DOC: Misc spell corrections

skimage/_shared/testing.py

@@ -48,7 +48,7 @@ def doctest_skip_parser(func):
         >>> something + else
         >>> something # skip if HAVE_BMODULE
 
-    This decorator will evaluate the expresssion after ``skip if``.  If this
+    This decorator will evaluate the expression after ``skip if``.  If this
     evaluates to True, then the comment is replaced by ``# doctest: +SKIP``.  If
     False, then the comment is just removed. The expression is evaluated in the
     ``globals`` scope of `func`.

skimage/color/colorconv.py

@@ -1350,7 +1350,7 @@ def lab2lch(lab):
 
 
 def _cart2polar_2pi(x, y):
-    """convert cartesian coordiantes to polar (uses non-standard theta range!)
+    """convert cartesian coordinates to polar (uses non-standard theta range!)
 
     NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than usual ``(-pi, +pi)``
     """

skimage/feature/texture.py

@@ -11,7 +11,7 @@ def greycomatrix(image, distances, angles, levels=256, symmetric=False,
                  normed=False):
     """Calculate the grey-level co-occurrence matrix.
 
-    A grey level co-occurence matrix is a histogram of co-occuring
+    A grey level co-occurrence matrix is a histogram of co-occurring
     greyscale values at a given offset over an image.
 
     Parameters

skimage/filters/lpi_filter.py

@@ -213,7 +213,7 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
     data : (M,N) ndarray
         Input data.
     K : float or (M,N) ndarray
-        Ratio between power spectrum of noise and undegraded
+        Ratio between power spectrum of noise and undergraded
         image.
     impulse_response : callable `f(r, c, **filter_params)`
         Impulse response of the filter.  See LPIFilter2D.__init__.

skimage/graph/_ncut.py

@@ -20,7 +20,7 @@ def DW_matrices(graph):
     -------
     D : csc_matrix
         The diagonal matrix of the graph. ``D[i, i]`` is the sum of weights of
-        all edges incident on `i`. All other enteries are `0`.
+        all edges incident on `i`. All other entries are `0`.
     W : csc_matrix
         The weight matrix of the graph. ``W[i, j]`` is the weight of the edge
         joining `i` to `j`.
@@ -38,7 +38,7 @@ def ncut_cost(cut, D, W):
     Parameters
     ----------
     cut : ndarray
-        The mask for the nodes in the graph. Nodes corressponding to a `True`
+        The mask for the nodes in the graph. Nodes corresponding to a `True`
         value are in one set.
     D : csc_matrix
         The diagonal matrix of the graph.

skimage/graph/graph_cut.py

@@ -10,10 +10,10 @@ from scipy.sparse import linalg
 
 
 def cut_threshold(labels, rag, thresh, in_place=True):
-    """Combine regions seperated by weight less than threshold.
+    """Combine regions separated by weight less than threshold.
 
     Given an image's labels and its RAG, output new labels by
-    combining regions whose nodes are seperated by a weight less
+    combining regions whose nodes are separated by a weight less
     than the given threshold.
 
     Parameters
@@ -228,7 +228,7 @@ def _ncut_relabel(rag, thresh, num_cuts):
     """Perform Normalized Graph cut on the Region Adjacency Graph.
 
     Recursively partition the graph into 2, until further subdivision
-    yields a cut greather than `thresh` or such a cut cannot be computed.
+    yields a cut greater than `thresh` or such a cut cannot be computed.
     For such a subgraph, indices to labels of all its nodes map to a single
     unique value.
 

skimage/graph/heap.pyx

@@ -711,7 +711,7 @@ cdef class FastUpdateBinaryHeap(BinaryHeap):
         Returns
         -------
         pushed : bool
-            True if an append/update occured, False if otherwise.
+            True if an append/update occurred, False if otherwise.
 
         Raises
         ------

skimage/graph/rag.py

@@ -26,7 +26,7 @@ def min_weight(graph, src, dst, n):
     """Callback to handle merging nodes by choosing minimum weight.
 
     Returns either the weight between (`src`, `n`) or (`dst`, `n`)
-    in `graph` or the minumum of the two when both exist.
+    in `graph` or the minimum of the two when both exist.
 
     Parameters
     ----------
@@ -41,7 +41,7 @@ def min_weight(graph, src, dst, n):
     -------
     weight : float
         The weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the
-        minumum of the two when both exist.
+        minimum of the two when both exist.
 
     """
 
@@ -200,7 +200,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
     """Compute the Region Adjacency Graph using mean colors.
 
     Given an image and its initial segmentation, this method constructs the
-    corresponsing Region Adjacency Graph (RAG). Each node in the RAG
+    corresponding Region Adjacency Graph (RAG). Each node in the RAG
     represents a set of pixels within `image` with the same label in `labels`.
     The weight between two adjacent regions represents how similar or
     dissimilar two regions are depending on the `mode` parameter.

skimage/measure/_structural_similarity.py

@@ -29,7 +29,7 @@ def structural_similarity(X, Y, win_size=7,
     Returns
     -------
     s : float
-        Strucutural similarity.
+        Structural similarity.
     grad : (N * N,) ndarray
         Gradient of the structural similarity index between X and Y.
         This is only returned if `gradient` is set to True.

skimage/restoration/uft.py

@@ -10,7 +10,7 @@ frequency is equal to
 
 .. math::  \frac{1}{\sqrt{n}} \sum_i x_i
 
-so the Fourier tranform has the same energy as the original image
+so the Fourier transform has the same energy as the original image
 (see ``image_quad_norm`` function). The transform is applied from the
 last axis for performance (assuming a C-order array input).
 
@@ -353,18 +353,18 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
         The impulse responses.
     shape : tuple of int
         A tuple of integer corresponding to the target shape of the
-        tranfer function.
+        transfer function.
     dim : int, optional
         The last axis along which to compute the transform. All
         axes by default.
-    is_real : boolean (optionnal, default True)
+    is_real : boolean (optional, default True)
        If True, imp_resp is supposed real and the Hermitian property
        is used with rfftn Fourier transform.
 
     Returns
     -------
     y : complex ndarray
-       The tranfer function of shape ``shape``.
+       The transfer function of shape ``shape``.
 
     See Also
     --------
@@ -382,7 +382,7 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
     Notes
     -----
     The input array can be composed of multiple-dimensional IR with
-    an arbitrary number of IR. The individual IR must be accesed
+    an arbitrary number of IR. The individual IR must be accessed
     through the first axes. The last ``dim`` axes contain the space
     definition.
     """

skimage/transform/finite_radon_transform.py

@@ -29,7 +29,7 @@ def frt2(a):
 
     Notes
     -----
-    The FRT has a unique inverse iff n is prime. [FRT]
+    The FRT has a unique inverse if n is prime. [FRT]
     The idea for this algorithm is due to Vlad Negnevitski.
 
     Examples
@@ -88,7 +88,7 @@ def ifrt2(a):
 
     Notes
     -----
-    The FRT has a unique inverse iff n is prime.
+    The FRT has a unique inverse if n is prime.
     See [1]_ for an overview.
     The idea for this algorithm is due to Vlad Negnevitski.