weights in kwargs (attempt)

[mathematicalmichael]

trying to fix #51

cc: @cdelcastillo21

[codecov[bot]]

Codecov Report

Merging #52 (f81dc2a) into main (ce312d0) will increase coverage by 2.56%. The diff coverage is 77.27%.

@@            Coverage Diff             @@
##             main      #52      +/-   ##
==========================================
+ Coverage   58.38%   60.94%   +2.56%     
==========================================
  Files           4        4              
  Lines         346      361      +15     
  Branches       66       67       +1     
==========================================
+ Hits          202      220      +18     
  Misses        107      107              
+ Partials       37       34       -3     

Impacted Files	Coverage Δ
src/mud/funs.py	41.11% <66.66%> (ø)
src/mud/base.py	76.92% <81.25%> (+6.33%)	:arrow_up:

---

Continue to review full report at Codecov.

Legend - Click here to learn more Δ = absolute <relative> (impact), ø = not affected, ? = missing data Powered by Codecov. Last update ce312d0...f81dc2a. Read the comment docs.

[mathematicalmichael]

really need to actually add tests to cover this expected behavior:

d.set_predicted(weights=d._in) where d is a DensityProblem

[mathematicalmichael]

@cdelcastillo21 I want to also add a unit test to cover your expected behavior.

This is my rough outline: (think identity map and unit vector for truth):

---

• set up density problem with instantiation of d from the class, all defaults.
• call get predicted to force problem solution.
• assert on the ratio vector d._r

---

• pass weights from ratio in initialization of new density problem as weights keyword.
• solve new density problem
• inspect predicted density, assert that it differs from the one in first assertion as evidence that we solved a new problem

---

normalizing weights would just be setting weights to d._r/sum(d._r) instead of just d._r, right? probably should use that in that first step of the second part of the outline if so.