refnx.analysis¶

class
refnx.analysis.
BaseObjective
(p, logl, logp=None, fcn_args=(), fcn_kwds=None, name=None)[source]¶ Bases:
object
Don’t necessarily have to use Parameters, could use np.array

covar
()[source]¶  Returns
covar – The covariance matrix for the fitting system
 Return type
np.ndarray

logl
(pvals=None)[source]¶ Loglikelihood probability function
 Parameters
pvals (np.ndarray) – Array containing the values to be tested.
 Returns
logl – loglikelihood probability.
 Return type

logp
(pvals=None)[source]¶ Logprior probability function
 Parameters
pvals (np.ndarray) – Array containing the values to be tested.
 Returns
logp – logprior probability
 Return type

logpost
(pvals=None)[source]¶ Logposterior probability function
 Parameters
pvals (np.ndarray) – Array containing the values to be tested.
 Returns
logpost – logprobability.
 Return type
Notes
The log probability is the sum is the sum of the logprior and loglikelihood probabilities. Does not set the parameter attribute.

nll
(pvals=None)[source]¶ Negative loglikelihood function
 Parameters
pvals (np.ndarray) – Array containing the values to be tested.
 Returns
nll – negative loglikelihood
 Return type

nlpost
(pvals=None)[source]¶ Negative logposterior function
 Parameters
pvals (np.ndarray) – Array containing the values to be tested.
 Returns
nlpost – negative logposterior
 Return type


class
refnx.analysis.
Bounds
(seed=None)[source]¶ Bases:
object
A base class that describes the probability distribution for a parameter

logp
(value)[source]¶ Calculate the logprior probability of a value with the probability distribution.


class
refnx.analysis.
CurveFitter
(objective, nwalkers=200, ntemps=1, **mcmc_kws)[source]¶ Bases:
object
Analyse a curvefitting system (with MCMC sampling)
 Parameters
objective (refnx.analysis.Objective) – The
refnx.analysis.Objective
to be analysed.nwalkers (int, optional) – How many walkers you would like the sampler to have. Must be an even number. The more walkers the better.
ntemps (int or None, optional) – If ntemps == 1, then an
emcee.EnsembleSampler
is used during the sample method. Otherwise, or if ntemps is None then parallel tempering is used with aptemcee.sampler.Sampler
object during the sample method, with ntemps specifing the number of temperatures. Can be None, in which case the Tmax keyword argument sets the maximum temperature. Parallel Tempering is useful if you expect your posterior distribution to be multimodal.mcmc_kws (dict) – Keywords used to create the
emcee.EnsembleSampler
orptemcee.sampler.Sampler
objects.
Notes
See the documentation at http://dan.iel.fm/emcee/current/api/ for further details on what keywords are permitted, and for further information on Parallel Tempering. The pool and threads keywords are ignored here. Specification of parallel threading is done with the pool argument in the sample method.

acf
(nburn=0, nthin=1)[source]¶ Calculate the autocorrelation function
 Returns
acfs – The autocorrelation function, acfs.shape=(lags, nvary)
 Return type
np.ndarray

property
chain
¶ MCMC chain belonging to CurveFitter.sampler
 Returns
chain – The MCMC chain with shape (steps, nwalkers, ndim) or (steps, ntemps, nwalkers, ndim).
 Return type
array
Notes
The chain returned here has swapped axes compared to the PTSampler.chain and EnsembleSampler.chain attributes

