Exemple #1
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def load_samples(file_root):
    """Check that input chain exist, at least in zipped format, and read them."""

    try:
        samples = loadMCSamples(file_root)
    except IOError:
        if os.path.exists(file_root + '.txt.gz'):
            print('unzip chain', file_root)
            cmd = 'gzip -dk ' + file_root + '.txt.gz'
            os.system(cmd)
            samples = loadMCSamples(file_root)
        else:
            raise IOError('No chains found (not even zipped): ' + file_root)

    return samples
Exemple #2
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    def testFileLoadPlot(self):
        samples = loadMCSamples(self.root, settings={'ignore_rows': 0.1})
        g = plots.getSinglePlotter(chain_dir=self.tempdir,
                                   analysis_settings={'ignore_rows': 0.1})
        self.assertEqual(
            g.sampleAnalyser.samplesForRoot('testchain').numrows,
            samples.numrows, "Inconsistent chain loading")
        self.assertEqual(
            g.sampleAnalyser.samplesForRoot('testchain').getTable().tableTex(),
            samples.getTable().tableTex(), 'Inconsistent load result')
        samples.getConvergeTests(0.95)
        self.assertAlmostEqual(
            0.0009368, samples.GelmanRubin, 5,
            'Gelman Rubin error, got ' + str(samples.GelmanRubin))

        g = plots.getSinglePlotter()
        g.plot_3d(samples, ['x', 'y', 'x'])
        g.export(self.root + '_plot.pdf')

        g = plots.getSinglePlotter(chain_dir=self.tempdir,
                                   analysis_settings={
                                       'ignore_rows': 0.1,
                                       'contours': [0.68, 0.95, 0.99]
                                   })
        g.settings.num_plot_contours = 3
        g.plot_2d('testchain', ['x', 'y'])
Exemple #3
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 def limits(self):
     """Infer param limits from <root>.yaml in cobaya format."""
     s = loadMCSamples(file_root=self.root)
     return {
         p.name: (s.ranges.getLower(p.name), s.ranges.getUpper(p.name))
         for p in s.paramNames.names
         if s.ranges.getLower(p.name) is not None
         or s.ranges.getUpper(p.name) is not None
     }
def test_read_cobayamcmc():
    np.random.seed(3)
    mcmc = MCMCSamples(root='./tests/example_data/cb')
    mcmc.plot_2d(['x0', 'x1'])
    mcmc.plot_1d(['x0', 'x1'])
    plt.close("all")

