Hi Ken:
It all depends whether it has to work with things like NCO, or just working
with Python and the like would be adequate. You original example of a file
would work fin with Python (I find the lat and lon indices I want, extract
those from the time series). But if you want something that will work with
NCO, see:
http://nco.sourceforge.net/nco.html#Irregular-grids.
I don't know about CDO, you would have to see any options it has for irregular
grids. The options described above for NCO would be easy to handle in R or
Python also.
HTH,
-Roy
> On Mar 18, 2019, at 4:25 PM, Ken Mankoff <mankoff@xxxxxxxxx> wrote:
>
>
> I could always grid the data - I have 20,000 outlets around the coast of
> Greenland at 90 m resolution. The total number of cells is 500 million. This
> seems inefficient, but the 499,980,000 empty cells do compress quite well. It
> is ~15 MB/day (5 GB/year). Maybe compressing multiple days improves on this?
> 5 GB/year is tractable for 40 years of data.
>
> But as someone who works with NetCDF, wouldn't this data presentation make
> you cringe? On the other hand, NetCDF works best when gridded, right?
>
> -k.
>
>
> On 2019-03-18 at 16:19 -0700, Gus Correa <gus@xxxxxxxxxxxxxxxxx> wrote...
>> Hi Ken
>>
>> That is true.
>> I suppose both CDO and NCO (ncks) assume the lat and lon are
>> monotonic (increasing or decreasing) coordinate variables, and that
>> runoff has (time,lat,lon) dimensions, not (time,ID).
>> ID is not a coordinate, it is just a label for your observation stations, I
>> guess.
>>
>> You could devise a more elaborate scheme to define lat and lon dimensions,
>> then,
>> lat(lat) and lon(lon) coordinate variables, and from there create a 3D
>> runoff(time,lat,lon) variable.
>> There are several hurdles though:
>> 1) The values of lat and lon in your CSV file may have repetitions (this
>> affects the actual lenght of each dimension, which may be <20000).
>> 2) The values of lat and lon in your CSV file may not be monotonically
>> ordered (either in increasing or decreasing order).
>> I didn't spot any repetitions in the sample file you sent (but the full
>> file may have repetitions),
>> but the lat and lon are definitely not monotonically ordered,
>> they can go up, then down, then up again ...
>> Bona fide coordinate variables must be monotonic.
>> 3) Even if you weed out repetitions in lat or lon, and sort them in
>> increasing or decreasing order,
>> you would have to exchange also the corresponding runoff values, so that
>> they continue to belong to the correct station/location/ID,
>> i.e. sort the whole file with (lat,lon) as primary and secondary keys.
>>
>> Maybe Python has a sort routine that does all that for you gracefully, some
>> variant of qsort perhaps.
>>
>> Gus
>>
>>
>> On Mon, Mar 18, 2019 at 6:50 PM Ken Mankoff <mankoff@xxxxxxxxx> wrote:
>>
>>> Hi Sourish, Gus, and Elizabeth,
>>>
>>> Thank you all for your suggestions. I think I've found something that
>>> works, except for one issue. Please excuse my likely incorrect use of
>>> terminology - being new to NetCDF creation I may say something incorrect,
>>> but I hope the data dump below speaks for itself.
>>>
>>> Because my data is 2D (time, ID), then those are the dimensions, and
>>> lon,lat,x,y become variables on the ID dimension. This means my standard
>>> netcdf tools for slicing based on spatial dimension don't work. For example,
>>>
>>> cdo sellonlatbox,83.5,85,-27,-28 ds.nc bar.nc
>>>
>>> or
>>>
>>> ncks -d lat,83.5,85 -d lon,-27,-28 ds.nc bar.nc
>>> # ncks: ERROR dimension lat is not in input file
>>>
>>> Is there a way to make the data 2D but have the 2nd dimension be
>>> (lon,lat)? Even if yes, I don't imagine the cdo and ncks tools would work
>>> on that dimension... Is there a cdo, nco, or ncks (or other) simple tool
>>> I'm missing that can work with this non-gridded data the way those tools do
>>> so easily work with gridded data?
>>>
>>>
>>> Anway, here is the Python xarray code I got working to produce the NetCDF
>>> file, reading in the 'foo.csv' from my previous email and generating ds.nc.
>>> Once I understood the NetCDF structure from the file Sourish provided, I
>>> was able to generate something similar using a higher level API - one that
>>> takes care of time units, calendar, etc. I leave out (x,y,elev) for brevity.
>>>
>>>
>>> -k.
