issue_comments: 373219624
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1989#issuecomment-373219624 | https://api.github.com/repos/pydata/xarray/issues/1989 | 373219624 | MDEyOklzc3VlQ29tbWVudDM3MzIxOTYyNA== | 1828519 | 2018-03-15T00:27:35Z | 2018-03-15T00:27:35Z | CONTRIBUTOR | Example: ``` import numpy as np import xarray as xr a = xr.DataArray(np.random.random((rows, cols)).astype(np.float32), dims=('y', 'x')) In [65]: np.sum(a).data Out[65]: array(499858.0625) In [66]: np.sum(a.data) Out[66]: 499855.19 In [67]: np.sum(a.data.astype(np.float64)) Out[67]: 499855.21635645436 In [68]: np.sum(a.data.astype(np.float32)) Out[68]: 499855.19 ``` I realized after making this example that nansum gives expected results: ``` a = xr.DataArray(np.random.random((rows, cols)).astype(np.float32), dims=('y', 'x')) In [83]: np.nansum(a.data) Out[83]: 500027.81 In [84]: np.nansum(a) Out[84]: 500027.81 In [85]: np.nansum(a.data.astype(np.float64)) Out[85]: 500027.77103802469 In [86]: np.nansum(a.astype(np.float64)) Out[86]: 500027.77103802469 ``` |
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