result.py 47.8 KB
Newer Older
Colm Talbot's avatar
Colm Talbot committed
1 2
from __future__ import division

3
import os
4
from distutils.version import LooseVersion
5
from collections import OrderedDict, namedtuple
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
6

7 8
import numpy as np
import pandas as pd
9
import corner
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
10
import json
11
import scipy.stats
12
import matplotlib
13
import matplotlib.pyplot as plt
14
from matplotlib import lines as mpllines
15

16
from . import utils
Colm Talbot's avatar
Colm Talbot committed
17
from .utils import (logger, infer_parameters_from_function,
Colm Talbot's avatar
Colm Talbot committed
18 19
                    check_directory_exists_and_if_not_mkdir,
                    BilbyJsonEncoder, decode_bilby_json)
Colm Talbot's avatar
Colm Talbot committed
20
from .prior import Prior, PriorDict, DeltaFunction
21

22

Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
23
def result_file_name(outdir, label, extension='json'):
24 25 26 27 28 29 30 31
    """ Returns the standard filename used for a result file

    Parameters
    ----------
    outdir: str
        Name of the output directory
    label: str
        Naming scheme of the output file
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
32 33
    extension: str, optional
        Whether to save as `hdf5` or `json`
34 35 36 37 38

    Returns
    -------
    str: File name of the output file
    """
39 40
    if extension in ['json', 'hdf5']:
        return '{}/{}_result.{}'.format(outdir, label, extension)
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
41
    else:
42
        raise ValueError("Extension type {} not understood".format(extension))
43 44


45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
def _determine_file_name(filename, outdir, label, extension):
    """ Helper method to determine the filename """
    if filename is not None:
        return filename
    else:
        if (outdir is None) and (label is None):
            raise ValueError("No information given to load file")
        else:
            return result_file_name(outdir, label, extension)


def read_in_result(filename=None, outdir=None, label=None, extension='json'):
    """ Reads in a stored bilby result object

    Parameters
    ----------
    filename: str
        Path to the file to be read (alternative to giving the outdir and label)
    outdir, label, extension: str
        Name of the output directory, label and extension used for the default
        naming scheme.

    """
    filename = _determine_file_name(filename, outdir, label, extension)

    # Get the actual extension (may differ from the default extension if the filename is given)
    extension = os.path.splitext(filename)[1].lstrip('.')
    if 'json' in extension:
        result = Result.from_json(filename=filename)
    elif ('hdf5' in extension) or ('h5' in extension):
        result = Result.from_hdf5(filename=filename)
    elif extension is None:
        raise ValueError("No filetype extension provided")
    else:
        raise ValueError("Filetype {} not understood".format(extension))
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
80
    return result
81 82 83 84 85 86 87 88 89 90


class Result(object):
    def __init__(self, label='no_label', outdir='.', sampler=None,
                 search_parameter_keys=None, fixed_parameter_keys=None,
                 priors=None, sampler_kwargs=None, injection_parameters=None,
                 meta_data=None, posterior=None, samples=None,
                 nested_samples=None, log_evidence=np.nan,
                 log_evidence_err=np.nan, log_noise_evidence=np.nan,
                 log_bayes_factor=np.nan, log_likelihood_evaluations=None,
91 92 93 94
                 log_prior_evaluations=None, sampling_time=None, nburn=None,
                 walkers=None, max_autocorrelation_time=None,
                 parameter_labels=None, parameter_labels_with_unit=None,
                 version=None):
95
        """ A class to store the results of the sampling run
96 97 98

        Parameters
        ----------
99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
        label, outdir, sampler: str
            The label, output directory, and sampler used
        search_parameter_keys, fixed_parameter_keys: list
            Lists of the search and fixed parameter keys. Elemenents of the
            list should be of type `str` and matchs the keys of the `prior`
        priors: dict, bilby.core.prior.PriorDict
            A dictionary of the priors used in the run
        sampler_kwargs: dict
            Key word arguments passed to the sampler
        injection_parameters: dict
            A dictionary of the injection parameters
        meta_data: dict
            A dictionary of meta data to store about the run
        posterior: pandas.DataFrame
            A pandas data frame of the posterior
        samples, nested_samples: array_like
            An array of the output posterior samples and the unweighted samples
        log_evidence, log_evidence_err, log_noise_evidence, log_bayes_factor: float
            Natural log evidences
        log_likelihood_evaluations: array_like
            The evaluations of the likelihood for each sample point
120 121
        log_prior_evaluations: array_like
            The evaluations of the prior for each sample point
122 123 124 125 126 127 128 129 130 131
        sampling_time: float
            The time taken to complete the sampling
        nburn: int
            The number of burn-in steps discarded for MCMC samplers
        walkers: array_like
            The samplers taken by a ensemble MCMC samplers
        max_autocorrelation_time: float
            The estimated maximum autocorrelation time for MCMC samplers
        parameter_labels, parameter_labels_with_unit: list
            Lists of the latex-formatted parameter labels
132 133 134
        version: str,
            Version information for software used to generate the result. Note,
            this information is generated when the result object is initialized
135

136 137 138 139
        Note
        ---------
        All sampling output parameters, e.g. the samples themselves are
        typically not given at initialisation, but set at a later stage.
140 141

