requirements-dev.lock

# generated by rye
# use `rye lock` or `rye sync` to update this lockfile
#
# last locked with the following flags:
#   pre: false
#   features: []
#   all-features: false
#   with-sources: false
#   generate-hashes: false
#   universal: false

-e file:.
absl-py==2.1.0
    # via chex
    # via ml-collections
    # via optax
    # via orbax-checkpoint
    # via tensorboard
aiohappyeyeballs==2.4.4
    # via aiohttp
aiohttp==3.11.10
    # via fsspec
aiosignal==1.3.2
    # via aiohttp
anndata==0.11.1
    # via doubletdetection
    # via mudata
    # via parafac2
    # via scanpy
    # via sccp
    # via scib
    # via scvi-tools
annoy==1.17.3
    # via pacmap
    # via scanorama
array-api-compat==1.8
    # via anndata
asttokens==2.4.1
    # via stack-data
attrs==24.3.0
    # via aiohttp
certifi==2024.8.30
    # via requests
charset-normalizer==3.3.2
    # via requests
chex==0.1.88
    # via optax
click==8.1.7
    # via dask
cloudpickle==3.0.0
    # via dask
colorcet==3.1.0
    # via datashader
comm==0.2.2
    # via ipywidgets
contourpy==1.3.0
    # via matplotlib
coverage==7.6.1
    # via pytest-cov
cupy-cuda12x==13.3.0
    # via parafac2
cycler==0.12.1
    # via matplotlib
dask==2024.8.2
    # via dask-expr
    # via datashader
    # via sccp
dask-expr==1.1.13
    # via dask
datashader==0.16.3
    # via sccp
decorator==5.1.1
    # via ipython
deprecated==1.2.14
    # via scib
docrep==0.3.2
    # via scvi-tools
doubletdetection==4.2
    # via sccp
etils==1.11.0
    # via optax
    # via orbax-checkpoint
executing==2.1.0
    # via stack-data
fastrlock==0.8.2
    # via cupy-cuda12x
fbpca==1.0
    # via geosketch
    # via scanorama
filelock==3.16.1
    # via torch
    # via triton
flax==0.10.2
    # via scvi-tools
fonttools==4.53.1
    # via matplotlib
frozenlist==1.5.0
    # via aiohttp
    # via aiosignal
fsspec==2024.9.0
    # via dask
    # via etils
    # via lightning
    # via pytorch-lightning
    # via torch
geosketch==1.3
    # via scanorama
grpcio==1.68.1
    # via tensorboard
gseapy==1.1.3
    # via sccp
h5netcdf==1.3.0
    # via sccp
h5py==3.11.0
    # via anndata
    # via h5netcdf
    # via scanpy
    # via scib
harmonypy==0.0.10
    # via sccp
humanize==4.11.0
    # via orbax-checkpoint
idna==3.8
    # via requests
    # via yarl
igraph==0.11.6
    # via leidenalg
    # via louvain
    # via scib
importlib-resources==6.4.5
    # via etils
iniconfig==2.0.0
    # via pytest
intervaltree==3.1.0
    # via scanorama
ipython==8.27.0
    # via ipywidgets
ipywidgets==8.1.5
    # via doubletdetection
jax==0.4.37
    # via chex
    # via flax
    # via numpyro
    # via optax
    # via orbax-checkpoint
    # via scvi-tools
jaxlib==0.4.36
    # via chex
    # via jax
    # via numpyro
    # via optax
    # via scvi-tools
jedi==0.19.1
    # via ipython
jinja2==3.1.4
    # via torch
joblib==1.4.2
    # via pynndescent
    # via scanpy
    # via scikit-learn
jupyterlab-widgets==3.0.13
    # via ipywidgets
kiwisolver==1.4.7
    # via matplotlib
legacy-api-wrap==1.4
    # via scanpy
leidenalg==0.10.2
    # via doubletdetection
    # via phenograph
    # via sccp
    # via scib
lightning==2.4.0
    # via scvi-tools
lightning-utilities==0.11.9
    # via lightning
    # via pytorch-lightning
    # via torchmetrics
llvmlite==0.43.0
    # via numba
    # via pynndescent
    # via scib
locket==1.0.0
    # via partd
louvain==0.8.2
    # via doubletdetection
markdown==3.7
    # via tensorboard
markdown-it-py==3.0.0
    # via rich
markupsafe==3.0.2
    # via jinja2
    # via werkzeug
matplotlib==3.9.2
    # via doubletdetection
    # via gseapy
    # via scanorama
    # via scanpy
    # via scib
    # via seaborn
    # via tlviz
matplotlib-inline==0.1.7
    # via ipython
mdurl==0.1.2
    # via markdown-it-py
ml-collections==1.0.0
    # via scvi-tools
