ANEW -histogram_demo

NEEDS -gaussj
NEEDS -gnuplot

DOC
(*
	#!/usr/bin/env python
	import numpy as np
	import matplotlib.mlab as mlab
	import matplotlib.pyplot as plt

	mu, sigma = 100, 15
	x = mu + sigma*np.random.randn(10000)

	# the histogram of the data
	n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)

	# add a 'best fit' line
	y = mlab.normpdf( bins, mu, sigma)
	l = plt.plot(bins, y, 'r--', linewidth=1)

	plt.xlabel('Smarts')
	plt.ylabel('Probability')
	plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
	plt.axis([40, 160, 0, 0.03])
	plt.grid(True)

	plt.show()

*)
ENDDOC

#10000 DOUBLE ARRAY x{ 

100e FCONSTANT mu
 15e FCONSTANT sigma

--            1 	      (x-mu)^2
--   ------------------	exp - ---------
--   sqrt(2*pi*sigma^2)       2 sigma^2

: NORM-PDF ( F: x -- y ) 
	PI*2 sigma FSQR F* FSQRT 1/F  
	FSWAP mu F- FSQR FNEGATE  sigma FSQR F2* F/ FEXP
	F* ;

: histogram_demo ( -- )
	0 FIGURE CLF  TRUE GRID
	S" Histogram of IQ: %\mu%=100, %\sigma%=15" TITLE
	S" Smarts"        XLABEL
	S" Probability"   YLABEL
	40e 160e 0e 0.03e AXIS!
	x{ mu sigma NORMRND  
	[PHOLD
	  x{ #100 HIST S" #green" PLOT
	  40e 160e S" red" ['] NORM-PDF #100 FPLOT
	PHOLD] ;