[SciPy-User] Vectorizing functions where not known if each arg is (broadcast compatible) scalar or ndarray

[Yosef Meller]

Hi,

The way I did this before was to use broadcast_arrays() at the beginning of 
the function, to make sure all arrays have the same size.

HTH,
Yosef.

On Tuesday 18 September 2012 15:04:28 William Furnass wrote:
> Hi,
> 
> I'm looking for a simple way to create a vectorized version of the
> following function that flexibly allows one or more of the inputs to
> be either an ndarray of constant length l or a scalar.
> 
> With simpler functions (such as the 'reynolds' function mentioned
> below) the np.vectorize function does the job but I'm not sure how to
> vectorize the following given the conditionals involving Re (which
> could be vector or scalar) e.g. how should the case for Re > 4000 be
> calculated and D or k_s be indexed if it is not without introducing
> quite a number of checks whether Re, D or k_s are ndarrays or scalar
> floats?
> 
> Also, is there a numpy function that allows one to check whether a
> number of scalar and ndarray are broadcast compatible?
> 
> Cheers,
> 
> Will
> 
> def friction_factor(D, Q, k_s, T = 10.0, den = 1000.0):
>     Re = reynolds(D, Q, T, den)
>     if Re == 0:
>         f = 0
>     elif Re < 2000:
>         f = 64 / Re
>     elif 2000 <= Re < 4000:
>         y3 = -0.86859 * np.log((k_s / (3.7 * D)) + (5.74 / (4000**0.9)))
>         y2 = (k_s / (3.7 * D)) + (5.74 / (Re**0.9))
>         fa = y3**-2
>         fb = fa * (2 - (0.00514215 / (y2*y3)))
>         r = Re / 2000.
>         x4 = r * (0.032 - (3. * fa) + (0.5 * fb))
>         x3 = -0.128 + (13. * fa) - (2.0 * fb)
>         x2 =  0.128 - (17. * fa) + (2.5 * fb)
>         x1 = (7 * fa) - fb
>         f = x1 + r * (x2 + r * (x3 + x4))
>     elif Re >= 4000:
>         f = 0.25 / (np.log10((k_s / (3.7 * D)) + (5.74 / (Re**0.9))))**2
>     return f
> friction_factor = np.vectorized(friction_factor)
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