![]() # New enough versions have offset_copy by Eric Firing: New enough versions of matplotlib (currently only the svn version) have an offset_copy function which does this automatically. All transformations can have an offset which can be modified with set_offset, and the copying is necessary to avoid modifying the transform of the data itself. The way this works is by first taking a shallow copy of transData and then adding an offset to it. Here is one way to do it try running this in an interactive backend, and zooming and panning the figure. Sometimes you want to specify that a label is shown a fixed pixel offset from the corresponding data point, regardless of zooming. Trans = blend(ax.transData, ax.transAxes)įor x,text in [(0.0, '|'), (N.pi/2, r'$\rm2\pi$'),Įxample: adding a pixel offset to data coords Here is an example that draws annotations below the tick labels, and uses a transformation to guarantee that the x coordinates of the annotation correspond to the x coordinates of the plot, but the y coordinates are at a fixed position, independent of the scale of the plot: import matplotlib as Mīlend = M.transforms.blend_xy_sep_transform If you find that the built-in tick labels of Matplotlib are not enough for you, you can use transformations to implement something similar. Please see the official Matplotlib documentation at for further reference. , but the four listed above arise in a lot of applications. Of course, you can define more general transformations, e.g. The default transformation for ax.text is ax.transData and the default transformation for fig.text is fig.transFigure. ![]() These transformations can be used for any kind of Artist, not just for text objects. There are four built-in transforms that you should be aware of (let ax be an Axes instance and fig a Figure instance): _transform() # display coordsĪx.transAxes # 0,0 is bottom,left of axes and 1,1 is top,rightįig.transFigure # 0,0 is bottom,left of figure and 1,1 is top,right However, using transforms, you can simply use axes.text(0.5, 0.5, "middle of graph", transform=ansAxes) If you specified this by the method above, then you would need to determine the minimum and maximum values of x and y to determine the middle. want to put a label in the exact middle of your graph. There are however other coordinates one can think of. ![]() Consider the following example axes.text(x,y, "my label")Ī label ‘my label’ is added to the axes at the coordinates x,y, or stated more clearly: The text is placed at the theoretical position of a data point (x,y). Whenever you pass coordinates to matplotlib, the question arises, what kind of coordinates you mean. Inline Weave With Basic Array Conversion (no Blitz) Using NumPy With Other Languages (Advanced) Parallel Programming with numpy and scipyįDTD Algorithm Applied to the Schrödinger Equation Numpy
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