Use black default line length

.editorconfig

@@ -5,7 +5,7 @@ charset = utf-8
 end_of_line = lf
 insert_final_newline = true
 trim_trailing_whitespace = true
-max_line_length = 80
+max_line_length = 88
 
 [*.py]
 indent_size = 4

conftest.py

@@ -13,9 +13,7 @@ def pytest_addoption(parser):
 
 def pytest_collection_modifyitems(config, items):
     run_internet = config.getoption("--internet-tests")
-    skip_internet = pytest.mark.skip(
-        reason="need --internet-tests option to run"
-    )
+    skip_internet = pytest.mark.skip(reason="need --internet-tests option to run")
     for item in items:
         # All tests marked with `pytest.mark.internet` get skipped unless
         # `--run-internet` passed

docs/conf.py

@@ -94,9 +94,7 @@
 
 
 html_theme = 'sphinx_rtd_theme'
-html_theme_options = dict(
-    navigation_depth=4, logo_only=True  # Only show the logo
-)
+html_theme_options = dict(navigation_depth=4, logo_only=True)  # Only show the logo
 html_context = dict(
     display_github=True,  # Integrate GitHub
     github_user='theislab',  # Username

docs/extensions/function_images.py

@@ -7,12 +7,7 @@
 
 
 def insert_function_images(
-    app: Sphinx,
-    what: str,
-    name: str,
-    obj: Any,
-    options: Options,
-    lines: List[str],
+    app: Sphinx, what: str, name: str, obj: Any, options: Options, lines: List[str],
 ):
     path = app.config.api_dir / f'{name}.png'
     if what != 'function' or not path.is_file():

docs/extensions/github_links.py

@@ -36,19 +36,13 @@ def register_links(app: Sphinx, config: Config):
         config.html_context
     )
     app.add_role('pr', AutoLink('pr', f'{gh_url}/pull/{{}}', 'PR {}'))
-    app.add_role(
-        'issue', AutoLink('issue', f'{gh_url}/issues/{{}}', 'issue {}')
-    )
-    app.add_role(
-        'noteversion', AutoLink('noteversion', f'{gh_url}/releases/tag/{{}}')
-    )
+    app.add_role('issue', AutoLink('issue', f'{gh_url}/issues/{{}}', 'issue {}'))
+    app.add_role('noteversion', AutoLink('noteversion', f'{gh_url}/releases/tag/{{}}'))
     # tutorial links
     scanpy_tutorials_url = 'https://scanpy-tutorials.readthedocs.io/en/latest/'
     app.add_role(
         'tutorial',
-        AutoLink(
-            'tutorial', f'{scanpy_tutorials_url}{{}}.html', '→ tutorial: {}'
-        ),
+        AutoLink('tutorial', f'{scanpy_tutorials_url}{{}}.html', '→ tutorial: {}'),
     )
 
 

docs/extensions/typed_returns.py

@@ -15,9 +15,7 @@ def process_return(lines):
 
 
 def scanpy_parse_returns_section(self, section):
-    lines_raw = list(
-        process_return(self._dedent(self._consume_to_next_section()))
-    )
+    lines_raw = list(process_return(self._dedent(self._consume_to_next_section())))
     lines = self._format_block(':returns: ', lines_raw)
     if lines and lines[-1]:
         lines.append('')

pyproject.toml

@@ -3,7 +3,7 @@ requires = ['setuptools', 'setuptools_scm', 'wheel']
 build-backend = 'setuptools.build_meta'
 
