如何从字符串读取numpy二维数组? [英] how to read numpy 2D array from string?

问题描述

如何从字符串读取numpy数组?像这样的字符串:

how can I read a numpy array from a string? take a string like:

[[ 0.5544  0.4456], [ 0.8811  0.1189]]

并将其转换为数组:

a = from_string("[[ 0.5544  0.4456], [ 0.8811  0.1189]]")

其中a成为对象:np.array([[0.5544, 0.4456], [0.8811, 0.1189]])

更新:

我正在寻找一个非常简单的界面.将2D数组(浮点数)转换为字符串，然后将其读回以重建数组的方法:

i'm looking for a very simple interface. a way to convert 2D arrays (of floats) to a string and then a way to read them back to reconstruct the array:

arr_to_string(array([[0.5544, 0.4456], [0.8811, 0.1189]]))应该返回"[[ 0.5544 0.4456], [ 0.8811 0.1189]]"

string_to_arr("[[ 0.5544 0.4456], [ 0.8811 0.1189]]")应该返回对象array([[0.5544, 0.4456], [0.8811, 0.1189]])

理想情况下，如果arr_to_string具有一个精度参数来控制转换为字符串的浮点精度，那将很好，这样您就不会得到像0.4444444999999999999999999这样的条目.

ideally it would be great if arr_to_string had a precision parameter that controlled the precision of floating points converted to strings, so that you wouldn't get entries like 0.4444444999999999999999999.

在numpy的文档中我找不到任何可以同时执行这两种操作的文档. np.save允许您创建一个字符串，但是无法将其加载回(np.load仅适用于文件.)

there's nothing i can find in numpy docs that does this both ways. np.save lets you make a string but then there's no way to load it back in (np.load only works for files.)

推荐答案

挑战在于不仅要保存数据缓冲区，还要保存shape和dtype. np.fromstring读取数据缓冲区，但作为一维数组；您必须从其他位置获取dtype和形状.

The challenge is to save not only the data buffer, but also the shape and dtype. np.fromstring reads the data buffer, but as a 1d array; you have to get the dtype and shape from else where.

In [184]: a=np.arange(12).reshape(3,4)

In [185]: np.fromstring(a.tostring(),int)
Out[185]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

In [186]: np.fromstring(a.tostring(),a.dtype).reshape(a.shape)
Out[186]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

保存Python对象的一种受人尊敬的机制是pickle，而numpy是pickle兼容的:

A time honored mechanism to save Python objects is pickle, and numpy is pickle compliant:

In [169]: import pickle

In [170]: a=np.arange(12).reshape(3,4)

In [171]: s=pickle.dumps(a*2)

In [172]: s
Out[172]: "cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I3\nI4\ntp6\ncnumpy\ndtype\np7\n(S'i4'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'<'\np11\nNNNI-1\nI-1\nI0\ntp12\nbI00\nS'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x08\\x00\\x00\\x00\\n\\x00\\x00\\x00\\x0c\\x00\\x00\\x00\\x0e\\x00\\x00\\x00\\x10\\x00\\x00\\x00\\x12\\x00\\x00\\x00\\x14\\x00\\x00\\x00\\x16\\x00\\x00\\x00'\np13\ntp14\nb."

In [173]: pickle.loads(s)
Out[173]: 
array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

有一个numpy函数可以读取泡菜字符串:

There's a numpy function that can read the pickle string:

In [181]: np.loads(s)
Out[181]: 
array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

您在字符串中提到了np.save，但是您不能使用np.load.一种解决方法是进一步进入代码，并使用np.lib.npyio.format.

You mentioned np.save to a string, but that you can't use np.load. A way around that is to step further into the code, and use np.lib.npyio.format.

In [174]: import StringIO

In [175]: S=StringIO.StringIO()  # a file like string buffer

In [176]: np.lib.npyio.format.write_array(S,a*3.3)

In [177]: S.seek(0)   # rewind the string

In [178]: np.lib.npyio.format.read_array(S)
Out[178]: 
array([[  0. ,   3.3,   6.6,   9.9],
       [ 13.2,  16.5,  19.8,  23.1],
       [ 26.4,  29.7,  33. ,  36.3]])

save字符串的标头带有dtype和shape信息:

The save string has a header with dtype and shape info:

In [179]: S.seek(0)

In [180]: S.readlines()
Out[180]: 
["\x93NUMPY\x01\x00F\x00{'descr': '<f8', 'fortran_order': False, 'shape': (3, 4), }          \n",
 '\x00\x00\x00\x00\x00\x00\x00\x00ffffff\n',
 '@ffffff\x1a@\xcc\xcc\xcc\xcc\xcc\xcc#@ffffff*@\x00\x00\x00\x00\x00\x800@\xcc\xcc\xcc\xcc\xcc\xcc3@\x99\x99\x99\x99\x99\x197@ffffff:@33333\xb3=@\x00\x00\x00\x00\x00\x80@@fffff&B@']

如果您想要人类可读的字符串，则可以尝试json.

If you want a human readable string, you might try json.

In [196]: import json

In [197]: js=json.dumps(a.tolist())

In [198]: js
Out[198]: '[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]'

In [199]: np.array(json.loads(js))
Out[199]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

json最明显的用途是去/从数组的列表表示形式.可能有人写了一个更复杂的数组json表示形式.

Going to/from the list representation of the array is the most obvious use of json. Someone may have written a more elaborate json representation of arrays.

您也可以采用csv格式-读/写csv数组还有很多问题.

You could also go the csv format route - there have been lots of questions about reading/writing csv arrays.

'[[ 0.5544  0.4456], [ 0.8811  0.1189]]'

是用于此目的的较差的字符串表示形式.它看起来确实很像数组的str()，但是用,而不是\n.但是，没有一种解析嵌套[]的干净方法，缺少分隔符是很痛苦的.如果它始终使用,，则json可以将其转换为列表.

is a poor string representation for this purpose. It does look a lot like the str() of an array, but with , instead of \n. But there isn't a clean way of parsing the nested [], and the missing delimiter is a pain. If it consistently uses , then json can convert it to list.

np.matrix接受类似字符串的MATLAB:

np.matrix accepts a MATLAB like string:

In [207]: np.matrix(' 0.5544,  0.4456;0.8811,  0.1189')
Out[207]: 
matrix([[ 0.5544,  0.4456],
        [ 0.8811,  0.1189]])

In [208]: str(np.matrix(' 0.5544,  0.4456;0.8811,  0.1189'))
Out[208]: '[[ 0.5544  0.4456]\n [ 0.8811  0.1189]]'

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