    # compare directly with getdist
    mcmc_gd = getdist.loadMCSamples(file_root="./tests/example_data/cb")
    assert_array_almost_equal(mcmc.logL, mcmc_gd.loglikes, decimal=15)
 def load_from_file(self,rootname,**kwargs):
     #Load from file
     #self.samples=[]
     #for f in rootname:
     idchain=kwargs.pop('idchain', 0)
     print('mcsample: rootname, idchain',rootname,idchain)
     self.samples=gd.loadMCSamples(rootname,**kwargs)#.makeSingle()
     if idchain>0:
         self.samples.samples=self.samples.getSeparateChains()[idchain-1].samples
         self.samples.loglikes=self.samples.getSeparateChains()[idchain-1].loglikes
         self.samples.weights=self.samples.getSeparateChains()[idchain-1].weights            
Exemple #6
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 def test_planck_chains(self):
     if os.path.exists(self.path):
         root = os.path.join(self.path, 'compare_devel_drag')
         samples = loadMCSamples(root, settings={'ignore_rows': 0.3}, no_cache=True)
         self.assertAlmostEqual(samples.mean('ombh2'), 0.0223749, 6)
         self.assertAlmostEqual(samples.getUpper('H0'), 100, 6)
         self.assertEqual(samples.getLower('sigma8'), None)
         samples.saveAsText(r'planck_test')
         ranges = ParamBounds('planck_test.ranges')
         for par in samples.paramNames.names:
             self.assertEqual(samples.getUpper(par.name), ranges.getUpper(par.name))
Exemple #7
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 def test_chains(self):
     if os.path.exists(self.path):
         root = os.path.join(self.path, 'DES_shear')
         samples = loadMCSamples(root, settings={'ignore_rows': 0.3}, no_cache=True)
         self.assertAlmostEqual(samples.mean('ombh2'), 0.02764592190482377, 6)
         pars = samples.getParamSampleDict(10)
         self.assertAlmostEqual(0.06, pars['mnu'], 6)
         self.assertAlmostEqual(samples.getUpper('ns'), 1.07, 6)
         self.assertAlmostEqual(samples.getLower('ns'), 0.87, 6)
         self.assertEqual(samples.getLower('DES_DzS2'), None)
         self.assertAlmostEqual(0, pars['omk'])
         from getdist.command_line import getdist_command
         res = getdist_command([root])
         self.assertTrue('-log(Like) = 95.49' in res, res)
Exemple #8
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 def __init__(self, Chains, ignore_rows=0.3):
     self.root = list(np.asarray(Chains)[:, 0])
     self.lengend = list(np.asarray(Chains)[:, 1])
     self.aic_g = False
     self.Samp = []
     self._n = len(Chains)
     self.minkaf = np.zeros(self._n)
     self.data_num = np.zeros(self._n)
     for i in list(range(self._n)):
         self.Samp.append(
             loadMCSamples('./chains/' + self.root[i],
                           settings={'ignore_rows': ignore_rows}))
     self.param_names = []
     for na in self.Samp[0].getParamNames().names:
         self.param_names.append(na.name)
 def setUp(self):
     # get path:
     self.here = os.path.dirname(os.path.abspath(__file__))
     # get chains:
     self.chain_1 = loadMCSamples(self.here + '/../../test_chains/DES')
     self.chain_2 = loadMCSamples(self.here +
                                  '/../../test_chains/Planck18TTTEEE')
     self.chain_12 = loadMCSamples(self.here +
                                   '/../../test_chains/Planck18TTTEEE_DES')
     self.chain_prior = loadMCSamples(self.here +
                                      '/../../test_chains/prior')
     # thin the chain:
     self.chain_1.getConvergeTests()
     self.chain_2.getConvergeTests()
     self.chain_12.getConvergeTests()
     self.chain_prior.getConvergeTests()
     self.chain_1.weighted_thin(int(self.chain_1.indep_thin))
     self.chain_2.weighted_thin(int(self.chain_2.indep_thin))
     self.chain_12.weighted_thin(int(self.chain_12.indep_thin))
     self.chain_prior.weighted_thin(int(self.chain_prior.indep_thin))
     # get difference chain:
     self.diff_chain = pd.parameter_diff_chain(self.chain_1,
                                               self.chain_2,
                                               boost=1)
Exemple #10
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def get_models_from_mcsamples(mcsamples,
                              lk_names,
                              extras=None,
                              n=None,
                              progress=True):
    if type(mcsamples) == str:
        mcsamples = loadMCSamples(mcsamples)

    if n is not None:
        samples = mcsamples.samples[np.random.choice(
            mcsamples.samples.shape[0], size=n)]
    else:
        samples = mcsamples.samples

    out = {}
    lk = load_likelihood_from_yaml(mcsamples.rootname + '.yml')

    top_level = True
    if isinstance(lk, LikelihoodContainer):
        top_level = False

    if top_level and len(lk_names) > 1:
        raise ValueError(
            "you have specified more lk_names than actually exist!")

    for params in tqdm(samples, disable=not progress):
        ctx = lk.get_ctx(params=params)
        model = lk.reduce_model(ctx=ctx, params=params)

        if top_level:
            ctx = {lk_names[0]: ctx}
            model = {lk_names[0]: model}

        for lk_name in lk_names:

            if lk_name not in out:
                out[lk_name] = {}

            if "model" not in out[lk_name]:
                out[lk_name]['model'] = []

            out[lk_name]['model'].append(model[lk_name])

            for extra in (extras or []):
                if extra in ctx[lk_name]:
                    out[lk_name][extra] = ctx[lk_name][extra]

    return out
Exemple #11
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def samples_from_getdist_chains(params, file_root, latex=False, **kwargs):
    """ Extract samples and weights from getdist chains.

