import sys

import boto3
from boto3.dynamodb.conditions import Key
import datetime
import calendar
import json
import os
import statistics

dynamo_region_map = {
    'us-west-1': 'us-west-1',
    'us-west-2': 'us-west-2',
    'us-east-1': 'us-east-1',
    'us-east-2': 'us-east-2',
    'ap-south-1': 'eu-north-1',
    'ap-northeast-3': 'ap-northeast-1',
    'ap-northeast-2': 'ap-northeast-1',
    'ap-southeast-1': 'ap-southeast-2',
    'ap-southeast-2': 'ap-southeast-2',
    'ap-northeast-1': 'ap-northeast-1',
    'ca-central-1': 'us-east-1',
    'eu-central-1': 'eu-north-1',
    'eu-west-1': 'eu-west-3',
    'eu-west-2': 'eu-west-3',
    'eu-west-3': 'eu-west-3',
    'eu-north-1': 'eu-north-1',
    'sa-east-1': 'sa-east-1',
    'eu-south-1': 'eu-north-1',

}  # This is a rough first pass at an intelligent region selector based on what is replicated
local_region = ''
if os.environ['AWS_REGION'] in dynamo_region_map:
    local_region = dynamo_region_map[os.environ['AWS_REGION']]
else:
    local_region = 'eu-west-3'


timestream_client = boto3.client('timestream-query', region_name='us-east-1')
dynamodb_client = boto3.resource('dynamodb', region_name=local_region)
table = dynamodb_client.Table('wow-token-price-recent')


def historical_data(time, region):
    # This shim is to permanently change the URL of 30d to 720h for local caching,
    # There seems to be at least 1 person using 30d (strangely with no .json) which was deprecated
    # as the data source for 1 month of data years ago
    if time == '30d':
        time = '720h'

    if time[-1] == 'h':
        return dynamo_data(time, region)
    else:
        return timestream_data(time, region)


def timestream_data(time, region):
    query_table = {
        '30d': {'interval': "INTERVAL '1' MONTH", 'bin': '15m'},
        '90d': {'interval': "INTERVAL '3' MONTH", 'bin': '30m'},
        '6m': {'interval': "INTERVAL '6' MONTH", 'bin': '1h'},
        '1y': {'interval': "INTERVAL '1' YEAR", 'bin': '2h'},
        '2y': {'interval': "INTERVAL '2' YEAR", 'bin': '2h'},
        'all': {'interval': "INTERVAL '10' YEAR", 'bin': '3h'},
    }
    timestream_query = 'WITH binned_query AS (' + \
                       'SELECT BIN(time, ' + query_table[time]['bin'] + ') AS binned_time,' + \
                       'CAST(AVG(measure_value::bigint) AS bigint) as average ' + \
                       'FROM "wow-token-price-history"."' + region + '-price-history" ' + \
                       "WHERE measure_name = 'price' " + \
                       'AND time > now() - (' + query_table[time]['interval'] + ') ' + \
                       'GROUP BY BIN(time, ' + query_table[time]['bin'] + "), 'price') " + \
                       'SELECT CREATE_TIME_SERIES(binned_time, average) AS timeseries_data ' + \
                       'FROM binned_query ' + \
                       'ORDER BY timeseries_data ASC'  # This is so ugly
    response = timestream_client.query(QueryString=timestream_query)
    rows = response['Rows'][0]['Data'][0]['TimeSeriesValue']
    data = []
    # TODO: Should we inject the aggregation functions into this loop to reduce the cost of looping?
    for row in rows:
        row_isotime = row['Time'].split('.')[0]
        row_datetime = datetime.datetime.fromisoformat(row_isotime).replace(
            tzinfo=datetime.timezone.utc)
        data.append({
            'time': row_datetime.isoformat(),
            'value': int(int(row['Value']['ScalarValue']) / 10000)
        })
    return data


def dynamo_data(time, region):
    time_stripped = int(time[:-1])
    start_time = datetime.datetime.utcnow() - datetime.timedelta(hours=time_stripped)
    start_time_utc = start_time.replace(tzinfo=datetime.timezone.utc)
    response = table.query(
        KeyConditionExpression=(
                Key('region').eq(region) &
                Key('timestamp').gte(int(start_time_utc.timestamp()))))
    data = []
    for item in response['Items']:
        item_time = datetime.datetime.utcfromtimestamp(int(item['timestamp'])).replace(
            tzinfo=datetime.timezone.utc).isoformat()
        data.append({
            'time': item_time,
            'value': int(int(item['price']) / 10000)
        })
    return data


def aggregate_data(aggregate_function: str, data: list):
    if aggregate_function == 'daily_max':
        return max_min(1, 1, data)
    elif aggregate_function == 'daily_min':
        return max_min(-1, 1, data)
    elif aggregate_function == 'daily_mean':
        return mean(1, data)
    elif aggregate_function == 'weekly_max':
        return max_min(1, 7, data)
    elif aggregate_function == 'weekly_min':
        return max_min(-1, 7, data)
    elif aggregate_function == 'weekly_mean':
        return mean(7, data)


