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fix conflicts
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@moustakas
moustakas committed Nov 7, 2024
2 parents dfe6738 + 99f2f44 commit 228501b
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Showing 2 changed files with 43 additions and 40 deletions.
38 changes: 19 additions & 19 deletions py/fastspecfit/continuum.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -539,7 +539,7 @@ def build_stellar_continuum(self, templateflux, templatecoeff,
Parameters
----------
templateflux : :class:`numpy.ndarray` [npix, ntemplates]
templateflux : :class:`numpy.ndarray` [ntemplates, npix]
Rest-frame, native-resolution template spectra corresponding to
`templatewave`.
templatecoeff : :class:`numpy.ndarray` [ntemplates]
Expand All @@ -566,7 +566,7 @@ def build_stellar_continuum(self, templateflux, templatecoeff,
"""
if conv_pre is None or vdisp > Templates.MAX_PRE_VDISP:
# Compute the weighted sum of the templates.
contmodel = templateflux.dot(templatecoeff)
contmodel = templatecoeff.dot(templateflux)

# Optionally convolve to the desired velocity dispersion.
if vdisp is not None:
Expand All @@ -578,13 +578,13 @@ def build_stellar_continuum(self, templateflux, templatecoeff,
flux_lohi, ft_flux_mid, fft_len = conv_pre

# Compute the weighted sum of the templates.
cont_lohi = flux_lohi.dot(templatecoeff)
ft_cont_mid = ft_flux_mid.dot(templatecoeff)
cont_lohi = templatecoeff.dot(flux_lohi)
ft_cont_mid = templatecoeff.dot(ft_flux_mid)

# Convolve to the desired velocity dispersion. Use the vdisp
# convolution that takes precomputed FT of flux for convolved
# region.
flux_len = templateflux.shape[0]
flux_len = templateflux.shape[1]
contmodel = self.templates.convolve_vdisp_from_pre(
cont_lohi, ft_cont_mid, flux_len, fft_len, vdisp)

Expand Down Expand Up @@ -759,7 +759,7 @@ def fit_stellar_continuum(self, templateflux, fit_vdisp=False, conv_pre=None,
Parameters
----------
templateflux : :class:`numpy.ndarray` [npix, ntemplate]
templateflux : :class:`numpy.ndarray` [ntemplate, npix]
Grid of input (model) spectra.
fit_vdisp : :class:`bool`
If `True`, solve for the velocity dispersion;
Expand Down Expand Up @@ -830,7 +830,7 @@ def fit_stellar_continuum(self, templateflux, fit_vdisp=False, conv_pre=None,
if vdisp_bounds is None:
vdisp_bounds = self.vdisp_bounds

ntemplates = templateflux.shape[1]
ntemplates = templateflux.shape[0]

# Unpack the input data to infer the fitting "mode" and the objective
# function.
Expand Down Expand Up @@ -995,7 +995,7 @@ def continuum_fastphot(redshift, objflam, objflamivar, CTools,

t0 = time.time()
tauv, _, coeff, resid = CTools.fit_stellar_continuum(
templates.flux_nomvdisp[:, agekeep], fit_vdisp=False,
templates.flux_nomvdisp[agekeep, :], fit_vdisp=False,
objflam=objflam, objflamistd=objflamistd,
synthphot=True, synthspec=False)
dt = time.time()-t0
Expand All @@ -1017,7 +1017,7 @@ def continuum_fastphot(redshift, objflam, objflamivar, CTools,
# Get the best-fitting model with and without line-emission.
sedmodel = CTools.optimizer_saved_contmodel
sedmodel_nolines = CTools.build_stellar_continuum(
templates.flux_nolines_nomvdisp[:, agekeep], coeff,
templates.flux_nolines_nomvdisp[agekeep, :], coeff,
tauv=tauv, vdisp=None, dust_emission=False)

# Measure Dn(4000) from the line-free model.
Expand All @@ -1042,7 +1042,7 @@ def continuum_fastphot(redshift, objflam, objflamivar, CTools,

for imonte in range(nmonte):
tauv1, _, coeff1, _ = CTools.fit_stellar_continuum(
templates.flux_nomvdisp[:, agekeep], fit_vdisp=False,
templates.flux_nomvdisp[agekeep, :], fit_vdisp=False,
tauv_guess=tauv_guess[imonte], objflam=objflam_monte[:, imonte],
objflamistd=objflamistd, synthphot=True, synthspec=False)

