PR Summary

As noted in#6664,Axes.__init__ is fairly slow due to the eventual call toAxis.reset_ticks.@efiring suggested lazy instantiation and@Kojoley was implementing, but I'm unsure of the status. In the meantime though, we still speed things up by avoiding callingcla too many times.

Using the example from#6664 withv2.0.x:

$ python3 -m timeit \  -s "from matplotlib.figure import Figure; from matplotlib.axes import Axes; fig = Figure()" \  "Axes(fig, (0, 0, 1 ,1))"10 loops, best of 3: 29.7 msec per loop

andmaster is already a bit faster:

$ python -m timeit \  -s "from matplotlib.figure import Figure; from matplotlib.axes import Axes; fig = Figure()" \  "Axes(fig, (0, 0, 1 ,1))"100 loops, best of 3: 17.7 msec per loop

The trouble withAxes.__init__ is that it does a bit too much clearing;Axes.__init__ calls:

• Axes._init_axis
  • Createsself.xaxis andself.yaxis
    • Axis.__init__ ->Axis.cla (2 calls perAxes)
  • Registers eachAxis with two spines viaSpine.register_axis
    • Axis.cla is called on the registeredAxis (2Spine * 2Axis = 4 calls perAxes)
• Axes.cla - Necessary to finish initializing theAxes in a clean state, though it's generic for any caller.
  • self.xaxis.cla() /self.yaxis.cla() (2 calls perAxes)
  • self.spines[:].cla() (2 spines perAxis = 4 calls perAxes)

That makes12 calls toAxis.cla for only2Axis objects. Adding some hidden argument to skip these calls as in this PR would reduce those calls by 6, though the speedup is only approximately 4 times:

$ python -m timeit \  -s "from matplotlib.figure import Figure; from matplotlib.axes import Axes; fig = Figure()" \  "Axes(fig, (0, 0, 1 ,1))"100 loops, best of 3: 4.45 msec per loop

PR Checklist

• [N/A] Has Pytest style unit tests
• Code is PEP 8 compliant
• [N/A] New features are documented, with examples if plot related
• [N/A] Documentation is sphinx and numpydoc compliant
• [N/A] Added an entry to doc/users/whats_new.rst if major new feature
• [N/A] Documented in doc/api/api_changes.rst if API changed in a backward-incompatible way