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  • dopplershift · 4 ✖

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  • float32 instead of float64 when decoding int16 with scale_factor netcdf var using xarray · 4 ✖

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  • CONTRIBUTOR · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
410782982 https://github.com/pydata/xarray/issues/2304#issuecomment-410782982 https://api.github.com/repos/pydata/xarray/issues/2304 MDEyOklzc3VlQ29tbWVudDQxMDc4Mjk4Mg== dopplershift 221526 2018-08-06T17:17:38Z 2018-08-06T17:17:38Z CONTRIBUTOR

Ah, ok, not scaling per-se (i.e. * 0.01), but a second round of value conversion. 

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   float32 instead of float64 when decoding int16 with scale_factor netcdf var using xarray  343659822
410779271 https://github.com/pydata/xarray/issues/2304#issuecomment-410779271 https://api.github.com/repos/pydata/xarray/issues/2304 MDEyOklzc3VlQ29tbWVudDQxMDc3OTI3MQ== dopplershift 221526 2018-08-06T17:06:22Z 2018-08-06T17:06:22Z CONTRIBUTOR

I'm not following why the data are scaled twice.

Your point about the rounding being different is well-taken, though.

 
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   float32 instead of float64 when decoding int16 with scale_factor netcdf var using xarray  343659822
410774955 https://github.com/pydata/xarray/issues/2304#issuecomment-410774955 https://api.github.com/repos/pydata/xarray/issues/2304 MDEyOklzc3VlQ29tbWVudDQxMDc3NDk1NQ== dopplershift 221526 2018-08-06T16:52:42Z 2018-08-06T16:52:53Z CONTRIBUTOR

@shoyer But since it's a downstream calculation issue, and does not impact the actual precision of what's being read from the file, what's wrong with saying "Use data.astype(np.float64)". It's completely identical to doing it internally to xarray.

 
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   float32 instead of float64 when decoding int16 with scale_factor netcdf var using xarray  343659822
410769706 https://github.com/pydata/xarray/issues/2304#issuecomment-410769706 https://api.github.com/repos/pydata/xarray/issues/2304 MDEyOklzc3VlQ29tbWVudDQxMDc2OTcwNg== dopplershift 221526 2018-08-06T16:34:44Z 2018-08-06T16:36:16Z CONTRIBUTOR

A float32 values has 24 bits of precision in the significand, which is more than enough to store the 16-bits in in the original data; the exponent (8 bits) will more or less take care of the * 0.01:

```python

import numpy as np np.float32(2194 * 0.01) 21.94 ```

What you're seeing is an artifact of printing out the values. I have no idea why something is printing out a float (only 7 decimal digits) out to 17 digits. Even float64 only has 16 digits (which is overkill for this application).

The difference in subtracting the 32- and 64-bit values above are in the 8th decimal place, which is beyond the actual precision of the data; what you've just demonstrated is the difference in precision between 32-bit and 64-bit values, but it had nothing to do whatsoever with the data.

If you're really worried about precision round-off for things like std. dev, you should probably calculate it using the raw integer values and scale afterwards. (I don't actually think this is necessary, though.)

 
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   float32 instead of float64 when decoding int16 with scale_factor netcdf var using xarray  343659822

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