Wheelodex » Projects » pyepidemics » pyepidemics-0.0.9-py3-none-any.whl

pyepidemics

View on PyPI — Reverse Dependencies (0)

0.0.9 pyepidemics-0.0.9-py3-none-any.whl

Wheel Details

Project: pyepidemics
Version: 0.0.9
Filename: pyepidemics-0.0.9-py3-none-any.whl
Download: [link]
Size: 40398
MD5: ac245f13fd17b89c863f6c861fbde79b
SHA256: 0c90c2a2bee96f889edd3f85c9a5ff8b34f6710f122cfcb794a2e94cee1cb104
Uploaded: 2020-12-10 17:01:59 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: pyepidemics
Version: 0.0.9
Summary: Open source epidemiological modeling in Python
Author: Theo Alves Da Costa
Author-Email: theo.alvesdacosta[at]ekimetrics.com
Home-Page: https://github.com/collectif-codata/pyepidemics
Project-Url: Documentation, https://collectif-codata.github.io/pyepidemics/
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Requires-Dist: scipy (==1.4.1)
Requires-Dist: numpy (==1.18.4)
Requires-Dist: pandas (>=1.0.0)
Requires-Dist: scikit-learn (==0.23.1)
Requires-Dist: matplotlib (==3.1.3)
Requires-Dist: optuna (==1.3.0)
Requires-Dist: pydeck (==0.3.0b2)
Requires-Dist: requests (==2.22.0)
Requires-Dist: plotly (==4.6.0)
Requires-Dist: tqdm (==4.46.0)
Requires-Dist: statsmodels (==0.10.1)
Requires-Dist: networkx (==2.3)
Requires-Dist: PyYAML (==5.3.1)
Requires-Dist: xlrd (>=1.0.0)
Description-Content-Type: text/markdown
[Description omitted; length: 4451 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.34.2)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
pyepidemics/__init__.py sha256=d64CFfrmR9ZUQVO2oLDG2tw013FyRkbtu87n56MiPWM 218
pyepidemics/curve_fitting.py sha256=E8bmVpaLf2jB1M4xIx3a7cydYlG_0C4I9FEsY8MMpqk 3749
pyepidemics/utils.py sha256=gKwUeTs-y2fQqntier0ZKBKKl5O6Y1YxGNK4AqXQ9Hs 1224
pyepidemics/dataset/__init__.py sha256=5XF5x_6FSdrkhfKAK6Qm-4IDAmMUi8bfU0SfQuLmkCs 723
pyepidemics/dataset/_base.py sha256=5lq3aWSi_lCYLuuAmsvsxOEVlAi01967cE0ugfLxhwY 586
pyepidemics/dataset/_build.py sha256=gljyGzw3MU1Vu5XwsML-_58On0RMlFMRtIjSz_VSN58 2822
pyepidemics/dataset/_fetch.py sha256=FsKa0qr0DM3pD1CEEcgYRWfIpr0AAXIFF8yjm7rNVRg 13645
pyepidemics/dataset/_fetch_bed.py sha256=QplUJ71gqISzv1NEtnOTuMQ4nVDBSqwZVb7FkH48nfc 3780
pyepidemics/dataset/_fetch_geojson.py sha256=yT9HTSVqI94B2zAC0BEq138W0QKDd6Vr7f3aO07l0Pk 1241
pyepidemics/models/__init__.py sha256=GyCTc9Bi3iB3i2rIY68MSBHyMkygZ3bR_427_0Lufxk 368
pyepidemics/models/model.py sha256=HMeVTHVYkEIHah00xxw2sfLa4qXh7MhvKLzdY4ZvRew 20090
pyepidemics/models/network.py sha256=bOfQDJCMnEXRomlWCfVyFJZwYPN6THLS1xXMEq9jCgI 3599
