Fix typos in docstring

examples/acrobat_2023/valis/affine_optimizer.py

@@ -1,10 +1,10 @@
 """Optimize rigid alignment
 
 Contains functions related to optimization, as well as the AffineOptimizer
-class that performs the optimzation. This class can be subclassed to implement
+class that performs the optimization. This class can be subclassed to implement
 custom optimization methods.
 
-There are several subclasses, but AffineOptimizerMattesMI is the
+There are several subclasses, but AffineOptimizerMattesMI is
 the fastest and most accurate, and so is default affine optimizer in VALIS.
 It's not recommended that the other subclasses be used, but they are kept
 to provide examples on how to subclass AffineOptimizer.
@@ -348,7 +348,7 @@ def build_P(A, B, r, mask):
 @nba.njit()
 def entropy(x):
     """
-    Caclulate Shannon's entropy for array x
+    Calculate Shannon's entropy for array x
 
     Parameters
     ----------
@@ -473,7 +473,7 @@ def MI(fixed, moving, nb, spacing):
 
 
 class AffineOptimizer(object):
-    """Class that optimizes ridid registration
+    """Class that optimizes rigid registration
 
     Attributes
     ----------
@@ -492,10 +492,10 @@ class AffineOptimizer(object):
         Type of transformation, "EuclideanTransform" or "SimilarityTransform"
 
     current_level : int
-        Current level of the Guassian pyramid that is being registered
+        Current level of the Gaussian pyramid that is being registered
 
     accepts_xy : bool
-        Bool declaring whether or not the optimizer will use corresponding points to optimize the registration
+        Boolean indicating whether the optimizer uses corresponding points to optimize registration.
 
     Methods
     -------
@@ -521,9 +521,9 @@ class AffineOptimizer(object):
     to have a new cost_fxn method. See AffineOptimizerDisplacement for an example implementing a new cost function
 
     Major overhauls are possible too. See AffineOptimizerMattesMI for an example on using SimpleITK's
-    optimization methods inside of an AffineOptimizer subclass
+    optimization methods inside an AffineOptimizer subclass
 
-    If the optimizer uses corressponding points, then the class attribute
+    If the optimizer uses corresponding points, then the class attribute
     accepts_xy needs to be set to True. The default is False.
 
     """
@@ -567,7 +567,7 @@ def setup(self, moving, fixed, mask, initial_M=None):
             Image moving is warped to align to
 
         mask : ndarray
-            2D array having non-zero pixel values, where values of 0 are ignnored during registration
+            2D array having non-zero pixel values, where values of 0 are ignored during registration
 
         initial_M : (3x3) array
             Initial transformation matrix
@@ -628,7 +628,7 @@ def align(self, moving, fixed, mask, initial_M=None, moving_xy=None, fixed_xy=No
             Image moving is warped to align with
 
         mask : ndarray
-            2D array having non-zero pixel values, where values of 0 are ignnored during registration
+            2D array having non-zero pixel values, where values of 0 are ignored during registration
 
         initial_M : (3x3) array
             Initial transformation matrix
@@ -813,7 +813,7 @@ def setup(self, moving, fixed, mask, initial_M=None, moving_xy=None, fixed_xy=No
             Image moving is warped to align to
 
         mask : ndarray
-            2D array having non-zero pixel values, where values of 0 are ignnored during registration
+            2D array having non-zero pixel values, where values of 0 are ignored during registration
 
         initial_M : (3x3) array
             Initial transformation matrix
@@ -880,7 +880,7 @@ def align(self, moving, fixed, mask, initial_M=None,
             Image moving is warped to align with
 
         mask : ndarray
-            2D array having non-zero pixel values, where values of 0 are ignnored during registration
+            2D array having non-zero pixel values, where values of 0 are ignored during registration
 
         initial_M : (3x3) array
             Initial transformation matrix
@@ -995,7 +995,7 @@ def shannon_entropy(self, X, k=1):
     def mutual_information(self, A, B):
 
         if self.HA_list[self.current_level] is None:
-            # Only need to caluclate once per level, becuase the fixed
+            # Only need to calculate once per level, because the fixed
             # image doesn't change
 
             self.HA_list[self.current_level] = self.shannon_entropy(A)
@@ -1098,7 +1098,7 @@ def calc_cost(self, p):
         return self.cost_fxn(fixed_intensities, moving_intensities, self.pyramid_mask[self.current_level])
 
     def cost_fxn(self, fixed_intensities, transformed_intensities, mask):
-        """
+        """Cost function
         """
         results, _, _ = np.histogram2d(fixed_intensities, transformed_intensities, bins=self.nbins)
         n = np.sum(results)

examples/acrobat_2023/valis/feature_detectors.py

@@ -27,7 +27,7 @@
 
 
 def filter_features(kp, desc, n_keep=MAX_FEATURES):
-    """Get keypoints with highest response
+    """Get keypoints with the highest response
 
     Parameters
     ----------
@@ -43,7 +43,7 @@ def filter_features(kp, desc, n_keep=MAX_FEATURES):
 
     Returns
     -------
-    Keypoints and and corresponding descriptors that the the n_keep highest
+    Keypoints and corresponding descriptors that the n_keep highest
     responses.
 
