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<section id="changes-for-0-82">
<h1>Changes for 0.82<a class="headerlink" href="#changes-for-0-82" title="Permalink to this heading">#</a></h1>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>- toolbar import change in GTKAgg, GTKCairo and WXAgg
- Added subplot config tool to GTK* backends -- note you must now
import the NavigationToolbar2 from your backend of choice rather
than from backend_gtk because it needs to know about the backend
specific canvas -- see examples/embedding_in_gtk2.py. Ditto for
wx backend -- see examples/embedding_in_wxagg.py
- hist bin change
Sean Richards notes there was a problem in the way we created
the binning for histogram, which made the last bin
underrepresented. From his post:
I see that hist uses the linspace function to create the bins
and then uses searchsorted to put the values in their correct
bin. That's all good but I am confused over the use of linspace
for the bin creation. I wouldn't have thought that it does
what is needed, to quote the docstring it creates a "Linear
spaced array from min to max". For it to work correctly
shouldn't the values in the bins array be the same bound for
each bin? (i.e. each value should be the lower bound of a
bin). To provide the correct bins for hist would it not be
something like
def bins(xmin, xmax, N):
if N==1: return xmax
dx = (xmax-xmin)/N # instead of N-1
return xmin + dx*arange(N)
This suggestion is implemented in 0.81. My test script with these
changes does not reveal any bias in the binning
from matplotlib.numerix.mlab import randn, rand, zeros, Float
from matplotlib.mlab import hist, mean
Nbins = 50
Ntests = 200
results = zeros((Ntests,Nbins), typecode=Float)
for i in range(Ntests):
print 'computing', i
x = rand(10000)
n, bins = hist(x, Nbins)
results[i] = n
print mean(results)
</pre></div>
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