{"action":"closed","issue":{"url":"https://api.github.com/repos/numpy/numpy/issues/6468","labels_url":"https://api.github.com/repos/numpy/numpy/issues/6468/labels{/name}","comments_url":"https://api.github.com/repos/numpy/numpy/issues/6468/comments","events_url":"https://api.github.com/repos/numpy/numpy/issues/6468/events","html_url":"https://github.com/numpy/numpy/issues/6468","id":111217692,"number":6468,"title":"numpy.interp uses 'left' value even when x[0]==xp[0]","user":{"login":"danielsf","id":1682854,"avatar_url":"https://avatars.githubusercontent.com/u/1682854?v=3","gravatar_id":"","url":"https://api.github.com/users/danielsf","html_url":"https://github.com/danielsf","followers_url":"https://api.github.com/users/danielsf/followers","following_url":"https://api.github.com/users/danielsf/following{/other_user}","gists_url":"https://api.github.com/users/danielsf/gists{/gist_id}","starred_url":"https://api.github.com/users/danielsf/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/danielsf/subscriptions","organizations_url":"https://api.github.com/users/danielsf/orgs","repos_url":"https://api.github.com/users/danielsf/repos","events_url":"https://api.github.com/users/danielsf/events{/privacy}","received_events_url":"https://api.github.com/users/danielsf/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/numpy/numpy/labels/component:%20numpy.lib","name":"component: numpy.lib","color":"ff0000"},{"url":"https://api.github.com/repos/numpy/numpy/labels/Regression","name":"Regression","color":"7F0000"}],"state":"closed","locked":false,"assignee":null,"milestone":{"url":"https://api.github.com/repos/numpy/numpy/milestones/39","html_url":"https://github.com/numpy/numpy/milestones/1.10.2%20release","labels_url":"https://api.github.com/repos/numpy/numpy/milestones/39/labels","id":1353924,"number":39,"title":"1.10.2 release","description":"","creator":{"login":"charris","id":77272,"avatar_url":"https://avatars.githubusercontent.com/u/77272?v=3","gravatar_id":"","url":"https://api.github.com/users/charris","html_url":"https://github.com/charris","followers_url":"https://api.github.com/users/charris/followers","following_url":"https://api.github.com/users/charris/following{/other_user}","gists_url":"https://api.github.com/users/charris/gists{/gist_id}","starred_url":"https://api.github.com/users/charris/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/charris/subscriptions","organizations_url":"https://api.github.com/users/charris/orgs","repos_url":"https://api.github.com/users/charris/repos","events_url":"https://api.github.com/users/charris/events{/privacy}","received_events_url":"https://api.github.com/users/charris/received_events","type":"User","site_admin":false},"open_issues":4,"closed_issues":77,"state":"open","created_at":"2015-10-13T19:48:01Z","updated_at":"2015-12-07T21:14:13Z","due_on":null,"closed_at":null},"comments":10,"created_at":"2015-10-13T16:26:41Z","updated_at":"2015-12-07T21:14:13Z","closed_at":"2015-12-07T21:14:13Z","body":"The following code should demonstrate that numpy.interp uses the padding value specified by the kwarg 'left' even when the 0th element of the x-values being interpolated onto is identical to the 0th element of the input x-values.  This problem arose when I upgraded to numpy version 1.10.1 from version 1.9\r\n\r\n```\r\nimport numpy as np\r\n\r\nx1 = np.arange(1.0, 10.0, 1.0)\r\ny1 = np.power(x1,  2)\r\nx2 = np.arange(1.0, 9.1, 0.5)\r\n\r\nnp.testing.assert_equal(x1[0],  x2[0])\r\nnp.testing.assert_equal(x1[-1],  x2[-1])\r\n\r\ny2 = np.interp(x2, x1, y1, left=np.NaN, right=np.NaN)\r\n\r\nprint y2[0]\r\n```"}}