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>
>>>>>>>> .
>>>>>>>>
>>>>>>> --
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>>>>>>> Groups "sympy" group.
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>>>>>>> send an email to sympy+unsubscr...@googlegroups.com.
>>>>>>> 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>
>>>>>>> .
>>>>>>
>>>>>>
>>>>>>> --
>>>>>> 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
>>>>
>>>> --
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>>>> To unsubscribe from this group and stop receiving emails from it, send
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>>>> 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>
>>>> .
>>>>
>>> --
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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>
>>> .
>>
>>
>>> --
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>> <https://groups.google.com/d/msgid/sympy/CAKgW%3D6%2BBQo_nWKJtbxPmi40V0Y6OgAaT78jSNSWKnwW8L3qmZQ%40mail.gmail.com?utm_medium=email&utm_source=footer>
>> .
>>
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--
Best regards,
Peter Stahlecker
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