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

[Tony S Yu]

On Oct 1, 2010, at 9:40 AM, Benjamin Root wrote: On Thu, Sep 30, 2010 at 8:44 PM, Tony S Yu <tsy...@gmail.com> wrote: I'd like to make something in between a box plot [1] and a histogram. Each histogram would be represented by a single, tall, rectangular patch (like the box in a box plot), and the patch would be subdivided by the bin edges of the histogram. The face color of each sub-patch would replace the bar height in the histogram. If any of that actually made sense: * Does this type of plot have a name? * Is there an easy way to do this in Matplotlib? * If there isn't an easy way, what would be a good starting point? Initial ideas: 1) Use pcolor or imshow and embed this axes in a larger axes, 2) represent the sub-patches as a PolyCollection. Thoughts? -Tony [1] e.g. http://matplotlib.sourceforge.net/examples/pylab_examples/boxplot_demo.html Tony, I am not quite sure I understand.   [snip] Or maybe something else in the gallery is more like what you want: http://matplotlib.sourceforge.net/gallery.html Ben Root I've checked the gallery, but I don't see anything that appears similar. In any case, I ended up hacking together something that works. I've attached an image of what I had in mind (created with the code at the very bottom of this reply). I ended up using mpl Rectangle objects and stringing them together using a PatchCollection. Maybe there's a more efficient way to do this, but this approach worked well-enough. Best, -Tony """ 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) 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)         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()

------------------------------------------------------------------------------
Start uncovering the many advantages of virtual appliances
and start using them to simplify application deployment and
accelerate your shift to cloud computing.
http://p.sf.net/sfu/novell-sfdev2dev
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users