Hello,
It looks like there are a couple of typos in the equations used in the
function.
On Friday 07 July 2006 12:17, Faheem Mitha wrote:
> I think the bivariate normal probability distribution function
> implementation in Matplotlib is buggy. See the example code below, and
> note that the change in the x directions and the y directions is not
> uniform as it should be.
> The implementation (in mlab.py) is appended.
[snip]
> z = Xmu**2/sigmax**2 + Ymu**2/sigmay - 2*rho*Xmu*Ymu/(sigmax*sigmay)
^ **2
> return 1.0/(2*pi*sigmax*sigmay*(1-rho**2)) * exp( -z/(2*(1-rho**2)))
^ sqrt()
Compare to equations (1) and (2) from the link in the docstring:
http://mathworld.wolfram.com/BivariateNormalDistribution.html
I've attached a patch.
Mike
Index: lib/matplotlib/mlab.py
===================================================================
--- lib/matplotlib/mlab.py (revision 2553)
+++ lib/matplotlib/mlab.py (working copy)
@@ -960,8 +960,8 @@
Ymu = Y-muy
rho = sigmaxy/(sigmax*sigmay)
- z = Xmu**2/sigmax**2 + Ymu**2/sigmay - 2*rho*Xmu*Ymu/(sigmax*sigmay)
- return 1.0/(2*pi*sigmax*sigmay*(1-rho**2)) * exp( -z/(2*(1-rho**2)))
+ z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay)
+ return 1.0/(2*pi*sigmax*sigmay*numerix.mlab.sqrt(1-rho**2)) * exp( -z/(2*(1-rho**2)))
-------------------------------------------------------------------------
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