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Command Comparison: Sherpa 3.4 vs. Sherpa 4.1
The table below provides the Sherpa 4.1 (Python) translation for a
list of commonly used Sherpa 3.4 commands, to assist with your
transition to the new syntax. See the Sherpa ahelp
pages for detailed information on each Sherpa 4.1
function, including the corresponding S-Lang syntax.
To view complete Sherpa 3.4 and 4.1 (Python or S-Lang) scripts side-by-side
for various 1-D and 2-D plotting and fitting scenarios, visit
the Sherpa Gallery
of Examples, which includes eighteen common Sherpa use cases.
The CXC is committed to helping Sherpa users transition to new the syntax as smoothly as possible. If you have existing Sherpa scripts or save files, submit them to us via the CXC Helpdesk and we will provide the Sherpa 4.1 syntax to you.
| Sherpa 3.4 |
Sherpa 4.1 (Python) |
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ANALYSIS
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set_analysis([id], quantity, [type, factor])
Assuming data set with given ID contains PHA data:
quantity - "energy", "wavelength", "channel", or "bin"
type - "rate", "counts"
factor - 0, 1, 2, ..., n
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BACK
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load_bkg([id], arg, [use_errors, bkg_id])
Assuming data set with given ID contains PHA data:
arg - filename and path | PHACrate obj | PyFITS HDUList obj
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BACKERRORS | BERRORS | BSTATERRORS
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get_staterror([id, filter, bkg_id])
get_error([id, filter, bkg_id])
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BACKGROUND | BG
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set_bkg_model([id], model, [bkg_id])
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BDCOUNTS
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calc_data_sum([lo, hi, id, bkg_id])
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BEFLUX
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calc_energy_flux([lo, hi, id, bkg_id])
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BGROUP
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group([id, bkg_id])
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BMCOUNTS
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calc_model_sum([lo, hi, id, bkg_id])
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BSYSERRORS
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Assuming data set with given ID contains PHA data:
get_syserror([id, filter, bkg_id])
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BUNGROUP
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ungroup([id, bkg_id])
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BYE | EXIT | QUIT
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Ctrl-D, exit
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CLOSE
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TBD
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COORD
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set_coord([id], coord)
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COVARIANCE
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covar([id, otherids ... , pars])
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CPLOT
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contour_data([id])
contour_model([id])
contour_fit([id])
contour_resid([id])
contour_ratio([id])
contour_psf([id])
contour_fit_resid([id])
or
contour(arg1, [id], arg2, [id], ...])
where arg* is one of "data", "model",
"fit", "psf", "resid", or "ratio"
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CREATE
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abs1 = xsphabs.abs1
or
abs1 = create_model_component("xsphabs", "abs1")
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DATA
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arg - filename and path | PHACrate obj | PyFITS HDUList obj
load_arf([id], arg, [resp_id, bkg_id])
load_ascii([id], arg, [ncols, colkeys, dstype, sep, comment])
load_data([id], filename, [...])
load_image([id], arg, [coord])
load_pha([id], arg, [use_errors])
load_rmf([id], arf, [resp_id, bkg_id])
load_table([id], arg, [ncols, colkeys, dstype])
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DATASPACE
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dataspace1d(start, stop, [step, id, bkg_id, dstype])
dataspace2d(dims, [id, dstype])
dstype - Data1DInt, Data2D, Data2DInt, DataARF, DataIMG, DataPHA, or DataRMF
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DCOUNTS
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calc_data_sum([lo, hi, id, bkg_id])
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ECHO
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For Python users, IPython's logging mechanism is an option.
%logstart
...
