def make_data_dict(self,drunkdict):
datadict = {}
for z in drunkdict.keys():
datadict[z] = [dd.open_drunk(drunkdict[z][0],skip_radii=drunkdict[z][2]),
drunkdict[z][1],
drunkdict[z][2]]
return datadict
def test(drunkdict,pardict,output,sample,burn=10):
# pardict = {'h_dust': [True, 8.43],
# 'Vr': [False, 200, 300],
# 'hrot': [False, 4, 5.],
# 'kappa_0': [True, 0.652],
# 'z_d': [True, 0.43]}
# drunkdict = {0:['ESO_z0_drunk.fits',False,[]]}
sax = mark_VI(drunkdict,pardict,'test',1001)
S = pymc.MCMC(sax.model)
S.sample(sample,burn=burn)
traces = {}
bestfit = {}
outdict = copy.deepcopy(pardict)
for k in outdict.keys():
if not outdict[k][0]:
trace = S.trace(k)[:]
mean = np.mean(trace)
traces[k] = trace
bestfit[k] = mean
outdict[k][1] = mean
bars = []
datadict = {}
for z in drunkdict.keys():
datadict[z] = [dd.open_drunk(drunkdict[z][0],skip_radii=drunkdict[z][2]),
drunkdict[z][1],
drunkdict[z][2]]
for z in datadict.keys():
simfile = trane.make_boring([outdict['Vr'][1]],
[outdict['hrot'][1]],
h_dust=outdict['h_dust'][1],
kappa_0=outdict['kappa_0'][1],
z_d=outdict['z_d'][1],
name='final',size=1001,z=z,
flarepars=False)[0]
bar = trane.moments_notice(datadict[z][0],simfile,
skip_radii=datadict[z][2],
flip=datadict[z][1])
bars.append(bar)
pp = PDF(output)
for k in traces.keys():
ax = plt.figure().add_subplot(111)
ax.hist(traces[k],bins=50,histtype='step')
ax.set_xlabel(k)
ax.set_ylabel('PDF')
ax.set_title('Most likely value:\n{:9.4f}'.format(bestfit[k]))
pp.savefig(ax.figure)
pp.close()
return bestfit, bars, traces
def get_data(self,drunkdict):
bigm1 = np.array([])
bigm2 = np.array([])
bigm3 = np.array([])
for z in drunkdict.keys():
_,_,_, m1, m2, m3 = dd.open_drunk(drunkdict[z][0],
skip_radii=drunkdict[z][2])
bigm1 = np.append(bigm1,m1[0])
bigm2 = np.append(bigm2,m2[0])
bigm3 = np.append(bigm3,m3[0])
out = np.r_[bigm1,bigm2]#,bigm3]
return out
