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