issues

Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Similarly to the 'construct' method for Rolling objects, I think it will make sense to have the same for Coarsen objects.

My use will be for my da: da(lon, lat, time) of dimension (2000, 1600, 240) I wish to coarsen the data by a factor 4 in lon and lat (Lon and Lat), but wish to keep the data in a new dimension, it would be: da(Lon, Lat, time, samples) of dim (500, 400, 240, 16)

my aim is to do da.median(dim=['time','samples'])

Describe the solution you'd like A clear and concise description of what you want to happen.

da.coarsen(lon=4, lat=4, , boundary="trim").construct('samples')

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

I am using Rolling objects, but it increases the size of the matrix.

Many thanks for your work

Is your feature request related to a problem? Please describe. I would like to calculate the mean on all variables of my dataset which is easy and straightforward with xarray, for example: ds.mean(dim='time) The issue is I have some circular quantities in this dataset (for example wind direction). I can use script.stats.circmean/std, but I have to discriminate myself when I have to use one or the other function.

Describe the solution you'd like I would like to add a parameter in the description of my variable mentioning it is circular. Every time I apply a function on the full dataset, it uses the circular equivalent if it exists.

There is perhaps some tricks to do it, but I didn't find any so far. Thank you.

I am combining a list of snapshots having all the same geometry but with different time. Some time information appears in the attributes. I can 'drop' it, but I would prefer keep it and add it using a define dimension (in my case, in time). I believe for v0.15.1 (the default was to drop it with the default compat='no_conflicts'), I think of this because I got an error on the default combine_attrs = 'no_conflicts' on v0.16.0.

I would like an option of type combine_attrs = {dim: 'time'} or even better only combine_attrs = 'dim' and somehow it finds which dimension it should use.

Thanks

I get the following error when I move from xarray 0.15.1 to 0.16.0 "cannot use subplot_kws with existing ax". I don't really understand why this error is raised, but as far as I use it (see below), I feel I need to use both ax and subplot_kws. Please find below a minimum case: ```python import numpy as np import pandas as pd import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs

x = np.arange(0,3) y = np.arange(0,5) z = np.arange(0,2) xx, yy, zz = np.meshgrid(x, y, z)

lat = 40 + xx0.1 + yy1 lon = 5 + xx1 + yy0.1 + zz5 data3 = xx + yy + 5zz data4 = np.stack([data3, data32, data34, data3*8])

ds = xr.Dataset({'data': (['time','x','y','z'], data4)}, coords={'time': pd.date_range("2020-01-01", periods=4), 'lon': (['x','y','z'], lon), 'lat': (['x','y','z'], lat)} ) ```

So I have lon/lat maps with two swaths (z-dimension). I want to plot both swaths on the same map, and as function of time ```python plt.figure() for ss in z: ds.data.isel(time=0, z=ss).plot(x='lon', y='lat', add_colorbar=False, vmin=0, vmax=12)

plt.figure() g = ds.data.isel(z=0).plot(x='lon', y='lat', col='time') for cc, ax in enumerate(g.axes.flat): for ss in z: ds.data.isel(time=cc, z=ss).plot(x='lon', y='lat', add_colorbar=False, vmin=0, vmax=50, ax=ax) ```

All is relatively fine, the issue is when I want to project it on a map using Cartopy: Still fine with only one map: python plt.figure() for ss in z: ds.data.isel(time=0, z=ss).plot(x='lon', y='lat', add_colorbar=False, vmin=0, vmax=12, transform=ccrs.PlateCarree(), subplot_kws={'projection': ccrs.Mercator()})

but the "ValueError: cannot use subplot_kws with existing ax" is raised when I try with the facets plot: python plt.figure() g = ds.data.isel(z=0).plot(x='lon', y='lat', col='time', transform=ccrs.PlateCarree(), subplot_kws={'projection': ccrs.Mercator()}) for cc, ax in enumerate(g.axes.flat): for ss in z: ds.data.isel(time=cc, z=ss).plot(x='lon', y='lat', add_colorbar=False, vmin=0, vmax=50, ax=ax, transform=ccrs.PlateCarree(), subplot_kws={'projection': ccrs.Mercator()})

On a side note, I don't understand why I am loosing one pixel per row and column using the cartopy projection.