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https://github.com/dynamicslab/pysensors

PySensors is a Python package for sparse sensor placement
https://github.com/dynamicslab/pysensors

optimization-tools sensor sensor-placement sensors

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PySensors is a Python package for sparse sensor placement

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README 

          
PySensors

=========

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**PySensors** is a Scikit-learn style Python package for the sparse placement of sensors, either for reconstruction or classification tasks.



.. contents:: Table of contents



Sparse sensor placement

-----------------------



Sparse sensor placement concerns the problem of selecting a small subset

of sensor or measurement locations in a way that allows one to perform

some task nearly as well as if one had access to measurements at *every*

location.



PySensors provides objects designed for the tasks of *reconstruction* and

*classification*. See Manohar et al. (2018) for more information about

the PySensors approach to reconstruction problems and Brunton et al.

(2016) for classification. de Silva et al. (2021) contains a full

literature review along with examples and additional tips for

using PySensors effectively.



Reconstruction

^^^^^^^^^^^^^^

Reconstruction deals with predicting the values of a quantity of interest at different locations other than those where sensors are located.

For example, one might predict the temperature at a point in the middle of a lake based on temperature readings taken at various other positions in the lake.



PySensors provides the ``SSPOR`` (Sparse Sensor Placement Optimization for Reconstruction) class to aid in the solution of reconstruction problems.



Take representative examples of the types of data to be reconstructed (in this case polynomials)



.. code-block:: python



  x = numpy.linspace(0, 1, 1001)

  data = numpy.vander(x, 11).T  # Create an array whose rows are powers of x



feed them to a ``SSPOR`` instance with 10 sensors, and



.. code-block:: python



  model = pysensors.reconstruction.SSPOR(n_sensors=10)

  model.fit(data)



Use the ``predict`` method to reconstruct a new function sampled at the chosen sensor locations:



.. code-block:: python



  f = numpy.abs(x[model.selected_sensors]**2 - 0.5)

  f_pred = model.predict(f)



.. figure:: docs/figures/vandermonde.png

  :align: center

  :alt: A plot showing the function to be reconstructed, the learned sensor locations, and the reconstruction.

  :figclass: align-center



Classification

^^^^^^^^^^^^^^

Classification is the problem of predicting which category an example belongs to, given a set of training data (e.g. determining whether digital photos are of dogs or cats).

The ``SSPOC`` (Sparse Sensor Placement Optimization for Classification) class is used to solve classification problems.

Users familiar with Scikit-learn will find it intuitive:



.. code-block:: python



  model = pysensors.classification.SSPOC()

  model.fit(x, y)  # Learn sensor locations and fit a linear classifier

  y_pred = model.predict(x_test[:, model.selected_sensors])  #  Get predictions



See our set of `classification examples `__ for more information.



Bases

^^^^^

The basis in which measurement data are represented can have a dramatic

effect on performance. PySensors implements the three bases most commonly

used for sparse sensor placement: raw measurements, SVD/POD/PCA modes, and random projections. Bases can be easily incorporated into ``SSPOR`` and ``SSPOC`` classes:



.. code-block:: python



  basis = pysensors.basis.SVD(n_basis_modes=20)

  recon_model = pysensors.reconstruction.SSPOR(basis=basis)

  class_model = pysensors.classification.SSPOC(basis=basis)



See `this example `__ for further discussion of these options.



Installation

-------------



Dependencies

^^^^^^^^^^^^

The high-level dependencies for PySensors are Linux or macOS and Python 3.6-3.8. ``pip`` is also recommended as is makes managing PySensors' other dependencies much easier. You can install it by following the instructions `here `__.



PySensors has not been tested on Windows.



Installing with pip

^^^^^^^^^^^^^^^^^^^



If you are using Linux or macOS you can install PySensors with pip from the command line/terminal:



.. code-block:: bash



  pip install python-sensors



**Note:** the name you type in here **is** ``python-sensors`` and is **not** ``pysensors``.



Once you have run the line above, you are ready to get started with PySensors. Have a look at the examples in our `documentation `__ to see what PySensors can do.



Installing from source

^^^^^^^^^^^^^^^^^^^^^^

First clone this repository:



.. code-block:: bash



  git clone https://github.com/dynamicslab/pysensors.git



Then, to install the package, run



.. code-block:: bash



  cd pysensors

  pip install .



