Sherpa Template Models
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Sherpa Threads (CIAO 4.17 Sherpa)
Overview
Synopsis:
The Sherpa template model is available for comparing a source data spectrum against a set of user-provided template models, in order to find the single template model which best matches the source data. A special grid-search fit optimization method, gridsearch, is used to fit a template model to a data set in Sherpa.
Starting with a directory of template model files conforming to a specific format, and a single index file listing the file contents of that directory, the models are loaded into the Sherpa session using the load_template_model function, an extension of load_table_model. After fitting the template model to the source data using the gridsearch fit optimization method, the parameters of the best matched template model are returned. Fit results may be examined in the usual way with the appropriate plotting commands.
The Sherpa template model supports linear, nearest-neighbor, and polynomial interpolation. Interpolation is used by the template model to match the data grid to the model grid—which must match before the fit statistics can be calculated for fitting.
Last Update: 17 Dec 2024 - updated for CIAO 4.17, revised for output formating changes.
Contents
- Getting Started: Setting up the Template Model Files
- Loading the Source Data
- Loading the Template Models
- Using the Grid-search Optimization Method
- Fitting the Template Model to the Spectrum
- Fitting a Template Model with an Additive, Continuous Component
- History
- Images
Getting Started: Setting up the Template Model Files
Model files
A collection of template models may be read into Sherpa from a directory full of template files using the load_template_model function, in order to compare a data set to all of the templates in that collection. The Sherpa model library accommodates ASCII template model files containing separated columns of x- and y-coordinates, such as wavelength, energy, or time, versus flux, counts, or intensity. In this thread, we consider a set of accretion disk models in the Kerr geometry (Siemiginowska et al., ApJ, 454, p.77, 1995), where there is a separate model file for each combination of the black hole mass, accretion rate, and inclination angle parameter values. All the data used in this thread can be also found in the tar file: template_model.tar.gz. Download and unpack this file, and go into the "template_model" directory before continuing.
The contents of the directory of Kerr model files is shown below:
unix% ls data/Kerr/ k10_001_01.dat k6_01_05.dat k7_01_1.dat k8_03_025.dat k9_01_075.dat k10_001_025.dat k6_01_075.dat k7_03_01.dat k8_03_05.dat k9_01_1.dat k10_001_05.dat k6_01_1.dat k7_03_025.dat k8_03_075.dat k9_03_01.dat k10_001_075.dat k6_03_01.dat k7_03_05.dat k8_03_1.dat k9_03_025.dat k10_001_1.dat k6_03_025.dat k7_03_075.dat k8_08_01.dat k9_03_05.dat k10_01_01.dat k6_03_05.dat k7_03_1.dat k8_08_025.dat k9_03_075.dat k10_01_025.dat k6_03_075.dat k7_08_01.dat k8_08_05.dat k9_03_1.dat k10_01_05.dat k6_03_1.dat k7_08_025.dat k8_08_075.dat k9_08_01.dat k10_01_075.dat k6_08_01.dat k7_08_05.dat k8_08_1.dat k9_08_025.dat k10_01_1.dat k6_08_025.dat k7_08_075.dat k8_1_01.dat k9_08_05.dat k10_03_01.dat k6_08_05.dat k7_08_1.dat k8_1_025.dat k9_08_075.dat k10_03_025.dat k6_08_075.dat k7_1_01.dat k8_1_05.dat k9_08_1.dat k10_03_05.dat k6_08_1.dat k7_1_025.dat k8_1_075.dat k9_1_01.dat k10_03_075.dat k6_1_01.dat k7_1_05.dat k8_1_1.dat k9_1_025.dat k10_03_1.dat k6_1_025.dat k7_1_075.dat k9_001_01.dat k9_1_05.dat k10_08_01.dat k6_1_05.dat k7_1_1.dat k9_001_025.dat k9_1_075.dat k10_08_025.dat k6_1_075.dat k8_01_01.dat k9_001_05.dat k9_1_1.dat k10_08_05.dat k6_1_1.dat k8_01_025.dat k9_001_075.dat templ_index.dat k10_08_075.dat k7_01_01.dat k8_01_05.dat k9_001_1.dat k10_08_1.dat k7_01_025.dat k8_01_075.dat k9_01_01.dat k6_01_01.dat k7_01_05.dat k8_01_1.dat k9_01_025.dat k6_01_025.dat k7_01_075.dat k8_03_01.dat k9_01_05.dat
and the contents of a selected file, k10_001_05.dat is shown below.
