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cristallina_envs/analysis-opencv.yml

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# Analysis environment with the rather large OpenCV package for image analysis
name: analysis-stable
channels:
- conda-forge
dependencies:
# essentials
- python=3.13 # keep at 3.13 beause of bitshuffle
- pip
- numpy>=1.26,<3 # also because of bitshuffle (perhaps not necessary and be deleted in the future)
- scipy
- pandas
- numba
- matplotlib
- ipython
- jupyterlab
- ipympl
- joblib
- tqdm # sfdata dependency but here given explicitly
- lmfit
- pytest
- bitshuffle=0.5.2 # 0.5.2 is compatible with Python 3.123.13 on conda-forge;
# Python >=3.14 requires new builds, not a new version (currently not well specified enough to upgrade)
- opencv # OpenCV package for more elaborate image analysis
# useful development packages
- black
- pytest # for testing the cristallina package
- h5py # sfdata dependency but here given explicitly
- pint
- line_profiler
- loguru
- pylint
- ipytest # used to be taken from pip, but here we test the conda-forge version
- sqlalchemy # X-ray transmission calculations and similar
- xraydb # X-ray transmission calculations and similar
- scikit-image # Image processing
- partialjson # For processing unfinished runs
# and extra control parts
- fabric
# Jupyterlab extensions
- nb_conda_kernels # for discovery of other kernels
# - jupyterlab-drawio
- jupyterlab_code_formatter
# - jupytext # needs to be tested manually
# PSI specific modules
- paulscherrerinstitute::sfdata
- paulscherrerinstitute::jungfrau_utils # should be a dependency of sfdata, but not listed there explicitly
- paulscherrerinstitute::data_api
- pip:
- -e /sf/cristallina/applications/cristallina # Creates a "local package" of cristallina .This shows a deprication warning and
# should be changed to using .toml in the future.
# If wanted, jupyterlab_hdf can be added for nicer tables
# pip install jupyterlab_hdf
# jupyter labextension install @jupyterlab/hdf5