I’m not sure whether it is a new feature in later versions of matplotlib, but at least for 1.3.1, this is simply: plt.title(figure_title, y=1.08) This also works for plt.suptitle(), but not (yet) for plt.xlabel(), etc.
Another interesting parameter for a lot of functions is transform (unlike orientation or pivot this parameter can also be used in e.g. plot). The transform parameter allows you to add a transformation, specified by a Transform object. For the sake of example, this is how you would rotate the plot of some random data: import … Read more
I think this would be best explained by the following picture: To initialize the above, one would type: import matplotlib.pyplot as plt fig = plt.figure() fig.add_subplot(221) #top left fig.add_subplot(222) #top right fig.add_subplot(223) #bottom left fig.add_subplot(224) #bottom right plt.show()
The formatting of tick labels is controlled by a Formatter object, which assuming you haven’t done anything fancy will be a ScalerFormatterby default. This formatter will use a constant shift if the fractional change of the values visible is very small. To avoid this, simply turn it off: plt.plot(arange(0,100,10) + 1000, arange(0,100,10)) ax = plt.gca() … Read more
First, what happens when T0 is not None? I would test that, then I would adjust the values I pass to plt.subplot(); maybe try values 131, 132, and 133, or values that depend whether or not T0 exists. Second, after plt.show() is called, a new figure is created. To deal with this, you can Call … Read more
Another way is to use the subplots function and pass the width ratio with gridspec_kw matplotlib Tutorial: Customizing Figure Layouts Using GridSpec and Other Functions matplotlib.gridspec.GridSpec has available gridspect_kw options import numpy as np import matplotlib.pyplot as plt # generate some data x = np.arange(0, 10, 0.2) y = np.sin(x) # plot it f, (a0, … Read more