On Thu, 22 Nov 2007, Rich Shepard wrote:
>> For parsimony, I think you're probably best off just using the
>> Gaussian equation:
>>
>> def fwhm2k(fwhm):
>> '''converts fwhm value to k (see above)'''
>> return fwhm/(2 * n.sqrt( n.log( 2 ) ) )
>>
>> def gauss1d(r, fwhm, c):
>> '''returns the 1d gaussian given by fwhm (full-width at half-max),
>> and c (centre) at positions given by r
>> '''
>> return exp( -(r-c)**2 / fwhm2k( fwhm )**2 )
>
> Thank you, Angus. I'll look at the Gaussian explanation to understand the
> input values.
It's been decades since I've needed to write any statistical or
distributional function code; I used SysStat in DOS for quite some time and
R for the past decade with linux. There's a great difference between using
working code and writing one's one code. :-)
Looking more carefully at the SciPy stats module, I see the normal
distribution is included as a class. However, I don't understand all the
functions in that class -- other than normal.pdf(x) -- and do not see where
loc and scale (representing mean and std. deviation) are used. Toward
understanding how to use this code to draw the curves I need, I extracted
the one method into a stand-alone file and tried to plot a Normal/Bell curve
from 0 to 100:
import matplotlib.numerix as nx
import pylab as p
from math import *
def normal(x):
return exp(-x**2.0/2.0)/sqrt(2.0*pi)
x = nx.arange(0, 100, 0.1)
N = normal(x)
p.plot(x, N, color='red', lw=2)
p.axis([0, 100, 0.0, 1.0])
p.xlabel('Universe of Discourse')
p.ylabel('Membership Grade')
p.show()
The error returned is:
Traceback (most recent call last):
File "normal-curve.py", line 17, in ?
N = normal(x)
File "normal-curve.py", line 14, in normal
return exp(-x**2.0/2.0)/sqrt(2.0*pi)
TypeError: only length-1 arrays can be converted to Python scalars
A clue stick to the meaning of this error message, and how to fix the
problem is needed.
Rich
--
Richard B. Shepard, Ph.D. | Integrity Credibility
Applied Ecosystem Services, Inc. | Innovation
<http://www.appl-ecosys.com> Voice: 503-667-4517 Fax: 503-667-8863
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