"your.utils.gpu.gpu_dedisp_and_dmt_crop",
"your.utils.gpu.get_gpu_memory_map",
],
"utils/misc.md": [
"your.utils.misc.crop",
"your.utils.misc.pad_along_axis",
"your.utils.misc.MyEncoder",
"your.utils.misc.YourArgparseFormatter",
],
}
# Generate documentation from the installed package
doc_generator = DocumentationGenerator(
pages, "https://github.com/thepetabyteproject/your/blob/master"
)
doc_generator.generate("./sources")
# Make readme as the start page
shutil.copyfile("../README.md", "sources/index.md")
shutil.copyfile("../CODE_OF_CONDUCT.md", "sources/CODE_OF_CONDUCT.md")
shutil.copyfile("../CONTRIBUTING.md", "sources/CONTRIBUTING.md")
remove_html("sources/index.md")
# Make the dir for tutorials
os.mkdir("sources/ipynb")
for nb in glob.glob("../examples/*ipynb"):
file_name = os.path.basename(nb)
os.symlink(os.path.abspath(nb), "sources/ipynb/" + file_name)
# make the dir for bin files and run argmark
pages = {
'AutoEncoder.md': [
"docclean.autoencoder.Autoencoder",
"docclean.autoencoder.Autoencoder.build_model",
"docclean.autoencoder.Autoencoder.train_model"
],
'CycleGAN.md':
["docclean.cycle_gan.CycleGan", "docclean.cycle_gan.CycleGan.train"],
'Utils.md': [
"docclean.utils.ImageMosaic",
"docclean.utils.ImageMosaic.get_powers_of_two",
"docclean.utils.ImageMosaic.normalise",
"docclean.utils.ImageMosaic.extend_image",
"docclean.utils.ImageMosaic.make_patches",
"docclean.utils.ImageMosaic.combine_patches",
"docclean.utils.normed_to_uint8", "docclean.utils.get_png_data",
"docclean.utils.get_kaggle_paired_data",
"docclean.utils.get_kaggle_data", "docclean.utils.normalize",
"docclean.utils.books_crop_and_augment",
"docclean.utils.kaggle_paired_augment"
]
}
doc_generator = DocumentationGenerator(pages)
doc_generator.generate('./sources')
shutil.copyfile('../README.md', 'sources/index.md')
os.makedirs(os.path.dirname('sources/data/'), exist_ok=True)
shutil.copyfile('../data/demo.gif', 'sources/data/demo.gif')
],
"operators.md": [],
},
}
repo_package_names = {
"compute-engine": "larq-compute-engine",
"larq": "larq",
"zoo": "larq-zoo",
}
for repo, api_pages in repo_apis.items():
version = pkg_resources.require(repo_package_names[repo])[0].version
doc_generator = DocumentationGenerator(
api_pages,
project_url=f"https://github.com/larq/{repo}/blob/v{version}",
template_dir=f"./docs/{repo}/api_page_templates"
if os.path.exists(f"./docs/{repo}/api_page_templates") else None,
extra_aliases={
"tensorflow.python.ops.variables.Variable": "tf.Variable",
"tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2":
"tf.keras.optimizers.Optimizer",
"tensorflow.python.framework.ops.Tensor": "tf.Tensor",
"tensorflow.python.keras.engine.training.Model": "tf.keras.Model",
"tensorflow.python.framework.dtypes.DType": "tf.dtypes.DType",
"numpy.ndarray": "np.ndarray",
},
max_signature_line_length=88,
)
doc_generator.generate(f"./docs/{repo}/api")