issue_comments

Another way to do this would be to use da.map_blocks(plotfunc, template=...) with

``` python def plotfunc(da): f = plt.figure() ... f.close()

return da.time  # or similar

```

I have used this successfully with a distributed cluster.

Closing for now. @apatlpo, feel free to reopen if there's more to discuss here.

@apatlpo sure, something like: ```python import threading import matplotlib.pyplot

MPL_LOCK = threading.Lock()

def save_imshow(data, path): with MPL_LOCK: fig, ax = plt.subplot() ax.imshow(data) fig.savefig(path) ``save_imshow()is now something you safely call in parallel (e.g., fromdask.array.map_blocks()`).

I agree that it is certainly a best practice to avoid relying on global state from the pyplot module.

That said, my experience has been that even if you avoid using pyplot functions matplotlib is still not thread-safe (StackOverflow backs me up here). So you probably will need a thread-lock around your use of any plotting functions, anyways.

You are lucky, we just added a contributing guide to xarray: http://xarray.pydata.org/en/latest/contributing.html ;-)

The recommended way to install xarray (and any other local library btw) is:

pip install -e .

Thanks for the report! Would you like to open a PR? I think you are the best placed to see if you proposed fixes solve the problem for you