# Least squares circle fit python

This page gathers different methods used to find the least squares circle fitting a set of 2D points x,y. Finding the least squares circle corresponds to finding the center of the circle xc, yc and its radius Rc which minimize the residu function defined below:. This is a nonlinear problem.

We well see three approaches to the problem, and compare there results, as well as their speeds. As detailed in this document this problem can be approximated by a linear one if we define the function to minimize as follow:. Scipy comes will several tools to solve the nonlinear problem above. Among them, scipy. Indeed, once the center of the circle is defined, the radius can be calculated directly and is equal to mean Ri. So there is only two parameters left: xc and yc.

To gain in speed, it is possible to tell optimize. Scipy has a dedicated package to deal with orthogonal distance regression, namely scipy. This package can handle both explict and implicit function definition, and we will used the second form in this case.

One of the advantages of the implicit function definition is that its derivatives are very easily calculated. Adding a function to compute the jacobian can lead to decrease the number of function calls by a factor of two to five. In this case, to use ODR seems a bit overkill but it can be very handy for more complex use cases like ellipses.

The algebraic approximation gives good results when the points are all around the circle but is limited when there is only an arc to fit. Indeed, the two errors functions to minimize are not equivalent when data points are not all exactly on a circle. The algebraic method leads in most of the case to a smaller radius than that of the least squares circle, as its error function is based on squared distances and not on the distance themselves.

SciPy Cookbook latest. This leads to the following method, using linalg. Using scipy. Here is the implicit definition of the circle:. This can be used to complete the model:.Update: I have added the code I used to make the plot of the 3D data and sphere! It may not be intuitive to fit a sphere to three dimensional data points using the least squares method. This post demonstrates how the equation of a sphere can be rearranged to formulate the least squares problem. A Python function, which determines the sphere of best fit, is then presented.

Well I have a sample data set that is well suited for a spherical fit using the least squares method. A plot of data points in three dimensional space can be seen in the following image. We must rearrange the terms of the equation in order to use the least squares method.

After expanding and rearranging the terms, the new equation of a sphere is expressed below. We now have an over-determined system suitable for the least squares method of a spherical fit. The new equation is seen below. The function then returns the radius and center coordinates of the sphere. We can easily fit a sphere to our original data set using this function.

The resulting sphere of best fit plotted with the original data points can be seen in the following image.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

### Least squares fitting with Numpy and Scipy

Latest commit Fetching latest commit…. Least Squares fitting of ellipses, python routine based on the publication Halir, R. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Update circleci config. Mar 21, Aug 31, Jan 20, Apr 16, Update DOI tag. Remove unnecessary comment. Include example file.

Update code to mimic sklearn api.The model function, f x, …. It must take the independent variable as the first argument and the parameters to fit as separate remaining arguments.

The independent variable where the data is measured. Should usually be an M-length sequence or an k,M -shaped array for functions with k predictors, but can actually be any object.

The dependent data, a length M array - nominally f xdata, Initial guess for the parameters length N. If None, then the initial values will all be 1 if the number of parameters for the function can be determined using introspection, otherwise a ValueError is raised. Determines the uncertainty in ydata. A 1-d sigma should contain values of standard deviations of errors in ydata. A 2-d sigma should contain the covariance matrix of errors in ydata. T inv sigma r. If True, sigma is used in an absolute sense and the estimated parameter covariance pcov reflects these absolute values.

If False, only the relative magnitudes of the sigma values matter. The returned parameter covariance matrix pcov is based on scaling sigma by a constant factor.

This constant is set by demanding that the reduced chisq for the optimal parameters popt when using the scaled sigma equals unity. In other words, sigma is scaled to match the sample variance of the residuals after the fit. If True, check that the input arrays do not contain nans of infs, and raise a ValueError if they do.

Setting this parameter to False may silently produce nonsensical results if the input arrays do contain nans. Default is True. Lower and upper bounds on parameters. Defaults to no bounds. Each element of the tuple must be either an array with the length equal to the number of parameters, or a scalar in which case the bound is taken to be the same for all parameters. Use np. Method to use for optimization. Function with signature jac x, It will be scaled according to provided sigma.

