"""
Utils for dealing with time
"""

from __future__ import annotations

import re
from datetime import datetime, timedelta, timezone

from flask import current_app
from flask_security.core import current_user
from humanize import naturaldate, naturaltime
import pandas as pd
from pandas.tseries.frequencies import to_offset
import pytz
from dateutil import tz


def server_now() -> datetime:
    """The current time (timezone aware), converted to the timezone of the FlexMeasures platform."""
    return datetime.now(get_timezone())


def ensure_local_timezone(
    dt: pd.Timestamp | datetime, tz_name: str = "Europe/Amsterdam"
) -> pd.Timestamp | datetime:
    """If no timezone is given, assume the datetime is in the given timezone and make it explicit.
    Otherwise, if a timezone is given, convert to that timezone."""
    if isinstance(dt, datetime):
        if dt.tzinfo is not None:
            return dt.astimezone(tz.gettz(tz_name))
        else:
            return dt.replace(tzinfo=tz.gettz(tz_name))
    if dt.tzinfo is not None:
        return dt.tz_convert(tz_name)
    else:
        return dt.tz_localize(tz_name)


def as_server_time(dt: datetime) -> datetime:
    """The datetime represented in the timezone of the FlexMeasures platform.
    If dt is naive, we assume it is UTC time.
    """
    return naive_utc_from(dt).replace(tzinfo=pytz.utc).astimezone(get_timezone())


def localized_datetime(dt: datetime) -> datetime:
    """
    Localise a datetime to the timezone of the FlexMeasures platform.
    Note: this will change nothing but the tzinfo field.
    """
    return get_timezone().localize(naive_utc_from(dt))


def naive_utc_from(dt: datetime) -> datetime:
    """
    Return a naive datetime, that is localised to UTC if it has a timezone.
    If dt is naive, we assume it is already in UTC time.
    """
    if not hasattr(dt, "tzinfo") or dt.tzinfo is None:
        # let's hope this is the UTC time you expect
        return dt
    else:
        return dt.astimezone(pytz.utc).replace(tzinfo=None)


def tz_index_naively(
    data: pd.DataFrame | pd.Series | pd.DatetimeIndex,
) -> pd.DataFrame | pd.Series | pd.DatetimeIndex:
    """Turn any DatetimeIndex into a tz-naive one, then return. Useful for bokeh, for instance."""
    if isinstance(data, pd.DatetimeIndex):
        return data.tz_localize(tz=None)
    if hasattr(data, "index") and isinstance(data.index, pd.DatetimeIndex):
        # TODO: if index is already naive, don't
        data.index = data.index.tz_localize(tz=None)
    return data


def localized_datetime_str(dt: datetime, dt_format: str = "%Y-%m-%d %I:%M %p") -> str:
    """
    Localise a datetime to the timezone of the FlexMeasures platform.
    If no datetime is passed in, use server_now() as basis.

    Hint: This can be set as a jinja filter, so we can display local time in the app, e.g.:
    app.jinja_env.filters['localized_datetime'] = localized_datetime_str
    """
    if dt is None:
        dt = server_now()
    local_tz = get_timezone()
    local_dt = naive_utc_from(dt).astimezone(local_tz)
    return local_dt.strftime(dt_format)


def naturalized_datetime_str(
    dt: datetime | str | None, now: datetime | None = None
) -> str:
    """
    Naturalise a datetime object (into a human-friendly string).
    The dt parameter (as well as the now parameter if you use it)
    can be either naive or tz-aware. We assume UTC in the naive case.
    If dt parameter is a string it is expected to look like 'Sun, 28 Apr 2024 08:55:58 GMT'.
    String format is supported for case when we make json from internal api response
    e.g ui/crud/users auditlog router

    We use the `humanize` library to generate a human-friendly string.
    If dt is not longer ago than 24 hours, we use humanize.naturaltime (e.g. "3 hours ago"),
    otherwise humanize.naturaldate (e.g. "one week ago")

