GitHub - fossasia/visdom: A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
Visdom has a simple set of features that can be composed for various use-cases. Windows The UI begins as a blank slate – you can populate it with plots, images, and text. These appear in windows that you can drag, drop, resize, and destroy. The windows live in envs and the state of envs is stored across sessions. You can download the content of windows – including your plots in svg. Tip: You can u
fonnesbeck/ScipySuperpack @ GitHub
This shell script will build and install the Python scientific stack, including Numpy, Scipy, Matplotlib, IPython, Pandas, Statsmodels, Scikit-Learn, and PyMC for OS X 10.10 (Yosemite) using the Homebrew package manager and pip. The script will use recent development code from each package, which means that though some bugs may be fixed and features added, they also may be more unstable than the o
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HJBlog > Building 64bit NumPy/SciPy/PyLab on Snow Leopard
Thu, Sep 3, 2009, 12:47am Building 64bit NumPy/SciPy/PyLab on Snow Leopard hat follows is my collection of notes about installing a full 64-bit stack of NumPy, SciPy, PyLab, and all that entails on a fresh Snow Leopard system. If it helps out someone else trying to do something similar that would be great. If you look through what I've done and find things that could have been done better or in a
Numpy and Scipy Documentation — Numpy and Scipy documentation
For newer versions see https://www.numpy.org/doc Numpy (development version) Reference Guide Numpy (development version) User Guide Numpy 1.17.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.17.0 User Guide, [PDF] Numpy 1.16.1 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.1 User Guide, [PDF] Numpy 1.16.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.0 User Guide, [PDF] Numpy 1.15.4 Reference Guide, [
Routines and objects by topic — NumPy v2.2 Manual
Routines and objects by topic# In this chapter routine docstrings are presented, grouped by functionality. Many docstrings contain example code, which demonstrates basic usage of the routine. The examples assume that NumPy is imported with: A convenient way to execute examples is the %doctest_mode mode of IPython, which allows for pasting of multi-line examples and preserves indentation.
NumPy — NumPy
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional containe
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NumPy reference — NumPy v2.2 Manual