def main():
namefile=["sol3ghz.dat","surftemp504.txt", "covidbrasil.dat"]
ilist=namefile
d=[]
for file in namefile:
fileread=open(file)
y=[]
for line in fileread:
y.append(float(line))
alfa,xdfa,ydfa, reta = funcs.dfa1d(y,1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = funcs.psd(y)
d.append([funcs.variance(y), funcs.skewness(y), funcs.kurtosis(y)+3, alfa, index, 0])
QTD=10
d2,ilist2,rawdata=makeseries(randomseries, [8192],QTD)
for i in d2:
d.append(i)
for j in ilist2:
ilist.append(j)
d2,ilist2,rawdata=makeseries(powerlaw_psd_gaussian, range(0,3), QTD//3)
for i in d2:
d.append(i)
for j in ilist2:
ilist.append(j)
d2,ilist2,rawdata=makeseries(pmodel, ["Exogenous", "Endogenous"] , QTD//2)
for i in d2:
d.append(i)
for j in ilist2:
ilist.append(j)
d2,ilist2,rawdata=makeseries(LogisticMap, ["Logistic"], QTD//2)
for i in d2:
d.append(i)
for j in ilist2:
ilist.append(j)
d2,ilist2,rawdata=makeseries(HenonMap, ["Henon"], QTD//2)
for i in d2:
d.append(i)
for j in ilist2:
ilist.append(j)
s2=[]
k=[]
alpha=[]
beta=[]
for i in range(len(d)):
s2.append(d[i][1]**2)
k.append(d[i][2])
alpha.append(d[i][3])
beta.append(d[i][4])
makespaces(s2,k,alpha,beta,"All signals and Brazil Covid data", "Brazil", ilist)
return d
def makeseries(func, iterationlist, amount):
values=[]
ilist=[]
rawdata=[]
for i in iterationlist:
for j in range(amount):
x,y=func(i)
alfa,xdfa,ydfa, reta = funcs.dfa1d(y,1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = funcs.psd(y)
values.append([funcs.variance(y), funcs.skewness(y), funcs.kurtosis(y)+3, alfa, index])
ilist.append(i)
rawdata.append([i,x,y, alfa, xdfa, ydfa, reta, freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM])
return values, ilist, rawdata
##############################################################################
################################### MAIN #####################################
##############################################################################
namefile="daily-cases-covid-19.csv"
l=pd.read_csv(namefile)
codes=list(set(l["Entity"]))
codes=codes[1:]
l=l.set_index("Entity")
values=[]
countries=["Brazil", "India", "Iran", "South Africa", "Egypt" ]
for i in codes:
y=list(l.filter(like=i, axis=0)["Daily confirmed cases (cases)"])
if len(y) > 50:
alfa,xdfa,ydfa, reta = funcs.dfa1d(y,1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = funcs.psd(y)
values.append([funcs.variance(y), funcs.skewness(y), funcs.kurtosis(y), alfa, index, mfdfa.makemfdfa(y), i])
skew2=[]
alfa=[]
kurt=[]
index=[]
psi=[]
for i in range(len(values)):
skew2.append(values[i][1]**2)
kurt.append(values[i][2])
alfa.append(values[i][3])
index.append(values[i][6])
skew2=np.array(skew2)
