Fast array utilities with minimal dependencies.
Project description
usage
fast-array-utils supports the following array types:
numpy.ndarray
scipy.sparse.cs{rc}_{array,matrix}
cupy.ndarray and cupyx.scipy.sparse.cs{rc}_matrix
dask.array.Array
h5py.Dataset and zarr.Array
anndata.abc.CS{CR}Dataset (only supported by .conv.to_dense at the moment)
Use fast_array_utils.conv.to_dense to densify arrays and optionally move them to CPU memory:
from fast_array_utils.conv import to_dense
numpy_arr = to_dense(sparse_arr_or_mat)
numpy_arr = to_dense(dask_or_cuda_arr, to_cpu_memory=True)
dense_dask_arr = to_dense(dask_arr)
dense_cupy_arr = to_dense(sparse_cupy_mat)Use fast_array_utils.conv.* to calculate statistics across one or both axes of a 2D array. All of them support an axis and dtype parameter:
from fast_array_utils import stats
all_equal = stats.is_constant(arr_2d)
col_sums = stats.sum(arr_2d, axis=0)
mean = stats.mean(arr_2d)
row_means, row_vars = stats.mean_var(arr_2d, axis=1)installation
To use fast_array_utils.stats or fast_array_utils.conv:
(uv) pip install 'fast-array-utils[accel]'To use testing.fast_array_utils:
(uv) pip install 'fast-array-utils[testing]'Project details
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