home / github

Menu
  • Search all tables
  • GraphQL API

issue_comments

Table actions
  • GraphQL API for issue_comments

7 rows where author_association = "NONE" and issue = 332471780 sorted by updated_at descending

✖
✖
✖

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: reactions, created_at (date), updated_at (date)

user 5

  • rsignell-usgs 2
  • kthyng 2
  • iuryt 1
  • angelra 1
  • stale[bot] 1

issue 1

  • Problem opening unstructured grid ocean forecasts with 4D vertical coordinates · 7 ✖

author_association 1

  • NONE · 7 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1078439763 https://github.com/pydata/xarray/issues/2233#issuecomment-1078439763 https://api.github.com/repos/pydata/xarray/issues/2233 IC_kwDOAMm_X85AR69T rsignell-usgs 1872600 2022-03-24T22:26:07Z 2023-07-16T15:13:39Z NONE

https://github.com/pydata/xarray/issues/2233#issuecomment-397602084 Would the new xarray index/coordinate internal refactoring now allow us to address this issue?

cc @kthyng

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
1171375793 https://github.com/pydata/xarray/issues/2233#issuecomment-1171375793 https://api.github.com/repos/pydata/xarray/issues/2233 IC_kwDOAMm_X85F0cax kthyng 3487237 2022-06-30T15:40:34Z 2022-06-30T15:40:34Z NONE

@benbovy Ah, I see you mean under "Relax all constraints related to “dimension (index) coordinates” in Xarray". Ok, thank you for clarifying that for me! (I wasn't sure what the second item meant in the list of lists.)

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
1171342518 https://github.com/pydata/xarray/issues/2233#issuecomment-1171342518 https://api.github.com/repos/pydata/xarray/issues/2233 IC_kwDOAMm_X85F0US2 kthyng 3487237 2022-06-30T15:11:00Z 2022-06-30T15:11:00Z NONE

I've looked through the github issues associated with the explicit indices, but can't quite tell if I can use them to load FVCOM model output. In any case I just updated and tried without doing anything new and it didn't work:

import xarray as xr url = 'https://opendap.co-ops.nos.noaa.gov/thredds/dodsC/NOAA/SFBOFS/MODELS/2022/06/30/nos.sfbofs.fields.f014.20220630.t09z.nc' # this file will not be available in a few days but one for the present day will be available ds = xr.open_dataset(url, drop_variables='Itime2')

Same error message as before:

MissingDimensionsError: 'siglay' has more than 1-dimension and the same name as one of its dimensions ('siglay', 'node'). xarray disallows such variables because they conflict with the coordinates used to label dimensions.

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
1066784566 https://github.com/pydata/xarray/issues/2233#issuecomment-1066784566 https://api.github.com/repos/pydata/xarray/issues/2233 IC_kwDOAMm_X84_ldc2 iuryt 5797727 2022-03-14T13:27:00Z 2022-03-14T13:30:24Z NONE

Hi

For now, I found a workaround loading and renaming the problematic coordinates with netCDF4.Dataset(). Soon I will post this and other solutions for this model output in iuryt/FVCOMpy.

For now, you could try: ``` import xarray as xr from netCDF4 import Dataset

define year and month to be read

year = 2019 month = 5

we could use this to run a loop through the years/months we need

list problematic coordinates

drop_variables = ['siglay','siglev']

base url for openDAP server

url = "".join(["http://www.smast.umassd.edu:8080/thredds/dodsC/models/fvcom/", f"NECOFS/Archive/NECOFS_GOM/{year}/gom4_{year}{month:02d}.nc?"])

lazy load of the data

ds = xr.open_dataset(url,drop_variables=drop_variables,decode_times=False)

load data with netCDF4

nc = Dataset(url)

load the problematic coordinates

coords = {name:nc[name] for name in drop_variables}

function to extract ncattrs from Dataset()

get_attrs = lambda name: {attr:coords[name].getncattr(attr) for attr in coords[name].ncattrs()}

function to convert from Dataset() to xr.DataArray()

nc2xr = lambda name: xr.DataArray(coords[name],attrs=get_attrs(name),name=f'{name}_coord',dims=(f'{name}','node'))

merge xr.DataArray() objects

coords = xr.merge([nc2xr(name) for name in coords.keys()])

reassign to the main xr.Dataset()

ds = ds.assign_coords(coords) ```

Leaving it here in case someone fall into the same problem.

