Veronika Vodeb / ctools

Commit a784941cfb5a10471d0196c2c61b25cdff110719

Authored by Luigi Tibaldo
1 parent f9eb25aa

fixed error in logic for new stacked residuals of unbinned observations

Inline Side-by-side
Showing 2 changed files with 12 additions and 419 deletions
cscripts/csresspec.py
  View file @a784941
... ... @@ -532,6 +532,9 @@ class csresspec(ctools.csobservation):
532 532  
533 533 # Run model cube
534 534 modelcube['edisp'] = self['edisp'].boolean()
  535 + # Reset base cube that was modified internally by ctmodel
  536 + if ccube != 'NONE':
  537 + modelcube.cube(ccube)
535 538 modelcube.run()
536 539  
537 540 # Extract spectrum of individual component
... ...
doc/source/users/tutorials/hess_dr1/unbinned.ipynb
  View file @a784941
... ... @@ -259,162 +259,10 @@
259 259 "cell_type": "code",
260 260 "execution_count": 5,
261 261 "metadata": {},
262   - "outputs": [
263   - {
264   - "name": "stdout",
265   - "output_type": "stream",
266   - "text": [
267   - "=== GModels ===\n",
268   - " Number of models ..........: 5\n",
269   - " Number of parameters ......: 86\n",
270   - "=== GModelSky ===\n",
271   - " Name ......................: Crab\n",
272   - " Instruments ...............: all\n",
273   - " Instrument scale factors ..: unity\n",
274   - " Observation identifiers ...: all\n",
275   - " Model type ................: PointSource\n",
276   - " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
277   - " Number of parameters ......: 6\n",
278   - " Number of spatial par's ...: 2\n",
279   - " RA .......................: 83.6192131308071 +/- 0 deg (free,scale=1)\n",
280   - " DEC ......................: 22.0199996472185 +/- 0 deg (free,scale=1)\n",
281   - " Number of spectral par's ..: 3\n",
282   - " Prefactor ................: 5.7e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
283   - " Index ....................: -2.48 +/- 0 [10,-10] (free,scale=-2.48,gradient)\n",
284   - " PivotEnergy ..............: 1000000 MeV (fixed,scale=300000,gradient)\n",
285   - " Number of temporal par's ..: 1\n",
286   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
287   - "=== GCTAModelBackground ===\n",
288   - " Name ......................: Background_023523\n",
289   - " Instruments ...............: HESS\n",
290   - " Instrument scale factors ..: unity\n",
291   - " Observation identifiers ...: 023523\n",
292   - " Model type ................: CTABackground\n",
293   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
294   - " Number of parameters ......: 20\n",
295   - " Number of spatial par's ...: 3\n",
296   - " 1:Sigma ..................: 3.19679118852325 +/- 0.148784221714502 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
297   - " 2:Grad_DETX ..............: 0.0882131070561384 +/- 0.0223563997131535 deg^-1 (free,scale=1,gradient)\n",
298   - " 2:Grad_DETY ..............: -0.000692481271743188 +/- 0.0227254581791992 deg^-1 (free,scale=1,gradient)\n",
299   - " Number of spectral par's ..: 16\n",
300   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
301   - " Intensity0 ...............: 0.00149714476600658 +/- 0.000185798282794166 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
302   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
303   - " Intensity1 ...............: 0.000275025454049597 +/- 1.50407490107521e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
304   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
305   - " Intensity2 ...............: 9.68931866899334e-05 +/- 5.86314382972939e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
306   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
307   - " Intensity3 ...............: 3.42414862721453e-05 +/- 2.61283857499118e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
308   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
309   - " Intensity4 ...............: 1.2134598847599e-05 +/- 1.19969987797427e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
310   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
311   - " Intensity5 ...............: 4.80035386464156e-06 +/- 6.14208278785316e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
312   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
313   - " Intensity6 ...............: 8.5455367005893e-07 +/- 1.75301261074233e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
314   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
315   - " Intensity7 ...............: 2.8110106615004e-07 +/- 1.02501352381156e-07 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
316   - " Number of temporal par's ..: 1\n",
317   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
318   - "=== GCTAModelBackground ===\n",
319   - " Name ......................: Background_023526\n",
320   - " Instruments ...............: HESS\n",
321   - " Instrument scale factors ..: unity\n",
322   - " Observation identifiers ...: 023526\n",
323   - " Model type ................: CTABackground\n",
324   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
325   - " Number of parameters ......: 20\n",
326   - " Number of spatial par's ...: 3\n",
327   - " 1:Sigma ..................: 3.30082205953218 +/- 0.156912956620767 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
328   - " 2:Grad_DETX ..............: 0.0153115422362867 +/- 0.0215680816698383 deg^-1 (free,scale=1,gradient)\n",
