env_regulation_api/app/controllers/targets.py

# -*- coding: utf-8 -*-

from abc import abstractmethod
from typing import Dict, Tuple, Optional

import arrow
import numpy as np
import pandas as pd
from httpx import AsyncClient
from loguru import logger

from app.controllers.controller import Controller
from app.resources.params import (
    TEMPERATURE_RELATED_FEEDBACK_WEIGHT,
    TEMPERATURE_RELATED_FEEDBACK,
    CO2_RELATED_FEEDBACK_WEIGHT,
    SWITCH_RELATED_FEEDBACK
)
from app.services.platform import DataPlatformService
from app.services.transfer import SpaceInfoService, Season
from app.utils.date import get_time_str, get_quarter_minutes, TIME_FMT


class TargetController(Controller):

    def __init__(
            self,
            realtime_data: float,
            feedback: Dict,
            is_customized: bool,
            is_temporary: bool,
            current_targets: pd.DataFrame,
    ) -> None:
        super(TargetController, self).__init__()
        self._realtime_data = realtime_data
        self._feedback = feedback
        self._is_customized = is_customized
        self._is_temporary = is_temporary
        self._current_targets = current_targets
        self._now_time = arrow.get(get_time_str(), TIME_FMT).time().strftime('%H%M%S')
        self._quarter_time = get_quarter_minutes(get_time_str())

    async def calculate_diff(self, weight: Dict) -> float:
        related_feedback = [v for k, v in self._feedback.items() if k in weight]
        related_feedback = np.array(related_feedback)
        weight = np.array(list(weight.values()))
        feedback_count = related_feedback.sum()
        diff = 0
        if feedback_count > 0:
            diff = np.dot(related_feedback, weight) / feedback_count

        return diff

    @abstractmethod
    async def init_temporary(self):
        pass

    @abstractmethod
    async def get_targets(self) -> float:
        pass

    async def generate_temporary(self, lower, upper):
        now_str = get_time_str()
        time_index = arrow.get(arrow.get(now_str, TIME_FMT).shift(minutes=15).timestamp
                               // (15 * 60) * (15 * 60)).time().strftime('%H%M%S')
        result = {time_index: [lower, upper]}
        self._results.update({'temporary_targets': result})

    async def readjust_global(self, latest_change: float, previous_changes: pd.DataFrame):
        previous_changes = pd.concat([
            pd.DataFrame({'timestamp': [self._now_time], 'value': [latest_change]}),
            previous_changes,
        ])
        previous_changes.reset_index(inplace=True)
        previous_changes['weight1'] = previous_changes['index'].apply(lambda x: (1 / (x + 1)) ** 3)
        new_targets = []
        time_index = self._current_targets.reset_index()['time']
        for item in time_index:
            previous_changes['delta'] = previous_changes['timestamp'].apply(
                lambda x: abs(arrow.get(str(x), 'HHmmss') - arrow.get(item, 'HHmmss')).seconds // (15 * 60)
            )
            previous_changes['weight2'] = previous_changes['delta'].apply(lambda x: 0.5 ** x)
            previous_changes['weight'] = previous_changes['weight1'] * previous_changes['weight2']
            new_targets.append(
                (previous_changes['value'] * previous_changes['weight']).sum() / previous_changes['weight'].sum()
            )
        self._current_targets['new_targets'] = new_targets

    @abstractmethod
    async def run(self):
        pass


class TemperatureTargetController(TargetController):

    def __init__(
            self,
            realtime_data: float,
            feedback: Dict,
            is_customized: bool,
            is_temporary: bool,
            current_targets: pd.DataFrame,
            season: Season,
            previous_changes: Optional[pd.DataFrame] = None
    ) -> None:
        super(TemperatureTargetController, self).__init__(
            realtime_data,
            feedback,
            is_customized,
            is_temporary,
            current_targets
        )
        self._season = season
        self._previous_changes = previous_changes