fit
(method='LBFGSB', target='nll', **kws)[source]¶ Obtain the maximum loglikelihood, or logposterior, estimate (mode) of the objective. Maximising the loglikelihood is equivalent to minimising chi2 in a least squares fit.
 Parameters
method (str) –
which method to use for the optimisation. One of:
’least_squares’: scipy.optimize.least_squares.
’LBFGSB’: LBFGSB
 ’differential_evolution’:
scipy.optimize.differential_evolution
 ’dual_annealing’:
scipy.optimize.dual_annealing (SciPy >= 1.2.0)
’shgo’: scipy.optimize.shgo (SciPy >= 1.2.0)
You can also choose many of the minimizers from
scipy.optimize.minimize
.target ({'nll', 'nlpost'}, optional) – Minimize the negative loglikelihood (‘nll’) or the negative logposterior (‘nlpost’). This is equivalent to maximising the likelihood or posterior probabilities respectively. Maximising the likelihood is equivalent to minimising chi^2 in a leastsquares fit. This option only applies to the differential_evolution, shgo, dual_annealing or LBFGSB methods. These optimisers require lower and upper (box) bounds for each parameter. If the Bounds on a parameter are not an Interval, but a PDF specifying a statistical distribution, then the lower and upper bounds are approximated as
PDF.rv.ppf([0.005, 0.995])
, covering 99 % of the statistical distribution.kws (dict) – Additional arguments are passed to the underlying minimization method.
 Returns
result, covar – result.x contains the best fit parameters result.covar is the covariance matrix for the fit. result.stderr is the uncertainties on each of the fit parameters.
 Return type
OptimizeResult, np.ndarray
Notes
If the objective supplies a residuals method then least_squares can be used. Otherwise the nll method of the objective is minimised. Use this method just before a sampling run. If self.objective.parameters is a Parameters instance, then each of the varying parameters has its value updated by the fit, and each Parameter has a stderr attribute which represents the uncertainty on the fit parameter.
The use of dual annealing and shgo requires that scipy >= 1.2.0 be installed.

initialise
(pos='covar')[source]¶ Initialise the emcee walkers.
 Parameters
pos (str or np.ndarray) –
Method for initialising the emcee walkers. One of:
’covar’, use the estimated covariance of the system.
’jitter’, add a small amount of gaussian noise to each parameter
’prior’, sample random locations from the prior
 pos, an array that specifies a snapshot of the walkers. Has shape
 (nwalkers, ndim), or (ntemps, nwalkers, ndim) if parallel
tempering is employed. You can also provide a previously created chain.

initialise_with_chain
(chain)[source]¶ Initialise sampler with a preexisting chain
 Parameters
chain (array) – Array of size (steps, ntemps, nwalkers, ndim) or (steps, nwalkers, ndim), containing a chain from a previous sampling run.

property
logpost
¶ Logprobability for each of the entries in self.chain

make_sampler
()[source]¶ Make the samplers for the Objective.
Use this method if the number of varying parameters changes.

property
nvary
¶

sample
(steps, nthin=1, random_state=None, f=None, callback=None, verbose=True, pool=1)[source]¶ Performs sampling from the objective.
 Parameters
steps (int) – Collect steps samples into the chain. The sampler will run a total of steps * nthin moves.
nthin (int, optional) – Each chain sample is separated by nthin iterations.
random_state (int or np.random.RandomState, optional) – If random_state is an int, a new np.random.RandomState instance is used, seeded with random_state. If random_state is already a np.random.RandomState instance, then that np.random.RandomState instance is used. Specify random_state for repeatable sampling
f (filelike or str) – File to incrementally save chain progress to. Each row in the file is a flattened array of size (nwalkers, ndim) or (ntemps, nwalkers, ndim). There are steps rows in the file.
callback (callable) – callback function to be called at each iteration step. Has the signature callback(coords, logprob).
verbose (bool, optional) – Gives updates on the sampling progress
pool (int or maplike object, optional) – If pool is an int then it specifies the number of threads to use for parallelization. If pool == 1, then all CPU’s are used. If pool is a maplike callable that follows the same calling sequence as the builtin map function, then this pool is used for parallelisation.
Notes
Please see
emcee.EnsembleSampler
for its detailed behaviour.>>> # we'll burn the first 500 steps >>> fitter.sample(500) >>> # after you've run those, then discard them by resetting the >>> # sampler. >>> fitter.sampler.reset() >>> # Now collect 40 steps, each step separated by 50 sampler >>> # generations. >>> fitter.sample(40, nthin=50)
One can also burn and thin in Curvefitter.process_chain.

class
refnx.analysis.
GlobalObjective
(objectives)[source]¶ Bases:
refnx.analysis.objective.Objective
Global Objective function for simultaneous fitting with refnx.analysis.CurveFitter
 Parameters
objectives (list) – list of
refnx.analysis.Objective
objects

logl
(pvals=None)[source]¶ Calculate the loglikelhood of the system
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
logl – loglikelihood probability
 Return type

logp
(pvals=None)[source]¶ Calculate the logprior of the system
 Parameters
pvals (arraylike or refnx.analysis.Parameters, optional) – values for the varying or entire set of parameters
 Returns
logp – logprior probability
 Return type

property
npoints
¶ int number of data points in all the objectives.

property
parameters
¶ refnx.analysis.Parameters
associated with all the objectives.

