Question

这就是我所拥有的

import numpy as np    
import scipy.special as sp

ni = input("Digite o valor N Inicial: ") #Ask for a initial N    
ne = input ("Digite o valor N Final: ") #Ask for Final N

vet= np.arange(ni,ne+1) #Arrange A Vector with the Ns given

x = np.linspace (-1, 1, 100)

def polinomios (vet, x):
    vetr = [0]*(ne-ni+1)
    for j in range (ne-ni+1):
        for p in range (ne-ni+1):
            vetr[p] = sp.legendre(vet[j])(x)

    return (vetr)

P = polinomios (vet, x)    
print P

我得到这样的

[array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ]), array([ 1.        , -0.24609375,  1.        ])]

我无法找到一种方法让它看起来像这样，但不要在第一个collunn上放下较小的数字：

[[-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]
 [-0.24609375  1.          1.        ]]

Answer 1

好的，我确实喜欢@askewchan说并使用了np.array（P）并且它有效。

[[ 1.         -0.5         1.        ]
 [-1.          0.          1.        ]
 [ 1.          0.375       1.        ]
 [-1.          0.          1.        ]
 [ 1.         -0.3125      1.        ]
 [-1.          0.          1.        ]
 [ 1.          0.2734375   1.        ]
 [-1.          0.          1.        ]
 [ 1.         -0.24609375  1.        ]]

使用np.sort排序但结果不正确

1 个答案: