1. 3. COVID-19 Finland short-term forecast
   4. based on data from other countries
   5. Python 3 script
   7. Input from internet site: cases, recovered, deaths.
   9. version 0001.0003
   10. 12.1.2021

import math as math import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker

import locale from datetime import datetime, timedelta import matplotlib.dates as mdates from scipy import interpolate import scipy.signal from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator, MaxNLocator)

from scipy.signal import savgol_filter

1. 1. ggplot
2. from plotnine import *

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

from bs4 import BeautifulSoup import requests import locale from datetime import datetime, timedelta import matplotlib.dates as mdates

import matplotlib.ticker as ticker import matplotlib.dates as mdates from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator)

from matplotlib.ticker import MaxNLocator from matplotlib.ticker import ScalarFormatter from statsmodels.tsa.statespace.sarimax import SARIMAX from statsmodels.tsa.arima.model import ARIMA from numpy import fft

import statistics

from scipy.interpolate import UnivariateSpline from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn import svm, metrics from pygam import LogisticGAM from pygam import LinearGAM from sklearn.neighbors import KernelDensity from scipy.interpolate import pchip_interpolate from scipy import stats from datetime import date from scipy.optimize import curve_fit

1. 1. 1. 2. asetukset parameters

1. 1. pohjadatan rajat: limits of base data

1. paiva1="2021-01-01"
2. paiva2="2021-03-19"

1. 1. ennusteen rajat: forecast limits
2. taika1="2021-03-19"
3. taika2="2021-06-01"

paiva1="2021-01-01" paiva2="2021-07-12"

1. 1. ennusteen rajat: forecast limits

taika1="2021-07-12" taika2="2021-09-01"

1. 1. y-akselin rajat

ymax1=1200 ymax2=20

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

today = date.today()

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

yesterday = datetime.today() - timedelta(days=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)) 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)

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 catch_forecasting_data(toka, last1): lentoka=len(toka)-2 jono=[] initu=lentoka for m in range(0,10): q=0 for n in range(initu,0, -1): cc=toka[n] jono.append(cc) if (cc<last1): initu=n-1 if(q==0): jono.pop() break

arra00=np.array(jono).astype(int) arra=arra00[::-1] return(arra)

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

1. countries=["Finland", "Denmark", "Israel", "Sweden"]

1. countries=["Finland", "Denmark", "Israel", "France", "Germany", "Italy", "Belgium", "Netherlands", "Portugal", "Austria", "Brazil", "Lithuania", "Mexico", "Argentina", "Latvia"]

countries=["Finland", "Denmark", "Germany"]

num_countries=len(countries)

dfs=[]

for n in range(0,num_countries): #print (countries[n]) dfs.append(load_country_cases(countries[n]))

1. for n in range(0,num_countries):
2. print(dfs[n])

dada=[]

for n in range(0,num_countries): df=dfs[n] #print(df) taily=df['Daily_Cases'] #print(taily) taily2=np.array(taily).astype(int) dada.append(taily2)

1. for n in range(0,num_countries):
2. print(dada[n])

1. 1. last cases num of "Finland" data

eka=dada[0]

numu=30

last1=eka[len(eka)-2]

arras=[]

for n in range(1,num_countries): toka=dada[n] arra=catch_forecasting_data(toka, last1) arras.append(arra)

1. print(arra)

1. quit(-1)

maaras=num_countries-1

parras=[]

for n in range(0,maaras): arra=arras[n] parra=arra[0:numu] #print (parra) parras.append(parra)

supercache=[] for m in range(0,numu): cache=[] for n in range(0,maaras): parra=parras[n] print(parra) mmm=parra[m] cache.append(mmm) supercache.append(np.array(cache).astype(int))

average00=[] deviation00=[] min00=[] max00=[]

for n in range(0,numu): average00.append(statistics.mean(supercache[n])) deviation00.append(statistics.stdev(supercache[n])) min00.append(np.min(supercache[n])) max00.append(np.max(supercache[n]))

average1=np.array(average00).astype(int) deviation1=np.array(deviation00).astype(int) min1=np.array(min00).astype(int) max1=np.array(max00).astype(int)

high1=average1+deviation1/2 low1=average1-deviation1/2

1. ystdev=statistics.stdev(ycasesminusavgol)
2. mean=statistics.mean(ysavgol)

1. print (parra)

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

today = date.today()

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

yesterday = datetime.today() - timedelta(days=1)

timedelta1= np.timedelta64(numu,'D')

beginday=today endday=beginday+timedelta(numu)

days1 = pd.date_range(beginday, endday, periods=numu).to_pydatetime()

