from . import DAILY_DIR
from bs4 import BeautifulSoup
from io import BytesIO
import logging
import pandas as pd
import pathlib
from urllib.parse import urlsplit, parse_qs, urlunsplit, urljoin
import requests
import zipfile

logger = logging.getLogger(__name__)


def download_from_secure_id(
    secure_id: str,
    brand: str,
    path: pathlib.Path,
    base_url="https://secmail.bankofamerica.com",
):
    password = {
        "ghorel@lmcg.com": "v4vdMvH9Qe9t",
        "nyops@lmcg.com": "a6lAkBfqDSHsrkGspYSS",
    }
    payload = {}
    with requests.Session() as session:
        r = session.get(
            urljoin(base_url, "formpostdir/securereader"),
            params={"id": secure_id, "brand": brand},
        )
        soup = BeautifulSoup(r.content, features="lxml")
        form = soup.find(id="dialog")
        if "messagenotfound" in form["action"]:
            raise ValueError("message not found")
        for inp in form.find_all("input"):
            payload[inp["name"]] = inp["value"]
        payload["dialog:password"] = password[payload["dialog:username"]]
        r = session.post(base_url + form["action"], data=payload)
        soup = BeautifulSoup(r.content, features="lxml")
        form = soup.find(id="readTB")
        payload = {
            "readTB": "readTB",
            "readTB:downloadZipButton": "readTB:downloadZipButton",
        }
        for inp in form.find_all("input"):
            if "ViewState" in inp["name"]:
                payload["javax.faces.ViewState"] = inp["value"]
        r = session.post(urljoin(base_url, "securereader/read.jsf"), data=payload)
        if r.headers["content-type"] == "application/octet-stream":
            with zipfile.ZipFile(BytesIO(r.content)) as z:
                for f in z.namelist():
                    if not f.endswith("html"):
                        z.extract(f, path=path)


def download_files(d=None, count=20):
    from exchange import ExchangeMessage

    DATA_DIR = DAILY_DIR / "BAML_ISDA_reports"
    em = ExchangeMessage()
    emails = em.get_msgs(path=["NYops", "Margin Calls Baml"], count=count)
    for msg in emails:
        if msg.sender.name == "us_otc_client_valuation@baml.com":
            soup = BeautifulSoup(msg.body, features="lxml")
            a = soup.find("a")
            url = urlsplit(a["href"])
            query = parse_qs(url.query)
            base_url = urlunsplit(url[:2] + ("",) * 3)
            try:
                download_from_secure_id(
                    query["id"][0], query["brand"][0], DATA_DIR, base_url
                )
            except ValueError as e:
                logger.error(e)
                continue
        if msg.sender.name == "bank_of_america_collateral_operations@bankofamerica.com":
            for attach in msg.attachments:
                if attach.name.endswith("xls"):
                    p = DATA_DIR / attach.name
                    if not p.exists():
                        p.write_bytes(attach.content)


def collateral(d, dawn_trades, *args):
    REPORTS_DIR = DAILY_DIR / "BAML_ISDA_reports"
    try:
        fname = next(REPORTS_DIR.glob(f"Collat_*{d:%m%d%Y}_*.xls"))
    except StopIteration:
        raise ValueError("no data for date {d}")
    df = pd.read_excel(fname, skiprows=6, skipfooter=6)
    df = df.drop(0, axis=0)
    collateral = float(df.Notional)
    fname = REPORTS_DIR / f"Interest Rates Trade Summary_{d:%d-%b-%Y}.xls"
    # TODO: make more robust
    df = pd.read_excel(fname, skiprows=6, nrows=1)
    df = df[["Trade ID", "MTM(USD)"]]
    df["Trade ID"] = df["Trade ID"].astype("str")
    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")
    df = df[["folder", "MTM(USD)", "ia"]]
    df = df.groupby("folder").sum()
    df = (df["ia"] - df["MTM(USD)"]).to_frame(name="Amount")
    df["Currency"] = "USD"
    df = df.reset_index()
    df.columns = ["Strategy", "Amount", "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")