CLN: format examples for readthedocs

Author: yb6599

examples/OPTI-TWIST_constrained_sensing.ipynb

@@ -9,11 +9,11 @@
     "\n",
     "The General QR algorithm was devised specifically to solve such problems. The `PySensors` object implementing this method is named `GQR` and in this notebook we'll demonstrate its use on a temperature field reconstruction problem.\n",
     "\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",
+    "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: Circle, Cylinder, Line, Parabola, Ellipse, and Polygon\n",
+    "The constrained shapes currently implemented are: `Circle`, `Cylinder`, `Line`, `Parabola`, `Ellipse`, and `Polygon`\n",
     "\n",
-    "A user can define their own constrained shape using UserDefinedConstraints 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,\n",

examples/Olivetti_constrained_sensing.ipynb

@@ -9,9 +9,9 @@
     "\n",
     "The General QR algorithm was devised specifically to solve such problems. The `PySensors` object implementing this method is named `GQR` and 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, sensor numbering, showcasing sifference between constrained and unconstrained sensor locations have also been developed.\n",
     "\n",
-    "The constrained shapes currently implemented are: Circle, Cylinder, Line, Parabola, Ellipse, and Polygon\n",
+    "The constrained shapes currently implemented are: `Circle`, `Cylinder`, `Line`, `Parabola`, `Ellipse`, and `Polygon`\n",
     "\n",
-    "A user can define their own constrained shape using UserDefinedConstraints 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,\n",

examples/index.rst

@@ -28,8 +28,11 @@ Reconstruction
 --------------
 These notebooks show how the `SSPOR` class (Sparse Sensor Placement Optimization for Reconstruction) can be used with different optimizers.
 The default optimizer for `SSPOR` is `QR`, which uses QR pivoting to select sensors in unconstrained problems.
+
 `GQR` (General QR) optimizer provides a more intrusive approach into the `QR` pivoting procedure to take into account spatial constraints. The `General QR Optimizer for Spatial Constraints <./spatial_constrained_qr.ipynb>`__ and `Functional Constraints for Olivetti Faces <./Olivetti_constrained_sensing.ipynb>`__ notebooks provide a detailed account of unconstrained and constrained sensor placement.
+
 `CCQR` (Cost Constrained QR) optimizer can be used to place sparse sensors when there are variable costs associated with different locations. The `Cost Constrained QR <./cost_constrained_qr.ipynb>`__ notebook showcases the `CCQR` optimizer.
+
 `TPGR` (Two Point GReedy) optimizer uses a thermodynamic approach to sensor placement that maps the complete landscape of sensor interactions induced by the training data and places sensors such that the marginal energy of each next placed sensor is minimized. The `TPGR <./two_point_greedy.ipynb>`__ notebook goes into detail about the optimizer and the one-point and two-point enery landscape computation. The `TPGR` optimizer requires prior and noise.
 
 There are two methods used for reconstruction: `Unregularized Reconstruction`, which uses the Moore-Penrose Pseudoinverse method, and `Regularized Reconstruction`, that uses a maximal likelihood reconstructor that requires a prior and noise.
@@ -48,7 +51,7 @@ The `Reconstruction Comparison <./reconstruction_comparison.ipynb>`__ notebook c
 Basis
 -----
 The `Basis Comparison <./basis_comparison.ipynb>`__ notebook compares the different basis options implemented in `PySensors` on a simple problem.
-`Cross Validation<./cross_validation.ipynb>`__ is also performed with `scikit-learn` objects to optimize the number of sensors and/or basis modes.
+`Cross Validation <./cross_validation.ipynb>`__ is also performed with `scikit-learn` objects to optimize the number of sensors and/or basis modes.
 
 .. toctree::
   :hidden:
@@ -60,7 +63,7 @@ The `Basis Comparison <./basis_comparison.ipynb>`__ notebook compares the differ
 Applications
 ------------
 These notebooks showcase the sensor placement optimization methods on datasets ranging from `Sea Surface Temperature <./sea_surface_temperature.ipynb>`__ to predicting the temperature within a `Fuel Rod <./OPTI-TWIST_constrained_sensing.ipynb>`__ with spatially constrained sensors.
-The `Polynomial Curve Fitting <./polynomial_curve_fitting>`__ notebook demonstrates how to use PySensors to select sensor locations for polynomial interpolation using the monomial basis $1, x, x^2, x^3, \dots, x^k$.
+The `Polynomial Curve Fitting <./polynomial_curve_fitting.ipynb>`__ notebook demonstrates how to use PySensors to select sensor locations for polynomial interpolation using the monomial basis :math:`1, x, x^2, x^3, \dots, x^k`.
 
 .. toctree::
   :hidden: