Thank millions I will give it a try.
Zohreh Karimzadeh
Contact me on
+989102116325
and at
z.karimza...@gmail.com
🌧️🌍🌱
On Sat, 20 Aug 2022, 11:32 Peter Stahlecker, <peter.stahlec...@gmail.com>
wrote:
> args is a standard key word with minimize, just look it up to see how it
> works.
> Say, you have F(a, d, c, m ,e, f) as a function.
> If you give the ‚starting guess‘ as x0 = (1, ,2, 3), it will minimize the
> function by varying the first three variables in the parameter list of F,
> that is a, d, c.
> Balance parameters you have to give with the args keyword.
> If you read the minimizer operations instructions, it will tell you how
> minimizer hands over the values in args to your function F. And your F must
> ‚understand this‘.
>
> On Sat 20. Aug 2022 at 13:24 Zohreh Karimzadeh <z.karimza...@gmail.com>
> wrote:
>
>> You mean I should make independent variables acceptable as args to
>> minimizer,?
>>
>>
>> Zohreh Karimzadeh
>>
>> Contact me on
>> +989102116325
>> and at
>> z.karimza...@gmail.com
>> 🌧️🌍🌱
>>
>>
>> On Sat, 20 Aug 2022, 09:35 Peter Stahlecker, <peter.stahlec...@gmail.com>
>> wrote:
>>
>>> But would then this not be a simpler way, without any lambdify ?
>>>
>>> def func_to_be_minimized(alpha, beta, gamma, eta, L, K, VA):
>>> ……
>>> ……
>>> return np.sum(…….)
>>>
>>> X0 = (…..)
>>> args = (L. K, VA)
>>> resultat = minimize(func_to_be_minimized, X0, args = args, bounds =…)
>>>
>>> args contains the variables of your function, which will not be
>>> minimized.
>>>
>>> You may have to play around with args = (L. K, VA) a bit, because
>>> minimize ‚hands over‘ whatever is in args to your function in a certain
>>> way, and your function must accept them this way. (I never know exactly how
>>> to do it, as I do not use it often at all, so I just play around until it
>>> works)
>>>
>>>
>>>
>>> On Sat 20. Aug 2022 at 11:48 Zohreh Karimzadeh <z.karimza...@gmail.com>
>>> wrote:
>>>
>>>> Exactly
>>>>
>>>> Zohreh Karimzadeh
>>>>
>>>> Contact me on
>>>> +989102116325
>>>> and at
>>>> z.karimza...@gmail.com
>>>> 🌧️🌍🌱
>>>>
>>>>
>>>> On Sat, 20 Aug 2022, 06:15 Peter Stahlecker, <
>>>> peter.stahlec...@gmail.com> wrote:
>>>>
>>>>> Maybe a dumb question from my part:
>>>>>
>>>>> do I understand you correctly:
>>>>>
>>>>> For *given* L, K, VA you try to find the alpha, beta, gamma, eta
>>>>> which minimize
>>>>> the function np.sum(….) ?
>>>>>
>>>>> Is my understanding correct?
>>>>>
>>>>> On Fri 19. Aug 2022 at 22:11 Zohreh Karimzadeh <z.karimza...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I am new at python and using your comment seems hard to me could
>>>>>> possibly let me know know it by example or any key words if is there.
>>>>>>
>>>>>> Zohreh Karimzadeh
>>>>>>
>>>>>> Contact me on
>>>>>> +989102116325
>>>>>> and at
>>>>>> z.karimza...@gmail.com
>>>>>> 🌧️🌍🌱
>>>>>>
>>>>>>
>>>>>> On Fri, 19 Aug 2022, 00:03 Aaron Meurer, <asmeu...@gmail.com> wrote:
>>>>>>
>>>>>>> Instead of generating a separate lambdified function for every
>>>>>>> input, you may find it simpler to lambdify a single function with your
>>>>>>> params as extra symbolic parameters, then pass those in using the args()
>>>>>>> argument to minimize().
>>>>>>>
>>>>>>> Aaron Meurer
>>>>>>>
>>>>>>> On Thu, Aug 18, 2022 at 4:35 AM Zohreh Karimzadeh <
>>>>>>> z.karimza...@gmail.com> wrote:
>>>>>>>
>>>>>>>> the following code is ok when expression is passed as :
>>>>>>>>
>>>>>>>> import numpy as np
>>>>>>>> from scipy.optimize import minimize, curve_fit
>>>>>>>> from lmfit import Model, Parameters
>>>>>>>>
>>>>>>>> L = np.array([0.299, 0.295, 0.290, 0.284, 0.279, 0.273, 0.268, 0.262,
>>>>>>>> 0.256, 0.250])
>>>>>>>> K = np.array([2.954, 3.056, 3.119, 3.163, 3.215, 3.274, 3.351, 3.410,
>>>>>>>> 3.446, 3.416])
>>>>>>>> VA = np.array([0.919, 0.727, 0.928, 0.629, 0.656, 0.854, 0.955, 0.981,
>>>>>>>> 0.908, 0.794])
>>>>>>>>
>>>>>>>>
>>>>>>>> def f(param):
>>>>>>>> gamma = param[0]
>>>>>>>> alpha = param[1]
>>>>>>>> beta = param[2]
>>>>>>>> eta = param[3]
>>>>>>>> VA_est = gamma - (1 / eta) * np.log(alpha * L ** -eta + beta * K
>>>>>>>> ** -eta)
>>>>>>>>
>>>>>>>> return np.sum((np.log(VA) - VA_est) ** 2)
>>>>>>>>
>>>>>>>>
>>>>>>>> bnds = [(1, np.inf), (0, 1), (0, 1), (-1, np.inf)]
>>>>>>>> x0 = (1, 0.01, 0.98, 1)
>>>>>>>> result = minimize(f, x0, bounds=bnds)
>>>>>>>> print(result.message)
>>>>>>>> print(result.x[0], result.x[1], result.x[2], result.x[3])
>>>>>>>>
>>>>>>>> but when the expression is passed as the following way:
>>>>>>>>
>>>>>>>> import numpy as np
>>>>>>>> import sympy as sp
>>>>>>>> from scipy.optimize import minimize, curve_fit
>>>>>>>> from lmfit import Model, Parameters
>>>>>>>>
>>>>>>>> L = np.array([0.299, 0.295, 0.290, 0.284, 0.279, 0.273, 0.268, 0.262,
>>>>>>>> 0.256, 0.250])
>>>>>>>> K = np.array([2.954, 3.056, 3.119, 3.163, 3.215, 3.274, 3.351, 3.410,
>>>>>>>> 3.446, 3.416])
>>>>>>>> VA = np.array([0.919, 0.727, 0.928, 0.629, 0.656, 0.854, 0.955, 0.981,
>>>>>>>> 0.908, 0.794])
>>>>>>>>
>>>>>>>>
>>>>>>>> def f(param):
>>>>>>>> gamma, alpha, beta, eta = sp.symbols('gamma, alpha, beta, eta')
>>>>>>>> gamma = param[0]
>>>>>>>> alpha = param[1]
>>>>>>>> beta = param[2]
>>>>>>>> eta = param[3]
>>>>>>>> Vi_est = gamma - (1 / eta) * sp.log(alpha * L ** -eta + beta * K
>>>>>>>> ** -eta)
>>>>>>>> Vlam_est = sp.lambdify((gamma, alpha, beta, eta), Vi_est)
>>>>>>>>
>>>>>>>> return np.sum((np.log(VA) - Vlam_est) ** 2)
>>>>>>>>
>>>>>>>>
>>>>>>>> bnds = [(1, np.inf), (0, 1), (0, 1), (-1, np.inf)]
>>>>>>>> x0 = (1, 0.01, 0.98, 1)
>>>>>>>>
>>>>>>>> result = minimize(f, x0, bounds=bnds)
>>>>>>>>
>>>>>>>> print(result.message)
>>>>>>>> print(result.x[0], result.x[1], result.x[2], result.x[3])
>>>>>>>>
>>>>>>>>
>>>>>>>> I face difficulty:
>>>>>>>> *********************************************
>>>>>>>> Traceback (most recent call last):
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\cache.py",
>>>>>>>> line 70, in wrapper
>>>>>>>> retval = cfunc(*args, **kwargs)
>>>>>>>> TypeError: unhashable type: 'numpy.ndarray'
>>>>>>>>
>>>>>>>> During handling of the above exception, another exception occurred:
>>>>>>>>
>>>>>>>> Traceback (most recent call last):
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\cache.py",
>>>>>>>> line 70, in wrapper
>>>>>>>> retval = cfunc(*args, **kwargs)
>>>>>>>> TypeError: unhashable type: 'numpy.ndarray'
>>>>>>>>
>>>>>>>> During handling of the above exception, another exception occurred:
>>>>>>>>
>>>>>>>> Traceback (most recent call last):
>>>>>>>> File
>>>>>>>> "F:\Zohreh\MainZohreh\postdoc-field\CSU\pythonProject\fit_test_2.py",
>>>>>>>> line
>>>>>>>> 26, in <module>
>>>>>>>> result = minimize(f, x0, bounds=bnds)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_minimize.py",