plot
(pvals=None, samples=0, parameter=None, fig=None)[source]¶ Plot the data/model for all the objectives in the GlobalObjective.
Matplotlib must be installed to use this method.
 Parameters
pvals (np.ndarray, optional) – Numeric values for the Parameter’s that are varying
samples (number, optional) – If the objective has been sampled, how many samples you wish to plot on the graph.
parameter (refnx.analysis.Parameter, optional) – Creates an interactive plot for the Parameter in Jupyter. Requires ipywidgets be installed. Use with %matplotlib notebook/qt.
fig (Figure instance, optional) – If fig is not supplied then a new figure is created. Otherwise the graph is created on the current axes on the supplied figure.
 Returns
fig, ax – matplotlib figure and axes objects.
 Return type
matplotlib.Figure
,matplotlib.Axes

residuals
(pvals=None)[source]¶ Concatenated residuals for each of the
refnx.analysis.Objective.residuals
. Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
residuals – Concatenated
refnx.analysis.Objective.residuals
 Return type
np.ndarray

property
weighted
¶ bool do all the datasets have y_err, and are all the objectives wanting to use weights?

class
refnx.analysis.
Interval
(lb=inf, ub=inf, seed=None)[source]¶ Bases:
refnx.analysis.bounds.Bounds
Describes a uniform probability distribution. May be open, semiopen, or closed.
 Parameters
Examples
>>> from refnx.analysis import Parameter, Interval >>> p = Parameter(1) >>> # closed interval >>> p.bounds = Interval(0, 10) >>> p.logp([5, 1]) array([2.30258509, inf])
A semiclosed interval will still prevent the fitter from accessing impossible locations.
>>> p.bounds = Interval(lb=10) >>> p.logp([5, 1]) array([0., 0.])

property
lb
¶ Lower bound of uniform distribution

property
ub
¶ Upper bound of uniform distribution

valid
(val)[source]¶ Checks whether a parameter value is within the support of the Interval. If it isn’t then it returns a value that is within the support. If the Interval is closed (i.e. lower and upper bounds are both specified) then invalid values will be corrected by random samples taken between the lower and upper bounds. If the interval is semiopen, only one of the bounds being specified, then invalid values will be corrected by the value being reflected by the same distance from the relevant limit.
 Parameters
val (arraylike) – values to examine
 Returns
valid – values within the support
 Return type
arraylike
Examples
>>> b = Interval(0, 10) >>> b.valid(11.5) 8.5

class
refnx.analysis.
MCMCResult
(name, param, stderr, chain, median)¶ Bases:
tuple

property
chain
¶ Alias for field number 3

property
median
¶ Alias for field number 4

property
name
¶ Alias for field number 0

property
param
¶ Alias for field number 1

property
stderr
¶ Alias for field number 2

property

class
refnx.analysis.
Model
(parameters, fitfunc=None, fcn_args=(), fcn_kwds=None)[source]¶ Bases:
object
Calculates a generative model (dependent variable), given parameters and independent variables.
 Parameters
parameters (array or refnx.analysis.Parameters) – Parameters to calculate the model with
fitfunc (callable, optional) – A function that calculates the generative model. Should have the signature
fitfunc(x, parameters, *fcn_args, **fcn__kwds)
where x is an arraylike specifying the independent variables, and parameters are the parameters required to calculate the model. fcn_args and fcn_kwds can be used to supply additional arguments to to fitfunc.fcn_args (sequence, optional) – Supplies extra arguments to fitfunc
fcn_kwds (dict, optional) – Supplies keyword arguments to fitfunc
Notes
It is not necessary to supply fitfunc to create a Model iff you are inheriting Model and are also overriding Model.model.

property
fitfunc
¶ The fitfunction associated with the model

logp
()[source]¶ The model can add additional terms to it’s logprobability. However, it should _not_ include logp from any of the parameters. That is calculated by Objective.logp.

model
(x, p=None, x_err=None)[source]¶ Calculates a generative model(dependent variable), given parameters and independent variables.
 Parameters
x (arraylike) – Independent variable.
p (arraylike or refnx.analysis.Parameters) – Parameters to supply to the generative function.
x_err (optional) – Uncertainty in x.
 Returns
generative
 Return type
arraylike or float
Notes
The interpretation of x, p, and x_err is up to the fitfunc supplied during construction of this object (or the overridden model method of this object).

