Read tilt axis from mdoc and send to tomographic alignment (#505)

The tilt axis is present in the mdoc files. While it isn't required for tomogram alignment, AreTomo seems less prone to predicting wrong axes when one is provided

---------

Co-authored-by: Eu Pin Tien <eu-pin.tien@diamond.ac.uk>

Author: stephen-riggs

src/murfey/client/contexts/tomo.py

@@ -79,6 +79,7 @@ class TomographyContext(Context):
         ProcessingParameter("pixel_size_on_image", "Pixel Size"),
         ProcessingParameter("motion_corr_binning", "Motion Correction Binning"),
         ProcessingParameter("frame_count", "Number of image frames"),
+        ProcessingParameter("tilt_axis", "Stage rotation angle"),
         ProcessingParameter("num_eer_frames", "Number of EER Frames"),
     ]
 
@@ -339,6 +340,9 @@ def _add_tilt(
                 "frame_count": environment.data_collection_parameters.get(
                     "frame_count", 0
                 ),
+                "tilt_axis": environment.data_collection_parameters.get(
+                    "tilt_axis", 85
+                ),
                 "mc_binning": environment.data_collection_parameters.get(
                     "motion_corr_binning", 1
                 ),
@@ -570,6 +574,7 @@ def gather_metadata(
             mdoc_metadata["experiment_type"] = "tomography"
             mdoc_metadata["voltage"] = float(mdoc_data["Voltage"])
             mdoc_metadata["frame_count"] = int(mdoc_data_block["NumSubFrames"])
+            mdoc_metadata["tilt_axis"] = float(mdoc_data_block["RotationAngle"])
             mdoc_metadata["image_size_x"] = int(mdoc_data["ImageSize"][0])
             mdoc_metadata["image_size_y"] = int(mdoc_data["ImageSize"][1])
             mdoc_metadata["magnification"] = int(mdoc_data_block["Magnification"])

src/murfey/server/__init__.py

@@ -2471,6 +2471,7 @@ def feedback_callback(header: dict, message: dict) -> None:
                         "dose_per_frame": preproc_params.dose_per_frame,
                         "frame_count": preproc_params.frame_count,
                         "kv": preproc_params.voltage,
+                        "tilt_axis": preproc_params.tilt_axis,
                         "pixel_size": preproc_params.pixel_size,
                         "manual_tilt_offset": -tilt_offset,
                         "node_creator_queue": machine_config.node_creator_queue,
@@ -2841,6 +2842,7 @@ def feedback_callback(header: dict, message: dict) -> None:
                     voltage=message["voltage"],
                     dose_per_frame=message["dose_per_frame"],
                     frame_count=message["frame_count"],
+                    tilt_axis=message["tilt_axis"],
                     motion_corr_binning=message["motion_corr_binning"],
                     gain_ref=message["gain_ref"],
                     eer_fractionation_file=message["eer_fractionation_file"],

src/murfey/server/api/__init__.py

@@ -735,6 +735,7 @@ def register_completed_tilt_series(
                     "dose_per_frame": preproc_params.dose_per_frame,
                     "frame_count": preproc_params.frame_count,
                     "kv": preproc_params.voltage,
+                    "tilt_axis": preproc_params.tilt_axis,
                     "pixel_size": preproc_params.pixel_size,
                     "manual_tilt_offset": -tilt_offset,
                     "node_creator_queue": machine_config.node_creator_queue,

src/murfey/util/db.py

@@ -459,6 +459,7 @@ class TomographyPreprocessingParameters(SQLModel, table=True):  # type: ignore
     pixel_size: float
     dose_per_frame: float
     frame_count: int
+    tilt_axis: float
     voltage: int
     eer_fractionation_file: Optional[str] = None
     motion_corr_binning: int = 1

src/murfey/util/models.py

@@ -316,6 +316,7 @@ class ProcessFile(BaseModel):  # Rename to TomoProcessFile
     pixel_size: float
     dose_per_frame: float
     frame_count: int
+    tilt_axis: Optional[float]
     mc_uuid: Optional[int] = None
     voltage: float = 300
     mc_binning: int = 1
@@ -351,6 +352,7 @@ class CompletedTiltSeries(BaseModel):
 class PreprocessingParametersTomo(BaseModel):
     dose_per_frame: float
     frame_count: int
+    tilt_axis: float
     gain_ref: Optional[str]
     experiment_type: str
     voltage: float