{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
641410484 https://github.com/pydata/xarray/issues/2233#issuecomment-641410484 https://api.github.com/repos/pydata/xarray/issues/2233 MDEyOklzc3VlQ29tbWVudDY0MTQxMDQ4NA== angelra 6598749 2020-06-09T16:18:21Z 2020-06-09T16:18:21Z NONE

I had to go around this issue and not use xarray but pandas instead or plain netdcf4

nc = netCDF4.Dataset(input_file) h = nc.variables[vname] times = nc.variables['time'] jd = netCDF4.num2date(times[:],times.units) hs = pd.Series(h[:,station],index=jd)

I would love to know if there is a way to do it over xarray since it is so nice to use. Best regards

{
    "total_count": 2,
    "+1": 2,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
629248902 https://github.com/pydata/xarray/issues/2233#issuecomment-629248902 https://api.github.com/repos/pydata/xarray/issues/2233 MDEyOklzc3VlQ29tbWVudDYyOTI0ODkwMg== stale[bot] 26384082 2020-05-15T13:53:58Z 2020-05-15T13:53:58Z 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 or remove the stale label; 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
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780
397596002 https://github.com/pydata/xarray/issues/2233#issuecomment-397596002 https://api.github.com/repos/pydata/xarray/issues/2233 MDEyOklzc3VlQ29tbWVudDM5NzU5NjAwMg== rsignell-usgs 1872600 2018-06-15T11:44:35Z 2018-06-15T11:44:35Z NONE

@rabernat , this unstructured grid model output follows the UGRID Conventions, which layer on top of the CF Conventions. The issue Xarray is having here is with the vertical coordinate however, so this issue could arise with any CF convention model where the vertical stretching function varies over the domain.