329   - " 2:Grad_DETY ..............: -0.00906905077562078 +/- 0.0213184759257543 deg^-1 (free,scale=1,gradient)\n",
330   - " Number of spectral par's ..: 16\n",
331   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
332   - " Intensity0 ...............: 0.00115207913907334 +/- 6.11872558313686e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
333   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
334   - " Intensity1 ...............: 0.000216074985117796 +/- 1.14346974881101e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
335   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
336   - " Intensity2 ...............: 9.80980717622882e-05 +/- 6.14243331439364e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
337   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
338   - " Intensity3 ...............: 2.64302082334315e-05 +/- 2.27978082923659e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
339   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
340   - " Intensity4 ...............: 1.03353701064884e-05 +/- 1.13230244143927e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
341   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
342   - " Intensity5 ...............: 3.57899467682822e-06 +/- 5.03727665165389e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
343   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
344   - " Intensity6 ...............: 5.95101204005172e-07 +/- 1.41251129058325e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
345   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
346   - " Intensity7 ...............: 2.81714577113613e-07 +/- 1.0384120888285e-07 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
347   - " Number of temporal par's ..: 1\n",
348   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
349   - "=== GCTAModelBackground ===\n",
350   - " Name ......................: Background_023559\n",
351   - " Instruments ...............: HESS\n",
352   - " Instrument scale factors ..: unity\n",
353   - " Observation identifiers ...: 023559\n",
354   - " Model type ................: CTABackground\n",
355   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
356   - " Number of parameters ......: 20\n",
357   - " Number of spatial par's ...: 3\n",
358   - " 1:Sigma ..................: 4.32704862685447 +/- 0.368685355107103 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
359   - " 2:Grad_DETX ..............: 0.018772378083067 +/- 0.0217106872366865 deg^-1 (free,scale=1,gradient)\n",
360   - " 2:Grad_DETY ..............: -0.126627961668458 +/- 0.0204738085196433 deg^-1 (free,scale=1,gradient)\n",
361   - " Number of spectral par's ..: 16\n",
362   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
363   - " Intensity0 ...............: 0.000915773345685034 +/- 4.89989530347851e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
364   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
365   - " Intensity1 ...............: 0.000197398656687683 +/- 1.07759903733823e-05 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
366   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
367   - " Intensity2 ...............: 6.93704083542102e-05 +/- 4.77505196699795e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
368   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
369   - " Intensity3 ...............: 2.57663537500693e-05 +/- 2.20136814658691e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
370   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
371   - " Intensity4 ...............: 6.82483279973139e-06 +/- 7.86680350899204e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
372   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
373   - " Intensity5 ...............: 3.3292540797357e-06 +/- 4.68826132796518e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
374   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
375   - " Intensity6 ...............: 8.19437245391855e-07 +/- 1.82363529352901e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
376   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
377   - " Intensity7 ...............: 1.44457348642334e-07 +/- 7.0968299684809e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
378   - " Number of temporal par's ..: 1\n",
379   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
380   - "=== GCTAModelBackground ===\n",
381   - " Name ......................: Background_023592\n",
382   - " Instruments ...............: HESS\n",
383   - " Instrument scale factors ..: unity\n",
384   - " Observation identifiers ...: 023592\n",
385   - " Model type ................: CTABackground\n",
386   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
387   - " Number of parameters ......: 20\n",
388   - " Number of spatial par's ...: 3\n",
389   - " 1:Sigma ..................: 3.6479543205698 +/- 0.238749563358304 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
390   - " 2:Grad_DETX ..............: 0.0290086310507671 +/- 0.0231654281693484 deg^-1 (free,scale=1,gradient)\n",
391   - " 2:Grad_DETY ..............: 0.165462911909218 +/- 0.0212932684624286 deg^-1 (free,scale=1,gradient)\n",
392   - " Number of spectral par's ..: 16\n",
393   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
394   - " Intensity0 ...............: 0.00147439433272687 +/- 0.000191693450881709 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