>>>>>>>> line 692, in minimize
>>>>>>>> res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_lbfgsb_py.py",
>>>>>>>> line 308, in _minimize_lbfgsb
>>>>>>>> sf = _prepare_scalar_function(fun, x0, jac=jac, args=args,
>>>>>>>> epsilon=eps,
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_optimize.py",
>>>>>>>> line 263, in _prepare_scalar_function
>>>>>>>> sf = ScalarFunction(fun, x0, args, grad, hess,
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_differentiable_functions.py",
>>>>>>>> line 158, in __init__
>>>>>>>> self._update_fun()
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_differentiable_functions.py",
>>>>>>>> line 251, in _update_fun
>>>>>>>> self._update_fun_impl()
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_differentiable_functions.py",
>>>>>>>> line 155, in update_fun
>>>>>>>> self.f = fun_wrapped(self.x)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\scipy\optimize\_differentiable_functions.py",
>>>>>>>> line 137, in fun_wrapped
>>>>>>>> fx = fun(np.copy(x), *args)
>>>>>>>> File
>>>>>>>> "F:\Zohreh\MainZohreh\postdoc-field\CSU\pythonProject\fit_test_2.py",
>>>>>>>> line
>>>>>>>> 17, in f
>>>>>>>> Vi_est = gamma - (1 / eta) * sp.log(alpha * L ** -eta + beta *
>>>>>>>> K ** -eta)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\cache.py",
>>>>>>>> line 74, in wrapper
>>>>>>>> retval = func(*args, **kwargs)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\function.py",
>>>>>>>> line 476, in __new__
>>>>>>>> result = super().__new__(cls, *args, **options)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\cache.py",
>>>>>>>> line 74, in wrapper
>>>>>>>> retval = func(*args, **kwargs)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\core\function.py",
>>>>>>>> line 288, in __new__
>>>>>>>> evaluated = cls.eval(*args)
>>>>>>>> File
>>>>>>>> "C:\Users\Zohreh\AppData\Roaming\Python\Python310\site-packages\sympy\functions\elementary\exponential.py",
>>>>>>>> line 718, in eval
>>>>>>>> coeff = arg.as_coefficient(I)
>>>>>>>> AttributeError: 'ImmutableDenseNDimArray' object has no attribute
>>>>>>>> 'as_coefficient'
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> Zohreh Karimzadeh
>>>>>>>> https://www.researchgate.net/profile/Zohreh-Karimzadeh
>>>>>>>> Skype Name 49a52224a8b6b38b
>>>>>>>> Twitter Account @zohrehkarimzad1
>>>>>>>> z.karimza...@gmail.com
>>>>>>>> +989102116325
>>>>>>>>
>>>>>>>> ((((((((((((((((Value Water)))))))))))))))
>>>>>>>>
>>>>>>>> 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 Thu, Aug 18, 2022 at 10:42 AM Peter Stahlecker <
>>>>>>>> peter.stahlec...@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> 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
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> --
>>>>>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>>>>>> Google Groups "sympy" group.
>>>>>>>>>>>>>> To unsubscribe from this group and stop receiving emails from
>>>>>>>>>>>>>> it, send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>>>>>> https://groups.google.com/d/msgid/sympy/1f0b313f-31c5-402e-991e-142a556016f4n%40googlegroups.com
>>>>>>>>>>>>>> <https://groups.google.com/d/msgid/sympy/1f0b313f-31c5-402e-991e-142a556016f4n%40googlegroups.com?utm_medium=email&utm_source=footer>
>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>> Best regards,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Peter Stahlecker
>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>>>>> Google Groups "sympy" group.
