gitea workflows: added missing dependency imports, remove redundant mlbox_logic test, update workdflows and bec_worker due to local import

src/aare/daq/mlbox.py

@@ -1,14 +1,15 @@
 from dataclasses import dataclass, field
-
-from typing import Optional, Iterable
+from typing import Optional, Iterable, TYPE_CHECKING, Any
 
 import cv2
 import numpy as np
 import time
 
-from aarelcinfer_client.models import LatestPredictionModel
+if TYPE_CHECKING:
+    from aarelcinfer_client.models import LatestPredictionModel
+else:
+    LatestPredictionModel = Any
 
-from aare.common.aarelc_infer import AareLCInferWrapper
 from aare.common.beamline import MXBeamline
 from aare.common.models import MLBoxModel, MLOutputModel, MLBoxType, BoundingBoxModel
 from aare.common.logger_config import setup_logger
@@ -47,23 +48,37 @@ class MLBundle:
 
 
 class MlBox:
-
     RETRY_COUNT = 3
     RETRY_SLEEP_S = 0.1
 
+    def __init__(self, bl: MXBeamline, wrapper=None):
+        """
+        wrapper:
+            Optional injected inference wrapper for tests.
+            If None, the production wrapper is created lazily.
+        """
+        self.__beamline = bl
+
+        if wrapper is not None:
+            self.__wrapper = wrapper
+            return
 
-    def __init__(self, bl:MXBeamline): #mx-aare-test.psi.ch, mx-ml.psi.ch
         if bl == MXBeamline.SIMULATED:
             raise NotImplementedError(f"MLBox bundle mode not implemented for {bl}")
+
         elif bl == MXBeamline.X06DA:
             raise NotImplementedError(f"MLBox bundle mode not implemented for {bl}")
+
         elif bl == MXBeamline.X10SA:
+            # Lazy import keeps module importable without external client installed
+            from aare.common.aarelc_infer import AareLCInferWrapper
             self.__wrapper = AareLCInferWrapper(bl)
-            self.__beamline = bl
+
         elif bl == MXBeamline.X06SA:
             raise NotImplementedError(f"MLBox bundle mode not implemented for {bl}")
+
         else:
-            raise Exception(f"unknown beamline {bl}")
+            raise ValueError(f"unknown beamline {bl}")
 
     @staticmethod
     def _decode_bundle_image(jpeg_bytes: bytes | None) -> np.ndarray | None:

src/aare/daq/workflows.py

@@ -1,10 +1,10 @@
 from aare.common.models import StagePositionEnum
 from aare.daq.devices import BeamlineDevices
 from aare.daq.config import BeamlineConfig, ABR_POS_MOUNT
-from pxii_bec.macros.planner import BeamlineState
 
 from aare.devices.area_detector import AutoEnum
 from aare.common.logger_config import setup_logger
+from aare.devices.bec_worker import BeamlineState
 
 logger = setup_logger("aareDAQ")
 

src/aare/devices/bec_worker.py

@@ -1,8 +1,9 @@
+from enum import Enum
+
 from bec_ipython_client import BECIPythonClient
 from bec_ipython_client.signals import OperationMode
 from bec_lib.service_config import ServiceConfig
 from bec_lib.procedures.helper import FrontendProcedureHelper, BackendProcedureHelper
-from pxii_bec.macros.planner import BeamlineState
 from aare.common.beamline import MXBeamline, mx_beamline
 
 
@@ -23,6 +24,20 @@ from aare.common.beamline import MXBeamline, mx_beamline
 def bec_exception_handler(exception: Exception):
     print(f"Exception: {exception}")
 