395   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
396   - " Intensity1 ...............: 0.000235250773930648 +/- 1.37598369145017e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
397   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
398   - " Intensity2 ...............: 9.20623251759662e-05 +/- 5.75965738880592e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
399   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
400   - " Intensity3 ...............: 3.22022945215284e-05 +/- 2.53414761162924e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
401   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
402   - " Intensity4 ...............: 1.00701723302328e-05 +/- 1.04797408736245e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
403   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
404   - " Intensity5 ...............: 3.29354066928594e-06 +/- 4.58158358563221e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
405   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
406   - " Intensity6 ...............: 8.71669675678694e-07 +/- 1.76346292129154e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
407   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
408   - " Intensity7 ...............: 3.12067435753287e-07 +/- 1.11366909796179e-07 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
409   - " Number of temporal par's ..: 1\n",
410   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
411   - ]
412   - }
413   - ],
  262 + "outputs": [],
414 263 "source": [
415 264 "models['Src001'].name('Crab')\n",
416   - "models['Crab']['PivotEnergy'].value(1.e6)\n",
417   - "print(models)"
  265 + "models['Crab']['PivotEnergy'].value(1.e6)"
418 266 ]
419 267 },
420 268 {
... ... @@ -483,9 +331,6 @@
483 331 " Number of free parameters .: 48\n",
484 332 " Number of iterations ......: 28\n",
485 333 " Lambda ....................: 0.1\n",
486   - "=== GModels ===\n",
487   - " Number of models ..........: 5\n",
488   - " Number of parameters ......: 86\n",
489 334 "=== GModelSky ===\n",
490 335 " Name ......................: Crab\n",
491 336 " Instruments ...............: all\n",
... ... @@ -502,137 +347,13 @@
502 347 " Index ....................: -2.70266617269294 +/- 0.0657789822882349 [10,-10] (free,scale=-2.48,gradient)\n",
503 348 " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
504 349 " Number of temporal par's ..: 1\n",
505   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
506   - "=== GCTAModelBackground ===\n",
507   - " Name ......................: Background_023523\n",
508   - " Instruments ...............: HESS\n",
509   - " Instrument scale factors ..: unity\n",
510   - " Observation identifiers ...: 023523\n",
511   - " Model type ................: CTABackground\n",
512   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
513   - " Number of parameters ......: 20\n",
514   - " Number of spatial par's ...: 3\n",
515   - " 1:Sigma ..................: 4.24796190309855 +/- 0.363828288662435 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
516   - " 2:Grad_DETX ..............: 0.0392578179130662 +/- 0.0227286956196355 deg^-1 (free,scale=1,gradient)\n",
517   - " 2:Grad_DETY ..............: -0.0047926918480354 +/- 0.0227934549114435 deg^-1 (free,scale=1,gradient)\n",
518   - " Number of spectral par's ..: 16\n",
519   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
520   - " Intensity0 ...............: 0.00123059171830173 +/- 0.000161445584365066 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
521   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
522   - " Intensity1 ...............: 0.000230815967235297 +/- 1.34286767529395e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
523   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
524   - " Intensity2 ...............: 7.8658051980231e-05 +/- 5.13886527368075e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
525   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
526   - " Intensity3 ...............: 2.85553145693065e-05 +/- 2.3289509180566e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
527   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
528   - " Intensity4 ...............: 1.00802987220908e-05 +/- 1.04765365573538e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
529   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
530   - " Intensity5 ...............: 4.28196020747232e-06 +/- 5.54326563541006e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
531   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
532   - " Intensity6 ...............: 7.67784226549877e-07 +/- 1.35531394639752e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
533   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
534   - " Intensity7 ...............: 2.14608694206011e-07 +/- 2.93661260498718e-08 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
535   - " Number of temporal par's ..: 1\n",
536   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
537   - "=== GCTAModelBackground ===\n",
538   - " Name ......................: Background_023526\n",
539   - " Instruments ...............: HESS\n",
540   - " Instrument scale factors ..: unity\n",
541   - " Observation identifiers ...: 023526\n",
542   - " Model type ................: CTABackground\n",
543   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
544   - " Number of parameters ......: 20\n",
