### All Submissions In Order Of Most Views

• #### 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.

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• revision 5
• by jeremy
• on 07 February 2016

• #### 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 ...

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 ... • #### 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 ... • 0 • revision 4 • by joseA • on 17 September 2013 • #### 100 Numpy exercices A collection of 100 numpy exercices gathered from the numpy mailing list, stack overflow and other places. • #### 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 ... • 0 • revision 2 • by stefan • on 23 September 2011 • #### 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 ... • #### 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 ...

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• revision 2
• by woiski
• on 03 September 2011

• #### 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 ...

• #### 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 ...

• 0
• by sr
• on 14 August 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 ...

• #### 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 least-squares 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 ...

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• revision 2
• by vilo
• on 21 March 2012

• #### 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 ...

• #### 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 ...

• #### 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 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We ...

• #### 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 : ndarray
Input / Predictor
y : ndarray
Input / Estimator
alpha : float
Confidence limit [default=0.05]
newx : float or ndarray
Values for which the fit and the ...

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