ml-dtypes==0.5.0
    # via jax
    # via jaxlib
    # via tensorstore
mpmath==1.3.0
    # via sympy
msgpack==1.1.0
    # via flax
    # via orbax-checkpoint
mudata==0.3.1
    # via scvi-tools
multidict==6.1.0
    # via aiohttp
    # via yarl
multipledispatch==1.0.0
    # via datashader
    # via numpyro
natsort==8.4.0
    # via anndata
    # via scanpy
nest-asyncio==1.6.0
    # via orbax-checkpoint
networkx==3.3
    # via scanpy
    # via torch
nodeenv==1.9.1
    # via pyright
numba==0.60.0
    # via datashader
    # via pacmap
    # via pynndescent
    # via scanpy
    # via scib
    # via scvi-tools
    # via sparse
    # via umap-learn
numpy==2.0.2
    # via anndata
    # via chex
    # via contourpy
    # via cupy-cuda12x
    # via dask
    # via datashader
    # via doubletdetection
    # via flax
    # via geosketch
    # via gseapy
    # via h5py
    # via harmonypy
    # via jax
    # via jaxlib
    # via matplotlib
    # via ml-dtypes
    # via numba
    # via numpyro
    # via optax
    # via orbax-checkpoint
    # via pacmap
    # via pandas
    # via parafac2
    # via patsy
    # via phenograph
    # via pyarrow
    # via pyro-ppl
    # via scanorama
    # via scanpy
    # via sccp
    # via scib
    # via scikit-learn
    # via scikit-misc
    # via scipy
    # via scvi-tools
    # via seaborn
    # via sparse
    # via statsmodels
    # via tensorboard
    # via tensorly
    # via tensorstore
    # via tlviz
    # via torchmetrics
    # via umap-learn
    # via xarray
numpyro==0.16.1
    # via scvi-tools
nvidia-cublas-cu12==12.4.5.8
    # via nvidia-cudnn-cu12
    # via nvidia-cusolver-cu12
    # via torch
nvidia-cuda-cupti-cu12==12.4.127
    # via torch
nvidia-cuda-nvrtc-cu12==12.4.127
    # via torch
nvidia-cuda-runtime-cu12==12.4.127
    # via torch
nvidia-cudnn-cu12==9.1.0.70
    # via torch
nvidia-cufft-cu12==11.2.1.3
    # via torch
nvidia-curand-cu12==10.3.5.147
    # via torch
nvidia-cusolver-cu12==11.6.1.9
    # via torch
nvidia-cusparse-cu12==12.3.1.170
    # via nvidia-cusolver-cu12
    # via torch
nvidia-nccl-cu12==2.21.5
    # via torch
nvidia-nvjitlink-cu12==12.4.127
    # via nvidia-cusolver-cu12
    # via nvidia-cusparse-cu12
    # via torch
nvidia-nvtx-cu12==12.4.127
    # via torch
opt-einsum==3.4.0
    # via jax
    # via pyro-ppl
optax==0.2.4
    # via flax
    # via scvi-tools
orbax-checkpoint==0.10.2
    # via flax
packaging==24.1
    # via anndata
    # via dask
    # via datashader
    # via h5netcdf
    # via lightning
    # via lightning-utilities
    # via matplotlib
    # via pytest
    # via pytorch-lightning
    # via scanpy
    # via statsmodels
    # via tensorboard
    # via torchmetrics
    # via xarray
pacmap==0.7.3
    # via parafac2
    # via sccp
pandas==2.2.2
    # via anndata
    # via dask
    # via dask-expr
    # via datashader
    # via doubletdetection
    # via gseapy
    # via harmonypy
    # via scanpy
    # via sccp
    # via scib
    # via scvi-tools
    # via seaborn
    # via statsmodels
    # via tlviz
    # via xarray
parafac2 @ git+https://github.com/meyer-lab/parafac2.git@6600c677a77d0f242668babf39559cf7953ff534
    # via sccp
param==2.1.1
    # via datashader
    # via pyct
parso==0.8.4
    # via jedi
partd==1.4.2
    # via dask
patsy==0.5.6
    # via scanpy
    # via statsmodels
pexpect==4.9.0
    # via ipython
phenograph==1.5.7
    # via doubletdetection
pillow==10.4.0
    # via datashader
    # via matplotlib
pluggy==1.5.0
    # via pytest
prompt-toolkit==3.0.47
    # via ipython
propcache==0.2.1
    # via aiohttp
    # via yarl
protobuf==5.29.1
    # via orbax-checkpoint
    # via tensorboard
psutil==6.0.0
    # via phenograph
ptyprocess==0.7.0
    # via pexpect
pure-eval==0.2.3
    # via stack-data
pyarrow==17.0.0
    # via dask-expr
pyct==0.5.0
    # via datashader
pydot==3.0.1
    # via scib
