAtCompileTime; + using Base::MaxColsAtCompileTime; + using Base::MaxSizeAtCompileTime; + using Base::IsVectorAtCompileTime; + using Base::Flags; + using Base::IsRowMajor; + + using Base::rows; + using Base::cols; + using Base::size; + using Base::coeff; + using Base::coeffRef; + using Base::lazyAssign; + using Base::eval; + + using Base::innerStride; + using Base::outerStride; + using Base::rowStride; + using Base::colStride; + + // bug 217 - compile error on ICC 11.1 + using Base::operator=; + + typedef typename Base::CoeffReturnType CoeffReturnType; + + /** \copydoc DenseBase::rows() */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return m_rows.value(); } + /** \copydoc DenseBase::cols() */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return m_cols.value(); } + + /** Returns a pointer to the first coefficient of the matrix or vector. + * + * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride(). + * + * \sa innerStride(), outerStride() + */ + EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; } + + /** \copydoc PlainObjectBase::coeff(Index,Index) const */ + EIGEN_DEVICE_FUNC + inline const Scalar& coeff(Index rowId, Index colId) const + { + return m_data[colId * colStride() + rowId * rowStride()]; + } + + /** \copydoc PlainObjectBase::coeff(Index) const */ + EIGEN_DEVICE_FUNC + inline const Scalar& coeff(Index index) const + { + EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) + return m_data[index * innerStride()]; + } + + /** \copydoc PlainObjectBase::coeffRef(Index,Index) const */ + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index rowId, Index colId) const + { + return this->m_data[colId * colStride() + rowId * rowStride()]; + } + + /** \copydoc PlainObjectBase::coeffRef(Index) const */ + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index index) const + { + EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) + return this->m_data[index * innerStride()]; + } + + /** \internal */ + template + inline PacketScalar packet(Index rowId, Index colId) const + { + return internal::ploadt + (m_data + (colId * colStride() + rowId * rowStride())); + } + + /** \internal */ + template + inline PacketScalar packet(Index index) const + { + EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) + return internal::ploadt(m_data + index * innerStride()); + } + + /** \internal Constructor for fixed size matrices or vectors */ + EIGEN_DEVICE_FUNC + explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime) + { + EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) + checkSanity(); + } + + /** \internal Constructor for dynamically sized vectors */ + EIGEN_DEVICE_FUNC + inline MapBase(PointerType dataPtr, Index vecSize) + : m_data(dataPtr), + m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)), + m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime)) + { + EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) + eigen_assert(vecSize >= 0); + eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize); + checkSanity(); + } + + /** \internal Constructor for dynamically sized matrices */ + EIGEN_DEVICE_FUNC + inline MapBase(PointerType dataPtr, Index rows, Index cols) + : m_data(dataPtr), m_rows(rows), m_cols(cols) + { + eigen_assert( (dataPtr == 0) + || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) + && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))); + checkSanity(); + } + + #ifdef EIGEN_MAPBASE_PLUGIN + #include EIGEN_MAPBASE_PLUGIN + #endif + + protected: + EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase) + EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase) + + template + EIGEN_DEVICE_FUNC + void checkSanity(typename internal::enable_if<(internal::traits::Alignment>0),void*>::type = 0) const + { +#if EIGEN_MAX_ALIGN_BYTES>0 + // innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value: + const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime); + EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride); + eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits::Alignment) == 0) + || (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits::Alignment ) && "data is not aligned"); +#endif + } + + template + EIGEN_DEVICE_FUNC + void checkSanity(typename internal::enable_if::Alignment==0,void*>::type = 0) const + {} + + PointerType m_data; + const internal::variable_if_dynamic m_rows; + const internal::variable_if_dynamic m_cols; +}; + +/** \ingroup Core_Module + * + * \brief Base class for non-const dense Map and Block expression with direct access + * + * This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of + * dense Map and Block objects with direct access. + * It inherits MapBase which defines the const variant for reading specific entries. + * + * \sa class Map, class Block + */ +template class MapBase + : public MapBase +{ + typedef MapBase ReadOnlyMapBase; + public: + + typedef MapBase Base; + + typedef typename Base::Scalar Scalar; + typedef typename Base::PacketScalar PacketScalar; + typedef typename Base::StorageIndex StorageIndex; + typedef typename Base::PointerType PointerType; + + using Base::derived; + using Base::rows; + using Base::cols; + using Base::size; + using Base::coeff; + using Base::coeffRef; + + using Base::innerStride; + using Base::outerStride; + using Base::rowStride; + using Base::colStride; + + typedef typename internal::conditional< + internal::is_lvalue::value, + Scalar, + const Scalar + >::type ScalarWithConstIfNotLvalue; + + EIGEN_DEVICE_FUNC + inline const Scalar* data() const { return this->m_data; } + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error + + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col) + { + return this->m_data[col * colStride() + row * rowStride()]; + } + + EIGEN_DEVICE_FUNC + inline ScalarWithConstIfNotLvalue& coeffRef(Index index) + { + EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) + return this->m_data[index * innerStride()]; + } + + template + inline void writePacket(Index row, Index col, const PacketScalar& val) + { + internal::pstoret + (this->m_data + (col * colStride() + row * rowStride()), val); + } + + template + inline void writePacket(Index index, const PacketScalar& val) + { + EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) + internal::pstoret + (this->m_data + index * innerStride(), val); + } + + EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {} + EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {} + EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {} + + EIGEN_DEVICE_FUNC + Derived& operator=(const MapBase& other) + { + ReadOnlyMapBase::Base::operator=(other); + return derived(); + } + + // In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base, + // see bugs 821 and 920. + using ReadOnlyMapBase::Base::operator=; + protected: + EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase) + EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase) +}; + +#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS + +} // end namespace Eigen + +#endif // EIGEN_MAPBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctions.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctions.h new file mode 100644 index 00000000..61b78f4f --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctions.h @@ -0,0 +1,2057 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2010 Benoit Jacob +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATHFUNCTIONS_H +#define EIGEN_MATHFUNCTIONS_H + +// TODO this should better be moved to NumTraits +// Source: WolframAlpha +#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406L +#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L +#define EIGEN_LN2 0.693147180559945309417232121458176568075500134360255254120680009493393621L + +namespace Eigen { + +// On WINCE, std::abs is defined for int only, so let's defined our own overloads: +// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too. +#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500 +long abs(long x) { return (labs(x)); } +double abs(double x) { return (fabs(x)); } +float abs(float x) { return (fabsf(x)); } +long double abs(long double x) { return (fabsl(x)); } +#endif + +namespace internal { + +/** \internal \class global_math_functions_filtering_base + * + * What it does: + * Defines a typedef 'type' as follows: + * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then + * global_math_functions_filtering_base::type is a typedef for it. + * - otherwise, global_math_functions_filtering_base::type is a typedef for T. + * + * How it's used: + * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions. + * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know + * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase. + * So we must make sure to use sin_impl > and not sin_impl, otherwise our partial specialization + * won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells it. + * + * How it's implemented: + * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you replace + * the typename dummy by an integer template parameter, it doesn't work anymore! + */ + +template +struct global_math_functions_filtering_base +{ + typedef T type; +}; + +template struct always_void { typedef void type; }; + +template +struct global_math_functions_filtering_base + ::type + > +{ + typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type; +}; + +#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl::type> +#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval::type>::type + +/**************************************************************************** +* Implementation of real * +****************************************************************************/ + +template::IsComplex> +struct real_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return x; + } +}; + +template +struct real_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + using std::real; + return real(x); + } +}; + +template struct real_impl : real_default_impl {}; + +#if defined(EIGEN_GPU_COMPILE_PHASE) +template +struct real_impl > +{ + typedef T RealScalar; + EIGEN_DEVICE_FUNC + static inline T run(const std::complex& x) + { + return x.real(); + } +}; +#endif + +template +struct real_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of imag * +****************************************************************************/ + +template::IsComplex> +struct imag_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar&) + { + return RealScalar(0); + } +}; + +template +struct imag_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + using std::imag; + return imag(x); + } +}; + +template struct imag_impl : imag_default_impl {}; + +#if defined(EIGEN_GPU_COMPILE_PHASE) +template +struct imag_impl > +{ + typedef T RealScalar; + EIGEN_DEVICE_FUNC + static inline T run(const std::complex& x) + { + return x.imag(); + } +}; +#endif + +template +struct imag_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of real_ref * +****************************************************************************/ + +template +struct real_ref_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar& run(Scalar& x) + { + return reinterpret_cast(&x)[0]; + } + EIGEN_DEVICE_FUNC + static inline const RealScalar& run(const Scalar& x) + { + return reinterpret_cast(&x)[0]; + } +}; + +template +struct real_ref_retval +{ + typedef typename NumTraits::Real & type; +}; + +/**************************************************************************** +* Implementation of imag_ref * +****************************************************************************/ + +template +struct imag_ref_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar& run(Scalar& x) + { + return reinterpret_cast(&x)[1]; + } + EIGEN_DEVICE_FUNC + static inline const RealScalar& run(const Scalar& x) + { + return reinterpret_cast(&x)[1]; + } +}; + +template +struct imag_ref_default_impl +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline Scalar run(Scalar&) + { + return Scalar(0); + } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline const Scalar run(const Scalar&) + { + return Scalar(0); + } +}; + +template +struct imag_ref_impl : imag_ref_default_impl::IsComplex> {}; + +template +struct imag_ref_retval +{ + typedef typename NumTraits::Real & type; +}; + +/**************************************************************************** +* Implementation of conj * +****************************************************************************/ + +template::IsComplex> +struct conj_default_impl +{ + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + return x; + } +}; + +template +struct conj_default_impl +{ + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + using std::conj; + return conj(x); + } +}; + +template::IsComplex> +struct conj_impl : conj_default_impl {}; + +template +struct conj_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of abs2 * +****************************************************************************/ + +template +struct abs2_impl_default +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return x*x; + } +}; + +template +struct abs2_impl_default // IsComplex +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return x.real()*x.real() + x.imag()*x.imag(); + } +}; + +template +struct abs2_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return abs2_impl_default::IsComplex>::run(x); + } +}; + +template +struct abs2_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of sqrt/rsqrt * +****************************************************************************/ + +template +struct sqrt_impl +{ + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE Scalar run(const Scalar& x) + { + EIGEN_USING_STD(sqrt); + return sqrt(x); + } +}; + +// Complex sqrt defined in MathFunctionsImpl.h. +template EIGEN_DEVICE_FUNC std::complex complex_sqrt(const std::complex& a_x); + +// Custom implementation is faster than `std::sqrt`, works on +// GPU, and correctly handles special cases (unlike MSVC). +template +struct sqrt_impl > +{ + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE std::complex run(const std::complex& x) + { + return complex_sqrt(x); + } +}; + +template +struct sqrt_retval +{ + typedef Scalar type; +}; + +// Default implementation relies on numext::sqrt, at bottom of file. +template +struct rsqrt_impl; + +// Complex rsqrt defined in MathFunctionsImpl.h. +template EIGEN_DEVICE_FUNC std::complex complex_rsqrt(const std::complex& a_x); + +template +struct rsqrt_impl > +{ + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE std::complex run(const std::complex& x) + { + return complex_rsqrt(x); + } +}; + +template +struct rsqrt_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of norm1 * +****************************************************************************/ + +template +struct norm1_default_impl; + +template +struct norm1_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + EIGEN_USING_STD(abs); + return abs(x.real()) + abs(x.imag()); + } +}; + +template +struct norm1_default_impl +{ + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + EIGEN_USING_STD(abs); + return abs(x); + } +}; + +template +struct norm1_impl : norm1_default_impl::IsComplex> {}; + +template +struct norm1_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of hypot * +****************************************************************************/ + +template struct hypot_impl; + +template +struct hypot_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of cast * +****************************************************************************/ + +template +struct cast_impl +{ + EIGEN_DEVICE_FUNC + static inline NewType run(const OldType& x) + { + return static_cast(x); + } +}; + +// Casting from S -> Complex leads to an implicit conversion from S to T, +// generating warnings on clang. Here we explicitly cast the real component. +template +struct cast_impl::IsComplex && NumTraits::IsComplex + >::type> +{ + EIGEN_DEVICE_FUNC + static inline NewType run(const OldType& x) + { + typedef typename NumTraits::Real NewReal; + return static_cast(static_cast(x)); + } +}; + +// here, for once, we're plainly returning NewType: we don't want cast to do weird things. + +template +EIGEN_DEVICE_FUNC +inline NewType cast(const OldType& x) +{ + return cast_impl::run(x); +} + +/**************************************************************************** +* Implementation of round * +****************************************************************************/ + +template +struct round_impl +{ + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + EIGEN_STATIC_ASSERT((!NumTraits::IsComplex), NUMERIC_TYPE_MUST_BE_REAL) +#if EIGEN_HAS_CXX11_MATH + EIGEN_USING_STD(round); +#endif + return Scalar(round(x)); + } +}; + +#if !EIGEN_HAS_CXX11_MATH +#if EIGEN_HAS_C99_MATH +// Use ::roundf for float. +template<> +struct round_impl { + EIGEN_DEVICE_FUNC + static inline float run(const float& x) + { + return ::roundf(x); + } +}; +#else +template +struct round_using_floor_ceil_impl +{ + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + EIGEN_STATIC_ASSERT((!NumTraits::IsComplex), NUMERIC_TYPE_MUST_BE_REAL) + // Without C99 round/roundf, resort to floor/ceil. + EIGEN_USING_STD(floor); + EIGEN_USING_STD(ceil); + // If not enough precision to resolve a decimal at all, return the input. + // Otherwise, adding 0.5 can trigger an increment by 1. + const Scalar limit = Scalar(1ull << (NumTraits::digits() - 1)); + if (x >= limit || x <= -limit) { + return x; + } + return (x > Scalar(0)) ? Scalar(floor(x + Scalar(0.5))) : Scalar(ceil(x - Scalar(0.5))); + } +}; + +template<> +struct round_impl : round_using_floor_ceil_impl {}; + +template<> +struct round_impl : round_using_floor_ceil_impl {}; +#endif // EIGEN_HAS_C99_MATH +#endif // !EIGEN_HAS_CXX11_MATH + +template +struct round_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of rint * +****************************************************************************/ + +template +struct rint_impl { + EIGEN_DEVICE_FUNC + static inline Scalar run(const Scalar& x) + { + EIGEN_STATIC_ASSERT((!NumTraits::IsComplex), NUMERIC_TYPE_MUST_BE_REAL) +#if EIGEN_HAS_CXX11_MATH + EIGEN_USING_STD(rint); +#endif + return rint(x); + } +}; + +#if !EIGEN_HAS_CXX11_MATH +template<> +struct rint_impl { + EIGEN_DEVICE_FUNC + static inline double run(const double& x) + { + return ::rint(x); + } +}; +template<> +struct rint_impl { + EIGEN_DEVICE_FUNC + static inline float run(const float& x) + { + return ::rintf(x); + } +}; +#endif + +template +struct rint_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of arg * +****************************************************************************/ + +// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs. +// This seems to be fixed in VS 2019. +#if EIGEN_HAS_CXX11_MATH && (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920) +// std::arg is only defined for types of std::complex, or integer types or float/double/long double +template::IsComplex || is_integral::value + || is_same::value || is_same::value + || is_same::value > +struct arg_default_impl; + +template +struct arg_default_impl { + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + #if defined(EIGEN_HIP_DEVICE_COMPILE) + // HIP does not seem to have a native device side implementation for the math routine "arg" + using std::arg; + #else + EIGEN_USING_STD(arg); + #endif + return static_cast(arg(x)); + } +}; + +// Must be non-complex floating-point type (e.g. half/bfloat16). +template +struct arg_default_impl { + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return (x < Scalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0); + } +}; +#else +template::IsComplex> +struct arg_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + return (x < RealScalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0); + } +}; + +template +struct arg_default_impl +{ + typedef typename NumTraits::Real RealScalar; + EIGEN_DEVICE_FUNC + static inline RealScalar run(const Scalar& x) + { + EIGEN_USING_STD(arg); + return arg(x); + } +}; +#endif +template struct arg_impl : arg_default_impl {}; + +template +struct arg_retval +{ + typedef typename NumTraits::Real type; +}; + +/**************************************************************************** +* Implementation of expm1 * +****************************************************************************/ + +// This implementation is based on GSL Math's expm1. +namespace std_fallback { + // fallback expm1 implementation in case there is no expm1(Scalar) function in namespace of Scalar, + // or that there is no suitable std::expm1 function available. Implementation + // attributed to Kahan. See: http://www.plunk.org/~hatch/rightway.php. + template + EIGEN_DEVICE_FUNC inline Scalar expm1(const Scalar& x) { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) + typedef typename NumTraits::Real RealScalar; + + EIGEN_USING_STD(exp); + Scalar u = exp(x); + if (numext::equal_strict(u, Scalar(1))) { + return x; + } + Scalar um1 = u - RealScalar(1); + if (numext::equal_strict(um1, Scalar(-1))) { + return RealScalar(-1); + } + + EIGEN_USING_STD(log); + Scalar logu = log(u); + return numext::equal_strict(u, logu) ? u : (u - RealScalar(1)) * x / logu; + } +} + +template +struct expm1_impl { + EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) + { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) + #if EIGEN_HAS_CXX11_MATH + using std::expm1; + #else + using std_fallback::expm1; + #endif + return expm1(x); + } +}; + +template +struct expm1_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of log * +****************************************************************************/ + +// Complex log defined in MathFunctionsImpl.h. +template EIGEN_DEVICE_FUNC std::complex complex_log(const std::complex& z); + +template +struct log_impl { + EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) + { + EIGEN_USING_STD(log); + return static_cast(log(x)); + } +}; + +template +struct log_impl > { + EIGEN_DEVICE_FUNC static inline std::complex run(const std::complex& z) + { + return complex_log(z); + } +}; + +/**************************************************************************** +* Implementation of log1p * +****************************************************************************/ + +namespace std_fallback { + // fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar, + // or that there is no suitable std::log1p function available + template + EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) + typedef typename NumTraits::Real RealScalar; + EIGEN_USING_STD(log); + Scalar x1p = RealScalar(1) + x; + Scalar log_1p = log_impl::run(x1p); + const bool is_small = numext::equal_strict(x1p, Scalar(1)); + const bool is_inf = numext::equal_strict(x1p, log_1p); + return (is_small || is_inf) ? x : x * (log_1p / (x1p - RealScalar(1))); + } +} + +template +struct log1p_impl { + EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) + { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) + #if EIGEN_HAS_CXX11_MATH + using std::log1p; + #else + using std_fallback::log1p; + #endif + return log1p(x); + } +}; + +// Specialization for complex types that are not supported by std::log1p. +template +struct log1p_impl > { + EIGEN_DEVICE_FUNC static inline std::complex run( + const std::complex& x) { + EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar) + return std_fallback::log1p(x); + } +}; + +template +struct log1p_retval +{ + typedef Scalar type; +}; + +/**************************************************************************** +* Implementation of pow * +****************************************************************************/ + +template::IsInteger&&NumTraits::IsInteger> +struct pow_impl +{ + //typedef Scalar retval; + typedef typename ScalarBinaryOpTraits >::ReturnType result_type; + static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y) + { + EIGEN_USING_STD(pow); + return pow(x, y); + } +}; + +template +struct pow_impl +{ + typedef ScalarX result_type; + static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y) + { + ScalarX res(1); + eigen_assert(!NumTraits::IsSigned || y >= 0); + if(y & 1) res *= x; + y >>= 1; + while(y) + { + x *= x; + if(y&1) res *= x; + y >>= 1; + } + return res; + } +}; + +/**************************************************************************** +* Implementation of random * +****************************************************************************/ + +template +struct random_default_impl {}; + +template +struct random_impl : random_default_impl::IsComplex, NumTraits::IsInteger> {}; + +template +struct random_retval +{ + typedef Scalar type; +}; + +template inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y); +template inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(); + +template +struct random_default_impl +{ + static inline Scalar run(const Scalar& x, const Scalar& y) + { + return x + (y-x) * Scalar(std::rand()) / Scalar(RAND_MAX); + } + static inline Scalar run() + { + return run(Scalar(NumTraits::IsSigned ? -1 : 0), Scalar(1)); + } +}; + +enum { + meta_floor_log2_terminate, + meta_floor_log2_move_up, + meta_floor_log2_move_down, + meta_floor_log2_bogus +}; + +template struct meta_floor_log2_selector +{ + enum { middle = (lower + upper) / 2, + value = (upper <= lower + 1) ? int(meta_floor_log2_terminate) + : (n < (1 << middle)) ? int(meta_floor_log2_move_down) + : (n==0) ? int(meta_floor_log2_bogus) + : int(meta_floor_log2_move_up) + }; +}; + +template::value> +struct meta_floor_log2 {}; + +template +struct meta_floor_log2 +{ + enum { value = meta_floor_log2::middle>::value }; +}; + +template +struct meta_floor_log2 +{ + enum { value = meta_floor_log2::middle, upper>::value }; +}; + +template +struct meta_floor_log2 +{ + enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower }; +}; + +template +struct meta_floor_log2 +{ + // no value, error at compile time +}; + +template +struct random_default_impl +{ + static inline Scalar run(const Scalar& x, const Scalar& y) + { + if (y <= x) + return x; + // ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself. + typedef typename make_unsigned::type ScalarU; + // ScalarX is the widest of ScalarU and unsigned int. + // We'll deal only with ScalarX and unsigned int below thus avoiding signed + // types and arithmetic and signed overflows (which are undefined behavior). + typedef typename conditional<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned>::type ScalarX; + // The following difference doesn't overflow, provided our integer types are two's + // complement and have the same number of padding bits in signed and unsigned variants. + // This is the case in most modern implementations of C++. + ScalarX range = ScalarX(y) - ScalarX(x); + ScalarX offset = 0; + ScalarX divisor = 1; + ScalarX multiplier = 1; + const unsigned rand_max = RAND_MAX; + if (range <= rand_max) divisor = (rand_max + 1) / (range + 1); + else multiplier = 1 + range / (rand_max + 1); + // Rejection sampling. + do { + offset = (unsigned(std::rand()) * multiplier) / divisor; + } while (offset > range); + return Scalar(ScalarX(x) + offset); + } + + static inline Scalar run() + { +#ifdef EIGEN_MAKING_DOCS + return run(Scalar(NumTraits::IsSigned ? -10 : 0), Scalar(10)); +#else + enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value, + scalar_bits = sizeof(Scalar) * CHAR_BIT, + shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)), + offset = NumTraits::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0 + }; + return Scalar((std::rand() >> shift) - offset); +#endif + } +}; + +template +struct random_default_impl +{ + static inline Scalar run(const Scalar& x, const Scalar& y) + { + return Scalar(random(x.real(), y.real()), + random(x.imag(), y.imag())); + } + static inline Scalar run() + { + typedef typename NumTraits::Real RealScalar; + return Scalar(random(), random()); + } +}; + +template +inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y) +{ + return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y); +} + +template +inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random() +{ + return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(); +} + +// Implementation of is* functions + +// std::is* do not work with fast-math and gcc, std::is* are available on MSVC 2013 and newer, as well as in clang. +#if (EIGEN_HAS_CXX11_MATH && !(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC>=1800) || (EIGEN_COMP_CLANG) +#define EIGEN_USE_STD_FPCLASSIFY 1 +#else +#define EIGEN_USE_STD_FPCLASSIFY 0 +#endif + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if::value,bool>::type +isnan_impl(const T&) { return false; } + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if::value,bool>::type +isinf_impl(const T&) { return false; } + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if::value,bool>::type +isfinite_impl(const T&) { return true; } + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if<(!internal::is_integral::value)&&(!NumTraits::IsComplex),bool>::type +isfinite_impl(const T& x) +{ + #if defined(EIGEN_GPU_COMPILE_PHASE) + return (::isfinite)(x); + #elif EIGEN_USE_STD_FPCLASSIFY + using std::isfinite; + return isfinite EIGEN_NOT_A_MACRO (x); + #else + return x<=NumTraits::highest() && x>=NumTraits::lowest(); + #endif +} + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if<(!internal::is_integral::value)&&(!NumTraits::IsComplex),bool>::type +isinf_impl(const T& x) +{ + #if defined(EIGEN_GPU_COMPILE_PHASE) + return (::isinf)(x); + #elif EIGEN_USE_STD_FPCLASSIFY + using std::isinf; + return isinf EIGEN_NOT_A_MACRO (x); + #else + return x>NumTraits::highest() || x::lowest(); + #endif +} + +template +EIGEN_DEVICE_FUNC +typename internal::enable_if<(!internal::is_integral::value)&&(!NumTraits::IsComplex),bool>::type +isnan_impl(const T& x) +{ + #if defined(EIGEN_GPU_COMPILE_PHASE) + return (::isnan)(x); + #elif EIGEN_USE_STD_FPCLASSIFY + using std::isnan; + return isnan EIGEN_NOT_A_MACRO (x); + #else + return x != x; + #endif +} + +#if (!EIGEN_USE_STD_FPCLASSIFY) + +#if EIGEN_COMP_MSVC + +template EIGEN_DEVICE_FUNC bool isinf_msvc_helper(T x) +{ + return _fpclass(x)==_FPCLASS_NINF || _fpclass(x)==_FPCLASS_PINF; +} + +//MSVC defines a _isnan builtin function, but for double only +EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x)!=0; } +EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x) { return _isnan(x)!=0; } +EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x) { return _isnan(x)!