>>>>>>>>>>>>> To unsubscribe from this group and stop receiving emails from
>>>>>>>>>>>>> it, send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>>>>> https://groups.google.com/d/msgid/sympy/CABKqA0ZoGwsadsk4SWCbJVMbCDwXcO_gNGumJH00GAeEFod7Cw%40mail.gmail.com
>>>>>>>>>>>>> <https://groups.google.com/d/msgid/sympy/CABKqA0ZoGwsadsk4SWCbJVMbCDwXcO_gNGumJH00GAeEFod7Cw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>>>>>>> .
>>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>>>> Google Groups "sympy" group.
>>>>>>>>>>>> To unsubscribe from this group and stop receiving emails from
>>>>>>>>>>>> it, send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>>>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLPRvXZ6jiJbUS_xpWNKqMuUH7Kt5evue%2BwKEwDMvGekBQ%40mail.gmail.com
>>>>>>>>>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLPRvXZ6jiJbUS_xpWNKqMuUH7Kt5evue%2BwKEwDMvGekBQ%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>>>>>> .
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>>> Google Groups "sympy" group.
>>>>>>>>>>> To unsubscribe from this group and stop receiving emails from
>>>>>>>>>>> it, send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>>> https://groups.google.com/d/msgid/sympy/CAKgW%3D6JfUmU7Uu%2BSrcA1STxVvWWm7bGWE%3Dit8CTchksTC0Qk7g%40mail.gmail.com
>>>>>>>>>>> <https://groups.google.com/d/msgid/sympy/CAKgW%3D6JfUmU7Uu%2BSrcA1STxVvWWm7bGWE%3Dit8CTchksTC0Qk7g%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>>>>> .
>>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>> Google Groups "sympy" group.
>>>>>>>>>> To unsubscribe from this group and stop receiving emails from it,
>>>>>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLPiCR%3DS2Fac3FZtjMpspqB7BRKtYEi45BVWPjkizVbNvw%40mail.gmail.com
>>>>>>>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLPiCR%3DS2Fac3FZtjMpspqB7BRKtYEi45BVWPjkizVbNvw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>>>> .
>>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Best regards,
>>>>>>>>>
>>>>>>>>> Peter Stahlecker
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> You received this message because you are subscribed to the Google
>>>>>>>>> Groups "sympy" group.
>>>>>>>>> To unsubscribe from this group and stop receiving emails from it,
>>>>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>>> To view this discussion on the web visit
>>>>>>>>> https://groups.google.com/d/msgid/sympy/CABKqA0b%3DF0akMH4oyg5%2By9dGvgrf_vvVJTnVhVduMP1f%2Bp1pFw%40mail.gmail.com
>>>>>>>>> <https://groups.google.com/d/msgid/sympy/CABKqA0b%3DF0akMH4oyg5%2By9dGvgrf_vvVJTnVhVduMP1f%2Bp1pFw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>>> .
>>>>>>>>>
>>>>>>>> --
>>>>>>>> You received this message because you are subscribed to the Google
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>>>>>>>> To unsubscribe from this group and stop receiving emails from it,
>>>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>>> To view this discussion on the web visit
>>>>>>>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLMK-fgpxc71GYzue5gJvd%3Dfj2sV6Dvhj8zrmVpPhiVk%2Bw%40mail.gmail.com
>>>>>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLMK-fgpxc71GYzue5gJvd%3Dfj2sV6Dvhj8zrmVpPhiVk%2Bw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>>> .
>>>>>>>
>>>>>>>
>>>>>>>> --
>>>>>>> You received this message because you are subscribed to the Google
>>>>>>> Groups "sympy" group.
>>>>>>> To unsubscribe from this group and stop receiving emails from it,
>>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>> To view this discussion on the web visit
>>>>>>> https://groups.google.com/d/msgid/sympy/CAKgW%3D6%2BBQo_nWKJtbxPmi40V0Y6OgAaT78jSNSWKnwW8L3qmZQ%40mail.gmail.com
>>>>>>> <https://groups.google.com/d/msgid/sympy/CAKgW%3D6%2BBQo_nWKJtbxPmi40V0Y6OgAaT78jSNSWKnwW8L3qmZQ%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>> .
