Glue: multidimensional data exploration.
From the webpage:
Glue is a Python library to explore relationships within and among related datasets. Its main features include:
- Linked Statistical Graphics. With Glue, users can create scatter plots, histograms and images (2D and 3D) of their data. Glue is focused on the brushing and linking paradigm, where selections in any graph propagate to all others.
- Flexible linking across data. Glue uses the logical links that exist between different data sets to overlay visualizations of different data, and to propagate selections across data sets. These links are specified by the user, and are arbitrarily flexible.
- Full scripting capability. Glue is written in Python, and built on top of its standard scientific libraries (i.e., Numpy, Matplotlib, Scipy). Users can easily integrate their own python code for data input, cleaning, and analysis.
There is a series of videos by Chris Beaumont on Glue:
Glue FAQ: How do I overplot a catalog on an image?
Glue Demo: World Wide Telescope
I like Glue because of its use of astronomy data for examples but it isn’t limited to astronomical data.
From the FAQ:
What data formats does Glue understand?
Glue relies on several libraries to parse different file formats:
Astropy for FITS images and tables, a
variety of ascii table formats, and VO
tables.scikit-image to read popular image
formats like .jpeg and .tiffh5py to read HDF5 files If Glue’s predefined data loaders don’t fit your needs, ou can also write your own loader, and plug it into Glue.
Searching for particular information or data is one task.
Exploring a data set to see what you may encounter is another.
What data sets do you want to explore with Glue?
I first saw this in Christophe Lalanne’s A bag of tweets / June 2013.
PS: The mapping function in “Getting Started With Glue” is particularly interesting. What mapping function will you plugin?