In your first return statement, where it works, you seem to return a number.
In your second return, your a ‚mixture‘ of numbers and functions:
Vlam_est is a *function*, which requires four arguments as per its
definition. Would you not have to return Vlam_est(alpha, beta, gamma, eta) ?
On Thu 18. Aug 2022 at 17:35 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.
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>>>>>>> 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
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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/CABKqA0ZoGwsadsk4SWCbJVMbCDwXcO_gNGumJH00GAeEFod7Cw%40mail.gmail.com
>>>>>> <https://groups.google.com/d/msgid/sympy/CABKqA0ZoGwsadsk4SWCbJVMbCDwXcO_gNGumJH00GAeEFod7Cw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>> .
>>>>>>
>>>>> --
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>>>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLPRvXZ6jiJbUS_xpWNKqMuUH7Kt5evue%2BwKEwDMvGekBQ%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>> .
>>>>
>>>>
>>>>> --
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>>>> <https://groups.google.com/d/msgid/sympy/CAKgW%3D6JfUmU7Uu%2BSrcA1STxVvWWm7bGWE%3Dit8CTchksTC0Qk7g%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>> .
>>>>
>>> --
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>>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLPiCR%3DS2Fac3FZtjMpspqB7BRKtYEi45BVWPjkizVbNvw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>> .
>>>
>> --
>> Best regards,
>>
>> Peter Stahlecker
>>
>> --
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> To view this discussion on the web visit
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>> <https://groups.google.com/d/msgid/sympy/CABKqA0b%3DF0akMH4oyg5%2By9dGvgrf_vvVJTnVhVduMP1f%2Bp1pFw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>> .
>>
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Best regards,
Peter Stahlecker
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