def ism_transmittance_chunk(qso_record_table):
start_offset = qso_record_table[0]['index']
# spectra = read_spectrum_hdf5.SpectraWithMetadata(qso_record_table, settings.get_qso_spectra_hdf5())
# continuum_fit_file = NpSpectrumContainer(True, filename=settings.get_continuum_fit_npy())
delta_transmittance_file = NpSpectrumContainer(
readonly=True,
filename=settings.get_delta_t_npy(),
max_wavelength_count=1000)
num_spectra = len(qso_record_table)
ism_delta_t = NpSpectrumContainer(False, num_spectra=num_spectra)
# warning: np.ndarray is not initialized by default. zeroing manually.
ism_delta_t.zero()
n = 0
for i in range(len(qso_record_table)):
qso_rec = QSORecord.from_row(qso_record_table[i])
index = qso_rec.index
# read original delta transmittance
ar_redshift = delta_transmittance_file.get_wavelength(index)
# ar_flux = delta_transmittance_file.get_flux(index)
ar_ivar = delta_transmittance_file.get_ivar(index)
# get correction to ISM
# ar_flux_new, ar_ivar_new, is_corrected = pre_process_spectrum.mw_lines.apply_correction(
# ar_wavelength, np.ones_like(ar_flux), ar_ivar, qso_rec.ra, qso_rec.dec)
ar_wavelength = (ar_redshift + 1) * lya_center # type: np.ndarray
# limit maximum bin number because higher extinction bins are not reliable
max_extinction_bin = max(20, ar_extinction_levels.size)
if np.isfinite(qso_rec.extinction_g):
extinction_bin = int(
np.round(
np.interp(qso_rec.extinction_g, ar_extinction_levels,
np.arange(max_extinction_bin))))
else:
extinction_bin = 0
l_print_no_barrier("extinction_bin = ", extinction_bin)
ar_ism_resampled = np.interp(
ar_wavelength,
extinction_spectra_list[extinction_bin][0],
extinction_spectra_list[extinction_bin][1],
left=np.nan,
right=np.nan)
extinction = ar_extinction_levels[extinction_bin]
# rescale according to QSO extinction
l_print_no_barrier(qso_rec.extinction_g, extinction)
ism_scale_factor = 1.
ar_flux_new = (ar_ism_resampled - 1
) * ism_scale_factor * qso_rec.extinction_g / extinction
mask = np.logical_and(np.isfinite(ar_flux_new), ar_ivar)
ism_delta_t.set_wavelength(i, ar_redshift[mask])
# use reciprocal to get absorption spectrum, then subtract 1 to get the delta
ism_delta_t.set_flux(i, ar_flux_new[mask])
# ism_delta_t.set_flux(i, np.ones_like(ar_flux) * qso_rec.extinction_g)
# use original ivar because we are not correcting an existing spectrum
ism_delta_t.set_ivar(i, ar_ivar[mask])
n += 1
l_print_no_barrier("chunk n =", n, "offset =", start_offset)
return ism_delta_t.as_np_array(), None
def delta_transmittance_chunk(qso_record_table):
start_offset = qso_record_table[0]['index']
spectra = read_spectrum_hdf5.SpectraWithMetadata(
qso_record_table, settings.get_qso_spectra_hdf5())
continuum_fit_file = ContinuumFitContainerFiles(False)
num_spectra = len(qso_record_table)
delta_t = NpSpectrumContainer(False, num_spectra=num_spectra)
# warning: np.ndarray is not initialized by default. zeroing manually.
delta_t.zero()
m = mean_transmittance.MeanTransmittance.from_file(
settings.get_mean_transmittance_npy())
# m = median_transmittance.MedianTransmittance.from_file(settings.get_median_transmittance_npy())
# for debugging with a small data set:
# ignore values with less than 20 sample points
ar_z_mean_transmittance, ar_mean_transmittance = m.get_weighted_mean_with_minimum_count(
20)
# ar_z_mean_transmittance, ar_mean_transmittance = m.get_weighted_median_with_minimum_count(20, weighted=True)
remove_dla = RemoveDlaSimple()
pixel_weight = pixel_weight_coefficients.PixelWeight(
pixel_weight_coefficients.DEFAULT_WEIGHT_Z_RANGE)
for n in range(len(qso_record_table)):
qso_spec_obj = spectra.return_spectrum(n)
index = qso_spec_obj.qso_rec.index
if not continuum_fit_file.get_is_good_fit(index):
local_delta_stats['bad_fit'] += 1
l_print_no_barrier("skipped QSO (bad fit): ", qso_spec_obj.qso_rec)
continue
ar_fit_spectrum = continuum_fit_file.get_flux(index)
