I have applied the update to the documentation (although that function
needs a general rewrite - later...)
On Mon, Aug 15, 2011 at 8:53 AM, Andrea Gavana andrea.gav...@gmail.comwrote:
Hi Chris and All,
On 12 August 2011 16:53, Christopher Jordan-Squire wrote:
Hi Andrea--An easy way to get
Hi Chris Brennan,
On 15 August 2011 00:59, Brennan Williams wrote:
You can use scipy.stats.truncnorm, can't you? Unless I misread, you want to
sample a normal distribution but with generated values only being within a
specified range? However you also say you want to do this with triangular
Hi Chris and All,
On 12 August 2011 16:53, Christopher Jordan-Squire wrote:
Hi Andrea--An easy way to get something like this would be
import numpy as np
import scipy.stats as stats
sigma = #some reasonable standard deviation for your application
x = stats.norm.rvs(size=1000, loc=125,
On Mon, Aug 15, 2011 at 8:53 AM, Andrea Gavana andrea.gav...@gmail.comwrote:
Hi Chris and All,
On 12 August 2011 16:53, Christopher Jordan-Squire wrote:
Hi Andrea--An easy way to get something like this would be
import numpy as np
import scipy.stats as stats
sigma = #some
You can use scipy.stats.truncnorm, can't you? Unless I misread, you want
to sample a normal distribution but with generated values only being
within a specified range? However you also say you want to do this with
triangular and log normal and for these I presume the easiest way is to
sample
Hi All,
I am working on something that appeared to be a no-brainer issue (at the
beginning), by my complete ignorance in statistics is overwhelming and I got
stuck.
What I am trying to do can be summarized as follows
Let's assume that I have to generate a sample of a 1,000 values for a
Hi Andrea--An easy way to get something like this would be
import numpy as np
import scipy.stats as stats
sigma = #some reasonable standard deviation for your application
x = stats.norm.rvs(size=1000, loc=125, scale=sigma)
x = x[x50]
x = x[x200]
That will give a roughly normal distribution to
