functions in random.i - r

             random_ipq(ipq_model, dimlist)  
 
     returns an array of double values with the given DIMLIST (see array  
     function, nil for a scalar result).  The numbers are distributed  
     according to a piecewise linear function (possibly with power law  
     or exponential tails) specified by the IPQ_MODEL.  The "IPQ" stands  
     for "inverse piecewise quadratic", which the type of function  
     required to transform a uniform random deviate into the piecewise  
     linear distribution.  Use the ipq_setup function to compute  
     IPQ_MODEL.  

interpreted function, defined at i/random.i   line 178  

             random_n(dimlist)  
 
     returns an array of normally distributed random double values with  
     the given DIMLIST (see array function, nil for a scalar result).  
     The mean is 0.0 and the standard deviation is 1.0.  
     The algorithm follows the Box-Muller method (see Numerical Recipes  
     by Press et al.).  

interpreted function, defined at i/random.i   line 129  

             random_rej(target_dist, ipq_model, dimlist)  

          or random_rej(target_dist, bounding_dist, bounding_rand, dimlist)  
 
     returns an array of double values with the given DIMLIST (see array  
     function, nil for a scalar result).  The numbers are distributed  
     according to the TARGET_DIST function:  
        func target_dist(x)  
     returning u(x)>=0 of same number and dimensionality as x, normalized  
     so that the integral of target_dist(x) from -infinity to +infinity  
     is 1.0.  The BOUNDING_DIST function must have the same calling  
     sequence as TARGET_DIST:  
        func bounding_dist(x)  
     returning b(x)>=u(x) everywhere.  Since u(x) is normalized, the  
     integral of b(x) must be >=1.0.  Finally, BOUNDING_RAND is a  
     function which converts an array of uniformly distributed random  
     numbers on (0,1) -- as returned by random -- into an array  
     distributed according to BOUNDING_DIST:  
        func bounding_rand(uniform_x_01)  
     Mathematically, BOUNDING_RAND is the inverse of the integral of  
     BOUNDING_DIST from -infinity to x, with its input scaled to (0,1).  
     If BOUNDING_DIST is not a function, then it must be an IPQ_MODEL  
     returned by the ipq_setup function.  In this case BOUNDING_RAND is  
     omitted -- ipq_compute will be used automatically.  

interpreted function, defined at i/random.i   line 198  

             random_u(a, b, dimlist)  
 
     return uniformly distributed random numbers between A and B.  
     (Will never exactly equal A or B.)  The DIMLIST is as for the  
     array function.  Same as (b-a)*random(dimlist)+a.  If A==0,  
     you are better off just writing B*random(dimlist).  

interpreted function, defined at i/random.i   line 112  

             random_x(dimlist)  
 
     same as random(DIMLIST), except that random_x calls random  
     twice at each point, to avoid the defect that random only  
     can produce about 2.e9 numbers on the interval (0.,1.) (see  
     random for an explanation of these bins).  
     You may set random=random_x to get these "better" random  
     numbers in every call to random.  
     Unlike random, there is a chance in 1.e15 or so that random_x  
     may return exactly 1.0 or 0.0 (the latter may not be possible  
     with IEEE standard arithmetic, while the former apparently is).  
     Since cosmic rays are far more likely, you may as well not  
     worry about this.  Also, because of rounding errors, some bit  
     patterns may still be more likely than others, but the 0.5e-9  
     wide bins of random will be absent.  

interpreted function, defined at i/random.i   line 73