%logstop
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EFLUX
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calc_energy_flux([lo, hi, id, bkg_id])
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EQWIDTH
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eqwidth(cont, cont+eline, [id, bkg_id])
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ERASE
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clean()
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ERRORS
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get_error([id, filter, bkg_id])
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FAKEIT
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fake_pha(id, arf, rmf, exposure, [backscal, areascal, grouping, grouped, quality, bkg])
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FEFFILE
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deprecated
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FEFPLOT
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deprecated
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FIT | RUN
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fit([id, otherids ..., outfile, clobber])
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FLUX
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calc_photon_flux([lo, hi, id, bkg_id])
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FREEZE
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freeze(list params or models)
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FTEST
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ftest(dof_1, stat_1, dof_2, stat_2)
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GETX
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TBD
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GETY
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TBD
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GOODNESS
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get_fit_results()
get_fit_results().statval
get_fit_results().numpoints
get_fit_results().dof
get_fit_results().qval
get_fit_results().rstat
get_fit_results().nfev
get_fit_results().modelvals
get_fit_results().parnames
get_fit_results().parvals
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GROUP
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group([id, bkg_id])
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IGNORE
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Apply to all data sets:
ignore(3, 5) # IGNORE FILTER 3:5
ignore(3) # IGNORE FILTER 3:
ignore(None | NULL, 5) # IGNORE FILTER :5
ignore("4,8:,1:3") # IGNORE FILTER 4, 8:, 1:3
ignore() # IGNORE ALL
Apply to data set by id:
ignore_id(id, 3, 5) # IGNORE id FILTER 3:5
ignore_id(id, 3) # IGNORE id FILTER 3:
ignore_id(id, None | NULL, 5) # IGNORE id FILTER :5
ignore_id(id, "4,8:,1:3") # IGNORE id FILTER 4, 8:, 1:3
ignore_id(id) # IGNORE id ALL
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IMAGE
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image_data()
image_model()
image_fit()
image_psf()
image_ratio()
image_resid()
image_source()
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INSTRUMENT | RESPONSE
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load_rmf()
load_arf()
load_psf()
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INTEGRATE
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abs1 = atten.abs1
abs1.integrate = True | 1
abs1.integrate = False | 0
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INTERVAL-PROJECTION | INT-PROJ
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int_proj(param, [id, otherids, replot, min, max, nloop, delv, fac, log, overplot])
Print reg_proj parameters
print(get_int_proj())
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INTERVAL-UNCERTAINTY | INT-UNC
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int_unc(param, [id, otherids, replot, min, max, nloop, delv, fac, log, overplot])
Print reg_unc parameters
print(get_int_unc())
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JOURNAL
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For Python users, IPython's history mechanism is an option.
!cat ~/.ipython-ciao/history-sherpa
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LINK
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link()
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LPLOT
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plot_arf([id, resp_id, replot, overplot])
plot_bkg([id, bkg_id, replot, overplot])
plot_bkg_chisqr([id, bkg_id, replot, overplot])
plot_bkg_delchi([id, bkg_id, replot, overplot])
plot_bkg_fit([id, bkg_id, replot, overplot])
plot_bkg_fit_delchi([id, bkg_id, replot, overplot])
plot_bkg_fit_resid([id, bkg_id, replot, overplot])
plot_bkg_model([id, bkg_id, replot, overplot])
plot_bkg_ratio([id, bkg_id, replot, overplot])
plot_bkg_resid([id, bkg_id, replot, overplot])
plot_bkg_source([id, lo, hi, bkg_id, replot, overplot])
plot_chisqr([id, replot, overplot])
plot_data([id, replot, overplot])
plot_delchi([id, replot, overplot])
plot_fit([id, replot, overplot])
plot_fit_delchi([id, replot, overplot])
plot_fit_resid([id, replot, overplot])
plot_model([id, replot, overplot])
plot_ratio([id, replot, overplot])
plot_resid([id, replot, overplot])
plot_source([id, lo, hi, replot, overplot])
or
plot(arg1, [id], arg2, [id], ...)
where arg* is one of "arf","bkg","bkgchisqr",
"bkgdelchi","bkgfit","bkgmodel","bkgratio",
"bkgresid","bkgsource","chisqr","data","delchi",
"fit","model","psf","ratio","resid","source"
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MCOUNTS
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calc_model_sum([lo, hi, id, bkg_id])
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METHOD | SEARCHMETHOD
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set_method(method_name)
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MLR
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mlr(delta_dof, delta_stat)
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NOTICE
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Apply to all data sets:
notice(3, 5) # NOTICE FILTER 3:5
notice(3) # NOTICE FILTER 3:
notice(None | NULL, 5) # NOTICE FILTER :5
notice("4,8:,1:3") # NOTICE FILTER 4, 8:, 1:3
notice() # NOTICE ALL
Apply to one data set:
notice_id(id, 3, 5) # NOTICE id FILTER 3:5
notice_id(id, 3) # NOTICE id FILTER 3:
notice_id(id, None | NULL, 5) # NOTICE id FILTER :5
notice_id(id, "4,8:,1:3") # NOTICE id FILTER 4, 8:, 1:3
notice_id(id) # NOTICE id ALL
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OPEN
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image_open()
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OPLOT
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plot_data(overplot=True)
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PARAMPROMPT
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N/A
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PLOTX
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Removed as of CIAO 3.0.2
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PLOTY
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TBD
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PROJECTION
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proj([id, otherids ..., param])
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PROMPT
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N/A
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READ
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arg - filename and path | PHACrate obj | PyFITS HDUList obj
load_arf([id], arg, [resp_id, bkg_id])
load_ascii([id], arg, [ncols, colkeys, dstype, sep, comment])
load_data([id], filename, [...])
