I'm assuming you answered your own questions here with your symbolic
nsolve.  Were there any issues that you didn't resolve?

Aaron Meurer

On Tue, Jan 10, 2012 at 9:34 AM, krastanov.ste...@gmail.com
<krastanov.ste...@gmail.com> wrote:
> The problem of "python functions evaluate immediately sympy functions don't"
> is obvious when one tries to plot/lambdify something that contains both Expr
> and some numerical routine. Try to imagine a way to plot
> nsolve(..)*some_Expr for example.
>
> On 10 January 2012 17:30, krastanov.ste...@gmail.com
> <krastanov.ste...@gmail.com> wrote:
>>
>> Or more likely it's better to use implemented_function that is imported
>> from utilities.lambdify?
>> It seems to me that implemented_function is quite important if one wants
>> for example to have complicated numerical routines accessible as sympy
>> expressions. In my opinion it's actually important enough to be mentioned in
>> the tutorial/pitfalls. Because defining a function to be used in sympy is
>> actually not as simple as defining a python function (python functions
>> evaluate immediately, sympy functions do not).
>>
>> Another question that I need help with is what is Lambda used for. Is
>> there something that Lambda does and implemented_function does not?
>>
>>
>> On 10 January 2012 16:39, krastanov.ste...@gmail.com
>> <krastanov.ste...@gmail.com> wrote:
>>>
>>> How can I represent an unevaluated call to nsolve as a sympy expression?
>>> Is Lambda the best (and standard) solution?
>>>
>>> The expression I want to represent looks like nsolve(x-tanh(x-h),[x],0).
>>> The free symbol is h. It's in the context of plotting phase transition
>>> diagrams.
>>
>>
>
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