优势流

import numpy as np
import pandas as pd
import matplotlib as plt
import math

from pandas import DataFrame

Tidetag1 = float(input("请输入主退潮流方向"))
tide = np.loadtxt('.\B大潮.txt', dtype=float, usecols=(2, 3, 4))  # 导入数据
# print(tide)
testdir = np.linspace(0, 180, num=3600, endpoint=False)  # 生成等间隔数据
# print(testdir)
tide1 = tide[:, 1]  # 流速
tide2 = (tide[:, 2])  # 流向
tidetag = tide2 * 0  # 潮汐标志

# print(tide1)
# print(tide2)
# for i in range(len(testdir)):
#     testsum = np.sum((abs(tide1[i]*np.sin((tide2[i]-testdir[i]/180)*math.pi))))
# print(range(len(testdir)))
a = len(tide2)
b = len(testdir)
testdir1 = np.full((a, b), np.nan)
list1 = []
# print(testdir1)
# 形成弧度制转化
# for i in range(len(testdir)):
#     testdir1[i] = np.deg2rad(testdir[i])
# print(testdir)
for j in range(len(tide2)):
    for m in range(len(testdir)):
        testdir1[j, m] = np.abs(tide1[j] * np.sin(np.deg2rad(tide2[j] - testdir[m])))
# print(testdir1)

a1 = (np.sum(testdir1, axis=0))
a2 = np.argmax(a1)
a3 = testdir[a2]  ##分割线值
print(a3)
# print(testdir[421])
if a3 <= Tidetag1 < (a3 + 180):  # 分割线以下为退潮
    t = np.where((tide2 >= a3) & (tide2 < a3 + 180))
    for i in t:
        tidetag[i] = 1  # 退潮标记

else:
    # 分割线以上为涨潮
    t = np.where((tide2 < a3) | (tide2 < a3 + 180))
    for i in t:
        tidetag[i] = 1  # 退潮标记
print(tidetag)  # 输出潮汐标志信息
if a3 <= 90:
    axis_dir1 = a3 + 90
else:
    axis_dir1 = a3 - 90
print(axis_dir1)
# 查询潮周期数据
tag_change = 0
tag_Start = 0
tag_End_One = 0
tag_End_Two = 0
l = len(tide2)
for i in range(1, l):
    if tidetag[i] != tidetag[i - 1]:
        tag_change += 1

        if tag_change == 1:
            tag_Start = i

        if tag_change == 3:
            tag_End_One = i - 1
        if tag_change == 5:
            tag_End_Two = i - 1

print(tag_Start)
print(tag_End_Two)
print(tag_End_One)
if (tag_End_One == 0) and (tag_End_Two == 0):
    Index_Cycle = np.nan
    Num_Cycle = 0
elif tag_End_Two == 0:
    Index_Cycle = tag_End_One - tag_Start
    num_Cycle = 1
else:
    Index_Cycle = tag_End_Two - tag_Start
    num_Cycle = 2
print(num_Cycle)
c1 = tag_Start
c2 = tag_End_One
c3 = tag_End_Two
# 重置行列，提取某周期数据
print(tidetag)
# tidetag[0:c3] = tidetag[c1:c3 + 1]
tidetag_new = tidetag[c1:c3]
c = pd.DataFrame(tide, columns=['dep', 'vel', 'dir'])

c['tidetag'] = tidetag
#
c4 = c[c1:c3+1]  # 提取全周期值
print(c4)
d = c4[c4['tidetag'] == 1]
f = c4[c4['tidetag'] == 0]
d = d.reset_index(drop=True)
f = f.reset_index(drop=True)
#
# # print(c4)
# # #
# # print(d)
# # print(f)
# # # 计算优势流
# # # # 确认退潮和涨潮的流速流向以及水深等，并且形成array格式
v_ebb = np.array(d['vel'])
print(v_ebb)
dir_ebb = np.array(d['dir'])
print(dir_ebb)
depth_ebb = np.array(d['dep'])
print(depth_ebb)
v_flood = np.array(f['vel'])
dir_flood = np.array(f['dir'])
depth_flood = np.array(f['dep'])
q_ebb = np.sum(v_ebb * depth_ebb * np.abs(np.cos(np.deg2rad(dir_ebb - axis_dir1)))) / num_Cycle
f_flood = np.sum(v_flood * depth_flood * np.abs(np.cos(np.deg2rad(dir_flood - axis_dir1)))) / num_Cycle

print(f_flood)
print(q_ebb)
you = q_ebb / (f_flood + q_ebb)
print(you)
# # 计算涨落急流速流向
# # #落潮
# d1 = np.array(d)
# max_x = np.array(np.argmax(d1, axis=0))
# # print(max_x)
# v_ebb_max = d1[max_x[1],1]
# dir_ebb_max = d1[max_x[1],2]
# depth_ebb_max = d1[max_x[1],0]
# # print(v_ebb_max)
# # print(dir_ebb_max)
# # print(depth_ebb_max)
#
#
# #涨潮
# f1 = np.array(f)
# max_x = np.array(np.argmax(f1, axis=0))
# # print(max_x)
# v_flood_max = f1[max_x[1],1]
# dir_flood_max = f1[max_x[1],2]
# depth_flood_max = f1[max_x[1],0]
# # print(v_flood_max)
# print(dir_flood_max)
# print(depth_flood_max)