def expensive_task_gen(num=8700):
    r"""
    Runs a task that takes some time

    Args:
        num (int): (default = 8700)

    CommandLine:
        python -m utool.util_alg expensive_task_gen --show

    Example:
        >>> # DISABLE_DOCTEST
        >>> from utool.util_alg import *  # NOQA
        >>> import utool as ut
        >>> #num = 8700
        >>> num = 40000
        >>> with ut.Timer('expanesive task'):
        >>>     time_list = list(ut.expensive_task_gen(num))
        >>> print(sum(time_list))
        >>> ut.quit_if_noshow()
        >>> import plottool as pt
        >>> #pt.plot(time_list)
        >>> from scipy.optimize import curve_fit
        >>> def func(x, a, b, c, d):
        >>>     return a * np.exp(-c * x) + d
        >>> #a*x**3 + b*x**2 +c*x + d
        >>> y = np.array(time_list)
        >>> y = np.array(ut.cumsum(y))
        >>> x = np.arange(len(y))
        >>> #popt, pcov = curve_fit(func, x, y, p0=(1, 1e-6, 1))
        >>> #print('pcov = %r' % (pcov,))
        >>> #print('popt = %r' % (popt,))
        >>> # http://stackoverflow.com/questions/3433486/-curve-fitting-in-python
        >>> pt.plt.plot(x[::num//50], y[::num//50], 'rx', label='measured data')
        >>> #x2 = np.arange(len(y) * 2)
        >>> #pt.plt.plot(x2, func(x2, *popt), 'b', label="Fitted Curve") #same as line above \/
        >>> #pt.plt.legend(loc='upper left')
        >>> ut.show_if_requested()
    """
    import utool as ut
    #time_list = []
    for x in range(0, num):
        with ut.Timer(verbose=False) as t:
            ut.is_prime(x)
        yield t.ellapsed
r"""
    Runs a task that takes some time

    Args:
        num (int): (default = 8700)

    CommandLine:
        python -m utool.util_alg expensive_task_gen --show

    Example:
        >>> # DISABLE_DOCTEST
        >>> from utool.util_alg import *  # NOQA
        >>> import utool as ut
        >>> #num = 8700
        >>> num = 40000
        >>> with ut.Timer('expanesive task'):
        >>>     time_list = list(ut.expensive_task_gen(num))
        >>> print(sum(time_list))
        >>> ut.quit_if_noshow()
        >>> import plottool as pt
        >>> #pt.plot(time_list)
        >>> from scipy.optimize import curve_fit
        >>> def func(x, a, b, c, d):
        >>>     return a * np.exp(-c * x) + d
        >>> #a*x**3 + b*x**2 +c*x + d
        >>> y = np.array(time_list)
        >>> y = np.array(ut.cumsum(y))
        >>> x = np.arange(len(y))
        >>> #popt, pcov = curve_fit(func, x, y, p0=(1, 1e-6, 1))
        >>> #print('pcov = %r' % (pcov,))
        >>> #print('popt = %r' % (popt,))
        >>> # http://stackoverflow.com/questions/3433486/-curve-fitting-in-python
        >>> pt.plt.plot(x[::num//50], y[::num//50], 'rx', label='measured data')
        >>> #x2 = np.arange(len(y) * 2)
        >>> #pt.plt.plot(x2, func(x2, *popt), 'b', label="Fitted Curve") #same as line above \/
        >>> #pt.plt.legend(loc='upper left')
        >>> ut.show_if_requested()