1. print (parra)

dateformat1 = mdates.DateFormatter('%d.%m')

plt.xticks(fontsize=16)

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

for n in range(0,maaras): plt.plot(days1, parras[n],color='#800000', alpha=0.1 )

plt.plot(days1, average1,color='#F00', linewidth=3)

plt.fill_between(days1, max1, min1, fc='#FF3F3F', alpha=0.25)

for n in range(0,6): high1=average1+(2*deviation1)/(n+1) low1=average1-(2*deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF3F3F', alpha=n*0.05)

for n in range(0,6): high1=average1+(deviation1)/(n+1) low1=average1-(deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF1F1F', alpha=n*0.05)

1. plt.plot(days1, parras[n],color='#800000', alpha=0.1 )

plt.ylim(0,6000) plt.show()

1. print (last1)

1. 3. covid forecast test program
   4. based on data from other countries
2. python 3 script
3. 1. NOTE: trend must be decreasing
4. 1. Python 3 script
   3. Input from internet site: cases, recovered, deaths.
   5. version 0000.0002
   6. 3.6.2021

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

import matplotlib.pyplot as plt import matplotlib.ticker as ticker import matplotlib.dates as mdates

from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator, MaxNLocator,ScalarFormatter)

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

from datetime import datetime, timedelta, date

import scipy.signal from scipy.signal import savgol_filter from scipy.signal import find_peaks

from bs4 import BeautifulSoup import requests import locale

from country_list import countries_for_language

1. 1. 1. 2. asetukset parameters

1. 1. current covid cases in a day

currentcases=145

1. 1. pohjadatan rajat: limits of base data

paiva1="2020-03-01" paiva2="2021-05-20"

1. 1. y-akselin rajat

1. ymax1=1200
2. ymax2=20

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

today = date.today() - timedelta(days=2)

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

yesterday = datetime.today() - timedelta(days=1)

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

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 find_curve_from_peak(leka):

lenleka1=len(leka)

if(lenleka1==0): taffel=np.zeros(99).astype(int) return (taffel)

last1=leka[len(leka)-2] #print(" Peaks ... ") ekay=np.array(leka).astype(int) ekasmoothedy = savgol_filter(ekay,45, 3) tokasmoothedy0=savgol_filter(ekasmoothedy,15, 3) tokasmoothedy=np.array(tokasmoothedy0).astype(int) ekapeaks = find_peaks(tokasmoothedy, distance=90 ) #print (ekapeaks) #print("P1") ekapeaksx=ekapeaks[0] ekapeaks1=ekapeaks[1] #ekapeaks2=ekapeaks[2] ekapeaksy = [ekasmoothedy[index] for index in ekapeaksx] ekaalen1=len(tokasmoothedy) numpeaks1=len(ekapeaksx)

if(numpeaks1==0): taffel=np.zeros(99).astype(int) return (taffel)

#taffel=np.zeros(ekaalen1).astype(int) taffel0=[]

nn=0

eleni1=ekaalen1-1

for m in range(0,1): #pe=ekapeaksx[m+1] pe=ekapeaksx[m] #print (pe, ekaalen1) edc=1000000 nn=0 for n in range(pe,eleni1): c=tokasmoothedy[n] #print("N",n,"c", c) deltac=edc-c deltadc=deltac-2 if (deltadc>0): #taffel[nn]=int(c) taffel0.append(int(c)) edc=c nn=nn+1

taffel=np.array(taffel0).astype(int)

#plt.plot(taffel)

#plt.show()

paluu=taffel

return(paluu)

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

1. countries=["Finland", "Denmark", "Israel", "Sweden"]

1. countries=["Finland", "Denmark", "Israel", "France", "Germany", "Italy", "Belgium", "Netherlands", "Portugal", "Austria", "Brazil", "Lithuania", "Mexico", "Argentina", "Latvia"]

1. countries=["Finland", "Denmark", "Israel", "France", "Germany", "Italy", "Belgium", "Netherlands", "Portugal", "Austria", "Brazil", "Lithuania", "Mexico", "Argentina", "Latvia"]

1. countries=["Finland", "Denmark", "Israel"]

1. countries=["Finland", "Israel", "Norway", "Denmark"]

1. countries=["Finland"]

kountries = dict(countries_for_language('en'))

lenkountries=len(kountries)-1

1. print(kountries)

print(lenkountries)

1. for n in range(0,lenkountries):
2. kountri= kountries.values()[n]
3. print(kountri)

countries=[]

maxcountries=lenkountries

1. maxcountries=8

nnn=0 for x in enumerate(kountries.items()): kountry=x[1][1] countries.append(kountry) nnn=nnn+1 if(nnn>maxcountries): break

num_countries=len(countries)

print (countries)

