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Basic linear regression
Expected result: See the official Scipy documentation for details about
linregress
. A more complete regression model can be obtained with the OLS function in the statsmodels library. 
Building a simple interactive 2ddata viewer with Matplotlib
This is a very simple, but practical 2ddata viewer, which uses only matplotlib widgets. Click: on the image or the plots to get a cross section  x or y depending ...
 0
 revision 5
 by lissacoffey
 on 08 April 2015

Find the points at which two given functions intersect
The code considers the case of finding the intersection of a polynomial, \(y=x^2\) and a line, \(y=x+1\). Write these functions in the form \(\mathbf{f(x ...
 0
 revision 3
 by lissacoffey
 on 08 April 2015

Using Prony's method to fit a sum of exponentials
This a basic implementation of Prony’s method. In this form, it is very susceptible to noise. Added by Jose: I had some problems using “lstsq” from scipy.linalg that ...

100 Numpy exercices
A collection of 100 numpy exercices gathered from the numpy mailing list, stack overflow and other places.
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 revision 2
 by Nicolas Rougier
 on 28 September 2015

Fitting a Gaussian to noisy datapoints
This script reproduces the plots from
Guo, A Simple Algorithm for Fitting a Gaussian Function, IEEE Signal Processing Magazine, September 2011, pp. 134–137. 
Simple interactive Matplotlib plots
Interactive graphs with Matplotlib have haunted me. So here I have collected a number of tricks that should make interactive use of plots simpler. The functions below show how to ...
 0
 by thomas.haslwanter
 on 30 March 2015

Plot an ellipse
The code will plot any ellipse where you specify the major and minor axis distances, optional translation by \(x, y\) units, and an optional rotation. This revision deploys numpy broadcasting ...

Principal components analysis (PCA) using a sequential method
The singular value decomposition is usually presented as the way to calculate the PCA decomposition of a data matrix. The NIPALS algorithm is a very computationally tractable way of calculating ...

finds the solitary and clustered pixels in a 2D array of pixels
Upgraded on Wed march 03 14:00:30 2013  Version 3 Following some user requests:  slight polishing of the outputs by adding an output vector of the sequence of the ...
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 revision 3
 by PLautridou
 on 03 March 2017

Learning Scipy
This repository contains source code programs and some notes to complement the book about the scientific Python module SciPy entitle [Learning SciPy for Numerical and Scientific Computing  Second Edition (2015 ...

Integrating an initial value problem (single ODE)
We want to integrate a single equation \(\displaystyle \frac{dy(t)}{dt} = f(t, y)\) with a given initial condition \(y(t=0)=y_0\). There are several integrators available in ...
 0
 revision 3
 by SciPy Central
 on 06 August 2011

pymls  solving bounded linear least squares problems
Based on mls_alloc from the matlab toolbox Qcat by Ola Harkegard, this package allows to solve bounded least squares problems. It is also possible to put weights on your columns ...

Error estimates for fit parameters resulting from leastsquares fits using bootstrap resampling
The function scipy.optimize.leastsq does not yield error estimates for fitted parameters. As a remedy one might use bootstrap resampling of the underlying data in order to obtain error ...

NumPy / SciPy Recipes for Data Science: Eigenvalues/Eigenvectors of Covariance Matrices
In this note, we discuss a potential pitfall in using NumPy or SciPy methods to compute eigen decompositions of covariance matrices and show how to avoid it. In short, we ...
 0
 by christianbauckhage
 on 09 November 2015

SciPy Lectures Notes
These are a consistent set of materials to learn the core aspects of the scientific Python ecosystem, from beginner to expert. They are written and maintained by a set of ...
 0
 by Nicolas Rougier
 on 28 September 2015

ROC curve demo
This script can be used to understand the relationship between the signal absent and signal present distributions and the ROC curve which they generate. The distributions are assumed to be ...

Matplotlib tutorial
Matplotlib is probably the single most used Python package for 2Dgraphics. It provides both a very quick way to visualize data from Python and publicationquality figures in many formats. We ...
 0
 by Nicolas Rougier
 on 29 September 2015

Generating confidence intervals via model comparsion.
Using leastsq to fit data it is possible to calculate an estimation of error using the diagonal of covariance matrix. This has some drawbacks, especially if there is a strong ...

Line Fit with Confidence Intervals
‘’‘ Linear regression fit Parameters
x : ndarrayInput / PredictorInput / EstimatorConfidence limit [default=0.05]Values for which the fit and the ... 0
 by thomas.haslwanter
 on 01 November 2012