def plot_tri(self, ind=None, npar=8, *args, **kwargs):
if ind is None:
ind = np.arange(0, self.ndim)
ind = np.atleast_1d(ind)
nplots = int(np.ceil(len(ind) / npar))
plt.rcParams['font.size'] = 8
start = 0
for ii in range(nplots):
ax = bu.triplot(self.chain[:,ind[start:npar+start]], interpolate=True,
labels=list(self.pars[ind[start:npar+start]]), tex=False,
figsize=(24,18), *args, **kwargs)
start += npar
if self.save:
plt.savefig(self.outdir + '/tri_{0}.png'.format(ii),
dpi=300, bbox_inches='tight')
def plot_tri(self, ind=None, npar=8, *args, **kwargs):
if ind is None:
ind = np.arange(0, self.ndim)
ind = np.atleast_1d(ind)
nplots = int(np.ceil(len(ind) / npar))
plt.rcParams["font.size"] = 8
start = 0
for ii in range(nplots):
ax = bu.triplot(
self.chain[:, ind[start : npar + start]],
interpolate=True,
labels=list(self.pars[ind[start : npar + start]]),
tex=False,
figsize=(24, 18),
*args,
**kwargs
)
start += npar
if self.save:
plt.savefig(self.outdir + "/tri_{0}.png".format(ii), dpi=300, bbox_inches="tight")
# number of iterations and number of effective samples
N = 100000
neff = 1000
# set up covariance matrix for jumps
cov = model.initJumpCovariance()
# define MCMC sampler
sampler = PALInferencePTMCMC.PTSampler(len(p0), loglike, logprior, cov,
outDir=args.outdir,
loglkwargs=loglkwargs,
groups=ind)
# add in prior draws for red noise
sampler.addProposalToCycle(model.drawFromRedNoisePrior, 5)
# sample
sampler.sample(p0, N, covUpdate=1000, AMweight=15, SCAMweight=30,
DEweight=50, neff=neff, KDEweight=0)
# read in chain
chain = np.loadtxt(args.outdir + '/chain_1.txt')
burn = int(0.25 * chain.shape[0])
pars = np.loadtxt(args.outdir + '/pars.txt', dtype='S42')
# make triangle plot
ax = bu.triplot(chain[burn:,-9:-4], labels=list(pars[-5:]), tex=False)
plt.savefig(args.outdir + '/bwm_triplot.png', bbox_inches='tight')
print 'ploting for ' + this_group
mask = []
for ii, this_param in enumerate(params):
if this_group in this_param:
mask.append(ii)
this_names = [params[ii] for ii in mask]
this_chain = np.array([chain_burned[:, ii] for ii in mask])
print this_names
# plot triangle plot for parameter group
size = np.array([4, 3]) * min(len(this_names) / 2., 5)
title = this_PSR + ' -- ' + this_group
plt.rcParams['font.size'] = 6
ax = bu.triplot(this_chain.T,
labels=this_names[:],
title=title,
tex=False,
figsize=tuple(size))
plt.savefig(plotsdir + '/' + this_group + '_tri.pdf')
# finish with other params (red noise)
print 'ploting for all others'
mask = []
for ii, this_param in enumerate(params):
isin = False
for this_group in groups:
if this_group in this_param:
isin = True
break
if not isin:
mask.append(ii)