>>>
>>>
>>>
>>> df = pd.read_csv('foo.csv', index_col=0, header=[0,1,2,3,4,5])
>>> df.index = pd.to_datetime(df.index)
>>>
>>> # Build the dataset
>>> ds = xr.Dataset()
>>> ds['lon'] = (('ID'), df.columns.get_level_values('lon'))
>>> ds['lat'] = (('ID'), df.columns.get_level_values('lat'))
>>> ds['runoff'] = (('time', 'ID'), df.values)
>>> ds['ID'] = df.columns.get_level_values('ID')
>>> ds['time'] = df.index
>>>
>>> # Add metadata
>>> ds['lon'].attrs['units'] = 'Degrees East'
>>> ds['lon'].attrs['long_name'] = 'Longitude'
>>> ds['lat'].attrs['units'] = 'Degrees North'
>>> ds['lat'].attrs['long_name'] = 'Latitude'
>>> ds['runoff'].attrs['units'] = 'm^3/day'
>>> ds['ID'].attrs['long_name'] = 'Basin ID'
>>>
>>> ds.to_netcdf('ds.nc')
>>>
>>>
>>>
>>>
>>> And here is the ncdump of the file
>>>
>>>
>>>
>>>
>>>
>>> netcdf ds {
>>> dimensions:
>>> ID = 10 ;
>>> time = 5 ;
>>> variables:
>>> string lon(ID) ;
>>> lon:units = "Degrees East" ;
>>> lon:long_name = "Longitude" ;
>>> string lat(ID) ;
>>> lat:units = "Degrees North" ;
>>> lat:long_name = "Latitude" ;
>>> double runoff(time, ID) ;
>>> runoff:_FillValue = NaN ;
>>> runoff:units = "m^3/day" ;
>>> runoff:long_name = "RACMO runoff" ;
>>> string ID(ID) ;
>>> ID:long_name = "Basin ID" ;
>>> int64 time(time) ;
>>> time:units = "days since 1980-01-01 00:00:00" ;
>>> time:calendar = "proleptic_gregorian" ;
>>>
>>> // global attributes:
>>> :Creator = "Ken Mankoff" ;
>>> :Contact = "kdm@xxxxxxx" ;
>>> :Institution = "GEUS" ;
>>> :Version = 0.1 ;
>>> data:
>>>
>>> lon = "-27.983", "-27.927", "-27.894", "-28.065", "-28.093", "-28.106",
>>> "-28.155", "-27.807", "-27.455", "-27.914" ;
>>>
>>> lat = "83.505", "83.503", "83.501", "83.502", "83.501", "83.499",
>>> "83.498",
>>> "83.485", "83.471", "83.485" ;
>>>
>>> runoff =
>>> 0.023, 0.01, 0.023, 0.005, 0, 0, 0, 0, 0, 0,
>>> 0.023, 0.01, 0.023, 0.005, 0, 0, 0, 0, 0, 0,
>>> 0.024, 0.013, 0.023, 0.005, 0, 0, 0, 0, 0, 0,
>>> 0.025, 0.012, 0.023, 0.005, 0, 42, 0, 0, 0, 0,
>>> 0.023, 0.005, 0.023, 0.005, 0, 0, 0, 0, 0, 0 ;
>>>
>>> ID = "1", "2", "5", "8", "9", "10", "12", "13", "15", "16" ;
>>>
>>> time = 0, 1, 2, 3, 4 ;
>>> }
>>>
>>> _______________________________________________
>>> NOTE: All exchanges posted to Unidata maintained email lists are
>>> recorded in the Unidata inquiry tracking system and made publicly
>>> available through the web. Users who post to any of the lists we
>>> maintain are reminded to remove any personal information that they
>>> do not want to be made public.
>>>
>>>
>>> netcdfgroup mailing list
>>> netcdfgroup@xxxxxxxxxxxxxxxx
>>> For list information or to unsubscribe, visit:
>>> http://www.unidata.ucar.edu/mailing_lists/
>>>
>
> _______________________________________________
> NOTE: All exchanges posted to Unidata maintained email lists are
> recorded in the Unidata inquiry tracking system and made publicly
> available through the web. Users who post to any of the lists we
> maintain are reminded to remove any personal information that they
> do not want to be made public.
>
>
> netcdfgroup mailing list
> netcdfgroup@xxxxxxxxxxxxxxxx
> For list information or to unsubscribe, visit:
> http://www.unidata.ucar.edu/mailing_lists/
**********************
"The contents of this message do not reflect any position of the U.S.
Government or NOAA."
**********************
Roy Mendelssohn
Supervisory Operations Research Analyst
NOAA/NMFS
Environmental Research Division
Southwest Fisheries Science Center
***Note new street address***
110 McAllister Way
Santa Cruz, CA 95060
Phone: (831)-420-3666
Fax: (831) 420-3980
e-mail: Roy.Mendelssohn@xxxxxxxx www: http://www.pfeg.noaa.gov/
"Old age and treachery will overcome youth and skill."
"From those who have been given much, much will be expected"
"the arc of the moral universe is long, but it bends toward justice" -MLK Jr.
