Dask arrays from HDF5 datasets
You have been tasked with analyzing European rainfall over the last 40 years. The monthly average rainfall in a grid of locations over Europe has been provided for you in HDF5 format. Since this file is pretty large, you decide to load and process it using Dask.
h5py has been imported for you, and dask.array has been imported as da.
Cet exercice fait partie du cours
Parallel Programming with Dask in Python
Instructions
- Open the
'data/era_eu.hdf5'file usingh5py. - Load the
'/precip'variable into a Dask array using thefrom_array()function, and set chunks of (12 months, 15 latitudes, and 15 longitudes). - Use array slicing to select every 12th index along the first axis - this selects the January data from all years.
- Take the mean of
january_rainfallsalong the time axis (axis0) to calculate the mean rainfall in January across Europe.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Open the HDF5 dataset using h5py
hdf5_file = ____.____(____)
# Load the file into a Dask array with a reasonable chunk size
precip = da.____(____, chunks=____)
# Select only the months of January
january_rainfalls = ____[____]
# Calculate the mean rainfall in January for each location
january_mean_rainfall = ____.____(axis=____)
plt.imshow(january_mean_rainfall.compute())
plt.show()