=0; } + +EIGEN_DEVICE_FUNC inline bool isinf_impl(const long double& x) { return isinf_msvc_helper(x); } +EIGEN_DEVICE_FUNC inline bool isinf_impl(const double& x) { return isinf_msvc_helper(x); } +EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x) { return isinf_msvc_helper(x); } + +#elif (defined __FINITE_MATH_ONLY__ && __FINITE_MATH_ONLY__ && EIGEN_COMP_GNUC) + +#if EIGEN_GNUC_AT_LEAST(5,0) + #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((optimize("no-finite-math-only"))) +#else + // NOTE the inline qualifier and noinline attribute are both needed: the former is to avoid linking issue (duplicate symbol), + // while the second prevent too aggressive optimizations in fast-math mode: + #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((noinline,optimize("no-finite-math-only"))) +#endif + +template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const long double& x) { return __builtin_isnan(x); } +template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const double& x) { return __builtin_isnan(x); } +template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const float& x) { return __builtin_isnan(x); } +template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const double& x) { return __builtin_isinf(x); } +template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const float& x) { return __builtin_isinf(x); } +template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const long double& x) { return __builtin_isinf(x); } + +#undef EIGEN_TMP_NOOPT_ATTRIB + +#endif + +#endif + +// The following overload are defined at the end of this file +template EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex& x); +template EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex& x); +template EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex& x); + +template T generic_fast_tanh_float(const T& a_x); +} // end namespace internal + +/**************************************************************************** +* Generic math functions * +****************************************************************************/ + +namespace numext { + +#if (!defined(EIGEN_GPUCC) || defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC)) +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) +{ + EIGEN_USING_STD(min) + return min EIGEN_NOT_A_MACRO (x,y); +} + +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) +{ + EIGEN_USING_STD(max) + return max EIGEN_NOT_A_MACRO (x,y); +} +#else +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) +{ + return y < x ? y : x; +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y) +{ + return fminf(x, y); +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y) +{ + return fmin(x, y); +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y) +{ +#if defined(EIGEN_HIPCC) + // no "fminl" on HIP yet + return (x < y) ? x : y; +#else + return fminl(x, y); +#endif +} + +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) +{ + return x < y ? y : x; +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y) +{ + return fmaxf(x, y); +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y) +{ + return fmax(x, y); +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y) +{ +#if defined(EIGEN_HIPCC) + // no "fmaxl" on HIP yet + return (x > y) ? x : y; +#else + return fmaxl(x, y); +#endif +} +#endif + +#if defined(SYCL_DEVICE_ONLY) + + +#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_char) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_short) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_int) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_long) +#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_char) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_short) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_int) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_long) +#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uint) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong) +#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uint) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong) +#define SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(NAME, FUNC) \ + SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \ + SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) +#define SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(NAME, FUNC) \ + SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \ + SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) +#define SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(NAME, FUNC) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \ + SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC,cl::sycl::cl_double) +#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(NAME, FUNC) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \ + SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC,cl::sycl::cl_double) +#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(NAME, FUNC, RET_TYPE) \ + SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_float) \ + SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_double) + +#define SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \ +template<> \ + EIGEN_DEVICE_FUNC \ + EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE& x) { \ + return cl::sycl::FUNC(x); \ + } + +#define SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, TYPE) \ + SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, TYPE, TYPE) + +#define SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE1, ARG_TYPE2) \ + template<> \ + EIGEN_DEVICE_FUNC \ + EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE1& x, const ARG_TYPE2& y) { \ + return cl::sycl::FUNC(x, y); \ + } + +#define SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \ + SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE, ARG_TYPE) + +#define SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, TYPE) \ + SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, TYPE, TYPE) + +SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(mini, min) +SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(mini, fmin) +SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(maxi, max) +SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(maxi, fmax) + +#endif + + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x) +{ + return internal::real_ref_impl::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x) +{ + return internal::imag_ref_impl::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x); +} + +EIGEN_DEVICE_FUNC +inline bool abs2(bool x) { return x; } + +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE T absdiff(const T& x, const T& y) +{ + return x > y ? x - y : y - x; +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE float absdiff(const float& x, const float& y) +{ + return fabsf(x - y); +} +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y) +{ + return fabs(x - y); +} + +#if !defined(EIGEN_GPUCC) +// HIP and CUDA do not support long double. +template<> +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) { + return fabsl(x - y); +} +#endif + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y) +{ + return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y); +} + +#if defined(SYCL_DEVICE_ONLY) + SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(hypot, hypot) +#endif + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log1p, log1p) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float log1p(const float &x) { return ::log1pf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double log1p(const double &x) { return ::log1p(x); } +#endif + +template +EIGEN_DEVICE_FUNC +inline typename internal::pow_impl::result_type pow(const ScalarX& x, const ScalarY& y) +{ + return internal::pow_impl::run(x, y); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(pow, pow) +#endif + +template EIGEN_DEVICE_FUNC bool (isnan) (const T &x) { return internal::isnan_impl(x); } +template EIGEN_DEVICE_FUNC bool (isinf) (const T &x) { return internal::isinf_impl(x); } +template EIGEN_DEVICE_FUNC bool (isfinite)(const T &x) { return internal::isfinite_impl(x); } + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isnan, isnan, bool) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isinf, isinf, bool) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isfinite, isfinite, bool) +#endif + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(rint, Scalar) rint(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(rint, Scalar)::run(x); +} + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(round, round) +#endif + +template +EIGEN_DEVICE_FUNC +T (floor)(const T& x) +{ + EIGEN_USING_STD(floor) + return floor(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(floor, floor) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float floor(const float &x) { return ::floorf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double floor(const double &x) { return ::floor(x); } +#endif + +template +EIGEN_DEVICE_FUNC +T (ceil)(const T& x) +{ + EIGEN_USING_STD(ceil); + return ceil(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(ceil, ceil) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float ceil(const float &x) { return ::ceilf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double ceil(const double &x) { return ::ceil(x); } +#endif + + +/** Log base 2 for 32 bits positive integers. + * Conveniently returns 0 for x==0. */ +inline int log2(int x) +{ + eigen_assert(x>=0); + unsigned int v(x); + static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 }; + v |= v >> 1; + v |= v >> 2; + v |= v >> 4; + v |= v >> 8; + v |= v >> 16; + return table[(v * 0x07C4ACDDU) >> 27]; +} + +/** \returns the square root of \a x. + * + * It is essentially equivalent to + * \code using std::sqrt; return sqrt(x); \endcode + * but slightly faster for float/double and some compilers (e.g., gcc), thanks to + * specializations when SSE is enabled. + * + * It's usage is justified in performance critical functions, like norm/normalize. + */ +template +EIGEN_DEVICE_FUNC +EIGEN_ALWAYS_INLINE EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x); +} + +// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool). +template<> +EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC +bool sqrt(const bool &x) { return x; } + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt) +#endif + +/** \returns the reciprocal square root of \a x. **/ +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T rsqrt(const T& x) +{ + return internal::rsqrt_impl::run(x); +} + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T log(const T &x) { + return internal::log_impl::run(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log, log) +#endif + + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float log(const float &x) { return ::logf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double log(const double &x) { return ::log(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +typename internal::enable_if::IsSigned || NumTraits::IsComplex,typename NumTraits::Real>::type +abs(const T &x) { + EIGEN_USING_STD(abs); + return abs(x); +} + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +typename internal::enable_if::IsSigned || NumTraits::IsComplex),typename NumTraits::Real>::type +abs(const T &x) { + return x; +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(abs, abs) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(abs, fabs) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float abs(const float &x) { return ::fabsf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double abs(const double &x) { return ::fabs(x); } + +template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float abs(const std::complex& x) { + return ::hypotf(x.real(), x.imag()); +} + +template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double abs(const std::complex& x) { + return ::hypot(x.real(), x.imag()); +} +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T exp(const T &x) { + EIGEN_USING_STD(exp); + return exp(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp, exp) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float exp(const float &x) { return ::expf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double exp(const double &x) { return ::exp(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +std::complex exp(const std::complex& x) { + float com = ::expf(x.real()); + float res_real = com * ::cosf(x.imag()); + float res_imag = com * ::sinf(x.imag()); + return std::complex(res_real, res_imag); +} + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +std::complex exp(const std::complex& x) { + double com = ::exp(x.real()); + double res_real = com * ::cos(x.imag()); + double res_imag = com * ::sin(x.imag()); + return std::complex(res_real, res_imag); +} +#endif + +template +EIGEN_DEVICE_FUNC +inline EIGEN_MATHFUNC_RETVAL(expm1, Scalar) expm1(const Scalar& x) +{ + return EIGEN_MATHFUNC_IMPL(expm1, Scalar)::run(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(expm1, expm1) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float expm1(const float &x) { return ::expm1f(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double expm1(const double &x) { return ::expm1(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T cos(const T &x) { + EIGEN_USING_STD(cos); + return cos(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cos,cos) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float cos(const float &x) { return ::cosf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double cos(const double &x) { return ::cos(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T sin(const T &x) { + EIGEN_USING_STD(sin); + return sin(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sin, sin) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float sin(const float &x) { return ::sinf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double sin(const double &x) { return ::sin(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T tan(const T &x) { + EIGEN_USING_STD(tan); + return tan(x); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tan, tan) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float tan(const float &x) { return ::tanf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double tan(const double &x) { return ::tan(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T acos(const T &x) { + EIGEN_USING_STD(acos); + return acos(x); +} + +#if EIGEN_HAS_CXX11_MATH +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T acosh(const T &x) { + EIGEN_USING_STD(acosh); + return static_cast(acosh(x)); +} +#endif + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acosh, acosh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float acos(const float &x) { return ::acosf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double acos(const double &x) { return ::acos(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T asin(const T &x) { + EIGEN_USING_STD(asin); + return asin(x); +} + +#if EIGEN_HAS_CXX11_MATH +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T asinh(const T &x) { + EIGEN_USING_STD(asinh); + return static_cast(asinh(x)); +} +#endif + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asinh, asinh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float asin(const float &x) { return ::asinf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double asin(const double &x) { return ::asin(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T atan(const T &x) { + EIGEN_USING_STD(atan); + return static_cast(atan(x)); +} + +#if EIGEN_HAS_CXX11_MATH +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T atanh(const T &x) { + EIGEN_USING_STD(atanh); + return static_cast(atanh(x)); +} +#endif + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atanh, atanh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float atan(const float &x) { return ::atanf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double atan(const double &x) { return ::atan(x); } +#endif + + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T cosh(const T &x) { + EIGEN_USING_STD(cosh); + return static_cast(cosh(x)); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cosh, cosh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float cosh(const float &x) { return ::coshf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double cosh(const double &x) { return ::cosh(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T sinh(const T &x) { + EIGEN_USING_STD(sinh); + return static_cast(sinh(x)); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sinh, sinh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float sinh(const float &x) { return ::sinhf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double sinh(const double &x) { return ::sinh(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T tanh(const T &x) { + EIGEN_USING_STD(tanh); + return tanh(x); +} + +#if (!defined(EIGEN_GPUCC)) && EIGEN_FAST_MATH && !defined(SYCL_DEVICE_ONLY) +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float tanh(float x) { return internal::generic_fast_tanh_float(x); } +#endif + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tanh, tanh) +#endif + +#if defined(EIGEN_GPUCC) +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float tanh(const float &x) { return ::tanhf(x); } + +template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double tanh(const double &x) { return ::tanh(x); } +#endif + +template +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T fmod(const T& a, const T& b) { + EIGEN_USING_STD(fmod); + return fmod(a, b); +} + +#if defined(SYCL_DEVICE_ONLY) +SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(fmod, fmod) +#endif + +#if defined(EIGEN_GPUCC) +template <> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float fmod(const float& a, const float& b) { + return ::fmodf(a, b); +} + +template <> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double fmod(const double& a, const double& b) { + return ::fmod(a, b); +} +#endif + +#if defined(SYCL_DEVICE_ONLY) +#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY +#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY +#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY +#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY +#undef SYCL_SPECIALIZE_INTEGER_TYPES_BINARY +#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY +#undef SYCL_SPECIALIZE_FLOATING_TYPES_BINARY +#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY +#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE +#undef SYCL_SPECIALIZE_GEN_UNARY_FUNC +#undef SYCL_SPECIALIZE_UNARY_FUNC +#undef SYCL_SPECIALIZE_GEN1_BINARY_FUNC +#undef SYCL_SPECIALIZE_GEN2_BINARY_FUNC +#undef SYCL_SPECIALIZE_BINARY_FUNC +#endif + +} // end namespace numext + +namespace internal { + +template +EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex& x) +{ + return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x)); +} + +template +EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex& x) +{ + return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x)); +} + +template +EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex& x) +{ + return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x)); +} + +/**************************************************************************** +* Implementation of fuzzy comparisons * +****************************************************************************/ + +template +struct scalar_fuzzy_default_impl {}; + +template +struct scalar_fuzzy_default_impl +{ + typedef typename NumTraits::Real RealScalar; + template EIGEN_DEVICE_FUNC + static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec) + { + return numext::abs(x) <= numext::abs(y) * prec; + } + EIGEN_DEVICE_FUNC + static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) + { + return numext::abs(x - y) <= numext::mini(numext::abs(x), numext::abs(y)) * prec; + } + EIGEN_DEVICE_FUNC + static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec) + { + return x <= y || isApprox(x, y, prec); + } +}; + +template +struct scalar_fuzzy_default_impl +{ + typedef typename NumTraits::Real RealScalar; + template EIGEN_DEVICE_FUNC + static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&) + { + return x == Scalar(0); + } + EIGEN_DEVICE_FUNC + static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&) + { + return x == y; + } + EIGEN_DEVICE_FUNC + static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&) + { + return x <= y; + } +}; + +template +struct scalar_fuzzy_default_impl +{ + typedef typename NumTraits::Real RealScalar; + template EIGEN_DEVICE_FUNC + static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec) + { + return numext::abs2(x) <= numext::abs2(y) * prec * prec; + } + EIGEN_DEVICE_FUNC + static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) + { + return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec; + } +}; + +template +struct scalar_fuzzy_impl : scalar_fuzzy_default_impl::IsComplex, NumTraits::IsInteger> {}; + +template EIGEN_DEVICE_FUNC +inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, + const typename NumTraits::Real &precision = NumTraits::dummy_precision()) +{ + return scalar_fuzzy_impl::template isMuchSmallerThan(x, y, precision); +} + +template EIGEN_DEVICE_FUNC +inline bool isApprox(const Scalar& x, const Scalar& y, + const typename NumTraits::Real &precision = NumTraits::dummy_precision()) +{ + return scalar_fuzzy_impl::isApprox(x, y, precision); +} + +template EIGEN_DEVICE_FUNC +inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, + const typename NumTraits::Real &precision = NumTraits::dummy_precision()) +{ + return scalar_fuzzy_impl::isApproxOrLessThan(x, y, precision); +} + +/****************************************** +*** The special case of the bool type *** +******************************************/ + +template<> struct random_impl +{ + static inline bool run() + { + return random(0,1)==0 ? false : true; + } + + static inline bool run(const bool& a, const bool& b) + { + return random(a, b)==0 ? false : true; + } +}; + +template<> struct scalar_fuzzy_impl +{ + typedef bool RealScalar; + + template EIGEN_DEVICE_FUNC + static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&) + { + return !x; + } + + EIGEN_DEVICE_FUNC + static inline bool isApprox(bool x, bool y, bool) + { + return x == y; + } + + EIGEN_DEVICE_FUNC + static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&) + { + return (!x) || y; + } + +}; + +} // end namespace internal + +// Default implementations that rely on other numext implementations +namespace internal { + +// Specialization for complex types that are not supported by std::expm1. +template +struct expm1_impl > { + EIGEN_DEVICE_FUNC static inline std::complex run( + const std::complex& x) { + EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar) + RealScalar xr = x.real(); + RealScalar xi = x.imag(); + // expm1(z) = exp(z) - 1 + // = exp(x + i * y) - 1 + // = exp(x) * (cos(y) + i * sin(y)) - 1 + // = exp(x) * cos(y) - 1 + i * exp(x) * sin(y) + // Imag(expm1(z)) = exp(x) * sin(y) + // Real(expm1(z)) = exp(x) * cos(y) - 1 + // = exp(x) * cos(y) - 1. + // = expm1(x) + exp(x) * (cos(y) - 1) + // = expm1(x) + exp(x) * (2 * sin(y / 2) ** 2) + RealScalar erm1 = numext::expm1(xr); + RealScalar er = erm1 + RealScalar(1.); + RealScalar sin2 = numext::sin(xi / RealScalar(2.)); + sin2 = sin2 * sin2; + RealScalar s = numext::sin(xi); + RealScalar real_part = erm1 - RealScalar(2.) * er * sin2; + return std::complex(real_part, er * s); + } +}; + +template +struct rsqrt_impl { + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE T run(const T& x) { + return T(1)/numext::sqrt(x); + } +}; + +#if defined(EIGEN_GPU_COMPILE_PHASE) +template +struct conj_impl, true> +{ + EIGEN_DEVICE_FUNC + static inline std::complex run(const std::complex& x) + { + return std::complex(numext::real(x), -numext::imag(x)); + } +}; +#endif + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_MATHFUNCTIONS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctionsImpl.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctionsImpl.h new file mode 100644 index 00000000..4eaaaa78 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MathFunctionsImpl.h @@ -0,0 +1,200 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com) +// Copyright (C) 2016 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATHFUNCTIONSIMPL_H +#define EIGEN_MATHFUNCTIONSIMPL_H + +namespace Eigen { + +namespace internal { + +/** \internal \returns the hyperbolic tan of \a a (coeff-wise) + Doesn't do anything fancy, just a 13/6-degree rational interpolant which + is accurate up to a couple of ulps in the (approximate) range [-8, 8], + outside of which tanh(x) = +/-1 in single precision. The input is clamped + to the range [-c, c]. The value c is chosen as the smallest value where + the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004] + the approxmation tanh(x) ~= x is used for better accuracy as x tends to zero. + + This implementation works on both scalars and packets. +*/ +template +T generic_fast_tanh_float(const T& a_x) +{ + // Clamp the inputs to the range [-c, c] +#ifdef EIGEN_VECTORIZE_FMA + const T plus_clamp = pset1(7.99881172180175781f); + const T minus_clamp = pset1(-7.99881172180175781f); +#else + const T plus_clamp = pset1(7.90531110763549805f); + const T minus_clamp = pset1(-7.90531110763549805f); +#endif + const T tiny = pset1(0.0004f); + const T x = pmax(pmin(a_x, plus_clamp), minus_clamp); + const T tiny_mask = pcmp_lt(pabs(a_x), tiny); + // The monomial coefficients of the numerator polynomial (odd). + const T alpha_1 = pset1(4.89352455891786e-03f); + const T alpha_3 = pset1(6.37261928875436e-04f); + const T alpha_5 = pset1(1.48572235717979e-05f); + const T alpha_7 = pset1(5.12229709037114e-08f); + const T alpha_9 = pset1(-8.60467152213735e-11f); + const T alpha_11 = pset1(2.00018790482477e-13f); + const T alpha_13 = pset1(-2.76076847742355e-16f); + + // The monomial coefficients of the denominator polynomial (even). + const T beta_0 = pset1(4.89352518554385e-03f); + const T beta_2 = pset1(2.26843463243900e-03f); + const T beta_4 = pset1(1.18534705686654e-04f); + const T beta_6 = pset1(1.19825839466702e-06f); + + // Since the polynomials are odd/even, we need x^2. + const T x2 = pmul(x, x); + + // Evaluate the numerator polynomial p. + T p = pmadd(x2, alpha_13, alpha_11); + p = pmadd(x2, p, alpha_9); + p = pmadd(x2, p, alpha_7); + p = pmadd(x2, p, alpha_5); + p = pmadd(x2, p, alpha_3); + p = pmadd(x2, p, alpha_1); + p = pmul(x, p); + + // Evaluate the denominator polynomial q. + T q = pmadd(x2, beta_6, beta_4); + q = pmadd(x2, q, beta_2); + q = pmadd(x2, q, beta_0); + + // Divide the numerator by the denominator. + return pselect(tiny_mask, x, pdiv(p, q)); +} + +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y) +{ + // IEEE IEC 6059 special cases. + if ((numext::isinf)(x) || (numext::isinf)(y)) + return NumTraits::infinity(); + if ((numext::isnan)(x) || (numext::isnan)(y)) + return NumTraits::quiet_NaN(); + + EIGEN_USING_STD(sqrt); + RealScalar p, qp; + p = numext::maxi(x,y); + if(p==RealScalar(0)) return RealScalar(0); + qp = numext::mini(y,x) / p; + return p * sqrt(RealScalar(1) + qp*qp); +} + +template +struct hypot_impl +{ + typedef typename NumTraits::Real RealScalar; + static EIGEN_DEVICE_FUNC + inline RealScalar run(const Scalar& x, const Scalar& y) + { + EIGEN_USING_STD(abs); + return positive_real_hypot(abs(x), abs(y)); + } +}; + +// Generic complex sqrt implementation that correctly handles corner cases +// according to https://en.cppreference.com/w/cpp/numeric/complex/sqrt +template +EIGEN_DEVICE_FUNC std::complex complex_sqrt(const std::complex& z) { + // Computes the principal sqrt of the input. + // + // For a complex square root of the number x + i*y. We want to find real + // numbers u and v such that + // (u + i*v)^2 = x + i*y <=> + // u^2 - v^2 + i*2*u*v = x + i*v. + // By equating the real and imaginary parts we get: + // u^2 - v^2 = x + // 2*u*v = y. + // + // For x >= 0, this has the numerically stable solution + // u = sqrt(0.5 * (x + sqrt(x^2 + y^2))) + // v = y / (2 * u) + // and for x < 0, + // v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2))) + // u = y / (2 * v) + // + // Letting w = sqrt(0.5 * (|x| + |z|)), + // if x == 0: u = w, v = sign(y) * w + // if x > 0: u = w, v = y / (2 * w) + // if x < 0: u = |y| / (2 * w), v = sign(y) * w + + const T x = numext::real(z); + const T y = numext::imag(z); + const T zero = T(0); + const T w = numext::sqrt(T(0.5) * (numext::abs(x) + numext::hypot(x, y))); + + return + (numext::isinf)(y) ? std::complex(NumTraits::infinity(), y) + : x == zero ? std::complex(w, y < zero ? -w : w) + : x > zero ? std::complex(w, y / (2 * w)) + : std::complex(numext::abs(y) / (2 * w), y < zero ? -w : w ); +} + +// Generic complex rsqrt implementation. +template +EIGEN_DEVICE_FUNC std::complex complex_rsqrt(const std::complex& z) { + // Computes the principal reciprocal sqrt of the input. + // + // For a complex reciprocal square root of the number z = x + i*y. We want to + // find real numbers u and v such that + // (u + i*v)^2 = 1 / (x + i*y) <=> + // u^2 - v^2 + i*2*u*v = x/|z|^2 - i*v/|z|^2. + // By equating the real and imaginary parts we get: + // u^2 - v^2 = x/|z|^2 + // 2*u*v = y/|z|^2. + // + // For x >= 0, this has the numerically stable solution + // u = sqrt(0.5 * (x + |z|)) / |z| + // v = -y / (2 * u * |z|) + // and for x < 0, + // v = -sign(y) * sqrt(0.5 * (-x + |z|)) / |z| + // u = -y / (2 * v * |z|) + // + // Letting w = sqrt(0.5 * (|x| + |z|)), + // if x == 0: u = w / |z|, v = -sign(y) * w / |z| + // if x > 0: u = w / |z|, v = -y / (2 * w * |z|) + // if x < 0: u = |y| / (2 * w * |z|), v = -sign(y) * w / |z| + + const T x = numext::real(z); + const T y = numext::imag(z); + const T zero = T(0); + + const T abs_z = numext::hypot(x, y); + const T w = numext::sqrt(T(0.5) * (numext::abs(x) + abs_z)); + const T woz = w / abs_z; + // Corner cases consistent with 1/sqrt(z) on gcc/clang. + return + abs_z == zero ? std::complex(NumTraits::infinity(), NumTraits::quiet_NaN()) + : ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex(zero, zero) + : x == zero ? std::complex(woz, y < zero ? woz : -woz) + : x > zero ? std::complex(woz, -y / (2 * w * abs_z)) + : std::complex(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz ); +} + +template +EIGEN_DEVICE_FUNC std::complex complex_log(const std::complex& z) { + // Computes complex log. + T a = numext::abs(z); + EIGEN_USING_STD(atan2); + T b = atan2(z.imag(), z.real()); + return std::complex(numext::log(a), b); +} + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_MATHFUNCTIONSIMPL_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Matrix.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Matrix.h new file mode 100644 index 00000000..f0e59a91 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Matrix.h @@ -0,0 +1,565 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2010 Benoit Jacob +// Copyright (C) 2008-2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATRIX_H +#define EIGEN_MATRIX_H + +namespace Eigen { + +namespace internal { +template +struct traits > +{ +private: + enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret }; + typedef typename find_best_packet<_Scalar,size>::type PacketScalar; + enum { + row_major_bit = _Options&RowMajor ? RowMajorBit : 0, + is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic, + max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols, + default_alignment = compute_default_alignment<_Scalar,max_size>::value, + actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0, + required_alignment = unpacket_traits::alignment, + packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0 + }; + +public: + typedef _Scalar Scalar; + typedef Dense StorageKind; + typedef Eigen::Index StorageIndex; + typedef MatrixXpr XprKind; + enum { + RowsAtCompileTime = _Rows, + ColsAtCompileTime = _Cols, + MaxRowsAtCompileTime = _MaxRows, + MaxColsAtCompileTime = _MaxCols, + Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret, + Options = _Options, + InnerStrideAtCompileTime = 1, + OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime, + + // FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase + EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit, + Alignment = actual_alignment + }; +}; +} + +/** \class Matrix + * \ingroup Core_Module + * + * \brief The matrix class, also used for vectors and row-vectors + * + * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen. + * Vectors are matrices with one column, and row-vectors are matrices with one row. + * + * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note"). + * + * The first three template parameters are required: + * \tparam _Scalar Numeric type, e.g. float, double, int or std::complex. + * User defined scalar types are supported as well (see \ref user_defined_scalars "here"). + * \tparam _Rows Number of rows, or \b Dynamic + * \tparam _Cols Number of columns, or \b Dynamic + * + * The remaining template parameters are optional -- in most cases you don't have to worry about them. + * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either + * \b #AutoAlign or \b #DontAlign. + * The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required + * for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size. + * \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note"). + * \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note"). + * + * Eigen provides a number of typedefs covering the usual cases. Here are some examples: + * + * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix) + * \li \c Vector4f is a vector of 4 floats (\c Matrix) + * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix) + * + * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix) + * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix) + * + * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix) + * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix) + * + * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs. + * + * You can access elements of vectors and matrices using normal subscripting: + * + * \code + * Eigen::VectorXd v(10); + * v[0] = 0.1; + * v[1] = 0.2; + * v(0) = 0.3; + * v(1) = 0.4; + * + * Eigen::MatrixXi m(10, 10); + * m(0, 1) = 1; + * m(0, 2) = 2; + * m(0, 3) = 3; + * \endcode + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN. + * + * Some notes: + * + *
+ *
\anchor dense Dense versus sparse:
+ *
This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module. + * + * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array. + * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.
+ * + *
\anchor fixedsize Fixed-size versus dynamic-size:
+ *
Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array + * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up + * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time. + * + * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime + * variables, and the array of coefficients is allocated dynamically on the heap. + * + * Note that \em dense matrices, be they Fixed-size or Dynamic-size, do not expand dynamically in the sense of a std::map. + * If you want this behavior, see the Sparse module.
+ * + *
\anchor maxrows _MaxRows and _MaxCols:
+ *
In most cases, one just leaves these parameters to the default values. + * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases + * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot + * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols + * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.
