Cleanup and fixes

.github/workflows/release.yml

@@ -10,11 +10,11 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout source
-        uses: actions/checkout@v1
+        uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.7
+          python-version: 3.11
       - name: Install dependencies
         run: pip install wheel
       - name: Build package

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ repos:
   rev: v5.0.0
   hooks:
     - id: check-added-large-files
-      args: ["--maxkb=775"]
+      args: ["--maxkb=900"]
     - id: check-merge-conflict
 - repo: https://github.com/PyCQA/isort
   rev: 5.12.0

README.rst

@@ -21,6 +21,12 @@ the PySensors approach to reconstruction problems and Brunton et al.
 literature review along with examples and additional tips for
 using PySensors effectively.
 
+The diagram below shows current Pysensors capabilities.
+
+.. figure:: docs/figures/pysensors-capabilities.png
+  :align: center
+  :alt: A diagram showing current pysensors capabilities.
+  :figclass: align-center
 
 Reconstruction
 ^^^^^^^^^^^^^^
@@ -43,12 +49,18 @@ feed them to a ``SSPOR`` instance with 10 sensors, and
   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:
+Use the ``predict`` method to reconstruct a new function sampled at the chosen sensor locations. There are two methods of reconstruction using ``predict``: ``Unregularized Reconstruction`` and ``Regularized Reconstruction``.
+
 
 .. code-block:: python
 
   f = numpy.abs(x[model.selected_sensors]**2 - 0.5)
-  f_pred = model.predict(f)
+  # Unregularized reconstruction can be used using the method ``unregularized``
+  f_pred_unregularized = model.predict(f, method='unregularized')
+  # Regularized reconstruction, on the other hand is the default method for predict. It also requires other parameters like prior and noise
+  f_pred_regularized = model.predict(f, prior, noise)
+
+See `reconstruction comparison example <https://python-sensors.readthedocs.io/en/latest/examples/reconstruction_comparison.html>`__ for more information on the methods of reconstruction.
 
 .. figure:: docs/figures/vandermonde.png
   :align: center
@@ -89,24 +101,10 @@ Three strategies to deal with constraints are currently developed:
 We have further provided functions to compute the sensors in the constrained regions. For example if the user provides the center and radius of a circular
 constrained region, the constraints in utils compute the constrained sensor indices. Direct constraint plotting capabilities have also been developed.
 
-The constrained shapes currently implemented are:
-
-* ``Circle``
-
-* ``Cylinder``
-
-* ``Line``
-
-* ``Parabola``
+The constrained shapes currently implemented are: ``Circle``, ``Cylinder``, ``Line``, ``Parabola``, ``Ellipse``, ``Polygon``.
+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.
 
-* ``Ellipse``
-
-* ``Polygon``
-
-* ``UserDefinedConstraints``
-
-  - This type of constraint has the ability to take in either a function from the user or a
-  .py file which contains a functional definition of the constrained region.
+See `this example <https://python-sensors.readthedocs.io/en/latest/examples/Olivetti_constrained_sensing.html>`__ for more information.
 
 Classification
 ^^^^^^^^^^^^^^
@@ -126,7 +124,7 @@ 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:
+used for sparse sensor placement: raw measurements, SVD/POD/PCA modes, and random projections. A user can also define their own custom basis. Bases can be easily incorporated into ``SSPOR`` and ``SSPOC`` classes:
 
 .. code-block:: python
 
@@ -141,7 +139,7 @@ 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 <https://packaging.python.org/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line>`__.
+The high-level dependencies for PySensors are Linux or macOS and Python 3.9-3.12. ``pip`` is also recommended as is makes managing PySensors' other dependencies much easier. You can install it by following the instructions `here <https://packaging.python.org/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line>`__.
 
 PySensors has not been tested on Windows.
 
@@ -191,10 +189,23 @@ The primary PySensors objects are the ``SSPOR`` and ``SSPOC`` classes, which are
 
   - ``Identity`` - use raw measurement data
   - ``SVD`` - efficiently compute first k left singular vectors
-  - ``RandomProjection`` - Gaussian random projections of measurements
+  - ``RandomProjection`` - gaussian random projections of measurements
+  - ``CustomBasis`` - user defined bases ranging from DMD modes to Chebyshev polynomials
+
+* ``optimizers`` - submodule implementing different optimizers to fit data
 
+  - ``QR`` - greedy QR optimizer
+  - ``CCQR`` - greedy cost constrained QR optimizer
+  - ``GQR`` - general QR optimizer
+  - ``TPGR`` - two point greedy optmizer
 * Convenience functions to aid in the analysis of error as number of sensors or basis modes are varied
 
+The diagram below outlines a flow chart of how a user can utilize pysensors.
+.. figure:: docs/figures/pysensors-methods.png
+  :align: center
+  :alt: A flow chart of pysensors methods.
+  :figclass: align-center
+
 Documentation
 -------------
 PySensors has a `documentation site <https://python-sensors.readthedocs.io/en/latest/index.html>`__ hosted by readthedocs.
@@ -300,12 +311,15 @@ References
    (2018): 2642-2656.
    `[DOI] <https://doi.org/10.1109/JSEN.2018.2887044>`__
 
--  Karnik, Niharika, Mohammad G. Abdo, Carlos E. Estrada-Perez, Jun Soo Yoo,
-    Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, and Krithika Manohar.
-   "Constrained Optimization of Sensor Plcaement for Nuclear Digital Twins" IEEE Sensors Journal 24, no. 9
+-  Karnik, Niharika, Mohammad G. Abdo, Carlos E. Estrada-Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, and Krithika Manohar.
+   "Constrained Optimization of Sensor Placement for Nuclear Digital Twins" IEEE Sensors Journal 24, no. 9
    (2024): 15501 - 15516.
    `[DOI] <https://doi.org/10.1109/JSEN.2024.3368875>`__
 
+- Klishin, Andrei A., J. Nathan Kutz, Krithika Manohar
+  "Data-Induced Interations of Sparse Sensors" (2023)
+  `[DOI] <https://doi.org/10.48550/arXiv.2307.11838>`__
+
 .. |Build| image:: https://github.com/dynamicslab/pysensors/actions/workflows/main.yml/badge.svg?branch=master
     :target: https://github.com/dynamicslab/pysensors/actions?query=workflow%3ACI
 

examples/OPTI-TWIST_constrained_sensing.ipynb

@@ -11,47 +11,30 @@
     "\n",
     "In this notebook we particularly showcase the functionalities offered by the constraints class.For example if the user provides the center and radius of a circular constrained region, the constraints in utils compute the constrained sensor indices. Direct constraint plotting capabilities have also been developed.\n",
     "\n",
-    "The constrained shapes currently implemented are:\n",
+    "The constrained shapes currently implemented are: Circle, Cylinder, Line, Parabola, Ellipse, and Polygon\n",
     "\n",
-    "Circle\n",
-    "\n",
-    "Cylinder\n",
-    "\n",
-    "Line\n",
-    "\n",
-    "Parabola\n",
-    "\n",
-    "Ellipse\n",
-    "\n",
-    "Polygon\n",
-    "\n",
-    "UserDefinedConstraints\n",
-    "\n",
-    "This type of constraint has the ability to take in either a function from the user or a\n",
+    "A user can define their own constrained shape using UserDefinedConstraints to take in either a function from the user or a\n",
     ".py file which contains a functional definition of the constrained region.\n",
     "\n",
-    "See the following reference for more information ([link1](https://ieeexplore.ieee.org/abstract/document/10453459), [link2](https://www.mdpi.com/1996-1073/17/13/3355))\n",
+    "See the following reference for more information,\n",
     "\n",
-    "Karnik N, Abdo MG, Estrada-Perez CE, Yoo JS, Cogliati JJ, Skifton RS, Calderoni P, Brunton SL, Manohar K. Constrained optimization of sensor placement for nuclear digital twins. IEEE Sensors Journal. 2024 Feb 28.\n",
+    "Karnik N, Abdo MG, Estrada-Perez CE, Yoo JS, Cogliati JJ, Skifton RS, Calderoni P, Brunton SL, Manohar K. Constrained optimization of sensor placement for nuclear digital twins. IEEE Sensors Journal. 2024 Feb 28. [(link)](https://ieeexplore.ieee.org/abstract/document/10453459)\n",
     "\n",
-    "Karnik N, Wang C, Bhowmik PK, Cogliati JJ, Balderrama Prieto SA, Xing C, Klishin AA, Skifton R, Moussaoui M, Folsom CP, Palmer JJ, Sabharwall P, Manohar K, Abdo MG., Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems. Energies (19961073). 2024 Jul 1;17(13).\n",
+    "Karnik N, Wang C, Bhowmik PK, Cogliati JJ, Balderrama Prieto SA, Xing C, Klishin AA, Skifton R, Moussaoui M, Folsom CP, Palmer JJ, Sabharwall P, Manohar K, Abdo MG., Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems. Energies (19961073). 2024 Jul 1;17(13). [(link)](https://www.mdpi.com/1996-1073/17/13/3355)\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from time import time\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from sklearn import datasets\n",
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
-    "import pysensors as ps\n",
-    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
+    "import pysensors as ps"
    ]
   },
   {