def makeseries(func, iterationlist, amount, title="Nada", graphs=False):
values = []
ilist = []
rawdata = []
for i in iterationlist:
for j in range(amount):
x, y = func(i)
alfa, xdfa, ydfa, reta = statsfuncs.dfa1d(y, 1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = statsfuncs.psd(
y)
psi, alphal, falpha = mfdfa.makemfdfa(y)
values.append([
statsfuncs.variance(y),
statsfuncs.skewness(y),
statsfuncs.kurtosis(y) + 3, alfa, index, psi
])
ilist.append(i)
if graphs == True:
plt.plot(alphal, falpha, 'ko-', label=str(j))
if graphs == True:
plt.title("Espectro de Singularidade, para: {} {}".format(
title, i))
plt.xlabel(r'$\alpha$')
plt.ylabel(r'$f(\alpha)$')
plt.grid('on', which='major')
plt.savefig("{}singularityspectrum{}".format(title, i))
plt.show()
rawdata.append([
i, x, y, alfa, xdfa, ydfa, reta, freqs, power, xdata, ydata, amp,
index, powerlaw, INICIO, FIM
])
return values, ilist, rawdata
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)
psi=mfdfa.makemfdfa(y)
values.append([funcs.variance(y), funcs.skewness(y), funcs.kurtosis(y)+3, alfa, index,psi])
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)
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)
codes = list(set(l["Entity"]))
codes = codes[1:]
l = l.set_index("Entity")
values = []
countries = ["Belgium", "Brazil", "France", "Portugal", "Spain"]
for i in codes:
y = list(l.filter(like=i, axis=0)["Daily confirmed cases (cases)"])
if i in countries:
result = waipy.cwt(y, 1, 1, 0.125, 2, 4 / 0.125, 0.72, 6, 'DOG', "x")
waipy.wavelet_plot(i, range(len(y)), y, 0.03125, result, savefig=True)
if len(y) > 50:
SOC(y, i)
alfa, xdfa, ydfa, reta = statsfuncs.dfa1d(y, 1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = statsfuncs.psd(
y)
a, b, c = mfdfa.makemfdfa(y)
values.append([
statsfuncs.variance(y),
statsfuncs.skewness(y),
statsfuncs.kurtosis(y), alfa, index, a, i
])
plt.show()
skew2 = []
alfa = []
kurt = []
index = []
psi = []
for i in range(len(values)):
skew2.append(values[i][1]**2)
kurt.append(values[i][2])
x.append(y[-2] + 1.0 - a * x[-1] * x[-1])
return y
grng = []
endo = []
exo = []
logis = []
henon = []
white = []
pink = []
red = []
plt.title("Espectro de singularidade: GRNG")
for i in range(30):
a, b, c = mfdfa.makemfdfa(randomseries(8192), False)
plt.plot(b, c, 'ko-')
plt.xlabel(r"$\alpha$")
plt.legend()
plt.ylabel(r"f($\alpha$)")
plt.show()
plt.title("Espectro de singularidade: pmodel")
for i in range(15):
if i == 0:
a, b, c = mfdfa.makemfdfa(pmodel("Endogenous"))
plt.plot(b, c, 'ro-', label="Endogenous")
a, b, c = mfdfa.makemfdfa(pmodel("Exogenous"))
plt.plot(b, c, 'bo-', label="Exogenous")
else:
a, b, c = mfdfa.makemfdfa(pmodel("Endogenous"))
plt.plot(b, c, 'ro-')
