That's a clever trick. I should have thought of that.

Is there any reason you let system = A*x - B instead of A*x + B? The
latter seems more natural.

Aaron Meurer

On Sat, Jun 7, 2014 at 12:28 AM, James Crist <[email protected]> wrote:
> I just answered this on gitter earlier today, but you can just take the
> jacobian of the system to get its matrix form. For example:
>
> In [1]: from sympy import *
>
> In [2]: a, b, c, d = symbols('a, b, c, d')
>
> In [3]: x1, x2, x3, x4 = symbols('x1:5')
>
> In [4]: x = Matrix([x1, x2, x3, x4])
>
> In [5]: system = Matrix([a*x1 + b*x2 + c,
>    ...: c*x1 + d*x3 + 2,
>    ...: c*x3 + b*x4 + a])
>
> In [6]: A = system.jacobian(x)
>
> In [7]: B = A*x - system
>
> In [8]: A
> Out[8]:
> Matrix([
> [a, b, 0, 0],
> [c, 0, d, 0],
> [0, 0, c, b]])
>
> In [9]: B
> Out[9]:
> Matrix([
> [-c],
> [-2],
> [-a]])
>
> In [10]: assert A*x - B == system
>
> The functionality I'm adding for my GSoC for linearizing a system of
> equations will also be able to return these matrices in a convenient form.
> But it's not terribly difficult to solve for these arrangements using the
> current functionality.
>
>
>
>
>
>
>
> On Thursday, June 5, 2014 4:22:52 PM UTC-5, Andrei Berceanu wrote:
>>
>> Was this implemented into sympy at any point? It could be the equivalent
>> of Mathematica's CoefficientArrays function.
>>
>> On Thursday, November 14, 2013 5:56:22 AM UTC+1, Chris Smith wrote:
>>>
>>> I forgot that as_independent, without the as_Add=True flag will treat
>>> Muls differently. The following will be more robust:
>>>
>>> def eqs2matrix(eqs, syms, augment=False):
>>>     """
>>>     >>> s
>>>     [x + 2*y == 4, 2*c + y/2 == 0]
>>>     >>> eqs2matrix(s, (x, c))
>>>     (Matrix([
>>>     [1, 0],
>>>     [0, 2]]), Matrix([
>>>     [-2*y + 4],
>>>     [    -y/2]]))
>>>     >>> eqs2matrix([2*c*(x+y)-4],(x, y))
>>>     (Matrix([[2*c, 2*c]]), Matrix([[4]]))
>>>     """
>>>     s = Matrix([si.lhs - si.rhs if isinstance(si, Equality) else si for
>>> si in eqs])
>>>     sym = syms
>>>     j = s.jacobian(sym)
>>>     rhs = -(s - j*Matrix(sym))
>>>     rhs.simplify()
>>>     if augment:
>>>         j.col_insert(0, rhs)
>>>     else:
>>>         j = (j, rhs)
>>>     return j
>>>
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