simu.lib.instru_x module

Section author: ECLAIRs GP team

Instrument simulation module of point source .. note:: TODO need to be update

Summary

Classes:

SimuECLAIRsMaskProjection	Simulation with geometric method square intersection
SimuInstruXBase	Generic simulation of X instrument coded mask

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

Bases: simu.lib.instru_x.SimuInstruXBase

Simulation with geometric method square intersection

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)

plot_proj_mask(p_title='')

simu_catalog(time_expose, verbose=False)

class SimuInstruXBase

Bases: object

Generic simulation of X instrument coded mask

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_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_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=None)

projection_mask_numba(mask_trans, det_tmp, rect_proj, max_p_mask, max_p_det, size_p_mask, size_p_det, trans_mask, trans_det)

projection and intersection with numba library