+ *
+ * + * ABI and storage layout + * + * The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3. + * + * + * + * + * + * + *
Matrix typeEquivalent C structure
\code Matrix \endcode\code + * struct { + * T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0 + * Eigen::Index rows, cols; + * }; + * \endcode
\code + * Matrix + * Matrix \endcode\code + * struct { + * T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0 + * Eigen::Index size; + * }; + * \endcode
\code Matrix \endcode\code + * struct { + * T data[Rows*Cols]; // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0 + * }; + * \endcode
\code Matrix \endcode\code + * struct { + * T data[MaxRows*MaxCols]; // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0 + * Eigen::Index rows, cols; + * }; + * \endcode
+ * Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two + * smaller to EIGEN_MAX_STATIC_ALIGN_BYTES. + * + * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy, + * \ref TopicStorageOrders + */ + +template +class Matrix + : public PlainObjectBase > +{ + public: + + /** \brief Base class typedef. + * \sa PlainObjectBase + */ + typedef PlainObjectBase Base; + + enum { Options = _Options }; + + EIGEN_DENSE_PUBLIC_INTERFACE(Matrix) + + typedef typename Base::PlainObject PlainObject; + + using Base::base; + using Base::coeffRef; + + /** + * \brief Assigns matrices to each other. + * + * \note This is a special case of the templated operator=. Its purpose is + * to prevent a default operator= from hiding the templated operator=. + * + * \callgraph + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other) + { + return Base::_set(other); + } + + /** \internal + * \brief Copies the value of the expression \a other into \c *this with automatic resizing. + * + * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), + * it will be initialized. + * + * Note that copying a row-vector into a vector (and conversely) is allowed. + * The resizing, if any, is then done in the appropriate way so that row-vectors + * remain row-vectors and vectors remain vectors. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase& other) + { + return Base::_set(other); + } + + /* Here, doxygen failed to copy the brief information when using \copydoc */ + + /** + * \brief Copies the generic expression \a other into *this. + * \copydetails DenseBase::operator=(const EigenBase &other) + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase &other) + { + return Base::operator=(other); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue& func) + { + return Base::operator=(func); + } + + /** \brief Default constructor. + * + * For fixed-size matrices, does nothing. + * + * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix + * is called a null matrix. This constructor is the unique way to create null matrices: resizing + * a matrix to 0 is not supported. + * + * \sa resize(Index,Index) + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Matrix() : Base() + { + Base::_check_template_params(); + EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } + + // FIXME is it still needed + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit Matrix(internal::constructor_without_unaligned_array_assert) + : Base(internal::constructor_without_unaligned_array_assert()) + { Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED } + +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible::value) + : Base(std::move(other)) + { + Base::_check_template_params(); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable::value) + { + Base::operator=(std::move(other)); + return *this; + } +#endif + +#if EIGEN_HAS_CXX11 + /** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&... args) + * + * Example: \include Matrix_variadic_ctor_cxx11.cpp + * Output: \verbinclude Matrix_variadic_ctor_cxx11.out + * + * \sa Matrix(const std::initializer_list>&) + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + : Base(a0, a1, a2, a3, args...) {} + + /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11 + * + * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients: + * + * Example: \include Matrix_initializer_list_23_cxx11.cpp + * Output: \verbinclude Matrix_initializer_list_23_cxx11.out + * + * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered. + * + * In the case of a compile-time column vector, implicit transposition from a single row is allowed. + * Therefore VectorXd{{1,2,3,4,5}} is legal and the more verbose syntax + * RowVectorXd{{1},{2},{3},{4},{5}} can be avoided: + * + * Example: \include Matrix_initializer_list_vector_cxx11.cpp + * Output: \verbinclude Matrix_initializer_list_vector_cxx11.out + * + * In the case of fixed-sized matrices, the initializer list sizes must exactly match the matrix sizes, + * and implicit transposition is allowed for compile-time vectors only. + * + * \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + */ + EIGEN_DEVICE_FUNC + explicit EIGEN_STRONG_INLINE Matrix(const std::initializer_list>& list) : Base(list) {} +#endif // end EIGEN_HAS_CXX11 + +#ifndef EIGEN_PARSED_BY_DOXYGEN + + // This constructor is for both 1x1 matrices and dynamic vectors + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit Matrix(const T& x) + { + Base::_check_template_params(); + Base::template _init1(x); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Matrix(const T0& x, const T1& y) + { + Base::_check_template_params(); + Base::template _init2(x, y); + } + + +#else + /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */ + EIGEN_DEVICE_FUNC + explicit Matrix(const Scalar *data); + + /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors + * + * This is useful for dynamic-size vectors. For fixed-size vectors, + * it is redundant to pass these parameters, so one should use the default constructor + * Matrix() instead. + * + * \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance, + * calling Matrix(1) will call the initialization constructor: Matrix(const Scalar&). + * For fixed-size \c 1x1 matrices it is therefore recommended to use the default + * constructor Matrix() instead, especially when using one of the non standard + * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives). + */ + EIGEN_STRONG_INLINE explicit Matrix(Index dim); + /** \brief Constructs an initialized 1x1 matrix with the given coefficient + * \sa Matrix(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&...) */ + Matrix(const Scalar& x); + /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns. + * + * This is useful for dynamic-size matrices. For fixed-size matrices, + * it is redundant to pass these parameters, so one should use the default constructor + * Matrix() instead. + * + * \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance, + * calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y). + * For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default + * constructor Matrix() instead, especially when using one of the non standard + * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives). + */ + EIGEN_DEVICE_FUNC + Matrix(Index rows, Index cols); + + /** \brief Constructs an initialized 2D vector with given coefficients + * \sa Matrix(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&...) */ + Matrix(const Scalar& x, const Scalar& y); + #endif // end EIGEN_PARSED_BY_DOXYGEN + + /** \brief Constructs an initialized 3D vector with given coefficients + * \sa Matrix(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&...) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z) + { + Base::_check_template_params(); + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3) + m_storage.data()[0] = x; + m_storage.data()[1] = y; + m_storage.data()[2] = z; + } + /** \brief Constructs an initialized 4D vector with given coefficients + * \sa Matrix(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&...) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w) + { + Base::_check_template_params(); + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4) + m_storage.data()[0] = x; + m_storage.data()[1] = y; + m_storage.data()[2] = z; + m_storage.data()[3] = w; + } + + + /** \brief Copy constructor */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other) + { } + + /** \brief Copy constructor for generic expressions. + * \sa MatrixBase::operator=(const EigenBase&) + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Matrix(const EigenBase &other) + : Base(other.derived()) + { } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const EIGEN_NOEXCEPT { return 1; } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); } + + /////////// Geometry module /////////// + + template + EIGEN_DEVICE_FUNC + explicit Matrix(const RotationBase& r); + template + EIGEN_DEVICE_FUNC + Matrix& operator=(const RotationBase& r); + + // allow to extend Matrix outside Eigen + #ifdef EIGEN_MATRIX_PLUGIN + #include EIGEN_MATRIX_PLUGIN + #endif + + protected: + template + friend struct internal::conservative_resize_like_impl; + + using Base::m_storage; +}; + +/** \defgroup matrixtypedefs Global matrix typedefs + * + * \ingroup Core_Module + * + * %Eigen defines several typedef shortcuts for most common matrix and vector types. + * + * The general patterns are the following: + * + * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, + * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd + * for complex double. + * + * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats. + * + * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is + * a fixed-size vector of 4 complex floats. + * + * With \cpp11, template alias are also defined for common sizes. + * They follow the same pattern as above except that the scalar type suffix is replaced by a + * template parameter, i.e.: + * - `MatrixSize` where `Size` can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size. + * - `MatrixXSize` and `MatrixSizeX` where `Size` can be \c 2,\c 3,\c 4 for hybrid dynamic/fixed matrices. + * - `VectorSize` and `RowVectorSize` for column and row vectors. + * + * With \cpp11, you can also use fully generic column and row vector types: `Vector` and `RowVector`. + * + * \sa class Matrix + */ + +#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ +/** \ingroup matrixtypedefs */ \ +typedef Matrix Matrix##SizeSuffix##TypeSuffix; \ +/** \ingroup matrixtypedefs */ \ +typedef Matrix Vector##SizeSuffix##TypeSuffix; \ +/** \ingroup matrixtypedefs */ \ +typedef Matrix RowVector##SizeSuffix##TypeSuffix; + +#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ +/** \ingroup matrixtypedefs */ \ +typedef Matrix Matrix##Size##X##TypeSuffix; \ +/** \ingroup matrixtypedefs */ \ +typedef Matrix Matrix##X##Size##TypeSuffix; + +#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ +EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \ +EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \ +EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \ +EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ +EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ +EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ +EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4) + +EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i) +EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f) +EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d) +EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex, cf) +EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex, cd) + +#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES +#undef EIGEN_MAKE_TYPEDEFS +#undef EIGEN_MAKE_FIXED_TYPEDEFS + +#if EIGEN_HAS_CXX11 + +#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix) \ +/** \ingroup matrixtypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Matrix##SizeSuffix = Matrix; \ +/** \ingroup matrixtypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Vector##SizeSuffix = Matrix; \ +/** \ingroup matrixtypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using RowVector##SizeSuffix = Matrix; + +#define EIGEN_MAKE_FIXED_TYPEDEFS(Size) \ +/** \ingroup matrixtypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Matrix##Size##X = Matrix; \ +/** \ingroup matrixtypedefs */ \ +/** \brief \cpp11 */ \ +template \ +using Matrix##X##Size = Matrix; + +EIGEN_MAKE_TYPEDEFS(2, 2) +EIGEN_MAKE_TYPEDEFS(3, 3) +EIGEN_MAKE_TYPEDEFS(4, 4) +EIGEN_MAKE_TYPEDEFS(Dynamic, X) +EIGEN_MAKE_FIXED_TYPEDEFS(2) +EIGEN_MAKE_FIXED_TYPEDEFS(3) +EIGEN_MAKE_FIXED_TYPEDEFS(4) + +/** \ingroup matrixtypedefs + * \brief \cpp11 */ +template +using Vector = Matrix; + +/** \ingroup matrixtypedefs + * \brief \cpp11 */ +template +using RowVector = Matrix; + +#undef EIGEN_MAKE_TYPEDEFS +#undef EIGEN_MAKE_FIXED_TYPEDEFS + +#endif // EIGEN_HAS_CXX11 + +} // end namespace Eigen + +#endif // EIGEN_MATRIX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/MatrixBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MatrixBase.h new file mode 100644 index 00000000..45c3a596 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/MatrixBase.h @@ -0,0 +1,547 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2009 Benoit Jacob +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATRIXBASE_H +#define EIGEN_MATRIXBASE_H + +namespace Eigen { + +/** \class MatrixBase + * \ingroup Core_Module + * + * \brief Base class for all dense matrices, vectors, and expressions + * + * This class is the base that is inherited by all matrix, vector, and related expression + * types. Most of the Eigen API is contained in this class, and its base classes. Other important + * classes for the Eigen API are Matrix, and VectorwiseOp. + * + * Note that some methods are defined in other modules such as the \ref LU_Module LU module + * for all functions related to matrix inversions. + * + * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc. + * + * When writing a function taking Eigen objects as argument, if you want your function + * to take as argument any matrix, vector, or expression, just let it take a + * MatrixBase argument. As an example, here is a function printFirstRow which, given + * a matrix, vector, or expression \a x, prints the first row of \a x. + * + * \code + template + void printFirstRow(const Eigen::MatrixBase& x) + { + cout << x.row(0) << endl; + } + * \endcode + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN. + * + * \sa \blank \ref TopicClassHierarchy + */ +template class MatrixBase + : public DenseBase +{ + public: +#ifndef EIGEN_PARSED_BY_DOXYGEN + typedef MatrixBase StorageBaseType; + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::StorageIndex StorageIndex; + typedef typename internal::traits::Scalar Scalar; + typedef typename internal::packet_traits::type PacketScalar; + typedef typename NumTraits::Real RealScalar; + + typedef DenseBase Base; + using Base::RowsAtCompileTime; + using Base::ColsAtCompileTime; + using Base::SizeAtCompileTime; + using Base::MaxRowsAtCompileTime; + using Base::MaxColsAtCompileTime; + using Base::MaxSizeAtCompileTime; + using Base::IsVectorAtCompileTime; + using Base::Flags; + + using Base::derived; + using Base::const_cast_derived; + using Base::rows; + using Base::cols; + using Base::size; + using Base::coeff; + using Base::coeffRef; + using Base::lazyAssign; + using Base::eval; + using Base::operator-; + using Base::operator+=; + using Base::operator-=; + using Base::operator*=; + using Base::operator/=; + + typedef typename Base::CoeffReturnType CoeffReturnType; + typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType; + typedef typename Base::RowXpr RowXpr; + typedef typename Base::ColXpr ColXpr; +#endif // not EIGEN_PARSED_BY_DOXYGEN + + + +#ifndef EIGEN_PARSED_BY_DOXYGEN + /** type of the equivalent square matrix */ + typedef Matrix SquareMatrixType; +#endif // not EIGEN_PARSED_BY_DOXYGEN + + /** \returns the size of the main diagonal, which is min(rows(),cols()). + * \sa rows(), cols(), SizeAtCompileTime. */ + EIGEN_DEVICE_FUNC + inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); } + + typedef typename Base::PlainObject PlainObject; + +#ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal Represents a matrix with all coefficients equal to one another*/ + typedef CwiseNullaryOp,PlainObject> ConstantReturnType; + /** \internal the return type of MatrixBase::adjoint() */ + typedef typename internal::conditional::IsComplex, + CwiseUnaryOp, ConstTransposeReturnType>, + ConstTransposeReturnType + >::type AdjointReturnType; + /** \internal Return type of eigenvalues() */ + typedef Matrix, internal::traits::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType; + /** \internal the return type of identity */ + typedef CwiseNullaryOp,PlainObject> IdentityReturnType; + /** \internal the return type of unit vectors */ + typedef Block, SquareMatrixType>, + internal::traits::RowsAtCompileTime, + internal::traits::ColsAtCompileTime> BasisReturnType; +#endif // not EIGEN_PARSED_BY_DOXYGEN + +#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase +#define EIGEN_DOC_UNARY_ADDONS(X,Y) +# include "../plugins/CommonCwiseBinaryOps.h" +# include "../plugins/MatrixCwiseUnaryOps.h" +# include "../plugins/MatrixCwiseBinaryOps.h" +# ifdef EIGEN_MATRIXBASE_PLUGIN +# include EIGEN_MATRIXBASE_PLUGIN +# endif +#undef EIGEN_CURRENT_STORAGE_BASE_CLASS +#undef EIGEN_DOC_UNARY_ADDONS + + /** Special case of the template operator=, in order to prevent the compiler + * from generating a default operator= (issue hit with g++ 4.1) + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const MatrixBase& other); + + // We cannot inherit here via Base::operator= since it is causing + // trouble with MSVC. + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator=(const DenseBase& other); + + template + EIGEN_DEVICE_FUNC + Derived& operator=(const EigenBase& other); + + template + EIGEN_DEVICE_FUNC + Derived& operator=(const ReturnByValue& other); + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator+=(const MatrixBase& other); + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator-=(const MatrixBase& other); + + template + EIGEN_DEVICE_FUNC + const Product + operator*(const MatrixBase &other) const; + + template + EIGEN_DEVICE_FUNC + const Product + lazyProduct(const MatrixBase &other) const; + + template + Derived& operator*=(const EigenBase& other); + + template + void applyOnTheLeft(const EigenBase& other); + + template + void applyOnTheRight(const EigenBase& other); + + template + EIGEN_DEVICE_FUNC + const Product + operator*(const DiagonalBase &diagonal) const; + + template + EIGEN_DEVICE_FUNC + typename ScalarBinaryOpTraits::Scalar,typename internal::traits::Scalar>::ReturnType + dot(const MatrixBase& other) const; + + EIGEN_DEVICE_FUNC RealScalar squaredNorm() const; + EIGEN_DEVICE_FUNC RealScalar norm() const; + RealScalar stableNorm() const; + RealScalar blueNorm() const; + RealScalar hypotNorm() const; + EIGEN_DEVICE_FUNC const PlainObject normalized() const; + EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const; + EIGEN_DEVICE_FUNC void normalize(); + EIGEN_DEVICE_FUNC void stableNormalize(); + + EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const; + EIGEN_DEVICE_FUNC void adjointInPlace(); + + typedef Diagonal DiagonalReturnType; + EIGEN_DEVICE_FUNC + DiagonalReturnType diagonal(); + + typedef typename internal::add_const >::type ConstDiagonalReturnType; + EIGEN_DEVICE_FUNC + ConstDiagonalReturnType diagonal() const; + + template struct DiagonalIndexReturnType { typedef Diagonal Type; }; + template struct ConstDiagonalIndexReturnType { typedef const Diagonal Type; }; + + template + EIGEN_DEVICE_FUNC + typename DiagonalIndexReturnType::Type diagonal(); + + template + EIGEN_DEVICE_FUNC + typename ConstDiagonalIndexReturnType::Type diagonal() const; + + typedef Diagonal DiagonalDynamicIndexReturnType; + typedef typename internal::add_const >::type ConstDiagonalDynamicIndexReturnType; + + EIGEN_DEVICE_FUNC + DiagonalDynamicIndexReturnType diagonal(Index index); + EIGEN_DEVICE_FUNC + ConstDiagonalDynamicIndexReturnType diagonal(Index index) const; + + template struct TriangularViewReturnType { typedef TriangularView Type; }; + template struct ConstTriangularViewReturnType { typedef const TriangularView Type; }; + + template + EIGEN_DEVICE_FUNC + typename TriangularViewReturnType::Type triangularView(); + template + EIGEN_DEVICE_FUNC + typename ConstTriangularViewReturnType::Type triangularView() const; + + template struct SelfAdjointViewReturnType { typedef SelfAdjointView Type; }; + template struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView Type; }; + + template + EIGEN_DEVICE_FUNC + typename SelfAdjointViewReturnType::Type selfadjointView(); + template + EIGEN_DEVICE_FUNC + typename ConstSelfAdjointViewReturnType::Type selfadjointView() const; + + const SparseView sparseView(const Scalar& m_reference = Scalar(0), + const typename NumTraits::Real& m_epsilon = NumTraits::dummy_precision()) const; + EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(); + EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols); + EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i); + EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i); + EIGEN_DEVICE_FUNC static const BasisReturnType UnitX(); + EIGEN_DEVICE_FUNC static const BasisReturnType UnitY(); + EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ(); + EIGEN_DEVICE_FUNC static const BasisReturnType UnitW(); + + EIGEN_DEVICE_FUNC + const DiagonalWrapper asDiagonal() const; + const PermutationWrapper asPermutation() const; + + EIGEN_DEVICE_FUNC + Derived& setIdentity(); + EIGEN_DEVICE_FUNC + Derived& setIdentity(Index rows, Index cols); + EIGEN_DEVICE_FUNC Derived& setUnit(Index i); + EIGEN_DEVICE_FUNC Derived& setUnit(Index newSize, Index i); + + bool isIdentity(const RealScalar& prec = NumTraits::dummy_precision()) const; + bool isDiagonal(const RealScalar& prec = NumTraits::dummy_precision()) const; + + bool isUpperTriangular(const RealScalar& prec = NumTraits::dummy_precision()) const; + bool isLowerTriangular(const RealScalar& prec = NumTraits::dummy_precision()) const; + + template + bool isOrthogonal(const MatrixBase& other, + const RealScalar& prec = NumTraits::dummy_precision()) const; + bool isUnitary(const RealScalar& prec = NumTraits::dummy_precision()) const; + + /** \returns true if each coefficients of \c *this and \a other are all exactly equal. + * \warning When using floating point scalar values you probably should rather use a + * fuzzy comparison such as isApprox() + * \sa isApprox(), operator!= */ + template + EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase& other) const + { return cwiseEqual(other).all(); } + + /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other. + * \warning When using floating point scalar values you probably should rather use a + * fuzzy comparison such as isApprox() + * \sa isApprox(), operator== */ + template + EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase& other) const + { return cwiseNotEqual(other).any(); } + + NoAlias EIGEN_DEVICE_FUNC noalias(); + + // TODO forceAlignedAccess is temporarily disabled + // Need to find a nicer workaround. + inline const Derived& forceAlignedAccess() const { return derived(); } + inline Derived& forceAlignedAccess() { return derived(); } + template inline const Derived& forceAlignedAccessIf() const { return derived(); } + template inline Derived& forceAlignedAccessIf() { return derived(); } + + EIGEN_DEVICE_FUNC Scalar trace() const; + + template EIGEN_DEVICE_FUNC RealScalar lpNorm() const; + + EIGEN_DEVICE_FUNC MatrixBase& matrix() { return *this; } + EIGEN_DEVICE_FUNC const MatrixBase& matrix() const { return *this; } + + /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix + * \sa ArrayBase::matrix() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper array() { return ArrayWrapper(derived()); } + /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix + * \sa ArrayBase::matrix() */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper array() const { return ArrayWrapper(derived()); } + +/////////// LU module /////////// + + inline const FullPivLU fullPivLu() const; + inline const PartialPivLU partialPivLu() const; + + inline const PartialPivLU lu() const; + + EIGEN_DEVICE_FUNC + inline const Inverse inverse() const; + + template + inline void computeInverseAndDetWithCheck( + ResultType& inverse, + typename ResultType::Scalar& determinant, + bool& invertible, + const RealScalar& absDeterminantThreshold = NumTraits::dummy_precision() + ) const; + + template + inline void computeInverseWithCheck( + ResultType& inverse, + bool& invertible, + const RealScalar& absDeterminantThreshold = NumTraits::dummy_precision() + ) const; + + EIGEN_DEVICE_FUNC + Scalar determinant() const; + +/////////// Cholesky module /////////// + + inline const LLT llt() const; + inline const LDLT ldlt() const; + +/////////// QR module /////////// + + inline const HouseholderQR householderQr() const; + inline const ColPivHouseholderQR colPivHouseholderQr() const; + inline const FullPivHouseholderQR fullPivHouseholderQr() const; + inline const CompleteOrthogonalDecomposition completeOrthogonalDecomposition() const; + +/////////// Eigenvalues module /////////// + + inline EigenvaluesReturnType eigenvalues() const; + inline RealScalar operatorNorm() const; + +/////////// SVD module /////////// + + inline JacobiSVD jacobiSvd(unsigned int computationOptions = 0) const; + inline BDCSVD bdcSvd(unsigned int computationOptions = 0) const; + +/////////// Geometry module /////////// + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /// \internal helper struct to form the return type of the cross product + template struct cross_product_return_type { + typedef typename ScalarBinaryOpTraits::Scalar,typename internal::traits::Scalar>::ReturnType Scalar; + typedef Matrix type; + }; + #endif // EIGEN_PARSED_BY_DOXYGEN + template + EIGEN_DEVICE_FUNC +#ifndef EIGEN_PARSED_BY_DOXYGEN + inline typename cross_product_return_type::type +#else + inline PlainObject +#endif + cross(const MatrixBase& other) const; + + template + EIGEN_DEVICE_FUNC + inline PlainObject cross3(const MatrixBase& other) const; + + EIGEN_DEVICE_FUNC + inline PlainObject unitOrthogonal(void) const; + + EIGEN_DEVICE_FUNC + inline Matrix eulerAngles(Index a0, Index a1, Index a2) const; + + // put this as separate enum value to work around possible GCC 4.3 bug (?) + enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical) + : ColsAtCompileTime==1 ? Vertical : Horizontal }; + typedef Homogeneous HomogeneousReturnType; + EIGEN_DEVICE_FUNC + inline HomogeneousReturnType homogeneous() const; + + enum { + SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1 + }; + typedef Block::ColsAtCompileTime==1 ? SizeMinusOne : 1, + internal::traits::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne; + typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType; + EIGEN_DEVICE_FUNC + inline const HNormalizedReturnType hnormalized() const; + +////////// Householder module /////////// + + EIGEN_DEVICE_FUNC + void makeHouseholderInPlace(Scalar& tau, RealScalar& beta); + template + EIGEN_DEVICE_FUNC + void makeHouseholder(EssentialPart& essential, + Scalar& tau, RealScalar& beta) const; + template + EIGEN_DEVICE_FUNC + void applyHouseholderOnTheLeft(const EssentialPart& essential, + const Scalar& tau, + Scalar* workspace); + template + EIGEN_DEVICE_FUNC + void applyHouseholderOnTheRight(const EssentialPart& essential, + const Scalar& tau, + Scalar* workspace); + +///////// Jacobi module ///////// + + template + EIGEN_DEVICE_FUNC + void applyOnTheLeft(Index p, Index q, const JacobiRotation& j); + template + EIGEN_DEVICE_FUNC + void applyOnTheRight(Index p, Index q, const JacobiRotation& j); + +///////// SparseCore module ///////// + + template + EIGEN_STRONG_INLINE const typename SparseMatrixBase::template CwiseProductDenseReturnType::Type + cwiseProduct(const SparseMatrixBase &other) const + { + return other.cwiseProduct(derived()); + } + +///////// MatrixFunctions module ///////// + + typedef typename internal::stem_function::type StemFunction; +#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \ + /** \returns an expression of the matrix Description of \c *this. \brief This function requires the unsupported MatrixFunctions module. To compute the coefficient-wise Description use ArrayBase::##Name . */ \ + const ReturnType Name() const; +#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \ + /** \returns an expression of the matrix Description of \c *this. \brief This function requires the unsupported MatrixFunctions module. To compute the coefficient-wise Description use ArrayBase::##Name . */ \ + const ReturnType Name(Argument) const; + + EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential) + /** \brief Helper function for the unsupported MatrixFunctions module.*/ + const MatrixFunctionReturnValue matrixFunction(StemFunction f) const; + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine) + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine) +#if EIGEN_HAS_CXX11_MATH + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine) + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine) + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine) +#endif + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine) + EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine) + EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root) + EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm) + EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue, pow, power to \c p, const RealScalar& p) + EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex& p) + + protected: + EIGEN_DEFAULT_COPY_CONSTRUCTOR(MatrixBase) + EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MatrixBase) + + private: + EIGEN_DEVICE_FUNC explicit MatrixBase(int); + EIGEN_DEVICE_FUNC MatrixBase(int,int); + template EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase&); + protected: + // mixing arrays and matrices is not legal + template Derived& operator+=(const ArrayBase& ) + {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} + // mixing arrays and matrices is not legal + template Derived& operator-=(const ArrayBase& ) + {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} +}; + + +/*************************************************************************** +* Implementation of matrix base methods +***************************************************************************/ + +/** replaces \c *this by \c *this * \a other. + * + * \returns a reference to \c *this + * + * Example: \include MatrixBase_applyOnTheRight.cpp + * Output: \verbinclude MatrixBase_applyOnTheRight.out + */ +template +template +inline Derived& +MatrixBase::operator*=(const EigenBase &other) +{ + other.derived().applyThisOnTheRight(derived()); + return derived(); +} + +/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=(). + * + * Example: \include MatrixBase_applyOnTheRight.cpp + * Output: \verbinclude MatrixBase_applyOnTheRight.out + */ +template +template +inline void MatrixBase::applyOnTheRight(const EigenBase &other) +{ + other.derived().applyThisOnTheRight(derived()); +} + +/** replaces \c *this by \a other * \c *this. + * + * Example: \include MatrixBase_applyOnTheLeft.cpp + * Output: \verbinclude MatrixBase_applyOnTheLeft.out + */ +template +template +inline void MatrixBase::applyOnTheLeft(const EigenBase &other) +{ + other.derived().applyThisOnTheLeft(derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_MATRIXBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/NestByValue.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NestByValue.h new file mode 100644 index 00000000..b4275768 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NestByValue.h @@ -0,0 +1,85 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_NESTBYVALUE_H +#define EIGEN_NESTBYVALUE_H + +namespace Eigen { + +namespace internal { +template +struct traits > : public traits +{ + enum { + Flags = traits::Flags & ~NestByRefBit + }; +}; +} + +/** \class NestByValue + * \ingroup Core_Module + * + * \brief Expression which must be nested by value + * + * \tparam ExpressionType the type of the object of which we are requiring nesting-by-value + * + * This class is the return type of MatrixBase::nestByValue() + * and most of the time this is the only way it is used. + * + * \sa MatrixBase::nestByValue() + */ +template class NestByValue + : public internal::dense_xpr_base< NestByValue >::type +{ + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue) + + EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {} + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); } + + EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; } + + EIGEN_DEVICE_FUNC const ExpressionType& nestedExpression() const { return m_expression; } + + protected: + const ExpressionType m_expression; +}; + +/** \returns an expression of the temporary version of *this. + */ +template +EIGEN_DEVICE_FUNC inline const NestByValue +DenseBase::nestByValue() const +{ + return NestByValue(derived()); +} + +namespace internal { + +// Evaluator of Solve -> eval into a temporary +template +struct evaluator > + : public evaluator +{ + typedef evaluator Base; + + EIGEN_DEVICE_FUNC explicit evaluator(const NestByValue& xpr) + : Base(xpr.nestedExpression()) + {} +}; +} + +} // end namespace Eigen + +#endif // EIGEN_NESTBYVALUE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/NoAlias.