从一维numpy数组中获取这种矩阵的最有效方法是什么？

Question

我有一个文件，共有4950值，例如：

0.012345678912345678

我使用以下文件读取文件：

a = numpy.genfromtxt(file_name, dtype=str, delimiter=',') # a.shape = (4950L, 1L) #dtype=str as I don't want to compromise accuracy
#say a == ['0.000000000000000001', -'0.000000000000000002', ...., '0.000000000004950']

我要实现的目标是获得大小为(100L, 100L)的矩阵b ：

1. 上三角值由numpy数组'a'中的值填充。
2. 下三角值由numpy数组'a'中的值填充，但乘以-1。
3. 对角线仅包含零。

示例（精度很重要）：

array = ['1','2','-3','-5','6','-7'] # In reality the data is up to 18 decimal places.

final_matrix = [
               ['0','1','2','-3'],
               ['-1',0,'-5','6'],
               ['-2','5','0','-7'],
               ['3','-6','7','0']
               ]

最有效的方法是什么？

Answer 1

不知道这是否是最有效的方法，但这似乎很有效。

import numpy

# create some random data for testing
sz = 100
a  = numpy.random.random(sz*sz/2 - sz/2).astype('S50')

# convert back to float for a test on minus signs,
# as it would be done if a is read as string values
amins = numpy.where(a.astype(float) <= 0, "", "-")

# get the values without minus signs
aplus = numpy.char.lstrip(a, "-")

# addup to negated string values
aminus = numpy.char.add(amins, aplus)

# create an empty matrix
m = numpy.zeros(shape=(sz,sz), dtype='S51')
# ids of the upper triangle
u_ids = numpy.triu_indices(sz,1)
# set upper values
m[u_ids] = a
# switch coordinates to set lower values
m[u_ids[1],u_ids[0]] = aminus
# fill diag with zeros
numpy.fill_diagonal(m, numpy.zeros(sz).astype('S51'))


print m