        """
142

143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163
        self.label = label
        self.outdir = os.path.abspath(outdir)
        self.sampler = sampler
        self.search_parameter_keys = search_parameter_keys
        self.fixed_parameter_keys = fixed_parameter_keys
        self.parameter_labels = parameter_labels
        self.parameter_labels_with_unit = parameter_labels_with_unit
        self.priors = priors
        self.sampler_kwargs = sampler_kwargs
        self.meta_data = meta_data
        self.injection_parameters = injection_parameters
        self.posterior = posterior
        self.samples = samples
        self.nested_samples = nested_samples
        self.walkers = walkers
        self.nburn = nburn
        self.log_evidence = log_evidence
        self.log_evidence_err = log_evidence_err
        self.log_noise_evidence = log_noise_evidence
        self.log_bayes_factor = log_bayes_factor
        self.log_likelihood_evaluations = log_likelihood_evaluations
164
        self.log_prior_evaluations = log_prior_evaluations
165
        self.sampling_time = sampling_time
166
        self.version = version
Colm Talbot's avatar
Colm Talbot committed
167
        self.max_autocorrelation_time = max_autocorrelation_time
168

169 170 171
        self.prior_values = None
        self._kde = None

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
    @classmethod
    def from_hdf5(cls, filename=None, outdir=None, label=None):
        """ Read in a saved .h5 data file

        Parameters
        ----------
        filename: str
            If given, try to load from this filename
        outdir, label: str
            If given, use the default naming convention for saved results file

        Returns
        -------
        result: bilby.core.result.Result

        Raises
        -------
        ValueError: If no filename is given and either outdir or label is None
                    If no bilby.core.result.Result is found in the path

        """
193
        import deepdish
194 195
        filename = _determine_file_name(filename, outdir, label, 'hdf5')

196
        if os.path.isfile(filename):
Moritz Huebner's avatar
Moritz Huebner committed
197 198
            dictionary = deepdish.io.load(filename)
            # Some versions of deepdish/pytables return the dictionanary as
199
            # a dictionary with a key 'data'
Moritz Huebner's avatar
Moritz Huebner committed
200 201 202 203 204 205
            if len(dictionary) == 1 and 'data' in dictionary:
                dictionary = dictionary['data']
            try:
                return cls(**dictionary)
            except TypeError as e:
                raise IOError("Unable to load dictionary, error={}".format(e))
206 207 208
        else:
            raise IOError("No result '{}' found".format(filename))

Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229
    @classmethod
    def from_json(cls, filename=None, outdir=None, label=None):
        """ Read in a saved .json data file

        Parameters
        ----------
        filename: str
            If given, try to load from this filename
        outdir, label: str
            If given, use the default naming convention for saved results file

        Returns
        -------
        result: bilby.core.result.Result

        Raises
        -------
        ValueError: If no filename is given and either outdir or label is None
                    If no bilby.core.result.Result is found in the path

        """
230 231
        filename = _determine_file_name(filename, outdir, label, 'json')

Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
232
        if os.path.isfile(filename):
233
            with open(filename, 'r') as file:
Colm Talbot's avatar
Colm Talbot committed
234
                dictionary = json.load(file, object_hook=decode_bilby_json)
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
235 236 237 238 239 240 241 242 243 244 245 246 247
            for key in dictionary.keys():
                # Convert the loaded priors to bilby prior type
                if key == 'priors':
                    for param in dictionary[key].keys():
                        dictionary[key][param] = str(dictionary[key][param])
                    dictionary[key] = PriorDict(dictionary[key])
            try:
                return cls(**dictionary)
            except TypeError as e:
                raise IOError("Unable to load dictionary, error={}".format(e))
        else:
            raise IOError("No result '{}' found".format(filename))

248
    def __str__(self):
249
        """Print a summary """
250 251
        if getattr(self, 'posterior', None) is not None:
            if getattr(self, 'log_noise_evidence', None) is not None:
252 253 254 255
                return ("nsamples: {:d}\n"
                        "log_noise_evidence: {:6.3f}\n"
                        "log_evidence: {:6.3f} +/- {:6.3f}\n"
                        "log_bayes_factor: {:6.3f} +/- {:6.3f}\n"
256
                        .format(len(self.posterior), self.log_noise_evidence, self.log_evidence,
257 258 259 260 261
                                self.log_evidence_err, self.log_bayes_factor,
                                self.log_evidence_err))
            else:
                return ("nsamples: {:d}\n"
                        "log_evidence: {:6.3f} +/- {:6.3f}\n"
262
                        .format(len(self.posterior), self.log_evidence, self.log_evidence_err))
263 264
        else:
            return ''
265

266 267 268 269 270 271
    @property
    def priors(self):
        if self._priors is not None:
            return self._priors
        else:
            raise ValueError('Result object has no priors')
272

273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290
    @priors.setter
    def priors(self, priors):
        if isinstance(priors, dict):
            self._priors = PriorDict(priors)
            if self.parameter_labels is None:
                self.parameter_labels = [self.priors[k].latex_label for k in
                                         self.search_parameter_keys]
            if self.parameter_labels_with_unit is None:
                self.parameter_labels_with_unit = [
                    self.priors[k].latex_label_with_unit for k in
                    self.search_parameter_keys]

        elif priors is None:
            self._priors = priors
            self.parameter_labels = self.search_parameter_keys
            self.parameter_labels_with_unit = self.search_parameter_keys
        else:
            raise ValueError("Input priors not understood")
291

292 293 294 295 296
    @property
    def samples(self):
        """ An array of samples """
        if self._samples is not None:
            return self._samples
297
        else:
298
            raise ValueError("Result object has no stored samples")
299

300 301 302
    @samples.setter
    def samples(self, samples):
        self._samples = samples
303

304 305 306 307 308 309 310
    @property
    def nested_samples(self):
        """" An array of unweighted samples """
        if self._nested_samples is not None:
            return self._nested_samples
        else:
            raise ValueError("Result object has no stored nested samples")
311

312 313 314
    @nested_samples.setter
    def nested_samples(self, nested_samples):
        self._nested_samples = nested_samples
315

316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351
    @property
    def walkers(self):
        """" An array of the ensemble walkers """
        if self._walkers is not None:
            return self._walkers
        else:
            raise ValueError("Result object has no stored walkers")

    @walkers.setter
    def walkers(self, walkers):
        self._walkers = walkers

    @property
    def nburn(self):
        """" An array of the ensemble walkers """
        if self._nburn is not None:
            return self._nburn
        else:
            raise ValueError("Result object has no stored nburn")

    @nburn.setter
    def nburn(self, nburn):
        self._nburn = nburn

    @property
    def posterior(self):
        """ A pandas data frame of the posterior """
        if self._posterior is not None:
            return self._posterior
        else:
            raise ValueError("Result object has no stored posterior")

    @posterior.setter
    def posterior(self, posterior):
        self._posterior = posterior

352 353 354 355 356 357 358 359 360 361 362
    @property
    def version(self):
        return self._version

    @version.setter
    def version(self, version):
        if version is None:
            self._version = 'bilby={}'.format(utils.get_version_information())
        else:
            self._version = version

363
    def _get_save_data_dictionary(self):
364
        # This list defines all the parameters saved in the result object
365 366 367 368 369
        save_attrs = [
            'label', 'outdir', 'sampler', 'log_evidence', 'log_evidence_err',
            'log_noise_evidence', 'log_bayes_factor', 'priors', 'posterior',
            'injection_parameters', 'meta_data', 'search_parameter_keys',
            'fixed_parameter_keys', 'sampling_time', 'sampler_kwargs',
370 371
            'log_likelihood_evaluations', 'log_prior_evaluations', 'samples',
            'nested_samples', 'walkers', 'nburn', 'parameter_labels',
372
            'parameter_labels_with_unit', 'version']
373 374 375 376 377 378 379 380
        dictionary = OrderedDict()
        for attr in save_attrs:
            try:
                dictionary[attr] = getattr(self, attr)
            except ValueError as e:
                logger.debug("Unable to save {}, message: {}".format(attr, e))
                pass
        return dictionary
381

Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
382
    def save_to_file(self, overwrite=False, outdir=None, extension='json'):
Colm Talbot's avatar
Colm Talbot committed
383
        """
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
384
        Writes the Result to a json or deepdish h5 file
Colm Talbot's avatar
Colm Talbot committed
385 386 387 388 389 390

        Parameters
        ----------
        overwrite: bool, optional
            Whether or not to overwrite an existing result file.
            default=False
391 392
        outdir: str, optional
            Path to the outdir. Default is the one stored in the result object.
393 394
        extension: str, optional {json, hdf5}
            Determines the method to use to store the data
Colm Talbot's avatar
Colm Talbot committed
395
        """
396
        outdir = self._safe_outdir_creation(outdir, self.save_to_file)
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
397
        file_name = result_file_name(outdir, self.label, extension)
398

399
        if os.path.isfile(file_name):
Colm Talbot's avatar
Colm Talbot committed
400 401 402 403 404 405 406 407
            if overwrite:
                logger.debug('Removing existing file {}'.format(file_name))
                os.remove(file_name)
            else:
                logger.debug(
                    'Renaming existing file {} to {}.old'.format(file_name,
                                                                 file_name))
                os.rename(file_name, file_name + '.old')
408

Gregory Ashton's avatar
Gregory Ashton committed
409
        logger.debug("Saving result to {}".format(file_name))
410 411

        # Convert the prior to a string representation for saving on disk
412
        dictionary = self._get_save_data_dictionary()
413 414 415
        if dictionary.get('priors', False):
            dictionary['priors'] = {key: str(self.priors[key]) for key in self.priors}

416
        # Convert callable sampler_kwargs to strings
417 418 419 420
        if dictionary.get('sampler_kwargs', None) is not None:
            for key in dictionary['sampler_kwargs']:
                if hasattr(dictionary['sampler_kwargs'][key], '__call__'):
                    dictionary['sampler_kwargs'][key] = str(dictionary['sampler_kwargs'])
421

Gregory Ashton's avatar
Fix #49  
Gregory Ashton committed
422
        try:
423 424
            if extension == 'json':
                with open(file_name, 'w') as file:
Colm Talbot's avatar
Colm Talbot committed
425
                    json.dump(dictionary, file, indent=2, cls=BilbyJsonEncoder)
426
            elif extension == 'hdf5':
427
                import deepdish
Sylvia Biscoveanu's avatar
Sylvia Biscoveanu committed
428 429
                deepdish.io.save(file_name, dictionary)
            else:
430
                raise ValueError("Extension type {} not understood".format(extension))
Gregory Ashton's avatar
Fix #49  
Gregory Ashton committed
431
        except Exception as e:
Gregory Ashton's avatar
Gregory Ashton committed
432
            logger.error("\n\n Saving the data has failed with the "
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
433
                         "following message:\n {} \n\n".format(e))
Gregory Ashton's avatar
Gregory Ashton committed
434

435
    def save_posterior_samples(self, outdir=None):
436
        """Saves posterior samples to a file"""
437 438
        outdir = self._safe_outdir_creation(outdir, self.save_posterior_samples)
        filename = '{}/{}_posterior_samples.txt'.format(outdir, self.label)
439 440
        self.posterior.to_csv(filename, index=False, header=True)

Gregory Ashton's avatar
Gregory Ashton committed
441
    def get_latex_labels_from_parameter_keys(self, keys):
442 443 444 445 446 447 448 449 450 451 452 453 454
        """ Returns a list of latex_labels corresponding to the given keys

        Parameters
        ----------
        keys: list
            List of strings corresponding to the desired latex_labels

        Returns
        -------
        list: The desired latex_labels

        """
        latex_labels = []
Gregory Ashton's avatar
Gregory Ashton committed
455 456 457
        for k in keys:
            if k in self.search_parameter_keys:
                idx = self.search_parameter_keys.index(k)
458
                latex_labels.append(self.parameter_labels_with_unit[idx])
Gregory Ashton's avatar
Gregory Ashton committed
459
            elif k in self.parameter_labels:
460
                latex_labels.append(k)
Gregory Ashton's avatar
Gregory Ashton committed
461
            else:
Colm Talbot's avatar
Colm Talbot committed
462
                logger.debug(
463 464
                    'key {} not a parameter label or latex label'.format(k))
                latex_labels.append(' '.join(k.split('_')))
465
        return latex_labels
Gregory Ashton's avatar
Gregory Ashton committed
466

Gregory Ashton's avatar
Gregory Ashton committed
467 468 469 470 471 472 473 474 475
    @property
    def covariance_matrix(self):
        """ The covariance matrix of the samples the posterior """
        samples = self.posterior[self.search_parameter_keys].values
        return np.cov(samples.T)

    @property
    def posterior_volume(self):
        """ The posterior volume """
Gregory Ashton's avatar
Gregory Ashton committed
476 477 478
        if self.covariance_matrix.ndim == 0:
            return np.sqrt(self.covariance_matrix)
        else:
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
479 480
            return 1 / np.sqrt(np.abs(np.linalg.det(
                1 / self.covariance_matrix)))
Gregory Ashton's avatar
Gregory Ashton committed
481

482 483
    @staticmethod
    def prior_volume(priors):
Gregory Ashton's avatar
Gregory Ashton committed
484 485 486 487 488 489
        """ The prior volume, given a set of priors """
        return np.prod([priors[k].maximum - priors[k].minimum for k in priors])

    def occam_factor(self, priors):
        """ The Occam factor,

490 491 492
        See Chapter 28, `Mackay "Information Theory, Inference, and Learning
        Algorithms" <http://www.inference.org.uk/itprnn/book.html>`_ Cambridge
        University Press (2003).
Gregory Ashton's avatar
Gregory Ashton committed
493 494 495 496

        """
        return self.posterior_volume / self.prior_volume(priors)

497
    def get_one_dimensional_median_and_error_bar(self, key, fmt='.2f',
498
                                                 quantiles=(0.16, 0.84)):
499 500 501 502 503 504 505 506
        """ Calculate the median and error bar for a given key

        Parameters
        ----------
        key: str
            The parameter key for which to calculate the median and error bar
        fmt: str, ('.2f')
            A format string
507 508
        quantiles: list, tuple
            A length-2 tuple of the lower and upper-quantiles to calculate
509 510 511 512
            the errors bars for.

        Returns
        -------
513 514
        summary: namedtuple
            An object with attributes, median, lower, upper and string
515 516

        """
517 518
        summary = namedtuple('summary', ['median', 'lower', 'upper', 'string'])

519 520 521 522 523
        if len(quantiles) != 2:
            raise ValueError("quantiles must be of length 2")

        quants_to_compute = np.array([quantiles[0], 0.5, quantiles[1]])
        quants = np.percentile(self.posterior[key], quants_to_compute * 100)
524 525 526
        summary.median = quants[1]
        summary.plus = quants[2] - summary.median
        summary.minus = summary.median - quants[0]
527 528

        fmt = "{{0:{0}}}".format(fmt).format
529 530 531 532 533
        string_template = r"${{{0}}}_{{-{1}}}^{{+{2}}}$"
        summary.string = string_template.format(
            fmt(summary.median), fmt(summary.minus), fmt(summary.plus))
        return summary

Colm Talbot's avatar
Colm Talbot committed
534 535 536
    def plot_single_density(self, key, prior=None, cumulative=False,
                            title=None, truth=None, save=True,
                            file_base_name=None, bins=50, label_fontsize=16,
537 538
                            title_fontsize=16, quantiles=(0.16, 0.84), dpi=300):
        """ Plot a 1D marginal density, either probability or cumulative.
Colm Talbot's avatar
Colm Talbot committed
539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566

        Parameters
        ----------
        key: str
            Name of the parameter to plot
        prior: {bool (True), bilby.core.prior.Prior}
            If true, add the stored prior probability density function to the
            one-dimensional marginal distributions. If instead a Prior
            is provided, this will be plotted.
        cumulative: bool
            If true plot the CDF
        title: bool
            If true, add 1D title of the median and (by default 1-sigma)
            error bars. To change the error bars, pass in the quantiles kwarg.
            See method `get_one_dimensional_median_and_error_bar` for further
            details). If `quantiles=None` is passed in, no title is added.
        truth: {bool, float}
            If true, plot self.injection_parameters[parameter].
            If float, plot this value.
        save: bool:
            If true, save plot to disk.
        file_base_name: str, optional
            If given, the base file name to use (by default `outdir/label_` is
            used)
        bins: int
            The number of histogram bins
        label_fontsize, title_fontsize: int
            The fontsizes for the labels and titles
567 568
        quantiles: tuple
            A length-2 tuple of the lower and upper-quantiles to calculate
Colm Talbot's avatar
Colm Talbot committed
569 570 571 572 573 574 575 576 577 578 579 580
            the errors bars for.
        dpi: int
            Dots per inch resolution of the plot

        Returns
        -------
        figure: matplotlib.pyplot.figure
            A matplotlib figure object
        """
        logger.info('Plotting {} marginal distribution'.format(key))
        label = self.get_latex_labels_from_parameter_keys([key])[0]
        fig, ax = plt.subplots()
581 582 583 584 585 586 587 588
        try:
            ax.hist(self.posterior[key].values, bins=bins, density=True,
                    histtype='step', cumulative=cumulative)
        except ValueError as e:
            logger.info(
                'Failed to generate 1d plot for {}, error message: {}'
                .format(key, e))
            return
Colm Talbot's avatar
Colm Talbot committed
589 590 591 592 593 594 595 596 597 598 599 600 601
        ax.set_xlabel(label, fontsize=label_fontsize)
        if truth is not None:
            ax.axvline(truth, ls='-', color='orange')

        summary = self.get_one_dimensional_median_and_error_bar(
            key, quantiles=quantiles)
        ax.axvline(summary.median - summary.minus, ls='--', color='C0')
        ax.axvline(summary.median + summary.plus, ls='--', color='C0')
        if title:
            ax.set_title(summary.string, fontsize=title_fontsize)

        if isinstance(prior, Prior):
            theta = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 300)
602
            ax.plot(theta, prior.prob(theta), color='C2')
Colm Talbot's avatar
Colm Talbot committed
603 604 605 606 607 608 609 610

        if save:
            fig.tight_layout()
            if cumulative:
                file_name = file_base_name + key + '_cdf'
            else:
                file_name = file_base_name + key + '_pdf'
            fig.savefig(file_name, dpi=dpi)
611 612 613
            plt.close(fig)
        else:
            return fig
Colm Talbot's avatar
Colm Talbot committed
614

615 616
    def plot_marginals(self, parameters=None, priors=None, titles=True,
                       file_base_name=None, bins=50, label_fontsize=16,
617 618
                       title_fontsize=16, quantiles=(0.16, 0.84), dpi=300,
                       outdir=None):
619 620 621 622 623 624 625
        """ Plot 1D marginal distributions

        Parameters
        ----------
        parameters: (list, dict), optional
            If given, either a list of the parameter names to include, or a
            dictionary of parameter names and their "true" values to plot.
626
        priors: {bool (False), bilby.core.prior.PriorDict}
627
            If true, add the stored prior probability density functions to the
628
            one-dimensional marginal distributions. If instead a PriorDict
629 630 631 632 633 634 635 636 637 638 639 640
            is provided, this will be plotted.
        titles: bool
            If true, add 1D titles of the median and (by default 1-sigma)
            error bars. To change the error bars, pass in the quantiles kwarg.
            See method `get_one_dimensional_median_and_error_bar` for further
            details). If `quantiles=None` is passed in, no title is added.
        file_base_name: str, optional
            If given, the base file name to use (by default `outdir/label_` is
            used)
        bins: int
            The number of histogram bins
        label_fontsize, title_fontsize: int
641 642 643
            The font sizes for the labels and titles
        quantiles: tuple
            A length-2 tuple of the lower and upper-quantiles to calculate
644 645 646
            the errors bars for.
        dpi: int
            Dots per inch resolution of the plot
647 648
        outdir: str, optional
            Path to the outdir. Default is the one store in the result object.
649 650 651 652 653 654

        Returns
        -------
        """
        if isinstance(parameters, dict):
            plot_parameter_keys = list(parameters.keys())
Colm Talbot's avatar
Colm Talbot committed
655
            truths = parameters
656
        elif parameters is None:
Colm Talbot's avatar
Colm Talbot committed
657 658 659 660 661
            plot_parameter_keys = self.posterior.keys()
            if self.injection_parameters is None:
                truths = dict()
            else:
                truths = self.injection_parameters
662 663
        else:
            plot_parameter_keys = list(parameters)
Colm Talbot's avatar
Colm Talbot committed
664 665 666 667
            if self.injection_parameters is None:
                truths = dict()
            else:
                truths = self.injection_parameters
668 669

        if file_base_name is None:
670 671
            outdir = self._safe_outdir_creation(outdir, self.plot_marginals)
            file_base_name = '{}/{}_1d/'.format(outdir, self.label)
Colm Talbot's avatar
Colm Talbot committed
672
            check_directory_exists_and_if_not_mkdir(file_base_name)
673 674

        if priors is True:
Colm Talbot's avatar
Colm Talbot committed
675 676
            priors = getattr(self, 'priors', dict())
        elif isinstance(priors, dict):
677
            pass
Colm Talbot's avatar
Colm Talbot committed
678 679
        elif priors in [False, None]:
            priors = dict()
680 681 682 683
        else:
            raise ValueError('Input priors={} not understood'.format(priors))

        for i, key in enumerate(plot_parameter_keys):
Colm Talbot's avatar
Colm Talbot committed
684 685
            if not isinstance(self.posterior[key].values[0], float):
                continue
Colm Talbot's avatar
Colm Talbot committed
686 687
            prior = priors.get(key, None)
            truth = truths.get(key, None)
Colm Talbot's avatar
Colm Talbot committed
688
            for cumulative in [False, True]:
689
                self.plot_single_density(
Colm Talbot's avatar
Colm Talbot committed
690 691
                    key, prior=prior, cumulative=cumulative, title=titles,
                    truth=truth, save=True, file_base_name=file_base_name,
Colm Talbot's avatar
Colm Talbot committed
692 693
                    bins=bins, label_fontsize=label_fontsize, dpi=dpi,
                    title_fontsize=title_fontsize, quantiles=quantiles)
694

695 696 697
    def plot_corner(self, parameters=None, priors=None, titles=True, save=True,
                    filename=None, dpi=300, **kwargs):
        """ Plot a corner-plot
Gregory Ashton's avatar
Gregory Ashton committed
698 699 700

        Parameters
        ----------
701 702 703
        parameters: (list, dict), optional
            If given, either a list of the parameter names to include, or a
            dictionary of parameter names and their "true" values to plot.
704
        priors: {bool (False), bilby.core.prior.PriorDict}
705
            If true, add the stored prior probability density functions to the
706
            one-dimensional marginal distributions. If instead a PriorDict
707
            is provided, this will be plotted.
708 709 710 711 712
        titles: bool
            If true, add 1D titles of the median and (by default 1-sigma)
            error bars. To change the error bars, pass in the quantiles kwarg.
            See method `get_one_dimensional_median_and_error_bar` for further
            details). If `quantiles=None` is passed in, no title is added.
713 714 715 716 717 718
        save: bool, optional
            If true, save the image using the given label and outdir
        filename: str, optional
            If given, overwrite the default filename
        dpi: int, optional
            Dots per inch resolution of the plot
719 720 721
        **kwargs:
            Other keyword arguments are passed to `corner.corner`. We set some
            defaults to improve the basic look and feel, but these can all be
722 723
            overridden. Also optional an 'outdir' argument which can be used
            to override the outdir set by the absolute path of the result object.
Gregory Ashton's avatar
Gregory Ashton committed
724

725 726 727 728 729 730
        Notes
        -----
            The generation of the corner plot themselves is done by the corner
            python module, see https://corner.readthedocs.io for more
            information.

Gregory Ashton's avatar
Gregory Ashton committed
731 732 733 734
        Returns
        -------
        fig:
            A matplotlib figure instance
735

Gregory Ashton's avatar
Gregory Ashton committed
736
        """
737 738

        # If in testing mode, not corner plots are generated
739 740
        if utils.command_line_args.test:
            return
Gregory Ashton's avatar
Gregory Ashton committed
741

Colm Talbot's avatar
Colm Talbot committed
742
        # bilby default corner kwargs. Overwritten by anything passed to kwargs
743 744 745
        defaults_kwargs = dict(
            bins=50, smooth=0.9, label_kwargs=dict(fontsize=16),
            title_kwargs=dict(fontsize=16), color='#0072C1',
746
            truth_color='tab:orange', quantiles=[0.16, 0.84],
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
747
            levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),
748
            plot_density=False, plot_datapoints=True, fill_contours=True,
749 750 751 752 753 754
            max_n_ticks=3)

        if LooseVersion(matplotlib.__version__) < "2.1":
            defaults_kwargs['hist_kwargs'] = dict(normed=True)
        else:
            defaults_kwargs['hist_kwargs'] = dict(density=True)
755

756 757 758 759
        if 'lionize' in kwargs and kwargs['lionize'] is True:
            defaults_kwargs['truth_color'] = 'tab:blue'
            defaults_kwargs['color'] = '#FF8C00'

760 761 762
        defaults_kwargs.update(kwargs)
        kwargs = defaults_kwargs

763 764 765 766 767 768 769 770 771 772 773 774 775 776 777
        # Handle if truths was passed in
        if 'truth' in kwargs:
            kwargs['truths'] = kwargs.pop('truth')
        if kwargs.get('truths'):
            truths = kwargs.get('truths')
            if isinstance(parameters, list) and isinstance(truths, list):
                if len(parameters) != len(truths):
                    raise ValueError(
                        "Length of parameters and truths don't match")
            elif isinstance(truths, dict) and parameters is None:
                parameters = kwargs.pop('truths')
            else:
                raise ValueError(
                    "Combination of parameters and truths not understood")

Gregory Ashton's avatar
Gregory Ashton committed
778 779 780 781 782 783 784 785
        # If injection parameters where stored, use these as parameter values
        # but do not overwrite input parameters (or truths)
        cond1 = getattr(self, 'injection_parameters', None) is not None
        cond2 = parameters is None
        if cond1 and cond2:
            parameters = {key: self.injection_parameters[key] for key in
                          self.search_parameter_keys}

786 787 788 789 790 791 792 793 794
        # If parameters is a dictionary, use the keys to determine which
        # parameters to plot and the values as truths.
        if isinstance(parameters, dict):
            plot_parameter_keys = list(parameters.keys())
            kwargs['truths'] = list(parameters.values())
        elif parameters is None:
            plot_parameter_keys = self.search_parameter_keys
        else:
            plot_parameter_keys = list(parameters)
795

796
        # Get latex formatted strings for the plot labels
797 798
        kwargs['labels'] = kwargs.get(
            'labels', self.get_latex_labels_from_parameter_keys(
799
                plot_parameter_keys))
800

801 802 803 804
        # Unless already set, set the range to include all samples
        # This prevents ValueErrors being raised for parameters with no range
        kwargs['range'] = kwargs.get('range', [1] * len(plot_parameter_keys))

805 806
        # Create the data array to plot and pass everything to corner
        xs = self.posterior[plot_parameter_keys].values
807
        fig = corner.corner(xs, **kwargs)
808
        axes = fig.get_axes()
809 810 811

        #  Add the titles
        if titles and kwargs.get('quantiles', None) is not None:
812 813
            for i, par in enumerate(plot_parameter_keys):
                ax = axes[i + i * len(plot_parameter_keys)]
814 815
                if ax.title.get_text() == '':
                    ax.set_title(self.get_one_dimensional_median_and_error_bar(
816
                        par, quantiles=kwargs['quantiles']).string,
817 818 819
                        **kwargs['title_kwargs'])

        #  Add priors to the 1D plots
820 821 822
        if priors is True:
            priors = getattr(self, 'priors', False)
        if isinstance(priors, dict):
823 824
            for i, par in enumerate(plot_parameter_keys):
                ax = axes[i + i * len(plot_parameter_keys)]
825 826
                theta = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 300)
                ax.plot(theta, priors[par].prob(theta), color='C2')
827 828 829 830
        elif priors in [False, None]:
            pass
        else:
            raise ValueError('Input priors={} not understood'.format(priors))
831

832
        if save:
833
            if filename is None:
834 835
                outdir = self._safe_outdir_creation(kwargs.get('outdir'), self.plot_corner)
                filename = '{}/{}_corner.png'.format(outdir, self.label)
Gregory Ashton's avatar
Gregory Ashton committed
836
            logger.debug('Saving corner plot to {}'.format(filename))
837
            fig.savefig(filename, dpi=dpi)
838
            plt.close(fig)
839

840
        return fig
841

Gregory Ashton's avatar
Gregory Ashton committed
842
    def plot_walkers(self, **kwargs):
MoritzThomasHuebner's avatar
A typo  
MoritzThomasHuebner committed
843
        """ Method to plot the trace of the walkers in an ensemble MCMC plot """
844
        if hasattr(self, 'walkers') is False:
Gregory Ashton's avatar
Gregory Ashton committed
845
            logger.warning("Cannot plot_walkers as no walkers are saved")
846
            return
847 848 849

        if utils.command_line_args.test:
            return
850 851 852

        nwalkers, nsteps, ndim = self.walkers.shape
        idxs = np.arange(nsteps)
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
853
        fig, axes = plt.subplots(nrows=ndim, figsize=(6, 3 * ndim))
854 855
        walkers = self.walkers[:, :, :]
        for i, ax in enumerate(axes):
MoritzThomasHuebner's avatar
MoritzThomasHuebner committed
856
            ax.plot(idxs[:self.nburn + 1], walkers[:, :self.nburn + 1, i].T,
857 858 859 860 861 862 863 864 865
                    lw=0.1, color='r')
            ax.set_ylabel(self.parameter_labels[i])

        for i, ax in enumerate(axes):
            ax.plot(idxs[self.nburn:], walkers[:, self.nburn:, i].T, lw=0.1,
                    color='k')
            ax.set_ylabel(self.parameter_labels[i])

        fig.tight_layout()
866 867
        outdir = self._safe_outdir_creation(kwargs.get('outdir'), self.plot_walkers)
        filename = '{}/{}_walkers.png'.format(outdir, self.label)
Gregory Ashton's avatar
Gregory Ashton committed
868
        logger.debug('Saving walkers plot to {}'.format('filename'))
869
        fig.savefig(filename)
870
        plt.close(fig)
871

872 873 874
    def plot_with_data(self, model, x, y, ndraws=1000, npoints=1000,
                       xlabel=None, ylabel=None, data_label='data',
                       data_fmt='o', draws_label=None, filename=None,
875
                       maxl_label='max likelihood', dpi=300, outdir=None):
876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900
        """ Generate a figure showing the data and fits to the data

        Parameters
        ----------
        model: function
            A python function which when called as `model(x, **kwargs)` returns
            the model prediction (here `kwargs` is a dictionary of key-value
            pairs of the model parameters.
        x, y: np.ndarray
            The independent and dependent data to plot
        ndraws: int
            Number of draws from the posterior to plot
        npoints: int
            Number of points used to plot the smoothed fit to the data
        xlabel, ylabel: str
            Labels for the axes
        data_label, draws_label, maxl_label: str
            Label for the data, draws, and max likelihood legend
        data_fmt: str
            Matpltolib fmt code, defaults to `'-o'`
        dpi: int
            Passed to `plt.savefig`
        filename: str
            If given, the filename to use. Otherwise, the filename is generated
            from the outdir and label attributes.
901 902
        outdir: str, optional
            Path to the outdir. Default is the one store in the result object.
903 904

        """
905 906 907 908 909 910

        # Determine model_posterior, the subset of the full posterior which
        # should be passed into the model
        model_keys = infer_parameters_from_function(model)
        model_posterior = self.posterior[model_keys]

911 912 913 914
        xsmooth = np.linspace(np.min(x), np.max(x), npoints)
        fig, ax = plt.subplots()
        logger.info('Plotting {} draws'.format(ndraws))
        for _ in range(ndraws):
915
            s = model_posterior.sample().to_dict('records')[0]
916 917
            ax.plot(xsmooth, model(xsmooth, **s), alpha=0.25, lw=0.1, color='r',
                    label=draws_label)
918 919 920
        try:
            if all(~np.isnan(self.posterior.log_likelihood)):
                logger.info('Plotting maximum likelihood')
Colm Talbot's avatar
Colm Talbot committed
921
                s = model_posterior.iloc[self.posterior.log_likelihood.idxmax()]
922 923
                ax.plot(xsmooth, model(xsmooth, **s), lw=1, color='k',
                        label=maxl_label)
924
        except (AttributeError, TypeError):
925 926
            logger.debug(
                "No log likelihood values stored, unable to plot max")
927 928 929 930 931 932 933 934 935 936 937 938 939 940

        ax.plot(x, y, data_fmt, markersize=2, label=data_label)

        if xlabel is not None:
            ax.set_xlabel(xlabel)
        if ylabel is not None:
            ax.set_ylabel(ylabel)

        handles, labels = plt.gca().get_legend_handles_labels()
        by_label = OrderedDict(zip(labels, handles))
        plt.legend(by_label.values(), by_label.keys())
        ax.legend(numpoints=3)
        fig.tight_layout()
        if filename is None:
941 942
            outdir = self._safe_outdir_creation(outdir, self.plot_with_data)
            filename = '{}/{}_plot_with_data'.format(outdir, self.label)
943
        fig.savefig(filename, dpi=dpi)
944
        plt.close(fig)
945

946 947
    def samples_to_posterior(self, likelihood=None, priors=None,
                             conversion_function=None):
948
        """
949 950 951
        Convert array of samples to posterior (a Pandas data frame)

        Also applies the conversion function to any stored posterior
952

953 954
        Parameters
        ----------
Colm Talbot's avatar
Colm Talbot committed
955
        likelihood: bilby.likelihood.GravitationalWaveTransient, optional
956 957
            GravitationalWaveTransient likelihood used for sampling.
        priors: dict, optional
958
            Dictionary of prior object, used to fill in delta function priors.
959
        conversion_function: function, optional
960 961
            Function which adds in extra parameters to the data frame,
            should take the data_frame, likelihood and prior as arguments.
962
        """
963 964 965
        try:
            data_frame = self.posterior
        except ValueError:
966 967
            data_frame = pd.DataFrame(
                self.samples, columns=self.search_parameter_keys)
968
            for key in priors:
969
                if isinstance(priors[key], DeltaFunction):
970
                    data_frame[key] = priors[key].peak
971 972 973 974
                elif isinstance(priors[key], float):
                    data_frame[key] = priors[key]
            data_frame['log_likelihood'] = getattr(
                self, 'log_likelihood_evaluations', np.nan)
975 976 977 978 979
            if self.log_prior_evaluations is None:
                data_frame['log_prior'] = self.priors.ln_prob(
                    data_frame[self.search_parameter_keys], axis=0)
            else:
                data_frame['log_prior'] = self.log_prior_evaluations
980
        if conversion_function is not None:
981
            data_frame = conversion_function(data_frame, likelihood, priors)
982
        self.posterior = data_frame
Gregory Ashton's avatar
Gregory Ashton committed
983

Colm Talbot's avatar
Colm Talbot committed
984
    def calculate_prior_values(self, priors):
985 986 987 988 989
        """
        Evaluate prior probability for each parameter for each sample.

        Parameters
        ----------
990
        priors: dict, PriorDict
991 992 993 994 995 996 997 998 999 1000 1001
            Prior distributions
        """
        self.prior_values = pd.DataFrame()
        for key in priors:
            if key in self.posterior.keys():
                if isinstance(priors[key], DeltaFunction):
                    continue
                else:
                    self.prior_values[key]\
                        = priors[key].prob(self.posterior[key].values)

Colm Talbot's avatar
Colm Talbot committed
1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044
    def get_all_injection_credible_levels(self):
        """
        Get credible levels for all parameters in self.injection_parameters

        Returns
        -------
        credible_levels: dict
            The credible levels at which the injected parameters are found.
        """
        if self.injection_parameters is None:
            raise(TypeError, "Result object has no 'injection_parameters'. "
                             "Cannot copmute credible levels.")
        credible_levels = {key: self.get_injection_credible_level(key)
                           for key in self.search_parameter_keys
                           if isinstance(self.injection_parameters[key], float)}
        return credible_levels

    def get_injection_credible_level(self, parameter):
        """
        Get the credible level of the injected parameter

        Calculated as CDF(injection value)

        Parameters
        ----------
        parameter: str
            Parameter to get credible level for
        Returns
        -------
        float: credible level
        """
        if self.injection_parameters is None:
            raise(TypeError, "Result object has no 'injection_parameters'. "
                             "Cannot copmute credible levels.")
        if parameter in self.posterior and\
                parameter in self.injection_parameters:
            credible_level =\
                sum(self.posterior[parameter].values <
                    self.injection_parameters[parameter]) / len(self.posterior)
            return credible_level
        else:
            return np.nan

1045
    def _check_attribute_match_to_other_object(self, name, other_object):
1046 1047