id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 578427969,MDU6SXNzdWU1Nzg0Mjc5Njk=,3853,Custom Table when opening GRIB Files,2444231,open,0,,,8,2020-03-10T08:58:42Z,2022-04-27T14:34:02Z,,NONE,,,,"Hello, I'd like to open some old-school Grib files from one of our climate models. I'm using the `PyNIO` backend for this; which works pretty well so far -- at least the data opens! However, I am getting errors: ``` warning:NclGRIB: Unrecognized parameter table (center 252, subcenter 1, table 128), defaulting to NCEP operational table: variable names and units may be incorrect ``` So, would it somehow be possible to provide a code table to be used when opening grb files? I have files next to my output where the codes are stored. An example is below. I can imagine something like: ```python ds = xr.open_dataset(""/path/to/file.grb"", engine=""pynio"", code_table=""/path/to/codetab"") ``` Would this be difficult to implement? Cheers, Paul ``` 130 47 st 0.00 1.00 temperature [K] 138 47 svo 0.00 1.00 vorticity [1/s] 152 1 lsp 0.00 1.00 log surface pressure [] 155 47 sd 0.00 1.00 divergence [1/s] 133 47 q 0.00 1.00 specific humidity [kg/kg] 153 47 xl 0.00 1.00 cloud water [kg/kg] 154 47 xi 0.00 1.00 cloud ice [kg/kg] 50 1 rsdscs 0.00 1.00 surface downwelling shortwave radiation flux (clear-sky) [W/m**2] 51 1 rsuscs 0.00 1.00 surface upwelling shortwave radiation flux (clear-sky) [W/m**2] 52 1 rsdscs_na 0.00 1.00 instantaneous surface downwelling shortwave radiation flux (clear-sky) [W/m**2] 53 1 rsuscs_na 0.00 1.00 instantaneous surface upwelling shortwave radiation flux (clear-sky) [W/m**2] 54 1 q2m 0.00 1.00 2m specific humidity [] 55 1 rh2m 0.00 1.00 2m relative humidity [] 56 1 rsdsiac 0.00 1.00 surface downwelling shortwave radiation over ice [W/m**2] 57 1 rsdswac 0.00 1.00 surface downwelling shortwave radiation over water [W/m**2] 58 1 rsusiac 0.00 1.00 surface upwelling shortwave radiation over ice [W/m**2] 59 1 rsuswac 0.00 1.00 surface upwelling shortwave radiation over water [W/m**2] 60 1 rldsiac 0.00 1.00 surface downwelling longwave radiation over ice [W/m**2] 61 1 rldswac 0.00 1.00 surface downwelling longwave radiation over water [W/m**2] 62 1 rlusiac 0.00 1.00 surface upwelling longwave radiation over ice [W/m**2] 63 1 rluswac 0.00 1.00 surface upwelling longwave radiation over water [W/m**2] 64 1 sh_vdiff 0.00 1.00 column heating due to vertical diffusion [W/m**2] 65 1 ev_vdiff 0.00 1.00 column moistening due to vertical diffusion [kg/m**2s] 66 1 ch_concloud 0.00 1.00 convective heating [W/m**s] 67 1 cw_concloud 0.00 1.00 convective moistening [kg/m**2s] 68 1 fage 0.00 1.00 aging factor of snow on ice [] 69 1 snifrac 0.00 1.00 fraction of ice covered with snow [] 70 1 barefrac 0.00 1.00 bare ice fraction [] 71 1 alsom 0.00 1.00 albedo of melt ponds [] 72 1 alsobs 0.00 1.00 albedo of bare ice and snow without ponds [] 73 1 sicepdw 0.00 1.00 melt pond depth on sea-ice [m] 74 1 sicepdi 0.00 1.00 ice thickness on melt pond [m] 75 1 tsicepdi 0.00 1.00 ice temperature on frozen melt pond [K] 76 1 sicepres 0.00 1.00 residual heat flux [W/m**2] 77 1 ameltdepth 0.00 1.00 total melt pond depth [m] 78 1 ameltfrac 0.00 1.00 fract area of melt ponds on sea-ice [] 79 1 albedo_vis_dir 0.00 1.00 surface albedo visible range direct [] 80 1 albedo_nir_dir 0.00 1.00 surface albedo NIR range direct [] 81 1 albedo_vis_dif 0.00 1.00 surface albedo visible range diffuse [] 82 1 albedo_nir_dif 0.00 1.00 surface albedo NIR range diffuse [] 