numpy already has a MATLAB-like sting parsing matrix creating function,
buried in the "Matrix" class:
In [1]: import numpy as np
In [2]: np.mat('1 2 3; 4 5 6')
Out[2]:
matrix([[1, 2, 3],
[4, 5, 6]])
That gives you a Matrix, not an array, butyou could wrap it in a little
utility to convert.
But:
1) Matrix is pretty much being phased out -- with the @ operator, it has
little use
2) Hardly anyone ever uses is anyway.
Yes, it's a bit more typing to have to use nested lists for literals, and
that does make a notable difference in a REPL, but still not a big deal
--how often do you need to type, at a REPL, a large array??
if you are doing that a lot, you really need a better workflow!
-CHB
On Sat, Nov 9, 2019 at 2:51 PM Todd <toddr...@gmail.com> wrote:
> I am pretty sure this is a backwards incompatible change. It isn't likely
> syntax, but I think it is possible.
>
> I also don't like having to wait until the end of the expression to find
> out it isn't a list. And also seems like it would be easy to miss in a
> non-trivial case.
>
> How would you be parsed?
>
> a = [1, 2, 3;
> 4, 5, 6]
>
>
> On Fri, Nov 8, 2019, 09:13 <yejus...@163.com> wrote:
>
>> In recent years, python has become very popular due to the rise of data
>> science and machine learning. This is mainly because Python is easy to
>> learn and has a large number of third-party libraries, thus accumulating a
>> large number of users.
>> When Python is applied to scientific computing, there are two
>> problems. One is that Python itself is not fast enough, and the other is
>> that matrix is not a basic data type. The first problem can be well solved
>> by rewriting key codes in C/C++, or by using numba. For the second one,
>> people have invented Numpy which has become the actual matrix computing
>> standard in Python. Although it can do linear algebra, limited by the
>> syntax of Python, using Numpy to initialize a matrix is always not simple
>> enough. We have to do it like this:
>> import numpy as np
>> a=np.array([1,2,3])
>> b=np.array([[1,2,3],[4,5,6]])
>> While, you know, in Matlab and Julia(A new ambitious and interesting
>> language) it is in this way:
>> a=[1,2,3] or a=[1 2 3]
>> b=[1,2,3;4,5,6] or b=[1 2 3;4 5 6]
>>
>> Of course, python, as a general-purpose language, is not limited to
>> scientific computing, but also used for crawlers, web development, and even
>> writing GUI programs. Therefore, many developers do not need matrix
>> operations, nor need to use numpy as a standard library.
>> Since numpy has become the cornerstone of Python scientific
>> computing, there is no need to reinvent another wheel, that is, to design
>> new matrix data types. I suggest adding some parsing rules to the List data
>> type to facilitate the initialization of a matrix.
>>
>> (1) Keeping the original syntax of List unchanged，for example：
>> a = [1,2,3] # will be parsed to a normal list.
>> b = [[1,2,3],[4,5,6]] # will be parsed to a normal list，too.
>> Simply put, all the original list syntax remains unchanged.
>> (2) Using semicolons as a flag to make a better integration of a
>> List-like data type and Numpy. The Python interpreter will check whether
>> the numpy library is installed. If not, it will stop running and remind the
>> user to install it. The expected syntax:
>> c = [1,2,3;] or c = [1 2 3;] or c = [1 2 3]
>> Notice the semicolon after the last number. If numpy is found, c
>> will be parsed as a Numpy ndarray. All these forms are equivelent to c =
>> np.array([1,2,3]). For a vector, the semicolon is the key for Python to
>> parse it as a Numpy ndarray.
>>
>> d=[1,2,3;4,5,6] or d=[1,2,3;4,5,6;] or d=[1 2 3;4 5 6] or d=[1 2
>> 3;4 5 6;]
>> Notice the semicolons. If numpy is found, d will be parsed as a
>> Numpy ndarray. All these forms are equivelent to
>> d=np.array([[1,2,3],[4,5,6]])
>> You see，for defining a matrix or a vector，it will be nearly as
>> simple as Matalab or Julia!
>> Thank you！
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--
Christopher Barker, PhD
Python Language Consulting
- Teaching
- Scientific Software Development
- Desktop GUI and Web Development
- wxPython, numpy, scipy, Cython
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