This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

Title : axarray Author : sylvain.guieu@gmail.com

# Introduction axarray is a numpy array where axes can be labeled.

The idea is to be able to manipulate array, and do operation on axis without knowing the array shape order but on knowing labels related to the ‘physical’ meaning of the axes.

Often in science, it is useful to name the array axes by an intelligible label. For instance, for 2d images taken at different time, axes name of the obtain cube could be [“time”, “y”, “x”]

axarray object aims to do that. For instance a.mean(axis=”time”) will execute the mean on the axis labeled “time” where ever it is.

Given a1 and a2, two axarray, binary operation like a1+a2 can be performed even if the two axarray has different axes order as long as they have matching axis labels.

# installation With pip ` > pip install axarray `

Or from git in your PYTHON_PATH

` > git clone https://github.com/SylvainGuieu/axarray.git `

# Examples

`python >>> a = axarray( np.random.random((10,4,5)), ["time", "y", "x"]) >>> b = a.transpose( ["x","time", "y"]) >>> b.axes ["x","time", "y"] `

can operate 2 transposed axarray as long as they match axis names

`python >>> (a+b).axes ["time", "y", "x"] ` use the numpy frunction with axis labels

`python >>> a.min(axis="time").shape (4,5) # similar to: >>> np.min(a , axis="time") `

axis can be alist of axis label

`python >>> a.mean(axis=["x","y"]).shape (10,) `

one can use the convenient apply method. Useful in non-direct call as in a plot func for instance

`python >>> a.apply(time_reduce=np.mean, y_idx=slice(0,2)).shape (2,5) `

transpose, reshape rename axes in one call

`python >>> at = a.transform( [("pixel", "y","x"), "time"]) >>> at.shape (20, 10)  # (4*5, 10) >>> at.axes ['pixel', 'time'] `

Extract a spectrum from image from named indices

`python ### make some indices >>> iy, ix = axarray( np.indices( (3,4)), [0 ,"spatial", "freq"]) >>> ax[:,iy,ix].axes ['time', 'spatial', 'freq'] `

Release History

Release History

0.1.1

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.1.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
axarray-0.1.1.tar.gz (2.1 kB) Copy SHA256 Checksum SHA256 Source Oct 12, 2015

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting