import pandas as pd
import datetime
import pickle
import os
import process_feature

from odps import ODPS
from datetime import datetime as dt
from config import set_config

config_ = set_config()


def get_data_from_odps(date, project, table, connect_timeout=3000, read_timeout=500000,
                       pool_maxsize=1000, pool_connections=1000):
    """
    从odps获取数据
    :param date: 日期 type-string '%Y%m%d'
    :param project: type-string
    :param table: 表名 type-string
    :param connect_timeout: 连接超时设置
    :param read_timeout: 读取超时设置
    :param pool_maxsize:
    :param pool_connections:
    :return: records
    """
    odps = ODPS(
        access_id='LTAI4FtW5ZzxMvdw35aNkmcp',
        secret_access_key='0VKnydcaHK3ITjylbgUsLubX6rwiwc',
        project=project,
        endpoint='http://service.cn.maxcompute.aliyun.com/api',
        connect_timeout=connect_timeout,
        read_timeout=read_timeout,
        pool_maxsize=pool_maxsize,
        pool_connections=pool_connections
    )
    records = odps.read_table(name=table, partition='dt=%s' % date)
    return records


def get_rov_feature_table(date, project, table):
    """
    从DataWorks表中获取对应的特征值
    :param date: 日期 type-string '%Y%m%d'
    :param project: type-string
    :param table: 表名 type-string
    :return: feature_array type-DataFrame
    """
    records = get_data_from_odps(date=date, project=project, table=table)
    feature_value_list = []
    for record in records:
        feature_value = {}
        for feature_name in process_feature.features:
            if feature_name == 'dt':
                feature_value[feature_name] = date
            else:
                feature_value[feature_name] = record[feature_name]
        feature_value_list.append(feature_value)
    feature_array = pd.DataFrame(feature_value_list)
    print(date, project, table, 'feature table finish')
    return feature_array


def get_data_with_date(date, delta_days, project, table):
    """
    获取某一时间范围的特征数据
    :param date: 标准日期，delta基准，type-string，'%Y%m%d'
    :param delta_days: 日期范围（天），type-int，「 >0: date前，<0: date后 」
    :param project: type-string
    :param table: DataWorks表名，type-string
    :return: data，type-DataFrame
    """
    base_date = dt.strptime(date, '%Y%m%d')
    data_list = []
    for days in range(0, delta_days):
        delta = datetime.timedelta(days=days)
        delta_date = base_date - delta
        # 获取特征数据
        delta_data = get_rov_feature_table(date=delta_date.strftime('%Y%m%d'), project=project, table=table)
        data_list.append(delta_data)
    data = pd.concat(data_list)
    # 重新进行索引
    data.reset_index(inplace=True)
    # 删除index列
    data = data.drop(columns=['index'])
    return data


def write_to_pickle(data, filename, filepath=config_.DATA_DIR_PATH):
    """
    将数据写入pickle文件中
    :param data: 数据
    :param filename: 写入的文件名
    :param filepath: 文件存放路径，默认为config_.DATA_DIR_PATH
    :return: None
    """
    if not os.path.exists(filepath):
        os.makedirs(filepath)
    file = os.path.join(filepath, filename)
    with open(file, 'wb') as wf:
        pickle.dump(data, wf)


def read_from_pickle(filename, filepath=config_.DATA_DIR_PATH):
    """
    从pickle文件读取数据
    :param filename: 文件名
    :param filepath: 文件存放路径，默认为config_.DATA_DIR_PATH
    :return: data
    """
    file = os.path.join(filepath, filename)
    if not os.path.exists(file):
        return None
    with open(file, 'rb') as rf:
        data = pickle.load(rf)
    return data


def get_train_predict_data():
    """
    获取训练和预测数据
    :return: None
    """
    now_date = datetime.datetime.today()
    # ###### 训练数据 - 从7天前获取前30天的数据，写入pickle文件
    train_dt = now_date - datetime.timedelta(days=config_.TRAIN_DIFF)
    train_date = dt.strftime(train_dt, '%Y%m%d')
    train_data = get_data_with_date(
        date=train_date,
        delta_days=config_.TRAIN_DELTA_DAYS,
        project=config_.TRAIN_PROJECT,
        table=config_.TRAIN_TABLE
    )
    write_to_pickle(data=train_data, filename=config_.TRAIN_DATA_FILENAME)

    # ###### 预测数据 - 从1天前获取前1天的数据，写入pickle文件
    predict_dt = now_date - datetime.timedelta(days=config_.PREDICT_DIFF)
    predict_date = dt.strftime(predict_dt, '%Y%m%d')
    predict_data = get_data_with_date(
        date=predict_date,
        delta_days=config_.PREDICT_DELTA_DAYS,
        project=config_.PREDICT_PROJECT,
        table=config_.PREDICT_TABLE
    )
    write_to_pickle(data=predict_data, filename=config_.PREDICT_DATA_FILENAME)


def process_data(data):
    """
    数据清洗、预处理
    :param data: type-DataFrame
    :return:
    """
    pass


if __name__ == '__main__':
    dt_test = '20211007'
    project_test = 'usercdm'
    table_test = 'rov_feature_add_v1'
    # res = get_rov_feature_table(dt_test, table_test)
    # res = get_data_with_date(date=dt_test, delta_days=2, project=project_test, table=table_test)
    # print(res.shape)
    # write_to_pickle(res, 'test.pickle')

    data = read_from_pickle('test.pickle')
    if data is not None:
        print(data.shape, type(data))
        print(list(data))
        print(data[data['futre7dayreturn']<0])
    else:
        print(data)