[SciPy-Dev] [ANN] Release of scikit-spatial package

[Tyler Reddy]

I've thought a little more about this. I didn't come up with a great first
choice for migrating functionality into scipy.spatial just yet, but this
was my thought process for one of your methods, are_collinear():

- the boolean return value may be useful for assessments of general
position in computational geometry, for rejection of array input to other
algorithms / functions by fiat
- the main problem is that the algorithm is effectively a NumPy one-liner
on array-input: numpy.linalg.matrix_rank(input) <= 1
- before I realized that, I also considered how you might transplant it if
relative simplicity were not an issue for a new public SciPy function
- I would suggest avoiding the object-oriented / ndarray subclassing
approach used in the scikit & matching our scipy.spatial distance-style
approach which generally takes an array-like into a simple function &
operates on it / returns something directly; avoiding ndarray subclassing
will probably reduce the controversy of a PR

So, not sure how helpful that is. I think the initial assessment on
collinear is: 1) very useful; but 2) perhaps a little too simple to justify
an exposed / new SciPy function?

Perhaps that evaluation strategy (usefulness + non-trivial implementation;
avoiding ndarray subclasses) may help you select the right candidate for
migrating over to SciPy? Others may have different thoughts though.

On Wed, 10 Apr 2019 at 19:17, Andrew Hynes <andrewjhynes at gmail.com> wrote:

> Hello Tyler,
>
> Some of the algorithms might be suitable for inclusion. Probably easier if
>> there's a nice paper / source for algorithm citation and it is clear that
>> we don't already have the functionality somewhere in SciPy.
>>
>
> My main reference for the functions has been http://mathworld.wolfram.com/.
> Some of the docstrings have a link to the relevant webpage.
>
> I'd like to try making a pull request for scipy.spatial. Is there an area
> of scikit-spatial that you think would be best to start?
>
> Also, would you prefer that the code is not object-oriented? For example,
> the collinearity function is a method of the Points class, but I can adapt
> it to be a regular function that takes a 2D ndarray as input. Or if you're
> fine with the object oriented style, I can work on a pull request to
> include the Point and Vector classes, which could be imported from a
> scipy.spatial.objects module.
>
> Thanks,
>
> Andrew
>
>
> On Tue, Apr 9, 2019 at 2:05 PM Tyler Reddy <tyler.je.reddy at gmail.com>
> wrote:
>
>> Some of the algorithms might be suitable for inclusion. Probably easier
>> if there's a nice paper / source for algorithm citation and it is clear
>> that we don't already have the functionality somewhere in SciPy.
>>
>> To give one example, just browsing through, I see a collinearity
>> algorithm. Since collinearity is often used in the assessment of general
>> position prior to embarking on an algorithm in computational geometry,
>> it may be sensible to check if the low-level library we vendor for many
>> comp geo routines (Qhull) has a routine for this. Even if it does, it might
>> not be easy to expose directly, but it is a consideration I think.
>>
>> Some of the operations, if ultimately suitable for inclusion, may fit in
>> the scipy.spatial.transform namespace, but probably case-by-case basis is
>> best in terms of assessing broad interest in the algorithm
>> and its suitability for inclusion.
>>
>> On Mon, 8 Apr 2019 at 16:08, Andrew Hynes <andrewjhynes at gmail.com> wrote:
>>
>>> Hi Stéfan,
>>>
>>>> This looks neat!  It reminds me a bit of the following book which I
>>>> enjoyed:
>>>>
>>>> http://www.geometricalgebra.net/
>>>>
>>>
>>> Thanks for the link - it looks like a good source for expanding the
>>> package.
>>>
>>> Can you tell us a bit more about how you see these functions typically
>>>> being applied in practice?
>>>>
>>> Sure, I can give an example from my masters project. It involved
>>> clinical gait analysis with a depth camera, so I used some of these
>>> functions to calculate gait parameters from positions in space.
>>>
>>> A basic walking stride consists of three positions: The initial swing
>>> foot, the stance foot, and the final swing foot. The stride length is the
>>> distance from the initial to final swing foot. The stride width is the
>>> length of the projection from the stance foot to the swing path (the line
>>> from the initial swing foot to the final).
>>>
>>> Here's a snippet of code using scikit-spatial to calculate gait
>>> parameters from three points: point_a_i (initial swing foot), point_b
>>> (stance foot), and point_a_f (final swing foot).
>>>
>>> from skspatial.objects import Vector, Line
>>>
>>> vector_a = Vector.from_points(point_a_i, point_a_f)
>>> line_a = Line(point=point_a_i, direction=vector_a)
>>>
>>> point_b_proj = line_a.project_point(point_b)
>>>
>>> stride_length = vector_a.norm()
>>> absolute_step_length = Vector.from_points(point_b, point_a_f).norm()
>>> step_length = Vector.from_points(point_b_proj, point_a_f).norm()
>>> stride_width = line_a.distance_point(point_b)
>>>
>>>
>>> Hopefully that gives an idea as to how the package can be used.
>>>
>>> Thanks,
>>>
>>> Andrew
>>>
>>> P. S. Great job on scikit-image!
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