We can use h5py to read data from an HDF5 file and convert it into a dask array for efficient parallel processing. Here's an example of how to extract a dask array from an HDF5 file containing earthquake data:
import h5pyimport dask.array as dawith h5py.File('earthquake_data.h5', 'r') as f: latitude = da.from_array(f['latitude'], chunks='auto') longitude = da.from_array(f['longitude'], chunks='auto') depth = da.from_array(f['depth'], chunks='auto') magnitude = da.from_array(f['magnitude'], chunks='auto') |
In this example, we use h5py.File() to open the HDF5 file containing the earthquake data. We then use da.from_array() from dask.array to create dask arrays from the latitude, longitude, depth, and magnitude datasets in the HDF5 file. We pass chunks='auto' to let dask determine the optimal chunk size based on the size of the data.
Once we have the dask arrays, we can use dask operations to analyze the data. For example, we can compute the mean magnitude of the earthquakes using:
mean_magnitude = magnitude.mean().compute() |
This will compute the mean magnitude of the earthquakes in parallel using dask.
We can also use dask and matplotlib to create a scatter plot of earthquake locations as follows:
import matplotlib.pyplot as pltplt.scatter(longitude.compute(), latitude.compute(), c=magnitude.compute(), cmap='magma')plt.xlabel('Longitude')plt.ylabel('Latitude')plt.colorbar(label='Magnitude')plt.show() |
In this example, we use longitude.compute() and latitude.compute() to compute the NumPy arrays for longitude and latitude, and magnitude.compute() to compute the NumPy array for the magnitude. We then use plt.scatter() to create a scatter plot of earthquake locations, with the magnitude of each earthquake represented by the color of the point. We use plt.colorbar() to add a colorbar to the plot. Note that we need to call .compute() on the dask arrays to convert them to NumPy arrays before passing them to plt.scatter().