Code Monkey home page Code Monkey logo

Comments (6)

jryon avatar jryon commented on June 29, 2024

I took a look at the discrepancies and consulted with Norman. Given that the differences are all in the 5th or 6th significant figure and affect <2.5% of pixels in the detector, they are minor enough to ignore. Let me know if there's anything else I can help with.

from acstools.

pllim avatar pllim commented on June 29, 2024

Thanks! I can look into adjusting the tests when I have time. This is not urgent for now.

from acstools.

pllim avatar pllim commented on June 29, 2024

I have #185 that would make the CI green but I did find some difference that require me to set the tolerance very high (atol=0.1). Is this something you would accept as a solution? If not, what was the new tolerance you were thinking about (how relaxed are you willing to go)?

from acstools.

pllim avatar pllim commented on June 29, 2024

I confirmed that the failures are caused by numpy 2.0.dev , not scipy nor astropy dev versions. See #187 (comment)

from acstools.

jryon avatar jryon commented on June 29, 2024

I ran some tests in a numpy 2.0 dev environment and an environment with current numpy (1.26.2) to track down the cause of the failures. It looks like numpy 2.0 keeps results of some arithmetic in the original dtypes, when the current numpy converts them to np.float64. These differences in dtypes compound floating point errors during subsequent calculations, resulting in the discrepancies we see as test failures. This may explain some of the behavior we're seeing: https://numpy.org/neps/nep-0050-scalar-promotion.html#table-comparing-new-and-old-behaviour

For the failures, I think setting atol = 0.01 should allow the tests to pass. That level of error is acceptable to us given the stripe uncertainties (0.9 e- RMS). If that level of absolute tolerance doesn't work, I'll have to revisit this.

from acstools.

pllim avatar pllim commented on June 29, 2024

Thanks for investigating, @jryon ! I think atol=0.01 worked. See #192

from acstools.

Related Issues (20)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.