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.

In [1]:
import psyplot.project as psy
import xarray as xr
%matplotlib inline
%config InlineBackend.close_figures = False
import numpy as np

/Users/psommer/miniconda3/lib/python3.6/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
  "`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)

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

In [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:

In [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.

In [4]:
p.update(plot='contourf', levels=5)

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

In [5]:

| 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

In [6]: