issues

• [x] Closes #2775
• [x] Tests added
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

This is really nice to have when producing faceted plots of satellite observations in various bands, and should be somewhere between useful and harmless in other cases.

Example code:

python ds = xr.Dataset({ k: xr.DataArray(np.random.random((2, 2)), dims="x y".split(), name=k) for k in "blue green red".split() }) xr.concat([ds.blue, ds.green, ds.red], dim="band").plot.imshow(col="band")

Before - facets have an index, colorbar has misleading label:

After - facets have meaningful labels, colorbar has no label:

Closes #2441 - Hypothesis 3.72.0 turned a common no-op into an explicit error. Apparently this was such a common misunderstanding that I had done it too :disappointed:

Anyway: while it hasn't been using the deadline at all until now, I've still translated it into the correct form rather than deleting it in order to avoid flaky tests if the Travis VM is slow.

• [x] Closes #1842
• [x] Tests added
• [x] Tests passed
• [x] Passes flake8 xarray (now part of tests)
• [x] Fully documented, including whats-new.rst for all changes

Most satellites produce images with color depth in the range of eight to sixteen bits, which are therefore often stored as unsigned integers (with the quality mask in another variable). If you're lucky, they also have a scale_factor attribute and Xarray can automatically convert the integers to floats representing albedo.

This is fantastically convenient, and avoids all the bit-depth bugs from misremembered specifications. However, loading data as float64 when float32 is sufficient doubles memory usage in IO (even on multi-TB datasets...). While immediately downcasting helps, it's no substitute for doing the right thing first.

So this patch does some conservative checks, and if we can be sure float32 is safe we use that instead.

• [x] Closes #1846
• [x] Tests added - you bet
• [x] Tests passed - well, the code under test hasn't changed...

This is a small property-based test suite, to give two examples of the kinds of tests that we could write for Xarray using Hypothesis.

1. For any array, encoding and decoding it with a CF coder outputs an identical array. As you would hope, these tests pass.
2. For any 2D array, you can call the 2D plotting methods without raising an exception. Alas, this is not the case, and Hypothesis will show you the failing inputs (and matplotlib-related tracebacks) to prove it. (Contributing a very small feature to matplotlib was shockingly painful, so I'm not planning to take a similar suite upstream myself unless something changes)

Things that I would like to know:

• Have I build-wrangled something reasonable here?
• Will anyone else contribute property-based tests? I'm happy to help people debug or work out how to test something, but I simply don't have the time to write another test suite for free.
• Is this something you want?

• [x] Closes #1966
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes

Not much more to say, really. Thanks to @fmaussion for pinging me - definitely faster to track down when you know the code!

• [x] Closes #1880
• [x] Tests added (for all bug fixes or enhancements)
• [ ] Tests passed (for all non-documentation changes)
• [x] Fully documented (bugfix for earlier change, no additional note)

The cause of the bug in #1880 was that I had forgotten to specify the dtype when creating an alpha channel, and therefore concatenating it cast the all the data to float64. I've fixed that, corrected the alpha value for integer arrays, and avoided a pointless copy to save memory.

• [x] Closes #1630
• [x] Tests passed
• [x] Passes git diff upstream/master **/*py | flake8 --diff
• [x] Fully documented, including whats-new.rst for all changes - details of label not previously documented

Follow-up to #1796, where normalisation and clipping of RGB[A] values were deferred so that we could match any upstream API. matplotlib/matplotlib#10220 implements clipping to the valid range, but a strong consensus against RGB normalisation in matplotlib has emerged.

This pull therefore implements normalisation, and clips values only where our normalisation has pushed them out of range.

Closes #1741 by @mrocklin (who did most of the work I'm presenting here). I had an evening free, so I rebased the previous pull on master, fixed the conflicts, and then made everything pass with flake8's default settings (including line length). My condolences to whoever gets to review this diff!

The single change any non-pedant will notice: Travis now fails if there is a flake8 warning anywhere. My experience in other projects is that this is the only way to actually keep flake8 passing - it's just unrealistic to expect perfect attention to detail from every contributor, but "make the build green before we merge" is widely understood 😄

• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Passes git diff upstream/master **/*py | flake8 --diff
• [x] Fully documented, including whats-new.rst for all changes

This patch brings xarray.plot.imshow up to parity with matplotlib.pyplot.imshow:

• As well as 2D images (greyscale / luminance, using a colormap), it now supports a third dimension for RGB or RGBA channels. For consistency with 2D arrays, missing data is plotted as transparent pixels
• Being Xarray, users need not care about the order of their dimensions - we infer the right one for color, and warn if it's ambiguous.
• ~~Using robust=True for easy saturation is really nice. Having it adjust each channel and facet in the same way is essential for this to work, which it does.~~
• ~~Matplotlib wraps out-of-range colors, leading to crazy maps and serious interpretation problems if it's only a small region. Xarray clips (ie saturates) to the valid range instead.~~

I'm going to implement clip-to-range and color normalization upstream in matplotlib, then open a second PR here so that Xarray can use the same interface.

And that's the commit log! It's not really a big feature, but each of the parts can be fiddly so I've broken the commits up logically 😄

Finally, a motivating example: visible-light Landsat data before, during (top-right), and after a fire at Sampson's Flat, Australia:

arr = ds['red green blue'.split()].to_array(dim='band') / (2 ** 12)
arr.plot.imshow(col='time', col_wrap=5, robust=True)

• [x] Closes #1780
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Passes git diff upstream/master **/*py | flake8 --diff (remove if you did not edit any Python files)
• [x] Fully documented, including whats-new.rst for all changes

CC @fmaussion for review; @BexDunn for interest

This pull updates the comparison between Xarray and Pandas ND-Panels, fixes the zenodo links, and improves our configuration for the docs build. Closes #1541.

NetCDF files often have tens of attributes, including multi-paragraph summaries or the full modification history of the file. It's great to have this available in the .attrs, but we can truncate it substantially in the repr! Hopefully this will stop people writing data.attrs = {} and discarding metadata in interactive workflows for the sake of cleaner output.

• [x] closes #1319
• [x] test data adjusted
• [x] passes git diff upstream/master | flake8 --diff
• [x] whatsnew entry