import logging
import pandas as pd
from io import BytesIO
from pikepdf import Pdf
from . import DAILY_DIR
from .common import load_pdf, get_col, parse_num

logger = logging.getLogger(__name__)

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

accounts = {
    "BowdSt": "909271",
    "Serenitas": "923550",
}

passwords = {"BowdSt": "tm64EO", "Serenitas": "JV3RJu"}


def load_file(d, fund):
    try:
        fname = next(
            (DAILY_DIR / fund / "JPM_reports").glob(
                f"CSCFTCSTMT-*-{d:%y%m%d}-{accounts[fund]}_2.pdf"
            )
        )
    except StopIteration:
        raise FileNotFoundError(f"JPM file not found for date {d}")
    return fname


def get_collateral(collat_page):
    return float(get_col(collat_page, 200, 300, 1000, 1100)[0].replace(",", ""))


def load_positions(positions_page):
    anchor = next(c for c in positions_page if c.text.startswith("Total Product Group"))
    bottom = int(anchor["top"]) - 30
    widths = (10, 160, 300, 380, 450, 500, 550, 635, 700, 780, 850, 960, 1000, 1200)
    cols = [
        get_col(positions_page, 200, bottom, l, r) for l, r in zip(widths, widths[1:])
    ]

    def combine(l):
        return [f"{l[0]} {l[1]}", *l[2:]]

    cols = [combine(c) if len(c) == (len(cols[0]) + 1) else c for c in cols]
    df = pd.DataFrame({c[0]: c[1:] for c in cols})
    for col in ["Pay Notional", "Rec Notional", "MTM Amount", "IM Amount"]:
        df[col] = df[col].apply(parse_num)
    for col in ["Trade Date", "Maturity Date"]:
        df[col] = pd.to_datetime(df[col], format="%d-%b-%y")
    df["Deal ID"] = "810RI" + df["Deal ID"].str.extract(r"([^-]*)-.*")
    return df


def download_files(em, count=20, *, fund="BowdSt", **kwargs):
    if fund not in paths:
        return
    emails = em.get_msgs(
        path=paths[fund], count=count, subject__contains=accounts[fund]
    )
    DATA_DIR = DAILY_DIR / fund / "JPM_reports"
    for msg in emails:
        for attach in msg.attachments:
            fname = attach.name
            p = DATA_DIR / fname
            if not p.exists():
                stream = BytesIO(attach.content)
                pdf = Pdf.open(stream, password=passwords[fund])
                pdf.save(p)


def collateral(d, dawn_trades, *, fund="BowdSt", **kwargs):
    pdf_file = load_file(d, fund)
    pages = load_pdf(pdf_file, pages=True)
    try:
        collat = get_collateral(pages[3])
    except IndexError:
        collat = 0.0
    df = load_positions(pages[4])
    df = df.merge(dawn_trades, how="left", left_on="Deal ID", right_on="cpty_id")
    missing_ids = df.loc[df.cpty_id.isnull(), "Deal ID"]
    if not missing_ids.empty:
        logger.error(f"{missing_ids.tolist()} not in the database")
    df = df[["folder", "MTM Amount", "IM Amount"]]
    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 = df.append(
        {
            "Strategy": "M_CSH_CASH",
            "Amount": -collat - df.Amount.sum(),
            "Currency": "USD",
        },
        ignore_index=True,
    )
    df["date"] = d
    return df.set_index("Strategy")