Wheelodex » Projects » timewise-sup

timewise-sup

View on PyPI — Reverse Dependencies (0)

0.5.0 timewise_sup-0.5.0-py3-none-any.whl

Wheel Details

Project: timewise-sup
Version: 0.5.0
Filename: timewise_sup-0.5.0-py3-none-any.whl
Download: [link]
Size: 77944
MD5: e56b03f17d324897b7991a70475bb81c
SHA256: b02f0c0c1cad7ac4b63a7c28ad8e7ca58295bf189b4a8574f37e272506685ffd
Uploaded: 2024-05-06 17:09:16 +0000

dist-info

METADATA · WHEEL · RECORD · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: timewise-sup
Version: 0.5.0
Summary: The Timewise Subtraction Pipeline produces mid-infrared difference photometry based on measurements by the WISE satelite
Author: Jannis Necker
Author-Email: jannis.necker[at]gmail.com
Home-Page: https://gitlab.desy.de/jannisnecker/timewise_sup
Project-Url: Documentation, https://jannisnecker.pages.desy.de/timewise_sup/docs/
Project-Url: Repository, https://gitlab.desy.de/jannisnecker/timewise_sup
License: BSD-3-Clause
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.10,<3.11
Requires-Dist: ampel-core (==0.8.4)
Requires-Dist: ampel-hu-astro[ztf] (==0.8.3a20)
Requires-Dist: corner (<3.0.0,>=2.2.1)
Requires-Dist: extcats (<3.0.0,>=2.4.3)
Requires-Dist: jupyterlab (<5.0.0,>=4.0.0); extra == "jupyter"
Requires-Dist: matplotlib-venn (<0.12.0,>=0.11.9)
Requires-Dist: more-itertools (<10.0.0,>=9.0.0)
Requires-Dist: nltk (<4.0,>=3.7)
Requires-Dist: scikit-learn (<2.0.0,>=1.1.2)
Requires-Dist: timewise (==0.4.10)
Requires-Dist: tkpdfviewer (<0.2,>=0.1)
Requires-Dist: uncertainties (<4.0.0,>=3.1.7)
Provides-Extra: fpbot
Provides-Extra: jupyter
Description-Content-Type: text/markdown
[Description omitted; length: 1179 characters]