    Hint: This can be set as a jinja filter, so we can display local time in the app, e.g.:
    app.jinja_env.filters['naturalized_datetime'] = naturalized_datetime_str
    """
    if dt is None:
        return "never"
    if isinstance(dt, str):
        dt = datetime.strptime(dt, "%a, %d %b %Y %H:%M:%S %Z")
    if now is None:
        now = datetime.now(timezone.utc)
    naive_utc_now = naive_utc_from(now)

    # Convert or localize to utc
    if dt.tzinfo is None:
        utc_dt = pd.Timestamp(dt).tz_localize("utc")
    else:
        utc_dt = pd.Timestamp(dt).tz_convert("utc")

    # decide which humanize call to use for naturalization
    if naive_utc_from(utc_dt) >= naive_utc_now - timedelta(hours=24):
        # return natural time (naive utc dt with respect to naive utc now)
        return naturaltime(
            utc_dt.replace(tzinfo=None),
            when=naive_utc_now,
        )
    else:
        # return natural date in the user's timezone
        local_dt = utc_dt.tz_convert(get_timezone(of_user=True))
        return naturaldate(local_dt)


def resolution_to_hour_factor(resolution: str | timedelta) -> float:
    """Return the factor with which a value needs to be multiplied in order to get the value per hour,
    e.g. 10 MW at a resolution of 15min are 2.5 MWh per time step.

    :param resolution: timedelta or pandas offset such as "15min" or "h"
    """
    if isinstance(resolution, timedelta):
        return resolution / timedelta(hours=1)
    return pd.Timedelta(resolution).to_pytimedelta() / timedelta(hours=1)


def decide_resolution(start: datetime | None, end: datetime | None) -> str:
    """
    Decide on a practical resolution given the length of the selected time period.
    Useful for querying or plotting.
    """
    if start is None or end is None:
        return "15min"  # default if we cannot tell period
    period_length = end - start
    if period_length > timedelta(weeks=16):
        resolution = "168h"  # So upon switching from days to weeks, you get at least 16 data points
    elif period_length > timedelta(days=14):
        resolution = "24h"  # So upon switching from hours to days, you get at least 14 data points
    elif period_length > timedelta(hours=48):
        resolution = "1h"  # So upon switching from 15min to hours, you get at least 48 data points
    elif period_length > timedelta(hours=8):
        resolution = "15min"
    else:
        resolution = "5min"  # we are (currently) not going lower than 5 minutes
    return resolution


def get_timezone(of_user=False) -> pytz.BaseTzInfo:
    """Return the FlexMeasures timezone, or if desired try to return the timezone of the current user."""
    default_timezone = pytz.timezone(
        current_app.config.get("FLEXMEASURES_TIMEZONE", "")
    )
    if not of_user:
        return default_timezone
    if current_user.is_anonymous:
        return default_timezone
    if current_user.timezone not in pytz.common_timezones:
        return default_timezone
    return pytz.timezone(current_user.timezone)


def round_to_closest_quarter(dt: datetime) -> datetime:
    new_hour = dt.hour
    new_minute = 15 * round((float(dt.minute) + float(dt.second) / 60) / 15)
    if new_minute == 60:
        new_hour += 1
        new_minute = 0
    return datetime(dt.year, dt.month, dt.day, new_hour, new_minute, tzinfo=dt.tzinfo)


def round_to_closest_hour(dt: datetime) -> datetime:
    if dt.minute >= 30:
        return datetime(dt.year, dt.month, dt.day, dt.hour + 1, tzinfo=dt.tzinfo)
    else:
        return datetime(dt.year, dt.month, dt.day, dt.hour, tzinfo=dt.tzinfo)


def get_most_recent_quarter() -> datetime:
    now = server_now()
    return now.replace(minute=now.minute - (now.minute % 15), second=0, microsecond=0)


def get_most_recent_hour() -> datetime:
    now = server_now()
    return now.replace(minute=now.minute - (now.minute % 60), second=0, microsecond=0)


def get_most_recent_clocktime_window(
    window_size_in_minutes: int,
    now: datetime | None = None,
    grace_period_in_seconds: int | None = 0,
) -> tuple[datetime, datetime]:
    """
    Calculate a recent time window, returning a start and end minute so that
    a full hour can be filled with such windows, e.g.:

    Calling this function at 15:01:xx with window size 5 -> (14:55:00, 15:00:00)
    Calling this function at 03:36:xx with window size 15 -> (03:15:00, 03:30:00)

    We can demand a grace period (of x seconds) to have passed before we are ready to accept that we're in a new window:
    Calling this function at 15:00:16 with window size 5 and grace period of 30 seconds -> (14:50:00, 14:55:00)

    window_size_in_minutes is assumed to > 0 and < = 60, and a divisor of 60 (1, 2, ..., 30, 60).

