B bd@sddlmZmZmZmZdZddlZddlmZmZddl m Z ddl Z ddl Z ddlZddlZddlmZddlZddlZddlZddlZddl m ZddlmZmZddlmZmZddlmZdd lmZdd l m!Z"dd l#m$Z$ddl%Z%yej&Z'ej(Z)ej*Z+Wne,Z'e-Z)e.Z+YnXe/e-e0ej1j2Z3dOd d Z4dPddZ5dQddZ6ddddgdddddddddddddddddd ddddddfd!d"Z7dRd$d%Z8ddddgd&ddddddd dddddddddfd'd(Z9dSd*d+Z:e;e j<d,kre j=>d-e jd0e jeTZXd?Yd@eXdAeXgZZeAeZeNeReSfqWeAdBeI[Z\e\ddd#f]dddCZTe\eTd,dfZ^e\eTdfZ_e\eTd#fZ`eKd8Zaee_de`eaZbebebUZbece^e_ebdDZde7edeFddEe jee@dFdGdHe jee@dIdGdHe fe9edeFddEe jee@dJdGdHe jee@dKdGdHe fe8edeFdLe jee@dMdGdHe jee@dNdGdHe fdS)T)absolute_importunicode_literalsprint_functiondivisionz Script that does default visualizations (marginal plots, 1-d and 2-d). Author: Johannes Buchner (C) 2013-2019 Author: Josh Speagle (MIT licensed) N)explog)range) MaxNLocator NullLocator)LinearSegmentedColormapcolorConverter)ScalarFormatter)spatial)gaussian_filter) gaussian_kdecCst|}t|}t|dks0t|dkr8td|dkrTt|td|St|}t|t|krvtdt|}||}t|dd}||d}t d|}t |||| }|SdS) a  Compute (weighted) quantiles from an input set of samples. Parameters ---------- x : `~numpy.ndarray` with shape (nsamps,) Input samples. q : `~numpy.ndarray` with shape (nquantiles,) The list of quantiles to compute from `[0., 1.]`. weights : `~numpy.ndarray` with shape (nsamps,), optional The associated weight from each sample. Returns ------- quantiles : `~numpy.ndarray` with shape (nquantiles,) The weighted sample quantiles computed at `q`. gg?z#Quantiles must be between 0. and 1.NgY@z+Dimension mismatch: len(weights) != len(x).r) np atleast_1dany ValueErrorZ percentilelistlenargsortcumsumappendinterptolist)xqweightsidxswZcdf quantilesr#s/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/../../../bin/multinest_marginals_fancy.py _quantile,s       r%c Cs|dkrtj}tt|dtkr,tdt|}|t||}tj|tj d}t |}d\}}x6||kr||||kr|||<|d7}qn|d7}qnW||S)a Resample a new set of points from the weighted set of inputs such that they all have equal weight. Each input sample appears in the output array either `floor(weights[i] * nsamples)` or `ceil(weights[i] * nsamples)` times, with `floor` or `ceil` randomly selected (weighted by proximity). Parameters ---------- samples : `~numpy.ndarray` with shape (nsamples,) Set of unequally weighted samples. weights : `~numpy.ndarray` with shape (nsamples,) Corresponding weight of each sample. rstate : `~numpy.random.RandomState`, optional `~numpy.random.RandomState` instance. Returns ------- equal_weight_samples : `~numpy.ndarray` with shape (nsamples,) New set of samples with equal weights. Examples -------- >>> x = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]) >>> w = np.array([0.6, 0.2, 0.15, 0.05]) >>> utils.resample_equal(x, w) array([[ 1., 1.], [ 1., 1.], [ 1., 1.], [ 3., 3.]]) Notes ----- Implements the systematic resampling method described in `Hol, Schon, and Gustafsson (2006) `_. Ng?zWeights do not sum to 1.)Zdtype)rr) rrandomabssumSQRTEPSrrarangezerosintr) samplesrZrstateZnsamplesZ positionsr Zcumulative_sumijr#r#r$resample_equalVs"    r1FTblueredc$s|dkrt}|dkrt}| dkr*t} |dd|d<|dd|d<| dd| d<| dd| d<|d }|d |d t|d }|d |d d}|d |ddt<t|}y|d}d}WnZt||d}|||dkr0td|ddddd}t||}YnX|rb|||dkrT|}nt dd}|t |t |t g}|rt t  |d}tdd|}|| |d<dkrdd|Dd}nd}ttdkrtdxXtD]L\}}y|\}}Wn0t|||t||f|<YnXq W|r|r|rt dddt dddf} n"ddt dddf} | d<|dkrtjddd d!\}fd"dDnf|\}yddWntd#YnXd$dDd%dDd&dDd'dDfd(dtdDfd)dtdDd*d+d,d-g}!|r|!dd.7<xt|D]\}}"|t|tr|n||| dkrjtnjt| | dkr:jtnjt| t|d/}#j|#j d6|j!|!|f||r|dkrj" |"fd1i|d2dDnF|r|dkrj#| |"fd1i|nj# |"fd1i||dkr.|r.fd3dtddD|rxj$| fd4dd5||dkrxj%|fd4dd5||dkr| dk rʈj%t | fd1| i| qW|fS)7a 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? linestylesolidr6niterlogvollogllogwtlogzrlogzerrgZ 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.rcSsg|]}ddt|fqS)gg?)max).0dr#r#r$ "szrunplot..Tz-More bounds provided in `span` than subplots!g433333@g?g@)rI)figsizecsg|]}dt fqS)g)min)rEax)r=r#r$rG9szCProvided axes do not match the required shape for plotting samples.cSsg|] }|qSr#)Zget_xlim)rErLr#r#r$rGCscSsg|] }|qSr#)get_ylim)rErLr#r#r$rGDscSsg|]}|dkr|ndqS))gg?)NNr#)rEtr#r#r$rGGscSsg|]}|dkr|ndqS))gg?)NNr#)rErNr#r#r$rGHsc s@g|]8}|td|dtt |dgqS)grr&)set_xlimrKrD)rEr/)axesr=xspanr#r$rGKsc sJg|]B}|t|d|dt|d|dgqS)rr&)set_ylimrKrD)rEr/)rPspanyspanr#r$rGNsz Live PointszLikelihood (normalized)zImportance WeightZEvidencez PDF) useMathText$-\ln X$colorcSsg|] }|qSr#)rM)rErLr#r#r$rGrsc s>g|]6}j t|t|ddqS)g?)rWr9) fill_betweenrr)rEs)rLcr=r@rAr#r$rGxsdashed)rWlslw)rV)&dictgetrDrisfiniterZonesr+rwarningswarnrrr1linspacepdfrr enumerateplsubplotsreshaper isinstancestr_typexaxisset_major_locatorr r yaxisr set_major_formatter set_xlabel set_ylabelZsemilogyplotaxvlineaxhline)$resultsrSZlogplotkdenkderW plot_kwargs label_kwargsZ lnz_errorZ lnz_truth truth_color truth_kwargsZ max_x_ticksZ max_y_ticks use_math_textZmark_final_livefigr<r>r?nsampsrBZ nlive_finalZlive_idxdatawt_kdeZ logvol_newZno_spanr/_ZyminZymaxZzspanlabelsrFsfr#) rLrPrZr=r@rArSrQrTr$runplots_  0 "          "rg?g?g333333?