I see!
I also noticed that I can use `DeferredVector`s instead of `MatrixSymbol`s
to get avoid those dict shenanigans. This
```
import numpy
import sympy
a = sympy.DeferredVector('a')
b = sympy.DeferredVector('b')
x = sympy.Symbol('x')
P = (a[0] + a[1]*x) / (1 + b[0]*x + b[1]*x**2 + b[2]*x**3)
val_a = numpy.random.rand(2)
val_b = numpy.random.rand(3)
Pa = sympy.lambdify((a, b), P)(val_a, val_b)
print(P)
print(Pa)
```
works. It's a bit weird to create a lambdify only to call it straight away
though. Also, I'm reading [1] that `DeferredVector` were once scheduled for
removal? Probably outdated.
[1] https://github.com/sympy/sympy/issues/6788
On Monday, March 19, 2018 at 11:24:59 PM UTC+1, Leonid Kovalev wrote:
>
> P_a = P.subs(dict(zip(a, val_a)))
>
> returns
>
> (0.450033195586719*x + 0.829322314411828)/(x**3*b[2, 0] + x**2*b[1, 0] + x
> *b[0, 0] + 1)
>
> You need to substitute individual MatrixElement objects like a[0], not the
> MatrixSymbol itself (which isn't even present in the expression P).
>
>
>
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