Welcome. SciPy Central is a collection of code snippets, modules and links for solving scientific problems with SciPy and related Python tools.

• Matplotlib tutorial

Matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We ...

• 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 ...

• Ten simple rules for better figures

This link to the sources of the figures of the article “Ten Simple Rules for better figures”, Nicolas P. Rougier, Michael Droettboom, Philip E. Bourne, PLOS Computational Biology, 2014. Article ...

• 100 Numpy exercices

A collection of 100 numpy exercices gathered from the numpy mailing list, stack overflow and other places.

• 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 ...

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• by sr
• on 14 August 2015

• numpy-groupies

Optimised tools for group-indexing operations: aggregated sum and more. aggregate(group_idx, a, func=’sum’):

See github repo for details. Available on PyPi.

• NumPy / SciPy Recipes for Data Science: Kernel Least Squares Optimization (1)

In this note, we show that least squares optimization is amenable to the kernel trick. This provides great flexibility in model fitting and we consider examples that illustrate this. In ...

• 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 ...

• NumPy / SciPy Recipes for Data Science: Squared Euclidean Distance Matrices

In this note, we explore and evaluate various ways of computing squared Euclidean distance matrices (EDMs) using NumPy or SciPy. In particular, we discuss 6 increasingly abstract code snippets where ...

• NumPy / SciPy Recipes for Data Science: k-Medoids Clustering

In this note, we study k-medoids clustering and show how to implement the algorithm using NumPy. To illustrate potential and practical use of this lesser known clustering method, we discuss ...

• Building a simple interactive 2d-data viewer with Matplotlib

This is a very simple, but practical 2d-data viewer, which uses only matplotlib widgets. Click: on the image or the plots to get a cross section - x or y depending ...

• Basic linear regression

Expected result: See the official Scipy documentation for details about linregress. A more complete regression model can be obtained here with the OLS function in the statsmodels library.

• 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 ...

• 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 ...

• 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 ...

• 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 ...

• Fitting a Gaussian to noisy data-points

This script reproduces the plots from

Guo, A Simple Algorithm for Fitting a Gaussian Function, IEEE Signal Processing Magazine, September 2011, pp. 134–137.
The paper describes how to fit ...

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• revision 2
• by stefan
• on 23 September 2011

• Python tools for oceanographic analysis

A collaborative effort to organize Python tools for the Oceanographic Community.

• SfePy (simple finite elements in Python)

SfePy is a software for solving systems of coupled partial differential equations (PDEs) by the finite element method in 2D and 3D. It can be viewed both as black-box PDE ...

• 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 ...

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• revision 4
• by joseA
• on 17 September 2013

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