import datetime
import logging
import pandas as pd
from . import DAILY_DIR
from .common import load_pdf, next_business_day

logger = logging.getLogger(__name__)

paths = {
    "Serenitas": ["NYops", "Margin Calls CS"],
    "BowdSt": ["BowdoinOps", "Margin CS"],
}


def download_files(em, count=20, *, fund="Serenitas", **kwargs):
    if fund not in paths:
        return
    DATA_DIR = DAILY_DIR / fund / "CS_reports"
    emails = em.get_msgs(path=paths[fund], count=count, subject__contains="DERV048829")
    for msg in emails:
        for attach in msg.attachments:
            fname = attach.name
            if fname.endswith("xlsx"):
                p = DATA_DIR / fname
                if not p.exists():
                    p.write_bytes(attach.content)

    emails = em.get_msgs(
        path=paths[fund],
        count=count,
        sender="americas.collateralmgt@credit-suisse.com",
    )

    for msg in emails:
        for attach in msg.attachments:
            fname = attach.name
            if "Serenitas CGMF" in fname:
                p = DATA_DIR / fname
                p = p.parent / f"{msg.datetime_sent:%Y-%m-%d} {p.stem}{p.suffix}"
                if not p.exists():
                    p.write_bytes(attach.content)
            else:
                p = DATA_DIR / fname
                if not p.exists():
                    p.write_bytes(attach.content)


def get_collateral(d: datetime.date, fund):
    DATA_DIR = DAILY_DIR / fund / "CS_reports"
    collat = 0
    full_name = {
        "Serenitas": "SerenitasCGMF",
        "BowdSt": "BostonBPStLLC",
    }
    for collat_type in ("RVM", "IM"):
        pdf_file = (
            DATA_DIR
            / f"CollateralCptyStatement161{full_name[fund]}{collat_type}_{d:%m%d%Y}.pdf"
        )
        g = iter(load_pdf(pdf_file))
        for e in g:
            if e.text == "Cash  USD   (US Dollar)":
                next(g)
                value = next(g).text
                collat += float(value.strip().replace(",", ""))
                break
    return collat


def load_cs_positions(d: datetime.date, fund: str, legacy=False):
    if not legacy:
        d = next_business_day(d)
    DATA_DIR = DAILY_DIR / fund / "CS_reports"
    full_name = {
        "Serenitas": "SerenitasCGMF",
        "BowdSt": "BostonBPStLLC",
    }
    if legacy and fund == "Serenitas":
        df = pd.read_excel(
            DATA_DIR / f"DERV048829_{d:%b%d%Y}.xlsx",
            header=9,
            skipfooter=50,
        )

        df = df[["Order No", "Mid Price", "Notional Currency"]]
        df["Mid Price"] = (
            df["Mid Price"]
            .str.replace(",", "")
            .apply(lambda s: -float(s[1:-1]) if s.startswith("(") else float(s))
        )
        df["Order No"] = df["Order No"].astype("str")
        df = df.rename(
            columns={
                "Mid Price": "PV (USD)",
                "Notional Currency": "Currency",
                "Order No": "Structure ID",
            }
        )
    else:
        df = pd.read_excel(
            DATA_DIR / f"CollateralCptyStatement161{full_name[fund]}RVM_{d:%m%d%Y}.xls",
            header=5,
            skipfooter=29,
        )
        if df.empty:
            raise ValueError(f"empty position statement for {d}")
        df.columns = [c.replace("\n", " ").strip() for c in df.columns]
        df = df[1:]
        for i, e in df["Structure ID"].items():
            try:
                int(e)
            except ValueError:
                df = df[: (i - 1)]
                break
        df["Trade ID"] = df["Trade ID"].astype("int").astype("str")
        df = df.rename(columns={"Notional1 CCY": "Currency"})
    return df


def collateral(d, dawn_trades, *, fund="Serenitas", **kwargs):
    collateral = get_collateral(next_business_day(d), fund)
    try:
        df = load_cs_positions(d, fund)
    except ValueError as e:
        logger.warning(e)
        return pd.DataFrame(
            {
                "Strategy": ["M_CSH_CASH"],
                "Amount": [-collateral],
                "Currency": ["USD"],
                "date": [d],
            }
        ).set_index("Strategy")
    df = df.merge(dawn_trades, how="left", left_on="Structure ID", right_on="cpty_id")
    missing_ids = df.loc[df.cpty_id.isnull(), "Structure ID"]
    if not missing_ids.empty:
        raise ValueError(f"{missing_ids.tolist()} not in the database")
    df.ia = df.ia.fillna(0.0)
    df["Amount"] = df.ia + df["PV (USD)"]
    df = df[["folder", "Amount", "Currency"]]
    df = df.groupby(["folder", "Currency"], as_index=False).sum()
    df = df.rename(columns={"folder": "Strategy"})
    df.Amount *= -1
    df = df.append(
        {
            "Strategy": "M_CSH_CASH",
            "Amount": -collateral - df.Amount.sum(),
            "Currency": "USD",
        },
        ignore_index=True,
    )
    df["date"] = d
    return df.set_index("Strategy")