How to plot a netCDF file with xarray in Python

Question

I have a mean precipitation netCDF file with the following characteristics:

<xarray.Dataset>
Dimensions:  (rlat: 412, rlon: 424)
Coordinates:
    lat      (rlat, rlon) float64 21.99 22.03 22.07 22.11 ... 66.81 66.75 66.69
    lon      (rlat, rlon) float64 -10.06 -9.964 -9.864 ... 64.55 64.76 64.96
  * rlat     (rlat) float64 -23.38 -23.26 -23.16 -23.05 ... 21.61 21.73 21.83
  * rlon     (rlon) float64 -28.38 -28.26 -28.16 -28.05 ... 17.93 18.05 18.16
Data variables:
    pr       (rlat, rlon) float32 0.0001465 0.000145 ... 0.007854 0.004692
Attributes: (12/22)
    CDI:                            Climate Data Interface version 1.3.2
    Conventions:                    CF-1.6
    NCO:                            4.4.2
    CDO:                            Climate Data Operators version 1.3.2 (htt...
    contact:                        Fredrik Boberg, Danish Meteorological Ins...
    creation_date:                  2019-10-15 18:05:48
    ...                             ...
    rcm_version_id:                 v1
    project_id:                     CORDEX
    CORDEX_domain:                  EUR-11
    product:                        output
    tracking_id:                    hdl:21.14103/a879aaf7-ddeb-436a-96fd-b717...
    c3s_disclaimer:                 This data has been produced in the contex...

I am trying the plot function mentioned in the xarray package documentation https://docs.xarray.dev/en/stable/user-guide/plotting.html

out_ds.plot()

but it produces the following error:

Dataset.plot cannot be called directly. Use an explicit plot method, e.g. ds.plot.scatter(...)

It would help if you provide a link to the dataset...

raphael
– raphael

2022-12-18 15:25:58 +00:00
Commented Dec 18, 2022 at 15:25 — raphael
– raphael, Commented Dec 18, 2022 at 15:25

raphael · Accepted Answer · 2022-12-18 15:43:06Z

With EOmaps this should do the job:

# Create a dummy-NetCDF file
import xarray as xar
import pandas as pd
import numpy as np

lon, lat = np.meshgrid(np.linspace(-10, 64, 412), np.linspace(21, 66, 424))
pr = np.random.normal(0,1, lon.shape)
ncdata = pd.DataFrame(dict(lon=lon.flat, lat=lat.flat, pr=pr.flat)
                      ).set_index(["lon", "lat"]).to_xarray()

print(ncdata)
# Dimensions:  (lon: 100, lat: 100)
# Coordinates:
#   * lon      (lon) float64 -10.0 -9.253 -8.505 -7.758 ... 61.76 62.51 63.25 64.0
#   * lat      (lat) float64 21.0 21.45 21.91 22.36 ... 64.64 65.09 65.55 66.0
# Data variables:
#     pr       (lon, lat) float64 0.1526 2.008 0.2311 ... -0.6103 -0.6002 -0.7471

from eomaps import Maps
m = Maps(crs=4326)
m.add_feature.preset.coastline()

m2 = m.new_layer_from_file.NetCDF(
        ncdata,
        coords=("lon", "lat"),
        shape="raster",
        parameter="pr",
        data_crs=4326,        
        )

msi_gerva · Accepted Answer · 2022-12-16 10:58:46Z

The direct plotting options with dataset out_ds are very limited, basically you can only do either quiver, streamplot or scatter and these are most likely not the kind of maps you need to do. Therefore I suggest making the plot using matplotlib and either pcolormesh for colored values of precipitation or contourf for filled contours (most of the cases I prefer pcolormesh to visualize the data).

Here is the code (I generated Dataset with lon, lat and variable pr) and below is the plotting routine:

#!/usr/bin/env ipython
# --------------------
import numpy as np
import xarray as xr
# -------------------
# make data:
nx,ny = 20,20;
lon = np.linspace(9,30,nx);
lat = np.linspace(54,66,ny);
prec = np.random.random((ny,nx))
out_ds = xr.Dataset(data_vars=dict(pr=(['lat','lon'],prec)),coords=dict(lon=(["lon"],lon),lat=(["lat"],lat)));
# -----------------------------------------------------
import matplotlib as mpl;
mpl.rcParams['font.size'] = 20
import matplotlib.pylab as plt

fig = plt.figure(figsize=(15,15));ax = fig.add_subplot(111);
p0 = ax.pcolormesh(out_ds.lon,out_ds.lat,out_ds.pr,vmin=0.0,vmax=1.0);
cb = plt.colorbar(p0,ax=ax)
plt.show()

Hope this helps!

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