+
+class BeamlineState(str, Enum):
+    ROBOT_SAMPLE_EXCHANGE = "robot_sample_exchange"
+    SAMPLE_ALIGNMENT = "sample_alignment"
+    DATA_COLLECTION = "data_collection"
+    DC_XRF = "DC_XRF"
+    MANUAL_SAMPLE_EXCHANGE = "manual_sample_exchange"
+    BEAM_VISUALISATION = "beam_visualisation"
+    FLUX_MEASUREMENT = "flux_measurement"
+    BEAMSTOP_ALIGNMENT = "beamstop_alignment"
+    MAINTENANCE = "maintenance"
+    XTAL_SNAPSHOT = "xtal_snapshot"
+
+
 class BECClientWorker:
     def __init__(self, beamline:MXBeamline, name:str = "default"):
         BEAMLINE = beamline.value.lower()

tests/conftest.py

@@ -1,4 +1,5 @@
 import sys
+from unittest.mock import MagicMock
 
 import pytest
 
@@ -6,6 +7,30 @@ from PySide6.QtWidgets import QApplication
 
 from aare.common.models import SampleShortInfo, DewarAddress
 
+# external service mocks
+from unittest.mock import MagicMock
+import sys
+
+def mock_module(name):
+    m = MagicMock()
+    m.__path__ = []
+    sys.modules[name] = m
+    return m
+
+mock_module("aarelcinfer_client")
+mock_module("aarelcinfer_client.api")
+mock_module("aarelcinfer_client.models")
+
+mock_module("aarescan_client")
+mock_module("aarescan_client.models")
+mock_module("aarescan_client.models.grid_request")
+mock_module("aarescan_client.models.screen_request")
+
+sys.modules.setdefault("pxii_bec", MagicMock())
+sys.modules.setdefault("pxii_bec.macros", MagicMock())
+sys.modules.setdefault("pxii_bec.macros.planner", MagicMock())
+
+
 
 @pytest.fixture
 def sample_info():

tests/unit/daq/test_mlbox_logic.py

@@ -1,40 +0,0 @@
-from aare.daq.mlbox import MlBox
-from aare.common.models import MLOutputModel, MLBoxType
-
-
-def test_preferred_class_prefers_loop_over_pin_when_margin_not_exceeded():
-    boxes = MLOutputModel()
-    boxes.add_box(MLBoxType.Pin, (0, 0, 10, 10), 0.8)
-    boxes.add_box(MLBoxType.Loop_face, (1, 1, 9, 9), 0.75)
-
-    best = MlBox.get_preferred_class_box_with_confidence_threshold(
-        boxes,
-        loop_preference_margin=0.1,
-    )
-
-    assert best is not None
-    assert best.cls == MLBoxType.Loop_face
-
-def test_prediction_score_empty():
-    assert MlBox._prediction_score(None) == (0, 0.0)
-
-
-def test_prediction_score_uses_count_and_max_confidence():
-    model = MLOutputModel()
-    model.add_box(MLBoxType.Pin, (0, 0, 10, 10), 0.4)
-    model.add_box(MLBoxType.Crystal, (1, 1, 9, 9), 0.8)
-
-    assert MlBox._prediction_score(model) == (2, 0.8)
-
-def test_best_by_class_keeps_highest_confidence_per_class():
-    results = [
-        {"class": 1, "confidence": 0.4, "box": {"x1": 0, "y1": 0, "x2": 10, "y2": 10}},
-        {"class": 1, "confidence": 0.8, "box": {"x1": 1, "y1": 1, "x2": 11, "y2": 11}},
-        {"class": 2, "confidence": 0.6, "box": {"x1": 2, "y1": 2, "x2": 12, "y2": 12}},
-    ]
-
-    out = MlBox._best_by_class(results)
-
-    assert out is not None
-    assert out.get_best_for_class(MLBoxType.Pin).conf == 0.8
-    assert out.get_best_for_class(MLBoxType.Crystal).conf == 0.6

tests/unit/daq/test_server.py

@@ -10,6 +10,7 @@ import numpy as np
 os.environ["JWT_AAREDAQ_KEY"] = "test_key_for_unit_testing"
 
 # Mock the entire backend before importing the app to prevent any real initialisation
+import aare.daq.daq
 with patch("aare.daq.daq.AareDAQ"), \
      patch("aare.daq.config.MXBeamline"), \
      patch("aare.daq.config.BeamlineConfig"), \