issues: 117002929

The following code snippet produces an average of ERA-Interim temperature on a map:

``` python import matplotlib.pyplot as plt import xray import cartopy.crs as ccrs import time

netcdf = xray.open_dataset('ERA-Int-Monthly-2mTemp.nc') t2_avg = netcdf.t2m.mean(dim='time')

start_time = time.time() ax = plt.axes(projection=ccrs.Robinson()) if xray.version == '0.6.0': t2_avg.plot(ax=ax, origin='upper', aspect='equal', transform=ccrs.PlateCarree()) else: t2_avg.plot(ax=ax, transform=ccrs.PlateCarree()) ax.coastlines() plt.savefig('t_xray.png') print("xray V{}: {:.2f} s".format(xray.version, time.time() - start_time)) ```

I've been very careful to check that my environments are exact same (mpl 1.4.3, numpy 1.10.1, cartopy 0.13.0).

See the output for V0.6.0 and 0.6.1 (output from the latest master is similar to 0.6.1):

0.6.0:

python python test_xray.py /home/mowglie/.bin/conda/envs/climate/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if self._edgecolors == str('face'): xray V0.6.0: 3.21 s

0.6.1:

python python test_xray.py /home/mowglie/.bin/conda/envs/upclim/lib/python3.4/site-packages/numpy/lib/shape_base.py:431: FutureWarning: in the future np.array_split will retain the shape of arrays with a zero size, instead of replacing them by `array([])`, which always has a shape of (0,). FutureWarning) /home/mowglie/.bin/conda/envs/upclim/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if self._edgecolors == str('face'): xray V0.6.1: 28.52 s

The first warning seems related to recent numpy. Note that a second warning appeared with xray V0.6.1.

It's interesting to mention that the bottleneck clearly is in the rendering (plt.savefig('t_xray.png')). Removing this line will make xray V0.6.1 faster than xray V0.6.0.