EVOLUTION-MANAGER

Edit File: interpnd_info.py

""" Here we perform some symbolic computations required for the N-D interpolation routines in `interpnd.pyx`. """ from __future__ import division, print_function, absolute_import from sympy import symbols, binomial, Matrix def _estimate_gradients_2d_global(): # # Compute # # f1, f2, df1, df2, x = symbols(['f1', 'f2', 'df1', 'df2', 'x']) c = [f1, (df1 + 3*f1)/3, (df2 + 3*f2)/3, f2] w = 0 for k in range(4): w += binomial(3, k) * c[k] * x**k*(1-x)**(3-k) wpp = w.diff(x, 2).expand() intwpp2 = (wpp**2).integrate((x, 0, 1)).expand() A = Matrix([[intwpp2.coeff(df1**2), intwpp2.coeff(df1*df2)/2], [intwpp2.coeff(df1*df2)/2, intwpp2.coeff(df2**2)]]) B = Matrix([[intwpp2.coeff(df1).subs(df2, 0)], [intwpp2.coeff(df2).subs(df1, 0)]]) / 2 print("A") print(A) print("B") print(B) print("solution") print(A.inv() * B)