def find_lines(self, arcspec, slit_cen, slit, debug=False):
"""
Find the lines for tracing
Wrapper to tracewave.tilts_find_lines()
Args:
arcspec:
slit_cen:
slit (int):
debug:
Returns:
ndarray, ndarray: Spectral, spatial positions of lines to trace
"""
if self.par['idsonly'] is not None:
# Put in some hook here for getting the lines out of the wave calib for i.e. LRIS ghosts.
only_these_lines = None
pass
else:
only_these_lines = None
tracethresh = self._parse_param(self.par, 'tracethresh', slit)
lines_spec, lines_spat = tracewave.tilts_find_lines(
arcspec, slit_cen, tracethresh=tracethresh, sig_neigh=self.par['sig_neigh'],
nfwhm_neigh=self.par['nfwhm_neigh'],only_these_lines=only_these_lines, fwhm=self.wavepar['fwhm'],
nonlinear_counts=self.nonlinear_counts, debug_peaks=False, debug_lines = debug)
self.steps.append(inspect.stack()[0][3])
return lines_spec, lines_spat
def find_lines(self, arcspec, slit_cen, slit_idx, bpm=None, debug=False):
"""
Find the lines for tracing
Wrapper to tracewave.tilts_find_lines()
Args:
arcspec:
slit_cen:
slit_idx (int):
Slit index, zero-based
bpm (`numpy.ndarray`_, optional):
debug (bool, optional):
Returns:
tuple: 2 objectcs
- `numpy.ndarray`_ or None: Spectral positions of lines to trace
- `numpy.ndarray`_ or None: Spatial positions of lines to trace
"""
# TODO: Implement this!
only_these_lines = None
if self.par['idsonly']:
# Put in some hook here for getting the lines out of the
# wave calib for i.e. LRIS ghosts.
raise NotImplementedError('Select lines with IDs for tracing not yet implemented.')
# TODO -- This should be order not slit!
tracethresh = self._parse_param(self.par, 'tracethresh', slit_idx)
lines_spec, lines_spat, good \
= tracewave.tilts_find_lines(arcspec, slit_cen, tracethresh=tracethresh,
sig_neigh=self.par['sig_neigh'],
nfwhm_neigh=self.par['nfwhm_neigh'],
only_these_lines=only_these_lines,
fwhm=self.wavepar['fwhm'],
nonlinear_counts=self.nonlinear_counts,
bpm=bpm, debug_peaks=False, debug_lines=debug)
if debug:
mean, median, stddev = stats.sigma_clipped_stats(self.mstilt.image, sigma=3.)
# vmin, vmax = visualization.ZScaleInterval().get_limits(self.mstilt.image)
vmin = median - 2*stddev
vmax = median + 2*stddev
plt.imshow(self.mstilt.image, origin='lower', interpolation='nearest', aspect='auto',
vmin=vmin, vmax=vmax)
plt.scatter(lines_spat[good], lines_spec[good], marker='x', color='k', lw=2, s=50)
plt.scatter(lines_spat[np.invert(good)], lines_spec[np.invert(good)], marker='x', color='C3', lw=2, s=50)
plt.show()
self.steps.append(inspect.stack()[0][3])
return (None, None) if lines_spec is None else (lines_spec[good], lines_spat[good])