"Stephen P. Molnar" <s.mol...@sbcglobal.net> writes: > I have an list generated by: s = np.linspace(start,finish,points) > > and an array D: > > [ 0. 2.059801 3.60937686 3.32591826 2.81569212] > [ 2.059801 0. 4.71452879 4.45776445 4.00467382] > [ 3.60937686 4.71452879 0. 5.66500917 5.26602175] > [ 3.32591826 4.45776445 5.66500917 0. 5.02324896] > [ 2.81569212 4.00467382 5.26602175 5.02324896 0. ] > > Now I can multiply the Array by one element of the list: > > s2 = 1.100334448160535050 (The first non-zero list element.) > s2_D = s2*np.array(D) > > which results in: > > [ 0. 2.26647 3.97152169 3.65962243 3.09820303] > [ 2.26647 0. 5.18755844 4.90503178 4.40648056] > [ 3.97152169 5.18755844 0. 6.23340474 5.79438514] > [ 3.65962243 4.90503178 6.23340474 0. 5.52725387] > [ 3.09820303 4.40648056 5.79438514 5.52725387 0. ] > > I checked this, rather laboriously, in a spreadsheet. > > However, what I want to do is multiply each element ob D by each > element of s and sum all of the products.
if I understand what you want to do, R_ij = sum_k(D_ij s_k) you can do it in a number of ways, say D has shape(i, j) and s has shape(k) Possibility 1 c = np.outer(D, s) # c has shape (i*j, k) as outer flattens its arguments c = sum(c, 1) # c has shape (i*j) because se summed over 2nd index c.reshape(D.shape) # c has the shape of D, i.e., (i, j) Possibility 2 # https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html stackoverflow.com/questions/26089893/understanding-numpys-einsum c = np.einsum('ij,k -> ij', D, s) _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: https://mail.python.org/mailman/listinfo/tutor