545   - " Number of spatial par's ...: 3\n",
546   - " 1:Sigma ..................: 4.36636859572374 +/- 0.379832906954414 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
547   - " 2:Grad_DETX ..............: 0.0604712204548451 +/- 0.0216539018019791 deg^-1 (free,scale=1,gradient)\n",
548   - " 2:Grad_DETY ..............: -0.00754910005835091 +/- 0.0212638441166568 deg^-1 (free,scale=1,gradient)\n",
549   - " Number of spectral par's ..: 16\n",
550   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
551   - " Intensity0 ...............: 0.000996035075546069 +/- 5.59120993880688e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
552   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
553   - " Intensity1 ...............: 0.00017939691621029 +/- 1.01847588440004e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
554   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
555   - " Intensity2 ...............: 8.04557588013258e-05 +/- 5.40970945375409e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
556   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
557   - " Intensity3 ...............: 2.15572626670497e-05 +/- 2.01452345513318e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
558   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
559   - " Intensity4 ...............: 8.91945213825107e-06 +/- 1.03367951769406e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
560   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
561   - " Intensity5 ...............: 2.92927392377115e-06 +/- 4.21684012454099e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
562   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
563   - " Intensity6 ...............: 7.13908507948257e-07 +/- 1.58821665808081e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
564   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
565   - " Intensity7 ...............: 1.50162311716159e-07 +/- 2.75900576804034e-08 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
566   - " Number of temporal par's ..: 1\n",
567   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
568   - "=== GCTAModelBackground ===\n",
569   - " Name ......................: Background_023559\n",
570   - " Instruments ...............: HESS\n",
571   - " Instrument scale factors ..: unity\n",
572   - " Observation identifiers ...: 023559\n",
573   - " Model type ................: CTABackground\n",
574   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
575   - " Number of parameters ......: 20\n",
576   - " Number of spatial par's ...: 3\n",
577   - " 1:Sigma ..................: 4.32787803318655 +/- 0.34920330996182 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
578   - " 2:Grad_DETX ..............: 0.0441721873998526 +/- 0.0208179618307136 deg^-1 (free,scale=1,gradient)\n",
579   - " 2:Grad_DETY ..............: -0.0241022622534679 +/- 0.0216381693616336 deg^-1 (free,scale=1,gradient)\n",
580   - " Number of spectral par's ..: 16\n",
581   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
582   - " Intensity0 ...............: 0.000944923393976291 +/- 4.13921155153639e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
583   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
584   - " Intensity1 ...............: 0.000174187028209964 +/- 9.43481339312775e-06 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
585   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
586   - " Intensity2 ...............: 6.33197176574426e-05 +/- 4.55299311943745e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
587   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
588   - " Intensity3 ...............: 2.49926178682329e-05 +/- 2.18710722617025e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
589   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
590   - " Intensity4 ...............: 6.48161136360194e-06 +/- 7.73005169107497e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
591   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
592   - " Intensity5 ...............: 3.14667456960752e-06 +/- 4.49878333923386e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
593   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
594   - " Intensity6 ...............: 8.35330105692583e-07 +/- 1.58938835265122e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
595   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
596   - " Intensity7 ...............: 1.17285419727088e-07 +/- 2.51939662546146e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
597   - " Number of temporal par's ..: 1\n",
598   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
599   - "=== GCTAModelBackground ===\n",
600   - " Name ......................: Background_023592\n",
601   - " Instruments ...............: HESS\n",
602   - " Instrument scale factors ..: unity\n",
603   - " Observation identifiers ...: 023592\n",
604   - " Model type ................: CTABackground\n",
605   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
606   - " Number of parameters ......: 20\n",
607   - " Number of spatial par's ...: 3\n",
608   - " 1:Sigma ..................: 3.66629489250145 +/- 0.242494214234099 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
609   - " 2:Grad_DETX ..............: 0.0293136783938525 +/- 0.0233143431453372 deg^-1 (free,scale=1,gradient)\n",