83 1 ocu 0.00 1.00 ocean eastward velocity [m/s] 84 1 ocv 0.00 1.00 ocean northward velocity [m/s] 85 1 tradl 0.00 1.00 thermal radiation 200mb [W/m**2] 86 1 sradl 0.00 1.00 solar radiation 200mb [W/m**2] 87 1 trafl 0.00 1.00 thermal radiation 200mb (clear sky) [W/m**2] 88 1 srafl 0.00 1.00 solar radiation 200mb (clear sky) [W/m**2] 89 1 amlcorac 0.00 1.00 mixed layer flux correction [W/m**2] 91 1 trfliac 0.00 1.00 LW flux over ice [W/m**2] 92 1 trflwac 0.00 1.00 LW flux over water [W/m**2] 93 1 trfllac 0.00 1.00 LW flux over land [W/m**2] 94 1 sofliac 0.00 1.00 SW flux over ice [W/m**2] 95 1 soflwac 0.00 1.00 SW flux over water [W/m**2] 96 1 sofllac 0.00 1.00 SW flux over land [W/m**2] 97 1 friac 0.00 1.00 ice cover (fraction of grid box) [] 100 1 albedo_vis 0.00 1.00 surface albedo visible range [] 101 1 albedo_nir 0.00 1.00 surface albedo NIR range [] 102 1 tsi 0.00 1.00 surface temperature of ice [K] 103 1 tsw 0.00 1.00 surface temperature of water [K] 104 1 ustri 0.00 1.00 zonal wind stress over ice [Pa] 105 1 vstri 0.00 1.00 meridional wind stress over ice [Pa] 106 1 ustrw 0.00 1.00 zonal wind stress over water [Pa] 107 1 vstrw 0.00 1.00 meridional wind stress over water [Pa] 108 1 ustrl 0.00 1.00 zonal wind stress over land [Pa] 109 1 vstrl 0.00 1.00 meridional wind stress over land [Pa] 110 1 ahfliac 0.00 1.00 latent heat flux over ice [W/m**2] 111 1 ahflwac 0.00 1.00 latent heat flux over water [W/m**2] 112 1 ahfllac 0.00 1.00 latent heat flux over land [W/m**2] 113 1 evapiac 0.00 1.00 evaporation over ice [kg/m**2s] 114 1 evapwac 0.00 1.00 evaporation over water [kg/m**2s] 115 1 evaplac 0.00 1.00 evaporation over land [kg/m**2s] 116 1 az0i 0.00 1.00 roughness length over ice [m] 117 1 az0w 0.00 1.00 roughness length over water [m] 118 1 az0l 0.00 1.00 roughness length over land [m] 119 1 ahfsiac 0.00 1.00 sensible heat flux over ice [W/m**2] 120 1 ahfswac 0.00 1.00 sensible heat flux over water [W/m**2] 121 1 ahfslac 0.00 1.00 sensible heat flux over land [W/m**2] 122 1 alsoi 0.00 1.00 albedo of ice [] 123 1 alsow 0.00 1.00 albedo of water [] 124 1 alsol 0.00 1.00 albedo of land [] 125 1 ahfice 0.00 1.00 conductive heat flux [W/m**2] 126 1 qres 0.00 1.00 residual heat flux for melting sea ice [W/m**2] 129 1 geosp 0.00 1.00 surface geopotential (orography) [m**2/s**2] 134 1 aps 0.00 1.00 surface pressure [Pa] 137 1 apmeb 0.00 1.00 vertic integr tendenc of water [kg/m**2s] 139 1 tslm1 0.00 1.00 surface temperature of land [K] 140 1 ws 0.00 1.00 soil wetness [m] 141 1 sn 0.00 1.00 snow depth [m] 142 1 aprl 0.00 1.00 large scale precipitation [kg/m**2s] 143 1 aprc 0.00 1.00 convective precipitation [kg/m**2s] 144 1 aprs 0.00 1.00 snow fall [kg/m**2s] 145 1 vdis 0.00 1.00 boundary layer dissipation [W/m**2] 146 1 ahfs 0.00 1.00 sensible heat flux [W/m**2] 147 1 ahfl 0.00 1.00 latent heat flux [W/m**2] 150 1 xivi 0.00 1.00 vertically integrated cloud ice [kg/m**2] 157 47 relhum 0.00 1.00 relative humidity [] 164 1 aclcov 0.00 1.00 total cloud cover [] 165 1 u10 0.00 1.00 10m u-velocity [m/s] 166 1 v10 0.00 1.00 10m v-velocity [m/s] 167 1 temp2 0.00 1.00 2m temperature [K] 168 1 dew2 0.00 1.00 2m dew point temperature [K] 169 1 tsurf 0.00 1.00 surface temperature [K] 171 1 wind10 0.00 1.00 10m windspeed [m/s] 172 1 slm 0.00 1.00 land sea mask (1=land, 0=sea/lakes) [] 175 1 albedo 0.00 1.00 surface albedo [] 176 1 srads 0.00 1.00 net surface solar radiation [W/m**2] 177 1 trads 0.00 1.00 net surface thermal radiation [W/m**2] 