File:Suomen koronavirustapaukset ja kuolemat paivittain kevat 2022 1.svg
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[edit]DescriptionSuomen koronavirustapaukset ja kuolemat paivittain kevat 2022 1.svg |
Suomi: Suomen koronavirustapaukset ja kuolemat päivittäin kevät 2021 |
Date | |
Source | Own work |
Author | Merikanto |
code to make image
- COVID-19 statistics from aggregated data from net site
- with Python
- Input from internet site: cases, recovered, deaths.
- Calculates active cases.
- version 0000.0015
- 12.5.2022
-
- parametrit
paiva1="2022-01-01"
paiva2="2022-07-07"
ymax1=15000
ymax2=50
- paiva1="2021-04-01"
- paiva2="2021-06-29"
- ymax1=400
- ymax2=10
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 dateutil import rrule, parser
from scipy import interpolate
import scipy.signal
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator, MaxNLocator)
from scipy.signal import savgol_filter
from bs4 import BeautifulSoup
import requests
import json
- locale.setlocale(locale.LC_ALL, 'fi_FI')
def format_func(value, tick_number):
N = int(np.round(value/10))
if N == 0:
return "0"
else:
return r"${0}\pv$".format(N)
- very basic exponential r0 calculation
def calculate_r0(time1, time2, val1, val2):
k=0
td=time2-time1
##
#optim
#td=1
gr0=math.log(val2/val1)
gr=gr0/td
if(gr!=0):
td= math.log(2.0)/gr
else:
return(1)
tau=5.0
k=math.log(2.0)/td
r0=math.exp(k*tau)
if(r0==32):
r0=1
if(r0>32):
r0=4
return(r0)
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 plottaa_tapaukset_kuolemat(paivat, tapaukset, kuolemat, kuolemat_liuku):
#left, right = plt.xlim()
fig, ax1 = plt.subplots(constrained_layout=True)
ax1.set_ylim(0,ymax2)
ax1.set_ylabel('Päivittäiset kuolemat', color='black',size=18)
ax1.tick_params(axis='both', which='major', labelsize=15)
ax1.bar(paivat,kuolemat, linewidth=2, color='black',label="Päivittäiset kuolemat")
ax1.plot(paivat,kuolemat_liuku, linewidth=20, color='grey', label="Kuolemat viikon keskiarvo")
ax2 = ax1.twinx()
ax2.set_ylim(0,ymax1)
ax2.tick_params(axis='both', which='major', labelsize=15)
ax2.set_xlabel('Päivämäärä', color='g',size=18)
ax2.set_ylabel('Päivittäiset uudet tapaukset', color='#7f0000',size=18)
ax2.set_title('Koronavirustapaukset Suomessa', color='b',size=22)
ax2.plot(paivat, tapaukset, linewidth=6.5, color='#af0000', label="Päivittäiset tapaukset")
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines1 + lines2, labels1 + labels2, loc='upper left', fontsize=16)
locator1 = mdates.MonthLocator()
dateformat1 = mdates.DateFormatter('%d.%m')
ax1.xaxis.set_major_formatter(dateformat1)
ax1.xaxis.set_major_locator(locator1)
ax2.yaxis.set_major_locator(MaxNLocator(integer=True))
plt.show()
plt.savefig('kuva.svg')
return(0)
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"]
data = {'Date':dada1,
'Tapauksia':casa1,
'Kuolemia':death1,
'Sairaalassa':hosp1,
'Teholla':icu1,
'Uusia_tapauksia':newcasa1,
'Uusia_kuolemia':newdeath1,
'R':rt,
'Uusia_tapauksia_ennuste':newcasauks,
}
df = pd.DataFrame(data)
return(df)
def get_ecdc_fi_hospital_data():
url="https://opendata.ecdc.europa.eu/covid19/hospitalicuadmissionrates/json/"
response = requests.get(url,allow_redirects=True)
open('ecdc_hoic.json', 'w').write(response.text)
with open('ecdc_hoic.json') as f:
sola1=pd.read_json(f)
#print(sola1.head())
sola2=sola1.loc[sola1["country"]=='Finland']
#sola2.to_csv (r'ecdc_hospital_finland_origo.csv', index = True, header=True, sep=';')
