Add args to generate_theoric_random_graph.py

generate_theoric_random_graph.py

 # coding = utf-8
-
+import os
 
 import networkx as nx
 import argparse
@@ -9,6 +9,11 @@ import random
 import copy
 from tqdm import tqdm
 
+# COMMAND PARSING
+parser = argparse.ArgumentParser()
+parser.add_argument("output_dir")
+args = parser.parse_args()
+
 def generate_sbm_prob_matrix(nb_of_blocks,prob_btw_block=0.1):
     M = np.zeros((nb_of_blocks,nb_of_blocks))
     np.fill_diagonal(M,[random.random() for i in range(nb_of_blocks)])
@@ -19,8 +24,11 @@ def generate_sbm_prob_matrix(nb_of_blocks,prob_btw_block=0.1):
             M[j,i] = prob_btw_block
     return M
 
+
 GRAPH_SIZE = [50,75,100]
-OUTPUT_DIR = "data/theoric_graph_1/"
+OUTPUT_DIR = args.output_dir
+if os.path.exist(OUTPUT_DIR):
+    raise FileExistsError("Output directory does not exists !")
 
 parameters = {
     "planted_partition_graph": {
@@ -35,7 +43,7 @@ parameters = {
     },
     "fast_gnp_random_graph": {
         "n": GRAPH_SIZE,
-        "p": [0.1,0.4,0.6,0.8]
+        "p": [0.1,0.4,0.6]
     },
     "random_powerlaw_tree_sequence": {  # configuration_model
         "n": GRAPH_SIZE,
@@ -43,12 +51,12 @@ parameters = {
     },
     "random_geometric_graph": {
         "n": GRAPH_SIZE,
-        "radius": [0.1,0.2,0.4,0.8]
+        "radius": [0.1,0.4,0.6]
     },
     "waxman_graph": {
         "n": GRAPH_SIZE,
-        "beta": [0.1,0.4,0.8],
-        "alpha": [0.1,0.4,0.8]
+        "beta": [0.1,0.4,0.6],
+        "alpha": [0.1,0.4,0.6]
     },
     "geographical_threshold_graph": {
         "n": GRAPH_SIZE,