(self,t, N=1)
Generate N simulations of jump times prior to some time t > startTime.
        
        parameters:
            t: the end of the processes. 
            N: the number of samples to return, -N to return a generator of size N
        returns:
            An irregular numpy array, with first dimension N, and each element an np.array 
            with random length.
            
        example:
        >> print renewal_ps.generate_sample_jumps( 1, N=2)
        np.array( [ [2,2.5,3.0], [1.0,2.0] ], dtype=0bject )
Generate N simulations of jump times prior to some time t > startTime.
        
        parameters:
            t: the end of the processes. 
            N: the number of samples to return, -N to return a generator of size N
        returns:
            An irregular numpy array, with first dimension N, and each element an np.array 
            with random length.
            
        example:
        >> print renewal_ps.generate_sample_jumps( 1, N=2)
        np.array( [ [2,2.5,3.0], [1.0,2.0] ], dtype=0bject )
Generate N simulations of jump times prior to some time t > startTime . parameters : t : the end of the processes . N : the number of samples to return - N to return a generator of size N returns : An irregular numpy array with first dimension N and each element an np . array with random length . example : >> print renewal_ps . generate_sample_jumps ( 1 N = 2 ) np . array ( [ [ 2 2 . 5 3 . 0 ] [ 1 . 0 2 . 0 ] ] dtype = 0bject )
def _sample_jumps(self,t, N=1):
        """        
        Generate N simulations of jump times prior to some time t > startTime.
        
        parameters:
            t: the end of the processes. 
            N: the number of samples to return, -N to return a generator of size N
        returns:
            An irregular numpy array, with first dimension N, and each element an np.array 
            with random length.
            
        example:
        >> print renewal_ps.generate_sample_jumps( 1, N=2)
        np.array( [ [2,2.5,3.0], [1.0,2.0] ], dtype=0bject )
        
        """
        if N<0:
            return (  self.__sample_jumps(t) for i in xrange(-N) )
        else:
            return np.asarray( [ self.__sample_jumps(t) for i in xrange(N) ] )
def _sample_jumps ( self , t , N = 1 ) : if N < 0 : return ( self . __sample_jumps ( t ) for i in xrange ( - N ) ) else : return np . asarray ( [ self . __sample_jumps ( t ) for i in xrange ( N ) ] )