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Last modified: December 2009

URL: http://cxc.harvard.edu/sherpa/ahelp/prof_fit.html
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AHELP for CIAO 4.6 Sherpa v1

prof_fit

Context: contrib

Synopsis

Plot a radial or elliptical profile of the fit (data and model) to imaging data.

Syntax

prof_fit( [id], [model=None, rstep=None, rmin=None, rmax=None,
rlo=None, rhi=None, xpos=None, ypos=None, ellip=None, theta=None,
group_counts=None, group_snr=None, label=True, recalc=True,
overplot=False] )

Description

The prof_fit command calculates the radial - or elliptical - profile of the imaging data and model and plots them. It is a combination of the plot_data() and plot_model() commands. The profile is defined by the existing model compenents, although it is possible to over-ride these values.

Loading the routine

The routine can be loaded into Sherpa by saying:

from sherpa_contrib.profiles import *

Argument options

The argument options are the same as for the prof_data() command, and are described in its ahelp page.

Changing the plot defaults

The plot created by prof_fit() is controlled by the data and model preferences - i.e. the values returned by get_data_prof_prefs() and get_model_prof_prefs(). Changing these settings will therefore change the appearance of any new plots created by prof_fit(). For example

sherpa> get_data_prof_prefs()["xlog"] = True
sherpa> get_model_prof_prefs()["linecolor"] = "green"

will cause any new fit plots to use logarithmic scaling for the X axis and draw the model using a green line. See the ahelp files for get_data_prof_prefs and get_model_prof_prefs for the available preference settings.

Example 1

sherpa> prof_fit()
sherpa> log_scale()
...
sherpa> prefs = get_data_prof_prefs()
sherpa> prefs["xlog"] = True
sherpa> prefs["ylog"] = True
sherpa> prof_fit()

The preferences are set so that both the x and y axes should be drawn using log scaling. Setting the get_data_prof_prefs values only affects plots made after the change; to change an existing plot you need to use ChIPS commands such as log_scale() and linear_scale().

Example 2

sherpa> prof_fit(group_snr=15)

The data is plotted after the bins have been grouped so that each bin has a signal to noise ratio of 15 or more.

Example 3

sherpa> prof_fit(rmin=10, rmax=100, rstep=5)

Calculate the profile in radial bins with low/high limits of 10-15, 15-20, 20-25, ..., 90-95, 95-100. If the group_counts or group_snr option was also given - e.g.

sherpa> prof_fit(rmin=10, rmax=100, rstep=5, group_counts=100)

then the grouping would be applied to these bins.

Example 4

sherpa> prof_fit(rlo=[0,10,20,40,60,100,200])

Calculate the profile in radial bins with low/high limits of 0-10, 10-20, 20-40, 40-60, 60-100, 100-200.

Example 5

sherpa> prof_fit(rstep=[1,10,2,20,5,50,10])

The radii used for the radial bins depends on the radius as shown in the table below:

radius bin width
0 to 10 1
10 to 20 2
20 to 50 5
above 50 10

The minimum and maximum radii used are taken from the data.

Example 6

sherpa> prof_fit("src1")
sherpa> prof_fit("src2", overplot=True)
sherpa> set_histogram(["line.color", "green"])

Plots the fit profiles (data and model) for the dataset called "src1" and then overplots the results from dataset "src2". The model for the src2 dataset is changed to use a green line.

Example 7

sherpa> load_image("img.fits")
sherpa> set_source(beta2d.clus + gauss2d.qso + const2d.bgnd)
sherpa> fit()
...
sherpa> prof_fit(model=qso)
sherpa> thaw(clus.ellip)
sherpa> thaw(clus.theta)
sherpa> fit()
...
sherpa> prof_fit(model=clus)

Since the model expression contains two components which have xpos, ypos, ellip, and theta parameters - namely clus and qso - the model argument is used to select the gauss2d (qso) values for the first plot and the beta2d (clus) values for the second plot.

If the clus model has a non-zero ellipticity then the second plot will have used elliptical annuli to calculate the profile. To use circular annuli in this case we can manually override the model's ellipticity parameter by saying:

sherpa> prof_fit(model=clus, ellip=0)

Bugs

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

See Also

contrib
get_data_prof, get_data_prof_prefs, get_delchi_prof, get_delchi_prof_prefs, get_fit_prof, get_model_prof, get_model_prof_prefs, get_resid_prof, get_resid_prof_prefs, get_source_prof, get_source_prof_prefs, plot_chart_spectrum, prof_data, prof_delchi, prof_fit_delchi, prof_fit_resid, prof_model, prof_resid, prof_source, sherpa_profiles
data
get_arf_plot, get_bkg_plot
modeling
normal_sample, t_sample, uniform_sample
plotting
get_energy_flux_hist, get_lrt_plot, get_lrt_results, get_photon_flux_hist, get_pvalue_plot, get_pvalue_results, get_split_plot, plot, plot_arf, plot_bkg, plot_cdf, plot_chisqr, plot_data, plot_delchi, plot_energy_flux, plot_fit, plot_lrt, plot_model, plot_model_component, plot_order, plot_pdf, plot_photon_flux, plot_pvalue, plot_ratio, plot_resid, plot_scatter, plot_source, plot_source_component, plot_trace, set_xlinear, set_xlog, set_ylinear, set_ylog
psfs
plot_kernel
statistics
get_chisqr_plot, get_delchi_plot
visualization
contour_resid

Last modified: December 2009
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