潮差

import eofs
from eofs.standard import Eof
import pandas as  pd
import numpy as np
import glob
import datetime
from matplotlib import pyplot as plt
import copy
import re
import time
from pylab import *
import matplotlib.dates as mdate
import matplotlib.patches as patches
import matplotlib.ticker as ticker
import xarray as ax
import copy
import geopandas as gpd
from pykrige.ok import OrdinaryKriging
from pykrige.kriging_tools import write_asc_grid
import pykrige.kriging_tools as kt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Path, PathPatch
from shapely.geometry import LineString
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from sklearn.linear_model import LinearRegression
import heapq
from typing import *

time_parse1 = lambda date: datetime.datetime.strptime(date, '%Y/%m/%d %H:%M')

df = pd.read_csv('./water_level_nanmen_1.csv',parse_dates=['date'],date_parser=time_parse1,encoding='utf-8')
print(df)
helper = pd.DataFrame({'date': pd.date_range(df['date'].min(), df['date'].max(), freq='5T')})
df1 = pd.merge(df,helper,on = 'date',how='outer').sort_values('date')
df1.reset_index(drop=True,inplace=True)
time_1 = pd.Series(list(df1['date']))

dt =list(copy.deepcopy(df1['water_level']))
dy = pd.Series(dt,index=time_1)
dy= dy.resample('5T').mean()
dy1 =dy.interpolate(method='spline', order=3)
# # print(dy1)
dty = pd.DataFrame(dy1,columns=['water_level'])
dty.to_csv('./nanmen_five.csv')
wt = dty['water_level']


df = pd.DataFrame({'B': [-10, 1, 2, 5, 4]},
                  index = [pd.Timestamp('20130101 09:00:00'),
                           pd.Timestamp('20130101 09:00:02'),
                           pd.Timestamp('20130101 09:00:03'),
                           pd.Timestamp('20130101 09:00:05'),
                           pd.Timestamp('20130101 09:00:06')])

class Solution:
    def minSlidingWindow(self, nums: List[int], k: int) -> List[int]:
        n = len(nums)
        # 注意 Python 默认的优先队列是小根堆
        # 将数组中的元素取反 5 -> -5 1 -> -1；此时：-5 < -1；5就可以排到1上面了。
        q = [(nums[i], i) for i in range(k)]
        heapq.heapify(q)
        ans1 = [q[0][0]]
        # print(ans)
        for i in range(k, n):
            heapq.heappush(q, (nums[i], i))   #右移一个
            #不断地移除堆顶的元素，直到堆顶的元素确实出现在滑动窗口中
            while q[0][1] <= i - k:
                heapq.heappop(q)
            ans1.append(q[0][0])

        return ans1

    def maxSlidingWindow(self, nums: List[int], k: int) -> List[int]:
        n = len(nums)
        # 注意 Python 默认的优先队列是小根堆
        # 将数组中的元素取反 5 -> -5 1 -> -1；此时：-5 < -1；5就可以排到1上面了。
        q = [(-nums[i], i) for i in range(k)]
        heapq.heapify(q)

        ans = [-q[0][0]]
        for i in range(k, n):
            heapq.heappush(q, (-nums[i], i))  # 右移一个
            # 不断地移除堆顶的元素，直到堆顶的元素确实出现在滑动窗口中
            while q[0][1] <= i - k:
                heapq.heappop(q)
            ans.append(-q[0][0])

        return ans



#
ans1 = Solution()
# # nums = input()
# # nums = [int(i) for i in nums.split()]
# # k = int(input())
# nums = [1,3,-1,-3,5,3,6,7]
# k = 3
# nums = df['B']
# k= 2

nums = wt
k =324

# print(k)
# print(nums)

tu = pd.Series(ans1.minSlidingWindow(nums,k))
ti= pd.Series(ans1.maxSlidingWindow(nums,k))
tp = ti-tu
tpo = pd.DataFrame(tp)
tpo.to_csv('./142.csv')