SciPy library main repository (by scipy)

SciPy Alternatives

Similar projects and alternatives to SciPy

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better SciPy alternative or higher similarity.

SciPy reviews and mentions

Posts with mentions or reviews of SciPy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-29.
  • Python
    3 projects | | 29 Dec 2022
  • What's Great about Julia?
    6 projects | | 7 Dec 2022
    Software has bugs. That's the way it is. You may think that Julia (but I suppose this is mostly about the ecosystem of packages around Julia) has too many bugs. Then you can use something else. Like Python. If you move from Julia to Python, you may want to use Numpy? Pretty cool project. It currently has 1,9k issues on Github and if you filter by bugs, it has 599 such labeled issues. How many of those are issues like in the post? I don't know. The same applies to Scipy. For example, the gaussian hypergeometric function returns wrong results for some input values This issue was filed in 2014. You can find similar old issues in Julia packages. That's how these things go. Luckily, many of the issues listed in the blog post are fixed.

    If you think that picking any language and any library combination with a semi-high requirement for the number of features you want to be already implemented will be able to fulfill the "this has to be completely correct or I won't use it for my research"-requirement you will have a hard time.

    The last part of the post seems to be about OffSetArrays.jl. Many people who have implemented libraries and who care about composability and generic input also agree that the Base AbstractArray interface is not perfect or complete and sometimes the issue is that the interface that does exist is not followed well enough for composability to work. A more complete, agreed upon, and generally adhered to interface for an "AbstractArray" would be nice and has been+is being worked on and discussed by many people in the community.

  • scipy?
    2 projects | | 13 Oct 2022
    git clone
    2 projects | | 13 Oct 2022
    If you want to build on device: (assuming gfortran, openblas, numpy and pybind11 already installed) git clone cd scipy python install -> SciPy 1.10.0.dev0+xxxx.xxxxxxx
  • were can i find advance ( hardest ) python projects with source code ?
    5 projects | | 15 Sep 2022
  • Choosing Julia, Matlab, Python or R in economics?
    5 projects | | 13 Aug 2022
  • “Why I still recommend Julia”
    11 projects | | 25 Jun 2022
    I don't see how it addresses the original complaint. Vishnevsky basically stated that if you are trying to run a scientific experiment on a supercomputer, maybe it's a risky idea to use a new programming language with a new stdlib and a bunch of OSS libraries vs using an old language like C with very stable set of existing code because new things tend to have unknown bugs? Vishnevsky has a point, but unless you are running some critical computations on supercomputers, maybe it doesn't apply to you?

    To be clear, in supercomputing environments people still use old versions of CentOS just to make sure that library version updates do not change their computation results. I don't think many people here would say "I am sticking to Ubuntu 16.04 because I am afraid that the updates to some library like gmplib will slightly change my computation results in a way that is hard for me to detect".

    Also, just staying with the old doesn't mean it's correct. You can also introduce bugs to your libs. I think NASA thought this through long time ago and solved it by making sure critical parts of the code are implemented twice using different stacks with different programmers.

    If you are NASA, CERN, LLNL, or a bank, maybe it's a good idea to implement your computations once in Python and once in Julia (by at least two different programmers) and compare the outputs. And I don't think in this situation Julia is any different from other languages (other than you may put too much trust into it and skip this dual implementation). Case in point:

  • Being 500x faster than python still means it's 10x slower than C
    6 projects | | 22 Jun 2022
    This is not that uncommon for performant python packages. Another example: scipy is 19% Fortran and 17% C.
  • Open source library unavoidably prints advertisement upon initialisation. My program runs parallel processes each using this library.
    2 projects | | 9 Jun 2022
    *********************************************************************************** This program is written in Python, an easy programming language for a first time programmer. Python is open software released under the PSF license ( *********************************************************************************** +-----------------------------------------------------------------+ | This program uses NumPy, an open source library for numerical | | computing with Python. NumPy is released under the modified BSD | | license ( | +-----------------------------------------------------------------+ +-----------------------------------------------------------------+ | This program uses SciPy, an open source library for scientific | | computing with Python. SciPy is released under the modified BSD | | license ( | +-----------------------------------------------------------------+ \|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||/ = This program contains Baba Saitaandas, an aghori for programmatic decapitation of = = header files and head files. Saitaandas is unleashed by the Max Plank Institute = = for the Science of Decapitation ( = /|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\
  • Discrete Algebraic Ricatti Equation Solver
    4 projects | | 10 May 2022
    I am the author who touched the DARE solver in SciPy introduced here and modified over the years. The iterative solvers are not more stable in fact it is the other way around but when arrays are too big for dense computations, decompositions become intractable and we resort back to iterative solvers.
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