SimuECLAIRsMaskProjection

class SimuECLAIRsMaskProjection(catalog_x: ecpi.common.sky.catalog.CatalogFovBasic = None, p_nb_pixel=None)

Bases: ecpi.simu.lib.instru_x.SimuInstruXBase

Simulation with geometric method square intersection

Constructor

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_proj_mask()
get_proj_sub_mask()
get_shadow_apparent_surface(elev, direc)	Return apparent surface
get_shadow_percent(elev, direc)
get_shadow_surface(elev, direc)
get_shadowgram()
get_snr_sources([shadowgram_tot])	compute the theoretical max snr of each source.
plot_proj_mask([p_title])
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)

Add one on pixel associated to position

Parameters:apos (numpy array (n,2)) – array plan position of photon

add_cxb(cxb_eclairs_level=1)

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)

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

Parameters:

• cxb_eclairs_level (float [count/cm2/s]) – intensity of CXB
• cxb_type (string) – type of CXB = ‘flat_nospectr’ or ‘shapebased_nospectr’. Default is ‘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()

get_proj_mask()

get_proj_sub_mask()

get_shadow_apparent_surface(elev, direc)

Return apparent surface

get_shadow_percent(elev, direc)

get_shadow_surface(elev, direc)

get_shadowgram()

get_snr_sources(shadowgram_tot=None)

compute the theoretical max snr of each source. Takes into account the mask is partially coded. SNRs 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_proj_mask(p_title='')

plot_shadowgram()

raz_nb_photon()

save_cal_evts(filename, t_start=0, l1=False)

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=<Time object: scale='tt' format='isot' value=2018-06-16T14:37:07.000>)

save_mask(name_fits)

save_stack_evt_image(name_fits)

set_catalog(catalog_x: ecpi.common.sky.catalog.CatalogFovBasic)

simu_catalog(time_expose, verbose=False)