Type Annotation Enhancement in mantidimaging/core/ Directory part 2

mantidimaging/core/io/instrument_log_implmentations.py

@@ -100,15 +100,15 @@ def read_imat_date(time_stamp: str) -> datetime:
             locale.setlocale(locale.LC_TIME, lc)
 
     @staticmethod
-    def _has_imat_header(line: str):
+    def _has_imat_header(line: str) -> bool:
         HEADERS = [
             "TIME STAMP,IMAGE TYPE,IMAGE COUNTER,COUNTS BM3 before image,COUNTS BM3 after image",
             "TIME STAMP  IMAGE TYPE   IMAGE COUNTER   COUNTS BM3 before image   COUNTS BM3 after image",
         ]
         return line.strip() in HEADERS
 
     @classmethod
-    def _has_imat_data_line(cls, line: str):
+    def _has_imat_data_line(cls, line: str) -> bool:
         try:
             _ = cls.read_imat_date(line[:24])
         except ValueError:

mantidimaging/core/io/saver.py

@@ -36,17 +36,20 @@
 package_version = CheckVersion().get_version()
 
 
-def write_fits(data: np.ndarray, filename: str, overwrite: bool = False, description: str | None = ""):
+def write_fits(data: np.ndarray, filename: str, overwrite: bool = False, description: str | None = "") -> None:
     hdu = fits.PrimaryHDU(data)
     hdulist = fits.HDUList([hdu])
     hdulist.writeto(filename, overwrite=overwrite)
 
 
-def write_img(data: np.ndarray, filename: str, overwrite: bool = False, description: str | None = ""):
+def write_img(data: np.ndarray, filename: str, overwrite: bool = False, description: str | None = "") -> None:
     tifffile.imwrite(filename, data, description=description, metadata=None, software="Mantid Imaging")
 
 
-def write_nxs(data: np.ndarray, filename: str, projection_angles: np.ndarray | None = None, overwrite: bool = False):
+def write_nxs(data: np.ndarray,
+              filename: str,
+              projection_angles: np.ndarray | None = None,
+              overwrite: bool = False) -> None:
     import h5py
     nxs = h5py.File(filename, 'w')
 
@@ -177,7 +180,7 @@ def image_save(images: ImageStack,
         return names
 
 
-def nexus_save(dataset: StrictDataset, path: str, sample_name: str, save_as_float: bool):
+def nexus_save(dataset: StrictDataset, path: str, sample_name: str, save_as_float: bool) -> None:
     """
     Uses information from a StrictDataset to create a NeXus file.
     :param dataset: The dataset to save as a NeXus file.
@@ -199,7 +202,7 @@ def nexus_save(dataset: StrictDataset, path: str, sample_name: str, save_as_floa
     nexus_file.close()
 
 
-def _nexus_save(nexus_file: h5py.File, dataset: StrictDataset, sample_name: str, save_as_float: bool):
+def _nexus_save(nexus_file: h5py.File, dataset: StrictDataset, sample_name: str, save_as_float: bool) -> None:
     """
     Takes a NeXus file and writes the StrictDataset information to it.
     :param nexus_file: The NeXus file.
@@ -252,7 +255,7 @@ def _nexus_save(nexus_file: h5py.File, dataset: StrictDataset, sample_name: str,
 
 
 def _save_processed_data_to_nexus(nexus_file: h5py.File, dataset: StrictDataset, rotation_angle: h5py.Dataset,
-                                  image_key: h5py.Dataset, save_as_float: bool):
+                                  image_key: h5py.Dataset, save_as_float: bool) -> None:
     data = nexus_file.create_group(NEXUS_PROCESSED_DATA_PATH)
     data["rotation_angle"] = rotation_angle
     data["image_key"] = image_key
@@ -266,7 +269,7 @@ def _save_processed_data_to_nexus(nexus_file: h5py.File, dataset: StrictDataset,
     process.create_dataset("version", data=np.bytes_(package_version))
 
