def convert_relation()

in evaluation/latex2sympy/latex2sympy2.py [0:0]

• 48 lines of code
• 19 McCabe index (conditional complexity)

def convert_relation(rel):
    if rel.expr():
        return convert_expr(rel.expr())

    lh = convert_relation(rel.relation(0))
    rh = convert_relation(rel.relation(1))
    if rel.LT():
        return sympy.StrictLessThan(lh, rh, evaluate=False)
    elif rel.LTE():
        return sympy.LessThan(lh, rh, evaluate=False)
    elif rel.GT():
        return sympy.StrictGreaterThan(lh, rh, evaluate=False)
    elif rel.GTE():
        return sympy.GreaterThan(lh, rh, evaluate=False)
    elif rel.EQUAL():
        return sympy.Eq(lh, rh, evaluate=False)
    elif rel.ASSIGNMENT():
        # !Use Global variances
        if lh.is_Symbol:
            # set value
            variances[lh] = rh
            var[str(lh)] = rh
            return rh
        else:
            # find the symbols in lh - rh
            equation = lh - rh
            syms = equation.atoms(sympy.Symbol)
            if len(syms) > 0:
                # Solve equation
                result = []
                for sym in syms:
                    values = sympy.solve(equation, sym)
                    for value in values:
                        result.append(sympy.Eq(sym, value, evaluate=False))
                return result
            else:
                return sympy.Eq(lh, rh, evaluate=False)
    elif rel.IN():
        # !Use Global variances
        if hasattr(rh, 'is_Pow') and rh.is_Pow and hasattr(rh.exp, 'is_Mul'):
            n = rh.exp.args[0]
            m = rh.exp.args[1]
            if n in variances:
                n = variances[n]
            if m in variances:
                m = variances[m]
            rh = sympy.MatrixSymbol(lh, n, m)
            variances[lh] = rh
            var[str(lh)] = rh
        else:
            raise Exception("Don't support this form of definition of matrix symbol.")
        return lh
    elif rel.UNEQUAL():
        return sympy.Ne(lh, rh, evaluate=False)