B db@s.dZddlZddlZddlZddlmZddlm Z m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZmZdd lmZdd lmZmZdd lmZeZeZ e!Z"d ddddddgZ#ddZ$ddZ%d4ddZ&ddZ'd5d#d Z(d6d*dZ)d7d+dZ*d8d-dZ+d9d1dZ,d:d2dZ-d;d3dZ.dS)t||d}|||dkr:td|ddddd}t ||}YnX|rl|||dkr^|}nt dd}|t |t ||r|nt |g}|rt t |d|d}tdd|}|| |d<|dkrdd|D}d}nd}t|}t|dkrtdx|t|D]p\}} y*t||t||dkrRtdWn:tk rt||||t||f||<YnXq"W|r|r|r|dd|d|dd |df}!n"dd!t |dd"|df}!|!|d<|pd}"t|ddd#d#\}}#|#}#d$d|#D}$|"rFd%d|#D}%n|}%fd&d|$D}$d'd|%D}%xtdD]}|$|ddkrd}&ntd|$|d}&|$|ddkrt }'ntt |$|d}'|%|ddkrd}(nt||d|%|d}(|%|ddkr,||d})nt||d|%|d})|#||&|'g|#||(|)gqvWd(d)d*|rd+nd,g}*|r|*dd-7<xt|D]\}}+|#|t|tr|},n||},| dkrjt njt!| | dkrj"t nj"t!| t#|d.}-j"$|-j%d7|j&|*|f||r|dkrj' |+fd0|,i|fd1d|#D}%nF|r|dkrƈj'| |+fd0|,i|nj' |+fd0|,i||dkr|r|rT|(dd2k}.xtddD]8}/j)|. ||/||.||/||.|,d3d4qWnHxFtddD]8}/j) t ||/|t ||/||,d3d4q`W|rj*| f|,d5dd6||dkrj+|f|,d5dd6||dkr| dk r|rj+| fd0| i| nj+t | fd0| i| qW||#fS)8a Plot live points, ln(likelihood), ln(weight), and ln(evidence) as a function of ln(prior volume). Parameters ---------- results : :class:`~dynesty.results.Results` instance A :class:`~dynesty.results.Results` instance from a nested sampling run. span : iterable with shape (4,), optional A list where each element is either a length-2 tuple containing lower and upper bounds *or* a float from `(0., 1.]` giving the fraction below the maximum. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:: span = [(0., 10.), 0.001, 0.2, (5., 6.)] Default is `(0., 1.05 * max(data))` for each element. logplot : bool, optional Whether to plot the evidence on a log scale. Default is `False`. kde : bool, optional Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is `True`. nkde : int, optional The number of grid points used when plotting the kernel density estimate. Default is `1000`. color : str or iterable with shape (4,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting the lines in each subplot. Default is `'blue'`. plot_kwargs : dict, optional Extra keyword arguments that will be passed to `plot`. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. lnz_error : bool, optional Whether to plot the 1, 2, and 3-sigma approximate error bars derived from the ln(evidence) error approximation over the course of the run. Default is `True`. lnz_truth : float, optional A reference value for the evidence that will be overplotted on the evidence subplot if provided. truth_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color used when plotting :data:`lnz_truth`. Default is `'red'`. truth_kwargs : dict, optional Extra keyword arguments that will be used for plotting :data:`lnz_truth`. max_x_ticks : int, optional Maximum number of ticks allowed for the x axis. Default is `8`. max_y_ticks : int, optional Maximum number of ticks allowed for the y axis. Default is `4`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. mark_final_live : bool, optional Whether to indicate the final addition of recycled live points (if they were added to the resulting samples) using a dashed vertical line. Default is `True`. fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the run onto the provided figure. Otherwise, by default an internal figure is generated. Returns ------- runplot : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output summary plot. N linewidthalphagffffff? linestylesolidrAniterlogvollogllogwtlogzlogzerrgZ samples_nFnliver zThe number of iterations and samples differ by an amount that isn't the number of final live points. `mark_final_live` has been disabled.r2)rstatercSsg|]}ddt|fqS)gg?)max).0drrr szrunplot..Tz-More bounds provided in `span` than subplots!zIncorrect span valueggffff>@g433333@g?g@cSsg|] }|qSr)Zget_xlim)rQr#rrrrS!scSsg|] }|qSr)get_ylim)rQr#rrrrS#scs&g|]}|dkr|n dt fqS))gg?g)min)rQr0)rHrrrS(scSsg|]}|dkr|ndqS))gg?)NNr)rQr0rrrrS)sz