issues

I seem to be having the same issue that was reported here: https://github.com/pydata/xarray/issues/1932, after upgrading pandas to 1.1.0. The error does not arise with pandas 1.0.3.

Example: import xarray as ds = xr.tutorial.load_dataset('air_temperature') da = ds.air da.sel(time=da.time[0])

Result is: `--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-12-31afccd16cef> in <module> ----> 1 da.sel(time=da.time[0]) 2 #da.time

~/miniconda3/envs/ats641/lib/python3.7/site-packages/xarray/core/dataarray.py in sel(self, indexers, method, tolerance, drop, indexers_kwargs) 1152 method=method, 1153 tolerance=tolerance, -> 1154 indexers_kwargs, 1155 ) 1156 return self._from_temp_dataset(ds)

~/miniconda3/envs/ats641/lib/python3.7/site-packages/xarray/core/dataset.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs) 2100 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel") 2101 pos_indexers, new_indexes = remap_label_indexers( -> 2102 self, indexers=indexers, method=method, tolerance=tolerance 2103 ) 2104 result = self.isel(indexers=pos_indexers, drop=drop)

~/miniconda3/envs/ats641/lib/python3.7/site-packages/xarray/core/coordinates.py in remap_label_indexers(obj, indexers, method, tolerance, **indexers_kwargs) 395 396 pos_indexers, new_indexes = indexing.remap_label_indexers( --> 397 obj, v_indexers, method=method, tolerance=tolerance 398 ) 399 # attach indexer's coordinate to pos_indexers

~/miniconda3/envs/ats641/lib/python3.7/site-packages/xarray/core/indexing.py in remap_label_indexers(data_obj, indexers, method, tolerance) 268 coords_dtype = data_obj.coords[dim].dtype 269 label = maybe_cast_to_coords_dtype(label, coords_dtype) --> 270 idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance) 271 pos_indexers[dim] = idxr 272 if new_idx is not None:

~/miniconda3/envs/ats641/lib/python3.7/site-packages/xarray/core/indexing.py in convert_label_indexer(index, label, index_name, method, tolerance) 188 else: 189 indexer = index.get_loc( --> 190 label.item(), method=method, tolerance=tolerance 191 ) 192 elif label.dtype.kind == "b":

~/miniconda3/envs/ats641/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance) 620 else: 621 # unrecognized type --> 622 raise KeyError(key) 623 624 try:

KeyError: 1356998400000000000`

Output of xr.show_versions() is: ` INSTALLED VERSIONS

commit: None python: 3.7.6 | packaged by conda-forge | (default, Jan 7 2020, 22:05:27) [Clang 9.0.1 ] python-bits: 64 OS: Darwin OS-release: 19.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.3

xarray: 0.16.0 pandas: 1.1.0 numpy: 1.18.1 scipy: 1.5.2 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: None Nio: 1.5.5 zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: 0.9.8.3 iris: None bottleneck: None dask: 2.20.0 distributed: None matplotlib: 3.1.3 cartopy: 0.17.0 seaborn: 0.10.1 numbagg: None pint: 0.15 setuptools: 49.6.0.post20200814 pip: 20.0.2 conda: None pytest: None IPython: 7.17.0 sphinx: None `

With pandas 1.0.3, da.sel(time=da.time[0]) works correctly and returns a slice.

Thanks for any help you can offer!

Having generated test.nc as the two-dimensional array from ```python import numpy as np import xarray as xr

a = np.random.random((1000, 100)) ds = xr.Dataset({'foo': xr.DataArray(a, [('x', np.arange(1000)), ('y', np.arange(100))])}) ds.to_netcdf('test.nc') ```

I am seeing a huge performance regression from libnetcdf=4.6.1 python In [2]: ds = xr.open_dataset('test.nc') ...: %time np.asarray(ds['foo'].transpose('y', 'x')) ...: CPU times: user 2.58 ms, sys: 191 µs, total: 2.77 ms in a conda environment created by conda create -n mwe-fast 'libnetcdf=4.6.1' netcdf4 xarray ipython to the environment created by conda create -n mwe-slow 'libnetcdf=4.6.2' netcdf4 xarray ipython python In [5]: ds = xr.open_dataset('test.nc') ...: %time np.asarray(ds['foo'].transpose('y', 'x')) ...: CPU times: user 4.09 s, sys: 12 ms, total: 4.11 s

Loading into memory mitigates the regression (on mwe-slow): python In [2]: ds = xr.open_dataset('test.nc') ...: %time np.asarray(ds['foo'].load().transpose('y', 'x')) ...: CPU times: user 1.12 ms, sys: 80 µs, total: 1.2 ms

Output of xr.show_versions()

I am trying to apply ufunc to my code to speed it up. Currently I am looping over lon and lat and it takes ages. After long reads I came up with this though am still not sure where I a going wrong. Suggestions and Ideas would be very helpful to get me going. thanks

``` python def get_grps(s, thresh=-1, Nmin=3): """ Nmin : int > 0 Min number of consecutive values below threshold. """ s = pd.Series(s) m = np.logical_and.reduce([s.shift(-i).le(thresh) for i in range(Nmin)]) if Nmin > 1: m = pd.Series(m, index=s.index).replace({False: np.NaN}).ffill(limit=Nmin-1).fillna(False) else: m = pd.Series(m, index=s.index)

# Form consecutive groups
gps = m.ne(m.shift(1)).cumsum().where(m)

# Return None if no groups, else the aggregations
if gps.isnull().all():
    return [0]
else:
    d = s.groupby(gps).agg([list, sum, 'size']).reset_index(drop=True)

    data = np.sum((d['size']))

    return data

```

 obj is 3d data of shape (324x180x360) (time,lat,lon)

``` python def consec_events(obj):

  time_nums = xr.DataArray(obj['time'].values.astype(np.float),
                         dims='time',
                         coords={'time': obj['time']},
                         name='time_nums')
   trend = xr.apply_ufunc(get_grps,time_nums, obj,
                       vectorize=True,
                       input_core_dims=[['time'], ['time']],
                       output_core_dims=[[]],
                       output_dtypes=[np.float],
                       dask='parallelized',output_sizes={'ntime':time_nums.shape[0]})

return trend

results = consec_events(spi) ```

ERROR IS HERE

... File "/Users/mada0007/anaconda3/envs/RESEARCH_QUESTION_TWO/lib/python3.7/site-packages/numpy/lib/function_base.py", line 2157, in _vectorize_call res = self._vectorize_call_with_signature(func, args) File "/Users/mada0007/anaconda3/envs/RESEARCH_QUESTION_TWO/lib/python3.7/site-packages/numpy/lib/function_base.py", line 2221, in _vectorize_call_with_signature output[index] = result ValueError: setting an array element with a sequence.

I am relatively new to apply this and I have been reading quite a lot. I would appreciatiate if I can be corrected on how to properly apply this over time dimension of my data and get resulting 2d array for each lon and lat?