Eirik Hatlen / ctools

Commit f1c567ecefeb98f9cf8332c0c1d6d4a45c8d9fe6

Authored by Jürgen Knödlseder
2 parents 1c81f76b 2940dac5

Merge branch '2712_HESSDR1_tutorials' into integration

Inline Side-by-side
Showing 21 changed files with 1996 additions and 916 deletions
cscripts/csbkgmodel.par
  View file @f1c567e
... ... @@ -29,7 +29,7 @@ spectral, s, a, NODES,PLAW|NODES,, "Spectral model component"
29 29 ebinalg, s, a, LOG,FILE|LIN|LOG,, "Algorithm for defining energy nodes"
30 30 emin, r, a, 0.1,,, "Lower energy limit (TeV)"
31 31 emax, r, a, 100.0,,, "Upper energy limit (TeV)"
32   -enumbins, i, a, 10,1,200, "Number of energy nodes"
  32 +enumbins, i, a, 8,1,200, "Number of energy nodes"
33 33 ebinfile, f, a, NONE,,, "Name of the file containing the energy node definition"
34 34 runwise, b, a, yes,,, "Generate runwise background model?"
35 35 rad, r, h, 2.0,,, "Radius for event selection (degrees)"
... ...
cscripts/csresspec.py
  View file @f1c567e
... ... @@ -102,7 +102,7 @@ class csresspec(ctools.csobservation):
102 102 # If there is more than one observation, and observations are all
103 103 # unbinned or all onoff query user to know if they wish stacked results
104 104 if self.obs().size() > 1 and \
105   - (n_unbinned == self.obs().size() or n_onoff == self.obs().size()):
  105 + (n_unbinned == self.obs().size() or n_onoff == self.obs().size()):
106 106 self._stack = self['stack'].boolean()
107 107 # If we are to stack event lists query parameters for cube creation
108 108 if self._stack and n_unbinned == self.obs().size():
... ... @@ -162,11 +162,11 @@ class csresspec(ctools.csobservation):
162 162  
163 163 # Log for unbinned observations
164 164 if n_unbinned != 0:
165   - msg = ' User defined energy binning will be used for %d unbinned '\
  165 + msg = ' User defined energy binning will be used for %d unbinned ' \
166 166 'observations.' % (n_unbinned)
167 167 self._log_string(gammalib.TERSE, msg)
168 168 if n_cta > n_unbinned:
169   - msg = ' The intrinsic binning will be used for the remaining '\
  169 + msg = ' The intrinsic binning will be used for the remaining ' \
170 170 '%d CTA observations.' % (n_notunbin)
171 171 self._log_string(gammalib.TERSE, msg)
172 172  
... ... @@ -179,51 +179,20 @@ class csresspec(ctools.csobservation):
179 179 # Return
180 180 return
181 181  
182   - def _stack_observations(self):
183   - """
184   - Stack multiple observations and replace observation container
185   - with single stacked observation
186   -
187   - Returns
188   - -------
189   - obs : `~gammalib.GObservations'
190   - Observation container with a single stacked observation
191   - """
192   - # Use first observation to determine the type and apply the
193   - # appropriate stacking method
194   -
195   - # If On/Off use the GCTAOnOffObservation constructor
196   - if self.obs()[0].classname() == 'GCTAOnOffObservation':
197   - msg = 'Stacking %d On/Off observations.' % (self.obs().size())
198   - self._log_string(gammalib.NORMAL, msg)
199   - stacked = gammalib.GCTAOnOffObservation(self.obs())
200   - stacked_obs = gammalib.GObservations()
201   - stacked_obs.append(stacked)
202   - stacked_obs.models(self.obs().models())
203   -
204   - # If event list then bin observations
205   - elif self.obs()[0].classname() == 'GCTAObservation':
206   - msg = 'Stacking %d event lists.' % (self.obs().size())
207   - self._log_string(gammalib.NORMAL, msg)
208   - stacked_obs = obsutils.get_stacked_obs(self, self.obs())
209   -
210   - # Return
211   - return stacked_obs
212   -
213 182 def _bin_evlist(self, obs):
214 183 """
215 184 Turn single event list into counts cube
216 185  
217 186 Parameters
218 187 ----------
219   - obs : `~gammalib.GObservations()`
  188 + obs : `~gammalib.GObservations`
220 189 Observation container with single event list
221 190  
222 191 Returns
223 192 -------
224   - obs : `~gammalib.GObservations()`
225   - Binned observation container dict with event list ROI and energy
226   - range information
  193 + obs, info : `~gammalib.GObservations`, dict
  194 + Binned observation container and dictionary with event list ROI
  195 + and energy range information
227 196 """
228 197 # Retrieve information about ROI in event list
229 198 roi = obs[0].roi()
... ... @@ -287,7 +256,7 @@ class csresspec(ctools.csobservation):
287 256  
288 257 Parameters
289 258 ----------
290   - cube : `~gammalib.GCTAEventCube()`
  259 + cube : `~gammalib.GCTAEventCube`
291 260 Event cube
292 261 ra : float (str 'INDEF' for no selection on direction)
293 262 Right Ascension (deg)
... ... @@ -302,7 +271,7 @@ class csresspec(ctools.csobservation):
302 271  
303 272 Returns
304 273 -------
305   - cube : `~gammalib.GCTAEventCube()`
  274 + cube : `~gammalib.GCTAEventCube`
306 275 Event cube
307 276 """
308 277 # Turn cube into observation container to feed to ctcubemask
... ... @@ -337,14 +306,14 @@ class csresspec(ctools.csobservation):
337 306  
338 307 Parameters
339 308 ----------
340   - cube : `~gammalib.GCTAEventCube()`
  309 + cube : `~gammalib.GCTAEventCube`
341 310 Event cube
342 311 evlist_info : dict
343 312 Dictionary with information on original event list
344 313  
345 314 Returns
346 315 -------
347   - array : `~gammalib.GNdarray()'
  316 + array : `~gammalib.GNdarray'
348 317 Counts spectrum
349 318 """
350 319 # If we started from event list mask the ROI only
... ... @@ -388,13 +357,13 @@ class csresspec(ctools.csobservation):
388 357 ----------
389 358 obs_id : str
390 359 Observation id
391   - ebounds : `~gammalib.GEbounds()'
  360 + ebounds : `~gammalib.GEbounds'
392 361 Energy boundaries
393   - counts : `~gammalib.GNdarray()'
  362 + counts : `~gammalib.GNdarray'
394 363 Counts spectrum
395   - model : `~gammalib.GNdarray()'
  364 + model : `~gammalib.GNdarray'
396 365 Model spectrum
397   - residuals : `~gammalib.GNdarray()'
  366 + residuals : `~gammalib.GNdarray'
398 367 Residual spectrum
399 368  
400 369 Returns
... ... @@ -403,7 +372,7 @@ class csresspec(ctools.csobservation):
403 372 Residual spectrum as FITS binary table
404 373 """
405 374 # Create FITS table columns
406   - nrows = ebounds.size()
  375 + nrows = ebounds.size()
407 376 energy_low = gammalib.GFitsTableDoubleCol('Emin', nrows)
408 377 energy_high = gammalib.GFitsTableDoubleCol('Emax', nrows)
409 378 counts_col = gammalib.GFitsTableDoubleCol('Counts', nrows)
... ... @@ -444,16 +413,16 @@ class csresspec(ctools.csobservation):
444 413  
445 414 Parameters
446 415 ----------
447   - table : `~gammalib.GFitsBinTable()'
  416 + table : `~gammalib.GFitsBinTable'
448 417 FITS binary table
449 418 name : str
450 419 Column name
451   - data : `~gammalib.GEbounds()'
  420 + data : `~gammalib.GEbounds'
452 421 Data to be filled into new column
453 422  
454 423 Returns
455 424 -------
456   - table : `~gammalib.GFitsBinTable()'
  425 + table : `~gammalib.GFitsBinTable'
457 426 FITS binary table
458 427 """
459 428 # Check size compatibility
... ... @@ -478,6 +447,202 @@ class csresspec(ctools.csobservation):
478 447 # Return modified table
479 448 return table
480 449  
  450 + def _residuals_3D(self, obs, obs_id, ccube='NONE'):
  451 + """
  452 + Calculate residuals for 3D observation
  453 +
  454 + Parameters
  455 + ----------
  456 + obs : `~gammalib.GObservations`
  457 + Observation container with a single observations of type GCTAObservation
  458 + obs_id : str
  459 + Observation ID
  460 + ccube : `~gammalib.GCTAEventCube'
  461 + Count cube with stacked events lists
  462 +
  463 + Returns
  464 + -------
  465 + table : `~gammalib.GFitsBinTable()'
  466 + Residual spectrum as FITS binary table
  467 + """
  468 +
  469 + # If binned data already exist set the evlist_info dictionary to have
  470 + # attribute was_list False
  471 + if obs[0].eventtype() == 'CountsCube' or ccube!='NONE':
  472 + evlist_info = {'was_list': False}
  473 +
  474 + # ... otherwise bin now
  475 + else:
  476 + # we remember if we binned an event list so that we can
  477 + # mask only the ROI for residual calculation
  478 + msg = 'Setting up binned observation'
  479 + self._log_string(gammalib.NORMAL, msg)
  480 + obs, evlist_info = self._bin_evlist(obs)
  481 +
  482 + # Calculate Model and residuals. If model cube is provided load
  483 + # it
  484 + if self._use_maps:
  485 + modcube = gammalib.GCTAEventCube(self['modcube'].filename())
  486 +
  487 + # ... otherwise calculate it now
  488 + else:
  489 + msg = 'Computing model cube'
  490 + self._log_string(gammalib.NORMAL, msg)
  491 + modelcube = ctools.ctmodel(obs)
  492 + if ccube != 'NONE':
  493 + modelcube.cube(ccube)
  494 + modelcube['edisp'] = self['edisp'].boolean()
  495 + modelcube.run()
  496 + modcube = modelcube.cube().copy()
  497 +
  498 + # Extract cntcube for residual computation
  499 + if ccube != 'NONE':
  500 + cntcube = ccube
  501 + else:
  502 + cntcube = obs[0].events().copy()
  503 +
  504 + # Derive count spectra from cubes
  505 + msg = 'Computing counts, model, and residual spectra'
  506 + self._log_string(gammalib.NORMAL, msg)
  507 + counts = self._cube_to_spectrum(cntcube, evlist_info)
  508 + model = self._cube_to_spectrum(modcube, evlist_info)
  509 +
  510 + # Calculate residuals
  511 + residuals = obsutils.residuals(self, counts, model)
  512 +
  513 + # Extract energy bounds
  514 + ebounds = cntcube.ebounds()
  515 +
  516 + # Fill results table
  517 + msg = 'Filling residual table'
  518 + self._log_string(gammalib.NORMAL, msg)
  519 + table = self._residuals_table(obs_id, ebounds, counts, model,
  520 + residuals)
  521 +
  522 + # Calculate models of individual components if requested
  523 + if self['components'].boolean():
  524 + for component in obs.models():
  525 + # Log component
  526 + self._log_value(gammalib.NORMAL,
  527 + 'Computing model component',
  528 + component.name())
  529 +
  530 + # Set model cube models to individual component
  531 + model_cont = gammalib.GModels()
  532 + model_cont.append(component)
  533 + modelcube.obs().models(model_cont)
  534 +
  535 + # Reset base cube that was modified internally by ctmodel
  536 + if ccube != 'NONE':
  537 + modelcube.cube(ccube)
  538 +
  539 + # Run model cube
  540 + modelcube['edisp'] = self['edisp'].boolean()
  541 + modelcube.run()
  542 +
  543 + # Extract spectrum of individual component
  544 + modcube = modelcube.cube().copy()
  545 + model = self._cube_to_spectrum(modcube, evlist_info)
  546 +
  547 + # append component to table
  548 + table = self._append_column(table, component.name(),
  549 + model)
  550 +
  551 + return table
  552 +
  553 + def _residuals_OnOff(self,obs,obs_id):
  554 + """
  555 + Calculate residual for OnOff observation
  556 +
  557 + Parameters
  558 + ----------
  559 + obs : `~gammalib.GObservations`
  560 + Observation container with a single observations of type GCTAOnOffObservation
  561 + obs_id : str
  562 + Observation ID
  563 +
  564 + Returns
  565 + -------
  566 + table : `~gammalib.GFitsBinTable'
  567 + Residual spectrum as FITS binary table
  568 + """
  569 +
  570 + # Calculate Counts, Model and residuals
  571 + msg = 'Computing counts, model, and residual spectra'
  572 + self._log_string(gammalib.NORMAL, msg)
  573 +
  574 + onoff = obs[0]
  575 +
  576 + # On spectrum
  577 + counts = onoff.on_spec().counts_spectrum()
  578 +
  579 + # Model for On region
  580 + background = onoff.model_background(obs.models()).counts_spectrum()
  581 + alpha = onoff.on_spec().backscal_spectrum()
  582 + model = background.copy()
  583 + model *= alpha
  584 + model += onoff.model_gamma(obs.models()).counts_spectrum()
  585 +
  586 + # On Residuals
  587 + residuals = obsutils.residuals(self, counts, model)
  588 +
  589 + # Extract energy bounds
  590 + ebounds = onoff.on_spec().ebounds()
  591 +
  592 + # Fill results table
  593 + msg = 'Filling residual table'
  594 + self._log_string(gammalib.NORMAL, msg)
  595 + table = self._residuals_table(obs_id, ebounds, counts, model,
  596 + residuals)
  597 +
  598 + # Get Off spectrum and add to table
  599 + msg = 'Computing counts, model, and residual spectra for Off regions'
  600 + self._log_string(gammalib.NORMAL, msg)
  601 + counts_off = onoff.off_spec().counts_spectrum()
  602 + table = self._append_column(table, 'Counts_Off',
  603 + counts_off)
  604 +
  605 + # Add background/Off model to table
  606 + table = self._append_column(table, 'Model_Off',
  607 + background)
  608 +
  609 + # Calculate Off residuals and add to table
  610 + residuals_off = obsutils.residuals(self, counts_off, background)
  611 + table = self._append_column(table, 'Residuals_Off',
  612 + residuals_off)
  613 +
  614 + # Calculate models of individual components if requested
  615 + if self['components'].boolean():
  616 + for component in obs.models():
  617 + # If background pass
  618 + # We always add the background at the end so that
  619 + # we accommodate WSTAT for which the background is not
  620 + # mandatory in the model
  621 + if component.classname() == 'GCTAModelIrfBackground':
  622 + pass
  623 + # Otherwise calculate gamma component and append to Table
  624 + else:
  625 + self._log_value(gammalib.NORMAL,
  626 + 'Computing model for component',
  627 + component.name())
  628 + # Create observation container for individual components
  629 + model_cont = gammalib.GModels()
  630 + model_cont.append(component)
  631 + # Calculate gamma model
  632 + model = onoff.model_gamma(model_cont)
  633 + model = model.counts_spectrum()
  634 + # Append to table
  635 + table = self._append_column(table, component.name(),
  636 + model)
  637 + # Add background already calculated
  638 + self._log_value(gammalib.NORMAL,
  639 + 'Computing model for component',
  640 + 'Background')
  641 + background *= onoff.on_spec().backscal_spectrum()
  642 + table = self._append_column(table, 'Background',
  643 + background)
  644 +
  645 + return table
481 646  
482 647 # Public methods
483 648 def run(self):
... ... @@ -494,192 +659,84 @@ class csresspec(ctools.csobservation):
494 659 # Write observation into logger
495 660 self._log_observations(gammalib.NORMAL, self.obs(), 'Observation')
496 661  
497   - # Stack observations if requested
498   - if self._stack:
499   - self._log_header1(gammalib.TERSE, 'Stacking Observations')
500   - self.obs(self._stack_observations())
  662 + # Stack On/Off observations if requested
  663 + if self._stack and self.obs()[0].classname() == 'GCTAOnOffObservation':
  664 + msg = 'Stacking %d On/Off observations.' % (self.obs().size())
  665 + self._log_string(gammalib.NORMAL, msg)
  666 + stacked = gammalib.GCTAOnOffObservation(self.obs())
  667 + stacked_obs = gammalib.GObservations()
  668 + stacked_obs.append(stacked)
  669 + stacked_obs.models(self.obs().models())
  670 + self.obs(stacked_obs)
501 671  
502 672 # Log processing header
503 673 self._log_header1(gammalib.TERSE, 'Processing Observations')
504 674  
505   - # Loop over observations and calculate residuals
506   - for s, observation in enumerate(self.obs()):
  675 + #If observations are unbinned and we stack
  676 + if self._stack and self.obs()[0].classname() == 'GCTAObservation':
507 677  
508   - # Retrieve and store obs id
509   - obs_id = observation.id()
  678 + msg = 'Computing count cube from multiple unbinned observations'
  679 + self._log_string(gammalib.NORMAL, msg)
510 680  
511   - # If observation id is empty and there is more than one observation
512   - # replace with incremental number
513   - if obs_id == '' and self.obs().size() > 1:
514   - obs_id = str(s)
  681 + # Build count cube