    Parameters
    ----------
    params: list(str)
        Names of parameters to be supplied to second argument of f(x|theta).

    file_root: str, optional
        Root name for getdist chains files. This variable automatically
        defines:
        - chains_file = file_root.txt
        - paramnames_file = file_root.paramnames
        but can be overidden by chains_file or paramnames_file.

    latex: bool, optional
        Also return an array of latex strings for those paramnames.

    Any additional keyword arguments are forwarded onto getdist, e.g:

        samples_from_getdist_chains(params, file_root,
                                    settings={'ignore_rows':0.5})

    Returns
    -------
    samples: numpy.array
        2D Array of samples. `shape=(len(samples), len(params))`

    weights: numpy.array
        Array of weights. `shape = (len(params),)`

    latex: list(str), optional
        list of latex strings for each parameter
        (if latex is provided as an argument)
    """

    import getdist
    samples = getdist.loadMCSamples(file_root, **kwargs)
    weights = samples.weights

    indices = [samples.index[p] for p in params]
    samps = samples.samples[:, indices]
    if latex:
        latex = [samples.parLabel(p) for p in params]
        return samps, weights, latex
    else:
        return samps, weights
Exemple #12
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    def testFileLoadPlot(self):
        samples = loadMCSamples(self.root, settings={'ignore_rows': 0.1})
        g = plots.getSinglePlotter(chain_dir=self.tempdir, analysis_settings={'ignore_rows': 0.1})
        self.assertEqual(g.sampleAnalyser.samplesForRoot('testchain').numrows, samples.numrows,
                         "Inconsistent chain loading")
        self.assertEqual(g.sampleAnalyser.samplesForRoot('testchain').getTable().tableTex(),
                         samples.getTable().tableTex(), 'Inconsistent load result')
        samples.getConvergeTests(0.95)
        self.assertAlmostEqual(0.0009368, samples.GelmanRubin, 5, 'Gelman Rubin error, got ' + str(samples.GelmanRubin))

        g = plots.getSinglePlotter()
        g.plot_3d(samples, ['x', 'y', 'x'])
        g.export(self.root + '_plot.pdf')

        g = plots.getSinglePlotter(chain_dir=self.tempdir,
                                   analysis_settings={'ignore_rows': 0.1, 'contours': [0.68, 0.95, 0.99]})
        g.settings.num_plot_contours = 3
        g.plot_2d('testchain', ['x', 'y'])
Exemple #13
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    def paramnames(self):
        r"""Read header of <root>.1.txt to infer the paramnames.

        This is the data file of the first chain. It should have as many
        columns as there are parameters (sampled and derived) plus an
        additional two corresponding to the weights (first column) and the
        logposterior (second column). The first line should start with a # and
        should list the parameter names corresponding to the columns. This
        will be used as label in the pandas array. Uses GetDist's
        `loadMCSamples` to infer the tex dictionary.
        """
        try:
            with open(self.root + ".1.txt") as f:
                header = f.readline()[1:]
                paramnames = header.split()[2:]
                s = loadMCSamples(file_root=self.root)
                tex = {p.name: '$' + p.label + '$' for p in s.paramNames.names}
                return paramnames, tex
        except IOError:
            return super().paramnames()
Exemple #14
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def callGetDist(chainsFilePrefix, plotName, nParams, plotLegend):
    """
    produces triangular posterior plots using getDist for first nParams
    parameters from chains file as labelled in that file and in .paramnames
    plotName should contain image type extension (e.g. .png)

    Args:

    chainsFilePrefix : string chains file excluding the '.txt' prefix

    plotName : string name of plot

    nParams : int dimensionality of parameter space

    plotLegend : list used for plot legend

    """
    print(plotName)
    try:
        import getdist.plots
        import getdist.loadMCSamples
    except ImportError:
        try:
            import getdist
        except ImportError:
            print("can't import getdist. Exiting...")
            sys.exit(1)
    save = True
    paramList = ['p' + str(i + 1) for i in range(nParams)]
    chains = [getdist.loadMCSamples(chain) for chain in chainsFilePrefix]
    g = getdist.plots.getSubplotPlotter(width_inch=6)
    g.triangle_plot(chains, paramList, filled=False, legend_labels=plotLegend)
    if save:
        g.export(plotName)
    else:
        plt.show()
    return g
Exemple #15
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day = f'{today.year}_{today.month}_{10}'

N = 2
GL_min = "8.0"
GL_max = 76.8
points = 10000
directory = f"graphs/{day}/"
tag = "parameter_range_run"
# directory = f"graphs/{today.year}_6_23/"

for model in [model27, model28]:
    if not os.path.isdir(directory):
        os.makedirs(directory)

    samples = loadMCSamples(
        f'posterior_data/{day}/{model.__name__}{tag}_N{N}_GLmin{GL_min}_GLmax{GL_max}_p{points}'
    )
    # samples = loadMCSamples(f'posterior_data/{day}/{model.__name__}small_except_c_omega_N{N}_GLmin{GL_min}_GLmax{GL_max}_p{points}')
    g = plots_edit.get_subplot_plotter()

    g.triangle_plot(samples, filled=True)
    # plt.savefig(f"{directory}2D_posterior_{model.__name__}.png")

    # plt.savefig(f"special_graphs/2D_posterior_{model.__name__}_edit.png")

    # fig = plt.gcf()

    # fig2, axis = plt.subplots()

    # fig.axes[0].scatter([0], [0])
Exemple #16
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def main():
    
    # Generate the x values of our data
    for i in range(len(xdata)):
        xdata[i] = float(i)
    
    # Set the noise stddev of the y values
    for i in range(len(ydata)):
        ydata_err[i] = 100.0
    
    params_value = [
        900,
        400,
        100,
        0
    ]
    # Generate the y values of our data
    for i in range(len(ydata)):
        # Evaluate gaussian
        ydata[i] = evaluate_gaussian(xdata[i], 
                                     params_value[0],
                                     params_value[1],
                                     params_value[2],
                                     params_value[3])
        # Add noise
        ydata[i] += random.gauss(0, ydata_err[i])
    
    filename = 'output/out'
    
    if not os.path.exists('./output'):
        os.makedirs('./output')
    
    # Run MultiNest!
    pymultinest.run(my_loglike, 
                    my_prior, 
                    params_count, 
                    outputfiles_basename=filename, 
                    resume=False, 
                    verbose=True,
                    importance_nested_sampling=True,
                    multimodal=False,
                    const_efficiency_mode=False,
                    n_live_points=100,
                    evidence_tolerance=0.3,
                    sampling_efficiency=0.8,
                    max_iter=0)
    
    # Perform result analysis
    analyzer = pymultinest.Analyzer(outputfiles_basename=filename, n_params=params_count)
    stats = analyzer.get_stats()
    
    # Pretty print results to a JSON file
    results_json = json.dumps(stats, sort_keys=True, indent=2)
    with open('pretty_results.json', 'w') as f:
        f.write(results_json)
      
    # Generate best-fit model
    ydata_best_fit = numpy.empty(data_length)
    for i in range(len(ydata_best_fit)):
        ydata_best_fit[i] = evaluate_gaussian(xdata[i],
                                              stats['modes'][0]['maximum'][0],
                                              stats['modes'][0]['maximum'][1],
                                              stats['modes'][0]['maximum'][2],
                                              stats['modes'][0]['maximum'][3])
    
    # Plot data vs best fit
    fig = pyplot.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(ydata, color='#5e3c99')
    ax.plot(ydata_best_fit, color='#e66101', linewidth=6)
    pyplot.savefig('data_vs_best_fit.png')
    
    # Corner plot
    samples = getdist.loadMCSamples(filename+'.txt')
    plotter = getdist.plots.getSubplotPlotter()
    plotter.triangle_plot([samples], filled=True)
    plotter.export('corner_plot.png')
Exemple #17
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#!/usr/bin/python
import getdist
import numpy as np
import os
import pyccl as ccl

