PyPlot not projecting netcdf as expected


#1

Hi,

I am ending up with

t2

when trying to plot the following netcdf data

As far as I can tell I have chosen the appropriate projections.

using NetCDF, PyPlot, PyCall;
@pyimport seaborn as sns;
@pyimport cartopy.crs as ccrs;
@pyimport cartopy.feature as cfeature;
sns.set()

lats = ncread("T2.nc", "XLAT");
lons = ncread("T2.nc", "XLONG");
sst = ncread("T2.nc", "T2");
ax = axes(projection = ccrs.RotatedPole())
contourf(lons, lats, sst, transform = ccrs.PlateCarree())
ax[:set_xlim](-80, 10);
ax[:set_ylim](-50, 5);

Any ideas on how why this would be happening?


#2

I think you need to convert the lats and lons to the projected coordinates of your map.

See here for example : https://github.com/Balinus/ClimateTools.jl/blob/master/src/mapping.jl

Around lines 60-62 for the projected transformation and lines 32-50 for the map definition. This is using Basemap though, I assume Cartopy is similar in their approach.

m = basemap[:Basemap](projection="cyl", llcrnrlat = -90, urcrnrlat = 90, llcrnrlon = 0, urcrnrlon = 360, resolution = "c")
lon2, lat2 = meshgrid(lon, lat)
x, y = m(lon2, lat2)

edit - Looked at your nc file and there is no time definition in your file.


#3

I get the following error when trying to map your data. You might want to check you lons and lats matrix. I get a similar contour plot as you have shown.

cs = m[:contourf](x, y, sst)
WARNING: x coordinate not montonically increasing - contour plot
may not be what you expect.  If it looks odd, your can either
adjust the map projection region to be consistent with your data, or
(if your data is on a global lat/lon grid) use the shiftgrid
function to adjust the data to be consistent with the map projection

edit - The lons and lats matrix are not consistent I think.

lons
223×152 Array{Float64,2}:
   80.0567    80.3708    80.6809    80.9873    81.2899    81.5889  …   108.866   109.042   109.218   109.395   109.573   109.752
   80.52      80.8341    81.1442    81.4505    81.753     82.0518      109.204   109.378   109.553   109.729   109.905   110.082
   80.9855    81.2995    81.6096    81.9157    82.2181    82.5168      109.542   109.715   109.888   110.063   110.237   110.413
   81.4532    81.7671    82.0771    82.3831    82.6853    82.9838      109.88    110.052   110.224   110.396   110.57    110.744
   81.923     82.2369    82.5467    82.8526    83.1546    83.4529      110.218   110.389   110.559   110.731   110.903   111.075
   82.3951    82.7089    83.0185    83.3242    83.6261    83.9241  …   110.557   110.726   110.895   111.065   111.236   111.407
   82.8695    83.1831    83.4925    83.798     84.0996    84.3974      110.896   111.063   111.231   111.4     111.569   111.739
   83.3461    83.6595    83.9688    84.2741    84.5754    84.8729      111.235   111.401   111.567   111.734   111.902   112.07 
   83.825     84.1382    84.4473    84.7523    85.0533    85.3505      111.574   111.739   111.904   112.069   112.236   112.402
   84.3063    84.6193    84.9281    85.2327    85.5335    85.8303      111.914   112.077   112.24    112.405   112.569   112.735
    ⋮                                                      ⋮       ⋱                                             ⋮              
 -148.565   -148.878   -149.187   -149.492   -149.793   -150.09       -176.314  -176.479  -176.644  -176.809  -176.976  -177.142
 -148.086   -148.399   -148.709   -149.014   -149.315   -149.613   …  -175.975  -176.141  -176.307  -176.474  -176.642  -176.81 
 -147.609   -147.923   -148.233   -148.538   -148.84    -149.137      -175.636  -175.803  -175.971  -176.14   -176.309  -176.478
 -147.135   -147.449   -147.758   -148.064   -148.366   -148.664      -175.297  -175.466  -175.635  -175.805  -175.976  -176.147
 -146.663   -146.977   -147.287   -147.593   -147.895   -148.193      -174.958  -175.129  -175.299  -175.471  -175.643  -175.815
 -146.193   -146.507   -146.817   -147.123   -147.425   -147.724      -174.62   -174.792  -174.964  -175.136  -175.31   -175.484
 -145.725   -146.039   -146.35    -146.656   -146.958   -147.257   …  -174.282  -174.455  -174.628  -174.803  -174.977  -175.153
 -145.26    -145.574   -145.884   -146.19    -146.493   -146.792      -173.944  -174.118  -174.293  -174.469  -174.645  -174.822
 -144.797   -145.111   -145.421   -145.727   -146.03    -146.329      -173.606  -173.782  -173.958  -174.135  -174.313  -174.492

#4

My understanding is that cylindrical equidistant is the equivalent to the rotatedpole projection in cartopy. But I will have another look in case I missed something in the cartopy documentation.

Thanks


#5

Well, the projection is not important here. I was simply suggesting to transform your lat-lon matrix to projected coordinates.

With that being said, I think you problem arise from your lat-lon matrix values.


#6

It looks like you were right, after looking through some ncl scripts, it turns out that the lons are adjusted; such that there are no negative values

lons[lons .<  0] = 360 + lons[lons .< 0]

Making that adjustment has fixed the issue

index

Thanks for the help


#7

Great!

What’s your colormap? I like it.


#8

It is a subset of RdBu_r


#9

Is there a Julia native alternative to do this kind of mapping? Cartopy looks like a very useful tool but I would like to have the freedom of using Plots.jl


#10

Sorry I am not aware of an Julia alternative. With that said, plots.jl uses pyplots, so it with a little bit of work it should be possible to do this using plots.jl


#11

GMT.jl can certainly do it, though not in one step because the nc file is not a simple grid but arrays of lon,lat,z

This example, although it still uses the the more hard-core syntax, may be helpful.