docs: add type hints for contour

dclab/features/bright.py

@@ -10,7 +10,12 @@
 
 def get_bright(mask: npt.NDArray[bool] | list[npt.NDArray[bool]],
                image: npt.NDArray | list[npt.NDArray],
-               ret_data: str = "avg,sd") -> float | npt.NDArray:
+               ret_data: str = "avg,sd") -> (
+        float |
+        npt.NDArray |
+        tuple[float, float] |
+        tuple[npt.NDArray, npt.NDArray]
+):
     """Compute avg and/or std of the event brightness
 
     The event brightness is defined by the gray-scale values of the

dclab/features/bright_bc.py

@@ -12,7 +12,12 @@ def get_bright_bc(mask: npt.NDArray[bool] | list[npt.NDArray[bool]],
                   image: npt.NDArray | list[npt.NDArray],
                   image_bg: npt.NDArray | list[npt.NDArray],
                   bg_off: float | npt.NDArray = None,
-                  ret_data: str = "avg,sd"):
+                  ret_data: str = "avg,sd") -> (
+        float |
+        npt.NDArray |
+        tuple[float, float] |
+        tuple[npt.NDArray, npt.NDArray]
+):
     """Compute avg and/or std of the background-corrected event brightness
 
     The background-corrected event brightness is defined by the

dclab/features/bright_perc.py

@@ -11,8 +11,10 @@
 def get_bright_perc(mask: npt.NDArray[bool] | list[npt.NDArray[bool]],
                     image: npt.NDArray | list[npt.NDArray],
                     image_bg: npt.NDArray | list[npt.NDArray],
-                    bg_off: float | npt.NDArray = None) -> \
-        tuple[float, float] | tuple[npt.NDArray, npt.NDArray]:
+                    bg_off: float | npt.NDArray = None) -> (
+        tuple[float, float] |
+        tuple[npt.NDArray, npt.NDArray]
+):
     """Compute 10th and 90th percentile of the bg-corrected event brightness
 
     The background-corrected event brightness is defined by the

dclab/features/contour.py

@@ -17,7 +17,7 @@ class NoValidContourFoundError(BaseException):
 
 
 class LazyContourList(object):
-    def __init__(self, masks: npt.ArrayLike, max_events: int = 1000):
+    def __init__(self, masks: npt.ArrayLike, max_events: int = 1000) -> None:
         """A list-like object that computes contours upon indexing
 
         Parameters
@@ -43,7 +43,7 @@ def __init__(self, masks: npt.ArrayLike, max_events: int = 1000):
         self.identifier = str(masks[0][:].tobytes())
         self.shape = len(masks), np.nan, 2
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx) -> npt.NDArray:
         """Compute contour(s) if not already in self.contours"""
         if not isinstance(idx, numbers.Integral):
             # slicing!
@@ -77,7 +77,7 @@ def __len__(self):
         return len(self.masks)
 
 
-def get_contour(mask):
+def get_contour(mask: npt.NDArray[bool]) -> npt.NDArray | list[npt.NDArray]:
     """Compute the image contour from a mask
 
     The contour is computed in a very inefficient way using scikit-image
@@ -130,7 +130,8 @@ def get_contour(mask):
         return contours[0]
 
 
-def get_contour_lazily(mask):
+def get_contour_lazily(mask: npt.NDArray[bool]) -> \
+        npt.NDArray | LazyContourList:
     """Like :func:`get_contour`, but computes contours on demand
 
     Parameters
@@ -156,7 +157,7 @@ def get_contour_lazily(mask):
     return cont
 
 
-def remove_duplicates(cont):
+def remove_duplicates(cont: npt.NDArray) -> npt.NDArray:
     """Remove duplicates in a circular contour"""
     x = np.resize(cont, (len(cont) + 1, 2))
     selection = np.ones(len(x), dtype=bool)