Closed florianboergel closed 1 year ago
I am experiencing a problem with the function land_check. However, this is limited to combined usage with dask.
land_check
land_check throws the following error related to the .dropna() call, ultimately causing an error in reshaping the array:
1 with dask.config.set(**{'array.slicing.split_large_chunks': True}): ----> 2 ts = land_check(temp, tdim=tdim, anynans=anynans) File /silos/conda_packages/boergel/miniconda3_4.12.0/OS_15.4/conda_env/dask/lib/python3.11/site-packages/xmhw/identify.py:523, in land_check(temp, tdim, anynans) 521 if anynans: 522 how = "any" --> 523 ts = ts.dropna(dim="cell", how=how) 524 # if ts.cell.shape is 0 then all points are land, quit 525 if ts.cell.shape == (0,): File /silos/conda_packages/boergel/miniconda3_4.12.0/OS_15.4/conda_env/dask/lib/python3.11/site-packages/xarray/core/dataarray.py:3232, in DataArray.dropna(self, dim, how, thresh) 3159 def dropna( 3160 self: T_DataArray, 3161 dim: Hashable, 3162 how: Literal["any", "all"] = "any", 3163 thresh: int | None = None, 3164 ) -> T_DataArray: 3165 """Returns a new array with dropped labels for missing values along 3166 the provided dimension. 3167 (...) 3230 Dimensions without coordinates: Y, X ... File /silos/conda_packages/boergel/miniconda3_4.12.0/OS_15.4/conda_env/dask/lib/python3.11/site-packages/dask/utils.py:1104, in __call__() 1103 def __call__(self, __obj, *args, **kwargs): -> 1104 return getattr(__obj, self.method)(*args, **kwargs) ValueError: cannot reshape array of size 11422080 into shape (17847,220)
My data is of the following type:
sst = temperatureData.drop("depth").TEMP sst = sst.chunk({'time': -1, 'lat': 'auto', 'lon': 'auto'}) sst.data
144 chunks in 3 graph layers float64 numpy.ndarray
Any idea?
OK, nevermind, avoiding splitting large chunks solves the problem.
I am experiencing a problem with the function
land_check
. However, this is limited to combined usage with dask.land_check
throws the following error related to the .dropna() call, ultimately causing an error in reshaping the array:My data is of the following type:
sst = temperatureData.drop("depth").TEMP sst = sst.chunk({'time': -1, 'lat': 'auto', 'lon': 'auto'}) sst.data
144 chunks in 3 graph layers float64 numpy.ndarray
Any idea?