610   - " 2:Grad_DETY ..............: 0.0898261954013249 +/- 0.0235233171089274 deg^-1 (free,scale=1,gradient)\n",
611   - " Number of spectral par's ..: 16\n",
612   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
613   - " Intensity0 ...............: 0.00148311594944104 +/- 0.000199309397663106 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
614   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
615   - " Intensity1 ...............: 0.00021580164783548 +/- 1.31112902718728e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
616   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
617   - " Intensity2 ...............: 8.66506018742962e-05 +/- 5.58825799846098e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
618   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
619   - " Intensity3 ...............: 2.97255510368276e-05 +/- 2.43341011824678e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
620   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
621   - " Intensity4 ...............: 9.78534456834352e-06 +/- 1.05251236979649e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
622   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
623   - " Intensity5 ...............: 3.01181433026923e-06 +/- 4.31185364729404e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
624   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
625   - " Intensity6 ...............: 1.03514642510031e-06 +/- 1.7968958618696e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
626   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
627   - " Intensity7 ...............: 2.27507557572176e-07 +/- 3.33351665963239e-08 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
628   - " Number of temporal par's ..: 1\n",
629 350 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
630 351 ]
631 352 }
632 353 ],
633 354 "source": [
634 355 "print(like.opt())\n",
635   - "print(like.obs().models())"
  356 + "print(like.obs().models()['Crab'])"
636 357 ]
637 358 },
638 359 {
... ... @@ -643,7 +364,7 @@
643 364 "\n",
644 365 "According to [SIMBAD](http://cdsportal.u-strasbg.fr/?target=Crab%20nebula), the Crab nebula is situated at a Right Ascension of 83.633 degrees and a Declination of 22.015 degrees, which is 0.013 degrees (0.82 arcmin) away from the fitted position.\n",
645 366 "\n",
646   - "The intensity at 1 TeV of the Crab was fitted to (4.89±0.28)×10−11 photons cm−2 s−1 TeV−1 the spectral index of the power law is −2.71±0.07. This can be compared to the values of (3.45±0.05)×10−11 photons cm−2 s−1 TeV−1 and −2.63±0.01 reported in [Aharonian et al. (2006), A&A, 457, 899](https://www.aanda.org/articles/aa/abs/2006/39/aa5351-06/aa5351-06.html) (note that the datasets, calibrations etc. used in our analysis are not the same as in that paper).\n",
  367 + "The intensity at 1 TeV of the Crab was fitted to (4.89±0.27)×10−11 photons cm−2 s−1 TeV−1 the spectral index of the power law is −2.70±0.07. This can be compared to the values of (3.45±0.05)×10−11 photons cm−2 s−1 TeV−1 and −2.63±0.01 reported in [Aharonian et al. (2006), A&A, 457, 899](https://www.aanda.org/articles/aa/abs/2006/39/aa5351-06/aa5351-06.html) (note that the datasets, calibrations etc. used in our analysis are not the same as in that paper).\n",
647 368 "\n",
648 369 "In the ctlike run above the energy dispersion, which relates the true photon energies to the energies of the reconstructed events, was not taken into account. By default **energy dispersion usage is disabled** since it involves an extra dimension in the data analysis which slows down the computations. We can run accounting for energy dispersion to compare the results."
649 370 ]
... ... @@ -677,9 +398,6 @@
677 398 " Number of free parameters .: 48\n",
678 399 " Number of iterations ......: 3\n",
679 400 " Lambda ....................: 1e-06\n",
680   - "=== GModels ===\n",
681   - " Number of models ..........: 5\n",
682   - " Number of parameters ......: 86\n",
683 401 "=== GModelSky ===\n",
684 402 " Name ......................: Crab\n",
685 403 " Instruments ...............: all\n",
... ... @@ -696,144 +414,20 @@
696 414 " Index ....................: -2.73454025222651 +/- 0.0699765314511747 [10,-10] (free,scale=-2.48,gradient)\n",
697 415 " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
698 416 " Number of temporal par's ..: 1\n",
699   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
700   - "=== GCTAModelBackground ===\n",
701   - " Name ......................: Background_023523\n",
702   - " Instruments ...............: HESS\n",
703   - " Instrument scale factors ..: unity\n",
704   - " Observation identifiers ...: 023523\n",
705   - " Model type ................: CTABackground\n",
706   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
707   - " Number of parameters ......: 20\n",
708   - " Number of spatial par's ...: 3\n",
709   - " 1:Sigma ..................: 4.25820549991485 +/- 0.366501927677894 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
710   - " 2:Grad_DETX ..............: 0.0389391720239684 +/- 0.0227296882445812 deg^-1 (free,scale=1,gradient)\n",
711   - " 2:Grad_DETY ..............: -0.00472613515684162 +/- 0.0227939286009025 deg^-1 (free,scale=1,gradient)\n",
712   - " Number of spectral par's ..: 16\n",
713   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
