Showvar: Line Plots¶

If showvar() is passed a one dimensional variable, it will produce a line plot (ultimately using matplotlib.plot()), automatically extracting the coordinates and values from the variable. If the axis is recognized by PyGeode as being a vertical axis, the plot will be transposed. This can be reversed if desired by setting the keyword argument transpose = True.

Like the plot command, the format of the line can be set by passing a string immediately following the command; moreover, any other keyword argument recognized by matplotlib.plot() will be passed through.

# A vertical plot
In [4]: pyg.showvar(t2.Temp.mean('time', 'lat', 'lon'), 'k--', lw=3.)
Out[4]: <pygeode.plot.wrappers.AxesWrapper at 0x7f373400a430>

# A transposed plot
In [5]: pyg.showvar(t2.Temp.mean('time', 'lat', 'lon'), transpose = True)
Out[5]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727a824c0>

Showvar: Contour Plots¶

The showvar command, if passed a two-dimensional variable, will also produce contour plots. If no arguments (beyond the variable to be plotted), a set of (filled) contour intervals and an appropriate colourmap is chosen based on whether it thinks the data is better presented as a ‘divergent’ quantity centred on zero, or a ‘sequential’ quantity; different algorithms for choosing the contour interval are used in each case.

# Sequential data
In [6]: pyg.showvar(t2.Temp(pres=200).mean('time'))
Out[6]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727d41430>

# Divergent data
In [7]: pyg.showvar(t2.U(pres=200).mean('time'))
Out[7]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727944fd0>

PyGeode tries to choose a reasonably simple contour interval and sets the colourmap and tick labeling accordingly. Since the temperature data is centred away from zero, PyGeode assumes this is better plotted with a ‘sequential’ colourmap, with gradients tending in a single direction. However, the wind data is centred around zero, so the contour intervals chosen are also centred around zero with a colourmap that emphasizes oppositely signed values.

# Create a sequential style colormap with three divisions, three
# contour lines and six filled intervals per division
In [8]: pyg.showvar(t2.U(pres=200).mean('time'), style='seq', ndiv=3, nl=3, nf=6)
Out[8]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727867460>

# Create a divergent style colormap with 4 divisions (two per side), each
# spanning 5. with five filled intervals (so that the filled interval is 1) and
# one contour line per division, omitting the zero contour line
In [9]: pyg.showvar(t2.U(pres=200).mean('time'), style='div', cdelt=5., ndiv=2, nf=5, nozero=True)
Out[9]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727734430>

The interval algorithm is based on a larger “division”, which sets the tick labels in the colorbar (and, along with the ‘style’, the colormap chosen by default). Each division is split up into an equal number of filled intervals (‘nf’) and lined intervals (‘nl’). The latter can be omitted entirely by setting ‘nl’ to zero. The width of each interval can be specified explicitly with ‘cdelt’; if this is not given an appropriate value will be guessed.

Other types of contouring can be produced by specifying the ‘type’ keyword argument; possible values include:

How the rest of the arguments passed to showvar() are parsed depends on this argument. In each case these make use of a particular helper function which determines the appropriate contour intervals and colourmap; keyword arguments are passed through to the underlying function. While these details are transparent at this level, this is carried out by returning a dictionary of keyword arguments which is then passed on to the underlying vcontour() command which provides lower level control over the contouring performed.

Showlines, Showgrid¶

One often wants to plot multiple variables at once. Two useful commands for this purpose are showlines() and showgrid(). Multiple 1-dimensional variables can be plotted on the same axes with showlines().

In [10]: tm = t2.Temp.mean('time', 'lon')

# Multiple profiles
#@savefig showlines_ex.png width=4in
#In [2]: pyg.showlines([tm(lat=l) for l in [-60, -30, 0, 30, 60]])

In a similar vein, multiple contour plots can be plotted in a grid with the same contour intervals and a common colorbar.

In [11]: tm = t2.Temp.mean('time')

# Multiple contour plots
In [12]: pyg.showgrid([tm(pres=p) for p in [1000, 500, 200, 100]], ncol=2, style='seq', min=180, cdelt=30, ndiv=5)
Out[12]: <pygeode.plot.wrappers.AxesWrapper at 0x7f3727644670>