idmodels/requirements/requirements.txt at 3f2c07d606131d2c0264df468b608f1dd1a83e19 · reichlab/idmodels · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
# This file was autogenerated by uv via the following command:
#    uv pip compile pyproject.toml -o requirements/requirements.txt
aiobotocore==2.15.2
    # via s3fs
aiohappyeyeballs==2.4.3
    # via aiohttp
aiohttp==3.10.10
    # via
    #   aiobotocore
    #   s3fs
aioitertools==0.12.0
    # via aiobotocore
aiosignal==1.3.1
    # via aiohttp
attrs==24.2.0
    # via
    #   aiohttp
    #   pymmwr
botocore==1.35.36
    # via aiobotocore
contourpy==1.3.0
    # via matplotlib
cycler==0.12.1
    # via matplotlib
fonttools==4.54.1
    # via matplotlib
frozenlist==1.5.0
    # via
    #   aiohttp
    #   aiosignal
fsspec==2024.10.0
    # via s3fs
iddata @ git+https://github.com/reichlab/iddata@7b86ad0e513423faa8d327426c18350dfdfa07f0
    # via idmodels (pyproject.toml)
idna==3.10
    # via yarl
jax==0.8.0
    # via
    #   numpyro
    #   sarix
jaxlib==0.8.0
    # via
    #   jax
    #   numpyro
jmespath==1.0.1
    # via botocore
joblib==1.4.2
    # via scikit-learn
kiwisolver==1.4.7
    # via matplotlib
lightgbm==4.5.0
    # via idmodels (pyproject.toml)
markdown-it-py==3.0.0
    # via rich
matplotlib==3.9.2
    # via sarix
mdurl==0.1.2
    # via markdown-it-py
ml-dtypes==0.5.0
    # via
    #   jax
    #   jaxlib
multidict==6.1.0
    # via
    #   aiohttp
    #   yarl
multipledispatch==1.0.0
    # via numpyro
numpy==2.1.3
    # via
    #   idmodels (pyproject.toml)
    #   contourpy
    #   iddata
    #   jax
    #   jaxlib
    #   lightgbm
    #   matplotlib
    #   ml-dtypes
    #   numpyro
    #   pandas
    #   sarix
    #   scikit-learn
    #   scipy
    #   timeseriesutils
numpyro==0.19.0
    # via sarix
opt-einsum==3.4.0
    # via jax
packaging==24.1
    # via matplotlib
pandas==2.2.3
    # via
    #   idmodels (pyproject.toml)
    #   iddata
    #   timeseriesutils
pillow==11.0.0
    # via matplotlib
propcache==0.2.0
    # via yarl
pygments==2.18.0
    # via rich
pymmwr==0.2.2
    # via iddata
pyparsing==3.2.0
    # via matplotlib
python-dateutil==2.9.0.post0
    # via
    #   botocore
    #   matplotlib
    #   pandas
pytz==2024.2
    # via pandas
rich==13.9.4
    # via iddata
s3fs==2024.10.0
    # via iddata
sarix @ git+https://github.com/reichlab/sarix@35eea2379a9790e0457b1aed41d13509e5d5056f
    # via idmodels (pyproject.toml)
scikit-learn==1.5.2
    # via idmodels (pyproject.toml)
scipy==1.14.1
    # via
    #   jax
    #   jaxlib
    #   lightgbm
    #   scikit-learn
    #   timeseriesutils
six==1.16.0
    # via python-dateutil
threadpoolctl==3.5.0
    # via scikit-learn
timeseriesutils @ git+https://github.com/reichlab/timeseriesutils@d25f33db30a8d5252329269ec9d205984f980f6c
    # via idmodels (pyproject.toml)
toml==0.10.2
    # via iddata
tqdm==4.67.0
    # via
    #   idmodels (pyproject.toml)
    #   numpyro
tzdata==2024.2
    # via pandas
urllib3==2.2.3
    # via botocore
wrapt==1.16.0
    # via aiobotocore
yarl==1.17.1
    # via aiohttp