; Using the K-S test routine, kstwo_plot.pro      5/4/05-dd

; Comparing two uniform distributions:
;
; 
data1 = randomu(seed, 100)
data2 = randomu(seed, 200)

kstwo_plot, data1, data2, D, prob

; change one in "width" (factor) and/or offset:

kstwo_plot, 1.1*data1, data2, D, prob
kstwo_plot, 1.2*data1, data2, D, prob
kstwo_plot, 1.2*data1-0.1, data2, D, prob
kstwo_plot, 1.2*data1-0.2, data2, D, prob
;  etc.


; Compare a uniform distibution (data2) to a Gaussian (data3):
;
data3=data2
for id=0,n_elements(data3)-1 do data3(id)=g_rand(seed)
kstwo_plot, data3, data2, D, prob
;   not very similar!
; Scale and offset the uniform distribution
; to approximate a Gaussian in RMS sense:
kstwo_plot, data3, (data2-0.5)/0.29, D, prob
;   not too bad...
; Using more data points the difference is clearer:
data2 = randomu(seed, 2000)
data3=data2
for id=0,n_elements(data3)-1 do data3(id)=g_rand(seed)
kstwo_plot, data3, (data2-0.5)/0.29, D, prob