load_image([id], arg, [coord])
load_pha([id], arg, [use_errors])
load_rmf([id], arf, [resp_id, bkg_id])
load_table([id], arg, [ncols, colkeys, dstype])
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RECORD
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For Python users
fit(outfile=<filename>, clobber=True)
For S-lang users
fit(;outfile=<filename>, clobber=1)
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REGION-PROJECTION | REG-PROJ
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reg_proj(param_1, param_2, [id, otherids, replot, min, max,
nloop, delv, fac, log, sigma, levels, overplot])
Print reg_proj parameters
print(get_reg_proj())
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REGION-UNCERTAINTY | REG-UNC
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reg_unc(param_1, param_2, [id, otherids, replot, min, max,
nloop, delv, fac, log, sigma, levels, overplot])
Print reg_unc parameters
print(get_reg_unc())
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RENAME
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modelb = gauss1d.modelb
modelB = modelb
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RESET
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reset(get_model())
reset(p1)
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RESPONSE | INSTRUMENT
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load_rmf()
load_arf()
load_psf()
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SAVE
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save([filename]) --> produces binary file
then later ...
restore([filename])
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SEARCHMETHOD | METHOD
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set_method(method_name)
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SETBACK
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Assuming data set with given ID contains PHA data:
set_counts([id], val, [bkg_id])
set_exposure([id], exptime, [bkg_id])
set_backscal([id], backscale, [bkg_id])
set_areascal([id], area, [bkg_id])
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SETDATA
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Assuming data set with given ID contains PHA data:
set_counts([id], val, [bkg_id])
set_exposure([id], exptime, [bkg_id])
set_backscal([id], backscale, [bkg_id])
set_areascal([id], area, [bkg_id])
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SHOW
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Show session objects
show_all([id, outfile, clobber])
show_data([id, outfile, clobber])
show_covar([outfile, clobber])
show_filter([id, outfile, clobber])
show_method([outfile, clobber])
show_model([id, outfile, clobber])
show_proj([outfile, clobber])
show_source([id, outfile, clobber])
show_stat([outfile, clobber])
List session state info
list_bkg_ids()
list_data_ids()
list_methods()
list_model_components()
list_model_ids()
list_models()
list_response_ids()
list_stats()
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SOURCE | SRC
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set_model()
set_source()
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SPLOT
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N/A
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STATISTIC
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set_stat(stat_name)
calc_stat([id, otherids ...])
calc_chisqr([id, otherids ...])
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SUBTRACT
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subtract([id])
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STATERRORS
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get_staterror([id, filter, bkg_id])
set_staterror([id], val, [fractional,] [bkg_id])
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SYSERRORS
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get_syserror([id, filter, bkg_id])
set_syserror([id], val, [fractional,] [bkg_id])
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THAW
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thaw(list params or model)
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TRUNCATE
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TBD
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UNCERTAINTY
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deprecated
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UNGROUP
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ungroup([id, bkg_id])
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UNLINK
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unlink(param)
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UNSUBTRACT
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unsubtract([id])
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USE
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Execute script in Python:
execfile(filename)
Execute script in S-Lang:
() = evalfile(filename)
Restore a previous Sherpa session
restore([filename])
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VERSION
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In Python:
import sherpa
sherpa.__version__
In S-Lang:
_sherpa_version
_sherpa_version_string
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WRITE
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TBD
Workaround for writing ASCII or FITS table files:
Step 1:
load_arrays("my_model", get_model_plot().x, get_model_plot().y) #non-PHA 1D data set
load_arrays("my_model", get_model_plot().xlo, get_model_plot().y) # PHA 1D data set
Step 2:
save_data("my_model", "filename") # ASCII table
save_table("my_model", "filename") # FITS table
For writing FITS image files:
cr_image = pack_image()
set_imagevals(cr_image, get_resid_image().y)
write_file(cr_image, "resid_image.fits")
set_imagevals(cr_image,get_model_image().y)
write_file(cr_image,"model_image.fits")
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XSPEC ABUNDAN
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set_xsabund(name)
get_xsabund()
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XSPEC XSECT
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set_xsxsect(name)
get_xsxsect()
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