{"action":"opened","issue":{"url":"https://api.github.com/repos/OpenChemistry/tomviz/issues/405","repository_url":"https://api.github.com/repos/OpenChemistry/tomviz","labels_url":"https://api.github.com/repos/OpenChemistry/tomviz/issues/405/labels{/name}","comments_url":"https://api.github.com/repos/OpenChemistry/tomviz/issues/405/comments","events_url":"https://api.github.com/repos/OpenChemistry/tomviz/issues/405/events","html_url":"https://github.com/OpenChemistry/tomviz/issues/405","id":156304666,"number":405,"title":"Downsample x2 not efficient. Memory fills up for 1GB datasets","user":{"login":"Hovden","id":6197997,"avatar_url":"https://avatars.githubusercontent.com/u/6197997?v=3","gravatar_id":"","url":"https://api.github.com/users/Hovden","html_url":"https://github.com/Hovden","followers_url":"https://api.github.com/users/Hovden/followers","following_url":"https://api.github.com/users/Hovden/following{/other_user}","gists_url":"https://api.github.com/users/Hovden/gists{/gist_id}","starred_url":"https://api.github.com/users/Hovden/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Hovden/subscriptions","organizations_url":"https://api.github.com/users/Hovden/orgs","repos_url":"https://api.github.com/users/Hovden/repos","events_url":"https://api.github.com/users/Hovden/events{/privacy}","received_events_url":"https://api.github.com/users/Hovden/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2016-05-23T15:38:54Z","updated_at":"2016-05-23T15:38:54Z","closed_at":null,"body":"We need a more efficient Python approach to downsampling by 2. \r\n\r\nSolution 1:\r\nChange python code from order 4 spline to order 3 (bilinear) and turn prefilter off.\r\n    result = scipy.ndimage.interpolation.zoom(array, (0.5, 0.5, 0.5),output=None, order=1,mode='constant', cval=0.0, prefilter=False)\r\n\r\nSolution 2:\r\nFFT image and throw away half of data at high-frequencies.\r\n\r\nProblem:\r\nWhen I load a large 1GB dataset (e.g. \"LARGE_PtCu_NanoParticles.tif\" also known as tomo_5 recon) the dataset takes up 2GB of system memory (probably 16bit to 32bit casting which is ok). However, when I run a python transform to downsize by a factor of 2, tomviz uses 10GB of memory.\r\n\r\nIf you have a volume render in place, it takes up an additional 5GB of memory and I run out of memory.\r\n\r\nI think it is OK to have large memory requirements (>32GB) when users want work with large datasets, but it is worrisome when downsampling requires 5x the memory of the original dataset."}}
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