MP_root = '/mnt/zfsusers/gravityls_3/codes/montepython_emilio'

des_root = os.path.join(MP_root, 'chains/cl_cross_corr_des_full_kappa_l8/')
des = getdist.loadMCSamples(os.path.join(des_root, '2020-03-09_1000000_'),
                            settings={'ignore_rows': 0.1})

growth_root = os.path.join(
    MP_root, 'chains/cl_cross_corr_des_growth_dpk0_fixed_kappa_l8/')
growth = getdist.loadMCSamples(os.path.join(growth_root,
                                            '2020-03-09_1000000_'),
                               settings={'ignore_rows': 0.1})


def S8z(mcmc, a, MG=False, size=None):
    p = mcmc.getParams()
    if size is None:
        size = p.A_s.size
    S8_ar = np.zeros((size, a.size))

    for i in range(size):
        cosmo = ccl.Cosmology(h=p.h[i],
                              Omega_c=p.Omega_c[i],
                              Omega_b=p.Omega_b[i],
                              A_s=1e-9 * p.A_s[i],
                              n_s=p.n_s[i],
Exemple #18
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from getdist import loadMCSamples, plots, covmat
import numpy as np
import os, fnmatch

samples = loadMCSamples('/home/projects/dea/mcmc/chains/mcmc_final_output',
                        settings={'ignore_rows': .0})

g = plots.getSinglePlotter()

g.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)

p = samples.getParams()

bestfit = samples.getLikeStats()

means = samples.setMeans()

stats = samples.getMargeStats()

stats.saveAsText('/home/projects/dea/mcmc/chains/1Dstatistics.txt')

print '1D STATISTICS FILE CREATED'

f = plots.getSubplotPlotter()

f.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)

f.plots_1d(samples)

f.export('/home/projects/dea/mcmc/figures/1D_plots.pdf')
Exemple #19
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#!/usr/bin/python26

from __future__ import print_function
from getdist import plots, MCSamples, loadMCSamples
import getdist, IPython

A611 = loadMCSamples('chains/examp/A611')
new_chains = raw_input('Path to new chains:').strip()
A6112 = loadMCSamples(new_chains)

g = plots.getSubplotPlotter(width_inch=8)
g.settings.axes_fontsize = 8
g.settings.alpha_filled_add = 0.4
g.triangle_plot([A611, A6112], ['1', '2', '3', '4', '5', '6'],
                filled_compare=True,
                legend_labels=['Example', 'New chains'],
                legend_loc='upper right',
                line_args=[{
                    'ls': '--',
                    'color': 'green'
                }, {
                    'lw': 2,
                    'color': 'darkblue'
                }],
                contour_colors=['green', 'darkblue'])
g.export('A611_tri.png')
 def load_from_file(rootname):
     #Load from file
     self.samples = []
     for f in rootname:
         self.samples.append(gd.loadMCSamples(rootname))
from getdist import loadMCSamples, plots

number_of_parameters = 12

samples = loadMCSamples('../output/mcmc_output', settings={'ignore_rows': 2})

g = plots.getSinglePlotter()

g.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)

g.triangle_plot(samples, filled=True)

g.export('triangle_figure.pdf')

bestfit = samples.getLikeStats()

means = samples.setMeans()

filebestfit = open("../output/bestfit.txt", 'w')

filemeans = open("../output/means.txt", 'w')

#filebestfit.write("-log(Like) = "+str(bestfit.logLike_sample)+"\n")

for index in range(number_of_parameters):

    filebestfit.write(str(bestfit.names[index].bestfit_sample) + "\n")

    filemeans.write(str(means[index]) + "\n")

filebestfit.close()
        "standard_neff_summnu_planck_szclusters"
        ]

colors=['blue', 'green', 'red', 'magenta', 'gray']
labels = ['Planck15', 'Planck15+BAO', 'Planck15+LRG', 'Planck15+WZ', 'Planck15+Clusters']

# Reorder list for plotting
order = [0, 4, 3, 1, 2]
dirs = [dirs[i] for i in order]
roots = [roots[i] for i in order]
colors = [colors[i] for i in order]
labels = [labels[i] for i in order]

samples = []
for root in [d+r for d, r in zip(dirs, roots)]:
    samples.append(loadMCSamples(root))

for samp in samples:
    p = samp.getParams()
    samp.addDerived((p.ombh2+p.omch2)/p.h**2, name="omm", label="\Omega_M")

# Single plot
#g = plots.getSinglePlotter()
#g.plot_2d(samples, "omm", "sigma8", filled=True)#, lims=[0.20, 0.40, 0.55, 0.80])
#g.settings.legend_fontsize = 12
#g.add_legend(["Planck15", "Planck15+BAO", "Planck15+LRG", "Planck15+WiggleZ", "Planck+Clusters"], legend_loc="lower left")