property
parameters
¶ The refnx.analysis.Parameters set associated with the model.

class
refnx.analysis.
Objective
(model, data, lnsigma=None, use_weights=True, transform=None, logp_extra=None, name=None)[source]¶ Bases:
refnx.analysis.objective.BaseObjective
Objective function for using with curvefitters such as refnx.analysis.curvefitter.CurveFitter.
 Parameters
model (refnx.analysis.Model) – the generative model function. One can also provide an object that inherits refnx.analysis.Model.
data (refnx.dataset.Data1D) – data to be analysed.
lnsigma (float or refnx.analysis.Parameter, optional) –
Used if the experimental uncertainty (data.y_err) underestimated by a constant fractional amount. The experimental uncertainty is modified as:
s_n**2 = y_err**2 + exp(lnsigma * 2) * model**2
See Objective.logl for more details.
use_weights (bool) – use experimental uncertainty in calculation of residuals and logl, if available. If this is set to False, then you should also set self.lnsigma.vary = False, it will have no effect on the fit.
transform (callable, optional) – the model, data and data uncertainty are transformed by this function before calculating the likelihood/residuals. Has the signature transform(data.x, y, y_err=None), returning the tuple (transformed_y, transformed_y_err).
logp_extra (callable, optional) – user specifiable logprobability term. This contribution is in addition to the logprior term of the model parameters, and model.logp, as well as the loglikelihood of the data. Has signature: logp_extra(model, data). The model will already possess updated parameters. Beware of including the same logprobability terms more than once.
name (str) – Name for the objective.
Notes
For parallelisation logp_extra needs to be picklable.

chisqr
(pvals=None)[source]¶ Calculates the chisquared value for a given fitting system.
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
chisqr – Chisquared value, np.sum(residuals**2).
 Return type
np.ndarray

corner
(**kwds)[source]¶ Corner plot of the chains belonging to the Parameters. Requires the corner and matplotlib packages.
 Parameters
kwds (dict) – passed directly to the corner.corner function
 Returns
fig
 Return type
matplotlib.Figure
object.

covar
()[source]¶ Estimates the covariance matrix of the curvefitting system.
 Returns
covar – Covariance matrix
 Return type
np.ndarray

generative
(pvals=None)[source]¶ Calculate the generative (dependent variable) function associated with the model.
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
model
 Return type
np.ndarray

logl
(pvals=None)[source]¶ Calculate the loglikelhood of the system
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
logl – loglikelihood probability
 Return type
Notes
The loglikelihood is calculated as:
logl = 0.5 * np.sum(((y  model) / s_n)**2 + np.log(2 * pi * s_n**2))
where
s_n**2 = y_err**2 + exp(2 * lnsigma) * model**2

logp
(pvals=None)[source]¶ Calculate the logprior of the system
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
logp – logprior probability
 Return type
Notes
The logprior is calculated as:
logp = np.sum(param.logp() for param in self.varying_parameters()) logp += self.model.logp() logp += self.logp_extra(self.model, self.data)
The major components of the logprior probability are from the varying parameters and the Model used to construct the Objective. self.model.logp should not include any contributions from self.model.parameters otherwise they’ll be counted more than once. The same argument applies to the user specifiable logp_extra function.

logpost
(pvals=None)[source]¶ Calculate the logprobability of the curvefitting system
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
logpost – logprobability
 Return type
Notes
The overall logprobability is the sum of the logprior and loglikelihood. The loglikelihood is not calculated if the logprior is impossible (logp == np.inf).

nll
(pvals=None)[source]¶ Negative loglikelihood function
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
nll – negative loglikelihood
 Return type

property
npoints
¶ int the number of points in the dataset.

property
parameters
¶ refnx.analysis.Parameters
, all the Parameters contained in the fitting system.

pgen
(ngen=1000, nburn=0, nthin=1)[source]¶ Yield random parameter vectors from the MCMC samples. The objective state is not altered.
 Parameters
 Yields
pvec (np.ndarray) – A randomly chosen parameter vector

plot
(pvals=None, samples=0, parameter=None, fig=None)[source]¶ Plot the data/model.
Requires matplotlib be installed.
 Parameters
pvals (np.ndarray, optional) – Numeric values for the Parameter’s that are varying
samples (number) – If the objective has been sampled, how many samples you wish to plot on the graph.
parameter (refnx.analysis.Parameter) – Creates an interactive plot for the Parameter in Jupyter. Requires ipywidgets be installed. Use with %matplotlib notebook/qt.
fig (Figure instance, optional) – If fig is not supplied then a new figure is created. Otherwise the graph is created on the current axes on the supplied figure.
 Returns
fig, ax – matplotlib figure and axes objects.
 Return type
matplotlib.Figure
,matplotlib.Axes

