def Burr(c, k, tag=None):
"""
A Burr random variate
Parameters
----------
c : scalar
The first shape parameter
k : scalar
The second shape parameter
"""
assert c>0 and k>0, 'Burr "c" and "k" parameters must be greater than zero'
return uv(ss.burr(c, k), tag=tag)
def Burr(c, k, tag=None):
"""
A Burr random variate
Parameters
----------
c : scalar
The first shape parameter
k : scalar
The second shape parameter
"""
assert c > 0 and k > 0, 'Burr "c" and "k" parameters must be greater than zero'
return uv(ss.burr(c, k), tag=tag)
def gen_f(data_E, update=False):
# polynomial weight
w0 = np.load('w0.npy')
# f_e weight (familarity_exploration)
w = np.load('w.npy')
# f weight (familarity_exploration)
w1 = np.load('w1.npy')
# risk and delay
k = np.load('k.npy')
w2 = np.load('w2.npy')
#time
w_t = np.load('w_t.npy')
# burr
m1 = np.load('m1.npy')
m2 = np.load('m2.npy')
m3 = np.load('m3.npy')
m4 = np.load('m4.npy')
w_c = np.load('w_c.npy')
w_cc = np.load('w_cc.npy')
w_e = np.load('w_e.npy')
w_far = np.load('w_far.npy')
if update == 1:
np.save('w0_old.npy', w0)
np.save('w_old.npy', w)
np.save('w1_old.npy', w1)
np.save('w2_old.npy', w2)
np.save('k_old.npy', k)
np.save('w_t_old.npy', w_t)
m1 = np.save('m1.npy', m1)
m2 = np.save('m2.npy', m2)
m3 = np.save('m3.npy', m3)
m4 = np.save('m4.npy', m4)
model_update(data_E)
stress = np.int8(data_E.iloc[:, 31])
tired = np.int8(data_E.iloc[:, 32])
st = np.int8(data_E.iloc[:, 31:33])
choice3 = np.int8(data_E.iloc[:, 37])
choice4 = np.int8(data_E.iloc[:, 38])
tend = np.int8(choice3) + np.int8(choice4)
x1 = stress
x2 = tired
f1 = w0[0] + w0[1] * np.int8(x1) + w0[2] * np.int8(x2) + w0[3] * np.int8(
x1)**2 + w0[4] * np.int8(x1) * np.int8(x2) + w0[5] * np.int8(x2)**2
f = data_E.iloc[:, 21:31]
f_e = np.zeros((data_E.shape[0], 10))
rv1 = burr(m1[0], m1[1], m1[2], m1[3])
rv2 = burr(m2[0], m2[1], m2[2], m2[3])
rv3 = burr(m3[0], m3[1], m3[2], m3[3])
rv4 = burr(m4[0], m4[1], m4[2], m4[3])
x = np.arange(0, 4000)
f_e[np.where(f == '0')] = 0
f_e[np.where(f == '1')] = x.dot(rv1.pdf(x))
f_e[np.where(f == '2')] = x.dot(rv2.pdf(x))
f_e[np.where(f == '3')] = x.dot(rv3.pdf(x))
f_e[np.where(f == '4')] = x.dot(rv4.pdf(x))
f2 = np.sum(np.int8(f_e) * w, axis=1)
f3 = np.sum(np.int8(f) * w1, axis=1)
risk = data_E.iloc[:, 34]
delay = data_E.iloc[:, 39]
m = [risk == '1']
n = [delay == '1']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp1 = [i for i in a[0] if i in b[0]]
m = [risk == '2']
n = [delay == '1']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp2 = [i for i in a[0] if i in b[0]]
m = [risk == '1']
