From b72a9606705655c4811ba141d5d3dbef7e2a88f3 Mon Sep 17 00:00:00 2001 From: Pratap Vardhan Date: Tue, 16 Dec 2014 11:41:24 +0530 Subject: [PATCH 1/3] DOC: Misc spell corrections --- skimage/_shared/testing.py | 2 +- skimage/color/colorconv.py | 2 +- skimage/feature/texture.py | 2 +- skimage/filters/lpi_filter.py | 2 +- skimage/graph/_ncut.py | 4 ++-- skimage/graph/graph_cut.py | 6 +++--- skimage/graph/heap.pyx | 2 +- skimage/graph/rag.py | 6 +++--- skimage/measure/_structural_similarity.py | 2 +- skimage/restoration/uft.py | 10 +++++----- skimage/transform/finite_radon_transform.py | 4 ++-- 11 files changed, 21 insertions(+), 21 deletions(-) diff --git a/skimage/_shared/testing.py b/skimage/_shared/testing.py index f8c7a88e..0cf65cb4 100644 --- a/skimage/_shared/testing.py +++ b/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`. diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py index 959fad3e..8e8c3b41 100644 --- a/skimage/color/colorconv.py +++ b/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)`` """ diff --git a/skimage/feature/texture.py b/skimage/feature/texture.py index b1e1024a..45d97b97 100644 --- a/skimage/feature/texture.py +++ b/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 diff --git a/skimage/filters/lpi_filter.py b/skimage/filters/lpi_filter.py index e160407a..36442eb9 100644 --- a/skimage/filters/lpi_filter.py +++ b/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__. diff --git a/skimage/graph/_ncut.py b/skimage/graph/_ncut.py index 2735bbd2..05be8cd1 100644 --- a/skimage/graph/_ncut.py +++ b/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. diff --git a/skimage/graph/graph_cut.py b/skimage/graph/graph_cut.py index b14b6a34..b4658bd1 100644 --- a/skimage/graph/graph_cut.py +++ b/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. diff --git a/skimage/graph/heap.pyx b/skimage/graph/heap.pyx index dde8bd60..a4368595 100644 --- a/skimage/graph/heap.pyx +++ b/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 ------ diff --git a/skimage/graph/rag.py b/skimage/graph/rag.py index 5950dfdc..5f148c5e 100644 --- a/skimage/graph/rag.py +++ b/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. diff --git a/skimage/measure/_structural_similarity.py b/skimage/measure/_structural_similarity.py index 01e4e9eb..d3150667 100644 --- a/skimage/measure/_structural_similarity.py +++ b/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. diff --git a/skimage/restoration/uft.py b/skimage/restoration/uft.py index 046c981c..dfece24f 100644 --- a/skimage/restoration/uft.py +++ b/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. """ diff --git a/skimage/transform/finite_radon_transform.py b/skimage/transform/finite_radon_transform.py index dd3bad33..9d3780b1 100644 --- a/skimage/transform/finite_radon_transform.py +++ b/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. From 6346d54be2bf97ee97870f7c785f004e6e3dac42 Mon Sep 17 00:00:00 2001 From: Pratap Vardhan Date: Tue, 16 Dec 2014 12:12:27 +0530 Subject: [PATCH 2/3] DOC: Example spell corrections --- doc/examples/plot_phase_unwrap.py | 2 +- doc/examples/plot_rag.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/examples/plot_phase_unwrap.py b/doc/examples/plot_phase_unwrap.py index ded87ada..f2bbbc4c 100644 --- a/doc/examples/plot_phase_unwrap.py +++ b/doc/examples/plot_phase_unwrap.py @@ -92,7 +92,7 @@ is clear: Without unwrapping (lower left), the regions above and below the masked boundary do not interact at all, resulting in an offset between the two regions of an arbitrary integer times two pi. We could just as well have unwrapped the regions as two separate images. With wrap around enabled for the -vertical direction (lower rigth), the situation changes: Unwrapping paths are +vertical direction (lower right), the situation changes: Unwrapping paths are now allowed to pass from the bottom to the top of the image and vice versa, in effect providing a way to determine the offset between the two regions. diff --git a/doc/examples/plot_rag.py b/doc/examples/plot_rag.py index f360af52..e44f4094 100644 --- a/doc/examples/plot_rag.py +++ b/doc/examples/plot_rag.py @@ -31,7 +31,7 @@ def max_edge(g, src, dst, n): g : RAG The graph under consideration. src, dst : int - The verices in `g` to be merged. + The vertices in `g` to be merged. n : int A neighbor of `src` or `dst` or both. From b15f55c3b414ca25c809438c7706515218969425 Mon Sep 17 00:00:00 2001 From: Pratap Vardhan Date: Tue, 16 Dec 2014 12:31:28 +0530 Subject: [PATCH 3/3] DOC: Revert to old --- skimage/filters/lpi_filter.py | 2 +- skimage/transform/finite_radon_transform.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/skimage/filters/lpi_filter.py b/skimage/filters/lpi_filter.py index 36442eb9..e160407a 100644 --- a/skimage/filters/lpi_filter.py +++ b/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 undergraded + Ratio between power spectrum of noise and undegraded image. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. diff --git a/skimage/transform/finite_radon_transform.py b/skimage/transform/finite_radon_transform.py index 9d3780b1..dd3bad33 100644 --- a/skimage/transform/finite_radon_transform.py +++ b/skimage/transform/finite_radon_transform.py @@ -29,7 +29,7 @@ def frt2(a): Notes ----- - The FRT has a unique inverse if n is prime. [FRT] + The FRT has a unique inverse iff 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 if n is prime. + The FRT has a unique inverse iff n is prime. See [1]_ for an overview. The idea for this algorithm is due to Vlad Negnevitski.