import pandas as pd
from . import DAILY_DIR
from exchangelib import FileAttachment
from serenitas.analytics.utils import get_fx

paths = {
    "Serenitas": ["NYops", "Margin calls MS"],
    "Brinker": ["NYops", "Margin Calls MS-Brinker"],
    "BowdSt": ["BowdoinOps", "Margin MS"],
    "Selene": ["SeleneOps", "Margin MS"],
}

subjects = {
    "Serenitas": "SERCX **Daily",
    "Brinker": "061761QY1***BRINKER",
    "BowdSt": "BOSTON PATRIOT BOWDOIN",
    "Selene": "ISO SELENE",
}


def download_files(em, count=20, *, fund="Serenitas", **kwargs):
    if fund not in paths:
        return
    emails = em.get_msgs(
        path=paths[fund],
        count=count,
        subject__contains=subjects[fund],
    )
    DATA_DIR = DAILY_DIR / fund / "MS_reports"
    for msg in emails:
        for attach in msg.attachments:
            if isinstance(attach, FileAttachment):
                if "NETSwaps" in attach.name:
                    fname = "Trade_Detail_" + attach.name.split("_")[1]
                elif "NETFX" in attach.name:
                    fname = "Trade_Detail_FX_" + attach.name.split("_")[1]
                elif "NET_Collateral" in attach.name:
                    fname = "Collateral_Detail_" + attach.name.rsplit("_", 1)[1]
                elif "Statement" in attach.name and attach.name.endswith("pdf"):
                    ending = attach.name.rsplit("_", 1)[1]
                    fname = "Statement_" + ending.split(".")[0] + ".pdf"
                else:
                    continue
            p = DATA_DIR / fname
            if not p.exists():
                p.write_bytes(attach.content)


def collateral(d, dawn_trades, *, fund="Serenitas", **kwargs):
    df = pd.read_excel(
        DAILY_DIR / fund / "MS_reports" / f"Collateral_Detail_{d:%Y%m%d}.xls"
    )
    collat = df.loc[1, "coll_val_ccy"].replace(",", "")
    if "(" in collat:
        collat = collat[1:-1]
        collat = -float(collat)
    else:
        collat = float(collat)
    df = pd.read_excel(DAILY_DIR / fund / "MS_reports" / f"Trade_Detail_{d:%Y%m%d}.xls")
    try:
        df_fx = pd.read_excel(
            DAILY_DIR / fund / "MS_reports" / f"Trade_Detail_FX_{d:%Y%m%d}.xls"
        )
        net_fx_exposure = (
            df_fx.loc[df_fx.buy_ccy == "EUR", "amt_buy_ccy"].sum()
            - df_fx.loc[df_fx.sell_ccy == "EUR", "amt_sell_ccy"].sum()
        )
        fx_ia = net_fx_exposure * 0.05 * get_fx(d, "EUR")
        df = pd.concat([df, df_fx])
    except FileNotFoundError:  # We don't always have FX files
        pass
    # df = df.dropna(subset=["trade_ccy"])
    df = df.merge(dawn_trades, how="left", left_on="trade_id", right_on="cpty_id")
    missing_ids = df.loc[df.cpty_id.isnull(), "trade_id"]
    if not missing_ids.empty:
        raise ValueError(f"{missing_ids.tolist()} not in the database for {fund}")
    df = df.groupby("folder")[["collat_req_in_agr_ccy"]].sum()
    df["Currency"] = "USD"
    df = df.reset_index()
    df.columns = ["Strategy", "Amount", "Currency"]
    try:
        df.loc[df.Strategy == "TCSH", "Amount"] -= fx_ia
    except UnboundLocalError:
        pass
    if "M_CSH_CASH" not in df.Strategy.array:
        df.loc[len(df.index)] = ["M_CSH_CASH", -collat - df.Amount.sum(), "USD"]
    else:
        df.loc[df.Strategy == "M_CSH_CASH", "Amount"] -= collat + df.Amount.sum()
    df["date"] = d
    return df.set_index("Strategy")