issue_comments

@jklymak Thanks for the explanation.

To get the old behaviour you simply need to do pcolormesh(x, y, Z[:-1, :-1], shading='flat') or make x and y one larger than Z in each dimension and specify the the corners of the quadrilaterals.

Method1: Subset value

This method works for the xarray tutorial data, but not for the TROPOMI polar-orbiting satellite data.

``` %matplotlib inline

import xarray as xr import cartopy.crs as ccrs import matplotlib.pyplot as plt

plt.figure(figsize=(14,6)) ax = plt.axes(projection=ccrs.PlateCarree())

ds = xr.open_dataset('./S5P_OFFL_L2__NO2____20190810T212136_20190810T230306_09456_01_010302_20190816T233944.nc', group='PRODUCT').isel(time=0)

m = ax.pcolormesh(ds['longitude'], ds['latitude'], ds['nitrogendioxide_tropospheric_column'][:-1, :-1], # ds['nitrogendioxide_tropospheric_column'], # shading='auto', transform=ccrs.PlateCarree(), vmin=0, vmax=1e-4, cmap='Spectral_r') ```

(The TROPOMI example data is uploaded to Google Drive)

Method2: bounds

This issue still exists with bounds data: ``` %matplotlib inline import numpy as np import xarray as xr import cartopy.crs as ccrs import matplotlib.pyplot as plt

def prepare_geo(bounds_data): """Prepare lat/lon bounds for pcolormesh. lat/lon bounds are ordered in the following way:: 3----2 | | 0----1 Extend longitudes and latitudes with one element to support "pcolormesh":: (X[i+1, j], Y[i+1, j]) (X[i+1, j+1], Y[i+1, j+1]) +--------+ | C[i,j] | +--------+ (X[i, j], Y[i, j]) (X[i, j+1], Y[i, j+1]) """ # Create the left array left = np.vstack([bounds_data[:, :, 0], bounds_data[-1:, :, 3]]) # Create the right array right = np.vstack([bounds_data[:, -1:, 1], bounds_data[-1:, -1:, 2]]) # Stack horizontally dest = np.hstack([left, right]) # Convert to DataArray dest = xr.DataArray(dest, dims=('y_bounds', 'x_bounds'), attrs=bounds_data.attrs ) return dest

ds = xr.open_dataset('./S5P_OFFL_L2__NO2_20190810T21213620190810T230306_09456_01_010302_20190816T233944.nc', group='PRODUCT').isel(time=0) ds_geo = xr.open_dataset('./S5P_OFFL_L2NO2____20190810T212136_20190810T230306_09456_01_010302_20190816T233944.nc', group='/PRODUCT/SUPPORT_DATA/GEOLOCATIONS').isel(time=0)

lon_bounds = prepare_geo(ds_geo['longitude_bounds']) lat_bounds = prepare_geo(ds_geo['latitude_bounds'])

plt.figure(figsize=(14,6)) ax = plt.axes(projection=ccrs.PlateCarree())

m = ax.pcolormesh(lon_bounds, lat_bounds, ds['nitrogendioxide_tropospheric_column'], # shading='auto', transform=ccrs.PlateCarree(), vmin=0, vmax=1e-4, cmap='Spectral_r') ```

@QuLogic Ha, it looks well with the latest cartopy (0.20.1). Thanks a lot.

@TomNicholas So, is it better to keep this open until the doc is updated?

@TomNicholas I checked the doc and this issue begins from v0.16.1. Note that there're also small spurious lines after v0.10.9. Before v0.10.9, the figure looks fine. It's may be also related to matplotlib ... CC @jklymak and @timhoffm.

BTW, the question on StackOverflow, which was raised by @gerritholl a long time ago, looks similar. I'm not sure whether this is the cartopy issue, CC @QuLogic, and @greglucas.