[WIP] add spatial correlogram function

[knaaptime]

add first draft of correlogram

[knaaptime]

supercedes #253

[knaaptime]

there's nothing harder than pulling off a successful rebase. I'll die on this hill.

[jGaboardi]

there's nothing harder than pulling off a successful rebase. I'll die on this hill.

[knaaptime]

this should be just about good to go, but it's still failing for stuff like join counts because the class stashes all sorts of things as attriubutes (like the W and its adjacency list) that cant get serialized by joblib, and LOSH/Spatial_Pearson which have a different signature that needs w.sparse

[knaaptime]

Smart. The issue I was runnning into was that w.to_adjlist sorts the neighbors, so once you convert and try to subset the W, it and df are no longer aligned, which is a mess

--

Elijah Knaap, Ph.D. Senior Research Scientist & Associate Director Center for Open Geographical Science San Diego State University knaaptime.com | @knaaptime On Aug 6, 2023 at 1:47 AM -0700, Martin Fleischmann @.***>, wrote:

@martinfleis commented on this pull request. In esda/correlogram.py:

• with NotImplementedError as e:
• raise e("Only I, G, and C statistics are currently implemented")
• if distance_type == "band":
• W = DistanceBand
• elif distance_type == "knn":
• if max(distances) > gdf.shape[0] - 1:
• with ValueError as e:
• raise e("max number of neighbors must be less than or equal to n-1")
• W = KNN
• else:
• with NotImplementedError as e:
• raise e("distance_type must be either band or knn ")

• should be able to build the tree once and reuse it?

• but in practice, im not seeing any real difference from starting a new W from scratch each time

  To keep the structure, you can just assign weight=0 to everything above the set distance threshold. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

[codecov[bot]]

Codecov Report

Merging #259 (c643524) into main (f6a8732) will decrease coverage by 0.2%. The diff coverage is 55.0%.

@@           Coverage Diff           @@
##            main    #259     +/-   ##
=======================================
- Coverage   73.4%   73.2%   -0.2%     
=======================================
  Files         24      25      +1     
  Lines       3285    3325     +40     
  Branches     518     527      +9     
=======================================
+ Hits        2411    2433     +22     
- Misses       708     721     +13     
- Partials     166     171      +5     

Files	Coverage Δ
esda/__init__.py	100.0% <100.0%> (ø)
esda/correlogram.py	53.8% <53.8%> (ø)

[knaaptime]

what other tests do folks think are necessary for this?

[ljwolf]

I think at least:

1. verify that the documented association measures work as expected
2. verify that a simple callable completes its computation

Then I think it's done!

[knaaptime]

cool. I think 1 is covered by the existing test using I? just need to add a generic callable test for 2?

[ljwolf]

covered by the test using I

Ah ok, in light of the previous discussion, fine! I meant that we should test all of the estimators we specify to ensure correctness.

Yes on the generic callable test!

Then, I think the last bit is just the test failures?