# Subplots
g = plots.getSubplotPlotter()
g.settings.figure_legend_frame = False
g.settings.legend_frac_subplot_margin = 0.2
Exemple #23
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import numpy as np
import os, fnmatch

filenames = fnmatch.filter(os.listdir("../output/chains/NC-CMB-run3/"),
                           "mcmc_*.txt")

for index in range(len(filenames)):

    os.rename(
        "../output/chains/NC-CMB-run3/" + str(filenames[index]),
        "../output/chains/NC-CMB-run3/mcmc_final_output_" + str(index + 1) +
        ".txt")

number_of_parameters = 11

samples = loadMCSamples('../output/chains/NC-CMB-run3/mcmc_final_output',
                        settings={'ignore_rows': 0.})

#print 'CONVERGENCE FOR SAMPLES WITH LENSING ', samples.getGelmanRubin()

g = plots.getSinglePlotter()

g.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)

p = samples.getParams()

samples.addDerived(np.log(1.e1**10 * p.A_s),
                   name='ln1010As',
                   label='\ln 10^{10}A_s')

samples.addDerived(np.log10(p.cs2_fld), name='logcs2fld', label='\log c_s^2')
Exemple #24
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args = parser.parse_args()

# Load the chains
chains = []
for path in args.chains:
    try:
        prefix = [x for x in os.listdir(path) if re.match('.*__..txt', x)]
    except FileNotFoundError:
        prefix = []
        print('WARNING: chains not found at {}'.format(path))
        continue
    prefix.sort()
    chain = loadMCSamples(os.path.join(path, prefix[0][:-6]),
                          no_cache=False,
                          settings={
                              'ignore_rows': args.ignore_rows,
                              'smooth_scale_1D': args.smooth_scale_1D,
                              'smooth_scale_2D': args.smooth_scale_2D
                          })
    chains.append(chain)

# Load legends
legends = []
if args.legends and len(args.legends) == len(chains):
    legends = args.legends
else:
    legends = [re.split('/', x.root)[-2] for x in chains]

# Load output file
if args.output:
    output_file = args.output
Exemple #25
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 def setUp(self):
     # get path:
     self.here = os.path.dirname(os.path.abspath(__file__))
     # get chains:
     self.chain = loadMCSamples(self.here + '/../../test_chains/DES')
Exemple #26
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def LoadDataset(label):
    return getdist.loadMCSamples(str(STRING_TO_CHAIN[label])) 
from getdist import plots, loadMCSamples

ednew = loadMCSamples('../results/chains/braneworld',
                      settings={'ignore_rows': 0.3})

g = plots.getSubplotPlotter()
g.plot_2d(ednew, ['l', 'z'], filled=True)
g.export('fig2.pdf')
Exemple #28
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import matplotlib.pyplot as plt
from matplotlib import rcParams
from getdist import plots, loadMCSamples

# Generic properties from matplotlib
rcParams['font.family'] = 'serif'
rcParams['text.usetex'] = True

path_to_chains = '../data/'
chain_file = ['Post_FM_MWP_WDMP_data3_MWdwarfs']
labels = [r'LSST MW Sats. + Spec.']

samplesMC = []
# Load samples
for cfile in chain_file:
    samplesMC.append(loadMCSamples(path_to_chains + cfile + '/' + cfile))

g = plots.getSinglePlotter()
g.settings.figure_legend_frame = True
g.settings.tight_layout = True
g.settings.legend_fontsize = 16
g.settings.lab_fontsize = 20
g.plot_2d(samplesMC, ['m_WDM', 'log_sigma_o_m'],
          filled=True,
          legend_loc='lower left',
          line_args=[{
              'lw': 1.5,
              'color': 'tab:blue'
          }, {
              'lw': 1.5,
              'color': '#85C1E9',
from getdist import loadMCSamples, plots

number_of_parameters = 14

samples = loadMCSamples('../output/chains/mcmc_final_output_HP',
                        settings={'ignore_rows': 2})

g = plots.getSinglePlotter()

g.settings.rcSizes(axes_fontsize=2, lab_fontsize=7)

g.triangle_plot(samples, filled=True)

g.export('../output/chains/triangle_figure_HP_R11_H.pdf')

bestfit = samples.getLikeStats()

means = samples.setMeans()

filebestfit = open("../output/chains/bestfit.txt", 'w')

filemeans = open("../output/chains/means.txt", 'w')

