{"action":"created","issue":{"url":"https://api.github.com/repos/numpy/numpy/issues/4464","labels_url":"https://api.github.com/repos/numpy/numpy/issues/4464/labels{/name}","comments_url":"https://api.github.com/repos/numpy/numpy/issues/4464/comments","events_url":"https://api.github.com/repos/numpy/numpy/issues/4464/events","html_url":"https://github.com/numpy/numpy/issues/4464","id":29036258,"number":4464,"title":"ENH: Implement matmul function","user":{"login":"abalkin","id":535197,"avatar_url":"https://avatars.githubusercontent.com/u/535197?v=3","gravatar_id":"","url":"https://api.github.com/users/abalkin","html_url":"https://github.com/abalkin","followers_url":"https://api.github.com/users/abalkin/followers","following_url":"https://api.github.com/users/abalkin/following{/other_user}","gists_url":"https://api.github.com/users/abalkin/gists{/gist_id}","starred_url":"https://api.github.com/users/abalkin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/abalkin/subscriptions","organizations_url":"https://api.github.com/users/abalkin/orgs","repos_url":"https://api.github.com/users/abalkin/repos","events_url":"https://api.github.com/users/abalkin/events{/privacy}","received_events_url":"https://api.github.com/users/abalkin/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":12,"created_at":"2014-03-09T03:53:29Z","updated_at":"2015-04-06T22:16:56Z","closed_at":null,"body":"The @-operator PEP (gh-4351) describes two operations that would be a valuable addition to numpy independently of the main purpose of the PEP.\r\n\r\nMost of the functionality is already available in various places (`np.dot`, `np.linalg.matrix_power`, etc.) but no single function conforms to the PEP."},"comment":{"url":"https://api.github.com/repos/numpy/numpy/issues/comments/90262200","html_url":"https://github.com/numpy/numpy/issues/4464#issuecomment-90262200","issue_url":"https://api.github.com/repos/numpy/numpy/issues/4464","id":90262200,"user":{"login":"nicktimko","id":5297556,"avatar_url":"https://avatars.githubusercontent.com/u/5297556?v=3","gravatar_id":"","url":"https://api.github.com/users/nicktimko","html_url":"https://github.com/nicktimko","followers_url":"https://api.github.com/users/nicktimko/followers","following_url":"https://api.github.com/users/nicktimko/following{/other_user}","gists_url":"https://api.github.com/users/nicktimko/gists{/gist_id}","starred_url":"https://api.github.com/users/nicktimko/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nicktimko/subscriptions","organizations_url":"https://api.github.com/users/nicktimko/orgs","repos_url":"https://api.github.com/users/nicktimko/repos","events_url":"https://api.github.com/users/nicktimko/events{/privacy}","received_events_url":"https://api.github.com/users/nicktimko/received_events","type":"User","site_admin":false},"created_at":"2015-04-06T22:16:56Z","updated_at":"2015-04-06T22:16:56Z","body":"Python 3.5.0a3 has added the `@` binary operator to the syntax, is there some NumPy branch available to test with? Naively trying to subclass from `np.array` fails because `np.ndarray` is actually the type and it gets really messy to recast everything (I don't think I can monkey-patch the class...)"}}