{Gz?plasma r8z.2fcA s|dkrt}|dkrt}| dkr*t} |dkr8t}|dkrFt}dkrTt|dd|d<|dd|d<| dd| d<ddd<d d d <|d }|d }yt|d |dd}Wn|d}YnX|r4tt| |}t|d|d|}|| } t| | | }!n|}!t |}t |j dkr^t |}nt |j d ksvt d|j}|j d|j dkst d|j \}"}#|jdkrtd|#|j dkrtd|jdkrtd|#|j dkrtd| r^y|d}$t|$}%WntdYnXy| d}&| }&Wntjj|%| dd}&YnX|dkrzddt|"D}t|}t ||"krtdxjt|D]^\}'}(y||'\})}*WnBd d ||'d d ||'g}+t||'|+|d!||'<YnXqW|dkr d"dt|"D}tts8ttrNfd#dt|"D|dkrvtj|"d d$d|"fd%\}},n.|\}},y|,|"d Wntd&YnXx t|D]\}'}-t |ddkr|,dn |,|'df| dk r t| tr| }.n| |'}.n|!}.t|tr |}/n||'}/d't | g!t |-t"|-g|dkrzj#$t%j&$t%n j#$t'|j&$t'|t(|d(}0j&)|0j*d;|j+||'f|j,| |-f|.|/d*| | r*x8|&D]0}1|$|1k}2j-||2 |-|2fd+| i|qW|dk r||'dk ryfd,d||'DWn&j.||'fd+iYnXt |ddkr|,dn |,|'dft|tr|}.n||'}.||'|dkrj#$t%j&$t%nj#$t'|j&$t%t(|d(}0j#)|0j*||'f||'}3t|3trʈj/|-f|3||.t0||'d-|\}4}5}(t1tt2|5dd|5dd3}6t1tt2|4|43}7nrt4t5d.|3}8tj6|-|8|t0||'d/\}4}5t7|4d.}4d |5dd|5dd}6|4}7j8|6|7fd+|.i|!d't"|7d0g|dk rt |dkrt|-||d!}9x |9D]}+j9|+dd1|.d2q~W|rt:d3t:||'d4dt2||9D|dk r$||'dk r$yfd5d||'DWn&j9||'fd+iYnX|rd}:|dk rt|-d6d d7g|d!\};}<}=|<|;|=|<}>}?d8;|j;}@d9}:|:;|@|<|@|>|@|?}:d:;||'|:}:j<|:f|qW||,fS)`_. 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`. 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 `True`. 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. Nr9gffffff?g333333?rYr6r:r;r7rCr.r=r?r@rrrr&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#)rEr/r#r#r$rGpsztraceplot..z,Dimension mismatch between samples and span.g?)rcSs g|]}dt|ddqS)z$x_{r&z}$)str)rEr/r#r#r$rG}scsg|]}qSr#r#)rEr/)smoothr#r$rGs )rJzCProvided axes do not match the required shape for plotting samples.g)rU$-\ln X$)rZcmaprWcs"g|]}j|fdiqS)rW)rs)rErN)rLryrzr#r$rGs)binsrrWrg$@)rrrg?r[)r]r\rWz Quantiles:cSsg|]}|qSr#r#)rEblobr#r#r$rGscs"g|]}j|fdiqS)rW)rr)rErN)rLryrzr#r$rGsg?g333333?z {{0:{0}}}z${{{0}}}_{{-{1}}}^{{+{2}}}$z {0} = {1})r)=r^r_rrrr1rcrdrrrshape atleast_2dAssertionErrorTndimruniquer'choicerrrer%riint_type float_typerfrgrhrjrOrKrRrDrkrlr rmr r rnrorpscatterrqrshistsortarrayzipflattenr-round histogramnorm_kderXrrprintformat set_title)ArtrSr"rZ post_colorZ post_kwargsrurvZ trace_cmapZ trace_colorZ trace_kwargsconnectZconnect_highlightZ connect_colorZconnect_kwargs max_n_ticksr{rrx show_titles title_fmt title_kwargstruthsryrzverboser|r.r=rr logvol_gridwt_gridZwtsrr}ruidZidsr/rxminxmaxrrPrrWrrr0selrYnbZx0Zy0rqstitleqlqmqhq_minusq_plusfmtr#)rLrryrzr$ traceplotsL        "                     (      rr&c+ sLdkrt| dkrt} |dkr*t}|dd|d<ddd<ddd<dd d<|d }|d }yt|d |d d}Wn|d}YnX|rtt| |}t|d|d|}|| }t| | |}t |}t |j dkrt |}nt |j dks4t d|j}|j d|j dksXt d|j \}}|jdkrvtd||j dkrtd|dk rt ||krtdxjt|D]^\}}y||\}}WnBdd||dd||g}t||||d||<YnXqW|dkr6ddt|D}|dkrD|}d}d|}d|}d} ||d||d| }!||!