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.bergesund@…982…> 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
- 2d interpolation: I got “segmentation fault” (on a quadcore machine with
8Gb of RAM)
Rbf (radial basis functions): I got wrong contours
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