WHEEL

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

RECORD

Path Digest Size
timewise_sup/__init__.py sha256=7Xq4bblNvZLyN52--kvauk04nEKntT9O7hhhOycTkiQ 387
timewise_sup/ampel_conf.py sha256=1zMGeIEjH8o0EYYv6NGY2WuaU3lFsWvI8eMX35x_OFY 8210
timewise_sup/analyse_lightcurves/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
timewise_sup/analyse_lightcurves/bayesian_blocks.py sha256=sUzJMmzAZenxKVMtao9Fd3KP_coE9xW2r3qnXziUiDM 1720
timewise_sup/analyse_lightcurves/cluster.py sha256=sohUzekt4Aga8tU1Zl6H7bUV6xdKUFdyPcN2OlDCI6g 12959
timewise_sup/analyse_lightcurves/create_job_file_yaml.py sha256=oOrVcT1tS-8Gl-RcKlI3hJ0COP2hk-AH32V6I40mIRI 13084
timewise_sup/config_loader.py sha256=LHujrBsA4pFxX0_Qk6_ElnlQM3TuWWNMDMqyHIq8EJI 5476
timewise_sup/environment.py sha256=n9c1CDkhyfGOK2ng0rUfWi_YoYBFKPOgrjQji6QPFHQ 740
timewise_sup/main.py sha256=ogSqsu3NJ-FC36abl_BK9sURWHNJPNl4M47jZ_4k2fo 5647
timewise_sup/meta_analysis/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
timewise_sup/meta_analysis/agn.py sha256=_o9woXTvgOBw0JIGmSpyKS7M1sMLJXqgnzbOJ016qYY 4550
timewise_sup/meta_analysis/baseline_subtraction.py sha256=jl-ZdrAuVojTywA33eMUWv6WdOzh7hAT0GYBHI37FDQ 13738
timewise_sup/meta_analysis/catalog_match.py sha256=vxGnIyxi9DnL1T0FaCYoycfi-viUvSp8REg_fQDxZbE 1232
timewise_sup/meta_analysis/diagnostics.py sha256=oBZ2Vba9aBk49M2kI_Xc2wdYdKCSuuPyG6u2cCmAqPE 18577
timewise_sup/meta_analysis/fluence.py sha256=LIDYM-tOquulTikav1oVzB--tsy0EdOgtjIhCa22V-c 5955
timewise_sup/meta_analysis/flux.py sha256=f9JyNHoX9i_rroKnYo9qp3591plJwWVPnkAaVQ1sEMI 11903
timewise_sup/meta_analysis/integrate_time.py sha256=nMox9chfvXo8bxCSnxAVObNkmwQuI44lvuoyCpw2o1g 3058
timewise_sup/meta_analysis/ir_energy.py sha256=OynX_mOH30jKvVCv0x3BCWJyxq7IEvK1uWq5AOUHvw0 9274
timewise_sup/meta_analysis/luminosity.py sha256=Z_rq8VrvZYLuWPrVlh2fR4esRhGser3v5NA-cbRhrPU 11364
timewise_sup/meta_analysis/plot_tns_matches.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
timewise_sup/meta_analysis/rejection_reason.py sha256=RUl6ypmpbzrNFRtBC8EIwU4erSvfc8fgZuLaa03YDNg 7031
timewise_sup/meta_analysis/separation.py sha256=zHC1IUlvYA29rEvhFTuoV-KYepUwLu0PfVghA6pEWVA 10926
timewise_sup/mongo.py sha256=B0sVGCUwboT99l5HTZfJxxDl8whJKybpkIta4Q_L9yk 32171
timewise_sup/plots/__init__.py sha256=JaWXvuoSxnI7ObjEg_ogBZOvtCmkGvQAJXx62I9w6Ls 1066
timewise_sup/plots/chi2_histograms.py sha256=fF3a4F5XcMhpxybuzm0bkvzT2MAMnXtCq9i9H26qtxM 2462
timewise_sup/plots/color_plots.py sha256=qStvtrTuUGtYgkujSc3K8mqVNPAedZfypsn6erdr-J8 8698
timewise_sup/plots/diagnostic_plots.py sha256=4z1zvkTz3HRcEnt2Dr3BUoE-rKGe__mNjijgDvMa97g 17829
timewise_sup/plots/ensemble_lightcurves.py sha256=_nyPmgl4N36uoQweL9vY1JIWhdDEx_pMaIjrNemxVtY 4406
timewise_sup/plots/ir_energies.py sha256=sba3zfHdhgTzXndqRGzWTir2nhC9ALluuytHWZdj79k 2088
timewise_sup/plots/plot_lightcurves.py sha256=yNXHjd0QPlsoPIg4kFkIHfq1E5U0KMVT6cAxXMEVHDE 5532
timewise_sup/plots/rise_fade_times.py sha256=_smUg990WjbbCvgNhQr6XkOTyo-OkrAEEVgpds1U0Vg 2768
timewise_sup/plots/test_data_res.py sha256=bFVmka7XTeIxG5vaaEAsrLDjoyqHTmIm1wR1-8U2oi4 2973
timewise_sup/samples/bts.py sha256=cmy4DLG2Asc3LJsdndx35q-3T3TDXpuOlj6siwikYWE 2822
timewise_sup/samples/data/test_sample.json sha256=DUShUE9WT_3B-N_3RNVz3Ases1vQG20cE4YbnkwjCn4 32086
timewise_sup/samples/sjoerts_flares.py sha256=1SFT5i3d1FtMM28VQIotrcmN7JWErEmuAm66fEcYEEY 2959
timewise_sup/sky_match.py sha256=VG0ygZTdpmo1F3VAXwuvoYJaag75GhIeGEQkiHJGGW8 3775
timewise_sup-0.5.0.dist-info/LICENSE sha256=fBTWLYfUnsVHvHGAvoCoMzr1WFd7m8LN7WFuwpLCME4 1542
timewise_sup-0.5.0.dist-info/METADATA sha256=H-K6-lM1xX0C98Q4X79ibWXqmqBCQQHPjSX69iYLwwA 2454
timewise_sup-0.5.0.dist-info/WHEEL sha256=sP946D7jFCHeNz5Iq4fL4Lu-PrWrFsgfLXbbkciIZwg 88
timewise_sup-0.5.0.dist-info/entry_points.txt sha256=Uu5jRslc6Apvl3Bxn1kxWquriHGGtXQstjELvg5TcJ8 104
timewise_sup-0.5.0.dist-info/RECORD — —

entry_points.txt

[console_scripts]
timewise_sup = timewise_sup.main:main
timewise_sup_bump = timewise_sup.main:bump_version