unix% more data/Kerr/k10_001_05.dat
13.3830 43.6970
13.4330 43.8500
13.4830 43.9320
13.5330 44.0030
13.5830 44.0720
13.6330 44.1400
13.6830 44.2080
13.7330 44.2760
.
.
.
16.4830 33.9740
16.5330 32.2860
16.5830 30.3930
16.6330 28.2710
16.6830 25.8880
16.7330 23.2100
16.7830 20.1980
16.8330 16.8090
16.8830 12.9930
Each of the example ASCII-format template files contains columns of frequency and luminosity values defining the predicted spectra emitted by an accretion disk surrounding a supermassive black hole (see section 4.1 of the referenced paper), where local modified blackbody emission is assumed. The first column has units of \(\log_{10}\nu\) frequency in Hz, and the second is the luminosity in logarithm of the rest-frame, integrated luminosity—\(\log_{10}\left(\nu L_{\nu}\right)\)—in erg/s.
Index File
The template model index file should contain a table with one line per template data file, with three groups of columns in the following order:
- Model parameter columns, an arbitrary number of columns
- The MODELFLAG column which separates the parameter list from the filenames/model arrays, and marks lines which are to be used or not: MODELFLAG = 1 — use the file; MODELFLAG = 0 — do not use the file
- The FILENAME column which points to the data file for that instance, including the absolute path to the file.
Sherpa reads the index file in order to set up the template model with the parameters specified in the first line, and the arrays from the columns given by the data files.
Our example index file appears below, with model flags (all having value 1) and filenames listed in the two right-most columns, and the black hole mass (\(\log_{10} \left(M_{bh}/M_{\odot}\right)\)), accretion rate (Eddington), and inclination angle (\(\cos \theta\)) model parameters listed from the left. The combination of the three parameter values sets the spectral shape of the model.
unix% more templ_index.dat # mass rate angle modelflag filename 6.0 0.1 0.1 1 data/Kerr/k6_01_01.dat 6.0 0.1 0.25 1 data/Kerr/k6_01_025.dat 6.0 0.1 0.5 1 data/Kerr/k6_01_05.dat 6.0 0.1 0.75 1 data/Kerr/k6_01_075.dat 6.0 0.1 1.0 1 data/Kerr/k6_01_1.dat 6.0 0.3 0.1 1 data/Kerr/k6_03_01.dat 6.0 0.3 0.25 1 data/Kerr/k6_03_025.dat 6.0 0.3 0.5 1 data/Kerr/k6_03_05.dat 6.0 0.3 0.75 1 data/Kerr/k6_03_075.dat 6.0 0.3 1.0 1 data/Kerr/k6_03_1.dat . . . 10.0 0.3 0.1 1 data/Kerr/k10_03_01.dat 10.0 0.3 0.25 1 data/Kerr/k10_03_025.dat 10.0 0.3 0.5 1 data/Kerr/k10_03_05.dat 10.0 0.3 0.75 1 data/Kerr/k10_03_075.dat 10.0 0.3 1.0 1 data/Kerr/k10_03_1.dat 10.0 0.8 0.1 1 data/Kerr/k10_08_01.dat 10.0 0.8 0.25 1 data/Kerr/k10_08_025.dat 10.0 0.8 0.5 1 data/Kerr/k10_08_05.dat 10.0 0.8 0.75 1 data/Kerr/k10_08_075.dat 10.0 0.8 1.0 1 data/Kerr/k10_08_1.dat
Loading the Source Data
After starting Sherpa, we read in our source data spectrum to be fitted by the template model in the usual way, using the appropriate load command. In this example, we use the load_ascii command to read the first three columns of ASCII data from file source.dat, namely the x (frequency), y (integrated luminosity), and y error columns.
