Fermi GBM Data Collections (gdt.missions.fermi.gbm.collections)

The GbmDetectorCollection class extends the functionality of DataCollection so that it can be used with GBM’s heterogenous detector types: NaI and BGO detectors. This specialized collection class allows us to apply the same action over a collection of detectors, but also to specify different parameters based on if a detector is an NaI or BGO.

As an example, let’s assume we have two NaI CSPEC files open and one BGO file open, and we read them into a collection:

>>> from gdt.missions.fermi.gbm.collection import GbmDetectorCollection
>>> cspecs = GbmDetectorCollection.from_list([cspec_n0, cspec_n1, cspec_b0])
>>> cspecs
<GbmDetectorCollection: 3 Cspec objects>

Just like the base DataCollection, we can retrieve properties of each item in the collection with a single call:

>>> # get the energy range for each item
>>> cspec.energy_range()
[(4.5702357, 2000.0), (4.089358, 2000.0), (113.00731, 50000.0)]

Or call a function that applies the same arguments/keywords to each item:

>>> # generate the lightcurve over from -10 to +10 s
>>> cspecs.to_lightcurve(time_range=[-10.0, 10.0])
[<TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>,
 <TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>,
 <TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>]

But what if we wanted to integrate over a different energy range for the NaI and BGO detectors to generate the lightcurves? For a situation like that, we specify NaI-specific arguments with nai_args and BGO-specific arguments with bgo_args. Similarly, if we need to pass different keywords, we would specify nai_kwargs and bgo_kwargs. Note that the xxx_args and xxx_kwargs follow the same convention as normal Python args and kwargs: args are passed as tuples containing the arguments, and kwargs are passed as dicts.

For example:

>>> nai_kwargs = {'energy_range': (8.0, 900.0)}
>>> bgo_kwargs = {'energy_range': (325, 35000.0)}
>>> cspecs.to_lightcurve(time_range=[-10.0, 10.0], nai_kwargs=nai_kwargs, bgo_kwargs=bgo_kwargs)
[<TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>,
 <TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>,
 <TimeBins: 14 bins;
  range (-13.824249982833862, 10.752128005027771);
  1 contiguous segments>]

Or similarly slice over different energies:

>>> cspecs.slice_energy(nai_args=((8.0, 900.0),), bgo_args=((325, 35000.0),))
[<Cspec:
  trigger time: 484477130.219298;
  time range (-4003.394767999649, 4001.3853880167007);
  energy range (7.3262835, 923.9744)>,
 <Cspec:
  trigger time: 484477130.219298;
  time range (-4003.394767999649, 4001.3853880167007);
  energy range (7.7042193, 912.946)>,
 <Cspec:
  trigger time: 484477130.219298;
  time range (-4003.394767999649, 4001.3853880167007);
  energy range (317.86954, 35982.97)>]

For other details about using collections, please see the documentation on Data Collections.

Reference/API

gdt.missions.fermi.gbm.collection Module

Classes

GbmDetectorCollection()

A container for a collection of GBM-specific data objects, such as a collection of GbmPhaii objects from different detectors.

Class Inheritance Diagram