As requested, here is the ncdump of this URL: ``` jovyan@jupyter-rsignell-2dusgs:~$ ncdump -h http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM3_FORECAST.nc netcdf NECOFS_GOM3_FORECAST { dimensions: time = UNLIMITED ; // (145 currently) maxStrlen64 = 64 ; nele = 99137 ; node = 53087 ; siglay = 40 ; three = 3 ; variables: float lon(node) ; lon:long_name = "nodal longitude" ; lon:standard_name = "longitude" ; lon:units = "degrees_east" ; float lat(node) ; lat:long_name = "nodal latitude" ; lat:standard_name = "latitude" ; lat:units = "degrees_north" ; float xc(nele) ; xc:long_name = "zonal x-coordinate" ; xc:units = "meters" ; float yc(nele) ; yc:long_name = "zonal y-coordinate" ; yc:units = "meters" ; float lonc(nele) ; lonc:long_name = "zonal longitude" ; lonc:standard_name = "longitude" ; lonc:units = "degrees_east" ; float latc(nele) ; latc:long_name = "zonal latitude" ; latc:standard_name = "latitude" ; latc:units = "degrees_north" ; float siglay(siglay, node) ; siglay:long_name = "Sigma Layers" ; siglay:standard_name = "ocean_sigma_coordinate" ; siglay:positive = "up" ; siglay:valid_min = -1. ; siglay:valid_max = 0. ; siglay:formula_terms = "sigma: siglay eta: zeta depth: h" ; float h(node) ; h:long_name = "Bathymetry" ; h:standard_name = "sea_floor_depth_below_geoid" ; h:units = "m" ; h:coordinates = "lat lon" ; h:type = "data" ; h:mesh = "fvcom_mesh" ; h:location = "node" ; int nv(three, nele) ; nv:long_name = "nodes surrounding element" ; nv:cf_role = "face_node_connnectivity" ; nv:start_index = 1 ; float time(time) ; time:long_name = "time" ; time:units = "days since 1858-11-17 00:00:00" ; time:format = "modified julian day (MJD)" ; time:time_zone = "UTC" ; time:standard_name = "time" ; float zeta(time, node) ; zeta:long_name = "Water Surface Elevation" ; zeta:units = "meters" ; zeta:standard_name = "sea_surface_height_above_geoid" ; zeta:coordinates = "time lat lon" ; zeta:type = "data" ; zeta:missing_value = -999. ; zeta:field = "elev, scalar" ; zeta:coverage_content_type = "modelResult" ; zeta:mesh = "fvcom_mesh" ; zeta:location = "node" ; int nbe(three, nele) ; nbe:long_name = "elements surrounding each element" ; float u(time, siglay, nele) ; u:long_name = "Eastward Water Velocity" ; u:units = "meters s-1" ; u:type = "data" ; u:missing_value = -999. ; u:field = "ua, scalar" ; u:coverage_content_type = "modelResult" ; u:standard_name = "eastward_sea_water_velocity" ; u:coordinates = "time siglay latc lonc" ; u:mesh = "fvcom_mesh" ; u:location = "face" ; float v(time, siglay, nele) ; v:long_name = "Northward Water Velocity" ; v:units = "meters s-1" ; v:type = "data" ; v:missing_value = -999. ; v:field = "va, scalar" ; v:coverage_content_type = "modelResult" ; v:standard_name = "northward_sea_water_velocity" ; v:coordinates = "time siglay latc lonc" ; v:mesh = "fvcom_mesh" ; v:location = "face" ; float ww(time, siglay, nele) ; ww:long_name = "Upward Water Velocity" ; ww:units = "meters s-1" ; ww:type = "data" ; ww:coverage_content_type = "modelResult" ; ww:standard_name = "upward_sea_water_velocity" ; ww:coordinates = "time siglay latc lonc" ; ww:mesh = "fvcom_mesh" ; ww:location = "face" ; float ua(time, nele) ; ua:long_name = "Vertically Averaged x-velocity" ; ua:units = "meters s-1" ; ua:type = "data" ; ua:missing_value = -999. ; ua:field = "ua, scalar" ; ua:coverage_content_type = "modelResult" ; ua:standard_name = "barotropic_eastward_sea_water_velocity" ; ua:coordinates = "time latc lonc" ; ua:mesh = "fvcom_mesh" ; ua:location = "face" ; float va(time, nele) ; va:long_name = "Vertically Averaged y-velocity" ; va:units = "meters s-1" ; va:type = "data" ; va:missing_value = -999. ; va:field = "va, scalar" ; va:coverage_content_type = "modelResult" ; va:standard_name = "barotropic_northward_sea_water_velocity" ; va:coordinates = "time latc lonc" ; va:mesh = "fvcom_mesh" ; va:location = "face" ; float temp(time, siglay, node) ; temp:long_name = "temperature" ; temp:standard_name = "sea_water_potential_temperature" ; temp:units = "degrees_C" ; temp:coordinates = "time siglay lat lon" ; temp:type = "data" ; temp:coverage_content_type = "modelResult" ; temp:mesh = "fvcom_mesh" ; temp:location = "node" ; float salinity(time, siglay, node) ; salinity:long_name = "salinity" ; salinity:standard_name = "sea_water_salinity" ; salinity:units = "0.001" ; salinity:coordinates = "time siglay lat lon" ; salinity:type = "data" ; salinity:coverage_content_type = "modelResult" ; salinity:mesh = "fvcom_mesh" ; salinity:location = "node" ; int fvcom_mesh ; fvcom_mesh:cf_role = "mesh_topology" ; fvcom_mesh:topology_dimension = 2 ; fvcom_mesh:node_coordinates = "lon lat" ; fvcom_mesh:face_coordinates = "lonc latc" ; fvcom_mesh:face_node_connectivity = "nv" ;

// global attributes: :title = "NECOFS GOM3 (FVCOM) - Northeast US - Latest Forecast" ; :institution = "School for Marine Science and Technology" ; :source = "FVCOM_3.0" ; :Conventions = "CF-1.0, UGRID-1.0" ; :summary = "Latest forecast from the FVCOM Northeast Coastal Ocean Forecast System using an newer, higher-resolution GOM3 mesh (GOM2 was the preceding mesh)" ;

```

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Problem opening unstructured grid ocean forecasts with 4D vertical coordinates 332471780

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options:

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);
Powered by Datasette · Queries took 247.816ms · About: xarray-datasette
  • Sort ascending
  • Sort descending
  • Facet by this
  • Hide this column
  • Show all columns
  • Show not-blank rows