def _r_and_a(row, passthrough, accumulate_result, find_pins=True, fiber=None, exposure=None):
grid = (skyexp_wlen_out if not use_loglam else skyexp_loglam_out) \
if use_skyexp_fid else (10**bdspec.fiducial_loglam if not use_loglam \
else bdspec.fiducial_loglam)
resampled_data = resample(row, ('wavelength' if not use_loglam else 'loglam'), grid, passthrough)
pins = None
if find_pins:
pins = _find_grid_pins(resampled_data, fiber, exposure)
return accumulate(accumulate_result, resampled_data, data_out=accumulate_result,
join=('wavelength' if not use_loglam else 'loglam'),
add=('flux'), weight='ivar'), \
pins
def build_observation_list(observations, start_wlen, end_wlen, delta_wlen):
''' This function takes a list of observations and redshifts them and
resamples them to be on the same grid. '''
wlen_range = np.arange(start_wlen, end_wlen, delta_wlen)
qsos = list()
for info, qso in observations.items():
rest_frame = redshift(z_in=qso.z, z_out=0, data_in=qso.data,
rules=[dict(name='wavelength', exponent=1),
dict(name='flux', exponent=-1),
dict(name='dflux', exponent=-1)])
resampled = resample(rest_frame, x_in='wavelength',
x_out=wlen_range, y=('flux', 'dflux'))
qsos.append(resampled)
return qsos
def _r_and_a(row,
passthrough,
accumulate_result,
find_pins=True,
fiber=None,
exposure=None):
grid = (skyexp_wlen_out if not use_loglam else skyexp_loglam_out) \
if use_skyexp_fid else (10**bdspec.fiducial_loglam if not use_loglam \
else bdspec.fiducial_loglam)
resampled_data = resample(
row, ('wavelength' if not use_loglam else 'loglam'), grid,
passthrough)
pins = None
if find_pins:
pins = _find_grid_pins(resampled_data, fiber, exposure)
return accumulate(accumulate_result, resampled_data, data_out=accumulate_result,
join=('wavelength' if not use_loglam else 'loglam'),
add=('flux'), weight='ivar'), \
pins
fiducial_grid = np.arange(1000.0, 3000.0)
rest_frame, resampled, spec_qso = None, None, None
for row in qso_table:
filename = finder.get_spec_path(plate=row["PLATE"], mjd=row["MJD"], fiber=row["FIBER"], lite=True)
spectrum = bossdata.spec.SpecFile(mirror.get(filename))
data = spectrum.get_valid_data(use_ivar=True, fiducial_grid=True)
rest_frame = speclite.redshift(
z_in=row["Z_VI"],
z_out=0,
data_in=data,
data_out=rest_frame,
rules=[dict(name="wavelength", exponent=+1), dict(name="flux", exponent=-1), dict(name="ivar", exponent=+2)],
)
resampled = speclite.resample(
rest_frame, x_in="wavelength", x_out=fiducial_grid, y=("flux", "ivar"), data_out=resampled
)
spec_qso = speclite.accumulate(spec_qso, resampled, data_out=spec_qso, join="wavelength", add="flux", weight="ivar")
plot_stack(spec_qso, truncate_percentile=99.5)
filename = finder.get_plate_spec_path(plate=6641, mjd=56383)
plate = bossdata.plate.PlateFile(mirror.get(filename))
plate_data = plate.get_valid_data(qso_table["FIBER"], use_ivar=True, fiducial_grid=True)
zorder = np.argsort(qso_table["Z_VI"])
z_in = qso_table["Z_VI"][:, np.newaxis]
plate_data = speclite.redshift(
z_in=z_in,
z_out=0,