It seems always an expression of parameters and independent variables is
needed to be passed to fit and find parameters.
Zohreh Karimzadeh
*https://www.researchgate.net/profile/Zohreh-Karimzadeh*
<https://www.researchgate.net/profile/Zohreh-Karimzadeh>
Skype Name 49a52224a8b6b38b
Twitter Account @zohrehkarimzad1
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((((((((((((((((Value Water)))))))))))))))
On Thu, Aug 18, 2022 at 3:28 PM Peter Stahlecker <peter.stahlec...@gmail.com>
wrote:
> 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
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
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>>>>>>>> To view this discussion on the web visit
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>>>>>>>> <https://groups.google.com/d/msgid/sympy/1f0b313f-31c5-402e-991e-142a556016f4n%40googlegroups.com?utm_medium=email&utm_source=footer>
>>>>>>>> .
>>>>>>>>
>>>>>>> --
>>>>>>> Best regards,
>>>>>>>
>>>>>>> Peter Stahlecker
>>>>>>>
>>>>>>> --
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>>>>>>> <https://groups.google.com/d/msgid/sympy/CABKqA0ZoGwsadsk4SWCbJVMbCDwXcO_gNGumJH00GAeEFod7Cw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>>>>>>> .
>>>>>>>
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>>>>>> To view this discussion on the web visit
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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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>>>> .
>>>>
>>> --
>>> Best regards,
>>>
>>> Peter Stahlecker
>>>
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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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>>>
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>> <https://groups.google.com/d/msgid/sympy/CA%2B1XYLMK-fgpxc71GYzue5gJvd%3Dfj2sV6Dvhj8zrmVpPhiVk%2Bw%40mail.gmail.com?utm_medium=email&utm_source=footer>
>> .
>>
> --
> Best regards,
>
> Peter Stahlecker
>
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