Here, we perform simple benchmarks to demonstrate basic performance.
import anndata as ad import scanpy as sc
adata = sc.datasets.pbmc3k()
AnnData object with n_obs × n_vars = 2700 × 32738 var: 'gene_ids'
Reading & writing#
Let us start by writing & reading anndata’s native HDF5 file format:
CPU times: user 93.9 ms, sys: 17.4 ms, total: 111 ms Wall time: 118 ms
%%time adata = ad.read('test.h5ad')
CPU times: user 51.2 ms, sys: 13.3 ms, total: 64.5 ms Wall time: 64.1 ms
We see that reading and writing is much faster than for loom files. The efficiency gain here is due to explicit storage of the sparse matrix structure.
CPU times: user 2.82 s, sys: 457 ms, total: 3.27 s Wall time: 3.31 s
%%time adata = ad.read_loom('test.loom')
CPU times: user 1.05 s, sys: 221 ms, total: 1.28 s Wall time: 1.28 s
/Users/alexwolf/repos/anndata/anndata/_core/anndata.py:120: ImplicitModificationWarning: Transforming to str index. warnings.warn("Transforming to str index.", ImplicitModificationWarning)