If None defaultthe Jacobian will be estimated numerically. The estimated covariance of popt. The diagonals provide the variance of the parameter estimate. Note that this algorithm can only deal with unconstrained problems. New in version 0. Previous topic scipy. Last updated on Dec 19, Created using Sphinx 2.Both Numpy and Scipy provide black box methods to fit one-dimensional data using linear least squaresin the first case, and non-linear least squaresin the latter. Let's dive into them:. In a vector notation, this will be:. The X matrix corresponds to a Vandermonde matrix of our x variable, but in our case, instead of the first column, we will set our last one to ones in the variable a.

Doing this and for consistency with the next examples, the result will be the array [m, c] instead of [c, m] for the linear equation.

We can use the lstsqs function from the linalg module to do the same:. As we can see, all of them calculate a good aproximation to the coefficients of the original function. In terms of speed, the first method is the fastest and the last one, a bit slower than the second method:. In the case of polynomial functions the fitting can be done in the same way as the linear functions. Using polyfitlike in the previous example, the array x will be converted in a Vandermonde matrix of the size n, mbeing n the number of coefficients the degree of the polymomial plus one and m the lenght of the data array.

Just to introduce the example and for using it in the next section, let's fit a polynomial function:. In this section we are going back to the previous post and make use of the optimize module of Scipy to fit data with non-linear equations.

## Least Squares Sphere Fit

Scipy's least square function uses Levenberg-Marquardt algorithm to solve a non-linear leasts square problems. Levenberg-Marquardt algorithm is an iterative method to find local minimums. In vector notation:. Here is the implementation of the previous example. In the following examples, non-polynomial functions will be used and the solution of the problems must be done using non-linear solvers.

Linear and Polynomial Regression in Python

Also, we will compare the non-linear least square fitting with the optimizations seen in the previous post. We should use non-linear least squares if the dimensionality of the output vector is larger than the number of parameters to optimize. Here, we can see the number of function evaluations of our last estimation of the coeffients:. Using as a example, a L-BFGS minimization we will achieve the minimization in more cost function evaluations:. We obtained a really bad fitting, in this case we will need a better initial guess.

Observing the data we have it is possible to set a better initial estimation:.Contents Introduction Using an algebraic approximation Using scipy. We well see three approaches to the problem, and compare there results, as well as their speeds. Among them, scipy. Indeed, once the center of the circle is defined, the radius can be calculated directly and is equal to mean Ri.

So there is only two parameters left: xc and yc. This package can handle both explict and implicit function definition, and we will used the second form in this case. This differs from the number of iteration as ODR can make multiple calls during an iteration.

Adding a function to compute the jacobian can lead to decrease the number of function calls by a factor of two to five. In this case, to use ODR seems a bit overkill but it can be very handy for more complex use cases like ellipses. The algebraic approximation gives good results when the points are all around the circle but is limited when there is only an arc to fit. Indeed, the two errors functions to minimize are not equivalent when data points are not all exactly on a circle.

The algebraic method leads in most of the case to a smaller radius than that of the least squares circle, as its error function is based on squared distances and not on the distance themselves.

This is an archival dump of old wiki content see scipy. More Actions: Rename Page Delete Page Subscribe User Remove Spam Revert to this revision.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

I'm trying to fit some data points in order to find the center of a circle. All of the following points are noisy data points around the circumference of the circle:. I was trying to use some library like Scipybut I'm having trouble using the available functions. But this seems to be using a single xy? Any ideas on how to plug this function to my data example? Your data points seem fairly clean and I see no outliers, so many circle fitting algorithms will work.

I recommend you to start with the Coope method, which works by magically linearizing the problem:. As a follow up to Bas Swinckels post, I figured I'd post my code implementing the Halir and Flusser method of fitting an ellipse.

I do not have any experience fitting circles, but I have worked with the more general case of fitting ellipses. Doing this in a correct way with noisy data is not trivial. For this problem, the algorithm described in Numerically stable direct least squares fitting of ellipses by Halir and Flusser works pretty well. The paper includes Matlab code, which should be straightforward to translate to Numpy. Maybe you could use this algorithm to fit an ellipse and then take the average of the two axis as the radius or so.

Some of the references in the paper also mention fitting circles, you might want to look those up.