residuals
(pvals=None)[source]¶ Calculates the residuals for a given fitting system.
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters
 Returns
residuals – Residuals, (data.y  model) / y_err.
 Return type
np.ndarray

setp
(pvals)[source]¶ Set the parameters from pvals.
 Parameters
pvals (arraylike or refnx.analysis.Parameters) – values for the varying or entire set of parameters

varying_parameters
()[source]¶  Returns
varying_parameters – The varying Parameter objects allowed to vary during the fit.
 Return type

property
weighted
¶ bool Does the data have weights (data.y_err), and is the objective using them?

class
refnx.analysis.
PDF
(rv, seed=None)[source]¶ Bases:
refnx.analysis.bounds.Bounds
A class that describes the probability distribution for a parameter.
 Parameters
rv (
scipy.stats.rv_continuous
or Object) – A continuous probability distribution. If rv is not an rv_continuous, then it must implement the logpdf and rvs methods.seed (int, float or np.random.RandomState) – Seed for random variates
Examples
>>> import scipy.stats as stats >>> from refnx.analysis import Parameter, PDF >>> p = Parameter(0.5) >>> # use a normal distribution for prior, mean=5 and sd=1. >>> p.bounds = PDF(stats.norm(5, 1)) >>> p.logp(), stats.norm.logpdf(0.5, 5, 1) (11.043938533204672, 11.043938533204672)

logp
(val)[source]¶ Calculate the logprior probability of a value with the probability distribution.

class
refnx.analysis.
Parameter
(value=0.0, name=None, bounds=None, vary=False, constraint=None)[source]¶ Bases:
refnx.analysis.parameter.BaseParameter
Class for specifying a variable.
 Parameters
value (float, optional) – Numerical Parameter value.
name (str, optional) – Name of the parameter.
bounds (refnx.analysis.Bounds, tuple, optional) – Sets the bounds for the parameter. Either supply a refnx.analysis.Bounds object (or one of its subclasses), or a (lower_bound, upper_bound) tuple.
vary (bool, optional) – Whether the Parameter is fixed during a fit.
constraint (expression, optional) – Python expression used to constrain the value during the fit.

property
constraint
¶

logp
(pval=None)[source]¶ Calculate the log probability of the parameter
 Returns
prob – log probability of the parameter
 Return type

setp
(value=None, vary=None, bounds=None, constraint=None)[source]¶ Set several attributes of the parameter at once.
 Parameters
value (float, optional) – Numerical Parameter value.
vary (bool, optional) – Whether the Parameter is fixed during a fit.
bounds (refnx.analysis.Bounds, tuple, optional) – Sets the bounds for the parameter. Either supply a refnx.analysis.Bounds object (or one of its subclasses), or a (lower_bound, upper_bound) tuple.
constraint (expression, optional) – Python expression used to constrain the value during the fit.

class
refnx.analysis.
Parameters
(data=(), name=None)[source]¶ Bases:
collections.UserList
A sequence of Parameters
 Parameters
data (sequence) – A sequence of
Parameter
orParameters
name (str) – Name of this
Parameters
instance

flattened
(unique=False)[source]¶ A list of all the
Parameter
contained in this object, including those contained withinParameters
at any depth.

logp
()[source]¶ Calculates logp for all the parameters
 Returns
logp – Log probability for all the parameters
 Return type

property
nparam
¶

property
parameters
¶

pgen
(ngen=1000, nburn=0, nthin=1)[source]¶ Yield random parameter vectors from MCMC samples.
 Parameters
 Yields
pvec (np.ndarray) – A randomly chosen parameter vector
Notes
The entire parameter vector is yielded, not only the varying parameters. The reason for this is that some parameters may possess a chain if they are not varying, because they are controlled by a constraint.

