Updating code for compatibility with Python 3

README.md

@@ -1,49 +1,54 @@
-Random Modular Network Generator
-================================
-This is the source code used for the following paper: 
+# Random Modular Network Generator
 
-Sah, Pratha*, Lisa O. Singh, Aaron Clauset, and Shweta Bansal. "Exploring community structure in biological networks with random graphs." BMC Bioinformatics 2014, 15:220. doi:10.1186/1471-2105-15-220
+This is the source code used for the following paper:
+
+Sah, Pratha\*, Lisa O. Singh, Aaron Clauset, and Shweta Bansal. "Exploring community structure in biological networks with random graphs." BMC Bioinformatics 2014, 15:220\. doi:10.1186/1471-2105-15-220
 
 This Python script generates undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible.
 
 email: ps875@georgetown.edu, sb753@georgetown.edu
 
 Please cite the paper above, if you use our code in any form or create a derivative work.
 
-Sample Output Graph
-================================
+# Dependencies
 
-![alt tag](https://github.com/prathasah/random-modular-network-generator/blob/master/Figure2.jpg)
+* Python 3.8 or higher
+* Networkx 3.3 or higher
+* NumPy 1.24 or higher
+* Matplotlib 3.7 or higher
 
-Modular random graphs with network size = 150, 375 edges, 3 modules (or communities) of size, *s* = 50 and degree distribution is power law with modularity values of: a) *Q* = 0.1; b) *Q* = 0.3; and c) *Q* = 0.6. 
+You can install all dependencies using pip:
+```bash
+pip install -r requirements.txt
+```
 
-From  Figure 2, Sah *et al.* (2014)
+# Usage
 
-Dependencies
-================================
-* [Python 2.7](http://python.org/)
-* [Networkx 2.2](https://networkx.github.io/)
+For a quick demo of the code, open the script "random\_modular\_generator\_variable\_modules.py" and scroll down to the _main_ function. Adjust the model parameters, degree distribution function and module size distribution function and run in terminal using the command:
 
-Link to install Networkx can be found [here](https://networkx.github.io/). The generator also requires sequence_generator.py script which is provided.
+```bash
+python random_modular_generator_variable_modules.py
+```
 
-Usage
-================================
+# Sample Code
 
-For a quick demo of the code, open the script "random_modular_generator_variable_modules.py" and scroll down to the *main* function. Adjust the model parameters, degree distribution function and module size distribution function and run in terminal using the command:
+For users unfamiliar with Python, I have uploaded a sample code file (mock\_code.py) demonstrating how the graph generator can be imported and used in a script. The mock code can be run using the command:
 
-`$ python random_modular_generator_variable_modules.py`
+```bash
+python mock_code.py
+```
 
+# Output
 
+The code saves the adjacency matrix of the generated random modular graph under the filename 'adjacency\_matrix.txt'. The graph is also saved in a graphml format under the filename "random\_modular\_graph.graphml". The graphml file can be uploaded in Gephi (<http://gephi.github.io/>) for graph visualization. Note that Gephi currently does not have a layout plugin to visualize modular graphs. In the near future, I am planning to add a function in random-modular-network-generator.py code to assign each node a particular coordinate that allows easy visualization of modules (I have done this in Figure 2). Shoot us an email if you need this feature sooner than later.
 
-Sample Code
-================================
-For users unfamiliar to Python, I have uploadeded a sample code file (mock_code.py) demonstrating how the graph generator can be imported and used in a script. The mock code can be run using the command
+# Sample Output Graph
 
-`$ python mock_code.py`
+![alt tag](https://github.com/prathasah/random-modular-network-generator/blob/master/Figure2.jpg)
+
+Modular random graphs with network size = 150, 375 edges, 3 modules (or communities) of size, *s* = 50 and degree distribution is power law with modularity values of: a) *Q* = 0.1; b) *Q* = 0.3; and c) *Q* = 0.6. 
 
-Output
-================================
-The code saves the the adjacency matrix of the generated random modular graph under the filename 'adjacency_matrix.txt'. The graph is also saved in a graphml format under the filename "random_modular_graph.graphml". The graphml file can be uploaded in Gephi (http://gephi.github.io/) for graph visualization. Note that Gephi currently does not have a layout plugin to visualize modular graphs.  In the near future, I am planning to add a function in random-modular-network-generator.py code to assign each node a particular coordinate that allows easy visualization of modules (I have done this in Figure 2). Shoot us an email if you need this feature sooner than later.  
+From Figure 2, Sah *et al.* (2014)
 
 =========
 

mock_code.py

@@ -29,9 +29,9 @@
 
 # generate the graph! 
 
-print "Generating a simple poisson random modular graph with modularity(Q)= " + str(Q)
-print "Graph has " + str(N) + " nodes, " +str(m)+ " modules, and a network mean degree of " + str(d)
-print "Generating graph....." 
+print("Generating a simple poisson random modular graph with modularity(Q)= " + str(Q))
+print("Graph has " + str(N) + " nodes, " +str(m)+ " modules, and a network mean degree of " + str(d))
+print("Generating graph.....") 
 G = rmg.generate_modular_networks(N, sfunction, modfunction, Q, m, d, verbose=True)
 #nx.write_graphml(G, "random_modular_graph_Q"+str(Q)+"_N"+str(N)+"_d"+str(d)+"_m"+str(m)+".graphml")
 nx.write_graphml(G, "random_graph_poisson_N"+str(N)+"_d"+str(d)+".graphml")