714   - " Intensity0 ...............: 0.00122272853241955 +/- 0.000160618928056305 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
715   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
716   - " Intensity1 ...............: 0.000230801318963207 +/- 1.34277054576628e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
717   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
718   - " Intensity2 ...............: 7.85700193503483e-05 +/- 5.13128928836297e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
719   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
720   - " Intensity3 ...............: 2.8528034358627e-05 +/- 2.32580980524765e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
721   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
722   - " Intensity4 ...............: 1.0070993162234e-05 +/- 1.04661279546369e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
723   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
724   - " Intensity5 ...............: 4.27644064704126e-06 +/- 5.53758251431206e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
725   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
726   - " Intensity6 ...............: 7.67410862228037e-07 +/- 1.35457554132055e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
727   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
728   - " Intensity7 ...............: 2.1448670829988e-07 +/- 2.9348534912121e-08 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
729   - " Number of temporal par's ..: 1\n",
730   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
731   - "=== GCTAModelBackground ===\n",
732   - " Name ......................: Background_023526\n",
733   - " Instruments ...............: HESS\n",
734   - " Instrument scale factors ..: unity\n",
735   - " Observation identifiers ...: 023526\n",
736   - " Model type ................: CTABackground\n",
737   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
738   - " Number of parameters ......: 20\n",
739   - " Number of spatial par's ...: 3\n",
740   - " 1:Sigma ..................: 4.38436187934916 +/- 0.384686334345721 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
741   - " 2:Grad_DETX ..............: 0.0610169522840539 +/- 0.0216545497577335 deg^-1 (free,scale=1,gradient)\n",
742   - " 2:Grad_DETY ..............: -0.00749394029587959 +/- 0.021263406213484 deg^-1 (free,scale=1,gradient)\n",
743   - " Number of spectral par's ..: 16\n",
744   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
745   - " Intensity0 ...............: 0.000992177590610287 +/- 5.57883414203466e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
746   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
747   - " Intensity1 ...............: 0.000179239581487694 +/- 1.01767165892755e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
748   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
749   - " Intensity2 ...............: 8.03214520133318e-05 +/- 5.39864861040088e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
750   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
751   - " Intensity3 ...............: 2.15226707440944e-05 +/- 2.01084845748612e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
752   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
753   - " Intensity4 ...............: 8.90701441601745e-06 +/- 1.03242625677532e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
754   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
755   - " Intensity5 ...............: 2.92419347455662e-06 +/- 4.21130464516625e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
756   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
757   - " Intensity6 ...............: 7.13597126357122e-07 +/- 1.58787319037983e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
758   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
759   - " Intensity7 ...............: 1.5000037797394e-07 +/- 2.75605844083001e-08 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
760   - " Number of temporal par's ..: 1\n",
761   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
762   - "=== GCTAModelBackground ===\n",
763   - " Name ......................: Background_023559\n",
764   - " Instruments ...............: HESS\n",
765   - " Instrument scale factors ..: unity\n",
766   - " Observation identifiers ...: 023559\n",
767   - " Model type ................: CTABackground\n",
768   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
769   - " Number of parameters ......: 20\n",
770   - " Number of spatial par's ...: 3\n",
771   - " 1:Sigma ..................: 4.32476767960198 +/- 0.348479244935753 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
772   - " 2:Grad_DETX ..............: 0.0441533105057133 +/- 0.0208180794506116 deg^-1 (free,scale=1,gradient)\n",
773   - " 2:Grad_DETY ..............: -0.0235367124597195 +/- 0.0216474437215724 deg^-1 (free,scale=1,gradient)\n",
774   - " Number of spectral par's ..: 16\n",
775   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
776   - " Intensity0 ...............: 0.000942938677578964 +/- 4.13349634164026e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
777   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
778   - " Intensity1 ...............: 0.000174363707126899 +/- 9.44440283970874e-06 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
779   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
