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-# CTBN_Project
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+PyCTBN
+======
+
+A Continuous Time Bayesian Networks Library
+
+Installation/Usage
+*******************
+Download the release in .tar.gz or .whl format and simply use pip install to install it::
+
+    $ pip install PyCTBN-1.0.tar.gz
+
+Documentation
+*************
+Please refer to https://philipmartini.github.io/PyCTBN/ for the full project documentation.
+
+Implementing your own data importer
+***********************************
+.. code-block:: python
+
+    """This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
+    The net in exam has three ternary nodes and no prior net structure.
+    """
+
+    from PyCTBN import AbstractImporter
+
+    class CSVImporter(AbstractImporter):
+
+        def __init__(self, file_path):
+            self._df_samples_list = None
+            super(CSVImporter, self).__init__(file_path)
+
+        def import_data(self):
+            self.read_csv_file()
+            self._sorter = self.build_sorter(self._df_samples_list[0])
+            self.import_variables()
+            self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
+
+        def read_csv_file(self):
+            df = pd.read_csv(self._file_path)
+            df.drop(df.columns[[0]], axis=1, inplace=True)
+            self._df_samples_list = [df]
+
+        def import_variables(self):
+            values_list = [3 for var in self._sorter]
+            # initialize dict of lists
+            data = {'Name':self._sorter, 'Value':values_list}
+            # Create the pandas DataFrame
+            self._df_variables = pd.DataFrame(data)
+
+        def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+            return list(sample_frame.columns)[1:]
+
+        def dataset_id(self) -> object:
+            pass
+
+Parameters Estimation Example
+*****************************
+
+.. code-block:: python
+
+    from PyCTBN import JsonImporter
+    from PyCTBN import SamplePath
+    from PyCTBN import NetworkGraph
+    from PyCTBN import ParametersEstimator
+
+
+    def main():
+        read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
+        #import data
+        importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        importer.import_data(0)
+        #Create a SamplePath Obj passing an already filled AbstractImporter object
+        s1 = SamplePath(importer)
+        #Build The trajectries and the structural infos
+        s1.build_trajectories()
+        s1.build_structure()
+        print(s1.structure.edges)
+        print(s1.structure.nodes_values)
+        #From The Structure Object build the Graph
+        g = NetworkGraph(s1.structure)
+        #Select a node you want to estimate the parameters
+        node = g.nodes[2]
+        print("Node", node)
+        #Init the _graph specifically for THIS node
+        g.fast_init(node)
+        #Use SamplePath and Grpah to create a ParametersEstimator Object
+        p1 = ParametersEstimator(s1.trajectories, g)
+        #Init the peEst specifically for THIS node
+        p1.fast_init(node)
+        #Compute the parameters
+        sofc1 = p1.compute_parameters_for_node(node)
+        #The est CIMS are inside the resultant SetOfCIms Obj
+        print(sofc1.actual_cims)
+
+Structure Estimation Example
+****************************
+
+.. code-block:: python
+
+    from PyCTBN import JsonImporter
+    from PyCTBN import SamplePath
+    from PyCTBN import StructureEstimator
+    
+    def structure_estimation_example():
+
+        # read the json files in ./data path
+        read_files = glob.glob(os.path.join('./data', "*.json"))
+        # initialize a JsonImporter object for the first file
+        importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        # import the data at index 0 of the outer json array
+        importer.import_data(0)
+        # construct a SamplePath Object passing a filled AbstractImporter
+        s1 = SamplePath(importer)
+        # build the trajectories
+        s1.build_trajectories()
+        # build the real structure
+        s1.build_structure()
+        # construct a StructureEstimator object
+        se1 = StructureEstimator(s1, 0.1, 0.1)
+        # call the ctpc algorithm
+        se1.ctpc_algorithm()
+        # the adjacency matrix of the estimated structure
+        print(se1.adjacency_matrix())
+        # save results to a json file
+        se1.save_results()