, 1 min read
Numerical Python versus SciPy core
If you are like me and use Python to do actual research, you probably know about Numerical Python which provides you with basic linear algebra, complex numbers, FFT and related code.
Two days ago, I had to reinstall Numerical Python and say that they are promoting a replacement called SciPy core. They warn you to stay away from the old Numerical Python. Don’t listen to them!
- On my machine, the code fails its basic unit testing . The function “linear_least_squares” is broken. Yet, there was no warning while installing. Specifically, here’s what I get:
>>> scipy.basic.linalg.linear_least_squares(array([[1,1],[1,1]]),array([2,2]))
Traceback (most recent call last):
File "", line 1, in ?
File "/usr/lib64/python2.4/site-packages/scipy/basic/basic_lite.py", line 457, in linear_least_squares
0,work,-1,iwork,0 )
lapack_lite.LapackError: Parameter b is not contiguous in lapack_lite.dgelsd
For this reason alone, I suggest you stay away from scipy! (This was verified with version 0.6.1, but it now works with version 0.8.4.) - The author figured out it was time to replace the good old “import LinearAlgebra” by “import scipy.basic.linalg” though he claims in the documentation that all you have to do is “import scipy.linalg”. Even so, I want “import LinearAlgebra”, not “import LinAlg” or “import scipy.linalg”. I think most users will agree with me. Cryptic package names are out. (Similarly, the good old “import Numeric” is replaced with “import scipy.base”… what’s wrong with “import scipy”?)- The inline documentation for SciPy Core is still incomplete. Typing “help(scipy.basic.linalg)” returns an empty page whereas “help(LinearAlgebra)” would give you a description of the package.
Update: please see a more positive recent post about this package.