  682 + binning = ctools.ctbin(self.obs())
  683 + binning['xref'] = self['xref'].real()
  684 + binning['yref'] = self['yref'].real()
  685 + binning['proj'] = self['proj'].string()
  686 + binning['coordsys'] = self['coordsys'].string()
  687 + binning['ebinalg'] = self['ebinalg'].string()
  688 + binning['nxpix'] = self['nxpix'].integer()
  689 + binning['nypix'] = self['nypix'].integer()
  690 + binning['binsz'] = self['binsz'].real()
  691 + if self['ebinalg'].string() == 'FILE':
  692 + binning['ebinfile'] = self['ebinfile'].filename().file()
  693 + else:
  694 + binning['enumbins'] = self['enumbins'].integer()
  695 + binning['emin'] = self['emin'].real()
  696 + binning['emax'] = self['emax'].real()
  697 + binning['chatter'] = self['chatter'].integer()
  698 + binning['clobber'] = self['clobber'].boolean()
  699 + binning['debug'] = self['debug'].boolean()
  700 + binning.run()
515 701  
516   - # Log processing of observation
517   - if self.obs().size() > 1:
518   - self._log_header2(gammalib.NORMAL,
519   - 'Processing observation %s' % obs_id)
  702 + #compute residuals using cube
  703 + table = self._residuals_3D(self.obs(),'',binning.cube())
520 704  
521   - # Turn into observation container and assign models
522   - obs = gammalib.GObservations()
523   - obs.append(observation)
524   - obs.models(self.obs().models())
  705 + # Append results table to output file
  706 + self._fits.append(table)
525 707  
526   - # If 3D observation
527   - if obs[0].classname() == 'GCTAObservation':
  708 + # Otherwise, loop over observations and calculate residuals
  709 + else:
  710 + for s, observation in enumerate(self.obs()):
528 711  
529   - # If already binned set the evlist_info dictionary to have
530   - # attribute was_list False
531   - if obs[0].eventtype() == 'CountsCube':
532   - evlist_info = {'was_list': False}
  712 + # Retrieve and store obs id
  713 + obs_id = observation.id()
533 714  
534   - # ... otherwise bin now
535   - else:
536   - # we remember if we binned an event list so that we can
537   - # mask only the ROI for residual calculation
538   - msg = 'Setting up binned observation'
539   - self._log_string(gammalib.NORMAL, msg)
540   - obs, evlist_info = self._bin_evlist(obs)
541   -
542   - # Calculate Model and residuals. If model cube is provided load
543   - # it
544   - if self._use_maps:
545   - modcube = gammalib.GCTAEventCube(self['modcube'].filename())
546   -
547   - # ... otherwise calculate it now
548   - else:
549   - msg = 'Computing model cube'
550   - self._log_string(gammalib.NORMAL, msg)
551   - modelcube = ctools.ctmodel(obs)
552   - modelcube['edisp'] = self['edisp'].boolean()
553   - modelcube.run()
554   - modcube = modelcube.cube().copy()
555   -
556   - # Extract cntcube for residual computation
557   - cntcube = obs[0].events().copy()
558   -
559   - # Derive count spectra from cubes
560   - msg = 'Computing counts, model, and residual spectra'
561   - self._log_string(gammalib.NORMAL, msg)
562   - counts = self._cube_to_spectrum(cntcube, evlist_info)
563   - model = self._cube_to_spectrum(modcube, evlist_info)
564   -
565   - # Calculate residuals
566   - residuals = obsutils.residuals(self, counts, model)
567   -
568   - # Extract energy bounds
569   - ebounds = cntcube.ebounds()
570   -
571   - # Fill results table
572   - msg = 'Filling residual table'
573   - self._log_string(gammalib.NORMAL, msg)
574   - table = self._residuals_table(obs_id, ebounds, counts, model,
575   - residuals)
576   -
577   - # Calculate models of individual components if requested
578   - if self['components'].boolean():
579   - for component in obs.models():
580   -
581   - # Log component
582   - self._log_value(gammalib.NORMAL,
583   - 'Computing model component',
584   - component.name())
585   -
586   - # Set model cube models to individual component
587   - model_cont = gammalib.GModels()
588   - model_cont.append(component)
589   - modelcube.obs().models(model_cont)
590   -
591   - # Run model cube
592   - modelcube['edisp'] = self['edisp'].boolean()
593   - modelcube.run()
594   -
595   - # Extract spectrum of individual component
596   - modcube = modelcube.cube().copy()
597   - model = self._cube_to_spectrum(modcube, evlist_info)
598   -
599   - # append component to table
600   - table = self._append_column(table, component.name(),
601   - model)
602   -
603   - # otherwise, if On/Off
604   - elif obs[0].classname() == 'GCTAOnOffObservation':
605   -
606   - onoff = obs[0]
607   -
608   - # Calculate Counts, Model and residuals
609   - msg = 'Computing counts, model, and residual spectra'
610   - self._log_string(gammalib.NORMAL, msg)
611   -
612   - # On spectrum
613   - counts = onoff.on_spec().counts_spectrum()
614   -
615   - # Model for On region
616   - background = onoff.model_background(obs.models()).counts_spectrum()
617   - alpha = onoff.on_spec().backscal_spectrum()
618   - model = background.copy()
619   - model *= alpha
620   - model += onoff.model_gamma(obs.models()).counts_spectrum()
621   -
622   - # On Residuals
623   - residuals = obsutils.residuals(self, counts, model)
624   -
625   - # Extract energy bounds
626   - ebounds = onoff.on_spec().ebounds()
627   -
628   - # Fill results table
629   - msg = 'Filling residual table'
630   - self._log_string(gammalib.NORMAL, msg)
631   - table = self._residuals_table(obs_id, ebounds, counts, model,
632   - residuals)
633   -
634   - # Get Off spectrum and add to table
635   - msg = 'Computing counts, model, and residual spectra for Off regions'
636   - self._log_string(gammalib.NORMAL, msg)
637   - counts_off = onoff.off_spec().counts_spectrum()
638   - table = self._append_column(table, 'Counts_Off',
639   - counts_off)
640   -
641   - # Add background/Off model to table
642   - table = self._append_column(table, 'Model_Off',
643   - background)
644   -
645   - # Calculate Off residuals and add to table
646   - residuals_off = obsutils.residuals(self, counts_off, background)
647   - table = self._append_column(table, 'Residuals_Off',
648   - residuals_off)
649   -
650   - # Calculate models of individual components if requested
651   - if self['components'].boolean():
652   - for component in obs.models():
653   - # If background pass
654   - # We always add the background at the end so that
655   - # we accommodate WSTAT for which the background is not
656   - # mandatory in the model
657   - if component.classname() == 'GCTAModelIrfBackground':
658   - pass
659   - # Otherwise calculate gamma component and append to Table
660   - else:
661   - self._log_value(gammalib.NORMAL,
662   - 'Computing model for component',
663   - component.name())
664   - # Create observation container for individual components
665   - model_cont = gammalib.GModels()
666   - model_cont.append(component)
667   - # Calculate gamma model
668   - model = onoff.model_gamma(model_cont)
669   - model = model.counts_spectrum()
670   - # Append to table
671   - table = self._append_column(table, component.name(),
672   - model)
673   - # Add background already calculated
674   - self._log_value(gammalib.NORMAL,
675   - 'Computing model for component',
676   - 'Background')
677   - background *= onoff.on_spec().backscal_spectrum()
678   - table = self._append_column(table, 'Background',
679   - background)
  715 + # If observation id is empty and there is more than one observation
  716 + # replace with incremental number
  717 + if obs_id == '' and self.obs().size() > 1:
  718 + obs_id = str(s)
680 719  
681   - # Append results table to output file
682   - self._fits.append(table)
  720 + # Log processing of observation
  721 + if self.obs().size() > 1:
  722 + self._log_header2(gammalib.NORMAL,
  723 + 'Processing observation %s' % obs_id)
  724 +
  725 + # Turn into observation container and assign models
  726 + obs = gammalib.GObservations()
  727 + obs.append(observation)
  728 + obs.models(self.obs().models())
  729 +
  730 + # If 3D observation
  731 + if obs[0].classname() == 'GCTAObservation':
  732 + table = self._residuals_3D(obs,obs_id)
  733 +
  734 + # otherwise, if On/Off
  735 + elif obs[0].classname() == 'GCTAOnOffObservation':
  736 + table = self._residuals_OnOff(obs,obs_id)
  737 +
  738 + # Append results table to output file
  739 + self._fits.append(table)
683 740  
684 741 # Return
685 742 return
... ...
cscripts/cssrcdetect.py
  View file @f1c567e
... ... @@ -38,12 +38,6 @@ class cssrcdetect(ctools.cscript):
38 38 """
39 39 Constructor
40 40 """
41   - # Initialise application by calling the base class constructor
42   - self._init_cscript(self.__class__.__name__, ctools.__version__, argv)
43   -
44   - # Set protected members
45   - self._models = gammalib.GModels()
46   -
47 41 # Initialise sky map from constructor arguments
48 42 if len(argv) > 0 and isinstance(argv[0], gammalib.GSkyMap):
49 43 self._map = argv[0]
... ... @@ -51,6 +45,12 @@ class cssrcdetect(ctools.cscript):
51 45 else:
52 46 self._map = gammalib.GSkyMap()
53 47  
  48 + # Initialise application by calling the base class constructor
  49 + self._init_cscript(self.__class__.__name__, ctools.__version__, argv)
  50 +
  51 + # Set protected members
  52 + self._models = gammalib.GModels()
  53 +
54 54 # Initialise other members
55 55 self._map_dirs = []
56 56  
... ...
doc/source/users/reference_manual/csbkgmodel.rst
  View file @f1c567e
... ... @@ -37,10 +37,9 @@ General parameters
37 37 Allow for a spatial gradient in the background event distribution?
38 38 This option only applies to the ``GAUSS`` spatial model component.
39 39  
40   -``spectral <PLAW|HESS> [string]``
  40 +``spectral <PLAW|NODES> [string]``
41 41 Spectral model component. ``PLAW`` specifies a simple power law model,
42   - ``HESS`` specifies a piecewise broken power-law using 8 energy nodes
43   - spread over the energy range from 100 GeV to 12.8 TeV.
  42 + ``NODES`` specifies a piecewise broken power-law.
44 43  
45 44 ``ebinalg <FILE|LIN|LOG> [string]``
46 45 Algorithm for defining energy nodes. For ``FILE``, the energy nodes are
... ...
doc/source/users/reference_manual/csresmap.rst
  View file @f1c567e
... ... @@ -18,10 +18,14 @@ binning and used for residual computation. Before residual computation,
18 18 the counts and model cubes are collapsed into maps by summing over all
19 19 energies. Four options exist then for residual computation:
20 20  
21   -- the subtraction of the model from the counts (SUB)
22   -- the subtraction and division by the model (SUBDIV)
23   -- the subtraction and division by the square root of the model (SUBDIVSQRT)
24   -- the computation of the significance using Li & Ma (SIGNIFICANCE)
  21 +- the subtraction of the model from the counts (``SUB``)
  22 +- the subtraction and division by the model (``SUBDIV``)
  23 +- the subtraction and division by the square root of the model (``SUBDIVSQRT``)
  24 +- the computation of the significance based on the likelihood-ratio
  25 + test for Poisson statistics (``SIGNIFICANCE``)
  26 +
  27 +The ``SUBDIVSQRT`` and ``SIGNIFICANCE`` algorithms approximate the residual
  28 +significance and may become inaccurate in the low-counting regime.
25 29  
26 30 The residual map is written into a FITS file.
27 31  
... ... @@ -108,7 +112,9 @@ General parameters
108 112  
109 113 ``SUBDIVSQRT``: :math:`(DATA - MODEL) / \sqrt{MODEL}`
110 114  
111   - ``SIGNIFICANCE``: :math:`{\rm sign}(DATA-MODEL) \times \sqrt{ 2 \times ( DATA \times \ln \left(\frac{DATA}{MODEL} \right) + MODEL - DATA ) }`
  115 + ``SIGNIFICANCE``: :math:`{\rm sign}(DATA-MODEL) \times \sqrt{ 2
  116 + \times ( DATA \times \ln \left(\frac{DATA}{MODEL} \right) +
  117 + MODEL - DATA ) }`
112 118  
113 119  
114 120 Standard parameters
... ...
doc/source/users/reference_manual/csresspec.rst
  View file @f1c567e
... ... @@ -32,10 +32,14 @@ residuals are calculated separately for each observation.
32 32  
33 33 Four options exist then for residual computation:
34 34  
35   -- the subtraction of the model from the counts (SUB)
36   -- the subtraction and division by the model (SUBDIV)
37   -- the subtraction and division by the square root of the model (SUBDIVSQRT)
38   -- the computation of the significance using Li & Ma (SIGNIFICANCE)
  35 +- the subtraction of the model from the counts (``SUB``)
  36 +- the subtraction and division by the model (``SUBDIV``)
  37 +- the subtraction and division by the square root of the model (``SUBDIVSQRT``)
  38 +- the computation of the significance based on the likelihood-ratio
  39 + test for Poisson statistics (``SIGNIFICANCE``)
  40 +
  41 +The ``SUBDIVSQRT`` and ``SIGNIFICANCE`` algorithms approximate the residual
  42 +significance and may become inaccurate in the low-counting regime.
39 43  
40 44 The counts, model, and residual spectra are written into a FITS file.
41 45  
... ... @@ -162,7 +166,9 @@ General parameters
162 166  
163 167 ``SUBDIVSQRT``: :math:`(DATA - MODEL) / \sqrt{MODEL}`
164 168  
165   - ``SIGNIFICANCE``: :math:`{\rm sign}(DATA-MODEL) \times \sqrt{ 2 \times ( DATA \times \ln \left(\frac{DATA}{MODEL} \right) + MODEL - DATA ) }`
  169 + ``SIGNIFICANCE``: :math:`{\rm sign}(DATA-MODEL) \times \sqrt{ 2
  170 + \times ( DATA \times \ln \left(\frac{DATA}{MODEL} \right) +
  171 + MODEL - DATA ) }`
166 172  
167 173  
168 174 Standard parameters
... ...
doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb 0 → 100644
  View file @f1c567e
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Generating a background model from the data"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/bkgmodel.ipynb\" download=\"bkgmodel.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn to generate a background model from the data**\n",
  22 + "\n",
  23 + "The public H.E.S.S. data do not contain a model for the instrumental background. We can generate one from the data using the csbkgmodel script.\n",
  24 + "\n",
  25 + "We start with the usual imports."
  26 + ]
  27 + },
  28 + {
  29 + "cell_type": "code",
  30 + "execution_count": 15,
  31 + "metadata": {},
  32 + "outputs": [],
  33 + "source": [
  34 + "import gammalib\n",
  35 + "import ctools\n",
  36 + "import cscripts"
  37 + ]
  38 + },
  39 + {
  40 + "cell_type": "markdown",
  41 + "metadata": {},
  42 + "source": [
  43 + "We will use the same events selected in a previous tutorial. For the spatial part we will model the background as a radial Gaussian modified by a bi-linear gradient. For the spectral part we will use a piece-wise power law with 8 nodes between 0.7 TeV and 30 TeV. Note that for a reasonable background fit the nodes should be within the energy range covered by the observations and where there are enough photon counts to constrain the model, hence the upper energy bound at 30 TeV."
  44 + ]
  45 + },
  46 + {
  47 + "cell_type": "code",
  48 + "execution_count": 16,
  49 + "metadata": {},
  50 + "outputs": [],
  51 + "source": [
  52 + "obsfile = \"obs_crab_selected.xml\"\n",
  53 + "outmodel = 'bkgmodel.xml'\n",
  54 + "\n",
  55 + "bkgmodel = cscripts.csbkgmodel()\n",
  56 + "bkgmodel['inobs'] = obsfile\n",
  57 + "bkgmodel['outmodel'] = outmodel\n",
  58 + "bkgmodel['instrument'] = 'HESS'\n",
  59 + "bkgmodel['spatial'] = 'GAUSS'\n",
  60 + "bkgmodel['gradient'] = True\n",
  61 + "bkgmodel['ebinalg'] = 'LOG'\n",
  62 + "bkgmodel['emin'] = 0.7\n",
  63 + "bkgmodel['emax'] = 30.\n",
  64 + "bkgmodel['enumbins'] = 8\n",
  65 + "bkgmodel['runwise'] = True\n",
  66 + "\n",
  67 + "bkgmodel.execute()"
  68 + ]
  69 + },
  70 + {
  71 + "cell_type": "markdown",
  72 + "metadata": {},
  73 + "source": [
  74 + "Let's look at the resulting background model."