    If now is not given, the current server time is used.
    if now / the current time lies within a boundary minute (e.g. 15 when window_size_in_minutes=5),
    then the window is not deemed over and the previous one is returned (in this case, [5, 10])

    Returns two datetime objects. They'll be in the timezone (if given) of the now parameter,
    or in the server timezone (see FLEXMEASURES_TIMEZONE setting).
    """
    assert window_size_in_minutes > 0
    assert 60 % window_size_in_minutes == 0
    if now is None:
        now = server_now()
    last_full_minute = now.replace(second=0, microsecond=0)
    if (
        grace_period_in_seconds is not None
        and grace_period_in_seconds > 0
        and (now - last_full_minute).seconds < grace_period_in_seconds
    ):
        last_full_minute -= timedelta(minutes=1 + grace_period_in_seconds // 60)
    last_round_minute = last_full_minute.minute - (
        last_full_minute.minute % window_size_in_minutes
    )
    begin_time = last_full_minute.replace(minute=last_round_minute) - timedelta(
        minutes=window_size_in_minutes
    )
    end_time = begin_time + timedelta(minutes=window_size_in_minutes)
    return begin_time, end_time


def get_first_day_of_next_month() -> datetime:
    return (datetime.now().replace(day=1) + timedelta(days=32)).replace(day=1)


def forecast_horizons_for(resolution: str | timedelta) -> list[str] | list[timedelta]:
    """Return a list of horizons that are supported per resolution.
    Return values or of the same type as the input."""
    if isinstance(resolution, timedelta):
        resolution_str = timedelta_to_pandas_freq_str(resolution)
    else:
        resolution_str = resolution
    horizons = []
    if resolution_str in ("5T", "5min", "10T", "10min"):
        horizons = ["1h", "6h", "24h"]
    elif resolution_str in ("15T", "15min", "1h", "H"):
        horizons = ["1h", "6h", "24h", "48h"]
    elif resolution_str in ("24h", "D"):
        horizons = ["24h", "48h"]
    elif resolution_str in ("168h", "7D"):
        horizons = ["168h"]
    if isinstance(resolution, timedelta):
        return [pd.to_timedelta(to_offset(h)) for h in horizons]
    else:
        return horizons


def supported_horizons() -> list[timedelta]:
    return [
        timedelta(hours=1),
        timedelta(hours=6),
        timedelta(hours=24),
        timedelta(hours=48),
    ]


def timedelta_to_pandas_freq_str(resolution: timedelta) -> str:
    return to_offset(resolution).freqstr


def duration_isoformat(duration: timedelta):
    """Adapted version of isodate.duration_isoformat for formatting a datetime.timedelta.

    The difference is that absolute days are not formatted as nominal days.
    Workaround for https://github.com/gweis/isodate/issues/74.
    """
    ret = []
    usecs = abs(
        (duration.days * 24 * 60 * 60 + duration.seconds) * 1000000
        + duration.microseconds
    )
    seconds, usecs = divmod(usecs, 1000000)
    minutes, seconds = divmod(seconds, 60)
    hours, minutes = divmod(minutes, 60)
    if hours or minutes or seconds or usecs:
        ret.append("T")
        if hours:
            ret.append("%sH" % hours)
        if minutes:
            ret.append("%sM" % minutes)
        if seconds or usecs:
            if usecs:
                ret.append(("%d.%06d" % (seconds, usecs)).rstrip("0"))
            else:
                ret.append("%d" % seconds)
            ret.append("S")
    # at least one component has to be there.
    repl = ret and "".join(ret) or "T0H"
    return re.sub("%P", repl, "P%P")


def determine_minimum_resampling_resolution(
    event_resolutions: list[timedelta],
) -> timedelta:
    """Return minimum non-zero event resolution, or zero resolution if none of the event resolutions is non-zero."""
    condition = list(
        event_resolution
        for event_resolution in event_resolutions
        if event_resolution > timedelta(0)
    )
    return min(condition) if any(condition) else timedelta(0)


def to_http_time(dt: pd.Timestamp | datetime) -> str:
    """Formats datetime using the Internet Message Format fixdate.

