我用从高斯分布中提取的值填充两个数组field_in_k_space_REAL
和field_in_k_space_IMAGINARY
,在对数组进行逆变换时,注意对称性以获得实场。代码如下:
field_in_k_space_REAL = zeros(n, float)
field_in_k_space_IMAGINARY = zeros(n, float)
field_in_k_space_REAL[0] = 0.0
for i in range(1, int(n/2+1)):
field_in_k_space_REAL[i] = np.random.normal(mu, math.sqrt((1/2)*math.exp(-(2*math.pi*i*sigma/L)*(2*math.pi*i*sigma/L))))
x = range(int(n/2+1), int(n))
y = range(1, int(n/2))
zipped = zip(x, y)
for j, j2 in zipped:
field_in_k_space_REAL[j] = field_in_k_space_REAL[j-2*j2]
field_in_k_space_IMAGINARY[0] = 0.0
for i in range(1, int(n/2)):
field_in_k_space_IMAGINARY[i] = np.random.normal(mu, math.sqrt((1/2)*math.exp(-(2*math.pi*i*sigma/L)*(2*math.pi*i*sigma/L))))
field_in_k_space_IMAGINARY[n/2] = 0.0
for j, j2 in zipped:
field_in_k_space_IMAGINARY[j] = - field_in_k_space_IMAGINARY[j-2*j2]
print 'field_k', field_in_k_space_REAL
但我一直有以下错误:
field_in_k_space_REAL[0] = 0.0
IndexError: index 0 is out of bounds for axis 0 with size 0
有人能解释一下为什么以及如何修复吗?
这是因为在您的close列中使用了NAN值。 例如:
我的猜测是数组
field_in_k_space_REAL
实际上是长度为0的,很可能是因为您在代码中进一步设置了n = 0
(您是否在循环中使用了n
?)。当我直接初始化长度为0的数组时,我可以重现错误。实际上,你最好使用:
而不是:
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