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‎README.md

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# tsl: a PyTorch library for spatiotemporal data processing
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**tsl** *(Torch Spatiotemporal)* is a library built to accelerate research on neural spatiotemporal data processing
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methods, with a focus on Graph Neural Networks.
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`tsl` is built on several libraries of the *Python* scientific computing ecosystem, with the final objective of providing a straightforward process that goes from data preprocessing to model prototyping.
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In particular, `tsl` offers a wide range of utilities to develop neural networks in *PyTorch* for processing spatiotemporal data signals.
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## Installation
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`tsl` is compatible with Python 3.7 and above. We recommend using Anaconda and the provided environment configuration by running the command:
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```conda env create -f tsl_env.yml```
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To install `tsl` from source, run this in a terminal:
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```bash
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git clone https://github.com/TorchSpatiotemporal/tsl.git
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cd tsl
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python setup.py install # Or 'pip install .'
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```
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## Tutorial
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The best way to start using `tsl` is by following the tutorial notebook in `examples/notebooks/a_gentle_introduction_to_tsl.ipynb`.
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## Documentation
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The documentation can be built and accessed from `docs`.
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## Credits
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[Andrea Cini](https://andreacini.github.io/), [Ivan Marisca](https://marshka.github.io/)

‎docs/Makefile

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SPHINXBUILD = sphinx-build
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SPHINXPROJ = tsl
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SOURCEDIR = source
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BUILDDIR = build
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(0)

‎docs/README.md

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# Building Documentation
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To build the documentation:
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1. Install [Sphinx](https://www.sphinx-doc.org/en/master/) via `pip install -r requirements.txt`.
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2. Generate the documentation file via:
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```
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cd docs
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make html
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```
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The documentation is now available to view by opening `docs/build/html/index.html`.

‎docs/requirements.txt

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sphinx==4.2.0
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sphinx_rtd_theme==1.0.0

‎docs/source/_static/css/custom.css

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‎docs/source/_static/img/tsl_logo.svg

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‎docs/source/conf.py

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import datetime
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import sphinx_rtd_theme
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import doctest
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import tsl
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.autosummary',
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'sphinx.ext.doctest',
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'sphinx.ext.intersphinx',
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'sphinx.ext.mathjax',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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'sphinx.ext.githubpages',
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]
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autosummary_generate = True
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templates_path = ['_templates']
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source_suffix = '.rst'
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master_doc = 'index'
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# exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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# -- Project information -----------------------------------------------------
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project = 'tsl'
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author = 'Andrea Cini, Ivan Marisca'
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copyright = '{}, {}'.format(datetime.datetime.now().year, author)
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version = tsl.__version__
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release = tsl.__version__
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html_theme = 'sphinx_rtd_theme'
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html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
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doctest_default_flags = doctest.NORMALIZE_WHITESPACE
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autodoc_member_order = 'bysource'
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intersphinx_mapping = {
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'python': ('https://docs.python.org/3/', None),
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'numpy': ('https://numpy.org/doc/stable/', None),
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'pd': ('https://pandas.pydata.org/docs/', None),
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'PyTorch': ('https://pytorch.org/docs/stable/', None),
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'pytorch_lightning': ('https://pytorch-lightning.readthedocs.io/en/latest/', None),
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'PyG': ('https://pytorch-geometric.readthedocs.io/en/latest/', None)
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}
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html_theme_options = {
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'collapse_navigation': False,
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'display_version': True,
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'logo_only': True,
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'navigation_depth': 2,
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}
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html_static_path = ['_static']
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html_logo = '_static/img/tsl_logo.svg'
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html_favicon = '_static/img/tsl_logo.svg'
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html_context = {'css_files': ['_static/css/custom.css']}
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rst_context = {'tsl': tsl}
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add_module_names = False
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def setup(app):
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def skip(app, what, name, obj, skip, options):
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members = [
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'__init__',
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'__repr__',
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'__weakref__',
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'__dict__',
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'__module__',
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]
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return True if name in members else skip
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def rst_jinja_render(app, docname, source):
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src = source[0]
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rendered = app.builder.templates.render_string(src, rst_context)
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source[0] = rendered
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app.connect('autodoc-skip-member', skip)
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app.connect("source-read", rst_jinja_render)

‎docs/source/index.rst

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:github_url: https://github.com/TorchSpatiotemporal/tsl
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Torch Spatiotemporal
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====================
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tsl (Torch Spatiotemporal) is a library built to accelerate research on neural spatiotemporal data processing methods, with a focus on Graph Neural Networks.
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It is built on several libraries of the Python scientific computing ecosystem, with the final objective of providing a straightforward process that goes from data preprocessing to model prototyping. In particular, tsl offers a wide range of utilities to develop neural networks in `PyTorch <https://pytorch.org/>`_ and `PyG <https://github.com/pyg-team/pytorch_geometric/>`_ for processing spatiotemporal data signals.
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Usage
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notes/data_format
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Package API
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modules/data
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modules/data_datamodule
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modules/data_preprocessing
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modules/datasets
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modules/nn
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modules/inference_modules
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Indices and Tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`

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