Live PointszLikelihood (normalized)zImportance Weightz log(Evidence)ZEvidencez PDF) useMathText$-\ln X$r%csg|] }qSr)rV)rQZ_ax)r#rrrSbs g?)r%rDdashed)r%lslw)rY),r getrPrisfiniter5KeyErrorZonesarangeappendwarningswarnexprr linspacepdflistrr4r(r)r flattenrangerWset_xlimset_ylim isinstancestr_typexaxisset_major_locatorrryaxisrset_major_formatter set_xlabel set_ylabelplotrV fill_betweenr&r')0resultsr7Zlogplotkdenkder% plot_kwargs label_kwargsZ lnz_errorZ lnz_truthr+r,Z max_x_ticksZ max_y_ticks use_math_textZmark_final_liverrOrGrIrJrKrMnsampsrNZ nlive_finalZlive_idxdatawt_kdeZ logvol_newZno_spanr9r:ZzspanZhad_figrZxspanZyspanZxminZxmaxZyminZymaxlabelsrRcsfmasksr)r#rHrr`sk     0 "         g?g?g333333?{Gz?plasmarZrC.2fcD s|dkr i}|dkri}| dkr$i} |dkr0i}|dkr}?}@|?|>|@|?}A}Bd4:|j:}Cd5}=|=:|C|?|C|A|C|B}=d6:||,|=}=|.j;|=f|qW||+fS)8a Plot traces and marginalized posteriors for each parameter. Parameters ---------- results : :class:`~dynesty.results.Results` instance A :class:`~dynesty.results.Results` instance from a nested sampling run. **Compatible with results derived from** `nestle `_. span : iterable with shape (ndim,), optional A list where each element is either a length-2 tuple containing lower and upper bounds or a float from `(0., 1.]` giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:: span = [(0., 10.), 0.95, (5., 6.)] Default is `0.999999426697` (5-sigma credible interval) for each parameter. quantiles : iterable, optional A list of fractional quantiles to overplot on the 1-D marginalized posteriors as vertical dashed lines. Default is `[0.025, 0.5, 0.975]` (the 95%/2-sigma credible interval). smooth : float or iterable with shape (ndim,), optional The standard deviation (either a single value or a different value for each subplot) for the Gaussian kernel used to smooth the 1-D marginalized posteriors, expressed as a fraction of the span. Default is `0.02` (2% smoothing). If an integer is provided instead, this will instead default to a simple (weighted) histogram with `bins=smooth`. thin : int, optional Thin the samples so that only each `thin`-th sample is plotted. Default is `1` (no thinning). dims : iterable of shape (ndim,), optional The subset of dimensions that should be plotted. If not provided, all dimensions will be shown. post_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting the histograms. Default is `'blue'`. post_kwargs : dict, optional Extra keyword arguments that will be used for plotting the marginalized 1-D posteriors. kde : bool, optional Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is `True`. nkde : int, optional The number of grid points used when plotting the kernel density estimate. Default is `1000`. trace_cmap : str or iterable with shape (ndim,), optional A `~matplotlib`-style colormap (either a single colormap or a different colormap for each subplot) used when plotting the traces, where each point is colored according to its weight. Default is `'plasma'`. trace_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different color for each subplot) used when plotting the traces. This overrides the `trace_cmap` option by giving all points the same color. Default is `None` (not used). trace_kwargs : dict, optional Extra keyword arguments that will be used for plotting the traces. connect : bool, optional Whether to draw lines connecting the paths of unique particles. Default is `False`. connect_highlight : int or iterable, optional If `connect=True`, highlights the paths of a specific set of particles. If an integer is passed, :data:`connect_highlight` random particle paths will be highlighted. If an iterable is passed, then the particle paths corresponding to the provided indices will be highlighted. connect_color : str, optional The color of the highlighted particle paths. Default is `'red'`. connect_kwargs : dict, optional Extra keyword arguments used for plotting particle paths. max_n_ticks : int, optional Maximum number of ticks allowed. Default is `5`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. labels : iterable with shape (ndim,), optional A list of names for each parameter. If not provided, the default name used when plotting will follow :math:`x_i` style. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. show_titles : bool, optional Whether to display a title above each 1-D marginalized posterior showing the 0.5 quantile along with the upper/lower bounds associated with the 0.025 and 0.975 (95%/2-sigma credible interval) quantiles. Default is `False`. title_quantiles : iterable, optional A list of fractional quantiles to use in the title. Default is `[0.025, 0.5, 0.975]` (median plus 95%/2-sigma credible interval). title_fmt : str, optional The format string for the quantiles provided in the title. Default is `'.2f'`. title_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_title` command. truths : iterable with shape (ndim,), optional A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using `None`. Default is `None`. truth_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting `truths`. Default is `'red'`. truth_kwargs : dict, optional Extra keyword arguments that will be used for plotting the vertical and horizontal lines with `truths`. verbose : bool, optional Whether to print the values of the computed quantiles associated with each parameter. Default is `False`. fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the traces and marginalized 1-D posteriors onto the provided figure. Otherwise, by default an internal figure is generated. Returns ------- traceplot : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output trace plot. NrDgffffff?g333333?rrA edgecolor edgecolorsrErFrBr2r8rH)rOrrLr zSamples must be 1- or 2-D.z'There are more dimensions than samples!zWeights must be 1-D.z+The number of weights and samples disagree!zLn(volume)'s must be 1-D.z0The number of ln(volume)'s and samples disagree! samples_idzSample IDs are not defined!F)sizereplacecSsg|]}dqS)g5?r)rQr9rrrrSsztraceplot..z,Dimension mismatch between samples and span.cSs g|]}dt|ddqS)z$x_{r z}$)str)rQr9rrrrSscsg|]}qSrr)rQr9)smoothrrrSs g)rX$-\ln X$)rcmapr%T)r.)binsr3r%rjg$@)rr3rjg?g?)r3r[)r]r\r%z Quantiles:)r-z {{0:{0}}}z${{{0}}}_{{-{1}}}^{{+{2}}}$z {0} = {1})r)`_. dims : iterable of shape (ndim,), optional The subset of dimensions that should be plotted. If not provided, all dimensions will be shown. thin : int, optional Thin the samples so that only each `thin`-th sample is plotted. Default is `1` (no thinning). span : iterable with shape (ndim,), optional A list where each element is either a length-2 tuple containing lower and upper bounds or a float from `(0., 1.]` giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:: span = [(0., 10.), 0.95, (5., 6.)] Default is `1.` for all parameters (no bound). cmap : str, optional A `~matplotlib`-style colormap used when plotting the points, where each point is colored according to its weight. Default is `'plasma'`. color : str, optional A `~matplotlib`-style color used when plotting the points. This overrides the `cmap` option by giving all points the same color. Default is `None` (not used). kde : bool, optional Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is `True`. nkde : int, optional The number of grid points used when plotting the kernel density estimate. Default is `1000`. plot_kwargs : dict, optional Extra keyword arguments that will be used for plotting the points. labels : iterable with shape (ndim,), optional A list of names for each parameter. If not provided, the default name used when plotting will follow :math:`x_i` style. