diff --git a/doc/source/user_manual/getting_started/python.rst b/doc/source/user_manual/getting_started/python.rst index d830088..3be6ef9 100644 --- a/doc/source/user_manual/getting_started/python.rst +++ b/doc/source/user_manual/getting_started/python.rst @@ -42,8 +42,8 @@ way as if it were executed from the command line. >>> sim = ctools.ctobssim() >>> sim["inmodel"].filename("${CTOOLS}/share/models/crab.xml") >>> sim["outevents"].filename("events.fits") - >>> sim["caldb"].string("dummy") - >>> sim["irf"].string("cta_dummy_irf") + >>> sim["caldb"].string("prod2") + >>> sim["irf"].string("South_50h") >>> sim["ra"].real(83.63) >>> sim["dec"].real(22.01) >>> sim["rad"].real(5.0) @@ -95,13 +95,13 @@ in that observation you may type: >>> print(sim.obs()[0].events()) === GCTAEventList === - Number of events ..........: 12152 + Number of events ..........: 31928 Time interval .............: 51544.5 - 51544.5 days === GEbounds === Number of intervals .......: 1 Energy range ..............: 100 GeV - 100 TeV === GCTARoi === - ROI centre ................: RA=83.63, DEC=22.01 [0, 0] + ROI centre ................: RA=83.63, DEC=22.01 [0,0] ROI radius ................: 5 deg The ``obs()[0]`` operator returns the first observation in the observation @@ -127,9 +127,9 @@ terrestrial times (TT) of all events. >>> for event in events: ... print(event) ... - Dir=RA=83.6477, DEC=22.0202 [0.000178879, 0.000286637] Energy=3.235 TeV Time=-3.15576e+08 s (TT) - Dir=RA=83.482, DEC=22.0189 [0.000155643, -0.00239471] Energy=141.949 GeV Time=-3.15576e+08 s (TT) - Dir=RA=83.6058, DEC=22.1586 [0.00259306, -0.000391919] Energy=316.376 GeV Time=-3.15576e+08 s (TT) + Dir=RA=83.5759, DEC=21.9757 [-0.000597839,-0.000875483] Energy=328.153 GeV Time=-3.15576e+08 s (TT) + Dir=RA=83.5775, DEC=22.0128 [4.88943e-05,-0.000849504] Energy=353.264 GeV Time=-3.15576e+08 s (TT) + Dir=RA=83.4302, DEC=21.931 [-0.00137616,-0.0032343] Energy=117.999 GeV Time=-3.15576e+08 s (TT) ... .. note:: @@ -150,13 +150,13 @@ terrestrial times (TT) of all events. >>> sim.run() >>> print(sim.obs()[0].events()) === GCTAEventList === - Number of events ..........: 6141 + Number of events ..........: 15964 Time interval .............: 51544.5 - 51544.5 days === GEbounds === Number of intervals .......: 1 Energy range ..............: 100 GeV - 100 TeV === GCTARoi === - ROI centre ................: RA=83.63, DEC=22.01 [0, 0] + ROI centre ................: RA=83.63, DEC=22.01 [0,0] ROI radius ................: 5 deg @@ -189,14 +189,14 @@ To check how the fit went you may inspect the optimiser class used by >>> print(like.opt()) === GOptimizerLM === - Optimized function value ..: 44578.761 + Optimized function value ..: 101048.382 Absolute precision ........: 0.005 Acceptable value decrease .: 2 Optimization status .......: converged - Number of parameters ......: 9 + Number of parameters ......: 10 Number of free parameters .: 4 - Number of iterations ......: 3 - Lambda ....................: 1e-06 + Number of iterations ......: 2 + Lambda ....................: 1e-05 Apparently, the fit converged after fitting 4 parameters in 3 iterations. To inspect the fit results you may print the model container that is a @@ -207,7 +207,7 @@ member of the observation container: >>> print(like.obs().models()) === GModels === Number of models ..........: 2 - Number of parameters ......: 9 + Number of parameters ......: 10 === GModelSky === Name ......................: Crab Instruments ...............: all @@ -220,24 +220,24 @@ member of the observation container: RA .......................: 83.6331 [-360,360] deg (fixed,scale=1) DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1) Number of spectral par's ..: 3 - Prefactor ................: 6.13265e-16 +/- 2.05734e-17 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient) - Index ....................: -2.50565 +/- 0.0250831 [-0,-5] (free,scale=-1,gradient) + Prefactor ................: 5.80565e-16 +/- 1.11147e-17 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient) + Index ....................: -2.49266 +/- 0.0163476 [-0,-5] (free,scale=-1,gradient) PivotEnergy ..............: 300000 [10000,1e+09] MeV (fixed,scale=1e+06,gradient) Number of temporal par's ..: 1 - Constant .................: 1 (relative value) (fixed,scale=1,gradient) - === GCTAModelRadialAcceptance === - Name ......................: Background + Normalization ............: 1 (relative value) (fixed,scale=1,gradient) + === GCTAModelIrfBackground === + Name ......................: CTABackgroundModel Instruments ...............: CTA Instrument scale factors ..: unity Observation identifiers ...: all - Model type ................: "Gaussian" * "FileFunction" * "Constant" - Number of parameters ......: 3 - Number of radial par's ....: 1 - Sigma ....................: 3.03693 +/- 0.0304896 [0.01,10] deg2 (free,scale=1,gradient) - Number of spectral par's ..: 1 - Normalization ............: 0.998667 +/- 0.0172585 [0,1000] (free,scale=1,gradient) + Model type ................: "PowerLaw" * "Constant" + Number of parameters ......: 4 + Number of spectral par's ..: 3 + Prefactor ................: 1.01701 +/- 0.0216563 [0.001,1000] ph/cm2/s/MeV (free,scale=1,gradient) + Index ....................: 0.0230864 +/- 0.0123006 [-5,5] (free,scale=1,gradient) + PivotEnergy ..............: 1e+06 [10000,1e+09] MeV (fixed,scale=1e+06,gradient) Number of temporal par's ..: 1 - Constant .................: 1 (relative value) (fixed,scale=1,gradient) + Normalization ............: 1 (relative value) (fixed,scale=1,gradient) Suppose you want to repeat the fit by optimising also the position of the point source. @@ -250,8 +250,8 @@ This is easy from Python: >>> like.run() >>> print(like.obs().models()) ... - RA .......................: 83.633 +/- 0.00137216 [-360,360] deg (free,scale=1) - DEC ......................: 22.0144 +/- 0.00127247 [-90,90] deg (free,scale=1) + RA .......................: 83.6341 +/- 0.000958947 [-360,360] deg (free,scale=1) + DEC ......................: 22.0139 +/- 0.000870912 [-90,90] deg (free,scale=1) The ``like.obs().models()`` method provides the model container, using the ``["Crab"]`` operator we access the Crab model in that container and using @@ -293,7 +293,7 @@ the ``fix()`` method. >>> print(like.obs()) === GObservations === Number of observations ....: 1 - Number of predicted events : 6141 + Number of predicted events : 15964 is okay as the ``ctools.ctlike(sim.obs())`` constructor will create a copy of the observation container that lives within the :ref:`ctlike`