unix% sherpa
-----------------------------------------------------
Welcome to Sherpa: CXC's Modeling and Fitting Package
-----------------------------------------------------
Sherpa 4.17.0
Python 3.11.6 | packaged by conda-forge | (main, Oct 3 2023, 10:40:35) [GCC 12.3.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.30.0 -- An enhanced Interactive Python. Type '?' for help.
IPython profile: sherpa
Using matplotlib backend: tkagg
sherpa> load_ascii('source.dat', ncols=3, dstype=Data1D)
The show_data and get_indep/get_dep commands may be used to check that the data was properly read:
sherpa> show_data()
Data Set: 1
Filter: 17.6983-7.4149 x
name = source.dat
x = Float64[46]
y = Float64[46]
staterror = Float64[46]
syserror = None
sherpa> get_indep()
(array([ 17.69831459, 15.22778313, 15.14764634, 15.04849851,
14.69471047, 14.57931705, 14.4512198 , 14.2154123 ,
10.4876855 , 10.34388301, 10.01346923, 10.01346923,
10.00024097, 10.00024097, 9.74586299, 9.74586299,
9.46062726, 9.46062726, 9.46062726, 9.46062726,
9.46062726, 9.46062726, 9.18956049, 9.18956049,
9.18956049, 8.92515939, 8.87679209, 8.56491923,
8.56491923, 8.56491923, 8.51861921, 8.49634282,
8.49634282, 8.24899768, 8.21758921, 8.09265048,
7.89428282, 7.89428282, 7.73445498, 7.73445498,
7.71243924, 7.6608522 , 7.61552922, 7.5372157 ,
7.48181656, 7.41486977]),)
sherpa> get_dep()
array([ 44.83025145, 46.20275955, 46.03082425, 46.04305871,
45.63597532, 45.47709506, 45.46993128, 45.41821408,
43.73912372, 43.74154589, 44.06194405, 44.0665398 ,
43.96097247, 43.95510354, 43.98644329, 43.9809108 ,
44.02583231, 44.03575947, 44.05023729, 44.05023729,
44.08004227, 44.04785759, 44.07782081, 44.10165342,
44.10165342, 44.14189464, 44.16799744, 44.18745992,
44.22553677, 44.15139818, 44.18572621, 44.16069821,
44.16436316, 44.24415224, 44.19602617, 44.18918674,
44.05729915, 44.13020888, 43.94007455, 43.94007455,
44.07334351, 43.86696745, 43.87414603, 44.02331465,
44.05495793, 43.96676315])
We may also visualize the data before fitting with plot_data, and utilize several Sherpa and matplotlib pyplot commands to customize the data plot.
sherpa> plot_data()
sherpa> set_xlinear()
sherpa> set_ylinear()
sherpa> plt.xlabel(r"log \nu [Hz]")
sherpa> plt.ylabel(r"log \nu L_{\nu} [erg/s]")
Figure 1: Plot of source data to be fitted with the template model
![[Fit residuals plot]](source_data.png)
Figure 1: Plot of source data to be fitted with the template model
Loading the Template Models
We may now use the load_template_model function to read in our collection of templates and assign them to a Sherpa model instance, as demonstrated below.
sherpa> load_template_model("kerr_templ", "templ_index.dat")
sherpa> set_model(kerr_templ)
Here, we load the Kerr disk model templates listed in the index file templ_index.dat into the Sherpa model instance kerr_templ, and assign this model to our source data set with set_model.