 
-def _save_image_stacks_to_nexus(dataset: StrictDataset, data_group: h5py.Group, save_as_float: bool):
+def _save_image_stacks_to_nexus(dataset: StrictDataset, data_group: h5py.Group, save_as_float: bool) -> None:
     combined_data_shape = (sum([len(arr) for arr in dataset.nexus_arrays]), ) + dataset.nexus_arrays[0].shape[1:]
 
     index = 0
@@ -305,7 +308,7 @@ def scale_row(row):
     return converted, factors
 
 
-def _save_recon_to_nexus(nexus_file: h5py.File, recon: ImageStack, sample_path: str):
+def _save_recon_to_nexus(nexus_file: h5py.File, recon: ImageStack, sample_path: str) -> None:
     """
     Saves a recon to a NeXus file.
     :param nexus_file: The NeXus file.
@@ -369,7 +372,7 @@ def _create_pixel_size_arrays(recon: ImageStack) -> tuple[np.ndarray, np.ndarray
     return x_arr, y_arr, z_arr
 
 
-def _set_nx_class(group: h5py.Group, class_name: str):
+def _set_nx_class(group: h5py.Group, class_name: str) -> None:
     """
     Sets the NX_class attribute of data in a NeXus file.
     :param group: The h5py group.

mantidimaging/core/net/help_pages.py

@@ -9,13 +9,13 @@
 SECTION_USER_GUIDE = f"{DOCS_BASE}/user_guide/"
 
 
-def open_user_operation_docs(operation_name: str):
+def open_user_operation_docs(operation_name: str) -> None:
     page_url = "operations/index"
     section = operation_name.lower().replace(" ", "-")
     open_help_webpage(SECTION_USER_GUIDE, page_url, section)
 
 
-def open_help_webpage(section_url: str, page_url: str, section: str | None = None):
+def open_help_webpage(section_url: str, page_url: str, section: str | None = None) -> None:
     if section is not None:
         url = f"{section_url}{page_url}.html#{section}"
     else:

mantidimaging/core/parallel/shared.py

@@ -33,7 +33,7 @@ def run_compute_func(func: ComputeFuncType,
                      num_operations: int,
                      arrays: list[pu.SharedArray] | pu.SharedArray,
                      params: dict[str, Any],
-                     progress=None):
+                     progress=None) -> None:
     if isinstance(arrays, pu.SharedArray):
         arrays = [arrays]
     all_data_in_shared_memory, data = _check_shared_mem_and_get_data(arrays)

mantidimaging/core/parallel/utility.py

@@ -23,7 +23,7 @@
 LOG = getLogger(__name__)
 
 
-def enough_memory(shape, dtype):
+def enough_memory(shape, dtype) -> bool:
     return full_size_KB(shape=shape, dtype=dtype) < system_free_memory().kb()
 
 
@@ -103,7 +103,7 @@ def multiprocessing_necessary(shape: int, is_shared_data: bool) -> bool:
     return True
 
 
-def execute_impl(img_num: int, partial_func: partial, is_shared_data: bool, progress: Progress, msg: str):
+def execute_impl(img_num: int, partial_func: partial, is_shared_data: bool, progress: Progress, msg: str) -> None:
     task_name = f"{msg}"
     progress = Progress.ensure_instance(progress, num_steps=img_num, task_name=task_name)
     indices_list = range(img_num)
@@ -128,7 +128,7 @@ def run_compute_func_impl(worker_func: Callable[[int], None],
                           num_operations: int,
                           is_shared_data: bool,
                           progress=None,
-                          msg: str = ""):
+                          msg: str = "") -> None:
     task_name = f"{msg}"
     progress = Progress.ensure_instance(progress, num_steps=num_operations, task_name=task_name)
     indices_list = range(num_operations)

mantidimaging/core/reconstruct/astra_recon.py

@@ -24,11 +24,11 @@
 # Full credit for following code to Daniil Kazantzev
 # Source:
 # https://github.com/dkazanc/ToMoBAR/blob/5990aaa264e2f08bd9b0069c8847e5021fbf2ee2/src/Python/tomobar/supp/astraOP.py#L20-L70
-def rotation_matrix2d(theta: float):
+def rotation_matrix2d(theta: float) -> np.ndarray:
     return np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
 
