集合卡尔曼滤波（ENKF）Python实现代码

集合卡尔曼滤波 Python实现代码

import pcse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os, sys
import copy
import datetime as dt
from pcse.fileinput import CABOFileReader, ExcelWeatherDataProvider
from pcse.util import WOFOST71SiteDataProvider
from pcse.base import ParameterProvider
from pcse.fileinput import YAMLAgroManagementReader
from pcse.models import Wofost71_WLP_FD

plt.style.use("ggplot")
print("This notebook was built with:")
print("python version: %s " % sys.version)
print("PCSE version: %s" % pcse.__version__)

data_dir = r'E:/ANN/data/wofost'

cropfile = os.path.join(data_dir, 'sug0601.crop')
cropdata = CABOFileReader(cropfile)

soilfile = os.path.join(data_dir, 'ec3.soil')
soildata = CABOFileReader(soilfile)
sitedata = WOFOST71SiteDataProvider(WAV=100, CO2=360)

parameters = ParameterProvider(cropdata=cropdata, soildata=soildata, sitedata=sitedata)

agromanagement_file = os.path.join(data_dir, 'sugarbeet_calendar.agro')
agromanagement = YAMLAgroManagementReader(agromanagement_file)

wdp = ExcelWeatherDataProvider(os.path.join(data_dir, 'nl1.xlsx'))

wofost = Wofost71_WLP_FD(parameters, wdp, agromanagement)
wofost1 = Wofost71_WLP_FD(parameters, wdp, agromanagement)
wofost.run_till_terminate()
df = pd.DataFrame(wofost.get_output()).set_index("day")
df.to_excel("wofost_results_enkf.xlsx")
output = wofost.get_output()

variables_for_DA = ["LAI", "SM"]
dates_of_observation = [dt.date(2006, 4, 9), dt.date(2006,