issue_comments

imshow with all NaNs works for me on matplotlib 1.5.2

ups yes, I might have been confused by the warning, but it does work with latest mpl yes. The warning says:

``` In [1]: import numpy as np

In [2]: import matplotlib.pyplot as plt

In [3]: a = np.full((2, 2), np.NaN)

In [4]: plt.imshow(a)
Out[4]: <matplotlib.image.AxesImage at 0x7f6e2a8ba710>

In [5]: plt.show() /home/mowglie/.pyvirtualenvs/py3/lib/python3.5/site-packages/matplotlib/colors.py:897: UserWarning: Warning: converting a masked element to nan. dtype = np.min_scalar_type(value) /home/mowglie/.pyvirtualenvs/py3/lib/python3.5/site-packages/numpy/ma/core.py:748: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok) ```

@fmaussion what version of matplotlib are you using? imshow with all NaNs works for me on matplotlib 1.5.2 (yes, we're stuck in the dark ages). This might be a regression in matplotlib.

I'm willing to write the patch, if that helps!

Yes, thanks! The best place to start would be to add a test to Common2dMixin (https://github.com/pydata/xarray/blob/master/xarray/tests/test_plot.py#L560).

I investigated a bit and it seems that in the case of all nan data you would probably have to force add_colorbar to False and do other things for matplotlib to accept your all nan data.

Actually I changed my mind a little bit ;-). Matplotlib won't plot all NaNs images either:

```python import numpy as np import matplotlib.pyplot as plt

a = np.full((2, 2), np.NaN) plt.imshow(a)
plt.show() # raises an error ``` So we have to decide if it's worth to add a bunch of logic in xarray to handle these cases or if it's the user's responsibility to check the data before plotting.

Yes, I agree. We inherited the logic from seaborn, which also has this problem:

```python In [2]: import seaborn as sns In [3]: import numpy as np In [4]: a = np.zeros((2, 2)) * np.NaN In [6]: sns.heatmap(a)

ValueError Traceback (most recent call last) <ipython-input-6-9ef15135ade2> in <module>() ----> 1 sns.heatmap(a)

~/.pyvirtualenvs/py3/lib/python3.5/site-packages/seaborn/matrix.py in heatmap(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs) 515 plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt, 516 annot_kws, cbar, cbar_kws, xticklabels, --> 517 yticklabels, mask) 518 519 # Add the pcolormesh kwargs here

~/.pyvirtualenvs/py3/lib/python3.5/site-packages/seaborn/matrix.py in init(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask) 166 # Determine good default values for the colormapping 167 self._determine_cmap_params(plot_data, vmin, vmax, --> 168 cmap, center, robust) 169 170 # Sort out the annotations

~/.pyvirtualenvs/py3/lib/python3.5/site-packages/seaborn/matrix.py in _determine_cmap_params(self, plot_data, vmin, vmax, cmap, center, robust) 205 calc_data = plot_data.data[~np.isnan(plot_data.data)] 206 if vmin is None: --> 207 vmin = np.percentile(calc_data, 2) if robust else calc_data.min() 208 if vmax is None: 209 vmax = np.percentile(calc_data, 98) if robust else calc_data.max()

~/.pyvirtualenvs/py3/lib/python3.5/site-packages/numpy/core/_methods.py in _amin(a, axis, out, keepdims) 27 28 def _amin(a, axis=None, out=None, keepdims=False): ---> 29 return umr_minimum(a, axis, None, out, keepdims) 30 31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):

ValueError: zero-size array to reduction operation minimum which has no identity

```

Will try to submit a fix later today