n = [delay == '2']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp3 = [i for i in a[0] if i in b[0]]
m = [risk == '2']
n = [delay == '2']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp4 = [i for i in a[0] if i in b[0]]
tend[tmp4]
f41 = np.zeros(data_E.shape[0])
f41[tmp1] = k[0, 0]
f41[tmp2] = k[1, 0]
f41[tmp3] = k[2, 0]
f41[tmp4] = k[3, 0]
f42 = np.zeros(data_E.shape[0])
f42[tmp1] = k[0, 1]
f42[tmp2] = k[1, 1]
f42[tmp3] = k[2, 1]
f42[tmp4] = k[3, 1]
f43 = np.zeros(data_E.shape[0])
f43[tmp1] = k[0, 2]
f43[tmp2] = k[1, 2]
f43[tmp3] = k[2, 2]
f43[tmp4] = k[3, 2]
f4 = np.sum(np.vstack((f42, f41, f43)).T * w2, axis=1)
time = np.float32(data_E.iloc[:, 1:11])
f5 = np.sum(time * w_t, axis=1)
choice = np.int8(data_E.iloc[:, 11:21])
f6 = choice.dot(w_c)
f7 = np.vstack((choice3, choice4)).T.dot(w_cc)
f8 = time.dot(w_e)
farmiliar = np.float32(data_E.iloc[:, 21:31])
f9 = farmiliar.dot(w_far)
return f1, f2, f3, f4, f5, f6, f7, f8, f9
def model_update(data_E):
# polynomial weight
w0 = np.load('w0.npy')
# f_e weight (familarity_exploration)
w = np.load('w.npy')
# f weight (familarity_exploration)
w1 = np.load('w1.npy')
# risk and delay
k = np.load('k.npy')
w2 = np.load('w2.npy')
#time
w_t = np.load('w_t.npy')
w_c = np.load('w_c.npy')
w_cc = np.load('w_cc.npy')
w_e = np.load('w_e.npy')
w_far = np.load('w_far.npy')
# burr
m1 = np.load('m1.npy')
m2 = np.load('m2.npy')
m3 = np.load('m3.npy')
m4 = np.load('m4.npy')
stress = np.int8(data_E.iloc[:, 31])
tired = np.int8(data_E.iloc[:, 32])
st = np.int8(data_E.iloc[:, 31:33])
choice3 = data_E.iloc[:, 37]
choice4 = data_E.iloc[:, 38]
tend = np.int8(choice3) + np.int8(choice4)
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(st)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, tend)
w0_1 = pol_reg.coef_
np.save('w0.npy', w0 + (w0_1 - w0) / 100)
data_p = np.zeros((data_E.shape[0] * 10, 13))
data_clean = np.array(data_E)
for i in range(0, data_E.shape[0]):
data_p[i * 10:i * 10 + 10, 0] = data_clean[i, 0] #ID
data_p[i * 10:i * 10 + 10, 1] = data_clean[i, 1:11] #time
data_p[i * 10:i * 10 + 10, 2] = data_clean[i, 11:21] #choice
data_p[i * 10:i * 10 + 10, 3] = data_clean[i, 21:31] #familarity
data_p[i * 10:i * 10 + 10, 4] = data_clean[i, 31] #stress
data_p[i * 10:i * 10 + 10, 5] = data_clean[i, 32] #tired
data_p[i * 10:i * 10 + 10, 6] = data_clean[i, 33] #pay off
data_p[i * 10:i * 10 + 10, 7] = data_clean[i, 34] #risk
data_p[i * 10:i * 10 + 10, 8] = data_clean[i, 35] #bounce 4
data_p[i * 10:i * 10 + 10, 9] = data_clean[i, 36] #bounce 3
data_p[i * 10:i * 10 + 10, 10] = data_clean[i,
37] #choice 4 include 3?
