Repository: PetroFit/petrofit (language: python)

Issue:
How much better is `DecisionTreeRegressor` for `PetrosianCorrection`
We have a working model of `DecisionTreeRegressor` but it is not tested yet.


Discussion / hints:
As of now, this seems to add complexity to the approximations, it seems like using this kind of method will require rethinking how the grid is generated as well as how the closest point is found. 

Fix patch:
diff --git a/petrofit/petrosian/core.py b/petrofit/petrosian/core.py
index 8a6d336..1cba678 100644
--- a/petrofit/petrosian/core.py
+++ b/petrofit/petrosian/core.py
@@ -1,13 +1,9 @@
-import yaml
-
 import numpy as np
 
 from scipy.interpolate import interp1d
 
 from matplotlib import pyplot as plt
 
-from sklearn.tree import DecisionTreeRegressor
-
 from ..utils import closest_value_index, get_interpolated_values, pixel_to_angular, mpl_tick_frame
 from ..photometry import radial_elliptical_aperture
 


Test patch:
diff --git a/.github/workflows/ci_tests.yml b/.github/workflows/ci_tests.yml
index ca9a831..44121d8 100644
--- a/.github/workflows/ci_tests.yml
+++ b/.github/workflows/ci_tests.yml
@@ -27,7 +27,7 @@ jobs:
 
           - os: ubuntu-latest
             python: '3.10'
-            tox_env: 'py310-test-astropy50'
+            tox_env: 'py310-test-astropy53'
             allow_failure: false
             prefix: ''