Fix logging levels

scanpy/datasets/__init__.py

@@ -76,7 +76,7 @@ def krumsiek11():
     """
     filename = os.path.dirname(__file__) + '/krumsiek11.txt'
     verbosity_save = sc.settings.verbosity
-    sc.settings.verbosity = 0  # suppress output...
+    sc.settings.verbosity = 'error'  # suppress output...
     adata = sc.read(filename, first_column_names=True)
     sc.settings.verbosity = verbosity_save
     adata.uns['iroot'] = 0

scanpy/neighbors/__init__.py

@@ -9,8 +9,9 @@
 
 from .. import settings
 from .. import logging as logg
-from .. utils import doc_params
 from .. import utils
+from ..utils import doc_params
+from ..logging import _settings_verbosity_greater_or_equal_than
 from ..tools._utils import choose_representation, doc_use_rep, doc_n_pcs
 
 N_DCS = 15  # default number of diffusion components
@@ -93,7 +94,7 @@ def neighbors(
     adata.uns['neighbors']['params'] = {'n_neighbors': n_neighbors, 'method': method}
     adata.uns['neighbors']['distances'] = neighbors.distances
     adata.uns['neighbors']['connectivities'] = neighbors.connectivities
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint(
         'added to `.uns[\'neighbors\']`\n'
         '    \'distances\', distances for each pair of neighbors\n'
@@ -956,11 +957,11 @@ def _test_embed(self):
         """
         # pl.semilogy(w,'x',label=r'$ \widetilde K$')
         # pl.show()
-        if settings.verbosity > 2:
+        if _settings_verbosity_greater_or_equal_than(3):
             # output of spectrum of K for comparison
             w, v = np.linalg.eigh(self.K)
             logg.msg('spectrum of K (kernel)')
-        if settings.verbosity > 3:
+        if _settings_verbosity_greater_or_equal_than(4):
             # direct computation of spectrum of T
             w, vl, vr = scipy.linalg.eig(self.T, left=True)
             logg.msg('spectrum of transition matrix (should be same as of Ktilde)')

scanpy/plotting/_anndata.py

@@ -1809,7 +1809,7 @@ def tracksplot(adata, var_names, groupby, use_raw=None, log=False,
 
     # get categories colors:
     if groupby + "_colors" not in adata.uns:
-        from scanpy.plotting._tools.scatterplots import _set_default_colors_for_categorical_obs
+        from ._tools.scatterplots import _set_default_colors_for_categorical_obs
         _set_default_colors_for_categorical_obs(adata, groupby)
 
     groupby_colors = adata.uns[groupby + "_colors"]

scanpy/preprocessing/_magic.py

@@ -1,11 +1,9 @@
 """Denoise high-dimensional data using MAGIC
 """
 
-import numbers
-import numpy as np
-
 from .. import settings
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 
 
 def magic(adata,
@@ -118,8 +116,8 @@ def magic(adata,
             "`copy=True`".format(name_list))
     adata = adata.copy() if copy else adata
     verbose = settings.verbosity if verbose is None else verbose
-    if isinstance(verbose, int):
-        verbose = verbose >= 2
+    if isinstance(verbose, (str, int)):
+        verbose = _settings_verbosity_greater_or_equal_than(2)
     n_jobs = settings.n_jobs if n_jobs is None else n_jobs
 
     X_magic = MAGIC(k=k,
@@ -133,7 +131,7 @@ def magic(adata,
                     **kwargs).fit_transform(adata,
                                             genes=name_list)
     logg.info('    finished', time=True,
-              end=' ' if settings.verbosity > 2 else '\n')
+              end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     # update AnnData instance
     if name_list == "pca_only":
         # special case - update adata.obsm with smoothed values

scanpy/settings.py

@@ -1,7 +1,7 @@
 """Settings
 """
 
-verbosity = 1
+verbosity = 'warning'
 """Set global verbosity level.
 
 Level 0: only show 'error' messages.

scanpy/tests/conftest.py

@@ -0,0 +1,3 @@
+import scanpy
+
+scanpy.settings.verbosity = 'hint'

scanpy/tests/test_combat.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pandas as pd
-import scanpy as sc
-import scanpy
 from sklearn.metrics import silhouette_score
+
+import scanpy as sc
 from scanpy.preprocessing._combat import stand_data
 
 

scanpy/tests/test_neighbors.py

@@ -1,7 +1,8 @@
 import numpy as np
+import pytest
 from anndata import AnnData
+
 from scanpy import Neighbors
-import pytest
 
 # the input data
 X = [[1, 0], [3, 0], [5, 6], [0, 4]]

scanpy/tests/test_rank_genes_groups_logreg.py

@@ -1,7 +1,7 @@
-import pandas as pd
 import numpy as np
 import scanpy as sc
 
+
 def test_rank_genes_groups_with_renamed_categories():
     adata = sc.datasets.blobs(n_variables=4, n_centers=3, n_observations=200)
     assert np.allclose(adata.X[1], [9.214668, -2.6487126, 4.2020774, 0.51076424])

scanpy/tools/_dpt.py

@@ -3,8 +3,9 @@
 import scipy as sp
 import networkx as nx
 from natsort import natsorted
-from .. import settings
+
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 from ..neighbors import Neighbors, OnFlySymMatrix
 
 
@@ -15,7 +16,7 @@ def _diffmap(adata, n_comps=15):
     dpt.compute_eigen(n_comps=n_comps)
     adata.obsm['X_diffmap'] = dpt.eigen_basis
     adata.uns['diffmap_evals'] = dpt.eigen_values
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('added\n'
               '    \'X_diffmap\', diffmap coordinates (adata.obsm)\n'
               '    \'diffmap_evals\', eigenvalues of transition matrix (adata.uns)')
@@ -130,7 +131,7 @@ def dpt(adata, n_dcs=10, n_branchings=0, min_group_size=0.01,
         for count, idx in enumerate(dpt.indices): ordering_id[idx] = count
         adata.obs['dpt_order'] = ordering_id
         adata.obs['dpt_order_indices'] = dpt.indices
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('added\n'
            + ('    \'dpt_pseudotime\', the pseudotime (adata.obs)'
               if dpt.iroot is not None else '')

scanpy/tools/_draw_graph.py

@@ -1,7 +1,8 @@
 import numpy as np
-from .. import settings
+
 from .. import utils
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 from ._utils import get_init_pos_from_paga
 
 
@@ -141,7 +142,7 @@ class of long-established algorithms for visualizing graphs. It has been
     adata.uns['draw_graph']['params'] = {'layout': layout, 'random_state': random_state}
     key_added = 'X_draw_graph_' + (layout if key_added_ext is None else key_added_ext)
     adata.obsm[key_added] = positions
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('added\n'
               '    \'{}\', graph_drawing coordinates (adata.obsm)'
               .format(key_added))

scanpy/tools/_leiden.py

@@ -7,8 +7,8 @@
 from scipy import sparse
 
 from .. import utils
-from .. import settings
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 
 try:
     from leidenalg.VertexPartition import MutableVertexPartition
@@ -132,7 +132,7 @@ def leiden(
         random_state=random_state,
         n_iterations=n_iterations,
     )
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('found {} clusters and added\n'
               '    \'{}\', the cluster labels (adata.obs, categorical)'
               .format(len(np.unique(groups)), key_added))

scanpy/tools/_louvain.py

@@ -7,8 +7,8 @@
 from scipy.sparse import spmatrix
 
 from .. import utils
-from .. import settings
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 
 try:
     from louvain.VertexPartition import MutableVertexPartition
@@ -168,7 +168,7 @@ def louvain(
             categories=natsorted(all_groups.unique()))
     adata.uns['louvain'] = {}
     adata.uns['louvain']['params'] = {'resolution': resolution, 'random_state': random_state}
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('found {} clusters and added\n'
               '    \'{}\', the cluster labels (adata.obs, categorical)'
               .format(n_clusters, key_added))

scanpy/tools/_paga.py

@@ -1,11 +1,13 @@
 from collections import namedtuple
+
 import numpy as np
 import scipy as sp
 from scipy.sparse.csgraph import minimum_spanning_tree
+
+from .. import utils
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 from ..neighbors import Neighbors
-from .. import utils
-from .. import settings
 
