SimuInstruXBase

class SimuInstruXBase[source]

Bases: object

Methods Summary

add_all_evt(apos) Add one on pixel associated to position
add_cxb([cxb_eclairs_level])
param cxb_eclairs_level:
 intensity of CXB
get_cxb([cxb_eclairs_level, cxb_type, …]) return a numpy array of detector with CXB count by pixel.
get_det_pixel_surface()
get_shadowgram()
get_snr_sources([shadowgram_tot]) compute the theoretical max snr of each source
plot_shadowgram()
raz_nb_photon()
save_cal_evts(filename[, t_start, l1]) t_start in s from mjdref
save_catal_log(filename) save a fits log file of the simulation of each source
save_evts(filename[, t_start])
save_mask(name_fits)
save_stack_evt_image(name_fits)
set_catalog(catalog_x)
simu_catalog(time_expose[, verbose])

Methods Documentation

add_all_evt(apos)[source]

Add one on pixel associated to position

Parameters:apos (numpy array (n,2)) – array plan position of photon
add_cxb(cxb_eclairs_level=1)[source]
Parameters:cxb_eclairs_level (float [count/cm2/s]) – intensity of CXB
get_cxb(cxb_eclairs_level=1, cxb_type='flat_nospectr', internal_noise=False, noise_level=0.003)[source]

return a numpy array of detector with CXB count by pixel.

method :

Parameters:
  • cxb_eclairs_level (float [count/cm2/s]) – intensity of CXB
  • cxb_type (string) – type of CXB = ‘flat_nospectr’ or ‘shapebased_nospectr’ (default=’flat_nospectr’)
  • internal_noise (bool) – flag to add internal noise
  • noise_level (float) – internal noise level in cts/sec/cm2/keV (default is 0.003)
Returns:

cxb detector image counts
Return type:

array(float)
get_det_pixel_surface()[source]
get_shadowgram()[source]
get_snr_sources(shadowgram_tot=None)[source]

compute the theoretical max snr of each source

the snr are listed in the order of the sources catalog

Parameters:shadowgram_tot (array(float)) – final shadowgram (default=from the simu object)
Returns:list of the snr of each source
Return type:list(float)
plot_shadowgram()[source]
raz_nb_photon()[source]
save_cal_evts(filename, t_start=0, l1=False)[source]

t_start in s from mjdref

save_catal_log(filename)[source]

save a fits log file of the simulation of each source

save_evts(filename, t_start=<Time object: scale='tt' format='isot' value=2018-06-16T14:37:07.000>)[source]
save_mask(name_fits)[source]
save_stack_evt_image(name_fits)[source]
set_catalog(catalog_x: common.sky.catalog.CatalogFovBasic)[source]
simu_catalog(time_expose, verbose=None)[source]