#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import os
from os.path import expanduser
from collections import OrderedDict
from datetime import timedelta

import logbook

import pandas as pd

from . treasuries import get_treasury_data
from . import benchmarks
from . benchmarks import get_benchmark_returns

from zipline.protocol import DailyReturn
from zipline.utils.tradingcalendar import trading_days

logger = logbook.Logger('Loader')

# TODO: Make this path customizable.
DATA_PATH = os.path.join(
    expanduser("~"),
    '.zipline',
    'data'
)


def get_datafile(name, mode='r'):
    """
    Returns a handle to data file.

    Creates containing directory, if needed.
    """

    if not os.path.exists(DATA_PATH):
        os.makedirs(DATA_PATH)

    return open(os.path.join(DATA_PATH, name), mode)


def dump_treasury_curves():
    """
    Dumps data to be used with zipline.

    Puts source treasury and data into zipline.
    """
    tr_data = {}

    for curve in get_treasury_data():
        # Not ideal but massaging data into expected format
        tr_data[curve['date']] = curve

    curves = pd.DataFrame(tr_data).T

    datafile = get_datafile('treasury_curves.csv', mode='wb')
    curves.to_csv(datafile)
    datafile.close()


def dump_benchmarks(symbol):
    """
    Dumps data to be used with zipline.

    Puts source treasury and data into zipline.
    """
    benchmark_data = []
    for daily_return in get_benchmark_returns(symbol):
        # Not ideal but massaging data into expected format
        benchmark = (daily_return.date, daily_return.returns)
        benchmark_data.append(benchmark)

    datafile = get_datafile(get_benchmark_filename(symbol), mode='wb')
    benchmark_returns = pd.Series(dict(benchmark_data))
    benchmark_returns.to_csv(datafile)
    datafile.close()


def update_treasury_curves(last_date):
    """
    Updates data in the zipline treasury curves message pack

    last_date should be a datetime object of the most recent data

    Puts source treasury and data into zipline.
    """
    datafile = get_datafile('treasury_curves.csv', mode='rb')
    curves = pd.DataFrame.from_csv(datafile).T
    datafile.close()

    for curve in get_treasury_data():
        curves[curve['date']] = curve

    updated_curves = curves.T

    datafile = get_datafile('treasury_curves.csv', mode='wb')
    updated_curves.to_csv(datafile)
    datafile.close()


def update_benchmarks(symbol, last_date):
    """
    Updates data in the zipline message pack

    last_date should be a datetime object of the most recent data

    Puts source benchmark into zipline.
    """
    datafile = get_datafile(get_benchmark_filename(symbol), mode='rb')
    saved_benchmarks = pd.Series.from_csv(datafile)
    datafile.close()

    try:
        start = last_date + timedelta(days=1)
        for daily_return in get_benchmark_returns(symbol, start_date=start):
            # Not ideal but massaging data into expected format
            benchmark = pd.Series({daily_return.date: daily_return.returns})
            saved_benchmarks.append(benchmark)

        datafile = get_datafile(get_benchmark_filename(symbol), mode='wb')
        saved_benchmarks.to_csv(datafile)
        datafile.close()
    except benchmarks.BenchmarkDataNotFoundError as exc:
        logger.warn(exc)
    return saved_benchmarks


def get_benchmark_filename(symbol):
    return "%s_benchmark.csv" % symbol


def load_market_data(bm_symbol='^GSPC'):
    try:
        fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")
    except IOError:
        print("""
data files aren't distributed with source.
Fetching data from Yahoo Finance.
""").strip()
        dump_benchmarks(bm_symbol)
        fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")

    saved_benchmarks = pd.Series.from_csv(fp_bm)
    fp_bm.close()

    # Find the offset of the last date for which we have trading data in our
    # list of valid trading days
    last_bm_date = saved_benchmarks.index[-1]
    last_bm_date_offset = trading_days.searchsorted(
        last_bm_date.strftime('%Y/%m/%d'))

    # If more than 1 trading days has elapsed since the last day where
    # we have data,then we need to update
    if len(trading_days) - last_bm_date_offset > 1:
        benchmark_returns = update_benchmarks(bm_symbol, last_bm_date)
    else:
        benchmark_returns = saved_benchmarks

    benchmark_returns = benchmark_returns.tz_localize('UTC')

    bm_returns = []
    for dt, returns in benchmark_returns.iterkv():
        daily_return = DailyReturn(date=dt, returns=returns)
        bm_returns.append(daily_return)

    try:
        fp_tr = get_datafile('treasury_curves.csv', "rb")
    except IOError:
        print("""
data msgpacks aren't distributed with source.
Fetching data from data.treasury.gov
""").strip()
        dump_treasury_curves()
        fp_tr = get_datafile('treasury_curves.csv', "rb")

    saved_curves = pd.DataFrame.from_csv(fp_tr)

    # Find the offset of the last date for which we have trading data in our
    # list of valid trading days
    last_tr_date = saved_curves.index[-1]
    last_tr_date_offset = trading_days.searchsorted(
        last_tr_date.strftime('%Y/%m/%d'))

    # If more than 1 trading days has elapsed since the last day where
    # we have data,then we need to update
    if len(trading_days) - last_tr_date_offset > 1:
        treasury_curves = update_treasury_curves(last_tr_date)
    else:
        treasury_curves = saved_curves

    treasury_curves = treasury_curves.tz_localize('UTC')

    tr_curves = {}
    for tr_dt, curve in treasury_curves.T.iterkv():
        # tr_dt = tr_dt.replace(hour=0, minute=0, second=0, microsecond=0,
        #                       tzinfo=pytz.utc)
        tr_curves[tr_dt] = curve.to_dict()

    fp_tr.close()

    tr_curves = OrderedDict(sorted(
                            ((dt, c) for dt, c in tr_curves.iteritems()),
                            key=lambda t: t[0]))

    return bm_returns, tr_curves