data_p[i * 10:i * 10 + 10, 11] = data_clean[i, 38] #choice 3
data_p[i * 10:i * 10 + 10, 12] = data_clean[i, 39] #delay
if data_E.shape[0] > 50:
for i in range(1, 5):
y = data_p[np.where(data_p[:, 3] == i), 1]
m = best_fit_distribution(y, bins=200, ax=None)
np.save('m' + str(i) + '.npy', np.array(m[1]))
m1 = np.load('m1.npy')
m2 = np.load('m2.npy')
m3 = np.load('m3.npy')
m4 = np.load('m4.npy')
f = data_E.iloc[:, 21:31]
f_e = np.zeros((data_E.shape[0], 10))
rv1 = burr(m1[0], m1[1], m1[2], m1[3])
rv2 = burr(m2[0], m2[1], m2[2], m2[3])
rv3 = burr(m3[0], m3[1], m3[2], m3[3])
rv4 = burr(m4[0], m4[1], m4[2], m4[3])
x = np.arange(0, 4000)
f_e[np.where(f == '0')] = 0
f_e[np.where(f == '1')] = x.dot(rv1.pdf(x))
f_e[np.where(f == '2')] = x.dot(rv2.pdf(x))
f_e[np.where(f == '3')] = x.dot(rv3.pdf(x))
f_e[np.where(f == '4')] = x.dot(rv4.pdf(x))
lin_reg = LinearRegression()
lin_reg.fit(np.int8(f_e), tend)
w_1 = lin_reg.coef_
np.save('w.npy', w + (w_1 - w) / 100)
lin_reg = LinearRegression()
lin_reg.fit(np.int8(f), tend)
w1_1 = lin_reg.coef_
np.save('w1.npy', w1 + (w1_1 - w1) / 100)
risk = data_E.iloc[:, 34]
delay = data_E.iloc[:, 39]
m = [risk == '1']
n = [delay == '1']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp1 = [i for i in a[0] if i in b[0]]
if np.array(tmp1).any():
#print(np.mean(tend[tmp1]))
k11 = np.mean(tend[tmp1])
#print(np.median(tend[tmp1]))
k12 = np.median(tend[tmp1])
counts = np.bincount(tend[tmp1])
#print(np.argmax(counts))
k13 = np.argmax(counts)
else:
k11 = k[0, 0]
k12 = k[0, 1]
k13 = k[0, 2]
m = [risk == '2']
n = [delay == '1']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp2 = [i for i in a[0] if i in b[0]]
if np.array(tmp2).any():
#tend[tmp2]
#plt.hist(tend[tmp2])
#print(np.mean(tend[tmp2]))
k21 = np.mean(tend[tmp2])
#print(np.median(tend[tmp2]))
k22 = np.median(tend[tmp2])
counts = np.bincount(tend[tmp2])
#print(np.argmax(counts))
k23 = np.argmax(counts)
else:
k21 = k[1, 0]
k22 = k[1, 1]
k23 = k[1, 2]
m = [risk == '1']
n = [delay == '2']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp3 = [i for i in a[0] if i in b[0]]
if np.array(tmp3).any():
#tend[tmp3]
#plt.hist(tend[tmp2])
#print(np.mean(tend[tmp2]))
k31 = np.mean(tend[tmp3])
#print(np.median(tend[tmp2]))
k32 = np.median(tend[tmp3])
counts = np.bincount(tend[tmp3])
#print(np.argmax(counts))
k33 = np.argmax(counts)
else:
k31 = k[2, 0]
k32 = k[2, 1]
k33 = k[2, 2]
m = [risk == '2']
n = [delay == '2']
a = np.array(np.where(np.array(m[0]) == 1))
b = np.array(np.where(np.array(n[0]) == 1))
tmp4 = [i for i in a[0] if i in b[0]]
if np.array(tmp4).any():
#tend[tmp4]
#plt.hist(tend[tmp2])
#print(np.mean(tend[tmp2]))
k41 = np.mean(tend[tmp4])
#print(np.median(tend[tmp2]))
k42 = np.median(tend[tmp4])
counts = np.bincount(tend[tmp4])
#print(np.argmax(counts))
k43 = np.argmax(counts)
else:
k41 = k[3, 0]
k42 = k[3, 1]
k43 = k[3, 2]
k_1 = np.zeros((4, 3))
k_1[0, 0] = k11
k_1[1, 0] = k21
k_1[2, 0] = k31
k_1[3, 0] = k41
k_1[0, 1] = k12
k_1[1, 1] = k22
k_1[2, 1] = k32
k_1[3, 1] = k42
k_1[0, 2] = k13
k_1[1, 2] = k23
k_1[2, 2] = k33
k_1[3, 2] = k43
np.save('k.npy', k + (k_1 - k) / 100)
f41 = np.zeros(data_E.shape[0])
f41[tmp1] = k[0, 0]
f41[tmp2] = k[1, 0]
f41[tmp3] = k[2, 0]
f41[tmp4] = k[3, 0]
f42 = np.zeros(data_E.shape[0])
f42[tmp1] = k[0, 1]
f42[tmp2] = k[1, 1]
f42[tmp3] = k[2, 1]
f42[tmp4] = k[3, 1]
f43 = np.zeros(data_E.shape[0])
f43[tmp1] = k[0, 2]
f43[tmp2] = k[1, 2]
f43[tmp3] = k[2, 2]
f43[tmp4] = k[3, 2]
lin_reg = LinearRegression()
lin_reg.fit(np.vstack((f42, f41, f43)).T, tend)
