{"action":"closed","issue":{"url":"https://api.github.com/repos/numpy/numpy/issues/8469","repository_url":"https://api.github.com/repos/numpy/numpy","labels_url":"https://api.github.com/repos/numpy/numpy/issues/8469/labels{/name}","comments_url":"https://api.github.com/repos/numpy/numpy/issues/8469/comments","events_url":"https://api.github.com/repos/numpy/numpy/issues/8469/events","html_url":"https://github.com/numpy/numpy/issues/8469","id":200521407,"number":8469,"title":"numpy.load fails loading large arrays on windows due to integer overflow","user":{"login":"tolga-b","id":6536229,"avatar_url":"https://avatars.githubusercontent.com/u/6536229?v=3","gravatar_id":"","url":"https://api.github.com/users/tolga-b","html_url":"https://github.com/tolga-b","followers_url":"https://api.github.com/users/tolga-b/followers","following_url":"https://api.github.com/users/tolga-b/following{/other_user}","gists_url":"https://api.github.com/users/tolga-b/gists{/gist_id}","starred_url":"https://api.github.com/users/tolga-b/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tolga-b/subscriptions","organizations_url":"https://api.github.com/users/tolga-b/orgs","repos_url":"https://api.github.com/users/tolga-b/repos","events_url":"https://api.github.com/users/tolga-b/events{/privacy}","received_events_url":"https://api.github.com/users/tolga-b/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2017-01-13T00:52:40Z","updated_at":"2017-01-13T01:05:37Z","closed_at":"2017-01-13T01:05:37Z","body":"Hi,\r\nThe problem is as follows:\r\nI have an array of size (1281024, 4096) (5247074304 elements) of type float32.\r\nI can save and load this array using numpy.save and numpy.load functions in Ubuntu 14.04 64bit, however when I try to load the same array in Windows 64bit I get an error:\r\n\"Total size of the array must be unchanged\" in numpy/lib/format.py.\r\n\r\nI found out that the read count is incorrectly set in numpy.multiple.reduce at line 626 in format.py due to int32 overflow for any array with larger than 2,147,483,647 elements (default C int is 32bit in 64bit Windows unlike Linux). Which in turn causes only a part of the file to be read and therefore setting the shape fails. Just fixing the read count caused the read array to contain a lot of \"nan\" values, so there must be other parts that are broken as well. \r\n\r\nI think this should be included in documentation if there is a file limit for numpy.save.\r\n\r\nI am using numpy version 1.11.1 with latest Anaconda distribution."}}
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