#print(sola2.head())
dada0=sola2["date"]
hosp0=sola2["value"]
country0=sola2["country"]
len1=len(dada0)
len2=int(len1/2)
#print (len2)
dada1=dada0[1:len2-1]
hosp1=np.array(hosp0[1:len2-1])
icu1=np.array(hosp0[len2:len1])
#print(dada1)
print (icu1)
quit(-1)
data = {'Date':dada1,
'Sairaalassa':hosp1,
'Teholla':icu1
}
df = pd.DataFrame(data)
df.to_csv (r'ecdc_hospital_finland.csv', index = True, header=True, sep=';')
return df
def get_thl_fi_open_data():
## thl open data, 1.2.2021
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}
response1 = requests.get(url1,headers=headers,allow_redirects=True)
open('thl_cases1.json', 'w').write(response1.text)
with open('thl_cases1.json') as json_file1:
data1 = json.load(json_file1)
#print(data1)
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}
response2 = requests.get(url2,headers=headers,allow_redirects=True)
open('thl_deaths1.json', 'w').write(response2.text)
with open('thl_deaths1.json') as json_file2:
data2 = json.load(json_file2)
#print(data1)
k2=data1['dataset']
k3=k2['dimension']
k4=k3['dateweek20200101']
k5=k4['category']
k6=k5['label']
k8a=k6.keys()
k8b=k6.values()
d1=k2['value']
m2=data2['dataset']
m3=m2['dimension']
m4=m3['dateweek20200101']
m5=m4['category']
m6=m5['label']
m8a=m6.keys()
m8b=m6.values()
d2=m2['value']
#print (d1)
d1a=d1.keys()
d1b=d1.values()
d2a=d2.keys()
d2b=d2.values()
#print (k8b)
#print (d1a)
#print (d1b)
#print (d2a)
#print (d2b)
len1=len(k8b)
#print(len1)
#dates0=np.datetime64(np.array(list(k8b)))
dates0=list(k8b)
casekeys=np.array(list(d1a)).astype(int)
cases0=np.array(list(d1b)).astype(int)
deathkeys=np.array(list(d2a)).astype(int)
deaths0=np.array(list(d2b)).astype(int)
#print(dates0)
#print(casekeys)
#print(cases0)
kasetab1=np.empty(len1).astype(int)
kasetab1[casekeys]=cases0
deathtab1=np.empty(len1).astype(int)
deathtab1[deathkeys]=deaths0
#print (len(dates0))
#print (len(kasetab1))
datax = {'Date':dates0,
'Uusia_tapauksia':kasetab1,
'Uusia_kuolemia':deathtab1
}
df = pd.DataFrame(datax)
return(df)
def load_thl_hospital_data():
#url1="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"
url_base1="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19care/fact_epirapo_covid19care.json"
#request1="?row=dateweek20200101-508804L&column=measure-547523.547516.547531.456732.&fo=1"
requesta=['?row=dateweek20200101-508804L&column=measure-547523','?row=dateweek20200101-508804L&column=measure-547516','?row=dateweek20200101-508804L&column=measure-547531','?row=dateweek20200101-508804L&column=measure-456732']
#request1="?row=dateweek20200101-508804L&column=measure-547523" ## esh
n=0
for x in requesta:
request1=requesta[n]
outname1='thl_hospital_'+str(n)+'.json'
url1=url_base1+request1
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}
response1 = requests.get(url1,headers=headers,allow_redirects=True)
open(outname1, 'w').write(response1.text)
n=n+1
datetabs=[]
kasetabs=[]
for n in range(0,4):
outname1='thl_hospital_'+str(n)+'.json'
with open(outname1) as json_file1:
data1 = json.load(json_file1)
#print(data1)
k2=data1['dataset']
k3=k2['dimension']
k4=k3['dateweek20200101']
k5=k4['category']
k6=k5['label']
k8a=k6.keys()
k8b=k6.values()
d1=k2['value']
#print(d1)
#print (json.dumps(k2))
#quit(-1)
d1a=d1.keys()
d1b=d1.values()
len1=len(k8b)
#print(len1)
#dates0=np.datetime64(np.array(list(k8b)))
dates0=list(k8b)