  75 + ]
  76 + },
  77 + {
  78 + "cell_type": "code",
  79 + "execution_count": 17,
  80 + "metadata": {},
  81 + "outputs": [
  82 + {
  83 + "name": "stdout",
  84 + "output_type": "stream",
  85 + "text": [
  86 + "=== GModels ===\n",
  87 + " Number of models ..........: 4\n",
  88 + " Number of parameters ......: 80\n",
  89 + "=== GCTAModelBackground ===\n",
  90 + " Name ......................: Background_023523\n",
  91 + " Instruments ...............: HESS\n",
  92 + " Instrument scale factors ..: unity\n",
  93 + " Observation identifiers ...: 023523\n",
  94 + " Model type ................: CTABackground\n",
  95 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  96 + " Number of parameters ......: 20\n",
  97 + " Number of spatial par's ...: 3\n",
  98 + " 1:Sigma ..................: 3.19679118852325 +/- 0.148784221714502 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  99 + " 2:Grad_DETX ..............: 0.0882131070561384 +/- 0.0223563997131535 deg^-1 (free,scale=1,gradient)\n",
  100 + " 2:Grad_DETY ..............: -0.000692481271743188 +/- 0.0227254581791992 deg^-1 (free,scale=1,gradient)\n",
  101 + " Number of spectral par's ..: 16\n",
  102 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  103 + " Intensity0 ...............: 0.00149714476600658 +/- 0.000185798282794166 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
  104 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  105 + " Intensity1 ...............: 0.000275025454049597 +/- 1.50407490107521e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
  106 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  107 + " Intensity2 ...............: 9.68931866899334e-05 +/- 5.86314382972939e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
  108 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  109 + " Intensity3 ...............: 3.42414862721453e-05 +/- 2.61283857499118e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
  110 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  111 + " Intensity4 ...............: 1.2134598847599e-05 +/- 1.19969987797427e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
  112 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  113 + " Intensity5 ...............: 4.80035386464156e-06 +/- 6.14208278785316e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
  114 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  115 + " Intensity6 ...............: 8.5455367005893e-07 +/- 1.75301261074233e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
  116 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  117 + " Intensity7 ...............: 2.8110106615004e-07 +/- 1.02501352381156e-07 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
  118 + " Number of temporal par's ..: 1\n",
  119 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  120 + "=== GCTAModelBackground ===\n",
  121 + " Name ......................: Background_023526\n",
  122 + " Instruments ...............: HESS\n",
  123 + " Instrument scale factors ..: unity\n",
  124 + " Observation identifiers ...: 023526\n",
  125 + " Model type ................: CTABackground\n",
  126 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  127 + " Number of parameters ......: 20\n",
  128 + " Number of spatial par's ...: 3\n",
  129 + " 1:Sigma ..................: 3.30082205953218 +/- 0.156912956620767 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  130 + " 2:Grad_DETX ..............: 0.0153115422362867 +/- 0.0215680816698383 deg^-1 (free,scale=1,gradient)\n",
  131 + " 2:Grad_DETY ..............: -0.00906905077562078 +/- 0.0213184759257543 deg^-1 (free,scale=1,gradient)\n",
  132 + " Number of spectral par's ..: 16\n",
  133 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  134 + " Intensity0 ...............: 0.00115207913907334 +/- 6.11872558313686e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
  135 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  136 + " Intensity1 ...............: 0.000216074985117796 +/- 1.14346974881101e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
  137 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  138 + " Intensity2 ...............: 9.80980717622882e-05 +/- 6.14243331439364e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
  139 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  140 + " Intensity3 ...............: 2.64302082334315e-05 +/- 2.27978082923659e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
  141 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  142 + " Intensity4 ...............: 1.03353701064884e-05 +/- 1.13230244143927e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
  143 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  144 + " Intensity5 ...............: 3.57899467682822e-06 +/- 5.03727665165389e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
  145 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  146 + " Intensity6 ...............: 5.95101204005172e-07 +/- 1.41251129058325e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
  147 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  148 + " Intensity7 ...............: 2.81714577113613e-07 +/- 1.0384120888285e-07 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
  149 + " Number of temporal par's ..: 1\n",
  150 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  151 + "=== GCTAModelBackground ===\n",
  152 + " Name ......................: Background_023559\n",
  153 + " Instruments ...............: HESS\n",
  154 + " Instrument scale factors ..: unity\n",
  155 + " Observation identifiers ...: 023559\n",
  156 + " Model type ................: CTABackground\n",
  157 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  158 + " Number of parameters ......: 20\n",
  159 + " Number of spatial par's ...: 3\n",
  160 + " 1:Sigma ..................: 4.32704862685447 +/- 0.368685355107103 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  161 + " 2:Grad_DETX ..............: 0.018772378083067 +/- 0.0217106872366865 deg^-1 (free,scale=1,gradient)\n",
  162 + " 2:Grad_DETY ..............: -0.126627961668458 +/- 0.0204738085196433 deg^-1 (free,scale=1,gradient)\n",
  163 + " Number of spectral par's ..: 16\n",
  164 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  165 + " Intensity0 ...............: 0.000915773345685034 +/- 4.89989530347851e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
  166 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  167 + " Intensity1 ...............: 0.000197398656687683 +/- 1.07759903733823e-05 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
  168 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  169 + " Intensity2 ...............: 6.93704083542102e-05 +/- 4.77505196699795e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
  170 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  171 + " Intensity3 ...............: 2.57663537500693e-05 +/- 2.20136814658691e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
  172 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  173 + " Intensity4 ...............: 6.82483279973139e-06 +/- 7.86680350899204e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
  174 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  175 + " Intensity5 ...............: 3.3292540797357e-06 +/- 4.68826132796518e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
  176 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  177 + " Intensity6 ...............: 8.19437245391855e-07 +/- 1.82363529352901e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
  178 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  179 + " Intensity7 ...............: 1.44457348642334e-07 +/- 7.0968299684809e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
  180 + " Number of temporal par's ..: 1\n",
  181 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  182 + "=== GCTAModelBackground ===\n",
  183 + " Name ......................: Background_023592\n",
  184 + " Instruments ...............: HESS\n",
  185 + " Instrument scale factors ..: unity\n",
  186 + " Observation identifiers ...: 023592\n",
  187 + " Model type ................: CTABackground\n",
  188 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  189 + " Number of parameters ......: 20\n",
  190 + " Number of spatial par's ...: 3\n",
  191 + " 1:Sigma ..................: 3.6479543205698 +/- 0.238749563358304 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  192 + " 2:Grad_DETX ..............: 0.0290086310507671 +/- 0.0231654281693484 deg^-1 (free,scale=1,gradient)\n",
  193 + " 2:Grad_DETY ..............: 0.165462911909218 +/- 0.0212932684624286 deg^-1 (free,scale=1,gradient)\n",
  194 + " Number of spectral par's ..: 16\n",
  195 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  196 + " Intensity0 ...............: 0.00147439433272687 +/- 0.000191693450881709 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
  197 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  198 + " Intensity1 ...............: 0.000235250773930648 +/- 1.37598369145017e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
  199 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  200 + " Intensity2 ...............: 9.20623251759662e-05 +/- 5.75965738880592e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
  201 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  202 + " Intensity3 ...............: 3.22022945215284e-05 +/- 2.53414761162924e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
  203 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  204 + " Intensity4 ...............: 1.00701723302328e-05 +/- 1.04797408736245e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
  205 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  206 + " Intensity5 ...............: 3.29354066928594e-06 +/- 4.58158358563221e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
  207 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  208 + " Intensity6 ...............: 8.71669675678694e-07 +/- 1.76346292129154e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
  209 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  210 + " Intensity7 ...............: 3.12067435753287e-07 +/- 1.11366909796179e-07 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
  211 + " Number of temporal par's ..: 1\n",
  212 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  213 + ]
  214 + }
  215 + ],
  216 + "source": [
  217 + "print(gammalib.GModels(outmodel))"
  218 + ]
  219 + }
  220 + ],
  221 + "metadata": {
  222 + "kernelspec": {
  223 + "display_name": "Python 3",
  224 + "language": "python",
  225 + "name": "python3"
  226 + },
  227 + "language_info": {
  228 + "codemirror_mode": {
  229 + "name": "ipython",
  230 + "version": 3
  231 + },
  232 + "file_extension": ".py",
  233 + "mimetype": "text/x-python",
  234 + "name": "python",
  235 + "nbconvert_exporter": "python",
  236 + "pygments_lexer": "ipython3",
  237 + "version": "3.5.4"
  238 + }
  239 + },
  240 + "nbformat": 4,
  241 + "nbformat_minor": 2
  242 +}
... ...
doc/source/users/tutorials/hess_dr1/classical.ipynb 0 → 100644
  View file @f1c567e
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Performing a classical On/Off analysis"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/classical.ipynb\" download=\"classical.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn to perform a classical On/Off analysis of the data.**\n",
  22 + "\n",
  23 + "We start by importing the gammalib, ctools, and cscripts Python modules."
  24 + ]
  25 + },
  26 + {
  27 + "cell_type": "code",
  28 + "execution_count": 4,
  29 + "metadata": {},
  30 + "outputs": [],
  31 + "source": [
  32 + "import gammalib\n",
  33 + "import ctools\n",
  34 + "import cscripts"
  35 + ]
  36 + },
  37 + {
  38 + "cell_type": "markdown",
  39 + "metadata": {},
  40 + "source": [
  41 + "We will also use matplotlib to display the results."
  42 + ]
  43 + },
  44 + {
  45 + "cell_type": "code",
  46 + "execution_count": 5,
  47 + "metadata": {},
  48 + "outputs": [],
  49 + "source": [
  50 + "%matplotlib inline\n",
  51 + "import matplotlib.pyplot as plt"
  52 + ]
  53 + },
  54 + {
  55 + "cell_type": "markdown",
  56 + "metadata": {},
  57 + "source": [
  58 + "And finally we add to our path the directory containing the example plotting scripts provided with the ctools installation."
  59 + ]
  60 + },
  61 + {
  62 + "cell_type": "code",
  63 + "execution_count": 6,
  64 + "metadata": {},
  65 + "outputs": [],
  66 + "source": [
  67 + "import sys\n",
  68 + "import os\n",
  69 + "sys.path.append(os.environ['CTOOLS']+'/share/examples/python/')"
  70 + ]
  71 + },
  72 + {
  73 + "cell_type": "markdown",
  74 + "metadata": {},
  75 + "source": [
  76 + "## Preparing the binned observations\n",
  77 + "\n",
  78 + "The first step for a classical analysis is to bin the events in bins of energy for an On (signal) region, and one or several Off (background regions).\n",
  79 + "\n",
  80 + "We will use the event selection performed in the previous tutorial."
  81 + ]
  82 + },
  83 + {
  84 + "cell_type": "code",
  85 + "execution_count": 21,
  86 + "metadata": {},
  87 + "outputs": [],
  88 + "source": [
  89 + "obsfile = \"obs_crab_selected.xml\"\n",
  90 + "\n",
  91 + "phagen = cscripts.csphagen()\n",
  92 + "phagen['inobs'] = obsfile\n",
  93 + "phagen['inmodel'] = 'NONE' # assume that the source is pointlike\n",
  94 + "phagen['ebinalg'] = 'LOG'\n",
  95 + "phagen['emin'] = 0.66\n",
  96 + "phagen['emax'] = 30.\n",
  97 + "phagen['enumbins'] = 20\n",
  98 + "phagen['coordsys'] = 'CEL'\n",
  99 + "phagen['ra'] = 83.63\n",
  100 + "phagen['dec'] = 22.01\n",
  101 + "phagen['rad'] = 0.2\n",
  102 + "phagen['bkgmethod'] = 'REFLECTED'\n",
  103 + "phagen['use_irf_bkg'] = False\n",
  104 + "phagen['maxoffset'] = 2.0\n",
  105 + "phagen['stack'] = True # treat observations separately in joint likelihood fit\n",
  106 + "\n",
  107 + "phagen.run()"
  108 + ]
  109 + },
  110 + {
  111 + "cell_type": "markdown",
  112 + "metadata": {},
  113 + "source": [
  114 + "Note that we have used the hidden parameter *use_irf_bkg* to specify that no background model should be used in the computation of the background normalisation factor, which is simply assumed to be the ratio of the solid angles of On/Off regions.\n",
  115 + "\n",
  116 + "## Likelihood fit\n",
  117 + "\n",
  118 + "The next step will be to fit a model for the source to the On data (along with a model for the background derived directly from the On/Off data). The model for the source is"
  119 + ]
  120 + },
  121 + {
  122 + "cell_type": "code",
  123 + "execution_count": 22,
  124 + "metadata": {},
  125 + "outputs": [
  126 + {
  127 + "name": "stdout",
  128 + "output_type": "stream",
  129 + "text": [
  130 + "=== GModels ===\n",
  131 + " Number of models ..........: 1\n",
  132 + " Number of parameters ......: 6\n",
  133 + "=== GModelSky ===\n",
  134 + " Name ......................: Crab\n",
  135 + " Instruments ...............: all\n",
  136 + " Instrument scale factors ..: unity\n",
  137 + " Observation identifiers ...: all\n",
  138 + " Model type ................: PointSource\n",
  139 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  140 + " Number of parameters ......: 6\n",
  141 + " Number of spatial par's ...: 2\n",
  142 + " RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)\n",
  143 + " DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)\n",
  144 + " Number of spectral par's ..: 3\n",
  145 + " Prefactor ................: 5.7e-17 +/- 0 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
  146 + " Index ....................: -2.48 +/- 0 [-0,-5] (free,scale=-1,gradient)\n",
  147 + " PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)\n",
  148 + " Number of temporal par's ..: 1\n",
  149 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  150 + ]
  151 + }
  152 + ],
  153 + "source": [
  154 + "srcmodel = os.environ['CTOOLS']+'/share/models/crab_nobkg.xml'\n",
  155 + "\n",
  156 + "print(gammalib.GModels(srcmodel))"
  157 + ]
  158 + },
  159 + {
  160 + "cell_type": "markdown",
  161 + "metadata": {},
  162 + "source": [
  163 + "We can now fit this model to the data. Note that we have to specify WSTAT as statistic to derive the background model from the data without prior information."
  164 + ]
  165 + },
  166 + {
  167 + "cell_type": "code",
  168 + "execution_count": 23,
  169 + "metadata": {},
  170 + "outputs": [],
  171 + "source": [
  172 + "like = ctools.ctlike(phagen.obs())\n",
  173 + "\n",
  174 + "like['statistic'] = 'WSTAT'\n",
  175 + "like['inmodel'] = srcmodel\n",
  176 + "\n",
  177 + "like.run()"
  178 + ]
  179 + },
  180 + {
  181 + "cell_type": "markdown",
  182 + "metadata": {},
  183 + "source": [
  184 + "We can now look at the results from the optimisation and the fitted model."
  185 + ]
  186 + },
  187 + {
  188 + "cell_type": "code",
  189 + "execution_count": 24,
  190 + "metadata": {},
  191 + "outputs": [
  192 + {
  193 + "name": "stdout",
  194 + "output_type": "stream",
  195 + "text": [
  196 + "=== GOptimizerLM ===\n",
  197 + " Optimized function value ..: 16.457\n",
  198 + " Absolute precision ........: 0.005\n",
  199 + " Acceptable value decrease .: 2\n",
  200 + " Optimization status .......: converged\n",
  201 + " Number of parameters ......: 6\n",
  202 + " Number of free parameters .: 2\n",
  203 + " Number of iterations ......: 4\n",
  204 + " Lambda ....................: 1e-07\n",
  205 + "=== GModels ===\n",
  206 + " Number of models ..........: 1\n",
  207 + " Number of parameters ......: 6\n",
  208 + "=== GModelSky ===\n",
  209 + " Name ......................: Crab\n",
  210 + " Instruments ...............: all\n",
  211 + " Instrument scale factors ..: unity\n",
  212 + " Observation identifiers ...: all\n",
  213 + " Model type ................: PointSource\n",
  214 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  215 + " Number of parameters ......: 6\n",
  216 + " Number of spatial par's ...: 2\n",
  217 + " RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)\n",
  218 + " DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)\n",
  219 + " Number of spectral par's ..: 3\n",
  220 + " Prefactor ................: 4.58230817013557e-17 +/- 3.14037370499303e-18 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
  221 + " Index ....................: -2.6252889331251 +/- 0.0832081186871608 [-0,-5] (free,scale=-1,gradient)\n",
  222 + " PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)\n",
  223 + " Number of temporal par's ..: 1\n",
  224 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  225 + ]
  226 + }
  227 + ],
  228 + "source": [
  229 + "print(like.opt())\n",
  230 + "print(like.obs().models())"
  231 + ]
  232 + },
  233 + {
  234 + "cell_type": "markdown",
  235 + "metadata": {},
  236 + "source": [
  237 + "The fit has properly converged and the results are consistent with those obtained with the unbinned analysis.\n",
  238 + "\n",
  239 + "We will also check the spectral residuals."
  240 + ]
  241 + },
  242 + {
  243 + "cell_type": "code",
  244 + "execution_count": 26,
  245 + "metadata": {},
  246 + "outputs": [
  247 + {
  248 + "data": {
  249 + "image/png": 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\n",
  250 + "text/plain": [
  251 + "<matplotlib.figure.Figure at 0x110a33908>"
  252 + ]
  253 + },
  254 + "metadata": {},
  255 + "output_type": "display_data"
  256 + }
  257 + ],
  258 + "source": [
  259 + "residuals = 'residuals_classical.fits'\n",
  260 + "res = cscripts.csresspec(like.obs())\n",
  261 + "\n",
  262 + "res['statistic'] = 'WSTAT'\n",
  263 + "res['components'] = True\n",
  264 + "res['algorithm'] = 'SIGNIFICANCE'\n",
  265 + "res['outfile'] = residuals\n",
  266 + "\n",
  267 + "res.execute()\n",
  268 + "\n",
  269 + "from show_residuals import plot_residuals\n",
  270 + "plot_residuals(residuals,'',0)"
  271 + ]
  272 + }
  273 + ],
  274 + "metadata": {
  275 + "kernelspec": {
  276 + "display_name": "Python 3",
  277 + "language": "python",
  278 + "name": "python3"
  279 + },
  280 + "language_info": {
  281 + "codemirror_mode": {
  282 + "name": "ipython",
  283 + "version": 3
  284 + },
  285 + "file_extension": ".py",
  286 + "mimetype": "text/x-python",
  287 + "name": "python",
  288 + "nbconvert_exporter": "python",
  289 + "pygments_lexer": "ipython3",
  290 + "version": "3.5.4"
  291 + }
  292 + },
  293 + "nbformat": 4,
  294 + "nbformat_minor": 2
  295 +}
... ...
doc/source/users/tutorials/hess_dr1/classical.rst deleted
View file @1c81f76
1   -.. _hess_dr1_classical:
2   -
3   -Performing a classical On/Off analysis
4   ---------------------------------------
5   -
6   - .. admonition:: What you will learn
7   -
8   - You will learn **how to perform a classical On/Off analysis of the
9   - data**.