# we assume the fit spectrum uses the same wavelengths.
lya_forest_transmittance = qso_transmittance(
qso_spec_obj,
ar_fit_spectrum,
local_delta_stats,
downsample_factor=settings.get_forest_downsample_factor())
ar_z = lya_forest_transmittance.ar_z
if ar_z.size:
# prepare the mean transmittance for the z range of this QSO
ar_mean_flux_for_z_range = np.asarray(
np.interp(ar_z, ar_z_mean_transmittance,
ar_mean_transmittance))
# delta transmittance is the change in relative transmittance vs the mean
# therefore, subtract 1.
ar_delta_t = lya_forest_transmittance.ar_transmittance / ar_mean_flux_for_z_range - 1
# finish the error estimation, and save it
ar_delta_t_ivar = pixel_weight.eval(
lya_forest_transmittance.ar_ivar,
ar_mean_flux_for_z_range * lya_forest_transmittance.ar_fit,
ar_z)
# simple DLA removal (without using a catalog)
if settings.get_enable_simple_dla_removal():
# remove DLA regions by setting the ivar of nearby pixels to 0
ar_dla_mask = remove_dla.get_mask(ar_delta_t)
if np.any(ar_dla_mask):
l_print_no_barrier("DLA(s) removed from QSO: ",
qso_spec_obj.qso_rec)
ar_delta_t_ivar[ar_dla_mask] = 0
# ignore nan or infinite values (in case m_mean has incomplete data because of a low sample size)
# Note: using wavelength field to store redshift
finite_mask = np.logical_and(np.isfinite(ar_delta_t),
np.isfinite(ar_delta_t_ivar))
finite_z = ar_z[finite_mask]
finite_delta_t = ar_delta_t[finite_mask]
finite_ivar = ar_delta_t_ivar[finite_mask]
# detrend forests with large enough range in comoving coordinates:
finite_distances = cd.fast_comoving_distance(finite_z)
if finite_distances[-1] - finite_distances[0] > 500:
delta_t_boxcar = nu_boxcar(finite_distances,
finite_delta_t,
lambda c: c - 300,
lambda c: c + 300,
weights=finite_ivar)
finite_delta_t = finite_delta_t - delta_t_boxcar
delta_t.set_wavelength(n, finite_z)
delta_t.set_flux(n, finite_delta_t)
delta_t.set_ivar(n, finite_ivar)
else:
# empty record
pass
delta_transmittance_chunk.num_spec += 1
l_print_no_barrier("finished chunk, num spectra:",
delta_transmittance_chunk.num_spec, " offset: ",
start_offset)
return delta_t.as_np_array(), None
class ContinuumFitContainer(object):
def __init__(self, num_spectra=-1):
self.num_spectra = num_spectra
self.np_spectrum = NpSpectrumContainer(readonly=False, num_spectra=num_spectra)
self.continuum_fit_metadata = table.Table()
self.continuum_fit_metadata.add_columns(
[table.Column(name='index', dtype='i8', unit=None, length=num_spectra),
table.Column(name='is_good_fit', dtype='b', unit=None, length=num_spectra),
table.Column(name='goodness_of_fit', dtype='f8', unit=None, length=num_spectra),
table.Column(name='snr', dtype='f8', unit=None, length=num_spectra)])
# initialize array
self.np_spectrum.zero()
def get_wavelength(self, n):
return self.np_spectrum.get_wavelength(n)
def get_flux(self, n):
return self.np_spectrum.get_flux(n)
def set_wavelength(self, n, data):
self.np_spectrum.set_wavelength(n, data)
def set_flux(self, n, data):
self.np_spectrum.set_flux(n, data)
def set_metadata(self, n, is_good_fit, goodness_of_fit, snr):
self.continuum_fit_metadata[n] = [n, is_good_fit, goodness_of_fit, snr]
def copy_metadata(self, n, metadata):
self.continuum_fit_metadata[n] = metadata
def get_metadata(self, n):
return self.continuum_fit_metadata[n]
def get_is_good_fit(self, n):
return self.get_metadata(n)['is_good_fit']
def get_goodness_of_fit(self, n):
return self.get_metadata(n)['goodness_of_fit']
def get_snr(self, n):
return self.get_metadata(n)['snr']
@classmethod
def from_np_array_and_object(cls, np_array, obj):
# TODO: consider refactoring.
np_spectrum = NpSpectrumContainer.from_np_array(np_array, readonly=True)
new_instance = cls(num_spectra=np_spectrum.num_spectra)
# replace spectrum container with existing data
new_instance.np_spectrum = np_spectrum
# replace metadata with existing metadata object
assert type(new_instance.continuum_fit_metadata) == type(obj)
new_instance.continuum_fit_metadata = obj
return new_instance
def as_object(self):
return self.continuum_fit_metadata
def as_np_array(self):
return self.np_spectrum.as_np_array()