If we had done the ScaledTranslation first, thenįirst be transformed to display coordinates ( onĪ 200-dpi monitor) and then those coordinates Is given the right dimensions in display space first and then moved Like you see in normal Cartesian coordinate systems, but not on
![ax transdata ax transdata](https://matplotlib.org/1.5.1/_images/inset_locator_demo.png)
This trick only works for separable transformations, Will implement the horizontal span here using a blended These blended lines and spans are so useful, we have built in X-axis regardless of the data limits, pan or zoom level, etc. Span which highlights some region of the y-data but spans across the show () Blended transformations ¶ĭrawing in blended coordinate spaces which mix axes with dataĬoordinates is extremely useful, for example to create a horizontal transAxes, facecolor = 'blue', alpha = 0.75 ) ax. plot ( x, y, 'go', alpha = 0.2 ) # plot some data in data coordinates circ = mpatches. Is a simple example that creates four panels and labels them 'A', 'B',įig, ax = plt. Pane, and have that location remain fixed when you pan or zoom. Want a text bubble in a fixed, location, e.g., the upper left of the axes This coordinate system isĮxtremely useful when placing text in your axes, because you often You can also refer to points outside the range, so (-0.1,ġ.1) is to the left and above your axes. Your axes or subplot, (0.5, 0.5) is the center, and (1.0, 1.0) is the Here the point (0, 0) is the bottom left of transform (( 5, 0 )) Out: array() Axes coordinates ¶Īfter the data coordinate system, axes is probably the second most transform (( 5, 0 )) Out: array() In : ax. annotate ( 'display = ( %.1f, %.1f )' % ( xdisplay, ydisplay ), ( xdisplay, ydisplay ), xytext = ( 0.5 * offset, - offset ), xycoords = 'figure pixels', textcoords = 'offset points', bbox = bbox, arrowprops = arrowprops ) plt. annotate ( 'data = ( %.1f, %.1f )' % ( xdata, ydata ), ( xdata, ydata ), xytext = ( - 2 * offset, offset ), textcoords = 'offset points', bbox = bbox, arrowprops = arrowprops ) disp = ax. transform (( xdata, ydata )) bbox = dict ( boxstyle = "round", fc = "0.8" ) arrowprops = dict ( arrowstyle = "->", connectionstyle = "angle,angleA=0,angleB=90,rad=10" ) offset = 72 ax. set_ylim ( - 1, 1 ) xdata, ydata = 5, 0 # This computing the transform now, if anything # (figure size, dpi, axes placement, data limits, scales.) # changes re-calling transform will get a different value. For example, in the figureīelow, the data limits stretch from 0 to 10 on the x-axis, and -1 to 1 on the Most commonly updated with the set_xlim() and Whenever you add data to the axes, Matplotlib updates the datalimits, Let's start with the most commonly used coordinate, the data coordinate Something other than the IdentityTransform() the default whenĪn artist is placed on an axes using add_artist is for the Printing or changing screen resolution, because the object can change locationįor artists placed in an axes or figure to have their transform set to Location if the dpi of the figure changes.
![ax transdata ax transdata](https://matplotlib.org/3.0.3/_images/sphx_glr_annotate_transform_001.png)
Note that specifying objects in display coordinates will change their Know where the mouse click or key-press occurred in your data Interface, which typically occur in display space, and you want to
![ax transdata ax transdata](https://matplotlib.org/2.2.3/_images/sphx_glr_transforms_tutorial_001.png)
This is particularly useful when processing events from the user Themselves, to go from display back to the native coordinate system.
#Ax transdata how to
The transformations also know how to invert None for the Transformation Object column - it already is inĭisplay coordinates. That is why the display coordinate system has Their coordinate system, and transform the input to the displayĬoordinate system. In the Transformation Object column,Īll of the transformation objects in the table above take inputs in Object you should use to work in that coordinate system, and theĭescription of that system. The tableīelow summarizes the some useful coordinate systems, the transformation Generation, it helps to have an understanding of these objects so youĬan reuse the existing transformations Matplotlib makes available to Happens under the hood, but as you push the limits of custom figure In 95% of your plotting, you won't need to think about this, as it The figure coordinate system, and the display coordinate system. The userland data coordinate system, the axes coordinate system, Transformation framework to easily move between coordinate systems, Like any graphics packages, Matplotlib is built on top of a
#Ax transdata full
To download the full example code Transformations Tutorial ¶