10   -
11   -
12   -.. code-block:: bash
13   -
14   - $ csphagen use_irf_bkg=no
15   - Input event list or observation definition XML file [obs.xml] obs_crab_selected.xml
16   - Input model definition XML file (if NONE, use point source) [NONE] crab_models.xml
17   - Source name [Crab]
18   - Algorithm for defining energy bins (FILE|LIN|LOG) [LOG]
19   - Start value for first energy bin in TeV [0.1] 0.66
20   - Stop value for last energy bin in TeV [100.0]
21   - Number of energy bins [120] 40
22   - Stack multiple observations into single PHA, ARF and RMF files? [no]
23   - Output observation definition XML file [onoff_obs.xml] onoff_obs_crab.xml
24   - Method for background estimation (REFLECTED|CUSTOM) [REFLECTED]
25   - Coordinate system (CEL - celestial, GAL - galactic) (CEL|GAL) [CEL]
26   - Right Ascension of source region centre (deg) (0-360) [83.63]
27   - Declination of source region centre (deg) (-90-90) [22.01]
28   - Radius of source region circle (deg) (0-180) [0.2]
29   -
30   -.. code-block:: xml
31   -
32   - <?xml version="1.0" encoding="UTF-8" standalone="no"?>
33   - <source_library title="source library">
34   - <source name="Crab" type="PointSource">
35   - <spectrum type="PowerLaw">
36   - <parameter name="Prefactor" value="1" scale="5.7e-18" min="0" free="1" />
37   - <parameter name="Index" value="1" scale="-2.48" min="-4" max="4" free="1" />
38   - <parameter name="PivotEnergy" value="1" scale="1000000" free="0" />
39   - </spectrum>
40   - <spatialModel type="PointSource">
41   - <parameter name="RA" value="83.633" scale="1" free="0" />
42   - <parameter name="DEC" value="22.015" scale="1" free="0" />
43   - </spatialModel>
44   - </source>
45   - </source_library>
46   -
47   -.. code-block:: bash
48   -
49   - $ ctlike statistic=wstat
50   - Input event list, counts cube or observation definition XML file [events.fits] onoff_obs_crab.xml
51   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_models_onoff.xml
52   - Output model definition XML file [crab_results.xml]
53   -
54   -.. code-block:: none
55   -
56   - 2018-09-13T11:49:06: +=========================================+
57   - 2018-09-13T11:49:06: | Maximum likelihood optimisation results |
58   - 2018-09-13T11:49:06: +=========================================+
59   - 2018-09-13T11:49:06: === GOptimizerLM ===
60   - 2018-09-13T11:49:06: Optimized function value ..: 60.048
61   - 2018-09-13T11:49:06: Absolute precision ........: 0.005
62   - 2018-09-13T11:49:06: Acceptable value decrease .: 2
63   - 2018-09-13T11:49:06: Optimization status .......: converged
64   - 2018-09-13T11:49:06: Number of parameters ......: 6
65   - 2018-09-13T11:49:06: Number of free parameters .: 2
66   - 2018-09-13T11:49:06: Number of iterations ......: 5
67   - 2018-09-13T11:49:06: Lambda ....................: 1e-08
68   - 2018-09-13T11:49:06: Maximum log likelihood ....: -60.048
69   - 2018-09-13T11:49:06: Observed events (Nobs) ...: 605.000
70   - 2018-09-13T11:49:06: Predicted events (Npred) ..: 604.191 (Nobs - Npred = 0.808986318662733)
71   - 2018-09-13T11:49:06: === GModels ===
72   - 2018-09-13T11:49:06: Number of models ..........: 1
73   - 2018-09-13T11:49:06: Number of parameters ......: 6
74   - 2018-09-13T11:49:06: === GModelSky ===
75   - 2018-09-13T11:49:06: Name ......................: Crab
76   - 2018-09-13T11:49:06: Instruments ...............: all
77   - 2018-09-13T11:49:06: Instrument scale factors ..: unity
78   - 2018-09-13T11:49:06: Observation identifiers ...: all
79   - 2018-09-13T11:49:06: Model type ................: PointSource
80   - 2018-09-13T11:49:06: Model components ..........: "PointSource" * "PowerLaw" * "Constant"
81   - 2018-09-13T11:49:06: Number of parameters ......: 6
82   - 2018-09-13T11:49:06: Number of spatial par's ...: 2
83   - 2018-09-13T11:49:06: RA .......................: 83.633 deg (fixed,scale=1)
84   - 2018-09-13T11:49:06: DEC ......................: 22.015 deg (fixed,scale=1)
85   - 2018-09-13T11:49:06: Number of spectral par's ..: 3
86   - 2018-09-13T11:49:06: Prefactor ................: 4.77687464316323e-17 +/- 3.12433509735449e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)
87   - 2018-09-13T11:49:06: Index ....................: -2.60709757251519 +/- 0.0767624316833204 [9.92,-9.92] (free,scale=-2.48,gradient)
88   - 2018-09-13T11:49:06: PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)
89   - 2018-09-13T11:49:06: Number of temporal par's ..: 1
90   - 2018-09-13T11:49:06: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
91   -
92   -The intensity at 1 TeV of the Crab was fitted to
93   -:math:`(4.78 \pm 0.31) \times 10^{-11}\,{\rm photons}\,{\rm cm}^{-2}\,{\rm s}^{-1}\,{\rm TeV}^{-1}`
94   -the spectral index of the power law is
95   -:math:`-2.61 \pm 0.08`.
96   -This can be compared to the values
97   -:math:`(4.64 \pm 0.26) \times 10^{-11}\,{\rm photons}\,{\rm cm}^{-2}\,{\rm s}^{-1}\,{\rm TeV}^{-1}`
98   -and
99   -:math:`-2.61 \pm 0.07`
100   -that were obtained before with the unbinned maximum likelihood fit using the
101   -energy dispersion.
102   -
103   -.. code-block:: bash
104   -
105   - $ csresspec components=yes
106   - Input event list, counts cube, or observation definition XML file [events.fits] onoff_obs_crab.xml
107   - Stack observations? [no] yes
108   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_results_onoff.xml
109   - Residuals computation algorithm (SUB|SUBDIV|SUBDIVSQRT|SIGNIFICANCE) [SIGNIFICANCE]
110   - Output residual spectrum file [resspec.fits] resspec_onoff.fits
111   -
112   -The script will produce the FITS file ``resspec.fits`` that contains the
113   -spectral residuals for each of the model components.
114   -To display the residuals you can use the ``show_residuals.py`` script in the
115   -example folder as follows:
116   -
117   -.. code-block:: bash
118   -
119   - $CTOOLS/share/examples/python/show_residuals.py resspec.fits
120   -
121   -The figure below shows the resulting residual spectrum.
122   -Although the model provides a reasonable first order approximation of the
123   -spectral distribution of the data, there are still significant spectral
124   -residuals that cannot be explained by the model.
125   -
126   -.. figure:: classical_resspec.png
127   - :width: 600px
128   - :align: center
129   -
130   - *Residual counts spectrum after model fitting*
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1   -.. _hess_dr1_fitting:
2   -
3   -Fitting a model to the data and inspecting fit residuals
4   ---------------------------------------------------------
5   -
6   - .. admonition:: What you will learn
7   -
8   - You will learn **how to fit a parametric model to the data** and
9   - **how to inspect the fit residuals**.
10   -
11   -Now you are ready to fit the model to the data.
12   -Since the :ref:`cssrcdetect` script sets some default spectral parameters,
13   -we suggest that you slightly adjust the
14   -:ref:`model definition file <glossary_moddef>`
15   -so that the model fit runs seamlessly.
16   -For example, the pivot energy of a power law model should be comprised
17   -within the energy interval of the data, hence you should set
18   -``PivotEnergy`` for example to 1 TeV.
19   -The ``error`` attributes are also not needed (although they do not harm),
20   -so we remove them.
21   -And finally we rename ``Src001`` to ``Crab``.
22   -After these little adjustments, your
23   -:ref:`model definition file <glossary_moddef>`
24   -should look like this:
25   -
26   -.. code-block:: xml
27   -
28   - <?xml version="1.0" encoding="UTF-8" standalone="no"?>
29   - <source_library title="source library">
30   - <source name="Crab" type="PointSource">
31   - <spectrum type="PowerLaw">
32   - <parameter name="Prefactor" value="1" scale="5.7e-18" min="0" free="1" />
33   - <parameter name="Index" value="1" scale="-2.48" min="-4" max="4" free="1" />
34   - <parameter name="PivotEnergy" value="1" scale="1000000" free="0" />
35   - </spectrum>
36   - <spatialModel type="PointSource">
37   - <parameter name="RA" value="83.6192131308071" scale="1" free="1" />
38   - <parameter name="DEC" value="22.0199996472185" scale="1" free="1" />
39   - </spatialModel>
40   - </source>
41   - <source name="Background" type="CTAAeffBackground">
42   - <spectrum type="PowerLaw">
43   - <parameter name="Prefactor" value="1" scale="1e-13" min="0" free="1" />
44   - <parameter name="Index" value="1" scale="-2.5" min="-4" max="4" free="1" />
45   - <parameter name="PivotEnergy" value="1" scale="1000000" free="0" />
46   - </spectrum>
47   - </source>
48   - </source_library>
49   -
50   -Now you are ready to do the model fit.
51   -For this you will use :ref:`ctlike` that you will run as follows:
52   -
53   -.. code-block:: bash
54   -
55   - $ ctlike
56   - Input event list, counts cube or observation definition XML file [events.fits] obs_crab_selected.xml
57   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_models.xml
58   - Output model definition XML file [crab_results.xml]
59   -
60   -After a few seconds the tool will have terminated and you can inspect the fit
61   -results.
62   -They can for example be found in the ``ctlike.log`` file for which an excerpt
63   -is shown below.
64   -
65   -.. code-block:: none
66   -
67   - 2018-09-11T21:17:31: +=========================================+
68   - 2018-09-11T21:17:31: | Maximum likelihood optimisation results |
69   - 2018-09-11T21:17:31: +=========================================+
70   - 2018-09-11T21:17:31: === GOptimizerLM ===
71   - 2018-09-11T21:17:31: Optimized function value ..: 98422.688
72   - 2018-09-11T21:17:31: Absolute precision ........: 0.005
73   - 2018-09-11T21:17:31: Acceptable value decrease .: 2
74   - 2018-09-11T21:17:31: Optimization status .......: converged
75   - 2018-09-11T21:17:31: Number of parameters ......: 10
76   - 2018-09-11T21:17:31: Number of free parameters .: 6
77   - 2018-09-11T21:17:31: Number of iterations ......: 24
78   - 2018-09-11T21:17:31: Lambda ....................: 0.1
79   - 2018-09-11T21:17:31: Maximum log likelihood ....: -98422.688
80   - 2018-09-11T21:17:31: Observed events (Nobs) ...: 9675.000
81   - 2018-09-11T21:17:31: Predicted events (Npred) ..: 9674.870 (Nobs - Npred = 0.129601074213497)
82   - 2018-09-11T21:17:31: === GModels ===
83   - 2018-09-11T21:17:31: Number of models ..........: 2
84   - 2018-09-11T21:17:31: Number of parameters ......: 10
85   - 2018-09-11T21:17:31: === GModelSky ===
86   - 2018-09-11T21:17:31: Name ......................: Crab
87   - 2018-09-11T21:17:31: Instruments ...............: all
88   - 2018-09-11T21:17:31: Instrument scale factors ..: unity
89   - 2018-09-11T21:17:31: Observation identifiers ...: all
90   - 2018-09-11T21:17:31: Model type ................: PointSource
91   - 2018-09-11T21:17:31: Model components ..........: "PointSource" * "PowerLaw" * "Constant"
92   - 2018-09-11T21:17:31: Number of parameters ......: 6
93   - 2018-09-11T21:17:31: Number of spatial par's ...: 2
94   - 2018-09-11T21:17:31: RA .......................: 83.6230374464172 +/- 0.00241074887155303 deg (free,scale=1)
95   - 2018-09-11T21:17:31: DEC ......................: 22.0249049416179 +/- 0.0022137104783551 deg (free,scale=1)
96   - 2018-09-11T21:17:31: Number of spectral par's ..: 3
97   - 2018-09-11T21:17:31: Prefactor ................: 4.81375364083447e-17 +/- 2.62970930530696e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)
98   - 2018-09-11T21:17:31: Index ....................: -2.71079050853091 +/- 0.0652734279744327 [9.92,-9.92] (free,scale=-2.48,gradient)
99   - 2018-09-11T21:17:31: PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)
100   - 2018-09-11T21:17:31: Number of temporal par's ..: 1
101   - 2018-09-11T21:17:31: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
102   - 2018-09-11T21:17:31: === GCTAModelAeffBackground ===
103   - 2018-09-11T21:17:31: Name ......................: Background
104   - 2018-09-11T21:17:31: Instruments ...............: all
105   - 2018-09-11T21:17:31: Instrument scale factors ..: unity
106   - 2018-09-11T21:17:31: Observation identifiers ...: all
107   - 2018-09-11T21:17:31: Model type ................: "PowerLaw" * "Constant"
108   - 2018-09-11T21:17:31: Number of parameters ......: 4
109   - 2018-09-11T21:17:31: Number of spectral par's ..: 3
110   - 2018-09-11T21:17:31: Prefactor ................: 1.80502240642969e-13 +/- 2.34991048262393e-15 [0,infty[ ph/cm2/s/MeV (free,scale=1e-13,gradient)
111   - 2018-09-11T21:17:31: Index ....................: -2.58303579414564 +/- 0.0128891124984818 [10,-10] (free,scale=-2.5,gradient)
112   - 2018-09-11T21:17:31: PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)
113   - 2018-09-11T21:17:31: Number of temporal par's ..: 1
114   - 2018-09-11T21:17:31: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
115   -
116   -The fit has converged and the source was fitted at a position of
117   -:math:`83.623 \pm 0.002` degrees in Right Ascension and
118   -:math:`22.025 \pm 0.002` degrees in Declination.
119   -The statistical :math:`1\sigma` positional uncertainty corresponds to 0.12 arcmin.
120   -Systematic uncertainties are not computed.
121   -This can be compared to the values of
122   -:math:`83.629 \pm 0.005` degrees in Right Ascension and
123   -:math:`22.012 \pm 0.001` degrees in Declination reported in
124   -`Holler et al. (2017) <https://arxiv.org/pdf/1707.04196.pdf>`_.
125   -
126   -According to
127   -`SIMBAD <http://cdsportal.u-strasbg.fr/?target=Crab%20nebula>`_,
128   -the Crab nebula is situated at a Right Ascension of 83.633 degrees and a
129   -Declination of 22.015 degrees, which is 0.013 degrees (0.82 arcmin) away
130   -from the fitted position.
131   -
132   -The intensity at 1 TeV of the Crab was fitted to
133   -:math:`(4.81 \pm 0.26) \times 10^{-11}\,{\rm photons}\,{\rm cm}^{-2}\,{\rm s}^{-1}\,{\rm TeV}^{-1}`
134   -the spectral index of the power law is
135   -:math:`-2.71 \pm 0.07`.
136   -This can be compared to the values of
137   -:math:`(3.45 \pm 0.05) \times 10^{-11}\,{\rm photons}\,{\rm cm}^{-2}\,{\rm s}^{-1}\,{\rm TeV}^{-1}`
138   -and
139   -:math:`-2.63 \pm 0.01`
140   -reported in
141   -`Aharonian et al. (2006), A&A, 457, 899 <https://www.aanda.org/articles/aa/abs/2006/39/aa5351-06/aa5351-06.html>`_.
142   -
143   -.. note::
144   - In the :ref:`ctlike` run above the energy dispersion, which relates the
145   - true photon energies to the energies of the reconstructed events, was
146   - not taken into account.
147   - **By default energy dispersion usage is disabled** since it involves an
148   - extra dimension in the data analysis which slows down the computations.
149   - To enable energy dispersion you have to run :ref:`ctlike` with the
150   - ``edisp=yes`` argument:
151   -
152   - .. code-block:: bash
153   -
154   - $ ctlike edisp=yes
155   - Input event list, counts cube or observation definition XML file [events.fits] obs_crab_selected.xml
156   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_models.xml
157   - Output model definition XML file [crab_results.xml]
158   -
159   - Taking into account the energy dispersion, the fitted source position is
160   - :math:`83.621 \pm 0.003` degrees in Right Ascension and
161   - :math:`22.024 \pm 0.002` degrees in Declination,
162   - the fitted source intensity at 1 TeV is
163   - :math:`(4.64 \pm 0.26) \times 10^{-11}\,{\rm photons}\,{\rm cm}^{-2}\,{\rm s}^{-1}\,{\rm TeV}^{-1}`
164   - and the fitted spectral index is
165   - :math:`-2.61 \pm 0.07`.
166   -
167   -Following a model fit, **you should always inspect the fit residuals.**
168   -First let's inspect the spectral residuals.
