I think this would be a great contribution to the SymPy physics
module.  Can you put some usage doc strings? Then, we can put it in
the appropriate places.

-- Andy

On Wed, Nov 10, 2010 at 3:10 AM, Philippe <[email protected]> wrote:
> some usage if needed..
> this is my unittest file.
> the lagrange and partial_derivative functions are in meca.py
>
> -  -  -  -  -  -  -  -  -  -  -  -
>
> import unittest
> from sympy import *
> import meca
>
> class MyTest(unittest.TestCase):
>
>    def test_lagrange_1(self):
>        t = Symbol('t')
>        x = Function('x')(t)
>        y = Function('y')(t)
>        dT_dqvt, dT_dq = meca.lagrange(diff(x, t)**2+diff(y, t)**2 + 5
> * x, [x, y])
>
>        dT_dqvt_0 = 2*diff(x, t, t)
>        dT_dqvt_1 = 2*diff(y, t, t)
>
>        self.assertEquals(dT_dqvt, [dT_dqvt_0, dT_dqvt_1])
>        self.assertEquals(dT_dq, [5, 0])
>
>    def test_lagrange_2(self):
>        t = Symbol('t')
>        k = Symbol('k')
>        x = Function('x')(t)
>        dT_dqvt, dT_dq = meca.lagrange(k*diff(x, t)**2 + k * x, [x])
>
>        dT_dqvt_0 = 2*k*diff(x, t, t)
>
>        self.assertEquals(dT_dqvt, [dT_dqvt_0])
>        self.assertEquals(dT_dq, [k])
>
>    def test_partial_derivative_1(self):
>        t = Symbol('t')
>        k = Symbol('k')
>        x = Function('x')(t)
>        dE_q = meca.partial_derivative(k*diff(x, t)**2 + k * x, x)
>        dE_q_sol = k
>        self.assertEquals(dE_q, dE_q_sol)
>
>    def test_partial_derivative_2(self):
>        t = Symbol('t')
>        k = Symbol('k')
>        x = Function('x')(t)
>        dE_q = meca.partial_derivative(k*diff(x, t)**2 + k * x,
> diff(x, t))
>        dE_q_sol = 2*k*diff(x, t)
>        self.assertEquals(dE_q, dE_q_sol)
>
>
> if __name__ == '__main__':
>    unittest.main()
>
> -  -  -  -  -  -  -  -  -  -  -  -
>
> On 9 nov, 17:18, Philippe <[email protected]> wrote:
>> from sympy import *
>>
>> t = Symbol('t')
>> x = Function('x')(t)
>> y = Function('y')(t)
>>
>> def partial_derivative(equa, param):
>>     param_sub = Symbol('param_sub')
>>     equa_subs1 = equa.subs(param, param_sub)
>>     dequa_param_sub = diff(equa_subs1, param_sub)
>>     dequa_param = dequa_param_sub.subs(param_sub, param)
>>     return dequa_param
>>
>> def lagrange(equa, param):
>>     """
>>     param is a list of Function of time
>>     must not use 'q1', 'q2', .... 'qv1', ... in the equation
>>     """
>>     vel_var = [diff(e, t) for e in param]
>>     dT_dqv = [partial_derivative(equa, p) for p in vel_var]
>>     dT_dq = [partial_derivative(equa, p) for p in param]
>>
>>     dT_dqvt = [diff(e, t) for e in dT_dqv]
>>
>>     return dT_dqvt, dT_dq
>>
>> On 31 oct, 20:51, Tim Lahey <[email protected]> wrote:
>>
>> > On Sun, Oct 31, 2010 at 3:41 PM, Aaron S. Meurer <[email protected]> 
>> > wrote:
>>
>> > > I am still trying to fully understand the code below, but if one of the 
>> > > subs is trying to do something like diff(f(x), x).subs(x, g(x)), then 
>> > > you will get an error in SymPy for the above reason.
>>
>> > > Aaron Meurer
>>
>> > The basic approach is to substitute a symbol for f(x) say f1 and then
>> > take the derivative
>> > with respect to that. You know the substitution, so you can reverse
>> > it. Once the derivative
>> > is done, you reverse the substitution. Then, you can take the
>> > derivative with respect to
>> > x.
>>
>> > So, the steps become.
>>
>> > 1. Build list of symbols for functions.
>> > 2. Build a list of substitutions for the functions and the reverse.
>> > 3. Substitute for the functions.
>> > 4. Run derivatives with respect to the symbols.
>> > 5. Substitute functions for the symbols.
>> > 6. Take any derivatives with respect to the function variables.
>>
>> > Hope that helps the explanation. A lot of what's done in the code is using
>> > zip and map to make the code fast, but I originally wrote it as above. 
>> > Python
>> > has appropriate functions that this should still be fast.
>>
>> > Cheers,
>>
>> > Tim.
>>
>> > --
>> > Tim Lahey
>> > PhD Candidate, Systems Design Engineering
>> > University of Waterloohttp://www.linkedin.com/in/timlahey
>>
>>
>
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