I use lambdify quite a bit, on rather large expressions.
Basically, it always works like this for me:
import sympy as sm
x1, x2, …, xn = sm.symbols(‚x1, x2, ….., xn‘)
….
…
expr = some expression of generally with me: sm.sin, sm.cos, sm.exp,
sm.sqrt,
sm.Heaviside, etc..
This expression may have 50,000 terms, may be an (axb) matrix, whatever.
expr_lam = sm.lambdify([x1, x2, …,xn], expr)
Now I can evaluate expr_lam(…) like I would evaluate any numpy function.
I have no idea, what expr_lam looks like, I would not know how to look at
it.
I assume, it converts sm.sin(..) to np.sin(…), etc
This is how it works for me.
As I do not really understand your points, like ‚dynamically created‘,
‚parse and subs‘, this may be of not help at all for you.
Peter
On Thu 18. Aug 2022 at 09:21 Zohreh Karimzadeh <z.karimza...@gmail.com>
wrote:
> Before run I import sp.sqrt or sp.exp but after run they get disappeared.
> My expression is big and dynamically created and not possible to parse and
> subs np.exp or sp.exp.
>
> Zohreh Karimzadeh
>
> Contact me on
> +989102116325
> and at
> z.karimza...@gmail.com
> 🌧️🌍🌱
>
>
> On Thu, 18 Aug 2022, 01:17 Aaron Meurer, <asmeu...@gmail.com> wrote:
>
>> Your expression uses "sqrt" but you haven't imported it from anywhere,
>> since you only did "import sympy as sp". You need to use sp.sqrt.
>>
>> Aaron Meurer
>>
>> On Wed, Aug 17, 2022 at 11:02 AM Zohreh Karimzadeh <
>> z.karimza...@gmail.com> wrote:
>>
>>> Here is my code:
>>>
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import sympy as sp
>>> import pandas as pd
>>> #exp_NaCl path: F:\Zohreh\MainZohreh\postdoc-field\CSU\Duplicat_Pure
>>> df =
>>> pd.read_excel(r'F:\Zohreh\MainZohreh\postdoc-field\CSU\Duplicat_Pure\data.xlsx',
>>> sheet_name='NaCl_exp')
>>> XNa = df['XNa']
>>> XCl = df['XCl']
>>> Xwater = df['Xwater']
>>> Y = df['gama_x']
>>> L=['WwaterNaCl', 'UwaterNaCl', 'VwaterNaCl', 'XCl', 'XNa', 'Xwater',
>>> 'BNaCl']
>>> for j in range(len(L)):
>>> locals()[L[j]] = sp.symbols(L[j])
>>> expr = -0.0118343195266272*BNaCl*XCl*XNa*(-2*(9.19238815542512*sqrt(XNa) +
>>> 9.19238815542512*sqrt(XCl + XNa) + 1)*exp(-9.19238815542512*sqrt(XNa) -
>>> 9.19238815542512*sqrt(XCl + XNa)) + 2)/((XCl + XNa)*(sqrt(XNa) + sqrt(XCl +
>>> XNa))**2) + 0.00591715976331361*BNaCl*XCl*(-2*(9.19238815542512*sqrt(XNa) +
>>> 9.19238815542512*sqrt(XCl + XNa) + 1)*exp(-9.19238815542512*sqrt(XNa) -
>>> 9.19238815542512*sqrt(XCl + XNa)) + 2)/(sqrt(XNa) + sqrt(XCl + XNa))**2 +
>>> 0.00591715976331361*BNaCl*XNa*(-2*(9.19238815542512*sqrt(XNa) +
>>> 9.19238815542512*sqrt(XCl + XNa) + 1)*exp(-9.19238815542512*sqrt(XNa) -
>>> 9.19238815542512*sqrt(XCl + XNa)) + 2)/(sqrt(XNa) + sqrt(XCl + XNa))**2 -
>>> 1.0*Cl*WwaterNaCl*Xwater*(0.5*XCl + 0.5*XNa + 0.5)/XCl -
>>> 0.5*Cl*WwaterNaCl/XCl - 4.0*UwaterNaCl*XCl*XNa*Xwater +
>>> 2.0*UwaterNaCl*XCl*Xwater + 2.0*UwaterNaCl*XNa*Xwater - 4.0*UwaterNaCl*XNa
>>> - 6.0*VwaterNaCl*XCl*XNa*Xwater**2 - 4.0*VwaterNaCl*XCl*Xwater**2 +
>>> 2.0*VwaterNaCl*XNa*Xwater**2 - 1.0*WwaterNaCl*Xwater*(0.5*XCl + 0.5*XNa +