169   -You can do this using the :ref:`csresspec` script as follows:
170   -
171   -.. code-block:: bash
172   -
173   - $ csresspec components=yes
174   - Input event list, counts cube, or observation definition XML file [events.fits] obs_crab_selected.xml
175   - Algorithm for defining energy bins (FILE|LIN|LOG) [LOG]
176   - Start value for first energy bin in TeV [0.1] 0.66
177   - Stop value for last energy bin in TeV [100.0]
178   - Number of energy bins (1-200) [20]
179   - Stack observations? [no] yes
180   - Coordinate System (CEL|GAL) [CEL]
181   - Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
182   - First coordinate of image/source region center in degrees (RA or galactic l) (0-360) [83.63]
183   - Second coordinate of image/source region center in degrees (DEC or galactic b) (-90-90) [22.51] 22.01
184   - Size of the X axis in pixels [200]
185   - Size of the Y axis in pixels [200]
186   - Pixel size (deg/pixel) [0.02]
187   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_results.xml
188   - Mask data to calculate residuals in ROI? [no]
189   - Residuals computation algorithm (SUB|SUBDIV|SUBDIVSQRT|SIGNIFICANCE) [SIGNIFICANCE]
190   - Output residual spectrum file [resspec.fits]
191   -
192   -The script will produce the FITS file ``resspec.fits`` that contains the
193   -spectral residuals for each of the model components.
194   -To display the residuals you can use the ``show_residuals.py`` script in the
195   -example folder as follows:
196   -
197   -.. code-block:: bash
198   -
199   - $CTOOLS/share/examples/python/show_residuals.py resspec.fits
200   -
201   -The figure below shows the resulting residual spectrum.
202   -Although the model provides a reasonable first order approximation of the
203   -spectral distribution of the data, there are still significant spectral
204   -residuals that cannot be explained by the model.
205   -
206   -.. figure:: fitting_resspec.png
207   - :width: 600px
208   - :align: center
209   -
210   - *Residual counts spectrum after model fitting*
211   -
212   -
213   -Finally you should also inspect the spatial residuals.
214   -You do this using the :ref:`csresmap` script as follows:
215   -
216   -.. code-block:: bash
217   -
218   - $ csresmap
219   - Input event list, counts cube, or observation definition XML file [events.fits] obs_crab_selected.xml
220   - Input model definition XML file [$CTOOLS/share/models/crab.xml] crab_results.xml
221   - First coordinate of image center in degrees (RA or galactic l) (0-360) [83.63]
222   - Second coordinate of image center in degrees (DEC or galactic b) (-90-90) [22.51] 22.01
223   - Coordinate System (CEL|GAL) [CEL]
224   - Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
225   - Size of the X axis in pixels [200]
226   - Size of the Y axis in pixels [200]
227   - Pixel size (deg/pixel) [0.02]
228   - Lower energy limit (TeV) [0.1] 0.66
229   - Upper energy limit (TeV) [100.0]
230   - Residual map computation algorithm (SUB|SUBDIV|SUBDIVSQRT|SIGNIFICANCE) [SIGNIFICANCE]
231   - Output residual map file [resmap.fits]
232   -
233   -The script will produce the FITS file ``resmap.fits`` that contains a map of
234   -the spatial residuals.
235   -You can display this map using
236   -`ds9 <http://ds9.si.edu>`_.
237   -The resulting residual map with a linear color scaling and a slight Gaussian
238   -smoothing applied is shown below.
239   -The map is relatively clean, although there are some small residuals
240   -south-east of the fitted Crab nebula position.
241   -
242   -.. figure:: fitting_resmap.png
243   - :width: 400px
244   - :align: center
245   -
246   - *Residual map with a slight Gaussian smoothing applied*
247   -
248   -.. note::
249   - If the energy dispersion should be taken into account you also have to
250   - specify the ``edisp=yes`` argument in the residual computations:
251   -
252   - .. code-block:: bash
253   -
254   - $ csresspec components=yes edisp=yes
255   - $ csresmap edisp=yes
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doc/source/users/tutorials/hess_dr1/index.rst
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... ... @@ -7,7 +7,8 @@ Analysing public H.E.S.S. data
7 7 :maxdepth: 1
8 8  
9 9 data
10   - select_events
  10 + selecting_events
11 11 skymap
12   - fitting
  12 + bkgmodel
  13 + unbinned
13 14 classical
... ...
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1   -.. _hess_dr1_select_events:
2   -
3   -Selecting the relevant events
4   ------------------------------
5   -
6   - .. admonition:: What you will learn
7   -
8   - You will learn **how to select the relevant events for a target**
9   - and **how to determine the useful energy range** for a set of
10   - observations.
11   -
12   -Now you are ready for your first H.E.S.S. data analysis.
13   -As first step you should create a folder that will contain your analysis
14   -results and any intermediate data products.
15   -Let's call this folder ``crab`` since we explain in this example how you
16   -can analyse the data from the Crab nebula.
17   -Create and step into the folder using
18   -
19   -.. code-block:: bash
20   -
21   - $ mkdir crab
22   - $ cd crab
23   -
24   -The first step of your analysis consists in selecting the relevant events
25   -from the observations.
26   -In this step you can select a specific energy range, time range or region
27   -of interest.
28   -In the example below you will select events according to the save energy
29   -thresholds that are defined in the effective area component of the
30   -:ref:`instrument response functions <glossary_irf>`.
31   -You do this by setting the hidden parameter ``usethres=DEFAULT``.
32   -In addition, you will select all events within 2 degrees of the pointing
33   -direction:
34   -
35   -.. code-block:: bash
36   -
37   - $ ctselect usethres=DEFAULT
38   - Input event list or observation definition XML file [events.fits] $HESSDATA/obs/obs_crab.xml
39   - Lower energy limit (TeV) [0.1] NONE
40   - Radius of ROI around pointing or specified RA/DEC (degrees) (0-180) [3.0] 2.0
41   - Start time (UTC string, JD, MJD or MET in seconds) [NONE]
42   - Output event list or observation definition XML file [selected_events.fits] obs_crab_selected.xml
43   -
44   -.. warning::
45   - If you encounter the error
46   -
47   - .. code-block:: none
48   -
49   - *** ERROR encounterted in the execution of ctselect. Run aborted ...
50   - *** ERROR in ctselect::run(): Invalid value. Invalid RoI selection: the new RoI must be enclosed in the original RoI
51   -
52   - then run the :ref:`ctselect` tool with the ``forcesel=yes`` option:
53   -
54   - .. code-block:: bash
55   -
56   - $ ctselect usethres=DEFAULT forcesel=yes
57   -
58   -
59   -Below is an excerpt of the log file that was created by :ref:`ctselect`.
60   -You will find that the following energy ranges were applied for the
61   -observations:
62   -
63   -* ``id=023523``: 0.871 - 100 TeV
64   -* ``id=023526``: 0.708 - 100 TeV
65   -* ``id=023559``: 0.661 - 100 TeV
66   -* ``id=023592``: 0.871 - 100 TeV
67   -
68   -.. code-block:: none
69   -
70   - 2018-09-11T14:05:15: +=================+
71   - 2018-09-11T14:05:15: | Event selection |
72   - 2018-09-11T14:05:15: +=================+
73   - 2018-09-11T14:05:15: === HESS observation "Crab" (id=023523) ===
74   - 2018-09-11T14:05:15: Input filename ............: /project-data/hess/hess_dl3_dr1/data/hess_dl3_dr1_obs_id_023523.fits.gz
75   - 2018-09-11T14:05:15: Event extension name ......: EVENTS
76   - 2018-09-11T14:05:15: GTI extension name ........: GTI
77   - 2018-09-11T14:05:16: === Event selection ===
78   - 2018-09-11T14:05:16: Selected energy range .....: 0.870964 - 100 TeV
79   - 2018-09-11T14:05:16: Requested RoI .............: Centre(RA,DEC)=(83.633333333333, 21.514444444444) deg, Radius=2 deg
80   - 2018-09-11T14:05:16: RoI of data ...............: Centre(RA,DEC)=(83.633333333333, 21.514444444444) deg, Radius=2 deg
81   - 2018-09-11T14:05:16: Selected RoI ..............: Centre(RA,DEC)=(83.633333333333, 21.514444444444) deg, Radius=2 deg
82   - 2018-09-11T14:05:16: cfitsio selection .........: ENERGY >= 0.87096359 && ENERGY <= 100.00000000 && ANGSEP(83.633333,21.514444,RA,DEC) <= 2.000000
83   - 2018-09-11T14:05:16: FITS filename .............: /var/tmp/tmp.0.QX6f0L[EVENTS][ENERGY >= 0.87096359 && ENERGY <= 100.00000000 && ANGSEP(83.633333,21.514444,RA,DEC) <= 2.000000]
84   - 2018-09-11T14:05:16: === HESS observation "Crab" (id=023526) ===
85   - 2018-09-11T14:05:16: Input filename ............: /project-data/hess/hess_dl3_dr1/data/hess_dl3_dr1_obs_id_023526.fits.gz
86   - 2018-09-11T14:05:16: Event extension name ......: EVENTS
87   - 2018-09-11T14:05:16: GTI extension name ........: GTI
88   - 2018-09-11T14:05:16: === Event selection ===
89   - 2018-09-11T14:05:16: Selected energy range .....: 0.707946 - 100 TeV
90   - 2018-09-11T14:05:16: Requested RoI .............: Centre(RA,DEC)=(83.633333333333, 22.514444444444) deg, Radius=2 deg
91   - 2018-09-11T14:05:16: RoI of data ...............: Centre(RA,DEC)=(83.633333333333, 22.514444444444) deg, Radius=2 deg
92   - 2018-09-11T14:05:16: Selected RoI ..............: Centre(RA,DEC)=(83.633333333333, 22.514444444444) deg, Radius=2 deg
93   - 2018-09-11T14:05:16: cfitsio selection .........: ENERGY >= 0.70794578 && ENERGY <= 100.00000000 && ANGSEP(83.633333,22.514444,RA,DEC) <= 2.000000
94   - 2018-09-11T14:05:16: FITS filename .............: /var/tmp/tmp.1.IoZj2b[EVENTS][ENERGY >= 0.70794578 && ENERGY <= 100.00000000 && ANGSEP(83.633333,22.514444,RA,DEC) <= 2.000000]
95   - 2018-09-11T14:05:16: === HESS observation "Crab" (id=023559) ===
96   - 2018-09-11T14:05:16: Input filename ............: /project-data/hess/hess_dl3_dr1/data/hess_dl3_dr1_obs_id_023559.fits.gz
97   - 2018-09-11T14:05:16: Event extension name ......: EVENTS
98   - 2018-09-11T14:05:16: GTI extension name ........: GTI
99   - 2018-09-11T14:05:16: === Event selection ===
100   - 2018-09-11T14:05:16: Selected energy range .....: 0.660693 - 100 TeV
101   - 2018-09-11T14:05:16: Requested RoI .............: Centre(RA,DEC)=(85.2533333381014, 22.014444444444) deg, Radius=2 deg
102   - 2018-09-11T14:05:16: RoI of data ...............: Centre(RA,DEC)=(85.2533333381014, 22.014444444444) deg, Radius=2 deg
103   - 2018-09-11T14:05:16: Selected RoI ..............: Centre(RA,DEC)=(85.2533333381014, 22.014444444444) deg, Radius=2 deg
104   - 2018-09-11T14:05:16: cfitsio selection .........: ENERGY >= 0.66069345 && ENERGY <= 100.00000000 && ANGSEP(85.253333,22.014444,RA,DEC) <= 2.000000
105   - 2018-09-11T14:05:16: FITS filename .............: /var/tmp/tmp.2.IAwBNH[EVENTS][ENERGY >= 0.66069345 && ENERGY <= 100.00000000 && ANGSEP(85.253333,22.014444,RA,DEC) <= 2.000000]
106   - 2018-09-11T14:05:16: === HESS observation "Crab" (id=023592) ===
107   - 2018-09-11T14:05:16: Input filename ............: /project-data/hess/hess_dl3_dr1/data/hess_dl3_dr1_obs_id_023592.fits.gz
108   - 2018-09-11T14:05:16: Event extension name ......: EVENTS
109   - 2018-09-11T14:05:16: GTI extension name ........: GTI
110   - 2018-09-11T14:05:16: === Event selection ===
111   - 2018-09-11T14:05:16: Selected energy range .....: 0.870964 - 100 TeV
112   - 2018-09-11T14:05:16: Requested RoI .............: Centre(RA,DEC)=(82.0133333285646, 22.014444444444) deg, Radius=2 deg
113   - 2018-09-11T14:05:16: RoI of data ...............: Centre(RA,DEC)=(82.0133333285646, 22.014444444444) deg, Radius=2 deg
114   - 2018-09-11T14:05:16: Selected RoI ..............: Centre(RA,DEC)=(82.0133333285646, 22.014444444444) deg, Radius=2 deg
115   - 2018-09-11T14:05:16: cfitsio selection .........: ENERGY >= 0.87096359 && ENERGY <= 100.00000000 && ANGSEP(82.013333,22.014444,RA,DEC) <= 2.000000
116   - 2018-09-11T14:05:16: FITS filename .............: /var/tmp/tmp.3.879wq4[EVENTS][ENERGY >= 0.87096359 && ENERGY <= 100.00000000 && ANGSEP(82.013333,22.014444,RA,DEC) <= 2.000000]
117   -
118   -You may use the :ref:`csobsinfo` script to display a summary of the selected
119   -events into the console:
120   -
121   -.. code-block:: bash
122   -
123   - $ csobsinfo debug=yes
124   - Input event list, counts cube, or observation definition XML file [$HESSDATA/obs/obs_crab.xml] obs_crab_selected.xml
125   - Output DS9 region file [ds9.reg]
126   -
127   -.. note::
128   - The ``debug=yes`` attribute instructs :ref:`csobsinfo` to direct the log
129   - file output also to the console. Duplication of log file output into
130   - the console using the ``debug=yes`` attribute works for any tool or script.
131   -
132   -The output of :ref:`csobsinfo` is shown below.
133   -:ref:`ctselect` selected 9675 events within an energy interval of
134   -0.66 - 100 TeV.
135   -This will be the energy range that you will from now on use for the data
136   -analysis.
137   -
138   -.. code-block:: none
139   -
140   - 2018-09-11T20:30:16: +=========+
141   - 2018-09-11T20:30:16: | Summary |
142   - 2018-09-11T20:30:16: +=========+
143   - 2018-09-11T20:30:16: === Observations ===
144   - 2018-09-11T20:30:16: Unbinned observations .....: 4
145   - 2018-09-11T20:30:16: Binned observations .......: 0
146   - 2018-09-11T20:30:16: === Events ===
147   - 2018-09-11T20:30:16: Number of events ..........: 9675
148   - 2018-09-11T20:30:16: Number of bins ............: 0
149   - 2018-09-11T20:30:16: === Pointings ===
150   - 2018-09-11T20:30:16: Mean offset angle .........: Unknown
151   - 2018-09-11T20:30:16: Mean zenith angle .........: 47.04 deg
152   - 2018-09-11T20:30:16: Mean azimuth angle ........: 13.76 deg
153   - 2018-09-11T20:30:16: === Energy range ===
154   - 2018-09-11T20:30:16: Minimum energy ............: 660.693448007596 GeV
155   - 2018-09-11T20:30:16: Maximum energy ............: 100 TeV
156   - 2018-09-11T20:30:16: === Time range ===
157   - 2018-09-11T20:30:16: MJD (days) ................: 53343.922 - 53347.933
158   - 2018-09-11T20:30:16: UTC .......................: 2004-12-04T22:07:06 - 2004-12-08T22:22:02
159   - 2018-09-11T20:30:16: Total ontime ..............: 6742.00 s = 112.37 min = 1.87 h
160   - 2018-09-11T20:30:16: Total livetime ............: 6313.81 s = 105.23 min = 1.75 h
doc/source/users/tutorials/hess_dr1/selecting_events.ipynb 0 → 100644
  View file @f1c567e
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Selecting the relevant events"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/selecting_events.ipynb\" download=\"selecting_events.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn how to select the relevant events for a target and how to determine the useful energy range for a set of observations.**\n",
  22 + "\n",
  23 + "Now you are ready for your first H.E.S.S. data analysis. As an exemple you will analyse the observations of the Crab nebula. Note that all the output from this notebook will go to the folder from which the notebook is executed.\n",
  24 + "\n",
  25 + "As usual start by importing the gammalib, ctools, and cscripts Python modules."
  26 + ]
  27 + },
  28 + {
  29 + "cell_type": "code",
  30 + "execution_count": 7,
  31 + "metadata": {},
  32 + "outputs": [],
  33 + "source": [
  34 + "import gammalib\n",
  35 + "import ctools\n",
  36 + "import cscripts"
  37 + ]
  38 + },
  39 + {
  40 + "cell_type": "markdown",
  41 + "metadata": {},
  42 + "source": [
  43 + "The first step of your analysis consists in selecting the relevant events from the observations. In this step you can select a specific energy range, time range, or region of interest. In the example below you will select events according to the safe energy thresholds that are defined in the effective area component of the instrument response functions. You do this by setting the hidden parameter usethres=DEFAULT. In addition, you will select all events within 2 degrees of the pointing direction. We will write the selected observations to disk for easier use in the following steps."
  44 + ]
  45 + },
  46 + {
  47 + "cell_type": "code",
  48 + "execution_count": 9,
  49 + "metadata": {},
  50 + "outputs": [],
  51 + "source": [
  52 + "obsfile = \"obs_crab_selected.xml\"\n",
  53 + "\n",
  54 + "select = ctools.ctselect()\n",
  55 + "select['usethres'] = 'DEFAULT'\n",
  56 + "select['inobs'] = '$HESSDATA/obs/obs_crab.xml'\n",
  57 + "select['emin'] = 'INDEF' # no manual energy selection\n",
  58 + "select['rad'] = 2 # by default select around pointing direction of each observations\n",
  59 + "select['tmin'] = 'INDEF' # no temporal selection\n",
  60 + "select['outobs'] = obsfile\n",
  61 + "\n",
  62 + "select.execute()"
  63 + ]
  64 + },
  65 + {
  66 + "cell_type": "markdown",
  67 + "metadata": {},
  68 + "source": [
  69 + "Below is a summary of the selected observations."
