chain_array = ChainArray.from_file(keys=['sample', 'accepted'],
path=sampler_output_pilot_path)
# %% Drop burn-in samples
chain_array.vals['sample'] = chain_array.vals['sample'][
diagnostic_iter_thres:, :]
chain_array.vals['accepted'] = chain_array.vals['accepted'][
diagnostic_iter_thres:]
# %% Plot traces of simulated chain
for i in range(model.num_params()):
ps.trace(chain_array.get_param(i),
title=r'Traceplot of $\theta_{{{}}}$'.format(i + 1),
xlabel='Iteration',
ylabel='Parameter value')
# %% Plot running means of simulated chain
for i in range(model.num_params()):
ps.running_mean(
chain_array.get_param(i),
title=r'Running mean plot of parameter $\theta_{{{}}}$'.format(i + 1),
xlabel='Iteration',
ylabel='Running mean')
# %% Plot histograms of marginals of simulated chain
for i in range(model.num_params()):
ps.hist(chain_array.get_param(i),
import matplotlib.pyplot as plt
from numpy import genfromtxt
from statistics import mean
import kanga.plots as ps
# %% Read chains
chains = genfromtxt('chain01.csv', delimiter=',')
num_iters, num_pars = chains.shape
# %% Trace plot of a single chain with default trace input arguments
ps.trace(chains[:, 0])
# %% Trace plot of a single chain with a custom range of x values
ps.trace(chains[:, 0], x=range(1000, 1000 + num_iters))
# %% Trace plot of a single chain with some non-default trace input arguments
ps.trace(chains[:, 0],
ylim=[-8, 10],
margins=0.01,
title=r'Trace plot of parameter $\theta_{{{}}}$'.format(1),
xlabel='Iteration',
ylabel='Parameter value')
# %% Overlaid trace plots of all three parameters