178 1 srad0 0.00 1.00 net top solar radiation [W/m**2] 179 1 trad0 0.00 1.00 top thermal radiation (OLR) [W/m**2] 180 1 ustr 0.00 1.00 u-stress [Pa] 181 1 vstr 0.00 1.00 v-stress [Pa] 182 1 evap 0.00 1.00 evaporation [kg/m**2s] 184 1 srad0d 0.00 1.00 top incoming solar radiation [W/m**2] 185 1 srafs 0.00 1.00 net surface solar radiation (clear sky) [W/m**2] 186 1 trafs 0.00 1.00 net surface therm radiation (clear sky) [W/m**2] 187 1 sraf0 0.00 1.00 net top solar radiation (clear sky) [W/m**2] 188 1 traf0 0.00 1.00 net top thermal radiation (clear sky) [W/m**2] 193 1 wl 0.00 1.00 skin reservoir content [m] 194 1 slf 0.00 1.00 sea land fraction [] 197 1 vdisgw 0.00 1.00 gravity wave dissipation [W/m**2] 201 1 t2max 0.00 1.00 maximum 2m temperature [K] 202 1 t2min 0.00 1.00 minimum 2m temperature [K] 203 1 srad0u 0.00 1.00 top solar radiation upward [W/m**2] 204 1 sradsu 0.00 1.00 surface solar radiation upward [W/m**2] 205 1 tradsu 0.00 1.00 surface thermal radiation upward [W/m**2] 208 1 ahfcon 0.00 1.00 conductive heat flux through ice [W/m**2] 209 1 ahfres 0.00 1.00 melting of ice [W/m**2] 210 1 seaice 0.00 1.00 ice cover (fraction of 1-SLM) [] 211 1 siced 0.00 1.00 ice depth [m] 213 1 gld 0.00 1.00 glacier depth [m] 214 1 sni 0.00 1.00 water equivalent of snow on ice [m] 216 1 wimax 0.00 1.00 maximum 10m-wind speed [m/s] 217 1 topmax 0.00 1.00 max height of conv cloud tops [Pa] 223 47 aclcac 0.00 1.00 cloud cover [] 229 1 wsmx 0.00 1.00 field capacity of soil [m] 230 1 qvi 0.00 1.00 vertically integrated water vapor [kg/m**2] 231 1 xlvi 0.00 1.00 vertically integrated cloud water [kg/m**2] 232 1 glac 0.00 1.00 fraction of land covered by glaciers [] 233 1 snc 0.00 1.00 snow depth at the canopy [m] 235 1 abso4 0.00 1.00 antropogenic sulfur burden [kg/m**2] 236 47 ao3 0.00 1.00 ipcc ozone [kg/kg] 237 1 tropo 0.00 1.00 WMO defined tropopause height [Pa] 238 1 thvsig 0.00 1.00 stddev virt pot temp at halflev klevm1 [K] 239 47 tpot 0.00 1.00 potential temperature [K] ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3853/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 894498459,MDU6SXNzdWU4OTQ0OTg0NTk=,5332,Progressbar for groupby operations?,2444231,open,0,,,2,2021-05-18T15:19:19Z,2021-05-19T01:27:55Z,,NONE,,,," I recently learned that `tqdm` can automatically create a progress bar for you when you are doing expensive groupby/map operations. See for instance here: https://stackoverflow.com/questions/18603270/progress-indicator-during-pandas-operations Would it be simple to implement something similar in Xarray? The documentation seems to read as if the groupby is heavily inspired by pandas. - - - - **Is your feature request related to a problem? Please describe.** No, everything works as expected, this would just be a ""quality of life"" improvement. **Describe the solution you'd like** The [implementation in tqdm](https://github.com/tqdm/tqdm/blob/bcce20f771a16cb8e4ac5cc5b2307374a2c0e535/tqdm/_tqdm_pandas.py) states: ``` Registers the given `tqdm` instance with `pandas.core.groupby.DataFrameGroupBy.progress_apply`. ``` I suppose something similar would need to be implemented in Xarray, and then we might be able to copy the tqdm logic. **Describe alternatives you've considered** I could loop over whatever dimension I have and make my own progress bar, but that seems like defeating the purpose of groupby. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5332/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue