automaton.letterize()

Split the transition labels into letters.

Preconditions:

Postconditions:

the output transitions are atomic, meaning that they are composed of one letter (as viewed by the labelset). The labelset is adapted to provide this guarantee.

Examples



In [1]:

    
import vcsn
ctx = vcsn.context("law_char, b")
ctx









    Out[1]:





$\{\ldots\}^*\rightarrow\mathbb{B}$



In [2]:

    
a = ctx.expression("(cat)+(dog)*").standard()
a









    Out[2]:



In [3]:

    
al = a.letterize()
al









    Out[3]:



In [4]:

    
al.context()









    Out[4]:





$(\{a, c, d, g, o, t\})^?\rightarrow\mathbb{B}$

Note that here, the transitions are no longer words (law) but instead letters (nullableset<lal_char>).



In [5]:

    
ctx_t = vcsn.context("lat<law_char, law_char, lal_char>, z")



In [6]:

    
b = ctx_t.expression(r"(<2>'cat,dog,c')*+'\e,b,d'").derived_term()
b









    Out[6]:



In [7]:

    
bl = b.letterize()
bl









    Out[7]:



In [8]:

    
bl.context()









    Out[8]:





$(\{a, c, t\})^? \times (\{b, d, g, o\})^? \times (\{c, d\})^?\rightarrow\mathbb{Z}$

Here, all three labelsets have been converted to letters or the empty word (nullableset<lal_char>).