The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Included are histogramming methods, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy setup of
... [More] an analysis system that can query and process the data interactively or in batch mode.
Thanks to the builtin C++ interpreter, ROOT can be used interactively with C++, as well as a C++ library. It has many language bindings, most notably an excellent dynamic Python binding.
ROOT is widely used by researchers in the field of High Energy Physics and has been developed at CERN (http://www.cern.ch). [Less]
A Python package for gamma-ray astronomy
Gammapy is an open-source Python package for gamma-ray astronomy built on Numpy, Scipy and Astropy.
It is used as core library for the Science Analysis tools of the Cherenkov Telescope Array (CTA), recommended by the H.E.S.S. collaboration to be used for
... [More] Science publications, and is already widely used in the analysis of existing gamma-ray instruments, such as MAGIC, VERITAS and HAWC. [Less]
Hipparchus is a library of lightweight, self-contained
mathematics and statistics components addressing the most common problems not available in the Java programming language.
JAG3D is a tool to estimate geodetic 1d, 2d and 3d-networks by a least-square-adjustment called Gauß-Markov-Model. Moreover, the software supports deformation-analysis. Add-ons are coordinate-transformation or the form-fitting-toolbox.
Program to estimate material properties from the measured first order thickness resonance in the ultrasonic transmission coefficient of a homogeneous plate at normal incidence.
More information in:
http://us-biomat.com/resources/code/
https://github.com/usbiomat/ultrasonic-thickness-resonance/wiki
Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on Levenberg-Marquardt algorithm of scipy.optimize.leastsq(), but also supports most of the optimization methods from scipy.optimize.
Features:
- Using Parameter objects instead
... [More] of plain floats as variables. A Parameter has a value that can be varied in the fit, fixed, have upper and/or lower bounds. It can even have a value that is constrained by an algebraic expression of other Parameter values.
- Ease of changing fitting algorithms.
- Improved estimation of confidence intervals.
- Improved curve-fitting with the Model class, which allows to turn a function into a model to fit the data.
- Many pre-built models for common lineshapes are included and ready to use. [Less]
This site uses cookies to give you the best possible experience.
By using the site, you consent to our use of cookies.
For more information, please see our
Privacy Policy