我已將 .m 檔案轉換為 python 檔案。.m 檔案由一個矩陣組成；這是一個名為“f”的變數的函式。現在我想把這個矩陣轉換成 numpy 陣列，這樣我就可以為不同的“f”值計算矩陣的條件數。我使用了一個名為 matlab2python 的 github 存盤庫將 .m 檔案轉換為 .py 檔案。轉換后，我嘗試對 numpy 陣列執行一些操作，但我面臨值錯誤。下面是我嘗試過的代碼。現在我。
import numpy as np
from sympy import *
f=symbols('f')
Rarz = eval(np.array([[(- 640227.0) np.multiply(195.539,f ** 2),4211.96 np.multiply((- 6.45961e-14),f ** 2),(- 5469.12) np.multiply((- 6.30001e-14),f ** 2),(- 7178.23) np.multiply((- 1.0631e-12),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.683542,0.0,0.151787],[4211.96 np.multiply((- 6.45961e-14),f ** 2),(- 10147500.0) np.multiply(193.704,f ** 2),(- 2958.16) np.multiply((- 3.10464e-13),f ** 2),(- 3882.6) np.multiply(1.14274e-12,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.369718,0.0,(- 0.947078)],[(- 5469.12) np.multiply((- 6.30001e-14),f ** 2),(- 2958.16) np.multiply((- 3.10464e-13),f ** 2),(- 51372700.0) np.multiply(193.708,f ** 2),5041.45 np.multiply((- 2.10639e-12),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 0.480068),0.0,(- 1.22348)],[(- 7178.23) np.multiply((- 1.0631e-12),f ** 2),(- 3882.6) np.multiply(1.14274e-12,f ** 2),5041.45 np.multiply((- 2.10639e-12),f ** 2),(- 162352000.0) np.multiply(193.695,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 0.630091),0.0,1.0085],[0.0,0.0,0.0,0.0,(- 1629440000.0) np.multiply(154.953,f ** 2),(- 11753.4) np.multiply(0.000111686,f ** 2),25800.5 np.multiply((- 6.59562e-05),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 9.50724e-06)],[0.0,0.0,0.0,0.0,(- 11753.4) np.multiply(0.000111686,f ** 2),(- 12381300000.0) np.multiply(154.953,f ** 2),(- 73445.5) np.multiply(5.30086e-05,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.27121e-05],[0.0,0.0,0.0,0.0,25800.5 np.multiply((- 6.59562e-05),f ** 2),(- 73445.5) np.multiply(5.30086e-05,f ** 2),(- 47583200000.0) np.multiply(154.953,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,3.63798e-12,(- 0.000219237)],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 1239040000.0) np.multiply(194.525,f ** 2),969793000.0 np.multiply((- 151.126),f ** 2),0.0,0.0,0.0,0.0,(- 0.680346),0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,969793000.0 np.multiply((- 151.126),f ** 2),(- 4199830000.0) np.multiply(227.587,f ** 2),0.0,0.0,0.0,0.0,1.33685e-12,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 12337000000.0) np.multiply(77.4764,f ** 2),0.0,0.0,(- 1.22465e-16),0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 49348000000.0) np.multiply(77.4764,f ** 2),0.0,2.44929e-16,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 111033000000.0) np.multiply(77.4764,f ** 2),(- 3.67394e-16),0.0,0.0],[0.683542,0.369718,(- 0.480068),(- 0.630091),0.0,0.0,0.0,0.0,0.0,(- 1.22465e-16),2.44929e-16,(- 3.67394e-16),0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.63798e-12,(- 0.680346),1.33685e-12,0.0,0.0,0.0,0.0,0.0,0.0],[0.151787,(- 0.947078),(- 1.22348),1.0085,(- 9.50724e-06),9.27121e-05,(- 0.000219237),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]]))
kk=Rarz.subs({f:2})
print(kk)
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由于您對純數字結果感興趣，因此在這種情況下您不需要使用 SymPy。我們可以使用eval，傳入一個表示運算式的字串，以及一個包含運算式中變數值的字典：
import numpy as np
s = """np.array([[(- 640227.0) np.multiply(195.539,f ** 2),4211.96 np.multiply((- 6.45961e-14),f ** 2),(- 5469.12) np.multiply((- 6.30001e-14),f ** 2),(- 7178.23) np.multiply((- 1.0631e-12),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.683542,0.0,0.151787],[4211.96 np.multiply((- 6.45961e-14),f ** 2),(- 10147500.0) np.multiply(193.704,f ** 2),(- 2958.16) np.multiply((- 3.10464e-13),f ** 2),(- 3882.6) np.multiply(1.14274e-12,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.369718,0.0,(- 0.947078)],[(- 5469.12) np.multiply((- 6.30001e-14),f ** 2),(- 2958.16) np.multiply((- 3.10464e-13),f ** 2),(- 51372700.0) np.multiply(193.708,f ** 2),5041.45 np.multiply((- 2.10639e-12),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 0.480068),0.0,(- 1.22348)],[(- 7178.23) np.multiply((- 1.0631e-12),f ** 2),(- 3882.6) np.multiply(1.14274e-12,f ** 2),5041.45 np.multiply((- 2.10639e-12),f ** 2),(- 162352000.0) np.multiply(193.695,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 0.630091),0.0,1.0085],[0.0,0.0,0.0,0.0,(- 1629440000.0) np.multiply(154.953,f ** 2),(- 11753.4) np.multiply(0.000111686,f ** 2),25800.5 np.multiply((- 6.59562e-05),f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 9.50724e-06)],[0.0,0.0,0.0,0.0,(- 11753.4) np.multiply(0.000111686,f ** 2),(- 12381300000.0) np.multiply(154.953,f ** 2),(- 73445.5) np.multiply(5.30086e-05,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,0.0,9.27121e-05],[0.0,0.0,0.0,0.0,25800.5 np.multiply((- 6.59562e-05),f ** 2),(- 73445.5) np.multiply(5.30086e-05,f ** 2),(- 47583200000.0) np.multiply(154.953,f ** 2),0.0,0.0,0.0,0.0,0.0,0.0,3.63798e-12,(- 0.000219237)],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 1239040000.0) np.multiply(194.525,f ** 2),969793000.0 np.multiply((- 151.126),f ** 2),0.0,0.0,0.0,0.0,(- 0.680346),0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,969793000.0 np.multiply((- 151.126),f ** 2),(- 4199830000.0) np.multiply(227.587,f ** 2),0.0,0.0,0.0,0.0,1.33685e-12,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 12337000000.0) np.multiply(77.4764,f ** 2),0.0,0.0,(- 1.22465e-16),0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 49348000000.0) np.multiply(77.4764,f ** 2),0.0,2.44929e-16,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,(- 111033000000.0) np.multiply(77.4764,f ** 2),(- 3.67394e-16),0.0,0.0],[0.683542,0.369718,(- 0.480068),(- 0.630091),0.0,0.0,0.0,0.0,0.0,(- 1.22465e-16),2.44929e-16,(- 3.67394e-16),0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.63798e-12,(- 0.680346),1.33685e-12,0.0,0.0,0.0,0.0,0.0,0.0],[0.151787,(- 0.947078),(- 1.22348),1.0085,(- 9.50724e-06),9.27121e-05,(- 0.000219237),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]])"""
t = eval(s, {"f": 2, "np": np})
print(t)
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