The template interpolator may also be disabled
sherpa> load_template_model("kerr_templ", "templ_index.dat", template_interpolator_name=None)
but requires the fit method to be gridsearch or explicitly set, by importing the interpolator and setting the 'template_interpolator_name' argument:
sherpa> from sherpa.utils import neville, nearest_interp, linear_interp
sherpa> from sherpa.models import KNNInterpolator
sherpa> load_template_interpolator("KNN",KNNInterpolator)
sherpa> load_template_model("kerr_templ", "templ_index.dat", template_interpolator_name="KNN")
We can examine the model parameters using show_model, where the parameter names contained in our index file are shown, along with the parameter values matching the first template in our list.
sherpa> show_model() Model: 1 templatemodel.kerr_templ Param Type Value Min Max Units ----- ---- ----- --- --- ----- kerr_templ.mass thawed 6 6 10 kerr_templ.rate thawed 0.1 0.01 1 kerr_templ.angle thawed 0.1 0.1 1
Using the Grid-search Optimization Method
In order to fit a Sherpa template model to a data set, gridsearch must be chosen as the fit optimization method. We do this using set_method, after checking the current method setting with show_method:
sherpa> show_method()
Optimization Method: LevMar
name = levmar
ftol = 1.1920928955078125e-07
xtol = 1.1920928955078125e-07
gtol = 1.1920928955078125e-07
maxfev = None
epsfcn = 1.1920928955078125e-07
factor = 100.0
numcores = 1
verbose = 0
sherpa> set_method("gridsearch")
sherpa> set_method_opt('sequence', kerr_templ.parvals)
sherpa> show_method()
Optimization Method: GridSearch
name = gridsearch
num = 16
sequence = Float64[315]
numcores = 1
maxfev = None
ftol = 1.1920928955078125e-07
method = None
verbose = 0
The grid-search method evaluates the fit statistic for each point in the parameter-space grid provided by the user, which is specified as a list in the 'sequence' parameter (the list which the method should iterate through to evaluate the model function). In this example, we have set the sequence parameter to use the full list of mass, rate, and angle parameter values associated with the template model files in our collection. The list of parameter values may be accessed as follows:
sherpa> print(kerr_templ.parvals)
[[ 6. 0.1 0.1 ]
[ 6. 0.1 0.25]
[ 6. 0.1 0.5 ]
[ 6. 0.1 0.75]
[ 6. 0.1 1. ]
[ 6. 0.3 0.1 ]
[ 6. 0.3 0.25]
[ 6. 0.3 0.5 ]
[ 6. 0.3 0.75]
[ 6. 0.3 1. ]
. . .
. . .
. . .
[ 10. 0.3 0.1 ]
[ 10. 0.3 0.25]
[ 10. 0.3 0.5 ]
[ 10. 0.3 0.75]
[ 10. 0.3 1. ]
[ 10. 0.8 0.1 ]
[ 10. 0.8 0.25]
[ 10. 0.8 0.5 ]
[ 10. 0.8 0.75]
[ 10. 0.8 1. ]]
The best match to the source spectrum in this sequence is the grid point with the lowest value of the fit statistic; gridsearch returns the parameter values associated with this point.
![[NOTE]](../../imgs/note.png)
If the \(\mathtt{sequence}\) parameter is kept at the default value of \(\mathtt{None}\), the \(\mathtt{gridsearch}\) \(\mathtt{num}\) parameter is used to generate a sequence of parameters to evaluate. A uniform grid of size \(n_{\mathrm{par}}^{\mathtt{num}}\) elements is generated.
Fitting the Template Model to the Spectrum
Once the source data has been loaded, the template model properly set, and the fit optimization method switched to gridsearch and customized, we may fit the template model to the source spectrum, with the supplied errors included, in the usual way using the fit() command.
sherpa> show_stat()
Statistic: Chi2Gehrels
Chi Squared with Gehrels variance.
The variance is estimated from the number of counts in each bin,
but unlike `Chi2DataVar`, the Gaussian approximation is not
used. This makes it more-suitable for use with low-count data.
