Virtual large arrays and lazy evaluation
Project description
Virtual large arrays and lazy evaluation.
For example, we can combine multiple array data sources into a single virtual array:
>>> first_time_series = OrthoArrayAdapter(hdf_var_a)
>>> second_time_series = OrthoArrayAdapater(hdf_var_b)
>>> print first_time_series.shape, second_time_series.shape
(52000, 800, 600) (56000, 800, 600)
>>> time_series = biggus.LinearMosaic([first_time_series, second_time_series], axis=0)
>>> time_series
<LinearMosaic shape=(108000, 800, 600) dtype=dtype('float32')>
Any biggus Array can then be indexed, independent of underlying data sources:
>>> time_series[51999:52001, 10, 12]
<LinearMosaic shape=(2,) dtype=dtype('float32')>
And an Array can be converted to a numpy ndarray on demand:
>>> time_series[51999:52001, 10, 12].ndarray() array([ 0.72151309, 0.54654914], dtype=float32)
Get in touch!
We’ve got lots of exciting plans underway for biggus, but we’re also very keen to hear from you.
How are you thinking of using biggus?
What capabilities does biggus need for it to be useful to you?
What capabilities does biggus already have that you find useful?
Further reading
To get more ideas of what Biggus can do, please browse the wiki, and its examples.
If you have any questions or feedback please feel free to post to the discussion group or raise an issue on the issue tracker.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file Biggus-0.6.0.tar.gz.
File metadata
- Download URL: Biggus-0.6.0.tar.gz
- Upload date:
- Size: 37.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b57a4a49c6e7de077b77da4b1f76dfd45b82a50a5cc0728667155d76663812c0 |
|
| MD5 | df5934ce5c45872c3bb34b48513cd2eb |
|
| BLAKE2b-256 | ccc1aa7c083c334e50b023ea517987655f8b5080159c04acf1c2fc460ae4a51a |
