wcxve / elisa Goto Github PK
View Code? Open in Web Editor NEWEfficient library for spectral analysis in high-energy astrophysics.
Home Page: https://elisa-lib.readthedocs.io
License: GNU General Public License v3.0
Efficient library for spectral analysis in high-energy astrophysics.
Home Page: https://elisa-lib.readthedocs.io
License: GNU General Public License v3.0
We should implement grouping method as in ftgrouppha.
Grouping method should also take data type into consideration, i.e., for poisson data with poisson background or gaussian background, we should use Li-Ma significance or GV significance formulae to assess the SNR.
The NUTS sampling procedure is first defined by PyMC/PyTensor, and "jaxified" later. This has the issue that the finite difference wraparound for Xspec's models library will not work.
Transforming backend from PyTensor to JAX can fix this.
When using a spectral model that includes some Xspec models (e.g., apec, phabs, ztbabs, tbfeo, tbgas, tbgrain, tbpcf, tbvarabs, and tbrel), parallel computing will fail.
As an alternative to tbabs or phabs, consider using elisa.models.mul.TBAbs
or elisa.models.mul.PhAbs
. For the other models, a sequential computing approach should be used.
A general simulation API should be provided, and used in CompiledModel.simulate
and FitHelper
's simulation functions.
Negative background data can be produced from normal distribution, which should be set to zeros when the total spectrum data follows Poisson distribution.
It is well known that profile likelihood can be biased. For W-statistics, numerical experiments showed re-binning can avoid this bias (see e.g., Giacomo's blog and Johannes's review).
For PG-statistics, a certain criteria,
This inefficiency occurs when some parameters are fixed during computation. The finite differences will be computed for these fixed parameters, which are not (?) optimized by JAX.
Details about how Xspec groups the area/background array, see the OGIP_92aData::groupArrays
function in this link.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.