Surprising error on lat and lon of a multilevel dataset. #68
Description
Describe the bug
When inspecting a multilevel dataset with xrlint, lat and lon get a surprising error message about the size of the dimension. But only starting with the 1 level. The base level does not show the error. Also when inspecting the levels directly themselves (e.g. 1.zarr) with xrlint, the error is not raised.
ds[0] warn Access latency exceeds threshold: 44.1 > 2.5 seconds. access-latency
ds[0] warn Missing attribute 'title'. content-desc
ds[0] warn Missing attribute 'history'. content-desc
ds[0].data_vars['B2_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['B2_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['B2_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['B2_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'institution'. content-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'source'. content-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'references'. content-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'comment'. content-desc
ds[0].data_vars['B2_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].data_vars['B3_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].data_vars['B4_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].data_vars['SD_q1_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].data_vars['chl_q1_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].data_vars['tur_nechad_865_q1_mean'].attrs warn Missing metadata, attributes are empty. no-empty-attrs
ds[0].coords['time'] error Missing timezone in encoding 'units': 'days since 1970-01-01'. time-coordinate
ds[0].coords['time_bnds'] error Missing timezone in encoding 'units': 'days since 1970-01-01'. time-coordinate
ds[0].data_vars['B2_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['B2_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['B3_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['B4_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'standard_name'. var-desc
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'long_name'. var-desc
ds[0].coords['lat'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].coords['lat_bnds'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].coords['lon'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].coords['lon_bnds'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].coords['time'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].coords['time_bnds'] warn Unexpected encoding '_FillValue', coordinates must not have missing data. var-missing-data
ds[0].data_vars['B2_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['B3_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['B4_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'units'. var-units
ds[0].data_vars['B2_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0].data_vars['B3_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0].data_vars['B4_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0].data_vars['SD_q1_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0].data_vars['chl_q1_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0].data_vars['tur_nechad_865_q1_mean'] warn Missing attribute 'color_bar_name'. xcube/data-var-colors
ds[0] error Missing attribute 'title'. xcube/dataset-title
ds[0] error Missing '.zlevels' meta-info file. xcube/ml-dataset-meta
ds[0].coords['time'] warn Number of chunks exceeds limit: 1375 > 5. xcube/no-chunked-coords
and then starting with the levels additionally these lines appear. Note that only the error for lat appears for level 1 and 2. From level 3 onwards the error also appears for lon.
ds[1] error Expected size of dimension 'lat' in level 1 to be 2479, but was 2480. xcube/ml-dataset-xy
ds[2] error Expected size of dimension 'lat' in level 2 to be 1239, but was 1240. xcube/ml-dataset-xy
ds[3] error Expected size of dimension 'lon' in level 3 to be 1203, but was 1204. xcube/ml-dataset-xy
ds[3] error Expected size of dimension 'lat' in level 3 to be 619, but was 620. xcube/ml-dataset-xy
ds[4] error Expected size of dimension 'lon' in level 4 to be 601, but was 602. xcube/ml-dataset-xy
ds[4] error Expected size of dimension 'lat' in level 4 to be 309, but was 310. xcube/ml-dataset-xy
ds[5] error Expected size of dimension 'lon' in level 5 to be 300, but was 301. xcube/ml-dataset-xy
ds[5] error Expected size of dimension 'lat' in level 5 to be 154, but was 155. xcube/ml-dataset-xy
We investigated the lon and lat values for this cube and cannot find any place from where the mismatch comes from. Internal cube, happy to provicde the location of it for testing.
Expected behavior
I would expect that the error does not occur.
Python Environment
- operating system:
- XRLint version, output of
xrlint --version: 0.5.1 - optional: packages and their versions, output of
pip listorconda list:
.