  70 + ]
  71 + },
  72 + {
  73 + "cell_type": "code",
  74 + "execution_count": 10,
  75 + "metadata": {},
  76 + "outputs": [
  77 + {
  78 + "name": "stdout",
  79 + "output_type": "stream",
  80 + "text": [
  81 + "=== GObservations ===\n",
  82 + " Number of observations ....: 4\n",
  83 + " Number of models ..........: 0\n",
  84 + " Number of observed events .: 9675\n",
  85 + " Number of predicted events : 0\n"
  86 + ]
  87 + }
  88 + ],
  89 + "source": [
  90 + "obs_container = gammalib.GObservations(obsfile)\n",
  91 + "print(obs_container)"
  92 + ]
  93 + },
  94 + {
  95 + "cell_type": "markdown",
  96 + "metadata": {},
  97 + "source": [
  98 + "Let's look at the properties of each individual observation."
  99 + ]
  100 + },
  101 + {
  102 + "cell_type": "code",
  103 + "execution_count": 11,
  104 + "metadata": {},
  105 + "outputs": [
  106 + {
  107 + "name": "stdout",
  108 + "output_type": "stream",
  109 + "text": [
  110 + "=== GCTAObservation ===\n",
  111 + " Name ......................: Crab\n",
  112 + " Identifier ................: 023523\n",
  113 + " Instrument ................: HESS\n",
  114 + " Event file ................: selected_hess_dl3_dr1_obs_id_023523.fits\n",
  115 + " Event type ................: EventList\n",
  116 + " Statistic .................: cstat\n",
  117 + " Ontime ....................: 1687 s\n",
  118 + " Livetime ..................: 1581.7368164 s\n",
  119 + " Deadtime correction .......: 0.937603329223474\n",
  120 + " User energy range .........: undefined\n",
  121 + "=== GCTAPointing ===\n",
  122 + " Pointing direction ........: (RA,Dec)=(83.633333333,21.514444444)\n",
  123 + "=== GCTAResponseIrf ===\n",
  124 + " Caldb mission .............: \n",
  125 + " Caldb instrument ..........: \n",
  126 + " Response name .............: \n",
  127 + " Energy dispersion .........: Not used\n",
  128 + " Safe energy range .........: 0.870963589956081 - 100 TeV\n",
  129 + "=== GCTAEventList ===\n",
  130 + " Number of events ..........: 2305 (disposed in \"selected_hess_dl3_dr1_obs_id_023523.fits\")\n",
  131 + " Time interval .............: 53343.9223400926 - 53343.9418655556 days\n",
  132 + " Energy interval ...........: 0.870963589956081 - 100 TeV\n",
  133 + " Region of interest ........: RA=83.633333333333, DEC=21.514444444444 [0,0] Radius=2 deg\n",
  134 + "=== GSkyRegions ===\n",
  135 + " Number of regions .........: 0\n",
  136 + "=== GCTAObservation ===\n",
  137 + " Name ......................: Crab\n",
  138 + " Identifier ................: 023526\n",
  139 + " Instrument ................: HESS\n",
  140 + " Event file ................: selected_hess_dl3_dr1_obs_id_023526.fits\n",
  141 + " Event type ................: EventList\n",
  142 + " Statistic .................: cstat\n",
  143 + " Ontime ....................: 1683 s\n",
  144 + " Livetime ..................: 1572.6867676 s\n",
  145 + " Deadtime correction .......: 0.934454407367796\n",
  146 + " User energy range .........: undefined\n",
  147 + "=== GCTAPointing ===\n",
  148 + " Pointing direction ........: (RA,Dec)=(83.633333333,22.514444444)\n",
  149 + "=== GCTAResponseIrf ===\n",
  150 + " Caldb mission .............: \n",
  151 + " Caldb instrument ..........: \n",
  152 + " Response name .............: \n",
  153 + " Energy dispersion .........: Not used\n",
  154 + " Safe energy range .........: 0.707945784384138 - 100 TeV\n",
  155 + "=== GCTAEventList ===\n",
  156 + " Number of events ..........: 2571 (disposed in \"selected_hess_dl3_dr1_obs_id_023526.fits\")\n",
  157 + " Time interval .............: 53343.9542150926 - 53343.9736942593 days\n",
  158 + " Energy interval ...........: 0.707945784384138 - 100 TeV\n",
  159 + " Region of interest ........: RA=83.633333333333, DEC=22.514444444444 [0,0] Radius=2 deg\n",
  160 + "=== GSkyRegions ===\n",
  161 + " Number of regions .........: 0\n",
  162 + "=== GCTAObservation ===\n",
  163 + " Name ......................: Crab\n",
  164 + " Identifier ................: 023559\n",
  165 + " Instrument ................: HESS\n",
  166 + " Event file ................: selected_hess_dl3_dr1_obs_id_023559.fits\n",
  167 + " Event type ................: EventList\n",
  168 + " Statistic .................: cstat\n",
  169 + " Ontime ....................: 1686 s\n",
  170 + " Livetime ..................: 1578.1235352 s\n",
  171 + " Deadtime correction .......: 0.936016331672598\n",
  172 + " User energy range .........: undefined\n",
  173 + "=== GCTAPointing ===\n",
  174 + " Pointing direction ........: (RA,Dec)=(85.253333338,22.014444444)\n",
  175 + "=== GCTAResponseIrf ===\n",
  176 + " Caldb mission .............: \n",
  177 + " Caldb instrument ..........: \n",
  178 + " Response name .............: \n",
  179 + " Energy dispersion .........: Not used\n",
  180 + " Safe energy range .........: 0.660693448007596 - 100 TeV\n",
  181 + "=== GCTAEventList ===\n",
  182 + " Number of events ..........: 2631 (disposed in \"selected_hess_dl3_dr1_obs_id_023559.fits\")\n",
  183 + " Time interval .............: 53345.9619812963 - 53345.9814951852 days\n",
  184 + " Energy interval ...........: 0.660693448007596 - 100 TeV\n",
  185 + " Region of interest ........: RA=85.2533333381014, DEC=22.014444444444 [0,0] Radius=2 deg\n",
  186 + "=== GSkyRegions ===\n",
  187 + " Number of regions .........: 0\n",
  188 + "=== GCTAObservation ===\n",
  189 + " Name ......................: Crab\n",
  190 + " Identifier ................: 023592\n",
  191 + " Instrument ................: HESS\n",
  192 + " Event file ................: selected_hess_dl3_dr1_obs_id_023592.fits\n",
  193 + " Event type ................: EventList\n",
  194 + " Statistic .................: cstat\n",
  195 + " Ontime ....................: 1686 s\n",
  196 + " Livetime ..................: 1581.2646484 s\n",
  197 + " Deadtime correction .......: 0.937879388137604\n",
  198 + " User energy range .........: undefined\n",
  199 + "=== GCTAPointing ===\n",
  200 + " Pointing direction ........: (RA,Dec)=(82.013333329,22.014444444)\n",
  201 + "=== GCTAResponseIrf ===\n",
  202 + " Caldb mission .............: \n",
  203 + " Caldb instrument ..........: \n",
  204 + " Response name .............: \n",
  205 + " Energy dispersion .........: Not used\n",
  206 + " Safe energy range .........: 0.870963589956081 - 100 TeV\n",
  207 + "=== GCTAEventList ===\n",
  208 + " Number of events ..........: 2168 (disposed in \"selected_hess_dl3_dr1_obs_id_023592.fits\")\n",
  209 + " Time interval .............: 53347.9131965741 - 53347.932710463 days\n",
  210 + " Energy interval ...........: 0.870963589956081 - 100 TeV\n",
  211 + " Region of interest ........: RA=82.0133333285646, DEC=22.014444444444 [0,0] Radius=2 deg\n",
  212 + "=== GSkyRegions ===\n",
  213 + " Number of regions .........: 0\n"
  214 + ]
  215 + }
  216 + ],
  217 + "source": [
  218 + "for obs in obs_container:\n",
  219 + " print(obs)"
  220 + ]
  221 + },
  222 + {
  223 + "cell_type": "markdown",
  224 + "metadata": {},
  225 + "source": [
  226 + "You will find that the following energy ranges were applied for the observations:\n",
  227 + "\n",
  228 + "* id=023523: 0.871 - 100 TeV\n",
  229 + "* id=023526: 0.708 - 100 TeV\n",
  230 + "* id=023559: 0.661 - 100 TeV\n",
  231 + "* id=023592: 0.871 - 100 TeV\n",
  232 + "\n",
  233 + "The overall energy range that you will from now on use for the data analysis is 0.66 - 100 TeV. "
  234 + ]
  235 + }
  236 + ],
  237 + "metadata": {
  238 + "kernelspec": {
  239 + "display_name": "Python 3",
  240 + "language": "python",
  241 + "name": "python3"
  242 + },
  243 + "language_info": {
  244 + "codemirror_mode": {
  245 + "name": "ipython",
  246 + "version": 3
  247 + },
  248 + "file_extension": ".py",
  249 + "mimetype": "text/x-python",
  250 + "name": "python",
  251 + "nbconvert_exporter": "python",
  252 + "pygments_lexer": "ipython3",
  253 + "version": "3.5.4"
  254 + }
  255 + },
  256 + "nbformat": 4,
  257 + "nbformat_minor": 2
  258 +}
... ...
doc/source/users/tutorials/hess_dr1/skymap.ipynb 0 → 100644
  View file @f1c567e
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Generating a sky map and finding sources"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/skymap.ipynb\" download=\"skymap.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn how to generate a sky map from the selected events and how to find potential sources in the data.**\n",
  22 + "\n",
  23 + "As next step you will make a sky map of the selected events to inspect what’s in the data. You do this using the ctskymap tool. \n",
  24 + "\n",
  25 + "We start with the usual gammalib, ctools, and cscripts imports."
  26 + ]
  27 + },
  28 + {
  29 + "cell_type": "code",
  30 + "execution_count": 2,
  31 + "metadata": {},
  32 + "outputs": [],
  33 + "source": [
  34 + "import gammalib\n",
  35 + "import ctools\n",
  36 + "import cscripts"
  37 + ]
  38 + },
  39 + {
  40 + "cell_type": "markdown",
  41 + "metadata": {},
  42 + "source": [
  43 + "We will use the matplotlib package to display the results."
  44 + ]
  45 + },
  46 + {
  47 + "cell_type": "code",
  48 + "execution_count": 3,
  49 + "metadata": {},
  50 + "outputs": [],
  51 + "source": [
  52 + "%matplotlib inline\n",
  53 + "import matplotlib.pyplot as plt"
  54 + ]
  55 + },
  56 + {
  57 + "cell_type": "markdown",
  58 + "metadata": {},
  59 + "source": [
  60 + "We will use the events selected in the previous step. Since the data correspond to observations of the Crab nebula we should centre the sky map on this source. The energy limits of the sky map should be set to the energy interval of the data (0.66 - 100 TeV). "
  61 + ]
  62 + },
  63 + {
  64 + "cell_type": "code",
  65 + "execution_count": 29,
  66 + "metadata": {},
  67 + "outputs": [],
  68 + "source": [
  69 + "obsfile = \"obs_crab_selected.xml\"\n",
  70 + "emin = 0.66\n",
  71 + "emax = 100.\n",
  72 + "\n",
  73 + "skymap = ctools.ctskymap()\n",
  74 + "skymap['inobs'] = obsfile\n",
  75 + "skymap['proj'] = 'CAR'\n",
  76 + "skymap['coordsys'] = 'CEL'\n",
  77 + "skymap['xref'] = 83.63\n",
  78 + "skymap['yref'] = 22.01\n",
  79 + "skymap['binsz'] = 0.02\n",
  80 + "skymap['nxpix'] = 200\n",
  81 + "skymap['nypix'] = 200\n",
  82 + "skymap['emin'] = emin\n",
  83 + "skymap['emax'] = emax\n",
  84 + "skymap['bkgsubtract'] = 'NONE'\n",
  85 + "\n",
  86 + "\n",
  87 + "skymap.run()"
  88 + ]
  89 + },
  90 + {
  91 + "cell_type": "markdown",
  92 + "metadata": {},
  93 + "source": [
  94 + "We can visualise the skymap."
  95 + ]
  96 + },
  97 + {
  98 + "cell_type": "code",
  99 + "execution_count": 30,
  100 + "metadata": {},
  101 + "outputs": [
  102 + {
  103 + "data": {
  104 + "image/png": 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\n",
  105 + "text/plain": [
  106 + "<matplotlib.figure.Figure at 0x11f6265f8>"
  107 + ]
  108 + },
  109 + "metadata": {},
  110 + "output_type": "display_data"
  111 + }
  112 + ],
  113 + "source": [
  114 + "#module used to stretch the color palette\n",
  115 + "import matplotlib.colors as colors\n",
  116 + "\n",
  117 + "ax = plt.subplot()\n",
  118 + "plt.imshow(skymap.skymap().array(),origin='lower',\n",
  119 + " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200],\n",
  120 + " # boundaries of the coord grid\n",
  121 + " norm=colors.PowerNorm(gamma=0.5),\n",
  122 + " cmap=plt.get_cmap('jet')) # square root scale \n",
  123 + "ax.set_xlabel('R.A. (deg)')\n",
  124 + "ax.set_ylabel('Dec (deg)')\n",
  125 + "cbar = plt.colorbar()\n",
  126 + "cbar.set_label('Counts')"
  127 + ]
  128 + },
  129 + {
  130 + "cell_type": "markdown",
  131 + "metadata": {},
  132 + "source": [
  133 + "Based on the sky map you can now extract a model for all potentially significant sources in the data. You do this using the cssrcdetect script, and to extract only significant sources, you should set the detection threshold for example to 5 sigma. We will make a copy of the skymap since the detection process involves smoothing and we do not want to modify the original map."
  134 + ]
  135 + },
  136 + {
  137 + "cell_type": "code",
  138 + "execution_count": 31,
  139 + "metadata": {},
  140 + "outputs": [],
  141 + "source": [
  142 + "srcmodel = 'crab.xml'\n",
  143 + "regfile = 'crab_sources.ds9'\n",
  144 + "\n",
  145 + "srcdetect = cscripts.cssrcdetect(skymap.skymap().copy())\n",
  146 + "srcdetect['srcmodel'] = 'POINT'\n",
  147 + "srcdetect['bkgmodel'] = 'NONE' # we will determine the background model in a later step\n",
  148 + "srcdetect['threshold'] = 5\n",
  149 + "srcdetect['corr_rad'] = 0.1\n",
  150 + "srcdetect['outmodel'] = srcmodel\n",
  151 + "srcdetect['outds9file'] = regfile\n",
  152 + "\n",
  153 + "srcdetect.execute()"
  154 + ]
  155 + },
  156 + {
  157 + "cell_type": "markdown",
  158 + "metadata": {},
  159 + "source": [
  160 + "Let's look at the resulting model. It contains a point source Src001 at the position of the Crab nebula."
  161 + ]
  162 + },
  163 + {
  164 + "cell_type": "code",
  165 + "execution_count": 32,
  166 + "metadata": {},
  167 + "outputs": [
  168 + {
  169 + "name": "stdout",
  170 + "output_type": "stream",
  171 + "text": [
  172 + "=== GModels ===\n",
  173 + " Number of models ..........: 1\n",
  174 + " Number of parameters ......: 6\n",
  175 + "=== GModelSky ===\n",
  176 + " Name ......................: Src001\n",
  177 + " Instruments ...............: all\n",
  178 + " Instrument scale factors ..: unity\n",
  179 + " Observation identifiers ...: all\n",
  180 + " Model type ................: PointSource\n",
  181 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  182 + " Number of parameters ......: 6\n",
  183 + " Number of spatial par's ...: 2\n",
  184 + " RA .......................: 83.6192131308071 +/- 0 deg (free,scale=1)\n",
  185 + " DEC ......................: 22.0199996472185 +/- 0 deg (free,scale=1)\n",
  186 + " Number of spectral par's ..: 3\n",
  187 + " Prefactor ................: 5.7e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  188 + " Index ....................: -2.48 +/- 0 [10,-10] (free,scale=-2.48,gradient)\n",
  189 + " PivotEnergy ..............: 300000 MeV (fixed,scale=300000,gradient)\n",
  190 + " Number of temporal par's ..: 1\n",
  191 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  192 + ]
  193 + }
  194 + ],
  195 + "source": [
  196 + "models = gammalib.GModels(srcmodel)\n",
  197 + "print(models)"
  198 + ]
  199 + },
  200 + {
  201 + "cell_type": "markdown",
  202 + "metadata": {},
  203 + "source": [
  204 + "We can overlay the position of the source we found to the smoothed skymap to make sure everything worked smoothly."
