Merge branch 'master' into dependabot/pip/jaxtyping-0.2.36

kwave/executor.py

@@ -33,7 +33,8 @@ def _make_binary_executable(self):
         binary_path.chmod(binary_path.stat().st_mode | stat.S_IEXEC)
 
     def run_simulation(self, input_filename: str, output_filename: str, options: str):
-        command = [str(self.execution_options.binary_path), "-i", input_filename, "-o", output_filename, options]
+        command = [str(self.execution_options.binary_path), "-i", input_filename, "-o", output_filename]
+        command.extend(options.split(' '))
 
         try:
             with subprocess.Popen(

kwave/ktransducer.py

@@ -644,7 +644,7 @@ def delay_mask(self, mode=None):
             ].min()  # -1s compatibility
         else:
             mask[unflatten_matlab_mask(mask, active_elements_index - 1)] += self.stored_beamforming_delays_offset  # -1s compatibility
-        return mask.astype(np.uint8)
+        return mask.astype(np.uint16)
 
     @property
     def elevation_beamforming_delays(self):

kwave/utils/kwave_array.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 from numpy import arcsin, pi, cos, size, array
-from numpy.linalg import linalg
+import numpy.linalg as linalg
 
 from kwave.data import Vector
 from kwave.kgrid import kWaveGrid

kwave/utils/matlab.py

@@ -1,5 +1,5 @@
 from typing import Tuple, Union, Optional, List
-
+from beartype import beartype as typechecker
 import numpy as np
 
 
@@ -75,6 +75,7 @@ def matlab_find(arr: Union[List[int], np.ndarray], val: int = 0, mode: str = "ne
     return np.expand_dims(arr, -1)  # compatibility, n => [n, 1]
 
 
+@typechecker
 def matlab_mask(arr: np.ndarray, mask: np.ndarray, diff: Optional[int] = None) -> np.ndarray:
     """
     Applies a mask to an array and returns the masked elements.
@@ -89,10 +90,14 @@ def matlab_mask(arr: np.ndarray, mask: np.ndarray, diff: Optional[int] = None) -
 
     """
 
+    if mask.dtype == "uint8":
+        mask = mask.astype("int8")
+
     if diff is None:
-        return np.expand_dims(arr.ravel(order="F")[mask.ravel(order="F")], axis=-1)  # compatibility, n => [n, 1]
+        flat_mask = mask.ravel(order="F")
     else:
-        return np.expand_dims(arr.ravel(order="F")[mask.ravel(order="F") + diff], axis=-1)  # compatibility, n => [n, 1]
+        flat_mask = mask.ravel(order="F") + diff
+    return np.expand_dims(arr.ravel(order="F")[flat_mask], axis=-1)  # compatibility, n => [n, 1]
 
 
 def unflatten_matlab_mask(arr: np.ndarray, mask: np.ndarray, diff: Optional[int] = None) -> Tuple[Union[int, np.ndarray], ...]: