issues

I think it would be nice to add a quiver plot function. I got this far in my current project:

``` python @xarray.plot.dataset_plot._dsplot def quiver(ds, x, y, ax, u, v, **kwargs): from xarray import broadcast

if x is None or y is None or u is None or v is None:
    raise ValueError("Must specify x, y, u, v for quiver plots.")

# matplotlib autoscaling algorithm
scale = kwargs.pop("scale", None)
if scale is None:
    npts = ds.dims[x] * ds.dims[y]
    # crude auto-scaling
    # scale is typical arrow length as a multiple of the arrow width
    scale = (
        1.8 * ds.to_array().median().values * np.maximum(10, np.sqrt(npts))
    )  # / span

ds = ds.squeeze()
x, y, u, v = broadcast(ds[x], ds[y], ds[u], ds[v])

# no support for these kwargs right now.
kwargs.pop("cmap_params")
kwargs.pop("hue")
kwargs.pop("hue_style")
hdl = ax.quiver(x.values, y.values, u.values, v.values, scale=scale, **kwargs)

return hdl

```

The autoscaling logic is quite crude; I tried to copy what matplotlib does but got somewhat confused. To get faceting to work properly, we'll need to estimate scale around here: https://github.com/pydata/xarray/blob/a36d0a1d4657c848dcdd76d0ecb9c783ad464057/xarray/plot/facetgrid.py#L339-L343

What happened:

The HTML repr is using the first element in a chunks tuple;

What you expected to happen:

it should be using whatever dask does in this case

Minimal Complete Verifiable Example:

```python

import xarray as xr import dask

test = xr.DataArray( dask.array.zeros( (12, 901, 1001), chunks=( (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (1, 899, 1), (1, 199, 1, 199, 1, 199, 1, 199, 1, 199, 1), ), ) ) test.to_dataset(name="a") ```

EDIT: The text repr has the same issue <xarray.Dataset> Dimensions: (dim_0: 12, dim_1: 901, dim_2: 1001) Dimensions without coordinates: dim_0, dim_1, dim_2 Data variables: a (dim_0, dim_1, dim_2) float64 dask.array<chunksize=(1, 1, 1), meta=np.ndarray>