Merge branch 'dev' of github.com:saezlab/networkcommons into dev

networkcommons/_visual/__init__.py

@@ -17,4 +17,5 @@
 Visualization methods for networks and analyses.
 """
 
-from . import networkx as networkx
+from . import vis_networkx as vis_networkx
+from . import yfiles as vis_yfiles

networkcommons/_visual/_aux.py

@@ -0,0 +1,11 @@
+
+def wrap_node_name(node_name):
+    if ":" in node_name:
+        node_name = node_name.replace(":", "_")
+    if node_name.startswith("COMPLEX"):
+        # remove the word COMPLEX with a separator (:/-, etc)
+        return node_name[8:]
+    else:
+        return node_name
+
+

networkcommons/_visual/_network_mock.py

@@ -0,0 +1,91 @@
+from graphviz import Digraph
+from IPython.display import display
+from yfiles_jupyter_graphs import GraphWidget
+from typing import Dict, List
+import networkx as nx
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import os
+from pypath.utils import mapping
+
+from _aux import wrap_node_name
+import yfiles
+
+class NetworkMock:
+
+    def __init__(self):
+        self.network = nx.DiGraph()
+        self.color_map = {}
+
+    def init_from_sif(self, filepath):
+        """
+        Initialize the network from a SIF file
+        """
+        with open(filepath, 'r') as f:
+            for line in f:
+                source, target = line.strip().split()
+                self.network.add_edge(source, target)
+        return self.network
+
+    def add_nodes(self, nodes):
+        # add nodes to networkx
+        self.network.add_nodes_from(nodes)
+        return self.network
+
+    def set_initial_nodes(self, initial_nodes):
+        self.update_node_property(initial_nodes, type="initial_node")
+
+    def set_nodes_of_interest(self, nodes):
+        self.update_node_property(nodes, type="noi", value="1")
+
+    def add_edges(self, edges):
+        self.network.add_edges_from(edges)
+
+    def update_node_property(self, node, type="color", value="blue"):
+        # add color to the node in networkx
+        if type == "color":
+            self.color_map[node] = value
+        elif type == "initial_node":
+            self.network.nodes[node]["initial_node"] = True
+        return self.color_map
+
+    def update_edge_property(self, edge, type="color", color="blue"):
+        # add color to the edge in networkx
+        if type == "color":
+            self.color_map[edge] = color
+        return self.color_map
+
+    def draw(self, filepath=None, render=False):
+        networkx_vis = yfiles.NetworkXVisualizer()
+        networkx_vis.visualise(render=render)
+
+    def mapping_node_identifier(self, node: str) -> List[str]:
+        complex_string = None
+        gene_symbol = None
+        uniprot = None
+
+        if mapping.id_from_label0(node):
+            uniprot = mapping.id_from_label0(node)
+            gene_symbol = mapping.label(uniprot)
+        elif node.startswith("COMPLEX"):
+            node = node[8:] #TODO change to wrap
+            node_list = node.split("_")
+            translated_node_list = [mapping.label(mapping.id_from_label0(item)) for item in node_list]
+            complex_string = "COMPLEX:" + "_".join(translated_node_list)
+        elif mapping.label(node):
+            gene_symbol = mapping.label(node)
+            uniprot = mapping.id_from_label0(gene_symbol)
+        else:
+            print("Error during translation, check syntax for ", node)
+
+        return [complex_string, gene_symbol, uniprot]
+
+    def convert_edges_into_genesymbol(self, edges: pd.DataFrame) -> pd.DataFrame:
+        def convert_identifier(x):
+            identifiers = self.mapping_node_identifier(x)
+            return identifiers[1]  # Using GeneSymbol
+        edges["source"] = edges["source"].apply(convert_identifier)
+        edges["target"] = edges["target"].apply(convert_identifier)
+        return edges
+

