Added `memory_profiler` and placed data and results in proper directories

.gitignore

@@ -0,0 +1,8 @@
+
+venv/
+.venv/
+
+data/
+res/
+
+*.dat

benchmark.py

@@ -2,19 +2,27 @@
 
 import glob
 import os
+from pathlib import Path
+from memory_profiler import profile
 
 from pyctbn.legacy import JsonImporter
 from pyctbn.legacy import SamplePath
 from pyctbn.legacy import StructureConstraintBasedEstimator
 
-
+@profile
 def structure_constraint_based_estimation_example():
+    Path("./data").mkdir(parents=True, exist_ok=True)
     # <read the json files in ./data path>
-    read_files = glob.glob(os.path.join('./', "*.json"))
+    read_files = glob.glob(os.path.join("./data/", "*.json"))
     # <initialize a JsonImporter object for the first file>
-    importer = JsonImporter(file_path=read_files[0], samples_label='samples',
+    importer = JsonImporter(
-                            structure_label='dyn.str', variables_label='variables',
+        file_path=read_files[0],
-                            time_key='Time', variables_key='Name')
+        samples_label='samples',
+        structure_label='dyn.str',
+        variables_label='variables',
+        time_key='Time',
+        variables_key='Name'
+    )
     # <import the data at index 0 of the outer json array>
     importer.import_data(0)
     # construct a SamplePath Object passing a filled AbstractImporter object
@@ -27,17 +35,23 @@ def structure_constraint_based_estimation_example():
     # and the independence tests significance, if you have prior knowledge about
     # the net structure create a list of tuples
     # that contains them and pass it as known_edges parameter
-    se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
+    se1 = StructureConstraintBasedEstimator(
-                                            known_edges=[], thumb_threshold=25)
+        sample_path=s1,
+        exp_test_alfa=0.1,
+        chi_test_alfa=0.1,
+        known_edges=[],
+        thumb_threshold=25
+    )
     # call the algorithm to estimate the structure
     se1.estimate_structure()
     # obtain the adjacency matrix of the estimated structure
     print(se1.adjacency_matrix())
+    Path("./res").mkdir(parents=True, exist_ok=True)
     # save the estimated structure  to a json file
     # (remember to specify the path AND the .json extension)....
-    se1.save_results('./results0.json')
+    se1.save_results("./res/results0.json")
     # ...or save it also in a graphical model fashion
     # (remember to specify the path AND the .png extension)
-    se1.save_plot_estimated_structure_graph('./result0.png')
+    se1.save_plot_estimated_structure_graph("./res/result0.png")
 
 structure_constraint_based_estimation_example()