More fitting algorithms and type hints

.gitignore

@@ -1 +1,160 @@
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LICENSE.txt

@@ -1,4 +1,4 @@
-Copyright (c) 2019 Michael Klear
+Copyright (c) 2022 Magne Lauritzen
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,21 +1,54 @@
-# Circle-Fit
-## Simple Circle Fitting Library for Python
-Fitting circles is a simple problem. Given a set of points in 2-D space, let's find a "best fit" circle. A simple least-squares algorithm is a simple and effective solution. A novel algorithm came out in 2011 called ["Hyper Fit"](https://www.sciencedirect.com/science/article/pii/S0167947310004809?via%3Dihub) by Kanatani, et al.
+# Circle-Fit 2
+## A Circle Fitting Library for Python
+Given a collection of points in 2D space, a common problem is finding the parameters of a circle that best approximate 
+these points. This library implements a collection of different circle fitting algorithms:
+
+```
+- hyperLSQ()      : Least squares circle fit with "hyperaccuracy" by Kenichi Kanatani, Prasanna Rangarajan
+- standardLSQ()   : Least squares circle fit, standard version.
+- riemannSWFLa()  : Riemann circle fit, SWFL version A
+- lm()            : Levenberg-Marquardt in the full (a,b,R) parameter space
+- prattSVD()      : Algebraic circle fit by V. Pratt
+- taubinSVD()     : Algebraic circle fit by G. Taubin
+- hyperSVD()      : Algebraic circle fit with "hyperaccuracy"
+- kmh()           : Consistent circle fit by A. Kukush, I. Markovsky, S. Van Huffel
+```
+
+Most of these algorithms are based on the original MATLAB implementations by Nikolai Chernov: 
+https://people.cas.uab.edu/~mosya/cl/MATLABcircle.html
+
+Each algorithm may work better in specific cases. If you are in doubt about which to use, `taubinSVD()`
+is a good starting point.
 
 ## Installation
+`circle-fits2` is not yet available from PyPi. Coming very soon! 
+
+## Example
+Fit a circle to four `(x,y)` points.
 ```
-pip install circle-fit
+from circle-fit2 import taubinSVD
+point_coordinates = [[1, 0], [-1, 0], [0, 1], [0, -1]]
+xc, yc, r, sigma = taubinSVD(point_coordinates)
 ```
 
-## Usage
-### Prepare the Data
-Your data must have at least two points in 2-D space. Circle-fit expects an array (or similar structure) of shape `(n, 2)`, where n is the number of points in your dataset.<br>
-There are two algorithms available: `hyper_fit` and `least_squares_circle`. Both return four values:
+## Data format
+Your data must have at least two points in 2-D space. The algorithms in `circle-fit2` expects either 
+a 2D List or numpy ndarray of shape `(n, 2)`, where n is the number of points in your dataset.
+
+All the algorithms available in this library return four values:
 ```
-- xc : x-coordinate of solution center (float)
-- yc : y-coordinate of solution center (float)
-- R : Radius of solution (float)
-- variance or residual (float)
+- xc    : x-coordinate of solution center (float)
+- yc    : y-coordinate of solution center (float)
+- r     : Radius of solution (float)
+- sigma : Residual error of solution (float)
+```
+
+### View the fit
+The function `plot_data_circle(coords, xc, yc, r)` can be used to open a plot window which shows you data points with
+a circle fit overlaid on top. Example use:
+```
+xc, yc, r, sigma = taubinSVD(point_coordinates)
+plot_data_circle(point_coordinates, xc, yc, r)
 ```
 
 ### Contributors and Maintainers
@@ -28,4 +61,5 @@ Please open a pull request with the changes you would like to contribute ([examp
 As we are volunteers, please be patient when requesting support. You can either open an issue if you think you've found a bug with the code, or contact one of us directly if you have a user issue:
 
  - michael.r.klear@gmail.com
- - etc.
+ - mag.lauritzen@gmail.com
+