In this chapter we will address methods for plotting a scalar function of two variables. Here are some examples:
- A photographic image, represented as a 2-D array of colors; the grid is regular, with each element of the array corresponding to a square pixel.
- Earth surface elevation and ocean bottom topography, represented as a 2-D array of heights; the grid is rectangular in latitude and longitude, but the latitude increment may not be uniform--often it will decrease toward the poles.
- A mathematical function of two variables, such as a bivariate Gaussian probability density.
Note: in this chapter we will assume the data to be plotted are on a grid. If you have scalar data of two variables, but the data are not on a grid - for example, sea level at island stations - then you will need to use an interpolation or other gridding routine before you can use any of the plotting methods we will discuss here.
As a 2-D plotting library, matplotlib offers two basic styles of plot for scalar functions of two variables: an image style and a contour style. The image style renders the data as either a continuously-varying field of color or a set of contiguous colored quadrilaterals. Hence, the image style is a direct representation of the data array. The contour style is less direct; isolines of the data are calculated and then either plotted as lines or used to delimit colored regions.
some text
image text and example
pcolor and pcolorfast, including quadmesh variant
contour and contourf