Virtual large arrays and lazy evaluation
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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