Merge pull request #148 from amueller/doc_error_message

DOC: Some sphinx and rst fixes.

DEVELOPMENT.txt

@@ -46,7 +46,7 @@ Test coverage
 Tests for a module should ideally cover all code in that module,
 i.e. statement coverage should be at 100%.
 
- To measure the test coverage, install
+To measure the test coverage, install
 `coverage.py <http://nedbatchelder.com/code/coverage/>`__
 (using ``easy_install coverage``) and then run::
 

doc/examples/plot_gabors_from_lena.py

@@ -32,10 +32,10 @@ is not rocket science.
 .. [4] http://en.wikipedia.org/wiki/K-means_clustering
 .. [5] http://en.wikipedia.org/wiki/Lateral_geniculate_nucleus
 .. [6] D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones
-in the Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
+    in the Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
 .. [7] D. H. Hubel and T. N. Wiesel Receptive Fields, Binocular
-Interaction and Functional Architecture in the Cat's Visual Cortex J.
-Physiol. 160 pp.  106-154 1962
+    Interaction and Functional Architecture in the Cat's Visual Cortex J.
+    Physiol. 160 pp.  106-154 1962
 """
 
 import numpy as np

doc/source/gitwash/forking_hell.txt

@@ -1,8 +1,8 @@
 .. _forking:
 
-==========================================
+============================================
 Making your own copy (fork) of scikits-image
-==========================================
+============================================
 
 You need to do this only once.  The instructions here are very similar
 to the instructions at http://help.github.com/forking/ |emdash| please see
@@ -10,7 +10,7 @@ that page for more detail.  We're repeating some of it here just to give the
 specifics for the scikits-image_ project, and to suggest some default names.
 
 Set up and configure a github account
-=====================================
+======================================
 
 If you don't have a github account, go to the github page, and make one.
 
@@ -18,7 +18,7 @@ You then need to configure your account to allow write access |emdash| see
 the ``Generating SSH keys`` help on `github help`_.
 
 Create your own forked copy of scikits-image_
-===========================================
+=============================================
 
 #. Log into your github account.
 #. Go to the scikits-image_ github home at `scikits-image github`_.

doc/source/gitwash/index.txt

@@ -1,7 +1,7 @@
 .. _using-git:
 
 Working with *scikits-image* source code
-======================================
+========================================
 
 Contents:
 

skimage/feature/greycomatrix.py

@@ -127,9 +127,9 @@ def greycoprops(P, prop='contrast'):
     - 'homogeneity': :math:`\\sum_{i,j=0}^{levels-1}\\frac{P_{i,j}}{1+(i-j)^2}`
     - 'ASM': :math:`\\sum_{i,j=0}^{levels-1} P_{i,j}^2`    
     - 'energy': :math:`\\sqrt{ASM}`
-    - 'correlation': 
-.. math:: \\sum_{i,j=0}^{levels-1} P_{i,j}\\left[\\frac{(i-\\mu_i) \\ 
-          (j-\\mu_j)}{\\sqrt{(\\sigma_i^2)(\\sigma_j^2)}}\\right]
+    - 'correlation':
+        .. math:: \\sum_{i,j=0}^{levels-1} P_{i,j}\\left[\\frac{(i-\\mu_i) \\ 
+                  (j-\\mu_j)}{\\sqrt{(\\sigma_i^2)(\\sigma_j^2)}}\\right]
 
     
     Parameters
@@ -140,6 +140,7 @@ def greycoprops(P, prop='contrast'):
         `P[i,j,d,theta]` is the number of times that grey-level j
         occurs at a distance d and at an angle theta from
         grey-level i.
+
     prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', \
             'correlation', 'ASM'}, optional
         The property of the GLCM to compute. The default is 'contrast'.

skimage/feature/hog.py

@@ -7,11 +7,12 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
     """Extract Histogram of Oriented Gradients (HOG) for a given image.
 
     Compute a Histogram of Oriented Gradients (HOG) by
-        1) (optional) global image normalisation
-        2) computing the gradient image in x and y
-        3) computing gradient histograms
-        3) normalising across blocks
-        4) flattening into a feature vector
+
+        1. (optional) global image normalisation
+        2. computing the gradient image in x and y
+        3. computing gradient histograms
+        4. normalising across blocks
+        5. flattening into a feature vector
 
     Parameters
     ----------

skimage/io/collection.py

@@ -21,15 +21,6 @@ class MultiImage(object):
         Whether to conserve memory by only caching a single frame. Default is
         True.
 
