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requirements-dev.lock
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requirements-dev.lock
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# generated by rye
# use `rye lock` or `rye sync` to update this lockfile
#
# last locked with the following flags:
# pre: false
# features: ["replication"]
# all-features: false
# with-sources: false
-e file:.
appnope==0.1.4
# via ipykernel
asttokens==2.4.1
# via stack-data
click==8.1.7
# via typer
cliffs-delta==1.0.0
# via neurojit
comm==0.2.2
# via ipykernel
contourpy==1.2.1
# via matplotlib
cycler==0.12.1
# via matplotlib
debugpy==1.8.1
# via ipykernel
decorator==5.1.1
# via ipython
executing==2.0.1
# via stack-data
fonttools==4.53.1
# via matplotlib
gitdb==4.0.11
# via gitpython
gitpython==3.1.43
# via pydriller
imageio==2.35.1
# via scikit-image
imbalanced-learn==0.12.3
# via imblearn
imblearn==0.0
# via neurojit
ipykernel==6.29.4
ipython==8.23.0
# via ipykernel
javalang==0.13.0
# via neurojit
jedi==0.19.1
# via ipython
joblib==1.4.2
# via imbalanced-learn
# via scikit-learn
jupyter-client==8.6.1
# via ipykernel
jupyter-core==5.7.2
# via ipykernel
# via jupyter-client
kiwisolver==1.4.5
# via matplotlib
lazy-loader==0.4
# via scikit-image
lime==0.2.0.1
# via neurojit
lizard==1.17.10
# via pydriller
markdown-it-py==3.0.0
# via rich
matplotlib==3.9.2
# via lime
# via neurojit
# via seaborn
matplotlib-inline==0.1.6
# via ipykernel
# via ipython
mdurl==0.1.2
# via markdown-it-py
nest-asyncio==1.6.0
# via ipykernel
networkx==3.3
# via scikit-image
numpy==1.26.4
# via contourpy
# via imageio
# via imbalanced-learn
# via lime
# via matplotlib
# via neurojit
# via pandas
# via patsy
# via scikit-image
# via scikit-learn
# via scipy
# via seaborn
# via statsmodels
# via tifffile
# via xgboost
packaging==24.0
# via ipykernel
# via lazy-loader
# via matplotlib
# via scikit-image
# via statsmodels
pandas==2.2.1
# via neurojit
# via seaborn
# via statsmodels
parso==0.8.4
# via jedi
patsy==0.5.6
# via statsmodels
pexpect==4.9.0
# via ipython
pillow==10.4.0
# via imageio
# via matplotlib
# via scikit-image
platformdirs==4.2.0
# via jupyter-core
prompt-toolkit==3.0.43
# via ipython
psutil==5.9.8
# via ipykernel
ptyprocess==0.7.0
# via pexpect
pure-eval==0.2.2
# via stack-data
pydriller==2.6
# via neurojit
pygments==2.17.2
# via ipython
# via rich
pyparsing==3.1.2
# via matplotlib
python-dateutil==2.9.0.post0
# via jupyter-client
# via matplotlib
# via pandas
pytz==2024.1
# via pandas
# via pydriller
pyzmq==25.1.2
# via ipykernel
# via jupyter-client
rich==13.7.1
# via neurojit
# via typer
scikit-image==0.24.0
# via lime
scikit-learn==1.5.1
# via imbalanced-learn
# via lime
# via neurojit
scipy==1.14.1
# via imbalanced-learn
# via lime
# via neurojit
# via scikit-image
# via scikit-learn
# via statsmodels
# via xgboost
seaborn==0.13.2
# via neurojit
shellingham==1.5.4
# via typer
six==1.16.0
# via asttokens
# via javalang
# via patsy
# via python-dateutil
smmap==5.0.1
# via gitdb
stack-data==0.6.3
# via ipython
statsmodels==0.14.2
# via neurojit
tabulate==0.9.0
# via neurojit
threadpoolctl==3.5.0
# via imbalanced-learn
# via scikit-learn
tifffile==2024.8.10
# via scikit-image
tornado==6.4
# via ipykernel
# via jupyter-client
tqdm==4.66.5
# via lime
traitlets==5.14.2
# via comm
# via ipykernel
# via ipython
# via jupyter-client
# via jupyter-core
# via matplotlib-inline
typer==0.12.4
# via neurojit
types-pytz==2024.1.0.20240203
# via pydriller
typing-extensions==4.12.2
# via typer
tzdata==2024.1
# via pandas
wcwidth==0.2.13
# via prompt-toolkit
xgboost==2.1.1
# via neurojit