{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-image/scikit-image/issues/1356","labels_url":"https://api.github.com/repos/scikit-image/scikit-image/issues/1356/labels{/name}","comments_url":"https://api.github.com/repos/scikit-image/scikit-image/issues/1356/comments","events_url":"https://api.github.com/repos/scikit-image/scikit-image/issues/1356/events","html_url":"https://github.com/scikit-image/scikit-image/pull/1356","id":55430830,"number":1356,"title":"Travis build docs","user":{"login":"blink1073","id":2096628,"avatar_url":"https://avatars.githubusercontent.com/u/2096628?v=3","gravatar_id":"","url":"https://api.github.com/users/blink1073","html_url":"https://github.com/blink1073","followers_url":"https://api.github.com/users/blink1073/followers","following_url":"https://api.github.com/users/blink1073/following{/other_user}","gists_url":"https://api.github.com/users/blink1073/gists{/gist_id}","starred_url":"https://api.github.com/users/blink1073/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/blink1073/subscriptions","organizations_url":"https://api.github.com/users/blink1073/orgs","repos_url":"https://api.github.com/users/blink1073/repos","events_url":"https://api.github.com/users/blink1073/events{/privacy}","received_events_url":"https://api.github.com/users/blink1073/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":{"url":"https://api.github.com/repos/scikit-image/scikit-image/milestones/4","labels_url":"https://api.github.com/repos/scikit-image/scikit-image/milestones/4/labels","id":827254,"number":4,"title":"0.11","description":"","creator":{"login":"stefanv","id":45071,"avatar_url":"https://avatars.githubusercontent.com/u/45071?v=3","gravatar_id":"","url":"https://api.github.com/users/stefanv","html_url":"https://github.com/stefanv","followers_url":"https://api.github.com/users/stefanv/followers","following_url":"https://api.github.com/users/stefanv/following{/other_user}","gists_url":"https://api.github.com/users/stefanv/gists{/gist_id}","starred_url":"https://api.github.com/users/stefanv/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/stefanv/subscriptions","organizations_url":"https://api.github.com/users/stefanv/orgs","repos_url":"https://api.github.com/users/stefanv/repos","events_url":"https://api.github.com/users/stefanv/events{/privacy}","received_events_url":"https://api.github.com/users/stefanv/received_events","type":"User","site_admin":false},"open_issues":13,"closed_issues":33,"state":"open","created_at":"2014-10-15T12:52:34Z","updated_at":"2015-01-28T21:31:51Z","due_on":"2016-02-07T08:00:00Z","closed_at":null},"comments":22,"created_at":"2015-01-25T21:23:41Z","updated_at":"2015-01-29T03:18:00Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/scikit-image/scikit-image/pulls/1356","html_url":"https://github.com/scikit-image/scikit-image/pull/1356","diff_url":"https://github.com/scikit-image/scikit-image/pull/1356.diff","patch_url":"https://github.com/scikit-image/scikit-image/pull/1356.patch"},"body":"Partially addresses #681, but does not handle Sphinx warnings.  I squashed some of them, but here's an example of two warnings I could not figure out how to fix:\r\n\r\n```\r\n/home/silvester/workspace/scikit-image/doc/source/api/skimage.novice.txt:46: WARNING: toctree references unknown document u'api/skimage.novice.Picture.path'\r\n```\r\n\r\n```\r\n[u'unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True, clip=True)', u':module: skimage.restoration', u'', u'', u'', u'Unsupervised Wiener-Hunt deconvolution.', u'', u'Return the deconvolution with a Wiener-Hunt approach, where the', u'hyperparameters are automatically estimated. The algorithm is a', u'stochastic iterative process (Gibbs sampler) described in the', u'reference below. See also ``wiener`` function.', u'', u':Parameters:', u'', u'    **image** : (M, N) ndarray', u'', u'        The input degraded image.', u'', u'    **psf** : ndarray', u'', u\"        The impulse response (input image's space) or the transfer\", u'        function (Fourier space). Both are accepted. The transfer', u'        function is automatically recognized as being complex', u'        (``np.iscomplexobj(psf)``).', u'', u'    **reg** : ndarray, optional', u'', u'        The regularisation operator. The Laplacian by default. It can', u'        be an impulse response or a transfer function, as for the psf.', u'', u'    **user_params** : dict', u'', u'        Dictionary of parameters for the Gibbs sampler. See below.', u'', u'    **clip** : boolean, optional', u'', u'        True by default. If true, pixel values of the result above 1 or', u'        under -1 are thresholded for skimage pipeline compatibility.', u'', u':Returns:', u'', u'    **x_postmean** : (M, N) ndarray', u'', u'        The deconvolved image (the posterior mean).', u'', u'    **chains** : dict', u'', u'        The keys ``noise`` and ``prior`` contain the chain list of', u'        noise and prior precision respectively.', u'', u':Other Parameters:', u'', u'    **The keys of ``user_params`` are:**', u'', u'        ', u'', u'    **threshold** : float', u'', u'        The stopping criterion: the norm of the difference between to', u'        successive approximated solution (empirical mean of object', u'        samples, see Notes section). 