def date_in_range(day_range: tuple, date: datetime.datetime):
    month_range = calendar.monthrange(date.year, date.month)
    if day_range[0] <= date.day < day_range[1]:
        return True
    elif date.day < day_range[1] and date.day < day_range[0]:
        # TODO: I am probably missing a sanity check here, come back to it
        return True
    else:
        return False


def day_bucket(bucket_size: int, date: datetime.datetime) -> tuple[datetime.datetime, datetime.datetime]:
    month_range = calendar.monthrange(date.year, date.month)
    days_to_reset = {0: 1, 1: 0, 2: 6, 3: 5, 4: 4, 5: 3, 6: 2}
    # We want the bucket boundaries for a bucket size of 7 to fall on
    # reset day (index 1), and for a month (31) to fall on the actual boundaries of that month
    # this means month-to-month, there are dynamic sizing of buckets
    # TODO: Monthly boundaries
    if bucket_size == 7 and date.weekday() != 1:
        # This is WoW, the week starts on Tuesday (datetime index 1)
        bucket_size = days_to_reset[date.weekday()]

    return tuple((date, date + datetime.timedelta(days=bucket_size)))


def is_new_bucket(d_datetime: datetime.datetime, current_bucket_day: datetime.datetime.day, bucket: tuple) -> bool:
    if d_datetime.day != current_bucket_day and (d_datetime >= bucket[1] or d_datetime.weekday() == 1):
        return True
    return False


def max_min(fn: int, bucket_size: int, data: list) -> list:
    new_data = []
    first_date = datetime.datetime.fromisoformat(data[0]['time'])
    current_bucket_day = first_date.day
    # I hate working with dates
    bucket = day_bucket(bucket_size, first_date)
    min_max = {'minimum': 999_999_999, 'maximum': 0}
    min_max_date = {'minimum_date': datetime.datetime.min, 'maximum_date': datetime.datetime.max}

    for d in data:
        d_datetime = datetime.datetime.fromisoformat(d['time'])
        # current_day is used to check if this 'if' has triggered for a new bucket and bypass if it has
        if is_new_bucket(d_datetime, current_bucket_day, bucket):
            current_bucket_day = d_datetime.day
            bucket = day_bucket(bucket_size, d_datetime)
            if fn == -1:  # Minimum function
                new_data.append({'time': min_max_date['minimum_date'], 'value': min_max['minimum']})
            elif fn == 1:  # Maximum function
                new_data.append({'time': min_max_date['maximum_date'], 'value': min_max['maximum']})
            min_max = {'minimum': 999_999_999, 'maximum': 0}
            min_max_date = {
                'minimum_date': datetime.datetime.min.isoformat(),
                'maximum_date': datetime.datetime.max.isoformat()
            }

        if d['value'] < min_max['minimum']:
            min_max['minimum'] = d['value']
            min_max_date['minimum_date'] = d_datetime.isoformat()

        if d['value'] > min_max['maximum']:
            min_max['maximum'] = d['value']
            min_max_date['maximum_date'] = d_datetime.isoformat()

    return new_data


def mean(bucket_size: int, data: list) -> list:
    new_data = []
    first_date = datetime.datetime.fromisoformat(data[0]['time'])
    current_bucket_day = first_date.day
    bucket = day_bucket(bucket_size, first_date)
    mean_bucket = []
    bucket_date = first_date

    for d in data:
        d_datetime = datetime.datetime.fromisoformat(d['time'])
        if is_new_bucket(d_datetime, current_bucket_day, bucket):
            current_bucket_day = d_datetime.day
            bucket = day_bucket(bucket_size, d_datetime)
            new_data.append({'time': bucket[0].isoformat(), 'value': int(statistics.mean(mean_bucket))})
            mean_bucket = []

        mean_bucket.append(d['value'])

    return new_data


def validate_path(split_uri: str) -> bool:
    return validate_region(split_uri[-2]) and validate_time(split_uri[-1].split('.')[0])


def validate_time(time: str) -> bool:
    valid_times = ['24h', '48h', '72h', '120h', '168h', '336h', '720h', '30d', '2190h', '90d', '6m', '1y', '2y', 'all']
    return time in valid_times


def validate_region(region: str) -> bool:
    valid_regions = ['us', 'eu', 'tw', 'kr']
    return region in valid_regions


def validate_aggregate(aggregate_function: str) -> bool:
    valid_aggregates = ['daily_max', 'daily_min', 'daily_mean', 'weekly_max', 'weekly_min', 'weekly_mean']
    return aggregate_function in valid_aggregates


def lambda_handler(event, context):
    uri = event['Records'][0]['cf']['request']['uri']
    split_uri = uri.split('/')
    if validate_path(split_uri):
        time = split_uri[-1].split('.')[0]
        region = split_uri[-2]
        aggregate_function = split_uri[-3]
        data = historical_data(time, region)

        if validate_aggregate(aggregate_function):
            data = aggregate_data(aggregate_function, data)

        response = {'status': '200', 'statusDescription': 'OK', 'headers': {}}
        response['headers']['content-type'] = [{'key': 'Content-Type', 'value': 'application/json'}]
        response['body'] = json.dumps(data)
        return response
    else:
        return {'status': '404', 'statusDescription': 'NotFound', 'headers': {}}