How to set the view_limits / range of an axis in Matplotlib

Question

I want some space before and after the last tick location on an axis. I would just use axis.set_xlim() for example but this interferes with my (custom) locator and reruns the tick generation. I found and overwritten the view_limits() method of the locator-classes but they don't seem to be called automatically and when called manually they don't have any impact on the resulting plot. I searched the docs and the source but haven't come up with a solution. Am I missing something?

For the greater picture I want to have a locator which gives me some space before and after the ticks and chooses tick points which are a multiple of 'base' like the MultipleLocator but scale the base automatically if the number of ticks exceeds a specified value. If there is another way to achieve this without subclassing a locator I am all ears :).

Here is my example code for the subclassed locator with overwritten view_limits-method:

import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import MaxNLocator class MyLocator(MaxNLocator): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def view_limits(self, dmin, dmax): bins = self.bin_boundaries(dmin, dmax) step = bins[1] - bins[0] result = np.array([bins[0] - step, bins[-1] + step]) print(result) return result a = 10.0 b = 99.0 t = np.arange(a, b, 0.1) s = np.sin(0.1*np.pi*t)*np.exp(-t*0.01) loc = MyLocator(9) fig, ax = plt.subplots() plt.plot(t, s) ax.xaxis.set_major_locator(loc) loc.autoscale() # results in [ 0. 110.] but doesnt change the plot plt.show()

ImportanceOfBeingErnest · Accepted Answer · 2016-09-29 18:43:45Z

Not sure, if I understood completely what your problem is, but if you only want to add extra space, you can still use MaxNLocator and add that space manually like here:

import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import MaxNLocator a = 10.0 b = 99.0 t = np.arange(a, b, 0.1) s = np.sin(0.1*np.pi*t)*np.exp(-t*0.01) loc = MaxNLocator(9) fig, ax = plt.subplots() plt.plot(t, s) ax.xaxis.set_major_locator(loc) ticks = ax.get_xticks() newticks = np.zeros(len(ticks)+2) newticks[0] = ticks[0]- (ticks[1]-ticks[0]) newticks[-1] = ticks[-1]+ (ticks[1]-ticks[0]) newticks[1:-1] = ticks ax.set_xticks(newticks) plt.show()

thanks for that trick! I can work with that. I would still rather know why the whole view_limits-method doesn't work as expected.

Wisperwind · Accepted Answer · 2020-10-22 12:14:52Z

One slightly hacky solution to avoid ticks close to the plot's edges is the following:

class PaddedMaxNLocator(mp.ticker.MaxNLocator): def __init__(self, *args, protected_width=0.25, **kwargs): # `prune` edge ticks that might now become visible super().__init__(*args, **kwargs, prune='both') # Clamp to some reasonable range self.protected_width = min(0.5, protected_width) def tick_values(self, vmin, vmax): diff = (vmax - vmin) * self.protected_width / 2 return super().tick_values(vmin + diff, vmax - diff)

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