Re: [Matplotlib-users] Combination of a box plot and a histogram

[Tony S Yu <ts...@gm...>]

On Oct 4, 2010, at 4:09 PM, Friedrich Romstedt wrote:

>> """
>> 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 believe collections.PatchCollection.__init__() is equivalent. In this instance, I don't think there are advantages (or disadvantages) to using super---it's just how I'm used to writing classes.

> 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().

Yes, you're right. This was actually my main question in the original post; i.e. what plotting function to start with. I'm not really sure how I overlooked pcolor(mesh) as a viable option. Thanks.

> 
>> 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?

As I mentioned, this was just a function I hacked together. I didn't try to make it general purpose (yet).

> 
> In my opinion this is too special to be added as a general matplotlib
> plotting feature.

I'd agree that it's pretty specialized; especially since I haven't been able to find any mention of it. I'm still curious if there's a name for this type of plot if anyone out there knows.

Best,
-Tony

> 
> Friedrich