<jupyter_start><jupyter_text>'D1' : 0,    'H4' : 1,    'H1' : 2,    'M30': 3,    'M15': 4,    'M5' : 5,    'M1' : 6<jupyter_code>class DatasetLoader: 
    def __init__(self,raw_data):
        self.dataset = []
        self.Last_D1 = None
        #self.label_D1 = None
        self.Last_H4 = None
        self.Last_H1 = None
        self.Last_M30 = None
        self.Last_M15 = None
        self.Last_M5 = None
        self.Last_M1 = None
        self.image = [None]
        
    def _update_image(self):
        self.image = [
            self.Last_D1,
            self.Last_H4,
            self.Last_H1,
            self.Last_M30,
            self.Last_M15,
            self.Last_M5,
            self.Last_M1
        ]
    def _update_label(self):
        
    def build_dataset(self,data):
        for k in  range(len(data)) :
            line = data[k]
            if line[1] == 0 :
                self.Last_D1 = line
            elif line[1] == 1 :
                self.Last_H4 = line
            elif line[1] == 2 :
                self.Last_H1 = line
            elif line[1] == 3 :
                self.Last_M30 = line
            elif line[1] == 4 :
                self.Last_M15 = line
            elif line[1] == 5 :
                self.Last_M5 = line
            elif line[1] == 6 :
                self.Last_M1 = line
            else : continue
            self._update_image()
            if None not in self.image :
                self.dataset.append(self.image)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10728476821192053, 'fraction_numerical': 0.038410596026490065, 'mean_word_length': 1.235207100591716, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '652549', 'license': 'MIT', 'n_tokens_mistral': 535, 'n_tokens_neox': 502, 'n_words': 100, 'path': 'read_data.ipynb', 'repo_name': 'bendidi/Time-Series-Tensorflow', 'size': '1510'}