Setting up dask

Both the threshold and detect functions are set up to use dask delayed automatically. I found this was a good away to make sure the main processes would be automatically run in parallel even if you are not experienced with dask. This approach add some overhead before the actual calculation starts, when dask is working out the task graph. This is usually negligible, but with a bigger grid size you might end up with too many tasks and a less efficinet graph. In that case, and anytime you are working with limited resources, it is more efficient to split the grid and run the code separately for each grid section. You can easily recompose the original grid by concatenating together the resulting datasets. Even when running the functions on the full grid it is important to setup proper chunks. The code will make sure that the timeseries for each grid point are all in the same chunk. This is important as it is requires by some of the calculation, it also makes sense as every operation is done cell by cell. > sst = sst.chunk({‘time’: -1})

This corresponds to have a chunk size (for time dimension) equal to the length of the timeseries. The code will not do anything for the other dimensions, so it is a good idea to make sure that once you have chunked the time dimension, you are left with resonable chunk sizes.

 # This will tell dask to automatically determine a good chunk size for the other dimensions
 # Assuming that the sst variable has (time, lat, lon) dimensions
 sst = sst.chunk({'time': -1, 'lat': 'auto', 'lon': 'auto'})

shows the chunks shape and their size. As the sst array is quite small we have only 1 chunk in this case, if this was a big grid we would want chunks around 200 MB of size. Below is an example running detect on a big grid by splitting the grid according to chunks. You can split the grid in different ways and advantage of this methid is that the data will be all in one chunk. So by managing the chunksize you can optimise the amount of memory used. Whichever way you are splitting the grid make sure that is somehow aligned with the dataset chunks.

# retrieve chunks information
# xt/xy will be lists the number of lat/lon points for each chunk
# As an example
# [20, 20, 20], [30, 30, 30]
# means we have 20 lat and 30 lon points for each chunk for a total of 600 grid cells
# we set these as out "steps"
dummy, xt, xy = sst.chunks
xtstep = xt[0]
ytstep = yt[0]
# the length of the list gives has the number of chunks, in the example 3
xtblocks = len(xt)
ytblocks = len(yt)
print(xtstep, ytstep, xtblocks, ytblocks)
# create an empty list to store the results
# loop first across the ytblocks and then xtblocks
# using xtstep and ytstep to determine the indexes to use with isel

results = []
for i in range(xtblocks):
    xt_from = i*xtstep
    xt_to = (i+1)*xtstep
    for j in range (ytblocks):
        yt_from = j*ytstep
        yt_to = (j+1)*ytstep
        ts = sst.isel(xt_ocean=slice(xt_from,xt_to),yt_ocean=slice(yt_from,yt_to))
        th = thresh.isel(xt_ocean=slice(xt_from,xt_to),yt_ocean=slice(yt_from,yt_to))
        se = seas.isel(xt_ocean=slice(xt_from,xt_to),yt_ocean=slice(yt_from,yt_to))
        # run function
        del ts, th, se
# Combine the results into one dataset
mhw = xr.combine_by_coords(results)