pygments==2.18.0
    # via ipython
    # via rich
pynndescent==0.5.13
    # via scanpy
    # via umap-learn
pyparsing==3.1.4
    # via matplotlib
    # via pydot
pyright==1.1.391
pyro-api==0.1.2
    # via pyro-ppl
pyro-ppl==1.9.1
    # via scvi-tools
pytest==8.3.3
    # via pytest-cov
pytest-cov==6.0.0
python-dateutil==2.9.0.post0
    # via matplotlib
    # via pandas
pytorch-lightning==2.4.0
    # via lightning
pytz==2024.2
    # via pandas
pyyaml==6.0.2
    # via dask
    # via flax
    # via lightning
    # via ml-collections
    # via orbax-checkpoint
    # via pytorch-lightning
requests==2.32.3
    # via datashader
    # via gseapy
    # via tlviz
rich==13.9.4
    # via flax
    # via scvi-tools
scanorama==1.7.4
    # via sccp
scanpy==1.10.4
    # via doubletdetection
    # via sccp
    # via scib
scib==1.1.5
    # via sccp
scikit-learn==1.6.0
    # via geosketch
    # via harmonypy
    # via pacmap
    # via parafac2
    # via phenograph
    # via pynndescent
    # via scanorama
    # via scanpy
    # via sccp
    # via scib
    # via scvi-tools
    # via umap-learn
scikit-misc==0.5.1
    # via scib
scipy==1.14.1
    # via anndata
    # via datashader
    # via doubletdetection
    # via gseapy
    # via harmonypy
    # via jax
    # via jaxlib
    # via parafac2
    # via phenograph
    # via pynndescent
    # via scanorama
    # via scanpy
    # via sccp
    # via scib
    # via scikit-learn
    # via scvi-tools
    # via sparse
    # via statsmodels
    # via tensorly
    # via tlviz
    # via umap-learn
scvi-tools==1.2.2.post1
    # via sccp
seaborn==0.13.2
    # via scanpy
    # via sccp
    # via scib
session-info==1.0.0
    # via scanpy
setuptools==74.1.2
    # via chex
    # via lightning-utilities
    # via phenograph
    # via tensorboard
    # via torch
simplejson==3.19.3
    # via orbax-checkpoint
six==1.16.0
    # via asttokens
    # via docrep
    # via ml-collections
    # via patsy
    # via python-dateutil
    # via tensorboard
sortedcontainers==2.4.0
    # via intervaltree
sparse==0.15.4
    # via scvi-tools
stack-data==0.6.3
    # via ipython
statsmodels==0.14.2
    # via scanpy
    # via sccp
    # via tlviz
stdlib-list==0.10.0
    # via session-info
sympy==1.13.1
    # via torch
tensorboard==2.18.0
    # via scvi-tools
tensorboard-data-server==0.7.2
    # via tensorboard
tensorly==0.8.1
    # via parafac2
    # via sccp
tensorstore==0.1.71
    # via flax
    # via orbax-checkpoint
texttable==1.7.0
    # via igraph
threadpoolctl==3.5.0
    # via scikit-learn
tlviz==0.1.1
    # via parafac2
    # via sccp
toolz==0.12.1
    # via chex
    # via dask
    # via datashader
    # via partd
torch==2.5.1
    # via lightning
    # via pyro-ppl
    # via pytorch-lightning
    # via scvi-tools
    # via torchmetrics
torchmetrics==1.6.0
    # via lightning
    # via pytorch-lightning
    # via scvi-tools
tqdm==4.66.5
    # via doubletdetection
    # via lightning
    # via numpyro
    # via parafac2
    # via pyro-ppl
    # via pytorch-lightning
    # via scanpy
    # via sccp
    # via scvi-tools
    # via umap-learn
traitlets==5.14.3
    # via comm
    # via ipython
    # via ipywidgets
    # via matplotlib-inline
triton==3.1.0
    # via torch
typing-extensions==4.12.2
    # via chex
    # via etils
    # via flax
    # via lightning
    # via lightning-utilities
    # via orbax-checkpoint
    # via pyright
    # via pytorch-lightning
    # via torch
tzdata==2024.1
    # via pandas
umap-learn==0.5.6
    # via scanpy
    # via scib
urllib3==2.2.3
    # via requests
wcwidth==0.2.13
    # via prompt-toolkit
werkzeug==3.1.3
    # via tensorboard
widgetsnbextension==4.0.13
    # via ipywidgets
wrapt==1.16.0
    # via deprecated
xarray==2024.9.0
    # via datashader
    # via scvi-tools
    # via tlviz
yarl==1.18.3
    # via aiohttp
zipp==3.20.2
    # via etils