how to read numpy 2D array from string?

Question

How can I read a Numpy array from a string? Take a string like:

"[[ 0.5544  0.4456], [ 0.8811  0.1189]]"

and convert it to an array:

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

where a becomes the object: np.array([[0.5544, 0.4456], [0.8811, 0.1189]]).

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]])) should return "[[ 0.5544 0.4456], [ 0.8811 0.1189]]".

string_to_arr("[[ 0.5544 0.4456], [ 0.8811 0.1189]]") should return the object array([[0.5544, 0.4456], [0.8811, 0.1189]]).

Ideally arr_to_string would have a precision parameter that controlled the precision of floating points converted to strings, so that you wouldn't get entries like 0.4444444999999999999999999.

There's nothing I can find in the 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).

Answer 1

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]])

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]])

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]])

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]])

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@']

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]])

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.

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]]'

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 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]]'

Answer 2

Forward to string:

import numpy as np
def array2str(arr, precision=None):
    s=np.array_str(arr, precision=precision)
    return s.replace('\n', ',')

Backward to array:

import re
import ast
import numpy as np
def str2array(s):
    # Remove space after [
    s=re.sub('\[ +', '[', s.strip())
    # Replace commas and spaces
    s=re.sub('[,\s]+', ', ', s)
    return np.array(ast.literal_eval(s))

If you use repr() to convert array to string, the conversion will be trivial.