    >>> to_http_time(pd.Timestamp("2022-12-13 14:06:23Z"))
    'Tue, 13 Dec 2022 14:06:23 GMT'

    References
    ----------
    IMF-fixdate: https://www.rfc-editor.org/rfc/rfc7231#section-7.1.1.1
    """
    return dt.strftime("%a, %d %b %Y %H:%M:%S GMT")


def get_max_planning_horizon(resolution: timedelta) -> timedelta | None:
    """Determine the maximum planning horizon for the given sensor resolution."""
    max_planning_horizon = current_app.config.get("FLEXMEASURES_MAX_PLANNING_HORIZON")
    if isinstance(max_planning_horizon, int):
        # Config setting specifies maximum number of planning steps
        max_planning_horizon *= resolution
    return max_planning_horizon


def apply_offset_chain(
    dt: pd.Timestamp | datetime, offset_chain: str
) -> pd.Timestamp | datetime:
    """Apply an offset chain to a date.

    An offset chain consist of multiple (pandas) offset strings separated by commas. Moreover,
    this function implements the offset string "DB", which stands for Day Begin, to
    get a date from a datetime, i.e. removing time details finer than a day.

    Args:
        dt (pd.Timestamp | datetime)
        offset_chain (str)

    Returns:
        pd.Timestamp | datetime (same type as given dt)
    """

    if len(offset_chain) == 0:
        return dt

    # We need a Pandas structure to apply the offsets
    if isinstance(dt, datetime):
        _dt = pd.Timestamp(dt)
    elif isinstance(dt, pd.Timestamp):
        _dt = dt
    else:
        raise TypeError()

    # Apply the offsets
    for offset in offset_chain.split(","):
        try:
            _dt += to_offset(offset.strip())
        except ValueError:
            if offset.strip().lower() == "db":  # db = day begin
                _dt = _dt.floor("D")
            elif offset.strip().lower() == "hb":  # hb = hour begin
                _dt = _dt.floor("h")

    # Return output in the same type as the input
    if isinstance(dt, datetime):
        return _dt.to_pydatetime()
    return _dt


def to_utc_timestamp(value):
    """
    Convert a datetime object or string to a UTC timestamp (seconds since epoch).

    The dt parameter can be either naive or tz-aware. We assume UTC in the naive case.
    If dt parameter is a string, it is expected to look like 'Sun, 28 Apr 2024 08:55:58 GMT'.
    String format is supported for cases when we process JSON from internal API responses,
    e.g., ui/crud/users auditlog router.


    Returns:
    - Float: seconds since Unix epoch (1970-01-01 00:00:00 UTC)
    - None: if input is None


    Hint: This can be set as a jinja filter to display UTC timestamps in the app, e.g.:
    app.jinja_env.filters['to_utc_timestamp'] = to_utc_timestamp

    Example usage:
    >>> to_utc_timestamp(datetime(2024, 4, 28, 8, 55, 58))
    1714294558.0
    >>> to_utc_timestamp("Sun, 28 Apr 2024 08:55:58 GMT")
    1714294558.0
    >>> to_utc_timestamp(None)
    """
    if value is None:
        return None
    if isinstance(value, str):
        # Parse string datetime in the format 'Tue, 13 Dec 2022 14:06:23 GMT'
        try:
            value = datetime.strptime(value, "%a, %d %b %Y %H:%M:%S %Z")
        except ValueError:
            # Return None or raise an error if the string is in an unexpected format
            return None
    if isinstance(value, datetime):
        if value.tzinfo is None:
            # If naive, assume UTC
            value = pd.Timestamp(value).tz_localize("utc")
        else:
            # Convert to UTC if already timezone-aware
            value = pd.Timestamp(value).tz_convert("utc")

    # Return Unix timestamp (seconds since epoch)
    return value.timestamp()