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