def plota_figura_curvas_m_varios_alpha(vcost, n, salva_fig=True):
for c in vcost:
beta_v = np.arange(0., 1.01, 0.01)
v_alpha = [0.1, 0.3, 0.5, 0.7, 0.9]
params = {'c': c, 'n': n, 'alpha': v_alpha}
fname = 'data/mxbeta_'+hashlib.md5(str(params)+\
str(beta_v)).hexdigest() + '.p'
if os.path.exists(fname):
df = pd.read_pickle(fname)
else:
df = pd.DataFrame(columns=['beta', 'm_eigen', 'alpha'])
cont = 0
for alpha in v_alpha:
for j, beta in enumerate(tqdm(beta_v)):
eigen_m = m_critico(c,n,alpha,beta)
df.loc[cont] = [beta, eigen_m, alpha]
cont += 1
df.to_pickle(fname)
w, h = plt.figaspect(1)
fig = plt.figure(figsize=(w,h),dpi=150)
ax = fig.add_subplot(111)
colors = ['blue', 'green', 'yellow', 'red', 'purple']
for i, alpha in enumerate(v_alpha):
print alpha
df_aux = df[df['alpha'] == alpha]
vetor_m_eigen = df_aux['m_eigen'].tolist()
vetor_beta = df_aux['beta'].tolist()
ax.plot(vetor_beta, vetor_m_eigen, label=r'$\alpha$= %.2f'%alpha, color=colors[i])
# Label
plt.xlabel(r"$\beta$")
plt.ylabel(r"$m_c$")
# Titulo
titulo = ("c=%f" %c).rstrip('0')
#plt.title(titulo)
# Limite e intervalos do eixo x
xlim([-0.005,1.005])
xticks(np.arange(0.0,1.1,0.1))
# Limite e intervalos do eixo y
if c == 0.03:
ylim([-0.005,1.005])
yticks(np.arange(0.0,1.1,0.1))
#plt.legend(loc='best')
handles, labels = ax.get_legend_handles_labels()
lgd = ax.legend(handles, labels, bbox_to_anchor=(1.05, 1),\
loc=2, borderaxespad=0.)
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
ax.grid(c='#8b8b83')
plt.tight_layout()
if salva_fig:
# Salva fig
nome = "figuras_/Varios_alphas_n=%s_c=%s.png" %(str(n), str(c))
fig.savefig(nome, bbox_extra_artists=(lgd,), bbox_inches='tight')
# Fecha fig
plt.close()
else:
plt.show()
return df
def plota_figura_curvas_m(c, alpha, n, salva_fig=True):
beta_v = np.arange(0., 1.01, 0.01)
params = '%f%f%d' %(c,alpha,n)
fname = 'data/mxbeta_'+hashlib.md5(str(params)+\
str(beta_v)).hexdigest() + '.p'
if os.path.exists(fname):
df = pd.read_pickle(fname)
else:
df = pd.DataFrame(columns=['beta', 'm_eigen', 'm_aprox', 'm_int'])
for i, beta in enumerate(tqdm(beta_v)):
eigen_m = m_critico(c,n,alpha,beta)
aprox_m = aprox_encontra_m_critico(c, alpha, beta, n)
m = encontra_m_critico(c, alpha, beta, n)
df.loc[i] = [beta, eigen_m, aprox_m, m]
df.to_pickle(fname)
vetor_m_eigen = df['m_eigen'].tolist()
vetor_m_aprox = df['m_aprox'].tolist()
vetor_m_int = df['m_int'].tolist()
def mc_formula(c,alpha,beta,n):
z = 1./(alpha*beta)
quadrado = math.sqrt((1-c)*(1-c)+z*z*c*c+2*c*(1+c)*z)
numerador = c*z+2*(1+c)
return (numerador/(c-1+quadrado))/(2.*n)
vetor_m_analitico = [mc_formula(c,alpha,beta,n) for beta in beta_v]
w, h = plt.figaspect(1)
fig = plt.figure(figsize=(w,h),dpi=100)
ax = fig.add_subplot(111)
# Mostra um grafico
ax.plot(beta_v, vetor_m_int,label=u'approximation',color='blue')
ax.plot(beta_v, vetor_m_aprox, label=ur'small $c$', color='red')
ax.plot(beta_v, vetor_m_analitico, label=ur'calculado na mao', color='purple', marker='*')
ax.plot(beta_v, vetor_m_eigen,linestyle='--', label=u'eigenvalue method', color='green')
# Label
plt.xlabel(r"$\beta$")
plt.ylabel(r"$m$")
# Titulo
titulo = ("c=%f" %c).rstrip('0')
#plt.title(titulo)
# Limite e intervalos do eixo x
xlim([-0.005,1.005])
xticks(np.arange(0.0,1.1,0.1))
#maximo = max(vetor_m_eigen)
#inc = float(maximo)/200.
#ylim([-inc, maximo+inc])
# Limite e intervalos do eixo y
#ylim([-0.005,0.105])
#yticks(np.arange(0.0,0.1001,0.025))
# Limite e intervalos do eixo y
a = -0.0005
b = 0.2
maximo = 0.25
inc = 0.05
if c == 0.03:
b = 1.005
maximo = 1.1
inc = 0.1
elif c == 0.15:
b = 0.2505
maximo = 0.3
inc = 0.05
elif c >= 0.5:
b = 0.0105
maximo = 0.011
inc = 0.005
ylim([a,b])
yticks(np.arange(0.0,maximo, inc))
plt.legend(loc='best')
plt.grid(c='#8b8b83')
plt.tight_layout()
handles, labels = ax.get_legend_handles_labels()
lgd = ax.legend(loc="center", bbox_to_anchor=(0.5,-0.2), fancybox=True, ncol=3)
if salva_fig:
# Salva fig
nome = "figuras_/new_Duas_curvas_viabilidade_alpha=%s_n=%s_c=%s.png" %(str(alpha), str(n), str(c))
plt.savefig(nome, bbox_extra_artists=(lgd,), bbox_inches='tight')
# Fecha fig
plt.close()
else:
plt.show()
return df