#filebestfit.write("-log(Like) = "+str(bestfit.logLike_sample)+"\n")

for index in range(number_of_parameters):

    filebestfit.write(str(bestfit.names[index].bestfit_sample) + "\n")

    filemeans.write(str(means[index]) + "\n")
        gd_pars = gd_root + '.paramnames'
        #print(gd_pars)
        if not osp.exists(gd_pars):

            # prevent race conditions: can have all processes trying
            # to create a symlink simultaneously, with the slow ones
            # finding it's already been created despite not existing
            # before this if statement
            try:
                os.symlink(template, gd_pars)
            except OSError as e:
                if e.errno != errno.EEXIST:
                    raise e

        # read in samples and fill in derived parameters
        gd_samples = gd.loadMCSamples(gd_root)
        pars = gd_samples.getParams()
        m_1, m_2 = chirp_q_to_comp_masses(pars.chirp_mass, \
                                          pars.mass_ratio)
        gd_samples.addDerived(m_1, name='mass_1', label=r'm_{\rm BH}')
        gd_samples.addDerived(m_2, name='mass_2', label=r'm_{\rm NS}')
        if aligned_spins:
            gd_samples.addDerived(np.abs(pars.chi_1), name='a_1', label='a_1')

        # optionally importance sample the input mass priors
        if imp_sample:

            # extract posterior samples relevant for reweighting
            m_c_samples = pars.chirp_mass
            q_inv_samples = pars.mass_ratio
            m_1_samples, m_2_samples = \
Exemple #31
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        ff.write('\n')

    ff.close()
    print(corrs)
    #np.savetxt(roots+'_'+pinfo+'.txt', corrs, fmt='%16.8f')
    return


#===================================
if __name__ == "__main__":
    arg = []
    for ss in sys.argv[1:]:
        arg.append('%s' % ss)

    roots = arg[0]
    samples = loadMCSamples(roots, dist_settings={'ignore_rows': 0.3})

    pname = samples.getParams()
    #la1 = np.pi*pname.DAstar*1000.0/pname.rstar
    #samples.addDerived(la1, name='lA1', label='l_A')
    la2 = np.pi / (pname.thetastar / 100.0)
    samples.addDerived(la2, name='lA', label='l_A')
    R = np.sqrt(
        pname.omegam * pname.H0**2) * pname.DAstar * 1000.0 / (c_light / 1000)
    samples.addDerived(R, name='R', label='R')

    samples.contours = np.array([0.68, 0.95, 0.99])
    samples.updateBaseStatistics()
    #triangleplot(samples)
    #outputcorrelation(roots,samples)
    outputdats(samples)
Exemple #32
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path, name = os.path.split(fileroot)
if path == '':
    fileroot = './' + fileroot
chains = fileroot + '.txt'
fparam = fileroot + '.paramnames'
frange = fileroot + '.ranges'
ofile = fileroot + 'mystats.txt'
if not os.path.isfile(chains):
    print('Error: cannot access {}'.format(chains), file=sys.stderr)
    sys.exit(1)

np.savetxt(fparam, np.transpose([names, labels]), fmt='%s')
np.savetxt(frange, np.transpose([names, lowbound, upbound]), fmt='%s')

# Load sample from FILE_ROOT.txt
sample = loadMCSamples(fileroot, \
    settings={'fine_bins_2D':1024,'fine_bins':8192})

stats = sample.getMargeStats()
par = stats.parWithName(names[0])
lower = par.limits[0].lower
upper = par.limits[0].upper
sigma = (upper - lower) * 0.5
best = (upper + lower) * 0.5

# Read evidence from FILE_ROOTstats.dat
fstat = fileroot + 'stats.dat'
with open(fstat, "r") as f:
    f.readline()
    line = f.readline()
    evi = float(line.split(':')[1].split()[0])