This work is supported by Continuum Analytics and the XDATA Program as part of the Blaze Project

tl;dr Dask.arrays can slice and stack. This is useful for weather data.

## Introduction

This is the sixth in a sequence of posts constructing an out-of-core nd-array using NumPy, and dask. You can view these posts here:

Now we talk about slicing and stacking. We use meteorological data as an example use case.

## Slicing

Dask.array now supports most of the NumPy slicing syntax. In particular it supports the following:

• Slicing by integers and slices x[0, :5]
• Slicing by a list/np.ndarray of integers x[[1, 2, 4]]
• Slicing by a list/np.ndarray of booleans x[[False, True, True, False, True]]

It does not currently support the following:

• Slicing one dask.array with another x[x > 0]
• Slicing with lists in multiple axes x[[1, 2, 3], [3, 2, 1]]

## Stack and Concatenate

We often store large arrays on disk in many different files. We want to stack or concatenate these arrays together into one logical array. Dask solves this problem with the stack and concatenate functions, which stitch many arrays together into a single array, either creating a new dimension with stack or along an existing dimension with concatenate.

### Stack

We stack many existing dask arrays into a new array, creating a new dimension as we go.

>>> import dask.array as da
>>> arrays = [from_array(np.ones((4, 4)), blockshape=(2, 2))
...            for i in range(3)]  # A small stack of dask arrays

>>> da.stack(arrays, axis=0).shape
(3, 4, 4)

>>> da.stack(arrays, axis=1).shape
(4, 3, 4)

>>> da.stack(arrays, axis=2).shape
(4, 4, 3)


This creates a new dimension with length equal to the number of slices

### Concatenate

We concatenate existing arrays into a new array, extending them along an existing dimension

>>> import dask.array as da
>>> arrays = [from_array(np.ones((4, 4)), blockshape=(2, 2))
...            for i in range(3)]  # small stack of dask arrays

>>> da.concatenate(arrays, axis=0).shape
(12, 4)

>>> da.concatenate(arrays, axis=1).shape
(4, 12)


## Case Study with Meteorological Data

To test this new functionality we download meteorological data from the European Centre for Medium-Range Weather Forecasts. In particular we have the temperature for the Earth every six hours for all of 2014 with spatial resolution of a quarter degree. We download this data using this script (please don’t hammer their servers unnecessarily) (Thanks due to Stephan Hoyer for pointing me to this dataset).

As a result, I now have a bunch of netCDF files!

\$ ls
2014-01-01.nc3  2014-03-18.nc3  2014-06-02.nc3  2014-08-17.nc3  2014-11-01.nc3
2014-01-02.nc3  2014-03-19.nc3  2014-06-03.nc3  2014-08-18.nc3  2014-11-02.nc3
2014-01-03.nc3  2014-03-20.nc3  2014-06-04.nc3  2014-08-19.nc3  2014-11-03.nc3
2014-01-04.nc3  2014-03-21.nc3  2014-06-05.nc3  2014-08-20.nc3  2014-11-04.nc3
...             ...             ...             ...             ...

>>> import netCDF4
>>> t = netCDF4.Dataset('2014-01-01.nc3').variables['t2m']
>>> t.shape
(4, 721, 1440)


The shape corresponds to four measurements per day (24h / 6h), 720 measurements North/South (180 / 0.25) and 1440 measurements East/West (360/0.25). There are 365 files.

Great! We collect these under one logical dask array, concatenating along the time axis.

>>> from glob import glob
>>> filenames = sorted(glob('2014-*.nc3'))
>>> temps = [netCDF4.Dataset(fn).variables['t2m'] for fn in filenames]

>>> arrays = [da.from_array(t, blockshape=(4, 200, 200)) for t in temps]
>>> x = da.concatenate(arrays, axis=0)

>>> x.shape
(1464, 721, 1440)


Now we can play with x as though it were a NumPy array.

avg = x.mean(axis=0)
diff = x[1000] - avg


If we want to actually compute these results we have a few options

>>> diff.compute()  # compute result, return as array, float, int, whatever is appropriate
>>> np.array(diff)  # compute result and turn into np.ndarray
>>> diff.store(anything_that_supports_setitem)  # For out-of-core storage


Alternatively, because many scientific Python libraries call np.array on inputs, we can just feed our da.Array objects directly in to matplotlib (hooray for the __array__ protocol!):

>>> from matplotlib import imshow
>>> imshow(x.mean(axis=0), cmap='bone')
>>> imshow(x[1000] - x.mean(axis=0), cmap='RdBu_r')


I suspect that the temperature scale is in Kelvin. It looks like the random day is taken during Northern Summer. Another fun one, lets look at the difference between the temperatures at 00:00 and at 12:00

>>> imshow(x[::4].mean(axis=0) - x[2::4].mean(axis=0), cmap='RdBu_r')


Even though this looks and feels like NumPy we’re actually operating off of disk using blocked algorithms. We execute these operations using only a small amount of memory. If these operations were computationally intense (they aren’t) then we also would also benefit from multiple cores.

## What just happened

To be totally clear the following steps just occurred:

1. Open up a bunch of netCDF files and located a temperature variable within each file. This is cheap.
2. For each of those temperature variables create a da.Array object, specifying how we want to block up that variable. This is also cheap.
3. Make a new da.Array by concatenating all of our da.Arrays for each day. This, like the other steps, is just book-keeping. We haven’t loaded data or computed anything yet.
4. Write numpy-style code x[::2].mean(axis=0) - x[2::2].mean(axis=0). This creates yet another da.Array with a more complex task graph. It takes a few hundred milliseconds to create this dictionary.
5. Callimshow on our da.Array object
6. imshow calls np.array on its input, this starts the multi-core task scheduler
7. A flurry of chunks fly out of all the netCDF files. These chunks meet various NumPy functions and create new chunks. Well organized magic occurs and an np.ndarray emerges.
8. Matplotlib makes a pretty graph

## Problems that Popped Up

The threaded scheduler is introducing significant overhead in its planning. For this workflow the single-threaded naive scheduler is actually significantly faster. We’ll have to find better solutions to reduce scheduling overhead.

## Conclusion

I hope that this shows off how dask.array can be useful when dealing with collections of on-disk arrays. As always I’m very happy to hear how we can make this project more useful for your work. If you have large n-dimensional datasets I’d love to hear about what you do and how dask.array can help. I can be reached either in the comments below or at [email protected].

## Acknowledgements

First, other projects can already do this. In particular if this seemed useful for your work then you should probably also know about Biggus, produced by the UK Met office, which has been around for much longer than dask.array and is used in production.

Second, this post shows off work from the following people:

1. Erik Welch (Continuum) wrote optimization passes to clean up dask graphs before execution.
2. Wesley Emeneker (Continuum) wrote a good deal of the slicing code
3. Stephan Hoyer (Climate Corp) talked me through the application and pointed me to the data. If you’d like to see dask integrated with xray then you should definitely bug Stephan :)