1e-4 by default.', u'', u'    **burnin** : int', u'', u'        The number of sample to ignore to start computation of the', u'        mean. 100 by default.', u'', u'    **min_iter** : int', u'', u'        The minimum number of iterations. 30 by default.', u'', u'    **max_iter** : int', u'', u'        The maximum number of iterations if ``threshold`` is not', u'        satisfied. 150 by default.', u'', u'    **callback** : callable (None by default)', u'', u'        A user provided callable to which is passed, if the function', u'        exists, the current image sample for whatever purpose. The user', u'        can store the sample, or compute other moments than the', u'        mean. It has no influence on the algorithm execution and is', u'        only for inspection.', u'', u'.. rubric:: Notes', u'', u'', u'The estimated image is design as the posterior mean of a', u'probability law (from a Bayesian analysis). The mean is defined as', u'a sum over all the possible images weighted by their respective', u'probability. Given the size of the problem, the exact sum is not', u'tractable. This algorithm use of MCMC to draw image under the', u'posterior law. The practical idea is to only draw highly probable', u'images since they have the biggest contribution to the mean. At the', u'opposite, the less probable images are drawn less often since', u'their contribution is low. Finally the empirical mean of these', u'samples give us an estimation of the mean, and an exact', u'computation with an infinite sample set.', u'', u'.. rubric:: References', u'', u'.. [R285] Fran\\xe7ois Orieux, Jean-Fran\\xe7ois Giovannelli, and Thomas', u'       Rodet, \"Bayesian estimation of regularization and point', u'       spread function parameters for Wiener-Hunt deconvolution\",', u'       J. Opt. Soc. Am. A 27, 1593-1607 (2010)', u'', u'       http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593', u'', u'       http://research.orieux.fr/files/papers/OGR-JOSA10.pdf', u'', u'.. only:: latex', u'', u'   [R285]_', u'', u'.. rubric:: Examples', u'', u'', u'>>> from skimage import color, data, restoration', u'>>> img = color.rgb2gray(data.astronaut())', u'>>> from scipy.signal import convolve2d', u'>>> psf = np.ones((5, 5)) / 25', u\">>> img = convolve2d(img, psf, 'same')\", u'>>> img += 0.1 * img.std() * np.random.standard_normal(img.shape)', u'>>> deconvolved_img = restoration.unsupervised_wiener(img, psf)']:96: WARNING: Duplicate explicit target name: \"r285\".\r\n```\r\n\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/scikit-image/scikit-image/issues/comments/71962466","html_url":"https://github.com/scikit-image/scikit-image/pull/1356#issuecomment-71962466","issue_url":"https://api.github.com/repos/scikit-image/scikit-image/issues/1356","id":71962466,"user":{"login":"coveralls","id":2354108,"avatar_url":"https://avatars.githubusercontent.com/u/2354108?v=3","gravatar_id":"","url":"https://api.github.com/users/coveralls","html_url":"https://github.com/coveralls","followers_url":"https://api.github.com/users/coveralls/followers","following_url":"https://api.github.com/users/coveralls/following{/other_user}","gists_url":"https://api.github.com/users/coveralls/gists{/gist_id}","starred_url":"https://api.github.com/users/coveralls/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coveralls/subscriptions","organizations_url":"https://api.github.com/users/coveralls/orgs","repos_url":"https://api.github.com/users/coveralls/repos","events_url":"https://api.github.com/users/coveralls/events{/privacy}","received_events_url":"https://api.github.com/users/coveralls/received_events","type":"User","site_admin":false},"created_at":"2015-01-29T03:18:00Z","updated_at":"2015-01-29T03:18:00Z","body":"\n[![Coverage Status](https://coveralls.io/builds/1825008/badge)](https://coveralls.io/builds/1825008)\n\nCoverage increased (+0.01%) to 92.58% when pulling **aa6d9183c40b2d6efdf51258490776570ef2a4de on blink1073:travis-build-docs** into **8b24d27155a0e44dba55235c836f230aa489101b on scikit-image:master**.\n"}}