File:Uusia koronavirustapauksia suomessa fourier ennuste 1.svg

1. 3. COVID-19 Finland cases short-term forecast
   4. Python 3 script
   6. Input from internet site: cases.
   8. version 0000.0006
   9. 15.4.2022

import locale import math as math import numpy as np import pandas as pd import statistics

import matplotlib.pyplot as plt from numpy import fft from scipy import interpolate import scipy.signal from scipy.signal import savgol_filter

from scipy.interpolate import UnivariateSpline from scipy.interpolate import pchip_interpolate from scipy import stats from scipy.optimize import curve_fit

from datetime import datetime, timedelta from datetime import date

import matplotlib.dates as mdates import matplotlib.ticker as ticker from matplotlib.ticker import ScalarFormatter

from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator, MaxNLocator)

1. from mizani.breaks import date_breaks, minor_breaks
2. from mizani.formatters import date_format

from bs4 import BeautifulSoup import requests

1. 1. 1. 2. asetukset parameters

1. 1. pohjadatan rajat: limits of base data

today = date.today()

1. print("Today's date:", today)

daybeforeyesterday = datetime.today() - timedelta(days=2)

paiva1="2022-03-01" paiva2="2022-04-05"

1. paiva2=daybeforeyesterday

1. 1. ennusteen rajat: forecast limits

taika1=paiva2

taika2="2022-07-01"

1. 1. y-akselin rajat

ymax1=12000 ymax2=50

vormat1='%Y-%m-%d'

1. locale.setlocale(locale.LC_TIME, "fi_FI")

def func(x, a, b):

   return (a*x+b)

def fourierExtrapolation(x, n_predict):

   n = x.size
   n_harm = 10                     # number of harmonics in model
   t = np.arange(0, n)
   p = np.polyfit(t, x, 1)         # find linear trend in x
   x_notrend = x - p[0] * t        # detrended x
   x_freqdom = fft.fft(x_notrend)  # detrended x in frequency domain
   f = fft.fftfreq(n)              # frequencies
   indexes = range(n)
   # sort indexes by frequency, lower -> higher
   #indexes.sort(key = lambda i: np.absolute(f[i]))

   t = np.arange(0, n + n_predict)
   restored_sig = np.zeros(t.size)
   for i in indexes[:1 + n_harm * 2]:
       ampli = np.absolute(x_freqdom[i]) / n   # amplitude
       phase = np.angle(x_freqdom[i])          # phase
       restored_sig += ampli * np.cos(2 * np.pi * f[i] * t + phase)
   return restored_sig + p[0] * t

def cut_by_dates(dfx, start_date, end_date): mask = (dfx['Date'] > start_date) & (dfx['Date'] <= end_date) dfx2 = dfx.loc[mask] #print(dfx2) return(dfx2)

def load_country_cases(maa): dfin = pd.read_csv('https://datahub.io/core/covid-19/r/countries-aggregated.csv', parse_dates=['Date']) countries = [maa] dfin = dfin[dfin['Country'].isin(countries)] #print (head(dfin)) #quit(-1) selected_columns = dfin"Date", "Confirmed", "Recovered", "Deaths" df2 = selected_columns.copy()

df=df2 len1=len(df["Date"]) aktiv2= [None] * len1 for n in range(0,len1-1): aktiv2[n]=0

dates=df['Date'] rekov1=df['Recovered'] konf1=df['Confirmed'] death1=df['Deaths'] #print(dates) spanni=6

#print(rekov1)

#quit(-1)

rulla = rekov1.rolling(window=spanni).mean() rulla2 = rulla.rolling(window=spanni).mean()

tulosrulla=rulla2 tulosrulla= tulosrulla.replace(np.nan, 0) tulosrulla=np.array(tulosrulla).astype(int) rulla2=tulosrulla

x=np.linspace(0,len1,len1); #print("kupla") #print(tulosrulla)

#print(konf1) #print(death1) #print(aktiv2) konf1=np.array(konf1).astype(int) death1=np.array(death1).astype(int) #print(konf1) #quit(-1)

for n in range(0,(len1-1)): #print("luzmu") rulla2[n]=tulosrulla[n] #print ("luzmu2") #aktiv2[n]=konf1[n]-death1[n]-rulla2[n] aktiv2[n]=konf1[n] #print(rulla2[n])

#quit(-1)

#aktiv3=np.array(aktiv2).astype(int)

dailycases1= [0] * len1 dailydeaths1= [0] * len1

for n in range(1,(len1-1)): dailycases1[n]=konf1[n]-konf1[n-1] if (dailycases1[n]<0): dailycases1[n]=0

for n in range(1,(len1-1)): dailydeaths1[n]=death1[n]-death1[n-1] if (dailydeaths1[n]<0): dailydeaths1[n]=0

#quit(-1) df.insert (2, "Daily_Cases", dailycases1) df.insert (3, "Daily_Deaths", dailydeaths1) df['ActiveEst']=aktiv2 #print (df) dfout = df'Date', 'Confirmed','Deaths','Recovered', 'ActiveEst','Daily_Cases','Daily_Deaths' #print(df) #print(dfout) print(".") return(dfout)

def load_fin_wiki_data(): url="https://fi.wikipedia.org/wiki/Suomen_koronaviruspandemian_aikajana" response = requests.get(url) soup = BeautifulSoup(response.text, 'lxml') table = soup.find_all('table')[0] # Grab the first table df = pd.read_html(str(table))[0] #print(df) #Päivä Tapauksia Uusia tapauksia Sairaalassa Teholla Kuolleita Uusia kuolleita Toipuneita

df2 = df'Tapauksia','Uusia tapauksia','Sairaalassa','Teholla','Kuolleita','Uusia kuolleita','Toipuneita'

kaikkiatapauksia=df['Tapauksia'] toipuneita=df['Toipuneita'] uusiatapauksia=df['Uusia tapauksia'] sairaalassa=df['Sairaalassa'] teholla=df['Teholla'] kuolleita=df['Kuolleita'] uusiakuolleita=df['Uusia kuolleita']

len1=len(kaikkiatapauksia)

kaikkiatapauksia2=[] toipuneita2=[] uusiatapauksia2=[] sairaalassa2=[] teholla2=[] kuolleita2=[] uusiakuolleita2=[]

for n in range(0,len1): elem0=kaikkiatapauksia[n] elem1 = .join(c for c in elem0 if c.isdigit()) elem2=int(elem1) kaikkiatapauksia2.append(elem2) elem0=toipuneita[n] elem1 = .join(c for c in elem0 if c.isdigit()) #toipuneita2.append(int(elem1)) if (elem1!=): toipuneita2.append(int(elem1)) else: toipuneita2.append(0)

elem0=uusiatapauksia[n] elem1 = .join(c for c in elem0 if c.isdigit()) uusiatapauksia2.append(int(elem1)) elem0=sairaalassa[n] #elem1 = .join(c for c in elem0 if c.isdigit()) sairaalassa2.append(int(elem0)) elem0=teholla[n] #elem1 = .join(c for c in elem0 if c.isdigit()) teholla2.append(int(elem0)) elem0=kuolleita[n] #elem1 = .join(c for c in elem0 if c.isdigit()) kuolleita2.append(int(elem0)) elem0=uusiakuolleita[n] #elem1 = .join(c for c in elem0 if c.isdigit()) uusiakuolleita2.append(int(elem0))

#kaikkiatapauksia3=np.array(kaikkiatapauksia2).astype(int) #print("---") #print(kaikkiatapauksia2) #print(toipuneita2)

kaikkiatapauksia3=np.array(kaikkiatapauksia2).astype(int) toipuneita3=np.array(toipuneita2).astype(int)

uusiatapauksia3=np.array(uusiatapauksia2).astype(int) sairaalassa3=np.array(sairaalassa2).astype(int) teholla3=np.array(teholla2).astype(int) kuolleita3=np.array(kuolleita2) .astype(int) uusiakuolleita3=np.array(uusiakuolleita2).astype(int)

for n in range(1,len1): toipu0=toipuneita3[n] if (toipu0==0): paikka=n toipui=toipu1 break toipu1=toipu0

## oletus: toipuu suureksi osaksi akuutista vaiheesta 3 viikossa ## todellisuudessa yli 60% kärsii ainakin yhdestä pitkäkestoisesta oireesta

for n in range(paikka,len1): toipui=toipui+uusiatapauksia3[n-21]-uusiakuolleita3[n] toipuneita3[n]=toipui #print(toipuneita3[n])

napapaiva1 = np.datetime64("2020-04-01") timedelta1= np.timedelta64(len(kaikkiatapauksia3),'D') napapaiva2 = napapaiva1+timedelta1

