Merge branch 'black_syntax' into 'master'

reformat with black

See merge request imi-sad/spikexplore!2

Author: naspert

.github/workflows/black.yaml

@@ -0,0 +1,15 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: psf/black@stable
+        with:
+          options: "--check -l 150"
+          src: "."
+          version: "~= 22.0"
+

.gitlab-ci.yml

@@ -1,6 +1,7 @@
 image: python:3.9
 
 stages:
+  - format
   - test
 
 variables:
@@ -22,6 +23,11 @@ before_script:
   - pip install --upgrade -r requirements.txt
   - pip install black pytest
 
+Formatting:
+  stage: format
+  script:
+    - black --check -l 150 .
+
 Tests:
   stage: test
   script:

setup.py

@@ -1,32 +1,24 @@
 from setuptools import setup
 
 setup(
-    name='spikexplore',
-    version='0.0.12',
-    description='Graph exploration using inhomogeneous filtered diffusion',
-    url='https://github.com/epfl-lts2/spikexplore',
-    author='Nicolas Aspert, Benjamin Ricaud',
-    author_email='[email protected], [email protected]',
-    license='Apache license',
-    packages=['spikexplore', 'spikexplore.backends'],
+    name="spikexplore",
+    version="0.0.12",
+    description="Graph exploration using inhomogeneous filtered diffusion",
+    url="https://github.com/epfl-lts2/spikexplore",
+    author="Nicolas Aspert, Benjamin Ricaud",
+    author_email="[email protected], [email protected]",
+    license="Apache license",
+    packages=["spikexplore", "spikexplore.backends"],
     scripts=[],
-    install_requires=['pandas',
-                      'numpy',
-                      'networkx',
-                      'tqdm',
-                      'twython',
-                      'wikipedia-api',
-                      'python-louvain',
-                      'TwitterAPI'
-                      ],
-    python_requires='>=3.6',
+    install_requires=["pandas", "numpy", "networkx", "tqdm", "twython", "wikipedia-api", "python-louvain", "TwitterAPI"],
+    python_requires=">=3.6",
     classifiers=[
-        'Development Status :: 4 - Beta',
-        'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache License',
-        'Operating System :: POSIX :: Linux',
-        'Programming Language :: Python :: 3.8',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.6'
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: Apache License",
+        "Operating System :: POSIX :: Linux",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.6",
     ],
 )

spikexplore/NodeInfo.py

@@ -1,12 +1,6 @@
-
 class NodeInfo:  # abstract interface
     def update(self, new_info):
         raise NotImplementedError
 
     def get_nodes(self):
         raise NotImplementedError
-
-
-
-
-

spikexplore/backends/synthetic.py

@@ -5,76 +5,76 @@
 
 
 class SyntheticNetwork:
-	class SynthNodeInfo(NodeInfo):
-		def __init__(self, nodes=pd.DataFrame()):
-			self.nodes = nodes
+    class SynthNodeInfo(NodeInfo):
+        def __init__(self, nodes=pd.DataFrame()):
+            self.nodes = nodes
 
-		def update(self, new_info):
-			return
+        def update(self, new_info):
+            return
 
-		def get_nodes(self):
-			return self.nodes
+        def get_nodes(self):
+            return self.nodes
 
-	def __init__(self, g, config):
-		# Instantiate an object
-		self.G = g
-		self.config = config
+    def __init__(self, g, config):
+        # Instantiate an object
+        self.G = g
+        self.config = config
 
-	def create_node_info(self):
-		return self.SynthNodeInfo()
+    def create_node_info(self):
+        return self.SynthNodeInfo()
 
-	def get_neighbors(self, node_id):
-		# collect info on the node and its (out going) edges
-		# return 2 dataframes, one with edges info and the other with the node info
-		G = self.G
-		if node_id not in G:
-			return self.SynthNodeInfo(pd.DataFrame()), pd.DataFrame()
-		# node data
-		node_df = pd.DataFrame([{'source': node_id, **G.nodes[node_id]}])
-		# Edges and edge data		
-		if nx.is_directed(G):
-			edges = G.out_edges(node_id, data=True)
-		else:
-			edges = G.edges(node_id, data=True)
+    def get_neighbors(self, node_id):
+        # collect info on the node and its (out going) edges
+        # return 2 dataframes, one with edges info and the other with the node info
+        G = self.G
+        if node_id not in G:
+            return self.SynthNodeInfo(pd.DataFrame()), pd.DataFrame()
+        # node data
+        node_df = pd.DataFrame([{"source": node_id, **G.nodes[node_id]}])
+        # Edges and edge data
+        if nx.is_directed(G):
+            edges = G.out_edges(node_id, data=True)
+        else:
+            edges = G.edges(node_id, data=True)
 
-		edgeprop_dic_list = []
-		for source, target, data in edges:
-			edge_dic = {'source': source, 'target': target, **data}
-			edgeprop_dic_list.append(edge_dic)
-		edges_df = pd.DataFrame(edgeprop_dic_list)
-		edges_df['weight'] = 1.0
-		return self.SynthNodeInfo(node_df), edges_df
+        edgeprop_dic_list = []
+        for source, target, data in edges:
+            edge_dic = {"source": source, "target": target, **data}
+            edgeprop_dic_list.append(edge_dic)
+        edges_df = pd.DataFrame(edgeprop_dic_list)
+        edges_df["weight"] = 1.0
+        return self.SynthNodeInfo(node_df), edges_df
 
-	def filter(self, node_info, edges_df):
-		if len(edges_df) < self.config.min_degree:
-			# discard the node
-			node_info = self.SynthNodeInfo(pd.DataFrame())
-			edges_df = pd.DataFrame()
-		# filter the edges
-		edges_df = self.filter_edges(edges_df)
-		return node_info, edges_df
+    def filter(self, node_info, edges_df):
+        if len(edges_df) < self.config.min_degree:
+            # discard the node
+            node_info = self.SynthNodeInfo(pd.DataFrame())
+            edges_df = pd.DataFrame()
+        # filter the edges
+        edges_df = self.filter_edges(edges_df)
+        return node_info, edges_df
 
-	def filter_edges(self, edges_df):
-		return edges_df
+    def filter_edges(self, edges_df):
+        return edges_df
 
-	def neighbors_list(self, edges_df):
-		if edges_df.empty:
-			return edges_df
-		neighbors = edges_df['target'].unique().tolist()
-		return neighbors
+    def neighbors_list(self, edges_df):
+        if edges_df.empty:
+            return edges_df
+        neighbors = edges_df["target"].unique().tolist()
+        return neighbors
 
-	def neighbors_with_weights(self, edges_df):
-		node_list = self.neighbors_list(edges_df)
-		node_dic = {}
-		for node in node_list:
-			node_dic[node] = len(edges_df) # degree of the node
-		return node_dic
+    def neighbors_with_weights(self, edges_df):
+        node_list = self.neighbors_list(edges_df)
+        node_dic = {}
+        for node in node_list:
+            node_dic[node] = len(edges_df)  # degree of the node
+        return node_dic
 
-	def reshape_node_data(self, nodes_df):
-		nodes_df.set_index('source', inplace=True)
-		return nodes_df
+    def reshape_node_data(self, nodes_df):
+        nodes_df.set_index("source", inplace=True)
+        return nodes_df
 
-	def add_graph_attributes(self, g, nodes_df, edges_df, nodes_info):
-		g = add_edges_attributes(g, edges_df)
-		g = add_node_attributes(g, self.reshape_node_data(nodes_df))
-		return g
+    def add_graph_attributes(self, g, nodes_df, edges_df, nodes_info):
+        g = add_edges_attributes(g, edges_df)
+        g = add_node_attributes(g, self.reshape_node_data(nodes_df))
+        return g