ensure rlnTomoName is correctly parsed

src/tomography_python_programs/_metadata_models/relion/tilt_series.py

@@ -14,9 +14,9 @@ class Config:
 
     @classmethod
     def from_star_file(cls, filename: os.PathLike):
-        star = starfile.read(filename, always_dict=True)
+        star = starfile.read(filename, always_dict=True, parse_as_string=['rlnTomoName'])
         tilt_series_id = list(star.keys())[0]
-        return cls(name=tilt_series_id, data=starfile.read(filename))
+        return cls(name=tilt_series_id, data=starfile.read(filename, parse_as_string=['rlnTomoName']))
 
     def write_star_file(self, filename: os.PathLike):
         starfile.write({self.name: self.data}, filename, overwrite=True)

src/tomography_python_programs/_metadata_models/relion/tilt_series_set.py

@@ -36,7 +36,7 @@ def from_star_file(
             filename: os.PathLike,
             tilt_series_id: Optional[str] = None
     ):
-        star = starfile.read(filename, always_dict=True)
+        star = starfile.read(filename, always_dict=True, parse_as_string=['rlnTomoName'])
         global_df = star['global'].set_index('rlnTomoName', drop=False)
         if tilt_series_id is not None:  # get single tilt-series
             global_df = global_df.loc[tilt_series_id]

src/tomography_python_programs/denoise/cryoCARE/predict.py

@@ -87,7 +87,7 @@ def cryoCARE_predict(
     else:
         predict_executable = "cryoCARE_predict.py"
 
-    global_star = starfile.read(tomogram_star_file, always_dict=True)['global']
+    global_star = starfile.read(tomogram_star_file, always_dict=True, parse_as_string=['rlnTomoName'])['global']
 
     if not model_file.exists():
         e = f'Could not find denoise model'

src/tomography_python_programs/denoise/cryoCARE/train.py

@@ -93,7 +93,7 @@ def cryoCARE_train(
         extract_train_data_executable = "cryoCARE_extract_train_data.py"
 
 
-    global_star = starfile.read(tomogram_star_file, always_dict=True)['global']
+    global_star = starfile.read(tomogram_star_file, always_dict=True, parse_as_string=['rlnTomoName'])['global']
 
     if not 'rlnTomoReconstructedTomogramHalf1' in global_star.columns:
         e = 'Could not find rlnTomoReconstructedTomogramHalf1 in tomogram star file.'

src/tomography_python_programs/get_particle_poses/filaments.py

@@ -32,12 +32,12 @@ def get_poses_along_filament_backbones(
                    "during refinement."
     )
 ):
-    global_df = starfile.read(tilt_series_star_file)
+    global_df = starfile.read(tilt_series_star_file, parse_as_string=['rlnTomoName'])
     global_df = global_df.set_index('rlnTomoName')
     annotation_files = annotations_directory.glob('*_filaments.star')
     dfs = []
     for file in annotation_files:
-        filament_df = starfile.read(file)
+        filament_df = starfile.read(file, parse_as_string=['rlnTomoName'])
         tilt_series_id = '_'.join(file.name.split('_')[:-1])
         pixel_size = float(
             global_df.loc[tilt_series_id, 'rlnTomoTiltSeriesPixelSize']

src/tomography_python_programs/get_particle_poses/particles.py

@@ -27,12 +27,12 @@ def combine_particle_annotations(
 ):
     console.log("Running get_particle_poses particles.")
 
-    global_df = starfile.read(tilt_series_star_file)
+    global_df = starfile.read(tilt_series_star_file, parse_as_string=['rlnTomoName'])
     global_df = global_df.set_index('rlnTomoName')
     annotation_files = annotations_directory.glob('*_particles.star')
     dfs = []
     for file in annotation_files:
-        df = starfile.read(file)
+        df = starfile.read(file, parse_as_string=['rlnTomoName'])
         tilt_series_id = '_'.join(file.name.split('_')[:-1])
         pixel_size = float(
             global_df.loc[tilt_series_id, 'rlnTomoTiltSeriesPixelSize'])
@@ -89,8 +89,8 @@ def create_annotations_from_previous_star_file(
 
     annotations_directory.mkdir(parents=True, exist_ok=True)
 
