Add draw script and debug

draw_graph_script.py

+# coding = utf-8
+
+import argparse
+import networkx as nx
+import pandas as pd
+import joblib,json
+
+from lib.draw import draw
+
+parser = argparse.ArgumentParser()
+parser.add_argument("input_file",help="edgelist format (sep = \",\" )")
+parser.add_argument("--encoder-file",help="LabelEncoder instance that allows to obtain a label for each node")
+parser.add_argument("--country",help="if country node",action="store_true")
+parser.add_argument("-w",action="store_true")
+parser.add_argument("output_file")
+
+args = parser.parse_args()
+
+if args.w:
+    df = pd.read_csv(args.input_file,header=None,names="source target weight".split())
+    G = nx.from_pandas_edgelist(df,edge_attr="weight")
+else:
+    df = pd.read_csv(args.input_file, header=None, names="source target weight".split())
+    G = nx.from_pandas_edgelist(df, edge_attr="weight")
+
+encoder = None
+labels_dict = {}
+
+if args.encoder_file:
+    encoder = joblib.load(args.encoder_file)
+    if args.country:
+        iso2_name = json.load(open("data/ISO3166-1.alpha2.json.txt"))
+    for node in list(G.nodes()):
+        if args.country:
+            labels_dict[node] = iso2_name[encoder.inverse_transform([node])[0]]
+        else:
+            labels_dict[node] = encoder.inverse_transform([node])[0]
+
+fig, ax = draw(G,labels_dict)
+fig.savefig("test.pdf")
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lib/draw.py

@@ -89,3 +89,4 @@ def draw(G, labels_dict={}, iteration_force_atlase=2000, figsize=(40, 20), font_
     if weight:
         plt.colorbar(edges)
     plt.axis("off")
+    return fig, ax

nx2gt.py

-# code from http://bbengfort.github.io/snippets/2016/06/23/graph-tool-from-networkx.html
-
-import networkx as nx
-import graph_tool as gt
-
-
-def get_prop_type(value, key=None):
-    """
-    Performs typing and value conversion for the graph_tool PropertyMap class.
-    If a key is provided, it also ensures the key is in a format that can be
-    used with the PropertyMap. Returns a tuple, (type name, value, key)
-    """
-
-    # Deal with the value
-    if isinstance(value, bool):
-        tname = 'bool'
-
-    elif isinstance(value, int):
-        tname = 'float'
-        value = float(value)
-
-    elif isinstance(value, float):
-        tname = 'float'
-
-    elif isinstance(value, dict):
-        tname = 'object'
-
-    else:
-        tname = 'string'
-        value = str(value)
-
-    return tname, value, key
-
-
-def nx2gt(nxG):
-    """
-    Converts a networkx graph to a graph-tool graph.
-    """
-    # Phase 0: Create a directed or undirected graph-tool Graph
-    gtG = gt.Graph(directed=nxG.is_directed())
-
-    # Add the Graph properties as "internal properties"
-    for key, value in nxG.graph.items():
-        # Convert the value and key into a type for graph-tool
-        tname, value, key = get_prop_type(value, key)
-
-        prop = gtG.new_graph_property(tname)  # Create the PropertyMap
-        gtG.graph_properties[key] = prop      # Set the PropertyMap
-        gtG.graph_properties[key] = value     # Set the actual value
-
-    # Phase 1: Add the vertex and edge property maps
-    # Go through all nodes and edges and add seen properties
-    # Add the node properties first
-    nprops = set()  # cache keys to only add properties once
-    for node, data in nxG.nodes(data=True):
-
-        # Go through all the properties if not seen and add them.
-        for key, val in data.items():
-            if key in nprops:
-                continue  # Skip properties already added
-
-            # Convert the value and key into a type for graph-tool
-            tname, _, key = get_prop_type(val, key)
-
-            prop = gtG.new_vertex_property(tname)  # Create the PropertyMap
-            gtG.vertex_properties[key] = prop      # Set the PropertyMap
-
-            # Add the key to the already seen properties
-            nprops.add(key)
-
-    # Also add the node id: in NetworkX a node can be any hashable type, but
-    # in graph-tool node are defined as indices. So we capture any strings
-    # in a special PropertyMap called 'id' -- modify as needed!
-    gtG.vertex_properties['id'] = gtG.new_vertex_property('string')
-
-    # Add the edge properties second
-    eprops = set()  # cache keys to only add properties once
-    for src, dst, data in nxG.edges(data=True):
-
-        # Go through all the edge properties if not seen and add them.
-        for key, val in data.items():
-            if key in eprops:
-                continue  # Skip properties already added
-
-            # Convert the value and key into a type for graph-tool
-            tname, _, key = get_prop_type(val, key)
-
-            prop = gtG.new_edge_property(tname)  # Create the PropertyMap
-            gtG.edge_properties[key] = prop      # Set the PropertyMap
-
-            # Add the key to the already seen properties
-            eprops.add(key)
-
-    # Phase 2: Actually add all the nodes and vertices with their properties
-    # Add the nodes
-    vertices = {}  # vertex mapping for tracking edges later
-    for node, data in nxG.nodes(data=True):
-
-        # Create the vertex and annotate for our edges later
-        v = gtG.add_vertex()
-        vertices[node] = v
-
-        # Set the vertex properties, not forgetting the id property
-        data['id'] = str(node)
-        for key, value in data.items():
-            gtG.vp[key][v] = value  # vp is short for vertex_properties
-
-    # Add the edges
-    for src, dst, data in nxG.edges(data=True):
-
-        # Look up the vertex structs from our vertices mapping and add edge.
-        e = gtG.add_edge(vertices[src], vertices[dst])
-
-        # Add the edge properties
-        for key, value in data.items():
-            gtG.ep[key][e] = value  # ep is short for edge_properties
-
-    # Done, finally!
-    return gtG
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