If you do not have pip you can instead use



.. code-block:: bash



  python setup.py install



If you do not have root access, you should add the ``--user`` option to the ``install`` commands above.



Features

--------

The primary PySensors objects are the ``SSPOR`` and ``SSPOC`` classes, which are used to choose sensor locations optimized for reconstruction and classification tasks, respectively. Other implemented objects include



* ``basis`` - submodule implementing different bases in which to represent data



  - ``Identity`` - use raw measurement data

  - ``SVD`` - efficiently compute first k left singular vectors

  - ``RandomProjection`` - Gaussian random projections of measurements



* Convenience functions to aid in the analysis of error as number of sensors or basis modes are varied



Documentation

-------------

PySensors has a `documentation site `__ hosted by readthedocs.

Examples are available `online `__, as static

`Jupyter notebooks `__ and as `interactive notebooks `__. To run the example notebooks locally you should install the dependencies in ``requirements-examples.txt``:



.. code-block:: bash



  pip install -r requirements-examples.txt



Community guidelines

--------------------



Getting support

^^^^^^^^^^^^^^^

You may create an issue for any questions that aren't answered by the `documentation `__ or `examples `__.



Contributing examples

^^^^^^^^^^^^^^^^^^^^^

If you have used PySensors to solve an interesting problem, please consider submitting an example Jupyter notebook showcasing

your work!



Contributing code

^^^^^^^^^^^^^^^^^

We welcome contributions to PySensors. To contribute a new feature please submit a pull request. To get started we recommend installing the packages in ``requirements-dev.txt`` via



.. code-block:: bash



    pip install -r requirements-dev.txt



This will allow you to run unit tests and automatically format your code. To be accepted your code should conform to PEP8 and pass all unit tests. Code can be tested by invoking



.. code-block:: bash



    pytest



We recommend using ``pre-commit`` to format your code. Once you have staged changes to commit



.. code-block:: bash



    git add path/to/changed/file.py



you can run the following to automatically reformat your staged code



.. code-block:: bash



    pre-commit



Note that you will then need to re-stage any changes ``pre-commit`` made to your code.



Reporting issues or bugs

^^^^^^^^^^^^^^^^^^^^^^^^

If you find a bug in the code or want to request a new feature, please open an issue.



Citing PySensors

----------------

We have published a short paper in the Journal of Open Source Software (JOSS). You can find the paper `here  `__.



If you use PySensors in your work, please consider citing it using:



.. code-block:: text



    de Silva et al., (2021). PySensors: A Python package for sparse sensor placement. Journal of Open Source Software, 6(58), 2828, https://doi.org/10.21105/joss.02828``



Bibtex:



.. code-block:: text



  @article{de Silva2021,

    doi = {10.21105/joss.02828},

    url = {https://doi.org/10.21105/joss.02828},

    year = {2021},

    publisher = {The Open Journal},

    volume = {6},

    number = {58},

    pages = {2828},

    author = {Brian M. de Silva and Krithika Manohar and Emily Clark and Bingni W. Brunton and J. Nathan Kutz and Steven L. Brunton},

    title = {PySensors: A Python package for sparse sensor placement},

    journal = {Journal of Open Source Software}

  }



References

------------

-  de Silva, Brian M., Krithika Manohar, Emily Clark, Bingni W. Brunton,

   Steven L. Brunton, J. Nathan Kutz.

   "PySensors: A Python package for sparse sensor placement."

   arXiv preprint arXiv:2102.13476 (2021). `[arXiv] `__



-  Manohar, Krithika, Bingni W. Brunton, J. Nathan Kutz, and Steven L. Brunton.

   "Data-driven sparse sensor placement for reconstruction: Demonstrating the

   benefits of exploiting known patterns."

   IEEE Control Systems Magazine 38, no. 3 (2018): 63-86.

   `[DOI] `__



-  Brunton, Bingni W., Steven L. Brunton, Joshua L. Proctor, and J Nathan Kutz.

   "Sparse sensor placement optimization for classification."

   SIAM Journal on Applied Mathematics 76.5 (2016): 2099-2122.

   `[DOI] `__



-  Clark, Emily, Travis Askham, Steven L. Brunton, and J. Nathan Kutz.

   "Greedy sensor placement with cost constraints." IEEE Sensors Journal 19, no. 7

   (2018): 2642-2656.

   `[DOI] `__



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