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) alfa=np.array(alfa)
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])