The standard deviation for each bin is calculated using the
approximation from [1]_:
sigma(i,S) = 1 + sqrt(N(i,s) + 0.75)
where the higher-order terms have been dropped. This is accurate
to approximately one percent. For data where the background has
not been subtracted then the error term is:
sigma(i) = sigma(i,S)
whereas with background subtraction,
sigma(i)^2 = sigma(i,S)^2 + [A(S)/A(B)]^2 sigma(i,B)^2
A(B) is the off-source "area", which could be
the size of the region from which the background is extracted, or
the length of a background time segment, or a product of the two,
etc.; and A(S) is the on-source "area". These terms may be defined
for a particular type of data: for example, PHA data sets A(B) to
`BACKSCAL * EXPOSURE` from the background data set and A(S) to
`BACKSCAL * EXPOSURE` from the source data set.
See Also
--------
Chi2DataVar, Chi2ModVar, Chi2XspecVar
Notes
-----
The accuracy of the error term when the background has been
subtracted has not been determined. A preferable approach to
background subtraction is to model the background as well as the
source signal.
References
----------
.. [1] "Confidence limits for small numbers of events in
astrophysical data", Gehrels, N. 1986, ApJ, vol 303,
p. 336-346.
http://adsabs.harvard.edu/abs/1986ApJ...303..336G
sherpa> notice(13.,17.)
dataset 1: 17.6983:7.41487 -> 15.2278:14.2154 x
sherpa> fit()
Dataset = 1
Method = gridsearch
Statistic = chi2
Initial fit statistic = 426180
Final fit statistic = 97.4647 at function evaluation 106
Data points = 7
Degrees of freedom = 4
Probability [Q-value] = 3.40753e-20
Reduced statistic = 24.3662
Change in statistic = 426083
kerr_templ.mass 9
kerr_templ.rate 0.3
kerr_templ.angle 1
WARNING: parameter value kerr_templ.angle is at its maximum boundary 1.0
The initial fit results indicate that, of all the template models in our collection, the one contained in template model file k9_03_1.dat represents the best match to the source data.
We can further examine the fit results using one of the plotting commands designed for this puropse, such as plot_fit_delchi or plot_fit_resid, to visualize the quality of the fit and manipulate the matplotlib figure object axes to change axis labels.
sherpa> plot_fit_resid()
sherpa> fig = plt.gcf()
sherpa> ax1,ax2 = fig.axes
sherpa> ax1.set_ylabel(r"log $\nu L_{\nu}$ [erg/s]")
sherpa> ax2.set_xlabel(r"log $\nu$ [Hz]")
sherpa> ax2.set_ylabel("Residuals")
Figure 2: Plot of fit residuals resulting from template model fit
![[Fit residuals plot]](fit_resid.png)
Figure 2: Plot of fit residuals resulting from template model fit
The confidence and projection commands available for estimating errors on model parameters, such as confidence and region projection, are incompatible with the gridsearch optimization method used for fitting template models to source data.
Similarly, with the interpolator enabled, other fitting optimization methods are useable besides gridsearch, as demonstrated below.
sherpa> delete_model()
sherpa> load_template_model("kerr_templ", "templ_index.dat", KNNInterpolator)
sherpa> set_model(kerr_templ)
sherpa> set_method("neldermead")
sherpa> fit()
Dataset = 1
Method = neldermead
Statistic = chi2
Initial fit statistic = 426180
Final fit statistic = 91.4691 at function evaluation 324
Data points = 7
Degrees of freedom = 4
Probability [Q-value] = 6.4175e-19
Reduced statistic = 22.8673
Change in statistic = 426089
kerr_templ.mass 8.98957
kerr_templ.rate 0.305722
kerr_templ.angle 0.995458
The k-nearest neighbors (KNN or k-NN) algorithm is a supervised learning method for classification and regression problems, interpolating based on the input of the k closest training examples (the Sherpa default is \(k=2\)) from the set of template models with the data set being fitted against. In the Sherpa implementation a "distance" between the data and training sets are compared by using the normalized difference between the corresponding values to select the k-points used to determine an interpolated value.
