File:Suomen koronavirustapaukset ja kuolemat paivittain syksy 2020 1.svg
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Summary
[edit]DescriptionSuomen koronavirustapaukset ja kuolemat paivittain syksy 2020 1.svg |
English: Suomen koronavirustapaukset ja kuolemat päivittäin syksyllä 2020 |
Date | |
Source | Own work |
Author | Merikanto |
Sources of data: THL, Helsingin Sanomat, Janne Solanpää, ECDC, 28.1.2021
Wikipedian koronaviruspandemian aikajana
https://fi.wikipedia.org/wiki/Suomen_koronaviruspandemian_aikajana
Internetin COVID-19 data aggregaatti
https://datahub.io/core/covid-19/r/countries-aggregated.csv
Janne Solanpään ennustesivu
josta
https://covid19.solanpaa.fi/data/fin_cases.json
Myös THL avoin data:
paivittaiset_tapaukset="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-444833&column=dateweek20200101-508804L"
paivittaiset_kuolemat="https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=measure-492118&column=dateweek20200101-508804L"
Python code to produce graph
- COVID-19 statistics from aggregated data from net site
- with Python
- Input from internet site: cases, recovered, deaths.
- Calculates active cases.
- version 0000.0009
- 16.2.2020
-
- parametrit
paiva1="2020-09-01"
paiva2="2021-02-12"
ymax1=800
ymax2=30
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):
#left, right = plt.xlim()
fig, ax1 = plt.subplots(constrained_layout=True)
ax1.tick_params(axis='both', which='major', labelsize=15)
ax1.set_xlabel('Päivämäärä', color='g',size=18)
ax1.set_ylabel('Päivittäiset uudet tapaukset', color='#7f0000',size=18)
ax1.set_title('Koronavirustapaukset Suomessa', color='b',size=22)
ax1.plot(paivat, tapaukset, linewidth=6.5, color='#af0000', label="Päivittäiset tapaukset")
ax2 = ax1.twinx()
ax1.set_ylim(0,ymax1)
ax2.set_ylim(0,ymax2)
ax2.set_ylabel('Päivittäiset kuolemat', color='black',size=18)
ax2.tick_params(axis='both', which='major', labelsize=15)
ax2.bar(paivat,kuolemat, linewidth=2, color='black',label="Päivittäiset kuolemat")
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 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)
- quit(-1)
- df=get_thl_fi_open_data()
df=get_solanpaa_fi_data()
df.to_csv (r'kovadata0.csv', index = True, header=True, sep=';')
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=';')
dates0=df2['Date']
- cases0=df2['Daily_Cases']
dailycases1=df2['Uusia_tapauksia']
dailydeaths1=df2['Uusia_kuolemia']
- dailycases1=df2['Daily_Cases']
- dailydeaths1=df2['Daily_Deaths']
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)
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)
print(".")
Licensing
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- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
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current | 08:22, 12 March 2021 | 961 × 365 (84 KB) | Merikanto (talk | contribs) | Update | |
13:10, 26 February 2021 | 1,040 × 431 (82 KB) | Merikanto (talk | contribs) | Update | ||
13:31, 16 February 2021 | 881 × 379 (77 KB) | Merikanto (talk | contribs) | Upload | ||
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08:16, 27 January 2021 | 994 × 515 (73 KB) | Merikanto (talk | contribs) | Upload | ||
14:07, 10 January 2021 | 830 × 403 (70 KB) | Merikanto (talk | contribs) | Update of layout | ||
13:15, 10 January 2021 | 930 × 424 (70 KB) | Merikanto (talk | contribs) | Update | ||
11:26, 31 December 2020 | 854 × 469 (112 KB) | Merikanto (talk | contribs) | Update of graph | ||
12:08, 16 December 2020 | 935 × 407 (108 KB) | Merikanto (talk | contribs) | Update of graph | ||
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