pyepidemics/models/params.py sha256=zeGYck86X2568DUqPAfi6Id1iugKE6e9N-Qtbxho6MM 1888
pyepidemics/models/state.py sha256=9umNIJIf47p30dDfxBQWiYreZIzxcyzta3rJyV-7ISc 349
pyepidemics/models/states.py sha256=S08wJ9uuCOxThxMpmxFraiFXpf5MbxbFtaHtR0BSgFc 4883
pyepidemics/models/examples/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pyepidemics/models/examples/covid.py sha256=nyc7bAS9RM8WegtGYiHkhXpb4MClZ9D6JTP8DjrHJ3k 3973
pyepidemics/models/examples/covid_category.py sha256=LTpoDqD2aV2yfMJ8jYYzu7BsZOsGVbuVxg6HjaGDhIs 5981
pyepidemics/models/examples/covid_prodromal.py sha256=XtFp8yZJu2J2GjYtUDXCfdOMZ_37AvIYHGoin4JE-qY 4614
pyepidemics/models/examples/granular_sir.py sha256=6FizZNOUO2xxto4fDtkywOI7D3kLpfrJUI48RVhkwpg 798
pyepidemics/models/examples/seidr.py sha256=Xm_SeGIJ_vQm55VOGtE2i_nFh1mQnZ371fL6th6rtug 1127
pyepidemics/models/examples/seihdr.py sha256=BB0LS5YsH43oE0FUI4T0EASoLTurkpYOSlag3ys-AQI 1795
pyepidemics/models/examples/seir.py sha256=_X6HxKqc_iGEjxIkDEekqiVUla1fK9zTuGajH2-gkOs 769
pyepidemics/models/examples/sir.py sha256=K04kxNfZoteb5SwF619V0WkwSrNzif7WM9T_hQv3uMQ 1162
pyepidemics/params/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pyepidemics/params/bayesian_optimizer.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pyepidemics/params/hyperopt_optimizer.py sha256=Tr0dRxQNNrcipV2n22P98tqRykafBmpXfAG9e5CR5pg 1794
pyepidemics/params/metrics.py sha256=PaeAYRihgx4KM17U_-F5EcLXrhVJrQvfXLcqSl6UWC4 1033
pyepidemics/params/neptune.py sha256=284tkMFCCIPCg_EAwr7mvJuACRbvN_0XVGuEitO3oWE 301
pyepidemics/params/optimizer.py sha256=cJLcVXwsmtYv6oXfpJkwyQrzpnKNc6Zw2tGPA0a_xE8 8752
pyepidemics/params/utils.py sha256=WXoXS4ZCBQac8HtqFoD2EGUKF40j9l9Hva8xEyVqzE8 117
pyepidemics/policies/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pyepidemics/policies/contact.py sha256=jqYMJQmMhEGFz--_TuCNGvyEoiGEBLG8YnyqfUtAgAg 1277
pyepidemics/policies/utils.py sha256=VSbcTJ_ejtqdmIyZzsPQKC5eTrrxWQweB4nEota72Lk 2034
pyepidemics/visualization/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
pyepidemics/visualization/map.py sha256=Ss95hAcUfPgqHvAzEh_f5cl4xJFrTp6nUTQIhwmTaJU 1873
pyepidemics-0.0.9.dist-info/LICENSE sha256=jUEK68G24xCnLSe_L5TI383hdCvYxuHCeWhUgV_Ebr8 1094
pyepidemics-0.0.9.dist-info/METADATA sha256=gC6VlUd5ihlRDl36nmAkw2m51pE4oQ9g2qBa5gk5jOQ 5533
pyepidemics-0.0.9.dist-info/WHEEL sha256=YUYzQ6UQdoqxXjimOitTqynltBCkwY6qlTfTh2IzqQU 97
pyepidemics-0.0.9.dist-info/top_level.txt sha256=NyUqq2fYKpVN09gfEzhdVTrXB870LJjMB8eUjheCKS8 12
pyepidemics-0.0.9.dist-info/RECORD — —

top_level.txt

pyepidemics