On 24-Apr-2015 12:58, Christian Alis wrote: > I had the same problem some time ago and what I did is to use bar() to > plot the histogram, which can be done in one line: > > hist, bin_edges = np.histogram(data) > plt.bar(bin_edges[:-1], hist) Very elegant Christian :-) > > Perhaps this trick can be added in the documentation? Well, I vote to add it. However, I did find the following nice example (after reading your email) that shows how the bar function might be used (http://matplotlib.org/examples/api/barchart_demo.html) for my problem. Had I seen this before, I probably would not have posted this request.:-[ > > I am willing to code Virgil's request if many will find this useful. > > > On Fri, Apr 24, 2015 at 11:33 AM, Virgil Stokes <v...@it.uu.se> wrote: >> I have some Python (2.7.9) code that processes some rather large data sets >> to determine the curvatures along 2D curves. One feature of these data that >> I like to look at is the distribution of the curvatures. I use NumPy to to >> determine histograms for each set, and save the histogram parameters >> returned from numpy.histogram in a file. >> >> I would like to use Matplotlib to plot histograms from the parameters >> returned in NumPy and stored in a file --- why? Because the size of my data >> sets does not allow for the use of the histogram plot function in Matplotlib >> (1.4.3); i.e., it needs the data sets to calculate the histogram, before >> doing the plot. I would like to have a histogram plot function in Matplotlib >> that could bypass the actual calculation of the bin counts and edges from >> the data, and use values of these found a priori. Of course, an obvious >> question is -- Why not write code to plot the rectangles yourself? Yes, I >> could do this; but, why not extend the Matplotlib histogram class to allow >> for this option? If I better understood Matplotlib, I would try this myself. >> Maybe it is possible to get this into the next planned release (1.5). :-) >> >> If this request is inappropriate for this list, then please accept my >> apology and direct me to where I should send this request. >> >> Best regards. >> >> ------------------------------------------------------------------------------ >> One dashboard for servers and applications across Physical-Virtual-Cloud >> Widest out-of-the-box monitoring support with 50+ applications >> Performance metrics, stats and reports that give you Actionable Insights >> Deep dive visibility with transaction tracing using APM Insight. >> http://ad.doubleclick.net/ddm/clk/290420510;117567292;y >> _______________________________________________ >> Matplotlib-users mailing list >> Matplotlib-users@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> Keep up the good work on Matplotlib and I look forward to vers. 1.5 :-)
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