warn if TV weight is large (#83)

* warn if weight is large

* update settings @alisiafadini feedback

meteor/settings.py

@@ -44,7 +44,8 @@
 
 # tv denoising
 TV_WEIGHT_DEFAULT: float = 0.01
-BRACKET_FOR_GOLDEN_OPTIMIZATION: tuple[float, float] = (0.0, 0.05)
+BRACKET_FOR_GOLDEN_OPTIMIZATION: tuple[float, float] = (0.0, 0.01)  # the braket can expand
+TV_MAX_WEIGHT_EXPECTED = 0.1  # this value sets the threshold for a warning to the user
 TV_STOP_TOLERANCE: float = 0.00000005  # inner loop; not for iterative-tv phase retrieval
 TV_MAX_NUM_ITER: int = 50  # inner loop; not for iterative-tv phase retrieval
 

meteor/tv.py

@@ -9,17 +9,21 @@
 from typing import Literal, overload
 
 import numpy as np
+import structlog
 from skimage.restoration import denoise_tv_chambolle
 
 from .rsmap import Map
 from .settings import (
     BRACKET_FOR_GOLDEN_OPTIMIZATION,
     MAP_SAMPLING,
     TV_MAX_NUM_ITER,
+    TV_MAX_WEIGHT_EXPECTED,
     TV_STOP_TOLERANCE,
 )
 from .validate import ScalarMaximizer, negentropy
 
+log = structlog.get_logger()
+
 
 @dataclass
 class TvDenoiseResult:
@@ -177,6 +181,13 @@ def negentropy_objective(tv_weight: float) -> float:
     else:
         maximizer.optimize_with_golden_algorithm(bracket=BRACKET_FOR_GOLDEN_OPTIMIZATION)
 
+    if maximizer.argument_optimum > TV_MAX_WEIGHT_EXPECTED:
+        log.warning(
+            "TV regularization weight much larger than expected, something probably went wrong",
+            weight=maximizer.argument_optimum,
+            limit=TV_MAX_WEIGHT_EXPECTED,
+        )
+
     # denoise using the optimized parameters and convert to an rs.DataSet
     final_realspace_map_as_array = _tv_denoise_array(
         map_as_array=realspace_map_array,