[ITP] python-h5py 2.2.0
Wed Jan 15 02:57:00 GMT 2014
It's been several weeks since I posted this ITP and there hasn't been
Version 2.2.1 of h5py is out, and I'm willing to package it for Cygwin
but I'll need to know if this package will be accepted. Please let me
know if I'm doing something incorrectly or if I should make any
I know there is a new package upload system, but it appears to be for
accepted packages only. Please let me know if I can use this upload
system for this proposed package.
On Wed, Dec 11, 2013 at 12:03 PM, Chris LeBlanc <email@example.com> wrote:
> H5py (www.h5py.org) is a useful package for managing large scientific
> datasets in Python. The hdf5 library packages already exist in
> Cygwin, so there are no additional dependencies and no changes were
> needed to compile the C Python extension modules under Cygwin.
> Maintaining this package should be pretty light work and I'm happy to
> do it.
> H5py is a standard package in Debian and Fedora:
> I used Cygport to create the archives and setup.hint for version 2.2.0
> of h5py. This is the first time I've used cygport so please let me
> know if I've done something incorrectly. I haven't seen a single repo
> for keeping track of these pacakge config files, so made my own github
> repo for this file: https://github.com/crleblanc/cygwin_h5py_package.
> The h5py package includes a set of unit tests (run with
> "h5py.run_tests()" in a Python shell). I've run these after building
> and installing, and they run fine on both 32 and 64 bit. I haven't
> run any large scale tests.
> Here are the packages I've created on fresh 32 and 64 bit Cygwin
> instances. I see there is a Cygwin web page for uploading packages,
> but this package hasn't been accepted yet so thought I'd do it the old
> fashioned way.
> category: Python
> requires: libhdf5_8 python python-numpy hdf5
> sdesc: "A Pythonic interface to the HDF5 binary data format"
> ldesc: "This package lets you store huge amounts of numerical
> data, and easily manipulate that data from NumPy. For example, you
> can slice into multi-terabyte datasets stored on disk, as if they
> were real NumPy arrays. Thousands of datasets can be stored in a
> single file, categorized and tagged however you want."
> Hopefully this is all the info you need. I noticed a possible
> dependency issue with NumPy, but I'll post that as a different message
> on the appropriate list.
> Chris LeBlanc
> GNS Science
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