def test_compute_trend():
numpy.random.seed(12812818)
xx = numpy.arange(100)
yy = 700 + 3.2 * (xx / 100.0)
yy = numpy.random.normal(loc=yy, scale=2.0)
trend = detrend(yy)
maxt = trend.max()
mint = trend.min()
expected = [704.812599, 698.595538331]
numpy.testing.assert_array_almost_equal([maxt, mint], expected)
def calibrate_wl(self, rss, lines_catalog, poldeg, tracemap, nlines,
threshold=0.27,
min_distance=30):
wv_master = lines_catalog[:, 0]
ntriplets_master, ratios_master_sorted, triplets_master_sorted_list = \
gen_triplets_master(wv_master)
nwinwidth = 5
error_contador = 0
missing_fib = 0
data_wlcalib = WavelengthCalibration(instrument='MEGARA')
data_wlcalib.total_fibers = tracemap.total_fibers
for trace in tracemap.contents:
fibid = trace.fibid
idx = trace.fibid - 1
if trace.valid:
row = rss[idx]
trend = detrend(row)
fibdata_detrend = row - trend
# A fix for LR-V Jun 2016 test images
# that only have lines there
row = fibdata_detrend
self.logger.info('Starting row %d, fibid %d', idx, fibid)
# find peaks (initial search providing integer numbers)
ipeaks_int = peak_local_max(row, threshold_rel=threshold,
min_distance=min_distance)[:, 0]
self.logger.debug('ipeaks_int.........: %s', ipeaks_int)
# Filter by flux, selecting a maximum number of brightest
# lines in each region
region_size = (len(row)-1)/(len(nlines))
ipeaks_int_filtered = numpy.array([], dtype=int)
for iregion, nlines_in_region in enumerate(nlines):
if nlines_in_region > 0:
imin = int(iregion * region_size)
imax = int((iregion + 1) * region_size)
if iregion > 0:
imin += 1
ipeaks_int_region = ipeaks_int[numpy.logical_and(
ipeaks_int >= imin, ipeaks_int <= imax
)]
if len(ipeaks_int_region) > 0:
peak_fluxes = row[ipeaks_int_region]
spos = peak_fluxes.argsort()
ipeaks_tmp = ipeaks_int_region[
spos[-nlines_in_region:]
]
ipeaks_tmp.sort() # in-place sort
self.logger.debug('ipeaks_in_region...: %s',
ipeaks_tmp)
ipeaks_int_filtered=numpy.concatenate(
(ipeaks_int_filtered, ipeaks_tmp)
)
self.logger.debug('ipeaks_int_filtered: %s', ipeaks_int_filtered)
ipeaks_float = refine_peaks(row, ipeaks_int_filtered, nwinwidth)[0]
# if idx==299:
if False:
import matplotlib.pyplot as plt
plt.title('fibid %d' % fibid)
plt.plot(row)
plt.plot(ipeaks_int, row[ipeaks_int], 'ro', alpha=.9, ms=7,
label="ipeaks_int")
plt.plot(ipeaks_int_filtered, row[ipeaks_int_filtered], 'ro', alpha=.9, ms=7,
label="ipeaks_int_filtered")
plt.legend()
plt.show()
