Fix flake8

Author: ddomingof

docs/source/diffusion.rst

@@ -95,12 +95,12 @@ References
 .. [3] Mostafavi, S., *et al.* (2008). Genemania: a real-time multiple association network integration algorithm for
     predicting gene function.Genome Biology. (9), S4.
 
-.. [4] Harchaoui, Z., *et al.* (2013). Kernel-based methods for hypothesis testing: a unified view. IEEE Signal Processing
-    Magazine. (30), 87–97.
+.. [4] Harchaoui, Z., *et al.* (2013). Kernel-based methods for hypothesis testing: a unified view. IEEE Signal
+    Processing Magazine. (30), 87–97.
 
 .. [5] Bersanelli, M. *et al.* (2016). Network diffusion-based analysis of high-throughput data for the detection of
     differentially enriched modules. Scientific Reports. (6), 34841.
 
-.. [6] Tsuda, K., *et al.* (2005).  Fast  protein  classification  with  multiple  networks. Bioinformatics, (21), 59–65.
+.. [6] Tsuda, K., *et al.* (2005).  Fast  protein  classification  with  multiple  networks. Bioinformatics, (21), 59–65
 
 

src/diffupy/diffuse_raw.py

@@ -20,11 +20,11 @@
 
 
 def calculate_scores(
-        col_ind: int,
-        scores: np.array,
-        diff: np.array,
-        const_mean: np.array,
-        const_var: np.array
+    col_ind: int,
+    scores: np.array,
+    diff: np.array,
+    const_mean: np.array,
+    const_var: np.array
 ) -> float:
     """Help function for diffuse_raw, which operate the z-scores calculation given a whole column of the score matrix.
 
@@ -51,10 +51,10 @@ def calculate_scores(
 
 
 def diffuse_raw(
-        graph: nx.Graph,
-        scores: Matrix,
-        z: bool = False,
-        k: Matrix = None,
+    graph: nx.Graph,
+    scores: Matrix,
+    z: bool = False,
+    k: Matrix = None,
 ) -> Matrix:
     """Compute the score diffusion procedure, given an initial state as a set of scores and a network where diffuse it.
 
@@ -103,9 +103,9 @@ def diffuse_raw(
     if not z:
         return Matrix(
             diff,
-            rows_labels = scores.rows_labels,
-            cols_labels = ['output diffusion scores'],
-            name = scores.name
+            rows_labels=scores.rows_labels,
+            cols_labels=['output diffusion scores'],
+            name=scores.name
         )
 
     logging.info('Normalization z-scores.')
@@ -140,7 +140,7 @@ def diffuse_raw(
             )
                 for i in range(diff.shape[1])
             ]),
-        rows_labels = scores.rows_labels,
-        cols_labels = ['output diffusion scores'],
-        name = scores.name
+        rows_labels=scores.rows_labels,
+        cols_labels=['output diffusion scores'],
+        name=scores.name
     )

src/diffupy/matrix.py

@@ -23,15 +23,15 @@ class Matrix:
     """Matrix class."""
 
     def __init__(
-            self,
-            mat=None,
-            rows_labels=None,
-            cols_labels=None,
-            graph=None,
-            quadratic=False,
-            name='',
-            init_value=None,
-            **kwargs
+        self,
+        mat=None,
+        rows_labels=None,
+        cols_labels=None,
+        graph=None,
+        quadratic=False,
+        name='',
+        init_value=None,
+        **kwargs
     ):
         """Initialize matrix.
 
@@ -87,7 +87,7 @@ def __str__(self):
         return f"\nmatrix {self.name} \n  {s} \n "
 
     def __iter__(self, **kargs):
-        """Helper method for the iteration of the Matrix."""
+        """Help method for the iteration of the Matrix."""
         self.i = -1
         self.j = 0
 
@@ -99,7 +99,7 @@ def __iter__(self, **kargs):
         return self
 
     def __next__(self):
-        """Helper method for the iteration of the Matrix."""
+        """Help method for the iteration of the Matrix."""
         if self.i >= len(self.rows_labels) - 1 and self.j >= len(self.cols_labels) - 1:
             self.get_labels = True
             self.get_indices = False
@@ -381,7 +381,7 @@ def match_mat(self, reference_matrix, match_quadratic=None):
 
         return mat_match
 
-    def match_missing_rows(self, reference_labels, missing_fill = 0):
+    def match_missing_rows(self, reference_labels, missing_fill=0):
         """Match method to set missing rows labels from reference labels with the missing_fill value."""
         if reference_labels == self.rows_labels:
             return self
@@ -482,7 +482,7 @@ def from_csv(self, csv_path):
 
     """Export"""
 
-    def to_dict(self, ordered = True):
+    def to_dict(self, ordered=True):
         """Export/convert matrix as a dictionary data structure."""
         if ordered:
             mat = self.order_rows()
@@ -496,15 +496,17 @@ def to_dict(self, ordered = True):
 
         return d
 
-    def to_csv(self, path, file_name = '_export.csv', index = False, ordered = True):
+    def to_csv(self, path, file_name='_export.csv', index=False, ordered=True):
         """Export matrix to csv file using the headers (row_labels, cols_labels) of the Matrix class."""
         # Generate dataframe
-        df = pd.DataFrame(data = self.to_dict(ordered))
+        df = pd.DataFrame(data=self.to_dict(ordered))
+
+        df.to_csv(os.path.join(path, self.name, file_name), index=index)
 
-        df.to_csv(os.path.join(path, self.name, file_name), index = index)
 
 class LaplacianMatrix(Matrix):
     """Laplacian matrix class."""
+
     def __init__(self, graph, normalized=False, name=''):
         """Initialize laplacian."""
         l_mat = get_laplacian(graph, normalized)

tests/test_input.py

@@ -10,6 +10,7 @@
 from diffupy.process_input import process_input, map_nodes
 from diffupy.utils import process_network
 from diffupy.validate_input import _validate_scores
+
 from tests.constants import *
 
 log = logging.getLogger(__name__)
@@ -135,8 +136,7 @@ def test_network(self):
         })
 
     def test_node_mapping(self):
-        """Test mapping of nodes in label_input to nodes in network"""
-
+        """Test mapping of nodes in label_input to nodes in network."""
         input = NODE_LOGFC_TEST_PATH
         input_labels_dict = process_input(
             input, method=RAW, binning=False, absolute_value=True, p_value=0.05, threshold=0.5,