ImagingMiniPipeline

class ImagingMiniPipeline(catalog_simu=<ecpi.common.sky.catalog.CatalogFovBasic object>, simulator=None, deconvolver=None, src_finder=None)

Bases: object

mini-pipeline block used in IMAGING.

includes simu shadowgrams, deconv sky, src finder and fit

Constructor

init with everything at 0

Parameters:

• catalog_simu (CatalogFovBasic) – sources catalog to be use by the simulator
• simulator (SimuInstruXBase) – simulation object to be use
• deconvolver (DeconvV1) – deconvolution object
• src_finder (MaxSourceFinder) – source-finder object to be use

Methods Summary

deconv([decon_step])	Build the sky image of the source seen by ECLAIRs Need self.shadowgrams to be set before.
fit_source([snr_min, peak_position])	Find then fit the source in the sky image Need self.sky_count and self.sky_snr to be set before.
reset([catalog_simu])	Reset all attributes to 0
simu_shadowgram(time_exp_s[, normalized])	Build the shadowgrams model of the source using the simulation module.

Methods Documentation

deconv(decon_step='just_decon')

Build the sky image of the source seen by ECLAIRs Need self.shadowgrams to be set before.

Set self.sky_count, self.sky_variance and self.sky_snr decon_step = with_init or with_prep (or just_decon)

Parameters:decon_step (string) – what deconvolution step to begin with. Choices={‘with_init’, ‘with_prep’}. Default=’just_decon’.

fit_source(snr_min=5, peak_position=None)

Find then fit the source in the sky image Need self.sky_count and self.sky_snr to be set before.

Parameters:

• snr_min (float) – snr limit for the source localisation. Default=5.
• peak_position ([float, float]) – position of the peak to be fitted, if already known. [pix_y, pix_z]. Default=None

reset(catalog_simu=<ecpi.common.sky.catalog.CatalogFovBasic object>)

Reset all attributes to 0

Parameters:catalog_simu (CatalogFovBasic) – sources catalog to be use by the simulator

simu_shadowgram(time_exp_s, normalized=False)

Build the shadowgrams model of the source using the simulation module. No background is considered in this simulation.

Set self.shadowgrams

Parameters:

• time_exp_s (float) – observation time in s
• normalized (bool) – to get shadowgram’s values between 0 and 1 (default=False)