 [tool.black]
-line-length = 80
+line-length = 88
 target-version = ['py36']
 skip-string-normalization = true
 exclude = '''
@@ -57,7 +57,6 @@ exclude = '''
         |test_neighbors
         |test_readwrite
         |test_clustering
-        |test_qc_metrics
         |test_preprocessing
         |test_rank_genes_groups
         |test_marker_gene_overlap

scanpy/datasets/__init__.py

@@ -117,14 +117,10 @@ def moignard15() -> AnnData:
     Annotated data matrix.
     """
     filename = settings.datasetdir / 'moignard15/nbt.3154-S3.xlsx'
-    backup_url = (
-        'http://www.nature.com/nbt/journal/v33/n3/extref/nbt.3154-S3.xlsx'
-    )
+    backup_url = 'http://www.nature.com/nbt/journal/v33/n3/extref/nbt.3154-S3.xlsx'
     adata = read(filename, sheet='dCt_values.txt', backup_url=backup_url)
     # filter out 4 genes as in Haghverdi et al. (2016)
-    gene_subset = ~np.in1d(
-        adata.var_names, ['Eif2b1', 'Mrpl19', 'Polr2a', 'Ubc']
-    )
+    gene_subset = ~np.in1d(adata.var_names, ['Eif2b1', 'Mrpl19', 'Polr2a', 'Ubc'])
     adata = adata[:, gene_subset]  # retain non-removed genes
     # choose root cell for DPT analysis as in Haghverdi et al. (2016)
     adata.uns["iroot"] = 532  # note that in Matlab/R, counting starts at 1

scanpy/datasets/_ebi_expression_atlas.py

@@ -40,8 +40,7 @@ def download_experiment(accession: str):
     experiment_dir.mkdir(parents=True, exist_ok=True)
 
     _download(
-        download_url + "experiment-design",
-        experiment_dir / "experimental_design.tsv",
+        download_url + "experiment-design", experiment_dir / "experimental_design.tsv",
     )
     _download(
         download_url + "quantification-filtered",
@@ -123,9 +122,7 @@ def ebi_expression_atlas(
 
     with ZipFile(experiment_dir / "expression_archive.zip", "r") as f:
         adata = read_expression_from_archive(f)
-    obs = pd.read_csv(
-        experiment_dir / "experimental_design.tsv", sep="\t", index_col=0
-    )
+    obs = pd.read_csv(experiment_dir / "experimental_design.tsv", sep="\t", index_col=0)
 
     adata.obs[obs.columns] = obs
     adata.write(dataset_path, compression="gzip")  # To be kind to disk space

scanpy/external/pp/_dca.py

@@ -149,9 +149,7 @@ def dca(
     try:
         from dca.api import dca
     except ImportError:
-        raise ImportError(
-            'Please install dca package (>= 0.2.1) via `pip install dca`'
-        )
+        raise ImportError('Please install dca package (>= 0.2.1) via `pip install dca`')
 
     return dca(
         adata,

scanpy/external/pp/_magic.py

@@ -15,9 +15,7 @@
 @legacy_api('k', 'a')
 def magic(
     adata: AnnData,
-    name_list: Union[
-        Literal['all_genes', 'pca_only'], Sequence[str], None
-    ] = None,
+    name_list: Union[Literal['all_genes', 'pca_only'], Sequence[str], None] = None,
     *,
     knn: int = 10,
     decay: int = 15,

scanpy/external/tl/_harmony_timeseries.py

@@ -208,18 +208,15 @@ def __init__(
                 print('"sample_names" not provided, constructed internally')
                 rep = range(len(self.data))
                 self.sample_names = [
-                    "_".join(["sample{}".format(n + 1), str(n + 1)])
-                    for n in rep
+                    "_".join(["sample{}".format(n + 1), str(n + 1)]) for n in rep
                 ]
                 print('"sample_names": {}'.format(self.sample_names))
 
             try:
                 assert self.timepoints
             except AssertionError:
                 try:
-                    self.timepoints = [
-                        i.split("_")[0] for i in self.sample_names
-                    ]
+                    self.timepoints = [i.split("_")[0] for i in self.sample_names]
                 except:
                     msg = (
                         '"timepoints" must be a list of Time Points!!\n\n\t'
@@ -242,9 +239,7 @@ def __init__(
                         assert isinstance(data[0].to_df(), pd.DataFrame)
                     else:
                         assert isinstance(data[0], pd.DataFrame)
-                    counts = self.load_from_AnnData(
-                        self.data, self.sample_names
-                    )
+                    counts = self.load_from_AnnData(self.data, self.sample_names)
                     logg.info("Input data is a list of scanpy.AnnData objects")
                 except:
                     raise RuntimeError(
@@ -286,9 +281,7 @@ def process(self, n_components: int = 1000):
             self.timepoint_connections = pd.DataFrame(columns=[0, 1])
             index = 0
             for i in range(len(self.timepoints) - 1):
-                self.timepoint_connections.loc[index, :] = self.timepoints[
-                    i : i + 2
-                ]
+                self.timepoint_connections.loc[index, :] = self.timepoints[i : i + 2]
                 index += 1
 