     """
@@ -145,11 +145,11 @@ def detect_and_compute(self, image, mask=None):
 
         Returns
         -------
-        kp : ndarry
-            (N, 2) array positions of keypoints in xy corrdinates for N
+        kp : np.ndarray
+            (N, 2) array positions of keypoints in xy coordinates for N
             keypoints
 
-        desc : ndarry
+        desc : np.ndarray
             (N, M) array containing M features for each of the N keypoints
 
         """
@@ -289,8 +289,8 @@ def detect(self, image):
 
         Uses keypoint info to create KeyPoint objects for OpenCV
 
-        Paramters
-        ---------
+        Parameters
+        ----------
         image : ndarray
             image from keypoints will be detected
 

examples/acrobat_2023/valis/micro_rigid_registrar.py

@@ -34,7 +34,7 @@ class MicroRigidRegistrar(object):
     Attributes
     ----------
     val_obj : Valis
-        The "parent" object that registers all of the slides.
+        The "parent" object that registers all the slides.
 
     feature_detector_cls : FeatureDD, optional
         Uninstantiated FeatureDD object that detects and computes
@@ -47,7 +47,7 @@ class MicroRigidRegistrar(object):
         Matcher object that will be used to match image features
 
     scale : float
-        Degree of downsampling to use for the reigistration, based on the
+        Degree of downsampling to use for the registration, based on the
         registered WSI shape (i.e. Slide.aligned_slide_shape_rc)
 
     tile_wh : int
@@ -74,7 +74,7 @@ def __init__(self, val_obj, feature_detector_cls=DEFAULT_FD,
         Parameters
         ----------
         val_obj : Valis
-            The "parent" object that registers all of the slides.
+            The "parent" object that registers all the slides.
 
         feature_detector_cls : FeatureDD, optional
             Uninstantiated FeatureDD object that detects and computes
@@ -87,7 +87,7 @@ def __init__(self, val_obj, feature_detector_cls=DEFAULT_FD,
             Matcher object that will be used to match image features
 
         scale : float
-            Degree of downsampling to use for the reigistration, based on the
+            Degree of downsampling to use for the registration, based on the
             registered WSI shape (i.e. Slide.aligned_slide_shape_rc)
 
         tile_wh : int

examples/acrobat_2023/valis/non_rigid_registrars.py

@@ -46,7 +46,7 @@ class NonRigidRegistrar(object):
 
     mask : ndarray
         2D array with shape (N,M) where non-zero pixel values are foreground,
-        and 0 is background, which is ignnored during registration. If None,
+        and 0 is background, which is ignored during registration. If None,
         then all non-zero pixels in images will be used to create the mask.
 
     shape : tuple
@@ -90,11 +90,11 @@ def __init__(self, params=None):
         Parameters
         ----------
         params : dictionary
-            Keyword: value dictionary of parameters to be used in reigstration.
+            Keyword: value dictionary of parameters to be used in registration.
             Will get used in the calc() method.
 
             In the case where simple ITK will be used, params should be
-            a SimpleITK.ParameterMap. Note that numeric values needd to be
+            a SimpleITK.ParameterMap. Note that numeric values need to be
             converted to strings.
 
         """
@@ -128,9 +128,9 @@ def apply_mask(self, mask):
         return masked_moving, masked_fixed
 
     def calc(self, moving_img, fixed_img, mask, *args, **kwargs):
-        """Cacluate displacement fields
+        """Calculate displacement fields
 
-        Can record subclass specific atrributes here too
+        Can record subclass specific attributes here too
 
         Parameters
         ----------
@@ -142,7 +142,7 @@ def calc(self, moving_img, fixed_img, mask, *args, **kwargs):
 
         mask : ndarray
             2D array with shape (N,M) where non-zero pixel values are foreground,
-            and 0 is background, which is ignnored during registration. If None,
+            and 0 is background, which is ignored during registration. If None,
             then all non-zero pixels in images will be used to create the mask.
 
         Returns
@@ -188,7 +188,7 @@ def register(self, moving_img, fixed_img, mask=None, **kwargs):
 
         mask : ndarray
             2D array with shape (N,M) where non-zero pixel values are foreground,
-            and 0 is background, which is ignnored during registration. If None,
+            and 0 is background, which is ignored during registration. If None,
             then all non-zero pixels in images will be used to create the mask.
 
         **kwargs : dict, optional
@@ -337,7 +337,7 @@ class NonRigidRegistrarXY(NonRigidRegistrar):
 
     mask : ndarray
         2D array with shape (N,M) where non-zero pixel values are foreground,
-        and 0 is background, which is ignnored during registration. If None,
+        and 0 is background, which is ignored during registration. If None,
         then all non-zero pixels in images will be used to create the mask.
 
     shape : tuple
@@ -388,11 +388,11 @@ def __init__(self, params=None):
         Parameters
         ----------
         params : dictionary
-            Keyword: value dictionary of parameters to be used in reigstration.
+            Keyword: value dictionary of parameters to be used in registration.
             Will get used in the calc() method.
 