#dada1 = np.linspace(napapaiva1.astype('f8'), napapaiva2.astype('f8'), dtype='<M8[D]') dada1 = pd.date_range(napapaiva1, napapaiva2, periods=len(kaikkiatapauksia3)).to_pydatetime()

#print(dada1)

data = {'Date':dada1, 'Kaikkia tapauksia':kaikkiatapauksia3, "Uusia tapauksia":uusiatapauksia3, "Sairaalassa":sairaalassa3, "Teholla":teholla3, "Kuolleita":kuolleita3, "Uusiakuolleita":uusiakuolleita3, "Toipuneita":toipuneita3 }

df2 = pd.DataFrame(data)

#print(kaikkiatapauksia3) #print ("Fin wiki data.") return(df2)

def get_solanpaa_fi_data(): url="https://covid19.solanpaa.fi/data/fin_cases.json" response = requests.get(url,allow_redirects=True) open('solanpaa_fi.json', 'w').write(response.text) with open('solanpaa_fi.json') as f: sola1=pd.read_json(f)

sola1_top = sola1.head() #print (sola1_top) #Rt […] #Rt_lower […] #Rt_upper […] #Rt_lower50 […] #Rt_upper50 […] #Rt_lower90 […] #Rt_upper90 […] #new_cases_uks […] #new_cases_uks_lower50 […] #new_cases_uks_upper50 […] #new_cases_uks_lower90 […] #new_cases_uks_upper90 […] #new_cases_uks_lower […] #new_cases_uks_upper […]

dada1=sola1["date"] casa1=sola1["cases"] death1=sola1["deaths"] newcasa1=sola1["new_cases"] newdeath1=sola1["new_deaths"] hosp1=sola1["hospitalized"] icu1=sola1["in_icu"] rt=sola1["Rt"] newcasauks=sola1["new_cases_uks"]

print(dada1) len1=len(dada1) toipuneita=np.zeros(len1) toipuneita=toipuneita*0 aktiivisia=toipuneita

toipui=0 for n in range(0,len1):

if(n>21): toipui=toipui+newcasa1[n-21]-newdeath1[n]

toipuneita[n]=toipui

aktiivisia=casa1-toipuneita-death1 #print(toipuneita)

#print("KKKK") #print (dada1[0+21])

data = {'Date':dada1, 'Tapauksia':casa1, 'Kuolemia':death1, 'Sairaalassa':hosp1, 'Teholla':icu1, 'Uusia_tapauksia':newcasa1, 'Uusia_kuolemia':newdeath1, 'R':rt, 'Uusia_tapauksia_ennuste':newcasauks, 'Toipuneita':toipuneita, 'Aktiivisia':aktiivisia, }

df = pd.DataFrame(data) return(df)

def calcu_fourier_1(cases,days3): hattuja=7 fatticache=[]

for n in range(3, hattuja-1,2): lugu1=(lencases1-n)*-1 casest=cases[lugu1:] sulen1=len2-lencases1 casest2=np.pad(casest, ((n), 0), 'constant') casest3=savgol_filter(cases, n, 2) casest3=cases yfat1=fourierExtrapolation(casest3,sulen1) fatit1=yfat1.astype(int) fatticache.append(fatit1)

hattuja2=len(fatticache)

for n in range(0, hattuja2): fatit1=fatticache[n]

fotticache1=[]

for n in range(0,len3-1): fotti=[] for m in range(0, hattuja2): fatit1=fatticache[m] elem1=fatit1[n] fotti.append(elem1)

fotti1=np.array(fotti) fotticache1.append(fotti1)

fottitaulu00=np.array(fotticache1) fottitaulu=fottitaulu00.reshape(len3-1, hattuja2)

devis00=[] meas00=[]

for n in range(0,len3-1): sarake=fottitaulu[n] devis00.append(statistics.stdev(sarake)) meas00.append(statistics.mean(sarake))

devis=np.array(devis00) meas=np.array(meas00) return(meas)

1. df=load_country_cases('Finland')
2. df2=cut_by_dates(df, paiva1,paiva2)

df=get_solanpaa_fi_data()

1. df=load_fin_wiki_data()
2. print(df)

df2=cut_by_dates(df, paiva1,paiva2)

print(df2)

print("...")