The order argument is used to specify the numpy.linalg.norm normalization method to compare the Euclidean distance between data points and training set, which the Sherpa implementation default is "ord=2". In this case, the normalization is based on the largest singular value in a matrix for the 2-norm or for a vector \(\mathbf{X}\), the normalization is generalized as:
\[ \left\| \mathbf{X} \right\|^{\mathcal{O}} = \sum_{n}\left| x_{n}\right|^{\mathcal{O}} \]for the orders, \(\mathcal{O} \in \{ -2,-1,1,2 \}\). The set of normalized distances are then sorted so that the k values closest to the data may be selected.
\[ d_{n} = \frac{x_{\mathrm{data}} - x_{\mathrm{template}_{n}}}{\left\| \mathbf{X} \right\|} \ . \]The number of closest training sets to the data and normalization order may be set with load_template_interpolator using the optional k and order arguments for an interpolator object named "k2_O2":
sherpa> from sherpa.models import KNNInterpolator
sherpa> load_template_interpolator("k2_O2",KNNInterpolator,k=2,order=2)
sherpa> load_template_model("kerr_templ", "templ_index.dat", "k2_O2")
In this implementation, the normalized k distances are then used to weigh the points from the training sets of interest where the weights are simply \(w_{n} = \frac{1}{d_{n}}\). The interpolated value is then the distance weighted mean of the set of k closest training data points:
\[ x_{\mathrm{interpolated}} = \frac{\sum_{i=1}^{k} w_{i} x_{\mathrm{template}_{i}}}{\sum_{i=1}^{k} w_{i}} \ . \]When selecting k, note that higher k-values will increase bias due to averaging over a large number of training points while a lower k-value increases uncertainty.
The fit results may also be saved to an ASCII file, in this case fit.out, which can be examined to see the parameter space that was explored by Sherpa.
sherpa> fit(outfile="fit.out") Dataset = 1 Method = neldermead Statistic = chi2 Initial fit statistic = 91.4691 Final fit statistic = 91.4691 at function evaluation 236 Data points = 7 Degrees of freedom = 4 Probability [Q-value] = 6.4175e-19 Reduced statistic = 22.8673 Change in statistic = 3.58653e-07 kerr_templ.mass 8.98991 kerr_templ.rate 0.305347 kerr_templ.angle 0.994408 sherpa> !cat fit.out # nfev statistic kerr_templ.mass kerr_templ.rate kerr_templ.angle 0.000000e+00 9.146914e+01 8.989570e+00 3.057219e-01 9.954583e-01 1.000000e+00 9.258227e+04 7.000000e+00 3.057219e-01 9.954583e-01 2.000000e+00 1.365636e+02 8.989570e+00 2.575000e-01 9.954583e-01 3.000000e+00 9.554157e+01 8.989570e+00 2.816109e-01 9.954583e-01 4.000000e+00 2.262246e+04 7.994785e+00 3.057219e-01 9.954583e-01 5.000000e+00 4.915118e+04 8.326380e+00 2.896479e-01 3.954583e-01 ... 2.310000e+02 9.146917e+01 9.004774e+00 3.019865e-01 9.886359e-01 2.320000e+02 9.146918e+01 9.004744e+00 3.020858e-01 9.886406e-01 2.330000e+02 9.146915e+01 9.004567e+00 3.023142e-01 9.885753e-01 2.340000e+02 9.146915e+01 9.004538e+00 3.024135e-01 9.885800e-01 2.350000e+02 9.146914e+01 9.004232e+00 3.027101e-01 9.884963e-01 2.360000e+02 9.146914e+01 8.989906e+00 3.053467e-01 9.944080e-01
Fitting a Template Model with an Additive, Continuous Component
In the next example, we add a continuous model component to the template model, in this particular case, simply a constant. The default KNN-interpolation method is used and the fit is varied on the grid parameters, with the constant model component frozen.
