from pykrige import OrdinaryKriging
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, Float, DateTime, select, create_engine
import uuid
from datetime import datetime, timezone
import pandas as pd
from .database_models import CurrentReduce, CurrentReflux
from sqlalchemy.schema import MetaData, Table
import geopandas as gpd

Base = declarative_base()

def create_dynamic_table(dataset_id, columns):
    """动态创建数据表"""
    # 动态构建列
    dynamic_columns = {
        'id': Column(Integer, primary_key=True, autoincrement=True)  # 为每个表添加一个主键
    }

    # 根据 columns 字典动态创建字段
    for col_name, col_type in columns.items():
        if col_type == 'str':
            dynamic_columns[col_name] = Column(String(255))
        elif col_type == 'int':
            dynamic_columns[col_name] = Column(Integer)
        elif col_type == 'float':
            dynamic_columns[col_name] = Column(Float)
        elif col_type == 'datetime':
            dynamic_columns[col_name] = Column(DateTime)

    # 动态生成模型类，表名使用 dataset_{dataset_id}
    table_name = f"dataset_{dataset_id}"

    # 在生成的类中添加 `__tablename__`
    dynamic_columns['__tablename__'] = table_name

    # 动态创建类
    dynamic_class = type(table_name, (Base,), dynamic_columns)

    # 打印调试信息
    print("table_name:", table_name)
    print("dynamic_columns:", dynamic_columns)

    # 创建数据库引擎
    engine = create_engine('sqlite:///SoilAcidification.db')  # 这里需要替换为你的数据库引擎
    Base.metadata.create_all(engine)  # 创建所有表格

    return dynamic_class

# 判断文件类型是否允许
def allowed_file(filename):
    ALLOWED_EXTENSIONS = {'xlsx', 'xls'}
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

# 生成唯一文件名
def generate_unique_filename(filename):
    # 获取文件的扩展名
    ext = filename.rsplit('.', 1)[1].lower()
    # 使用 UUID 和当前时间戳生成唯一文件名（使用 UTC 时区）
    unique_filename = f"{uuid.uuid4().hex}_{datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')}.{ext}"
    return unique_filename

def infer_column_types(df):
    type_map = {
        'object': 'str',
        'int64': 'int',
        'float64': 'float',
        'datetime64[ns]': 'datetime'  # 适应Pandas datetime类型
    }
    # 提取列和其数据类型
    return {col: type_map.get(str(df[col].dtype), 'str') for col in df.columns}

def clean_column_names(dataframe):
    # Strip whitespace and replace non-breaking spaces and other non-printable characters
    dataframe.columns = [col.strip().replace('\xa0', '') for col in dataframe.columns]
    return dataframe


# 建立excel文件的列名和数据库模型字段之间的映射
def rename_columns_for_model(dataframe, dataset_type):
    if dataset_type == 'reduce':
        rename_map = {
            '1/b': 'Q_over_b',
            'pH': 'pH',
            'OM': 'OM',
            'CL': 'CL',
            'H': 'H',
            'Al': 'Al'
        }
    elif dataset_type == 'reflux':
        rename_map = {
            'OM': 'OM',
            'CL': 'CL',
            'CEC': 'CEC',
            'H+': 'H_plus',
            'N': 'N',
            'Al3+': 'Al3_plus',
            'ΔpH': 'Delta_pH'
        }

    # 使用 rename() 方法更新列名
    dataframe = dataframe.rename(columns=rename_map)
    return dataframe

# 建立前端参数和模型预测字段之间的映射
def rename_columns_for_model_predict(dataframe, dataset_type):
    if dataset_type == 'reduce':
        rename_map = {
            'init_pH': 'pH',
            'OM': 'OM',
            'CL': 'CL',
            'H': 'H',
            'Al': 'Al'
        }
    elif dataset_type == 'reflux':
        rename_map = {
            "OM": "OM",
            "CL": "CL",
            "CEC": "CEC",
            "H+": "H_plus",
            "N": "N",
            "Al3+": "Al3_plus"
        }

    # 使用 rename() 方法更新列名
    dataframe = dataframe.rename(columns=rename_map)
    return dataframe


def insert_data_into_existing_table(session, dataframe, model_class):
    """Insert data from a DataFrame into an existing SQLAlchemy model table."""
    for index, row in dataframe.iterrows():
        record = model_class(**row.to_dict())
        session.add(record)

def insert_data_into_dynamic_table(session, dataset_df, dynamic_table_class):
    for _, row in dataset_df.iterrows():
        record_data = row.to_dict()
        session.execute(dynamic_table_class.__table__.insert(), [record_data])

def insert_data_by_type(session, dataset_df, dataset_type):
    if dataset_type == 'reduce':
        for _, row in dataset_df.iterrows():
            record = CurrentReduce(**row.to_dict())
            session.add(record)
    elif dataset_type == 'reflux':
        for _, row in dataset_df.iterrows():
            record = CurrentReflux(**row.to_dict())
            session.add(record)


def get_current_data(session, data_type):
    # 根据数据类型选择相应的表模型
    if data_type == 'reduce':
        model = CurrentReduce
    elif data_type == 'reflux':
        model = CurrentReflux
    else:
        raise ValueError("Invalid data type provided. Choose 'reduce' or 'reflux'.")

    # 从数据库中查询所有记录
    result = session.execute(select(model))

    # 将结果转换为DataFrame
    dataframe = pd.DataFrame([dict(row) for row in result])
    return dataframe

def get_dataset_by_id(session, dataset_id):
    # 动态获取表的元数据
    metadata = MetaData(bind=session.bind)
    dataset_table = Table(dataset_id, metadata, autoload=True, autoload_with=session.bind)

    # 从数据库中查询整个表的数据
    query = select(dataset_table)
    result = session.execute(query).fetchall()

    # 检查是否有数据返回
    if not result:
        raise ValueError(f"No data found for dataset {dataset_id}.")

    # 将结果转换为DataFrame
    dataframe = pd.DataFrame(result, columns=[column.name for column in dataset_table.columns])

    return dataframe

def predict_to_Q(predictions, init_ph, target_ph):
    # 将预测结果转换为Q
    Q = predictions * (target_ph - init_ph)
    return Q

# 说明：Q指生石灰投加量，单位是%，例如1%代表100g土壤中施加1g生石灰。
# 其中，土壤是指表层20cm土壤。# 如果Q的单位换算为吨/公顷，即t/ha，则需要乘以25。
# ΔpH=目标pH-初始pH
def Q_to_t_ha(Q):
    return Q * 25


def create_kriging(file_name, emission_column, points):
    # 从 Excel 读取数据
    df = pd.read_excel(file_name)
    print(df)

    # 转换为 GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['longitude'], df['latitude']))
    print(gdf)

    # 初始化并运行克里金插值
    OK = OrdinaryKriging(
        gdf.geometry.x,
        gdf.geometry.y,
        gdf[emission_column],
        variogram_model='spherical',
        verbose=True,
        enable_plotting=False
    )

    # 提取输入点的经度和纬度
    input_lons = [point[0] for point in points]
    input_lats = [point[1] for point in points]

    # 对输入的点进行插值
    z, ss = OK.execute('points', input_lons, input_lats)

    result = {
        "message": "Kriging interpolation for points completed successfully",
        "interpolated_concentrations": z.tolist()
    }
    return result