def test_apextract_weights():
import tarfile
file_name = 'master_weights_LCB_10img_1exp.tar'
data = fits.getdata('fiberflat_frame.fits')
rss = apextract_weights(data, tarfile.open(file_name, 'r'))
hdu_rss = fits.PrimaryHDU(rss)
final = fits.HDUList([hdu_rss])
final.writeto('rss.fits', clobber=True)
assert True
def run_args(self, obresult, weights, wlcalib, parameters):
# Basic processing
self.logger.info('twilight fiber flat reduction started')
reduced = self.bias_process_common(obresult, parameters)
_logger.info('extract fibers')
rssdata = apextract_weights(reduced[0].data, weights)
# FIXME: we are ignoring here all the possible bad pixels
# and WL distortion when doing the normalization
# rssdata /= rssdata.mean() #Originally uncomment
template_header = reduced[0].header
rsshdu = fits.PrimaryHDU(rssdata, header=template_header)
rss = fits.HDUList([rsshdu])
self.logger.info('extraction completed')
_logger.info('resampling spectra')
final, wcsdata = self.resample_rss_flux(rsshdu.data, wlcalib)
# This value was ~0.4% and now is 4e-6 %
# (abs(final.sum()-hdu_t.data.sum())/hdu_t.data.sum()*100)
# Measure values in final
start = 200
end = 2100
_logger.info('doing mean between columns %d-%d', start, end)
colapse = final[:, start:end].mean(axis=1)
normalized = numpy.tile(colapse[:, numpy.newaxis], 4096)
master_t_hdu = fits.PrimaryHDU(normalized, header=template_header)
master_t = fits.HDUList([master_t_hdu])
_logger.info('twilight fiber flat reduction ended')
result = self.create_result(reduced_frame=reduced, reduced_rss=rss,
master_twilight_flat=master_t)
return result
