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)
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We’ve got lots of exciting plans underway for biggus, but we’re also very keen to hear from you.
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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.
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