 _AVAIL_MODELS = {'v1.0', 'v1.2'}
 
@@ -94,7 +96,7 @@ def paga(
         adata.uns['paga']['transitions_confidence'] = paga.transitions_confidence
         # adata.uns['paga']['transitions_ttest'] = paga.transitions_ttest
     adata.uns['paga']['groups'] = groups
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     if use_rna_velocity:
         logg.hint(
             'added\n'

scanpy/tools/_phate.py

@@ -3,6 +3,7 @@
 
 from .. import settings
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 
 
 def phate(
@@ -112,8 +113,8 @@ def phate(
     logg.info('computing PHATE', r=True)
     adata = adata.copy() if copy else adata
     verbose = settings.verbosity if verbose is None else verbose
-    if isinstance(verbose, int):
-        verbose = verbose >= 2
+    if isinstance(settings.verbosity, (str, int)):
+        verbose = _settings_verbosity_greater_or_equal_than(2)
     n_jobs = settings.n_jobs if n_jobs is None else n_jobs
     try:
         import phate
@@ -138,7 +139,7 @@ def phate(
         **kwargs
     ).fit_transform(adata)
     logg.info('    finished', time=True,
-              end=' ' if settings.verbosity > 2 else '\n')
+              end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     # update AnnData instance
     adata.obsm['X_phate'] = X_phate  # annotate samples with PHATE coordinates
     logg.hint('added\n'

scanpy/tools/_rank_genes_groups.py

@@ -8,8 +8,8 @@
 from scipy.sparse import issparse
 
 from .. import utils
-from .. import settings
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 from ..preprocessing._simple import _get_mean_var
 
 
@@ -396,7 +396,7 @@ def rank_genes_groups(
             [n for n in rankings_gene_pvals_adj],
             dtype=[(rn, 'float64') for rn in groups_order_save])
 
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint(
         'added to `.uns[\'{}\']`\n'
         '    \'names\', sorted np.recarray to be indexed by group ids\n'

scanpy/tools/_rna_velocity.py

@@ -1,6 +1,8 @@
-import numba
 import numpy as np
+from numpy.linalg import norm
+
 from .. import logging as logg
+from ..neighbors import Neighbors, get_indices_distances_from_sparse_matrix
 
 
 def compute_velocity_graph(adata, adata_u, X_du):
@@ -9,14 +11,12 @@ def compute_velocity_graph(adata, adata_u, X_du):
         or X_du.shape[0] != adata.shape[0]):
         raise ValueError('Number of cells do not match.')
 
-    from scanpy.neighbors import Neighbors, get_indices_distances_from_sparse_matrix
     neigh = Neighbors(adata)
     knn_indices, knn_distances = get_indices_distances_from_sparse_matrix(
         neigh.distances, neigh.n_neighbors)
     n_obs = adata.n_obs
     n_neighbors = neigh.n_neighbors
 
-    from numpy.linalg import norm
     X_u = adata_u.X.toarray()
     X_du = X_du.astype('float32').toarray()
 
@@ -58,28 +58,6 @@ def fill_graph():
     return graph.tocsr()
 
 
-def comppute_arrows_embedding():
-    if 'rna_velocity' not in adata.uns:
-        raise ValueError('`arrows=True` requires `tl.rna_velocity` to be run before.')
-    adjacency = adata.uns['rna_velocity']['graph']
-    # loop over columns of adjacency, this is where transitions start from
-    from numpy.linalg import norm
-    V = np.zeros((adjacency.shape[0], 2), dtype='float32')
-    for i, n in enumerate(adjacency.T):  # loop over columns (note the transposition)
-        for j in n.nonzero()[1]:  # these are row indices
-            diff = adata.obsm['X_' + basis][j] - adata.obsm['X_' + basis][i]
-            # need the normalized difference vector: the distance in the embedding
-            # might be completely meaningless
-            diff /= norm(diff)
-            V[i] += adjacency[j, i] * diff
-    logg.hint('added \'V_{}\' to `.obsm`'.format(basis))
-    adata.obsm['V_' + basis] = V
-    X = adata.obsm['X_' + basis]
-    for ax in axs:
-        quiver_kwds = arrows_kwds if arrows_kwds is not None else {}
-        ax.quiver(X[:, 0], X[:, 1], V[:, 0], V[:, 1], **quiver_kwds)    
-
-
 def rna_velocity(adata, loomfile, copy=False):
     """Estimate RNA velocity [LaManno17]_
 

scanpy/tools/_score_genes.py

@@ -3,10 +3,10 @@
 
 import numpy as np
 import pandas as pd
-import scipy.sparse
 from scipy.sparse import issparse
-from .. import settings
+
 from .. import logging as logg
+from ..logging import _settings_verbosity_greater_or_equal_than
 
 
 def score_genes(
@@ -128,7 +128,7 @@ def score_genes(
 
     adata.obs[score_name] = pd.Series(np.array(score).ravel(), index=adata.obs_names)
 
-    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
+    logg.info('    finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
     logg.hint('added\n'
               '    \'{}\', score of gene set (adata.obs)'.format(score_name))
     return adata if copy else None