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 [email protected] +989102116325 ((((((((((((((((Value Water)))))))))))))))
On Thu, Aug 18, 2022 at 3:28 PM Peter Stahlecker <[email protected]> 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 <[email protected]> > 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 >> [email protected] >> +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 >> [email protected] >> +989102116325 >> ((((((((((((((((Value Water))))))))))))))) >> >> >> On Thu, Aug 18, 2022 at 10:42 AM Peter Stahlecker < >> [email protected]> 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 <[email protected]> >>> 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 >>>> [email protected] >>>> 🌧️🌍🌱 >>>> >>>> >>>> On Thu, 18 Aug 2022, 01:17 Aaron Meurer, <[email protected]> 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 < >>>>> [email protected]> 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 >>>>>> [email protected] >>>>>> +989102116325 >>>>>> >>>>>> ((((((((((((((((Value Water))))))))))))))) >>>>>> >>>>>> >>>>>> On Wed, Aug 17, 2022 at 7:46 PM Peter Stahlecker < >>>>>> [email protected]> 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 < >>>>>>> [email protected]> 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 [email protected]. >>>>>>>> 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 [email protected]. >>>>>>> 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 [email protected]. >>>>>> 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 [email protected]. >>>>> 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 [email protected]. >>>> 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 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