[Numpy-discussion] Using normal()

Zachary Pincus zachary.pincus@yale....
Thu Apr 24 12:28:38 CDT 2008

>   The syntax is normal(loc=0.0, scale=1.0, size=None), but I've not  
> seen
> what those represent, nor how to properly invoke this function. A  
> clue will
> be much appreciated.
>   I want to call normal() passing at least the width of the curve(at  
> the end
> points where y=0.0), and the center (where y=1.0). Being able to  
> specify the
> y value of the inflection point (by default 0.5) would also be  
> helpful.

The 'normal dstribution' is the Gaussian function:
N(x) = 1/(std*sqrt(2*pi))*exp(-(x-mean)**2/(2*std**2)), where N(x) is  
the probability density at position x, given a normal distribution  
characterized by 'mean' and 'std' (standard deviation).


Now, numpy.random.normal gives random samples distributed according to  
the above probability density function. The only remaining mystery is  
how 'loc' and 'scale' -- the parameters of numpy.random.normal -- map  
to 'mean' and 'standard deviation', which is how a normal distribution  
is usually parameterized. Fortunately, the documentation reveals this:

 >>> print numpy.random.normal.__doc__
Normal distribution (mean=loc, stdev=scale).

         normal(loc=0.0, scale=1.0, size=None) -> random values

If you need an alternate parameterization of the normal (e.g. in terms  
of the y value of the inflection point), just solve that out  
analytically from the definition of the normal in terms of mean and std.

However, it looks like you're trying to plot the normal function, not  
get random samples. Just evaluate the function (as above) at the x  

mean, std = (0, 1)
x = numpy.linspace(-10, 10, 200) # 200 evenly-spaced points from -10  
to 10
y = 1/(std*numpy.sqrt(2*numpy.pi))*numpy.exp(-(x-mean)**2/(2*std**2))


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