I want calculate vegetation phenology from ndvi netcdf dataset, I follow yours tutorials (DEA Australia and DEA Africa) to apply xr_phenology
function , defined here. To avoid killed kernel of my python jupyter, I import my daily NDVI dataset (.nc) as dask using .chunk argument
After I smooth my daily ndvi dataset with the rolling function :
window=4
veg_smooth= ndvi_doy.rolling(time=window, min_periods=1).mean()
veg_smooth
For calculating the phenology statistics , I use your code, like this :
pheno_stats = ['SOS','vSOS','POS','vPOS','EOS','vEOS','Trough','LOS','AOS'] #,'ROG','ROS'
method_sos = 'first'
method_eos = 'last'
with HiddenPrints():
phen=ts.xr_phenology(
veg_smooth.ndvi,
method_sos=method_sos,
method_eos=method_eos,
stats=pheno_stats
)
print(phen)
But when I want to display the results with the ".compute" function , I have this error and I can't display the results :
phen=phen.compute()
phen
ValueError: All-NaN slice encountered
To avoid the NaN values produced during the calculation of the "pheno stats" => A function "dea_tools.temporal.allNaN_arg" has been defined in your web site but I can't apply it ?
Can you help me in these step in order to ignore the NaN values and display the rest of results correctly.
Below the detail of this error :
Phenology...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[11], line 1
----> 1 phen=phen.compute()
2 phen
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/dataset.py:958, in Dataset.compute(self, **kwargs)
939 """Manually trigger loading and/or computation of this dataset's data
940 from disk or a remote source into memory and return a new dataset.
941 Unlike load, the original dataset is left unaltered.
(...)
955 dask.compute
956 """
957 new = self.copy(deep=False)
--> 958 return new.load(**kwargs)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/dataset.py:792, in Dataset.load(self, **kwargs)
789 chunkmanager = get_chunked_array_type(*lazy_data.values())
791 # evaluate all the chunked arrays simultaneously
--> 792 evaluated_data = chunkmanager.compute(*lazy_data.values(), **kwargs)
794 for k, data in zip(lazy_data, evaluated_data):
795 self.variables[k].data = data
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/daskmanager.py:70, in DaskManager.compute(self, *data, **kwargs)
67 def compute(self, *data: DaskArray, **kwargs) -> tuple[np.ndarray, ...]:
68 from dask.array import compute
---> 70 return compute(*data, **kwargs)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/dask/base.py:628, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
625 postcomputes.append(x.__dask_postcompute__())
627 with shorten_traceback():
--> 628 results = schedule(dsk, keys, **kwargs)
630 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/parallel.py:268, in map_blocks.<locals>._wrapper(func, args, kwargs, arg_is_array, expected)
258 """
259 Wrapper function that receives datasets in args; converts to dataarrays when necessary;
260 passes these to the user function `func` and checks returned objects for expected shapes/sizes/etc.
261 """
263 converted_args = [
264 dataset_to_dataarray(arg) if is_array else arg
265 for is_array, arg in zip(arg_is_array, args)
266 ]
--> 268 result = func(*converted_args, **kwargs)
270 # check all dims are present
271 missing_dimensions = set(expected["shapes"]) - set(result.sizes)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/deafrica_tools/temporal.py:350, in xr_phenology(da, stats, method_sos, method_eos, verbose)
348 trough = _trough(da)
349 aos = _aos(vpos, trough)
--> 350 vsos = _vsos(da, pos, method_sos=method_sos)
351 sos = _sos(vsos)
352 veos = _veos(da, pos, method_eos=method_eos)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/deafrica_tools/temporal.py:120, in _vsos(da, pos, method_sos)
116 distance = pos_greenup - median
118 if method_sos == "first":
119 # find index (argmin) where distance is most negative
--> 120 idx = allNaN_arg(distance, "time", "min").astype("int16")
122 if method_sos == "median":
123 # find index (argmin) where distance is smallest absolute value
124 idx = allNaN_arg(np.fabs(distance), "time", "min").astype("int16")
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/deafrica_tools/temporal.py:59, in allNaN_arg(da, dim, stat)
57 if stat == "min":
58 y = da.fillna(float(da.max() + 1))
---> 59 y = y.argmin(dim=dim, skipna=True).where(~mask)
60 return y
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/dataarray.py:5969, in DataArray.argmin(self, dim, axis, keep_attrs, skipna)
5875 def argmin(
5876 self,
5877 dim: Dims = None,
(...)