1. quit(-1)

dfs=[]

for n in range(0,num_countries): print (countries[n]) dfs.append(load_country_cases(countries[n]))

1. for n in range(0,num_countries):
2. print(dfs[n])

dada=[]

for n in range(0,num_countries): df=dfs[n] #print(df) taily=df['Daily_Cases'] #print(taily) taily2=np.array(taily).astype(int) dada.append(taily2)

1. for n in range(0,num_countries):
2. print(dada[n])

1. 1. cases num of "Finland" data

eka=dada[0]

multitab=[]

num_countries2=0

for n in range(0,num_countries): bufu0=dada[n] taffel=find_curve_from_peak(bufu0) if(np.max(taffel)==0): print("Nuls.") else: multitab.append(taffel) num_countries2=num_countries2+1

num_countries2=num_countries2-1

many0=[] nummany1=0

itemnum=40

for n in range(0,num_countries2): bufu0=np.array(multitab[n]).astype(float) maxi=np.max(bufu0).astype(float) skaled0=bufu0/maxi lennu1=len(skaled0) if(lennu1>itemnum): skaled1=skaled0[0:itemnum] many0.append(skaled1) nummany1=nummany1+1

1. for n in range(0,nummany1):

#yy=many0[n] #yys.append(yy) #plt.plot(yy)

smoothed0=savgol_filter(eka,15, 3)

lensmoothed0=len(smoothed0)-1

1. 1. currentsmoothedcases=smoothed0[lensmoothed0-5]

currentsmoothedcases=currentcases

1. print(eka)

1. print(currentsmoothedcases)

today = date.today()

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

yesterday = datetime.today() - timedelta(days=1)

timedelta1= np.timedelta64(itemnum,'D')

beginday=today endday=beginday+timedelta(itemnum)

days1 = pd.date_range(beginday, endday, periods=itemnum).to_pydatetime()

1. print (parra)

dateformat1 = mdates.DateFormatter('%d.%m')

plt.xticks(fontsize=16)

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

yys=[]

for n in range(0,nummany1): bufu0=many0[n] yy=currentsmoothedcases*bufu0 yys.append(yy)

1. plt.plot(days1, yy,color='#800000', alpha=0.7 )
2. plt.plot(yy)

print ("Process ...")

print(nummany1) print(itemnum)

supercache=[]

for m in range(0,itemnum): cache=[] for n in range(0,nummany1): parra=yys[n] mmm=parra[m] cache.append(mmm)

supercache.append(np.array(cache).astype(int))

average00=[] deviation00=[] min00=[] max00=[]

for n in range(0,itemnum): average00.append(statistics.mean(supercache[n])) deviation00.append(statistics.stdev(supercache[n])) min00.append(np.min(supercache[n])) max00.append(np.max(supercache[n]))

average1=np.array(average00).astype(int) deviation1=np.array(deviation00).astype(int) min1=np.array(min00).astype(int) max1=np.array(max00).astype(int)

high1=average1+deviation1/2 low1=average1-deviation1/2

for n in range(0,nummany1): yy=yys[n] plt.plot(days1,yy,color='#800000', alpha=0.3)

plt.plot(days1, average1,color='#F00', linewidth=3)

plt.fill_between(days1, max1, min1, fc='#FF3F3F', alpha=0.25)

for n in range(0,6): high1=average1+(2*deviation1)/(n+1) low1=average1-(2*deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF3F3F', alpha=n*0.05)

for n in range(0,6): high1=average1+(deviation1)/(n+1) low1=average1-(deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF1F1F', alpha=n*0.05)

plt.show()

print(".")

1. 3. COVID-19 Finland short-term forecast
   4. based on data from other countries
   5. Python 3 script
   7. Input from internet site: cases, recovered, deaths.
   9. version 0001.0004
   10. 23.3.2021

import math as math import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker

import locale from datetime import datetime, timedelta import matplotlib.dates as mdates from scipy import interpolate import scipy.signal from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator, MaxNLocator)

from scipy.signal import savgol_filter

1. 1. ggplot

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

from bs4 import BeautifulSoup import requests import locale from datetime import datetime, timedelta import matplotlib.dates as mdates

import matplotlib.ticker as ticker import matplotlib.dates as mdates from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,

                              AutoMinorLocator)

from matplotlib.ticker import MaxNLocator from matplotlib.ticker import ScalarFormatter from statsmodels.tsa.statespace.sarimax import SARIMAX from statsmodels.tsa.arima.model import ARIMA from numpy import fft

import statistics

from scipy.interpolate import UnivariateSpline from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn import svm, metrics from pygam import LogisticGAM from pygam import LinearGAM from sklearn.neighbors import KernelDensity from scipy.interpolate import pchip_interpolate from scipy import stats from datetime import date from scipy.optimize import curve_fit