-    Attributes
-    ----------
-    filename : str
-        The complete path to the image file.
-    conserve_memory : bool
-        Whether memory is conserved by only caching a single frame.
-    numframes : int
-        The number of frames in the image.
-
     Notes
     -----
     If ``conserve_memory=True`` the memory footprint can be reduced, however

skimage/segmentation/random_walker_segmentation.py

@@ -182,22 +182,22 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True):
         Mode for solving the linear system in the random walker
         algorithm.
 
-        - 'bf' (brute force, default): an LU factorization of the
-        Laplacian is computed. This is fast for small images (<1024x1024),
-        but very slow (due to the memory cost) and memory-consuming for
-        big images (in 3-D for example).
+        - 'bf' (brute force, default): an LU factorization of the Laplacian is
+          computed. This is fast for small images (<1024x1024), but very slow
+          (due to the memory cost) and memory-consuming for big images (in 3-D
+          for example).
 
-        - 'cg' (conjugate gradient): the linear system is solved
-        iteratively using the Conjugate Gradient method from
-        scipy.sparse.linalg. This is less memory-consuming than the
-        brute force method for large images, but it is quite slow.
+        - 'cg' (conjugate gradient): the linear system is solved iteratively
+          using the Conjugate Gradient method from scipy.sparse.linalg. This is
+          less memory-consuming than the brute force method for large images,
+          but it is quite slow.
 
-        - 'cg_mg' (conjugate gradient with multigrid preconditioner):
-        a preconditioner is computed using a multigrid solver, then
-        the solution is computed with the Conjugate Gradient method.
-        This mode requires that the pyamg module
-        (http://code.google.com/p/pyamg/) is installed. For images of
-        size > 512x512, this is the recommended (fastest) mode.
+        - 'cg_mg' (conjugate gradient with multigrid preconditioner): a
+          preconditioner is computed using a multigrid solver, then the
+          solution is computed with the Conjugate Gradient method.  This mode
+          requires that the pyamg module (http://code.google.com/p/pyamg/) is
+          installed. For images of size > 512x512, this is the recommended
+          (fastest) mode.
 
     tol : float
         tolerance to achieve when solving the linear system, in
@@ -240,18 +240,20 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True):
     the other coefficients are looked for). Each pixel is attributed the label
     for which it has a maximal value of x. The Laplacian L of the image
     is defined as:
+
        - L_ii = d_i, the number of neighbors of pixel i (the degree of i)
        - L_ij = -w_ij if i and j are adjacent pixels
+
     The weight w_ij is a decreasing function of the norm of the local gradient.
     This ensures that diffusion is easier between pixels of similar values.
 
-    When the Laplacian is decomposed into blocks of marked and unmarked pixels
+    When the Laplacian is decomposed into blocks of marked and unmarked pixels::
 
         L = M B.T
             B A
 
     with first indices corresponding to marked pixels, and then to unmarked
-    pixels, minimizing x.T L x for one phase amount to solving
+    pixels, minimizing x.T L x for one phase amount to solving::
 
         A x = - B x_m
 

skimage/transform/finite_radon_transform.py

@@ -97,7 +97,7 @@ def ifrt2(a):
     Notes
     -----
     The FRT has a unique inverse iff n is prime.
-    See [FRT] for an overview.
+    See [1]_ for an overview.
     The idea for this algorithm is due to Vlad Negnevitski.
 
     Examples
@@ -120,7 +120,7 @@ def ifrt2(a):
 
     References
     ----------
-    .. [FRT] A. Kingston and I. Svalbe, "Projective transforms on periodic
+    .. [1] A. Kingston and I. Svalbe, "Projective transforms on periodic
              discrete image arrays," in P. Hawkes (Ed), Advances in Imaging
              and Electron Physics, 139 (2006)
 

skimage/transform/radon_transform.py

@@ -17,6 +17,9 @@ import numpy as np
 from scipy.fftpack import fftshift, fft, ifft
 from ._project import homography
 
+__all__ = ["radon", "iradon"] 
+
+
 def radon(image, theta=None):
     """
     Calculates the radon transform of an image given specified