Use .clf or .cla on your figure object instead of creating a new figure. From @DavidZwicker Assuming you have imported pyplot as import matplotlib.pyplot as plt plt.cla() clears an axis, i.e. the currently active axis in the current figure. It leaves the other axes untouched. plt.clf() clears the entire current figure with all its axes, … Read more
Quite a busy one-liner, but here it is: First ensure your NumPy array, myarray, is normalised with the max value at 1.0. Apply the colormap directly to myarray. Rescale to the 0-255 range. Convert to integers, using np.uint8(). Use Image.fromarray(). And you’re done: from PIL import Image from matplotlib import cm im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255)) with … Read more
This should work: legend(numpoints=1) BTW, if you add the line legend.numpoints : 1 # the number of points in the legend line to your matplotlibrc file, then this will be the new default. [See also scatterpoints, depending on your plot.] API: Link to API docs
You should use the OO interface to matplotlib, rather than the state machine interface. Almost all of the plt.* function are thin wrappers that basically do gca().*. plt.subplot returns an axes object. Once you have a reference to the axes object you can plot directly to it, change its limits, etc. import matplotlib.pyplot as plt … Read more
This combination (and values near to these) seems to “magically” work for me to keep the colorbar scaled to the plot, no matter what size the display. plt.colorbar(im,fraction=0.046, pad=0.04) It also does not require sharing the axis which can get the plot out of square.
I had a problem with the answer by @mwaskorn, namely that g.set_xticklabels(rotation=30) fails, because this also requires the labels. A bit easier than the answer by @Aman is to just add plt.xticks(rotation=45)
You can add a Rectangle patch to the matplotlib Axes. For example (using the image from the tutorial here): import matplotlib.pyplot as plt import matplotlib.patches as patches from PIL import Image im = Image.open(‘stinkbug.png’) # Create figure and axes fig, ax = plt.subplots() # Display the image ax.imshow(im) # Create a Rectangle patch rect = … Read more
One way to automatically do this is the bbox_inches=”tight” kwarg to plt.savefig. E.g. import matplotlib.pyplot as plt import numpy as np data = np.arange(3000).reshape((100,30)) plt.imshow(data) plt.savefig(‘test.png’, bbox_inches=”tight”) Another way is to use fig.tight_layout() import matplotlib.pyplot as plt import numpy as np xs = np.linspace(0, 1, 20); ys = np.sin(xs) fig = plt.figure() axes = fig.add_subplot(1,1,1) … Read more
The following calls allow you to access the figure (Seaborn 0.8.1 compatible): swarm_plot = sns.swarmplot(…) fig = swarm_plot.get_figure() fig.savefig(“out.png”) as seen previously in this answer. The suggested solutions are incompatible with Seaborn 0.8.1. They give the following errors because the Seaborn interface has changed: AttributeError: ‘AxesSubplot’ object has no attribute ‘fig’ When trying to access … Read more
Specify the keyword args linestyle and/or marker in your call to plot. For example, using a dashed line and blue circle markers: plt.plot(range(10), linestyle=”–“, marker=”o”, color=”b”, label=”line with marker”) plt.legend() A shortcut call for the same thing: plt.plot(range(10), ‘–bo’, label=”line with marker”) plt.legend() Here is a list of the possible line and marker styles: ================ … Read more