One-dimensional arrays can also be stacked in dask using the da.stack() function. This can be useful, for example, when we want to combine arrays of earthquake magnitudes or depths from multiple sources.
Here's an example of how to stack two one-dimensional arrays of earthquake magnitudes to create a new array:
import dask.array as dawith h5py.File('earthquake_data.h5', 'r') as f: magnitudes1 = da.from_array(f['magnitudes1'], chunks='auto') magnitudes2 = da.from_array(f['magnitudes2'], chunks='auto')magnitudes = da.stack([magnitudes1, magnitudes2]) |
In this example, we use da.from_array() to create dask arrays from the magnitudes1 and magnitudes2 datasets in the HDF5 file. We pass chunks='auto' to let dask determine the optimal chunk size based on the size of the data.
We then use da.stack() to stack the magnitudes1 and magnitudes2 arrays along a new axis, which creates a new array with two rows (one for each input array) and N columns, where N is the length of the input arrays.
We can also stack more than two one-dimensional arrays by passing a list of arrays to da.stack(). For example, to stack three arrays of earthquake depths, we could do:
import dask.array as dawith h5py.File('earthquake_data.h5', 'r') as f: depths1 = da.from_array(f['depths1'], chunks='auto') depths2 = da.from_array(f['depths2'], chunks='auto') depths3 = da.from_array(f['depths3'], chunks='auto')depths = da.stack([depths1, depths2, depths3]) |
In this case, the resulting array would have three rows and N columns.