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NoAlias.h new file mode 100644 index 00000000..570283d9 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NoAlias.h @@ -0,0 +1,109 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_NOALIAS_H +#define EIGEN_NOALIAS_H + +namespace Eigen { + +/** \class NoAlias + * \ingroup Core_Module + * + * \brief Pseudo expression providing an operator = assuming no aliasing + * + * \tparam ExpressionType the type of the object on which to do the lazy assignment + * + * This class represents an expression with special assignment operators + * assuming no aliasing between the target expression and the source expression. + * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression. + * It is the return type of MatrixBase::noalias() + * and most of the time this is the only way it is used. + * + * \sa MatrixBase::noalias() + */ +template class StorageBase> +class NoAlias +{ + public: + typedef typename ExpressionType::Scalar Scalar; + + EIGEN_DEVICE_FUNC + explicit NoAlias(ExpressionType& expression) : m_expression(expression) {} + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase& other) + { + call_assignment_no_alias(m_expression, other.derived(), internal::assign_op()); + return m_expression; + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase& other) + { + call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op()); + return m_expression; + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase& other) + { + call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op()); + return m_expression; + } + + EIGEN_DEVICE_FUNC + ExpressionType& expression() const + { + return m_expression; + } + + protected: + ExpressionType& m_expression; +}; + +/** \returns a pseudo expression of \c *this with an operator= assuming + * no aliasing between \c *this and the source expression. + * + * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag. + * Currently, even though several expressions may alias, only product + * expressions have this flag. Therefore, noalias() is only useful when + * the source expression contains a matrix product. + * + * Here are some examples where noalias is useful: + * \code + * D.noalias() = A * B; + * D.noalias() += A.transpose() * B; + * D.noalias() -= 2 * A * B.adjoint(); + * \endcode + * + * On the other hand the following example will lead to a \b wrong result: + * \code + * A.noalias() = A * B; + * \endcode + * because the result matrix A is also an operand of the matrix product. Therefore, + * there is no alternative than evaluating A * B in a temporary, that is the default + * behavior when you write: + * \code + * A = A * B; + * \endcode + * + * \sa class NoAlias + */ +template +NoAlias EIGEN_DEVICE_FUNC MatrixBase::noalias() +{ + return NoAlias(derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_NOALIAS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/NumTraits.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NumTraits.h new file mode 100644 index 00000000..72eac5a9 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/NumTraits.h @@ -0,0 +1,335 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_NUMTRAITS_H +#define EIGEN_NUMTRAITS_H + +namespace Eigen { + +namespace internal { + +// default implementation of digits10(), based on numeric_limits if specialized, +// 0 for integer types, and log10(epsilon()) otherwise. +template< typename T, + bool use_numeric_limits = std::numeric_limits::is_specialized, + bool is_integer = NumTraits::IsInteger> +struct default_digits10_impl +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { return std::numeric_limits::digits10; } +}; + +template +struct default_digits10_impl // Floating point +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { + using std::log10; + using std::ceil; + typedef typename NumTraits::Real Real; + return int(ceil(-log10(NumTraits::epsilon()))); + } +}; + +template +struct default_digits10_impl // Integer +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { return 0; } +}; + + +// default implementation of digits(), based on numeric_limits if specialized, +// 0 for integer types, and log2(epsilon()) otherwise. +template< typename T, + bool use_numeric_limits = std::numeric_limits::is_specialized, + bool is_integer = NumTraits::IsInteger> +struct default_digits_impl +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { return std::numeric_limits::digits; } +}; + +template +struct default_digits_impl // Floating point +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { + using std::log; + using std::ceil; + typedef typename NumTraits::Real Real; + return int(ceil(-log(NumTraits::epsilon())/log(static_cast(2)))); + } +}; + +template +struct default_digits_impl // Integer +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static int run() { return 0; } +}; + +} // end namespace internal + +namespace numext { +/** \internal bit-wise cast without changing the underlying bit representation. */ + +// TODO: Replace by std::bit_cast (available in C++20) +template +EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) { +#if EIGEN_HAS_TYPE_TRAITS + // The behaviour of memcpy is not specified for non-trivially copyable types + EIGEN_STATIC_ASSERT(std::is_trivially_copyable::value, THIS_TYPE_IS_NOT_SUPPORTED); + EIGEN_STATIC_ASSERT(std::is_trivially_copyable::value && std::is_default_constructible::value, + THIS_TYPE_IS_NOT_SUPPORTED); +#endif + + EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED); + Tgt tgt; + EIGEN_USING_STD(memcpy) + memcpy(&tgt, &src, sizeof(Tgt)); + return tgt; +} +} // namespace numext + +/** \class NumTraits + * \ingroup Core_Module + * + * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen. + * + * \tparam T the numeric type at hand + * + * This class stores enums, typedefs and static methods giving information about a numeric type. + * + * The provided data consists of: + * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real, + * then \c Real is just a typedef to \a T. If \a T is \c std::complex then \c Real + * is a typedef to \a U. + * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values, + * such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives + * \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to + * take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is + * only intended as a helper for code that needs to explicitly promote types. + * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex, Literal is defined as \c U. + * Of course, this type must be fully compatible with \a T. In doubt, just use \a T here. + * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what + * this means, just use \a T here. + * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex + * type, and to 0 otherwise. + * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int, + * and to \c 0 otherwise. + * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed + * to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers. + * Stay vague here. No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost. + * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned. + * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must + * be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise. + * \li An epsilon() function which, unlike std::numeric_limits::epsilon(), + * it returns a \a Real instead of a \a T. + * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default + * value by the fuzzy comparison operators. + * \li highest() and lowest() functions returning the highest and lowest possible values respectively. + * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is + * the analogue of std::numeric_limits::digits + * which is used as the default implementation if specialized. + * \li digits10() function returning the number of decimal digits that can be represented without change. This is + * the analogue of std::numeric_limits::digits10 + * which is used as the default implementation if specialized. + * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively, + * such that the radix raised to the power exponent-1 is a normalized floating-point number. These are equivalent to + * std::numeric_limits::min_exponent/ + * std::numeric_limits::max_exponent. + * \li infinity() function returning a representation of positive infinity, if available. + * \li quiet_NaN function returning a non-signaling "not-a-number", if available. + */ + +template struct GenericNumTraits +{ + enum { + IsInteger = std::numeric_limits::is_integer, + IsSigned = std::numeric_limits::is_signed, + IsComplex = 0, + RequireInitialization = internal::is_arithmetic::value ? 0 : 1, + ReadCost = 1, + AddCost = 1, + MulCost = 1 + }; + + typedef T Real; + typedef typename internal::conditional< + IsInteger, + typename internal::conditional::type, + T + >::type NonInteger; + typedef T Nested; + typedef T Literal; + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline Real epsilon() + { + return numext::numeric_limits::epsilon(); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline int digits10() + { + return internal::default_digits10_impl::run(); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline int digits() + { + return internal::default_digits_impl::run(); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline int min_exponent() + { + return numext::numeric_limits::min_exponent; + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline int max_exponent() + { + return numext::numeric_limits::max_exponent; + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline Real dummy_precision() + { + // make sure to override this for floating-point types + return Real(0); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline T highest() { + return (numext::numeric_limits::max)(); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline T lowest() { + return IsInteger ? (numext::numeric_limits::min)() + : static_cast(-(numext::numeric_limits::max)()); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline T infinity() { + return numext::numeric_limits::infinity(); + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline T quiet_NaN() { + return numext::numeric_limits::quiet_NaN(); + } +}; + +template struct NumTraits : GenericNumTraits +{}; + +template<> struct NumTraits + : GenericNumTraits +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline float dummy_precision() { return 1e-5f; } +}; + +template<> struct NumTraits : GenericNumTraits +{ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline double dummy_precision() { return 1e-12; } +}; + +template<> struct NumTraits + : GenericNumTraits +{ + EIGEN_CONSTEXPR + static inline long double dummy_precision() { return 1e-15l; } +}; + +template struct NumTraits > + : GenericNumTraits > +{ + typedef _Real Real; + typedef typename NumTraits<_Real>::Literal Literal; + enum { + IsComplex = 1, + RequireInitialization = NumTraits<_Real>::RequireInitialization, + ReadCost = 2 * NumTraits<_Real>::ReadCost, + AddCost = 2 * NumTraits::AddCost, + MulCost = 4 * NumTraits::MulCost + 2 * NumTraits::AddCost + }; + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline Real epsilon() { return NumTraits::epsilon(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline Real dummy_precision() { return NumTraits::dummy_precision(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline int digits10() { return NumTraits::digits10(); } +}; + +template +struct NumTraits > +{ + typedef Array ArrayType; + typedef typename NumTraits::Real RealScalar; + typedef Array Real; + typedef typename NumTraits::NonInteger NonIntegerScalar; + typedef Array NonInteger; + typedef ArrayType & Nested; + typedef typename NumTraits::Literal Literal; + + enum { + IsComplex = NumTraits::IsComplex, + IsInteger = NumTraits::IsInteger, + IsSigned = NumTraits::IsSigned, + RequireInitialization = 1, + ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits::ReadCost), + AddCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits::AddCost), + MulCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits::MulCost) + }; + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline RealScalar epsilon() { return NumTraits::epsilon(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + static inline RealScalar dummy_precision() { return NumTraits::dummy_precision(); } + + EIGEN_CONSTEXPR + static inline int digits10() { return NumTraits::digits10(); } +}; + +template<> struct NumTraits + : GenericNumTraits +{ + enum { + RequireInitialization = 1, + ReadCost = HugeCost, + AddCost = HugeCost, + MulCost = HugeCost + }; + + EIGEN_CONSTEXPR + static inline int digits10() { return 0; } + +private: + static inline std::string epsilon(); + static inline std::string dummy_precision(); + static inline std::string lowest(); + static inline std::string highest(); + static inline std::string infinity(); + static inline std::string quiet_NaN(); +}; + +// Empty specialization for void to allow template specialization based on NumTraits::Real with T==void and SFINAE. +template<> struct NumTraits {}; + +template<> struct NumTraits : GenericNumTraits {}; + +} // end namespace Eigen + +#endif // EIGEN_NUMTRAITS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/PartialReduxEvaluator.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PartialReduxEvaluator.h new file mode 100644 index 00000000..29abf35b --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PartialReduxEvaluator.h @@ -0,0 +1,232 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2011-2018 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_PARTIALREDUX_H +#define EIGEN_PARTIALREDUX_H + +namespace Eigen { + +namespace internal { + + +/*************************************************************************** +* +* This file provides evaluators for partial reductions. +* There are two modes: +* +* - scalar path: simply calls the respective function on the column or row. +* -> nothing special here, all the tricky part is handled by the return +* types of VectorwiseOp's members. They embed the functor calling the +* respective DenseBase's member function. +* +* - vectorized path: implements a packet-wise reductions followed by +* some (optional) processing of the outcome, e.g., division by n for mean. +* +* For the vectorized path let's observe that the packet-size and outer-unrolling +* are both decided by the assignement logic. So all we have to do is to decide +* on the inner unrolling. +* +* For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h, +* but be need to be careful to specify correct increment. +* +***************************************************************************/ + + +/* logic deciding a strategy for unrolling of vectorized paths */ +template +struct packetwise_redux_traits +{ + enum { + OuterSize = int(Evaluator::IsRowMajor) ? Evaluator::RowsAtCompileTime : Evaluator::ColsAtCompileTime, + Cost = OuterSize == Dynamic ? HugeCost + : OuterSize * Evaluator::CoeffReadCost + (OuterSize-1) * functor_traits::Cost, + Unrolling = Cost <= EIGEN_UNROLLING_LIMIT ? CompleteUnrolling : NoUnrolling + }; + +}; + +/* Value to be returned when size==0 , by default let's return 0 */ +template +EIGEN_DEVICE_FUNC +PacketType packetwise_redux_empty_value(const Func& ) { return pset1(0); } + +/* For products the default is 1 */ +template +EIGEN_DEVICE_FUNC +PacketType packetwise_redux_empty_value(const scalar_product_op& ) { return pset1(1); } + +/* Perform the actual reduction */ +template::Unrolling +> +struct packetwise_redux_impl; + +/* Perform the actual reduction with unrolling */ +template +struct packetwise_redux_impl +{ + typedef redux_novec_unroller Base; + typedef typename Evaluator::Scalar Scalar; + + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE + PacketType run(const Evaluator &eval, const Func& func, Index /*size*/) + { + return redux_vec_unroller::OuterSize>::template run(eval,func); + } +}; + +/* Add a specialization of redux_vec_unroller for size==0 at compiletime. + * This specialization is not required for general reductions, which is + * why it is defined here. + */ +template +struct redux_vec_unroller +{ + template + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE PacketType run(const Evaluator &, const Func& f) + { + return packetwise_redux_empty_value(f); + } +}; + +/* Perform the actual reduction for dynamic sizes */ +template +struct packetwise_redux_impl +{ + typedef typename Evaluator::Scalar Scalar; + typedef typename redux_traits::PacketType PacketScalar; + + template + EIGEN_DEVICE_FUNC + static PacketType run(const Evaluator &eval, const Func& func, Index size) + { + if(size==0) + return packetwise_redux_empty_value(func); + + const Index size4 = (size-1)&(~3); + PacketType p = eval.template packetByOuterInner(0,0); + Index i = 1; + // This loop is optimized for instruction pipelining: + // - each iteration generates two independent instructions + // - thanks to branch prediction and out-of-order execution we have independent instructions across loops + for(; i(i+0,0),eval.template packetByOuterInner(i+1,0)), + func.packetOp(eval.template packetByOuterInner(i+2,0),eval.template packetByOuterInner(i+3,0)))); + for(; i(i,0)); + return p; + } +}; + +template< typename ArgType, typename MemberOp, int Direction> +struct evaluator > + : evaluator_base > +{ + typedef PartialReduxExpr XprType; + typedef typename internal::nested_eval::type ArgTypeNested; + typedef typename internal::add_const_on_value_type::type ConstArgTypeNested; + typedef typename internal::remove_all::type ArgTypeNestedCleaned; + typedef typename ArgType::Scalar InputScalar; + typedef typename XprType::Scalar Scalar; + enum { + TraversalSize = Direction==int(Vertical) ? int(ArgType::RowsAtCompileTime) : int(ArgType::ColsAtCompileTime) + }; + typedef typename MemberOp::template Cost CostOpType; + enum { + CoeffReadCost = TraversalSize==Dynamic ? HugeCost + : TraversalSize==0 ? 1 + : int(TraversalSize) * int(evaluator::CoeffReadCost) + int(CostOpType::value), + + _ArgFlags = evaluator::Flags, + + _Vectorizable = bool(int(_ArgFlags)&PacketAccessBit) + && bool(MemberOp::Vectorizable) + && (Direction==int(Vertical) ? bool(_ArgFlags&RowMajorBit) : (_ArgFlags&RowMajorBit)==0) + && (TraversalSize!=0), + + Flags = (traits::Flags&RowMajorBit) + | (evaluator::Flags&(HereditaryBits&(~RowMajorBit))) + | (_Vectorizable ? PacketAccessBit : 0) + | LinearAccessBit, + + Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized + }; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType xpr) + : m_arg(xpr.nestedExpression()), m_functor(xpr.functor()) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(TraversalSize==Dynamic ? HugeCost : (TraversalSize==0 ? 1 : int(CostOpType::value))); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Scalar coeff(Index i, Index j) const + { + return coeff(Direction==Vertical ? j : i); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Scalar coeff(Index index) const + { + return m_functor(m_arg.template subVector(index)); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketType packet(Index i, Index j) const + { + return packet(Direction==Vertical ? j : i); + } + + template + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC + PacketType packet(Index idx) const + { + enum { PacketSize = internal::unpacket_traits::size }; + typedef Block PanelType; + + PanelType panel(m_arg, + Direction==Vertical ? 0 : idx, + Direction==Vertical ? idx : 0, + Direction==Vertical ? m_arg.rows() : Index(PacketSize), + Direction==Vertical ? Index(PacketSize) : m_arg.cols()); + + // FIXME + // See bug 1612, currently if PacketSize==1 (i.e. complex with 128bits registers) then the storage-order of panel get reversed + // and methods like packetByOuterInner do not make sense anymore in this context. + // So let's just by pass "vectorization" in this case: + if(PacketSize==1) + return internal::pset1(coeff(idx)); + + typedef typename internal::redux_evaluator PanelEvaluator; + PanelEvaluator panel_eval(panel); + typedef typename MemberOp::BinaryOp BinaryOp; + PacketType p = internal::packetwise_redux_impl::template run(panel_eval,m_functor.binaryFunc(),m_arg.outerSize()); + return p; + } + +protected: + ConstArgTypeNested m_arg; + const MemberOp m_functor; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_PARTIALREDUX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/PermutationMatrix.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PermutationMatrix.h new file mode 100644 index 00000000..69401bf4 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PermutationMatrix.h @@ -0,0 +1,605 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Benoit Jacob +// Copyright (C) 2009-2015 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_PERMUTATIONMATRIX_H +#define EIGEN_PERMUTATIONMATRIX_H + +namespace Eigen { + +namespace internal { + +enum PermPermProduct_t {PermPermProduct}; + +} // end namespace internal + +/** \class PermutationBase + * \ingroup Core_Module + * + * \brief Base class for permutations + * + * \tparam Derived the derived class + * + * This class is the base class for all expressions representing a permutation matrix, + * internally stored as a vector of integers. + * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix + * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have: + * \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f] + * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have: + * \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f] + * + * Permutation matrices are square and invertible. + * + * Notice that in addition to the member functions and operators listed here, there also are non-member + * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase) + * on either side. + * + * \sa class PermutationMatrix, class PermutationWrapper + */ +template +class PermutationBase : public EigenBase +{ + typedef internal::traits Traits; + typedef EigenBase Base; + public: + + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename Traits::IndicesType IndicesType; + enum { + Flags = Traits::Flags, + RowsAtCompileTime = Traits::RowsAtCompileTime, + ColsAtCompileTime = Traits::ColsAtCompileTime, + MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = Traits::MaxColsAtCompileTime + }; + typedef typename Traits::StorageIndex StorageIndex; + typedef Matrix + DenseMatrixType; + typedef PermutationMatrix + PlainPermutationType; + typedef PlainPermutationType PlainObject; + using Base::derived; + typedef Inverse InverseReturnType; + typedef void Scalar; + #endif + + /** Copies the other permutation into *this */ + template + Derived& operator=(const PermutationBase& other) + { + indices() = other.indices(); + return derived(); + } + + /** Assignment from the Transpositions \a tr */ + template + Derived& operator=(const TranspositionsBase& tr) + { + setIdentity(tr.size()); + for(Index k=size()-1; k>=0; --k) + applyTranspositionOnTheRight(k,tr.coeff(k)); + return derived(); + } + + /** \returns the number of rows */ + inline EIGEN_DEVICE_FUNC Index rows() const { return Index(indices().size()); } + + /** \returns the number of columns */ + inline EIGEN_DEVICE_FUNC Index cols() const { return Index(indices().size()); } + + /** \returns the size of a side of the respective square matrix, i.e., the number of indices */ + inline EIGEN_DEVICE_FUNC Index size() const { return Index(indices().size()); } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + void evalTo(MatrixBase& other) const + { + other.setZero(); + for (Index i=0; i=0 && j>=0 && i=0 && j>=0 && i + void assignTranspose(const PermutationBase& other) + { + for (Index i=0; i + void assignProduct(const Lhs& lhs, const Rhs& rhs) + { + eigen_assert(lhs.cols() == rhs.rows()); + for (Index i=0; i + inline PlainPermutationType operator*(const PermutationBase& other) const + { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); } + + /** \returns the product of a permutation with another inverse permutation. + * + * \note \blank \note_try_to_help_rvo + */ + template + inline PlainPermutationType operator*(const InverseImpl& other) const + { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); } + + /** \returns the product of an inverse permutation with another permutation. + * + * \note \blank \note_try_to_help_rvo + */ + template friend + inline PlainPermutationType operator*(const InverseImpl& other, const PermutationBase& perm) + { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); } + + /** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation. + * + * This function is O(\c n) procedure allocating a buffer of \c n booleans. + */ + Index determinant() const + { + Index res = 1; + Index n = size(); + Matrix mask(n); + mask.fill(false); + Index r = 0; + while(r < n) + { + // search for the next seed + while(r=n) + break; + // we got one, let's follow it until we are back to the seed + Index k0 = r++; + mask.coeffRef(k0) = true; + for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k)) + { + mask.coeffRef(k) = true; + res = -res; + } + } + return res; + } + + protected: + +}; + +namespace internal { +template +struct traits > + : traits > +{ + typedef PermutationStorage StorageKind; + typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType; + typedef _StorageIndex StorageIndex; + typedef void Scalar; +}; +} + +/** \class PermutationMatrix + * \ingroup Core_Module + * + * \brief Permutation matrix + * + * \tparam SizeAtCompileTime the number of rows/cols, or Dynamic + * \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it. + * \tparam _StorageIndex the integer type of the indices + * + * This class represents a permutation matrix, internally stored as a vector of integers. + * + * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix + */ +template +class PermutationMatrix : public PermutationBase > +{ + typedef PermutationBase Base; + typedef internal::traits Traits; + public: + + typedef const PermutationMatrix& Nested; + + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename Traits::IndicesType IndicesType; + typedef typename Traits::StorageIndex StorageIndex; + #endif + + inline PermutationMatrix() + {} + + /** Constructs an uninitialized permutation matrix of given size. + */ + explicit inline PermutationMatrix(Index size) : m_indices(size) + { + eigen_internal_assert(size <= NumTraits::highest()); + } + + /** Copy constructor. */ + template + inline PermutationMatrix(const PermutationBase& other) + : m_indices(other.indices()) {} + + /** Generic constructor from expression of the indices. The indices + * array has the meaning that the permutations sends each integer i to indices[i]. + * + * \warning It is your responsibility to check that the indices array that you passes actually + * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the + * array's size. + */ + template + explicit inline PermutationMatrix(const MatrixBase& indices) : m_indices(indices) + {} + + /** Convert the Transpositions \a tr to a permutation matrix */ + template + explicit PermutationMatrix(const TranspositionsBase& tr) + : m_indices(tr.size()) + { + *this = tr; + } + + /** Copies the other permutation into *this */ + template + PermutationMatrix& operator=(const PermutationBase& other) + { + m_indices = other.indices(); + return *this; + } + + /** Assignment from the Transpositions \a tr */ + template + PermutationMatrix& operator=(const TranspositionsBase& tr) + { + return Base::operator=(tr.derived()); + } + + /** const version of indices(). */ + const IndicesType& indices() const { return m_indices; } + /** \returns a reference to the stored array representing the permutation. */ + IndicesType& indices() { return m_indices; } + + + /**** multiplication helpers to hopefully get RVO ****/ + +#ifndef EIGEN_PARSED_BY_DOXYGEN + template + PermutationMatrix(const InverseImpl& other) + : m_indices(other.derived().nestedExpression().size()) + { + eigen_internal_assert(m_indices.size() <= NumTraits::highest()); + StorageIndex end = StorageIndex(m_indices.size()); + for (StorageIndex i=0; i + PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs) + : m_indices(lhs.indices().size()) + { + Base::assignProduct(lhs,rhs); + } +#endif + + protected: + + IndicesType m_indices; +}; + + +namespace internal { +template +struct traits,_PacketAccess> > + : traits > +{ + typedef PermutationStorage StorageKind; + typedef Map, _PacketAccess> IndicesType; + typedef _StorageIndex StorageIndex; + typedef void Scalar; +}; +} + +template +class Map,_PacketAccess> + : public PermutationBase,_PacketAccess> > +{ + typedef PermutationBase Base; + typedef internal::traits Traits; + public: + + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename Traits::IndicesType IndicesType; + typedef typename IndicesType::Scalar StorageIndex; + #endif + + inline Map(const StorageIndex* indicesPtr) + : m_indices(indicesPtr) + {} + + inline Map(const StorageIndex* indicesPtr, Index size) + : m_indices(indicesPtr,size) + {} + + /** Copies the other permutation into *this */ + template + Map& operator=(const PermutationBase& other) + { return Base::operator=(other.derived()); } + + /** Assignment from the Transpositions \a tr */ + template + Map& operator=(const TranspositionsBase& tr) + { return Base::operator=(tr.derived()); } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** This is a special case of the templated operator=. Its purpose is to + * prevent a default operator= from hiding the templated operator=. + */ + Map& operator=(const Map& other) + { + m_indices = other.m_indices; + return *this; + } + #endif + + /** const version of indices(). */ + const IndicesType& indices() const { return m_indices; } + /** \returns a reference to the stored array representing the permutation. */ + IndicesType& indices() { return m_indices; } + + protected: + + IndicesType m_indices; +}; + +template class TranspositionsWrapper; +namespace internal { +template +struct traits > +{ + typedef PermutationStorage StorageKind; + typedef void Scalar; + typedef typename _IndicesType::Scalar StorageIndex; + typedef _IndicesType IndicesType; + enum { + RowsAtCompileTime = _IndicesType::SizeAtCompileTime, + ColsAtCompileTime = _IndicesType::SizeAtCompileTime, + MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime, + MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime, + Flags = 0 + }; +}; +} + +/** \class PermutationWrapper + * \ingroup Core_Module + * + * \brief Class to view a vector of integers as a permutation matrix + * + * \tparam _IndicesType the type of the vector of integer (can be any compatible expression) + * + * This class allows to view any vector expression of integers as a permutation matrix. + * + * \sa class PermutationBase, class PermutationMatrix + */ +template +class PermutationWrapper : public PermutationBase > +{ + typedef PermutationBase Base; + typedef internal::traits Traits; + public: + + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename Traits::IndicesType IndicesType; + #endif + + inline PermutationWrapper(const IndicesType& indices) + : m_indices(indices) + {} + + /** const version of indices(). */ + const typename internal::remove_all::type& + indices() const { return m_indices; } + + protected: + + typename IndicesType::Nested m_indices; +}; + + +/** \returns the matrix with the permutation applied to the columns. + */ +template +EIGEN_DEVICE_FUNC +const Product +operator*(const MatrixBase &matrix, + const PermutationBase& permutation) +{ + return Product + (matrix.derived(), permutation.derived()); +} + +/** \returns the matrix with the permutation applied to the rows. + */ +template +EIGEN_DEVICE_FUNC +const Product +operator*(const PermutationBase &permutation, + const MatrixBase& matrix) +{ + return Product + (permutation.derived(), matrix.derived()); +} + + +template +class InverseImpl + : public EigenBase > +{ + typedef typename PermutationType::PlainPermutationType PlainPermutationType; + typedef internal::traits PermTraits; + protected: + InverseImpl() {} + public: + typedef Inverse InverseType; + using EigenBase >::derived; + + #ifndef EIGEN_PARSED_BY_DOXYGEN + typedef typename PermutationType::DenseMatrixType DenseMatrixType; + enum { + RowsAtCompileTime = PermTraits::RowsAtCompileTime, + ColsAtCompileTime = PermTraits::ColsAtCompileTime, + MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime + }; + #endif + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + void evalTo(MatrixBase& other) const + { + other.setZero(); + for (Index i=0; i friend + const Product + operator*(const MatrixBase& matrix, const InverseType& trPerm) + { + return Product(matrix.derived(), trPerm.derived()); + } + + /** \returns the matrix with the inverse permutation applied to the rows. + */ + template + const Product + operator*(const MatrixBase& matrix) const + { + return Product(derived(), matrix.derived()); + } +}; + +template +const PermutationWrapper MatrixBase::asPermutation() const +{ + return derived(); +} + +namespace internal { + +template<> struct AssignmentKind { typedef EigenBase2EigenBase Kind; }; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_PERMUTATIONMATRIX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/PlainObjectBase.