class
refnx.analysis.
Transform
(form)[source]¶ Bases:
object
Mathematical transforms of numeric data.
 Parameters
One of:
 ’lin’
No transform is made
 ’logY’
log10 transform
 ’YX4’
YX**4 transform
 ’YX2’
YX**2 transform
 None
No transform is made
Notes
You ask for a transform to be carried out by calling the Transform object directly.
>>> x = np.linspace(0.01, 0.1, 11) >>> y = np.linspace(100, 1000, 11) >>> y_err = np.sqrt(y) >>> t = Transform('logY') >>> ty, te = t(x, y, y_err) >>> ty array([2. , 2.2787536 , 2.44715803, 2.56820172, 2.66275783, 2.74036269, 2.80617997, 2.86332286, 2.91381385, 2.95904139, 3. ])

refnx.analysis.
autocorrelation_chain
(chain, nburn=0, nthin=1)[source]¶ Calculate the autocorrelation function
 Parameters
chain (np.ndarray) – The MCMC chain  (nsteps, nwalkers, ndim) or (nsteps, ntemps, nwalkers, ndim)
 Returns
acfs – The autocorrelation function, acfs.shape=(lags, nvary)
 Return type
np.ndarray

refnx.analysis.
integrated_time
(x, c=5, tol=50, quiet=False)[source]¶ Estimate the integrated autocorrelation time of a time series.
This estimate uses the iterative procedure described on page 16 of Sokal’s notes to determine a reasonable window size.
 Parameters
x – The time series. If multidimensional, set the time axis using the
axis
keyword argument and the function will be computed for every other axis.c (Optional[float]) – The step size for the window search. (default:
5
)tol (Optional[float]) – The minimum number of autocorrelation times needed to trust the estimate. (default:
50
)quiet (Optional[bool]) – This argument controls the behavior when the chain is too short. If
True
, give a warning instead of raising anAutocorrError
. (default:False
)
 Returns
 An estimate of the integrated autocorrelation time of
the time series
x
computed along the axisaxis
.
 Return type
float or array
 Raises
 AutocorrError: If the autocorrelation time can’t be reliably estimated
from the chain and
quiet
isFalse
. This normally means that the chain is too short.

refnx.analysis.
load_chain
(f)[source]¶ Loads a chain from disk. Does not change the state of a CurveFitter object.
 Parameters
f (str or filelike) – File containing the chain.
 Returns
chain – The loaded chain  (nsteps, nwalkers, ndim) or (nsteps, ntemps, nwalkers, ndim)
 Return type
array

refnx.analysis.
possibly_create_parameter
(value, name='')[source]¶ If supplied with a Parameter return it. If supplied with float, wrap it in a Parameter instance.
 Parameters
value (float or refnx.analysis.Parameter) –
 Returns
parameter
 Return type

refnx.analysis.
process_chain
(objective, chain, nburn=0, nthin=1, flatchain=False)[source]¶ Process the chain produced by a sampler for a given Objective
 Parameters
objective (refnx.analysis.Objective) – The Objective function that the Posterior was sampled for
chain (array) – The MCMC chain
nburn (int, optional) – discard this many steps from the start of the chain
nthin (int, optional) – only accept every nthin samples from the chain
flatchain (bool, optional) – collapse the walkers down into a single dimension.
 Returns
[(param, stderr, chain)] – List of (param, stderr, chain) tuples. If isinstance(objective.parameters, Parameters) then param is a Parameter instance. param.value, param.stderr and param.chain will contain the median, stderr and chain samples, respectively. Otherwise param will be a float representing the median of the chain samples. stderr is the half width of the [15.87, 84.13] spread (similar to standard deviation) and chain is an array containing the MCMC samples for that parameter.
 Return type
Notes
The chain should have the shape (iterations, nwalkers, nvary) or (iterations, ntemps, nwalkers, nvary) if parallel tempering was employed. The burned and thinned chain is created via: chain[nburn::nthin]. Note, if parallel tempering is employed, then only the lowest temperature of the parallel tempering chain is processed and returned as it corresponds to the (lowest energy) target distribution. If flatten is True then the burned/thinned chain is reshaped and arr.reshape(1, nvary) is returned. This function has the effect of setting the parameter stderr’s.

refnx.analysis.
pymc_objective
(objective)[source]¶ Creates a pymc3 model from an Objective. Will not be able to use the NUTS sampler because gradients aren’t evaluable.
Requires theano and pymc3 be installed. This is an experimental feature.
 Parameters
objective (refnx.analysis.Objective) –
 Returns
model
 Return type
pymc3.Model
Notes
The varying parameters are renamed ‘p0’, ‘p1’, etc, as it’s vital in pymc3 that all parameters have their own unique name.