1. quit(-1)

1. dates0=df2['Date']
2. cases0=df2['Daily_Cases']

dates0=df2['Date']

1. cases0=df2['Daily_Cases']

dailycases1=df2['Uusia_tapauksia'] dailydeaths1=df2['Uusia_kuolemia']

cases0=dailycases1

1. quit(-1)

cases=np.array(cases0).astype(int)

1. dates=np.array(dates0).to_pydatetime()

dates=dates0

savgol=savgol_filter(cases, 7, 2) casesminusavgol=cases-savgol stdev=statistics.stdev(casesminusavgol) mean=statistics.mean(savgol)

1. print (mean)

1. quit(-1)

start1 = np.datetime64(paiva1) end1 = np.datetime64(paiva2)

start2 = np.datetime64(taika1) end2 = np.datetime64(taika2)

1. days1 = np.linspace(start1.astype('f8'), end1.astype('f8'), dtype='<M8[D]')

1. napapaiva1 = np.datetime64("2020-04-01")

timedelta1= np.timedelta64(len(cases),'D')

1. napapaiva2 = napapaiva1+timedelta1

1. dada1 = np.linspace(napapaiva1.astype('f8'), napapaiva2.astype('f8'), dtype='<M8[D]')

days1 = pd.date_range(start1, end1, periods=len(cases)).to_pydatetime()

days2 = np.linspace(start2.astype('f8'), end2.astype('f8'), dtype='<M8[D]') days3 = np.linspace(start1.astype('f8'), end2.astype('f8'), dtype='<M8[D]')

len1=len(days1) len2=len(days2) len3=len(days3) x1 = np.linspace(0, len1, len1) x2 = np.linspace(len1, len2, len2) x3 = np.linspace(0, len3, len3) lenx1=len(x1) lenx2=len(x2) lenx3=len(x3)

1. print(days2)

1. print (dates)

print (cases) print (len(cases)) print (len2)

1. quit(-1)

1. print (len(dates))
2. print (len(cases))

lencases1=len(cases)

1. print(lencases1)

1. quit(-1)

start1 = np.datetime64(paiva1) days3 = np.linspace(start1.astype('f8'), end2.astype('f8'), dtype='<M8[D]') meas=calcu_fourier_1(cases,days3) logmeas=calcu_fourier_1(np.log(cases),days3) expmeas=np.exp(logmeas) alkumeas=savgol_filter(meas, 3, 2) silitettymeas=meas

1. days4=days3[:-1]

1. nym=1
2. for nym in range(0,10):
3. start1=start1+np.timedelta64(nym,'D')
4. days3 = np.linspace(start1.astype('f8'), end2.astype('f8'), dtype='<M8[D]')
5. days4=days3[:-1]

#meas=calcu_fourier_1(cases,days3) #plt.plot(days4,meas, color="red", alpha=0.3)

1. plt.show()
2. quit(-1)

1. upper0=meas+devis
2. lower0=meas-devis

1. meas=silitettymeas

upper=meas+stdev lower=meas-stdev upper2=meas+stdev*2.4 lower2=meas-stdev*1.2 upper3=meas+stdev*0.5 lower3=meas-stdev*0.5

days4=days3[:-1]

1. days4=days3

1. plt.plot(days4,meas)

1. plt.plot(days4,expmeas)

1. plt.plot(days4,upper)
2. plt.plot(days4,lower)

plt.fill_between(days4,upper2, lower2,color="#ffa0a0") plt.fill_between(days4,upper, lower,color="#af8080") plt.fill_between(days4,upper3, lower3,color="#8f6060")

plt.plot(days4,meas)

plt.scatter(days1,cases, color='k') dateformat1 = mdates.DateFormatter('%d.%m')

plt.xticks(fontsize=16)

plt.yticks(fontsize=16, rotation=0) ax1=plt.gca() plt.title("Koronavirustapauksia - Fourier-ennuste", fontsize=18) ax1.set_xlabel('Pvm', color='g',size=18) ax1.set_ylabel('Koronatapauksia päivässä', color='#800000',size=18) ax1.xaxis.set_major_formatter(dateformat1)

plt.ylim(0,ymax1) plt.show() print(".")