import pandas as pd
from bs4 import BeautifulSoup
import requests

# Table names:
# fact_sales_order
# fact_purchase_orders
# fact_payment
# dim_transaction
# dim_staff
# dim_payment_type
# dim_location
# dim_design
# dim_date
# dim_currency
# dim_counterparty


def create_fact_sales_order(dict_of_df):
    df_sales = dict_of_df["sales_order"]
    df_sales.index.name = "sales_record_id"
    df_sales["created_date"] = pd.to_datetime(df_sales["created_at"]).dt.date
    df_sales["created_time"] = pd.to_datetime(df_sales["created_at"]).dt.time
    df_sales["last_updated_date"] = pd.to_datetime(
        df_sales["last_updated"]).dt.date
    df_sales["last_updated_time"] = pd.to_datetime(
        df_sales["last_updated"]).dt.time
    fact_sales_order = df_sales.loc[
        :,
        [
            "sales_record_id",
            "sales_order_id",
            "created_date",
            "created_time",
            "last_updated_date",
            "last_updated_time",
            "sales_staff_id",
            "counterparty_id",
            "units_sold",
            "unit_price",
            "currency_id",
            "design_id",
            "agreed_payment_date",
            "agreed_delivery_date",
            "agreed_delivery_location_id",
        ],
    ]
    return fact_sales_order


# fact_purchase_order from purchase_order


def create_fact_purchase_orders(dict_of_df):
    df_po = dict_of_df["purchase_order"]
    df_po.index.name = "purchase_record_id"
    df_po["created_date"] = df_po["created_at"].date()
    df_po["created_time"] = df_po["created_at"].dt.time
    df_po["last_updated_date"] = df_po["last_updated_at"].date()
    df_po["last_updated_time"] = df_po["last_updated_at"].dt.time
    df_po["agreed_delivery_date"] = pd.to_datetime(
        df_po["agreed_delivery_date"], format="%Y-%m-%d"
    )
    df_po["agreed_payment_date"] = pd.to_datetime(
        df_po["agreed_payment_date"], format="%Y-%m-%d"
    )
    df_po.drop(labels=["created_at", "last_updated_at"], axis=1, inplace=True)
    return df_po


def create_fact_payment(dict_of_df):
    df_payment = dict_of_df["payment"]
    df_payment.index.name = "payment_record_id"
    df_payment["created_date"] = df_payment["created_at"].date()
    df_payment["created_time"] = df_payment["created_at"].time
    df_payment["last_updated_date"] = df_payment["last_updated"].date()
    df_payment["last_updated_time"] = df_payment["last_updated"].time
    df_payment["payment_date"] = pd.to_datetime(
        df_payment["payment_date"], format="%Y-%m-%d")
    fact_payment = df_payment.loc[
        :,
        [
            "payment_record_id",
            "payment_id",
            "created_date",
            "created_time",
            "last_updated_date",
            "last_updated_time",
            "transaction_id",
            "counterparty_id",
            "payment_amount",
            "currency_id",
            "payment_type_id",
            "paid",
            "payment_date",
        ],
    ]
    return fact_payment


# test passed


def create_dim_transaction(dict_of_df):
    df_transaction = dict_of_df["transaction"].drop(
        labels=["created_at", "last_updated"], axis=1
    )
    return df_transaction


# test passed
def create_dim_location(dict_of_df):
    df_loc = (
        dict_of_df["address"]
        .drop(labels=["created_at", "last_updated"], axis=1)
        .rename(columns={"address_id": "location_id"}))
    return df_loc
    


def create_dim_counterparty(dict_of_df):
    df_prefixed_address=dict_of_df["address"].add_prefix(
        "counterparty_legal_", axis=1
    )
    df_cp=pd.merge(
        dict_of_df["counterparty"],
        df_prefixed_address,
        left_on="legal_address_id",
        right_on="counterparty_legal_address_id",
        how="outer",
    )
    df_cp.drop(
        columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True
    )
    return df_cp


# test passed


def create_dim_date(dict_of_df):
    fact_dfs=[
        create_fact_payment(dict_of_df),
        create_fact_purchase_orders(dict_of_df),
        create_fact_sales_order(dict_of_df),
    ]
    date_col_names=[
        col_name for col_name in list(fact_dfs[0].columns) if "date" in col_name
    ]
    list_of_date_columns=[]
    for df in fact_dfs:
        for col in date_col_names:
            list_of_date_columns.append(df[col])
    sr_date=pd.array(pd.concat(list_of_date_columns), dtype="datetime64[ns]")
    df_date=pd.DataFrame(data=sr_date, columns=["date_id"])
    df_date.drop_duplicates(inplace=True)
    df_date["year"]=df_date["date_id"].dt.year
    df_date["month"]=df_date["date_id"].dt.month
    df_date["day"]=df_date["date_id"].dt.day
    df_date["day_of_week"]=df_date["date_id"].dt.dayofweek
    df_date["day_name"]=df_date["date_id"].dt.day_name()
    df_date["month_name"]=df_date["date_id"].dt.month_name()
    df_date["quarter"]=df_date["date_id"].dt.quarter
    return df_date


# tests passed
def scrape_currency_names():
    response=requests.get("https://www.xe.com/currency/").content
    soup=BeautifulSoup(response, "html.parser")
    currency=[
        item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"})
    ]
    sr=pd.Series(currency)
    df_cur=sr.str.split(pat=" - ", expand=True).rename(
        {0: "currency_code", 1: "currency_name"}, axis=1
    )
    return df_cur

# tests passed


def create_dim_currency(dict_of_df, names=scrape_currency_names()):
    df_cur=dict_of_df["currency"].drop(
        labels=["created_at", "last_updated"], axis=1)
    dim_cur=pd.merge(
        df_cur, names, left_on="currency_code", right_on="currency_code", how="inner"
    )
    return dim_cur


# tests passed

def create_dim_payment_type(dict_of_df):
    df_payment_type=dict_of_df["payment_type"]
    dim_payment_type=df_payment_type.loc[:, [
        "payment_type_id", "payment_type_name"]]
    return dim_payment_type



# tests passed


def create_dim_design(dict_of_df):
    df_design=dict_of_df["design"]
    dim_design=df_design.loc[
        :, ["design_id", "design_name", "file_name", "file_location"]
    ]
    return dim_design



# tests passed

def create_dim_staff(dict_of_df):
    staff_department=pd.merge(
        dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left"
    )
    dim_staff=staff_department.loc[
        :,
        [
            "staff_id",
            "first_name",
            "last_name",
            "department_name",
            "location",
            "email_address",
        ],
    ]
    return dim_staff