How to use the getdist.MCSamples function in getdist

in_root = ini.params['file_root']
    if not in_root:
        do_error('Chain Root file name not given ')
    rootname = os.path.basename(in_root)

    if args.ignore_rows is not None:
        ignorerows = args.ignore_rows
    else:
        ignorerows = ini.float('ignore_rows', 0.0)

    samples_are_chains = ini.bool('samples_are_chains', True)

    paramnames = ini.string('parameter_names', '')

    # Create instance of MCSamples
    mc = MCSamples(in_root, ini=ini, files_are_chains=samples_are_chains, paramNamesFile=paramnames)

    if ini.bool('adjust_priors', False) or ini.bool('map_params', False):
        do_error('To adjust priors or define new parameters, use a separate python script; '
                 'see the python getdist docs for examples')

    plot_ext = ini.string('plot_ext', 'py')
    finish_run_command = ini.string('finish_run_command', '')

    no_plots = ini.bool('no_plots', no_plots)
    plots_only = ini.bool('plots_only', False)
    no_tests = plots_only or ini.bool('no_tests', False)

    thin_factor = ini.int('thin_factor', 0)
    thin_cool = ini.float('thin_cool', 1.0)

    cool = ini.float('cool', 1.0)

print()

            for log_dir in log_dim_dir:

                with open(os.path.join(log_dir, 'info', 'params.txt')) as f:
                    data = json.load(f)

                if os.path.exists(os.path.join(log_dir, 'chains', 'chain.txt')):
                    names = ['p%i' % i for i in range(int(data['x_dim']))]
                    labels = [r'x_{%i}' % i for i in range(int(data['x_dim']))]
                    files = getdist.chains.chainFiles(os.path.join(log_dir, 'chains', 'chain.txt'))
                    if data['sampler'] == 'nested':
                        mc = getdist.MCSamples(os.path.join(log_dir, 'chains', 'chain.txt'), names=names, labels=labels,
                                               ignore_rows=0.0, sampler='nested')
                    else:
                        mc = getdist.MCSamples(os.path.join(log_dir, 'chains', 'chain.txt'), names=names, labels=labels,
                                               ignore_rows=0.3)
                    mc.readChains(files)
                    print(mc.getMargeStats())

                    if not args.no_plot:
                        g = getdist.plots.getSubplotPlotter()
                        g.triangle_plot(mc, filled=True)
                        g.export(os.path.join(os.path.join(log_dir, 'plots', 'triangle.png')))

                if data['sampler'] == 'nested':
                    if os.path.exists(os.path.join(log_dir, 'results', 'final.csv')):
                        results = pd.read_csv(os.path.join(log_dir, 'results', 'final.csv'))
                        print(results)
                        logzs.append(results['logz'])
                        dlogzs.append(results['logzerr'])
                        nlikes.append(results['ncall'])

print('--------')
                print('Dim: %s' % x_dims[ix])
                print('--------')
                print()

            for log_dir in log_dim_dir:

                with open(os.path.join(log_dir, 'info', 'params.txt')) as f:
                    data = json.load(f)

                if os.path.exists(os.path.join(log_dir, 'chains', 'chain.txt')):
                    names = ['p%i' % i for i in range(int(data['x_dim']))]
                    labels = [r'x_{%i}' % i for i in range(int(data['x_dim']))]
                    files = getdist.chains.chainFiles(os.path.join(log_dir, 'chains', 'chain.txt'))
                    if data['sampler'] == 'nested':
                        mc = getdist.MCSamples(os.path.join(log_dir, 'chains', 'chain.txt'), names=names, labels=labels,
                                               ignore_rows=0.0, sampler='nested')
                    else:
                        mc = getdist.MCSamples(os.path.join(log_dir, 'chains', 'chain.txt'), names=names, labels=labels,
                                               ignore_rows=0.3)
                    mc.readChains(files)
                    print(mc.getMargeStats())

                    if not args.no_plot:
                        g = getdist.plots.getSubplotPlotter()
                        g.triangle_plot(mc, filled=True)
                        g.export(os.path.join(os.path.join(log_dir, 'plots', 'triangle.png')))

                if data['sampler'] == 'nested':
                    if os.path.exists(os.path.join(log_dir, 'results', 'final.csv')):
                        results = pd.read_csv(os.path.join(log_dir, 'results', 'final.csv'))
                        print(results)

def triangle_plot(self):
        if not HAS_GETDIST:
            raise RuntimeError(
                'you need to install getdist to get the triangle plot.')
        if 0 < self.m_plot < self.dim:
            plot_data = self._data[:, :self.m_plot]
        else:
            plot_data = self._data
        samples = MCSamples(samples=plot_data)
        g = plots.getSubplotPlotter()
        g.triangle_plot([samples,], filled=True, contour_args={'alpha':0.8},
                        diag1d_kwargs={'normalized':True})
        if self.i_iter:
            plt.suptitle("triangle plot after iteration " + str(self.i_iter),
                         fontsize=plot_data.shape[-1] * 4, ha='left')
        else:
            plt.suptitle('triangle plot for the initial data',
                         fontsize=plot_data.shape[-1] * 4, ha='left')
        plt.show()

def triangle_plot(self, samples = None, weights = None, savefig = False, filename = None):
        # Set samples to the posterior samples by default
        if samples is None:
            samples = self.posterior_samples
        mc_samples = [MCSamples(samples=s, weights=weights[i], names=self.names, labels=self.labels, ranges=self.ranges) for i, s in enumerate(samples)]

        # Triangle plot
        plt.close()
        with mpl.rc_context():
            g = plots.getSubplotPlotter(width_inch = 12)
            g.settings.figure_legend_frame = False
            g.settings.alpha_filled_add=0.6
            g.settings.axes_fontsize=14
            g.settings.legend_fontsize=16
            g.settings.lab_fontsize=20
            g.triangle_plot(mc_samples, filled_compare=True, normalized=True)
            for i in range(0, len(samples[0][0,:])):
                for j in range(0, i+1):
                    ax = g.subplots[i,j]
                    xtl = ax.get_xticklabels()
                    ax.set_xticklabels(xtl, rotation=45)