issue_comments

@mmartini-usgs - Thanks for the questions. I'm going to close this now as it seems like you're up and going. In the future, we try to keep our "Usage Questions" to the xarray users google group or StackOverflow. Cheers!

Many thanks! I will try this. OK, since you asked for more details:

I have used xarray resample successfully on a file with ~3 million single ping ADCP ensembles, 17 variables of these with 1D and 2D data. Three lines of code in a handful of minutes to reduce that. On a middling laptop. Amazing.

Unintended behaviors from resample that I need to figure out:
-- a time offset is introduced. Hourly mean data is offset by 16 hours in one use case. 16 hours is odd. I may have my time set up wrong. Or it is a start transient from a filter. I've been reading code trying to figure out exactly what kind of algorithm pandas is using in resample (as best I can tell this is where xarray is getting its resample method). -- NaNs are introduced. I think I need to learn how to set mask_and_scale to prevent this.
-- when outputting to netCDF the "time' dimension got added to a variable that didn't and shouldn't have it.

On the menu next to learn/use: -- Multi-indexing and data reshaping -- slicing -- Separating an uneven time base into separate time series -- Calculations involving several of the variables at the same time (e.g. using xarray to perform the ADCP beam to earth rotations)

Where is all this going? Ultimately to produce mean current profile time series for data release (think hourly time-depth shaped dataframes) and bursts of hourly data (think hourly time-depth-sample shaped dataframes) on which to perform a variety of wave analysis.

This is my learn-python project, so apologies for the non-pythonic approach. I also need to preserve backwards compatibility with existing code and conventions (EPIC, historically, CF and thredds, going forward). The project is here: https://github.com/mmartini-usgs/ADCPy

@mmartini-usgs, an entire netCDF file (as long as it only has 1 group, which it most likely does if we're talking about standard atmospheric/oceanic data) would be the equivalent of an xarray.Dataset. Each variable could be represented as a pandas.DataFrame, but with a MultiIndex - an index with multiple levels, but which are consist across each level.

To start with, you should read in your data using the chunks keyword to open_dataset(); this turns all of the data you read into dask arrays. Then, you use xarray Dataset and DataArray operations to manipulate them. So you can start, instead, by opening your data:

python ds = xr.open_dataset('hugefile.nc', chunks={<fill me in>}) ds_lp = ds.resample('H','time','mean')

You'd have to choose chunks based on the dimensions of your data. Like @rabernat previously mentioned, it's very likely you can perform your entire workflow within xarray without every having to drop down to pandas; let us know if you can share more details

Hello Ryan,

I have read a bit about dask. Am I missing the Pandas Panel analog in Dask?

My data is in netcdf4, and the files can have as many as 17 variables or more. It's not clear how to get this easily into dask. In Pandas I think the entire netCDF file equates to a Panel. A single variable would be a DataFrame.

Rather than wandering around in the weeds, I could use a hint here. Do I really need to open the netCDF4 file, then iterate over my variables and deal them into a series of dask data frames? That seems very un-pythonic.

I tried this... presumably from here: http://www.unidata.ucar.edu/software/netcdf/docs/interoperability_hdf5.html I can open a netCDF4 file as "HDF5" using dask. Let's try a dask example (http://dask.pydata.org/en/latest/examples/dataframe-hdf5.html) with one of my netCDF files:

df = dd.read_hdf('reallybignetCDF4file.nc',key='/c') # this does not work

Thanks, Marinna

Many thanks, I will go learn about dask dataframes. Marinna

Marinna,

You are correct. In the present release of Xarray, converting to a pandas dataframe loads all of the data eagerly into memory as a regular pandas object, giving up dask's parallel capabilities and potentially consuming lots of memory. With chunked Xarray data, It would be preferable instead to convert to a dask.dataframe, rather than a regular pandas dataframe, which would carry over some of the performance benefits.

This is a known issue: https://github.com/pydata/xarray/issues/1462

With a solution in the works: https://github.com/pydata/xarray/pull/1489

So hopefully a release of Xarray in the near future will have the feature you seek.

Alternatively, if you describe the filtering, masking, and other QA/QC that you need to do in more detail, we may be able to help you accomplish this entirely within Xarray.

Good luck! Ryan

On Mon, Aug 28, 2017 at 2:02 PM, Marinna Martini notifications@github.com wrote:

Apologies for what is probably a very newbie question:

If I convert such a large file to pandas using to_dataframe() to gain access to more pandas methods, will I lose the speed and dask capabillity that is so wonderful in xarray?

I have a very large netCDF file (3 GB with 3 Million data points of 1-2 Hz ADCP data) that needs to be reduced to hourly or 10 min averages. xarray is perfect for this. I am exploring resample and other methods. It is amazingly fast doing this:

ds = xr.open_dataset('hugefile.nc') ds_lp = ds.resample('H','time','mean')

And an offset of about half a day is introduced to the data. Probably user error or due to filtering. To figure this out, I am looking at using resample in pandas directly, or multindexing and reshaping using methods that are not inherited from pandas by xarray, then back to xarray using to_xarray. I will also need to be masking data (and other things pandas can do) during a QA/QC process. It appears that pandas can do masking and xarray does not inherit masking?

Am I understanding the relationship between xarray and pandas correctly?

Thanks, Marinna

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