In dask, we can put array blocks together using the da.concatenate() function. This can be useful when we want to combine earthquake data from different sources that have the same dimensions.

For example, let's say we have two datasets of earthquake magnitudes and depths, with the same dimensions, that we want to combine into a single dataset:

import dask.array as da
 
with h5py.File('earthquake_data1.h5', 'r') as f:
    magnitudes1 = da.from_array(f['magnitudes'], chunks='auto')
    depths1 = da.from_array(f['depths'], chunks='auto')
 
with h5py.File('earthquake_data2.h5', 'r') as f:
    magnitudes2 = da.from_array(f['magnitudes'], chunks='auto')
    depths2 = da.from_array(f['depths'], chunks='auto')
 
magnitudes = da.concatenate([magnitudes1, magnitudes2])
depths = da.concatenate([depths1, depths2])

In this example, we use da.from_array() to create dask arrays from the magnitudes and depths datasets in each 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.concatenate() to concatenate the magnitudes1 and magnitudes2 arrays horizontally, and the depths1 and depths2 arrays vertically, which creates new arrays magnitudes and depths, respectively.

Note that the da.concatenate() function expects a list of arrays to concatenate, and the arrays must have the same dimensions along the axis of concatenation. In this example, we concatenate the arrays along different axes, because the input datasets have different shapes.