PyCTBN/main_package/classes/json_importer.py

import os
import glob
import pandas as pd
import json
import typing
from abstract_importer import AbstractImporter
from line_profiler import LineProfiler


class JsonImporter(AbstractImporter):
    """
    Implementa l'interfaccia AbstractImporter e aggiunge i metodi necessari a costruire le trajectories e la struttura della rete
    del dataset in formato json con la seguente struttura:
    [] 0
        |_ dyn.cims
        |_ dyn.str
        |_ samples
        |_ variabels

    :df_samples_list: lista di dataframe, ogni dataframe contiene una traj
    :df_structure: dataframe contenente la struttura della rete
    :df_variables: dataframe contenente le infromazioni sulle variabili della rete

    """

    def __init__(self, files_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
                 variables_key: str):
        self.samples_label = samples_label
        self.structure_label = structure_label
        self.variables_label = variables_label
        self.time_key = time_key
        self.variables_key = variables_key
        self.df_samples_list = []
        self._df_structure = pd.DataFrame()
        self._df_variables = pd.DataFrame()
        self._concatenated_samples = None
        self.sorter = None
        super(JsonImporter, self).__init__(files_path)

    def import_data(self):
        raw_data = self.read_json_file()
        self.import_variables(raw_data)
        self.import_trajectories(raw_data)
        self.compute_row_delta_in_all_samples_frames(self.time_key)
        self.clear_data_frame_list()
        self.import_structure(raw_data)
        # self.import_variables(raw_data, self.sorter)

    def import_trajectories(self, raw_data: pd.DataFrame):
        self.normalize_trajectories(raw_data, 0, self.samples_label)

    def import_structure(self, raw_data: pd.DataFrame):
        self._df_structure = self.one_level_normalizing(raw_data, 0, self.structure_label)

    def import_variables(self, raw_data: pd.DataFrame):
        self._df_variables = self.one_level_normalizing(raw_data, 0, self.variables_label)
        self.sorter = self._df_variables[self.variables_key].to_list()
        self.sorter.sort()
        print("Sorter:", self.sorter)
        self._df_variables[self.variables_key] = self._df_variables[self.variables_key].astype("category")
        self._df_variables[self.variables_key] = self._df_variables[self.variables_key].cat.set_categories(self.sorter)
        self._df_variables = self._df_variables.sort_values([self.variables_key])

    def read_json_file(self) -> typing.List:
        """
        Legge il primo file .json nel path self.filepath

        Parameters:
              void
        Returns:
              :data: il contenuto del file json

        """
        try:
            read_files = glob.glob(os.path.join(self.files_path, "*.json"))
            if not read_files:
                raise ValueError('No .json file found in the entered path!')
            with open(read_files[0]) as f:
                data = json.load(f)
                return data
        except ValueError as err:
            print(err.args)

    def one_level_normalizing(self, raw_data: pd.DataFrame, indx: int, key: str) -> pd.DataFrame:
        """
        Estrae i dati innestati di un livello, presenti nel dataset raw_data,
        presenti nel json array all'indice indx nel json object key

        Parameters:
            :raw_data: il dataset json completo
            :indx: l'indice del json array da cui estrarre i dati
            :key: il json object da cui estrarre i dati
        Returns:
            Il dataframe contenente i dati normalizzati

        """
        return pd.DataFrame(raw_data[indx][key])

    def normalize_trajectories(self, raw_data: pd.DataFrame, indx: int, trajectories_key: str):
        """
        Estrae le traiettorie presenti in rawdata nel json array all'indice indx, nel json object trajectories_key.
        Aggiunge le traj estratte nella lista di dataframe self.df_samples_list

        Parameters:
            void
        Returns:
            void
        """
        self.df_samples_list = [pd.DataFrame(sample) for sample in raw_data[indx][trajectories_key]]
        #for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]):
            #self.df_samples_list.append(pd.DataFrame(sample))
        #self.sorter = list(self.df_samples_list[0].columns.values)[1:]

    def compute_row_delta_sigle_samples_frame(self, sample_frame: pd.DataFrame, time_header_label: str,
                                              columns_header: typing.List, shifted_cols_header: typing.List) \
            -> pd.DataFrame:
        sample_frame[time_header_label] = sample_frame[time_header_label].diff().shift(-1)
        shifted_cols = sample_frame[columns_header].shift(-1).fillna(0).astype('int32')
        #print(shifted_cols)
        shifted_cols.columns = shifted_cols_header
        sample_frame = sample_frame.assign(**shifted_cols)
        sample_frame.drop(sample_frame.tail(1).index, inplace=True)
        return sample_frame

    def compute_row_delta_in_all_samples_frames(self, time_header_label: str):
        #columns_header = list(self.df_samples_list[0].columns.values)
        #self.sorter = columns_header[1:]
        shifted_cols_header = [s + "S" for s in self.sorter]
        for indx, sample in enumerate(self.df_samples_list):
            self.df_samples_list[indx] = self.compute_row_delta_sigle_samples_frame(sample,
                                                        time_header_label, self.sorter, shifted_cols_header)
        self._concatenated_samples = pd.concat(self.df_samples_list)
        complete_header = self.sorter[:]
        complete_header.insert(0, 'Time')
        complete_header.extend(shifted_cols_header)
        print("Complete Header", complete_header)
        self._concatenated_samples = self._concatenated_samples[complete_header]
        print("Concat Samples", self._concatenated_samples)

    def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
        """
        Costruisce una lista contenente le colonne presenti nel dataframe data_frame convertendole in numpy_array
        Parameters:
            :data_frame: il dataframe da cui estrarre e convertire le colonne
        Returns:
            :columns_list: la lista contenente le colonne convertite in numpyarray

        """
        columns_list = [data_frame[column].to_numpy() for column in data_frame]
        #for column in data_frame:
            #columns_list.append(data_frame[column].to_numpy())
        return columns_list

    def clear_concatenated_frame(self):
        """
        Rimuove tutti i valori contenuti nei data_frames presenti in df_samples_list
        Parameters:
            void
        Returns:
            void
         """
        self._concatenated_samples = self._concatenated_samples.iloc[0:0]

    def clear_data_frame_list(self):
        for indx in range(len(self.df_samples_list)):  # Le singole traj non servono più
            self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]

    def import_sampled_cims(self, raw_data: pd.DataFrame, indx: int, cims_key: str) -> typing.Dict:
        cims_for_all_vars = {}
        for var in raw_data[indx][cims_key]:
            sampled_cims_list = []
            cims_for_all_vars[var] = sampled_cims_list
            for p_comb in raw_data[indx][cims_key][var]:
                cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy())
        return cims_for_all_vars


    @property
    def concatenated_samples(self):
        return self._concatenated_samples

    @property
    def variables(self):
        return self._df_variables

    @property
    def structure(self):
        return self._df_structure