Expected type 'Union[ndarray, Iterable]' warning in Python instruction

Question

I have translated a Matlab function to create an Overcomplete Discrete Cosine Transform matrix to represent 1D signals in such vectorial space, to Python language.

Matlab Function

function D = odctdict(n,L)
%ODCTDICT Overcomplete DCT dictionary.
%  D = ODCTDICT(N,L) returns the overcomplete DCT dictionary of size NxL
%  for signals of length N.
%
%  See also ODCT2DICT, ODCT3DICT, ODCTNDICT.    

D = zeros(n,L);
D(:,1) = 1/sqrt(n);
for k = 2:L
  v = cos((0:n-1)*pi*(k-1)/L)';
  v = v-mean(v);
  D(:,k) = v/norm(v);
end

Python translated function

import numpy as np


def odct1dict(n, l):
    """
    1-D Overcomplete DCT dictionary.

    D = odct1dict(N, L) returns the overcomplete DCT dictionary of size NxL
    for signals of length N.

    :param n: signal size
    :type n: int
    :param l: number of atoms
    :type l: int
    :return: 1-D Overcomplete DCT dictionary NumPy array
    """

    d = np.zeros((n, l))
    d[:, 0] = 1 / np.sqrt(n)

    for k in range(1, l):
        v = np.transpose(np.cos(np.arange(0, n) * np.pi * k * l))
        v = v - np.mean(v)
        d[:, k] = v / np.linalg.norm(v)

    return d

I'm using PyCharm as Python IDE, and this software raises a warning, that I don't understand, in the instruction v = np.transpose(np.cos(np.arange(0, n) * np.pi * k * l)) inside the for loop, specifically for the argument of the np.transpose function, np.cos(np.arange(0, n) * np.pi * k * l).

Expected type 'Union[ndarray, Iterable]', got 'int' instead less...

This inspection detects type errors in function call expressions. Due to dynamic dispatch and duck typing, this is possible in a limited but useful number of cases. Types of function parameters can be specified in docstrings or in Python 3 function annotations.

Can you explain this warning for me? And how to correct it? What is the proper way to write such instruction?

Answer 1

I'm guessing that PyCharm does not fully understand numpy. It looks and acts like valid Python:

Using my IDE, Ipython, I can do:

In [84]: n,k,l=3, .4, 1

In [85]: v = np.transpose(np.cos(np.arange(0, n) * np.pi * k * l))

In [86]: v
Out[86]: array([ 1.        ,  0.30901699, -0.80901699])