import datetime
import pandas as pd
from . import DAILY_DIR

paths = {
    "Serenitas": ["NYops", "Margin Calls BNP"],
    "BowdSt": ["BowdoinOps", "Margin BNP"],
    "Selene": ["SeleneOps", "Margin BNP"],
}


def download_files(em, count: int = 20, *, fund="Serenitas", **kwargs):
    if fund not in paths:
        return
    emails = em.get_msgs(
        path=paths[fund],
        count=count,
        sender="bnppnycollateralmgmt@us.bnpparibas.com",
    )
    DATA_DIR = DAILY_DIR / fund / "BNP_reports"
    for msg in emails:
        for attach in msg.attachments:
            p = DATA_DIR / attach.name
            if not p.exists() and hasattr(attach, "content"):
                p.write_bytes(attach.content)


def load_file(d: datetime.date, report_type: str, fund: str):
    fund_mapping = {
        "Serenitas": "SERENITAS CREDIT GAMMA MASTER FUND, LP",
        "BowdSt": "BOSTON PATRIOT BOWDOIN ST LLC",
        "Selene": "ISO SELENE INC.",
    }
    fname = (
        f"{report_type} - BNP PARIBAS - {fund_mapping[fund]} " f"- COB {d:%Y%m%d}.XLS"
    )
    return pd.read_excel(DAILY_DIR / fund / "BNP_reports" / fname, skiprows=7)


def collateral(
    d: datetime.date, dawn_trades: pd.DataFrame, *, fund="Serenitas", **kwargs
):
    df = load_file(d, "Collateral Positions", fund)
    if df.at[0, "Held/Posted"] == "Posted":
        sign = 1.0
    else:
        sign = -1.0
    collateral = sign * df.at[0, "Mkt Val (Agmt Ccy)"]
    df = load_file(d, "Exposure Statement", fund)
    df = df[["Trade Ref", "Exposure Amount (Agmt Ccy)", "Lock Up (Agmt Ccy)"]]
    df["Trade Ref"] = df["Trade Ref"].str.replace("(FOC-|MBO-)", "", regex=True)
    df = df.merge(dawn_trades, how="left", left_on="Trade Ref", right_on="cpty_id")
    missing_ids = df.loc[df.cpty_id.isnull(), "Trade Ref"]
    if not missing_ids.empty:
        raise ValueError(f"{missing_ids.tolist()} not in the database")
    df = df[["folder", "Exposure Amount (Agmt Ccy)", "Lock Up (Agmt Ccy)"]]
    df = df.groupby("folder").sum()
    df = df.sum(axis=1).to_frame(name="Amount")
    df["Currency"] = "USD"
    df = df.reset_index()
    df.columns = ["Strategy", "Amount", "Currency"]
    df.Amount *= -1
    df.loc[len(df.index)] = ["M_CSH_CASH", collateral - df.Amount.sum(), "USD"]
    df["date"] = d
    return df.set_index("Strategy")