 
-def vec_geom_init2d(angles_rad: ProjectionAngles, detector_spacing_x: float, center_rot_offset: float):
+def vec_geom_init2d(angles_rad: ProjectionAngles, detector_spacing_x: float, center_rot_offset: float) -> np.ndarray:
     angles_value = angles_rad.value
     s0 = [0.0, -1.0]  # source
     u0 = [detector_spacing_x, 0.0]  # detector coordinates

mantidimaging/core/reconstruct/base_recon.py

@@ -20,7 +20,7 @@ def find_cor(images: ImageStack, slice_idx: int, start_cor: float, recon_params:
         raise NotImplementedError("Base class call")
 
     @staticmethod
-    def prepare_sinogram(data: np.ndarray, recon_params: ReconstructionParameters):
+    def prepare_sinogram(data: np.ndarray, recon_params: ReconstructionParameters) -> np.ndarray:
         logged_data = BaseRecon.negative_log(data)
         if recon_params.beam_hardening_coefs is not None:
             coefs = np.array([0.0, 1.0] + recon_params.beam_hardening_coefs)

mantidimaging/core/reconstruct/tomopy_recon.py

@@ -36,7 +36,7 @@ def single_sino(sino: np.ndarray,
                     cor: ScalarCoR,
                     proj_angles: ProjectionAngles,
                     recon_params: ReconstructionParameters,
-                    progress: Progress | None = None):
+                    progress: Progress | None = None) -> np.ndarray:
         sino = BaseRecon.prepare_sinogram(sino, recon_params)
         volume = tomopy.recon(tomo=[sino],
                               sinogram_order=True,

mantidimaging/core/rotation/data_model.py

@@ -130,19 +130,19 @@ def angle_in_degrees(self) -> Degrees:
         return Degrees(-np.rad2deg(np.arctan(self.gradient.value)))
 
     @property
-    def has_results(self):
+    def has_results(self) -> bool:
         return self._cached_gradient is not None and self._cached_cor is not None
 
     @property
-    def empty(self):
+    def empty(self) -> bool:
         return not self._points
 
     @property
-    def num_points(self):
+    def num_points(self) -> int:
         return len(self._points)
 
     @property
-    def stack_properties(self):
+    def stack_properties(self) -> dict:
         return {
             # TODO remove float casts
             const.COR_TILT_ROTATION_CENTRE: float(self.cor.value),

mantidimaging/core/rotation/polyfit_correlation.py

@@ -17,7 +17,7 @@
 
 
 def do_calculate_correlation_err(store: np.ndarray, search_index: int, p0_and_180: tuple[np.ndarray, np.ndarray],
-                                 image_width: int):
+                                 image_width: int) -> None:
     """
     Calculates squared sum error in the difference between the projection at 0 degrees, and the one at 180 degrees
     """
@@ -68,7 +68,7 @@ def find_center(images: ImageStack, progress: Progress) -> tuple[ScalarCoR, Degr
     return ScalarCoR(images.h_middle + -offset), theta
 
 
-def compute_correlation_error(index: int, arrays: list[Any], params: dict[str, Any]):
+def compute_correlation_error(index: int, arrays: list[Any], params: dict[str, Any]) -> None:
     min_correlation_error = arrays[0]
     shared_projections = arrays[1]
     shared_search_range = arrays[2]
@@ -79,7 +79,8 @@ def compute_correlation_error(index: int, arrays: list[Any], params: dict[str, A
                                  (shared_projections[0], shared_projections[1]), image_width)
 
 
-def _find_shift(images: ImageStack, search_range: range, min_correlation_error: np.ndarray, shift: np.ndarray):
+def _find_shift(images: ImageStack, search_range: range, min_correlation_error: np.ndarray,
+                shift: np.ndarray) -> np.ndarray:
     # Then we just find the index of the minimum one (minimum error)
     min_correlation_error = np.transpose(min_correlation_error)
     # argmin returns a list of where the minimum argument is found