w2_1 = lin_reg.coef_
np.save('w2.npy', w2 + (w2_1 - w2) / 100)
lin_reg = LinearRegression()
time = np.float32(data_E.iloc[:, 1:11])
lin_reg.fit(time, tend)
w_t_1 = lin_reg.coef_
np.save('w_t.npy', w_t + (w_t_1 - w_t) / 100)
choice = np.int8(data_E.iloc[:, 11:21])
payoff = np.float32(data_E.iloc[:, 33])
lin_reg = LinearRegression()
lin_reg.fit(choice, payoff)
w_c_1 = lin_reg.coef_
np.save('w_c.npy', w_c + (w_c_1 - w_c) / 100)
lin_reg = LinearRegression()
lin_reg.fit(np.vstack((choice3, choice4)).T, payoff)
w_cc_1 = lin_reg.coef_
np.save('w_cc.npy', w_cc + (w_cc_1 - w_cc) / 100)
lin_reg = LinearRegression()
lin_reg.fit(time, payoff)
w_e_1 = lin_reg.coef_
np.save('w_e.npy', w_e + (w_e_1 - w_e) / 100)
farmiliar = np.float32(data_E.iloc[:, 21:31])
lin_reg = LinearRegression()
lin_reg.fit(farmiliar, payoff)
w_far_1 = lin_reg.coef_
np.save('w_far.npy', w_far + (w_far_1 - w_far) / 100)
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() #burr Continuous distributions¶ from scipy.stats import burr import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots(1, 1) #Calculate a few first moments: c, d = 10.5, 4.3 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') #Display the probability density function (pdf): x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) ax.plot(x, burr.pdf(x, c, d), 'r-', lw=5, alpha=0.6, label='burr pdf') #Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. #Freeze the distribution and display the frozen pdf: rv = burr(c, d) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = burr.ppf([0.001, 0.5, 0.999], c, d) np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d)) True #Generate random numbers: r = burr.rvs(c, d, size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
def gen_v():
data = pd.read_csv("data.csv")
data[data == ' '] = np.nan
data = data.dropna()
data.head(5)
data_E = data.iloc[0:56, :]
data_T = data.iloc[56::, :]
stress = np.int8(data_E.iloc[:, 31])
tired = np.int8(data_E.iloc[:, 32])
st = np.int8(data_E.iloc[:, 31:33])
choice3 = np.int8(data_E.iloc[:, 37])
choice4 = np.int8(data_E.iloc[:, 38])
tend = np.int8(choice3) + np.int8(choice4)
risk = np.int8(data_E.iloc[:, 34])
delay = np.int8(data_E.iloc[:, 39])
fst = np.zeros((3, 3))
for i in range(0, 3):
for j in range(0, 3):
fst[i, j] = np.array([
i for i in np.where(st[:, 0] == i)[0]
if i in np.where(st[:, 1] == j)[0]
]).size
fst /= fst.sum()
frd = np.zeros((2, 2))
for i in range(0, 2):
for j in range(0, 2):
frd[i, j] = np.array([
i for i in np.where(risk == i + 1)[0]
if i in np.where(delay == j + 1)[0]
]).size
frd /= frd.sum()
frd
time = np.float32(data_E.iloc[:, 1:11])
mu = np.zeros(10)
std = np.zeros(10)
for i in range(0, 10):
mu[i], std[i] = norm.fit(time[:, i])
farmiliar = np.float32(data_E.iloc[:, 21:31])
mu_f = np.zeros(10)
std_f = np.zeros(10)
for i in range(0, 10):
mu_f[i], std_f[i] = norm.fit(farmiliar[:, i])
m1 = np.load('m1.npy')
m2 = np.load('m2.npy')
m3 = np.load('m3.npy')
m4 = np.load('m4.npy')
f = data_E.iloc[:, 21:31]
f_e = np.zeros((data_E.shape[0], 10))
rv1 = burr(m1[0], m1[1], m1[2], m1[3])
rv2 = burr(m2[0], m2[1], m2[2], m2[3])
rv3 = burr(m3[0], m3[1], m3[2], m3[3])
rv4 = burr(m4[0], m4[1], m4[2], m4[3])
x = np.arange(0, 4000)
f_e[np.where(f == '0')] = 0
f_e[np.where(f == '1')] = x.dot(rv1.pdf(x))
f_e[np.where(f == '2')] = x.dot(rv2.pdf(x))
f_e[np.where(f == '3')] = x.dot(rv3.pdf(x))
f_e[np.where(f == '4')] = x.dot(rv4.pdf(x))
mu_fe = np.zeros(10)
std_fe = np.zeros(10)
for i in range(0, 10):