# FIXME: xchannel ???
# This comes from Nico's code, so probably pixels
# will start in 1
naxis1 = row.shape[0]
crpix1 = 1.0
xchannel = numpy.arange(1, naxis1 + 1)
finterp_channel = interp1d(range(xchannel.size), xchannel,
kind='linear',
bounds_error=False,
fill_value=0.0)
xpeaks_refined = finterp_channel(ipeaks_float)
wv_range_catalog = lines_catalog[-1][0] - lines_catalog[0][0]
delta_wv = 0.05 * wv_range_catalog
wv_ini_search = int(lines_catalog[0][0] - delta_wv)
wv_end_search = int(lines_catalog[-1][0] + delta_wv)
try:
self.logger.info('wv_ini_search %s', wv_ini_search)
self.logger.info('wv_end_search %s', wv_end_search)
list_of_wvfeatures = arccalibration_direct(
wv_master,
ntriplets_master,
ratios_master_sorted,
triplets_master_sorted_list,
xpeaks_refined,
naxis1,
crpix1=crpix1,
wv_ini_search=wv_ini_search,
wv_end_search=wv_end_search,
error_xpos_arc=2.3, # initially: 2.0
times_sigma_r=3.0,
frac_triplets_for_sum=0.50,
times_sigma_theil_sen=10.0,
poly_degree_wfit=poldeg,
times_sigma_polfilt=10.0,
times_sigma_cook=10.0,
times_sigma_inclusion=5.0
)
self.logger.info('Solution for row %d completed', idx)
self.logger.info('Fitting solution for row %d', idx)
solution_wv = fit_list_of_wvfeatures(
list_of_wvfeatures,
naxis1_arc=naxis1,
crpix1=crpix1,
poly_degree_wfit=poldeg,
weighted=False,
debugplot=0,
plot_title=None
)
self.logger.info('linear crval1, cdelt1: %f %f',
solution_wv.cr_linear.crval,
solution_wv.cr_linear.cdelt)
self.logger.info('fitted coefficients %s',
solution_wv.coeff)
trace_pol = trace.polynomial
# Update feature with measurements of Y coord in original image
# Peak and FWHM in RSS
for feature in solution_wv.features:
# Compute Y
feature.ypos = trace_pol(feature.xpos)
# FIXME: check here FITS vs PYTHON coordinates, etc
peak_int = int(feature.xpos)
try:
peak, fwhm = self.calc_fwhm_of_line(row, peak_int, lwidth=20)
except Exception as error:
self.logger.error("%s", error)
self.logger.error('error in feature %s', feature)
# workaround
peak = row[peak_int]
fwhm = 0.0
# I would call this peak instead...
feature.peak = peak
feature.fwhm = fwhm
# if True:
# plt.title('fibid %d' % fibid)
# plt.plot(row)
# plt.plot(ipeaks_int, row[ipeaks_int],'ro', alpha=.9, ms=7, label="ipeaks_int")
# # # plt.plot(ipeaks_int2, row[ipeaks_int2],'gs', alpha=.5 , ms=10)
# plt.legend()
# plt.show()
new = FiberSolutionArcCalibration(fibid, solution_wv)
data_wlcalib.contents.append(new)
except (ValueError, TypeError, IndexError) as error:
self.logger.error("%s", error)
self.logger.error('error in row %d, fibid %d', idx, fibid)
traceback.print_exc()
data_wlcalib.error_fitting.append(fibid)
if False:
import matplotlib.pyplot as plt
plt.title('fibid %d' % fibid)
rrow = row[::-1]
rpeaks = 4096-ipeaks_int_filtered[::-1]
plt.plot(rrow)
plt.plot(rpeaks, rrow[rpeaks], 'ro', alpha=.9, ms=7, label="ipeaks_int_filtered")
# # plt.plot(ipeaks_int2, row[ipeaks_int2],'gs', alpha=.5 , ms=10)
plt.legend()
plt.show()
error_contador += 1
else:
self.logger.info('skipping row %d, fibid %d, not extracted', idx, fibid)
missing_fib += 1
data_wlcalib.missing_fibers.append(fibid)
self.logger.info('Errors in fitting: %s', error_contador)
self.logger.info('Missing fibers: %s', missing_fib)
self.logger.info('Generating fwhm_image...')
image = self.generate_fwhm_image(data_wlcalib.contents)
fwhm_image = fits.PrimaryHDU(image)
fwhm_hdulist = fits.HDUList([fwhm_image])
self.logger.info('End arc calibration')
return data_wlcalib, fwhm_hdulist