Expand All @@ -1051,7 +1051,7 @@ def continuum_fastphot(redshift, objflam, objflamivar, CTools,

sedmodel_monte[:, imonte] = CTools.optimizer_saved_contmodel
sedmodel_nolines_monte[:, imonte] = CTools.build_stellar_continuum(
templates.flux_nolines_nomvdisp[:, agekeep], coeff1,
templates.flux_nolines_nomvdisp[agekeep, :], coeff1,
tauv=tauv1, vdisp=None, dust_emission=False)

dn4000_model1, _ = Photometry.get_dn4000(
Expand Down Expand Up @@ -1158,7 +1158,7 @@ def continuum_fastspec(redshift, objflam, objflamivar, CTools,
# the IR fluxes put an additional constraint on the dust content.
init_tauv_bounds = (0., 1.)
tauv_nomvdisp, _, coeff_nomvdisp, resid_nomvdisp = CTools.fit_stellar_continuum(
templates.flux_nolines_nomvdisp[:, agekeep], fit_vdisp=False, conv_pre=None,
templates.flux_nolines_nomvdisp[agekeep, :], fit_vdisp=False, conv_pre=None,
tauv_bounds=init_tauv_bounds, specflux=specflux, specistd=specistd,
dust_emission=False, synthspec=True)

Expand All @@ -1178,7 +1178,7 @@ def continuum_fastspec(redshift, objflam, objflamivar, CTools,
input_conv_pre_nolines = templates.conv_pre_select(templates.conv_pre_nolines, agekeep)

tauv_withvdisp, vdisp, coeff_withvdisp, resid_withvdisp = CTools.fit_stellar_continuum(
templates.flux_nolines[:, agekeep], fit_vdisp=True,
templates.flux_nolines[agekeep, :], fit_vdisp=True,
conv_pre=input_conv_pre_nolines,
vdisp_guess=templates.vdisp_nominal,
tauv_bounds=init_tauv_bounds, specflux=specflux,
Expand All @@ -1198,8 +1198,8 @@ def continuum_fastspec(redshift, objflam, objflamivar, CTools,
contmodel = contmodel_withvdisp

# convolve the templates at the derived vdisp
input_templateflux = templates.convolve_vdisp(templates.flux[:, agekeep], vdisp)
input_templateflux_nolines = templates.convolve_vdisp(templates.flux_nolines[:, agekeep], vdisp)
input_templateflux = templates.convolve_vdisp(templates.flux[agekeep, :], vdisp)
input_templateflux_nolines = templates.convolve_vdisp(templates.flux_nolines[agekeep, :], vdisp)
else:
tauv = tauv_nomvdisp
coeff = coeff_nomvdisp
Expand All @@ -1219,7 +1219,7 @@ def continuum_fastspec(redshift, objflam, objflamivar, CTools,

for imonte in range(nmonte):
tauv1, vdisp1, coeff1, _ = CTools.fit_stellar_continuum(
templates.flux_nolines[:, agekeep], fit_vdisp=True,
templates.flux_nolines[agekeep, :], fit_vdisp=True,
conv_pre=input_conv_pre_nolines,
vdisp_guess=vdisp_guess[imonte],
tauv_guess=tauv_guess[imonte],
Expand Down Expand Up @@ -1302,9 +1302,9 @@ def continuum_fastspec(redshift, objflam, objflamivar, CTools,
tauv = tauv_nomvdisp
coeff = coeff_nomvdisp
vdisp = templates.vdisp_nominal
input_templateflux = templates.flux_nomvdisp[:, agekeep]
input_templateflux_nolines = templates.flux_nolines_nomvdisp[:, agekeep]
contmodel = CTools.optimizer_saved_contmodel.copy() # copy needed??
input_templateflux = templates.flux_nomvdisp[agekeep, :]
input_templateflux_nolines = templates.flux_nolines_nomvdisp[agekeep, :]
contmodel = CTools.optimizer_saved_contmodel.copy()