networkcommons/_visual/network_stats.py

@@ -0,0 +1,128 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+from typing import List, Dict
+
+
+def plot_n_nodes_edges(
+        networks: Dict[str, nx.DiGraph],
+        filepath=None,
+        render=False,
+        orientation='vertical',
+        color_palette='Set2',
+        size=10,
+        linewidth=2,
+        marker='o',
+        show_nodes=True,
+        show_edges=True
+):
+    """
+    Plot the number of nodes and edges in the networks using a lollipop plot.
+
+    Args:
+        networks (Dict[str, nx.DiGraph]): A dictionary of network names and their corresponding graphs.
+        filepath (str): Path to save the plot. Default is None.
+        render (bool): Whether to display the plot. Default is False.
+        orientation (str): 'vertical' or 'horizontal'. Default is 'vertical'.
+        color_palette (str): Matplotlib color palette. Default is 'Set2'.
+        size (int): Size of the markers. Default is 10.
+        linewidth (int): Line width of the lollipops. Default is 2.
+        marker (str): Marker style for the lollipops. Default is 'o'.
+        show_nodes (bool): Whether to show nodes count. Default is True.
+        show_edges (bool): Whether to show edges count. Default is True.
+    """
+    if not show_nodes and not show_edges:
+        raise ValueError("At least one of 'show_nodes' or 'show_edges' must be True.")
+
+    # Set the color palette
+    palette = plt.get_cmap(color_palette)
+    colors = palette.colors if hasattr(palette, 'colors') else palette(range(len(networks)))
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    for idx, (network_name, network) in enumerate(networks.items()):
+        # Get the number of nodes and edges
+        n_nodes = len(network.nodes)
+        n_edges = len(network.edges)
+        categories = []
+        values = []
+
+        if show_nodes:
+            categories.append('Nodes')
+            values.append(n_nodes)
+        if show_edges:
+            categories.append('Edges')
+            values.append(n_edges)
+
+        color = colors[idx % len(colors)]
+
+        if orientation == 'vertical':
+            positions = [f"{network_name} {cat}" for cat in categories]
+            ax.vlines(x=positions, ymin=0, ymax=values, color=color, linewidth=linewidth, label=network_name)
+            ax.scatter(positions, values, color=color, s=size ** 2, marker=marker, zorder=3)
+
+            # Annotate the values
+            for i, value in enumerate(values):
+                offset = size * 0.1 if value < 10 else size * 0.2
+                ax.text(positions[i], value + offset, str(value), ha='center', va='bottom', fontsize=size)
+        else:
+            positions = [f"{network_name} {cat}" for cat in categories]
+            ax.hlines(y=positions, xmin=0, xmax=values, color=color, linewidth=linewidth, label=network_name)
+            ax.scatter(values, positions, color=color, s=size ** 2, marker=marker, zorder=3)
+
+            # Annotate the values
+            for i, value in enumerate(values):
+                offset = size * 0.1 if value < 10 else size * 0.2
+                ax.text(value + offset, positions[i], str(value), va='center', ha='left', fontsize=size)
+
+    # Set the axis labels
+    if orientation == 'vertical':
+        ax.set_xlabel("Network and Type")
+        ax.set_ylabel("Count")
+    else:
+        ax.set_ylabel("Network and Type")
+        ax.set_xlabel("Count")
+
+    # Set the title depending on the categories
+    title = "Number of Nodes and Edges"
+    if show_nodes and not show_edges:
+        title = "Number of Nodes"
+    elif show_edges and not show_nodes:
+        title = "Number of Edges"
+    ax.set_title(title)
+
+    # Add a legend
+    ax.legend()
+
+    # Save the plot
+    if filepath is not None:
+        plt.savefig(filepath)
+
+    # Render the plot
+    if render:
+        plt.show()
+
+
+# Test the function with sample networks
+# if __name__ == "__main__":
+#     # Create sample directed graphs
+#     G1 = nx.DiGraph()
+#     G1.add_nodes_from(range(10))  # Adding 10 nodes
+#     G1.add_edges_from([(i, i + 1) for i in range(9)])  # Adding 9 edges
+#
+#     G2 = nx.DiGraph()
+#     G2.add_nodes_from(range(15))  # Adding 15 nodes
+#     G2.add_edges_from([(i, i + 1) for i in range(14)])  # Adding 14 edges
+#
+#     G3 = nx.DiGraph()
+#     G3.add_nodes_from(range(5))  # Adding 5 nodes
+#     G3.add_edges_from([(i, (i + 1) % 5) for i in range(5)])  # Adding 5 edges
+#
+#     G4 = nx.DiGraph()
+#     G4.add_nodes_from(range(20))  # Adding 20 nodes
+#     G4.add_edges_from([(i, i + 1) for i in range(19)])  # Adding 19 edges
+#
+#     networks = {'Network1': G1, 'Network2': G2, 'Network3': G3, 'Network4': G4}
+#
+#     plot_n_nodes_edges(networks, filepath="nodes_edges_plot.png", render=True, orientation='horizontal',
+#                        color_palette='Set2', size=12, linewidth=2, marker='o', show_nodes=True, show_edges=True)