clean up before merge

src/blop/agent.py

@@ -67,7 +67,7 @@ def __init__(
         dofs: Sequence[DOF],
         objectives: Sequence[Objective],
         db: Broker = None,
-        detectors: Sequence[Signal] = [],
+        detectors: Sequence[Signal] = None,
         acquistion_plan=default_acquisition_plan,
         digestion: Callable = default_digestion_function,
         digestion_kwargs: dict = {},
@@ -128,7 +128,7 @@ def __init__(
 
         self.dofs = DOFList(list(np.atleast_1d(dofs)))
         self.objectives = ObjectiveList(list(np.atleast_1d(objectives)))
-        self.detectors = list(np.atleast_1d(detectors))
+        self.detectors = list(np.atleast_1d(detectors or []))
 
         _validate_dofs_and_objs(self.dofs, self.objectives)
 
@@ -838,8 +838,6 @@ def _set_hypers(self, hypers):
         self.validity_constraint.load_state_dict(hypers["validity_constraint"])
 
     def constraint(self, x):
-        # x = self.dofs(active=True).transform(x)
-
         p = torch.ones(x.shape[:-1])
         for obj in self.objectives(active=True):
             # if the targeting constraint is non-trivial

src/blop/digestion/__init__.py

@@ -1,46 +1,6 @@
-import numpy as np
 import pandas as pd
-import scipy as sp
 
-
-def get_beam_stats(image, threshold=0.5):
-    ny, nx = image.shape
-
-    fim = image.copy()
-    fim -= np.median(fim, axis=0)
-    fim -= np.median(fim, axis=1)[:, None]
-
-    fim = sp.ndimage.median_filter(fim, size=3)
-    fim = sp.ndimage.gaussian_filter(fim, sigma=1)
-
-    m = fim > threshold * fim.max()
-    area = m.sum()
-
-    cs_x = np.cumsum(m.sum(axis=0)) / area
-    cs_y = np.cumsum(m.sum(axis=1)) / area
-
-    q_min, q_max = [0.15865, 0.84135]  # one sigma
-    q_min, q_max = [0.05, 0.95]  # 90%
-
-    x_min, x_max = np.interp([q_min, q_max], cs_x, np.arange(nx))
-    y_min, y_max = np.interp([q_min, q_max], cs_y, np.arange(ny))
-
-    stats = {
-        "max": fim.max(),
-        "sum": fim.sum(),
-        "area": area,
-        "cen_x": (x_min + x_max) / 2,
-        "cen_y": (y_min + y_max) / 2,
-        "wid_x": x_max - x_min,
-        "wid_y": y_max - y_min,
-        "x_min": x_min,
-        "x_max": x_max,
-        "y_min": y_min,
-        "y_max": y_max,
-        "bbox": [[x_min, x_max, x_max, x_min, x_min], [y_min, y_min, y_max, y_max, y_min]],
-    }
-
-    return stats
+from ..utils import get_beam_stats
 
 
 def default_digestion_function(df: pd.DataFrame) -> pd.DataFrame:

src/blop/sim/beamline.py

@@ -111,8 +111,7 @@ def unstage(self):
     def collect_asset_docs(self):
         items = list(self._asset_docs_cache)
         self._asset_docs_cache.clear()
-        for item in items:
-            yield item
+        yield from items
 
     def generate_beam(self, noise: bool = True):
         nx, ny = self.image_shape.get()

src/blop/utils/__init__.py

@@ -37,6 +37,10 @@ def get_beam_stats(image, threshold=0.5):
         "cen_y": (y_min + y_max) / 2,
         "wid_x": x_max - x_min,
         "wid_y": y_max - y_min,
+        "x_min": x_min,
+        "x_max": x_max,
+        "y_min": y_min,
+        "y_max": y_max,
         "bbox": [[x_min, x_max, x_max, x_min, x_min], [y_min, y_min, y_max, y_max, y_min]],
     }