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PlainObjectBase.h new file mode 100644 index 00000000..e2ddbd1d --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/PlainObjectBase.h @@ -0,0 +1,1128 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2009 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_DENSESTORAGEBASE_H +#define EIGEN_DENSESTORAGEBASE_H + +#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO) +# define EIGEN_INITIALIZE_COEFFS +# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i::quiet_NaN(); +#else +# undef EIGEN_INITIALIZE_COEFFS +# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED +#endif + +namespace Eigen { + +namespace internal { + +template struct check_rows_cols_for_overflow { + template + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE void run(Index, Index) + { + } +}; + +template<> struct check_rows_cols_for_overflow { + template + EIGEN_DEVICE_FUNC + static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols) + { + // http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242 + // we assume Index is signed + Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed + bool error = (rows == 0 || cols == 0) ? false + : (rows > max_index / cols); + if (error) + throw_std_bad_alloc(); + } +}; + +template +struct conservative_resize_like_impl; + +template struct matrix_swap_impl; + +} // end namespace internal + +#ifdef EIGEN_PARSED_BY_DOXYGEN +namespace doxygen { + +// This is a workaround to doxygen not being able to understand the inheritance logic +// when it is hidden by the dense_xpr_base helper struct. +// Moreover, doxygen fails to include members that are not documented in the declaration body of +// MatrixBase if we inherits MatrixBase >, +// this is why we simply inherits MatrixBase, though this does not make sense. + +/** This class is just a workaround for Doxygen and it does not not actually exist. */ +template struct dense_xpr_base_dispatcher; +/** This class is just a workaround for Doxygen and it does not not actually exist. */ +template +struct dense_xpr_base_dispatcher > + : public MatrixBase {}; +/** This class is just a workaround for Doxygen and it does not not actually exist. */ +template +struct dense_xpr_base_dispatcher > + : public ArrayBase {}; + +} // namespace doxygen + +/** \class PlainObjectBase + * \ingroup Core_Module + * \brief %Dense storage base class for matrices and arrays. + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN. + * + * \tparam Derived is the derived type, e.g., a Matrix or Array + * + * \sa \ref TopicClassHierarchy + */ +template +class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher +#else +template +class PlainObjectBase : public internal::dense_xpr_base::type +#endif +{ + public: + enum { Options = internal::traits::Options }; + typedef typename internal::dense_xpr_base::type Base; + + typedef typename internal::traits::StorageKind StorageKind; + typedef typename internal::traits::Scalar Scalar; + + typedef typename internal::packet_traits::type PacketScalar; + typedef typename NumTraits::Real RealScalar; + typedef Derived DenseType; + + using Base::RowsAtCompileTime; + using Base::ColsAtCompileTime; + using Base::SizeAtCompileTime; + using Base::MaxRowsAtCompileTime; + using Base::MaxColsAtCompileTime; + using Base::MaxSizeAtCompileTime; + using Base::IsVectorAtCompileTime; + using Base::Flags; + + typedef Eigen::Map MapType; + typedef const Eigen::Map ConstMapType; + typedef Eigen::Map AlignedMapType; + typedef const Eigen::Map ConstAlignedMapType; + template struct StridedMapType { typedef Eigen::Map type; }; + template struct StridedConstMapType { typedef Eigen::Map type; }; + template struct StridedAlignedMapType { typedef Eigen::Map type; }; + template struct StridedConstAlignedMapType { typedef Eigen::Map type; }; + + protected: + DenseStorage m_storage; + + public: + enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits::Alignment>0) }; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) + + EIGEN_DEVICE_FUNC + Base& base() { return *static_cast(this); } + EIGEN_DEVICE_FUNC + const Base& base() const { return *static_cast(this); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const EIGEN_NOEXCEPT { return m_storage.rows(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const EIGEN_NOEXCEPT { return m_storage.cols(); } + + /** This is an overloaded version of DenseCoeffsBase::coeff(Index,Index) const + * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts. + * + * See DenseCoeffsBase::coeff(Index) const for details. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const + { + if(Flags & RowMajorBit) + return m_storage.data()[colId + rowId * m_storage.cols()]; + else // column-major + return m_storage.data()[rowId + colId * m_storage.rows()]; + } + + /** This is an overloaded version of DenseCoeffsBase::coeff(Index) const + * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts. + * + * See DenseCoeffsBase::coeff(Index) const for details. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const + { + return m_storage.data()[index]; + } + + /** This is an overloaded version of DenseCoeffsBase::coeffRef(Index,Index) const + * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts. + * + * See DenseCoeffsBase::coeffRef(Index,Index) const for details. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId) + { + if(Flags & RowMajorBit) + return m_storage.data()[colId + rowId * m_storage.cols()]; + else // column-major + return m_storage.data()[rowId + colId * m_storage.rows()]; + } + + /** This is an overloaded version of DenseCoeffsBase::coeffRef(Index) const + * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts. + * + * See DenseCoeffsBase::coeffRef(Index) const for details. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) + { + return m_storage.data()[index]; + } + + /** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index). + * It is provided for convenience. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const + { + if(Flags & RowMajorBit) + return m_storage.data()[colId + rowId * m_storage.cols()]; + else // column-major + return m_storage.data()[rowId + colId * m_storage.rows()]; + } + + /** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index). + * It is provided for convenience. */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const + { + return m_storage.data()[index]; + } + + /** \internal */ + template + EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const + { + return internal::ploadt + (m_storage.data() + (Flags & RowMajorBit + ? colId + rowId * m_storage.cols() + : rowId + colId * m_storage.rows())); + } + + /** \internal */ + template + EIGEN_STRONG_INLINE PacketScalar packet(Index index) const + { + return internal::ploadt(m_storage.data() + index); + } + + /** \internal */ + template + EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val) + { + internal::pstoret + (m_storage.data() + (Flags & RowMajorBit + ? colId + rowId * m_storage.cols() + : rowId + colId * m_storage.rows()), val); + } + + /** \internal */ + template + EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val) + { + internal::pstoret(m_storage.data() + index, val); + } + + /** \returns a const pointer to the data array of this matrix */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const + { return m_storage.data(); } + + /** \returns a pointer to the data array of this matrix */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() + { return m_storage.data(); } + + /** Resizes \c *this to a \a rows x \a cols matrix. + * + * This method is intended for dynamic-size matrices, although it is legal to call it on any + * matrix as long as fixed dimensions are left unchanged. If you only want to change the number + * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t). + * + * If the current number of coefficients of \c *this exactly matches the + * product \a rows * \a cols, then no memory allocation is performed and + * the current values are left unchanged. In all other cases, including + * shrinking, the data is reallocated and all previous values are lost. + * + * Example: \include Matrix_resize_int_int.cpp + * Output: \verbinclude Matrix_resize_int_int.out + * + * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t) + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void resize(Index rows, Index cols) + { + eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime) + && EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime) + && EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime) + && EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime) + && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."); + internal::check_rows_cols_for_overflow::run(rows, cols); + #ifdef EIGEN_INITIALIZE_COEFFS + Index size = rows*cols; + bool size_changed = size != this->size(); + m_storage.resize(size, rows, cols); + if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + #else + m_storage.resize(rows*cols, rows, cols); + #endif + } + + /** Resizes \c *this to a vector of length \a size + * + * \only_for_vectors. This method does not work for + * partially dynamic matrices when the static dimension is anything other + * than 1. For example it will not work with Matrix. + * + * Example: \include Matrix_resize_int.cpp + * Output: \verbinclude Matrix_resize_int.out + * + * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t) + */ + EIGEN_DEVICE_FUNC + inline void resize(Index size) + { + EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase) + eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0); + #ifdef EIGEN_INITIALIZE_COEFFS + bool size_changed = size != this->size(); + #endif + if(RowsAtCompileTime == 1) + m_storage.resize(size, 1, size); + else + m_storage.resize(size, size, 1); + #ifdef EIGEN_INITIALIZE_COEFFS + if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + #endif + } + + /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange + * as in the example below. + * + * Example: \include Matrix_resize_NoChange_int.cpp + * Output: \verbinclude Matrix_resize_NoChange_int.out + * + * \sa resize(Index,Index) + */ + EIGEN_DEVICE_FUNC + inline void resize(NoChange_t, Index cols) + { + resize(rows(), cols); + } + + /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange + * as in the example below. + * + * Example: \include Matrix_resize_int_NoChange.cpp + * Output: \verbinclude Matrix_resize_int_NoChange.out + * + * \sa resize(Index,Index) + */ + EIGEN_DEVICE_FUNC + inline void resize(Index rows, NoChange_t) + { + resize(rows, cols()); + } + + /** Resizes \c *this to have the same dimensions as \a other. + * Takes care of doing all the checking that's needed. + * + * Note that copying a row-vector into a vector (and conversely) is allowed. + * The resizing, if any, is then done in the appropriate way so that row-vectors + * remain row-vectors and vectors remain vectors. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void resizeLike(const EigenBase& _other) + { + const OtherDerived& other = _other.derived(); + internal::check_rows_cols_for_overflow::run(other.rows(), other.cols()); + const Index othersize = other.rows()*other.cols(); + if(RowsAtCompileTime == 1) + { + eigen_assert(other.rows() == 1 || other.cols() == 1); + resize(1, othersize); + } + else if(ColsAtCompileTime == 1) + { + eigen_assert(other.rows() == 1 || other.cols() == 1); + resize(othersize, 1); + } + else resize(other.rows(), other.cols()); + } + + /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. + * + * The method is intended for matrices of dynamic size. If you only want to change the number + * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or + * conservativeResize(Index, NoChange_t). + * + * Matrices are resized relative to the top-left element. In case values need to be + * appended to the matrix they will be uninitialized. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols) + { + internal::conservative_resize_like_impl::run(*this, rows, cols); + } + + /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. + * + * As opposed to conservativeResize(Index rows, Index cols), this version leaves + * the number of columns unchanged. + * + * In case the matrix is growing, new rows will be uninitialized. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t) + { + // Note: see the comment in conservativeResize(Index,Index) + conservativeResize(rows, cols()); + } + + /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. + * + * As opposed to conservativeResize(Index rows, Index cols), this version leaves + * the number of rows unchanged. + * + * In case the matrix is growing, new columns will be uninitialized. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols) + { + // Note: see the comment in conservativeResize(Index,Index) + conservativeResize(rows(), cols); + } + + /** Resizes the vector to \a size while retaining old values. + * + * \only_for_vectors. This method does not work for + * partially dynamic matrices when the static dimension is anything other + * than 1. For example it will not work with Matrix. + * + * When values are appended, they will be uninitialized. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void conservativeResize(Index size) + { + internal::conservative_resize_like_impl::run(*this, size); + } + + /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched. + * + * The method is intended for matrices of dynamic size. If you only want to change the number + * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or + * conservativeResize(Index, NoChange_t). + * + * Matrices are resized relative to the top-left element. In case values need to be + * appended to the matrix they will copied from \c other. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase& other) + { + internal::conservative_resize_like_impl::run(*this, other); + } + + /** This is a special case of the templated operator=. Its purpose is to + * prevent a default operator= from hiding the templated operator=. + */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other) + { + return _set(other); + } + + /** \sa MatrixBase::lazyAssign() */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase& other) + { + _resize_to_match(other); + return Base::lazyAssign(other.derived()); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue& func) + { + resize(func.rows(), func.cols()); + return Base::operator=(func); + } + + // Prevent user from trying to instantiate PlainObjectBase objects + // by making all its constructor protected. See bug 1074. + protected: + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase() : m_storage() + { +// _check_template_params(); +// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } + +#ifndef EIGEN_PARSED_BY_DOXYGEN + // FIXME is it still needed ? + /** \internal */ + EIGEN_DEVICE_FUNC + explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert) + : m_storage(internal::constructor_without_unaligned_array_assert()) + { +// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } +#endif + +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC + PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT + : m_storage( std::move(other.m_storage) ) + { + } + + EIGEN_DEVICE_FUNC + PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT + { + _check_template_params(); + m_storage = std::move(other.m_storage); + return *this; + } +#endif + + /** Copy constructor */ + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other) + : Base(), m_storage(other.m_storage) { } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols) + : m_storage(size, rows, cols) + { +// _check_template_params(); +// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } + + #if EIGEN_HAS_CXX11 + /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11 + * + * \only_for_vectors + * + * This constructor is for 1D array or vectors with more than 4 coefficients. + * There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients. + * + * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this + * constructor must match the the fixed number of rows (resp. columns) of \c *this. + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) + : m_storage() + { + _check_template_params(); + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4); + m_storage.data()[0] = a0; + m_storage.data()[1] = a1; + m_storage.data()[2] = a2; + m_storage.data()[3] = a3; + Index i = 4; + auto x = {(m_storage.data()[i++] = args, 0)...}; + static_cast(x); + } + + /** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer + * lists \cpp11 + */ + EIGEN_DEVICE_FUNC + explicit EIGEN_STRONG_INLINE PlainObjectBase(const std::initializer_list>& list) + : m_storage() + { + _check_template_params(); + + size_t list_size = 0; + if (list.begin() != list.end()) { + list_size = list.begin()->size(); + } + + // This is to allow syntax like VectorXi {{1, 2, 3, 4}} + if (ColsAtCompileTime == 1 && list.size() == 1) { + eigen_assert(list_size == static_cast(RowsAtCompileTime) || RowsAtCompileTime == Dynamic); + resize(list_size, ColsAtCompileTime); + std::copy(list.begin()->begin(), list.begin()->end(), m_storage.data()); + } else { + eigen_assert(list.size() == static_cast(RowsAtCompileTime) || RowsAtCompileTime == Dynamic); + eigen_assert(list_size == static_cast(ColsAtCompileTime) || ColsAtCompileTime == Dynamic); + resize(list.size(), list_size); + + Index row_index = 0; + for (const std::initializer_list& row : list) { + eigen_assert(list_size == row.size()); + Index col_index = 0; + for (const Scalar& e : row) { + coeffRef(row_index, col_index) = e; + ++col_index; + } + ++row_index; + } + } + } + #endif // end EIGEN_HAS_CXX11 + + /** \sa PlainObjectBase::operator=(const EigenBase&) */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase &other) + : m_storage() + { + _check_template_params(); + resizeLike(other); + _set_noalias(other); + } + + /** \sa PlainObjectBase::operator=(const EigenBase&) */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase &other) + : m_storage() + { + _check_template_params(); + resizeLike(other); + *this = other.derived(); + } + /** \brief Copy constructor with in-place evaluation */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue& other) + { + _check_template_params(); + // FIXME this does not automatically transpose vectors if necessary + resize(other.rows(), other.cols()); + other.evalTo(this->derived()); + } + + public: + + /** \brief Copies the generic expression \a other into *this. + * \copydetails DenseBase::operator=(const EigenBase &other) + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& operator=(const EigenBase &other) + { + _resize_to_match(other); + Base::operator=(other.derived()); + return this->derived(); + } + + /** \name Map + * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects, + * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned + * \a data pointers. + * + * Here is an example using strides: + * \include Matrix_Map_stride.cpp + * Output: \verbinclude Matrix_Map_stride.out + * + * \see class Map + */ + //@{ + static inline ConstMapType Map(const Scalar* data) + { return ConstMapType(data); } + static inline MapType Map(Scalar* data) + { return MapType(data); } + static inline ConstMapType Map(const Scalar* data, Index size) + { return ConstMapType(data, size); } + static inline MapType Map(Scalar* data, Index size) + { return MapType(data, size); } + static inline ConstMapType Map(const Scalar* data, Index rows, Index cols) + { return ConstMapType(data, rows, cols); } + static inline MapType Map(Scalar* data, Index rows, Index cols) + { return MapType(data, rows, cols); } + + static inline ConstAlignedMapType MapAligned(const Scalar* data) + { return ConstAlignedMapType(data); } + static inline AlignedMapType MapAligned(Scalar* data) + { return AlignedMapType(data); } + static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size) + { return ConstAlignedMapType(data, size); } + static inline AlignedMapType MapAligned(Scalar* data, Index size) + { return AlignedMapType(data, size); } + static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols) + { return ConstAlignedMapType(data, rows, cols); } + static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols) + { return AlignedMapType(data, rows, cols); } + + template + static inline typename StridedConstMapType >::type Map(const Scalar* data, const Stride& stride) + { return typename StridedConstMapType >::type(data, stride); } + template + static inline typename StridedMapType >::type Map(Scalar* data, const Stride& stride) + { return typename StridedMapType >::type(data, stride); } + template + static inline typename StridedConstMapType >::type Map(const Scalar* data, Index size, const Stride& stride) + { return typename StridedConstMapType >::type(data, size, stride); } + template + static inline typename StridedMapType >::type Map(Scalar* data, Index size, const Stride& stride) + { return typename StridedMapType >::type(data, size, stride); } + template + static inline typename StridedConstMapType >::type Map(const Scalar* data, Index rows, Index cols, const Stride& stride) + { return typename StridedConstMapType >::type(data, rows, cols, stride); } + template + static inline typename StridedMapType >::type Map(Scalar* data, Index rows, Index cols, const Stride& stride) + { return typename StridedMapType >::type(data, rows, cols, stride); } + + template + static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, const Stride& stride) + { return typename StridedConstAlignedMapType >::type(data, stride); } + template + static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, const Stride& stride) + { return typename StridedAlignedMapType >::type(data, stride); } + template + static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, Index size, const Stride& stride) + { return typename StridedConstAlignedMapType >::type(data, size, stride); } + template + static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, Index size, const Stride& stride) + { return typename StridedAlignedMapType >::type(data, size, stride); } + template + static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride& stride) + { return typename StridedConstAlignedMapType >::type(data, rows, cols, stride); } + template + static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride& stride) + { return typename StridedAlignedMapType >::type(data, rows, cols, stride); } + //@} + + using Base::setConstant; + EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val); + EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val); + EIGEN_DEVICE_FUNC Derived& setConstant(NoChange_t, Index cols, const Scalar& val); + EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, NoChange_t, const Scalar& val); + + using Base::setZero; + EIGEN_DEVICE_FUNC Derived& setZero(Index size); + EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols); + EIGEN_DEVICE_FUNC Derived& setZero(NoChange_t, Index cols); + EIGEN_DEVICE_FUNC Derived& setZero(Index rows, NoChange_t); + + using Base::setOnes; + EIGEN_DEVICE_FUNC Derived& setOnes(Index size); + EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols); + EIGEN_DEVICE_FUNC Derived& setOnes(NoChange_t, Index cols); + EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, NoChange_t); + + using Base::setRandom; + Derived& setRandom(Index size); + Derived& setRandom(Index rows, Index cols); + Derived& setRandom(NoChange_t, Index cols); + Derived& setRandom(Index rows, NoChange_t); + + #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN + #include EIGEN_PLAINOBJECTBASE_PLUGIN + #endif + + protected: + /** \internal Resizes *this in preparation for assigning \a other to it. + * Takes care of doing all the checking that's needed. + * + * Note that copying a row-vector into a vector (and conversely) is allowed. + * The resizing, if any, is then done in the appropriate way so that row-vectors + * remain row-vectors and vectors remain vectors. + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase& other) + { + #ifdef EIGEN_NO_AUTOMATIC_RESIZING + eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size()) + : (rows() == other.rows() && cols() == other.cols()))) + && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined"); + EIGEN_ONLY_USED_FOR_DEBUG(other); + #else + resizeLike(other); + #endif + } + + /** + * \brief Copies the value of the expression \a other into \c *this with automatic resizing. + * + * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), + * it will be initialized. + * + * Note that copying a row-vector into a vector (and conversely) is allowed. + * The resizing, if any, is then done in the appropriate way so that row-vectors + * remain row-vectors and vectors remain vectors. + * + * \sa operator=(const MatrixBase&), _set_noalias() + * + * \internal + */ + // aliasing is dealt once in internal::call_assignment + // so at this stage we have to assume aliasing... and resising has to be done later. + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& _set(const DenseBase& other) + { + internal::call_assignment(this->derived(), other.derived()); + return this->derived(); + } + + /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which + * is the case when creating a new matrix) so one can enforce lazy evaluation. + * + * \sa operator=(const MatrixBase&), _set() + */ + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase& other) + { + // I don't think we need this resize call since the lazyAssign will anyways resize + // and lazyAssign will be called by the assign selector. + //_resize_to_match(other); + // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because + // it wouldn't allow to copy a row-vector into a column-vector. + internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op()); + return this->derived(); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if::type* = 0) + { + const bool t0_is_integer_alike = internal::is_valid_index_type::value; + const bool t1_is_integer_alike = internal::is_valid_index_type::value; + EIGEN_STATIC_ASSERT(t0_is_integer_alike && + t1_is_integer_alike, + FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED) + resize(rows,cols); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if::type* = 0) + { + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2) + m_storage.data()[0] = Scalar(val0); + m_storage.data()[1] = Scalar(val1); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1, + typename internal::enable_if< (!internal::is_same::value) + && (internal::is_same::value) + && (internal::is_same::value) + && Base::SizeAtCompileTime==2,T1>::type* = 0) + { + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2) + m_storage.data()[0] = Scalar(val0); + m_storage.data()[1] = Scalar(val1); + } + + // The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array, + // then the argument is meant to be the size of the object. + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if< (Base::SizeAtCompileTime!=1 || !internal::is_convertible::value) + && ((!internal::is_same::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>::type* = 0) + { + // NOTE MSVC 2008 complains if we directly put bool(NumTraits::IsInteger) as the EIGEN_STATIC_ASSERT argument. + const bool is_integer_alike = internal::is_valid_index_type::value; + EIGEN_UNUSED_VARIABLE(is_integer_alike); + EIGEN_STATIC_ASSERT(is_integer_alike, + FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED) + resize(size); + } + + // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted) + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if::value,T>::type* = 0) + { + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1) + m_storage.data()[0] = val0; + } + + // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type) + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Index& val0, + typename internal::enable_if< (!internal::is_same::value) + && (internal::is_same::value) + && Base::SizeAtCompileTime==1 + && internal::is_convertible::value,T*>::type* = 0) + { + EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1) + m_storage.data()[0] = Scalar(val0); + } + + // Initialize a fixed size matrix from a pointer to raw data + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Scalar* data){ + this->_set_noalias(ConstMapType(data)); + } + + // Initialize an arbitrary matrix from a dense expression + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const DenseBase& other){ + this->_set_noalias(other); + } + + // Initialize an arbitrary matrix from an object convertible to the Derived type. + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Derived& other){ + this->_set_noalias(other); + } + + // Initialize an arbitrary matrix from a generic Eigen expression + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const EigenBase& other){ + this->derived() = other; + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const ReturnByValue& other) + { + resize(other.rows(), other.cols()); + other.evalTo(this->derived()); + } + + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const RotationBase& r) + { + this->derived() = r; + } + + // For fixed-size Array + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Scalar& val0, + typename internal::enable_if< Base::SizeAtCompileTime!=Dynamic + && Base::SizeAtCompileTime!=1 + && internal::is_convertible::value + && internal::is_same::XprKind,ArrayXpr>::value,T>::type* = 0) + { + Base::setConstant(val0); + } + + // For fixed-size Array + template + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void _init1(const Index& val0, + typename internal::enable_if< (!internal::is_same::value) + && (internal::is_same::value) + && Base::SizeAtCompileTime!=Dynamic + && Base::SizeAtCompileTime!=1 + && internal::is_convertible::value + && internal::is_same::XprKind,ArrayXpr>::value,T*>::type* = 0) + { + Base::setConstant(val0); + } + + template + friend struct internal::matrix_swap_impl; + + public: + +#ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal + * \brief Override DenseBase::swap() since for dynamic-sized matrices + * of same type it is enough to swap the data pointers. + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void swap(DenseBase & other) + { + enum { SwapPointers = internal::is_same::value && Base::SizeAtCompileTime==Dynamic }; + internal::matrix_swap_impl::run(this->derived(), other.derived()); + } + + /** \internal + * \brief const version forwarded to DenseBase::swap + */ + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void swap(DenseBase const & other) + { Base::swap(other.derived()); } + + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void _check_template_params() + { + EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor) + && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0) + && ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0)) + && ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0)) + && ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0)) + && ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0)) + && (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic) + && (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic) + && (Options & (DontAlign|RowMajor)) == Options), + INVALID_MATRIX_TEMPLATE_PARAMETERS) + } + + enum { IsPlainObjectBase = 1 }; +#endif + public: + // These apparently need to be down here for nvcc+icc to prevent duplicate + // Map symbol. + template friend class Eigen::Map; + friend class Eigen::Map; + friend class Eigen::Map; +#if EIGEN_MAX_ALIGN_BYTES>0 + // for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice. + friend class Eigen::Map; + friend class Eigen::Map; +#endif +}; + +namespace internal { + +template +struct conservative_resize_like_impl +{ + #if EIGEN_HAS_TYPE_TRAITS + static const bool IsRelocatable = std::is_trivially_copyable::value; + #else + static const bool IsRelocatable = !NumTraits::RequireInitialization; + #endif + static void run(DenseBase& _this, Index rows, Index cols) + { + if (_this.rows() == rows && _this.cols() == cols) return; + EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) + + if ( IsRelocatable + && (( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows + (!Derived::IsRowMajor && _this.rows() == rows) )) // column-major and we change only the number of columns + { + internal::check_rows_cols_for_overflow::run(rows, cols); + _this.derived().m_storage.conservativeResize(rows*cols,rows,cols); + } + else + { + // The storage order does not allow us to use reallocation. + Derived tmp(rows,cols); + const Index common_rows = numext::mini(rows, _this.rows()); + const Index common_cols = numext::mini(cols, _this.cols()); + tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); + _this.derived().swap(tmp); + } + } + + static void run(DenseBase& _this, const DenseBase& other) + { + if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; + + // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index), + // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the + // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or + // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like + // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good. + EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) + EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived) + + if ( IsRelocatable && + (( Derived::IsRowMajor && _this.cols() == other.cols()) || // row-major and we change only the number of rows + (!Derived::IsRowMajor && _this.rows() == other.rows()) )) // column-major and we change only the number of columns + { + const Index new_rows = other.rows() - _this.rows(); + const Index new_cols = other.cols() - _this.cols(); + _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols()); + if (new_rows>0) + _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows); + else if (new_cols>0) + _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols); + } + else + { + // The storage order does not allow us to use reallocation. + Derived tmp(other); + const Index common_rows = numext::mini(tmp.rows(), _this.rows()); + const Index common_cols = numext::mini(tmp.cols(), _this.cols()); + tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); + _this.derived().swap(tmp); + } + } +}; + +// Here, the specialization for vectors inherits from the general matrix case +// to allow calling .conservativeResize(rows,cols) on vectors. +template +struct conservative_resize_like_impl + : conservative_resize_like_impl +{ + typedef conservative_resize_like_impl Base; + using Base::run; + using Base::IsRelocatable; + + static void run(DenseBase& _this, Index size) + { + const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size; + const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1; + if(IsRelocatable) + _this.derived().m_storage.conservativeResize(size,new_rows,new_cols); + else + Base::run(_this.derived(), new_rows, new_cols); + } + + static void run(DenseBase& _this, const DenseBase& other) + { + if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; + + const Index num_new_elements = other.size() - _this.size(); + + const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows(); + const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1; + if(IsRelocatable) + _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols); + else + Base::run(_this.derived(), new_rows, new_cols); + + if (num_new_elements > 0) + _this.tail(num_new_elements) = other.tail(num_new_elements); + } +}; + +template +struct matrix_swap_impl +{ + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE void run(MatrixTypeA& a, MatrixTypeB& b) + { + a.base().swap(b); + } +}; + +template +struct matrix_swap_impl +{ + EIGEN_DEVICE_FUNC + static inline void run(MatrixTypeA& a, MatrixTypeB& b) + { + static_cast(a).m_storage.swap(static_cast(b).m_storage); + } +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_DENSESTORAGEBASE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Product.