Thank you so much, for the answer.

Il giorno martedì 11 giugno 2019 12:49:36 UTC+2, Shekhar Prasad Rajak ha 
scritto:
>
> Also, using solveset we can get the solution pretty fast : 
>
> ```
> solveset(omega_nf - 942.5 , J_u) 
> {0.00235331614197391}
>
> ```
>
> Regards,
> Shekhar
>
> On Thursday, 6 June 2019 02:50:27 UTC+5:30, Aaron Meurer wrote:
>>
>> What version of SymPy are you using? For me in 1.4, solve(omega_nf - 
>> 942.5, J_u, dict=True) returns [{J_u: 0.00235331614197392}] 
>>
>> In general, solve() only returns closed-form solutions, so if it 
>> doesn't return a solution, it may just mean that it couldn't find one 
>> in closed-form. If you know that you want a numeric solution, you may 
>> be better off starting with nsolve. 
>>
>> I believe nsolve() gives the wrong answer because you passed 
>> verify=False. Without it, it gives an error that it couldn't find the 
>> root. The default Newton's method solver has a hard time with this 
>> equation, but you can use a different one. For instance, 
>> nsolve(omega_nf - 942.5, [0, 1], solver='bisect') immediately returns 
>> the solution 0.00235331614197389. See the docstring of nsolve. 
>>
>> Aaron Meurer 
>>
>> On Wed, Jun 5, 2019 at 7:20 AM pull_over93 <[email protected]> 
>> wrote: 
>> > 
>> > Probably I'm not using sympy correctly, here I have this equation : 
>> > 
>> > omega_nf = sqrt(2)*sqrt(87791997.5351708 - 
>> 12563210.5479217*sqrt(-0.00144380926150678*J_u + 48.4180817181289*(J_u + 
>> 0.00027375075)**2 - 3.58991000729848e-7)/(J_u + 0.00027375075) + 
>> 2392.05862861605/(J_u + 0.00027375075))/2 
>> > and I want to solve this equation : 
>> > 942.5 = omega_nf 
>> > 
>> > I have tried the classic sympy.solve() 
>> > 
>> > eq_solution = sym.solve(omega_nf - 942.5 , J_u, dict = True) 
>> > 
>> > 
>> > But I do not have any output. 
>> > Than I've tried using sympy.nsolve() 
>> > eq_solution = sym.nsolve(omega_nf - 942.5, J_u, 0.0023, verify=False) 
>> > 
>> > But I don't get the right answer. (Right answer: J_u =  0.0023) 
>> > There's a smarter way to use sympy ? 
>> > 
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>>
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>>
>

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