---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [25], in <cell line: 3>()
      1 # Pint with xarray chunk
      2 ds_pint_chunk = ds_pint.chunk({'time': 1})
----> 3 ds_pint_chunk.resample(time="D").mean().compute()

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/xarray/core/dataarray.py:1083, in DataArray.compute(self, **kwargs)
   1064 """Manually trigger loading of this array's data from disk or a
   1065 remote source into memory and return a new array. The original is
   1066 left unaltered.
   (...)
   1080 dask.compute
   1081 """
   1082 new = self.copy(deep=False)
-> 1083 return new.load(**kwargs)

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/xarray/core/dataarray.py:1057, in DataArray.load(self, **kwargs)
   1039 def load(self: T_DataArray, **kwargs) -> T_DataArray:
   1040     """Manually trigger loading of this array's data from disk or a
   1041     remote source into memory and return this array.
   1042 
   (...)
   1055     dask.compute
   1056     """
-> 1057     ds = self._to_temp_dataset().load(**kwargs)
   1058     new = self._from_temp_dataset(ds)
   1059     self._variable = new._variable

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/xarray/core/dataset.py:734, in Dataset.load(self, **kwargs)
    731 import dask.array as da
    733 # evaluate all the dask arrays simultaneously
--> 734 evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    736 for k, data in zip(lazy_data, evaluated_data):
    737     self.variables[k].data = data

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    597     keys.append(x.__dask_keys__())
    598     postcomputes.append(x.__dask_postcompute__())
--> 600 results = schedule(dsk, keys, **kwargs)
    601 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
     86     elif isinstance(pool, multiprocessing.pool.Pool):
     87         pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
     90     pool.submit,
     91     pool._max_workers,
     92     dsk,
     93     keys,
     94     cache=cache,
     95     get_id=_thread_get_id,
     96     pack_exception=pack_exception,
     97     **kwargs,
     98 )
    100 # Cleanup pools associated to dead threads
    101 with pools_lock:

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
    509         _execute_task(task, data)  # Re-execute locally
    510     else:
--> 511         raise_exception(exc, tb)
    512 res, worker_id = loads(res_info)
    513 state["cache"][key] = res

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/local.py:319, in reraise(exc, tb)
    317 if exc.__traceback__ is not tb:
    318     raise exc.with_traceback(tb)
--> 319 raise exc

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    222 try:
    223     task, data = loads(task_info)
--> 224     result = _execute_task(task, data)
    225     id = get_id()
    226     result = dumps((result, id))

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/optimization.py:990, in SubgraphCallable.__call__(self, *args)
    988 if not len(args) == len(self.inkeys):
    989     raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 990 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/core.py:149, in get(dsk, out, cache)
    147 for key in toposort(dsk):
    148     task = dsk[key]
--> 149     result = _execute_task(task, cache)
    150     cache[key] = result
    151 result = _execute_task(out, cache)

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/toolz/functoolz.py:487, in Compose.__call__(self, *args, **kwargs)
    486 def __call__(self, *args, **kwargs):
--> 487     ret = self.first(*args, **kwargs)
    488     for f in self.funcs:
    489         ret = f(ret)

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/flox/core.py:689, in chunk_reduce(array, by, func, expected_groups, axis, fill_value, dtype, reindex, engine, kwargs, sort)
    687     result = reduction(group_idx, array, **kwargs)
    688 else:
--> 689     result = generic_aggregate(
    690         group_idx, array, axis=-1, engine=engine, func=reduction, **kwargs
    691     ).astype(dt, copy=False)
    692 if np.any(props.nanmask):
    693     # remove NaN group label which should be last
    694     result = result[..., :-1]

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/flox/aggregations.py:49, in generic_aggregate(group_idx, array, engine, func, axis, size, fill_value, dtype, **kwargs)
     44 else:
     45     raise ValueError(
     46         f"Expected engine to be one of ['flox', 'numpy', 'numba']. Received {engine} instead."
     47     )
---> 49 return method(
     50     group_idx, array, axis=axis, size=size, fill_value=fill_value, dtype=dtype, **kwargs
     51 )

File ~/miniconda3/envs/analysis_py39/lib/python3.9/site-packages/flox/aggregate_flox.py:33, in _np_grouped_op(group_idx, array, op, axis, size, fill_value, dtype, out)
     26     out = np.full(array.shape[:-1] + (size,), fill_value=fill_value, dtype=dtype)
     28 if (len(uniques) == size) and (uniques == np.arange(size)).all():
     29     # The previous version of this if condition
     30     #     ((uniques[1:] - uniques[:-1]) == 1).all():
     31     # does not work when group_idx is [1, 2] for e.g.
     32     # This happens  during binning
---> 33     op.reduceat(array, inv_idx, axis=axis, dtype=dtype, out=out)
     34 else:
     35     out[..., uniques] = op.reduceat(array, inv_idx, axis=axis, dtype=dtype)

TypeError: operand type(s) all returned NotImplemented from __array_ufunc__(<ufunc 'add'>, 'reduceat', <Quantity([0], 'kelvin')>, array([0]), axis=-1, dtype=dtype('int64'), out=(array([0]),)): 'Quantity', 'ndarray', 'ndarray'