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. truths : iterable with shape (ndim,), optional A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using `None`. Default is `None`. truth_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting `truths`. Default is `'red'`. truth_kwargs : dict, optional Extra keyword arguments that will be used for plotting the vertical and horizontal lines with `truths`. max_n_ticks : int, optional Maximum number of ticks allowed. Default is `5`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the points onto the provided figure object. Otherwise, by default an internal figure is generated. Returns ------- cornerpoints : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output (sub-)corner plot of (weighted) samples. Nrr rrrErFrBr2rDgffffff?r8rHrrLzSamples must be 1- or 2-D.z'There are more dimensions than samples!zWeights must be 1-D.z+The number of weights and samples disagree!z2cornerpoints does not make sense for 1-D posteriorz,Dimension mismatch between samples and span.cSs g|]}dt|ddqS)z$x_{r z}$)r)rQr9rrrrSsz cornerpoints..g@g?g?g?g?)leftbottomrighttopwspacehspaceFlower)prune)rXr!g333333ӿybool)rrT)r-)r.)-r^rrr rrfrgrrr5rrrrrrr<rjr subplots_adjustr4rkrl set_frame_on set_xticks set_yticksrorprrqrrrrset_xticklabelsr$rsset_label_coordsset_yticklabelsrtZ ones_likeZastypermrrr1)*rwrrr7rr%rxryrzrr{r*r+r,rr|rr8rHr3rrrrr}factorlbdimtrdimwhspaceplotdimdimrlbtrr9r!rrr#rZ in_boundsZ cur_colorrrrr1sn                            $$   blackc> sl|dkr g}|dkri}| dkr$i} |dkr0i}|dkr`_. dims : iterable of shape (ndim,), optional The subset of dimensions that should be plotted. If not provided, all dimensions will be shown. span : iterable with shape (ndim,), optional A list where each element is either a length-2 tuple containing lower and upper bounds or a float from `(0., 1.]` giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:: span = [(0., 10.), 0.95, (5., 6.)] Default is `0.999999426697` (5-sigma credible interval). quantiles : iterable, optional A list of fractional quantiles to overplot on the 1-D marginalized posteriors as vertical dashed lines. Default is `[0.025, 0.5, 0.975]` (spanning the 95%/2-sigma credible interval). color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting the histograms. Default is `'black'`. smooth : float or iterable with shape (ndim,), optional The standard deviation (either a single value or a different value for each subplot) for the Gaussian kernel used to smooth the 1-D and 2-D marginalized posteriors, expressed as a fraction of the span. Default is `0.02` (2% smoothing). If an integer is provided instead, this will instead default to a simple (weighted) histogram with `bins=smooth`. quantiles_2d : iterable with shape (nquant,), optional The quantiles used for plotting the smoothed 2-D distributions. If not provided, these default to 0.5, 1, 1.5, and 2-sigma contours roughly corresponding to quantiles of `[0.1, 0.4, 0.65, 0.85]`. hist_kwargs : dict, optional Extra keyword arguments to send to the 1-D (smoothed) histograms. hist2d_kwargs : dict, optional Extra keyword arguments to send to the 2-D (smoothed) histograms. labels : iterable with shape (ndim,), optional A list of names for each parameter. If not provided, the default name used when plotting will follow :math:`x_i` style. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. show_titles : bool, optional Whether to display a title above each 1-D marginalized posterior showing the 0.5 quantile along with the upper/lower bounds associated with the 0.025 and 0.975 (95%/2-sigma credible interval) quantiles. Default is `False`. title_quantiles : iterable, optional A list of fractional quantiles to use in the title. Default is `[0.025, 0.5, 0.975]` (median plus 95%/2-sigma credible interval). title_fmt : str, optional The format string for the quantiles provided in the title. Default is `'.2f'`. title_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_title` command. truths : iterable with shape (ndim,), optional A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using `None`. Default is `None`. truth_color : str or iterable with shape (ndim,), optional A `~matplotlib`-style color (either a single color or a different value for each subplot) used when plotting `truths`. Default is `'red'`. truth_kwargs : dict, optional Extra keyword arguments that will be used for plotting the vertical and horizontal lines with `truths`. max_n_ticks : int, optional Maximum number of ticks allowed. Default is `5`. top_ticks : bool, optional Whether to label the top (rather than bottom) ticks. Default is `False`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. verbose : bool, optional Whether to print the values of the computed quantiles associated with each parameter. Default is `False`. fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the traces and marginalized 1-D posteriors onto the provided figure. Otherwise, by default an internal figure is generated. Returns ------- cornerplot : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output corner plot. NrDg333333?levelsrErFrBr2gffffff?r8r zSamples must be 1- or 2-D.rz'There are more dimensions than samples!zWeights must be 1-D.z+The number of weights and samples disagree!cSsg|]}dqS)g5?r)rQr9rrrrSszcornerplot..z,Dimension mismatch between samples and span.cSs g|]}dt|ddqS)z$x_{r z}$)r)rQr9rrrrSscsg|]}qSrr)rQr9)rrrrS sg@g?g?g?g?)rrrrrrr)r)rXrr!g333333ӿ)rr3r%rjg$@)rr3rjrL)rr3rjr%gg?)r3r[)r]r\r%z Quantiles:T)r-z {{0:{0}}}z${{{0}}}_{{-{1}}}^{{+{2}}}$z {0} = {1}Fr fill_contours plot_contours)r#r7r3r%r)r.)6r^rrrr5rrrrrrrjrhr<rmrrr rr4rkrorprrqrrrrZset_ticks_positionr$rrsrrrrrrrrlrPr6r&rrr1rrrrrrrtr)>rwrr7rr%rZ quantiles_2dZ hist_kwargsZ hist2d_kwargsrr{rrrrr*r+r,rZ top_ticksr|rrr8r3rr}rrrrrrrrrr9r!r#rsxrrr:rZb0rr;rrrrrrrrrZsyZcheck_ixZcheck_iyrrr)rrr<sp                                               gray darkvioletc2s| dkr i} | dkri} |dkr$i}|dkr4|dksD|dk rL|dk rLtd| dd| d<| dd| d<| dd| d<| d d | d <|dd|d<|dd|d<|dd|d<y |d }Wntk rtd YnXt|d }t|dj|||dk rl||kr tdyt|d}Wntk rPtdYnX|dkrbd}n||}nF||kr~tdy |d}Wntk rtdYnX||}||}|r|y|d}|d}|||krtd|d}|d}|jd}t ||f}|| d||| d<|dkrB|d|}x:t d||dD]$}|| } || }!|| ||!<qVWWn|d|}"|d|"}#|d|"k}$t |$|#}%|d|$}|d|$}|t |8}|jd}t |#|f}||# d|||# d<|dkr&|d|}t |$d|d}&x:t d|%|&dD]$}|#| } || }!|| ||!<qNWYnXt }'t|tjtjfs|j||'d}(n|sy|d}|d}|||krtd|d}|d}|jd}t ||f}|| d||| d<|dkr.|d|}x:t d||dD]$}|| } || }!|| ||!<qBWWntdYnX|j|||'d}(|dkr|(dd|fj\})}*|r@|dd|fj\}+},npfdd |(D}-ttt||(|-}.|.dd|fj\})}*|r@ttt||}/|/dd|fj\}+},t|ddd!d!\}}0|0d"}0|0j|)|*f|dd#| |r|0j|+|,f|d$d#||dk r|0|d|0|d| dkr|0jt|0jtn |0jt| |0jt| t| d%}1|0j |1|0j |1| dk rV|0j!| df| |0j"| df| nD|0j!d&t#|ddd'f| |0j"d&t#|ddd'f| ||0fS)(a Return the bounding distribution used to propose either (1) live points at a given iteration or (2) a specific dead point during the course of a run, projected onto the two dimensions specified by `dims`. Parameters ---------- results : :class:`~dynesty.results.Results` instance A :class:`~dynesty.results.Results` instance from a nested sampling run. dims : length-2 tuple The dimensions used to plot the bounding. it : int, optional If provided, returns the bounding distribution at the specified iteration of the nested sampling run. **Note that this option and `idx` are mutually exclusive.** idx : int, optional If provided, returns the bounding distribution used to propose the dead point at the specified iteration of the nested sampling run. **Note that this option and `it` are mutually exclusive.** prior_transform : func, optional The function transforming samples within the unit cube back to samples in the native model space. If provided, the transformed bounding distribution will be plotted in the native model space. periodic : iterable, optional A list of indices for parameters with periodic boundary conditions. These parameters *will not* have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is `None` (i.e. no periodic boundary conditions). reflective : iterable, optional A list of indices for parameters with reflective boundary conditions. These parameters *will not* have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is `None` (i.e. no reflective boundary conditions). ndraws : int, optional The number of random samples to draw from the bounding distribution when plotting. Default is `5000`. color : str, optional The color of the points randomly sampled from the bounding distribution. Default is `'gray'`. plot_kwargs : dict, optional Extra keyword arguments used when plotting the bounding draws. labels : iterable with shape (ndim,), optional A list of names for each parameter. If not provided, the default name used when plotting will follow :math:`x_i` style. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. max_n_ticks : int, optional Maximum number of ticks allowed. Default is `5`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. show_live : bool, optional Whether the live points at a given iteration (for `it`) or associated with the bounding (for `idx`) should be highlighted. Default is `False`. In the dynamic case, only the live points associated with the batch used to construct the relevant bound are plotted. live_color : str, optional The color of the live points. Default is `'darkviolet'`. live_kwargs : dict, optional Extra keyword arguments used when plotting the live points. span : iterable with shape (2,), optional A list where each element is a length-2 tuple containing lower and upper bounds. Default is `None` (no bound). fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the draws onto the provided figure. Otherwise, by default an internal figure is generated. Returns ------- bounding_plot : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output plot of the bounding distribution. Nz1You must specify either an iteration or an index!markerorENone markersizer rDg?boundz$No bounds were saved in the results!r8rzHThe iteration requested goes beyond the number of iterations in the run. bound_iterzrCannot reconstruct the bound used at the specified iteration since bound iterations were not saved in the results.zAThe index requested goes beyond the number of samples in the run. samples_boundzCannot reconstruct the bound used to compute the specified dead point since sample bound indices were not saved in the results.rNrGz^Cannot reconstruct bound because the final set of live points are not included in the results. samples_ur samples_it samples_batch batch_nlive)rOzKLive point tracking currently not implemented for dynamic sampling results.csg|]}t|qSr)r )rQpoint) nonboundedrrrSszboundplot..)rr)r%zorderr2)rXz$x_{z}$)$rr^r`r5rrrrremptyrjsumrWr rmr RadFriends SupFriendsr8rrhmapr rurkrlrorprrqrrrrrsrtr)2rwritidxprior_transformperiodic reflectivendrawsr%rzrr{rr| show_live live_color live_kwargsr7rboundsr}rpidxrrrNrGr8rrlive_ur9ruidxbatchnbatchbsel niter_effit_effrOpsampsx1Zx2l1l2rZvsampslsampsrrr)rrrsw                                   ""c9s| dkr i} | dkri} |dkr$i}|dkr4|dksD|dk rL|dk rLtd| dd| d<| dd| d<| dd| d<| d d | d <|dd|d<|dd|d<|dd|d<y |d }Wntk rtd YnX|d j\}}|dkrtdt|dj|||dk r||kr4tdyt|d}Wntk rdtdYnX|dkrvd}n||}nF||krtdy |d}Wntk rtdYnX||}||}|ry|d}|d}|||krtd|d}|d}|jd}t||f}|| d||| d<|dkrV|d|}x:t d||dD]$}|| } || }!|| ||!<qjWWn|dk r|d|}"n |d|}"|d|"}#|d|"k}$t |$|#}%|d|$}|d|$}|t |8}|jd}t|#|f}||# d|||# d<|dkrT|d|}t |$d|d}&x:t d|%|&dD]$}|#| } || }!|| ||!<q|WYnXt }'t |tjtjfs|j||'d}(n|s|d}|d}|||krtd|d}|d}|jd}t||f}|| d||| d<|dkrZ|d|}x:t d||dD]$}|| } || }!|| ||!<qnW|j|||'d}(|dkr|(j}(|r|j})nPfdd |(D}*ttt||(|*}(|(j}(|rttt||})|)j})|dk r4|(|}(|r4|)|})|(jd}| dkrZd!d t |D} d"}+d#|+},d$|+}-d%}.|+|d&|+|d"|.}/|,|/|-}0t||d|d|0|0\}}1|,|0}2|,|/|0}3|j|2|2|3|3|.|.d'xt|(ddD]\}}4xt|(dd(D]\}5}6|1||5f}7|dk rN|7||5|7|||5|krz|7d)|7g|7gq| dkr|7jt|7j tn(|7jt!| d*d+|7j t!| d*d+t"| d,}8|7j#|8|7j #|8||d-kr|7$gn*t%|7d.|7j&| |5f| |7j'd#d/|5dkrF|7(gn.t%|7d0|7j)| |df| |7j 'd/d#|7j*|6|4fd1|i| |r|7j*|)|5|)|dfd1|i|qWqW||1fS)2a Return the bounding distribution used to propose either (1) live points at a given iteration or (2) a specific dead point during the course of a run, projected onto all pairs of dimensions. Parameters ---------- results : :class:`~dynesty.results.Results` instance A :class:`~dynesty.results.Results` instance from a nested sampling run. it : int, optional If provided, returns the bounding distribution at the specified iteration of the nested sampling run. **Note that this option and `idx` are mutually exclusive.** idx : int, optional If provided, returns the bounding distribution used to propose the dead point at the specified iteration of the nested sampling run. **Note that this option and `it` are mutually exclusive.** dims : iterable of shape (ndim,), optional The subset of dimensions that should be plotted. If not provided, all dimensions will be shown. prior_transform : func, optional The function transforming samples within the unit cube back to samples in the native model space. If provided, the transformed bounding distribution will be plotted in the native model space. periodic : iterable, optional A list of indices for parameters with periodic boundary conditions. These parameters *will not* have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is `None` (i.e. no periodic boundary conditions). reflective : iterable, optional A list of indices for parameters with reflective boundary conditions. These parameters *will not* have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is `None` (i.e. no reflective boundary conditions). ndraws : int, optional The number of random samples to draw from the bounding distribution when plotting. Default is `5000`. color : str, optional The color of the points randomly sampled from the bounding distribution. Default is `'gray'`. plot_kwargs : dict, optional Extra keyword arguments used when plotting the bounding draws. labels : iterable with shape (ndim,), optional A list of names for each parameter. If not provided, the default name used when plotting will be in :math:`x_i` style. label_kwargs : dict, optional Extra keyword arguments that will be sent to the `~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel` methods. max_n_ticks : int, optional Maximum number of ticks allowed. Default is `5`. use_math_text : bool, optional Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using `e`. Default is `False`. show_live : bool, optional Whether the live points at a given iteration (for `it`) or associated with the bounding (for `idx`) should be highlighted. Default is `False`. In the dynamic case, only the live points associated with the batch used to construct the relevant bound are plotted. live_color : str, optional The color of the live points. Default is `'darkviolet'`. live_kwargs : dict, optional Extra keyword arguments used when plotting the live points. span : iterable with shape (2,), optional A list where each element is a length-2 tuple containing lower and upper bounds. Default is `None` (no bound). fig : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`), optional If provided, overplot the draws onto the provided figure. Otherwise, by default an internal figure is generated. Returns ------- cornerbound : (`~matplotlib.figure.Figure`, `~matplotlib.axes.Axes`) Output corner plot of the bounding distribution. Nz1You must specify either an iteration or an index!rrrErrr rDg?rz$No bounds were saved in the results!r8z,cornerbound does not work for 1-D posteriorsrzHThe iteration requested goes beyond the number of iterations in the run.rzrCannot reconstruct the bound used at the specified iteration since bound iterations were not saved in the results.zAThe index requested goes beyond the number of samples in the run.rzCannot reconstruct the bound used to compute the specified dead point since sample bound indices were not saved in the results.rNrGz^Cannot reconstruct bound because the final set of live points are not included in the results.rrrrr)rOcsg|]}t|qSr)r )rQr)rrrrSszcornerbound..cSs g|]}dt|ddqS)z$x_{r z}$)r)rQr9rrrrS(sg@g?g?g?g?)rrrrrrrLFr)r)rXr2r!g333333ӿrr)+rr^r`rrrrrrrjrrWr rmrrrr8rrhrr rr4rkrlrrrrorprrqrrrrrr$rsrrrtru)9rwrrrrrrrr%rzrr{rr|rrrr7rrr}rrrrrrNrGr8rrr9rrr r r r r rOrrrrrrrrrrrrr!rrr#rr)rrrsJx                                                  0c( CsD|dkrt}||g}|dkr2ddtdD}t|}t|dkrNtdt||||dkrdtdt dd dd}t d |d g}t j d d d gdd}t |}t|}t|g||g}x0t|D]$}||dt||d9<qWt|ttfr||g}g}g}xP|D]H}t|trJ|||dn |ttd||dq$Wy2tj|||tttj||d\}}}Wntk rtdYnXt|dkst||}|}t|ddd}||}t|}||d}tt|} xHt|D]<\}}!y|||!kd| |<Wn|d| |<YnXq.W| t| dk}"t |"r| rt!"dt|"rt!"dnHx>t |"r| t#|"ddd9<t| dk}"qW| d|dd|ddd|dd|dd}#}$|$t%|j&dd|j&ddf}%||%ddddf<|dddf|%dddf<|dddf|%dddf<|d|%dddf<|d|%dddf<|d|%d<|d|%d<|d|%d <|d!|%d"<t'|#dt(ddgt|#dd|#|#dt(ddgt|#ddg}&t'|$dt(ddgt|$dd|$|$dt(ddgt|$ddg}'|r(|dkri}|)d#||d#<|)d$d|d$<|)d%d&|d%<|)d'd(|d'<|j*||d)fdd*d+|| s4| r^| s^|j+|&|'|%j,| $|-g|d,d-| r| r|dkrxi}|)d.||d.<|)d/d,|d/<|j+|&|'|%j,t'dg| |-d0ggf|n"| r|j.|||-|j,|d1| r$| dkri} | )d.|| d.<|j/|&|'|%j,| f| |0|d|1|ddS)2a Internal function called by :meth:`cornerplot` used to generate a a 2-D histogram/contour of samples. Parameters ---------- x : interable with shape (nsamps,) Sample positions in the first dimension. y : iterable with shape (nsamps,) Sample positions in the second dimension. span : iterable with shape (ndim,), optional A list where each element is either a length-2 tuple containing lower and upper bounds or a float from `(0., 1.]` giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:: span = [(0., 10.), 0.95, (5., 6.)] Default is `0.999999426697` (5-sigma credible interval). weights : iterable with shape (nsamps,) Weights associated with the samples. Default is `None` (no weights). levels : iterable, optional The contour levels to draw. Default are `[0.5, 1, 1.5, 2]`-sigma. ax : `~matplotlib.axes.Axes`, optional An `~matplotlib.axes.axes` instance on which to add the 2-D histogram. If not provided, a figure will be generated. color : str, optional The `~matplotlib`-style color used to draw lines and color cells and contours. Default is `'gray'`. plot_datapoints : bool, optional Whether to plot the individual data points. Default is `False`. plot_density : bool, optional Whether to draw the density colormap. Default is `True`. plot_contours : bool, optional Whether to draw the contours. Default is `True`. no_fill_contours : bool, optional Whether to add absolutely no filling to the contours. This differs from `fill_contours=False`, which still adds a white fill at the densest points. Default is `False`. fill_contours : bool, optional Whether to fill the contours. Default is `True`. contour_kwargs : dict Any additional keyword arguments to pass to the `contour` method. contourf_kwargs : dict Any additional keyword arguments to pass to the `contourf` method. data_kwargs : dict Any additional keyword arguments to pass to the `plot` method when adding the individual data points. 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