from . import DAILY_DIR
from bs4 import BeautifulSoup
from io import BytesIO
from pandas.tseries.offsets import BDay
import logging
import pandas as pd
import pathlib
import requests
import xlrd
import zipfile

from urllib.parse import urlsplit, parse_qs, urlunsplit, urljoin
from xlrd import open_workbook, xldate_as_tuple

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},
            verify="secmail-bankofamerica-com-chain.pem",
        )
        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") and not (path / f).exists():
                        z.extract(f, path=path)


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


def download_files(em, d=None, count=20, *, fund="Serenitas", **kwargs):
    DATA_DIR = DAILY_DIR / fund / "BoA_reports"
    if fund not in paths:
        return
    emails = em.get_msgs(path=paths[fund], count=count)
    for msg in emails:
        if (
            msg.sender.name == "us_otc_client_valuation@baml.com"
            or msg.sender.name == "us_otc_client_valuation@bofa.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 load_excel_old(fname):
    wb = open_workbook(fname)
    s = wb.sheet_by_index(0)
    headers = s.row_values(6, 0)
    i = 7
    rows = []
    while s.cell(i, 0).value != "":
        r = []
        for e in s.row_slice(i, 0):
            if e.ctype == xlrd.XL_CELL_DATE:
                r.append(pd.Timestamp(*xldate_as_tuple(e.value, wb.datemode)))
            else:
                r.append(e.value)
        rows.append(r)
        i += 1
    df = pd.DataFrame.from_records(rows, columns=headers)
    for col in ["Original Notional", "Notional"]:
        df[col] = pd.to_numeric(df[col].str.replace(",", ""))
    return df


def load_excel(fname):
    wb = open_workbook(fname)
    s = wb.sheet_by_index(0)
    headers = s.row_values(13, 0)
    i = 19
    rows = []
    while s.cell(i, 0).ctype != xlrd.XL_CELL_DATE:
        if s.cell(i, 0).value == "" or s.cell(i, 0).value.startswith("Credit"):
            i += 1
            continue
        r = []
        for e in s.row_slice(i, 0):
            if e.ctype == xlrd.XL_CELL_DATE:
                r.append(pd.Timestamp(*xldate_as_tuple(e.value, wb.datemode)))
            else:
                r.append(e.value)
        rows.append(r)
        i += 1
    df = pd.DataFrame.from_records(rows, columns=headers)
    df = df.rename(
        columns={
            "Contract ID ": "Trade ID",
            "Market Value Amount": "local_nav",
            "Market Value Amount in CA Ccy": "base_nav",
            "Trade Date ": "Trade Date",
            "Cpty Independent Amount": "ia",
        }
    )
    df[["local_nav", "base_nav"]] *= -1.0
    return df


def collateral(d, dawn_trades, *, fund="Serenitas", **kwargs):
    report_date = d + BDay()
    REPORTS_DIR = DAILY_DIR / fund / "BoA_reports"
    try:
        fname = next(REPORTS_DIR.glob(f"Collat_*{report_date:%m%d%Y}_*.xls"))
    except StopIteration:
        raise ValueError(f"no collateral data for date {report_date}")
    df = pd.read_excel(fname, skiprows=6, skipfooter=6)
    df = df.drop(0, axis=0)
    try:
        collateral = float(df.Notional)
    except TypeError:
        collateral = df.Notional.sum()
    try:
        tag = "SLP" if fund == "Serenitas" else "LLC"
        fname = next(
            REPORTS_DIR.glob(f"301__LMCG_INVESTMENT{tag}_CSA_{report_date:%m%d%Y}_*")
        )
    except StopIteration:
        raise ValueError(f"no trade-level data for date {report_date}")
    df = load_excel(fname)
    df = df[["Trade ID", "base_nav", "ia"]]
    df = df.merge(
        dawn_trades.drop("ia", axis=1),
        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", "base_nav", "ia"]]
    df = df.groupby("folder").sum()
    df = (df["ia"] - df["base_nav"]).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")