1. 1. 1. 2. asetukset parameters

1. 1. pohjadatan rajat: limits of base data

paiva1="2020-11-15" paiva2="2021-01-20"

1. 1. ennusteen rajat: forecast limits

taika1="2021-01-20" taika2="2021-02-20"

1. 1. y-akselin rajat

ymax1=1200 ymax2=20

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

today = date.today()

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

yesterday = datetime.today() - timedelta(days=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)) 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)

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 catch_forecasting_data(toka, last1): lentoka=len(toka)-2 jono=[] initu=lentoka for m in range(0,10): q=0 for n in range(initu,0, -1): cc=toka[n] jono.append(cc) if (cc<last1): initu=n-1 if(q==0): jono.pop() break

arra00=np.array(jono).astype(int) arra=arra00[::-1] return(arra)

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

1. countries=["Finland", "Denmark", "Israel", "Sweden"]

1. countries=["Finland", "Denmark", "Israel", "France", "Germany", "Italy", "Belgium", "Netherlands", "Portugal", "Austria", "Brazil", "Lithuania", "Mexico", "Argentina", "Latvia"]

countries=["Finland", "Denmark", "Israel", "France", "Germany", "Italy", "Belgium", "Netherlands", "Portugal", "Austria", "Brazil", "Lithuania", "Mexico", "Argentina", "Latvia"]

num_countries=len(countries)

dfs=[]

for n in range(0,num_countries): #print (countries[n]) dfs.append(load_country_cases(countries[n]))

1. for n in range(0,num_countries):
2. print(dfs[n])

dada=[]

for n in range(0,num_countries): df=dfs[n] #print(df) taily=df['Daily_Cases'] #print(taily) taily2=np.array(taily).astype(int) dada.append(taily2)

1. for n in range(0,num_countries):
2. print(dada[n])

1. 1. last cases num of "Finland" data

eka=dada[0]

numu=30

last1=eka[len(eka)-2]

arras=[]

for n in range(1,num_countries): toka=dada[n] arra=catch_forecasting_data(toka, last1) arras.append(arra)

print(arra)

1. quit(-1)

maaras=num_countries-1

parras=[]

maaraz=0

for n in range(0,maaras): arra=arras[n] parra=arra[0:numu] print (parra) qualik=len(parra) if (qualik>12): parras.append(parra) maaraz=maaraz+1

print ("Precess")

supercache=[] for m in range(0,numu): cache=[] for n in range(0,maaraz): parra=parras[n] print (parra) mmm=parra[m] cache.append(mmm) supercache.append(np.array(cache).astype(int))

average00=[] deviation00=[] min00=[] max00=[]

for n in range(0,numu): average00.append(statistics.mean(supercache[n])) deviation00.append(statistics.stdev(supercache[n])) min00.append(np.min(supercache[n])) max00.append(np.max(supercache[n]))

average1=np.array(average00).astype(int) deviation1=np.array(deviation00).astype(int) min1=np.array(min00).astype(int) max1=np.array(max00).astype(int)

high1=average1+deviation1/2 low1=average1-deviation1/2

1. ystdev=statistics.stdev(ycasesminusavgol)
2. mean=statistics.mean(ysavgol)

1. print (parra)

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

today = date.today()

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

yesterday = datetime.today() - timedelta(days=1)

timedelta1= np.timedelta64(numu,'D')

beginday=today endday=beginday+timedelta(numu)

days1 = pd.date_range(beginday, endday, periods=numu).to_pydatetime()

1. print (parra)

dateformat1 = mdates.DateFormatter('%d.%m')

plt.xticks(fontsize=16)

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

for n in range(0,maaraz): plt.plot(days1, parras[n],color='#800000', alpha=0.1 )

plt.plot(days1, average1,color='#F00', linewidth=3)

plt.fill_between(days1, max1, min1, fc='#FF3F3F', alpha=0.25)

for n in range(0,6): high1=average1+(2*deviation1)/(n+1) low1=average1-(2*deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF3F3F', alpha=n*0.05)

for n in range(0,6): high1=average1+(deviation1)/(n+1) low1=average1-(deviation1)/(n+1) plt.fill_between(days1, high1, low1, fc='#FF1F1F', alpha=n*0.05)

1. plt.plot(days1, parras[n],color='#800000', alpha=0.1 )

plt.ylim(0,6000) plt.show()

1. print (last1)