From: John Hunter <jdhunter@ac...>  20041201 05:21:45

>>>>> "Jos=E9" =3D=3D Jos=E9 Alexandre Nalon <nalon@...> writes: Jos=E9> That, and setting the x_lim inverted, worked as I wanted, Jos=E9> thanks. Great Jos=E9> But one more thing: while I was searching for a specific Jos=E9> command to do that, I found something (but not much) about Jos=E9> transforms, which I couldn't exactly understand what they do Jos=E9> and how they work, but they might be handy in the near Jos=E9> future. What are they exactly, and how can they help? The official documentation is at http://matplotlib.sf.net/matplotlib.transforms.html , but this may be of limited help because it is not a tutorial. matplotlib is divided conceptually into three parts: the "matlab interface" which are the plot functions that most people use and see, the object oriented "artists" which are classes that store the figure information and the "backends" which actually put the ink on the paper. The "artists" are things like lines, text, rectangles, images and so on, and all of them are concrete classes derived from matplotlib.artist.Artist. Stupid name, I know, but I'm weak when it comes to naming. There are, at a minimum, two coordinate systems to consider when you plot, the "world" coordinates, ie your data coordinates, and the display coordinates, which are physical locations on the display device. The mapping between these two is given by a transformation. matplotlib transformations generally store the information about the data coordinates and display coordinates, and possibly also a nonlinear function (eg, polar, log). Every artist stores it's own transform, and you can place different artists in the figure using different coordinate systems (transforms). The vast majority of all plot commands use the default data>display transform and this is invisible in normal usage. But for convenience and internal usage, matplotlib provides a couple of extra transformations that you can make use of. For example, suppose you wanted to place a rectangle in the middle of the axes that was 25% from the left, bottom, top and right. You would place this rectangle in the axes in what I refer to as axes coordinates, where 0 is the left side of the axes, 1 is the width, 0.5 is the middle and so on. This hypothetical rectangle has left, bottom=3D0.25, 0.25 and width, height=3D0.5, 0.5 from matplotlib.matlab import * from matplotlib.patches import Rectangle ax =3D subplot(111) plot([1,2,3]) trans =3D ax.transAxes r =3D Rectangle( (0.25, 0.25), 0.5, 0.5, transform=3Dtrans) ax.add_patch(r) show() =20 Likewise, the figure instance provides a default transformation to allow you to place objects in figure coordinates, where 0,0 is the bottom left of the figure, 0.5, 0.5 is the middle and 1,1 is the upper right. =20 Internally, I use this all the time. For example, I may want to place the xticklabels a few points under the bottom of the y location of the xaxis, and I have a transformation that allows me to do this with minimal coding overhead in the presence o figure resizing, dpi changes, etc. These are examples of the transformations that are provided by default, and they tend to cover most of the things people want to do. But if you want to do something more, you can use affine transformation (matplotlib.transforms.Affine) or otherwise and set this for a given artist to place the object into the figure as you like it. The example examples/alignment_test.py in the matplotlib src distribution makes heavy use of placing text in axes coordinates, so you may want to take a look at this for an example. JDH 