mu_fe[i], std_fe[i] = norm.fit(f_e[:, i])
v = [fst, frd, mu, std, mu_fe, std_fe, mu_f, std_f]
return v, tend, stress, tired, risk, delay, time, farmiliar, f_e
def all_dists():
# dists param were taken from scipy.stats official
# documentaion examples
# Total - 89
return {
"alpha":
stats.alpha(a=3.57, loc=0.0, scale=1.0),
"anglit":
stats.anglit(loc=0.0, scale=1.0),
"arcsine":
stats.arcsine(loc=0.0, scale=1.0),
"beta":
stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0),
"betaprime":
stats.betaprime(a=5, b=6, loc=0.0, scale=1.0),
"bradford":
stats.bradford(c=0.299, loc=0.0, scale=1.0),
"burr":
stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0),
"cauchy":
stats.cauchy(loc=0.0, scale=1.0),
"chi":
stats.chi(df=78, loc=0.0, scale=1.0),
"chi2":
stats.chi2(df=55, loc=0.0, scale=1.0),
"cosine":
stats.cosine(loc=0.0, scale=1.0),
"dgamma":
stats.dgamma(a=1.1, loc=0.0, scale=1.0),
"dweibull":
stats.dweibull(c=2.07, loc=0.0, scale=1.0),
"erlang":
stats.erlang(a=2, loc=0.0, scale=1.0),
"expon":
stats.expon(loc=0.0, scale=1.0),
"exponnorm":
stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
"exponweib":
stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0),
"exponpow":
stats.exponpow(b=2.7, loc=0.0, scale=1.0),
"f":
stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0),
"fatiguelife":
stats.fatiguelife(c=29, loc=0.0, scale=1.0),
"fisk":
stats.fisk(c=3.09, loc=0.0, scale=1.0),
"foldcauchy":
stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
"foldnorm":
stats.foldnorm(c=1.95, loc=0.0, scale=1.0),
# "frechet_r": stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
# "frechet_l": stats.frechet_l(c=3.63, loc=0.0, scale=1.0),
"genlogistic":
stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
"genpareto":
stats.genpareto(c=0.1, loc=0.0, scale=1.0),
"gennorm":
stats.gennorm(beta=1.3, loc=0.0, scale=1.0),
"genexpon":
stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0),
"genextreme":
stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
"gausshyper":
stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0),
"gamma":
stats.gamma(a=1.99, loc=0.0, scale=1.0),
"gengamma":
stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0),
"genhalflogistic":
stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
"gilbrat":
stats.gilbrat(loc=0.0, scale=1.0),
"gompertz":
stats.gompertz(c=0.947, loc=0.0, scale=1.0),
"gumbel_r":
stats.gumbel_r(loc=0.0, scale=1.0),
"gumbel_l":
stats.gumbel_l(loc=0.0, scale=1.0),
"halfcauchy":
stats.halfcauchy(loc=0.0, scale=1.0),
"halflogistic":
stats.halflogistic(loc=0.0, scale=1.0),
"halfnorm":
stats.halfnorm(loc=0.0, scale=1.0),
"halfgennorm":
stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
"hypsecant":
stats.hypsecant(loc=0.0, scale=1.0),
"invgamma":
stats.invgamma(a=4.07, loc=0.0, scale=1.0),
"invgauss":
stats.invgauss(mu=0.145, loc=0.0, scale=1.0),
"invweibull":
stats.invweibull(c=10.6, loc=0.0, scale=1.0),
"johnsonsb":
stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0),
"johnsonsu":
stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
"ksone":
stats.ksone(n=1e03, loc=0.0, scale=1.0),
"kstwobign":
stats.kstwobign(loc=0.0, scale=1.0),
"laplace":
stats.laplace(loc=0.0, scale=1.0),
"levy":
stats.levy(loc=0.0, scale=1.0),
"levy_l":
stats.levy_l(loc=0.0, scale=1.0),
"levy_stable":
stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
"logistic":
stats.logistic(loc=0.0, scale=1.0),
"loggamma":
stats.loggamma(c=0.414, loc=0.0, scale=1.0),
"loglaplace":
