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?

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

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.

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

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.

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.

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.

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.