I haven't been very careful about insisting on a clean git history so far, so don't worry about rebase/squash.

Ok, going to merge once the build passes. Will continue with the rest of the stuff next week. @shoyer should I rebase or squash these commits?

I think this is pretty much good to go. Merge at your discretion!

@shoyer Latest commit also added a little more to the docs on how to use the attributes data axes, and name_dicts.

For the simple example in the doc -- use imshow instead of contourf? The result is definitely more aesthetically pleasing:

see https://github.com/clarkfitzg/xray/pull/1

By the way, I believe this is to a point where we could easily tune any of the aesthetics- say shrinking the width of the colorbar or using a different number of ticks / sizes on the labels.

Here's what's left to do: - [ ] convenience method darray.plot(row='z') - [ ] map example using matplotlib quiver plot - [ ] port any more methods / features from Seaborn FacetGrid

@shoyer, am I forgetting anything?

Ready for this PR to be reviewed now. I can finish the rest in a follow up PR.

For this PR: - [x] facet on rows and columns - [x] Align label on colorbar - [x] Label figure axes - [x] Use xray plotting logic to make nice colorbar - [x] Make and test choices around colorbar

EDIT - going to revisit plt.contour(color='k') after #537 gets worked out. - [x] separate iteration over data from plotting - edge case: require the index to actually be unique .is_unique. - [x] Use core string formatting for titles - [x] tests for all of above - [x] test col_wrap - [x] write and organize docs - [x] docs for public attribute- axes and such

On this PR or possibly a different one: - [ ] Convenience method to easily make faceted plots- something like darray.plot(col='time') - [x] port Seaborn's aspect capabilities - [ ] matplotlib quiver plot for map example - [x] Create new xray.tutorial module with function like load_example_dataset for plotting examples

Different PR: - [ ] Facets and maps - [ ] Inline documentation and plots for API reference

@shoyer multiple labels on x / y axes compared to the one above.

@clarkfitzg was that second plot supposed to be different?

Starting to agree with @shoyer and Seaborn on the multiple labels:

Getting better...

Thanks for the encouragement @jhamman - I'll take what I can get! Seem to be finding a way around it by setting custom ax.xaxis.set_major_locator

The colorbar looks great though :wink:

Aaaaaaahhh!!

Issues seem to be mostly caused by the colorbar.

I believe it's possible to fix automatically. Somehow. Will look into it further tomorrow.

@clarkfitzg Is that something that can fixed simply by adjusting the size argument? Does matplotlib use figure size when determining tick labels? I'm not sure there's much we can do to fix this automatically.

For reference, here's what is happening once aspect is adjusted. x axes are all crowded.

Some issues cropping up with using plt.tight_layout when manipulating the aspect and size.

take aspects and figure sizes from seaborn

Done

add ability to set margin_titles=True.

TODO- don't really understand the use case for this.

yep, could definitely do that. I'm still not crazy about repeating the x, y labels like that. Let me see if I can come up with a nice alternative using text on the figure.

Skipping the missing block looks pretty nice to me!

I'm pretty happy with the aesthetics, though there's a case for copying the behavior from Seaborn, e.g.,

python attend = sns.load_dataset("attention").query('subject < 6') g = sns.FacetGrid(attend, col="subject", col_wrap=3, size=2, ylim=(0, 10), aspect=0.8) g = g.map(plt.plot, "solutions", "score")

Here's what we have now with one missing block.

Not sure about these aesthetics.

todo- make sure to seed random number generator or else use deterministic

Done

add matplotlib quiver plot for map example.

Here's where the progress stands:

g = xray.plot.FacetGrid(t5, col='time', col_wrap=2) g.map_dataarray(xray.plot.contour, color='k')

Looks like the column wrapping does the right thing by leaving the last axes blank.

Observe #537 is still an issue here. We'll have to make sure that when that is fixed it also works for these facets.

On Monday morning I can clean the code up, take out the seaborn chunks, reorganize the docs, and then I'll be ready to ask for a review.

Thanks! The colorbar label spacing is a matplotlib thing- it only became a problem after rotating it to match the row labels. So I expect that I can fix it by changing the anchor point or placing it manually.

@clarkfitzg - These are looking really great. I think this is going to be a really useful tool.

The colorbar label is too close to the colorbar tick label, are we manually placing that or is this a matplotlib thing?

Here's what a 4d array looks like.

``` In [26]: t4d = xray.concat([t2, t2 + 50], pd.Index(['normal', 'hot'], name='fourth_dim'))

This is a 4d array

In [27]: t4d.coords Out[27]: Coordinates: * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 ... * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 ... * time (time) datetime64[ns] 2013-01-01 2013-03-04T12:00:00 * fourth_dim (fourth_dim) object 'normal' 'hot'

In [28]: g = xray.plot.FacetGrid(t4d, col='time', row='fourth_dim')

In [29]: g.map_dataarray(xray.plot.imshow, 'lon', 'lat') Out[29]: <xray.plot.facetgrid.FacetGrid at 0x10f5aad90> ```

@shoyer Here's what we currently have:

``` In [15]: g = xray.plot.FacetGrid(t, col='time', col_wrap=2)

In [16]: g.map_dataarray(xray.plot.contourf, 'lon', 'lat') Out[16]: <xray.plot.facetgrid.FacetGrid at 0x10de082d0> ```

Right now this assumes that the function for map_dataarray has the same signature as 2d xray plotting methods.