Merge pull request #468 from EIT-ALIVE/add_global_roi_definition

Add global ROI definition

Author: psomhorst

eitprocessing/roi/__init__.py

@@ -4,21 +4,8 @@
 module is `PixelMask`. Any type of region of interest selection results in a `PixelMask` object. A mask can be applied
 to any pixel dataset (EITData, PixelMap) with the same shape.
 
-Several default masks have been predefined. NB: the right side of the patient is to the left side of the EIT image and
-vice versa.
-
-- `VENTRAL_MASK` includes only the first 16 rows;
-- `DORSAL_MASK` includes only the last 16 rows;
-- `ANATOMICAL_RIGHT_MASK` includes only the first 16 columns;
-- `ANATOMICAL_LEFT_MASK` includes only the last 16 columns;
-- `QUADRANT_1_MASK` includes the top right quadrant;
-- `QUADRANT_2_MASK` includes the top left quadrant;
-- `QUADRANT_3_MASK` includes the bottom right quadrant;
-- `QUADRANT_4_MASK` includes the bottom left quadrant;
-- `LAYER_1_MASK` includes only the first 8 rows;
-- `LAYER_2_MASK` includes only the second set of 8 rows;
-- `LAYER_3_MASK` includes only the third set of 8 rows;
-- `LAYER_4_MASK` includes only the last 8 rows.
+Predefined default masks can be created using `get_geometric_mask()`. These masks are based on common anatomical regions
+of interest.
 """
 
 from __future__ import annotations
@@ -295,7 +282,7 @@ def __eq__(self, other: object) -> bool:
         return self.mask.shape == other.mask.shape and np.array_equal(self.mask, other.mask, equal_nan=True)
 
 
-def get_geometric_mask(mask: str, shape: tuple[int, int] = (32, 32)) -> PixelMask:
+def get_geometric_mask(mask: str, shape: tuple[int, int] = (32, 32)) -> PixelMask:  # noqa: PLR0911
     """Get a geometric mask by name.
 
     Masks can be generated for appropriates shapes, provided by the shape` argument, a tuple of two integers
@@ -305,6 +292,7 @@ def get_geometric_mask(mask: str, shape: tuple[int, int] = (32, 32)) -> PixelMas
     The function accepts both full names (e.g., "layer 1") and abbreviations (e.g., "L1").
 
     The following masks are available:
+    - "global" or "G": all pixels in the EIT image.
     - "ventral" or "V": the first half rows of the EIT image.
     - "dorsal" or "D": the last half rows of the EIT image.
     - "anatomical right" or "R": the first half columns of the EIT image.
@@ -331,23 +319,6 @@ def get_geometric_mask(mask: str, shape: tuple[int, int] = (32, 32)) -> PixelMas
         ValueError: If an unknown mask name is provided.
         ValueError: If the shape is not compatible with the requested mask.
     """
-
-    def _check_dimensions(
-        name: str, shape: tuple[int, int], *, height_divisor: int | None = None, width_divisor: int | None = None
-    ) -> None:
-        total_height, total_width = shape
-        if isinstance(height_divisor, int) and total_height % height_divisor != 0:
-            msg = (
-                f"Shape {shape} is not compatible with a {name} mask. "
-                "The height must be a multiple of {height_divisor}."
-            )
-            raise ValueError(msg)
-        if isinstance(width_divisor, int) and total_width % width_divisor != 0:
-            msg = (
-                f"Shape {shape} is not compatible with a {name} mask. The width must be a multiple of {width_divisor}."
-            )
-            raise ValueError(msg)
-
     total_height, total_width = shape
     n_layers_quadrants = 4
 
@@ -356,6 +327,8 @@ def _check_dimensions(
     mask = re.sub(r"^L([1-4]{1})$", r"layer \1", mask)
 
     match mask.split(" "):
+        case ["global"] | ["G"]:
+            return PixelMask(np.ones(shape), label="global")
         case ["ventral"] | ["V"]:
             _check_dimensions("ventral", shape, height_divisor=2)
             height = total_height // 2
@@ -386,7 +359,7 @@ def _check_dimensions(
 
         case ["layer", num_str] if num_str.isdigit() and 1 <= int(num_str) <= n_layers_quadrants:
             num = int(num_str)
-            _check_dimensions("layer", shape, height_divisor=4)
+            _check_dimensions("layer", shape, height_divisor=n_layers_quadrants)
             height = total_height // n_layers_quadrants
             return PixelMask(
                 np.concatenate(
@@ -410,3 +383,17 @@ def _check_dimensions(
         case _:
             msg = f"Unknown mask name: {mask}."
             raise ValueError(msg)
+
+
+def _check_dimensions(
+    name: str, shape: tuple[int, int], *, height_divisor: int | None = None, width_divisor: int | None = None
+) -> None:
+    total_height, total_width = shape
+    if isinstance(height_divisor, int) and total_height % height_divisor != 0:
+        msg = (
+            f"Shape {shape} is not compatible with a {name} mask. The height must be a multiple of {{height_divisor}}."
+        )
+        raise ValueError(msg)
+    if isinstance(width_divisor, int) and total_width % width_divisor != 0:
+        msg = f"Shape {shape} is not compatible with a {name} mask. The width must be a multiple of {width_divisor}."
+        raise ValueError(msg)

tests/test_pixelmask.py

@@ -220,6 +220,14 @@ def test_pixelmask_subtract():
     assert np.array_equal(pm3.mask, np.array([[np.nan, np.nan, 1, np.nan], [0.1, np.nan, np.nan, 0.5]]), equal_nan=True)
 
 
+@pytest.mark.parametrize("shape", [(32, 1), (64, 1), (16, 1), (100, 1), (4, 1)])
+def test_predefined_global_mask(shape: tuple[int, int]):
+    global_mask = get_geometric_mask("global", shape)
+    assert global_mask.label == "global"
+    assert global_mask.shape == shape
+    assert np.all(global_mask.mask == 1.0)
+
+
 @pytest.mark.parametrize("shape", [(32, 1), (64, 1), (16, 1), (100, 1), (4, 1)])
 def test_predefined_layer_masks(shape: tuple[int, int]):
     quarter_height = shape[0] // 4