|}"|dkrtj|d|d|"|"fd\}}#n>y(|\}}#t|#|d|df}#WntdYnX||"}$||!|"}%|j|$|$|%|%| | d x(t|ddD]\}}&xt|ddD]\}'}(|#||'f|dk r||'|||'|krd!ggqJ| dkrڈjt j!t n(jt"| d"d#j!t"| d"d#t#|d$})j$|)j!$|)||dkr>%gn2d%d&Dj'||'f| j(dd&|'dkr)gn6d'd*Dj+||df| j!(d&dt,|(-d(}*|dk r||dk r|*|&||dk|&||dk@9}*|dk rD||'dk rD|*|(||'dk|(||'dk@9}*j.|(|*dd||&|*dd|f||d)|| dk rJ| |'dk ryfd*d| |'DWn&j/| |'fd+iYnX| |ddk rJy"fd,d| |dDWn*j0| |dfd+iYnXqJWq,W||#fS)-a Generate a (sub-)corner plot of (weighted) samples. Parameters ---------- results : :class:`~dynesty.results.Results` instance A :class:`~dynesty.results.Results` instance from a nested sampling run. **Compatible with results derived from** `nestle `_. 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. NrYr&r:r;r7rCr9gffffff?r.r=r?r@rrrzSamples 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!z,Dimension mismatch between samples and span.g?)rcSs g|]}dt|ddqS)z$x_{r&z}$)r)rEr/r#r#r$rGsz cornerpoints..g@g?g?g?)rJz$Mismatch between axes and dimension.)leftbottomrighttopwspacehspaceFlower)prune)rUcSsg|]}|dqS)-) set_rotation)rElr#r#r$rGsg333333ӿcSsg|]}|dqS)r)r)rErr#r#r$rGsbool)rZrcs"g|]}j|fdiqS)rW)rr)rErN)rLryrzr#r$rGsrWcs"g|]}j|fdiqS)rW)rs)rErN)rLryrzr#r$rGs)1r^r_rrrr1rcrdrrrrrrrrrrer%rrfrgrrhsubplots_adjustrOrR set_frame_on set_xticks set_yticksrkrlr rmr r rnset_xticklabelsget_xticklabelsroset_label_coordsset_yticklabelsget_yticklabelsrpZ ones_likeZastyperrrrs)+rtZthinrSrrWrurvrwrrxrryrzrr{r|r.r=rrrrrr}r/rrrrfactorlbdimtrdimwhspaceplotdimdimrPlbtrrr0yrZ in_boundsr#)rLryrzr$ cornerpoints s^       "    "                     $$& rblackc< sB |dkr g}dkrt|dkr(t}| dkr6t} |dkrDt}|dkrRt}|dd|d<|dd|d<ddd<ddd<ddd<|d }yt|d |d d }Wn|d }YnXt|}t|jdkrt|}nt|jdkstd|j }|jd|jdks@td|j\}}|j dkr^t d||jdkrvt d|dkrddt |D}t |}t||krt dxjt|D]^\}}y||\}}WnBdd||dd||g}t||||d||<YnXqW|dkr8ddt |D}ttsPttrffddt |Dd}d|}d|}d} ||||d| }!||!|}"|dkrtj|||"|"fd\}}#n6y |\}}#t|#||f}#Wnt d YnX||"}$||!|"}%|j|$|$|%|%| | d!xt|D]\}}&t|ddkrV|#n |#||f|||dkrjtjtn"jt|d"d#jtt|d$}'j|'||dkr|rj d%d&d!Dn "gn2d'd!Dj#||f|j$dd(|}(t|(trj%|&f|(||t&||d)|\})}*}nt't(d*|(}+tj)|&|+|t&||d+\})}*t*|)d*})d|*dd|*dd },j%|,f|*|)t&|||d,|\})}*}+d-t,|)d.g|dk rt|dkrt|&||d}-x |-D]}j-|dd/|d0qHW|rt.d1t.||d2dt/||-D| dk r| |dk ryfd3d| |DWn&j-| |fd4iYnX| rvd}.| dk rvt|&d5dd6g|d\}/}0}1|0|/|1|0}2}3d70| j0}4d8}.|.0|4|0|4|2|4|3}.d90|||.}.j1|.f| xt|D]\}5}6t|ddkr|#n |#||5f|5|kr2d:3g4gqn|5|krq|dkrjtjtn(jt|d"d#jt|d"d#t|d$}'j|'j|'||dkrz"gn2d;d!Dj#||5f|j$dd(|5dkrˆ5gn2d|:|d><|d?|;|d?<t8|6|&f||5||g|||7|(gd@|| dk r| |5dk ryfdAd| |5DWn&j-| |5fd4iYnX| |dk ryfdBd| |DWn&j9| |fd4iYnXqWq2W||#fS)Cau Generate a corner plot of the 1-D and 2-D marginalized posteriors. 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). 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`. 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 `True`. 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. Nr9g333333?r:r;r7rCgffffff?r.r?r@rrr&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#)rEr/r#r#r$rGszcornerplot..z,Dimension mismatch between samples and span.g?)rcSs g|]}dt|ddqS)z$x_{r&z}$)r)rEr/r#r#r$rGscsg|]}qSr#r#)rEr/)rr#r$rGsg@g?g?g?)rJz$Mismatch between axes and dimension.)rrrrrrr)r)rUrcSsg|]}|dqS)r)r)rErr#r#r$rGscSsg|]}|dqS)r)r)rErr#r#r$rGsg333333ӿ)rrrWrg$@)rrr)rrrrWgg?r[)r]r\rWz Quantiles:cSsg|]}|qSr#r#)rErr#r#r$rGscs"g|]}j|fdiqS)rW)rr)rErN)rLryrzr#r$rGsrWg?g333333?z {{0:{0}}}z${{{0}}}_{{-{1}}}^{{+{2}}}$z {0} = {1}FcSsg|]}|dqS)r)r)rErr#r#r$rGBscSsg|]}|dqS)r)r)rErr#r#r$rGHsT fill_contours plot_contours)rLrSrrWrcs"g|]}j|fdiqS)rW)rr)rErN)rLryrzr#r$rG`scs"g|]}j|fdiqS)rW)rs)rErN)rLryrzr#r$rGgs):r^r_rrrrrrrrrrrrrer%rirrrfrgrrhrrOrkrlr rmr r rnZset_ticks_positionrrrorrrr-rrrrRrDrrrrrrrrrrrrp_hist2drs)t |&rJ|$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~|dkr"t)}|*d$||d$<|*d%d|d%<|*d&d'|d&<|*d(d)|d(<|j+||d*fdd+d,|| s| r| s|j,|*|+|)j-|$$|.g|d-d.| r | r |dkrt)}|*d/||d/<|*d0d-|d0<|j,|*|+|)j-t'dg|$|.d1ggf|n"| rB|j/|| |.|j-|d2| r~| dkrXt)} | *d/|| d/<|j0|*|+|)j-|$f| |1|d|2|ddS)3a 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. NcSsg|]}dqS)g5?r#)rEr/r#r#r$rGsz_hist2d..rCz,Dimension mismatch between samples and span.g?)rg?gg@ density_cmap)r&r&r&r white_cmap)r&r&r&)Ncsg|] }tqSr#)r)rEr) rgba_colorr#r$rGsrr&gg@)rrrzHIt looks like at least one of your sample columns have no dynamic range.rz(Too few points to create valid contours.gH.?rH)rr)r&r&)rr)r&r)rr)rr&)rr)rrrWmsZmecnoner9g?oT)ZzorderZ rasterizedF)r antialiasedcolorsrgqh?)r)3rfZgcarrrrrer%rrr+r from_listr Zto_rgbafloatrirrrr-rZ histogram2drmaprallrrremptydiffrloggingwarningwhererKr,rZ concatenaterr^r_rqZcontourfrrDZpcolorZcontourrOrR),rrrrSrZlevelsrLrWZplot_datapointsZ plot_densityrZno_fill_contoursrZcontour_kwargsZcontourf_kwargsZ data_kwargskwargsr~r/rrrrrrZ contour_cmaprrZsvaluesrYHXYZHflatZindssmVZv0mZX1ZY1ZH2ZX2ZY2r#)rr$rqsF      *        :*    ,,,,     ( rrCzSYNOPSIS: %s output-root: Where the output of a MultiNest run has been written to. Example: chains/1- %sz model "%s"z params.jsonzExpected the file %sparams.json with the parameter names. 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