Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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72 lines
2.3 KiB
72 lines
2.3 KiB
import os
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import glob
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import pandas as pd
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import json
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import numpy as np
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from abstract_importer import AbstractImporter
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class JsonImporter(AbstractImporter):
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def __init__(self, files_path):
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self.df_samples_list = []
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self.df_structure = pd.DataFrame()
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self.df_variables = pd.DataFrame()
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super(JsonImporter, self).__init__(files_path)
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def import_data(self):
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raw_data = self.read_json_file()
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self.import_trajectories(raw_data)
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self.import_structure(raw_data)
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self.import_variables(raw_data)
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def import_trajectories(self, raw_data):
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self.normalize_trajectories(raw_data, 0, 'samples')
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def import_structure(self, raw_data):
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self.df_structure = self.one_level_normalizing(raw_data, 0, 'dyn.str')
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def import_variables(self, raw_data):
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self.df_variables = self.one_level_normalizing(raw_data, 0, 'variables')
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def read_json_file(self):
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try:
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read_files = glob.glob(os.path.join(self.files_path, "*.json"))
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for file_name in read_files:
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with open(file_name) as f:
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data = json.load(f)
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return data
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except ValueError as err:
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print(err.args)
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def one_level_normalizing(self, raw_data, indx, variables_key):
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return pd.json_normalize(raw_data[indx][variables_key])
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def normalize_trajectories(self, raw_data, indx, trajectories_key):
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for sample_indx, sample in enumerate(raw_data[indx][trajectories_key]):
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self.df_samples_list.append(pd.json_normalize(raw_data[indx][trajectories_key][sample_indx]))
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def build_list_of_samples_array(self, data_frame):
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columns_list = []
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for column in data_frame:
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columns_list.append(data_frame[column].to_numpy())
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return columns_list
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def clear_data_frames(self):
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"""
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Rimuove tutti i valori contenuti nei data_frames presenti in df_list
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Parameters:
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void
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Returns:
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void
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"""
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for indx in range(len(self.df_samples_list)):
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self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
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"""ij = JsonImporter("../data")
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ij.import_data()
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#print(ij.df_samples_list[7])
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print(ij.df_structure)
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print(ij.df_variables)
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print((ij.build_list_of_samples_array(0)[1].size))"""
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