Examples
=============

Installation/Usage:
*******************


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.
    """

    from .abstract_importer 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.import_structure()
            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 import_structure(self):
            data = {'From':['X','Y','Z'], 'To':['Z','Z','Y']}
            self._df_structure = pd.DataFrame(data)

        def dataset_id(self) -> object:
            pass

Parameters Estimation Example
*****************************

.. code-block:: python

    from PyCTBN.PyCTBN.json_importer import JsonImporter
    from PyCTBN.PyCTBN.sample_path import SamplePath
    from PyCTBN.PyCTBN.network_graph import NetworkGraph
    from PyCTBN.PyCTBN.parameters_estimator 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.PyCTBN.json_importer import JsonImporter
    from PyCTBN.PyCTBN.sample_path import SamplePath
    from PyCTBN.PyCTBN.structure_estimator 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()