def render(self):
# Set global font to ensure ability to display Cyrillic
plt.rc('font', **{'sans-serif': ['Helvetica', 'Arial'],
'family': 'sans-serif'})
for lang in self.langs:
figure, ax_array = self.figures[lang]
figure, title = self._set_up_figure(figure, lang)
self._get_axis_limits()
self._render_axes(ax_array, lang)
self._save_fig(figure, title, lang)
for m, phi in enumerate(phis):
print n,m,alphas.size
lamb = phi*np.sqrt(2*np.pi*alpha)
eps[n,m] = get_eq_eps( gamma, eta, alpha, lamb )
stoc_eps[n,m] = np.mean(full_stoc_sigma(0.01, dt, N, -gamma,
eta, alpha, lamb, 200,
discard=discard))
with open("figure_5_3.pik","w") as fi:
print "Writing pickle to figure_5_3.pik"
pic.dump({'alphas':alphas,'phis':phis,'mean_field':eps,'stochastic':stoc_eps},fi)
print "Plotting..."
font = {'size':12}
plt.rc('font',**font)
fig, (ax1,ax2) = ppl.subplots(1,2,figsize = (12,5))
alphas2,phis2 = np.meshgrid(np.arange(alphas.min(),alphas.max()+dalpha,dalpha)-dalpha/2,
np.arange(phis.min(),phis.max()+dphi,dphi)-dphi/2)
yellorred = brewer2mpl.get_map('YlOrRd','Sequential',9).mpl_colormap
p = ax1.pcolormesh(alphas2,phis2,eps.T,cmap=yellorred, rasterized=True)
ax1.axis([alphas2.min(),alphas2.max(),phis2.min(),phis2.max()])
#xticks = np.arange(alphas.min(),alphas.max(),0.5)
#xlabels = np.arange(alphas.min(),alphas.max(),0.5)-alphas.min()
#yticks = np.arange(phis.min(),phis.max(),0.5)
tau = 0.2
#phi = 1.0
thetas = np.arange(-4.0,4.0,0.4)
deltas = np.arange(0.0,2.1,0.5)
phis = np.arange(0.0,100.0,0.1)
alpha = 0.5
#alphas = np.arange(0.001,4.0,0.05)
ndeltas = len(deltas)
nphis = len(phis)
#nalphas = len(alphas)
epss = np.zeros((ndeltas,nphis))
ls = np.zeros((ndeltas,nphis))
if __name__=='__main__':
plt.rc('text',usetex=True)
plt.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
try:
print 'data loaded'
data = np.load(open('../data/mean_field_ratedist.npz','rb'))
epss = data['eps']
phis = data['phis']
deltas = data['deltas']
tau = data['tau']
ls = data['ls']
except:
print 'no data, running again'
for nd,delta in enumerate(deltas):
y = eta**2/(2*gamma)
## DO principales + vitalicios
## numero de socios por categoria
numero_socios = [str(len(principales) + len(vitalicios)),
str(250-len(principales)-len(vitalicios))]
print "Socios privilegiados con el voto " + str(len(principales) +
len(vitalicios) + len(activos))
y = [princi_money + vitali_money, activo_money + otros_money]
annotate = [locale.format("%d", y[0], grouping=True) + " S/.",
locale.format("%d", y[1], grouping=True) + " S/."]
width = 0.35
bar_color = ["r", "#66c2a5"]
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(8,6))
ind = np.arange(2)
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.4)
ax.set_xticklabels(["principales y vitalicios\n(" + numero_socios[0] + " socios)",
"otros socios\n(" + numero_socios[1] + " socios)",
],
rotation="horizontal", multialignment="center")
ax.autoscale()
ax.set_title(u'Ganancias de socios principales y vitalicios\n comparados con el resto de socios',
fontdict = {'fontsize':22}
)
y_labels = ["0", "1,000,000", "2,000,000", "3,000,000", "4,000,000",
return out
observation = pm.Poisson("obs", lambda_, value=datos, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(50000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
plt.rc('font', **{'family': 'DejaVu Sans'})
plt.subplot(311)
plt.title(u'''Distribución posterior de las variables
$\lambda_1,\;\lambda_2,\;tau$''')
plt.hist(lambda_1_samples,
histtype="stepfilled",
bins=30,
alpha=0.85,
normed=True)
plt.xlim([150000, 250000])
plt.xlabel("valor de $\lambda_1$")
plt.subplot(312)
#ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples,
histtype="stepfilled",
##import matplotlib
##matplotlib.use('Agg')
##import matplotlib.pyplot as plt
import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np
import cPickle as pic
import sys
plt.rc('text',usetex=True)
try:
sys.argv[1]
data = pic.load(open(sys.argv[1],"rb"))
#data = np.load(open(sys.argv[1]))
except:
fi = open("../data/pickle_alpha_1","rb")
data = np.load(fi)
taus = data[3]
alphas = data[2]
eps = data[0]
delta = 0.1
string = [r'$\tau\delta$ = %.2f' % float(delta*t) for t in taus]
#string2 = [r'$\tau$ = '+str(data[3][i]) for i in range(5)]
maxmmse = np.max(eps)
#maxf = np.max(data[1])
def f(x):
ppl.plot(alphas,eps[:,x]/maxmmse,label=string[x],ax=ax1)
# ax2.plot(data[2],data[1][:,x]/maxf,label=string2[x])
#plt.close()
def main():
description = """Simple bar plot of table. Input is a table from a file or
standard input."""
parser = argparse.ArgumentParser(
description=description,
formatter_class=RawTextHelpFormatter
)
parser.add_argument(
'-f',
'--filename',
action='store',
metavar='table.csv',
help='''A table of the format:
x_title, y_title, Main_title
x_label1, value1
x_label2, value2''',
required=False,
dest='filename',
)
args = parser.parse_args()
if args.filename:
filename = args.filename.strip()
else:
parser.print_help()
sys.exit(1)
y = []
x = []
for line in open(filename, "r").readlines():
line = line.strip()
res = re.search("[0-9]+", line)
if not res:
line = line.split(",")
x_lab = line[0]
y_lab = line[1]
main = line[2]
else:
line = line.split(",")
if len(line) < 2:
y.append(float(line[0]))
else:
x.append(line[0])
y.append(float(line[1]))
print(line)
if len(x) < 1:
x = range(1, len(y) + 1)
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(8, 6))
width = 0.2
ind = np.arange(len(y))
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.5)
ax.set_xticklabels(x)
ax.autoscale()
ax.set_title(
main,
fontdict={'fontsize': 20},
)
plt.ylabel(y_lab, fontdict={'fontsize': 15})
plt.xlabel(x_lab, fontdict={'fontsize': 15})
plt.tick_params(axis="y", which="major", labelsize=10)
plt.tick_params(axis="x", which="major", labelsize=10)
ppl.bar(ax, np.arange(len(y)), y, grid="y")
plt.tight_layout()
print("The plot has been saved as ``out.png``")
fig.savefig("out.png")