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Product.h new file mode 100644 index 00000000..70a6c106 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Product.h @@ -0,0 +1,191 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2011 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_PRODUCT_H +#define EIGEN_PRODUCT_H + +namespace Eigen { + +template class ProductImpl; + +namespace internal { + +template +struct traits > +{ + typedef typename remove_all::type LhsCleaned; + typedef typename remove_all::type RhsCleaned; + typedef traits LhsTraits; + typedef traits RhsTraits; + + typedef MatrixXpr XprKind; + + typedef typename ScalarBinaryOpTraits::Scalar, typename traits::Scalar>::ReturnType Scalar; + typedef typename product_promote_storage_type::ret>::ret StorageKind; + typedef typename promote_index_type::type StorageIndex; + + enum { + RowsAtCompileTime = LhsTraits::RowsAtCompileTime, + ColsAtCompileTime = RhsTraits::ColsAtCompileTime, + MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime, + MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime, + + // FIXME: only needed by GeneralMatrixMatrixTriangular + InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime), + + // The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator. + Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit + : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0 + : ( ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit)) + || ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit + : NoPreferredStorageOrderBit + }; +}; + +} // end namespace internal + +/** \class Product + * \ingroup Core_Module + * + * \brief Expression of the product of two arbitrary matrices or vectors + * + * \tparam _Lhs the type of the left-hand side expression + * \tparam _Rhs the type of the right-hand side expression + * + * This class represents an expression of the product of two arbitrary matrices. + * + * The other template parameters are: + * \tparam Option can be DefaultProduct, AliasFreeProduct, or LazyProduct + * + */ +template +class Product : public ProductImpl<_Lhs,_Rhs,Option, + typename internal::product_promote_storage_type::StorageKind, + typename internal::traits<_Rhs>::StorageKind, + internal::product_type<_Lhs,_Rhs>::ret>::ret> +{ + public: + + typedef _Lhs Lhs; + typedef _Rhs Rhs; + + typedef typename ProductImpl< + Lhs, Rhs, Option, + typename internal::product_promote_storage_type::StorageKind, + typename internal::traits::StorageKind, + internal::product_type::ret>::ret>::Base Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(Product) + + typedef typename internal::ref_selector::type LhsNested; + typedef typename internal::ref_selector::type RhsNested; + typedef typename internal::remove_all::type LhsNestedCleaned; + typedef typename internal::remove_all::type RhsNestedCleaned; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) + { + eigen_assert(lhs.cols() == rhs.rows() + && "invalid matrix product" + && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR + Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const LhsNestedCleaned& lhs() const { return m_lhs; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const RhsNestedCleaned& rhs() const { return m_rhs; } + + protected: + + LhsNested m_lhs; + RhsNested m_rhs; +}; + +namespace internal { + +template::ret> +class dense_product_base + : public internal::dense_xpr_base >::type +{}; + +/** Conversion to scalar for inner-products */ +template +class dense_product_base + : public internal::dense_xpr_base >::type +{ + typedef Product ProductXpr; + typedef typename internal::dense_xpr_base::type Base; +public: + using Base::derived; + typedef typename Base::Scalar Scalar; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator const Scalar() const + { + return internal::evaluator(derived()).coeff(0,0); + } +}; + +} // namespace internal + +// Generic API dispatcher +template +class ProductImpl : public internal::generic_xpr_base, MatrixXpr, StorageKind>::type +{ + public: + typedef typename internal::generic_xpr_base, MatrixXpr, StorageKind>::type Base; +}; + +template +class ProductImpl + : public internal::dense_product_base +{ + typedef Product Derived; + + public: + + typedef typename internal::dense_product_base Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Derived) + protected: + enum { + IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) && + (ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic), + EnableCoeff = IsOneByOne || Option==LazyProduct + }; + + public: + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const + { + EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS); + eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) ); + + return internal::evaluator(derived()).coeff(row,col); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const + { + EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS); + eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) ); + + return internal::evaluator(derived()).coeff(i); + } + + +}; + +} // end namespace Eigen + +#endif // EIGEN_PRODUCT_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ProductEvaluators.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ProductEvaluators.h new file mode 100644 index 00000000..8cf294b2 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ProductEvaluators.h @@ -0,0 +1,1179 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2008-2010 Gael Guennebaud +// Copyright (C) 2011 Jitse Niesen +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + + +#ifndef EIGEN_PRODUCTEVALUATORS_H +#define EIGEN_PRODUCTEVALUATORS_H + +namespace Eigen { + +namespace internal { + +/** \internal + * Evaluator of a product expression. + * Since products require special treatments to handle all possible cases, + * we simply defer the evaluation logic to a product_evaluator class + * which offers more partial specialization possibilities. + * + * \sa class product_evaluator + */ +template +struct evaluator > + : public product_evaluator > +{ + typedef Product XprType; + typedef product_evaluator Base; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {} +}; + +// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B" +// TODO we should apply that rule only if that's really helpful +template +struct evaluator_assume_aliasing, + const CwiseNullaryOp, Plain1>, + const Product > > +{ + static const bool value = true; +}; +template +struct evaluator, + const CwiseNullaryOp, Plain1>, + const Product > > + : public evaluator > +{ + typedef CwiseBinaryOp, + const CwiseNullaryOp, Plain1>, + const Product > XprType; + typedef evaluator > Base; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) + : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs()) + {} +}; + + +template +struct evaluator, DiagIndex> > + : public evaluator, DiagIndex> > +{ + typedef Diagonal, DiagIndex> XprType; + typedef evaluator, DiagIndex> > Base; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) + : Base(Diagonal, DiagIndex>( + Product(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()), + xpr.index() )) + {} +}; + + +// Helper class to perform a matrix product with the destination at hand. +// Depending on the sizes of the factors, there are different evaluation strategies +// as controlled by internal::product_type. +template< typename Lhs, typename Rhs, + typename LhsShape = typename evaluator_traits::Shape, + typename RhsShape = typename evaluator_traits::Shape, + int ProductType = internal::product_type::value> +struct generic_product_impl; + +template +struct evaluator_assume_aliasing > { + static const bool value = true; +}; + +// This is the default evaluator implementation for products: +// It creates a temporary and call generic_product_impl +template +struct product_evaluator, ProductTag, LhsShape, RhsShape> + : public evaluator::PlainObject> +{ + typedef Product XprType; + typedef typename XprType::PlainObject PlainObject; + typedef evaluator Base; + enum { + Flags = Base::Flags | EvalBeforeNestingBit + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit product_evaluator(const XprType& xpr) + : m_result(xpr.rows(), xpr.cols()) + { + ::new (static_cast(this)) Base(m_result); + +// FIXME shall we handle nested_eval here?, +// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.) +// typedef typename internal::nested_eval::type LhsNested; +// typedef typename internal::nested_eval::type RhsNested; +// typedef typename internal::remove_all::type LhsNestedCleaned; +// typedef typename internal::remove_all::type RhsNestedCleaned; +// +// const LhsNested lhs(xpr.lhs()); +// const RhsNested rhs(xpr.rhs()); +// +// generic_product_impl::evalTo(m_result, lhs, rhs); + + generic_product_impl::evalTo(m_result, xpr.lhs(), xpr.rhs()); + } + +protected: + PlainObject m_result; +}; + +// The following three shortcuts are enabled only if the scalar types match exactly. +// TODO: we could enable them for different scalar types when the product is not vectorized. + +// Dense = Product +template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar> +struct Assignment, internal::assign_op, Dense2Dense, + typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type> +{ + typedef Product SrcXprType; + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op &) + { + Index dstRows = src.rows(); + Index dstCols = src.cols(); + if((dst.rows()!=dstRows) || (dst.cols()!=dstCols)) + dst.resize(dstRows, dstCols); + // FIXME shall we handle nested_eval here? + generic_product_impl::evalTo(dst, src.lhs(), src.rhs()); + } +}; + +// Dense += Product +template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar> +struct Assignment, internal::add_assign_op, Dense2Dense, + typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type> +{ + typedef Product SrcXprType; + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op &) + { + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); + // FIXME shall we handle nested_eval here? + generic_product_impl::addTo(dst, src.lhs(), src.rhs()); + } +}; + +// Dense -= Product +template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar> +struct Assignment, internal::sub_assign_op, Dense2Dense, + typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type> +{ + typedef Product SrcXprType; + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op &) + { + eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); + // FIXME shall we handle nested_eval here? + generic_product_impl::subTo(dst, src.lhs(), src.rhs()); + } +}; + + +// Dense ?= scalar * Product +// TODO we should apply that rule if that's really helpful +// for instance, this is not good for inner products +template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain> +struct Assignment, const CwiseNullaryOp,Plain>, + const Product >, AssignFunc, Dense2Dense> +{ + typedef CwiseBinaryOp, + const CwiseNullaryOp,Plain>, + const Product > SrcXprType; + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func) + { + call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func); + } +}; + +//---------------------------------------- +// Catch "Dense ?= xpr + Product<>" expression to save one temporary +// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct + +template +struct evaluator_assume_aliasing::Scalar>, const OtherXpr, + const Product >, DenseShape > { + static const bool value = true; +}; + +template +struct evaluator_assume_aliasing::Scalar>, const OtherXpr, + const Product >, DenseShape > { + static const bool value = true; +}; + +template +struct assignment_from_xpr_op_product +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/) + { + call_assignment_no_alias(dst, src.lhs(), Func1()); + call_assignment_no_alias(dst, src.rhs(), Func2()); + } +}; + +#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \ + template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \ + struct Assignment, const OtherXpr, \ + const Product >, internal::ASSIGN_OP, Dense2Dense> \ + : assignment_from_xpr_op_product, internal::ASSIGN_OP, internal::ASSIGN_OP2 > \ + {} + +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_sum_op,add_assign_op); +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op); +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op); + +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_difference_op,sub_assign_op); +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op); +EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op); + +//---------------------------------------- + +template +struct generic_product_impl +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum(); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum(); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); } +}; + + +/*********************************************************************** +* Implementation of outer dense * dense vector product +***********************************************************************/ + +// Column major result +template +void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&) +{ + evaluator rhsEval(rhs); + ei_declare_local_nested_eval(Lhs,lhs,Rhs::SizeAtCompileTime,actual_lhs); + // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored + // FIXME not very good if rhs is real and lhs complex while alpha is real too + const Index cols = dst.cols(); + for (Index j=0; j +void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&) +{ + evaluator lhsEval(lhs); + ei_declare_local_nested_eval(Rhs,rhs,Lhs::SizeAtCompileTime,actual_rhs); + // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored + // FIXME not very good if lhs is real and rhs complex while alpha is real too + const Index rows = dst.rows(); + for (Index i=0; i +struct generic_product_impl +{ + template struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {}; + typedef typename Product::Scalar Scalar; + + // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose + struct set { template EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } }; + struct add { template EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } }; + struct sub { template EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } }; + struct adds { + Scalar m_scale; + explicit adds(const Scalar& s) : m_scale(s) {} + template void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const { + dst.const_cast_derived() += m_scale * src; + } + }; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major()); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major()); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major()); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major()); + } + +}; + + +// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo +template +struct generic_product_impl_base +{ + typedef typename Product::Scalar Scalar; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); } + +}; + +template +struct generic_product_impl + : generic_product_impl_base > +{ + typedef typename nested_eval::type LhsNested; + typedef typename nested_eval::type RhsNested; + typedef typename Product::Scalar Scalar; + enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight }; + typedef typename internal::remove_all::type>::type MatrixType; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + // Fallback to inner product if both the lhs and rhs is a runtime vector. + if (lhs.rows() == 1 && rhs.cols() == 1) { + dst.coeffRef(0,0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0)); + return; + } + LhsNested actual_lhs(lhs); + RhsNested actual_rhs(rhs); + internal::gemv_dense_selector::HasUsableDirectAccess) + >::run(actual_lhs, actual_rhs, dst, alpha); + } +}; + +template +struct generic_product_impl +{ + typedef typename Product::Scalar Scalar; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // Same as: dst.noalias() = lhs.lazyProduct(rhs); + // but easier on the compiler side + call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op()); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // dst.noalias() += lhs.lazyProduct(rhs); + call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op()); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) + { + // dst.noalias() -= lhs.lazyProduct(rhs); + call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op()); + } + + // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h + // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance: + // dst {,+,-}= (s1*A)*(B*s2) + // will be rewritten as: + // dst {,+,-}= (s1*s2) * (A.lazyProduct(B)) + // There are at least four benefits of doing so: + // 1 - huge performance gain for heap-allocated matrix types as it save costly allocations. + // 2 - it is faster than simply by-passing the heap allocation through stack allocation. + // 3 - it makes this fallback consistent with the heavy GEMM routine. + // 4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices. + // (see https://stackoverflow.com/questions/54738495) + // For small fixed sizes matrices, howver, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower, + // and the behavior depends also a lot on the compiler... This is why this re-writting strategy is currently + // enabled only when falling back from the main GEMM. + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func &func) + { + enum { + HasScalarFactor = blas_traits::HasScalarFactor || blas_traits::HasScalarFactor, + ConjLhs = blas_traits::NeedToConjugate, + ConjRhs = blas_traits::NeedToConjugate + }; + // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto + // this is important for real*complex_mat + Scalar actualAlpha = combine_scalar_factors(lhs, rhs); + + eval_dynamic_impl(dst, + blas_traits::extract(lhs).template conjugateIf(), + blas_traits::extract(rhs).template conjugateIf(), + func, + actualAlpha, + typename conditional::type()); + } + +protected: + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s /* == 1 */, false_type) + { + EIGEN_UNUSED_VARIABLE(s); + eigen_internal_assert(s==Scalar(1)); + call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func); + } + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s, true_type) + { + call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func); + } +}; + +// This specialization enforces the use of a coefficient-based evaluation strategy +template +struct generic_product_impl + : generic_product_impl {}; + +// Case 2: Evaluate coeff by coeff +// +// This is mostly taken from CoeffBasedProduct.h +// The main difference is that we add an extra argument to the etor_product_*_impl::run() function +// for the inner dimension of the product, because evaluator object do not know their size. + +template +struct etor_product_coeff_impl; + +template +struct etor_product_packet_impl; + +template +struct product_evaluator, ProductTag, DenseShape, DenseShape> + : evaluator_base > +{ + typedef Product XprType; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit product_evaluator(const XprType& xpr) + : m_lhs(xpr.lhs()), + m_rhs(xpr.rhs()), + m_lhsImpl(m_lhs), // FIXME the creation of the evaluator objects should result in a no-op, but check that! + m_rhsImpl(m_rhs), // Moreover, they are only useful for the packet path, so we could completely disable them when not needed, + // or perhaps declare them on the fly on the packet method... We have experiment to check what's best. + m_innerDim(xpr.lhs().cols()) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits::MulCost); + EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits::AddCost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); +#if 0 + std::cerr << "LhsOuterStrideBytes= " << LhsOuterStrideBytes << "\n"; + std::cerr << "RhsOuterStrideBytes= " << RhsOuterStrideBytes << "\n"; + std::cerr << "LhsAlignment= " << LhsAlignment << "\n"; + std::cerr << "RhsAlignment= " << RhsAlignment << "\n"; + std::cerr << "CanVectorizeLhs= " << CanVectorizeLhs << "\n"; + std::cerr << "CanVectorizeRhs= " << CanVectorizeRhs << "\n"; + std::cerr << "CanVectorizeInner= " << CanVectorizeInner << "\n"; + std::cerr << "EvalToRowMajor= " << EvalToRowMajor << "\n"; + std::cerr << "Alignment= " << Alignment << "\n"; + std::cerr << "Flags= " << Flags << "\n"; +#endif + } + + // Everything below here is taken from CoeffBasedProduct.h + + typedef typename internal::nested_eval::type LhsNested; + typedef typename internal::nested_eval::type RhsNested; + + typedef typename internal::remove_all::type LhsNestedCleaned; + typedef typename internal::remove_all::type RhsNestedCleaned; + + typedef evaluator LhsEtorType; + typedef evaluator RhsEtorType; + + enum { + RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime, + ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime, + InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime), + MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime, + MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime + }; + + typedef typename find_best_packet::type LhsVecPacketType; + typedef typename find_best_packet::type RhsVecPacketType; + + enum { + + LhsCoeffReadCost = LhsEtorType::CoeffReadCost, + RhsCoeffReadCost = RhsEtorType::CoeffReadCost, + CoeffReadCost = InnerSize==0 ? NumTraits::ReadCost + : InnerSize == Dynamic ? HugeCost + : InnerSize * (NumTraits::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost)) + + (InnerSize - 1) * NumTraits::AddCost, + + Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT, + + LhsFlags = LhsEtorType::Flags, + RhsFlags = RhsEtorType::Flags, + + LhsRowMajor = LhsFlags & RowMajorBit, + RhsRowMajor = RhsFlags & RowMajorBit, + + LhsVecPacketSize = unpacket_traits::size, + RhsVecPacketSize = unpacket_traits::size, + + // Here, we don't care about alignment larger than the usable packet size. + LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))), + RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))), + + SameType = is_same::value, + + CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1), + CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1), + + EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 + : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 + : (bool(RhsRowMajor) && !CanVectorizeLhs), + + Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit) + | (EvalToRowMajor ? RowMajorBit : 0) + // TODO enable vectorization for mixed types + | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0) + | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0), + + LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)), + RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)), + + Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment) + : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment) + : 0, + + /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside + * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner + * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect + * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI. + */ + CanVectorizeInner = SameType + && LhsRowMajor + && (!RhsRowMajor) + && (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit) + && (int(InnerSize) % packet_traits::size == 0) + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const + { + return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum(); + } + + /* Allow index-based non-packet access. It is impossible though to allow index-based packed access, + * which is why we don't set the LinearAccessBit. + * TODO: this seems possible when the result is a vector + */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const CoeffReturnType coeff(Index index) const + { + const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index; + const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0; + return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum(); + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const PacketType packet(Index row, Index col) const + { + PacketType res; + typedef etor_product_packet_impl PacketImpl; + PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res); + return res; + } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const PacketType packet(Index index) const + { + const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index; + const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0; + return packet(row,col); + } + +protected: + typename internal::add_const_on_value_type::type m_lhs; + typename internal::add_const_on_value_type::type m_rhs; + + LhsEtorType m_lhsImpl; + RhsEtorType m_rhsImpl; + + // TODO: Get rid of m_innerDim if known at compile time + Index m_innerDim; +}; + +template +struct product_evaluator, LazyCoeffBasedProductMode, DenseShape, DenseShape> + : product_evaluator, CoeffBasedProductMode, DenseShape, DenseShape> +{ + typedef Product XprType; + typedef Product BaseProduct; + typedef product_evaluator Base; + enum { + Flags = Base::Flags | EvalBeforeNestingBit + }; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit product_evaluator(const XprType& xpr) + : Base(BaseProduct(xpr.lhs(),xpr.rhs())) + {} +}; + +/**************************************** +*** Coeff based product, Packet path *** +****************************************/ + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res) + { + etor_product_packet_impl::run(row, col, lhs, rhs, innerDim, res); + res = pmadd(pset1(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet(Index(UnrollingIndex-1), col), res); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res) + { + etor_product_packet_impl::run(row, col, lhs, rhs, innerDim, res); + res = pmadd(lhs.template packet(row, Index(UnrollingIndex-1)), pset1(rhs.coeff(Index(UnrollingIndex-1), col)), res); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res) + { + res = pmul(pset1(lhs.coeff(row, Index(0))),rhs.template packet(Index(0), col)); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res) + { + res = pmul(lhs.template packet(row, Index(0)), pset1(rhs.coeff(Index(0), col))); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res) + { + res = pset1(typename unpacket_traits::type(0)); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res) + { + res = pset1(typename unpacket_traits::type(0)); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res) + { + res = pset1(typename unpacket_traits::type(0)); + for(Index i = 0; i < innerDim; ++i) + res = pmadd(pset1(lhs.coeff(row, i)), rhs.template packet(i, col), res); + } +}; + +template +struct etor_product_packet_impl +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res) + { + res = pset1(typename unpacket_traits::type(0)); + for(Index i = 0; i < innerDim; ++i) + res = pmadd(lhs.template packet(row, i), pset1(rhs.coeff(i, col)), res); + } +}; + + +/*************************************************************************** +* Triangular products +***************************************************************************/ +template +struct triangular_product_impl; + +template +struct generic_product_impl + : generic_product_impl_base > +{ + typedef typename Product::Scalar Scalar; + + template + static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + triangular_product_impl + ::run(dst, lhs.nestedExpression(), rhs, alpha); + } +}; + +template +struct generic_product_impl +: generic_product_impl_base > +{ + typedef typename Product::Scalar Scalar; + + template + static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + triangular_product_impl::run(dst, lhs, rhs.nestedExpression(), alpha); + } +}; + + +/*************************************************************************** +* SelfAdjoint products +***************************************************************************/ +template +struct selfadjoint_product_impl; + +template +struct generic_product_impl + : generic_product_impl_base > +{ + typedef typename Product::Scalar Scalar; + + template + static EIGEN_DEVICE_FUNC + void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + selfadjoint_product_impl::run(dst, lhs.nestedExpression(), rhs, alpha); + } +}; + +template +struct generic_product_impl +: generic_product_impl_base > +{ + typedef typename Product::Scalar Scalar; + + template + static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) + { + selfadjoint_product_impl::run(dst, lhs, rhs.nestedExpression(), alpha); + } +}; + + +/*************************************************************************** +* Diagonal products +***************************************************************************/ + +template +struct diagonal_product_evaluator_base + : evaluator_base +{ + typedef typename ScalarBinaryOpTraits::ReturnType Scalar; +public: + enum { + CoeffReadCost = int(NumTraits::MulCost) + int(evaluator::CoeffReadCost) + int(evaluator::CoeffReadCost), + + MatrixFlags = evaluator::Flags, + DiagFlags = evaluator::Flags, + + _StorageOrder = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor + : (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor + : MatrixFlags & RowMajorBit ? RowMajor : ColMajor, + _SameStorageOrder = _StorageOrder == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor), + + _ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft) + ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)), + _SameTypes = is_same::value, + // FIXME currently we need same types, but in the future the next rule should be the one + //_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))), + _Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) + && _SameTypes + && (_SameStorageOrder || (MatrixFlags&LinearAccessBit)==LinearAccessBit) + && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))), + _LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0, + Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0), + Alignment = evaluator::Alignment, + + AsScalarProduct = (DiagonalType::SizeAtCompileTime==1) + || (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft) + || (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight) + }; + + EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag) + : m_diagImpl(diag), m_matImpl(mat) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits::MulCost); + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const + { + if(AsScalarProduct) + return m_diagImpl.coeff(0) * m_matImpl.coeff(idx); + else + return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx); + } + +protected: + template + EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const + { + return internal::pmul(m_matImpl.template packet(row, col), + internal::pset1(m_diagImpl.coeff(id))); + } + + template + EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const + { + enum { + InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime, + DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator::Alignment)) // FIXME hardcoded 16!! + }; + return internal::pmul(m_matImpl.template packet(row, col), + m_diagImpl.template packet(id)); + } + + evaluator m_diagImpl; + evaluator m_matImpl; +}; + +// diagonal * dense +template +struct product_evaluator, ProductTag, DiagonalShape, DenseShape> + : diagonal_product_evaluator_base, OnTheLeft> +{ + typedef diagonal_product_evaluator_base, OnTheLeft> Base; + using Base::m_diagImpl; + using Base::m_matImpl; + using Base::coeff; + typedef typename Base::Scalar Scalar; + + typedef Product XprType; + typedef typename XprType::PlainObject PlainObject; + typedef typename Lhs::DiagonalVectorType DiagonalType; + + + enum { StorageOrder = Base::_StorageOrder }; + + EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr) + : Base(xpr.rhs(), xpr.lhs().diagonal()) + { + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const + { + return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col); + } + +#ifndef EIGEN_GPUCC + template + EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const + { + // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case. + // See also similar calls below. + return this->template packet_impl(row,col, row, + typename internal::conditional::type()); + } + + template + EIGEN_STRONG_INLINE PacketType packet(Index idx) const + { + return packet(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx); + } +#endif +}; + +// dense * diagonal +template +struct product_evaluator, ProductTag, DenseShape, DiagonalShape> + : diagonal_product_evaluator_base, OnTheRight> +{ + typedef diagonal_product_evaluator_base, OnTheRight> Base; + using Base::m_diagImpl; + using Base::m_matImpl; + using Base::coeff; + typedef typename Base::Scalar Scalar; + + typedef Product XprType; + typedef typename XprType::PlainObject PlainObject; + + enum { StorageOrder = Base::_StorageOrder }; + + EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr) + : Base(xpr.lhs(), xpr.rhs().diagonal()) + { + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const + { + return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col); + } + +#ifndef EIGEN_GPUCC + template + EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const + { + return this->template packet_impl(row,col, col, + typename internal::conditional::type()); + } + + template + EIGEN_STRONG_INLINE PacketType packet(Index idx) const + { + return packet(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx); + } +#endif +}; + +/*************************************************************************** +* Products with permutation matrices +***************************************************************************/ + +/** \internal + * \class permutation_matrix_product + * Internal helper class implementing the product between a permutation matrix and a matrix. + * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h + */ +template +struct permutation_matrix_product; + +template +struct permutation_matrix_product +{ + typedef typename nested_eval::type MatrixType; + typedef typename remove_all::type MatrixTypeCleaned; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) + { + MatrixType mat(xpr); + const Index n = Side==OnTheLeft ? mat.rows() : mat.cols(); + // FIXME we need an is_same for expression that is not sensitive to constness. For instance + // is_same_xpr, Block >::value should be true. + //if(is_same::value && extract_data(dst) == extract_data(mat)) + if(is_same_dense(dst, mat)) + { + // apply the permutation inplace + Matrix mask(perm.size()); + mask.fill(false); + Index r = 0; + while(r < perm.size()) + { + // search for the next seed + while(r=perm.size()) + break; + // we got one, let's follow it until we are back to the seed + Index k0 = r++; + Index kPrev = k0; + mask.coeffRef(k0) = true; + for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k)) + { + Block(dst, k) + .swap(Block + (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev)); + + mask.coeffRef(k) = true; + kPrev = k; + } + } + } + else + { + for(Index i = 0; i < n; ++i) + { + Block + (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i) + + = + + Block + (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i); + } + } + } +}; + +template +struct generic_product_impl +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) + { + permutation_matrix_product::run(dst, lhs, rhs); + } +}; + +template +struct generic_product_impl +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) + { + permutation_matrix_product::run(dst, rhs, lhs); + } +}; + +template +struct generic_product_impl, Rhs, PermutationShape, MatrixShape, ProductTag> +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse& lhs, const Rhs& rhs) + { + permutation_matrix_product::run(dst, lhs.nestedExpression(), rhs); + } +}; + +template +struct generic_product_impl, MatrixShape, PermutationShape, ProductTag> +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse& rhs) + { + permutation_matrix_product::run(dst, rhs.nestedExpression(), lhs); + } +}; + + +/*************************************************************************** +* Products with transpositions matrices +***************************************************************************/ + +// FIXME could we unify Transpositions and Permutation into a single "shape"?? + +/** \internal + * \class transposition_matrix_product + * Internal helper class implementing the product between a permutation matrix and a matrix. + */ +template +struct transposition_matrix_product +{ + typedef typename nested_eval::type MatrixType; + typedef typename remove_all::type MatrixTypeCleaned; + + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr) + { + MatrixType mat(xpr); + typedef typename TranspositionType::StorageIndex StorageIndex; + const Index size = tr.size(); + StorageIndex j = 0; + + if(!is_same_dense(dst,mat)) + dst = mat; + + for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k +struct generic_product_impl +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) + { + transposition_matrix_product::run(dst, lhs, rhs); + } +}; + +template +struct generic_product_impl +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) + { + transposition_matrix_product::run(dst, rhs, lhs); + } +}; + + +template +struct generic_product_impl, Rhs, TranspositionsShape, MatrixShape, ProductTag> +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose& lhs, const Rhs& rhs) + { + transposition_matrix_product::run(dst, lhs.nestedExpression(), rhs); + } +}; + +template +struct generic_product_impl, MatrixShape, TranspositionsShape, ProductTag> +{ + template + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose& rhs) + { + transposition_matrix_product::run(dst, rhs.nestedExpression(), lhs); + } +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_PRODUCT_EVALUATORS_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Random.