>>> 0.5) + 2.0*WwaterNaCl*Xwater - 0.5*WwaterNaCl -
>>> 1.45739430799067*(0.707106781186548*sqrt(XNa) + 0.707106781186548*sqrt(XCl
>>> + XNa))*(-XCl - XNa + 1)/(9.19238815542512*sqrt(XNa) +
>>> 9.19238815542512*sqrt(XCl + XNa) + 1) -
>>> 1.45739430799067*(0.707106781186548*sqrt(XNa) + 0.707106781186548*sqrt(XCl
>>> + XNa))*(-1.4142135623731*sqrt(XNa) - 1.4142135623731*sqrt(XCl + XNa) +
>>> 1)/(9.19238815542512*sqrt(XNa) + 9.19238815542512*sqrt(XCl + XNa) + 1) -
>>> 0.448429017843282*log(9.19238815542512*sqrt(XNa) +
>>> 9.19238815542512*sqrt(XCl + XNa) + 1)
>>> model_func = sp.lambdify(L, expr )
>>>
>>> def f(param):
>>> BNaCl = param[0]
>>> UwaterNaCl = param[1]
>>> VwaterNaCl = param[2]
>>> WwaterNaCl = param[3]
>>> Y_est = model_func
>>> return np.sum((np.log(Y) - Y_est)**2)
>>>
>>>
>>> bnds = [(1, np.inf), (0, 1), (0, 1), (-1, np.inf)]
>>> x0 = (1, 0.01, 0.98, 1)
>>> con = {"type": "eq", "fun": c}
>>>
>>> result = minimize(f, x0, bounds=bnds)
>>>
>>> print(result.fun)
>>> print(result.message)
>>> print(result.x[0], result.x[1], result.x[2], result.x[3])
>>>
>>> while I got :
>>> NameError: name 'sqrt' is not defined
>>>
>>> Zohreh Karimzadeh
>>> *https://www.researchgate.net/profile/Zohreh-Karimzadeh*
>>> <https://www.researchgate.net/profile/Zohreh-Karimzadeh>
>>> Skype Name 49a52224a8b6b38b
>>> Twitter Account @zohrehkarimzad1
>>> z.karimza...@gmail.com
>>> +989102116325
>>> ((((((((((((((((Value Water)))))))))))))))
>>>
>>>
>>> On Wed, Aug 17, 2022 at 7:46 PM Peter Stahlecker <
>>> peter.stahlec...@gmail.com> wrote:
>>>
>>>> I use lambdify(....) a lot, but always like this:
>>>>
>>>> x = sympy.symbols('x')
>>>> expr = symy.S(10.) * sympy.sqrt(x)
>>>> expr_lam = sympy.lambdify([x], expr)
>>>>
>>>> a = expr_lam(10.)
>>>>
>>>> This seems to work for me.
>>>>
>>>> On Wed 17. Aug 2022 at 20:38, Zohreh Karimzadeh <z.karimza...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dear sympy group
>>>>> Thanks for your sympy.
>>>>>
>>>>> I am working on a code, after creating my big expression using sympy
>>>>> it includes sqrt.
>>>>>
>>>>> I need to lambdify my expression to make it consistent with numpy and
>>>>> other suffs.
>>>>>
>>>>> expr =10 * sp.sqrt(sp.symbols('x'))
>>>>>
>>>>> model_func = sp.lambdify('x', expr)
>>>>>
>>>>> But I found my expression after lambdifying becomes somethings like
>>>>> this:
>>>>>
>>>>> 10*sqrt(x)
>>>>>
>>>>> while I need :
>>>>>
>>>>> 10*numpy.sqrt(x)
>>>>>
>>>>> Could possibly let me know how get sqrt to work with numpy?
>>>>>
>>>>> Regards,
>>>>>
>>>>> Zohreh
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> --
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>>>>> .
>>>>>
>>>> --
>>>> Best regards,
>>>>
>>>> Peter Stahlecker
>>>>
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>>>>
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>>
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Best regards,
Peter Stahlecker
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