sherpa> reset()
sherpa> notice(13.5,16.)
dataset 1: 15.2278:14.2154 x (unchanged)
sherpa> set_method("gridsearch")
sherpa> set_method_opt("sequence",None)
sherpa> load_template_model("kerr_templ","templ_index.dat")
sherpa> set_model(kerr_templ+const1d.c1)
sherpa> set_par(c1.c0, min=0.1, max=10)
sherpa> freeze(c1)
sherpa> fit()
Dataset = 1
Method = gridsearch
Statistic = chi2
Initial fit statistic = 287265
Final fit statistic = 83.1809 at function evaluation 4097
Data points = 7
Degrees of freedom = 4
Probability [Q-value] = 3.68823e-17
Reduced statistic = 20.7952
Change in statistic = 287182
kerr_templ.mass 8.93333
kerr_templ.rate 0.01
kerr_templ.angle 0.76
WARNING: parameter value kerr_templ.rate is at its minimum boundary 0.01
sherpa> show_model()
Model: 1
template.kerr_templ + const1d.c1
Param Type Value Min Max Units
----- ---- ----- --- --- -----
kerr_templ.mass thawed 8.93333 6 10
kerr_templ.rate thawed 0.01 0.01 1
kerr_templ.angle thawed 0.76 0.1 1
c1.c0 frozen 1 0.1 10
Since the template fit converges on a parameter boundary, we then try thawing the constant model component and re-fit.
sherpa> thaw(c1.c0) sherpa> fit() Dataset = 1 Method = gridsearch Statistic = chi2 Initial fit statistic = 83.1809 Final fit statistic = 82.174 at function evaluation 65537 Data points = 7 Degrees of freedom = 3 Probability [Q-value] = 1.04869e-17 Reduced statistic = 27.3913 Change in statistic = 1.00688 kerr_templ.mass 8.93333 kerr_templ.rate 0.142 kerr_templ.angle 0.76 c1.c0 0.76 sherpa> show_model() Model: 1 template.kerr_templ1 + const1d.c1 Param Type Value Min Max Units ----- ---- ----- --- --- ----- kerr_templ1.mass thawed 8.93333 6 10 kerr_templ1.rate thawed 0.142 0.01 1 kerr_templ1.angle thawed 0.76 0.1 1 c1.c0 thawed 0.76 0.1 10
It should be noted that when using continuous model components, an interpolator is required in order to perform the grid-search, otherwise an error will be raised:
sherpa> reset()
sherpa> load_template_model("kerr_templ","templ_index.dat",template_interpolator_name=None)
sherpa> set_model(kerr_templ+const1d.c2)
sherpa> fit()
ModelErr: Interpolation of template parameters was disabled for this
model, but parameter values not in the template library have been
requested. Please use gridsearch method and make sure the sequence
option is consistent with the template library
History
| 17 Jan 2012 | The Sherpa template model is new in CIAO 4.4. |
| 10 Dec 2013 | updated for CIAO 4.6, new examples with continuous model component added, demonstrated setting interpolator. |
| 17 Mar 2015 | updated for CIAO 4.7, added missing commands dealing with loading in an interpolator. |
| 09 Dec 2015 | reviewed for CIAO 4.8, no content change. |
| 16 Nov 2016 | reviewed for CIAO 4.9; linked tarball with template models, and updated examples to use the tarball directory struction. Fixed typos. |
| 29 May 2018 | reviewed for CIAO 4.10, no content change. |
| 14 Dec 2018 | reviewed for CIAO 4.11, no content change. |
| 29 Mar 2022 | reviewed for CIAO 4.14, updated for matplotlib usage. |
| 07 Jun 2022 | added description of the k-NN algorithm implemented in Sherpa. |
| 09 Dec 2022 | updated for CIAO 4.15, no content change. |
| 01 Dec 2023 | updated for CIAO 4.16, no content change. |
| 17 Dec 2024 | updated for CIAO 4.17, revised for output formating changes. |