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Random.h new file mode 100644 index 00000000..dab2ac8e --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Random.h @@ -0,0 +1,218 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_RANDOM_H +#define EIGEN_RANDOM_H + +namespace Eigen { + +namespace internal { + +template struct scalar_random_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op) + inline const Scalar operator() () const { return random(); } +}; + +template +struct functor_traits > +{ enum { Cost = 5 * NumTraits::MulCost, PacketAccess = false, IsRepeatable = false }; }; + +} // end namespace internal + +/** \returns a random matrix expression + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * The parameters \a rows and \a cols are the number of rows and of columns of + * the returned matrix. Must be compatible with this MatrixBase type. + * + * \not_reentrant + * + * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, + * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used + * instead. + * + * + * Example: \include MatrixBase_random_int_int.cpp + * Output: \verbinclude MatrixBase_random_int_int.out + * + * This expression has the "evaluate before nesting" flag so that it will be evaluated into + * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected + * behavior with expressions involving random matrices. + * + * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators. + * + * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random() + */ +template +inline const typename DenseBase::RandomReturnType +DenseBase::Random(Index rows, Index cols) +{ + return NullaryExpr(rows, cols, internal::scalar_random_op()); +} + +/** \returns a random vector expression + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * The parameter \a size is the size of the returned vector. + * Must be compatible with this MatrixBase type. + * + * \only_for_vectors + * \not_reentrant + * + * This variant is meant to be used for dynamic-size vector types. For fixed-size types, + * it is redundant to pass \a size as argument, so Random() should be used + * instead. + * + * Example: \include MatrixBase_random_int.cpp + * Output: \verbinclude MatrixBase_random_int.out + * + * This expression has the "evaluate before nesting" flag so that it will be evaluated into + * a temporary vector whenever it is nested in a larger expression. This prevents unexpected + * behavior with expressions involving random matrices. + * + * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random() + */ +template +inline const typename DenseBase::RandomReturnType +DenseBase::Random(Index size) +{ + return NullaryExpr(size, internal::scalar_random_op()); +} + +/** \returns a fixed-size random matrix or vector expression + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you + * need to use the variants taking size arguments. + * + * Example: \include MatrixBase_random.cpp + * Output: \verbinclude MatrixBase_random.out + * + * This expression has the "evaluate before nesting" flag so that it will be evaluated into + * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected + * behavior with expressions involving random matrices. + * + * \not_reentrant + * + * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index) + */ +template +inline const typename DenseBase::RandomReturnType +DenseBase::Random() +{ + return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op()); +} + +/** Sets all coefficients in this expression to random values. + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * \not_reentrant + * + * Example: \include MatrixBase_setRandom.cpp + * Output: \verbinclude MatrixBase_setRandom.out + * + * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index) + */ +template +EIGEN_DEVICE_FUNC inline Derived& DenseBase::setRandom() +{ + return *this = Random(rows(), cols()); +} + +/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values. + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * \only_for_vectors + * \not_reentrant + * + * Example: \include Matrix_setRandom_int.cpp + * Output: \verbinclude Matrix_setRandom_int.out + * + * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random() + */ +template +EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setRandom(Index newSize) +{ + resize(newSize); + return setRandom(); +} + +/** Resizes to the given size, and sets all coefficients in this expression to random values. + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * \not_reentrant + * + * \param rows the new number of rows + * \param cols the new number of columns + * + * Example: \include Matrix_setRandom_int_int.cpp + * Output: \verbinclude Matrix_setRandom_int_int.out + * + * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random() + */ +template +EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setRandom(Index rows, Index cols) +{ + resize(rows, cols); + return setRandom(); +} + +/** Resizes to the given size, changing only the number of columns, and sets all + * coefficients in this expression to random values. For the parameter of type + * NoChange_t, just pass the special value \c NoChange. + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * \not_reentrant + * + * \sa DenseBase::setRandom(), setRandom(Index), setRandom(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Random() + */ +template +EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setRandom(NoChange_t, Index cols) +{ + return setRandom(rows(), cols); +} + +/** Resizes to the given size, changing only the number of rows, and sets all + * coefficients in this expression to random values. For the parameter of type + * NoChange_t, just pass the special value \c NoChange. + * + * Numbers are uniformly spread through their whole definition range for integer types, + * and in the [-1:1] range for floating point scalar types. + * + * \not_reentrant + * + * \sa DenseBase::setRandom(), setRandom(Index), setRandom(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Random() + */ +template +EIGEN_STRONG_INLINE Derived& +PlainObjectBase::setRandom(Index rows, NoChange_t) +{ + return setRandom(rows, cols()); +} + +} // end namespace Eigen + +#endif // EIGEN_RANDOM_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Redux.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Redux.h new file mode 100644 index 00000000..b6790d11 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Redux.h @@ -0,0 +1,515 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008 Gael Guennebaud +// Copyright (C) 2006-2008 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_REDUX_H +#define EIGEN_REDUX_H + +namespace Eigen { + +namespace internal { + +// TODO +// * implement other kind of vectorization +// * factorize code + +/*************************************************************************** +* Part 1 : the logic deciding a strategy for vectorization and unrolling +***************************************************************************/ + +template +struct redux_traits +{ +public: + typedef typename find_best_packet::type PacketType; + enum { + PacketSize = unpacket_traits::size, + InnerMaxSize = int(Evaluator::IsRowMajor) + ? Evaluator::MaxColsAtCompileTime + : Evaluator::MaxRowsAtCompileTime, + OuterMaxSize = int(Evaluator::IsRowMajor) + ? Evaluator::MaxRowsAtCompileTime + : Evaluator::MaxColsAtCompileTime, + SliceVectorizedWork = int(InnerMaxSize)==Dynamic ? Dynamic + : int(OuterMaxSize)==Dynamic ? (int(InnerMaxSize)>=int(PacketSize) ? Dynamic : 0) + : (int(InnerMaxSize)/int(PacketSize)) * int(OuterMaxSize) + }; + + enum { + MightVectorize = (int(Evaluator::Flags)&ActualPacketAccessBit) + && (functor_traits::PacketAccess), + MayLinearVectorize = bool(MightVectorize) && (int(Evaluator::Flags)&LinearAccessBit), + MaySliceVectorize = bool(MightVectorize) && (int(SliceVectorizedWork)==Dynamic || int(SliceVectorizedWork)>=3) + }; + +public: + enum { + Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal) + : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) + : int(DefaultTraversal) + }; + +public: + enum { + Cost = Evaluator::SizeAtCompileTime == Dynamic ? HugeCost + : int(Evaluator::SizeAtCompileTime) * int(Evaluator::CoeffReadCost) + (Evaluator::SizeAtCompileTime-1) * functor_traits::Cost, + UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize)) + }; + +public: + enum { + Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling + }; + +#ifdef EIGEN_DEBUG_ASSIGN + static void debug() + { + std::cerr << "Xpr: " << typeid(typename Evaluator::XprType).name() << std::endl; + std::cerr.setf(std::ios::hex, std::ios::basefield); + EIGEN_DEBUG_VAR(Evaluator::Flags) + std::cerr.unsetf(std::ios::hex); + EIGEN_DEBUG_VAR(InnerMaxSize) + EIGEN_DEBUG_VAR(OuterMaxSize) + EIGEN_DEBUG_VAR(SliceVectorizedWork) + EIGEN_DEBUG_VAR(PacketSize) + EIGEN_DEBUG_VAR(MightVectorize) + EIGEN_DEBUG_VAR(MayLinearVectorize) + EIGEN_DEBUG_VAR(MaySliceVectorize) + std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl; + EIGEN_DEBUG_VAR(UnrollingLimit) + std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl; + std::cerr << std::endl; + } +#endif +}; + +/*************************************************************************** +* Part 2 : unrollers +***************************************************************************/ + +/*** no vectorization ***/ + +template +struct redux_novec_unroller +{ + enum { + HalfLength = Length/2 + }; + + typedef typename Evaluator::Scalar Scalar; + + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func& func) + { + return func(redux_novec_unroller::run(eval,func), + redux_novec_unroller::run(eval,func)); + } +}; + +template +struct redux_novec_unroller +{ + enum { + outer = Start / Evaluator::InnerSizeAtCompileTime, + inner = Start % Evaluator::InnerSizeAtCompileTime + }; + + typedef typename Evaluator::Scalar Scalar; + + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func&) + { + return eval.coeffByOuterInner(outer, inner); + } +}; + +// This is actually dead code and will never be called. It is required +// to prevent false warnings regarding failed inlining though +// for 0 length run() will never be called at all. +template +struct redux_novec_unroller +{ + typedef typename Evaluator::Scalar Scalar; + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); } +}; + +/*** vectorization ***/ + +template +struct redux_vec_unroller +{ + template + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func& func) + { + enum { + PacketSize = unpacket_traits::size, + HalfLength = Length/2 + }; + + return func.packetOp( + redux_vec_unroller::template run(eval,func), + redux_vec_unroller::template run(eval,func) ); + } +}; + +template +struct redux_vec_unroller +{ + template + EIGEN_DEVICE_FUNC + static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func&) + { + enum { + PacketSize = unpacket_traits::size, + index = Start * PacketSize, + outer = index / int(Evaluator::InnerSizeAtCompileTime), + inner = index % int(Evaluator::InnerSizeAtCompileTime), + alignment = Evaluator::Alignment + }; + return eval.template packetByOuterInner(outer, inner); + } +}; + +/*************************************************************************** +* Part 3 : implementation of all cases +***************************************************************************/ + +template::Traversal, + int Unrolling = redux_traits::Unrolling +> +struct redux_impl; + +template +struct redux_impl +{ + typedef typename Evaluator::Scalar Scalar; + + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE + Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr) + { + eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix"); + Scalar res; + res = eval.coeffByOuterInner(0, 0); + for(Index i = 1; i < xpr.innerSize(); ++i) + res = func(res, eval.coeffByOuterInner(0, i)); + for(Index i = 1; i < xpr.outerSize(); ++i) + for(Index j = 0; j < xpr.innerSize(); ++j) + res = func(res, eval.coeffByOuterInner(i, j)); + return res; + } +}; + +template +struct redux_impl + : redux_novec_unroller +{ + typedef redux_novec_unroller Base; + typedef typename Evaluator::Scalar Scalar; + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE + Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/) + { + return Base::run(eval,func); + } +}; + +template +struct redux_impl +{ + typedef typename Evaluator::Scalar Scalar; + typedef typename redux_traits::PacketType PacketScalar; + + template + static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr) + { + const Index size = xpr.size(); + + const Index packetSize = redux_traits::PacketSize; + const int packetAlignment = unpacket_traits::alignment; + enum { + alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned), + alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Evaluator::Alignment) + }; + const Index alignedStart = internal::first_default_aligned(xpr); + const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize); + const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize); + const Index alignedEnd2 = alignedStart + alignedSize2; + const Index alignedEnd = alignedStart + alignedSize; + Scalar res; + if(alignedSize) + { + PacketScalar packet_res0 = eval.template packet(alignedStart); + if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop + { + PacketScalar packet_res1 = eval.template packet(alignedStart+packetSize); + for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize) + { + packet_res0 = func.packetOp(packet_res0, eval.template packet(index)); + packet_res1 = func.packetOp(packet_res1, eval.template packet(index+packetSize)); + } + + packet_res0 = func.packetOp(packet_res0,packet_res1); + if(alignedEnd>alignedEnd2) + packet_res0 = func.packetOp(packet_res0, eval.template packet(alignedEnd2)); + } + res = func.predux(packet_res0); + + for(Index index = 0; index < alignedStart; ++index) + res = func(res,eval.coeff(index)); + + for(Index index = alignedEnd; index < size; ++index) + res = func(res,eval.coeff(index)); + } + else // too small to vectorize anything. + // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. + { + res = eval.coeff(0); + for(Index index = 1; index < size; ++index) + res = func(res,eval.coeff(index)); + } + + return res; + } +}; + +// NOTE: for SliceVectorizedTraversal we simply bypass unrolling +template +struct redux_impl +{ + typedef typename Evaluator::Scalar Scalar; + typedef typename redux_traits::PacketType PacketType; + + template + EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr) + { + eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix"); + const Index innerSize = xpr.innerSize(); + const Index outerSize = xpr.outerSize(); + enum { + packetSize = redux_traits::PacketSize + }; + const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize; + Scalar res; + if(packetedInnerSize) + { + PacketType packet_res = eval.template packet(0,0); + for(Index j=0; j(j,i)); + + res = func.predux(packet_res); + for(Index j=0; j::run(eval, func, xpr); + } + + return res; + } +}; + +template +struct redux_impl +{ + typedef typename Evaluator::Scalar Scalar; + + typedef typename redux_traits::PacketType PacketType; + enum { + PacketSize = redux_traits::PacketSize, + Size = Evaluator::SizeAtCompileTime, + VectorizedSize = (int(Size) / int(PacketSize)) * int(PacketSize) + }; + + template + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE + Scalar run(const Evaluator &eval, const Func& func, const XprType &xpr) + { + EIGEN_ONLY_USED_FOR_DEBUG(xpr) + eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix"); + if (VectorizedSize > 0) { + Scalar res = func.predux(redux_vec_unroller::template run(eval,func)); + if (VectorizedSize != Size) + res = func(res,redux_novec_unroller::run(eval,func)); + return res; + } + else { + return redux_novec_unroller::run(eval,func); + } + } +}; + +// evaluator adaptor +template +class redux_evaluator : public internal::evaluator<_XprType> +{ + typedef internal::evaluator<_XprType> Base; +public: + typedef _XprType XprType; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + explicit redux_evaluator(const XprType &xpr) : Base(xpr) {} + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename XprType::PacketScalar PacketScalar; + + enum { + MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime, + MaxColsAtCompileTime = XprType::MaxColsAtCompileTime, + // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator + Flags = Base::Flags & ~DirectAccessBit, + IsRowMajor = XprType::IsRowMajor, + SizeAtCompileTime = XprType::SizeAtCompileTime, + InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + CoeffReturnType coeffByOuterInner(Index outer, Index inner) const + { return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); } + + template + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketType packetByOuterInner(Index outer, Index inner) const + { return Base::template packet(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); } + +}; + +} // end namespace internal + +/*************************************************************************** +* Part 4 : public API +***************************************************************************/ + + +/** \returns the result of a full redux operation on the whole matrix or vector using \a func + * + * The template parameter \a BinaryOp is the type of the functor \a func which must be + * an associative operator. Both current C++98 and C++11 functor styles are handled. + * + * \warning the matrix must be not empty, otherwise an assertion is triggered. + * + * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise() + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::redux(const Func& func) const +{ + eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix"); + + typedef typename internal::redux_evaluator ThisEvaluator; + ThisEvaluator thisEval(derived()); + + // The initial expression is passed to the reducer as an additional argument instead of + // passing it as a member of redux_evaluator to help + return internal::redux_impl::run(thisEval, func, derived()); +} + +/** \returns the minimum of all coefficients of \c *this. + * In case \c *this contains NaN, NaNPropagation determines the behavior: + * NaNPropagation == PropagateFast : undefined + * NaNPropagation == PropagateNaN : result is NaN + * NaNPropagation == PropagateNumbers : result is minimum of elements that are not NaN + * \warning the matrix must be not empty, otherwise an assertion is triggered. + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::minCoeff() const +{ + return derived().redux(Eigen::internal::scalar_min_op()); +} + +/** \returns the maximum of all coefficients of \c *this. + * In case \c *this contains NaN, NaNPropagation determines the behavior: + * NaNPropagation == PropagateFast : undefined + * NaNPropagation == PropagateNaN : result is NaN + * NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN + * \warning the matrix must be not empty, otherwise an assertion is triggered. + */ +template +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::maxCoeff() const +{ + return derived().redux(Eigen::internal::scalar_max_op()); +} + +/** \returns the sum of all coefficients of \c *this + * + * If \c *this is empty, then the value 0 is returned. + * + * \sa trace(), prod(), mean() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::sum() const +{ + if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) + return Scalar(0); + return derived().redux(Eigen::internal::scalar_sum_op()); +} + +/** \returns the mean of all coefficients of *this +* +* \sa trace(), prod(), sum() +*/ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::mean() const +{ +#ifdef __INTEL_COMPILER + #pragma warning push + #pragma warning ( disable : 2259 ) +#endif + return Scalar(derived().redux(Eigen::internal::scalar_sum_op())) / Scalar(this->size()); +#ifdef __INTEL_COMPILER + #pragma warning pop +#endif +} + +/** \returns the product of all coefficients of *this + * + * Example: \include MatrixBase_prod.cpp + * Output: \verbinclude MatrixBase_prod.out + * + * \sa sum(), mean(), trace() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +DenseBase::prod() const +{ + if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) + return Scalar(1); + return derived().redux(Eigen::internal::scalar_product_op()); +} + +/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal. + * + * \c *this can be any matrix, not necessarily square. + * + * \sa diagonal(), sum() + */ +template +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits::Scalar +MatrixBase::trace() const +{ + return derived().diagonal().sum(); +} + +} // end namespace Eigen + +#endif // EIGEN_REDUX_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Ref.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Ref.h new file mode 100644 index 00000000..c2a37ead --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Ref.h @@ -0,0 +1,381 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2012 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_REF_H +#define EIGEN_REF_H + +namespace Eigen { + +namespace internal { + +template +struct traits > + : public traits > +{ + typedef _PlainObjectType PlainObjectType; + typedef _StrideType StrideType; + enum { + Options = _Options, + Flags = traits >::Flags | NestByRefBit, + Alignment = traits >::Alignment + }; + + template struct match { + enum { + IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime, + HasDirectAccess = internal::has_direct_access::ret, + StorageOrderMatch = IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)), + InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic) + || int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime) + || (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1), + OuterStrideMatch = IsVectorAtCompileTime + || int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime), + // NOTE, this indirection of evaluator::Alignment is needed + // to workaround a very strange bug in MSVC related to the instantiation + // of has_*ary_operator in evaluator. + // This line is surprisingly very sensitive. For instance, simply adding parenthesis + // as "DerivedAlignment = (int(evaluator::Alignment))," will make MSVC fail... + DerivedAlignment = int(evaluator::Alignment), + AlignmentMatch = (int(traits::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment + ScalarTypeMatch = internal::is_same::value, + MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch + }; + typedef typename internal::conditional::type type; + }; + +}; + +template +struct traits > : public traits {}; + +} + +template class RefBase + : public MapBase +{ + typedef typename internal::traits::PlainObjectType PlainObjectType; + typedef typename internal::traits::StrideType StrideType; + +public: + + typedef MapBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(RefBase) + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const + { + return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1; + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const + { + return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer() + : IsVectorAtCompileTime ? this->size() + : int(Flags)&RowMajorBit ? this->cols() + : this->rows(); + } + + EIGEN_DEVICE_FUNC RefBase() + : Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime), + // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values: + m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime, + StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime) + {} + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase) + +protected: + + typedef Stride StrideBase; + + // Resolves inner stride if default 0. + static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveInnerStride(Index inner) { + return inner == 0 ? 1 : inner; + } + + // Resolves outer stride if default 0. + static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveOuterStride(Index inner, Index outer, Index rows, Index cols, bool isVectorAtCompileTime, bool isRowMajor) { + return outer == 0 ? isVectorAtCompileTime ? inner * rows * cols : isRowMajor ? inner * cols : inner * rows : outer; + } + + // Returns true if construction is valid, false if there is a stride mismatch, + // and fails if there is a size mismatch. + template + EIGEN_DEVICE_FUNC bool construct(Expression& expr) + { + // Check matrix sizes. If this is a compile-time vector, we do allow + // implicitly transposing. + EIGEN_STATIC_ASSERT( + EIGEN_PREDICATE_SAME_MATRIX_SIZE(PlainObjectType, Expression) + // If it is a vector, the transpose sizes might match. + || ( PlainObjectType::IsVectorAtCompileTime + && ((int(PlainObjectType::RowsAtCompileTime)==Eigen::Dynamic + || int(Expression::ColsAtCompileTime)==Eigen::Dynamic + || int(PlainObjectType::RowsAtCompileTime)==int(Expression::ColsAtCompileTime)) + && (int(PlainObjectType::ColsAtCompileTime)==Eigen::Dynamic + || int(Expression::RowsAtCompileTime)==Eigen::Dynamic + || int(PlainObjectType::ColsAtCompileTime)==int(Expression::RowsAtCompileTime)))), + YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES + ) + + // Determine runtime rows and columns. + Index rows = expr.rows(); + Index cols = expr.cols(); + if(PlainObjectType::RowsAtCompileTime==1) + { + eigen_assert(expr.rows()==1 || expr.cols()==1); + rows = 1; + cols = expr.size(); + } + else if(PlainObjectType::ColsAtCompileTime==1) + { + eigen_assert(expr.rows()==1 || expr.cols()==1); + rows = expr.size(); + cols = 1; + } + // Verify that the sizes are valid. + eigen_assert( + (PlainObjectType::RowsAtCompileTime == Dynamic) || (PlainObjectType::RowsAtCompileTime == rows)); + eigen_assert( + (PlainObjectType::ColsAtCompileTime == Dynamic) || (PlainObjectType::ColsAtCompileTime == cols)); + + + // If this is a vector, we might be transposing, which means that stride should swap. + const bool transpose = PlainObjectType::IsVectorAtCompileTime && (rows != expr.rows()); + // If the storage format differs, we also need to swap the stride. + const bool row_major = ((PlainObjectType::Flags)&RowMajorBit) != 0; + const bool expr_row_major = (Expression::Flags&RowMajorBit) != 0; + const bool storage_differs = (row_major != expr_row_major); + + const bool swap_stride = (transpose != storage_differs); + + // Determine expr's actual strides, resolving any defaults if zero. + const Index expr_inner_actual = resolveInnerStride(expr.innerStride()); + const Index expr_outer_actual = resolveOuterStride(expr_inner_actual, + expr.outerStride(), + expr.rows(), + expr.cols(), + Expression::IsVectorAtCompileTime != 0, + expr_row_major); + + // If this is a column-major row vector or row-major column vector, the inner-stride + // is arbitrary, so set it to either the compile-time inner stride or 1. + const bool row_vector = (rows == 1); + const bool col_vector = (cols == 1); + const Index inner_stride = + ( (!row_major && row_vector) || (row_major && col_vector) ) ? + ( StrideType::InnerStrideAtCompileTime > 0 ? Index(StrideType::InnerStrideAtCompileTime) : 1) + : swap_stride ? expr_outer_actual : expr_inner_actual; + + // If this is a column-major column vector or row-major row vector, the outer-stride + // is arbitrary, so set it to either the compile-time outer stride or vector size. + const Index outer_stride = + ( (!row_major && col_vector) || (row_major && row_vector) ) ? + ( StrideType::OuterStrideAtCompileTime > 0 ? Index(StrideType::OuterStrideAtCompileTime) : rows * cols * inner_stride) + : swap_stride ? expr_inner_actual : expr_outer_actual; + + // Check if given inner/outer strides are compatible with compile-time strides. + const bool inner_valid = (StrideType::InnerStrideAtCompileTime == Dynamic) + || (resolveInnerStride(Index(StrideType::InnerStrideAtCompileTime)) == inner_stride); + if (!inner_valid) { + return false; + } + + const bool outer_valid = (StrideType::OuterStrideAtCompileTime == Dynamic) + || (resolveOuterStride( + inner_stride, + Index(StrideType::OuterStrideAtCompileTime), + rows, cols, PlainObjectType::IsVectorAtCompileTime != 0, + row_major) + == outer_stride); + if (!outer_valid) { + return false; + } + + ::new (static_cast(this)) Base(expr.data(), rows, cols); + ::new (&m_stride) StrideBase( + (StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride, + (StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride ); + return true; + } + + StrideBase m_stride; +}; + +/** \class Ref + * \ingroup Core_Module + * + * \brief A matrix or vector expression mapping an existing expression + * + * \tparam PlainObjectType the equivalent matrix type of the mapped data + * \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned. + * The default is \c #Unaligned. + * \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1), + * but accepts a variable outer stride (leading dimension). + * This can be overridden by specifying strides. + * The type passed here must be a specialization of the Stride template, see examples below. + * + * This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies. + * A Ref<> object can represent either a const expression or a l-value: + * \code + * // in-out argument: + * void foo1(Ref x); + * + * // read-only const argument: + * void foo2(const Ref& x); + * \endcode + * + * In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered. + * By default, a Ref can reference any dense vector expression of float having a contiguous memory layout. + * Likewise, a Ref can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with + * the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension) + * can be greater than the number of rows. + * + * In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function. + * Here are some examples: + * \code + * MatrixXf A; + * VectorXf a; + * foo1(a.head()); // OK + * foo1(A.col()); // OK + * foo1(A.row()); // Compilation error because here innerstride!