issue_comments

@jhamman Sorry for the delayed response (and the even more delayed PR)! I'd love to finish this up, apologies for having this fall to the wayside.

Lemme look at it a bit after work and see how much work it would take to resolve the merge conflicts, though it seems like the issue is only in the test_conventions.py file, so it may not be that difficult to resolve.

This seems a little too magical to me. How would we know if the dataset dimension was added intentionally or not?

Yeah, that's a fair point. I'll put together something that uses an optional list of dimensions to concatenate over. Thanks!

Hey @shoyer,

Sorry for the delayed response. Passing a list of dimensions over which to concatenate over seems like it would be the easiest workaround with the fewest questions asked. As you mentioned, every dimension gets a variable by the time it is a dataset, so another option (that I'll admit I haven't thought all the way through and may not even work) would be to first check if decode_cf is working on a Dataset or AbstractDataStore (e: which it already does anyway), and then decide whether to concatenate over a dimension or not. I could see the latter idea not working out so well, but I'd be curious about your thoughts.

Either way, I can put something together this week and open up a PR.

Redeeming myself (only a little bit) from my previous message here:

@akrherz Was messing around with this a bit, this seems to work ok. This gets rid of unnecessary dimensions, concatenates string arrays, and turns it into a pandas DataFrame:

``` [In [1]: import xarray as xr

In [2]: ds = xr.open_dataset('20160430_1600.nc', decode_cf=True, mask_and_scale=False, decode_times=False) # xarray has issue decoding the times, so you'll have to do this in pandas.

In [3]: vars_to_drop = [k for k in ds.variables.iterkeys() if ('recNum' not in ds[k].dims)]

In [4]: ds = ds.drop(vars_to_drop)

In [5]: df = ds.to_dataframe()

In [6]: df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 6277 entries, 0 to 6276 Data columns (total 93 columns): invTime 6277 non-null int32 prevRecord 6277 non-null int32 isOverflow 6277 non-null int32 secondsStage1_2 6277 non-null int32 secondsStage3 6277 non-null int32 providerId 6277 non-null object stationId 6277 non-null object handbook5Id 6277 non-null object](url) ~snip~ ```

A bit hacky, but it works.

@shoyer: You're right in that MADIS netCDF files are (imo) poorly formatted. There is also the issue of xarray.decode_cf() not concatenating the string arrays even after fixing the _FillValue, missing_value conflict (hence requiring passing decode_cf=False when opening up the MADIS netCDF file). After looking at the decode_cf code, though, I don't think this is a bug per se (some quick debugging revealed that it doesn't seem like any variable in this netCDF file gets past this check), though if you feel this may in fact be a bug, I can look a bit more into it.

Unfortunately, this does mean I have to do a lot of "manual cleaning" of the netCDF file before exporting as a DataFrame, but it is easy to write a set of functions to accomplish this for you. That said, I can't c/p the exact code (for work-related reasons). I'm not sure how helpful this is, but when working with MADIS netCDF data, I more or less do the following as a workaround: 1. Open up the MADIS netCDF file, fix the _FillValue and missing_value conflict in the variables. 2. Drop the variables I don't want (and there is a lot of filler in MADIS netCDF files). 3. Concatenate the string arrays (e.g. stationId, dataProvider). 4. Turn into a pandas DataFrame.

Though reading over it, that is kind of a draw the owl-esque response, though. :/