  205 + ]
  206 + },
  207 + {
  208 + "cell_type": "code",
  209 + "execution_count": 45,
  210 + "metadata": {},
  211 + "outputs": [
  212 + {
  213 + "name": "stderr",
  214 + "output_type": "stream",
  215 + "text": [
  216 + "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1238: UserWarning: Power-law scaling on negative values is ill-defined, clamping to 0.\n",
  217 + " warnings.warn(\"Power-law scaling on negative values is \"\n"
  218 + ]
  219 + },
  220 + {
  221 + "data": {
  222 + "text/plain": [
  223 + "<matplotlib.collections.PathCollection at 0x121307c88>"
  224 + ]
  225 + },
  226 + "execution_count": 45,
  227 + "metadata": {},
  228 + "output_type": "execute_result"
  229 + },
  230 + {
  231 + "name": "stderr",
  232 + "output_type": "stream",
  233 + "text": [
  234 + "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1199: RuntimeWarning: invalid value encountered in power\n",
  235 + " np.power(resdat, gamma, resdat)\n"
  236 + ]
  237 + },
  238 + {
  239 + "data": {
  240 + "image/png": 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\n",
  241 + "text/plain": [
  242 + "<matplotlib.figure.Figure at 0x120d52278>"
  243 + ]
  244 + },
  245 + "metadata": {},
  246 + "output_type": "display_data"
  247 + }
  248 + ],
  249 + "source": [
  250 + "ax1 = plt.subplot()\n",
  251 + "plt.imshow(srcdetect._map.array(),origin='lower',\n",
  252 + " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200],\n",
  253 + " # boundaries of the coord grid\n",
  254 + " norm=colors.PowerNorm(gamma=0.5),\n",
  255 + " cmap=plt.get_cmap('jet')) # square root scale \n",
  256 + "ax1.set_xlabel('R.A. (deg)')\n",
  257 + "ax1.set_ylabel('Dec (deg)')\n",
  258 + "cbar = plt.colorbar()\n",
  259 + "cbar.set_label('Counts')\n",
  260 + "\n",
  261 + "crab = models['Src001']\n",
  262 + "\n",
  263 + "ax1.scatter(crab['RA'].value(),crab['DEC'].value(),\n",
  264 + " marker='x',c='white')"
  265 + ]
  266 + }
  267 + ],
  268 + "metadata": {
  269 + "kernelspec": {
  270 + "display_name": "Python 3",
  271 + "language": "python",
  272 + "name": "python3"
  273 + },
  274 + "language_info": {
  275 + "codemirror_mode": {
  276 + "name": "ipython",
  277 + "version": 3
  278 + },
  279 + "file_extension": ".py",
  280 + "mimetype": "text/x-python",
  281 + "name": "python",
  282 + "nbconvert_exporter": "python",
  283 + "pygments_lexer": "ipython3",
  284 + "version": "3.5.4"
  285 + }
  286 + },
  287 + "nbformat": 4,
  288 + "nbformat_minor": 2
  289 +}
... ...
doc/source/users/tutorials/hess_dr1/skymap.rst deleted
View file @1c81f76
1   -.. _hess_dr1_sky_map:
2   -
3   -Generating a sky map and finding sources
4   -----------------------------------------
5   -
6   - .. admonition:: What you will learn
7   -
8   - You will learn **how to generate a sky map from the selected events**
9   - and **how to find potential sources in the data**.
10   -
11   -As next step you will make a sky map of the selected events to inspect what's
12   -in the data.
13   -You do this using the :ref:`ctskymap` tool.
14   -Since the data correspond to observations of the Crab nebula you should centre
15   -the sky map on this source.
16   -The energy limits of the sky map should be set to the energy interval of the
17   -data (0.66 - 100 TeV).
18   -To create the sky map, type:
19   -
20   -.. code-block:: bash
21   -
22   - $ ctskymap
23   - Input event list or observation definition XML file [events.fits] obs_crab_selected.xml
24   - Coordinate system (CEL - celestial, GAL - galactic) (CEL|GAL) [CEL]
25   - Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
26   - First coordinate of image center in degrees (RA or galactic l) (0-360) [83.63]
27   - Second coordinate of image center in degrees (DEC or galactic b) (-90-90) [22.01]
28   - Image scale (in degrees/pixel) [0.02]
29   - Size of the X axis in pixels [200]
30   - Size of the Y axis in pixels [200]
31   - Lower energy limit (TeV) [0.1] 0.66
32   - Upper energy limit (TeV) [100.0]
33   - Background subtraction method (NONE|IRF|RING) [NONE]
34   - Output skymap file [skymap.fits]
35   -
36   -The resulting sky map, displayed using
37   -`ds9 <http://ds9.si.edu>`_ with a square-root color scaling, is shown below:
38   -
39   -.. figure:: skymap.png
40   - :width: 400px
41   - :align: center
42   -
43   - *Sky map of the selected events centred on the Crab nebula*
44   -
45   -Based on the sky map you can now extract a model for all potentially significant
46   -sources in the data.
47   -You do this using the :ref:`cssrcdetect` script, and to extract only significant
48   -sources, you should set the detection threshold for example to 5 sigma.
49   -For convenience, the :ref:`cssrcdetect` script can also add a background model
50   -component to the output
51   -:ref:`model definition XML file <glossary_moddef>`.
52   -Since the H.E.S.S. Instrument Response Function does not include a background
53   -template, the ``IRF`` option will not work.
54   -While sophisticated methods for the analysis of H.E.S.S. data still need to
55   -be developed, you may try the effective area model ``AEFF`` that uses the
56   -effective area as spatial template for the background component.
57   -So, now type:
58   -
59   -.. code-block:: bash
60   -
61   - $ cssrcdetect
62   - Input sky map file [skymap.fits]
63   - Source model type (POINT) [POINT]
64   - Background model type (NONE|IRF|AEFF|CUBE|RACC) [NONE] AEFF
65   - Detection threshold (Gaussian sigma) [5.0]
66   - Correlation kernel radius (deg) [0.1]
67   - Output model definition XML file [models.xml] crab_models.xml
68   - Output DS9 region file [ds9.reg] crab.reg
69   -
70   -Below an excerpt of the ``cssrcdetect.log`` log file.
71   -Only one significant source was found, and its position is consistent with the
72   -Crab nebula.
73   -This is also illustrated in the figure below where the region file
74   -``crab.reg`` that was produced by :ref:`cssrcdetect` was overlaid with
75   -`ds9 <http://ds9.si.edu>`_
76   -on the sky map.
77   -
78   -.. code-block:: none
79   -
80   - 2018-09-11T20:32:20: +==================+
81   - 2018-09-11T20:32:20: | Source detection |
82   - 2018-09-11T20:32:20: +==================+
83   - 2018-09-11T20:32:20: === Iteration 1 ===
84   - 2018-09-11T20:32:20: Map maximum ...............: 11.1003120131
85   - 2018-09-11T20:32:20: Src001 position ...........: (RA,Dec)=(83.6192131308071,22.0199996472185)
86   - 2018-09-11T20:32:20: === Iteration 2 ===
87   - 2018-09-11T20:32:20: Map maximum ...............: None above threshold
88   -
89   -.. figure:: skymap_crab.png
90   - :width: 400px
91   - :align: center
92   -
93   - *Sky map of the selected events centred on the Crab nebula*
94   -
95   -The
96   -:ref:`model definition file <glossary_moddef>`
97   -``crab_models.xml`` that was generated by :ref:`cssrcdetect` is shown below.
98   -It contains a point source ``Src001`` at the position of the Crab nebula
99   -and an effective area background model scaled by a spectral power law as
100   -background component.
101   -
102   -.. code-block:: xml
103   -
104   - <?xml version="1.0" encoding="UTF-8" standalone="no"?>
105   - <source_library title="source library">
106   - <source name="Src001" type="PointSource">
107   - <spectrum type="PowerLaw">
108   - <parameter name="Prefactor" value="1" error="0" scale="5.7e-18" min="0" free="1" />
109   - <parameter name="Index" value="1" error="-0" scale="-2.48" min="-4.03225806451613" max="4.03225806451613" free="1" />
110   - <parameter name="PivotEnergy" value="1" scale="300000" free="0" />
111   - </spectrum>
112   - <spatialModel type="PointSource">
113   - <parameter name="RA" value="83.6192131308071" error="0" scale="1" free="1" />
114   - <parameter name="DEC" value="22.0199996472185" error="0" scale="1" free="1" />
115   - </spatialModel>
116   - </source>
117   - <source name="Background" type="CTAAeffBackground">
118   - <spectrum type="PowerLaw">
119   - <parameter name="Prefactor" value="1" error="0" scale="1e-13" min="0" free="1" />
120   - <parameter name="Index" value="1" error="-0" scale="-2.5" min="-4" max="4" free="1" />
121   - <parameter name="PivotEnergy" value="1" scale="1000000" free="0" />
122   - </spectrum>
123   - </source>
124   - </source_library>
doc/source/users/tutorials/hess_dr1/unbinned.ipynb 0 → 100644
  View file @f1c567e
  1 +{
  2 + "cells": [
  3 + {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "# Performing an unbinned analysis"
  8 + ]
  9 + },
  10 + {
  11 + "cell_type": "raw",
  12 + "metadata": {},
  13 + "source": [
  14 + "<div align=\"right\"><a href=\"../../../_downloads/unbinned.ipynb\" download=\"unbinned.ipynb\"><img src=\"../../../_static/download-notebook.jpg\" alt=\"Download Notebook\" height=\"40\"></a></div>"
  15 + ]
  16 + },
  17 + {
  18 + "cell_type": "markdown",
  19 + "metadata": {},
  20 + "source": [
  21 + "**In this tutorial you will learn to fit a parametric model to the event data (unbinned fit) and how to inspect the fit residuals**\n",
  22 + "\n",
  23 + "Now you are ready to fit the models for the source and the background to the data.\n",
  24 + "\n",
  25 + "We start by importing gammalib, ctools, and cscripts."
  26 + ]
  27 + },
  28 + {
  29 + "cell_type": "code",
  30 + "execution_count": 1,
  31 + "metadata": {},
  32 + "outputs": [],
  33 + "source": [
  34 + "import gammalib\n",
  35 + "import ctools\n",
  36 + "import cscripts"
  37 + ]
  38 + },
  39 + {
  40 + "cell_type": "markdown",
  41 + "metadata": {},
  42 + "source": [
  43 + "We will also use matplotlib to display the results."
  44 + ]
  45 + },
  46 + {
  47 + "cell_type": "code",
  48 + "execution_count": 2,
  49 + "metadata": {},
  50 + "outputs": [],
  51 + "source": [
  52 + "%matplotlib inline\n",
  53 + "import matplotlib.pyplot as plt"
  54 + ]
  55 + },
  56 + {
  57 + "cell_type": "markdown",
  58 + "metadata": {},
  59 + "source": [
  60 + "Finally we add to our path the directory containing the example plotting scripts provided with the ctools installation."
  61 + ]
  62 + },
  63 + {
  64 + "cell_type": "code",
  65 + "execution_count": 3,
  66 + "metadata": {},
  67 + "outputs": [],
  68 + "source": [
  69 + "import sys\n",
  70 + "import os\n",
  71 + "sys.path.append(os.environ['CTOOLS']+'/share/examples/python/')"
  72 + ]
  73 + },
  74 + {
  75 + "cell_type": "markdown",
  76 + "metadata": {},
  77 + "source": [
  78 + "## Preparing the model\n",
  79 + "\n",
  80 + "First, we will merge the two models we derived in the previous tutorials for source and background."
  81 + ]
  82 + },
  83 + {
  84 + "cell_type": "code",
  85 + "execution_count": 4,
  86 + "metadata": {},
  87 + "outputs": [
  88 + {
  89 + "name": "stdout",
  90 + "output_type": "stream",
  91 + "text": [
  92 + "=== GModels ===\n",
  93 + " Number of models ..........: 5\n",
  94 + " Number of parameters ......: 86\n",
  95 + "=== GModelSky ===\n",
  96 + " Name ......................: Src001\n",
  97 + " Instruments ...............: all\n",
  98 + " Instrument scale factors ..: unity\n",
  99 + " Observation identifiers ...: all\n",
  100 + " Model type ................: PointSource\n",
  101 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  102 + " Number of parameters ......: 6\n",
  103 + " Number of spatial par's ...: 2\n",
  104 + " RA .......................: 83.6192131308071 +/- 0 deg (free,scale=1)\n",
  105 + " DEC ......................: 22.0199996472185 +/- 0 deg (free,scale=1)\n",
  106 + " Number of spectral par's ..: 3\n",
  107 + " Prefactor ................: 5.7e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  108 + " Index ....................: -2.48 +/- 0 [10,-10] (free,scale=-2.48,gradient)\n",
  109 + " PivotEnergy ..............: 300000 MeV (fixed,scale=300000,gradient)\n",
  110 + " Number of temporal par's ..: 1\n",
  111 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  112 + "=== GCTAModelBackground ===\n",
  113 + " Name ......................: Background_023523\n",
  114 + " Instruments ...............: HESS\n",
  115 + " Instrument scale factors ..: unity\n",
  116 + " Observation identifiers ...: 023523\n",
  117 + " Model type ................: CTABackground\n",
  118 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  119 + " Number of parameters ......: 20\n",
  120 + " Number of spatial par's ...: 3\n",
  121 + " 1:Sigma ..................: 3.19679118852325 +/- 0.148784221714502 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  122 + " 2:Grad_DETX ..............: 0.0882131070561384 +/- 0.0223563997131535 deg^-1 (free,scale=1,gradient)\n",
  123 + " 2:Grad_DETY ..............: -0.000692481271743188 +/- 0.0227254581791992 deg^-1 (free,scale=1,gradient)\n",
  124 + " Number of spectral par's ..: 16\n",
  125 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  126 + " Intensity0 ...............: 0.00149714476600658 +/- 0.000185798282794166 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
  127 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  128 + " Intensity1 ...............: 0.000275025454049597 +/- 1.50407490107521e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
  129 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  130 + " Intensity2 ...............: 9.68931866899334e-05 +/- 5.86314382972939e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
  131 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  132 + " Intensity3 ...............: 3.42414862721453e-05 +/- 2.61283857499118e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
  133 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  134 + " Intensity4 ...............: 1.2134598847599e-05 +/- 1.19969987797427e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
  135 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  136 + " Intensity5 ...............: 4.80035386464156e-06 +/- 6.14208278785316e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
  137 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  138 + " Intensity6 ...............: 8.5455367005893e-07 +/- 1.75301261074233e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
  139 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  140 + " Intensity7 ...............: 2.8110106615004e-07 +/- 1.02501352381156e-07 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
  141 + " Number of temporal par's ..: 1\n",
  142 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  143 + "=== GCTAModelBackground ===\n",
  144 + " Name ......................: Background_023526\n",
  145 + " Instruments ...............: HESS\n",
  146 + " Instrument scale factors ..: unity\n",
  147 + " Observation identifiers ...: 023526\n",
  148 + " Model type ................: CTABackground\n",
  149 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  150 + " Number of parameters ......: 20\n",
  151 + " Number of spatial par's ...: 3\n",
  152 + " 1:Sigma ..................: 3.30082205953218 +/- 0.156912956620767 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  153 + " 2:Grad_DETX ..............: 0.0153115422362867 +/- 0.0215680816698383 deg^-1 (free,scale=1,gradient)\n",
  154 + " 2:Grad_DETY ..............: -0.00906905077562078 +/- 0.0213184759257543 deg^-1 (free,scale=1,gradient)\n",
  155 + " Number of spectral par's ..: 16\n",
  156 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  157 + " Intensity0 ...............: 0.00115207913907334 +/- 6.11872558313686e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
  158 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  159 + " Intensity1 ...............: 0.000216074985117796 +/- 1.14346974881101e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
  160 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  161 + " Intensity2 ...............: 9.80980717622882e-05 +/- 6.14243331439364e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
  162 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  163 + " Intensity3 ...............: 2.64302082334315e-05 +/- 2.27978082923659e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
  164 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  165 + " Intensity4 ...............: 1.03353701064884e-05 +/- 1.13230244143927e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
  166 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  167 + " Intensity5 ...............: 3.57899467682822e-06 +/- 5.03727665165389e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
  168 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  169 + " Intensity6 ...............: 5.95101204005172e-07 +/- 1.41251129058325e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
  170 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  171 + " Intensity7 ...............: 2.81714577113613e-07 +/- 1.0384120888285e-07 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
  172 + " Number of temporal par's ..: 1\n",
  173 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  174 + "=== GCTAModelBackground ===\n",
  175 + " Name ......................: Background_023559\n",
  176 + " Instruments ...............: HESS\n",
  177 + " Instrument scale factors ..: unity\n",
  178 + " Observation identifiers ...: 023559\n",
  179 + " Model type ................: CTABackground\n",
  180 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  181 + " Number of parameters ......: 20\n",
  182 + " Number of spatial par's ...: 3\n",
  183 + " 1:Sigma ..................: 4.32704862685447 +/- 0.368685355107103 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  184 + " 2:Grad_DETX ..............: 0.018772378083067 +/- 0.0217106872366865 deg^-1 (free,scale=1,gradient)\n",
  185 + " 2:Grad_DETY ..............: -0.126627961668458 +/- 0.0204738085196433 deg^-1 (free,scale=1,gradient)\n",
  186 + " Number of spectral par's ..: 16\n",
  187 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  188 + " Intensity0 ...............: 0.000915773345685034 +/- 4.89989530347851e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
  189 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  190 + " Intensity1 ...............: 0.000197398656687683 +/- 1.07759903733823e-05 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
  191 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  192 + " Intensity2 ...............: 6.93704083542102e-05 +/- 4.77505196699795e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
  193 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  194 + " Intensity3 ...............: 2.57663537500693e-05 +/- 2.20136814658691e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
  195 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  196 + " Intensity4 ...............: 6.82483279973139e-06 +/- 7.86680350899204e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
  197 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  198 + " Intensity5 ...............: 3.3292540797357e-06 +/- 4.68826132796518e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
  199 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  200 + " Intensity6 ...............: 8.19437245391855e-07 +/- 1.82363529352901e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
  201 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  202 + " Intensity7 ...............: 1.44457348642334e-07 +/- 7.0968299684809e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
  203 + " Number of temporal par's ..: 1\n",
  204 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  205 + "=== GCTAModelBackground ===\n",
  206 + " Name ......................: Background_023592\n",
  207 + " Instruments ...............: HESS\n",
  208 + " Instrument scale factors ..: unity\n",
  209 + " Observation identifiers ...: 023592\n",
  210 + " Model type ................: CTABackground\n",
  211 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  212 + " Number of parameters ......: 20\n",
  213 + " Number of spatial par's ...: 3\n",
  214 + " 1:Sigma ..................: 3.6479543205698 +/- 0.238749563358304 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  215 + " 2:Grad_DETX ..............: 0.0290086310507671 +/- 0.0231654281693484 deg^-1 (free,scale=1,gradient)\n",
  216 + " 2:Grad_DETY ..............: 0.165462911909218 +/- 0.0212932684624286 deg^-1 (free,scale=1,gradient)\n",
  217 + " Number of spectral par's ..: 16\n",
  218 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  219 + " Intensity0 ...............: 0.00147439433272687 +/- 0.000191693450881709 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
  220 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  221 + " Intensity1 ...............: 0.000235250773930648 +/- 1.37598369145017e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
  222 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  223 + " Intensity2 ...............: 9.20623251759662e-05 +/- 5.75965738880592e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
  224 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  225 + " Intensity3 ...............: 3.22022945215284e-05 +/- 2.53414761162924e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
  226 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  227 + " Intensity4 ...............: 1.00701723302328e-05 +/- 1.04797408736245e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
  228 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  229 + " Intensity5 ...............: 3.29354066928594e-06 +/- 4.58158358563221e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
  230 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  231 + " Intensity6 ...............: 8.71669675678694e-07 +/- 1.76346292129154e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
  232 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  233 + " Intensity7 ...............: 3.12067435753287e-07 +/- 1.11366909796179e-07 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
  234 + " Number of temporal par's ..: 1\n",
  235 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  236 + ]
  237 + }
  238 + ],
  239 + "source": [
  240 + "srcmodel = 'crab.xml'\n",
  241 + "bkgmodel = 'bkgmodel.xml'\n",
  242 + "\n",
  243 + "models = gammalib.GModels()\n",
  244 + "for inmodels in [srcmodel,bkgmodel]:\n",
  245 + " for model in gammalib.GModels(inmodels):\n",
  246 + " models.append(model)\n",
  247 + " \n",
  248 + "print(models)"
  249 + ]
  250 + },
  251 + {
  252 + "cell_type": "markdown",
  253 + "metadata": {},
  254 + "source": [
  255 + "Note how the source found by cssrcdetect was named *Src001*. We will call it *Crab* instead. Also, spectral parameters are set to default values, but you should make sure they are appropriate so that the model fit runs seamlessly. In this case it is best to set the value of the pivot energy for the Crab within our energy range (> 0.66 TeV). We will set it to 1 TeV."
