linked documentation of axes in reconstruction

simpa/core/simulation_modules/reconstruction_module/reconstruction_utils.py

@@ -452,8 +452,7 @@ def compute_image_dimensions(field_of_view_in_mm: np.ndarray, spacing_in_mm: flo
     """
     Computes size of beamformed image from field of view of detection geometry given the spacing.
 
-    :param field_of_view_in_mm: field of view in mm as list of xdim_start, xdim_end, ydim_start, ydim_end, 
-    zdim_start, zdim_end
+    :param field_of_view_in_mm: field of view in mm as list of xdim_start, xdim_end, ydim_start, ydim_end, zdim_start, zdim_end
     :type field_of_view_in_mm: numpy ndarray
     :param spacing_in_mm: space betwenn pixels in mm
     :type spacing_in_mm: float
@@ -474,6 +473,8 @@ def compute_for_one_dimension(start_in_mm: float, end_in_mm: float) -> Tuple[int
         larger or smaller than the given field of view. The maximal deviation amounts to a quarter spacing on each side.
         The algorithm spaces the pixels symmetrically between start and end.
 
+        For nomenclature of the x, y and z dimension, please read :doc:`Understanding SIMPA <understanding_simpa>` within the README.
+
         :param start_in_mm: lower limit of the field of view in this dimension
         :type start_in_mm: float
         :param start_in_mm: upper limit of the field of view in this dimension
@@ -514,13 +515,49 @@ def compute_delay_and_sum_values(time_series_sensor_data: Tensor, sensor_positio
                                  zdim_start: int, zdim_end: int, spacing_in_mm: float, speed_of_sound_in_m_per_s: float,
                                  time_spacing_in_ms: float, logger: Logger, torch_device: torch.device,
                                  component_settings: Settings) -> Tuple[torch.tensor, int]:
+    """Perform the core computation of Delay and Sum, without summing up the delay dependend values.
+
+    For nomenclature of the x, y and z dimension, please read :doc:`Understanding SIMPA <understanding_simpa>` within the README.
+
+    :param time_series_sensor_data: time series data to be reconstructed
+    :type time_series_sensor_data: Tensor
+    :param sensor_positions: position of the detection geometry sensor elements
+    :type sensor_positions: torch.tensor
+    :param xdim: number of pixels in x dimension
+    :type xdim: int
+    :param ydim: number of pixels in y dimension
+    :type ydim: int
+    :param zdim: number of pixels in z dimension
+    :type zdim: int
+    :param xdim_start: at which pixels the x dimension starts
+    :type xdim_start: int
+    :param xdim_end: at which pixels the x dimension ends
+    :type xdim_end: int
+    :param ydim_start: at which pixels the y dimension starts
+    :type ydim_start: int
+    :param ydim_end: at which pixels the y dimension ends
+    :type ydim_end: int
+    :param zdim_start: at which pixels the z dimension starts
+    :type zdim_start: int
+    :param zdim_end: at which pixels the z dimension ends
+    :type zdim_end: int
+    :param spacing_in_mm: pixel spacing in mm
+    :type spacing_in_mm: float
+    :param speed_of_sound_in_m_per_s: speed of sound in m/s
+    :type speed_of_sound_in_m_per_s: float
+    :param time_spacing_in_ms: time steps spacing of data
+    :type time_spacing_in_ms: float
+    :param logger: object to be used for logging
+    :type logger: Logger
+    :param torch_device: cpu or cuda device used for computation
+    :type torch_device: torch.device
+    :param component_settings: reconstruction component settings
+    :type component_settings: Settings
+    :return: values (torch tensor) of the time series data corrected for delay and sensor positioning, ready to be summed up and n_sensor_elements (int) which might be used for later computations
+    :rtype: Tuple[torch.tensor, int]
     """
-    Perform the core computation of Delay and Sum, without summing up the delay dependend values.
-
-    Returns
-    - values (torch tensor) of the time series data corrected for delay and sensor positioning, ready to be summed up
-    - and n_sensor_elements (int) which might be used for later computations
-    """
+
+
 
     if time_series_sensor_data.shape[0] < sensor_positions.shape[0]:
         logger.warning("Warning: The time series data has less sensor element entries than the given sensor positions. "