# Next, estimate the aperture correction.
apercorrs = np.ones(len(phot.synth_bands))
Expand Down
45 changes: 24 additions & 21 deletions py/fastspecfit/templates.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,9 @@ def __init__(self, template_file=None, template_version=None, imf=None,
templateinfo = T['METADATA'].read()
templatehdr = T['METADATA'].read_header()

templateflux = np.transpose(templateflux).copy()
templatelineflux = np.transpose(templatelineflux).copy()

self.version = T[0].read_header()['VERSION']

self.imf = templatehdr['IMF']
Expand All @@ -87,8 +90,8 @@ def __init__(self, template_file=None, template_version=None, imf=None,
keeplo = np.searchsorted(templatewave, mintemplatewave, 'left')
keephi = np.searchsorted(templatewave, maxtemplatewave, 'right')
self.wave = templatewave[keeplo:keephi]
self.flux = templateflux[keeplo:keephi, :]
self.flux_nolines = self.flux - templatelineflux[keeplo:keephi, :]
self.flux = templateflux[:, keeplo:keephi]
self.flux_nolines = self.flux - templatelineflux[:, keeplo:keephi]
self.npix = len(self.wave)

# dust attenuation curve
Expand Down Expand Up @@ -196,7 +199,7 @@ def convolve_vdisp_pre(self, templateflux):
Parameters
----------
templateflux: :class:`np.ndarray`
[nwavelengths x ntemplates] array of templates
[ntemplates x nwavelengths] array of templates
Returns
-------
Expand All @@ -215,43 +218,43 @@ def convolve_vdisp_pre(self, templateflux):
lo, hi = self.pixkms_bounds

# extract the un-convolved ranges of templateflux
flux_lo = templateflux[:lo, :]
flux_hi = templateflux[hi:, :]
flux_mid = templateflux[lo:hi, :]
flux_lo = templateflux[:, :lo]
flux_hi = templateflux[:, hi:]
flux_mid = templateflux[:, lo:hi]

mid_len = flux_mid.shape[0]
mid_len = flux_mid.shape[1]

fft_len = sc_fft.next_fast_len(mid_len + kernel_size - 1,
real=True)
ft_flux_mid = sc_fft.rfft(flux_mid, n=fft_len, axis=0)
ft_flux_mid = sc_fft.rfft(flux_mid, n=fft_len)

return (np.vstack((flux_lo, flux_hi)), ft_flux_mid, fft_len)
return (np.hstack((flux_lo, flux_hi)), ft_flux_mid, fft_len)


@staticmethod
def conv_pre_select(conv_pre, cols):
def conv_pre_select(conv_pre, rows):
"""
Filter a set of preprocessing data for vdisp convolution
to use only the specified columns (templates)
to use only the specified rows (templates)
Parameters
----------
conv_pre: :class:`tuple` or None
Preprocessing structure for vdisp convolution (may be None)
cols: :class:`int`
Which columns (templates) from the preprocessing
rows: :class:`int`
Which rows (templates) from the preprocessing
data should we keep?
Returns
-------
Revised preprocessing data with only the selected columns
Revised preprocessing data with only the selected rows
"""
if conv_pre is None:
return None
else:
flux_lohi, ft_flux_mid, fft_len = conv_pre
return (flux_lohi[:, cols], ft_flux_mid[:, cols], fft_len)
return (flux_lohi[rows, :], ft_flux_mid[rows, :], fft_len)


def convolve_vdisp_from_pre(self, flux_lohi, ft_flux_mid, flux_len, fft_len, vdisp):
Expand Down Expand Up @@ -322,7 +325,7 @@ def convolve_vdisp(self, templateflux, vdisp):
----------
templateflux: :class:`np.ndarray`
Either a 1D template array of fluxes, or a 2D array of size
[nfluxes x ntemplates] representing ntemplates fluxes
[ntemplates x nfluxes] representing ntemplates fluxes
vdisp: :class:`float64`
Velocity dispersion to convolve with fluxes
Expand Down Expand Up @@ -354,12 +357,12 @@ def convolve_vdisp(self, templateflux, vdisp):
output[:lo] = templateflux[:lo]
output[hi:] = templateflux[hi:]
else:
ntemplates = templateflux.shape[1]
ntemplates = templateflux.shape[0]
for ii in range(ntemplates):
output[lo:hi, ii] = self.convolve(
templateflux[lo:hi, ii], kernel, mode='same')
output[:lo, :] = templateflux[:lo, :]
output[hi:, :] = templateflux[hi:, :]
output[ii, lo:hi] = self.convolve(
templateflux[ii, lo:hi], kernel, mode='same')
output[:, :lo] = templateflux[:, :lo]
output[:, hi:] = templateflux[:, hi:]

return output

Expand Down

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