    @staticmethod
    def _cut(value: float) -> float:
        _LOWER_LIMIT = 22.0
        _UPPER_LIMIT = 28.0
        value = min(value, _UPPER_LIMIT)
        value = max(value, _LOWER_LIMIT)

        return value

    async def init_temporary(self) -> Tuple[float, float]:
        _VAR = 2
        _RANGE = 1
        new_target = 24.0
        new_lower_bound, new_upper_bound = new_target - 1.0, new_target + 1.0
        if not np.isnan(self._realtime_data):
            if self._season == Season.cooling:
                if ('a little hot' in self._feedback
                        or 'so hot' in self._feedback
                        or 'switch on' in self._feedback):
                    mid = self._realtime_data - _VAR
                    mid = self._cut(mid)
                    new_lower_bound = mid - _RANGE
                    new_upper_bound = mid + _RANGE
            elif self._season == Season.heating:
                if ('a little cold' in self._feedback
                        or 'so cold' in self._feedback
                        or 'switch on' in self._feedback):
                    mid = self._realtime_data + _VAR
                    mid = self._cut(mid)
                    new_lower_bound = mid - _RANGE
                    new_upper_bound = mid + _RANGE

        return new_lower_bound, new_upper_bound

    async def get_targets(self) -> float:
        current_lower_target = self._current_targets['temperatureMin'].loc[self._quarter_time]
        current_upper_target = self._current_targets['temperatureMax'].loc[self._quarter_time]
        if np.isnan(current_lower_target):
            current_lower_target = 23.0
        if np.isnan(current_upper_target):
            current_upper_target = 25.0

        return (current_lower_target + current_upper_target) / 2

    async def readjust_current(self, current: float, diff: float) -> float:
        _RANGE = 2
        new_target = current
        if not np.isnan(self._realtime_data):
            if self._season == Season.cooling:
                standard = current + 1.0
            elif self._season == Season.heating:
                standard = current - 1.0
            else:
                standard = current

            if (diff > 0 and self._realtime_data + _RANGE > standard
                    or diff < 0 and self._realtime_data - _RANGE < standard):
                new_target = self._realtime_data + diff

        return new_target

    async def generate_global(self):
        _RANGE = 1
        new_targets = self._current_targets['new_targets'].apply(lambda x: [self._cut(x) - _RANGE,
                                                                            self._cut(x) + _RANGE])
        time_index = self._current_targets.reset_index()['time']
        result = {}
        for i in range(len(time_index)):
            result.update({time_index[i]: new_targets[i]})
        self._results.update({'global_targets': result})

    async def run(self):
        diff = await self.calculate_diff(TEMPERATURE_RELATED_FEEDBACK_WEIGHT)
        if diff != 0:
            if not self._is_customized:
                lower_bound, upper_bound = await self.init_temporary()
                await self.generate_temporary(lower_bound, upper_bound)
            else:
                current_target = await self.get_targets()
                new_target = await self.readjust_current(current_target, diff)
                new_target = self._cut(new_target)
                if not self._is_temporary:
                    self._results.update({'latest_change': new_target})
                    await self.readjust_global(new_target, self._previous_changes)
                    await self.generate_global()
                else:
                    await self.generate_temporary(new_target - 1.0, new_target + 1.0)
        else:
            return


class Co2TargetController(TargetController):

    def __init__(
            self,
            realtime_data: float,
            feedback: Dict,
            is_customized: bool,
            is_temporary: bool,
            current_targets: pd.DataFrame,
            previous_changes: Optional[pd.DataFrame] = None
    ) -> None:
        super(Co2TargetController, self).__init__(
            realtime_data,
            feedback,
            is_customized,
            is_temporary,
            current_targets
        )
        self._previous_changes = previous_changes