=1 + * foo2(A.row()); // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object + * foo2(A.row().transpose()); // The row is copied into a contiguous temporary + * foo2(2*a); // The expression is evaluated into a temporary + * foo2(A.col().segment(2,4)); // No temporary + * \endcode + * + * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters. + * Here is an example accepting an innerstride!=1: + * \code + * // in-out argument: + * void foo3(Ref > x); + * foo3(A.row()); // OK + * \endcode + * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more + * expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a + * template function, e.g.: + * \code + * // in the .h: + * void foo(const Ref& A); + * void foo(const Ref >& A); + * + * // in the .cpp: + * template void foo_impl(const TypeOfA& A) { + * ... // crazy code goes here + * } + * void foo(const Ref& A) { foo_impl(A); } + * void foo(const Ref >& A) { foo_impl(A); } + * \endcode + * + * See also the following stackoverflow questions for further references: + * - Correct usage of the Eigen::Ref<> class + * + * \sa PlainObjectBase::Map(), \ref TopicStorageOrders + */ +template class Ref + : public RefBase > +{ + private: + typedef internal::traits Traits; + template + EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase& expr, + typename internal::enable_if::MatchAtCompileTime),Derived>::type* = 0); + public: + + typedef RefBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Ref) + + + #ifndef EIGEN_PARSED_BY_DOXYGEN + template + EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase& expr, + typename internal::enable_if::MatchAtCompileTime),Derived>::type* = 0) + { + EIGEN_STATIC_ASSERT(bool(Traits::template match::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH); + // Construction must pass since we will not create temprary storage in the non-const case. + const bool success = Base::construct(expr.derived()); + EIGEN_UNUSED_VARIABLE(success) + eigen_assert(success); + } + template + EIGEN_DEVICE_FUNC inline Ref(const DenseBase& expr, + typename internal::enable_if::MatchAtCompileTime),Derived>::type* = 0) + #else + /** Implicit constructor from any dense expression */ + template + inline Ref(DenseBase& expr) + #endif + { + EIGEN_STATIC_ASSERT(bool(internal::is_lvalue::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY); + EIGEN_STATIC_ASSERT(bool(Traits::template match::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH); + EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY); + // Construction must pass since we will not create temporary storage in the non-const case. + const bool success = Base::construct(expr.const_cast_derived()); + EIGEN_UNUSED_VARIABLE(success) + eigen_assert(success); + } + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref) + +}; + +// this is the const ref version +template class Ref + : public RefBase > +{ + typedef internal::traits Traits; + public: + + typedef RefBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Ref) + + template + EIGEN_DEVICE_FUNC inline Ref(const DenseBase& expr, + typename internal::enable_if::ScalarTypeMatch),Derived>::type* = 0) + { +// std::cout << match_helper::HasDirectAccess << "," << match_helper::OuterStrideMatch << "," << match_helper::InnerStrideMatch << "\n"; +// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n"; +// std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n"; + construct(expr.derived(), typename Traits::template match::type()); + } + + EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) { + // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy + } + + template + EIGEN_DEVICE_FUNC inline Ref(const RefBase& other) { + construct(other.derived(), typename Traits::template match::type()); + } + + protected: + + template + EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type) + { + // Check if we can use the underlying expr's storage directly, otherwise call the copy version. + if (!Base::construct(expr)) { + construct(expr, internal::false_type()); + } + } + + template + EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type) + { + internal::call_assignment_no_alias(m_object,expr,internal::assign_op()); + Base::construct(m_object); + } + + protected: + TPlainObjectType m_object; +}; + +} // end namespace Eigen + +#endif // EIGEN_REF_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Replicate.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Replicate.h new file mode 100644 index 00000000..ab5be7e6 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Replicate.h @@ -0,0 +1,142 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_REPLICATE_H +#define EIGEN_REPLICATE_H + +namespace Eigen { + +namespace internal { +template +struct traits > + : traits +{ + typedef typename MatrixType::Scalar Scalar; + typedef typename traits::StorageKind StorageKind; + typedef typename traits::XprKind XprKind; + typedef typename ref_selector::type MatrixTypeNested; + typedef typename remove_reference::type _MatrixTypeNested; + enum { + RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic + ? Dynamic + : RowFactor * MatrixType::RowsAtCompileTime, + ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic + ? Dynamic + : ColFactor * MatrixType::ColsAtCompileTime, + //FIXME we don't propagate the max sizes !!! + MaxRowsAtCompileTime = RowsAtCompileTime, + MaxColsAtCompileTime = ColsAtCompileTime, + IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1 + : MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0 + : (MatrixType::Flags & RowMajorBit) ? 1 : 0, + + // FIXME enable DirectAccess with negative strides? + Flags = IsRowMajor ? RowMajorBit : 0 + }; +}; +} + +/** + * \class Replicate + * \ingroup Core_Module + * + * \brief Expression of the multiple replication of a matrix or vector + * + * \tparam MatrixType the type of the object we are replicating + * \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic. + * \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic. + * + * This class represents an expression of the multiple replication of a matrix or vector. + * It is the return type of DenseBase::replicate() and most of the time + * this is the only way it is used. + * + * \sa DenseBase::replicate() + */ +template class Replicate + : public internal::dense_xpr_base< Replicate >::type +{ + typedef typename internal::traits::MatrixTypeNested MatrixTypeNested; + typedef typename internal::traits::_MatrixTypeNested _MatrixTypeNested; + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Replicate) + typedef typename internal::remove_all::type NestedExpression; + + template + EIGEN_DEVICE_FUNC + inline explicit Replicate(const OriginalMatrixType& matrix) + : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor) + { + EIGEN_STATIC_ASSERT((internal::is_same::type,OriginalMatrixType>::value), + THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE) + eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic); + } + + template + EIGEN_DEVICE_FUNC + inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor) + : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor) + { + EIGEN_STATIC_ASSERT((internal::is_same::type,OriginalMatrixType>::value), + THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE) + } + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); } + + EIGEN_DEVICE_FUNC + const _MatrixTypeNested& nestedExpression() const + { + return m_matrix; + } + + protected: + MatrixTypeNested m_matrix; + const internal::variable_if_dynamic m_rowFactor; + const internal::variable_if_dynamic m_colFactor; +}; + +/** + * \return an expression of the replication of \c *this + * + * Example: \include MatrixBase_replicate.cpp + * Output: \verbinclude MatrixBase_replicate.out + * + * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate + */ +template +template +EIGEN_DEVICE_FUNC const Replicate +DenseBase::replicate() const +{ + return Replicate(derived()); +} + +/** + * \return an expression of the replication of each column (or row) of \c *this + * + * Example: \include DirectionWise_replicate_int.cpp + * Output: \verbinclude DirectionWise_replicate_int.out + * + * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate + */ +template +EIGEN_DEVICE_FUNC const typename VectorwiseOp::ReplicateReturnType +VectorwiseOp::replicate(Index factor) const +{ + return typename VectorwiseOp::ReplicateReturnType + (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1); +} + +} // end namespace Eigen + +#endif // EIGEN_REPLICATE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reshaped.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reshaped.h new file mode 100644 index 00000000..52de73b6 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reshaped.h @@ -0,0 +1,454 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2017 Gael Guennebaud +// Copyright (C) 2014 yoco +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_RESHAPED_H +#define EIGEN_RESHAPED_H + +namespace Eigen { + +/** \class Reshaped + * \ingroup Core_Module + * + * \brief Expression of a fixed-size or dynamic-size reshape + * + * \tparam XprType the type of the expression in which we are taking a reshape + * \tparam Rows the number of rows of the reshape we are taking at compile time (optional) + * \tparam Cols the number of columns of the reshape we are taking at compile time (optional) + * \tparam Order can be ColMajor or RowMajor, default is ColMajor. + * + * This class represents an expression of either a fixed-size or dynamic-size reshape. + * It is the return type of DenseBase::reshaped(NRowsType,NColsType) and + * most of the time this is the only way it is used. + * + * However, in C++98, if you want to directly maniputate reshaped expressions, + * for instance if you want to write a function returning such an expression, you + * will need to use this class. In C++11, it is advised to use the \em auto + * keyword for such use cases. + * + * Here is an example illustrating the dynamic case: + * \include class_Reshaped.cpp + * Output: \verbinclude class_Reshaped.out + * + * Here is an example illustrating the fixed-size case: + * \include class_FixedReshaped.cpp + * Output: \verbinclude class_FixedReshaped.out + * + * \sa DenseBase::reshaped(NRowsType,NColsType) + */ + +namespace internal { + +template +struct traits > : traits +{ + typedef typename traits::Scalar Scalar; + typedef typename traits::StorageKind StorageKind; + typedef typename traits::XprKind XprKind; + enum{ + MatrixRows = traits::RowsAtCompileTime, + MatrixCols = traits::ColsAtCompileTime, + RowsAtCompileTime = Rows, + ColsAtCompileTime = Cols, + MaxRowsAtCompileTime = Rows, + MaxColsAtCompileTime = Cols, + XpxStorageOrder = ((int(traits::Flags) & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor, + ReshapedStorageOrder = (RowsAtCompileTime == 1 && ColsAtCompileTime != 1) ? RowMajor + : (ColsAtCompileTime == 1 && RowsAtCompileTime != 1) ? ColMajor + : XpxStorageOrder, + HasSameStorageOrderAsXprType = (ReshapedStorageOrder == XpxStorageOrder), + InnerSize = (ReshapedStorageOrder==int(RowMajor)) ? int(ColsAtCompileTime) : int(RowsAtCompileTime), + InnerStrideAtCompileTime = HasSameStorageOrderAsXprType + ? int(inner_stride_at_compile_time::ret) + : Dynamic, + OuterStrideAtCompileTime = Dynamic, + + HasDirectAccess = internal::has_direct_access::ret + && (Order==int(XpxStorageOrder)) + && ((evaluator::Flags&LinearAccessBit)==LinearAccessBit), + + MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits::size) == 0) + && (InnerStrideAtCompileTime == 1) + ? PacketAccessBit : 0, + //MaskAlignedBit = ((OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0, + FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0, + FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, + FlagsRowMajorBit = (ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0, + FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0, + Flags0 = traits::Flags & ( (HereditaryBits & ~RowMajorBit) | MaskPacketAccessBit), + + Flags = (Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit | FlagsDirectAccessBit) + }; +}; + +template class ReshapedImpl_dense; + +} // end namespace internal + +template class ReshapedImpl; + +template class Reshaped + : public ReshapedImpl::StorageKind> +{ + typedef ReshapedImpl::StorageKind> Impl; + public: + //typedef typename Impl::Base Base; + typedef Impl Base; + EIGEN_GENERIC_PUBLIC_INTERFACE(Reshaped) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reshaped) + + /** Fixed-size constructor + */ + EIGEN_DEVICE_FUNC + inline Reshaped(XprType& xpr) + : Impl(xpr) + { + EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE) + eigen_assert(Rows * Cols == xpr.rows() * xpr.cols()); + } + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC + inline Reshaped(XprType& xpr, + Index reshapeRows, Index reshapeCols) + : Impl(xpr, reshapeRows, reshapeCols) + { + eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==reshapeRows) + && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==reshapeCols)); + eigen_assert(reshapeRows * reshapeCols == xpr.rows() * xpr.cols()); + } +}; + +// The generic default implementation for dense reshape simply forward to the internal::ReshapedImpl_dense +// that must be specialized for direct and non-direct access... +template +class ReshapedImpl + : public internal::ReshapedImpl_dense >::HasDirectAccess> +{ + typedef internal::ReshapedImpl_dense >::HasDirectAccess> Impl; + public: + typedef Impl Base; + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl) + EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr) : Impl(xpr) {} + EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr, Index reshapeRows, Index reshapeCols) + : Impl(xpr, reshapeRows, reshapeCols) {} +}; + +namespace internal { + +/** \internal Internal implementation of dense Reshaped in the general case. */ +template +class ReshapedImpl_dense + : public internal::dense_xpr_base >::type +{ + typedef Reshaped ReshapedType; + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense) + + typedef typename internal::ref_selector::non_const_type MatrixTypeNested; + typedef typename internal::remove_all::type NestedExpression; + + class InnerIterator; + + /** Fixed-size constructor + */ + EIGEN_DEVICE_FUNC + inline ReshapedImpl_dense(XprType& xpr) + : m_xpr(xpr), m_rows(Rows), m_cols(Cols) + {} + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC + inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols) + : m_xpr(xpr), m_rows(nRows), m_cols(nCols) + {} + + EIGEN_DEVICE_FUNC Index rows() const { return m_rows; } + EIGEN_DEVICE_FUNC Index cols() const { return m_cols; } + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** \sa MapBase::data() */ + EIGEN_DEVICE_FUNC inline const Scalar* data() const; + EIGEN_DEVICE_FUNC inline Index innerStride() const; + EIGEN_DEVICE_FUNC inline Index outerStride() const; + #endif + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC + const typename internal::remove_all::type& + nestedExpression() const { return m_xpr; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC + typename internal::remove_reference::type& + nestedExpression() { return m_xpr; } + + protected: + + MatrixTypeNested m_xpr; + const internal::variable_if_dynamic m_rows; + const internal::variable_if_dynamic m_cols; +}; + + +/** \internal Internal implementation of dense Reshaped in the direct access case. */ +template +class ReshapedImpl_dense + : public MapBase > +{ + typedef Reshaped ReshapedType; + typedef typename internal::ref_selector::non_const_type XprTypeNested; + public: + + typedef MapBase Base; + EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType) + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense) + + /** Fixed-size constructor + */ + EIGEN_DEVICE_FUNC + inline ReshapedImpl_dense(XprType& xpr) + : Base(xpr.data()), m_xpr(xpr) + {} + + /** Dynamic-size constructor + */ + EIGEN_DEVICE_FUNC + inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols) + : Base(xpr.data(), nRows, nCols), + m_xpr(xpr) + {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all::type& nestedExpression() const + { + return m_xpr; + } + + EIGEN_DEVICE_FUNC + XprType& nestedExpression() { return m_xpr; } + + /** \sa MapBase::innerStride() */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index innerStride() const + { + return m_xpr.innerStride(); + } + + /** \sa MapBase::outerStride() */ + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index outerStride() const + { + return ((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows(); + } + + protected: + + XprTypeNested m_xpr; +}; + +// Evaluators +template struct reshaped_evaluator; + +template +struct evaluator > + : reshaped_evaluator >::HasDirectAccess> +{ + typedef Reshaped XprType; + typedef typename XprType::Scalar Scalar; + // TODO: should check for smaller packet types + typedef typename packet_traits::type PacketScalar; + + enum { + CoeffReadCost = evaluator::CoeffReadCost, + HasDirectAccess = traits::HasDirectAccess, + +// RowsAtCompileTime = traits::RowsAtCompileTime, +// ColsAtCompileTime = traits::ColsAtCompileTime, +// MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, +// MaxColsAtCompileTime = traits::MaxColsAtCompileTime, +// +// InnerStrideAtCompileTime = traits::HasSameStorageOrderAsXprType +// ? int(inner_stride_at_compile_time::ret) +// : Dynamic, +// OuterStrideAtCompileTime = Dynamic, + + FlagsLinearAccessBit = (traits::RowsAtCompileTime == 1 || traits::ColsAtCompileTime == 1 || HasDirectAccess) ? LinearAccessBit : 0, + FlagsRowMajorBit = (traits::ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0, + FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0, + Flags0 = evaluator::Flags & (HereditaryBits & ~RowMajorBit), + Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit | FlagsDirectAccessBit, + + PacketAlignment = unpacket_traits::alignment, + Alignment = evaluator::Alignment + }; + typedef reshaped_evaluator reshaped_evaluator_type; + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : reshaped_evaluator_type(xpr) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } +}; + +template +struct reshaped_evaluator + : evaluator_base > +{ + typedef Reshaped XprType; + + enum { + CoeffReadCost = evaluator::CoeffReadCost /* TODO + cost of index computations */, + + Flags = (evaluator::Flags & (HereditaryBits /*| LinearAccessBit | DirectAccessBit*/)), + + Alignment = 0 + }; + + EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr) + { + EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); + } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + typedef std::pair RowCol; + + inline RowCol index_remap(Index rowId, Index colId) const + { + if(Order==ColMajor) + { + const Index nth_elem_idx = colId * m_xpr.rows() + rowId; + return RowCol(nth_elem_idx % m_xpr.nestedExpression().rows(), + nth_elem_idx / m_xpr.nestedExpression().rows()); + } + else + { + const Index nth_elem_idx = colId + rowId * m_xpr.cols(); + return RowCol(nth_elem_idx / m_xpr.nestedExpression().cols(), + nth_elem_idx % m_xpr.nestedExpression().cols()); + } + } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index rowId, Index colId) + { + EIGEN_STATIC_ASSERT_LVALUE(XprType) + const RowCol row_col = index_remap(rowId, colId); + return m_argImpl.coeffRef(row_col.first, row_col.second); + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index rowId, Index colId) const + { + const RowCol row_col = index_remap(rowId, colId); + return m_argImpl.coeffRef(row_col.first, row_col.second); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const + { + const RowCol row_col = index_remap(rowId, colId); + return m_argImpl.coeff(row_col.first, row_col.second); + } + + EIGEN_DEVICE_FUNC + inline Scalar& coeffRef(Index index) + { + EIGEN_STATIC_ASSERT_LVALUE(XprType) + const RowCol row_col = index_remap(Rows == 1 ? 0 : index, + Rows == 1 ? index : 0); + return m_argImpl.coeffRef(row_col.first, row_col.second); + + } + + EIGEN_DEVICE_FUNC + inline const Scalar& coeffRef(Index index) const + { + const RowCol row_col = index_remap(Rows == 1 ? 0 : index, + Rows == 1 ? index : 0); + return m_argImpl.coeffRef(row_col.first, row_col.second); + } + + EIGEN_DEVICE_FUNC + inline const CoeffReturnType coeff(Index index) const + { + const RowCol row_col = index_remap(Rows == 1 ? 0 : index, + Rows == 1 ? index : 0); + return m_argImpl.coeff(row_col.first, row_col.second); + } +#if 0 + EIGEN_DEVICE_FUNC + template + inline PacketScalar packet(Index rowId, Index colId) const + { + const RowCol row_col = index_remap(rowId, colId); + return m_argImpl.template packet(row_col.first, row_col.second); + + } + + template + EIGEN_DEVICE_FUNC + inline void writePacket(Index rowId, Index colId, const PacketScalar& val) + { + const RowCol row_col = index_remap(rowId, colId); + m_argImpl.const_cast_derived().template writePacket + (row_col.first, row_col.second, val); + } + + template + EIGEN_DEVICE_FUNC + inline PacketScalar packet(Index index) const + { + const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index, + RowsAtCompileTime == 1 ? index : 0); + return m_argImpl.template packet(row_col.first, row_col.second); + } + + template + EIGEN_DEVICE_FUNC + inline void writePacket(Index index, const PacketScalar& val) + { + const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index, + RowsAtCompileTime == 1 ? index : 0); + return m_argImpl.template packet(row_col.first, row_col.second, val); + } +#endif +protected: + + evaluator m_argImpl; + const XprType& m_xpr; + +}; + +template +struct reshaped_evaluator +: mapbase_evaluator, + typename Reshaped::PlainObject> +{ + typedef Reshaped XprType; + typedef typename XprType::Scalar Scalar; + + EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr) + : mapbase_evaluator(xpr) + { + // TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime + eigen_assert(((internal::UIntPtr(xpr.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator::Alignment)) == 0) && "data is not aligned"); + } +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_RESHAPED_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/ReturnByValue.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ReturnByValue.h new file mode 100644 index 00000000..4dad13ea --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/ReturnByValue.h @@ -0,0 +1,119 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009-2010 Gael Guennebaud +// Copyright (C) 2009-2010 Benoit Jacob +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_RETURNBYVALUE_H +#define EIGEN_RETURNBYVALUE_H + +namespace Eigen { + +namespace internal { + +template +struct traits > + : public traits::ReturnType> +{ + enum { + // We're disabling the DirectAccess because e.g. the constructor of + // the Block-with-DirectAccess expression requires to have a coeffRef method. + // Also, we don't want to have to implement the stride stuff. + Flags = (traits::ReturnType>::Flags + | EvalBeforeNestingBit) & ~DirectAccessBit + }; +}; + +/* The ReturnByValue object doesn't even have a coeff() method. + * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix. + * So internal::nested always gives the plain return matrix type. + * + * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ?? + * Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators + */ +template +struct nested_eval, n, PlainObject> +{ + typedef typename traits::ReturnType type; +}; + +} // end namespace internal + +/** \class ReturnByValue + * \ingroup Core_Module + * + */ +template class ReturnByValue + : public internal::dense_xpr_base< ReturnByValue >::type, internal::no_assignment_operator +{ + public: + typedef typename internal::traits::ReturnType ReturnType; + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue) + + template + EIGEN_DEVICE_FUNC + inline void evalTo(Dest& dst) const + { static_cast(this)->evalTo(dst); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return static_cast(this)->rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return static_cast(this)->cols(); } + +#ifndef EIGEN_PARSED_BY_DOXYGEN +#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT + class Unusable{ + Unusable(const Unusable&) {} + Unusable& operator=(const Unusable&) {return *this;} + }; + const Unusable& coeff(Index) const { return *reinterpret_cast(this); } + const Unusable& coeff(Index,Index) const { return *reinterpret_cast(this); } + Unusable& coeffRef(Index) { return *reinterpret_cast(this); } + Unusable& coeffRef(Index,Index) { return *reinterpret_cast(this); } +#undef Unusable +#endif +}; + +template +template +EIGEN_DEVICE_FUNC Derived& DenseBase::operator=(const ReturnByValue& other) +{ + other.evalTo(derived()); + return derived(); +} + +namespace internal { + +// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that +// when a ReturnByValue expression is assigned, the evaluator is not constructed. +// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world + +template +struct evaluator > + : public evaluator::ReturnType> +{ + typedef ReturnByValue XprType; + typedef typename internal::traits::ReturnType PlainObject; + typedef evaluator Base; + + EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) + : m_result(xpr.rows(), xpr.cols()) + { + ::new (static_cast(this)) Base(m_result); + xpr.evalTo(m_result); + } + +protected: + PlainObject m_result; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_RETURNBYVALUE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reverse.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reverse.h new file mode 100644 index 00000000..28cdd76a --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Reverse.h @@ -0,0 +1,217 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob +// Copyright (C) 2009 Ricard Marxer +// Copyright (C) 2009-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_REVERSE_H +#define EIGEN_REVERSE_H + +namespace Eigen { + +namespace internal { + +template +struct traits > + : traits +{ + typedef typename MatrixType::Scalar Scalar; + typedef typename traits::StorageKind StorageKind; + typedef typename traits::XprKind XprKind; + typedef typename ref_selector::type MatrixTypeNested; + typedef typename remove_reference::type _MatrixTypeNested; + enum { + RowsAtCompileTime = MatrixType::RowsAtCompileTime, + ColsAtCompileTime = MatrixType::ColsAtCompileTime, + MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, + Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit) + }; +}; + +template struct reverse_packet_cond +{ + static inline PacketType run(const PacketType& x) { return preverse(x); } +}; + +template struct reverse_packet_cond +{ + static inline PacketType run(const PacketType& x) { return x; } +}; + +} // end namespace internal + +/** \class Reverse + * \ingroup Core_Module + * + * \brief Expression of the reverse of a vector or matrix + * + * \tparam MatrixType the type of the object of which we are taking the reverse + * \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections + * + * This class represents an expression of the reverse of a vector. + * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse() + * and most of the time this is the only way it is used. + * + * \sa MatrixBase::reverse(), VectorwiseOp::reverse() + */ +template class Reverse + : public internal::dense_xpr_base< Reverse >::type +{ + public: + + typedef typename internal::dense_xpr_base::type Base; + EIGEN_DENSE_PUBLIC_INTERFACE(Reverse) + typedef typename internal::remove_all::type NestedExpression; + using Base::IsRowMajor; + + protected: + enum { + PacketSize = internal::packet_traits::size, + IsColMajor = !IsRowMajor, + ReverseRow = (Direction == Vertical) || (Direction == BothDirections), + ReverseCol = (Direction == Horizontal) || (Direction == BothDirections), + OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, + OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1, + ReversePacket = (Direction == BothDirections) + || ((Direction == Vertical) && IsColMajor) + || ((Direction == Horizontal) && IsRowMajor) + }; + typedef internal::reverse_packet_cond reverse_packet; + public: + + EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { } + + EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse) + + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); } + EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR + inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); } + + EIGEN_DEVICE_FUNC inline Index innerStride() const + { + return -m_matrix.innerStride(); + } + + EIGEN_DEVICE_FUNC const typename internal::remove_all::type& + nestedExpression() const + { + return m_matrix; + } + + protected: + typename MatrixType::Nested m_matrix; +}; + +/** \returns an expression of the reverse of *this. + * + * Example: \include MatrixBase_reverse.cpp + * Output: \verbinclude MatrixBase_reverse.out + * + */ +template +EIGEN_DEVICE_FUNC inline typename DenseBase::ReverseReturnType +DenseBase::reverse() +{ + return ReverseReturnType(derived()); +} + + +//reverse const overload moved DenseBase.h due to a CUDA compiler bug + +/** This is the "in place" version of reverse: it reverses \c *this. + * + * In most cases it is probably better to simply use the reversed expression + * of a matrix. However, when reversing the matrix data itself is really needed, + * then this "in-place" version is probably the right choice because it provides + * the following additional benefits: + * - less error prone: doing the same operation with .reverse() requires special care: + * \code m = m.reverse().eval(); \endcode + * - this API enables reverse operations without the need for a temporary + * - it allows future optimizations (cache friendliness, etc.) + * + * \sa VectorwiseOp::reverseInPlace(), reverse() */ +template +EIGEN_DEVICE_FUNC inline void DenseBase::reverseInPlace() +{ + if(cols()>rows()) + { + Index half = cols()/2; + leftCols(half).swap(rightCols(half).reverse()); + if((cols()%2)==1) + { + Index half2 = rows()/2; + col(half).head(half2).swap(col(half).tail(half2).reverse()); + } + } + else + { + Index half = rows()/2; + topRows(half).swap(bottomRows(half).reverse()); + if((rows()%2)==1) + { + Index half2 = cols()/2; + row(half).head(half2).swap(row(half).tail(half2).reverse()); + } + } +} + +namespace internal { + +template +struct vectorwise_reverse_inplace_impl; + +template<> +struct vectorwise_reverse_inplace_impl +{ + template + static void run(ExpressionType &xpr) + { + const int HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2; + Index half = xpr.rows()/2; + xpr.topRows(fix(half)) + .swap(xpr.bottomRows(fix(half)).colwise().reverse()); + } +}; + +template<> +struct vectorwise_reverse_inplace_impl +{ + template + static void run(ExpressionType &xpr) + { + const int HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2; + Index half = xpr.cols()/2; + xpr.leftCols(fix(half)) + .swap(xpr.rightCols(fix(half)).rowwise().reverse()); + } +}; + +} // end namespace internal + +/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this. + * + * In most cases it is probably better to simply use the reversed expression + * of a matrix. However, when reversing the matrix data itself is really needed, + * then this "in-place" version is probably the right choice because it provides + * the following additional benefits: + * - less error prone: doing the same operation with .reverse() requires special care: + * \code m = m.reverse().eval(); \endcode + * - this API enables reverse operations without the need for a temporary + * + * \sa DenseBase::reverseInPlace(), reverse() */ +template +EIGEN_DEVICE_FUNC void VectorwiseOp::reverseInPlace() +{ + internal::vectorwise_reverse_inplace_impl::run(m_matrix); +} + +} // end namespace Eigen + +#endif // EIGEN_REVERSE_H diff --git a/tools/SampleAnalyzer/Commons/Eigen/src/Core/Select.h b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Select.h new file mode 100644 index 00000000..7c86bf87 --- /dev/null +++ b/tools/SampleAnalyzer/Commons/Eigen/src/Core/Select.h @@ -0,0 +1,164 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SELECT_H +#define EIGEN_SELECT_H + +namespace Eigen { + +/** \class Select + * \ingroup Core_Module + * + * \brief Expression of a coefficient wise version of the C++ ternary operator ?: + * + * \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix + * \param ThenMatrixType the type of the \em then expression + * \param ElseMatrixType the type of the \em else expression + * + * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:. + * It is the return type of DenseBase::select() and most of the time this is the only way it is used. + * + * \sa DenseBase::select(const DenseBase&, const DenseBase&) const + */ + +namespace internal { +template +struct traits > + : traits +{ + typedef typename traits::Scalar Scalar; + typedef Dense StorageKind; + typedef typename traits::XprKind XprKind; + typedef typename ConditionMatrixType::Nested ConditionMatrixNested; + typedef typename ThenMatrixType::Nested ThenMatrixNested; + typedef typename ElseMatrixType::Nested ElseMatrixNested; + enum { + RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime, + ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime, + MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime, + MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime, + Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit + }; +}; +} + +template +class Select : public internal::dense_xpr_base< Select >::type, + internal::no_assignment_operator +{ + public: + + typedef typename internal::dense_xpr_base