File:Suomen korona epidemia parantuneita sairaana kuolleita tapauksia syksy 2020.svg
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Summary
[edit]DescriptionSuomen korona epidemia parantuneita sairaana kuolleita tapauksia syksy 2020.svg |
English: Suomen koronaepidemia THL:n datan mukaan syksyllä 2020. |
Date | |
Source | Own work |
Author | Merikanto |
Source of data is covid-19 aggregate datta in net
Python3 code to produce image
- COVID-19 statistics from aggregated data from net site
- with Python
- Input from internet site: cases, recovered, deaths.
- Calculates active cases.
- v. 2000.08
- 13.5.2021
-
apv="2020-07-01"
lpv="2021-05-12"
paiva1=apv
paiva2=lpv
import math as math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import locale
from datetime import datetime, timedelta
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 bs4 import BeautifulSoup
import requests
from scipy.signal import savgol_filter
from scipy import interpolate
import scipy.signal
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)
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 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)
- set dates
- main proge
- df=load_fin_wiki_data()
df=get_solanpaa_fi_data()
- print(df)
- quit(-1)
df2=cut_by_dates(df, paiva1,paiva2)
- print(df2)
- quit(-1)
dates0=df2['Date']
- cases0=df2['Daily_Cases']
dailycases1=df2['Uusia_tapauksia']
dailydeaths1=df2['Uusia_kuolemia']
konf1=np.array(df2['Tapauksia'])
rekov1=np.array(df2['Toipuneita'])
death1=np.array(df2['Kuolemia'])
konf_alussa=konf1[0]
rekov_alussa=rekov1[0]
death_alussa=death1[0]
konf2=np.array(konf1)-konf_alussa
- print(konf2)
- quit(-1)
rekov2=np.array(rekov1)-rekov_alussa
death2=np.array(death1)-death_alussa
aktiv_b=np.array(konf2)-np.array(rekov2)-np.array(death2)
aktiv_c = scipy.signal.savgol_filter(aktiv_b,7, 1)
dates_a=dates0
fig, ax = plt.subplots(constrained_layout=True)
ax.legend(fontsize=14)
ax2=ax.twinx()
- secax = ax.secondary_xaxis('top')
- secax.set_xlabel('paivia')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18, rotation=0)
ax.set_xlabel('Kuukausi 2020-2021', color='g',size=16)
ax.set_ylabel('Kuolleita', color='k',size=18)
ax.set_title('Koronavirusepidemia', color='b',size=22)
ax2.set_ylabel('Tapauksia', color='#7f0000', size=18)
ax.tick_params(axis='both', which='major', labelsize=18)
ax2.plot(dates_a, konf2,'y.-',linewidth=4.0, label='tapauksia')
ax2.plot(dates_a, aktiv_c,'r-',linewidth=4.0, label='sairaana')
ax2.plot(dates_a, rekov2,'g-',linewidth=4.0, label='akuutein vaihe ohi')
ax.plot(dates_a, death2,'k-',linewidth=4.0, label='kuolleita')
locator1 = mdates.MonthLocator() # every month
dateformat1 = mdates.DateFormatter('%b')
ax.xaxis.set_major_formatter(dateformat1)
ax.xaxis.set_major_locator(locator1)
ax2.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.legend(fontsize=14, loc="upper left")
ax2.legend(fontsize=14, loc="center left")
plt.show()
plt.savefig('kuva.svg')
Licensing
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- to share – to copy, distribute and transmit the work
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- 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.
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 06:56, 16 July 2021 | 1,282 × 589 (104 KB) | Merikanto (talk | contribs) | update | |
07:31, 25 June 2021 | 1,143 × 570 (104 KB) | Merikanto (talk | contribs) | Update | ||
07:58, 4 June 2021 | 1,092 × 375 (95 KB) | Merikanto (talk | contribs) | Update | ||
13:40, 13 May 2021 | 1,135 × 437 (94 KB) | Merikanto (talk | contribs) | Update | ||
11:49, 22 April 2021 | 947 × 398 (89 KB) | Merikanto (talk | contribs) | Update | ||
12:48, 16 April 2021 | 1,031 × 432 (87 KB) | Merikanto (talk | contribs) | update | ||
12:19, 23 March 2021 | 1,110 × 431 (85 KB) | Merikanto (talk | contribs) | Upload | ||
19:30, 26 February 2021 | 992 × 432 (76 KB) | Merikanto (talk | contribs) | Upload | ||
14:25, 22 January 2021 | 884 × 473 (74 KB) | Merikanto (talk | contribs) | Update | ||
11:57, 22 January 2021 | 913 × 455 (68 KB) | Merikanto (talk | contribs) | Update of layout |
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