780   - " Intensity2 ...............: 6.33467401106213e-05 +/- 4.55240953149486e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
781   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
782   - " Intensity3 ...............: 2.5027087500254e-05 +/- 2.18857619046531e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
783   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
784   - " Intensity4 ...............: 6.4881110986594e-06 +/- 7.73251638510271e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
785   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
786   - " Intensity5 ...............: 3.15359357632865e-06 +/- 4.50420576117203e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
787   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
788   - " Intensity6 ...............: 8.35524917884156e-07 +/- 1.58962858252832e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
789   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
790   - " Intensity7 ...............: 1.17324472770595e-07 +/- 2.51994043035288e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
791   - " Number of temporal par's ..: 1\n",
792   - " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
793   - "=== GCTAModelBackground ===\n",
794   - " Name ......................: Background_023592\n",
795   - " Instruments ...............: HESS\n",
796   - " Instrument scale factors ..: unity\n",
797   - " Observation identifiers ...: 023592\n",
798   - " Model type ................: CTABackground\n",
799   - " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
800   - " Number of parameters ......: 20\n",
801   - " Number of spatial par's ...: 3\n",
802   - " 1:Sigma ..................: 3.66626462649009 +/- 0.242490141656862 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
803   - " 2:Grad_DETX ..............: 0.0293452282396526 +/- 0.023316177109999 deg^-1 (free,scale=1,gradient)\n",
804   - " 2:Grad_DETY ..............: 0.0891133371444171 +/- 0.0235407463729163 deg^-1 (free,scale=1,gradient)\n",
805   - " Number of spectral par's ..: 16\n",
806   - " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
807   - " Intensity0 ...............: 0.00147305220501243 +/- 0.000198250554876098 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
808   - " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
809   - " Intensity1 ...............: 0.000215871666238352 +/- 1.31191938274719e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
810   - " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
811   - " Intensity2 ...............: 8.66362040842439e-05 +/- 5.58638958155396e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
812   - " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
813   - " Intensity3 ...............: 2.97356553620933e-05 +/- 2.43319537629507e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
814   - " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
815   - " Intensity4 ...............: 9.79234289592872e-06 +/- 1.05273492170173e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
816   - " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
817   - " Intensity5 ...............: 3.01397150082141e-06 +/- 4.3133625268575e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
818   - " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
819   - " Intensity6 ...............: 1.03513366565804e-06 +/- 1.79672410172364e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
820   - " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
821   - " Intensity7 ...............: 2.27532291313674e-07 +/- 3.33316565382873e-08 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
822   - " Number of temporal par's ..: 1\n",
823 417 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
824 418 ]
825 419 }
826 420 ],
827 421 "source": [
828 422 "print(like.opt())\n",
829   - "print(like.obs().models())"
  423 + "print(like.obs().models()['Crab'])"
830 424 ]
831 425 },
832 426 {
833 427 "cell_type": "markdown",
834 428 "metadata": {},
835 429 "source": [
836   - "The results are broadly consistents with those obtained ignoring the energy dispersion.\n",
  430 + "You can verify that the results are broadly consistents with those obtained ignoring the energy dispersion.\n",
837 431 "\n",
838 432 "## Inspecting the fit residuals\n",
839 433 "\n",
... ... @@ -847,9 +441,9 @@
847 441 "outputs": [
848 442 {
849 443 "data": {
850   - "image/png": 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\n",
  444 + "image/png": 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\n",
851 445 "text/plain": [
852   - "<matplotlib.figure.Figure at 0x116226da0>"
  446 + "<matplotlib.figure.Figure at 0x10fee3c88>"
853 447 ]
854 448 },
855 449 "metadata": {},
... ... @@ -895,10 +489,6 @@
895 489 "metadata": {},
896 490 "outputs": [],
897 491 "source": [
898   - "#obs = like.obs().copy()\n",
899   - "#obs1 = gammalib.GObservations()\n",
900   - "#obs1.append(obs[0])\n",
901   - "\n",
902 492 "resmap = cscripts.csresmap(like.obs())\n",
903 493 "resmap['emin'] = 0.66\n",
904 494 "resmap['emax'] = 100.\n",
... ... @@ -932,7 +522,7 @@
932 522 "data": {
933 523 "image/png": 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\n",
934 524 "text/plain": [
935   - "<matplotlib.figure.Figure at 0x1173b4ef0>"
  525 + "<matplotlib.figure.Figure at 0x103e6f978>"
936 526 ]
937 527 },
938 528 "metadata": {},
... ...