  256 + ]
  257 + },
  258 + {
  259 + "cell_type": "code",
  260 + "execution_count": 5,
  261 + "metadata": {},
  262 + "outputs": [],
  263 + "source": [
  264 + "models['Src001'].name('Crab')\n",
  265 + "models['Crab']['PivotEnergy'].value(1.e6)"
  266 + ]
  267 + },
  268 + {
  269 + "cell_type": "markdown",
  270 + "metadata": {},
  271 + "source": [
  272 + "We will save this model to disk for later use."
  273 + ]
  274 + },
  275 + {
  276 + "cell_type": "code",
  277 + "execution_count": 6,
  278 + "metadata": {},
  279 + "outputs": [],
  280 + "source": [
  281 + "modelfile = 'crab_models.xml'\n",
  282 + "models.save(modelfile)"
  283 + ]
  284 + },
  285 + {
  286 + "cell_type": "markdown",
  287 + "metadata": {},
  288 + "source": [
  289 + "## Fitting the model to the data\n",
  290 + "\n",
  291 + "The model fit is performed by ctlike. We will use the previously selected events."
  292 + ]
  293 + },
  294 + {
  295 + "cell_type": "code",
  296 + "execution_count": 7,
  297 + "metadata": {},
  298 + "outputs": [],
  299 + "source": [
  300 + "obsfile = \"obs_crab_selected.xml\"\n",
  301 + "\n",
  302 + "like = ctools.ctlike()\n",
  303 + "like['inobs'] = obsfile\n",
  304 + "like['inmodel'] = modelfile\n",
  305 + "\n",
  306 + "like.run()"
  307 + ]
  308 + },
  309 + {
  310 + "cell_type": "markdown",
  311 + "metadata": {},
  312 + "source": [
  313 + "We can now look at the results from the optimisation and the fitted model."
  314 + ]
  315 + },
  316 + {
  317 + "cell_type": "code",
  318 + "execution_count": 8,
  319 + "metadata": {},
  320 + "outputs": [
  321 + {
  322 + "name": "stdout",
  323 + "output_type": "stream",
  324 + "text": [
  325 + "=== GOptimizerLM ===\n",
  326 + " Optimized function value ..: 98200.140\n",
  327 + " Absolute precision ........: 0.005\n",
  328 + " Acceptable value decrease .: 2\n",
  329 + " Optimization status .......: converged\n",
  330 + " Number of parameters ......: 86\n",
  331 + " Number of free parameters .: 48\n",
  332 + " Number of iterations ......: 28\n",
  333 + " Lambda ....................: 0.1\n",
  334 + "=== GModelSky ===\n",
  335 + " Name ......................: Crab\n",
  336 + " Instruments ...............: all\n",
  337 + " Instrument scale factors ..: unity\n",
  338 + " Observation identifiers ...: all\n",
  339 + " Model type ................: PointSource\n",
  340 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  341 + " Number of parameters ......: 6\n",
  342 + " Number of spatial par's ...: 2\n",
  343 + " RA .......................: 83.6230528971337 +/- 0.00239926913139826 deg (free,scale=1)\n",
  344 + " DEC ......................: 22.0247811584824 +/- 0.0021892515628272 deg (free,scale=1)\n",
  345 + " Number of spectral par's ..: 3\n",
  346 + " Prefactor ................: 4.89251809524732e-17 +/- 2.67898412101055e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  347 + " Index ....................: -2.70266617269294 +/- 0.0657789822882349 [10,-10] (free,scale=-2.48,gradient)\n",
  348 + " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
  349 + " Number of temporal par's ..: 1\n",
  350 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  351 + ]
  352 + }
  353 + ],
  354 + "source": [
  355 + "print(like.opt())\n",
  356 + "print(like.obs().models()['Crab'])"
  357 + ]
  358 + },
  359 + {
  360 + "cell_type": "markdown",
  361 + "metadata": {},
  362 + "source": [
  363 + "The statistical 1-sigma positional uncertainty corresponds to 0.12 arcmin. Systematic uncertainties are not computed. The fitted position can be compared to the values of 83.629±0.005 degrees in Right Ascension and 22.012±0.001 degrees in Declination reported in [Holler et al. (2017)](https://arxiv.org/pdf/1707.04196.pdf).\n",
  364 + "\n",
  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",
  366 + "\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",
  368 + "\n",
  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."
  370 + ]
  371 + },
  372 + {
  373 + "cell_type": "code",
  374 + "execution_count": 9,
  375 + "metadata": {},
  376 + "outputs": [],
  377 + "source": [
  378 + "like['edisp'] = True\n",
  379 + "\n",
  380 + "like.run()"
  381 + ]
  382 + },
  383 + {
  384 + "cell_type": "code",
  385 + "execution_count": 10,
  386 + "metadata": {},
  387 + "outputs": [
  388 + {
  389 + "name": "stdout",
  390 + "output_type": "stream",
  391 + "text": [
  392 + "=== GOptimizerLM ===\n",
  393 + " Optimized function value ..: 98197.286\n",
  394 + " Absolute precision ........: 0.005\n",
  395 + " Acceptable value decrease .: 2\n",
  396 + " Optimization status .......: converged\n",
  397 + " Number of parameters ......: 86\n",
  398 + " Number of free parameters .: 48\n",
  399 + " Number of iterations ......: 3\n",
  400 + " Lambda ....................: 1e-06\n",
  401 + "=== GModelSky ===\n",
  402 + " Name ......................: Crab\n",
  403 + " Instruments ...............: all\n",
  404 + " Instrument scale factors ..: unity\n",
  405 + " Observation identifiers ...: all\n",
  406 + " Model type ................: PointSource\n",
  407 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  408 + " Number of parameters ......: 6\n",
  409 + " Number of spatial par's ...: 2\n",
  410 + " RA .......................: 83.6216748574258 +/- 0.00272066304506809 deg (free,scale=1)\n",
  411 + " DEC ......................: 22.0244889319664 +/- 0.0024860621013803 deg (free,scale=1)\n",
  412 + " Number of spectral par's ..: 3\n",
  413 + " Prefactor ................: 4.14814740728779e-17 +/- 2.00490449017781e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  414 + " Index ....................: -2.73454025222651 +/- 0.0699765314511747 [10,-10] (free,scale=-2.48,gradient)\n",
  415 + " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
  416 + " Number of temporal par's ..: 1\n",
  417 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  418 + ]
  419 + }
  420 + ],
  421 + "source": [
  422 + "print(like.opt())\n",
  423 + "print(like.obs().models()['Crab'])"
  424 + ]
  425 + },
  426 + {
  427 + "cell_type": "markdown",
  428 + "metadata": {},
  429 + "source": [
  430 + "You can verify that the results are broadly consistents with those obtained ignoring the energy dispersion.\n",
  431 + "\n",
  432 + "## Inspecting the fit residuals\n",
  433 + "\n",
  434 + "Following a model fit, you should always inspect the fit residuals. First let’s inspect the spectral residuals. You can do this using the csresspec script."
  435 + ]
  436 + },
  437 + {
  438 + "cell_type": "code",
  439 + "execution_count": 11,
  440 + "metadata": {},
  441 + "outputs": [
  442 + {
  443 + "data": {
  444 + "image/png": 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\n",
  445 + "text/plain": [
  446 + "<matplotlib.figure.Figure at 0x10fee3c88>"
  447 + ]
  448 + },
  449 + "metadata": {},
  450 + "output_type": "display_data"
  451 + }
  452 + ],
  453 + "source": [
  454 + "residuals = 'residuals.fits'\n",
  455 + "\n",
  456 + "resspec = cscripts.csresspec(like.obs())\n",
  457 + "resspec['stack'] = True\n",
  458 + "resspec['components'] = True\n",
  459 + "resspec['ebinalg'] = 'LOG'\n",
  460 + "resspec['emin'] = 0.66\n",
  461 + "resspec['emax'] = 100.\n",
  462 + "resspec['enumbins'] = 20\n",
  463 + "resspec['proj'] = 'CAR'\n",
  464 + "resspec['coordsys'] = 'CEL'\n",
  465 + "resspec['xref'] = 83.63\n",
  466 + "resspec['yref'] = 22.01\n",
  467 + "resspec['binsz'] = 0.02\n",
  468 + "resspec['nxpix'] = 200\n",
  469 + "resspec['nypix'] = 200\n",
  470 + "resspec['algorithm'] = 'SIGNIFICANCE'\n",
  471 + "resspec['outfile'] = residuals\n",
  472 + "\n",
  473 + "resspec.execute()\n",
  474 + "\n",
  475 + "from show_residuals import plot_residuals\n",
  476 + "plot_residuals(residuals,'',0)"
  477 + ]
  478 + },
  479 + {
  480 + "cell_type": "markdown",
  481 + "metadata": {},
  482 + "source": [
  483 + "The spectral fit looks satisfactory. Finally you should also inspect the spatial residuals. You do this using the csresmap script."
  484 + ]
  485 + },
  486 + {
  487 + "cell_type": "code",
  488 + "execution_count": 12,
  489 + "metadata": {},
  490 + "outputs": [],
  491 + "source": [
  492 + "resmap = cscripts.csresmap(like.obs())\n",
  493 + "resmap['emin'] = 0.66\n",
  494 + "resmap['emax'] = 100.\n",
  495 + "resmap['proj'] = 'CAR'\n",
  496 + "resmap['coordsys'] = 'CEL'\n",
  497 + "resmap['xref'] = 83.63\n",
  498 + "resmap['yref'] = 22.01\n",
  499 + "resmap['binsz'] = 0.02\n",
  500 + "resmap['nxpix'] = 200\n",
  501 + "resmap['nypix'] = 200\n",
  502 + "resmap['algorithm'] = 'SUBDIV'\n",
  503 + "\n",
  504 + "resmap.run()"
  505 + ]
  506 + },
  507 + {
  508 + "cell_type": "markdown",
  509 + "metadata": {},
  510 + "source": [
  511 + "We will inspect the residual map with a slight smoothing to suppress statistical fluctuations."
  512 + ]
  513 + },
  514 + {
  515 + "cell_type": "code",
  516 + "execution_count": 13,
  517 + "metadata": {
  518 + "code_folding": []
  519 + },
  520 + "outputs": [
  521 + {
  522 + "data": {
  523 + "image/png": 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\n",
  524 + "text/plain": [
  525 + "<matplotlib.figure.Figure at 0x103e6f978>"
  526 + ]
  527 + },
  528 + "metadata": {},
  529 + "output_type": "display_data"
  530 + }
  531 + ],
  532 + "source": [
  533 + "resmap._resmap.smooth('GAUSSIAN',0.1)\n",
  534 + "\n",
  535 + "ax = plt.subplot()\n",
  536 + "plt.imshow(resmap._resmap.array(),origin='lower',\n",
  537 + " cmap='bwr',vmin=-1,vmax=1,\n",
  538 + " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200])\n",
  539 + " # Boundaries of the coord grid\n",
  540 + "ax.set_xlabel('R.A. (deg)')\n",
  541 + "ax.set_ylabel('Dec (deg)')\n",
  542 + "cbar = plt.colorbar()\n",
  543 + "cbar.set_label('Residuals/Total Counts')"
  544 + ]
  545 + },
  546 + {
  547 + "cell_type": "markdown",
  548 + "metadata": {},
  549 + "source": [
  550 + "The residuals look flat across the field analysed."
  551 + ]
  552 + }
  553 + ],
  554 + "metadata": {
  555 + "kernelspec": {
  556 + "display_name": "Python 3",
  557 + "language": "python",
  558 + "name": "python3"
  559 + },
  560 + "language_info": {
  561 + "codemirror_mode": {
  562 + "name": "ipython",
  563 + "version": 3
  564 + },
  565 + "file_extension": ".py",
  566 + "mimetype": "text/x-python",
  567 + "name": "python",
  568 + "nbconvert_exporter": "python",
  569 + "pygments_lexer": "ipython3",
  570 + "version": "3.5.4"
  571 + }
  572 + },
  573 + "nbformat": 4,
  574 + "nbformat_minor": 2
  575 +}
... ...
doc/source/users/tutorials/notebooks.rst
  View file @f1c567e
... ... @@ -19,4 +19,10 @@ this page.
19 19 * :download:`How to connect observations to specific models? <howto/howto_connect_models.ipynb>`
20 20 * :download:`How to perform a stacked analysis? <howto/howto_stacked_analysis.ipynb>`
21 21 * :download:`How to do advanced model manipulation and fitting in Python? <howto/howto_advanced_python_fitting.ipynb>`
22   -* :download:`Data preparation for 1DC How-tos <howto/howto_prepare_1dc.ipynb>`
  22 +* :download:`Data preparation for 1DC How-to <howto/howto_prepare_1dc.ipynb>`
  23 +* :download:`Selecting events for H.E.S.S. public data <hess_dr1/selecting_events.ipynb>`
  24 +* :download:`Generating a sky map and finding sources for H.E.S.S. public data <hess_dr1/skymap.ipynb>`
  25 +* :download:`Generating a background model from H.E.S.S. public data <hess_dr1/bkgmodel.ipynb>`
  26 +* :download:`Performing an unbinned analysis of H.E.S.S. public data <hess_dr1/unbinned.ipynb>`
  27 +* :download:`Classical analysis of H.E.S.S. public data <hess_dr1/classical.ipynb>`
  28 +
... ...
models/Makefile.am
  View file @f1c567e
... ... @@ -31,6 +31,7 @@ models = 1dc_howto.xml \
31 31 crab.xml \
32 32 crab_beyond.xml \
33 33 crab_2bkg.xml \
  34 + crab_nobkg.xml \
34 35 crab_poly.xml \
35 36 crab_profile.xml \
36 37 disk.xml \
... ...
models/crab_nobkg.xml 0 → 100755
  View file @f1c567e
  1 +<?xml version="1.0" standalone="no"?>
  2 +<source_library title="source library">
  3 + <source name="Crab" type="PointSource">
  4 + <spectrum type="PowerLaw">
  5 + <parameter name="Prefactor" scale="1e-17" value="5.7" min="1e-07" max="1000.0" free="1"/>
  6 + <parameter name="Index" scale="-1" value="2.48" min="0.0" max="+5.0" free="1"/>
  7 + <parameter name="PivotEnergy" scale="1e6" value="1." min="0.01" max="1000.0" free="0"/>
  8 + </spectrum>
  9 + <spatialModel type="PointSource">
  10 + <parameter name="RA" scale="1.0" value="83.6331" min="-360" max="360" free="0"/>
  11 + <parameter name="DEC" scale="1.0" value="22.0145" min="-90" max="90" free="0"/>
  12 + </spatialModel>
  13 + </source>
  14 +</source_library>
... ...