average, bincount, ceil, clip, conj, conjugate, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue, sort, std, sum, trace, transpose, var, vdot, vectorize, where 。
average, bincount, ceil, clip, conj, conjugate, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue, sort, std, sum, trace, transpose, var, vdot, vectorize, where 参见:...
矩阵对象的属性: matrix.T transpose:返回矩阵的转置矩阵 matrix.H hermitian (conjugate) transpose:返回复数矩阵的共轭元素矩阵 matrix.I inverse:返回矩阵的逆矩阵 matrix.A base array:返回矩阵基于的数组 矩阵对象的方法: all([axis, out]) :沿给定的轴判断矩阵所有元素是否为真(非0即为真) any([axis, out...
average, bincount, ceil, clip, conj, conjugate, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue, sort, std, sum, trace, transpose, var, vdot, vectorize, where 参见:...
conjugate(), eig_vec)) components.append((exp_factor, proj)) return components Example #7Source File: m_c2r.py From pyscf with Apache License 2.0 6 votes def __init__(self, j): self._j = j self._c2r = np.zeros( (2*j+1, 2*j+1), dtype=np.complex128) self._c2r[j,j]=...
>>> c = array( [ [1,2], [3,4] ], dtype=complex )>>>c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) 通常,数组的元素开始都是未知的,但是它的大小已知。因此,NumPy提供了一些使用占位符创建数组的函数。这最小化了扩展数组的需要和高昂的运算代价。
getH()Returns the (complex) conjugate transpose of self. getI()Returns the (multiplicative) inverse of invertible self. getT()Returns the transpose of the matrix. getfield(dtype[, offset])Returns a field of the given array as a certain type. ...
HReturns the (complex) conjugate transpose of self. IReturns the (multiplicative) inverse of invertible self. 下面是从nadarray继承的属性 矩阵的属性属性说明 TReturns the transpose of the matrix. baseBase object if memory is from some other object. ...
proj = np.zeros((8,8), dtype=np.complex128) nontrivial_indices = np.array([3,5,6], dtype=np.intp) proj[nontrivial_indices[:, np.newaxis], nontrivial_indices] = ( np.outer(eig_vec.conjugate(), eig_vec)) components.append((exp_factor, proj))returncomponents ...
complex) # 生成一段噪音,长度是(2*nf+1)**2/2 noise = [np.complex(x, y) for x, y in np.random.uniform(-1,1,((2*nf+1)**2/2, 2))] # 傅里叶频谱的每一项和其共轭关于中心对称 noisy_block = np.concatenate((noise, [0j], np.conjugate(noise[::-1]))) # 将生成的频谱作为...