-    star_data = starfile.read(in_star_file)
-    tomo_data = starfile.read(tomograms_file)
+    star_data = starfile.read(in_star_file, parse_as_string=['rlnTomoName'])
+    tomo_data = starfile.read(tomograms_file, parse_as_string=['rlnTomoName'])
     tomo_data = tomo_data.set_index('rlnTomoName')
 
     if isinstance(star_data, pd.DataFrame):

src/tomography_python_programs/get_particle_poses/spheres.py

@@ -28,12 +28,12 @@ def derive_poses_on_spheres(
         ..., help="target spacing between particle poses on spheres in angstroms."
     ),
 ):
-    global_df = starfile.read(tilt_series_star_file)
+    global_df = starfile.read(tilt_series_star_file, parse_as_string=['rlnTomoName'])
     global_df = global_df.set_index('rlnTomoName')
     annotation_files = annotations_directory.glob('*_spheres.star')
     dfs = []
     for file in annotation_files:
-        sphere_df = starfile.read(file)
+        sphere_df = starfile.read(file, parse_as_string=['rlnTomoName'])
         tilt_series_id = '_'.join(file.name.split('_')[:-1])
         pixel_size = float(
             global_df.loc[tilt_series_id, 'rlnTomoTiltSeriesPixelSize'])

src/tomography_python_programs/pick/filaments.py

@@ -108,7 +108,7 @@ def save_particles(self):
     def load_particles(self) -> N3dPaths:
         if not self.current_particle_star_file.exists():
             raise FileNotFoundError
-        df = starfile.read(self.current_particle_star_file)
+        df = starfile.read(self.current_particle_star_file, parse_as_string=['rlnTomoName'])
         paths = []
         for _, _df in df.groupby('rlnTomoManifoldIndex'):
             zyx = _df[['rlnCoordinateZ', 'rlnCoordinateY', 'rlnCoordinateX']].to_numpy()

src/tomography_python_programs/pick/particles.py

@@ -108,7 +108,7 @@ def save_particles(self):
     def load_particles(self) -> N3dPoints:
         if not self.current_particle_star_file.exists():
             raise FileNotFoundError
-        df = starfile.read(self.current_particle_star_file)
+        df = starfile.read(self.current_particle_star_file, parse_as_string=['rlnTomoName'])
         zyx = df[['rlnCoordinateZ', 'rlnCoordinateY', 'rlnCoordinateX']].to_numpy()
 
         return N3dPoints(data=zyx)

src/tomography_python_programs/pick/spheres.py

@@ -106,7 +106,7 @@ def save_particles(self):
     def load_particles(self) -> N3dSpheres:
         if not self.current_particle_star_file.exists():
             raise FileNotFoundError
-        df = starfile.read(self.current_particle_star_file)
+        df = starfile.read(self.current_particle_star_file, parse_as_string=['rlnTomoName'])
         zyx = df[['rlnCoordinateZ', 'rlnCoordinateY', 'rlnCoordinateX']].to_numpy()
         radii = df['rlnSphereRadius'].to_numpy()
         return N3dSpheres(centers=zyx, radii=radii)

src/tomography_python_programs/view/particles.py

@@ -174,15 +174,15 @@ def view_particles(
     )
 ):
     # read in particle STAR file and get particle dataframe per tomogram
-    star = starfile.read(particle_star_file, always_dict=True)
+    star = starfile.read(particle_star_file, always_dict=True, parse_as_string=['rlnTomoName'])
     particle_df = star['particles']
     if 'optics' in star:
         particle_df = particle_df.merge(star['optics'], on='rlnOpticsGroup')
     particle_df_grouped = particle_df.groupby('rlnTomoName')
 
     # if requested, read in tomogram STAR file and get dataframe per tomogram
     if tilt_series_star_file is not None:
-        tomogram_df = starfile.read(tilt_series_star_file)
+        tomogram_df = starfile.read(tilt_series_star_file, parse_as_string=['rlnTomoName'])
         tomogram_df_grouped = tomogram_df.groupby('rlnTomoName')
 
     # construct a widget for switching between different tomograms