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Note

2D plots¶

Demonstration of the 2D plot capabilities

The plot2d plot method make plots of 2-dimensional scalar data using matplotlibs pcolormesh or the contourf functions.

Note that this method is extended by the mapplot plot method of the psy-maps plugin for visualization on the projected globe.

[1]:

import psyplot.project as psy
import xarray as xr
%matplotlib inline
%config InlineBackend.close_figures = False
import numpy as np


First we create some sample data in the form of a 2D parabola

[2]:

x = np.linspace(-1, 1.)
y = np.linspace(-1, 1.)
x2d, y2d = np.meshgrid(x, y)
z = - x2d**2 - y2d**2
ds = xr.Dataset(
{'z': xr.Variable(('x', 'y'), z)},
{'x': xr.Variable(('x', ), x), 'y': xr.Variable(('y', ), y)})


For a simple 2D plot of a scalar field, we can use the plot2d plot method:

[3]:

p = psy.plot.plot2d(ds, cmap='Reds', name='z')


The plot formatoption controls, how the plot is made. The default is a pcolormesh plot, but we can also make a filled contour plot. The levels of the contour plot are determined through the levels formatoption.

[4]:

p.update(plot='contourf', levels=5)
p.show()


The plot2d method has several formatoptions controlling the color coding of your plot:

[5]:

p.keys('colors')

+-------------+-------------+-------------+-------------+
| levels      | miss_color  | cmap        | bounds      |
+-------------+-------------+-------------+-------------+
| extend      | cbar        | cbarspacing | cticksize   |
+-------------+-------------+-------------+-------------+
| ctickweight | ctickprops  |             |             |
+-------------+-------------+-------------+-------------+


The most important ones are

• cbar: To specify the location of the colorbar

• bounds: To specify the boundaries for the color coding, i.e. the categories which data range belongs to which color

• cmap: To specify the colormap

[6]:

psy.close('all')