issue_comments

Ok. The strange thing with the spatial dimensions is, that the new syntax forces the user to tell exactly, on which dimension the mathematical operator for resampling (like sum) should be applied. The syntax is now data.resample(time="M").sum(dim="time",min_count=1).

That weird - two times giving the dimension. However, doing so the xarray is not summing up for, e.g. the first month, all values he finds in a specific dataaray, but only those values along the dimension time.

AND NOW, the good new is that I got the following picture with your 'min_count=0' and 'min_count=1':

To wrap it up. Your implementation works for timeseries - data. There is something strange with time-space data, which should be fixed. If this is fixed, it is worth to test in my evaluation environment. Do you have a feeling, why the new syntax is giving such strange behaviour? Shall we put the bug onto the issue list?

And maybe, it would be interesting to have in the future the min_count argument also available for the old syntax and not only the new. The reason: The dimension name is not flexible anymore - it cannot be a variable like dim=${dim}

Thanks, @fujiisoup .

I have good news and i have bad news.

A) Your min_count argument still seems to work only if using the new syntax for resample, i.e. data.resample($dim=$freq).sum(). I guess, this is due to the cancellation of the old syntax in the future. Using the old syntax data.resample(dim=$dim,freq=$freq,how=$oper) your code seems to ignore the min_count argument.

B) Your min_count argument is not allowed for type 'dataset' but only for type 'dataarray'. Starting with the dataset located here: https://swiftbrowser.dkrz.de/public/dkrz_c0725fe8741c474b97f291aac57f268f/GregorMoeller/ I've got the following message: data.resample(time="M").sum(min_count=1) TypeError: sum() got an unexpected keyword argument 'min_count'

Thus, I have tested your implementation only on dataarrays. I take the netcdf - array 'TOT_PREC' and try to compute the monthly sum: In [39]: data = array.open_dataset("eObs_gridded_0.22deg_rot_v14.0.TOT_PREC.1950-2016.nc_CutParamTimeUnitCor_FinalEvalGrid") In [40]: datamonth = data["TOT_PREC"].resample(time="M").sum() In [41]: datamonth Out[41]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 551833.25 , 465640.09375, 328445.90625, 836892.1875 , 503601.5 ], dtype=float32) Coordinates: time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ...

So, the xarray-package is still throwing away the dimensions in x- and y-direction. It has nothing to do with any min-count argument. THIS MUST BE A BUG OF XARRAY. The afore-mentioned dimensions only survive using the old-syntax: ``` In [41]: datamonth = data["TOT_PREC"].resample(dim="time",freq="M",how="sum") /usr/bin/ipython3:1: FutureWarning: .resample() has been modified to defer calculations. Instead of passing 'dim' and how="sum", instead consider using .resample(time="M").sum('time') #!/usr/bin/env python3

In [42]: datamonth Out[42]: <xarray.DataArray 'TOT_PREC' (time: 5, rlat: 136, rlon: 144)> array([[[ 0. , 0. , ..., 0. , 0. ], [ 0. , 0. , ..., 0. , 0. ], ..., [ 0. , 0. , ..., 44.900028, 41.400024], [ 0. , 0. , ..., 49.10001 , 46.5 ]]], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... * rlon (rlon) float32 -22.6 -22.38 -22.16 -21.94 -21.72 -21.5 -21.28 ... * rlat (rlat) float32 -12.54 -12.32 -12.1 -11.88 -11.66 -11.44 -11.22 ...

```

Nevertheless, I have started to use your min_count argument only at one point (the x- and y-dimensions do not matter). In that case, your implementation works fine: ``` In [46]: pointdata = data.isel(rlon=10,rlat=10) In [47]: pointdata["TOT_PREC"] Out[47]: <xarray.DataArray 'TOT_PREC' (time: 153)> array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, ... nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32) Coordinates: rlon float32 -20.4 rlat float32 -10.34 * time (time) datetime64[ns] 2006-05-01T12:00:00 2006-05-02T12:00:00 ... Attributes: standard_name: precipitation_amount long_name: Precipitation units: kg m-2 grid_mapping: rotated_pole cell_methods: time: sum

In [48]: pointdata["TOT_PREC"].resample(time="M").sum(min_count=1) Out[48]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ nan, nan, nan, nan, nan]) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... rlon float32 -20.4 rlat float32 -10.34

In [49]: pointdata["TOT_PREC"].resample(time="M").sum(min_count=0) Out[49]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 0., 0., 0., 0., 0.], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... rlon float32 -20.4 rlat float32 -10.34

