issue_comments

Hi @nickdoty — I'm less experienced in these than others, but the float conversion issue makes sense.

Generally I would use something like a tolerance for floats; relying on exact values is liable to hit these sorts of things at the moment.

I agree the current state isn't ideal. I think there are issues for float conversions on the tracker, and feel free to create a new one if there isn't.

Thanks for trying @nickdoty

Here's that slightly modified — does this help explain the 44.99 point?

```python

In [3]: import xarray as xr ...: ...: data = xr.open_dataset('https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/' ...: 'v4.1/2018/030/20180130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc') ...: print(data['analysed_sst'].sel(lat=slice(None, 10.45), lon=-68.00).indexes['lat']) ...: print(data['analysed_sst'].sel(lat=slice(10.45, None), lon=-68.00).indexes['lat']) ...: Float64Index([-89.98999786376953, -89.9800033569336, -89.97000122070312, -89.95999908447266, -89.94999694824219, -89.94000244140625, -89.93000030517578, -89.91999816894531, -89.91000366210938, -89.9000015258789, ... 10.359999656677246, 10.369999885559082, 10.380000114440918, 10.390000343322754, 10.399999618530273, 10.40999984741211, 10.420000076293945, 10.430000305175781, 10.4399995803833, 10.449999809265137], dtype='float64', name='lat', length=10045) Float64Index([10.460000038146973, 10.470000267028809, 10.479999542236328, 10.489999771118164, 10.5, 10.510000228881836, 10.520000457763672, 10.529999732971191, 10.539999961853027, 10.550000190734863, ... 89.9000015258789, 89.91000366210938, 89.91999816894531, 89.93000030517578, 89.94000244140625, 89.94999694824219, 89.95999908447266, 89.97000122070312, 89.9800033569336, 89.98999786376953], dtype='float64', name='lat', length=7954) ```

@nickdoty please show the output where this is happening. The output showing in my response above shows a value (10.449999809265137) less than 10.45.

Oh and check out the tolerance keyword to sel

Thanks for explaining @nickdoty . This does seem to support the idea that this is happening because the value is 10.449999809265137, and not 10.45.

If that's not the case, could you show a working example of selecting something with 10.45 failing to select a value that's equal or above 10.45. Probably the easiest way is to select the values along lat and show the result.

Sorry if I'm being slow in understanding. My question was whether this was caused by the 10.449999809265137 value being slightly below 10.45.

If not, could you show in the example both the values being selected and the values that it's selecting? Probably the easiest way is to select the values along lat and show the result.

Thanks for the issue @nickdoty . Is it a rounding issue?

```python

In [5]: import xarray as xr ...: longitude = -68.0 ...: ...: data = xr.open_dataset('https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/' ...: 'v4.1/2018/030/20180130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc') ...: ex_1 = data['analysed_sst'].sel(lat=slice(10.47, 10.52), lon=longitude).indexes['lat'] ...: ex_2 = data['analysed_sst'].sel(lat=slice(10.45, 10.52), lon=longitude).indexes['lat'] ...: ex_3 = data['analysed_sst'].sel(lat=slice(10.449, 10.52), lon=longitude).indexes['lat'] ...: ...: print(ex_1) ...: print(ex_2) ...: print(ex_3) Float64Index([10.470000267028809, 10.479999542236328, 10.489999771118164, 10.5, 10.510000228881836], dtype='float64', name='lat') Float64Index([10.460000038146973, 10.470000267028809, 10.479999542236328, 10.489999771118164, 10.5, 10.510000228881836], dtype='float64', name='lat') Float64Index([10.449999809265137, 10.460000038146973, 10.470000267028809, 10.479999542236328, 10.489999771118164, 10.5, 10.510000228881836], dtype='float64', name='lat') ```