verbose = False

[Laolga]

The method prints a lot of logs even though the verbose parameter is set to False:

100%
10/10 [00:53<00:00, 5.42s/it]
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 41 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 37 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 40 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 39 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 39 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 36 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 37 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 36 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 39 clusters and added
    'clusters', the cluster labels (adata.obs, categorical) (0:00:00)
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
    with n_comps=30
    finished (0:00:04)

[adamgayoso]

Is it possible you've changed the global verbosity of the scanpy package? These look like scanpy messages