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)