> From: C M [mailto:cmpyt...@gmail.com]
> Sent: Friday, February 12, 2010 17:15
>
> I would like to understand your approach better. So far, I can't get
> your code to produce the "margins" indicated--but I'm probably
> applying it wrongly. I don't know how to force an autoscale, for
> example. Your code is tough for me to understand because there are a
> number of things you make use of that I'm not familiar with yet. I
> could ask a number of questions but don't want to burden the list with
> that unless people are up for it.
Okay. The basic idea is that axes.autoscale_view(tight=False) already has the
capacity to obtain whatever view limits are returned by each axis' major tick
locator, so we don't need to alter the autoscale_view code within matplotlib;
we just implement a locator that yields limits we like. I based my locator on
MaxNLocator, but you could use a different base. Only two methods inherited
from MaxNLocator need to be modified -- the __init__ method to store the
margin, and the view_limits method to implement the looser limits. We attach
an instance of the locator to an axis using the axis' set_major_locator
method, and if we've already plotted and need to autoscale, we invoke
axes.autoscale_view. To later change the margin (or the parameters handled by
MaxNLocator), we can attach a new instance.
I updated my code for matplotlib 0.99.1, added some comments and examples, and
have attached it below. I hope it's helpful as an example.
----
import numpy as np
import matplotlib as mpl
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
class LooseMaxNLocator(mticker.MaxNLocator):
"""
Select no more than N intervals at nice locations with view
limits loosely fitted to the data. Unlike MaxNLocator, the
view limits do not necessarily coincide with tick locations.
"""
def __init__(self, margin = 0.0, **kwargs):
"""
Keyword arguments:
*margin*
Specifies the minimum size of both the lower and upper
margins (between the view limits and the data limits) as
a fraction of the data range. Must be non-negative.
Remaining keyword arguments are passed to MaxNLocator.
"""
mticker.MaxNLocator.__init__(self, **kwargs)
if margin < 0:
raise ValueError('The margin must be non-negative.')
self._margin = margin
def view_limits(self, dmin, dmax):
# begin partial duplication of MaxNLocator.view_limits
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=0.05)
# end duplication
margin = self._margin * (dmax - dmin) # fraction of data range
vmin = dmin - margin # expand the view
vmax = dmax + margin
bin_boundaries = self.bin_boundaries(vmin, vmax)
# locate ticks with MaxNLocator
# Note: If the lines below change vmin or vmax, the bin boundaries
# later calculated by MaxNLocator.__call__ may differ from those
# calculated here.
vmin = min(vmin, max(bin_boundaries[bin_boundaries <= dmin]))
# expand view to the highest tick below or touching the data
vmax = max(vmax, min(bin_boundaries[bin_boundaries >= dmax]))
# expand view to the lowest tick above or touching the data
return np.array([vmin, vmax])
# Examples
import matplotlib.pyplot as plt
fig1 = plt.figure()
ax1 = fig1.add_subplot(1, 1, 1)
ax1.set_xlabel('default locator')
ax1.set_ylabel('LooseMaxNLocator')
ax1.yaxis.set_major_locator(
LooseMaxNLocator(nbins=9, steps=[1, 2, 5, 10], margin=0.125))
# Set our locator before we plot.
ax1.plot([0, 0.95], [0, 0.95]) # Our locator's view limits are used.
fig2 = plt.figure()
ax2 = fig2.add_subplot(1, 1, 1)
ax2.set_xlabel('default locator')
ax2.set_ylabel('LooseMaxNLocator')
ax2.plot([0, 1.05], [0, 1.05]) # The default locator's view limits are used.
ax2.yaxis.set_major_locator(
LooseMaxNLocator(nbins=9, steps=[1, 2, 5, 10], margin=0.125))
# Now set our locator.
ax2.autoscale_view() # Autoscale activates our locator's view limits.
------------------------------------------------------------------------------
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