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/822#issuecomment-464950816,https://api.github.com/repos/pydata/xarray/issues/822,464950816,MDEyOklzc3VlQ29tbWVudDQ2NDk1MDgxNg==,2448579,2019-02-19T02:11:31Z,2019-02-19T02:11:31Z,MEMBER,Fixed upstream.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-458132212,https://api.github.com/repos/pydata/xarray/issues/822,458132212,MDEyOklzc3VlQ29tbWVudDQ1ODEzMjIxMg==,26384082,2019-01-28T13:29:44Z,2019-01-28T13:29:44Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity
If this issue remains relevant, please comment here; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208719528,https://api.github.com/repos/pydata/xarray/issues/822,208719528,MDEyOklzc3VlQ29tbWVudDIwODcxOTUyOA==,206773,2016-04-12T05:54:43Z,2016-04-12T05:54:43Z,NONE,"Fantastic, thanks!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208706109,https://api.github.com/repos/pydata/xarray/issues/822,208706109,MDEyOklzc3VlQ29tbWVudDIwODcwNjEwOQ==,1217238,2016-04-12T04:57:47Z,2016-04-12T04:57:47Z,MEMBER,"@forman Just a note -- if h5py can read the data correctly, you can read the data into xarray using [h5netcdf](https://github.com/shoyer/h5netcdf) (with `engine='h5netcdf'` in open_dataset).
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208339095,https://api.github.com/repos/pydata/xarray/issues/822,208339095,MDEyOklzc3VlQ29tbWVudDIwODMzOTA5NQ==,206773,2016-04-11T13:21:25Z,2016-04-11T13:21:25Z,NONE,"With [`h5py`](http://www.h5py.org/) the data is read correctly too.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208243175,https://api.github.com/repos/pydata/xarray/issues/822,208243175,MDEyOklzc3VlQ29tbWVudDIwODI0MzE3NQ==,206773,2016-04-11T09:10:41Z,2016-04-11T09:10:41Z,NONE,"Ok, I'll submit a netCDF issue then.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208214490,https://api.github.com/repos/pydata/xarray/issues/822,208214490,MDEyOklzc3VlQ29tbWVudDIwODIxNDQ5MA==,206773,2016-04-11T08:02:30Z,2016-04-11T09:08:34Z,NONE,"Just found that the `valid_min`/`valid_max` attributes are not directly part of the CF convention but the NUG convention and applying to `byte` only [according to CF Section 2.2 Data Types](http://cf-conventions.readthedocs.org/en/latest/ch02.html?highlight=valid_min)
> All integer types are treated by the netCDF interface as signed. It is possible to treat the byte type as unsigned by using the NUG convention of indicating the unsigned range using the `valid_min`, `valid_max`, or `valid_range` attributes.
As for for #821, Panoply shows the correct values for the same file:
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208237051,https://api.github.com/repos/pydata/xarray/issues/822,208237051,MDEyOklzc3VlQ29tbWVudDIwODIzNzA1MQ==,10050469,2016-04-11T08:53:43Z,2016-04-11T08:54:20Z,MEMBER,"Note that `ncview` also displays the output correctly.
It seems that the problem occurs at the necdf4 level already:
``` python
import netCDF4
import matplotlib.pyplot as plt
f = '/home/mowglie/Downloads/20100101120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_LT-v02.0-fv01.1.nc'
d = netCDF4.Dataset(f)
v = d['analysed_sst']
v.set_auto_maskandscale(False)
plt.imshow(v[0, ...], origin='lower')
plt.show()
```
To be compared to the equivalent output from [IDL](https://en.wikipedia.org/wiki/IDL_%28programming_language%29) at the netcdf backend level:
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-208088579,https://api.github.com/repos/pydata/xarray/issues/822,208088579,MDEyOklzc3VlQ29tbWVudDIwODA4ODU3OQ==,1217238,2016-04-10T23:14:50Z,2016-04-10T23:14:50Z,MEMBER,"I just opened this up with `decode_cf=False`. Here's what the raw data looks like:
```
ds.analysed_sst[0].plot.imshow()
```
with attributes:
```
Attributes:
_FillValue: -32768
units: kelvin
scale_factor: 0.01
add_offset: 273.15
long_name: analysed sea surface temperature
valid_min: -300
valid_max: 4500
standard_name: sea_water_temperature
depth: 20 cm
source: ATSR<1,2>-ESACCI-L3U-v1.0, AATSR-ESACCI-L3U-v1.0, AVHRR<12,14,15,16,17,18>_G-ESACCI-L2P-v1.0, AVHRRMTA-ESACCI-L2P-v1.0
comment: SST analysis produced for ESA SST CCI project using the OSTIA system in reanalysis mode.
```
To me, It looks like somebody just mis-prepared this dataset, given the smooth transitions in the ocean from dark red to dark blue, which would correspond to numeric overflow. If not, there are lots of places in the ocean where the temperature is in negative degrees Kelvin.
I don't have a strong opinion on whether or not we should automatically masking values outside `valid_min`/`valid_max`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-207552831,https://api.github.com/repos/pydata/xarray/issues/822,207552831,MDEyOklzc3VlQ29tbWVudDIwNzU1MjgzMQ==,1217238,2016-04-08T18:46:49Z,2016-04-08T18:46:49Z,MEMBER,"So we actually use our own value scaling logic, independent of netCDF4. It's interesting that we have the same issue, though!
Possibly it's because we don't use the `valid_min`/`valid_max` arguments in determining what should be masked? I can download your dataset later and take a more in depth look.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-207510870,https://api.github.com/repos/pydata/xarray/issues/822,207510870,MDEyOklzc3VlQ29tbWVudDIwNzUxMDg3MA==,10050469,2016-04-08T16:51:25Z,2016-04-08T16:51:25Z,MEMBER,"For what its worth, I've tested your file with my IDL library, and I get:
```
IDL> print, min(data), max(data)
-54.5300 306.440
IDL> print, TIME_to_STR(time)
01.01.2010 12:00:00
```
you should fill a report in netcdf4 as @rabernat suggests.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-207510306,https://api.github.com/repos/pydata/xarray/issues/822,207510306,MDEyOklzc3VlQ29tbWVudDIwNzUxMDMwNg==,1197350,2016-04-08T16:50:22Z,2016-04-08T16:50:22Z,MEMBER,"@fmaussion then it's a netCDF bug
https://github.com/Unidata/netcdf4-python/issues
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644
https://github.com/pydata/xarray/issues/822#issuecomment-207508190,https://api.github.com/repos/pydata/xarray/issues/822,207508190,MDEyOklzc3VlQ29tbWVudDIwNzUwODE5MA==,10050469,2016-04-08T16:43:47Z,2016-04-08T16:43:47Z,MEMBER,"It seems that the problem is happening at the NetCDF4 level already:
``` python
import netCDF4
import numpy as np
f = '/home/mowglie/Downloads/20100101120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_LT-v02.0-fv01.1.nc'
d = netCDF4.Dataset(f)
da = d['analysed_sst'][:]
print(np.max(da), np.min(da))
print(netCDF4.num2date(d['time'][:], d['time'].units))
```
prints
```
600.82 -54.53
[datetime.datetime(1947, 5, 12, 9, 58, 14)]
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146975644