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ligo.em-bright

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1.2.2 ligo_em_bright-1.2.2-py3-none-any.whl

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Project: ligo.em-bright
Version: 1.2.2
Filename: ligo_em_bright-1.2.2-py3-none-any.whl
Download: [link]
Size: 30272
MD5: 2635f1a2fdf7c6223a75a74e397b5d7d
SHA256: 1c3ef50b9d537019d9b6bdd2f60322f8ded26d598034d6d453b2b2246434dd64
Uploaded: 2024-09-25 13:44:03 +0000

dist-info

METADATA · WHEEL · RECORD · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: ligo.em-bright
Version: 1.2.2
Summary: Possibility and properties of Electromagnetically-bright sources of gravitational-wave events
Author: Deep Chatterjee
Author-Email: deep.chatterjee[at]ligo.org
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Requires-Dist: astropy (<7.0,>=6.0)
Requires-Dist: h5py (<4.0,>=3.11)
Requires-Dist: lalsuite (<8.0,>=7.23)
Requires-Dist: numpy
Requires-Dist: pandas (<3.0,>=2.2)
Requires-Dist: scikit-learn (==1.5.1)
Description-Content-Type: text/markdown
[Description omitted; length: 978 characters]

WHEEL

Wheel-Version: 1.0
Generator: poetry-core 1.9.0
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
ligo/em_bright/__init__.py sha256=0DtEupbn2I8byIHcMySHjVrnSxocsUhdYNoXW2yt7JI 875
ligo/em_bright/categorize.py sha256=ytAqVVJaiAqp6EghwYjChAKw9xTvp1HdwePjTOoWS78 13353
ligo/em_bright/computeDiskMass.py sha256=a5rz5vmaMSGnOxCQf6eu85T7fJj9oADUx7Wi6hhfuGw 9917
ligo/em_bright/dag_writer.py sha256=sC2pVxayYRQC943-8bStfvrHXhZ0t8xOVzHn_Oi5_SY 18387
ligo/em_bright/data/__init__.py sha256=mkutHIQFfcf8PgDKuHZ9goFEBX3wQpeY37v6vEVA_mo 2322
ligo/em_bright/em_bright.py sha256=DGb1VabjHXE16k8kpyPFDYkDnHXDGRh0_nRLNOO9s9E 15287
ligo/em_bright/tests/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
ligo/em_bright/tests/data/test_categorize_data.tbl sha256=F7D4vszbhp5W4rLiRH2Vu2agcOCbGjFVgffm8roQo9c 303
ligo/em_bright/tests/data/test_categorize_ssm_data.csv sha256=9FVVf0D3nfbk2_JxUAUuGXIF77YBvZpUrnxRwKlfBw4 598
ligo/em_bright/tests/test_em_bright.py sha256=55r_R2Gticjj6K-aEFreVoNFMH0dsOnFWmGlWt3seyE 13926
ligo/em_bright/utils.py sha256=VFwrJ_NMiHLA4yOYaXrNqzmKqRJARZRADDVGaNsnSRA 28412
ligo_em_bright-1.2.2.dist-info/LICENSE sha256=EvBA7MIkZ9dSRO9CLe__ma1DYuJv0v4wUPE_DVtyZ98 1072
ligo_em_bright-1.2.2.dist-info/METADATA sha256=BpqPXP4_2bOSf8tE3EC7CUAdOZYNh1YfStaf4jSG03g 1734
ligo_em_bright-1.2.2.dist-info/WHEEL sha256=sP946D7jFCHeNz5Iq4fL4Lu-PrWrFsgfLXbbkciIZwg 88
ligo_em_bright-1.2.2.dist-info/entry_points.txt sha256=2fbwcSSkUoXna6faxfwktagUxSwfAE2rM9TihTl_vVs 390
ligo_em_bright-1.2.2.dist-info/RECORD — —

entry_points.txt

[console_scripts]
em_bright_categorize = ligo.em_bright.categorize:main
em_bright_categorize_all_eos = ligo.em_bright.categorize:main_all
em_bright_create_param_sweep_plot = ligo.em_bright.utils:param_sweep_plot
em_bright_dag_writer = ligo.em_bright.dag_writer:main
em_bright_extract = ligo.em_bright.utils:extract
em_bright_join = ligo.em_bright.utils:join
em_bright_train = ligo.em_bright.utils:train