On Thursday, December 19, 2019 at 2:53:18 AM UTC-8, lampahome wrote:
> I meet performance is low when I use struct.unpack to unpack binary data.
>
> So I tried to use numpy.ndarray
> But meet error when I want to unpack multiple dtypes
>
> Can anyone teach me~
>
> Code like below:
> # python3
> import struct
> import numpy as np
> s1 = struct.Struct("@QIQ")
> ss1 = s1.pack(1,11,111)
> np.ndarray((3,), [('Q','I','Q')], ss1)
> # ValueError: mismatch in size of old and new data-descriptor.
Is there some reason that you're calling the np.ndarray constructor directly
instead of the np.array convenience function? The arguments that you pass to
each are slightly different. Generally, you want to use np.array.
The first argument to np.ndarray is the shape. You appear to want three
elements.
The second argument to np.ndarray is the data type (dtype). It needs to be a
SINGLE data type, like 'float' or 'int' or 'object'. All elements in in array
are of the same dtype. If you want different behavior, I think that you want
lists, not arrays. You can set the dtype to 'object' and you should be able to
store any data type as an array element. But I don't think that offers you any
advantages over using ordinary Python lists.
In any case, it appears that you aren't passing a dtype as your second
argument. You're trying to pass the data itself, as a tuple wrapped in a
single list. This seems to be certain to raise an error.
The third argument to np.ndarray is supposed to be the buffer which contains
your data.
https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy.array
ile:///usr/share/doc/python-numpy-doc/html/reference/generated/numpy.array.html#numpy.array
--
https://mail.python.org/mailman/listinfo/python-list
