Metadata-Version: 2.1
Name: serial-data-handler-zxw
Version: 0.3.6
Summary: A simple library to handle time gaps in data , especially in AI 
 人工智能中时间数据预处理
Home-page: UNKNOWN
Author: xuewei zhang
Author-email: jingmu_predict@qq.com
License: UNKNOWN
Description: # process serial data for AI training
        # 人工智能训练中,对于时间断层数据的处理工具包
        
        - 根据断点,分配数据集,保证训练数据的连续性
        - allocate data set according to breakpoints, to ensure the continuity of training data
        
        ## code demo
        
        ```python
        # Attention:
        # 传入的数据必须指定时间列(或其他顺序列)
        # You must specify the time column (or other sequential column) of the incoming data
        # 传入的数据必须已按顺序排列好
        # The incoming data must be sorted in order
        
        from datetime import timedelta
        from serial_data_handler_zxw import 生成训练数据_避开时间断点, 时间列_三角函数化
        import pandas as pd
        
        csv_path = "/Volumes/time_serial_data.csv"
        data = pd.read_csv(csv_path)
        
        # specific the time column named '收盘时间' , 
        # and set the gap is 2 minutes , 
        # it means that if the gap between two adjacent data > 2 minutes, it will be considered as a breakpoint
        # 生成训练数据
        x = 生成训练数据_避开时间断点(data, column_timestamp='收盘时间', gap=timedelta(minutes=2))
        print(x.断点)
        训练数据index = x.数据划分_避开断点(input长度=100, output长度=100, step=1)
        print(len(训练数据index))
        
        # If your data interval < 1s, please do the corresponding multiplication conversion
        # for example: 1ms data, you should multiply by 1000, convert to second-level data
        # time column trigonometric function
        # 如果您的数据间隔小于1秒,请做相应的乘法转换, 例如: 1毫秒的数据,请乘以1000,转换为秒级数据
        # 时间列_三角函数化
        data['收盘时间'] = pd.to_datetime(data['收盘时间'])
        data['收盘时间'] = 时间列_三角函数化(data['收盘时间'], 周期=timedelta(days=1))
        print(data['收盘时间'])
        ```
        
        ## code demo2
        
        ```python   
        import pandas as pd
        from datetime import datetime
        from serial_data_handler_zxw import 时间序列_数据对齐
        
        data = pd.read_csv('/Volumes/AI_1505056/量化交易/币安_K线数据_1d/BTCUSDT-1m-201909-202308.csv')
        # to datetime
        data['收盘时间'] = pd.to_datetime(data['收盘时间'])
        
        # drop part of columns if it exits
        data.drop(columns=['开盘时间戳', '收盘时间戳'], inplace=True)
        
        #
        数据预处理 = 时间序列_数据对齐(data, '收盘时间')
        i = 数据预处理.查找_时间范围(datetime(2023, 8, 29, 16, 54, 0), 查找精度='1d')
        print(i)
        ```
        
        
        ## code demo3 神经网络训练数据预处理
        
        ```python
        from serial_data_handler_zxw import 金融K线_AI数据预处理 as k_ai
        
        # print help
        k_ai.金融K线_AI数据预处理.help()
        
        # 
        csv_file = "/Volumes/AI_1505056/量化交易/币安_K线数据/BTCUSDT-1m-201909-202308.csv"
        x = k_ai.金融K线_AI数据预处理(csv_file, 100, 100)
        xn = x.标准化(x.data)
        xn.to_csv("/Volumes/AI_1505056/量化交易/币安_K线数据/BTCUSDT-1m-201909-202308_normalize.csv", index=False)
        
        print(x.data.head())
        print(xn.head())
        ```
        
        
        python setup.py sdist bdist_wheel
        twine upload dist/*
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
