import logging
import pandas as pd
from . import DAILY_DIR

logger = logging.getLogger(__name__)

paths = {
    "Serenitas": ["NYops", "Margin Calls Barclays"],
    "Selene": ["SeleneOps", "Margin Barclays"],
}


def load_file(d, fund, pattern):
    file_mapping = {
        "Serenitas": "Serenitas Credit Gamma Master Fund LP",
        "Selene": "ISO Selene",  # WIll replace to the correct name once we get it
    }
    try:
        fname = next(
            (DAILY_DIR / fund / "BARCLAYS_reports").glob(
                f"{pattern}*{file_mapping[fund]} - Regulatory as at {d:%d %b %Y}*"
            )
        )
    except StopIteration:
        raise FileNotFoundError(f"BARCLAYS file not found for date {d}")
    return pd.read_excel(fname, skiprows=3, skipfooter=23)


def download_files(em, count=20, *, fund="Serenitas", **kwargs):
    if fund not in paths:
        return
    emails = em.get_msgs(path=paths[fund], count=count)
    DATA_DIR = DAILY_DIR / fund / "BARCLAYS_reports"
    for msg in emails:
        for attach in msg.attachments:
            fname = attach.name
            if fname.startswith("CreditSupport") or fname.startswith("Exposure"):
                p = DATA_DIR / fname
                if not p.exists():
                    p.write_bytes(attach.content)


def collateral(d, dawn_trades, *, fund="Serenitas", **kwargs):
    collateral = 0
    df = load_file(d, fund, "Exposure Report")
    df = df[["Trade Reference", "Exposure (USD)", "Ind Amt (USD)"]]
    df["Trade Reference"] = df["Trade Reference"].astype(str)
    df = df.merge(
        dawn_trades, how="left", left_on="Trade Reference", right_on="cpty_id"
    )
    missing_ids = df.loc[df.cpty_id.isnull(), "Trade Reference"]
    if not missing_ids.empty:
        raise ValueError(f"{missing_ids.tolist()} not in the database for {fund}")
    df = df[["folder", "Exposure (USD)", "Ind Amt (USD)"]]
    df = df.groupby("folder", dropna=False).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")