5880 skipna: bool | None = None,
5881 ) -> DataArray | dict[Hashable, DataArray]:
5882 """Index or indices of the minimum of the DataArray over one or more dimensions.
5883
5884 If a sequence is passed to 'dim', then result returned as dict of DataArrays,
(...)
5967 Dimensions without coordinates: y
5968 """
-> 5969 result = self.variable.argmin(dim, axis, keep_attrs, skipna)
5970 if isinstance(result, dict):
5971 return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/variable.py:2842, in Variable.argmin(self, dim, axis, keep_attrs, skipna)
2799 def argmin(
2800 self,
2801 dim: Dims = None,
(...)
2804 skipna: bool | None = None,
2805 ) -> Variable | dict[Hashable, Variable]:
2806 """Index or indices of the minimum of the Variable over one or more dimensions.
2807 If a sequence is passed to 'dim', then result returned as dict of Variables,
2808 which can be passed directly to isel(). If a single str is passed to 'dim' then
(...)
2840 DataArray.argmin, DataArray.idxmin
2841 """
-> 2842 return self._unravel_argminmax("argmin", dim, axis, keep_attrs, skipna)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/variable.py:2763, in Variable._unravel_argminmax(self, argminmax, dim, axis, keep_attrs, skipna)
2754 dim = self.dims
2755 if (
2756 dim is None
2757 or axis is not None
(...)
2761 # Return int index if single dimension is passed, and is not part of a
2762 # sequence
-> 2763 return self.reduce(
2764 argminmax_func, dim=dim, axis=axis, keep_attrs=keep_attrs, skipna=skipna
2765 )
2767 # Get a name for the new dimension that does not conflict with any existing
2768 # dimension
2769 newdimname = "_unravel_argminmax_dim_0"
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/variable.py:2012, in Variable.reduce(self, func, dim, axis, keep_attrs, keepdims, **kwargs)
2008 if isinstance(axis, tuple) and len(axis) == 1:
2009 # unpack axis for the benefit of functions
2010 # like np.argmin which can't handle tuple arguments
2011 axis = axis[0]
-> 2012 data = func(self.data, axis=axis, **kwargs)
2013 else:
2014 data = func(self.data, **kwargs)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:382, in _create_nan_agg_method.<locals>.f(values, axis, skipna, **kwargs)
380 with warnings.catch_warnings():
381 warnings.filterwarnings("ignore", "All-NaN slice encountered")
--> 382 return func(values, axis=axis, **kwargs)
383 except AttributeError:
384 if not is_duck_dask_array(values):
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/nanops.py:82, in nanargmin(a, axis)
79 fill_value = dtypes.get_pos_infinity(a.dtype)
80 return _nan_argminmax_object("argmin", fill_value, a, axis=axis)
---> 82 return nputils.nanargmin(a, axis=axis)
File ~/anaconda3/envs/anaconda_env/lib/python3.11/site-packages/xarray/core/nputils.py:175, in _create_bottleneck_method.<locals>.f(values, axis, **kwargs)
163 if (
164 _USE_BOTTLENECK
165 and OPTIONS["use_bottleneck"]
(...)
172 ):
173 # bottleneck does not take care dtype, min_count
174 kwargs.pop("dtype", None)
--> 175 result = bn_func(values, axis=axis, **kwargs)
176 else:
177 result = getattr(npmodule, name)(values, axis=axis, **kwargs)
ValueError: All-NaN slice encountered
