linting

Author: florian-huber

graphconstructor/__init__.py

@@ -1,6 +1,6 @@
 from .graph import Graph
-from .pipeline import Pipeline
 from .importers import from_ann, from_csr, from_dense, from_knn
+from .pipeline import Pipeline
 from .types import CSRMatrix, MatrixMode
 
 

graphconstructor/adapters.py

@@ -2,9 +2,8 @@
 from typing import Optional
 import numpy as np
 from numpy.typing import NDArray
-
 from .types import MatrixMode
-from .utils import _validate_square_matrix, _to_numpy
+from .utils import _to_numpy, _validate_square_matrix
 
 
 @dataclass

graphconstructor/graph.py

@@ -1,10 +1,10 @@
 from dataclasses import dataclass
 from typing import Iterable, Literal, Sequence
-
 import numpy as np
 import pandas as pd
 import scipy.sparse as sp
 
+
 SymOp = Literal["max", "min", "average"]
 
 
@@ -104,7 +104,8 @@ def from_edges(
 
         if weights is None:
             data = np.ones_like(rows, dtype=float)
-            weighted_eff = False if not weighted else False if weights is None else True  # edges w/o weights → unweighted
+            # edges w/o weights → unweighted
+            weighted_eff = False if not weighted else False if weights is None else True
         else:
             data = np.asarray(weights, dtype=float)
             if data.shape[0] != rows.shape[0]:

graphconstructor/operators/__init__.py

@@ -2,7 +2,7 @@
 from .disparity import DisparityFilter
 from .doubly_stochastic import DoublyStochastic
 from .knn_selector import KNNSelector
-from .locally_adaptive_sparsification import LocallyAdaptiveSparsification 
+from .locally_adaptive_sparsification import LocallyAdaptiveSparsification
 from .marginal_likelihood import MarginalLikelihoodFilter
 from .noise_corrected import NoiseCorrected
 from .weight_threshold import WeightThreshold

graphconstructor/operators/doubly_stochastic.py

@@ -1,6 +1,5 @@
 from dataclasses import dataclass
 import numpy as np
-import scipy.sparse as sp
 from ..graph import Graph
 from .base import GraphOperator