annotate ( 'function minium \n relative to data', xy = ( 0, 0 ), xycoords = 'data', xytext = ( 2, 3 ), arrowprops = dict ( facecolor = 'black', shrink = 0.05 ), horizontalalignment = 'left', verticalalignment = 'top' ) # second annotation relative to the axis limits bbox_props = dict ( boxstyle = "round,pad=0.5", fc = "w", ec = "k", lw = 2 ) ax. plot ( x, y ) # first annotation relative to the data ax. Import numpy as np import matplotlib.pyplot as plt # if using a Jupyter notebook, include: % matplotlib inline x = np. The next code section builds a figure with three annotation arrows. The chart below summarizes Matplotlib’s ax.annotate() keyword arguments.Īnnotation location relative to figure windowĭefine bounding box properties with a dictionaryĭefine arrow properties with a dictionary Since xy=(20, 75), the third annotation arrow points 20 pixels to the right and 75 pixels up from the bottom left corner of the figure window. This means the third annotation is placed relative to the figure window. In the third annotation, xycoords='figure pixels'. Since xy=(0, 0.5), the annotation arrow points all the way to the left edge of the x-axis and half way up the y-axis. This means the second annotation is placed relative to the axis. In the second annotation, xycoords='axes fraction'. Since xy=(0, 0), the annotation arrow points to the data point 0,0. This means the annotation is placed relative to the data. In the first annotation, xycoords='data'. The annotation can be located relative to the plot data, located relative to the axis, or located relative to the figure window. Each of the three annotations in the next figure has a different xycoords= keyword argument. The keyword argument to pay attention to in the next code section is xycoords=. annotate ( 'text', xy =, xycoodrs =, xytext =, arrowprops = ) You might want to add a small buffer etc, but the idea would stay the same.Ax. Rect = patches.Rectangle((xmin,ymin),xmax-xmin,ymax-ymin, facecolor='grey', alpha=0.2, transform=fig.transFigure) T3 = axs.text(0.6,0.4, 'Hello world line 3', ha='center', color='blue', weight='bold', transform=axs.transAxes) T2 = axs.text(0.5,0.5, 'Hello world line 2', ha='center', color='green', weight='bold', transform=axs.transAxes) T1 = axs.text(0.4,0.6, 'Hello world line 1', ha='center', color='red', weight='bold', transform=axs.transAxes) Perhaps my example can be improved, transformations always confuse me a bit. Among other things, it allows you to place your text at an offsent in points from a particular data position.Ī solution could be to explore the boundingbox from the text objects and generate a box yourself. (Which is rather annoying for simple things like padding, though it makes the implementation simpler behind-the-scenes.)Īlso, if you're labeling things in your plot, you'll probably find that annotate is a better choice. The last two are "Fancy" bbox patches, so the padding, etc is set in a different manner. I assume you're confused on how to set the color, etc, of the box? As a quick example: import matplotlib.pyplot as pltībox=dict(facecolor='none', edgecolor='red'))ībox=dict(facecolor='none', edgecolor='blue', pad=10.0))ībox=dict(facecolor='none', edgecolor='green', boxstyle='round'))ībox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) As the example you linked to mentions, you can use the bbox kwarg to add a box.
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