slice_data_and_shift_indexes¶
slice_data_and_shift_indexes(da, indexes_tuple)Slice data using min/max indexes, and shift indexes.
smart_read¶
smart_read(da, indexes_tuple, dask_more_efficient=10, chunks='auto')Read from a xarray.DataArray given a tuple indexes.
Try to do it fast and smartly. There is a lot of improvement to be made here, but this is how it is currently done.
The data we read is going to be unstructured but they tend to be rather localized. For example, the lagrangian particles read data from the same time step. Currently, using dask/xarray’s unstructured read does not really take advantage of the locality. This function figures out which chunks stores the data, convert them into numpy arrays, and then read the data from the converted ones.
Parameters
da (xarray.DataArray) -- DataArray to read from
indexes_tuple (tuple of numpy.ndarray) -- The indexes of points of interest, each element does not need to be 1D
dask_more_efficient (int, default 100) -- When the number of chunks is larger than this, and the data points are few, it may make sense to directly use dask’s vectorized read.
chunks (int, str, default: “auto”) -- Chunks for indexes
Returns
+ values (numpy.ndarray) -- The values of the points of interest. Has the same shape as the elements in indexes_tuple.