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CLN: add figures and lint
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.pre-commit-config.yaml

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rev: v5.0.0
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hooks:
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- id: check-added-large-files
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args: ["--maxkb=775"]
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args: ["--maxkb=900"]
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- id: check-merge-conflict
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- repo: https://github.com/PyCQA/isort
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rev: 5.12.0

README.rst

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# Regularized reconstruction, on the other hand is the default method for predict. It also requires other parameters like prior and noise
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f_pred_regularized = model.predict(f, prior, noise)
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See `reconstruction comparison example <https://python-sensors.readthedocs.io/en/latest/examples/reconstruction_comparison.html>` for more information on the methods of reconstruction.
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See `reconstruction comparison example <https://python-sensors.readthedocs.io/en/latest/examples/reconstruction_comparison.html>`__ for more information on the methods of reconstruction.
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.. figure:: docs/figures/vandermonde.png
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:align: center
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The constrained shapes currently implemented are: ``Circle``, ``Cylinder``, ``Line``, ``Parabola``, ``Ellipse``, ``Polygon``.
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A user can also define their own constraints using ``UserDefinedConstraints``, this type of constraint has the ability to take in either a function or a .py file which contains a functional definition of the constrained region.
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See `this example <https://python-sensors.readthedocs.io/en/latest/examples/Olivetti_constrained_sensing.html>` for more information.
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See `this example <https://python-sensors.readthedocs.io/en/latest/examples/Olivetti_constrained_sensing.html>`__ for more information.
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Classification
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^^^^^^^^^^^^^^
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- Klishin, Andrei A., J. Nathan Kutz, Krithika Manohar
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"Data-Induced Interations of Sparse Sensors" (2023)
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`[DOI] <https://doi.org/10.48550/arXiv.2307.11838>`
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`[DOI] <https://doi.org/10.48550/arXiv.2307.11838>`__
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.. |Build| image:: https://github.com/dynamicslab/pysensors/actions/workflows/main.yml/badge.svg?branch=master
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:target: https://github.com/dynamicslab/pysensors/actions?query=workflow%3ACI
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