    @staticmethod
    def _cut(value: float) -> float:
        _UPPER_LIMIT = 1000.0
        value = min(value, _UPPER_LIMIT)

        return value

    async def init_temporary(self) -> float:
        new_target = 1000
        diff = await self.calculate_diff(CO2_RELATED_FEEDBACK_WEIGHT)
        if not np.isnan(self._realtime_data):
            new_target += diff

        return self._cut(new_target)

    async def get_targets(self) -> float:
        current_upper_target = self._current_targets['co2Max'].loc[self._quarter_time]
        if np.isnan(current_upper_target):
            current_upper_target = 500.0

        return current_upper_target

    async def readjust_current(self, lower: float, upper: float, diff: float) -> float:
        new_target = upper - lower
        if np.isnan(self._realtime_data):
            new_target += diff
        else:
            if (diff > 50 and self._realtime_data + 100 > upper
                    or diff < -50 and self._realtime_data - 100 < upper):
                new_target = self._realtime_data + diff

        return self._cut(new_target)

    async def generate_global(self):
        new_targets = self._current_targets['new_targets'].apply(lambda x: [0, x])
        time_index = self._current_targets.reset_index()['time']
        result = {}
        for i in range(len(time_index)):
            result.update({time_index[i]: new_targets[i]})
        self._results.update({'global_targets': result})

    async def run(self):
        diff = await self.calculate_diff(CO2_RELATED_FEEDBACK_WEIGHT)
        if diff != 0:
            if not self._is_customized:
                upper_bound = await self.init_temporary()
                await self.generate_temporary(0, upper_bound)
            else:
                current_upper = await self.get_targets()
                upper_bound = await self.readjust_current(0, current_upper, diff)
                if not self._is_temporary:
                    self._results.update({'latest_change': upper_bound})
                    await self.readjust_global(upper_bound, self._previous_changes)
                    await self.generate_global()
                else:
                    await self.generate_temporary(0, upper_bound)
        else:
            return


@logger.catch()
async def readjust_all_target(
        project_id: str,
        space_id: str,
        wechat_time: Optional[str] = None,
        feedback: Optional[Dict] = None
):
    async with AsyncClient() as client:
        transfer = SpaceInfoService(client, project_id, space_id)
        platform = DataPlatformService(client, project_id)

        realtime_temperature = await platform.get_realtime_temperature(space_id)
        current_targets = await transfer.get_custom_target()
        if wechat_time:
            feedback = await transfer.get_feedback(wechat_time)
        is_customized = await transfer.is_customized()
        is_temporary = await transfer.is_temporary()
        season = await transfer.get_season()
        previous_changes = await transfer.env_database_get()

    if feedback.get('switch off') and feedback.get('switch off') > 0:
        need_switch_off = True
        for item in SWITCH_RELATED_FEEDBACK:
            if feedback.get(item) and feedback.get(item) > 0:
                need_switch_off = False
                break
    else:
        need_switch_off = False

    need_run_room_control = False
    if need_switch_off:
        need_run_room_control = True
        async with AsyncClient() as client:
            transfer = SpaceInfoService(client, project_id, space_id)
            await transfer.set_temporary_custom()

        return need_run_room_control

    temperature_results = {}
    for item in TEMPERATURE_RELATED_FEEDBACK:
        if feedback.get(item) and feedback.get(item) > 0:
            temperature_controller = TemperatureTargetController(
                realtime_temperature,
                feedback,
                is_customized,
                is_temporary,
                current_targets[['temperatureMin', 'temperatureMax']].copy(),
                season,
                previous_changes['temperature']
            )
            await temperature_controller.run()
            temperature_results = temperature_controller.get_results()
            break

    if temperature_results:
        need_run_room_control = True
        async with AsyncClient() as client:
            transfer = SpaceInfoService(client, project_id, space_id)

            if temperature_results.get('temporary_targets'):
                await transfer.set_custom_target('temperature', temperature_results.get('temporary_targets'), '0')
            if temperature_results.get('global_targets'):
                await transfer.set_custom_target('temperature', temperature_results.get('global_targets'), '1')
            if temperature_results.get('latest_change'):
                await transfer.env_database_set('temperature', temperature_results.get('latest_change'))

    return need_run_room_control