             In the case where simple ITK will be used, params should be
-            a SimpleITK.ParameterMap. Note that numeric values needd to be
+            a SimpleITK.ParameterMap. Note that numeric values need to be
             converted to strings.
 
         moving_xy : ndarray, optional
@@ -427,7 +427,7 @@ def register(self, moving_img, fixed_img, mask=None, moving_xy=None,
 
         mask : ndarray
             2D array with shape (N,M) where non-zero pixel values are foreground,
-            and 0 is background, which is ignnored during registration. If None,
+            and 0 is background, which is ignored during registration. If None,
             then all non-zero pixels in images will be used to create the mask.
 
         moving_xy : ndarray, optional
@@ -497,7 +497,7 @@ class NonRigidRegistrarGroupwise(NonRigidRegistrar):
 
     mask : ndarray
         2D array with shape (N,M) where non-zero pixel values are foreground,
-        and 0 is background, which is ignnored during registration. If None,
+        and 0 is background, which is ignored during registration. If None,
         then all non-zero pixels in images will be used to create the mask.
 
     shape : tuple of int
@@ -789,7 +789,7 @@ def run_elastix(self, moving_img, fixed_img, moving_xy=None, fixed_xy=None,
 
         mask : ndarray, optional
             2D array with shape (N,M) where non-zero pixel values are
-            foreground, and 0 is background, which is ignnored during
+            foreground, and 0 is background, which is ignored during
             registration. If None, then all non-zero pixels in images
             will be used to create the mask.
 
@@ -929,7 +929,7 @@ def __init__(self, params=None, optical_flow_obj=None,
         Parameters
         ----------
         params : dictionary
-            Keyword: value dictionary of parameters to be used in reigstration.
+            Keyword: value dictionary of parameters to be used in registration.
             Will get used in the calc() method.
 
         optical_flow_obj : object
@@ -964,7 +964,7 @@ def __init__(self, params=None, optical_flow_obj=None,
             Folded regions will then be removed using inpainting.
 
             If "regularize", folded regions will be detected and
-            regularized using the method fescribed in
+            regularized using the method described in
             "Foldover-free maps in 50 lines of code" Garanzha et al. 2021.
 
             If "None" then no smoothing will be applied.
@@ -1032,7 +1032,7 @@ class SimpleElastixGroupwiseWarper(NonRigidRegistrarGroupwise):
 
     mask : ndarray
         2D array with shape (N,M) where non-zero pixel values are foreground,
-        and 0 is background, which is ignnored during registration. If None,
+        and 0 is background, which is ignored during registration. If None,
         then all non-zero pixels in images will be used to create the mask.
 
     shape : tuple of int
@@ -1170,7 +1170,7 @@ class NonRigidTileRegistrar(object):
 
     mask : pyvips.Image
         2D array with shape (N,M) where non-zero pixel values are foreground,
-        and 0 is background, which is ignnored during registration. If None,
+        and 0 is background, which is ignored during registration. If None,
         then all non-zero pixels in images will be used to create the mask.
 
     shape : tuple
@@ -1197,11 +1197,11 @@ def __init__(self, params=None, tile_wh=512, tile_buffer=100):
         Parameters
         ----------
         params : dictionary
-            Keyword: value dictionary of parameters to be used in reigstration.
+            Keyword: value dictionary of parameters to be used in registration.
             Will get used when initializing the `non_rigid_registrar_cls`
 
             In the case where simple ITK will be used, params should be
-            a SimpleITK.ParameterMap. Note that numeric values needd to be
+            a SimpleITK.ParameterMap. Note that numeric values need to be
             converted to strings.
 
         tile_wh : int
@@ -1354,7 +1354,7 @@ def reg_tile(self, tile_idx, lock):
             self.pbar.update(1)
 
     def calc(self, *args, **kwargs):
-        """Cacluate displacement fields
+        """Calculate displacement fields
         Each tile is registered and then stitched together
         """
 
@@ -1390,18 +1390,18 @@ def register(self, moving_img, fixed_img, mask=None, non_rigid_registrar_cls=Opt
 
         mask : ndarray, pyvips.Image
             2D array with shape (N,M) where non-zero pixel values are foreground,
-            and 0 is background, which is ignnored during registration. If None,
+            and 0 is background, which is ignored during registration. If None,
             then all non-zero pixels in images will be used to create the mask.
 
         non_rigid_registrar_cls : NonRigidRegistrar, optional
             Uninstantiated NonRigidRegistrar class that will be used
             to calculate the deformation fields between images.
 
-        processing_cls : preprocessing.ImageProcesser, optional
-            preprocessing.ImageProcesser used to process the images
+        processing_cls : preprocessing.ImageProcessor, optional
+            preprocessing.ImageProcessor used to process the images
 
         processing_kwargs : dict
-            Dictionary of keyward arguments to be passed to `processing_cls`
+            Dictionary of keyword arguments to be passed to `processing_cls`
 
         target_stats : ndarray
             Target stats used to normalize each tile after being processed.