SimuEclairsEnergyChannel

class SimuEclairsEnergyChannel(mdl_effect=None)

Bases: object

ECLAIRs simulation with energy chanel during stable pointing

User Guide: * defined first the context of simulation with context attribut : ** t_start, duration ** if necessary attitude for catalog swift and earth ** if necessary sat pos for earth * add some sky sources ** with methods like add_srcxxxx() ** from catalog with method set_src_point_with_catalog_xxx() * set energy band of simulation with * simulate sky source in photon with simu_src() * add some detector effect ** simu_arf_xxx ** simu_rmf (to check !!) * add poisson noise * result : image detector by channel in count

Constructor

Parameters:mdl_effect (ECLAIRsDetectorEffect) – ECLAIRs detector default.

Methods Summary

add_nonuniformity([factor])	Add non uniformity on the pixels of the detector.
add_src(model_src_obj)	add a source to the list
add_src_astroparticule()	Add flat internal noise from particles on the detector.
add_src_cxb([cxb_mode])	Add CXB source depending on model name cxb_mode.
add_src_point_with_catalog_astro(cat_astro)	add all sources in cat_astro that are in FOV
add_src_point_with_catalog_fov(cat_fov[, …])	add all sources from cat_fov
add_src_point_with_catalog_swift(use_irf)	reset all source l_ponc_src and init with swift catalog
create_evts()	Create events for every energy channel.
del_src(name_src)
del_src_all()
get_list_src()
get_shadow_channel(idx)
plot_channel(idx[, p_mes])
print_nb_photon()
raz_chan_shadows()	raz all channel with float64 type
set_chan_shadows(chan_shadows_)	Setter for chan_shadows class attribute.
set_range_channel(start_chan, end_chan)	Set energy range [start_chan, end_chan] of simulation
set_src_point_with_custom_catalog(path_cat, …)	reset all source l_ponc_src and init with swift catalog
simu_arf_binomial()	apply arf with binomial process to conserve integer value
simu_arf_mean()	This method introduces real (ie not integer) number of photon can be replaced by binomial random processing.
simu_global_noise_poisson()	Simulation of random Poisson noise for each shadowgram (ie.
simu_rmf()	RMF random simulation
simu_src([idx_chan, l_src])	simulation of sources
write_events(working_directory, observation)	Write ECL-EVT-SEC .fits files.
write_shadowgrams(working_directory, …[, …])	Write shadowgrams in .fits files.

Methods Documentation

add_nonuniformity(factor=0.8)

Add non uniformity on the pixels of the detector.

Parameters:factor –

???

add_src(model_src_obj)

add a source to the list

Parameters:model_src_obj (mdl_src.ModelSrcInterface) – initialized source object Returns:status of add Return type:boolean

add_src_astroparticule()

Add flat internal noise from particles on the detector.

Returns:status of add Return type:boolean

add_src_cxb(cxb_mode='shape_moretti')

Add CXB source depending on model name cxb_mode.

Note

to set properly the levels in ‘flat’ and ‘shape’ modes

with specific level, do

flat = ModelFlat(my_level) add_src(flat)

Parameters:cxb_mode – CXB model to be added. Default is “no_cxb”.

Possible choices are: flat”, “flat_moretti”, “shape”, “shape_moretti”, “shape_moretti_earth”. :type cxb_mode: str :return: status of add :rtype: boolean

add_src_point_with_catalog_astro(cat_astro, use_irf=False)

add all sources in cat_astro that are in FOV cat_astro is an user catalog can by used for debug, analysis and test .. cat_fov must have intensity array shape

Parameters:

• cat_astro (CatalogAstroXXXX) – sidx_chanources catalog with energy
• use_irf (bool) – whether or not to use IRF.

add_src_point_with_catalog_fov(cat_fov, use_irf=False)

add all sources from cat_fov cat_fov is an user catalog can by used for debug, analysis and test .. cat_fov must have intensity array shape

Parameters:

• cat_fov (CatalogFovBasic) – sources catalog with energy
• use_irf (bool) – whether or not to use IRF.

add_src_point_with_catalog_swift(use_irf)

reset all source l_ponc_src and init with swift catalog

Parameters:use_irf (bool) – whether or not to use IRF.

create_evts()

Create events for every energy channel.

del_src(name_src)

del_src_all()

get_list_src()

get_shadow_channel(idx)

plot_channel(idx, p_mes='')

print_nb_photon()

raz_chan_shadows()

raz all channel with float64 type

set_chan_shadows(chan_shadows_)

Setter for chan_shadows class attribute.

Parameters:chan_shadows (3D array (channel, shadowgram)) – shadowgrams per energy channel

set_range_channel(start_chan, end_chan)

Set energy range [start_chan, end_chan] of simulation

Parameters:

• start_chan (integer) –
• end_chan (integer) –

Returns:

True if check start_chan, end_chan are ok

Return type:

Boolean

set_src_point_with_custom_catalog(path_cat, use_irf)

reset all source l_ponc_src and init with swift catalog

Parameters:

• path_cat (str) – path to custom source catalog.
• use_irf (bool) – whether or not to use IRF.

simu_arf_binomial()

apply arf with binomial process to conserve integer value

simu_arf_mean()

This method introduces real (ie not integer) number of photon can be replaced by binomial random processing. Add poisson noise at end

simu_global_noise_poisson()

Simulation of random Poisson noise for each shadowgram (ie. each energy channel)

simu_rmf()

RMF random simulation

input: attribut array shadowgram is incident photon by channel ouput: attribut array shadowgram becomes detected photon by channel

simu_src(idx_chan=None, l_src=None)

simulation of sources

Parameters:

• idx_chan (int >0) – channel index to be simulated. Default=None for all channel
• l_src (list(string)) – list of sources to be simulated. Default=None for all initiated sources

write_events(working_directory, observation)

Write ECL-EVT-SEC .fits files.

Parameters:

• working_dir (str) – PATH/directory where to save files
• observation (EclairsObservation) – observation parameters

write_shadowgrams(working_directory, simulation_id, limit_shadowgrams=50)

Write shadowgrams in .fits files.

Parameters:

• working_dir (str) – PATH/directory where to save files
• simulation_id (int) – source ID