```

Okay. Using the old resample-nomenclature I tried to compare the results with your modified code (min_count = 2) and with the tagged version 0.10.7 (no min_count argument). But I am not sure, if this also works. Do you examine the keywords given from the old-resample method?.

However, in the comparison I did not saw the nan's I expected over water.

!!!Correction!!!. The resample-example above gives also a missing lon-lat dimensions in case of unmodified model code. The resulting numbers are the same.

data_aggreg = data["TOT_PREC"].resample(time="M").sum() data_aggreg <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 551833.25 , 465640.09375, 328445.90625, 836892.1875 , 503601.5 ], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ...

Now I am a little bit puzzled. But ...

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IT SEEMS TO BE A BUG!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

If I do the resample process using the old nomenclature: data_aggreg = data["TOT_PREC"].resample(dim="time",how="sum",freq="M") it works. DO we have a bug in xarray?

Hello from me hopefully contributing some needfull things.

At first, I would like to comment that I checked out your code.

I ran the following code example using a datafile uploaded under the following link: https://swiftbrowser.dkrz.de/public/dkrz_c0725fe8741c474b97f291aac57f268f/GregorMoeller/

``` import xarray import matplotlib.pyplot as plt

data = xarray.open_dataset("eObs_gridded_0.22deg_rot_v14.0.TOT_PREC.1950-2016.nc_CutParamTimeUnitCor_FinalEvalGrid") data <xarray.Dataset> Dimensions: (rlat: 136, rlon: 144, time: 153) Coordinates: * rlon (rlon) float32 -22.6 -22.38 -22.16 -21.94 -21.72 -21.5 ... * rlat (rlat) float32 -12.54 -12.32 -12.1 -11.88 -11.66 -11.44 ... * time (time) datetime64[ns] 2006-05-01T12:00:00 ... Data variables: rotated_pole int32 ... TOT_PREC (time, rlat, rlon) float32 ... Attributes: CDI: Climate Data Interface version 1.8.0 (http://m... Conventions: CF-1.6 history: Thu Jun 14 12:34:59 2018: cdo -O -s -P 4 remap... CDO: Climate Data Operators version 1.8.0 (http://m... cdo_openmp_thread_number: 4

data_aggreg = data["TOT_PREC"].resample(time="M").sum(min_count=0) data_aggreg2 = data["TOT_PREC"].resample(time="M").sum(min_count=1) I have recognized that the min_count option at recent state technically only works for DataArrays and not for DataSets. However, more interesting is the fact that the dimensions are destroyed: data_aggreg <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 551833.25 , 465640.09375, 328445.90625, 836892.1875 , 503601.5 ], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... no longitude and latitude survives your operation. If I would use the the sum-operator on the full dataset (where maybe the code was not modified?) I got

data_aggreg = data.resample(time="M").sum() data_aggreg <xarray.Dataset> Dimensions: (rlat: 136, rlon: 144, time: 5) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... * rlon (rlon) float32 -22.6 -22.38 -22.16 -21.94 -21.72 -21.5 ... * rlat (rlat) float32 -12.54 -12.32 -12.1 -11.88 -11.66 -11.44 ... Data variables: rotated_pole (time) int64 1 1 1 1 1 TOT_PREC (time, rlat, rlon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... ```