Dear Joe, finally I had time to come back to my python scritp for the contour plots. You're code works very nicelly and does exactly what I need.
Thank you for the help Francesco 2010/7/26 Joe Kington <jking...@wisc.edu>: > It sounds like you're wanting a gaussian kernel density estimate (KDE) (not > the desktop!). The other options you mentioned are for interpolation, and > are not at all what you're wanting to do. > > You can use scipy.stats.kde.gaussian_kde(). However, it currently doesn't > take a weights array, so you'll need to modify it for your use case. > > If you prefer, I have faster version of a gaussian KDE that can take a > weights array. It's actually slower than the scipy's gaussian kde for a low > number of points, but for hundreds, thousands, or millions of points, it's > several orders of magnitude faster. (Though the speedup depends on the > covariance of the points... higher covariance = slower, generally speaking) > > Here's a quick pastebin of the code. http://pastebin.com/LNdYCZgw > > To use it, you do something like the below... (assuming the code in the > pastebin is saved in a file called fast_kde.py) > > import numpy as np > import matplotlib.pyplot as plt > from fast_kde import fast_kde > > # From your description of your data... > weights, x, y = np.loadtxt('chain.txt', usecols=(0,4,6)).T > > kde_grid = fast_kde(x, y, gridsize=(200,200), weights=weights) > > # Plot the grid > plt.figure() > plt.imshow(kde_grid, extent=(x.min(), x.max(), y.max(), y.min()) > > # Reverse the y-axis > plt.gca().invert_yaxis() > > plt.show() > > Hope that helps a bit, > -Joe > > > > > On Sat, Jul 24, 2010 at 3:56 AM, montefra <franz.berges...@googlemail.com> > wrote: >> >> Hi, >> >> I am writing a program that reads three columns (one column containing the >> weights, the other two containing the values I want to plot) from a file >> containing the results from a MonteCarlo Markov Chain. The file contains >> thousends of lines. Then create the 2D histogram and make contourplots. >> Here >> is a sample of the code (I don't know if is correct, it's just to show >> what >> I do) >> >> >>> import numpy as np >> >>> import matplotlib.pyplot as mplp >> >>> chain = np.loadtxt("chain.txt", usecols=[0,4,6]) #read columns 0 >> >>> (the >> >>> weights), 4 and 6 (the data), from the file "chain.txt" >> >>> h2D, xe, ye = np.histogram2D(chain[:,1],chain[:,2], >> >>> weights=chain[:,0]) >> >>> #create the 2D histogram >> >>> x = (xe[:-1] + xe[1:])/2. #x and y values for the plot (I use the mean >> >>> of each bin) >> >>> y = (ye[:-1] + ye[1:])/2. >> >>> mplp.figure() #open the figure >> >>> mplp.contourf(x, y, h2D.T, origin='lower') #contour plot >> >> As it is the contours are not smooth and they look not that nice. After >> days >> of searches I've found three methods and tried, unsuccesfully, to apply >> them >> 1) 2d interpolation: I got "segmentation fault" (on a quadcore machine >> with >> 8Gb of RAM) >> 2) Rbf (radial basis functions): I got wrong contours >> 3) ndimage: it creates spurious features (like secondary peaks parallel to >> the direction of the main one) >> >> Before beginning with Python, I used to use IDL to plot, and there is a >> function 'smooth' that smooth for you 2D histograms. I haven't found >> anything similar for Python. >> Does anyone have an idea or suggestion on how to do it? >> >> Thank in advance >> Francesco >> >> -- >> View this message in context: >> http://old.nabble.com/Smooth-contourplots-tp29253884p29253884.html >> Sent from the matplotlib - users mailing list archive at Nabble.com. >> >> >> >> ------------------------------------------------------------------------------ >> The Palm PDK Hot Apps Program offers developers who use the >> Plug-In Development Kit to bring their C/C++ apps to Palm for a share >> of $1 Million in cash or HP Products. Visit us here for more details: >> http://ad.doubleclick.net/clk;226879339;13503038;l? >> http://clk.atdmt.com/CRS/go/247765532/direct/01/ >> _______________________________________________ >> Matplotlib-users mailing list >> Matplotlib-users@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > -- personals: monty...@yahoo.it, monte_...@hotmail.com (messenger), franz.berges...@googlemail.com. work: monte...@mpe.mpg.de http://picasaweb.google.it/franz.bergesund ------------------------------------------------------------------------------ This SF.net Dev2Dev email is sponsored by: Show off your parallel programming skills. Enter the Intel(R) Threading Challenge 2010. http://p.sf.net/sfu/intel-thread-sfd _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
