import pandas as pd
from . import DAILY_DIR
from pandas.tseries.offsets import BDay
from .common import load_pdf


def download_files(em, count=20, **kwargs):
    DATA_DIR = DAILY_DIR / "CS_reports"
    emails = em.get_msgs(
        path=["NYops", "Margin Calls CS"], 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=["NYops", "Margin Calls CS"],
        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):
    DATA_DIR = DAILY_DIR / "CS_reports"
    collat = 0
    for collat_type in ("RVM", "IM"):
        pdf_file = (
            DATA_DIR
            / f"CollateralCptyStatement161SerenitasCGMF{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 collateral(d, dawn_trades, **kwargs):
    collateral = get_collateral(d + BDay())
    df = pd.read_excel(
        f"/home/serenitas/Daily/CS_reports/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.merge(dawn_trades, how="left", left_on="Order No", right_on="cpty_id")
    missing_ids = df.loc[df.cpty_id.isnull(), "Order No"]
    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["Mid Price"]
    df = df[["folder", "Amount", "Notional Currency"]]
    df = df.groupby(["folder", "Notional Currency"], as_index=False).sum()
    df = df.rename(columns={"folder": "Strategy", "Notional Currency": "Currency"})
    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")