> """
> First attempt at a histogram strip chart (made up name).
> if-main block taken from [1] except that I've replaced uniform distributions
> with normal distributions.
> [1]
> http://matplotlib.sourceforge.net/examples/pylab_examples/boxplot_demo3.html
> """
> import numpy as np
> import matplotlib.pyplot as plt
> from matplotlib import collections
>
> NORM_TYPES = dict(max=max, sum=sum)
> class BinCollection(collections.PatchCollection):
> def __init__(self, hist, bin_edges, x=0, width=1, cmap=plt.cm.gray_r,
> norm_type='max', **kwargs):
> yy = (bin_edges[:-1] + bin_edges[1:])/2.
> heights = np.diff(bin_edges)
> bins = [plt.Rectangle((x, y), width, h) for y, h in zip(yy, heights)]
> norm = NORM_TYPES[norm_type]
> fc = cmap(np.asarray(hist, dtype=float)/norm(hist))
> super(BinCollection, self).__init__(bins, facecolors=fc, **kwargs)
Is this equivalent to writing collections.PatchCollection.__init__()
and what are the advantages of super()?
I think you can use axes.pcolor() to replace BinCollection. pcolor()
just adds a collection similar to what you do now by hand for you.
With appropriate arguments it should do the job. You can also look
into pcolorfast() and pcolormesh().
> def histstrip(x, positions=None, widths=None, ax=None):
> if ax is None:
> ax = plt.gca()
> if positions is None:
> positions = range(1, len(x) + 1)
> if widths is None:
> widths = np.min(np.diff(positions)) / 2. * np.ones(len(positions))
> for data, x_pos, w in zip(x, positions, widths):
> x_pos -= w/2.
> hist, bin_edges = np.histogram(data)
No other arguments to numpy.histogram() allowed?
> bins = BinCollection(hist, bin_edges, width=w, x=x_pos)
> ax.add_collection(bins, autolim=True)
> ax.set_xticks(positions)
> ax.autoscale_view()
> if __name__ == '__main__':
> import matplotlib.pyplot as plt
> import numpy as np
> np.random.seed(2)
> inc = 0.1
> e1 = np.random.normal(0,1, size=(500,))
> e2 = np.random.normal(0,1, size=(500,))
> e3 = np.random.normal(0,1 + inc, size=(500,))
> e4 = np.random.normal(0,1 + 2*inc, size=(500,))
> treatments = [e1,e2,e3,e4]
> fig, ax = plt.subplots()
> pos = np.array(range(len(treatments)))+1
> histstrip(treatments, ax=ax)
> ax.set_xlabel('treatment')
> ax.set_ylabel('response')
> fig.subplots_adjust(right=0.99,top=0.99)
> plt.show()
In my opinion this is too special to be added as a general matplotlib
plotting feature.
Friedrich
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