def plot(self, key):
time_data = key.load_state(self.meta.topdir)['times']
plot_y_times = time_data#.T
#print('plot times='+str(plot_y_times))
#print('times of shape '+str(np.shape(plot_y_times)))
self.sps_times.scatter([(key.r+1)*self.meta.miniters]*self.meta.nwalkers,#[i_run.iternos[r]]*meta.params[p_run.p],
plot_y_times,
c='k',#self.meta.colors[key.n+key.i],
alpha=self.a_times,
linewidth=0.1,
s=self.meta.nbins,
rasterized=True)
timesaver(self.meta,'times',key)
frac_data = key.load_state(self.meta.topdir)['fracs']
plot_y_fracs = frac_data.T
self.sps_fracs.scatter([(key.r+1)*self.meta.miniters]*self.meta.nwalkers,#[i_run.iternos[r]] * n_run.nwalkers,
plot_y_fracs,
c='k',#self.meta.colors[key.i+key.n],
alpha=self.a_fracs,
linewidth=0.1,
s=self.meta.nbins,
rasterized=True)
timesaver(self.meta,'fracs',key)
def plot(self,key):
if key.burnin == False:
# start_time = timeit.default_timer()
data = key.load_state(self.meta.topdir)['chains']
plot_y_ls = np.swapaxes(data,0,1)
plot_y_s = np.exp(plot_y_ls)
randsteps = random.sample(xrange(self.meta.ntimes),self.meta.nwalkers)
for w in self.randwalks:
for x in randsteps:
self.sps_samps[0].hlines(plot_y_ls[x][w],
self.meta.binlos,
self.meta.binhis,
color=self.meta.colors[key.r%self.ncolors],
alpha=self.a_samp,
rasterized=True)
self.sps_samps[1].hlines(plot_y_s[x][w],
self.meta.binlos,
self.meta.binhis,
color=self.meta.colors[key.r%self.ncolors],
alpha=self.a_samp,
rasterized=True)
timesaver(self.meta,'samps',key)
def plot(self,key):
data = key.load_state(self.meta.topdir)['probs']
plot_y = np.swapaxes(data,0,1).T
for w in xrange(self.meta.nwalkers):
self.sps.plot(np.arange(key.r*self.meta.ntimes,(key.r+1)*self.meta.ntimes)*self.meta.thinto,#,(key.r+1)*self.meta.miniters),#key.i_run.eachtimenos[r],
plot_y[w],
c=self.meta.colors[w%self.ncolors],
alpha=self.a_probs,
rasterized=True)
timesaver(self.meta,'probs',key)
def plot(self,key):
# start_time = timeit.default_timer()
data = key.load_state(self.meta.topdir)['chains']
plot_y_c = np.swapaxes(data,0,1).T
randsteps = random.sample(xrange(self.meta.ntimes),self.meta.nwalkers)
for k in xrange(self.meta.nbins):
mean = np.sum(plot_y_c[k])/(self.meta.ntimes*self.meta.nwalkers)
self.sps_chains[k].plot(np.arange(key.r*self.meta.ntimes,(key.r+1)*self.meta.ntimes)*self.meta.thinto,#i_run.eachtimenos[r],
[mean]*self.meta.ntimes,
color = 'k',
rasterized = True)
for x in xrange(self.meta.ntimes):
for w in self.randwalks:
self.sps_chains[k].plot(np.arange(key.r*self.meta.ntimes,(key.r+1)*self.meta.ntimes)*self.meta.thinto,#i_run.eachtimenos[r],
plot_y_c[k][w],
color = self.meta.colors[w%self.ncolors],
alpha = self.a_chain,
rasterized = True)
timesaver(self.meta,'chains',key)