             # compute the augmented and non-augmented affinity matrices
@@ -317,19 +310,14 @@ def process(self, n_components: int = 1000):
             self.harmony_adata.uns["aug_aff"] = self.aug_aff
             self.harmony_adata.uns["sample_names"] = self.sample_names
             self.harmony_adata.uns["timepoints"] = self.timepoints
-            self.harmony_adata.uns[
-                "timepoint_connections"
-            ] = self.timepoint_connections
+            self.harmony_adata.uns["timepoint_connections"] = self.timepoint_connections
 
             logg.info("End of processing, start plotting.")
 
             return self.harmony_adata
 
         def load_from_AnnData(
-            self,
-            data_list: list,
-            sample_names: list = None,
-            min_cell_count: int = 10,
+            self, data_list: list, sample_names: list = None, min_cell_count: int = 10,
         ):
             """
             Read in scRNA-seq data from :class:`~anndata.AnnData` list
@@ -375,32 +363,18 @@ def load_from_AnnData(
             # Concatenate cells and genes
             print("Concatenating data..")
             all_cells = list(
-                chain(
-                    *[
-                        list(counts_dict[sample].index)
-                        for sample in sample_names
-                    ]
-                )
+                chain(*[list(counts_dict[sample].index) for sample in sample_names])
             )
             all_genes = list(
-                chain(
-                    *[
-                        list(counts_dict[sample].columns)
-                        for sample in sample_names
-                    ]
-                )
+                chain(*[list(counts_dict[sample].columns) for sample in sample_names])
             )
             all_genes = list(set(all_genes))
 
             # Counts matrix
-            counts = pd.DataFrame(
-                0, index=all_cells, columns=all_genes, dtype=np.int16
-            )
+            counts = pd.DataFrame(0, index=all_cells, columns=all_genes, dtype=np.int16)
             for sample in sample_names:
                 sample_counts = counts_dict[sample]
-                counts.loc[
-                    sample_counts.index, sample_counts.columns
-                ] = sample_counts
+                counts.loc[sample_counts.index, sample_counts.columns] = sample_counts
 
             # Filter out low detection genes
             gs = counts.sum()
@@ -415,9 +389,7 @@ def log_transform(self):
         @log_transform.setter
         def log_transform(self, value):
             if value is True:
-                self.data_df = self.harmony_timeseries.utils.log_transform(
-                    self.data_df
-                )
+                self.data_df = self.harmony_timeseries.utils.log_transform(self.data_df)
                 logg.info("data log transformed ...")
 
     def wrapper_cls(data, func=None, **kargs):

scanpy/external/tl/_trimap.py

@@ -17,9 +17,7 @@ def trimap(
     n_inliers: int = 10,
     n_outliers: int = 5,
     n_random: int = 5,
-    metric: Literal[
-        'angular', 'euclidean', 'hamming', 'manhattan'
-    ] = 'euclidean',
+    metric: Literal['angular', 'euclidean', 'hamming', 'manhattan'] = 'euclidean',
     weight_adj: float = 500.0,
     lr: float = 1000.0,
     n_iters: int = 400,
@@ -89,9 +87,7 @@ def trimap(
     try:
         from trimap import TRIMAP
     except ImportError:
-        raise ImportError(
-            '\nplease install trimap: \n\n\tsudo pip install trimap'
-        )
+        raise ImportError('\nplease install trimap: \n\n\tsudo pip install trimap')
     adata = adata.copy() if copy else adata
     start = logg.info('computing TriMap')
     adata = adata.copy() if copy else adata