Apr 29, 2025 · xarray.Dataset.coarsen # Dataset.coarsen(dim=None, boundary='exact', side='left', coord_func='mean', **window_kwargs) [source] # Coarsen object for Datasets. Parameters: …

Mar 13, 2019 · I'd like to find a way to coarsen this data set up into user defined number of contiguous bins (or zones), each of which is described by a single mean value and lower …

coarsen is a simple way to reduce the size of your data along one or more axes. It is very similar to resample when operating on time dimensions; the key difference is that coarsen only …

xarray.DataArray.coarsen ¶ DataArray.coarsen(dim=None, boundary='exact', side='left', coord_func='mean', keep_attrs=None, **window_kwargs) ¶ Coarsen object. Parameters dim …

xarray.DataArray.coarsen # DataArray.coarsen(dim=None, boundary='exact', side='left', coord_func='mean', **window_kwargs) [source] # Coarsen object for DataArrays. Parameters: …

Recently the coarsen method has been added to xarray and I think it's the best way for spatially downsampling, even though it's not possible to use it setting a desired final resolution and …

Dec 14, 2021 · One way to do this is through DataArrayCoarsen.construct, which allows you to more easily operate on individual windows at a time: weights = xr.DataArray(gkern(5), …

Apr 29, 2025 · Reshaping and reorganizing data refers to the process of changing the structure or organization of data by modifying dimensions, array shapes, order of values, or indexes. …

Jan 7, 2023 · Depending on your application, you may want to use the axis argument (tuple of integers) in place of your [0] and [1] indexing steps. Note: I've added np.atleast_2d here to …

>>> x = np.array([1, 2, 3, 4, 5, 6]) >>> coarsen(np.sum, x, {0: 2}) array([ 3, 7, 11]) >>> coarsen(np.max, x, {0: 3}) array([3, 6])