Automatic source detection

In most of SITELLE data cubes there are numerous point-like emission-line objects which velocity is unknown. There is a simple algorithm which can help in their discovery. This algorithm is described in Martin et al. 2017 (http://arxiv.org/abs/1707.01366) and is based on the measurement of the highest emission of each pixel with respect to its neighbourhood. It delivers a map if the net emission which helps in detecting point-like emission but minimizes the diffuse emission.

Two versions of the algorithm (one fast and the one much slower) can be choosen. the fast version will take less than half an hour and will deliver a map with some artifacts but which gives a good idea of what can be obtained with the much slower algorithm.

# import base class for the manipulation of a SITELLE spectral cube: HDFCube
from orcs.process import SpectralCube
import pylab as pl
import numpy as np
cube = SpectralCube('/home/thomas/M31_SN3.merged.cm1.1.0.hdf5')

master.03e73|INFO| Cube is level 3
master.03e73|INFO| shape: (2048, 2064, 840)
master.03e73|INFO| wavenumber calibration: True
master.03e73|INFO| flux calibration: True
master.03e73|INFO| wcs calibration: True

cube.detect_sources(fast=True)

master.03e73|INFO| Source detection using fast algorithm
master.03e73|INFO| Signal range: [14598.54014599 15439.24656477] cm-1, (277, 597) pixels
master.03e73|INFO| Extracting frames: 277 to 312 (35/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| Closing parallel processing server
master.03e73|WARNING| /home/thomas/Astro/Python/ORB/Orcs/orcs/process.py:516: ComplexWarning: Casting complex values to real discards the imaginary part
  det_frame[new_det] = imax_frame[new_det]

master.03e73|INFO| Extracting frames: 312 to 348 (71/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 348 to 383 (106/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 383 to 419 (142/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 419 to 454 (177/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 454 to 490 (213/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 490 to 525 (248/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 525 to 561 (284/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Extracting frames: 561 to 597 (320/320 frames)
master.03e73|INFO| Init of the parallel processing server with 32 threads
master.03e73|INFO| parallel processing closed
master.03e73|INFO| Closing parallel processing server
master.03e73|INFO| Data written as ./M31_SN3/M31_SN3.SpectralCube.detection_frame.fits in 0.14 s
master.03e73|INFO| Data written as ./M31_SN3/M31_SN3.SpectralCube.detection_pos_frame.fits in 0.14 s
master.03e73|INFO| parallel processing closed

Here the file ./M31_SN3/M31_SN3.SpectralCube.detection_frame.fits is the generated detection map. You can see below a small excerpt of the detection map where numerous emission sources are visible which were undetectable in the deep frame.

import orb.utils.io as io
detect_map = io.read_fits('./M31_SN3/M31_SN3.SpectralCube.detection_frame.fits')
pl.figure(figsize=(8,8))
pl.imshow(detect_map.T, vmin=np.nanpercentile(detect_map,5),
         vmax = np.nanpercentile(detect_map, 99))
pl.xlim((400,800))
pl.ylim((800,1200))
pl.grid()
pl.title('detection frame')

Text(0.5, 1.0, 'detection image')

df = cube.get_deep_frame()
df.imshow(wcs=False, figsize=(8,8), perc=95)
pl.xlim((400,800))
pl.ylim((800,1200))
pl.grid()
pl.title('deep frame')

Text(0.5, 1.0, 'deep frame')