casekeys=np.array(list(d1a)).astype(int)
cases0=np.array(list(d1b)).astype(int)
kasetab1=np.empty(len1).astype(int)
kasetab1[casekeys]=cases0
kasetab1[kasetab1<0] = 0
kasetab1[kasetab1>100000] = 0
datetabs.append(dates0)
kasetabs.append(kasetab1)
daates=datetabs[0]
teholla=kasetabs[3]
sairaalassa=kasetabs[0]+kasetabs[1]+kasetabs[2]
sairaalassa=sairaalassa+teholla
datax = {'Date':dates0,
'Sairaalassa':sairaalassa,
'Teholla':teholla
}
df = pd.DataFrame(datax)
return(df)
def cut_country_data_by_current(dfx, start_date):
mask = (dfx['Date'] >= start_date)
dfx2 = dfx.loc[mask]
dfx2.drop(df.tail(1).index,inplace=True)
#print(dfx2)
return(dfx2)
- main proge
- df=load_country_cases("Finland")
- df.to_csv (r'kovadata1.csv', index = True, header=True, sep=';')
- df=load_fin_wiki_data()
- print(df)
- df=load_thl_hospital_data()
- datel=df['Date']
- sairal=df['Sairaalassa']
- tehol=df['Tehol']
- df=get_thl_fi_open_data()
df=get_solanpaa_fi_data()
df.to_csv (r'kovadata0.csv', index = True, header=True, sep=';', decimal=",")
df2=cut_by_dates(df, paiva1,paiva2)
- df2=cut_country_data_by_current(df, paiva1)
print(df2)
- quit(-1)
df2.to_csv (r'kovadata2.csv', index = True, header=True, sep=';', decimal=",")
dates0=df2['Date']
- cases0=df2['Daily_Cases']
dailycases1=df2['Uusia_tapauksia']
dailydeaths1=df2['Uusia_kuolemia']
- dailycases1=df2['Daily_Cases']
- dailydeaths1=df2['Daily_Deaths']
df3 = df2"Date","Sairaalassa", "Teholla","Uusia_kuolemia","Uusia_tapauksia"
- dates1=dates0.dt.strftime('%d.%m.%Y')
dates1=dates0.dt.strftime('%d.%m.')
df3["Date"]=dates1
df3.rename(columns = {'Date' : 'Pvm' }, inplace = True)
num = df3._get_numeric_data()
num[num < 0] = 0
- print(dates1)
- quit(-1)
print(df3)
df3.to_csv (r'epidemia.csv', index = False, header=True, sep=';', decimal=",")
date1 = paiva1
date2 = paiva2
datesx = list(rrule.rrule(rrule.DAILY, dtstart=parser.parse(date1), until=parser.parse(date2)))
- dates_a=dates0
dates_a=datesx
dailycases_savgol_1 = scipy.signal.savgol_filter(dailycases1,7, 1)
dailydeaths_savgol_1 = scipy.signal.savgol_filter(dailydeaths1,7, 1)
pos1=len(dailycases_savgol_1)-2
time2=pos1-0
time1=pos1-21
val1=dailycases_savgol_1[time1]
val2=dailycases_savgol_1[time2]
ro00=calculate_r0(time1, time2, val1, val2)
ro=round(ro00,2)
print("R0 = ",ro)
plottaa_tapaukset_kuolemat(dates_a, dailycases1, dailydeaths1, dailydeaths_savgol_1)
print(".")
Licensing
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 16:25, 23 July 2022 | 977 × 521 (80 KB) | Merikanto (talk | contribs) | Update | |
18:01, 13 April 2022 | 957 × 435 (66 KB) | Merikanto (talk | contribs) | update | ||
17:43, 13 April 2022 | 1,112 × 545 (68 KB) | Merikanto (talk | contribs) | Update | ||
07:36, 15 March 2022 | 826 × 496 (62 KB) | Merikanto (talk | contribs) | Update | ||
06:30, 16 February 2022 | 995 × 511 (91 KB) | Merikanto (talk | contribs) | Update | ||
18:56, 11 January 2022 | 1,039 × 432 (127 KB) | Merikanto (talk | contribs) | Update od data | ||
15:30, 7 December 2021 | 1,215 × 432 (120 KB) | Merikanto (talk | contribs) | Update | ||
07:17, 25 October 2021 | 989 × 410 (105 KB) | Merikanto (talk | contribs) | update | ||
05:07, 6 October 2021 | 1,139 × 444 (110 KB) | Merikanto (talk | contribs) | Update | ||
06:00, 20 September 2021 | 978 × 480 (106 KB) | Merikanto (talk | contribs) | Update |
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