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def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

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import numpy as np

def object_einsum(string, *arrays):
    """Simplified object einsum, not as much error checking
    
    does not support "..." or list input and will see "...", etc. as three times
    an axes identifier, tries normal einsum first!
    
    NOTE: This is untested, and not fast, but object type is
    never really fast anyway...

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#!/usr/bin/env python3

from pathlib import Path
import ast
import tokenize


class FindFuncs(ast.NodeVisitor):
    # Very dirtily adapted and stolen from NumPy tests...
    def __init__(self, filename, include_attributes=True):

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"""
Simply run the script to try to guess some information about how numpy
is linked.

If there is something odd going on, run/import the script after
your real import of numpy.

All versions tested on Linux, MKL is confusing me a bit since both lower and
upper case versions exist.

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Sebastian's Philosophy of the Array Object Types and DTypes

This object is solely for me to organize my thoughts, it probably is a horrific thing if seen by a theoretical computer scientist, and gets a lot of the terminalogy wrong. Type probably usually means a theoretical type, which I think is defined as the set of all possible instances.

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import numpy as np
from collections import defaultdict as _dd

def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------

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Downloading both files and running the cells (takes a while) should give you drop downs with the NumPy namespace (dark green names should be the ones that end up in the main numpy namespace).

After clicking one of them, tab will go to the next, and 1, 2, 3 , and 4 will allow to fairly quickly choose how you feel about the function. I left many blank (at least the ones that are not part of the main namespace). Then you press "save" and after gathering a few of them, maybe we can get some nice result out of the data.

Unfortunately the Jupyter widgets are failry slow, although with the keyboard it was not an issue for me.

My take on the categories:

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