get_covar

Synopsis

Return settings or results for the covariance method.

Syntax

get_covar()
get_covar_results()
get_covar_opt( [name] )

Description

get_covar

The get_covar command returns the current covariance estimation method object. The function accepts no arguments. The returned information is:

sigma - to get 1, 2, or 3-sigma limits, default=1
eps - precision of resulting limits, default=0.01
maxiters- maximum number of tries to bracket desired limits, before returning, default=200
soft_limits - a switch which allows the search to go all the way out to the hard parameter limits; if switched on, the search will be restricted to occur within the soft limits, which can be changed by the user, default=False

get_covar_results

The get_covar_results command accesses results from the last time covariance was run. The function accepts no arguments. The results include the following attributes:

datasets - Data sets in fit
methodname - Estimation method name
fitname - Fitting method name
statname - Statistic name
percent
sigma - Change in statistic
parnames - Model parameter names
parvals - Model parameter fit values
parmins - Model parameter minimum values
parmaxes - Model parameter maximum values
extra_output - the covariance matrix

get_covar_opt

The get_covar_opt command returns a covariance option by name. If the name isn't specified, it returns a dictionary of all options that govern how covariance is run. The covariance options are modified using the set_covar_opt command.

sigma - to get 1, 2, or 3-sigma limits, default=1
eps - precision of resulting limits, default=0.01
maxiters- maximum number of tries to bracket desired limits, before returning, default=200
soft_limits - a switch which allows the search to go all the way out to the hard parameter limits; if switched on, the search will be restricted to occur within the soft limits, which can be changed by the user, default=False

Example 1

sherpa> get_covar()

Return a variable listing of all settings that govern how the covariance method calculates confidence limits. Sample output is

sherpa> print(get_covar())
name        = covariance
soft_limits = False
maxiters    = 200
sigma       = 1
eps         = 0.01

Example 2

sherpa> get_covar_results()

Return a variable listing of all results from the last time covariance was run, e.g.

sherpa> print(get_covar_results())
data sets   = (2,)
methodname = covariance
fitname    = levmar
statname   = chi2gehrels
sigma      = 1
percent    = 68.2689492137
parnames   = ('p2.gamma', 'p2.ampl')
parvals    = (1.0, 1.0)
parmins    = (-2.5123712497785912e-05, -1.8031410361942839e-05)
parmaxes   = (2.5123712497785912e-05, 1.8031410361942839e-05)
nfits      = 0

Example 3

sherpa> get_covar_opt("sigma")
1
sherpa> set_covar_opt("sigma",3)

Return the current value of the covariance sigma option ('1' in this example), and change it to the desired value ('3').

Example 4

sherpa> print get_covar_results().extra_output
[[ 1.00688203e-07 1.94309062e-07 1.63077981e-07 -2.14579320e-08
-2.33234708e-07]
[ 1.94309062e-07 5.97149180e-08 6.14497850e-07 5.95866729e-07
-3.20550581e-07]
[ 1.63077981e-07 6.14497850e-07 1.36168008e-06 9.66523748e-07
-1.53815319e-06]
[ -2.14579320e-08 5.95866729e-07 9.66523748e-07 4.53031826e-06
-1.61278260e-06]
[ -2.33234708e-07 -3.20550581e-07 -1.53815319e-06 -1.61278260e-06
1.74050730e-06]]

Retrieve the covariance matrix as a numpy N-D array.

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.