# create conda environment
$ mamba env create -f env.yml
# update conda environment
$ mamba env update -n litds --file env.yml
pip install -e .
# install from pypi
pip install litds
# activate conda environment
$ conda activate litds
# make sure the litds package is installed in development mode
$ pip install -e .
# make changes under nbs/ directory
# ...
# compile to have changes apply to the litds package
$ nbdev_prepare
# publish to pypi
$ nbdev_pypi
# publish to conda
$ nbdev_conda --build_args '-c conda-forge'
# $ nbdev_conda --mambabuild --build_args '-c conda-forge -c pytorch -c bioconda -c dsm-72'
Install latest from the GitHub repository:
$ pip install git+https://github.com/dsm-72/litds.git
or from conda
$ conda install -c dsm-72 litds
or from pypi
$ pip install litds
Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.
dd = DiamondsDataset()
dd.getone()
(tensor([[ 0.0491, 0.0015],
[ 0.1555, 0.3419],
[-0.3727, 0.3598],
[ 0.6811, 0.3734],
[ 0.4492, -0.7379]]),
tensor([0., 1., 2., 3., 4.]))
dd.plot(palette='mako_r')
od = OrbitsDataset()
od.getone()
(tensor([[ 0.0203, -0.2043],
[-0.3889, -0.1781],
[ 0.0290, 0.6299],
[-0.3386, 0.8402],
[ 0.9571, 0.0337]]),
tensor([0., 1., 2., 3., 4.]))
od.plot(palette='mako_r')
eb = EmbryoidBodies2018DataModule(
primary='pca',
batch_size=8,
)
eb.setup()
EB Loader: 0%| | 0/126 [00:00<?, ?it/s]
Loading data
Data ready!
dl = eb.train_dataloader()
eb.train_ds.df[eb.train_ds.df.columns[-5:]].head()
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d97 | d98 | d99 | d100 | samples | |
---|---|---|---|---|---|
4854 | -0.633847 | -0.701649 | -1.466198 | -0.381950 | Day 06-09 |
6121 | 0.432789 | 1.089373 | -0.968371 | -0.211686 | Day 06-09 |
7620 | 0.042552 | 0.870117 | -1.264836 | -1.664296 | Day 12-15 |
12560 | 1.770667 | -0.594136 | 1.893129 | -1.064998 | Day 18-21 |
10336 | -2.947320 | -1.072601 | 1.554807 | 0.170369 | Day 18-21 |