stats.loglaplace(c=3.25, loc=0.0, scale=1.0),
"lognorm":
stats.lognorm(s=0.954, loc=0.0, scale=1.0),
"lomax":
stats.lomax(c=1.88, loc=0.0, scale=1.0),
"maxwell":
stats.maxwell(loc=0.0, scale=1.0),
"mielke":
stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0),
"nakagami":
stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
"ncx2":
stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0),
"ncf":
stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
"nct":
stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0),
"norm":
stats.norm(loc=0.0, scale=1.0),
"pareto":
stats.pareto(b=2.62, loc=0.0, scale=1.0),
"pearson3":
stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
"powerlaw":
stats.powerlaw(a=1.66, loc=0.0, scale=1.0),
"powerlognorm":
stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
"powernorm":
stats.powernorm(c=4.45, loc=0.0, scale=1.0),
"rdist":
stats.rdist(c=0.9, loc=0.0, scale=1.0),
"reciprocal":
stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0),
"rayleigh":
stats.rayleigh(loc=0.0, scale=1.0),
"rice":
stats.rice(b=0.775, loc=0.0, scale=1.0),
"recipinvgauss":
stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
"semicircular":
stats.semicircular(loc=0.0, scale=1.0),
"t":
stats.t(df=2.74, loc=0.0, scale=1.0),
"triang":
stats.triang(c=0.158, loc=0.0, scale=1.0),
"truncexpon":
stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
"truncnorm":
stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0),
"tukeylambda":
stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
"uniform":
stats.uniform(loc=0.0, scale=1.0),
"vonmises":
stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
"vonmises_line":
stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0),
"wald":
stats.wald(loc=0.0, scale=1.0),
"weibull_min":
stats.weibull_min(c=1.79, loc=0.0, scale=1.0),
"weibull_max":
stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
"wrapcauchy":
stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0),
}
fig = plt.figure(dpi=1300)
ax1 = fig.add_subplot(1, 2, 1)
sm.qqplot(logeados,
stats.mielke(parametros_mielke[0], parametros_mielke[1],
parametros_mielke[2], parametros_mielke[3]),
line="45",
ax=ax1)
ax1.set_title('mielke', size=11.0)
ax1.set_xlabel("")
ax1.set_ylabel("")
ax2 = fig.add_subplot(1, 2, 2)
sm.qqplot(logeados,
stats.burr(parametros_burr[0], parametros_burr[1],
parametros_burr[2], parametros_burr[3]),
line="45",
ax=ax2)
ax2.set_title('burr', size=11.0)
ax2.set_xlabel("")
ax2.set_ylabel("")
fig.tight_layout(pad=0.7)
fig.text(0.5, 0, 'Cuantiles teóricos', ha='center', va='center')
fig.text(0.,
0.5,
'Cuantiles observados',
ha='center',
va='center',
rotation='vertical')
fig = plt.figure(dpi = 1300)
ax1 = fig.add_subplot(1, 2, 1)
sm.qqplot(logeados, stats.mielke(parametros_mielke[0],
parametros_mielke[1],
parametros_mielke[2],
parametros_mielke[3]),
line = "45", ax = ax1)
ax1.set_title('mielke', size = 11.0)
ax1.set_xlabel("")
ax1.set_ylabel("")
ax2 = fig.add_subplot(1, 2, 2)
sm.qqplot(logeados, stats.burr(parametros_burr[0],
parametros_burr[1],
parametros_burr[2],
parametros_burr[3]),
line = "45",ax = ax2)
ax2.set_title('burr', size = 11.0)
ax2.set_xlabel("")
ax2.set_ylabel("")
fig.tight_layout(pad=0.7)
fig.text(0.5, 0, 'Cuantiles teóricos', ha='center', va='center')
fig.text(0., 0.5, 'Cuantiles observados', ha='center', va='center', rotation='vertical')
# fig.suptitle('Gráfico de cuantiles distribuciones ajustadas')
fig.subplots_adjust(top=0.86)
# plt.savefig('P1.QQDist.png', format='png', dpi=1300)
plt.show()