>>>>>>>
>>>>>> --
>>>>>> You received this message because you are subscribed to the Google
>>>>>> Groups "sympy" group.
>>>>>> To unsubscribe from this group and stop receiving emails from it,
>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>> To view this discussion on the web visit
>>>>>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLNg4ScH5bK%3DeW7%3DVL_2%2BWdWT4kf5ud41LR5hh%2BJkCJR2g%40mail.gmail.com
>>>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLNg4ScH5bK%3DeW7%3DVL_2%2BWdWT4kf5ud41LR5hh%2BJkCJR2g%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>> .
>>>>>>
>>>>> --
>>>>> Best regards,
>>>>>
>>>>> Peter Stahlecker
>>>>>
>>>> --
>>>>> You received this message because you are subscribed to the Google
>>>>> Groups "sympy" group.
>>>>> To unsubscribe from this group and stop receiving emails from it, send
>>>>> an email to sympy+unsubscr...@googlegroups.com.
>>>>> To view this discussion on the web visit
>>>>> https://groups.google.com/d/msgid/sympy/CABKqA0aP_Rx9rDQ-%2BumFq_9W8C4LDTRMGrFif_5GZ_dRTpA2VA%40mail.gmail.com
>>>>> <https://groups.google.com/d/msgid/sympy/CABKqA0aP_Rx9rDQ-%2BumFq_9W8C4LDTRMGrFif_5GZ_dRTpA2VA%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>> .
>>>>>
>>>> --
>>>> You received this message because you are subscribed to the Google
>>>> Groups "sympy" group.
>>>> To unsubscribe from this group and stop receiving emails from it, send
>>>> an email to sympy+unsubscr...@googlegroups.com.
>>>> To view this discussion on the web visit
>>>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLOmpYg0iHjow4D5hvp_%3DFSfmKEgaWBiDFoweEX4Vo4jxw%40mail.gmail.com
>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLOmpYg0iHjow4D5hvp_%3DFSfmKEgaWBiDFoweEX4Vo4jxw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>> .
>>>>
>>> --
>>> Best regards,
>>>
>>> Peter Stahlecker
>>>
>>> --
>>> You received this message because you are subscribed to the Google
>>> Groups "sympy" group.
>>> To unsubscribe from this group and stop receiving emails from it, send
>>> an email to sympy+unsubscr...@googlegroups.com.
>>>
>> To view this discussion on the web visit
>>> https://groups.google.com/d/msgid/sympy/CABKqA0YJ7gvfrAKV4%2B%2BWU%3Dd_MP_%2BUMDAeRh434KdFesS%3DxiAvA%40mail.gmail.com
>>> <https://groups.google.com/d/msgid/sympy/CABKqA0YJ7gvfrAKV4%2B%2BWU%3Dd_MP_%2BUMDAeRh434KdFesS%3DxiAvA%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>> .
>>>
>> --
>> You received this message because you are subscribed to the Google Groups
>> "sympy" group.
>> To unsubscribe from this group and stop receiving emails from it, send an
>> email to sympy+unsubscr...@googlegroups.com.
>> To view this discussion on the web visit
>> https://groups.google.com/d/msgid/sympy/CA%2B1XYLP8YcB6puF4Tg0BVOUKv8wCHnE1o113AeKXoMZnJ-NfpA%40mail.gmail.com
>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLP8YcB6puF4Tg0BVOUKv8wCHnE1o113AeKXoMZnJ-NfpA%40mail.gmail.com?utm_medium=email&utm_source=footer>
>> .
>>
> --
> Best regards,
>
> Peter Stahlecker
>
> --
> You received this message because you are subscribed to the Google Groups
> "sympy" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to sympy+unsubscr...@googlegroups.com.
> To view this discussion on the web visit
> https://groups.google.com/d/msgid/sympy/CABKqA0ZZCaRbYjVVhEHs1mdnVtrbXr8kYR8Vk8_ZQ81YV%2Btg_Q%40mail.gmail.com
> <https://groups.google.com/d/msgid/sympy/CABKqA0ZZCaRbYjVVhEHs1mdnVtrbXr8kYR8Vk8_ZQ81YV%2Btg_Q%40mail.gmail.com?utm_medium=email&utm_source=footer>
> .
>
--
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